Intelligent self-adaptive industrial robot control system and method thereof

Abstract

The invention discloses an intelligent self-adaptive industrial robot control system and method, and the method comprises the steps: carrying out the automatic recognition and self-inspection of a sensor, an actuator and a controller module through a standardized interface when the system is started; various sensors are used for environment monitoring, acquired data are stored in a cloud end, and a sensor fusion technology is applied for integration; the system state and the environment condition are monitored in real time, machine learning and an artificial intelligence algorithm are used for analyzing the operation state and the task effect, and a corresponding operation strategy is formulated; based on task execution and environment information, an execution path is generated by adopting a path planning algorithm, and an operation strategy and parameters are dynamically adjusted according to real-time feedback; a real-time safety monitoring mechanism is introduced, and a safety threshold value is set; sensor data are analyzed regularly, potential faults are identified, and fault prediction is carried out; according to the intelligent self-adaptive industrial robot control system and method, when the robot is designed, the safety of man-machine cooperation is considered, and safety protection measures are added.

Description

Technical Field

[0001] The present invention belongs to the field of adaptive industrial robot control, and in particular relates to an intelligent adaptive industrial robot control system and a method thereof. Background Art

[0002] An intelligent adaptive industrial robot control system is a system that uses advanced technology to improve the flexibility and adaptability of industrial robots so that they can operate efficiently in a constantly changing production environment. The system can dynamically adjust the robot's control strategy based on changes in the external environment and internal state; through machine learning and artificial intelligence technology, the system can analyze and learn data during the operation process to optimize decision-making and improve work efficiency; real-time monitoring and feedback, the system can monitor the robot's working status in real time and make adjustments based on feedback to ensure accuracy and reliability.

[0003] However, despite the many advantages of intelligent adaptive industrial robot control systems, they also face some challenges. When robots work together with human workers, there may be safety hazards and robots may cause unexpected injuries when performing tasks. Summary of the invention

[0004] In view of the deficiencies in the prior art, the purpose of the present invention is to provide an intelligent adaptive industrial robot control system and method thereof, and to consider the safety of human-machine collaboration and add safety protection measures when designing the robot.

[0005] The technical solution adopted by the present invention to solve its technical problem is:

[0006] Intelligent adaptive industrial robot control system, including:

[0007] Sensor module, used to integrate sensor vision, force, temperature, and acceleration data to monitor environmental information in real time;

[0008] The control module is used to introduce an AI-based reinforcement learning adaptive control algorithm and generate a fault-tolerant mechanism so that the industrial robot control system automatically switches to a backup solution in the event of a fault;

[0009] The execution module is used to use a servo motor and an actuator to realize the grabbing, handling and welding operations through the servo motor and the actuator;

[0010] User interface module, used to design intuitive and easy-to-use operation interface for visual monitoring and manual intervention functions;

[0011] Intelligent decision-making module, which is used to collect and analyze data during operation, identify performance bottlenecks, analyze and process data using machine learning algorithms, generate intelligent decisions and optimize production processes, and introduce online learning algorithms, through which the system updates and adjusts its control strategy during operation;

[0012] Safety and stability modules are used to implement buffer and emergency stop button safety protection measures, and implement predictive maintenance technology to identify potential failures in advance by monitoring system status.

[0013] Intelligent adaptive industrial robot control method, including:

[0014] Initialize the intelligent adaptive industrial robot control system. When the system starts, it automatically identifies sensors, actuators, and controller modules through standardized interfaces, and performs equipment self-checks on the functions and status of each component of the system.

[0015] Based on the use of vision, force, temperature, and humidity sensors for environmental monitoring, the collected environmental data is stored in the cloud, and sensor fusion technology is applied to integrate data from different sensors;

[0016] By monitoring system status and environmental conditions in real time, machine learning and artificial intelligence algorithms are used to analyze the current operating status and task execution effect data, and operating strategies are formulated based on current status data and historical data;

[0017] Based on the current task execution and environmental information data, a path planning algorithm is used to generate an execution path. During the task execution process, the system dynamically adjusts the operation strategy and execution parameters according to real-time feedback and environmental changes;

[0018] Introduce a safety monitoring mechanism to monitor the system operation status in real time and set safety thresholds. When an abnormality occurs, it will automatically alarm and take protective measures.

