Electromechanical equipment intelligent operation monitoring and optimization control system fusing AI and big data
Through the intelligent operation monitoring and optimization control system of electromechanical equipment that integrates AI and big data, the problem of poor adaptability to equipment aging and environmental changes in traditional monitoring and optimization technologies is solved, the stability and energy efficiency of equipment operation are improved, the failure rate and downtime are reduced, and the intelligent level of equipment management and operation and maintenance efficiency are improved.
Patent Information
- Application Number
- CN202510288180.6
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-03-12
- Publication Date
- 2025-08-08
AI Technical Summary
The existing mechanical and electrical equipment monitoring and optimization technologies rely on traditional sensor networks and fixed threshold alarms, and cannot adapt to equipment aging and environmental changes, resulting in high false alarm rates, frequent unplanned downtime, insufficient or excessive maintenance, and inability to predict failures, and data fragmentation and decision-making lag, resulting in passive operation and maintenance of equipment management.
The intelligent operation monitoring and optimization control system of electromechanical equipment integrating AI and big data is achieved through data acquisition, edge computing, cloud big data analysis, real-time monitoring and alarm, abnormal processing and optimization control, combined with human-computer interaction, comprehensive monitoring and optimization control of equipment status, and dynamically adjust strategies to adapt to equipment aging and environmental changes.
It improves the stability and energy efficiency of equipment operation, reduces the failure rate and downtime, realizes preventive maintenance and adaptive control, improves the intelligent level of equipment management and operation and maintenance efficiency, and enhances the operability and flexibility of the system.
Smart Images
Figure CN120447353A_ABST
Abstract
Description
Technical Field
[0001] The present invention belongs to the field of computer technology, and specifically relates to an intelligent operation monitoring and optimization control system for electromechanical equipment that integrates AI and big data. Background Art
[0002] Electromechanical equipment is a device that integrates mechanical and electronic technologies. It realizes automated and intelligent operation and control by integrating sensors, controllers, actuators and other components. These devices are widely used in industrial manufacturing, construction, transportation, agriculture and other fields. They not only improve production efficiency and product quality, but also promote the effective use of energy and sustainable development of the environment. From simple production line automation equipment to complex integrated systems, electromechanical equipment plays a vital role in modern society and is an important force in promoting scientific and technological progress and industrial upgrading.
[0003] The stable operation of electromechanical equipment is crucial to the continuity and safety of production. The monitoring and daily optimization of electromechanical equipment should not be ignored. Traditional electromechanical equipment operation monitoring and optimization technology mainly relies on traditional sensor networks and fixed threshold alarm mechanisms. Although this method can alarm when the threshold is reached, it still has some shortcomings. The setting of sensor thresholds relies on manual experience and cannot adapt to the drift of normal operating conditions caused by equipment aging and environmental changes. The false alarm rate is high. In addition, the optimization and maintenance of equipment mostly rely on the passive mode of post-maintenance or regular inspections. Sudden failures cannot be predicted, resulting in frequent unplanned downtime, excessive maintenance and insufficient maintenance coexist, and it is difficult to balance the spare parts inventory cost and equipment life loss. Existing technologies are limited by data fragmentation, decision lag and intelligent bottlenecks, resulting in equipment management being in a passive operation and maintenance state for a long time, so it needs to be improved. Summary of the Invention
[0004] The purpose of the present invention is to provide an intelligent operation monitoring and optimization control system for electromechanical equipment that integrates AI and big data to solve the problems raised in the above background technology.
[0005] To achieve the above objectives, the present invention provides the following technical solution: an intelligent operation monitoring and optimization control system for electromechanical equipment that integrates AI and big data, comprising:
[0006] Data acquisition module: responsible for collecting real-time operating data from various electromechanical equipment, including but not limited to key parameters such as temperature, pressure, vibration, current, voltage, and speed;
[0007] Edge computing module: cleans, denoises, and formats the collected raw data to ensure data quality, and is also responsible for lightweight AI reasoning;
[0008] Cloud-based big data platform module: This module includes big data on normal and abnormal operating conditions of related equipment in factory settings, equipment degradation curves, environmental data, work order records, and supply chain information.
