Equipment operation and maintenance safety behavior management and control system based on intelligent monitoring and data analysis

Abstract

The invention discloses an equipment operation and maintenance safety behavior management and control system based on intelligent monitoring and data analysis, and the system comprises a data collection module, a data processing and analysis module, an intelligent early warning and decision module, an execution and feedback module, and a behavior monitoring and management and control module. The safety behavior management and control capability in the operation and maintenance process of the industrial equipment is comprehensively improved, and the problems that an existing equipment monitoring system is poor in real-time performance, insufficient in prediction capability, much in manual intervention, lack of multi-dimensional collaborative management and control and the like are solved. According to the equipment operation and maintenance safety behavior management and control system based on intelligent monitoring and data analysis, through the technical means of real-time data acquisition, intelligent data analysis, an early warning mechanism, automatic management and control, a feedback mechanism and the like, management and control of equipment safety behaviors are effectively enhanced, and the safety, timeliness and accuracy of equipment operation and maintenance are improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of industrial equipment operation and maintenance, and in particular relates to an equipment operation and maintenance safety behavior management and control system based on intelligent monitoring and data analysis. Background Art

[0002] In the process of industrial equipment operation and maintenance, equipment safety behavior control has always been a key link to ensure efficient and stable operation of equipment. However, the current traditional equipment monitoring and operation and maintenance control methods have significant confusion and difficulties, which urgently need to be solved through innovative technologies.

[0003] First, the traditional operation and maintenance system relies too much on manual inspections and fixed sensor monitoring, which greatly reduces the real-time and comprehensiveness of monitoring. Although manual inspections can cover a certain range of equipment inspections, due to the fixed inspection cycle and reliance on manual judgment, it is often impossible to achieve comprehensive and real-time monitoring, and there is a risk of misjudgment and omissions. Although sensor data collection can provide some information about equipment operation, traditional sensors can only monitor the local status of specific equipment and cannot comprehensively and continuously monitor the operating parameters of all equipment, which makes early warning and prevention of potential failures difficult.

[0004] Secondly, there is a lack of intelligent data analysis capabilities during the equipment operation and maintenance process. The existing system cannot effectively process the large amount of real-time data generated by the equipment, resulting in its insufficient capabilities in equipment failure prediction, anomaly detection, and safety behavior control. Traditional operation and maintenance rely more on manual experience and post-event investigation. Often, the cause analysis and treatment are not started until the equipment fails or a safety accident occurs. Such post-event treatment not only increases maintenance costs, but also increases equipment downtime, affecting production efficiency. More importantly, the current management and control methods are usually isolated, and there is a lack of effective coordination between various monitoring and analysis tools, making it impossible to form a unified and efficient safety management and control system. This decentralized and inefficient management model cannot fully respond to the ever-changing needs in equipment operation and maintenance.

[0005] In addition, the current equipment safety behavior management and control methods generally lack foresight and intelligent early warning mechanisms based on data analysis. Since most management and control rely on fixed inspection processes and manual judgment, they often miss the opportunity to identify potential failure risks or unsafe behaviors. Factors such as equipment failure, improper operation, and environmental changes may appear as minor anomalies in the early stages, and the existing management and control systems cannot respond in time, resulting in the gradual accumulation of problems and eventually developing into equipment failures or safety accidents. Although some industries have introduced equipment monitoring systems based on data analysis, these systems often find it difficult to achieve efficient and real-time multi-dimensional data fusion, and it is difficult to provide sufficiently accurate and timely safety warnings. Summary of the invention

[0006] Based on the above technical problems, it is urgent to use intelligent monitoring and data analysis technology to make up for the shortcomings of existing management and control methods. Accurate fault prediction and anomaly detection through real-time data collection, deep learning and machine learning algorithms can improve the safety of equipment, reduce the failure rate, and further improve operation and maintenance efficiency. In addition, building an integrated and automated operation and maintenance control platform and strengthening all-round safety behavior monitoring of equipment, personnel and environment can achieve more accurate and intelligent operation and maintenance safety control, helping industrial enterprises to achieve efficient and reliable operation of equipment in complex and changing production environments.

[0007] The present invention aims to comprehensively improve the safety behavior control capabilities in the operation and maintenance of industrial equipment through intelligent monitoring and data analysis technologies, and to solve problems such as poor real-time performance, insufficient predictive capabilities, frequent manual intervention, and lack of multi-dimensional collaborative control in existing equipment monitoring systems.

[0008] The present invention relates to an equipment operation and maintenance safety behavior management and control system based on intelligent monitoring and data analysis, including a data acquisition module, a data processing and analysis module, and an intelligent early warning and decision-making module;

[0009] The data acquisition module is used to monitor the equipment status and personnel operation behavior using a variety of sensors, including equipment operation status data, equipment environment data and personnel data. Data acquisition not only covers the equipment operation status, but also includes personnel operation behavior and external environment changes, forming multi-dimensional data monitoring, and providing comprehensive data support for equipment health assessment. According to the characteristics and security level of the equipment, the data acquisition frequency is selected to be real-time or periodic to meet the needs of different equipment and environments. The multiple sensors are connected to the edge gateway device, and the raw data is cleaned using the data processing algorithm to unify the data into a fusionable format.

