AN INTELLIGENT REAL-TIME THREAT DETECTION AND RESPONSE SYSTEM AND METHOD THEREOF

Abstract

Disclosed herein is an intelligent real-time threat detection and response system and method thereof (100) that comprises a user device (102) that receives alerts and provides a remote interface, alongside a camera (104) and multiple surveillance cameras (106) for comprehensive visual data capture. The system utilizes a communication network (108) for seamless data transmission, while a processor (110) executes real-time analysis and decision-making through an adaptive threat detection algorithm (112) and a multi-source data fusion module (114). With cross-platform integration (116) and scalable architecture (118), the system adapts to various environments. Additional features include predictive analytics (120), enhanced data privacy (122), a contextual learning unit (124), energy-efficient power management (126), customizable threat profiles (128), and a multi-language user interface (130). A memory unit (132) supports data storage and retrieval, allowing for improved threat detection and response efficacy.

Specification

Description:FIELD OF DISCLOSURE
[0001] The present disclosure generally relates to the field of artificial intelligence, computer vision, and public safety, more specifically, relates to intelligent real-time threat detection and response system and method thereof.
BACKGROUND OF THE DISCLOSURE
[0002] The intelligent real-time threat detection and response system and method thereof continuously enhances public safety by using deep convolutional neural networks (CNNs) to detect and classify threats with high precision. By leveraging advanced neural networks, the system maintains a consistently high level of accuracy in identifying threats like fires, smoke, hazardous materials, and other emergency conditions. This accuracy is crucial for timely responses, as even the slightest delay or misclassification can compromise safety and increase risk. The system continuously analyses data from multiple sources, allowing for nuanced recognition of threats across diverse scenarios and environments.
[0003] Intelligent real-time threat detection and response system and method thereof sustains efficient communication channels with public safety agencies, enabling immediate threat awareness and swift response coordination. By connecting with relevant emergency responders, the system ensures that critical threat data reaches those who need it without delay. This real-time integration not only accelerates response times but also enhances the effectiveness of emergency interventions, as responders are better informed before arrival. Additionally, the platform facilitates a structured communication approach, optimizing resource allocation and potentially saving more lives by minimizing response time across various emergency situations.
[0004] The system exhibits remarkable adaptability and scalability, accommodating a range of environments, from urban infrastructures to industrial facilities. By integrating machine learning algorithms, intelligent real-time threat detection and response system and method thereof continuously adapts to changing data patterns and evolving threat types, ensuring ongoing relevance and accuracy in diverse settings. This adaptability includes scaling the system up or down based on the size of the monitored area and the nature of the threats specific to that environment. Its versatility serves industries, residential areas, and government agencies alike, making it a robust solution capable of adjusting to both high-density and remote areas with minimal adjustment requirements.
[0005] Existing threat monitoring systems often lack the precision needed to distinguish between genuine threats and benign events, leading to a higher frequency of false positives and missed detections. These systems frequently rely on basic pattern recognition or rule-based algorithms, which struggle with complex scenarios or environments where multiple factors contribute to potential threats. This inaccuracy not only creates unnecessary interruptions but also desensitizes personnel to alerts, ultimately increasing the risk of ignoring actual emergencies due to repeated false alarms.
[0006] Current systems generally operate with limited real-time connectivity, resulting in slower response times and delayed communication with emergency responders. Due to outdated or rigid communication infrastructures, these systems cannot seamlessly coordinate with public safety networks, often resulting in response lag during critical events. The lack of direct, instantaneous communication with responders reduces the effectiveness of interventions, putting lives and property at greater risk, as emergency services remain uninformed or underinformed until they reach the site.
[0007] Many traditional systems are designed to operate within a fixed set of parameters, making it challenging to scale them across varying environments or adapt to new types of threats. These systems often perform sub optimally when applied to settings outside their original design scope, such as different environmental conditions, levels of occupancy, or specific threat types. This lack of scalability and adaptability limits the system's effectiveness in diverse contexts, reducing its overall functionality in environments that demand dynamic and flexible threat response solutions.
[0008] Thus, in light of the above-stated discussion, there exists a need for an intelligent real-time threat detection and response system and method thereof.
SUMMARY OF THE DISCLOSURE
[0009] The following is a summary description of illustrative embodiments of the invention. It is provided as a preface to assist those skilled in the art to more rapidly assimilate the detailed design discussion which ensues and is not intended in any way to limit the scope of the claims which are appended hereto in order to particularly point out the invention.
[0010] According to illustrative embodiments, the present disclosure focuses on an intelligent real-time threat detection and response system and method thereof which overcomes the above-mentioned disadvantages or provide the users with a useful or commercial choice.
[0011] An objective of the present disclosure is to achieve highly accurate and real-time threat detection using advanced algorithmic models, ensuring effective identification of potential threats without delays.
[0012] Another objective of the present disclosure is to enhance safety in various environments by continuously monitoring for suspicious activities, enabling preventive measures before threats escalate.
[0013] Another objective of the present disclosure is to reduce false alarms through adaptive learning, refining threat assessment over time to minimize unnecessary disruptions and focus on genuine threats.
[0014] Another objective of the present disclosure is to seamlessly integrate with existing communication systems to provide rapid alerts to authorities and stakeholders, ensuring prompt response in critical situations.
[0015] Another objective of the present disclosure is to offer scalable implementation options across different environments, making the system adaptable for varied security needs in settings such as public spaces, corporate facilities, and private properties.
[0016] Another objective of the present disclosure is to use data from multiple sources, including surveillance feeds and sensor inputs, to provide a comprehensive analysis of potential risks, increasing detection accuracy.
[0017] Another objective of the present disclosure is to incorporate user-defined parameters and customizable settings, allowing individuals or organizations to tailor the system based on specific threat concerns and requirements.
[0018] Another objective of the present disclosure is to operate with minimal maintenance needs, ensuring that the system continues to function efficiently and reliably over extended periods.
[0019] Yet another objective of the present disclosure is to provide real-time data visualization and insights, allowing users to monitor threat levels, historical data, and response patterns for improved decision-making.
[0020] Yet another objective of the present disclosure is to support multi-language and cross-platform compatibility, enabling accessibility and ease of use for diverse users across different regions and devices.
[0021] In light of the above, in one aspect of the present disclosure, an intelligent real-time threat detection and response system and method thereof is disclosed herein. The system comprises a user device configured to receive real-time alerts and notifications and provide an interface for remote monitoring and control. The system includes a camera operatively connected to the user device and configured for providing visual data to the system. The system also includes a plurality of surveillance cameras configured to capture and transmit real-time video feeds from various locations. The system also includes a communication network operatively connected to the user device, the camera, and the plurality of surveillance cameras and configured to enable seamless data transmission across these components and supports real-time alerts, notifications, and updates across multiple platforms and devices. The system also includes a processor connected to the user device and configured to execute real-time data analysis, decision-making, and algorithmic updates. The system also includes an adaptive threat detection algorithm integrated within the processor and configured to continuously analyse and update threat identification based on new data inputs. The system also includes a multi-source data fusion module integrated within the processor and configured to continuously aggregate data from video surveillance feeds, biometric sensors, and internet of things (IoT). The system also includes a cross-platform integration module integrated within the processor and configured to support real-time system connectivity across various platforms and devices. The system also includes a scalable system architecture integrated within the processor and configured to facilitate seamless system expansion for varied environments ranging from small installations to large-scale deployments. The system also includes a predictive analytics module integrated within the processor and configured to anticipate potential threats based on historical data and emerging patterns. The system also includes an enhanced data privacy and security management module integrated within the processor and configured to enforce encryption protocols and privacy controls for safeguarding sensitive information and ensuring compliance with data privacy standards. The system also includes a contextual learning unit connected to the processor and configured to identify genuine threats versus non-threatening activities by analysing behavioural patterns. The system also includes an energy-efficient power management unit operatively coupled with the user device configured to reduce energy consumption and minimize maintenance requirements. The system also includes a customizable threat profile interface connected to the user device and configured to enable user-defined threat profiles and risk parameters. The system also includes a multi-language user interface module connected to all system components and operatively integrated within the customizable threat profile interface, wherein the multi-language user interface module provides a user-friendly experience adaptable to various regional and cultural needs. The system also includes a memory unit communicatively coupled with the processor configured to store data processed by the adaptive threat detection algorithm, multi-source data fusion module, predictive analytics module, and contextual learning unit, allowing for data retrieval and analysis essential for real-time threat detection, pattern recognition, and system improvement.