[0019] Based on the introduced safety monitoring mechanism, the fault prediction algorithm of the intelligent adaptive industrial robot control system regularly analyzes sensor data, identifies potential faults, and performs maintenance and repairs before faults occur.

[0020] As a preferred method, the intelligent adaptive industrial robot control system is initialized, and when the system is started, the sensor, actuator, and controller modules are automatically identified through the standardized interface, and the function and status of each component of the system are self-checked by the following method:

[0021] The connection is established with the sensor, actuator, and controller modules through the CAN standardized communication protocol. The controller sends an identification request signal to each module to query its identity and status;

[0022] The controller compares the received identity information with the predefined module list to confirm the function and type of the module;

[0023] During the self-check process, if any module is found to be malfunctioning, the system will record the error information and notify the operator through the alarm system.

[0024] As a preferred method, based on the use of vision, force, temperature, and humidity sensors for environmental monitoring, the collected environmental data is stored in the cloud, and the sensor fusion technology is applied to integrate the data from different sensors as follows:

[0025] Through the selected vision, force, temperature, and humidity sensors, the sensors are deployed in the location scene of the robot or equipment to collect various data in real time;

[0026] The sensor regularly collects environmental data through the I2C standardized interface. The collected data should include timestamp, sensor type, sensor ID, and measurement value information data, and send the real-time collected data to the cloud server through the Wi-Fi wireless network;

[0027] Based on the collected environmental data, the collected raw data is cleaned and preprocessed, and sensor fusion technology is used to integrate the data from different sensors into a unified environmental status description, and the fused environmental status information is used to form a unified environmental monitoring report.

[0028] As a preferred method, by real-time monitoring of system status and environmental conditions, using machine learning and artificial intelligence algorithms to analyze the current operating status and task execution effect data, and formulating operating strategies based on current status data and historical data:

[0029] Adopt regression analysis machine learning models according to task execution requirements, use historical data to train and validate the selected machine learning models, and use cross-validation techniques to evaluate the performance of the models;

[0030] Apply the trained model to analyze real-time data, predict the current operation status and task execution effect, compare the analysis results with the set thresholds or standards, and identify potential problems or improvement opportunities;

[0031] Based on the analysis results of current status data and historical data, the speed and load working parameters are automatically adjusted to adapt to environmental changes.

[0032] As a preferred method, based on the current task execution and environmental information data, a path planning algorithm is used to generate an execution path. During the task execution process, the system dynamically adjusts the operation strategy and execution parameters according to real-time feedback and environmental changes.

[0033] Based on the objectives and requirements of the task, including the starting point, end point, obstacle location, priority and execution time information, the Dijkstra path planning algorithm is used to generate the optimal path;

[0034] Generate an execution path based on the task and environment information, including the path points and travel direction of each stage, and set the basic parameters of the path execution, including speed, acceleration and steering angle;

[0035] During the execution of the task, feedback data is collected in real time, including position, speed, and environmental change information. Through sensor monitoring and data analysis, dynamic obstacles that affect execution are identified;

[0036] Based on real-time feedback information, dynamically adjust the operation strategy and execution parameters including:

[0037] Path adjustment: if real-time data indicates that the current path is not feasible or inefficient, the system will recalculate the path or modify the current path points; speed and acceleration adjustment: adjust the speed and acceleration of the robot's movement according to the complexity of the environment and the distance of obstacles; task priority adjustment: if environmental conditions change or new obstacles appear, adjust the task priority or execution order.

[0038] As a preferred method, a safety monitoring mechanism is introduced to monitor the system operation status in real time and set safety thresholds. When an abnormality occurs, an automatic alarm is issued and protective measures are taken.

[0039] Deploy temperature, pressure, humidity, vibration, and flow sensors to monitor the key operating status of the equipment in real time, and transmit the data collected by each sensor to the central monitoring system via wireless network;

[0040] According to the characteristics of the equipment and environment, set the temperature upper limit and pressure limit safety thresholds. The thresholds are set according to the equipment's working specifications and industry standards, and the safety thresholds are regularly evaluated and adjusted;

[0041] Use visualization tools to display the status data of each sensor in real time through the monitoring system, including the comparison between the current value and the threshold value;

[0042] Set up an alarm mechanism in the monitoring system, including sound and light alarms, SMS notifications, emails or APP push notifications, set different levels of alarms, and send different alarms according to the severity of the abnormality;

[0043] According to the alarm type, protective measures are automatically executed. The protective measures include automatically starting the cooling system when the temperature exceeds the standard, automatically closing the valve or shutting down when the pressure is abnormal, and automatically stopping the equipment operation when abnormal vibration is detected to prevent damage.