[0009] Intelligent comparative analysis module: used to analyze the current operating data of electromechanical equipment with big data, automatically generate or adjust equipment operation strategies, and achieve energy efficiency optimization, fault warning and adaptive control;
[0010] Real-time monitoring and alarm module: monitors the equipment's operating status in real time. Once an anomaly is detected or an impending failure is predicted, the alarm mechanism is immediately triggered to notify relevant personnel to take countermeasures.
[0011] Exception handling module: Once the real-time monitoring and alarm module detects an anomaly, the exception handling module is immediately activated. Based on historical experience in the anomaly database and the processing suggestions generated by the intelligent comparative analysis module, emergency response measures are quickly formulated and implemented.
[0012] Optimization control module: converts the analysis results of the intelligent comparison and analysis module into PLC executable instructions, thereby generating optimization strategies and directly controlling the operating parameters of electromechanical equipment;
[0013] Human-computer interaction module: used to display device status and control AI decision-making.
[0014] Preferably, the cloud-based big data platform module includes an abnormal database unit and a normal database unit. The abnormal database unit can store historical abnormal events and their processing records, including abnormal type, abnormal data, occurrence time, impact range, processing measures and results; the normal database unit can record various types of data of the equipment under normal working conditions.
[0015] Preferably, the intelligent comparative analysis module includes a dynamic baseline generation unit and an AI hybrid model analysis unit. The dynamic baseline generation unit can construct an equipment health status baseline based on the normal operating condition data in the equipment factory settings in the cloud big data platform module, and generate an equipment operation status baseline based on the data cleaned by the edge computing module. The AI hybrid model analysis unit can analyze the operation status of the equipment through the data cleaned by the edge computing module and the equipment health status baseline and the equipment operation status baseline.
[0016] Preferably, the AI hybrid model analysis unit avoids purely data-driven counterintuitive decisions by embedding physical equations as AI training constraints and device design parameters as constraints into model training.
[0017] Preferably, the AI hybrid model analysis unit adopts a baseline optimization method based on reinforcement learning, so that the AI hybrid model analysis unit can automatically identify baseline drift caused by equipment aging, process upgrades, etc., and dynamically adjust the normal range.
[0018] Preferably, the optimization control module includes a weighting unit, which enables the optimization control module to comprehensively consider the downtime cost and potential failure risk when generating equipment maintenance decisions, and dynamically adjust the priority of the maintenance strategy by setting a weight factor, wherein the weight calculation formula is:
[0019]
[0020] Among them, C stop is the real-time downtime cost; C max is the maximum downtime cost threshold allowed by the system; R fault (t is the time-varying failure risk probability; R critical is the enterprise risk tolerance threshold; S priority is the production plan urgency index; a(t) is the time-varying factor of cost sensitivity; ω is the artificial weighting parameter, with a standardized value of 0-1.
[0021] Preferably, the human-computer interaction module supports a three-dimensional visualization interface for displaying the equipment health status baseline and the equipment operation status baseline, and allows engineers to manually adjust the maintenance strategy recommended by AI. The system records the results of manual decisions and feeds them back to the anomaly database.
[0022] Preferably, the optimization control module directly controls the operating parameters of the electromechanical equipment, including adjusting the motor speed and opening and closing valves, to achieve intelligent regulation. The operator can limit and release the control authority of the optimization control module in the human-computer interaction module, including the controllable operating parameter range and the controllable equipment range.
[0023] Preferably, the edge computing module is responsible for lightweight AI reasoning, which is used to monitor abnormal data with excessive fluctuations and reduce cloud transmission bandwidth usage.
[0024] The beneficial effects of the present invention are as follows:
[0025] Through functional modules such as real-time data collection, edge computing processing, cloud big data analysis, intelligent comparative analysis, real-time monitoring and alarm, exception handling, optimization control, and human-computer interaction, comprehensive monitoring and optimization control of the operating status of electromechanical equipment is achieved. This not only improves the energy efficiency and stability of equipment operation, reduces the failure rate and downtime, but also realizes preventive maintenance and adaptive control through intelligent decision support, significantly improving the intelligence level of equipment management and operation and maintenance efficiency. At the same time, the human-computer interaction module provides an intuitive equipment status display and AI decision-making control interface, enhancing the operability and flexibility of the system. BRIEF DESCRIPTION OF THE DRAWINGS
[0026] Figure 1 This is a system block diagram of the present invention. DETAILED DESCRIPTION
[0027] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.