[0022] In light of the above, in one aspect of the present disclosure, a method for real-time threat detection and response in an intelligent threat detection system is disclosed herein. The method comprising continuously receiving real-time alerts and notifications from a user device connected to a communication network. The method includes capturing and transmitting real-time video feeds and sensory data from various monitored locations to a multi-source data fusion module connected to a processor via a plurality of surveillance cameras. The method also includes processing the aggregated data through an adaptive threat detection algorithm connected to the processor. The method also includes identifying genuine threats by analysing behavioural patterns by a contextual learning unit operatively connected to the processor. The method also includes transmitting real-time alerts and notifications across various platforms and user devices through multiple communication channels by a cross-platform integration module operatively connected to the processor. The method also includes enabling user-defined threat profiles and risk parameters by a customizable threat profile interface in operative connection with the user device. The method also includes expanding the system's ability to accommodate varied environments, from small installations to large-scale deployments by a scalable system architecture integrated with the processor. The method also includes anticipating potential threats through analysis of historical data and emerging patterns by a predictive analytics module integrated within the processor. The method also includes safeguarding sensitive data wherein the data privacy and security management system enforce encryption protocols and privacy controls by an enhanced data privacy and security management system operatively connected to all data collection components. The method also includes managing energy consumption reducing power requirements across components and minimizing system maintenance needs by an energy-efficient power management system coupled with the user device and processor.
[0023] These and other advantages will be apparent from the present application of the embodiments described herein.
BRIEF DESCRIPTION OF THE DRAWINGS
[0024] The advantages and features of the present disclosure will become better understood with reference to the following detailed description taken in conjunction with the accompanying drawing, in which:
[0025] FIG. 1 illustrates a block diagram of an intelligent real-time threat detection and response system and method thereof, in accordance with an exemplary embodiment of the present disclosure;
[0026] FIG. 2 illustrates a flowchart of an intelligent real-time threat detection and response system, in accordance with an exemplary embodiment of the present disclosure;
[0027] FIG. 3 illustrates a flowchart of a method for real-time threat detection and response in an intelligent threat detection system, in accordance with an exemplary embodiment of the present disclosure;
[0028] FIG. 4 illustrates a perspective view of the use case design of the system, in accordance with an exemplary embodiment of the present disclosure;
[0029] FIG. 5 illustrates a perspective view of the proposed system, in accordance with an exemplary embodiment of the present disclosure;
[0030] FIG. 6 illustrates a perspective view of the concept of contrastive pre-training, in accordance with an exemplary embodiment of the present disclosure;
[0031] FIG. 7 illustrates a perspective view of the concept of dataset classifier and use of zero-shot prediction, in accordance with an exemplary embodiment of the present disclosure;
[0032] FIG. 8 illustrates a perspective view of the training loss over epochs, in accordance with an exemplary embodiment of the present disclosure;
[0033] FIG. 9 illustrates a perspective view of the cloud fire store, in accordance with an exemplary embodiment of the present disclosure;
[0034] FIG. 10 illustrates a perspective view of the threat manager login window, in accordance with an exemplary embodiment of the present disclosure;
[0035] FIG. 11 illustrates a perspective view of the signup window for a new user, in accordance with an exemplary embodiment of the present disclosure;
[0036] FIG. 12 illustrates a perspective view of the detection of fight on a street, in accordance with an exemplary embodiment of the present disclosure;
[0037] FIG. 13 illustrates a perspective view of the detection of violence in office, in accordance with an exemplary embodiment of the present disclosure;
[0038] FIG. 14 illustrates a perspective view of the detection of car crash, in accordance with an exemplary embodiment of the present disclosure;
[0039] FIG. 15 illustrates a perspective view of the detection of fire on a street and office, in accordance with an exemplary embodiment of the present disclosure;
[0040] FIG. 16 illustrates a perspective view of the medical squad user interface, in accordance with an exemplary embodiment of the present disclosure;
[0041] FIG. 17 illustrates a perspective view of the details of threat received by medical squad, in accordance with an exemplary embodiment of the present disclosure;
[0042] FIG. 18 illustrates a perspective view of the threat information received in the respective department, in accordance with an exemplary embodiment of the present disclosure.
[0043] Like reference, numerals refer to like parts throughout the description of several views of the drawing.
[0044] The intelligent real-time threat detection and response system and method thereof is illustrated in the accompanying drawings, which like reference letters indicate corresponding parts in the various figures. It should be noted that the accompanying figure is intended to present illustrations of exemplary embodiments of the present disclosure. This figure is not intended to limit the scope of the present disclosure. It should also be noted that the accompanying figure is not necessarily drawn to scale.
DETAILED DESCRIPTION OF THE DISCLOSURE
[0045] The following is a detailed description of embodiments of the disclosure depicted in the accompanying drawings. The embodiments are in such detail as to communicate the disclosure. However, the amount of detail offered is not intended to limit the anticipated variations of embodiments; on the contrary, the intention is to cover all modifications, equivalents, and alternatives falling within the spirit and scope of the present disclosure.
[0046] The terms "a" and "an" herein do not denote a limitation of quantity, but rather denote the presence of at least one of the referenced items.
[0047] The terms "having", "comprising", "including", and variations thereof signify the presence of a component.
[0048] Referring now to FIG. 1 to FIG. 18 to describe various exemplary embodiments of the present disclosure. FIG. 1 illustrates a perspective view of an intelligent real-time threat detection and response system and method thereof 100, in accordance with an exemplary embodiment of the present disclosure.
[0049] The system 100 may include a user device 102 configured to receive real-time alerts and notifications and provide an interface for remote monitoring and control, a camera 104 operatively connected to the user device 102 and configured for providing visual data to the system, a plurality of surveillance cameras 106 configured to capture and transmit real-time video feeds from various locations, a communication network 108 operatively connected to the user device 102, the camera 104, and the plurality of surveillance cameras 106 and configured to enable seamless data transmission across these components and supports real-time alerts, notifications, and updates across multiple platforms and devices, a processor 110 connected to the user device 102 and configured to execute real-time data analysis, decision-making, and algorithmic updates, an adaptive threat detection algorithm 112 integrated within the processor 110 and configured to continuously analyse and update threat identification based on new data inputs, a multi-source data fusion module 114 integrated within the processor 110 and configured to continuously aggregate data from video surveillance feeds, biometric sensors, and internet of things (IoT), a cross-platform integration module 116 integrated within the processor 110 and configured to support real-time system connectivity across various platforms and devices, a scalable system architecture 118 integrated within the processor 110 and configured to facilitate seamless system expansion for varied environments ranging from small installations to large-scale deployments, a predictive analytics module 120 integrated within the processor 110 and configured to anticipate potential threats based on historical data and emerging patterns, an enhanced data privacy and security management module 122 integrated within the processor 110 and configured to enforce encryption protocols and privacy controls for safeguarding sensitive information and ensuring compliance with data privacy standards, a contextual learning unit 124 connected to the processor 110 and configured to identify genuine threats versus non-threatening activities by analysing behavioural patterns, an energy-efficient power management unit 126 operatively coupled with the user device 102 configured to reduce energy consumption and minimize maintenance requirements, a customizable threat profile interface 128 connected to the user device 102 and configured to enable user-defined threat profiles and risk parameters, a multi-language user interface module 130 connected to all system components and operatively integrated within the customizable threat profile interface 128, wherein the multi-language user interface module 130 provides a user-friendly experience adaptable to various regional and cultural needs, a memory unit 132 communicatively coupled with the processor 110 configured to store data processed by the adaptive threat detection algorithm 112, multi-source data fusion module 114, predictive analytics module 120, and contextual learning unit 124, allowing for data retrieval and analysis essential for real-time threat detection, pattern recognition, and system improvement.
[0050] The user device 102 further comprises a display screen configured to present real-time video feeds and analytics information from the plurality of surveillance cameras 106.
[0051] The communication network 108 comprises a wireless network protocol, including but not limited to Wi-Fi, 4G, or 5G, enabling secure and efficient transmission of data between the user device 102, camera 104, and plurality of surveillance cameras 106.
[0052] The adaptive threat detection algorithm 112 further incorporates a machine learning module trained to improve detection accuracy by learning from previous threat patterns and user feedback, thereby enhancing the system's ability to detect emerging threats.
[0053] The multi-source data fusion module 114 is further configured to aggregate data from additional sources, including environmental sensors and audio input devices, for a more comprehensive threat profile and improved detection accuracy in diverse environments.
[0054] The cross-platform integration module 116 further comprises an application programming interface (API) layer that supports interoperability with third-party security systems, providing enhanced system compatibility and extending the range of accessible platforms and devices.
[0055] The contextual learning unit 124 further comprises a behavioural pattern recognition module that continuously adapts to user-defined parameters, allowing the system to refine its distinction between potential threats and benign activities according to the specific context of the monitored environment.
[0056] The energy-efficient power management unit 126 further comprises a low-power mode that activates during periods of inactivity, significantly reducing power consumption and extending the operational lifespan of the user device 102 and other system components.
[0057] The customizable threat profile interface 128 further comprises an alert prioritization setting that allows users to designate high-priority threat levels, ensuring that critical alerts are delivered promptly, with an audible or visual signal to immediately notify users.
[0058] The method 100 may include continuously receiving real-time alerts and notifications from a user device 102 connected to a communication network 108, capturing and transmitting real-time video feeds and sensory data from various monitored locations to a multi-source data fusion module 114 connected to a processor 110 via a plurality of surveillance cameras 106, processing the aggregated data through an adaptive threat detection algorithm 112 connected to the processor 110, identifying genuine threats by analysing behavioural patterns by a contextual learning unit 124 operatively connected to the processor 110, transmitting real-time alerts and notifications across various platforms and user device 102 through multiple communication channels by a cross-platform integration module 116 operatively connected to the processor 110, enabling user-defined threat profiles and risk parameters by a customizable threat profile interface 128 in operative connection with the user device 102, expanding the system's ability to accommodate varied environments, from small installations to large-scale deployments by a scalable system architecture 118 integrated with the processor 110, anticipating potential threats through analysis of historical data and emerging patterns by a predictive analytics module 120 integrated within the processor 110, safeguarding sensitive data using the data privacy and security management module 122 via the processor 110, managing energy consumption reducing power requirements across components and minimizing system maintenance needs by an energy-efficient power management system coupled with the user device 102 and processor 110.
