Management system of nuclear accident emergency compatible decontamination waste liquid pool

Abstract

The invention relates to the technical field of emergency management, and discloses a management system of a nuclear accident emergency compatible decontamination waste liquid pool. The system comprises a dual-mode switching control module, a water circulation treatment module and a gas treatment module, and can be switched between a daily swimming pool mode and a nuclear accident emergency decontamination mode. The system further comprises an assembly type equipment management module which is used for equipment storage, rapid deployment, state monitoring, maintenance reminding, performance testing and updating management. In addition, the intelligent monitoring and alarm module realizes anomaly detection, risk assessment and emergency response. The water circulation module and the gas treatment module adopt corresponding strategies according to different modes, real-time monitoring, treatment and optimization of water quality and air quality are achieved, and effective operation of the system in the two modes is ensured. According to the invention, the dual utilization of facilities is realized, the resource efficiency is improved, and the emergency response capability of nuclear accidents is enhanced.

Description

Technical Field

[0001] The present application relates to the field of emergency management technology, and in particular to a management system for a nuclear accident emergency compatible decontamination waste liquid pool. Background Art

[0002] For the decontamination phase of nuclear emergency, the daily practice is to build a decontamination station for decontamination. There are two main ways to deal with the generated decontamination waste liquid: fixed decontamination waste liquid pool and temporary decontamination waste liquid storage equipment. The advantages of fixed waste liquid pool are fixed site, full configuration, large capacity, good effect, no need for temporary deployment, and can be put into use at any time; the disadvantages are high construction and maintenance costs, long-term occupation of public resources, long daily idle time, low efficiency, etc.

[0003] Currently, temporary decontamination stations commonly include mobile decontamination vehicles, decontamination cabins, decontamination tents and other temporary decontamination equipment. Although they can solve the problem of long-term occupation of public resources, they have their own characteristics, such as the need for temporary scheduling to increase the emergency response time, few supporting functions such as warmth, rain and snow protection, low decontamination work efficiency, weak radioactive pollution disposal capabilities, and high risk of pollution leakage. Summary of the invention

[0004] The present application provides a management system for a nuclear accident emergency compatible decontamination waste liquid pool, which is used to achieve dual utilization of facilities, improve resource efficiency, and enhance nuclear accident emergency response capabilities.

[0005] In a first aspect, the present application provides a management system for a nuclear accident emergency compatible decontamination waste liquid pool, the system comprising: a dual-mode switching control module, a water circulation processing module and a gas processing module; The dual-mode switching control module is used to: automatically identify the current mode through preset trigger conditions, the current mode being the daily swimming pool mode or the nuclear accident emergency decontamination mode; send a switching instruction according to the current mode, and adjust the operating parameters and working status according to the switching instruction; during the mode switching process, start the safety lock program and complete the disinfection and isolation measures; record the time, reason and process of each mode switching; the management personnel manually trigger the mode switching and monitor the system status in real time through the human-computer interactive operation interface; The water circulation treatment module is used to: adopt different treatment schemes according to the current mode. If the current mode is the daily swimming pool mode, a conventional water quality treatment scheme is adopted, and the conventional water quality treatment scheme includes filtration, disinfection and pH value adjustment, and constant temperature control is performed through a heat exchanger to maintain a constant water temperature, and water quality parameters are monitored in real time to ensure compliance with swimming pool hygiene standards; if the current mode is the nuclear accident emergency decontamination mode, a preset filtration device is used to remove radioactive particles in the water, a neutralizer or flocculant is added, dissolved radioactive substances are treated, and the heat exchanger is used to reduce the temperature to reduce the volume of waste liquid, and the radioactive indicators of the waste liquid are continuously monitored; The gas processing module is used for: if the current mode is the daily swimming pool mode, the indoor environment is maintained by adjusting the temperature and humidity, the irritating gas generated by the volatilization of chlorine in the water is removed, and the air supply is ensured through air circulation; if the current mode is the decontamination waste liquid mode, the particulate air filter and the activated carbon filter are used to remove radioactive aerosols, create a negative pressure environment, prevent the spread of pollution, monitor the content of radioactive substances in the air in real time, and discharge the multiple filtered gases through a preset dedicated pipeline.

[0006] In combination with the first aspect, in a first implementation of the first aspect of the present application, the management system of the nuclear accident emergency compatible decontamination waste liquid pool further includes: an assembled equipment management module; the assembled equipment management module specifically includes: An equipment storage unit is used to store emergency equipment in the daily swimming pool mode, wherein the emergency equipment includes: a decontamination water treatment device and a gas treatment device; A rapid deployment unit, configured to install the emergency equipment after receiving the switching instruction; An equipment status monitoring unit, used for regularly checking the equipment status of the emergency equipment to ensure that the emergency equipment is in a usable state; A maintenance reminder unit, used to generate a corresponding target maintenance plan according to the usage and storage time of the emergency equipment; An equipment performance testing unit, used to regularly perform equipment function tests on the emergency equipment and obtain equipment function test results; The update management unit is used to record the update information of the emergency equipment and ensure that the emergency equipment complies with the nuclear accident response standards based on the update information.