[0010] Data processing and analysis module: including data processing unit and data analysis unit;

[0011] The data processing unit is used to unify the sensor timestamps of the data in the data acquisition module, interpolate the discrete data in the data, check the transmission delay, remove the data packets that exceed the time limit, ensure data integrity, and ensure that multi-source data is analyzed in the same time window; through the use of ETL process, the equipment ID, location coordinates and operator information are used for data association, the equipment, environment and personnel data are logically associated, the data source is loaded and integrated on demand, and the interaction model is established; the time series data fusion technology is used to integrate the operation data from different sources, generate health assessment indicators, and the evaluation indicator data is optimized based on the LSTM fusion result of deep learning to generate a new data view;

[0012] The data analysis unit is used to use a deep neural network DNN algorithm to perform pattern recognition on the behavior of the equipment, and by identifying the normal behavior pattern of the equipment and comparing it with historical data, predict potential safety risks or fault points, and improve the fault warning capability; according to the data view, integrate the health index algorithm, combine the collected real-time data of equipment operation parameters and environmental parameters, and calculate the score of the current status of the equipment; predict the equipment operation trend through the Prophet model, so as to identify potential faults and anomalies, and thus build an equipment health assessment model; by integrating the environment and equipment data and combining the Pearson coefficient algorithm to find out the main influencing factors, using regression models or random forest algorithms to evaluate the direct impact of environmental changes on equipment performance, the impact of various environmental conditions on equipment operation efficiency and product quality, and build an environmental impact assessment model that meets the current production enterprises; by analyzing the relationship between the operator's operation trajectory and equipment performance, the posture recognition algorithm is used to detect the accuracy and compliance of the operation action, so as to monitor the operator's behavior pattern, improve the standardization and efficiency of operation, and build a personnel behavior analysis model that meets the enterprise;

[0013] The intelligent early warning and decision-making module includes an equipment operation parameter prediction unit, an operator behavior prediction unit, an early warning model construction unit, and a decision model construction unit;

[0014] The equipment operation parameter prediction unit is used to establish a change trend model by using regression analysis on the historical data of the equipment's long-term operation. Through the accumulation and analysis of historical data, the rule engine and model parameters are continuously optimized to improve the accuracy and response speed of early warning. The change trend model is used to analyze the real-time collected equipment operation data to identify abnormal trends. When key parameters deviate from the safe range, the system generates an early warning and provides maintenance suggestions, notifying relevant personnel to intervene. After each abnormality occurs, the system automatically generates a report, including a data change trend chart and possible cause analysis, for the operation and maintenance team to optimize the strategy.

[0015] The operator behavior prediction unit is used to define the operator's behavior norms during equipment operation and maintenance using operation and maintenance rules and regulations to ensure that the operator's behavior complies with the specified safety standards and work processes; collect operator behavior data, integrate and analyze the operator's operation behavior data with the equipment status and environmental monitoring data, so as to more comprehensively evaluate the impact of operation behavior on equipment performance and safety; make real-time predictions on operation behavior based on real-time data inputs such as operator behavior and equipment status to determine whether there are potential safety risks. When possible safety hazards are predicted, the system will automatically issue an early warning to remind the operator to adjust the operation behavior or take preventive measures; collect operator behavior data, count indicators such as violation frequency and key action efficiency, automatically generate analysis reports, and mark high-frequency problem points to provide a basis for optimized training, thereby achieving operation improvement and behavior optimization of operation and maintenance operators;

[0016] The early warning model construction unit is used to use historical normal and abnormal operating condition data to generate the normal mode of equipment operation based on the isolation forest algorithm; then use the AutoEncoder model to capture abnormal data characteristics and identify potential risks, dynamically predict safety risks based on the deviation between real-time data and historical patterns, verify the accuracy of data through massive real-time data, continuously optimize the abnormal model, and improve the accuracy of the model;

[0017] The decision model building unit is used to establish emergency plan templates based on different types of security incidents, and use knowledge graph technology to automatically match the best plan based on event characteristics.

[0018] Preferably, in the equipment operation and maintenance safety behavior control system based on intelligent monitoring and data analysis of the present invention, the equipment operation status data includes the temperature, pressure and vibration of the equipment, the environmental data includes gas concentration, humidity and noise, and the personnel data includes operation trajectory and behavior pattern.

[0019] Preferably, in the equipment operation and maintenance safety behavior control system based on intelligent monitoring and data analysis of the present invention, the data processing algorithm is implemented using moving average filtering.

[0020] Preferably, in the equipment operation and maintenance safety behavior control system based on intelligent monitoring and data analysis of the present invention, the data processing unit uses the NTP protocol synchronization mechanism to unify the sensor timestamps of the data in the data acquisition module.

[0021] Preferably, in the equipment operation and maintenance safety behavior control system based on intelligent monitoring and data analysis of the present invention, in the operator behavior prediction unit, the behavior specifications include operation steps, frequency, duration, and operation standards.

