Safety management risk dynamic monitoring system for safe production

Abstract

The present invention discloses a dynamic monitoring system for safety management risks for safe production, and belongs to the technical field of industrial safety management. The present invention integrates sensors such as toxic gas, temperature and pressure, and video monitoring equipment to collect monitoring parameter data in the enterprise in real time, and combines the three-dimensional map annotation function to dynamically display the risk distribution of major hazard sources and key areas, identify high-risk areas and potential hidden dangers through risk classification assessment and hidden danger association analysis, dynamically adjust the response mechanism according to the risk level, combine the emergency plan and remote equipment control function, quickly link and deal with major risks, and significantly reduce the possibility of accident expansion. The intelligent inspection module realizes efficient and accurate inspection tasks, collects and analyzes inspection data in real time, generates inspection reports including task completion rate and abnormal conditions, realizes hidden danger rectification and continuous optimization through closed-loop management, realizes real-time monitoring of safety risks, and effectively reduces the incidence of safe production accidents.

Description

Technical Field

[0001] The present invention relates to the technical field of industrial safety management, and in particular to a safety management risk dynamic monitoring system for safe production. Background Art

[0002] The safety risks in the production process of hazardous chemical enterprises are gradually increasing, including toxic gas leakage, loss of control of high-temperature and high-pressure equipment, and improper operation of personnel, which can easily lead to safety accidents. Traditional safety management methods mainly rely on manual inspections, paper records and single parameter monitoring, which not only have problems of information lag and limited coverage, but also make it difficult to achieve overall control and dynamic early warning of complex production environments.

[0003] Through the integration of sensors, video surveillance equipment and information platforms, real-time monitoring of the production process of enterprises can be achieved. However, most of the safety management systems on the market currently have single functions and lack the ability to comprehensively analyze multi-dimensional monitoring data, making it difficult to dynamically assess risk levels and generate targeted emergency response plans. In addition, some systems still rely on manual labor in the inspection process, which has the limitations of low efficiency and insufficient coverage. Summary of the invention

[0004] The purpose of the present invention is to provide a dynamic monitoring system for safety management risks in production safety, so as to solve the problems raised in the above-mentioned background technology.

[0005] To achieve the above object, the present invention provides the following technical solution: a dynamic monitoring system for safety management risks in production safety, comprising:

[0006] Data acquisition module for:

[0007] Integrate sensors and video surveillance equipment to collect real-time monitoring parameter data related to production safety in hazardous chemical enterprises. The sensors include toxic gas sensors, temperature sensors, and pressure sensors, create a three-dimensional model of the plant area and perform risk zoning;

[0008] Data processing module for:

[0009] Obtain real-time monitoring parameter data from the data acquisition module, clean, format and standardize the collected raw data, set the safety threshold of each monitoring parameter, and draw the monitoring parameter data curve in real time;

[0010] Data analysis module for:

[0011] Conduct graded assessment of regional and equipment risks based on real-time monitoring parameter data and generate comprehensive analysis reports;

[0012] Early warning module, used to:

[0013] Set risk levels, and define different response mechanisms and notification scopes based on different risk levels. Monitor real-time monitoring parameter data based on preset safety thresholds, push immediate warning information when abnormalities are found, record all warning events, and remotely control emergency relief valves in conjunction with emergency plans when warning events occur;

[0014] Intelligent inspection module, used for:

[0015] Set inspection routes and tasks based on risk zoning, configure intelligent inspection robots and drones, collect real-time video and gas composition data, record the movement trajectories of inspection personnel and equipment in real time, perform pattern recognition on inspection data, and detect abnormal behavior and substandard areas.

[0016] Furthermore, the data acquisition module includes:

[0017] Sensor integration unit for:

[0018] Integrate toxic gas sensors, temperature sensors and pressure sensors into the system, automatically detect and register sensor devices when the system is initialized, set the sensor acquisition frequency and communication protocol, and periodically perform status detection on the sensor, including the device online status, communication stability and parameter acquisition accuracy;

[0019] Video surveillance unit for:

[0020] Through the surveillance cameras installed in the area, real-time video collection is carried out on key areas of the enterprise, and the surveillance images are analyzed in real time to identify risky behaviors including leaving the post, not wearing a safety helmet, and irregular operations, and generate behavioral event records;

[0021] Dynamically overlay risk area information on the video screen, mark the boundaries of high-risk areas and sensor locations, and archive identified risky behaviors.

[0022] Furthermore, the data acquisition module further includes:

[0023] A sensor operation parameter acquisition module, used to extract the sensor operation parameters of the detection period corresponding to the state detection of the sensor; wherein the sensor operation parameters include data acquisition response time, zero drift deviation and range drift deviation;

[0024] A data collection response time extraction module is used to extract the data collection response time of each sensor's detection period;

[0025] A response time determination coefficient acquisition module, used to acquire a response time determination coefficient by using the data collection response time of the detection period of each sensor;

[0026] The response time determination coefficient is obtained by the following formula:

[0027] ;

[0028] Where S represents the response time determination coefficient; n represents the number of data collection times of each sensor during the detection period; T i represents the data collection response time corresponding to the i-th data collection; T c Indicates the preset response time reference value; T bi represents the standard deviation of the data collection response time corresponding to the i-th data collection; T z Indicates the duration corresponding to the detection period;

[0029] A first comparison module, used for comparing the response time determination coefficient with a preset determination coefficient threshold;

[0030] A sensor operation parameter retrieving module, for retrieving the sensor operation parameters of the detection period in which the response time determination coefficient exceeds the preset determination coefficient threshold when the response time determination coefficient exceeds the preset determination coefficient threshold;

[0031] The cycle duration adjustment determination module is used to determine whether it is necessary to adjust the cycle duration of the sensor for state detection by using the sensor operating parameters in the detection period where the response duration determination coefficient exceeds the preset determination coefficient threshold.

