DEEP LEARNING-ENABLED ARTIFICIAL INTELLIGENCE CONTROL SYSTEMS

Abstract

ABSTRACT Deep Learning-Enabled Artificial Intelligence Control Systems The present disclosure introduces deep learning-enabled artificial intelligence control systems 100 which is designed to enhance real-time decision-making, adaptability, and efficiency across various applications. The system features sensors and data acquisition modules 102 for collecting real-time data, and preprocessing and data normalization module 104 for structuring the raw data. The core of the system comprises of deep learning models 106 for analyzing data and generating insights. A controller module 108 executes actions based on these insights, while an adaptive learning and feedback mechanism 110 continuously updates the models to improve performance. The system incorporates an edge computing module 112 for low-latency tasks, ensuring real-time operation. Distributed AI models 116 allow cooperative decision-making in multi-agent environments, and the predictive maintenance module 120 analyzes data to identify potential failures, enabling proactive maintenance. This comprehensive system optimizes control processes, and ensures operational efficiency in dynamic environments. Reference Fig 1

Specification

Description:Deep Learning-Enabled Artificial Intelligence Control Systems
TECHNICAL FIELD
[0001] The present innovation relates to the integration of deep learning techniques within artificial intelligence control systems to enhance real-time decision-making, adaptability, and operational efficiency.

BACKGROUND

[0002] Artificial intelligence (AI) control systems have become integral in various industries such as autonomous vehicles, robotics, manufacturing automation, and smart infrastructure. Traditional AI control systems typically rely on machine learning algorithms, pre-defined rules, and reinforcement learning to make decisions in real time. While these methods provide a degree of automation and efficiency, they face significant limitations in handling complex, unstructured, or high-dimensional data, adapting to dynamic environments, and scaling across various applications. These traditional systems often lack the capability for continuous learning from new data, limiting their adaptability and responsiveness. Furthermore, their reliance on static models means they struggle to perform in unpredictable environments, leading to inefficiencies, system failures, and increased maintenance costs.

[0003] Existing solutions for improving control systems include the use of basic machine learning models and heuristic algorithms, which are generally limited in their ability to process large volumes of data, and lack the real-time learning and decision-making capabilities needed for complex, multi-agent systems. Additionally, these options do not offer predictive maintenance features, increasing operational downtime due to system failures or inefficiencies.

[0004] The invention of Deep Learning-Enabled Artificial Intelligence Control Systems addresses these challenges by integrating deep learning techniques, such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and reinforcement learning with deep Q-learning, to enable more advanced real-time decision-making, self-adaptation, and predictive maintenance. Unlike traditional systems, this invention can continuously learn from new data, adapt to changing environments, and handle unstructured data inputs like sensor data and video feeds. The novel features include real-time learning, self-tuning algorithms, and the ability to deploy distributed AI models for scalability. This system significantly improves efficiency, reduces maintenance costs through predictive analytics, and provides greater adaptability in dynamic environments, offering a more advanced and reliable AI control solution.

OBJECTS OF THE INVENTION

[0005] The primary object of the invention is to enhance the performance of AI control systems by integrating deep learning techniques for more accurate and efficient real-time decision-making.

[0006] Another object of the invention is to improve the adaptability of control systems in dynamic environments through continuous learning and self-adjustment based on real-time data.

[0007] Another object of the invention is to enable predictive maintenance in industrial and autonomous systems, reducing downtime and maintenance costs by detecting anomalies and predicting failures before they occur.

[0008] Another object of the invention is to handle complex and unstructured data, such as sensor inputs and video feeds, through advanced deep learning models like CNNs and RNNs, ensuring more robust decision-making.
[0009] Another object of the invention is to offer scalable AI control solutions capable of functioning in both small-scale autonomous devices and large-scale industrial automation systems by leveraging distributed AI models.

[00010] Another object of the invention is to improve the security and robustness of AI control systems against adversarial attacks through the inclusion of adversarial training and input sanitization techniques.

[00011] Another object of the invention is to reduce latency in decision-making processes by incorporating edge computing, allowing real-time control and adaptation without heavy reliance on cloud infrastructure.