[0028] like Figure 1 As shown, the embodiment of the present invention provides an intelligent operation monitoring and optimization control system for electromechanical equipment that integrates AI and big data, including:
[0029] Data acquisition module: responsible for collecting real-time operating data from various electromechanical equipment, including but not limited to key parameters such as temperature, pressure, vibration, current, voltage, and speed;
[0030] Edge computing module: cleans, denoises, and formats the collected raw data to ensure data quality, and is also responsible for lightweight AI reasoning;
[0031] Cloud-based big data platform module: This module includes big data on normal and abnormal operating conditions of related equipment in factory settings, equipment degradation curves, environmental data, work order records, and supply chain information.
[0032] Intelligent comparative analysis module: used to analyze the current operating data of electromechanical equipment with big data, automatically generate or adjust equipment operation strategies, and achieve energy efficiency optimization, fault warning and adaptive control;
[0033] Real-time monitoring and alarm module: monitors the equipment's operating status in real time. Once an anomaly is detected or an impending failure is predicted, the alarm mechanism is immediately triggered to notify relevant personnel to take countermeasures.
[0034] Exception handling module: Once the real-time monitoring and alarm module detects an anomaly, the exception handling module is immediately activated. Based on historical experience in the anomaly database and the processing suggestions generated by the intelligent comparative analysis module, emergency response measures are quickly formulated and implemented.
[0035] Optimization control module: converts the analysis results of the intelligent comparison and analysis module into PLC executable instructions, thereby generating optimization strategies and directly controlling the operating parameters of electromechanical equipment;
[0036] Human-computer interaction module: used to display device status and control AI decision-making.
[0037] By collecting key operating parameters in real time and conducting intelligent analysis, the system can accurately grasp the operating status of the equipment and adjust the equipment operation strategy in a timely manner, thereby effectively improving the equipment's operating efficiency and energy utilization. In addition, by using big data analysis and AI technology, it can deeply mine the equipment's operating data, discover potential fault hazards in advance, and implement fault warnings. At the same time, the optimization control module can directly control the equipment's operating parameters based on the analysis results, realize adaptive control, and enable the equipment to maintain optimal operating status under different working conditions.
[0038] Among them, the cloud-based big data platform module includes an abnormal database unit and a normal database unit. The abnormal database unit can store historical abnormal events and their processing records, including abnormal type, abnormal data, occurrence time, impact range, processing measures and results; the normal database unit can record various data of the equipment under normal working conditions.
[0039] By setting up abnormal database units and normal database units, rich samples are provided for AI analysis, helping the system learn and identify different types of abnormal patterns, while providing rapid comparison and reference processing solutions for newly emerging abnormalities.
[0040] Among them, the intelligent comparative analysis module includes a dynamic baseline generation unit and an AI hybrid model analysis unit. The dynamic baseline generation unit can build an equipment health status baseline based on the normal operating condition data in the equipment factory settings in the cloud big data platform module, and generate an equipment operation status baseline based on the data cleaned by the edge computing module. The AI hybrid model analysis unit can analyze the equipment's operation status through the data cleaned by the edge computing module, the equipment health status baseline, and the equipment operation status baseline.
[0041] The dynamic baseline generation unit in the intelligent comparative analysis module can accurately construct the baseline of equipment health status and operating status. Combined with the in-depth analysis of cleaned data by the AI hybrid model analysis unit, it can accurately evaluate the equipment operating status in real time and detect potential faults in a timely manner, providing strong support for optimizing equipment operation strategies and preventing faults, significantly improving the intelligence level of equipment management and operation and maintenance efficiency.
[0042] Among them, the AI hybrid model analysis unit avoids purely data-driven counterintuitive decisions by using physical equations as AI training constraints and embedding equipment design parameters as constraints into model training.
