DEEP LEARNING-BASED AI CONTROLLER FOR ENHANCED AUTOMATION

Abstract

ABSTRACT Deep Learning-Based AI Controller for Enhanced Automation The present disclosure introduces deep learning based AI controller for enhanced automation 100 which is designed to optimize decision-making, real-time data processing, and adaptive control across various industries. The system includes a data acquisition module 102 to collect real-time data from sensors and IoT devices, and a preprocessing module 104 to filter and normalize the data. A deep learning engine 106 analyzes the data and generates insights, while the decision-making module 108 selects optimal actions based on these insights. The other key components are control interface 110, real-time monitoring system 112, adaptive learning system 114, predictive maintenance engine 116, resource optimization module 118, multi-modal data fusion system 120, self-learning feedback loop 122, user interface 124, decentralized control architecture 126, energy-aware optimization algorithms 128, anomaly detection and root cause analysis system 130 and real-time simulation and modelling engine 132. Reference Fig 1

Specification

Description:Deep Learning-Based AI Controller for Enhanced Automation
TECHNICAL FIELD
[0001] The present innovation relates to artificial intelligence (AI) and automation, specifically a deep learning-based AI controller for optimizing automation processes across various industries.

BACKGROUND

[0002] In recent years, advancements in artificial intelligence (AI) and machine learning (ML) have transformed industries by driving automation. However, traditional automation systems often rely on rule-based algorithms, which require explicit programming and struggle to adapt to dynamic and complex environments. These systems may fail to optimize processes or respond adequately to unexpected changes, leading to inefficiencies, increased downtime, and higher operational costs. While some solutions integrate basic AI components, they often lack the real-time adaptability required to handle complex decision-making, resulting in suboptimal performance.

[0003] The available options for automation typically include conventional controllers or semi-autonomous systems that follow predefined rules. While these systems work well in static environments, they cannot adapt when the conditions change rapidly, such as in manufacturing where supply chain disruptions or machine failures can occur. Additionally, many current solutions do not effectively leverage the vast amounts of data generated by the Internet of Things (IoT) devices, sensors, and other digital systems, limiting their ability to make intelligent, data-driven decisions.

[0004] The present invention-a deep learning-based AI controller-differentiates itself from existing solutions by incorporating advanced neural network architectures capable of learning from real-time data and autonomously adjusting system operations. Unlike traditional systems, this controller can process complex datasets, detect patterns, and make decisions without manual intervention. It addresses the shortcomings of rule-based automation by offering adaptive control, predictive maintenance, and resource optimization, making it suitable for dynamic environments.

[0005] The novelty of the invention lies in its real-time data processing, self-learning feedback loops, and multi-modal data fusion capabilities. These features enable the AI controller to optimize processes continuously, enhance operational efficiency, and respond proactively to changing conditions, offering a more intelligent and adaptable solution than existing automation technologies.

OBJECTS OF THE INVENTION
[0006] The primary object of the invention is to enhance automation processes by leveraging deep learning-based AI to optimize decision-making and control across various industries.

[0007] Another object of the invention is to provide real-time adaptive learning capabilities, allowing the system to continuously improve performance based on evolving data and conditions.

[0008] Another object of the invention is to reduce operational inefficiencies by enabling predictive maintenance and proactive process management, minimizing downtime and extending equipment lifespan.

[0009] Another object of the invention is to integrate multi-modal data fusion, allowing the AI controller to process and analyze diverse data sources for more comprehensive insights and improved decision-making.

[00010] Another object of the invention is to enhance resource optimization by dynamically adjusting resource allocation based on real-time demand, thereby reducing waste and promoting sustainable practices.

[00011] Another object of the invention is to improve the adaptability of automation systems in dynamic environments, ensuring the controller can respond to unexpected changes and optimize processes autonomously.

[00012] Another object of the invention is to support seamless integration with existing automation systems, enabling organizations to upgrade their automation capabilities without overhauling their current infrastructure.