[0032] Furthermore, the cycle duration adjustment determination module includes:

[0033] A first target drift amount acquisition module is used to extract the zero drift deviation included in the sensor operation parameters in the detection period where the response time determination coefficient exceeds a preset determination coefficient threshold as the first target drift amount;

[0034] A second target drift amount acquisition module is used to extract the range drift deviation contained in the sensor operation parameters in the detection period where the response time determination coefficient exceeds a preset determination coefficient threshold as the second target drift amount;

[0035] A first observed drift acquisition module, used for retrieving the zero drift deviation corresponding to all detection periods in the historical records of the sensor as the first observed drift;

[0036] A second observed drift amount acquisition module, used for retrieving the range drift deviations corresponding to all detection time periods in the historical records of the sensor as the second observed drift amount;

[0037] A deviation evaluation coefficient acquisition module, used to acquire a deviation evaluation coefficient using the first target drift, the second target drift, the first observed drift, and the second observed drift;

[0038] The deviation evaluation coefficient is obtained by the following formula:

[0039] ;

[0040] Wherein, P represents the deviation evaluation coefficient; k represents the number of drift values ​​contained in the first observed drift; m represents the number of drift values ​​contained in the second observed drift; X 01m Indicates the corresponding value of the first target drift; X 02m Indicates the corresponding value of the second target drift; X 01i represents the value corresponding to the i-th first observed drift; X 02i represents the value corresponding to the i-th second observed drift; X 01bi represents the standard deviation of the drift corresponding to the first observed drift of the ith time; X 02bi represents the standard deviation of the drift corresponding to the i-th second observed drift; X 01b represents the standard deviation of the drift corresponding to the k first observed drifts; X 02b represents the standard deviation of the drift corresponding to the m second observed drifts; X cmax Indicates the preset drift difference reference value;

[0041] A second comparison module, used for comparing the deviation evaluation coefficient with a preset deviation evaluation coefficient threshold;

[0042] The adjustment determination module is used to determine that the cycle duration of the sensor for state detection needs to be adjusted when the deviation evaluation coefficient exceeds a preset deviation evaluation coefficient threshold, and adjust the cycle duration.

[0043] Furthermore, the adjustment determination module includes:

[0044] A target sensor determination module is used to call a sensor whose cycle duration of state detection needs to be adjusted as a target sensor;

[0045] A coefficient retrieving module, used to retrieve the deviation assessment coefficient and response time determination coefficient of the target sensor;

[0046] A first cycle adjustment coefficient acquisition module, used to acquire the first cycle adjustment coefficient by using the deviation evaluation coefficient and the response time determination coefficient of the target sensor;

[0047] The first cycle adjustment coefficient is obtained by the following formula:

[0048] ;

[0049] Among them, A 01represents the first cycle adjustment coefficient; P represents the deviation evaluation coefficient; S represents the response time determination coefficient; P0 represents the preset deviation evaluation coefficient threshold; S0 represents the preset determination coefficient threshold;

[0050] A cycle adjustment time difference retrieving module, used to retrieve each cycle adjustment time difference contained in the cycle adjustment history record of the target sensor;

[0051] A second cycle adjustment coefficient acquisition module, configured to acquire a second cycle adjustment coefficient by using a time difference of each cycle adjustment included in a cycle adjustment history record of the target sensor;

[0052] The second period adjustment coefficient is obtained by the following formula:

[0053] ;

[0054] Among them, A 02 represents the second cycle adjustment coefficient; e represents the number of cycle adjustments; C i represents the period adjustment time difference corresponding to the i-th period adjustment; C b represents the standard deviation of the period adjustment time difference corresponding to n period adjustments; P0 represents the preset deviation evaluation coefficient threshold; S0 represents the preset determination coefficient threshold; P i represents the deviation assessment coefficient corresponding to the i-th period adjustment; S i represents the response time determination coefficient corresponding to the i-th period adjustment;

[0055] The cycle adjustment execution module is used to adjust the cycle duration of the state detection of the target sensor by using the first cycle adjustment coefficient and the second cycle adjustment coefficient, wherein the adjusted cycle duration is obtained by the following formula:

[0056] ;

[0057] Among them, T st Indicates the adjusted cycle length; T s Indicates the length of the cycle before adjustment; A 01 Represents the first cycle adjustment coefficient; A 02 Represents the second period adjustment coefficient.

[0058] Furthermore, the data acquisition module further includes:

[0059] 3D map annotation unit, used for:

[0060] According to the plant floor plan and equipment layout provided by the enterprise, a three-dimensional model of the plant is created in the system and risk zones are divided. Major hazard sources, key areas and data acquisition equipment are marked in the model.

[0061] Mark the location and attribute information of major hazardous sources, including the area name, responsible department and risk level; mark the location of sensors and monitoring equipment for data acquisition equipment, and attach real-time status data of data acquisition equipment, including whether it is online, current parameter values ​​and whether an alarm is triggered.

[0062] Furthermore, the data processing module includes:

[0063] Data cleaning unit, used for:

[0064] Clean the collected original monitoring parameter data, remove invalid data, correct erroneous data and complete missing data;

[0065] Data formatting unit, used to:

[0066] Format the cleaned monitoring parameter data, convert it into a unified standard format, and record it in a unified timestamp format;

[0067] Data normalization unit for:

[0068] Assign safety threshold ranges to monitoring parameters based on equipment attributes and risk levels, map data to standardized values, store standardized monitoring data in real time in the database, and dynamically update based on new collected data;

[0069] Using the time series of monitoring data, each parameter is dynamically plotted in the form of a curve, and the parameter change trend, maximum value and minimum value are marked.

[0070] Furthermore, the data analysis module includes:

[0071] Risk classification assessment unit for:

[0072] Receive standardized monitoring data from the data processing module, combined with the historical risk level information of the equipment as input parameters;

[0073] The risk level of the monitored objects including equipment, areas and operating activities is calculated by risk classification, and the risk assessment results are generated according to the calculation results of the assessment model, and the risk level of each monitored object is generated, and the monitored objects are classified according to the risk level, which includes major risk, relatively large risk, general risk and low risk;

[0074] When the monitoring parameter data of the monitored object continuously exceeds the standard or fluctuates frequently, the assessment model is automatically triggered to recalculate the risk level of the monitored object;

[0075] Analysis report generation unit, used to:

[0076] Based on the real-time monitoring parameter data and risk assessment results, a comprehensive analysis report is generated, the content of which includes risk level distribution, major hazard source status and hidden danger rectification completion rate.

[0077] Furthermore, the early warning module includes:

[0078] Risk level response unit, used to:

[0079] Based on the calculation results of the risk classification assessment unit, the risks are classified into major risks, relatively large risks, general risks and low risks, and a corresponding response mechanism is preset for each level, the response mechanism including notification priority and handling measures;

[0080] Dynamically select the notification scope based on the risk level. For major risks, notify the management, emergency response team and equipment maintenance team. For high, medium and low risks, only notify the regional manager.

[0081] When the monitoring parameter data exceeds the set safety threshold or continues to fluctuate, the corresponding response mechanism is triggered in real time, and the triggering conditions, processing flow and results of each response event are recorded;

[0082] Emergency linkage unit, used for:

[0083] According to the risk source and the type of monitoring equipment, the warning event is automatically matched with the enterprise's preset emergency plan, and the corresponding disposal process is loaded;

[0084] When the warning event meets the major risk conditions, the external equipment control module will be linked to automatically perform remote control operations, and the warning event and emergency operations will be synchronously notified to relevant personnel through the real-time communication interface;

[0085] After completing the emergency response, record the execution process and results of all linkage operations.