[0059] The user device 102 is configured to act as a central interface for monitoring and controlling the intelligent threat detection system 100, providing real-time alerts and notifications to the user. The user device 102 receives continuous data from various system components, including visual data from the camera 104 and aggregated video feeds from the plurality of surveillance cameras 106, enabling the user to monitor live events as they occur. The user device 102 displays these feeds on a display screen, presenting video feeds, analytics information, and threat profiles, ensuring comprehensive and actionable information for immediate response.
[0060] In addition, the user device 102 remains in constant communication with the communication network 108, supporting seamless data transmission with other connected system components. Through the integration of the cross-platform integration module 116, the user device 102 ensures instant connectivity and alert dissemination across multiple communication channels, enhancing user accessibility and enabling real-time response coordination. The customizable threat profile interface 128 embedded in the user device 102 further empowers users to define and adjust threat profiles and risk parameters according to specific situational needs.
[0061] The energy-efficient power management unit 126 operatively coupled with the user device 102 optimizes energy consumption, reducing power demand and maintaining system reliability. Through these combined functionalities, the user device 102 ensures real-time, adaptable monitoring, providing a comprehensive and user-friendly interface for remote threat detection and response.
[0062] The camera 104 is configured to serve as a primary visual data source within the intelligent threat detection and response system 100, capturing real-time footage that feeds directly into the system. The camera 104 connects operatively to the user device 102, providing the user with immediate access to high-quality video data, allowing continuous monitoring of specified locations and supporting prompt assessment and response to potential threats. The footage generated by the camera 104 integrates into the broader data collection process, contributing critical visual information that enhances threat detection accuracy.
[0063] Through its connection to the communication network 108, the camera 104 transmits captured video data seamlessly to the system's multi-source data fusion module 114, where the video data combines with inputs from other devices such as biometric sensors and internet of things (IoT) devices. This multi-layered data aggregation enhances the depth of threat profiles created by the system, allowing the adaptive threat detection algorithm 112 to perform a more comprehensive analysis based on rich, diverse data sources. The camera 104's role in providing real-time visual data is crucial for identifying immediate threats and contributes to building an extensive historical data library, which the predictive analytics module 120 later leverages to identify emerging patterns and support proactive threat prevention.
[0064] The plurality of surveillance cameras 106 is configured to continuously monitor various designated areas within the environment, providing real-time video feeds that contribute to comprehensive security coverage. Each surveillance camera within the plurality of surveillance cameras 106 captures high-resolution video, allowing the intelligent threat detection and response system 100 to collect a vast array of visual data from different perspectives and locations. This continuous monitoring enables the system to detect potential security risks in real time, enhancing its ability to respond promptly and effectively to any detected threats.
[0065] Each surveillance camera in the plurality of surveillance cameras 106 is operatively connected to the communication network 108, allowing seamless data transmission from multiple sources to the system's central processing units. The communication network 108 manages the flow of data between the surveillance cameras and other system components, ensuring that all visual information is received and processed without interruption. This constant transmission enables the adaptive threat detection algorithm 112 integrated within the processor 110 to access video data from each surveillance camera, enhancing the algorithm's capability to analyse and identify unusual activities or patterns in the monitored spaces.
[0066] The plurality of surveillance cameras 106 integrates closely with the multi-source data fusion module 114, which continuously aggregates visual data from these cameras along with inputs from other devices such as biometric sensors and internet of things (IoT) devices. This integration allows the multi-source data fusion module 114 to create a comprehensive threat profile that incorporates visual, biometric, and environmental data. The data from the plurality of surveillance cameras 106, thus, plays a critical role in providing a detailed view of potential security risks, ensuring that the system can detect, assess, and respond to threats with a high degree of accuracy.
[0067] By supporting the adaptive threat detection algorithm 112, the plurality of surveillance cameras 106 enhances the system's ability to differentiate between routine and potentially dangerous activities. The contextual learning unit 124 further processes the visual data from each surveillance camera, analysing behavioural patterns and distinguishing genuine threats from non-threatening events. This process minimizes false alarms and increases the precision of threat detection, making the data collected by the plurality of surveillance cameras 106 a key element in improving overall system reliability.
[0068] The plurality of surveillance cameras 106 also contributes valuable information to the predictive analytics module 120, allowing it to analyse historical data and emerging patterns. Over time, the video data captured by the plurality of surveillance cameras 106 enables the predictive analytics module 120 to anticipate potential threats based on recurring patterns and past incidents, supporting a proactive approach to threat prevention. This predictive capability strengthens the system's ability to identify vulnerabilities before they escalate, offering an additional layer of protection through the use of the video data collected by the plurality of surveillance cameras 106.
[0069] Moreover, the energy-efficient power management unit 126 optimizes the functionality of the plurality of surveillance cameras 106 by managing energy consumption across these components. By employing power-saving measures during periods of lower activity, the energy-efficient power management unit 126 reduces operational costs and extends the service life of each surveillance camera. This efficient energy utilization further enhances the durability and reliability of the plurality of surveillance cameras 106, ensuring continuous monitoring capabilities without requiring excessive power resources.
[0070] Additionally, the plurality of surveillance cameras 106 is configured to work in conjunction with the customizable threat profile interface 128, allowing users to set specific threat parameters and define monitoring priorities according to different zones or locations. This customization enables each surveillance camera in the plurality of surveillance cameras 106 to adjust its sensitivity and response triggers based on user-defined criteria, creating a security setup that is highly adaptable to various environments. Through this feature, the data collected by each surveillance camera aligns with the unique security needs of each monitored area, allowing the system to deliver more targeted and effective threat detection.
[0071] Finally, the memory unit 132 plays an essential role in supporting the plurality of surveillance cameras 106 by storing all captured video data for future analysis and retrieval. The memory unit 132 retains information processed by the adaptive threat detection algorithm 112, multi-source data fusion module 114, and predictive analytics module 120, ensuring that the data from the plurality of surveillance cameras 106 is accessible for retrospective analysis and pattern identification. This capability allows the system to improve its threat detection algorithms over time by learning from previously recorded data, making the plurality of surveillance cameras 106 a foundational component in the system's ongoing enhancement and adaptability.
[0072] The communication network 108 is operatively connected to the user device 102, the camera 104, and the plurality of surveillance cameras 106, establishing a seamless connection between these components. This communication network 108 enables uninterrupted data transmission across the system, ensuring that each connected device consistently receives real-time information. By facilitating constant communication, the communication network 108 supports the system's ability to deliver live updates, alerts, and notifications across multiple platforms and devices, allowing the user device 102 to access real-time data from the camera 104 and the plurality of surveillance cameras 106 for comprehensive threat monitoring.
[0073] The communication network 108 is configured to handle high data volumes, efficiently managing video feeds, alerts, and other data from multiple sources. This network integrates wireless protocols, including Wi-Fi, 4G, or 5G, enabling secure, high-speed data transmission. Such advanced connectivity features allow the communication network 108 to operate smoothly even in dynamic environments, ensuring that the adaptive threat detection algorithm 112 and other system components receive timely data to perform accurate threat assessments.
[0074] Furthermore, the communication network 108 plays a crucial role in supporting cross-platform integration through the cross-platform integration module 116, maintaining system connectivity across diverse platforms and ensuring compatibility with various devices. By enabling robust and secure data exchange, the communication network 108 sustains the operational efficiency of the intelligent threat detection and response system 100.
[0075] The processor 110 is connected to the user device 102, serving as the central operational unit of the intelligent threat detection and response system 100. This processor 110 continuously manages data processing, real-time analysis, and execution of system algorithms to ensure swift, precise threat detection. Through its integration with the adaptive threat detection algorithm 112, the processor 110 continuously analyses incoming data, updating threat identifications based on new information to enhance detection accuracy.
[0076] Additionally, the processor 110 supports the multi-source data fusion module 114, aggregating data from the plurality of surveillance cameras 106, biometric sensors, and IoT devices. By coordinating with this module, the processor 110 synthesizes various data inputs, constructing a comprehensive threat profile. The processor 110 also enables cross-platform integration through the cross-platform integration module 116, which maintains connectivity across multiple devices and platforms, allowing real-time alerts to reach the user device 102 without delay.
[0077] The processor 110 incorporates the scalable system architecture 118, making the system adaptable to different operational sizes and environments, from small setups to extensive networks. Additionally, with the predictive analytics module 120, the processor 110 proactively assesses potential threats based on historical and current data, offering preventive insights. The processor 110 thus orchestrates all components, ensuring coherent and efficient threat detection and response.