[0044] As a preferred method, based on the introduced safety monitoring mechanism, the fault prediction algorithm of the intelligent adaptive industrial robot control system regularly analyzes sensor data, identifies potential faults, and performs maintenance and repair before the fault occurs:

[0045] According to the execution requirements, the machine learning algorithm fault prediction algorithm is used to extract the mean, standard deviation, peak value, and frequency component features from the sensor data to provide input for model training;

[0046] Use historical data to train and validate the prediction model, use cross-validation and other methods to evaluate model performance, and optimize the model;

[0047] During operation, the system continuously monitors sensor data, uses the trained fault prediction model to analyze data in real time, identifies potential faults, calculates the probability of fault occurrence, and sets a threshold for the fault prediction result. If the predicted fault probability exceeds the set value, the system will mark it as a potential fault and trigger an alarm mechanism.

[0048] Based on the fault prediction results, a preventive maintenance plan is formulated to conduct regular inspections and maintenance before a fault occurs, and automated repairs are set up to perform repair operations such as restarting the equipment or adjusting working parameters through the automated system.

[0049] Another technical problem to be solved by the present invention is to provide an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, an intelligent adaptive industrial robot control system and method as described above are implemented.

[0050] Another technical problem to be solved by the present invention is to provide a computer-readable storage medium on which a computer program is stored, and when the program is executed by a processor, an intelligent adaptive industrial robot control system and method thereof are implemented.

[0051] The beneficial effects of the present invention are:

[0052] Automatically identify sensors, actuators and controller modules through standardized interfaces, and perform self-inspections on each component to ensure normal operation of the system at startup, reducing the need for manual intervention and the possibility of system failure; use multiple sensors such as vision, force, temperature and humidity for environmental monitoring, combined with sensor fusion technology to achieve a comprehensive understanding of the environment. This integration enhances the system's environmental perception capabilities, enabling it to respond more accurately in complex environments; formulate intelligent operation strategies by analyzing current operating status and historical data. This capability enables the robot to continuously optimize operations based on real-time feedback and environmental changes, improve work efficiency and reduce resource waste; based on real-time monitored environmental information and task requirements, a path planning algorithm is used to generate an execution path. During task execution, the system can dynamically adjust the path and operation strategy. To cope with environmental changes and emergencies, improve operational flexibility; introduce a safety monitoring mechanism to monitor the system's operating status in real time and set safety thresholds. When an abnormal situation is detected, the system can automatically alarm and take protective measures to ensure the safety of personnel and equipment; regularly analyze sensor data to identify potential faults, and perform maintenance and repairs before faults occur; through integrated control methods, intelligent adaptive industrial robots can achieve higher operating efficiency in complex production environments, reduce operating costs, and ultimately improve overall productivity; the modular design of the system allows different sensors and actuators to be replaced and upgraded as needed, enhancing the system's scalability and adaptability, and can quickly adapt to different task requirements; through environmental and operational data stored in the cloud, the system can continuously learn and improve to promote technological innovation. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 The figure is a flow chart of the intelligent adaptive industrial robot control system and method of the present invention. DETAILED DESCRIPTION

[0054] The principles and features of the present invention are described below, and the examples are only used to explain the present invention and are not used to limit the scope of the present invention. The present invention is described more specifically by way of example in the following paragraphs. According to the following description and claims, the advantages and features of the present invention will become clearer.

[0055] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art of the present invention. The terms used herein in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The term "and / or" used herein includes any and all combinations of one or more related listed items.

[0056] Example

[0057] Intelligent adaptive industrial robot control system, including:

[0058] Sensor module, used to integrate sensor vision, force, temperature, and acceleration data to monitor environmental information in real time;

[0059] The control module is used to introduce an AI-based reinforcement learning adaptive control algorithm and generate a fault-tolerant mechanism so that the industrial robot control system automatically switches to a backup solution in the event of a fault;

[0060] The execution module is used to use a servo motor and an actuator to realize the grabbing, handling and welding operations through the servo motor and the actuator;

[0061] User interface module, used to design intuitive and easy-to-use operation interface for visual monitoring and manual intervention functions;

[0062] Intelligent decision-making module, which is used to collect and analyze data during operation, identify performance bottlenecks, analyze and process data using machine learning algorithms, generate intelligent decisions and optimize production processes, and introduce online learning algorithms, through which the system updates and adjusts its control strategy during operation;

[0063] Safety and stability modules are used to implement buffer and emergency stop button safety protection measures, and implement predictive maintenance technology to identify potential failures in advance by monitoring system status.