[0043] The AI hybrid model analysis unit uses physical equations as AI training constraints and embeds equipment design parameters into model training, effectively avoiding physically infeasible solutions caused by pure data-driven methods, thereby improving the accuracy and reliability of equipment status analysis, ensuring the scientific nature and rationality of equipment operation strategies, and providing solid technical support for the stable operation and efficient maintenance of equipment.
[0044] Among them, the AI hybrid model analysis unit adopts a baseline optimization method based on reinforcement learning, so that the AI hybrid model analysis unit can automatically identify baseline drift caused by equipment aging, process upgrades, etc., and dynamically adjust the normal range.
[0045] The AI hybrid model analysis unit adopts a baseline optimization method based on reinforcement learning. It can intelligently identify and adapt to baseline drift caused by equipment aging, process upgrades, etc., and dynamically adjust the normal range to ensure the timeliness and accuracy of the analysis results, effectively extend equipment life, optimize production processes, and improve overall operational efficiency.
[0046] The optimization control module includes a weight unit, which allows the optimization control module to comprehensively consider the downtime cost and potential failure risk when generating equipment maintenance decisions. By setting the weight factor, the priority of the maintenance strategy is dynamically adjusted. The weight calculation formula is:
[0047]
[0048] Among them, C stop is the real-time downtime cost; C max is the maximum downtime cost threshold allowed by the system; R fault (t is the time-varying failure risk probability; R critical is the enterprise risk tolerance threshold; S priority is the production plan urgency index; a(t) is the time-varying factor of cost sensitivity; ω is the artificial weighting parameter, with a standardized value of 0-1.
[0049] By setting weight units, it is possible to comprehensively consider downtime costs and potential failure risks, and set weight factors to dynamically adjust the priority of maintenance strategies, ensuring the economy and safety of equipment maintenance decisions, effectively balancing production efficiency and equipment reliability, and reducing maintenance costs.
[0050] Among them, the human-computer interaction module supports a three-dimensional visualization interface for displaying the equipment health status baseline and the equipment operation status baseline, and allows engineers to manually adjust the maintenance strategy recommended by AI. The system records the results of manual decisions and feeds them back to the anomaly database.
[0051] By setting up a human-computer interaction module, the equipment health and operating status baseline is intuitively displayed, which greatly improves engineers' understanding of the equipment status. They can also manually adjust the maintenance strategies recommended by AI to ensure flexibility and accuracy in decision-making.
[0052] Among them, the optimization control module directly controls the operating parameters of the electromechanical equipment, including adjusting the motor speed and opening and closing valves to achieve intelligent regulation. The operator can limit and release the control authority of the optimization control module in the human-computer interaction module, including the controllable operating parameter range and the controllable equipment range.
[0053] The optimization control module adjusts the operating equipment parameters so that the equipment can maintain the best operating state under different working conditions. The operator can flexibly limit and release the control authority of the optimization control module through the human-computer interaction module, including setting the controllable operating parameter range and controllable equipment range. This not only ensures the safety and controllability of equipment operation, but also enables the system to adapt to different production needs and equipment status.
[0054] Among them, the edge computing module is responsible for lightweight AI reasoning, which is used to monitor abnormal data with excessive fluctuations and reduce cloud transmission bandwidth usage.
[0055] Through lightweight AI reasoning, it is possible to monitor and process abnormal data with large fluctuations in real time, reducing bandwidth usage, thereby improving data transmission efficiency and system response speed, enabling the system to identify and process equipment anomalies more quickly, and improving the real-time and accuracy of overall monitoring and control.
[0056] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.
[0057] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.
