SENSOR SYSTEM ARCHITECTURE FOR INTELLIGENT DATA PROCESSING AND REALIZATION

Abstract

ABSTRACT Sensor System Architecture for Intelligent Data Processing and Realization The present disclosure introduces sensor system architecture for intelligent data processing and realization 100 that optimizes data collection, fusion, and analysis across various sensor modalities. The architecture incorporates sensor nodes 102 for gathering environmental data and communication module 104 for transmitting the collected data. A data fusion engine 106 aggregates the data, reducing noise and redundancy, while the intelligent processing unit 108 applies machine learning algorithms for real-time analysis. The other components of the invention are user interface and visualization tools 110, power management system 112, cloud integration architecture 114, edge computing module 116, anomaly detection system 118, data integrity and security protocols 120, predictive maintenance framework 122, health monitoring for sensor nodes 124, inter-sensor communication protocols 126, automated reporting and insights generation 128, customizable algorithm framework 130, geospatial data integration system 132, user-defined event triggers 134, blockchain-based data integrity verification 136, environmental adaptation module 138. Reference Fig 1

Specification

Description:Sensor System Architecture for Intelligent Data Processing and Realization
TECHNICAL FIELD
[0001] The present innovation relates to an integrated sensor system architecture designed for intelligent data processing and realization, leveraging advanced algorithms and machine learning for optimized sensor data collection, fusion, and analysis.

BACKGROUND

[0002] The rapid advancement of sensor technologies has led to the proliferation of sensor systems across various industries, such as healthcare, environmental monitoring, and industrial automation. While these systems generate vast amounts of data, a major challenge has emerged: the inability to process, analyze, and realize the full potential of this data in real time. Traditional sensor systems often operate as standalone devices with limited data processing capabilities, resulting in data overload and delays in critical decision-making. Moreover, raw sensor data frequently contains noise and redundancy, making it difficult to extract meaningful insights without advanced preprocessing. Users currently rely on a range of data management tools and processing frameworks, but these options either lack the ability to integrate multiple sensor modalities effectively or are not designed to scale for large sensor networks.

[0003] Existing systems also face issues with accuracy, latency, and data security. Many conventional sensor architectures do not employ adaptive learning techniques, leading to outdated models that fail to evolve with new data. Additionally, these systems often lack robust data fusion capabilities, which compromises the quality of the information gathered from diverse sensors.

[0004] The present invention addresses these challenges by introducing an intelligent sensor system architecture that integrates advanced algorithms and machine learning for real-time data processing, fusion, and analysis. The invention's novelty lies in its adaptive learning capabilities, which enable the system to improve over time based on evolving data patterns. Furthermore, the architecture's modular design allows for seamless integration of various sensor types, ensuring flexibility and scalability. The use of advanced data fusion algorithms and real-time anomaly detection with contextual awareness sets this invention apart, offering greater accuracy, reduced latency, and improved decision-making across applications. This system overcomes the limitations of existing solutions by offering a comprehensive, intelligent, and secure approach to sensor data processing.


OBJECTS OF THE INVENTION

[0005] The primary object of the invention is to provide an intelligent sensor system architecture that optimizes data processing and realization through advanced algorithms and machine learning techniques.

[0006] Another object of the invention is to enable real-time data fusion from multiple sensor modalities, improving the accuracy and reliability of sensor-generated insights.

[0007] Another object of the invention is to reduce data overload by employing intelligent processing methods that filter noise and redundancy, allowing for efficient decision-making.

[0008] Another object of the invention is to offer a modular and scalable sensor system that allows users to easily integrate various sensor types without overhauling the entire architecture.

[0009] Another object of the invention is to enhance system adaptability by incorporating adaptive learning algorithms that continuously refine predictions and analysis based on evolving data patterns.

[00010] Another object of the invention is to improve data integrity and security by incorporating encryption and validation protocols, particularly for applications requiring high data confidentiality, such as healthcare.

[00011] Another object of the invention is to provide real-time anomaly detection with contextual awareness, allowing the system to differentiate between normal variations and critical anomalies.

[00012] Another object of the invention is to enable seamless integration with existing IoT frameworks, supporting cross-domain interoperability and facilitating comprehensive data analysis.

[00013] Another object of the invention is to promote sustainability by utilizing energy-efficient sensor nodes and incorporating resource optimization features, such as energy harvesting for prolonged operation.