[0022] Preferably, in the equipment operation and maintenance safety behavior control system based on intelligent monitoring and data analysis of the present invention, in the operator behavior prediction unit, the means of collecting the operator's behavior data include: through sensors, cameras and operation log systems.

[0023] Preferably, in the equipment operation and maintenance safety behavior management and control system based on intelligent monitoring and data analysis of the present invention, in the decision model construction unit, the emergency plan template includes response steps, responsibility allocation and resource call using knowledge graph technology, and the event characteristics include leakage type and impact range.

[0024] Preferably, the equipment operation and maintenance safety behavior management and control system based on intelligent monitoring and data analysis of the present invention also includes an execution and feedback module, which is used to record detailed information of each safety incident, including the triggering cause of the incident, the response process, the decision-making plan, and the actual processing results, to ensure the integrity and traceability of the event data; by analyzing the timeliness, resource scheduling, and execution steps of each link in the emergency response process, the key links that affect the response efficiency and effectiveness are identified, and the efficiency and effectiveness of the emergency response are judged by quantitatively evaluating the time, resource utilization, and personnel response of event processing; based on the results of critical path analysis, the emergency response process is optimized to reduce time delays and waste of resources; using data mining technology to analyze the patterns and laws that occurred in historical events, thereby optimizing emergency plans; based on the analysis results, the existing emergency plans are improved, the decision-making process is optimized, and the feasibility and accuracy of the plan execution are improved.

[0025] Preferably, in the equipment operation and maintenance safety behavior control system based on intelligent monitoring and data analysis of the present invention, in the execution and feedback module, the data mining technology includes association rules and Apriori algorithm.

[0026] Preferably, the equipment operation and maintenance safety behavior management and control system based on intelligent monitoring and data analysis of the present invention also includes a behavior monitoring and control module, which is used to monitor and control personnel's operating behavior. By combining the personnel's operating frequency, operating standardization, operating time and other behavioral data, it evaluates and identifies potential unsafe operating behaviors; combines equipment status and environmental monitoring data to generate safety behavior reports, and provides operating standards and behavior improvement suggestions; through comprehensive analysis of personnel operating behavior and equipment operation, safety hazards are discovered, and then the operating strategy is automatically or manually adjusted to prevent equipment failures or safety accidents due to human factors.

[0027] In general, the equipment operation and maintenance safety behavior control system based on intelligent monitoring and data analysis of the present invention, in order to solve the problems of weak safety behavior control, delayed response, untimely early warning, etc. in the current equipment operation and maintenance, effectively strengthens the control of equipment safety behavior through real-time data collection, intelligent data analysis, early warning mechanism, automated control and feedback mechanism and other technical means, and improves the safety, timeliness and accuracy of equipment operation and maintenance.

[0028] Specifically, the present invention has the following beneficial effects:

[0029] Improve equipment operation safety: Through real-time monitoring and intelligent analysis, potential equipment failures and abnormal operations can be discovered in a timely manner, reducing the occurrence of equipment failures and safety accidents, and improving equipment reliability and production continuity.

[0030] Reduce manual intervention and operational risks: The system's automatic warning, emergency response and behavior control mechanisms reduce manual intervention, making equipment operation and maintenance more intelligent and reducing human errors and operational risks.

[0031] Improve emergency response efficiency: The system can quickly initiate emergency response measures when a security incident occurs, automatically adjust equipment operating parameters or start backup equipment, significantly shortening the response time and improving the efficiency and accuracy of emergency response.

[0032] Optimize the operational standardization of personnel: By monitoring the behavior of operators, timely discovering irregular operations and giving improvement suggestions, the operational standardization of employees is enhanced and the safety hazards caused by human factors are reduced.

[0033] Enhance the accuracy and traceability of decisions: The system provides accurate early warning and processing strategies through data-driven decision-making models, and stores all operation process records, which improves the accuracy of decisions and provides data support for subsequent audits and optimizations. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] The present invention will be further described below with reference to the accompanying drawings and embodiments, in which:

[0035] Figure 1 It is a schematic diagram of an embodiment of an equipment operation and maintenance safety behavior control system based on intelligent monitoring and data analysis of the present invention. DETAILED DESCRIPTION

[0036] In order to have a clearer understanding of the technical features, purposes and effects of the present invention, specific embodiments of the present invention are now described in detail with reference to the accompanying drawings.