[0078] The adaptive threat detection algorithm 112 integrated within the processor 110 continuously analyses data inputs, enabling the intelligent threat detection and response system 100 to identify potential threats with high accuracy. This adaptive threat detection algorithm 112 continuously updates its threat identification protocols based on real-time data, learning from each new data set it processes. Through this adaptive learning, the adaptive threat detection algorithm 112 improves its detection accuracy, refining threat identification by recognizing emerging patterns and behaviours that signify potential risks.
[0079] In conjunction with the multi-source data fusion module 114, the adaptive threat detection algorithm 112 aggregates and processes data from a range of inputs, including video feeds from the plurality of surveillance cameras 106, biometric sensors, and IoT devices. This integration allows the adaptive threat detection algorithm 112 to operate on comprehensive data, capturing a multidimensional perspective of potential threats. This aggregated data also includes behavioural patterns, making the adaptive threat detection algorithm 112 capable of distinguishing between genuine threats and non-threatening activities, reducing the occurrence of false alarms and improving response accuracy.
[0080] The adaptive threat detection algorithm 112 connects with the predictive analytics module 120, allowing it to leverage historical data in its threat analysis. By referencing past threat patterns and emerging behaviours, the adaptive threat detection algorithm 112 provides proactive threat detection, alerting the user device 102 through the communication network 108 when potential risks arise. Through its integration with the customizable threat profile interface 128, the adaptive threat detection algorithm 112 adapts to user-defined parameters, allowing customization for different environments and threat sensitivities.
[0081] Through these advanced processes, the adaptive threat detection algorithm 112 plays a central role in enabling the intelligent threat detection and response system 100 to effectively respond to real-time security challenges.
[0082] The multi-source data fusion module 114 continuously aggregates data from various sources, including video feeds from the plurality of surveillance cameras 106, biometric sensors, and internet of things (IoT) devices, creating a comprehensive threat profile within the intelligent threat detection and response system 100. This multi-source data fusion module 114 integrated within the processor 110 operates by synthesizing information from multiple sources to provide a more robust and complete picture of the monitored environment, enhancing accuracy in identifying potential threats.
[0083] By incorporating inputs from diverse sources, the multi-source data fusion module 114 captures a wide range of situational data. This aggregation process helps ensure that the system 100 remains adaptable to different monitoring needs and scenarios, as the multi-source data fusion module 114 continuously adjusts to the dynamic conditions of the monitored environment. Through real-time data aggregation, the multi-source data fusion module 114 supplies the adaptive threat detection algorithm 112 with an enriched dataset, improving the algorithm's ability to detect and analyse threat patterns with heightened accuracy.
[0084] The multi-source data fusion module 114 communicates with the contextual learning unit 124, providing a basis for the system 100 to differentiate between genuine threats and benign activities by understanding behavioural patterns. The multi-source data fusion module 114, by combining varied data types and inputs, reduces the potential for false alarms and enables a more nuanced response to perceived threats, thereby optimizing the system's performance.
[0085] Furthermore, the multi-source data fusion module 114 integrates seamlessly with the cross-platform integration module 116, allowing data collected from different hardware and software components to be unified. This integration enhances the system's operational efficiency and ensures that users receive timely and accurate information. In collaboration with other components, the multi-source data fusion module 114 supports the system's adaptability to varied environments and requirements, making it a critical element in the intelligent threat detection and response system 100.
[0086] The cross-platform integration module 116 establishes real-time connectivity across diverse platforms and devices within the intelligent threat detection and response system 100. Integrated within the processor 110, the cross-platform integration module 116 supports seamless data and alert communication, allowing users to monitor and respond to threats from multiple types of devices, including mobile phones, tablets, and desktop computers. This cross-platform integration module 116 enables users to receive real-time notifications and alerts across any connected device, ensuring accessibility and continuous monitoring regardless of platform constraints.
[0087] Operating as a bridge between system components, the cross-platform integration module 116 optimizes the communication network 108, facilitating efficient data transfer and real-time system updates. The cross-platform integration module 116 provides interoperability and enhances flexibility by allowing the system 100 to interact with third-party security systems, contributing to a more comprehensive threat response. Through this interoperability, the cross-platform integration module 116 broadens the range of devices and applications compatible with the system 100, providing extensive adaptability to user environments.
[0088] Additionally, the cross-platform integration module 116 enhances user experience by enabling interaction with user-defined threat profiles and risk parameters through the customizable threat profile interface 128. This connection allows users to fine-tune system preferences, ensuring that the system 100 aligns with specific security needs across different devices and platforms. Working alongside the multi-source data fusion module 114, the cross-platform integration module 116 ensures that data from various sources is readily available on all connected devices, delivering an integrated, unified view of the monitored environment.
[0089] The cross-platform integration module 116 plays a crucial role in supporting the scalability and flexibility of the system 100. By enabling cross-device and cross-platform functionality, the cross-platform integration module 116 ensures that the system 100 remains adaptable to different security environments, from smaller installations to complex, large-scale setups. This integration, combined with system compatibility and real-time functionality, establishes the cross-platform integration module 116 as a key component in ensuring the system's comprehensive monitoring and response capabilities.
[0090] The scalable system architecture 118 within the intelligent threat detection and response system 100 provides the essential framework for expanding and adapting the system to a range of environments, from small installations to extensive, large-scale deployments. Integrated within the processor 110, the scalable system architecture 118 enables the system 100 to accommodate varying levels of security needs by allowing seamless addition or modification of system components, such as the plurality of surveillance cameras 106, biometric sensors, and internet of things (IoT) devices. Through this flexible and robust infrastructure, the scalable system architecture 118 ensures efficient performance across environments of differing sizes and complexities.
[0091] Supporting optimal resource allocation, the scalable system architecture 118 dynamically distributes processing tasks across available system components, enhancing operational efficiency. By optimizing the distribution of data processing and real-time analysis through the processor 110, the scalable system architecture 118 ensures consistent performance as system demands change. This adaptive approach minimizes delays in threat detection and response, ensuring that the system 100 continues to deliver timely insights and alerts, regardless of the environment or installation scale.
[0092] The scalable system architecture 118 further enhances system adaptability by supporting integration with third-party security solutions and cross-platform devices, facilitated by the cross-platform integration module 116. This compatibility expands the operational scope of the system 100, allowing seamless connections with external systems and offering users a unified, comprehensive security network. By promoting compatibility and interoperability, the scalable system architecture 118 supports user-defined customization and future-proofing, allowing the system 100 to evolve with emerging security needs.
[0093] The scalability provided by the scalable system architecture 118 not only ensures that the system 100 meets current security requirements but also prepares the infrastructure for future developments. This feature maintains a high level of system resilience, allowing users to adapt the security configuration as new threats emerge or as specific requirements evolve. Through its flexible, forward-looking structure, the scalable system architecture 118 plays a crucial role in ensuring the longevity and reliability of the intelligent threat detection and response system 100.
[0094] The predictive analytics module 120 within the intelligent threat detection and response system 100 plays a vital role in anticipating potential threats through data analysis. Integrated within the processor 110, the predictive analytics module 120 processes historical data and emerging patterns, enabling proactive threat assessment and response. By identifying correlations and trends, the predictive analytics module 120 continuously updates its analysis, adapting to changes in threat patterns and providing timely alerts.
[0095] Through advanced algorithms and machine learning, the predictive analytics module 120 analyses vast amounts of data sourced from the plurality of surveillance cameras 106, biometric sensors, and other integrated devices. This ongoing analysis allows the predictive analytics module 120 to recognize subtle indicators that may signal future security risks. The component thereby enhances the overall efficiency of the system 100 by generating predictive insights, which enable pre-emptive measures, reducing the likelihood of potential incidents.
[0096] The predictive analytics module 120 also enhances decision-making capabilities by providing actionable data to the user device 102. Through real-time communication, facilitated by the communication network 108, users receive predictive alerts, enabling quick responses to anticipated risks. This capability allows the system 100 to effectively prioritize high-risk situations, directing attention where it is most needed and optimizing resource allocation within the intelligent threat detection and response system 100.
[0097] Furthermore, the predictive analytics module 120 supports seamless integration with other critical modules, such as the adaptive threat detection algorithm 112 and the multi-source data fusion module 114. This interconnected functionality enriches the system's capacity for threat anticipation by providing a more comprehensive threat profile. The predictive analytics module 120 thereby strengthens the overall threat management framework of the intelligent threat detection and response system 100, ensuring proactive and effective threat mitigation. Through its robust predictive abilities, the predictive analytics module 120 establishes a powerful layer of security, contributing to a resilient and adaptable security infrastructure.
[0098] The enhanced data privacy and security management module 122 within the intelligent threat detection and response system 100 performs a critical function by safeguarding sensitive information through advanced encryption protocols and privacy controls. Integrated within the processor 110, the enhanced data privacy and security management module 122 continuously enforces data protection measures, ensuring secure handling and storage of all data captured, transmitted, and processed by the system 100.
[0099] Through encryption protocols, the enhanced data privacy and security management module 122 secures data from unauthorized access, maintaining confidentiality during transmission across the communication network 108. The component operates with strong authentication mechanisms, ensuring that only authorized users access critical data and system functions. These security protocols also support compliance with relevant data privacy standards, reinforcing the system's adherence to legal and regulatory requirements.