[0064] The sensor module integrates multiple sensors (vision, force, temperature, acceleration) and can monitor environmental conditions in real time. This real-time data collection helps to respond to environmental changes in a timely manner and improve operational accuracy and safety. The control module uses artificial intelligence reinforcement learning algorithms to automatically adjust control strategies based on real-time data. This adaptive ability enables the system to optimize performance and improve operational efficiency. The execution module uses servo motors and actuators to achieve multiple operations such as grabbing, handling, and welding, which improves the flexibility and diversity of the robot and meets the needs of different industrial tasks. The user interface module provides visual monitoring and manual intervention functions, allowing operators to monitor and control the robot more conveniently, improving user experience and operational efficiency. The intelligent decision-making module uses machine learning algorithms to analyze operational data, identify performance bottlenecks, generate intelligent decisions, and optimize production processes. This data-driven approach can continuously improve system performance. The safety and stability module uses measures such as buffers and emergency stop buttons to ensure that power can be cut off in an emergency to protect personnel and equipment safety. By monitoring system status and implementing predictive maintenance technology, potential faults can be identified in advance, downtime and maintenance costs can be reduced, and system reliability can be improved.

[0065] Intelligent adaptive industrial robot control method, including:

[0066] Initialize the intelligent adaptive industrial robot control system. When the system starts, it automatically identifies sensors, actuators, and controller modules through standardized interfaces, and performs equipment self-checks on the functions and status of each component of the system.

[0067] Based on the use of vision, force, temperature, and humidity sensors for environmental monitoring, the collected environmental data is stored in the cloud, and sensor fusion technology is applied to integrate data from different sensors;

[0068] By monitoring system status and environmental conditions in real time, machine learning and artificial intelligence algorithms are used to analyze the current operating status and task execution effect data, and operating strategies are formulated based on current status data and historical data;

[0069] Based on the current task execution and environmental information data, a path planning algorithm is used to generate an execution path. During the task execution process, the system dynamically adjusts the operation strategy and execution parameters according to real-time feedback and environmental changes;

[0070] Introduce a safety monitoring mechanism to monitor the system operation status in real time and set safety thresholds. When an abnormality occurs, it will automatically alarm and take protective measures.

[0071] Based on the introduced safety monitoring mechanism, the fault prediction algorithm of the intelligent adaptive industrial robot control system regularly analyzes sensor data, identifies potential faults, and performs maintenance and repairs before faults occur.

[0072] When the system starts, it automatically identifies and self-checks each module to ensure that all components are working properly, thereby improving the reliability and availability of the system. It uses a variety of sensors (vision, force, temperature, and humidity) to conduct comprehensive environmental monitoring, store data in the cloud, and integrate data through sensor fusion technology, which helps to obtain more comprehensive environmental information. By analyzing real-time and historical data through machine learning and artificial intelligence algorithms, the system can dynamically formulate and optimize operating strategies to improve operational efficiency and accuracy. It uses a path planning algorithm to generate execution paths, and adjusts operating strategies and parameters based on real-time feedback, thereby improving the robot's ability to adapt to dynamic environments. It introduces a safety monitoring mechanism to monitor the system status in real time and set safety thresholds to ensure that alarms are issued in a timely manner and protective measures are taken in abnormal situations to ensure the safety of personnel and equipment. It regularly analyzes sensor data, identifies potential faults, and performs maintenance before a fault occurs, effectively reducing downtime and repair costs and improving system reliability and maintenance efficiency.

[0073] Initialize the intelligent adaptive industrial robot control system. When the system starts, automatically identify the sensor, actuator, and controller modules through the standardized interface, and perform a self-check on the functions and status of each component of the system.

[0074] The connection is established with the sensor, actuator, and controller modules through the CAN standardized communication protocol. The controller sends an identification request signal to each module to query its identity and status;

[0075] The controller compares the received identity information with the predefined module list to confirm the function and type of the module;

[0076] During the self-check process, if any module is found to be malfunctioning, the system will record the error information and notify the operator through the alarm system.