[00013] Another object of the invention is to provide a decentralized control architecture that allows distributed decision-making, enhancing system resilience and reducing bottlenecks in large-scale operations.

[00014] Another object of the invention is to offer a customizable user interface that facilitates better interaction with the AI controller, making it accessible to users with varying technical expertise.

[00015] Another object of the invention is to promote sustainability by incorporating energy-aware optimization algorithms, minimizing energy consumption while maintaining high performance across industrial applications.

SUMMARY OF THE INVENTION

[00016] In accordance with the different aspects of the present invention, Deep learning Based AI controller for enhanced automation is presented. It is designed to enhance automation across industries by enabling intelligent decision-making, real-time data processing, and adaptive control. It utilizes advanced neural network architectures to optimize resource allocation, predictive maintenance, and process management. The controller integrates multi-modal data fusion and self-learning capabilities, allowing for continuous improvement and adaptability in dynamic environments. It seamlessly integrates with existing systems and promotes sustainability through energy-aware algorithms. This solution offers a significant advancement in automation, improving efficiency, flexibility, and operational performance.

[00017] 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.

[00018] 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
[00019] 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.

[00020] Embodiments of the present disclosure will now be described, by way of example only, with reference to the following diagrams wherein:

[00021] FIG. 1 is component wise drawing for deep learning based AI controller for enhanced automation.

[00022] FIG 2 is working methodology of deep learning based AI controller for enhanced automation.


DETAILED DESCRIPTION

[00023] 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.

[00024] The description set forth below in connection with the appended drawings is intended as a description of certain embodiments of Deep learning based AI controller for enhanced automation 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.

[00025] 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.

[00026] 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.

[00027] 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.

[00028] 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.

[00029] Referring to Fig. 1, deep learning based AI controller for enhanced automation 100 is disclosed, in accordance with one embodiment of the present invention. It comprises of data acquisition module 102, preprocessing module 104, deep learning engine 106, decision-making module 108, control interface 110, real-time monitoring system 112, adaptive learning system 114, predictive maintenance engine 116, resource optimization module 118, multi-modal data fusion system 120, self-learning feedback loop 122, user interface 124, decentralized control architecture 126, energy-aware optimization algorithms 128, anomaly detection and root cause analysis system 130 and real-time simulation and modelling engine 132.

[00030] Referring to Fig. 1, the present disclosure provides details of deep learning based AI controller for enhanced automation 100 across various industries. It is designed to optimize decision-making, real-time data processing, and adaptive control in dynamic environments. The AI controller comprises key components such as data acquisition module 102, preprocessing module 104, and deep learning engine 106 to collect, preprocess, and analyze data. The decision-making module 108 evaluates insights, while the control interface 110 translates decisions into actions. Additionally, the system includes real-time monitoring system 112 and adaptive learning system 114 to continuously improve performance, and predictive maintenance engine 116 for proactive maintenance. Other components such as resource optimization module 118 and multi-modal data fusion system 120 ensure efficiency and data integration.

[00031] Referring to Fig. 1, the deep learning-based AI controller for enhanced automation 100 is provided with data acquisition module 102, which collects data from various sources such as sensors, IoT devices, and existing automation systems. This module handles both structured and unstructured data, ensuring a continuous flow of information into the system. The data acquisition module 102 works in close coordination with preprocessing module 104 to ensure that raw data is effectively captured and ready for further processing by other components, such as the deep learning engine 106.


[00032] Referring to Fig. 1, the deep learning-based AI controller for enhanced automation 100 is provided with preprocessing module 104, which is responsible for cleaning and organizing the data collected by the data acquisition module 102. It removes noise, normalizes the data, and formats it for input into the deep learning engine 106. This ensures that the system works efficiently by reducing errors and inconsistencies. The preprocessing module 104 acts as an essential bridge between data acquisition and the analysis performed by the deep learning engine 106.