[0037] refer to Figure 1 , an equipment operation and maintenance safety behavior control system based on intelligent monitoring and data analysis, including a data acquisition module, a data processing and analysis module, an intelligent early warning and decision-making module, an execution and feedback module, and a monitoring and control module;

[0038] The data acquisition module is used to monitor the equipment status and personnel operation behavior using a variety of sensors, including equipment operation status data (such as temperature, pressure and vibration), equipment environment data (such as gas concentration, humidity and noise) and personnel data (including operation trajectory and behavior pattern). Pressure data can usually come from the fluid system pressure inside the equipment (such as hydraulic oil pressure of industrial equipment, air pressure of gas compressor), power transmission link pressure (such as automobile tire pressure, aircraft hydraulic system pressure used to control component movement) and pipeline pressure (such as natural gas pipeline pressure, infusion pump infusion pipeline pressure) and other aspects; temperature and vibration data are the temperature and vibration of components on the equipment respectively. Data acquisition not only covers the equipment operation status, but also includes personnel operation behavior and external environmental changes, forming multi-dimensional data monitoring, providing comprehensive data support for equipment health assessment; according to the characteristics and safety level of the equipment, the data acquisition frequency selects real-time acquisition (every second or less) or periodic acquisition to meet the needs of different equipment and environments; the multiple sensors are connected to the edge gateway device, and the data processing algorithm is used, such as moving average filtering to clean the original data, and the data is unified into a fusionable format.

[0039] Among them, sensors are arranged in key parts of the equipment to ensure comprehensive monitoring of the equipment and personnel. The collected data includes equipment operating status, personnel operating behavior and external environmental changes. The data accuracy of the sensor needs to meet the requirements of refined equipment management and have anti-interference capabilities to ensure the accuracy of the data.

[0040] In this embodiment, the specific steps are as follows:

[0041] a) The system registers the sensor information of each device, and the system needs to associate it with the device number, device location and other information. The device status data (such as temperature, pressure, etc.) is received in real time through the sensor data interface. These data are transmitted to the central database through the acquisition unit to form a real-time monitoring panel.

[0042] b) Receive data from sensors according to the preset frequency and update the operating status of the equipment in real time. During the operation of the equipment, the collected data is displayed on the monitoring panel to ensure that the operator is aware of the status of the equipment at any time.

[0043] c) Verify the accuracy of sensor data through data verification algorithms. If the data of temperature, pressure and other sensors exceeds the normal range, the system will automatically issue a warning message and mark the sensor as "abnormal" to facilitate technical personnel to handle

[0044] d) The equipment management system needs to display real-time data through the user interface and monitor the equipment status in real time. The system should support charts and graphical displays so that operators can intuitively see the equipment operation trends. When the data exceeds the preset threshold, the system should trigger an alarm and send SMS, email or APP push notifications to relevant personnel.

[0045] e) Store the collected data in the database for subsequent query and analysis. The system should support long-term storage of data and facilitate data archiving; regularly analyze historical data to detect whether the equipment has potential failure trends and provide data support for equipment maintenance and replacement.

[0046] f) By analyzing the historical data and real-time data of the equipment, cluster analysis methods are used to identify patterns in the equipment data, and the fault point is predicted based on the equipment's operating trend. The system generates a fault report to indicate the possible fault type and probability of occurrence of the equipment.

[0047] g) Support the generation of equipment data reports to facilitate management personnel to analyze and make decisions. The reports should include equipment operating status, fault records, maintenance logs, etc. Provide data export function, users can export data to Excel, CSV and other formats for further processing or archiving.

[0048] Data processing and analysis module: This module uses cloud computing platform and big data technology to centrally store, process and analyze the collected data, and uses deep neural network algorithm to identify patterns of equipment behavior, predict possible security risks, and identify abnormal behaviors based on historical data and real-time data. Through efficient data processing capabilities, it can process large amounts of data streams in real time or near real time, support fault prediction and behavior pattern recognition, and ensure the system's response speed and decision-making accuracy. Data processing and analysis module: includes data processing unit and data analysis unit, as follows.

[0049] The data processing unit is used to unify the sensor timestamps of the data in the data acquisition module using the NTP protocol synchronization mechanism, interpolate the discrete data in the data, check the transmission delay, remove the data packets that exceed the time limit, ensure data integrity, and ensure that multi-source data is analyzed in the same time window; through the use of ETL process, the equipment ID, location coordinates and operator information are used for data association, the equipment, environment and personnel data are logically associated, the data source is loaded and integrated on demand, and an interaction model is established; the time series data fusion technology is used to integrate the operation data from different sources, generate health assessment indicators, and the evaluation indicator data is optimized based on the LSTM fusion results of deep learning to generate new data views;

[0050] The data analysis unit is used to use a deep neural network (DNN) algorithm to perform pattern recognition on the behavior of the equipment, and by identifying the normal behavior pattern of the equipment and comparing it with historical data, predict potential safety risks or fault points, and improve the fault warning capability; according to the data view, integrate the health index algorithm, combine the collected real-time data of equipment operation parameters and environmental parameters, and calculate the score of the current status of the equipment; predict the equipment operation trend through the Prophet model, so as to identify potential faults and anomalies, and thus build an equipment health assessment model; by integrating the environment and equipment data and combining the Pearson coefficient algorithm to find out the main influencing factors, use the regression model or random forest algorithm to evaluate the direct impact of environmental changes on equipment performance, the impact of various environmental conditions on equipment operation efficiency and product quality, and build an environmental impact assessment model that meets the current production enterprises; by analyzing the relationship between the operator's operation trajectory and equipment performance, the posture recognition algorithm is used to detect the accuracy and compliance of the operation action, so as to monitor the operator's behavior pattern, improve the standardization and efficiency of operation, and build a personnel behavior analysis model that meets the enterprise.