[0077] Through system self-check, potential problems can be discovered in time before the system is officially put into operation, reducing the equipment failure rate and improving the overall reliability of the system; the system monitors the status of all modules at startup to ensure that all components are in normal working condition during operation, which helps to prevent production interruptions due to component failure; automatic identification and recording of error information can provide reference for subsequent maintenance, reduce the time and effort of manual inspection, and improve maintenance efficiency; the use of CAN protocol and standardized interface can achieve interoperability between modules of different manufacturers, facilitating system upgrades and expansions; timely alarm mechanism ensures that operators can respond quickly when problems occur, avoid accidents and losses, and improve system safety.

[0078] Based on the use of vision, force, temperature, and humidity sensors for environmental monitoring, the collected environmental data is stored in the cloud, and the sensor fusion technology is applied to integrate the data from different sensors as follows:

[0079] Through the selected vision, force, temperature, and humidity sensors, the sensors are deployed in the location scene of the robot or equipment to collect various data in real time;

[0080] The sensor regularly collects environmental data through the I2C standardized interface. The collected data should include timestamp, sensor type, sensor ID, and measurement value information data, and send the real-time collected data to the cloud server through the Wi-Fi wireless network;

[0081] Based on the collected environmental data, the collected raw data is cleaned and preprocessed, and sensor fusion technology is used to integrate the data from different sensors into a unified environmental status description, and the fused environmental status information is used to form a unified environmental monitoring report.

[0082] Through real-time data collection from multiple sensors, the real-time status of the environment can be accurately grasped to provide support for the operation of robots and equipment; sensor fusion technology can eliminate the errors of single sensor data and improve the accuracy of environmental monitoring by integrating data from multiple sensors; cloud storage of data makes data access, analysis and management more convenient while ensuring data security and reliability; through data cleaning and preprocessing steps, interference in subsequent analysis can be effectively reduced and the effectiveness of analysis results can be improved; a unified environmental monitoring report is generated so that data from different sources can be displayed in a centralized manner, facilitating operators to quickly understand and make decisions.

[0083] By monitoring the system status and environmental conditions in real time, using machine learning and artificial intelligence algorithms to analyze the current operating status and task execution effect data, the method of formulating operating strategies based on current status data and historical data is as follows:

[0084] Adopt regression analysis machine learning models according to task execution requirements, use historical data to train and validate the selected machine learning models, and use cross-validation techniques to evaluate the performance of the models;

[0085] Apply the trained model to analyze real-time data, predict the current operation status and task execution effect, compare the analysis results with the set thresholds or standards, and identify potential problems or improvement opportunities;

[0086] Based on the analysis results of current status data and historical data, the speed and load working parameters are automatically adjusted to adapt to environmental changes.

[0087] Through real-time monitoring and analysis, the system can respond to environmental changes in a timely manner and improve operational efficiency and safety; the application of machine learning models makes decisions based on data rather than experience, thereby improving the accuracy and effectiveness of operating strategies; by continuously learning historical data and real-time data, the system can continuously optimize its operating strategies, identify and improve potential problems, and improve overall performance; automatic adjustment of operating strategies reduces reliance on human intervention, reduces operating costs, and improves work efficiency; the solution can be adjusted and expanded according to different tasks and environmental conditions, and has good adaptability.

[0088] Based on the current task execution and environmental information data, the path planning algorithm is used to generate the execution path. During the task execution process, the system dynamically adjusts the operation strategy and execution parameters according to real-time feedback and environmental changes as follows:

[0089] Based on the objectives and requirements of the task, including the starting point, end point, obstacle location, priority and execution time information, the Dijkstra path planning algorithm is used to generate the optimal path;

[0090] Generate an execution path based on the task and environment information, including the path points and travel direction of each stage, and set the basic parameters of the path execution, including speed, acceleration and steering angle;

[0091] During the execution of the task, feedback data is collected in real time, including position, speed, and environmental change information. Through sensor monitoring and data analysis, dynamic obstacles that affect execution are identified;

[0092] Based on real-time feedback information, dynamically adjust the operation strategy and execution parameters including:

[0093] Path adjustment: if real-time data indicates that the current path is not feasible or inefficient, the system will recalculate the path or modify the current path points; speed and acceleration adjustment: adjust the speed and acceleration of the robot's movement according to the complexity of the environment and the distance of obstacles; task priority adjustment: if environmental conditions change or new obstacles appear, adjust the task priority or execution order.