[00033] Referring to Fig. 1, the deep learning-based AI controller for enhanced automation 100 is provided with deep learning engine 106, which serves as the core analytical component. It uses advanced neural network architectures to detect patterns in the preprocessed data from preprocessing module 104 and generate insights for decision-making. The deep learning engine 106 interacts directly with decision-making module 108 to apply learned patterns and provide actionable decisions. It also continuously improves through real-time feedback from the adaptive learning system 114.

[00034] Referring to Fig. 1, the deep learning-based AI controller for enhanced automation 100 is provided with decision-making module 108, which evaluates the insights produced by the deep learning engine 106 and selects the optimal course of action. This module is key for real-time decision-making, allowing the system to adapt to changing conditions. The decision-making module 108 works closely with the control interface 110 to translate its conclusions into commands that can be executed by automation systems, such as robotic arms or energy management systems.

[00035] Referring to Fig. 1, the deep learning-based AI controller for enhanced automation 100 is provided with control interface 110, which serves as the communication layer between the AI controller and external hardware or automation systems. It translates the decisions made by the decision-making module 108 into executable commands that interact with physical devices. The control interface 110 ensures seamless integration with existing automation systems, enhancing the system's ability to manage processes efficiently. It also supports feedback mechanisms that are relayed to other modules like the real-time monitoring system 112.

[00036] Referring to Fig. 1, the deep learning-based AI controller for enhanced automation 100 is provided with real-time monitoring system 112, which continuously tracks the performance and operational parameters of the automation system. This component ensures that any deviations from the expected behavior are promptly detected and addressed. The real-time monitoring system 112 works in tandem with the decision-making module 108 to provide feedback that helps adjust system operations on the fly. It also collaborates with the predictive maintenance engine 116 to detect early signs of equipment failure.

[00037] Referring to Fig. 1, the deep learning-based AI controller for enhanced automation 100 is provided with adaptive learning system 114, which allows the controller to learn from new data and improve its decision-making capabilities over time. By continuously analyzing the feedback from real-time monitoring system 112 and deep learning engine 106, the adaptive learning system 114 refines its algorithms. This helps the system adapt to changing conditions and ensures that the overall automation process remains efficient and responsive.

[00038] Referring to Fig. 1, the deep learning-based AI controller for enhanced automation 100 is provided with predictive maintenance engine 116, which analyzes both historical and real-time data to predict equipment failures or maintenance requirements. This engine works closely with the real-time monitoring system 112 to gather data on system performance and use that information to forecast maintenance needs. By doing so, the predictive maintenance engine 116 minimizes downtime and extends the lifespan of machinery, enhancing overall operational efficiency


[00039] Referring to Fig. 1, the deep learning-based AI controller for enhanced automation 100 is provided with resource optimization module 118, which is responsible for dynamically adjusting resource allocation based on current operational needs and predicted demand. This module analyzes data from the deep learning engine 106 and works alongside the decision-making module 108 to optimize the use of resources such as energy, materials, and labor. The resource optimization module 118 contributes significantly to reducing waste and improving sustainability in industrial operations.

[00040] Referring to Fig. 1, the deep learning-based AI controller for enhanced automation 100 is provided with multi-modal data fusion system 120, which integrates data from diverse sources, including sensors, video feeds, and logs, into a unified dataset for analysis. This system ensures that the deep learning engine 106 has access to comprehensive, accurate data for generating insights. The multi-modal data fusion system 120 interacts with both the data acquisition module 102 and preprocessing module 104 to manage the flow of information and ensure that all relevant data is included in the decision-making process.

[00041] Referring to Fig. 1, the deep learning-based AI controller for enhanced automation 100 is provided with self-learning feedback loop 122, which enables the system to continuously refine its learning algorithms based on operational outcomes. The feedback loop works in collaboration with the adaptive learning system 114 and the deep learning engine 106, ensuring that the AI controller becomes more efficient over time. The self-learning feedback loop 122 plays a critical role in improving the system's adaptability to changing conditions and evolving operational demands.