[00012] Another object of the invention is to enhance the flexibility of AI control systems by providing modular architecture, allowing easy integration of additional deep learning models or algorithms based on specific application needs.

[00013] Another object of the invention is to support multi-agent systems, enabling improved coordination, resource management, and process optimization in complex environments like smart cities and industrial networks.

[00014] Another object of the invention is to promote sustainability by reducing energy consumption and resource wastage through optimized control and predictive maintenance, aligning with modern environmental and industrial goals

SUMMARY OF THE INVENTION

[00015] In accordance with the different aspects of the present invention, deep learning enabled artificial intelligence control system is presented. By integrating deep learning techniques such as CNNs, RNNs, and reinforcement learning, the system handles complex, unstructured data and enables continuous learning. The invention also incorporates predictive maintenance, edge computing for reduced latency, and distributed AI models for scalability. Its security and robustness features ensure resilience against adversarial attacks, making it suitable for various applications, including autonomous vehicles, robotics, and industrial automation. This system offers improved performance, adaptability, and sustainability over traditional AI control systems.

[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 deep learning enabled artificial intelligence control systems.

[00021] FIG 2 is working methodology of plates for deep learning enabled artificial intelligence control systems.

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 deep learning enabled artificial intelligence control systems 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, deep learning enabled artificial intelligence control systems 100 is disclosed, in accordance with one embodiment of the present invention. It comprises of sensors and data acquisition modules 102, preprocessing and data normalization module 104, deep learning models 106, controller module 108, adaptive learning and feedback mechanism 110, edge computing module 112, cloud integration module 114, distributed AI models 116, security and robustness features 118, predictive maintenance module 120.

[00029] Referring to Fig. 1, the present disclosure provides details of deep learning enabled artificial intelligence control systems 100 which integrates advanced deep learning models to enhance real-time decision-making, adaptability, and system efficiency across various applications. The invention is composed of key components such as sensors and data acquisition modules 102, preprocessing and data normalization module 104, and deep learning models 106 that process unstructured data for accurate control. A controller module 108 executes actions based on the processed data, while the adaptive learning and feedback mechanism 110 enables continuous performance improvement. The system also incorporates edge computing module 112 for low-latency decision-making, and distributed AI models 116 for scalability in multi-agent environments. Furthermore, predictive maintenance module 120 and security and robustness features 118 ensure operational reliability and protection against adversarial threats.

[00030] Referring to Fig. 1, deep learning-enabled artificial intelligence control systems 100 is provided with sensors and data acquisition modules 102, which continuously collect real-time data from various sources such as cameras, LIDAR, GPS, and infrared sensors. These sensors provide the essential input required for processing by the deep learning models 106. The data acquired is raw and often noisy, requiring further preprocessing. The sensors and data acquisition modules 102 play a critical role in supplying the system with the necessary information to make informed decisions, interacting directly with the preprocessing and data normalization module 104 to ensure the data is structured and ready for analysis.
[00031] Referring to Fig. 1, deep learning-enabled artificial intelligence control systems 100 is provided with preprocessing and data normalization module 104, which cleans, normalizes, and structures raw data collected by the sensors and data acquisition modules 102. This module ensures that the data is in a usable format, free from noise or errors, and organized for efficient processing by the deep learning models 106. The preprocessing and data normalization module 104 plays a crucial role in ensuring high-quality input for the decision-making process, collaborating closely with the deep learning models 106 to optimize performance and accuracy.

[00032] Referring to Fig. 1, deep learning-enabled artificial intelligence control systems 100 is provided with deep learning models 106, which form the core of the system's decision-making capabilities. These models include CNNs, RNNs, and reinforcement learning algorithms, tailored to handle different types of data and decision-making tasks. The deep learning models 106 process the structured data from preprocessing and data normalization module 104 and generate actionable insights or predictions. These models work closely with the controller module 108, which executes the commands based on the output of the deep learning models 106, ensuring real-time adaptability.