[0086] Furthermore, the intelligent inspection module includes:

[0087] Inspection route planning unit, used for:

[0088] Acquire plant area risk zoning data from a data acquisition module, wherein the plant area risk zoning data includes the location distribution of high-risk areas and key equipment;

[0089] The shortest inspection route is generated by path optimization based on the inspection starting point, end point and risk points that need to be inspected. When the system detects new hidden dangers or changes in risk levels, the inspection route is automatically adjusted to cover the latest high-risk areas.

[0090] According to the area scope of the inspection task and the duties and authority of the inspection personnel, the planned paths and tasks are dynamically allocated to specific inspection personnel and equipment;

[0091] Unmanned inspection unit, used for:

[0092] According to the inspection route generated by the inspection path planning unit, the inspection robot or drone is dispatched to perform the task, wherein the inspection robot is used for ground inspection and the drone is suitable for aerial inspection;

[0093] During the inspection process, the gas composition data, video surveillance images and environmental parameters in the area are collected in real time, and the data collected during the inspection process is preliminarily processed to detect gas leaks, high temperatures and abnormal unauthorized activities in the area in real time, and generate abnormal event records.

[0094] Furthermore, the intelligent inspection module further includes:

[0095] Inspection data analysis unit, used for:

[0096] Classify, store and organize the multi-dimensional inspection data collected during the inspection process, and divide the time and area labels according to the inspection tasks. The inspection data includes video, gas composition and ambient temperature;

[0097] Perform pattern recognition on the collected data and generate an inspection report, the inspection report including task completion rate, abnormal conditions and improvement suggestions;

[0098] Push the inspection report to the management system and the person in charge.

[0099] Compared with the prior art, the present invention has the following beneficial effects:

[0100] 1. The present invention realizes comprehensive monitoring of key areas and hazardous sources within an enterprise through the sensor integration and video monitoring functions of the data acquisition module, combined with three-dimensional map annotation, real-time collection of monitoring parameter data such as toxic gases, temperature, and pressure, and intelligent identification of personnel behavior in the picture, timely discovery of risk behaviors such as leaving the post and irregular operation, dynamic display of risk distribution and real-time status, and improvement of monitoring coverage and accuracy.

[0101] 2. The present invention ensures the high quality and standardization of data by cleaning, formatting and standardizing the monitoring data. The data analysis module combines risk classification assessment, historical hidden danger records and trend analysis to dynamically generate risk assessment reports to help managers make scientific decisions. The early warning module sets a graded response mechanism based on the risk level, dynamically adjusts the notification scope, and combines the emergency linkage function to achieve rapid disposal of major risks, such as remote control of emergency relief valves, effectively reducing the impact of accidents.

[0102] 3. The intelligent inspection module of the present invention utilizes path optimization algorithms and unmanned inspection equipment to automatically plan inspection routes and execute tasks, collect gas composition, video and environmental parameter data, and reduce the risks of manual inspections. The inspection report generated by the system can intuitively display the task completion rate and abnormal conditions, provide improvement suggestions, and realize dynamic investigation and rectification of safety hazards through closed-loop management. The system as a whole has closed-loop functions from data collection, analysis, early warning, inspection to emergency response, which will help enterprises establish a comprehensive, multi-level dynamic monitoring system for production safety risks. BRIEF DESCRIPTION OF THE DRAWINGS

[0103] Figure 1 It is a schematic diagram of the module of the safety management risk dynamic monitoring system for safe production of the present invention. DETAILED DESCRIPTION

[0104] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. 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 creative work are within the scope of protection of the present invention.

[0105] See also Figure 1 , the present invention provides the following technical solutions:

[0106] The safety management risk dynamic monitoring system for safe production includes:

[0107] Data acquisition module for:

[0108] Integrate sensors and video surveillance equipment to collect real-time monitoring parameter data related to production safety in hazardous chemical enterprises. The sensors include toxic gas sensors, temperature sensors, and pressure sensors, create a three-dimensional model of the plant area and perform risk zoning;

[0109] Data processing module for:

[0110] Obtain real-time monitoring parameter data from the data acquisition module, clean, format and standardize the collected raw data, set the safety threshold of each monitoring parameter, and draw the monitoring parameter data curve in real time;

[0111] Data analysis module for:

[0112] Conduct graded assessment of regional and equipment risks based on real-time monitoring parameter data and generate comprehensive analysis reports;

[0113] Early warning module, used to:

[0114] Set risk levels, and define different response mechanisms and notification scopes based on different risk levels. Monitor real-time monitoring parameter data based on preset safety thresholds, push immediate warning information when abnormalities are found, record all warning events, and remotely control emergency relief valves in conjunction with emergency plans when warning events occur;

[0115] Intelligent inspection module, used for:

[0116] Set inspection routes and tasks based on risk zoning, configure intelligent inspection robots and drones, collect real-time video and gas composition data, record the movement trajectories of inspection personnel and equipment in real time, perform pattern recognition on inspection data, and detect abnormal behavior and substandard areas.

[0117] In the above embodiments, through the use of this system, real-time monitoring of safety risks can be achieved, thereby effectively reducing the incidence of production safety accidents. Through modular design, it integrates data collection, processing, analysis, early warning and intelligent inspection functions, providing a full-process dynamic management solution for the safe production of hazardous chemical enterprises. The system can monitor key parameters in the production process (such as toxic gas concentration, temperature, pressure, etc.) in real time, and dynamically analyze behaviors and scenes in combination with video surveillance to ensure rapid discovery and response of risk information.

[0118] In the above embodiment, AI algorithms, path optimization and unmanned inspection equipment are integrated to automatically identify abnormal behaviors, optimize inspection paths, and generate data reports through intelligent analysis, thus realizing full-link intelligence from data collection to decision support. Through risk classification, dynamic early warning and emergency linkage, the system can quickly take emergency measures (such as remote control of the release valve) to minimize the probability and degree of harm of accidents.

[0119] In the above embodiment, the system introduces a three-dimensional map function to intuitively display the factory layout, the location of major hazards and real-time monitoring data, providing managers with a comprehensive view of production safety and improving management efficiency. The system covers the entire process of risk identification, early warning, inspection, hidden danger rectification and feedback optimization, realizing a closed loop of safety management with dynamic monitoring and continuous improvement.