[0051] In this embodiment, the specific steps of the data processing and analysis module are as follows:

[0052] a) The data acquisition module transmits the data of equipment and environmental sensors to the cloud platform for centralized storage. Real-time data is quickly processed through stream processing technology. The system processes real-time data according to preset thresholds and monitoring rules and updates the data in real time. Real-time monitoring data is merged with historical data to ensure the comprehensiveness and accuracy of the analysis.

[0053] b) Use historical data to train a deep neural network model to identify normal device behavior and potential abnormal patterns. The training process includes data preprocessing, feature extraction, and model optimization. When new data is input, the system will automatically determine whether the device behavior conforms to the expected pattern. If there is a deviation, an alarm will be triggered.

[0054] c) Use deep learning models and historical data to predict the probability of equipment failure, and combine real-time data to evaluate the current health status of the equipment. The prediction module generates prediction results based on the real-time data of the equipment (such as temperature, pressure, vibration, etc.), feeds the prediction results back to the operator through the interface, and performs automatic management based on the predicted risk level.

[0055] d) Based on real-time data and the historical behavior patterns of the equipment, the system identifies abnormal behavior through pattern recognition algorithms. The system monitors every operation of the equipment and determines in real time whether there are potential anomalies. When the system detects a situation that does not match the normal behavior pattern, it will trigger an alarm mechanism and generate a fault report. The report provides detailed information about the abnormal event and sends an alarm message to the equipment manager to remind him of possible risks.

[0056] e) The system provides a visual decision support interface that displays information such as risk prediction, fault diagnosis, and equipment health status.

[0057] By optimizing the prediction model through historical data and feedback information, the system can continuously improve and increase the response speed according to the real-time situation of the equipment.

[0058] f) Automatically generate detailed reports on equipment health status, fault prediction results and safety risk assessment through the report module. Users can customize the content and format of the report as needed and export relevant data for further analysis.

[0059] Based on the analysis module, the intelligent early warning and decision-making module automatically generates safety warnings and emergency response decisions based on the behavioral data, environmental data and prediction models of equipment and personnel. When potential safety risks or abnormal behaviors are detected, the system will automatically generate early warning information and notify relevant operation and maintenance personnel through SMS, email, APP push, etc. This module relies on the logistic regression algorithm to generate accurate safety warnings and automatically adjust the processing strategy according to real-time data to avoid risks in time. The intelligent early warning and decision-making module includes the equipment operation parameter prediction unit, the operator behavior prediction unit, the early warning model construction unit and the decision model construction unit, as follows.

[0060] The equipment operation parameter prediction unit is used to establish a change trend model by using regression analysis on the historical data of the equipment's long-term operation. Through the accumulation and analysis of historical data, the rule engine and model parameters are continuously optimized to improve the accuracy and response speed of early warning. The change trend model is used to analyze the real-time collected equipment operation data to identify abnormal trends. When key parameters deviate from the safe range, the system generates an early warning and provides maintenance suggestions, notifying relevant personnel to intervene. After each abnormality occurs, the system automatically generates a report, including a data change trend chart and possible cause analysis, for the operation and maintenance team to optimize the strategy.

[0061] The operator behavior prediction unit is used to define the operator's behavior norms during equipment operation and maintenance by adopting operation and maintenance rules and regulations, including operation steps, frequency, duration, and operation standards, to ensure that the operator's behavior complies with the prescribed safety standards and work processes; the operator's behavior data is collected through sensors, cameras, and operation log systems, and the operator's operation behavior data is integrated and analyzed with the equipment status and environmental monitoring data, so as to more comprehensively evaluate the impact of operation behavior on equipment performance and safety; the operation behavior is predicted in real time based on the real-time data input of the operator's behavior and equipment status to determine whether there are potential safety risks. When possible safety hazards are predicted, the system will automatically issue an early warning to remind the operator to adjust the operation behavior or take preventive measures; the operator's behavior data is collected, and the frequency of violations and the efficiency of key actions are counted. The analysis report is automatically generated, and high-frequency problem points are marked to provide a basis for optimized training, thereby achieving operation improvement and behavior optimization of operation and maintenance operators;

[0062] The early warning model construction unit is used to use historical normal and abnormal operating condition data to generate the normal mode of equipment operation based on the isolation forest algorithm; then use the AutoEncoder model to capture abnormal data characteristics and identify potential risks, dynamically predict safety risks based on the deviation between real-time data and historical patterns, verify the accuracy of data through massive real-time data, continuously optimize the abnormal model, and improve the accuracy of the model;

[0063] The decision model building unit is used to establish emergency plan templates based on different types of security incidents, including response steps, responsibility allocation and resource call. It uses knowledge graph technology to automatically match the best plan based on event characteristics, such as leakage type and impact scope.