[00014] Another object of the invention is to offer user-centric data visualization tools and customizable interfaces that enhance operational efficiency and allow real-time monitoring across various sectors.


SUMMARY OF THE INVENTION

[00015] In accordance with the different aspects of the present invention, sensor system architecture for intelligent data processing and realisation is presented. It is designed for optimized data processing and realization across various applications. It integrates advanced algorithms, machine learning, and data fusion techniques to enhance real-time decision-making and operational efficiency. The architecture is modular and scalable, allowing seamless integration of multiple sensor modalities while ensuring data integrity, security, and adaptability. By incorporating adaptive learning, anomaly detection, and cross-domain interoperability, the system addresses the limitations of existing sensor networks. This invention supports sustainable practices through energy-efficient design and resource optimization.

[00016] Additional aspects, advantages, features and objects of the present disclosure would be made apparent from the drawings and the detailed description of the illustrative embodiments constructed in conjunction with the appended claims that follow.

[00017] It will be appreciated that features of the present disclosure are susceptible to being combined in various combinations without departing from the scope of the present disclosure as defined by the appended claims.

BRIEF DESCRIPTION OF DRAWINGS
[00018] The summary above, as well as the following detailed description of illustrative embodiments, is better understood when read in conjunction with the appended drawings. For the purpose of illustrating the present disclosure, exemplary constructions of the disclosure are shown in the drawings. However, the present disclosure is not limited to specific methods and instrumentalities disclosed herein. Moreover, those in the art will understand that the drawings are not to scale. Wherever possible, like elements have been indicated by identical numbers.

[00019] Embodiments of the present disclosure will now be described, by way of example only, with reference to the following diagrams wherein:

[00020] FIG. 1 is component wise drawing for sensor system architecture for intelligent data processing and realisation.

[00021] FIG 2 is working methodology of sensor system architecture for intelligent data processing and realisation.

DETAILED DESCRIPTION

[00022] The following detailed description illustrates embodiments of the present disclosure and ways in which they can be implemented. Although some modes of carrying out the present disclosure have been disclosed, those skilled in the art would recognise that other embodiments for carrying out or practising the present disclosure are also possible.

[00023] The description set forth below in connection with the appended drawings is intended as a description of certain embodiments of sensor system architecture for intelligent data processing and realisation and is not intended to represent the only forms that may be developed or utilised. The description sets forth the various structures and/or functions in connection with the illustrated embodiments; however, it is to be understood that the disclosed embodiments are merely exemplary of the disclosure that may be embodied in various and alternative forms. The figures are not necessarily to scale; some features may be exaggerated or minimised to show details of particular components. Therefore, specific structural and functional details disclosed herein are not to be interpreted as limiting, but merely as a representative basis for teaching one skilled in the art to variously employ the present invention.

[00024] While the disclosure is susceptible to various modifications and alternative forms, specific embodiment thereof has been shown by way of example in the drawings and will be described in detail below. It should be understood, however, that it is not intended to limit the disclosure to the particular forms disclosed, but on the contrary, the disclosure is to cover all modifications, equivalents, and alternatives falling within the scope of the disclosure.

[00025] The terms "comprises", "comprising", "include(s)", or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a setup, or system that comprises a list of components or steps does not include only those components or steps but may include other components or steps not expressly listed or inherent to such setup or system. In other words, one or more elements in a system or apparatus preceded by "comprises... a" does not, without more constraints, preclude the existence of other elements or additional elements in the system or apparatus.

[00026] In the following detailed description of the embodiments of the disclosure, reference is made to the accompanying drawings and which are shown by way of illustration-specific embodiments in which the disclosure may be practiced. These embodiments are described in sufficient detail to enable those skilled in the art to practice the disclosure, and it is to be understood that other embodiments may be utilized and that changes may be made without departing from the scope of the present disclosure. The following description is, therefore, not to be taken in a limiting sense.

[00027] The present disclosure will be described herein below with reference to the accompanying drawings. In the following description, well-known functions or constructions are not described in detail since they would obscure the description with unnecessary detail.