[0120] Data acquisition module, including:

[0121] Sensor integration unit for:

[0122] Integrate toxic gas sensors, temperature sensors and pressure sensors into the system, automatically detect and register sensor devices when the system is initialized, set the sensor acquisition frequency and communication protocol, and periodically perform status detection on the sensor, including the device online status, communication stability and parameter acquisition accuracy;

[0123] Video surveillance unit for:

[0124] Through the surveillance cameras installed in the area, real-time video collection is carried out on key areas of the enterprise, and the surveillance images are analyzed in real time to identify risky behaviors including leaving the post, not wearing a safety helmet, and irregular operations, and generate behavioral event records;

[0125] Dynamically overlay risk area information on the video screen, mark the boundaries of high-risk areas and sensor locations, and archive identified risky behaviors;

[0126] Specifically, the data acquisition module further includes:

[0127] A sensor operation parameter acquisition module, used to extract the sensor operation parameters of the detection period corresponding to the state detection of the sensor; wherein the sensor operation parameters include data acquisition response time, zero drift deviation and range drift deviation;

[0128] A data collection response time extraction module is used to extract the data collection response time of each sensor's detection period;

[0129] A response time determination coefficient acquisition module, used to acquire a response time determination coefficient by using the data collection response time of the detection period of each sensor;

[0130] The response time determination coefficient is obtained by the following formula:

[0131] ;

[0132] Where S represents the response time determination coefficient; n represents the number of data collection times of each sensor during the detection period; T i represents the data collection response time corresponding to the i-th data collection; T c Indicates the preset response time reference value; T bi represents the standard deviation of the data collection response time corresponding to the i-th data collection; T z Indicates the duration corresponding to the detection period;

[0133] A first comparison module, used for comparing the response time determination coefficient with a preset determination coefficient threshold;

[0134] A sensor operation parameter retrieving module, for retrieving the sensor operation parameters of the detection period in which the response time determination coefficient exceeds the preset determination coefficient threshold when the response time determination coefficient exceeds the preset determination coefficient threshold;

[0135] The cycle duration adjustment determination module is used to determine whether it is necessary to adjust the cycle duration of the sensor for state detection by using the sensor operating parameters in the detection period where the response duration determination coefficient exceeds the preset determination coefficient threshold.

[0136] The technical effect of the above technical solution is: through the sensor operation parameter acquisition module, the key operation parameters of the sensor in a specific detection period can be extracted, including data acquisition response time, zero drift deviation and range drift deviation. These parameters are important indicators for evaluating sensor performance and stability. The data acquisition response time extraction module and the response time determination coefficient acquisition module work together to calculate a response time determination coefficient S based on the response time of each data acquisition. This coefficient comprehensively considers the number of data acquisitions, the response time of each data acquisition, the standard deviation of the response time and the total duration of the detection period, thereby providing a comprehensive and dynamic indicator for evaluating the sensor response performance. The first comparison module compares the calculated response time determination coefficient with the preset determination coefficient threshold. If it exceeds the threshold, it indicates that the sensor response performance may be abnormal. At this time, the sensor operation parameter retrieval module will retrieve the sensor operation parameters of the corresponding detection period for further analysis and processing. The cycle time adjustment determination module uses the retrieved sensor operation parameters to determine whether it is necessary to adjust the cycle time of the sensor for state detection. This helps to flexibly adjust the detection frequency according to actual conditions, ensuring the timeliness of monitoring while avoiding unnecessary waste of resources. This technical solution improves the efficiency and accuracy of sensor status monitoring through refined management and dynamic evaluation. It can detect potential problems in sensor performance at the first time and take corresponding measures to prevent and correct them, thereby ensuring the reliability and stability of monitoring data.

[0137] In summary, this technical solution achieves accurate monitoring and dynamic evaluation of sensor operating parameters through a series of carefully designed modules and processes, providing strong support for timely detection and handling of sensor performance anomalies. It also lays a solid foundation for optimizing the status detection cycle and improving monitoring efficiency and accuracy.

[0138] Specifically, the cycle duration adjustment determination module includes:

[0139] A first target drift amount acquisition module is used to extract the zero drift deviation included in the sensor operation parameters in the detection period where the response time determination coefficient exceeds a preset determination coefficient threshold as the first target drift amount;

[0140] A second target drift amount acquisition module is used to extract the range drift deviation contained in the sensor operation parameters in the detection period where the response time determination coefficient exceeds a preset determination coefficient threshold as the second target drift amount;

[0141] A first observed drift acquisition module, used for retrieving the zero drift deviation corresponding to all detection periods in the historical records of the sensor as the first observed drift;

[0142] A second observed drift amount acquisition module, used for retrieving the range drift deviations corresponding to all detection time periods in the historical records of the sensor as the second observed drift amount;

[0143] A deviation evaluation coefficient acquisition module, used to acquire a deviation evaluation coefficient using the first target drift, the second target drift, the first observed drift, and the second observed drift;

[0144] The deviation evaluation coefficient is obtained by the following formula:

[0145] ;

[0146] Wherein, P represents the deviation evaluation coefficient; k represents the number of drift values ​​contained in the first observed drift; m represents the number of drift values ​​contained in the second observed drift; X 01m Indicates the corresponding value of the first target drift; X 02m Indicates the corresponding value of the second target drift; X 01i represents the value corresponding to the i-th first observed drift; X 02i represents the value corresponding to the i-th second observed drift; X 01bi represents the standard deviation of the drift corresponding to the first observed drift of the ith time; X 02bi represents the standard deviation of the drift corresponding to the i-th second observed drift; X 01b represents the standard deviation of the drift corresponding to the k first observed drifts; X 02b represents the standard deviation of the drift corresponding to the m second observed drifts; X cmax Indicates the preset drift difference reference value;

[0147] A second comparison module, used for comparing the deviation evaluation coefficient with a preset deviation evaluation coefficient threshold;

[0148] The adjustment determination module is used to determine that the cycle duration of the sensor for state detection needs to be adjusted when the deviation evaluation coefficient exceeds a preset deviation evaluation coefficient threshold, and adjust the cycle duration.

[0149] The technical effect of the above technical solution is: through the first target drift acquisition module and the second target drift acquisition module, the zero drift deviation and range drift deviation of the sensor in the abnormal period of the response time determination coefficient can be accurately extracted, providing basic data for subsequent deviation evaluation. At the same time, the first observation drift acquisition module and the second observation drift acquisition module retrieve the drift data of all detection periods in the historical records of the sensor, providing rich data support for the comprehensive evaluation of the drift of the sensor. The deviation evaluation coefficient acquisition module uses a variety of drift data (including target drift and observation drift) and their corresponding standard deviations to calculate a deviation evaluation coefficient P through a complex formula. This coefficient comprehensively considers the values ​​of zero drift and range drift, the discrete degree of observation data, and the preset drift difference reference value, thereby providing a comprehensive and accurate indicator for evaluating the drift of the sensor. The second comparison module compares the calculated deviation evaluation coefficient with the preset deviation evaluation coefficient threshold. If it exceeds the threshold, it indicates that the drift of the sensor may have reached the level where the detection cycle needs to be adjusted. At this time, the adjustment determination module will determine whether the cycle duration of the sensor's status detection needs to be adjusted, and adjust the cycle duration according to the actual situation. This intelligent adjustment method helps ensure the timeliness and accuracy of monitoring while avoiding unnecessary waste of resources.