[0094] By using the optimal path and dynamic adjustment mechanism, the system can quickly find the execution plan in a complex environment and improve the efficiency of task completion; the real-time monitoring and feedback mechanism makes the system highly flexible and can make rapid adjustments when the environment changes to adapt to different task requirements; by dynamically identifying obstacles and adjusting the path and speed, the risk of accidental collisions and accidents can be effectively reduced, and operational safety can be improved; the strategy of dynamically adjusting speed, acceleration and task priority can optimize the use of resources according to actual conditions and avoid waste of resources; the system accumulates real-time data during the execution process, which can provide richer historical data support for subsequent path planning and strategy adjustments, and promote the improvement of intelligent decision-making capabilities.

[0095] Introduce a safety monitoring mechanism to monitor the system operation status in real time and set safety thresholds. When an abnormality occurs, the system will automatically alarm and take protective measures.

[0096] Deploy temperature, pressure, humidity, vibration, and flow sensors to monitor the key operating status of the equipment in real time, and transmit the data collected by each sensor to the central monitoring system via wireless network;

[0097] According to the characteristics of the equipment and environment, set the temperature upper limit and pressure limit safety thresholds. The thresholds are set according to the equipment's working specifications and industry standards, and the safety thresholds are regularly evaluated and adjusted;

[0098] Use visualization tools to display the status data of each sensor in real time through the monitoring system, including the comparison between the current value and the threshold value;

[0099] Set up an alarm mechanism in the monitoring system, including sound and light alarms, SMS notifications, emails or APP push notifications, set different levels of alarms, and send different alarms according to the severity of the abnormality;

[0100] According to the alarm type, protective measures are automatically executed. The protective measures include automatically starting the cooling system when the temperature exceeds the standard, automatically closing the valve or shutting down when the pressure is abnormal, and automatically stopping the equipment operation when abnormal vibration is detected to prevent damage.

[0101] Through real-time monitoring and alarm mechanisms, equipment anomalies can be discovered and handled in a timely manner to prevent accidents and ensure safe operation of equipment. By monitoring equipment status and taking timely protective measures, the maintenance and downtime costs caused by equipment failure can be reduced and the service life of equipment can be increased. Through automated monitoring and alarm mechanisms, the need for manual intervention is reduced, allowing operators to focus on other more important tasks and improve overall work efficiency. Through a visual monitoring system, managers can understand the operating status of equipment in real time, conduct data analysis and decision-making, and improve the level of intelligent management. By setting safety thresholds and conducting regular assessments, ensure that equipment operation complies with industry safety standards and reduce legal risks and compliance issues.

[0102] Based on the introduced safety monitoring mechanism, the fault prediction algorithm of the intelligent adaptive industrial robot control system regularly analyzes sensor data, identifies potential faults, and performs maintenance and repair before the fault occurs.

[0103] According to the execution requirements, the machine learning algorithm fault prediction algorithm is used to extract the mean, standard deviation, peak value, and frequency component features from the sensor data to provide input for model training;

[0104] Use historical data to train and validate the prediction model, use cross-validation and other methods to evaluate model performance, and optimize the model;

[0105] During operation, the system continuously monitors sensor data, uses the trained fault prediction model to analyze data in real time, identifies potential faults, calculates the probability of fault occurrence, and sets a threshold for the fault prediction result. If the predicted fault probability exceeds the set value, the system will mark it as a potential fault and trigger an alarm mechanism.

[0106] Based on the fault prediction results, a preventive maintenance plan is formulated to conduct regular inspections and maintenance before a fault occurs, and automated repairs are set up to perform repair operations such as restarting the equipment or adjusting working parameters through the automated system.

[0107] Through real-time monitoring and data analysis, potential problems can be identified before failures occur, reducing the risk of production stagnation due to equipment failures; through preventive maintenance plans, emergency repair costs and equipment downtime losses caused by sudden failures can be reduced, improving overall economic benefits; the use of machine learning algorithms can more accurately identify failure modes, thereby improving the reliability and stability of equipment operation and reducing failure rates; through data-driven maintenance decisions, maintenance personnel and resources can be reasonably arranged, maintenance operations can be optimized, and work efficiency can be improved; combined with intelligent monitoring and fault prediction technology, intelligent equipment management can be realized, the level of automation in the production process can be improved, and the competitiveness of the enterprise can be enhanced.