[00033] Referring to Fig. 1, deep learning-enabled artificial intelligence control systems 100 is provided with the controller module 108, responsible for executing actions based on the output from deep learning models 106. The controller module 108 interacts with actuators, motors, or other system components to adjust system behavior in real time, ensuring that decisions made by the deep learning models 106 are implemented. This module operates in close coordination with the adaptive learning and feedback mechanism 110, enabling the system to refine its decision-making process dynamically based on real-time outcomes.

[00034] Referring to Fig. 1, deep learning-enabled artificial intelligence control systems 100 is provided with adaptive learning and feedback mechanism 110, which continuously updates the deep learning models 106 with real-time data and feedback from control outcomes. This component ensures that the system can learn from its environment and improve its performance over time. The adaptive learning and feedback mechanism 110 also interacts with the controller module 108 to fine-tune the actions based on ongoing performance metrics, creating a closed-loop system that adapts and evolves in dynamic environments.

[00035] Referring to Fig. 1, deep learning-enabled artificial intelligence control systems 100 is provided with edge computing module 112, which handles time-sensitive tasks requiring low latency. The edge computing module 112 processes critical data locally on hardware-accelerated devices, such as GPUs, reducing the need for cloud-based processing and ensuring real-time decision-making. This module works closely with the sensors and data acquisition modules 102 and deep learning models 106, optimizing computational efficiency for applications like autonomous vehicles where speed and responsiveness are paramount.

[00036] Referring to Fig. 1, deep learning-enabled artificial intelligence control systems 100 is provided with cloud integration module 114, which is designed to handle more resource-intensive tasks such as model training and large-scale data analysis. This module works in tandem with edge computing module 112 to offload non-time-critical processes, ensuring optimal system performance. The cloud integration module 114 is also used for updating deep learning models 106, enabling the system to scale and adapt by leveraging cloud-based computational resources.

[00037] Referring to Fig. 1, deep learning-enabled artificial intelligence control systems 100 is provided with distributed AI models 116, enabling the deployment of AI across multiple devices or control nodes in complex, multi-agent environments. These distributed AI models 116 allow the system to coordinate actions and optimize decision-making across interconnected devices. The distributed architecture works closely with the controller module 108 and adaptive learning and feedback mechanism 110 to enhance scalability and ensure smooth operation in applications such as smart cities or industrial networks.

[00038] Referring to Fig. 1, deep learning-enabled artificial intelligence control systems 100 is provided with security and robustness features 118, designed to protect the system from adversarial attacks and ensure safe operation in critical environments. These features include adversarial training and input sanitization techniques to safeguard the deep learning models 106 from manipulation. The security and robustness features 118 work in conjunction with the adaptive learning and feedback mechanism 110 to ensure that the system remains resilient and can recover from unexpected disruptions.

[00039] Referring to Fig. 1, deep learning-enabled artificial intelligence control systems 100 is provided with predictive maintenance module 120, which analyzes real-time and historical data to identify potential failures or inefficiencies in the system. The predictive maintenance module 120 uses deep learning models 106 to detect patterns of wear or degradation, allowing proactive maintenance before critical failures occur. This module works closely with the sensors and data acquisition modules 102 to monitor equipment health and optimize operational efficiency, reducing downtime and maintenance costs.

[00040] Referring to Fig 2, there is illustrated method 200 for deep learning-enabled artificial intelligence control systems 100. The method comprises:

At step 202, method 200 includes the system collecting real-time data through the sensors and data acquisition modules 102 from various sources such as cameras, LIDAR, and temperature sensors;

At step 204, method 200 includes preprocessing the collected data in the preprocessing and data normalization module 104 to clean, normalize, and structure the data for further processing;

At step 206, method 200 includes passing the preprocessed data to the deep learning models 106, where it is analyzed using various architectures such as CNNs and RNNs for decision-making;

At step 208, method 200 includes the deep learning models 106 generating actionable insights or predictions, which are then sent to the controller module 108 for executing necessary actions;

At step 210, method 200 includes the controller module 108 issuing commands to system actuators or motors, adjusting the system's behavior based on the output of the deep learning models 106;

At step 212, method 200 includes the adaptive learning and feedback mechanism 110 continuously updating the deep learning models 106 based on feedback from real-time outcomes, improving future decision-making;

At step 214, method 200 includes the edge computing module 112 processing critical tasks locally to ensure real-time performance, reducing latency, especially in time-sensitive applications such as autonomous vehicles.