[0150] This technical solution optimizes the duration of the sensor status monitoring cycle by accurately evaluating sensor drift, comprehensively considering multiple factors to evaluate deviations, and intelligently determining and adjusting the detection cycle. This helps improve monitoring efficiency and reliability, and ensures that the sensor can continuously and stably provide accurate monitoring data.

[0151] In summary, this technical solution achieves accurate evaluation and intelligent judgment of sensor drift through a series of carefully designed modules and processes, providing strong support for optimizing the duration of the status detection cycle. This not only improves monitoring efficiency and reliability, but also lays a solid foundation for the intelligent and automated development of sensor status monitoring.

[0152] Specifically, the adjustment determination module includes:

[0153] A target sensor determination module is used to call a sensor whose cycle duration of state detection needs to be adjusted as a target sensor;

[0154] A coefficient retrieving module, used to retrieve the deviation assessment coefficient and response time determination coefficient of the target sensor;

[0155] A first cycle adjustment coefficient acquisition module, used to acquire the first cycle adjustment coefficient by using the deviation evaluation coefficient and the response time determination coefficient of the target sensor;

[0156] The first cycle adjustment coefficient is obtained by the following formula:

[0157] ;

[0158] Among them, A 01 represents the first cycle adjustment coefficient; P represents the deviation evaluation coefficient; S represents the response time determination coefficient; P0 represents the preset deviation evaluation coefficient threshold; S0 represents the preset determination coefficient threshold;

[0159] A cycle adjustment time difference retrieving module, used to retrieve each cycle adjustment time difference contained in the cycle adjustment history record of the target sensor;

[0160] A second cycle adjustment coefficient acquisition module, configured to acquire a second cycle adjustment coefficient by using a time difference of each cycle adjustment included in a cycle adjustment history record of the target sensor;

[0161] The second period adjustment coefficient is obtained by the following formula:

[0162] ;

[0163] Among them, A 02 represents the second cycle adjustment coefficient; e represents the number of cycle adjustments; C i represents the period adjustment time difference corresponding to the i-th period adjustment; C b represents the standard deviation of the period adjustment time difference corresponding to n period adjustments; P0 represents the preset deviation evaluation coefficient threshold; S0 represents the preset determination coefficient threshold; P i represents the deviation assessment coefficient corresponding to the i-th period adjustment; S i represents the response time determination coefficient corresponding to the i-th period adjustment;

[0164] The cycle adjustment execution module is used to adjust the cycle duration of the state detection of the target sensor by using the first cycle adjustment coefficient and the second cycle adjustment coefficient, wherein the adjusted cycle duration is obtained by the following formula:

[0165] ;

[0166] Among them, T st Indicates the adjusted cycle length; T s Indicates the length of the cycle before adjustment; A 01 Represents the first cycle adjustment coefficient; A 02 Represents the second period adjustment coefficient.

[0167] The technical effect of the above technical solution is that the target sensor determination module can accurately identify the sensor that needs to adjust the cycle duration, which is the basis of the entire adjustment process. The coefficient retrieval module retrieves the deviation evaluation coefficient and response time determination coefficient of the target sensor. These two coefficients reflect the drift and response performance of the sensor respectively, and are important bases for the subsequent calculation of the adjustment coefficient. The first cycle adjustment coefficient acquisition module and the second cycle adjustment coefficient acquisition module respectively use the deviation evaluation coefficient, the response time determination coefficient and the time difference data in the cycle adjustment history record to calculate the two adjustment coefficients through complex formulas. These two coefficients comprehensively consider the current state and historical adjustment of the sensor, and provide accurate data support for the subsequent cycle duration adjustment. The cycle adjustment execution module uses the calculated first cycle adjustment coefficient and the second cycle adjustment coefficient to accurately adjust the state detection cycle duration of the target sensor. This adjustment method not only takes into account the current performance status of the sensor, but also its historical adjustment status, thereby ensuring that the adjusted cycle duration is more reasonable and accurate. By comprehensively evaluating the deviation and response performance of the sensor and intelligently adjusting its state detection cycle duration, the technical solution helps to improve monitoring efficiency and reliability. On the one hand, a reasonable cycle duration can ensure that the sensor can accurately capture data changes at critical moments, thereby improving the timeliness of monitoring; on the other hand, it avoids unnecessary frequent detection and reduces resource consumption and costs. The technical solution also enhances the adaptability of the system by introducing the time difference data in the cycle adjustment history records to calculate the second cycle adjustment coefficient. This means that the system can predict and adjust the future cycle duration based on the historical adjustment of the sensor to make it more in line with the actual operating conditions of the sensor. The technical solution realizes the intelligent management of the sensor status detection cycle duration through comprehensive evaluation, intelligent adjustment and precise adjustment. This not only improves the monitoring efficiency and reliability, but also provides strong support for the intelligent development of sensor status monitoring systems.

[0168] In summary, this technical solution achieves precise adjustment and optimized management of the sensor status detection cycle through a series of carefully designed modules and processes. This helps to improve monitoring efficiency and reliability, enhance the system's adaptability, and promote the intelligent development of sensor status monitoring systems.

[0169] 3D map annotation unit, used for:

[0170] According to the plant floor plan and equipment layout provided by the enterprise, a three-dimensional model of the plant is created in the system and risk zones are divided. Major hazard sources, key areas and data acquisition equipment are marked in the model.

[0171] Mark the location and attribute information of major hazardous sources, including the area name, responsible department and risk level; mark the location of sensors and monitoring equipment for data acquisition equipment, and attach real-time status data of data acquisition equipment, including whether it is online, current parameter values ​​and whether an alarm is triggered.

[0172] In the above embodiment, the data acquisition module is the core data entry of the system, which has multi-functional integration and high-precision acquisition capabilities. Through the sensor integration unit, the system can collect multiple monitoring parameters such as toxic gas concentration, temperature and pressure in real time, and at the same time combine video monitoring equipment to collect real-time images of key areas to ensure comprehensive monitoring of the production environment, generate a three-dimensional model based on the plant layout, dynamically mark major hazardous sources, key areas and monitoring equipment locations, combine real-time monitoring data, and intuitively display risk distribution and status, which is convenient for management personnel to make decisions, automatically identify risk behaviors such as leaving the post and not wearing a helmet, and generate event records, which greatly improves the monitoring ability of personnel violations. The periodic status detection function can monitor the online status of the equipment, communication stability and parameter acquisition accuracy to ensure the reliability and accuracy of the collected data.