[0100] The enhanced data privacy and security management module 122 manages privacy settings and access controls that limit data visibility based on user roles, preventing unauthorized personnel from accessing restricted information. By maintaining such control, the module reduces security vulnerabilities within the system 100. The enhanced data privacy and security management module 122 further performs regular updates to adapt to evolving security threats, ensuring resilience against new cyber risks.
[0101] In close integration with other modules, including the multi-source data fusion module 114 and the adaptive threat detection algorithm 112, the enhanced data privacy and security management module 122 enhances the reliability of the system 100. By securing data at every stage-capturing, transmitting, and storing-the module ensures the integrity of information processed by various components, from the plurality of surveillance cameras 106 to the user device 102.
[0102] The enhanced data privacy and security management module 122 thereby strengthens the system's overall capability to protect sensitive data, enabling it to function efficiently and securely across diverse environments. Through its robust data protection and compliance features, the enhanced data privacy and security management module 122 establishes a secure framework, ensuring the intelligent threat detection and response system 100 meets the high standards required for data integrity and privacy.
[0103] The contextual learning unit 124 within the intelligent threat detection and response system 100 operates to distinguish genuine threats from benign activities, providing an advanced layer of accuracy in threat identification. Connected to the processor 110, the contextual learning unit 124 leverages real-time data and continuously refines its algorithms based on behavioural analysis, enabling the system 100 to adapt dynamically to new information and evolving environmental conditions. By monitoring patterns over time, the contextual learning unit 124 enhances the system's understanding of typical versus atypical behaviours, reducing the incidence of false alarms.
[0104] The contextual learning unit 124 integrates deeply with the adaptive threat detection algorithm 112, supplying it with behavioural insights that improve threat classification. Through this integration, the contextual learning unit 124 analyses complex data streams from multiple sources, including the multi-source data fusion module 114 and the plurality of surveillance cameras 106. This comprehensive approach enables the unit to process vast amounts of information, identifying nuanced distinctions in activities that other modules within the system 100 might overlook.
[0105] In addition to behavioural pattern recognition, the contextual learning unit 124 adjusts its parameters according to user-defined settings from the customizable threat profile interface 128. This customization allows the contextual learning unit 124 to accommodate specific user requirements, aligning the system's threat detection sensitivity with the unique security needs of different environments. By consistently refining its parameters and using historical data, the contextual learning unit 124 develops a sophisticated understanding of context, distinguishing relevant threats with higher precision.
[0106] Through its connection with the memory unit 132, the contextual learning unit 124 retrieves previously processed data, enabling it to draw on a history of events that inform its current analyses. This historical data access further improves the contextual learning unit 124's ability to adapt to new patterns while maintaining a robust framework for assessing security risks. The contextual learning unit 124 thus ensures that the intelligent threat detection and response system 100 delivers accurate and context-aware threat detection, enhancing system reliability and user confidence.
[0107] The energy-efficient power management unit 126 within the intelligent threat detection and response system 100 focuses on minimizing energy consumption across all connected components, promoting sustainability and reducing maintenance needs. This energy-efficient power management unit 126 directly interacts with the user device 102 and processor 110, coordinating power distribution to ensure each component receives optimal power for operation without unnecessary energy expenditure.
[0108] To further conserve energy, the energy-efficient power management unit 126 supports low-power modes that activate during inactive periods, significantly extending the operational lifespan of critical components within the system 100. By dynamically adjusting power levels, the energy-efficient power management unit 126 addresses fluctuations in system demand, thereby reducing the load on both user device 102 and processor 110 while maintaining system responsiveness.
[0109] The energy-efficient power management unit 126 also incorporates advanced monitoring features that assess energy usage in real time, providing insights that enable predictive maintenance and system efficiency improvements. Through this focused approach, the energy-efficient power management unit 126 ensures that the intelligent threat detection and response system 100 remains highly efficient, eco-friendly, and reliable across diverse operational conditions.
[0110] The customizable threat profile interface 128 in the intelligent threat detection and response system 100 provides a user-centric platform for defining and managing specific threat parameters, allowing users to tailor system responses according to unique security needs. This customizable threat profile interface 128 connects with the user device 102, providing a seamless and accessible means for adjusting threat levels, alert prioritization, and monitoring configurations, ensuring that users remain informed about critical events in real time.
[0111] Through its intuitive design, the customizable threat profile interface 128 enables users to set personalized alert parameters that distinguish between different threat levels, allowing for varying degrees of response. This interface supports the integration of high-priority threat levels, ensuring that the system 100 promptly delivers immediate notifications and activates suitable countermeasures based on the user-defined profile. In connection with the cross-platform integration module 116, the customizable threat profile interface 128 remains responsive across multiple platforms, ensuring that users can access and adjust their threat profiles from any connected device.
[0112] The customizable threat profile interface 128 also facilitates continuous feedback, allowing users to refine their profiles based on system performance and evolving security needs. Integrated with the adaptive threat detection algorithm 112 and contextual learning unit 124, the customizable threat profile interface 128 optimizes detection accuracy by adjusting parameters in response to previous alerts and user inputs. This adaptability enhances the system's 100 overall efficacy, ensuring that the intelligent threat detection and response system 100 consistently aligns with the user's expectations, providing a personalized yet robust security management experience.
[0113] Through these capabilities, the customizable threat profile interface 128 empowers users with greater control, creating a responsive and adaptable threat management framework that reflects the specific security concerns and preferences of each individual or organization.
[0114] The multi-language user interface module 130 in the intelligent threat detection and response system 100 ensures accessibility by supporting various languages, making it adaptable to diverse user needs across different regions. This multi-language user interface module 130 remains directly connected to all system components, allowing users to interact seamlessly in their preferred language without compromising functionality.
[0115] Designed for a user-friendly experience, the multi-language user interface module 130 works closely with the customizable threat profile interface 128, enabling users to view and modify threat settings, receive alerts, and review system status in their native language. By providing real-time alerts, notifications, and interactive settings in multiple languages, the multi-language user interface module 130 accommodates varied regional requirements, enhancing user satisfaction and engagement with the system 100.
[0116] Through its cross-platform functionality, the multi-language user interface module 130 remains operable across various devices and platforms, ensuring that language support is available consistently wherever the user interacts with the intelligent threat detection and response system 100. This multi-language user interface module 130 not only enriches user experience but also aligns with the system's goal of delivering a personalized, globally adaptable security solution, supporting seamless communication and efficient response in critical situations.
[0117] The memory unit 132 in the intelligent threat detection and response system 100 plays a central role in data storage and retrieval, supporting the system's ability to analyse, learn, and improve over time. Communicatively coupled with the processor 110, the memory unit 132 continuously stores data processed by the adaptive threat detection algorithm 112, multi-source data fusion module 114, predictive analytics module 120, and contextual learning unit 124, making it a comprehensive repository for critical information.
[0118] By maintaining a detailed record of past events, patterns, and system performance, the memory unit 132 enables the intelligent threat detection and response system 100 to access historical data essential for threat pattern recognition, predictive analysis, and future improvements. This stored data empowers the processor 110 to use accumulated information to refine threat detection and response, ensuring the system 100 remains highly accurate and context-aware in real-time operation.
[0119] The memory unit 132 also facilitates quick retrieval of past records, supporting fast decision-making and system optimization without delays. This function aligns with the system's real-time operational requirements, allowing the intelligent threat detection and response system 100 to manage and process vast data inputs effectively, reinforcing overall system reliability and responsiveness in diverse security situations.
[0120] The intelligent real-time threat detection and response system 100 operates as an interconnected network designed to enhance security through continuous monitoring, data analysis, and adaptive threat detection. The user device 102, configured to receive real-time alerts and notifications, acts as the primary interface for remote monitoring, control, and user-defined settings, establishing a direct link between the system and its operators. The user device 102 connects to various system components, ensuring accessibility and real-time responsiveness through an intuitive interface.
[0121] The camera 104 provides visual data by capturing essential imagery for preliminary threat assessment, while a plurality of surveillance cameras 106 strategically positioned at various locations, capture and transmit real-time video feeds to deliver comprehensive monitoring coverage. The communication network 108 seamlessly connects the user device 102, camera 104, and plurality of surveillance cameras 106, facilitating continuous data flow and real-time alerts, notifications, and updates across platforms, which enhances accessibility on multiple devices.
[0122] The processor 110 forms the computational core of the system, executing real-time data analysis, decision-making, and algorithmic updates. The adaptive threat detection algorithm 112 within the processor 110 performs continuous analysis on incoming data, dynamically refining threat identification based on new inputs and evolving patterns. Integrating a multi-source data fusion module 114, the processor 110 aggregates data from video feeds, biometric sensors, and the internet of things (IoT), which supports multi-dimensional threat profiling. The cross-platform integration module 116 within the processor 110 ensures consistent real-time connectivity across devices and platforms, allowing the system 100 to function in diverse environments and expand system interoperability.