Claims
1. Intelligent operation monitoring and optimization control system for electromechanical equipment integrating AI and big data, characterized by: include: Data acquisition module: responsible for collecting real-time operating data from various electromechanical equipment, including but not limited to key parameters such as temperature, pressure, vibration, current, voltage, and speed; Edge computing module: cleans, denoises, and formats the collected raw data to ensure data quality, and is also responsible for lightweight AI reasoning; Cloud-based big data platform module: This module includes big data on normal and abnormal operating conditions of related equipment in factory settings, equipment degradation curves, environmental data, work order records, and supply chain information. Intelligent comparative analysis module: used to analyze the current operating data of electromechanical equipment with big data, automatically generate or adjust equipment operation strategies, and achieve energy efficiency optimization, fault warning and adaptive control; Real-time monitoring and alarm module: monitors the equipment's operating status in real time. Once an anomaly is detected or an impending failure is predicted, the alarm mechanism is immediately triggered to notify relevant personnel to take countermeasures. Exception handling module: Once the real-time monitoring and alarm module detects an anomaly, the exception handling module is immediately activated. Based on historical experience in the anomaly database and the processing suggestions generated by the intelligent comparative analysis module, emergency response measures are quickly formulated and implemented. Optimization control module: converts the analysis results of the intelligent comparison and analysis module into PLC executable instructions, thereby generating optimization strategies and directly controlling the operating parameters of electromechanical equipment; Human-computer interaction module: used to display device status and control AI decision-making.
2. The intelligent operation monitoring and optimization control system for electromechanical equipment integrating AI and big data according to claim 1 is characterized by: The cloud-based big data platform module includes an abnormal database unit and a normal database unit. The abnormal database unit can store historical abnormal events and their processing records, including abnormal type, abnormal data, occurrence time, impact range, processing measures and results; the normal database unit can record various types of data of the equipment under normal working conditions.
3. The intelligent operation monitoring and optimization control system for electromechanical equipment integrating AI and big data according to claim 1 is characterized by: The intelligent comparative analysis module includes a dynamic baseline generation unit and an AI hybrid model analysis unit. The dynamic baseline generation unit can construct an equipment health status baseline based on the normal operating condition data in the equipment factory settings in the cloud big data platform module, and generate an equipment operation status baseline based on the data cleaned by the edge computing module. The AI hybrid model analysis unit can analyze the operation status of the equipment through the data cleaned by the edge computing module and the equipment health status baseline and the equipment operation status baseline.
4. The intelligent operation monitoring and optimization control system for electromechanical equipment integrating AI and big data according to claim 3 is characterized by: The AI hybrid model analysis unit avoids purely data-driven counterintuitive decisions by embedding physical equations as AI training constraints and device design parameters as constraints into model training.
5. The intelligent operation monitoring and optimization control system for electromechanical equipment integrating AI and big data according to claim 3 is characterized by: The AI hybrid model analysis unit adopts a baseline optimization method based on reinforcement learning, so that the AI hybrid model analysis unit can automatically identify baseline drift caused by equipment aging, process upgrades, etc., and dynamically adjust the normal range.
6. The intelligent operation monitoring and optimization control system for electromechanical equipment integrating AI and big data according to claim 1 is characterized by: The optimization control module includes a weight unit, which allows the optimization control module to comprehensively consider the downtime cost and potential failure risk when generating equipment maintenance decisions. By setting weight factors, the priority of the maintenance strategy is dynamically adjusted, where the weight calculation formula is: Among them, C stop is the real-time downtime cost; C max is the maximum downtime cost threshold allowed by the system; R fault (t is the time-varying failure risk probability; R critical is the enterprise risk tolerance threshold; S priority is the production plan urgency index; a(t) is the time-varying factor of cost sensitivity; ω is the artificial weighting parameter, with a standardized value of 0-1.
7. The intelligent operation monitoring and optimization control system for electromechanical equipment integrating AI and big data according to claim 1 is characterized by: The human-computer interaction module supports a three-dimensional visualization interface for displaying the equipment health status baseline and the equipment operation status baseline, and allows engineers to manually adjust the maintenance strategy recommended by AI. The system records the results of manual decisions and feeds them back to the anomaly database.
8. The intelligent operation monitoring and optimization control system for electromechanical equipment integrating AI and big data according to claim 1 is characterized by: The optimization control module directly controls the operating parameters of the electromechanical equipment, including adjusting the motor speed and opening and closing valves to achieve intelligent regulation. The operator can limit and release the control authority of the optimization control module in the human-computer interaction module, including the controllable operating parameter range and the controllable equipment range.
9. The intelligent operation monitoring and optimization control system for electromechanical equipment integrating AI and big data according to claim 1 is characterized by: The edge computing module is responsible for lightweight AI reasoning, which is used to monitor abnormal data with excessive fluctuations and reduce cloud transmission bandwidth usage.