[00042] Referring to Fig. 1, the deep learning-based AI controller for enhanced automation 100 is provided with user interface 124, which offers a customizable platform for users to interact with the system. This component allows operators to monitor system performance, control various automation processes, and receive real-time feedback. The user interface 124 connects to the control interface 110, enabling seamless interaction between human users and the AI system, making it accessible and intuitive for users with varying technical expertise.

[00043] Referring to Fig. 1, the deep learning-based AI controller for enhanced automation 100 is provided with decentralized control architecture 126, which allows for distributed decision-making across multiple nodes within the network. This architecture enhances system resilience and reduces processing bottlenecks by enabling localized responses to operational changes. The decentralized control architecture 126 works in conjunction with the decision-making module 108 and control interface 110 to ensure that decisions can be executed efficiently across various sections of the automation system.

[00044] Referring to Fig. 1, the deep learning-based AI controller for enhanced automation 100 is provided with energy-aware optimization algorithms 128, which manage energy consumption efficiently without compromising system performance. These algorithms are integrated into the resource optimization module 118, ensuring that the system can dynamically adjust energy usage based on real-time operational needs. The energy-aware optimization algorithms 128 play a crucial role in supporting sustainability initiatives by minimizing energy waste.

[00045] Referring to Fig. 1, the deep learning-based AI controller for enhanced automation 100 is provided with anomaly detection and root cause analysis system 130, which identifies deviations from normal system performance and conducts in-depth analysis to determine the underlying causes. This system works closely with the real-time monitoring system 112 and the deep learning engine 106 to provide proactive maintenance solutions. The anomaly detection and root cause analysis system 130 helps prevent system failures and contributes to continuous process improvement.

[00046] Referring to Fig. 1, the deep learning-based AI controller for enhanced automation 100 is provided with real-time simulation and modeling engine 132, which allows the system to simulate different operational scenarios and evaluate potential outcomes before executing decisions. This component is particularly useful for optimizing decision-making in complex environments. The real-time simulation and modeling engine 132 interacts with the decision-making module 108 and resource optimization module 118 to enhance the accuracy and efficiency of the AI controller's actions.


[00047] Referring to Fig 2, there is illustrated method 200 for deep learning based AI controller for enhanced automation 100. The method comprises:

At step 202, method 200 includes the data acquisition module 102 collecting data from sensors and IoT devices;

At step 204, method 200 includes the preprocessing module 104 filtering and normalizing the raw data to prepare it for analysis;

At step 206, method 200 includes the deep learning engine 106 analyzing the preprocessed data, identifying patterns, and generating insights for decision-making;

At step 208, method 200 includes the decision-making module 108 evaluating the insights and selecting the optimal action based on real-time conditions;

At step 210, method 200 includes the control interface 110 translating the decisions into commands for execution by the connected automation systems;

At step 212, method 200 includes the real-time monitoring system 112 continuously tracking system performance and operational parameters, sending feedback to other modules;

At step 214, method 200 includes the adaptive learning system 114 refining algorithms based on feedback from real-time operations, improving future decision-making;

At step 216, method 200 includes the predictive maintenance engine 116 analyzing historical and real-time data to forecast equipment maintenance needs;

At step 218, method 200 includes the resource optimization module 118 dynamically adjusting resource allocation based on operational demands and efficiency;

At step 220, method 200 includes the multi-modal data fusion system 120 integrating diverse data sources to enhance the accuracy and comprehensiveness of the insights generated by the deep learning engine 106;

At step 222, method 200 includes the self-learning feedback loop 122 continuously improving the deep learning engine 106 by updating it with operational outcomes;

At step 224, method 200 includes the user interface 124 providing real-time performance feedback and control options to operators, enabling smooth interaction with the system.


[00048] 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.

[00049] 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.