[00028] Referring to Fig. 1, sensor system architecture for intelligent data processing and realisation 100 is disclosed, in accordance with one embodiment of the present invention. It comprises of sensor nodes 102, communication module 104, data fusion engine 106, intelligent processing unit 108, user interface and visualization tools 110, power management system 112, cloud integration architecture 114, edge computing module 116, anomaly detection system 118, data integrity and security protocols 120, predictive maintenance framework 122, health monitoring for sensor nodes 124, inter-sensor communication protocols 126, automated reporting and insights generation 128, customizable algorithm framework 130, geospatial data integration system 132, user-defined event triggers 134, blockchain-based data integrity verification 136, environmental adaptation module 138.

[00029] Referring to Fig. 1, the present disclosure provides details of sensor system architecture for intelligent data processing and realisation 100 which designed for intelligent data processing and realization, optimizing data collection and analysis across diverse sensor types. The system integrates sensor nodes 102 for collecting data from various modalities, connected via the communication module 104. The data fusion engine 106 synthesizes information from multiple sensors, while the intelligent processing unit 108 applies machine learning to improve predictions and anomaly detection. The system includes user interface and visualization tools 110 for real-time data monitoring and reporting. The power management system 112 ensures energy efficiency, and cloud integration architecture 114 allows scalable data storage and analysis. Additionally, edge computing module 116 enables real-time local data processing, enhancing responsiveness in dynamic environments.

[00030] Referring to Fig. 1, sensor system architecture for intelligent data processing and realization 100 is provided with sensor nodes 102, which collect data from various environmental modalities such as temperature, humidity, pressure, and motion. The sensor nodes 102 are designed to work autonomously, continuously gathering data from the environment. They interact closely with communication module 104, transmitting the collected data to a central hub for further processing. These nodes are energy-efficient, allowing for long-term deployment in remote locations and harsh environments.

[00031] Referring to Fig. 1, sensor system architecture for intelligent data processing and realization 100 is provided with communication module 104, which handles the wireless transmission of data collected by the sensor nodes 102. It supports various communication protocols like Wi-Fi, Zigbee, and LoRaWAN to ensure reliable and flexible connectivity. The communication module 104 works in conjunction with data fusion engine 106 to send raw data for processing, maintaining stable network connections even in challenging conditions.

[00032] Referring to Fig. 1, sensor system architecture for intelligent data processing and realization 100 is provided with data fusion engine 106, which aggregates data from multiple sensor nodes 102 to create a unified and coherent data set. The data fusion engine 106 uses advanced algorithms to eliminate redundancies, reduce noise, and improve the overall accuracy of the information. It collaborates with the intelligent processing unit 108 to ensure that the fused data is analyzed and interpreted in real time.

[00033] Referring to Fig. 1, sensor system architecture for intelligent data processing and realization 100 is provided with intelligent processing unit 108, which applies machine learning algorithms to the fused data from data fusion engine 106. The intelligent processing unit 108 detects patterns, predicts outcomes, and identifies anomalies in the sensor data. This processing unit continuously learns from historical data, refining its analysis and predictions over time, and sending the results to user interface and visualization tools 110.

[00034] Referring to Fig. 1, sensor system architecture for intelligent data processing and realization 100 is provided with user interface and visualization tools 110, which allow users to interact with and monitor the sensor data in real time. These tools display analytics, alerts, and reports generated by the intelligent processing unit 108. The user interface and visualization tools 110 can be customized based on user preferences, enabling efficient monitoring and decision-making across various applications.

[00035] Referring to Fig. 1, sensor system architecture for intelligent data processing and realization 100 is provided with power management system 112, which optimizes energy usage across all sensor nodes 102. The power management system 112 utilizes energy harvesting techniques, such as solar power or kinetic energy, to extend the operational life of the sensor network. It works in tandem with the communication module 104 to ensure that data transmission is not interrupted due to power limitations, thereby maintaining continuous system performance.

[00036] Referring to Fig. 1, sensor system architecture for intelligent data processing and realization 100 is provided with cloud integration architecture 114, which allows the system to store large volumes of sensor data in a scalable cloud environment. The cloud integration architecture 114 supports real-time analytics and historical data analysis by enabling seamless communication with the edge computing module 116. This architecture ensures that data is accessible across multiple platforms and can be processed in real time for decision-making.

[00037] Referring to Fig. 1, sensor system architecture for intelligent data processing and realization 100 is provided with edge computing module 116, which performs local data processing close to the sensor nodes 102. This reduces latency and minimizes bandwidth usage by processing essential data at the source rather than transmitting all raw data to the cloud. The edge computing module 116 works closely with the intelligent processing unit 108 to ensure that only critical data is sent to the cloud for further analysis.