[0173] Data processing module, including:

[0174] Data cleaning unit, used for:

[0175] Clean the collected original monitoring parameter data, remove invalid data, correct erroneous data and complete missing data;

[0176] Data formatting unit, used to:

[0177] Format the cleaned monitoring parameter data, convert it into a unified standard format, and record it in a unified timestamp format;

[0178] Data normalization unit for:

[0179] Assign safety threshold ranges to monitoring parameters based on equipment attributes and risk levels, map data to standardized values, store standardized monitoring data in real time in the database, and dynamically update based on new collected data;

[0180] Using the time series of monitoring data, each parameter is dynamically plotted in the form of a curve, and the parameter change trend, maximum value and minimum value are marked.

[0181] In the above embodiment, the data processing module realizes efficient cleaning, normalization and standardization of the collected data, ensuring the quality and availability of the data. Through the data cleaning unit, the system can effectively remove invalid data, correct erroneous data and complete missing data, providing high-quality basic data support for subsequent analysis and early warning, converting the processed data into a unified standard format, and sorting them according to timestamps, ensuring the integration and timing of multi-source data, providing standardized input for the analysis module, standardizing the monitoring parameters according to the safety threshold, generating curves in real time and marking trends, maximum values ​​and minimum values, facilitating dynamic tracking of changes in monitoring parameters and timely detection of anomalies. The data processing flow in the module focuses on the dynamic update mechanism, which can update the database in real time after receiving new collected data, and provide historical data support for subsequent long-term trend analysis and hidden danger investigation.

[0182] Data analysis modules, including:

[0183] Risk classification assessment unit for:

[0184] Receive standardized monitoring data from the data processing module, combined with the historical risk level information of the equipment as input parameters;

[0185] The risk level of the monitored objects including equipment, areas and operating activities is calculated by risk classification, and the risk assessment results are generated according to the calculation results of the assessment model, and the risk level of each monitored object is generated, and the monitored objects are classified according to the risk level, which includes major risk, relatively large risk, general risk and low risk;

[0186] When the monitoring parameter data of the monitored object continuously exceeds the standard or fluctuates frequently, the assessment model is automatically triggered to recalculate the risk level of the monitored object;

[0187] Analysis report generation unit, used for:

[0188] Based on the real-time monitoring parameter data and risk assessment results, a comprehensive analysis report is generated, the content of which includes risk level distribution, major hazard source status and hidden danger rectification completion rate.

[0189] In the above embodiment, the data analysis module provides a comprehensive safety analysis of equipment and areas through multi-dimensional data processing and intelligent risk assessment. Combined with real-time monitoring data and historical risk level information, comprehensive risk classification is carried out for equipment, areas and operating activities, and major risks, relatively large risks, general risks and low risks are clearly classified to provide managers with a scientific basis for risk management, dynamically track the changes in the risk level of the monitored objects, and automatically re-evaluate when there is an over-standard or fluctuation, help identify potential risk points and optimize risk control measures, and generate a comprehensive analysis report based on risk assessment results and hidden danger rectification progress, covering risk level distribution, major hazard source status and hidden danger rectification completion rate, support management to make quick decisions, accurately identify hidden danger prone areas or equipment through correlation analysis of historical hidden danger records and real-time data, and propose improvement suggestions such as maintenance plans, inspection route optimization and key area monitoring, forming a closed loop of hidden danger rectification.

[0190] Early warning module, including:

[0191] Risk level response unit, used to:

[0192] Based on the calculation results of the risk classification assessment unit, the risks are classified into major risks, relatively large risks, general risks and low risks, and a corresponding response mechanism is preset for each level, the response mechanism including notification priority and handling measures;

[0193] Dynamically select the notification scope based on the risk level. For major risks, notify the management, emergency response team and equipment maintenance team. For high, medium and low risks, only notify the regional manager.

[0194] When the monitoring parameter data exceeds the set safety threshold or continues to fluctuate, the corresponding response mechanism is triggered in real time, and the triggering conditions, processing flow and results of each response event are recorded;

[0195] Emergency linkage unit, used for:

[0196] According to the risk source and the type of monitoring equipment, the warning event is automatically matched with the enterprise's preset emergency plan, and the corresponding disposal process is loaded;

[0197] When the warning event meets the major risk conditions, the external equipment control module will be linked to automatically perform remote control operations, and the warning event and emergency operations will be synchronously notified to relevant personnel through the real-time communication interface;

[0198] After completing the emergency response, record the execution process and results of all linkage operations.

[0199] In the above embodiment, the early warning module provides the system with efficient abnormal response capabilities and a comprehensive emergency linkage mechanism. The response scope and notification mechanism are dynamically adjusted according to different risk levels to ensure that major risks are handled first, while general risks are monitored normally, optimizing the utilization efficiency of system resources. The module records the triggering conditions, processing procedures and results of each response event, providing important data support for subsequent risk analysis and emergency plan optimization. Combined with the preset emergency plan, it can automatically perform operations such as remote control of the discharge valve, effectively shortening the emergency response time and reducing the risk of accident expansion. Through the real-time communication interface, the module can notify relevant personnel of the early warning events and emergency operations, realize multi-party collaborative disposal, and improve the execution efficiency of emergency response.

[0200] Intelligent inspection module, including:

[0201] Inspection route planning unit, used for:

[0202] Acquire plant area risk zoning data from a data acquisition module, wherein the plant area risk zoning data includes the location distribution of high-risk areas and key equipment;

[0203] The shortest inspection route is generated by path optimization based on the inspection starting point, end point and risk points that need to be inspected. When the system detects new hidden dangers or changes in risk levels, the inspection route is automatically adjusted to cover the latest high-risk areas.

[0204] According to the area scope of the inspection task and the duties and authority of the inspection personnel, the planned paths and tasks are dynamically allocated to specific inspection personnel and equipment;

[0205] Unmanned inspection unit, used for:

[0206] According to the inspection route generated by the inspection path planning unit, the inspection robot or drone is dispatched to perform the task, wherein the inspection robot is used for ground inspection and the drone is suitable for aerial inspection;

[0207] During the inspection process, the gas composition data, video surveillance images and environmental parameters in the area are collected in real time, and the data collected during the inspection process are preliminarily processed to detect gas leaks, high temperatures and abnormal unauthorized activities in the area in real time, and generate abnormal event records;

[0208] Inspection data analysis unit, used for:

[0209] Classify, store and organize the multi-dimensional inspection data collected during the inspection process, and divide the time and area labels according to the inspection tasks. The inspection data includes video, gas composition and ambient temperature;

[0210] Perform pattern recognition on the collected data and generate an inspection report, the inspection report including task completion rate, abnormal conditions and improvement suggestions;

[0211] Push the inspection report to the management system and the person in charge.