[00050] 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 based AI controller for enhanced automation 100 comprising of
data acquisition module 102 to collect data from various sensors and IoT devices;
preprocessing module 104 to filter and normalize raw data for analysis;
deep learning engine 106 to analyze preprocessed data and generate insights;
decision-making module 108 to evaluate insights and select optimal actions;
control interface 110 to translate decisions into executable commands for automation systems;
real-time monitoring system 112 to continuously track system performance and send feedback;
adaptive learning system 114 to refine algorithms based on real-time operational feedback;
predictive maintenance engine 116 to analyze data and forecast maintenance needs;
resource optimization module 118 to dynamically adjust resource allocation based on demand;
multi-modal data fusion system 120 to integrate diverse data sources for comprehensive analysis;
self-learning feedback loop 122 to continuously update the system with operational outcomes;
user interface 124 to provide real-time performance feedback and control options to users;
decentralized control architecture 126 to enable distributed decision-making across multiple nodes;
energy-aware optimization algorithms 128 to manage energy consumption efficiently while maintaining performance;
anomaly detection and root cause analysis system 130 to identify deviations and analyze their causes; and
real-time simulation and modeling engine 132 to simulate operational scenarios and evaluate outcomes.

2. The deep learning-based AI controller for enhanced automation 100 as claimed in claim 1, wherein data acquisition module 102 is configured to collect real-time data from various sensors and IoT devices, ensuring continuous data flow for processing and decision-making.

3. The deep learning-based AI controller for enhanced automation as claimed in claim 1, wherein preprocessing module 104 is configured to filter, normalize, and transform the raw data into a suitable format for analysis by the deep learning engine 106.

4. The deep learning-based AI controller for enhanced automation 100 as claimed in claim 1, wherein deep learning engine 106 is configured to analyze preprocessed data using advanced neural network architectures, detect patterns, and generate insights for decision-making.

5. The deep learning-based AI controller for enhanced automation 100 as claimed in claim 1, wherein decision-making module 108 is configured to evaluate the insights provided by the deep learning engine 106, selecting the optimal course of action based on real-time data.

6. The deep learning-based AI controller for enhanced automation 100 as claimed in claim 1, wherein control interface 110 is configured to translate the decisions made by decision-making module 108 into executable commands for external automation systems or devices.

7. The deep learning-based AI controller for enhanced automation 100 as claimed in claim 1, wherein real-time monitoring system 112 is configured to continuously track system performance and operational parameters, providing feedback to other modules for improved efficiency and error detection.

8. The deep learning-based AI controller for enhanced automation 100 as claimed in claim 1, wherein adaptive learning system 114 is configured to refine algorithms and improve decision-making capabilities based on feedback from real-time operations and changing environmental conditions.

9. The deep learning-based AI controller for enhanced automation 100 as claimed in claim 1, wherein resource optimization module 118 is configured to dynamically adjust resource allocation and energy consumption, ensuring efficient operation based on real-time demand and usage patterns.

10. The deep learning based AI controller for enhanced automation 100 as claimed in claim 1, wherein method comprises of
data acquisition module 102 collecting data from sensors and IoT devices;
preprocessing module 104 filtering and normalizing the raw data to prepare it for analysis;
deep learning engine 106 analyzing the preprocessed data, identifying patterns, and generating insights for decision-making;
decision-making module 108 evaluating the insights and selecting the optimal action based on real-time conditions;
control interface 110 translating the decisions into commands for execution by the connected automation systems;

real-time monitoring system 112 continuously tracking system performance and operational parameters, sending feedback to other modules;
adaptive learning system 114 refining algorithms based on feedback from real-time operations, improving future decision-making;
predictive maintenance engine 116 analyzing historical and real-time data to forecast equipment maintenance needs;
resource optimization module 118 dynamically adjusting resource allocation based on operational demands and efficiency;
multi-modal data fusion system 120 integrating diverse data sources to enhance the accuracy and comprehensiveness of the insights generated by the deep learning engine 106;
self-learning feedback loop 122 continuously improving the deep learning engine 106 by updating it with operational outcomes; and
user interface 124 providing real-time performance feedback and control options to operators, enabling smooth interaction with the system.

Documents