[00041] 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.

[00042] 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.

[00043] 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 deep learning-enabled artificial intelligence control systems 100 comprising of
sensors and data acquisition modules 102 to collect real-time data from various sources such as cameras and lidar;
preprocessing and data normalization module 104 to clean and structure raw data for efficient processing;
deep learning models 106 to analyze data and generate actionable insights for decision-making;
controller module 108 to execute actions based on the output from the deep learning models;
adaptive learning and feedback mechanism 110 to continuously update models with real-time feedback for improved performance;
edge computing module 112 to process critical tasks locally, reducing latency in real-time operations;
cloud integration module 114 to handle resource-intensive tasks like model training with cloud-based computing;
distributed AI models 116 to enable cooperative decision-making across multiple control nodes or devices;
security and robustness features 118 to protect the system against adversarial attacks and ensure safe operation; and
predictive maintenance module 120 to detect potential failures and optimize system maintenance through real-time data analysis.

2. The deep learning-enabled artificial intelligence control systems 100 as claimed in claim 1, wherein sensors and data acquisition modules 102 are configured to collect real-time data from various sources, including cameras, LIDAR, GPS, and temperature sensors, enabling accurate and up-to-date input for the system's decision-making process.

3. The deep learning-enabled artificial intelligence control systems 100 as claimed in claim 1, wherein preprocessing and data normalization module 104 is configured to clean, normalize, and structure raw data from sensors to ensure high-quality input for the deep learning models 106, improving system efficiency and accuracy.

4. The deep learning-enabled artificial intelligence control systems 100 as claimed in claim 1, wherein deep learning models 106 are configured to analyze structured data using architectures such as CNNs, RNNs, and reinforcement learning, generating real-time insights for system control and optimization.

5. The deep learning-enabled artificial intelligence control systems 100 as claimed in claim 1, wherein controller module 108 is configured to execute actions based on the outputs from deep learning models 106, adjusting system behavior through commands to actuators and motors in real time.

6. The deep learning-enabled artificial intelligence control systems 100 as claimed in claim 1, wherein adaptive learning and feedback mechanism 110 is configured to continuously update deep learning models 106 based on real-time performance data and feedback, enabling the system to improve decision-making dynamically.

7. The deep learning-enabled artificial intelligence control systems 100 as claimed in claim 1, wherein edge computing module 112 is configured to process time-sensitive tasks locally on hardware-accelerated devices, reducing latency and ensuring real-time decision-making for critical applications.

8. The deep learning-enabled artificial intelligence control systems 100 as claimed in claim 1, wherein distributed AI models 116 are configured to allow cooperative decision-making across multiple devices or control nodes, optimizing the performance of complex multi-agent environments such as smart cities or industrial networks.

9. The deep learning-enabled artificial intelligence control systems 100 as claimed in claim 1, wherein predictive maintenance module 120 is configured to analyze real-time and historical data to identify patterns of equipment degradation or failure, enabling proactive maintenance and minimizing system downtime.

10. The deep learning-enabled artificial intelligence control systems 100 wherein method comprises of
system collecting real-time data through the sensors and data acquisition modules 102 from various sources such as cameras, LIDAR, and temperature sensors;

preprocessing the collected data in the preprocessing and data normalization module 104 to clean, normalize, and structure the data for further processing;

passing the preprocessed data to the deep learning models 106, where it is analyzed using various architectures such as CNNs and RNNs for decision-making;

deep learning models 106 generating actionable insights or predictions, which are then sent to the controller module 108 for executing necessary actions;

controller module 108 issuing commands to system actuators or motors, adjusting the system's behavior based on the output of the deep learning models 106;

adaptive learning and feedback mechanism 110 continuously updating the deep learning models 106 based on feedback from real-time outcomes, improving future decision-making;
edge computing module 112 processing critical tasks locally to ensure real-time performance, reducing latency, especially in time-sensitive applications such as autonomous vehicles.

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