[00038] Referring to Fig. 1, sensor system architecture for intelligent data processing and realization 100 is provided with anomaly detection system 118, which monitors the sensor data for unusual patterns or outliers. This system continuously compares incoming data with historical trends to detect potential anomalies in real time. The anomaly detection system 118 interacts with the data fusion engine 106 to validate detected anomalies, ensuring high accuracy in critical applications such as healthcare or industrial monitoring.

[00039] Referring to Fig. 1, sensor system architecture for intelligent data processing and realization 100 is provided with data integrity and security protocols 120, which ensure the confidentiality and reliability of the transmitted sensor data. These protocols include encryption and data validation mechanisms to protect the system from unauthorized access or data corruption. The data integrity and security protocols 120 work in conjunction with the blockchain-based data integrity verification 136 for enhanced security in sensitive applications.

[00040] Referring to Fig. 1, sensor system architecture for intelligent data processing and realization 100 is provided with predictive maintenance framework 122, which leverages machine learning algorithms to forecast equipment failures based on sensor data. This framework analyzes historical performance data to predict when maintenance is needed, helping to reduce downtime and operational costs. The predictive maintenance framework 122 interacts with both the intelligent processing unit 108 and the anomaly detection system 118 to deliver timely alerts.

[00041] Referring to Fig. 1, sensor system architecture for intelligent data processing and realization 100 is provided with health monitoring for sensor nodes 124, which tracks the operational metrics of each sensor node 102 in real time. This system monitors factors like battery life, data transmission quality, and sensor accuracy. Health monitoring for sensor nodes 124 ensures that each node is functioning optimally, and it works closely with the power management system 112 to maintain the overall health of the sensor network.

[00042] Referring to Fig. 1, sensor system architecture for intelligent data processing and realization 100 is provided with inter-sensor communication protocols 126, which enable direct communication and data sharing between sensor nodes 102. These protocols facilitate collaborative data collection, improving the system's efficiency in large-scale deployments. Inter-sensor communication protocols 126 are designed to work seamlessly with the communication module 104, ensuring stable connectivity across all nodes.

[00043] Referring to Fig. 1, sensor system architecture for intelligent data processing and realization 100 is provided with automated reporting and insights generation 128, which automatically generates reports and actionable insights from the processed sensor data. This component allows users to customize the parameters of the reports and receive updates at regular intervals. Automated reporting and insights generation 128 is closely integrated with the user interface and visualization tools 110, providing users with easy access to key metrics and trends.

[00044] Referring to Fig. 1, sensor system architecture for intelligent data processing and realization 100 is provided with customizable algorithm framework 130, which allows users to adapt the system's data processing algorithms according to specific use cases. This framework supports various machine learning models and can be fine-tuned based on the requirements of different applications. The customizable algorithm framework 130 works directly with the intelligent processing unit 108 to enhance the system's flexibility and accuracy.

[00045] Referring to Fig. 1, sensor system architecture for intelligent data processing and realization 100 is provided with geospatial data integration system 132, which incorporates location-based data from sensor nodes 102 into the overall analysis. This system allows sensor data to be mapped onto geographical information, enhancing applications in urban planning, environmental monitoring, and disaster response. Geospatial data integration system 132 works with the user interface and visualization tools 110 to provide users with real-time geographic insights.

[00046] Referring to Fig. 1, sensor system architecture for intelligent data processing and realization 100 is provided with user-defined event triggers 134, which allow users to set specific data thresholds or patterns that trigger automated alerts or actions. These event triggers are customizable based on application needs and are integrated with the intelligent processing unit 108 to ensure timely responses to critical conditions.

[00047] Referring to Fig. 1, sensor system architecture for intelligent data processing and realization 100 is provided with blockchain-based data integrity verification 136, which ensures the traceability and immutability of the sensor data. This component uses blockchain technology to log sensor data transactions, providing enhanced security and accountability, particularly in sectors like healthcare and finance. It works in conjunction with data integrity and security protocols 120.