[0064] In this embodiment, the intelligent early warning and decision-making module has the following specific steps:

[0065] a) The data acquisition module acquires data in real time from equipment sensors, personnel behavior records, and environmental monitoring data sources, and transmits it to the intelligent early warning and decision-making module. The data includes the operating status of the equipment, the operator's behavior data (such as operation duration, frequency, and standardization), and environmental change data (such as temperature, humidity, and vibration). The intelligent early warning module integrates these multi-source data, uses logistic regression algorithms to analyze historical data and real-time data, identifies potential safety risks, generates safety warning information, and transmits it to operation and maintenance personnel.

[0066] b) After receiving real-time data, the system automatically uses the preset logistic regression algorithm model to analyze the data and identify abnormal patterns in equipment or personnel behavior. Based on equipment status, personnel behavior and environmental data, the system predicts possible future safety risks and uses the model to predict risks. The prediction model can automatically adjust the threshold of the warning to ensure that potential safety hazards are accurately captured.

[0067] c) The system is integrated with the PLC control system. After generating early warning information, it automatically generates emergency response decisions based on the rule base or artificial intelligence model. Emergency responses may include automatically reducing equipment load, suspending equipment operation, or requiring operators to change operating methods. If the security risk is high, the system automatically triggers relevant operations and notifies the operation and maintenance personnel to take immediate action through SMS, email, APP, etc. If the risk is low, it will improve the operation by slightly adjusting the equipment settings or notifying the operation and maintenance personnel.

[0068] d) The system sends warning information to relevant operation and maintenance personnel or management personnel through multiple channels (SMS, email, APP push, etc.). Warning information includes specific risk content, urgency, handling suggestions, etc., to ensure that decision makers can respond quickly. For high-risk situations, the system will use high-priority notification methods to remind, ensuring that the information can be conveyed to the operation and maintenance personnel as soon as possible. Adjust the notification method and frequency according to different warning levels to ensure the timeliness and accuracy of information transmission.

[0069] e) Record each warning and emergency response process and analyze it through machine learning algorithms. The system can identify which emergency response measures are most effective in actual situations and optimize the prediction model and emergency decision-making process. After multiple iterations, the system can achieve more efficient and accurate intelligent warning and emergency decision-making.

[0070] The execution and feedback module performs automated emergency response operations according to the instructions issued by the intelligent early warning module, such as adjusting equipment operating parameters, triggering equipment shutdown protection, starting backup equipment, adjusting operating procedures, etc. At the same time, the system will feed back the execution results to the background system in real time for subsequent analysis and adjustment. Through high automation and real-time response capabilities, it can quickly initiate emergency response measures when a fault warning is issued, reducing human intervention and processing delays. Specifically, the execution and feedback module is used to record detailed information of each security incident, including the triggering cause of the incident, the response process, the decision-making plan, and the actual processing results, to ensure the integrity and traceability of the event data; by analyzing the timeliness, resource scheduling, and execution steps of each link in the emergency response process, identify the key links that affect the efficiency and effectiveness of the response, and by conducting quantitative evaluations of the time, resource utilization, and personnel response of event processing, judge the efficiency and effectiveness of the emergency response; based on the results of critical path analysis, optimize the emergency response process to reduce time delays and waste of resources; use data mining techniques, such as association rules and Apriori algorithms, to analyze the patterns and laws that occurred in historical events, thereby optimizing emergency plans; based on the analysis results, improve existing emergency plans, optimize decision-making processes, and improve the feasibility and accuracy of plan execution.

[0071] In this implementation, the execution and feedback module execution steps are as follows:

[0072] a) The execution and feedback module realizes data interaction through the interface with the equipment management system. When the warning conditions are met, the execution module will control the equipment through the equipment management system (such as stopping operation, adjusting parameters, etc.). Every step of the execution will be recorded and fed back to the system for subsequent analysis.

[0073] b) The system will automatically adjust the equipment parameters when the equipment fails or is in a critical state, and feed back to the equipment management system for status update for subsequent operation and analysis.

[0074] c) After completing the automated emergency response operation, the execution and feedback module will feed back the execution results to the equipment management system in real time. The system will record detailed information of the operation, such as equipment downtime, adjustment parameter values, and activation of backup equipment. Further analysis will be conducted based on the feedback data to optimize the emergency response strategy and generate reports for subsequent improvements.

[0075] The behavior monitoring and control module is used to monitor and control personnel operation behaviors. By combining the personnel's operation frequency, operation standardization, operation duration and other behavioral data, it evaluates and identifies potential unsafe operation behaviors; combined with equipment status and environmental monitoring data, it generates safety behavior reports and provides operation standards and behavior improvement suggestions; through comprehensive analysis of personnel operation behaviors and equipment operations, it discovers safety hazards, and then automatically or manually adjusts the operation strategy to prevent equipment failures or safety accidents caused by human factors.

[0076] In this step, the specific steps of behavior monitoring and control are as follows:

[0077] a) The system collects real-time data on personnel operation behavior, equipment operation status and environment through sensors, RFID equipment, operation recording systems, etc. These data are transmitted to the cloud platform through the data acquisition module for centralized storage and processing. The real-time transmission of data streams is achieved through the interface with the data acquisition unit to ensure data synchronization between operation behavior and equipment status. The system will evaluate the safety of operation behavior in real time according to the equipment's operating specifications.