[0212] In the above embodiment, the intelligent inspection module combines risk zoning planning, unmanned inspection and data analysis functions to greatly improve the inspection efficiency and accuracy. Generate the shortest path and dynamically adjust it according to the changes in hidden dangers or risk levels to ensure that the inspection task covers the latest high-risk areas, optimize resource allocation, dispatch robots and drones, conduct ground and air inspections, collect gas composition, video monitoring and environmental parameter data, reduce the safety hazards of manual inspections, and the inspection equipment can detect abnormal conditions such as gas leaks, high temperatures or unauthorized activities in real time, generate abnormal event records, provide real-time prevention and control capabilities for safe production, organize and analyze inspection data, and generate inspection reports containing task completion rates, abnormal conditions and improvement suggestions, provide optimization support for the management system, and improve the level of safe production management.

[0213] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A dynamic monitoring system for safety management risks in production safety, characterized in that: include: Data acquisition module for: Integrate sensors and video surveillance equipment to collect real-time monitoring parameter data related to production safety in hazardous chemical enterprises. The sensors include toxic gas sensors, temperature sensors, and pressure sensors, create a three-dimensional model of the plant area and perform risk zoning; Data processing module for: Obtain real-time monitoring parameter data from the data acquisition module, clean, format and standardize the collected raw data, set safety thresholds for monitoring parameters, and draw monitoring parameter data curves in real time; Data analysis module for: Conduct graded assessment of regional and equipment risks based on real-time monitoring parameter data and generate comprehensive analysis reports; Early warning module, used to: Set risk levels, and define different response mechanisms and notification scopes based on different risk levels. Monitor real-time monitoring parameter data based on preset safety thresholds, push immediate warning information when abnormalities are found, record all warning events, and remotely control emergency relief valves in conjunction with emergency plans when warning events occur; Intelligent inspection module, used for: Set inspection routes and tasks based on risk zoning, configure intelligent inspection robots and drones, collect real-time video and gas composition data, record the movement trajectories of inspection personnel and equipment in real time, perform pattern recognition on inspection data, and detect abnormal behaviors and non-compliant areas; Wherein, the data acquisition module includes: Sensor integration unit for: Integrate toxic gas sensors, temperature sensors and pressure sensors into the system, automatically detect and register sensor devices when the system is initialized, set the sensor acquisition frequency and communication protocol, and periodically perform status detection on the sensor, including the device online status, communication stability and parameter acquisition accuracy; Video surveillance unit for: Through the surveillance cameras installed in the area, real-time video collection is carried out on key areas of the enterprise, and the surveillance images are analyzed in real time to identify risky behaviors including leaving the post, not wearing a safety helmet, and irregular operations, and generate behavioral event records; Dynamically overlay risk area information on the video screen, mark the boundaries of high-risk areas and sensor locations, and archive identified risky behaviors; Wherein, the data acquisition module further includes: A sensor operation parameter acquisition module, used to extract the sensor operation parameters of the detection period corresponding to the state detection of the sensor; wherein the sensor operation parameters include data acquisition response time, zero drift deviation and range drift deviation; A data collection response time extraction module is used to extract the data collection response time of each sensor's detection period; A response time determination coefficient acquisition module, used to acquire a response time determination coefficient by using the data collection response time of the detection period of each sensor; The response time determination coefficient is obtained by the following formula: ； Where S represents the response time determination coefficient; n represents the number of data collection times of each sensor during the detection period; T i represents the data collection response time corresponding to the i-th data collection; T c Indicates the preset response time reference value; T bi represents the standard deviation of the data collection response time corresponding to the i-th data collection; T z Indicates the duration corresponding to the detection period; A first comparison module, used for comparing the response time determination coefficient with a preset determination coefficient threshold; A sensor operation parameter retrieving module, for retrieving the sensor operation parameters of the detection period in which the response time determination coefficient exceeds the preset determination coefficient threshold when the response time determination coefficient exceeds the preset determination coefficient threshold; A cycle time adjustment determination module, used to determine whether it is necessary to adjust the cycle time of the sensor for state detection by using the sensor operation parameters in the detection period in which the response time determination coefficient exceeds the preset determination coefficient threshold; Wherein, the cycle duration adjustment determination module includes: A first target drift amount acquisition module is used to extract the zero drift deviation included in the sensor operation parameters in the detection period where the response time determination coefficient exceeds a preset determination coefficient threshold as the first target drift amount; A second target drift amount acquisition module is used to extract the range drift deviation contained in the sensor operation parameters in the detection period where the response time determination coefficient exceeds a preset determination coefficient threshold as the second target drift amount; A first observed drift acquisition module, used for retrieving the zero drift deviation corresponding to all detection periods in the historical records of the sensor as the first observed drift; A second observed drift amount acquisition module, used for retrieving the range drift deviation corresponding to all detection time periods in the historical records of the sensor as the second observed drift amount; A deviation evaluation coefficient acquisition module, used to acquire a deviation evaluation coefficient using the first target drift, the second target drift, the first observed drift, and the second observed drift; The deviation evaluation coefficient is obtained by the following formula: ； Wherein, P represents the deviation evaluation coefficient; k represents the number of drift values ​​contained in the first observed drift; m represents the number of drift values ​​contained in the second observed drift; X 01m Indicates the corresponding value of the first target drift; X 02m Indicates the corresponding value of the second target drift; X 01i represents the value corresponding to the i-th first observed drift; X 02i represents the value corresponding to the i-th second observed drift; X 01bi represents the standard deviation of the drift corresponding to the first observed drift of the ith time; X 02bi represents the standard deviation of the drift corresponding to the i-th second observed drift; X 01b represents the standard deviation of the drift corresponding to the k first observed drifts; X 02b represents the standard deviation of the drift corresponding to the m second observed drifts; X cmax Indicates the preset drift difference reference value; A second comparison module, used for comparing the deviation evaluation coefficient with a preset deviation evaluation coefficient threshold; The adjustment determination module is used to determine that the cycle duration of the sensor for state detection needs to be adjusted when the deviation evaluation coefficient exceeds a preset deviation evaluation coefficient threshold, and adjust the cycle duration.