[0123] The scalable system architecture 118 within the processor 110 enables easy system expansion, whether for small or large installations, allowing flexible adaptation to different environments. The predictive analytics module 120, also embedded within the processor 110, analyses historical data and emerging patterns to anticipate potential threats, proactively enhancing the system's preventative capabilities. The enhanced data privacy and security management module 122 enforces encryption protocols and privacy controls, safeguarding sensitive data and ensuring compliance with stringent data privacy standards, critical in preserving user trust and system integrity.
[0124] The contextual learning unit 124 identifies genuine threats by analysing behavioural patterns, distinguishing between actual threats and non-threatening activities with high accuracy. The energy-efficient power management unit 126 connected to the user device 102 regulates energy usage, minimizing system power requirements and reducing maintenance needs, thereby ensuring sustained operational efficiency. The customizable threat profile interface 128 offers users the ability to define specific threat profiles and risk parameters tailored to their unique security needs. Operatively integrated with the user device 102, the customizable threat profile interface 128 provides real-time, user-centric control.
[0125] The multi-language user interface module 130, which integrates across all system components, enhances the user experience by offering multi-language support, ensuring that the system remains accessible to diverse users across different regions. The memory unit 132, communicatively coupled with the processor 110, serves as a critical repository for data processed by various modules, including the adaptive threat detection algorithm 112, multi-source data fusion module 114, predictive analytics module 120, and contextual learning unit 124. This memory unit 132 enables efficient data retrieval essential for threat detection, pattern recognition, and system improvement.
[0126] Overall, each component within the intelligent real-time threat detection and response system 100 is interconnected, creating a powerful security system that remains responsive, adaptive, and effective in varied environments. Through seamless data exchange, real-time processing, and user-defined customization, the system 100 continuously monitors, assesses, and responds to threats with high precision, supported by advanced analytics, adaptive learning, and a robust privacy framework. This design ensures that the intelligent real-time threat detection and response system 100 remains reliable, user-friendly, and efficient, fulfilling the need for a scalable, high-performance security solution.
[0127] FIG. 2 illustrates a flowchart of an intelligent real-time threat detection and response system, in accordance with an exemplary embodiment of the present disclosure.
[0128] At 202, the system acquires real-time video streams from surveillance cameras. User devices may also capture images or videos and input them into the system.
[0129] At 204, the acquired video and image data undergoes preprocessing, which involves noise reduction, normalization, and feature extraction.
[0130] At 206, the convolutional neural network (CNN) analyses the pre-processed data to identify potential threats such as fire, accidents, and violence. Alternatively, the clip model, a pre-trained model, can be used to classify threats based on image-text relationships.
[0131] At 208, data from various sources, including biometric sensors and IoT devices, can be integrated to provide a comprehensive threat assessment.
[0132] At 210, identified threats are categorized into specific types, such as fire, violence, or accident.
[0133] At 212, the system analyses behavioural patterns to distinguish genuine threats from non-threatening activities.
[0134] At 214, upon detecting a threat, the system generates alerts with specific details, including location, severity, and other relevant information.
[0135] At 216, the generated alerts are sent to relevant authorities or individuals, such as emergency responders or hospitals. Emergency response procedures are initiated based on the nature of the threat.
[0136] At 218, threat data is stored in the system's memory unit for future analysis and improvement. Historical data is analysed by the predictive analytics module to anticipate potential threats.
[0137] At 220, the user device provides a user-friendly interface for monitoring system status, configuring alerts, and accessing historical data. The customizable threat profile interface allows users to tailor the system to specific needs.
[0138] At 222, the system continuously updates its algorithms and models to improve performance. The energy-efficient power management unit optimizes power consumption
[0139] At 224, the system incorporates robust data privacy and security measures to protect sensitive information and comply with relevant regulations.
[0140] FIG. 3 illustrates a flowchart of a method for real-time threat detection and response in an intelligent threat detection system, in accordance with an exemplary embodiment of the present disclosure.
[0141] At 302, continuously receive real-time alerts and notifications from a user device connected to a communication network.
[0142] At 304, capture and transmit real-time video feeds and sensory data from various monitored locations to a multi-source data fusion module connected to a processor via a plurality of surveillance cameras.
[0143] At 306, process the aggregated data through an adaptive threat detection algorithm connected to the processor.
[0144] At 308, identify genuine threats by analysing behavioural patterns by a contextual learning unit operatively connected to the processor.
[0145] At 310, transmit real-time alerts and notifications across various platforms and user device through multiple communication channels by a cross-platform integration module operatively connected to the processor.
[0146] At 312, enable user-defined threat profiles and risk parameters by a customizable threat profile interface in operative connection with the user device.
[0147] At 314, expand the system's ability to accommodate varied environments, from small installations to large-scale deployments by a scalable system architecture integrated with the processor.
[0148] At 316, anticipate potential threats through analysis of historical data and emerging patterns by a predictive analytics module integrated within the processor.
[0149] At 318, safeguard sensitive data using the data privacy and security management module via the processor.
[0150] At 320, manage energy consumption reducing power requirements across components and minimizing system maintenance needs by an energy-efficient power management system coupled with the user device and processor.
[0151] FIG. 4 illustrates a perspective view of the use case design of the system, in accordance with an exemplary embodiment of the present disclosure.
[0152] The user 402 in the intelligent real-time threat detection and response system 100 serves as a vital component in identifying and responding to accidents. The user 402 actively engages with the system 408, utilizing its features to see nearby accidents 404 and report an accident 406. This interaction enables real-time updates and information dissemination, enhancing situational awareness.
[0153] Through this active participation, the user 402 contributes valuable insights about the accident scene, which the system 408 processes to inform the emergency squad 412. The user's involvement streamlines communication and facilitates a swift response, ensuring effective reach and rescue 414 operations
[0154] The see nearby accident 404 function in the intelligent real-time threat detection and response system 100 enables users to identify incidents occurring in proximity. Users access this feature through the system 408, receiving real-time visual data and alerts regarding accidents in their vicinity.
[0155] The see nearby accident 404 function enhances situational awareness, allowing users to observe ongoing events and assess potential threats. As users engage with the see nearby accident 404 function, they gather critical information that informs their subsequent actions, such as reporting an accident 406. This process promotes a quicker response to emergencies and ensures better coordination with the emergency squad 412.
[0156] The report an accident 406 function in the intelligent real-time threat detection and response system 100 allows users to swiftly communicate incidents to the appropriate authorities. Upon observing an accident through the see nearby accident 404 feature, users initiate the reporting process by interacting with the system 408.
[0157] The report an accident 406 function ensures that critical information about the accident is transmitted in real-time, including location details and visual data captured by surveillance cameras. The report an accident 406 feature streamlines communication with the emergency squad 412, facilitating a coordinated response. This immediate reporting enhances the overall effectiveness of the reach and rescue 414 operations, ensuring timely assistance.
[0158] The system 408 in the intelligent real-time threat detection and response system 100 serves as the central hub for processing and managing data related to incidents. This system 408 connects various components, including the user 402, see nearby accident 404, report an accident 406, and other modules.
[0159] The system 408 facilitates seamless communication and data flow among all elements, ensuring that alerts and notifications reach the relevant users efficiently. The system 408 processes incoming information from the report an accident 406 feature and integrates real-time video feeds and sensory data. Through this integration, the system 408 enhances situational awareness and enables a coordinated response by the emergency squad 412, optimizing reach and rescue 414 efforts.
[0160] The see accident details 410 feature in the intelligent real-time threat detection and response system 100 provides critical insights into the specifics of reported incidents. Through this feature, users access comprehensive information about the accident scene, including video feeds, location data, and real-time updates.
[0161] The see accident details 410 interface presents users with a clear visualization of events as they unfold, facilitating informed decision-making. Users interact with the system 408 to analyse the situation, enhancing their understanding of the context surrounding the accident. This information plays a vital role in coordinating efforts with the emergency squad 412, ensuring a swift and effective reach and rescue 414 operation.
[0162] The emergency squad 412 plays a crucial role in the intelligent real-time threat detection and response system 100, responding promptly to incidents reported by users. Upon receiving notifications from the system 408, the emergency squad 412 mobilizes resources and personnel to the accident site.
[0163] The emergency squad 412 utilizes real-time information gathered from the see accident details 410 feature to assess the situation effectively. This data informs their approach, ensuring that they are equipped to handle the specific circumstances of the incident. By coordinating efforts with the user 402 and leveraging insights from the system 408, the emergency squad 412 executes a rapid reach and rescue 414 operation, prioritizing the safety and well-being of individuals involved in the accident.
[0164] The reach and rescue 414 operation are a critical phase in the intelligent real-time threat detection and response system 100, ensuring timely assistance to individuals in distress. Upon receiving alerts from the emergency squad 412, the operation activates rapidly, mobilizing necessary resources and personnel to the accident scene.
[0165] The reach and rescue 414 team coordinates with the user 402 and utilizes data provided by the see accident details 410 feature to inform their approach. This real-time information enhances situational awareness, allowing the team to navigate effectively to the location. By executing efficient rescue tactics, the reach and rescue 414 operation prioritizes saving lives and mitigating further harm to those affected by the incident.
[0166] FIG. 5 illustrates a perspective view of the proposed system, in accordance with an exemplary embodiment of the present disclosure.