[00048] Referring to Fig. 1, sensor system architecture for intelligent data processing and realization 100 is provided with environmental adaptation module 138, which adjusts the system's operational parameters based on surrounding environmental conditions. This module ensures that the sensor nodes 102 collect relevant data while optimizing power usage and sensor performance. The environmental adaptation module 138 interacts with the power management system 112 to maintain efficient operation in diverse environments.

[00049] Referring to Fig 2, there is illustrated method 200 for sensor system architecture for intelligent data processing and realization 100. The method comprises:

At step 202, method 200 includes sensor nodes 102 collecting data from the environment, such as temperature, humidity, pressure, or motion;

At step 204, method 200 includes communication module 104 transmitting the collected data wirelessly to the central processing unit;

At step 206, method 200 includes data fusion engine 106 aggregating the transmitted data from multiple sensor nodes 102 to create a unified data set;

At step 208, method 200 includes intelligent processing unit 108 analyzing the fused data using machine learning algorithms to identify patterns, anomalies, and trends;

At step 210, method 200 includes user interface and visualization tools 110 displaying real-time data, insights, and alerts to the users for monitoring and decision-making;

At step 212, method 200 includes power management system 112 optimizing energy usage across sensor nodes 102, ensuring prolonged system operation;

At step 214, method 200 includes cloud integration architecture 114 storing the processed data in the cloud, enabling large-scale data storage and advanced analytics;

At step 216, method 200 includes edge computing module 116 performing local data processing at the sensor node level to minimize latency and bandwidth usage;

At step 218, method 200 includes anomaly detection system 118 continuously monitoring the sensor data and identifying any deviations or critical anomalies in real time;

At step 220, method 200 includes data integrity and security protocols 120 validating and encrypting data to ensure secure transmission and prevent unauthorized access;

At step 222, method 200 includes predictive maintenance framework 122 analyzing historical data to forecast equipment failures, helping users schedule timely maintenance;

At step 224, method 200 includes health monitoring for sensor nodes 124 tracking operational metrics such as battery life, transmission quality, and sensor performance;

At step 226, method 200 includes inter-sensor communication protocols 126 facilitating direct communication between sensor nodes 102 to improve data collection and collaboration across the network;

At step 228, method 200 includes automated reporting and insights generation 128 generating customized reports based on processed data and sending them to users at regular intervals;

At step 230, method 200 includes customizable algorithm framework 130 allowing users to modify the data processing algorithms to fit their specific requirements;

At step 232, method 200 includes geospatial data integration system 132 incorporating location-based data into the sensor data, enabling geographic insights for various applications.

[00050] In the description of the present invention, it is also to be noted that, unless otherwise explicitly specified or limited, the terms "fixed" "attached" "disposed," "mounted," and "connected" are to be construed broadly, and may for example be fixedly connected, detachably connected, or integrally connected, either mechanically or electrically. They may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meaning of the above terms in the present invention can be understood in specific cases to those skilled in the art.

[00051] Modifications to embodiments of the present disclosure described in the foregoing are possible without departing from the scope of the present disclosure as defined by the accompanying claims. Expressions such as "including", "comprising", "incorporating", "have", "is" used to describe and claim the present disclosure are intended to be construed in a non- exclusive manner, namely allowing for items, components or elements not explicitly described also to be present. Reference to the singular is also to be construed to relate to the plural where appropriate.

[00052] Although embodiments have been described with reference to a number of illustrative embodiments thereof, it should be understood that numerous other modifications and embodiments can be devised by those skilled in the art that will fall within the spirit and scope of the principles of this disclosure. More particularly, various variations and modifications are possible in the component parts and/or arrangements of the subject combination arrangement within the scope of the present disclosure, the drawings and the appended claims. In addition to variations and modifications in the component parts and/or arrangements, alternative uses will also be apparent to those skilled in the art.
, Claims:WE CLAIM:
1. A sensor system architecture for intelligent data processing and realization 100 comprising of
sensor nodes 102 to collect data from various environmental modalities;
communication module 104 to transmit the collected data wirelessly to the central processing unit;
data fusion engine 106 to aggregate and process data from multiple sensor nodes;
intelligent processing unit 108 to apply machine learning algorithms for data analysis and anomaly detection;
user interface and visualization tools 110 to display real-time data and insights for user monitoring;
power management system 112 to optimize energy usage and prolong sensor operation;
cloud integration architecture 114 to enable scalable data storage and advanced analytics in the cloud;
edge computing module 116 to perform local data processing and minimize latency;
anomaly detection system 118 to continuously monitor data and detect critical anomalies in real time;
data integrity and security protocols 120 to ensure secure transmission and validation of sensor data;
predictive maintenance framework 122 to forecast equipment failures and schedule maintenance;
health monitoring for sensor nodes 124 to track operational metrics like battery life and sensor performance;
inter-sensor communication protocols 126 to facilitate direct communication and collaboration between sensor nodes;
automated reporting and insights generation 128 to create customized reports and insights for users;
customizable algorithm framework 130 to allow users to modify data processing algorithms for specific needs; and
geospatial data integration system 132 to incorporate location-based data for enhanced analysis and decision-making.