[0108] This embodiment also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the intelligent adaptive industrial robot control system and method described above are implemented.

[0109] This embodiment also provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the intelligent adaptive industrial robot control system and method thereof as described above are implemented.

[0110] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0111] Those skilled in the art will clearly understand that for the sake of convenience and brevity of description, only the division of the above-mentioned functional units and modules is used as an example. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the system can be divided into different functional units or modules to complete all or part of the functions described above.

[0112] The above embodiments of the present invention are not intended to limit the protection scope of the present invention, and the implementation modes of the present invention are not limited thereto. All other modifications, replacements or changes made to the above structures of the present invention based on the above contents of the present invention, in accordance with common technical knowledge and customary means in the art, without departing from the above basic technical ideas of the present invention, should fall within the protection scope of the present invention.

Claims

1. Intelligent adaptive industrial robot control system, characterized in that: Included are: Sensor module, used to integrate sensor vision, force, temperature, and acceleration data to monitor environmental information in real time; The control module is used to introduce an AI-based reinforcement learning adaptive control algorithm and generate a fault-tolerant mechanism so that the industrial robot control system automatically switches to a backup solution in the event of a fault; The execution module is used to use a servo motor and an actuator to realize the operation modes of grabbing, carrying and welding through the servo motor and the actuator; User interface module, used to design intuitive and easy-to-use operation interface for visual monitoring and manual intervention functions; Intelligent decision-making module, which is used to collect and analyze data during operation, identify performance bottlenecks, analyze and process data using machine learning algorithms, generate intelligent decisions and optimize production processes, and introduce online learning algorithms, through which the system updates and adjusts its control strategy during operation; Safety and stability modules are used to implement buffer and emergency stop button safety protection measures, and implement predictive maintenance technology to identify potential failures in advance by monitoring system status.

2. An intelligent adaptive industrial robot control method, characterized in that: Including; Initialize the intelligent adaptive industrial robot control system. When the system starts, it automatically identifies sensors, actuators, and controller modules through standardized interfaces, and performs equipment self-checks on the functions and status of each component of the system. Based on the use of vision, force, temperature, and humidity sensors for environmental monitoring, the collected environmental data is stored in the cloud, and sensor fusion technology is applied to integrate data from different sensors; By monitoring system status and environmental conditions in real time, machine learning and artificial intelligence algorithms are used to analyze the current operating status and task execution effect data, and operating strategies are formulated based on current status data and historical data; Based on the current task execution and environmental information data, a path planning algorithm is used to generate an execution path. During the task execution process, the system dynamically adjusts the operation strategy and execution parameters according to real-time feedback and environmental changes; Introduce a safety monitoring mechanism to monitor the system operation status in real time and set safety thresholds. When an abnormality occurs, it will automatically alarm and take protective measures. Based on the introduced safety monitoring mechanism, the fault prediction algorithm of the intelligent adaptive industrial robot control system regularly analyzes sensor data, identifies potential faults, and performs maintenance and repairs before faults occur.

3. The intelligent adaptive industrial robot control method according to claim 2, characterized in that: Initialize the intelligent adaptive industrial robot control system. When the system starts, automatically identify the sensor, actuator, and controller modules through the standardized interface, and perform a self-check on the functions and status of each component of the system. The controller establishes connections with sensors, actuators, and controller modules through the CAN standardized communication protocol, and sends identification request signals to each module to query its identity and status; The controller compares the received identity information with the predefined module list to confirm the function and type of the module; During the self-check process, if any module is found to be malfunctioning, the system will record the error information and notify the operator through the alarm system.

4. The intelligent adaptive industrial robot control method according to claim 3, characterized in that: Based on the use of vision, force, temperature, and humidity sensors for environmental monitoring, the collected environmental data is stored in the cloud, and the sensor fusion technology is applied to integrate the data from different sensors as follows: Through the selected vision, force, temperature, and humidity sensors, the sensors are deployed in the location scene of the robot or equipment to collect various data in real time; The sensor regularly collects environmental data through the I2C standardized interface. The collected data should include timestamp, sensor type, sensor ID, and measurement value information data, and send the real-time collected data to the cloud server through the Wi-Fi wireless network; Based on the collected environmental data, the collected raw data is cleaned and preprocessed, and sensor fusion technology is used to integrate the data from different sensors into a unified environmental status description, and the fused environmental status information is used to form a unified environmental monitoring report.