[0167] The text labels 502 serve as essential identifiers within the intelligent real-time threat detection and response system 100. These labels provide critical information and context to users interacting with the system, facilitating a clearer understanding of each operational step.
[0168] By displaying relevant data, the text labels 502 guide users through the functionalities of the system, enhancing overall usability. They inform users about the current status and operational modes, such as the clip mode 504 and alert model 506. This continuous flow of information ensures that users engage effectively with the system, ultimately leading to a more efficient response during critical incidents.
[0169] The clip mode 504 functions as a crucial feature within the intelligent real-time threat detection and response system 100. This mode focuses on capturing significant events or actions occurring within the monitored environment. By activating the clip mode 504, users initiate a process that records specific segments of video input 510, ensuring that important moments are not overlooked.
[0170] While in clip mode 504, the system prioritizes the storage of relevant footage, allowing for efficient retrieval and review later. This functionality enhances the overall effectiveness of the alert system 512, as users can access precise video clips when responding to alerts generated by the alert model 506.
[0171] The alert model 506 serves as a critical component within the intelligent real-time threat detection and response system 100. This model analyses inputs from the video input 510 and processes data to identify potential threats or unusual activities. When detecting such events, the alert model 506 triggers the alert system 512, ensuring timely notifications are generated.
[0172] Through advanced algorithms, the alert model 506 continuously monitors the environment, providing real-time assessments. This proactive approach enhances security measures by facilitating quick responses to incidents. The efficiency of the alert model 506 significantly contributes to the overall effectiveness of the notification 514 process, promoting safety and awareness.
[0173] The backend system 508 plays a vital role in the intelligent real-time threat detection and response system 100 by managing data processing and storage. This system integrates inputs from various components, including the alert model 506 and video input 510, ensuring efficient data flow. Through robust algorithms, the backend system 508 analyses incoming information and facilitates communication among different steps in the workflow.
[0174] The backend system 508 also handles user requests, providing necessary data to the alert system 512 for generating timely notifications. Its seamless operation underpins the overall functionality of the intelligent system, enhancing performance and responsiveness to potential threats.
[0175] The video input 510 serves as a crucial element in the intelligent real-time threat detection and response system 100 by capturing live footage and transmitting it to the backend system 508. This component continuously monitors the environment, ensuring that visual data is readily available for analysis. The video input 510 works in conjunction with the alert model 506 to detect potential threats by interpreting visual cues.
[0176] By providing high-quality video streams, the video input 510 enhances the accuracy of threat detection, allowing the system to react swiftly to emerging situations. Its integration with the alert system 512 ensures that relevant information reaches users promptly, improving overall safety.
[0177] The alert system 512 plays a vital role in the intelligent real-time threat detection and response system 100 by ensuring that critical notifications reach users effectively. This system actively monitors data from the backend system 508 and processes information received from the alert model 506. By analysing potential threats detected through the video input 510, the alert system 512 generates timely alerts and warnings.
[0178] Through efficient communication channels, the alert system 512 ensures that notifications 514 are dispatched promptly to relevant stakeholders. This proactive approach enhances situational awareness and facilitates swift responses to threats, contributing significantly to the overall safety of the environment.
[0179] The notification 514 acts as a crucial element within the intelligent real-time threat detection and response system 100, delivering essential alerts to users regarding potential threats. Following the detection process initiated by the alert model 506, the notification 514 promptly informs users of critical incidents through various communication channels.
[0180] This system emphasizes clarity and immediacy, ensuring that notifications 514 convey relevant information effectively. By leveraging the data processed by the alert system 512, the notification 514 enhances user awareness and facilitates rapid decision-making. This function supports timely responses to threats, ultimately contributing to a safer environment for all stakeholders involved.
[0181] FIG. 6 illustrates a perspective view of the concept of contrastive pre-training in accordance with an exemplary embodiment of the present disclosure. This figure represents how contrastive pre-training utilizes paired data samples to differentiate between similar and dissimilar data points. Through this approach, the system 100 learns to establish meaningful representations by pulling similar data closer in the feature space and pushing dissimilar data farther apart.
[0182] The process in FIG. 6 enhances the system's ability to recognize patterns, which supports improved accuracy in downstream tasks. Contrastive pre-training, as depicted, enables efficient learning, allowing the model to better capture underlying relationships between data points while ensuring robust and reliable performance.
[0183] FIG. 7 illustrates a perspective view of the concept of a dataset classifier and the use of zero-shot prediction in accordance with an exemplary embodiment of the present disclosure. This figure highlights how the dataset classifier processes and categorizes data based on learned features, even when encountering new, unseen classes.
[0184] The zero-shot prediction capability in FIG. 7 enables the system 100 to make accurate predictions for classes that are not part of the initial training data. Through this mechanism, the model generalizes effectively, applying learned representations to novel data categories. The integration of zero-shot prediction enhances adaptability, making the system more versatile in various applications.
[0185] FIG. 8 illustrates a perspective view of the training loss over epochs, in accordance with an exemplary embodiment of the present disclosure. This figure demonstrates how the training loss metric behaves as the model progresses through multiple epochs during the training phase.
[0186] The training loss in FIG. 8 consistently decreases over successive epochs, indicating that the model effectively optimizes its parameters to improve performance. Lower training loss signifies improved accuracy in predictions, as the model learns from data patterns and refines its understanding. By monitoring the training loss over epochs, the system 100 assesses convergence and evaluates training efficiency, ensuring robust model performance.
[0187] FIG. 9 illustrates a perspective view of the cloud fire store, in accordance with an exemplary embodiment of the present disclosure. The cloud fire store serves as a flexible, scalable database that supports data storage and real-time synchronization, essential for handling vast datasets efficiently.
[0188] In FIG. 9, the cloud fire store stores structured data, allowing the system 100 to access, update, and retrieve information as needed. The cloud fire store also supports secure, managed access to data, ensuring data integrity and compliance with privacy protocols. By leveraging cloud fire store, the system 100 ensures seamless data management and scalability, enabling robust functionality across multiple platforms and devices.
[0189] FIG. 10 illustrates a perspective view of the threat manager login window, in accordance with an exemplary embodiment of the present disclosure. The threat manager login window presents a secure interface that authenticates user credentials before granting access to the threat management system.
[0190] The threat manager login window verifies the identity of the user 402, ensuring only authorized personnel interact with sensitive data and threat-related functionalities. Through the threat manager login window, users gain access to the system 100, allowing them to monitor, assess, and respond to potential threats. This secure entry point reinforces data security by controlling access to critical components and maintaining privacy standards within the system 408.
[0191] FIG. 11 illustrates a perspective view of the signup window for a new user, in accordance with an exemplary embodiment of the present disclosure. The signup window for a new user provides a structured interface for new users to create an account within the system 100.
[0192] The signup window for a new user requests essential information, such as name, email, and password, ensuring that the new user 402 registers securely. This interface in the signup window for a new user promotes a streamlined onboarding process, guiding the new user 402 through account creation steps to access system features effectively. By securely capturing and storing user data, the signup window for a new user contributes to enhanced data integrity and access control within the system 100.
[0193] FIG. 12 illustrates a perspective view of the detection of fight on a street, in accordance with an exemplary embodiment of the present disclosure. The detection of fight on a street demonstrates the system's 100 ability to identify suspicious or violent activity within public spaces, ensuring timely alerts and interventions.
[0194] The detection of fight on a street utilizes advanced algorithms to analyse real-time video footage, identifying aggressive behaviour patterns and distinguishing them from regular activities. By continuously monitoring through connected cameras and sensors, the detection of fight on a street enables the system 100 to promptly notify relevant authorities or emergency squad 412 when a potential fight occurs, enhancing public safety and facilitating quick responses.
[0195] FIG. 13 illustrates a perspective view of the detection of violence in office, in accordance with an exemplary embodiment of the present disclosure. The detection of violence in office focuses on identifying harmful actions within a workplace setting, using the system's 100 capabilities to recognize threatening behaviour, thereby promoting a secure environment.
[0196] The detection of violence in office relies on sophisticated algorithms that analyse office surveillance footage, identifying unusual or aggressive interactions among individuals. Through continuous monitoring, the detection of violence in office enables the system 100 to send alerts to security personnel or management upon recognizing violent behaviour, ensuring swift action to protect employees and maintain a safe work environment.
[0197] By leveraging the detection of violence in office, the system 100 upholds workplace safety standards and supports timely intervention, reducing potential harm and fostering a secure, peaceful office atmosphere.
[0198] FIG. 14 illustrates a perspective view of the detection of car crash, in accordance with an exemplary embodiment of the present disclosure. The detection of car crash enables the system 100 to recognize vehicle collisions, providing immediate situational awareness that supports prompt response from emergency services.
[0199] The detection of car crash analyses real-time data from sensors and cameras integrated within the system 100, identifying the occurrence of a crash based on sudden deceleration, impact sounds, or abrupt changes in position. Upon detecting a car crash, the system 100 transmits alerts to designated authorities, including emergency squads 412, initiating rapid response efforts to assist those affected.
[0200] By deploying the detection of car crash, the system 100 enhances road safety, minimizing response times and improving the chances of saving lives during critical accidents. The detection of car crash thus contributes significantly to a comprehensive emergency management framework.