2. The safety management risk dynamic monitoring system for safe production according to claim 1, characterized in that: The adjustment determination module comprises: A target sensor determination module is used to call a sensor whose cycle duration of state detection needs to be adjusted as a target sensor; A coefficient retrieving module, used to retrieve the deviation assessment coefficient and response time determination coefficient of the target sensor; A first cycle adjustment coefficient acquisition module, used to acquire the first cycle adjustment coefficient by using the deviation evaluation coefficient and the response time determination coefficient of the target sensor; The first cycle adjustment coefficient is obtained by the following formula: ； Among them, A 01 represents the first cycle adjustment coefficient; P represents the deviation evaluation coefficient; S represents the response time determination coefficient; P0 represents the preset deviation evaluation coefficient threshold; S0 represents the preset determination coefficient threshold; A cycle adjustment time difference retrieving module, used to retrieve each cycle adjustment time difference contained in the cycle adjustment history record of the target sensor; A second cycle adjustment coefficient acquisition module, configured to acquire a second cycle adjustment coefficient by using a time difference of each cycle adjustment included in a cycle adjustment history record of the target sensor; The second period adjustment coefficient is obtained by the following formula: ； Among them, A 02 represents the second cycle adjustment coefficient; e represents the number of cycle adjustments; C i represents the period adjustment time difference corresponding to the i-th period adjustment; C b represents the standard deviation of the period adjustment time difference corresponding to n period adjustments; P0 represents the preset deviation evaluation coefficient threshold; S0 represents the preset determination coefficient threshold; P i represents the deviation assessment coefficient corresponding to the i-th period adjustment; S i represents the response time determination coefficient corresponding to the i-th period adjustment; The cycle adjustment execution module is used to adjust the cycle duration of the state detection of the target sensor by using the first cycle adjustment coefficient and the second cycle adjustment coefficient, wherein the adjusted cycle duration is obtained by the following formula: ； Among them, T st Indicates the adjusted cycle length; T s Indicates the length of the cycle before adjustment; A 01 Represents the first cycle adjustment coefficient; A 02 Represents the second period adjustment coefficient.

3. The safety management risk dynamic monitoring system for safe production according to claim 1, characterized in that: The data acquisition module further includes: 3D map annotation unit, used for: According to the plant floor plan and equipment layout provided by the enterprise, a three-dimensional model of the plant is created in the system and risk zones are divided. Major hazard sources, key areas and data acquisition equipment are marked in the model. Mark the location and attribute information of major hazardous sources, including the area name, responsible department and risk level; mark the location of sensors and monitoring equipment for data acquisition equipment, and attach real-time status data of data acquisition equipment, including whether it is online, current parameter values ​​and whether an alarm is triggered.

4. The safety management risk dynamic monitoring system for safe production according to claim 1, characterized in that: The data processing module comprises: Data cleaning unit, used for: Clean the collected original monitoring parameter data, remove invalid data, correct erroneous data and complete missing data; Data formatting unit, used to: Format the cleaned monitoring parameter data, convert it into a unified standard format, and record it in a unified timestamp format; Data normalization unit for: Assign safety threshold ranges to monitoring parameters based on equipment attributes and risk levels, map data to standardized values, store standardized monitoring data in real time in the database, and dynamically update based on new collected data; Using the time series of monitoring data, each parameter is dynamically plotted in the form of a curve, and the parameter change trend, maximum value and minimum value are marked.

5. The safety management risk dynamic monitoring system for safe production according to claim 1, characterized in that: The data analysis module comprises: Risk classification assessment unit for: Receive standardized monitoring data from the data processing module, combined with the historical risk level information of the equipment as input parameters; The risk level of the monitored objects including equipment, areas and operating activities is calculated by risk classification, and the risk assessment results are generated according to the calculation results of the assessment model, and the risk level of each monitored object is generated, and the monitored objects are classified according to the risk level, which includes major risk, relatively large risk, general risk and low risk; When the monitoring parameter data of the monitored object continuously exceeds the standard or fluctuates frequently, the assessment model is automatically triggered to recalculate the risk level of the monitored object; Analysis report generation unit, used for: Based on the real-time monitoring parameter data and risk assessment results, a comprehensive analysis report is generated, the content of which includes risk level distribution, major hazard source status and hidden danger rectification completion rate.

6. The safety management risk dynamic monitoring system for safe production according to claim 1, characterized in that: The early warning module comprises: Risk level response unit, used to: Based on the calculation results of the risk classification assessment unit, the risks are classified into major risks, relatively large risks, general risks and low risks, and a corresponding response mechanism is preset for each level, the response mechanism including notification priority and handling measures; Dynamically select the notification scope based on the risk level. For major risks, notify the management, emergency response team and equipment maintenance team. For high, medium and low risks, only notify the regional manager. When the monitoring parameter data exceeds the set safety threshold or continues to fluctuate, the corresponding response mechanism is triggered in real time, and the triggering conditions, processing flow and results of each response event are recorded; Emergency linkage unit, used for: According to the risk source and the type of monitoring equipment, the warning event is automatically matched with the enterprise's preset emergency plan, and the corresponding disposal process is loaded; When the warning event meets the major risk conditions, the external equipment control module will be linked to automatically perform remote control operations, and the warning event and emergency operations will be synchronously notified to relevant personnel through the real-time communication interface; After completing the emergency response, record the execution process and results of all linkage operations.

7. The safety management risk dynamic monitoring system for safe production according to claim 1, characterized in that: The intelligent inspection module comprises: Inspection route planning unit, used for: Acquire plant area risk zoning data from a data acquisition module, wherein the plant area risk zoning data includes the location distribution of high-risk areas and key equipment; The shortest inspection route is generated by path optimization based on the inspection starting point, end point and risk points that need to be inspected. When the system detects new hidden dangers or changes in risk levels, the inspection route is automatically adjusted to cover the latest high-risk areas. According to the area scope of the inspection task and the duties and authority of the inspection personnel, the planned paths and tasks are dynamically allocated to specific inspection personnel and equipment; Unmanned inspection unit, used for: According to the inspection route generated by the inspection path planning unit, the inspection robot or drone is dispatched to perform the task, wherein the inspection robot is used for ground inspection and the drone is suitable for aerial inspection; During the inspection process, the gas composition data, video surveillance images and environmental parameters in the area are collected in real time, and the data collected during the inspection process are preliminarily processed to detect gas leaks, high temperatures and abnormal unauthorized activities in the area in real time, and generate abnormal event records; Inspection data analysis unit, used for: Classify, store and organize the multi-dimensional inspection data collected during the inspection process, and divide the time and area labels according to the inspection tasks. The inspection data includes video, gas composition and ambient temperature; Perform pattern recognition on the collected data and generate an inspection report, the inspection report including task completion rate, abnormal conditions and improvement suggestions; Push the inspection report to the management system and the person in charge.

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