[0078] b) By formulating operation and maintenance rules, the operation and maintenance personnel will monitor the operation behavior and equipment status of the personnel when they receive the maintenance tools and start the maintenance, and identify potential abnormal behaviors (such as frequent operation or timeout, violation of operation specifications) through algorithms. The anomaly detection system will identify irregular operations in the real-time data stream and issue an alarm immediately.

[0079] c) The system generates safety behavior reports regularly or in real time, which include the operator's operation standardization score, the identification results of potential safety hazards, equipment operation status analysis, etc. The report will provide specific improvement suggestions, which may include improving operating habits, optimizing equipment operation processes, and risk avoidance measures. Based on historical report data, the system gradually optimizes operating specifications to ensure that operators continuously improve work efficiency and safety.

[0080] d) The equipment management system can adjust the equipment operation mode in real time through integration with the equipment control system. The system will automatically or manually trigger the equipment protection mode according to the operation behavior and equipment data to ensure the safe operation of the equipment. When abnormal operation behavior is found, the system will automatically adjust the operation strategy according to the situation. The system can also automatically push operation prompts or safety warnings to operators by setting safety limits to remind them to comply with the operation specifications. The system can automatically or manually adjust the working status of the equipment according to the analysis results of the operation behavior, such as adjusting parameters such as temperature and pressure to avoid potential risks.

[0081] e) The system records each behavior monitoring and adjustment process, including the detection, processing, and adjustment measures of unsafe behaviors. All operation data and events will be archived to form a traceable data chain. Based on the collected feedback data, the system optimizes the behavior monitoring model through machine learning and deep learning algorithms to improve the accuracy of identifying unsafe behaviors, and ultimately achieve comprehensive intelligent equipment operation and maintenance.

[0082] The embodiments of the present invention are described above in conjunction with the accompanying drawings, but the present invention is not limited to the above-mentioned specific implementation modes, which are merely illustrative rather than restrictive. Under the guidance of the present invention, ordinary technicians in this field can also make many forms without departing from the scope of protection of the present invention and the claims, all of which are within the protection of the present invention.

Claims

1. Equipment operation and maintenance safety behavior control system based on intelligent monitoring and data analysis, characterized by: It includes data collection module, data processing and analysis module and intelligent early warning and decision-making module; The data acquisition module is used to monitor the equipment status and personnel operation behavior using a variety of sensors, including equipment operation status data, equipment environment data and personnel data. Data acquisition not only covers the equipment operation status, but also includes personnel operation behavior and external environment changes, forming multi-dimensional data monitoring, and providing comprehensive data support for equipment health assessment. According to the characteristics and security level of the equipment, the data acquisition frequency is selected to be real-time or periodic to meet the needs of different equipment and environments. The multiple sensors are connected to the edge gateway device, and the raw data is cleaned using the data processing algorithm to unify the data into a fusionable format. Data processing and analysis module: including data processing unit and data analysis unit; The data processing unit is used to unify the sensor timestamps of the data in the data acquisition module, interpolate the discrete data in the data, check the transmission delay, remove the data packets that exceed the time limit, ensure data integrity, and ensure that multi-source data is analyzed in the same time window; through the use of ETL process, the equipment ID, location coordinates and operator information are used for data association, the equipment, environment and personnel data are logically associated, the data source is loaded and integrated on demand, and the interaction model is established; the time series data fusion technology is used to integrate the operation data from different sources, generate health assessment indicators, and the evaluation indicator data is optimized based on the LSTM fusion result of deep learning to generate a new data view; The data analysis unit is used to use a deep neural network DNN algorithm to perform pattern recognition on the behavior of the equipment, and by identifying the normal behavior pattern of the equipment and comparing it with historical data, predict potential safety risks or fault points, and improve the fault warning capability; according to the data view, integrate the health index algorithm, combine the collected real-time data of equipment operation parameters and environmental parameters, and calculate the score of the current status of the equipment; predict the equipment operation trend through the Prophet model, so as to identify potential faults and anomalies, and thus build an equipment health assessment model; by integrating the environment and equipment data and combining the Pearson coefficient algorithm to find out the main influencing factors, using regression models or random forest algorithms to evaluate the direct impact of environmental changes on equipment performance, the impact of various environmental conditions on equipment operation efficiency and product quality, and build an environmental impact assessment model that meets the current production enterprises; by analyzing the relationship between the operator's operation trajectory and equipment performance, the posture recognition algorithm is used to detect the accuracy and compliance of the operation action, so as to monitor the operator's behavior pattern, improve the standardization and efficiency of operation, and build a personnel behavior analysis model that meets the enterprise; The intelligent early warning and decision-making module includes an equipment operation parameter prediction unit, an operator behavior prediction unit, an early warning model construction unit, and a decision model construction unit; The equipment operation parameter prediction unit is used to establish a change trend model by using regression analysis on the historical data of the long-term operation of the equipment. Through the accumulation and analysis of historical data, the rule engine and model parameters are continuously optimized to improve the accuracy and response speed of early warning. Use the change trend model to analyze the real-time collected equipment operation data and identify abnormal trends. When key parameters deviate from the safe range, the system generates an early warning and provides maintenance suggestions, notifying relevant personnel to intervene. After each exception occurs, the system automatically generates a report, including a data change trend chart and possible cause analysis, for the operation and maintenance team to optimize the strategy; The operator behavior prediction unit is used to define the operator's behavior norms during equipment operation and maintenance by adopting operation and maintenance rules and regulations to ensure that the operator's behavior complies with the prescribed safety standards and work processes; collect operator behavior data, integrate and analyze the operator's operation behavior data with the equipment status and environmental monitoring data, so as to more comprehensively evaluate the impact of operation behavior on equipment performance and safety; make real-time predictions on operation behavior based on real-time data inputs such as operator behavior and equipment status to determine whether there are potential safety risks. When possible safety hazards are predicted, the system will automatically issue an early warning to remind the operator to adjust the operation behavior or take preventive measures; Collect operator behavior data, count violation frequency, key action efficiency and other indicators, automatically generate analysis reports, mark high-frequency problem points to provide a basis for optimized training, and thus achieve operational improvement and behavior optimization of operation and maintenance operators; The early warning model construction unit is used to use historical normal and abnormal operating condition data to generate the normal mode of equipment operation based on the isolation forest algorithm; then use the AutoEncoder model to capture abnormal data characteristics and identify potential risks, dynamically predict safety risks based on the deviation between real-time data and historical patterns, verify the accuracy of data through massive real-time data, continuously optimize the abnormal model, and improve the accuracy of the model; The decision model building unit is used to establish emergency plan templates based on different types of security incidents, and use knowledge graph technology to automatically match the best plan based on event characteristics.