[0201] FIG. 15 illustrates a perspective view of the detection of fire on a street and office, showcasing an exemplary embodiment of the present disclosure. In this depiction, various components of the fire detection system are visible, highlighting their strategic placements for optimal coverage.
[0202] The system 100 includes advanced sensors that actively monitor the environment for signs of fire, ensuring prompt detection. Positioned within both the street and office areas, these sensors facilitate rapid assessment of potential fire hazards. The comprehensive layout enables the system to identify and communicate threats effectively, thereby enhancing overall safety and response measures in urban settings.
[0203] FIG. 16 illustrates a perspective view of the medical squad user interface, showcasing an exemplary embodiment of the present disclosure. This interface features an intuitive layout designed for easy access to critical information.
[0204] The medical squad user interface incorporates various components, including real-time communication tools and data displays, ensuring that medical personnel receive timely updates during emergencies. By utilizing this user interface, medical squad members efficiently coordinate their response efforts. The design emphasizes clarity and functionality, allowing users to navigate essential features with minimal distraction. This integration significantly enhances the overall effectiveness of the medical squad's operations in urgent situations.
[0205] FIG. 17 illustrates a perspective view of the details of threats received by the medical squad, in accordance with an exemplary embodiment of the present disclosure. This view highlights the comprehensive information relay that the medical squad receives during emergency situations.
[0206] The details of threats feature essential data, including the nature of the threat, location coordinates, and urgency level, allowing for rapid assessment and response. The medical squad utilizes this information to prioritize actions and allocate resources effectively. By presenting clear and structured details, the system 100 enhances situational awareness, ensuring that medical personnel remain informed and prepared to address various threats promptly.
[0207] FIG. 18 illustrates a perspective view of the threat information received in the respective department, in accordance with an exemplary embodiment of the present disclosure. This view provides a detailed overview of the incoming threat data that various departments process during emergency situations.
[0208] The threat information encompasses critical aspects such as threat type, severity, and specific location, facilitating efficient communication and coordination among teams. Each department utilizes this information to assess risks and formulate appropriate responses. By presenting the threat information in a structured manner, the system 100 enhances collaboration and enables departments to act swiftly and effectively in addressing potential dangers.
[0209] While the invention has been described in connection with what is presently considered to be the most practical and various embodiments, it will be understood that the invention is not to be limited to the disclosed embodiments, but on the contrary, is intended to cover various modifications and equivalent arrangements included within the scope of the appended claims.
[0210] A person of ordinary skill in the art may be aware that, in combination with the examples described in the embodiments disclosed in this specification, units and algorithm steps may be implemented by electronic hardware, computer software, or a combination thereof.
[0211] The foregoing descriptions of specific embodiments of the present disclosure have been presented for purposes of illustration and description. They are not intended to be exhaustive or to limit the present disclosure to the precise forms disclosed, and many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described to best explain the principles of the present disclosure and its practical application, and to thereby enable others skilled in the art to best utilize the present disclosure and various embodiments with various modifications as are suited to the particular use contemplated. It is understood that various omissions and substitutions of equivalents are contemplated as circumstances may suggest or render expedient, but such omissions and substitutions are intended to cover the application or implementation without departing from the scope of the present disclosure.
[0212] In a case that no conflict occurs, the embodiments in the present disclosure and the features in the embodiments may be mutually combined. The foregoing descriptions are merely specific implementations of the present disclosure, but are not intended to limit the protection scope of the present disclosure. Any variation or replacement readily figured out by a person skilled in the art within the technical scope disclosed in the present disclosure shall fall within the protection scope of the present disclosure. Therefore, the protection scope of the present disclosure shall be subject to the protection scope of the claims.
, Claims:I/We Claim:
1. An intelligent real-time threat detection and response system (100), the system (100) comprises:
a user device (102) configured to receive real-time alerts and notifications and provide an interface for remote monitoring and control;
a camera (104) operatively connected to the user device (102) and configured for providing visual data to the system;
a plurality of surveillance cameras (106) configured to capture and transmit real-time video feeds from various locations;
a communication network (108) operatively connected to the user device (102), the camera (104), and the plurality of surveillance cameras (106) and configured to enable seamless data transmission across these components and supports real-time alerts, notifications, and updates across multiple platforms and devices;
a processor (110) connected to the user device (102) and configured to execute real-time data analysis, decision-making, and algorithmic updates;
an adaptive threat detection algorithm (112) integrated within the processor (110) and configured to continuously analyse and update threat identification based on new data inputs;
a multi-source data fusion module (114) integrated within the processor (110) and configured to continuously aggregate data from video surveillance feeds, biometric sensors, and internet of things (IoT);
a cross-platform integration module (116) integrated within the processor (110) and configured to support real-time system connectivity across various platforms and devices;
a scalable system architecture (118) integrated within the processor (110) and configured to facilitate seamless system expansion for varied environments ranging from small installations to large-scale deployments;
a predictive analytics module (120) integrated within the processor (110) and configured to anticipate potential threats based on historical data and emerging patterns;
an enhanced data privacy and security management module (122) integrated within the processor (110) and configured to enforce encryption protocols and privacy controls for safeguarding sensitive information and ensuring compliance with data privacy standards;
a contextual learning unit (124) connected to the processor (110) and configured to identify genuine threats versus non-threatening activities by analysing behavioural patterns;
an energy-efficient power management unit (126) operatively coupled with the user device (102) configured to reduce energy consumption and minimize maintenance requirements;
a customizable threat profile interface (128) connected to the user device (102) and configured to enable user-defined threat profiles and risk parameters;
a multi-language user interface module (130) connected to all system components and operatively integrated within the customizable threat profile interface (128), wherein the multi-language user interface module (130) provides a user-friendly experience adaptable to various regional and cultural needs;
a memory unit (132) communicatively coupled with the processor (110) configured to store data processed by the adaptive threat detection algorithm (112), multi-source data fusion module (114), predictive analytics module (120), and contextual learning unit (124), allowing for data retrieval and analysis essential for real-time threat detection, pattern recognition, and system improvement.
2. The system (100) as claimed in claim 1, wherein the user device (102) further comprises a display screen configured to present real-time video feeds and analytics information from the plurality of surveillance cameras (106).
3. The system (100) as claimed in claim 1, wherein the communication network (108) comprises a wireless network protocol, including but not limited to Wi-Fi, 4G, or 5G, enabling secure and efficient transmission of data between the user device (102), camera (104), and plurality of surveillance cameras (106).
4. The system (100) as claimed in claim 1, wherein the adaptive threat detection algorithm (112) further incorporates a machine learning module trained to improve detection accuracy by learning from previous threat patterns and user feedback, thereby enhancing the system's ability to detect emerging threats.
5. The system (100) as claimed in claim 1, wherein the multi-source data fusion module (114) is further configured to aggregate data from additional sources, including environmental sensors and audio input devices, for a more comprehensive threat profile and improved detection accuracy in diverse environments.
6. The system (100) as claimed in claim 1, wherein the cross-platform integration module (116) further comprises an application programming interface (API) layer that supports interoperability with third-party security systems, providing enhanced system compatibility and extending the range of accessible platforms and devices.
7. The system (100) as claimed in claim 1, wherein the contextual learning unit (124) further comprises a behavioural pattern recognition module that continuously adapts to user-defined parameters, allowing the system to refine its distinction between potential threats and benign activities according to the specific context of the monitored environment.
8. The system (100) claimed in claim 1, wherein the energy-efficient power management unit (126) further comprises a low-power mode that activates during periods of inactivity, significantly reducing power consumption and extending the operational lifespan of the user device (102) and other system components.
9. The system (100) as claimed in claim 1, wherein the customizable threat profile interface (128) further comprises an alert prioritization setting that allows users to designate high-priority threat levels, ensuring that critical alerts are delivered promptly, with an audible or visual signal to immediately notify users.
10. A method for real-time threat detection and response in an intelligent threat detection system (100), the method (100) comprising:
continuously receiving real-time alerts and notifications from a user device (102) connected to a communication network (108);
capturing and transmitting real-time video feeds and sensory data from various monitored locations to a multi-source data fusion module (114) connected to a processor (110) via a plurality of surveillance cameras (106);
processing the aggregated data through an adaptive threat detection algorithm (112) connected to the processor (110);
identifying genuine threats by analysing behavioural patterns by a contextual learning unit (124) operatively connected to the processor (110);
transmitting real-time alerts and notifications across various platforms and user device (102) through multiple communication channels by a cross-platform integration module (116) operatively connected to the processor (110);
enabling user-defined threat profiles and risk parameters by a customizable threat profile interface (128) in operative connection with the user device (102);
expanding the system's ability to accommodate varied environments, from small installations to large-scale deployments by a scalable system architecture (118) integrated with the processor (110);
anticipating potential threats through analysis of historical data and emerging patterns by a predictive analytics module (120) integrated within the processor (110);
safeguarding sensitive data using the data privacy and security management module (122) via the processor (110);
managing energy consumption reducing power requirements across components and minimizing system maintenance needs by an energy-efficient power management system coupled with the user device (102) and processor (110).

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