5. The intelligent adaptive industrial robot control method according to claim 4, characterized in that: By monitoring the system status and environmental conditions in real time, using machine learning and artificial intelligence algorithms to analyze the current operating status and task execution effect data, the method of formulating operating strategies based on current status data and historical data is as follows: Adopt regression analysis machine learning models according to task execution requirements, use historical data to train and validate the selected machine learning models, and use cross-validation techniques to evaluate the performance of the models; Apply the trained model to analyze real-time data, predict the current operation status and task execution effect, compare the analysis results with the set thresholds or standards, and identify potential problems or improvement opportunities; According to the analysis results of current status data and historical data, the speed and load working parameters are automatically adjusted to adapt to environmental changes.

6. The intelligent adaptive industrial robot control method according to claim 5, characterized in that: Based on the current task execution and environmental information data, the path planning algorithm is used to generate the execution path. During the task execution process, the system dynamically adjusts the operation strategy and execution parameters according to real-time feedback and environmental changes as follows: Based on the objectives and requirements of the task, including the starting point, end point, obstacle location, priority and execution time information, the Dijkstra path planning algorithm is used to generate the optimal path; Generate an execution path based on the task and environment information, including the path points and travel direction of each stage, and set the basic parameters of the path execution, including speed, acceleration and steering angle; During the execution of the task, feedback data is collected in real time, including position, speed, and environmental change information. Through sensor monitoring and data analysis, dynamic obstacles that affect execution are identified; Based on real-time feedback information, dynamically adjust the operation strategy and execution parameters including: Path adjustment: if real-time data indicates that the current path is not feasible or inefficient, the system recalculates the path or modifies the current path points; speed and acceleration adjustment: adjust the speed and acceleration of the robot's movement according to the complexity of the environment and the distance to obstacles; Task priority adjustment: if environmental conditions change or new obstacles appear, adjust task priority or execution order.

7. The intelligent adaptive industrial robot control method according to claim 6, characterized in that: Introduce a safety monitoring mechanism to monitor the system operation status in real time and set safety thresholds. When an abnormality occurs, the system will automatically alarm and take protective measures. Deploy temperature, pressure, humidity, vibration, and flow sensors to monitor the key operating status of the equipment in real time, and transmit the data collected by each sensor to the central monitoring system via wireless network; According to the characteristics of the equipment and environment, set the temperature upper limit and pressure limit safety thresholds. The thresholds are set according to the equipment's working specifications and industry standards, and the safety thresholds are regularly evaluated and adjusted; Use visualization tools to display the status data of each sensor in real time through the monitoring system, including the comparison between the current value and the threshold value; Set up an alarm mechanism in the monitoring system, including sound and light alarms, SMS notifications, emails or APP push notifications, set different levels of alarms, and send different alarms according to the severity of the abnormality; According to the alarm type, protective measures are automatically executed. The protective measures include automatically starting the cooling system when the temperature exceeds the standard, automatically closing the valve or shutting down when the pressure is abnormal, and automatically stopping the equipment operation when abnormal vibration is detected to prevent damage.

8. The intelligent adaptive industrial robot control method according to claim 7, characterized in that: Based on the introduced safety monitoring mechanism, the fault prediction algorithm of the intelligent adaptive industrial robot control system regularly analyzes sensor data, identifies potential faults, and performs maintenance and repair before the fault occurs. According to the execution requirements, the machine learning algorithm fault prediction algorithm is used to extract the mean, standard deviation, peak value, and frequency component features from the sensor data to provide input for model training; Use historical data to train and validate the prediction model, use cross-validation and other methods to evaluate model performance, and optimize the model; During operation, the sensor data is continuously monitored, and the trained fault prediction model is used to analyze the data in real time, identify potential faults, calculate the probability of fault occurrence, and set a threshold for the fault prediction result. If the predicted fault probability exceeds the set value, the system will mark it as a potential fault and trigger an alarm mechanism; Based on the fault prediction results, a preventive maintenance plan is formulated to conduct regular inspections and maintenance before a fault occurs, and automated repairs are set up to restart the equipment or adjust working parameters through the automated system.

9. An electronic device, characterized in that: It comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the intelligent adaptive industrial robot control method as claimed in any one of claims 2 to 7 is implemented; 10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the intelligent adaptive industrial robot control method as described in any one of claims 2 to 7 is implemented.

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