2. According to claim 1, the equipment operation and maintenance safety behavior control system based on intelligent monitoring and data analysis is characterized in that: The equipment operation status data includes the temperature, pressure and vibration of the equipment, the environmental data includes gas concentration, humidity and noise, and the personnel data includes operation trajectory and behavior pattern.

3. The equipment operation and maintenance safety behavior control system based on intelligent monitoring and data analysis according to claim 1 is characterized in that: The data processing algorithm is implemented by moving average filtering.

4. The equipment operation and maintenance safety behavior control system based on intelligent monitoring and data analysis according to claim 1 is characterized in that: The data processing unit uses the NTP protocol synchronization mechanism to unify the sensor timestamps of the data in the data acquisition module.

5. The equipment operation and maintenance safety behavior control system based on intelligent monitoring and data analysis according to claim 1 is characterized in that: In the operator behavior prediction unit, the behavior specifications include operation steps, frequency, duration, and operation standards.

6. The equipment operation and maintenance safety behavior control system based on intelligent monitoring and data analysis according to claim 1 is characterized in that: In the operator behavior prediction unit, the means of collecting operator behavior data include: through sensors, cameras and operation log systems.

7. The equipment operation and maintenance safety behavior control system based on intelligent monitoring and data analysis according to claim 1 is characterized in that: In the decision model construction unit, the emergency plan template includes response steps, responsibility allocation and resource calling using knowledge graph technology, and the event characteristics include leakage type and impact scope.

8. The equipment operation and maintenance safety behavior control system based on intelligent monitoring and data analysis according to claim 1 is characterized in that: It also includes an execution and feedback module, which is used to record detailed information of each security incident, including the triggering cause of the incident, the response process, the decision-making plan, and the actual processing results, to ensure the integrity and traceability of the event data; by analyzing the timeliness, resource scheduling, and execution steps of each link in the emergency response process, the key links that affect the efficiency and effectiveness of the response are identified, and the efficiency and effectiveness of the emergency response are judged by quantitatively evaluating the time, resource utilization, and personnel response of event processing; based on the results of critical path analysis, the emergency response process is optimized to reduce time delays and waste of resources; using data mining technology to analyze the patterns and laws that occurred in historical events, thereby optimizing emergency plans; based on the analysis results, the existing emergency plans are improved, the decision-making process is optimized, and the feasibility and accuracy of the plan execution are improved.

9. The equipment operation and maintenance safety behavior control system based on intelligent monitoring and data analysis according to claim 8 is characterized in that: In the execution and feedback module, the data mining technology includes association rules and Apriori algorithm.

10. The equipment operation and maintenance safety behavior control system based on intelligent monitoring and data analysis according to claim 1 is characterized in that: It also includes a behavior monitoring and control module, which is used to monitor and control personnel's operating behaviors. By combining the personnel's operating frequency, operating standardization, and operating time, these behavioral data can be used to evaluate and identify potential unsafe operating behaviors. Combined with equipment status and environmental monitoring data, a safety behavior report is generated to provide operating specifications and behavior improvement suggestions. Through comprehensive analysis of personnel operating behavior and equipment operation, safety hazards are discovered, and operating strategies are automatically or manually adjusted to prevent equipment failures or safety accidents caused by human factors.

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