2. The sensor system architecture for intelligent data processing and realization 100 as claimed in claim 1, wherein sensor nodes 102 are configured to collect environmental data such as temperature, humidity, pressure, and motion, enabling comprehensive data acquisition across diverse conditions.

3. The sensor system architecture for intelligent data processing and realization 100 as claimed in claim 1, wherein communication module 104 is configured to wirelessly transmit collected data from sensor nodes to a central processing unit, supporting multiple communication protocols for reliable data transfer.

4. The sensor system architecture for intelligent data processing and realization 100 as claimed in claim 1, wherein data fusion engine 106 is configured to aggregate and process data from multiple sensor nodes 102, reducing noise and redundancies to create a unified data set for more accurate analysis.

5. The sensor system architecture for intelligent data processing and realization 100 as claimed in claim 1, wherein intelligent processing unit 108 is configured to apply machine learning algorithms to the fused data, enabling real-time pattern recognition, anomaly detection, and predictive insights.

6. The sensor system architecture for intelligent data processing and realization 100 as claimed in claim 1, wherein user interface and visualization tools 110 are configured to display real-time data, analytics, and alerts, offering customizable dashboards and monitoring tools for users.

7. The sensor system architecture for intelligent data processing and realization 100 as claimed in claim 1, wherein power management system 112 is configured to optimize energy consumption across sensor nodes 102 through energy harvesting techniques, extending the operational life of the system in remote environments.

8. The sensor system architecture for intelligent data processing and realization 100 as claimed in claim 1, wherein anomaly detection system 118 is configured to continuously monitor sensor data for deviations, identifying anomalies in real time and enabling timely intervention in critical applications.

9. The sensor system architecture for intelligent data processing and realization 100 as claimed in claim 1, wherein cloud integration architecture 114 is configured to store and manage large-scale sensor data, supporting scalable cloud-based analytics and providing real-time access to historical data for enhanced decision-making.

10. The sensor system architecture for intelligent data processing and realization 100 as claimed in claim 1, wherein method comprises of
sensor nodes 102 collecting data from the environment, such as temperature, humidity, pressure, or motion;
communication module 104 transmitting the collected data wirelessly to the central processing unit;
data fusion engine 106 aggregating the transmitted data from multiple sensor nodes 102 to create a unified data set;
intelligent processing unit 108 analyzing the fused data using machine learning algorithms to identify patterns, anomalies, and trends;
user interface and visualization tools 110 displaying real-time data, insights, and alerts to the users for monitoring and decision-making;
power management system 112 optimizing energy usage across sensor nodes 102, ensuring prolonged system operation;
cloud integration architecture 114 storing the processed data in the cloud, enabling large-scale data storage and advanced analytics;
edge computing module 116 performing local data processing at the sensor node level to minimize latency and bandwidth usage;
anomaly detection system 118 continuously monitoring the sensor data and identifying any deviations or critical anomalies in real time;
data integrity and security protocols 120 validating and encrypting data to ensure secure transmission and prevent unauthorized access;
predictive maintenance framework 122 analyzing historical data to forecast equipment failures, helping users schedule timely maintenance;
health monitoring for sensor nodes 124 tracking operational metrics such as battery life, transmission quality, and sensor performance;
inter-sensor communication protocols 126 facilitating direct communication between sensor nodes 102 to improve data collection and collaboration across the network;
automated reporting and insights generation 128 generating customized reports based on processed data and sending them to users at regular intervals;
customizable algorithm framework 130 allowing users to modify the data processing algorithms to fit their specific requirements; and
geospatial data integration system 132 incorporating location-based data into the sensor data, enabling geographic insights for various applications

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