[0007] In combination with the first aspect, in a second implementation of the first aspect of the present application, the device status monitoring unit is specifically used to: Capturing appearance images of the emergency equipment to obtain equipment appearance images, and performing equipment integrity analysis on the equipment appearance images to obtain an appearance integrity score; Classifying the emergency equipment according to the appearance integrity score to obtain a list of intact equipment and a list of equipment to be inspected, and performing ultrasonic scanning on the equipment in the list of equipment to be inspected to obtain internal structure data; Constructing a three-dimensional model according to the internal structure data to obtain a three-dimensional structure model of the device, and performing comparative analysis between the three-dimensional structure model of the device and a standard structure model to obtain structure deviation data; Performing stress analysis on the emergency equipment according to the structural deviation data to obtain a stress distribution diagram, and performing threshold judgment on the stress distribution diagram to obtain a list of potential fault points; Performing a simulated operation test on the emergency equipment according to the potential failure point list to obtain a performance parameter set, and comparing the performance parameter set with preset standard parameters to obtain a performance degradation index; The remaining life of the emergency equipment is predicted according to the performance degradation index to obtain an equipment status assessment report, and risk levels are divided according to the equipment status assessment report to obtain graded maintenance suggestions.

[0008] In combination with the first aspect, in a third implementation of the first aspect of the present application, the maintenance reminder unit is specifically used to: Performing data mining on the usage records of the emergency equipment to obtain equipment usage frequency data, and constructing a Markov chain model based on the equipment usage frequency data to obtain a predicted value of equipment usage intensity; Performing sensor monitoring on the storage environment of the emergency equipment to obtain an environmental parameter set, and calculating an environmental impact factor based on the environmental parameter set to obtain an equipment aging rate; A two-factor weighted model is established according to the predicted value of the equipment usage intensity and the equipment aging rate to obtain an equipment loss index, and a time series analysis is performed on the equipment loss index to obtain a maintenance demand curve; Performing dynamic programming calculation according to the maintenance demand curve and the preset maintenance cycle threshold to obtain a preliminary maintenance time point set, and performing cluster analysis on the preliminary maintenance time point set to obtain an optimized maintenance time point set; According to the optimized maintenance time point set, association rule mining is performed on historical maintenance records to obtain a maintenance item association matrix, and the maintenance item association matrix is ​​analyzed to obtain a key maintenance item list; According to the list of key maintenance items and the optimized maintenance time point set, a multi-objective optimization model is constructed to obtain a preliminary maintenance plan; the preliminary maintenance plan is simulated to obtain a maintenance plan reliability evaluation result, and the preliminary maintenance plan is adjusted according to the maintenance plan reliability evaluation result to obtain a target maintenance plan.

[0009] In combination with the first aspect, in a fourth implementation of the first aspect of the present application, the device performance testing unit is specifically used to: Performing a standard load test on the emergency equipment to obtain an equipment operation parameter set, and constructing an equipment performance benchmark curve based on the equipment operation parameter set; Performing Fourier transformation on the equipment performance benchmark curve to obtain frequency domain characteristic data, and performing wavelet decomposition based on the frequency domain characteristic data to obtain multi-scale performance indicators; Calculating equipment performance scores according to the multi-scale performance indicators, and performing time series analysis on the equipment performance scores to construct a performance trend prediction model; Generate a virtual load sequence according to the performance trend prediction model to obtain a simulation test plan, and execute the simulation test plan on the emergency equipment to obtain simulation test result data; Performing performance calculation on the simulation test result data to obtain key performance factors, and classifying the emergency equipment into performance levels according to the key performance factors to obtain equipment performance levels; The equipment reliability distribution is calculated according to the equipment performance level and the performance trend prediction model, and the equipment reliability distribution is updated by Bayesian method to obtain equipment function test results.

[0010] In combination with the first aspect, in a fifth implementation of the first aspect of the present application, the update management unit is specifically used to: Regularly scan the nuclear accident response standard database to obtain the latest standard update information, and construct a knowledge graph based on the latest standard update information to obtain a standard association network; Performing graph theory analysis on the standard association network to obtain key standard nodes, and extracting a device update indicator set based on the key standard nodes; Performing a performance matching evaluation on the emergency equipment according to the equipment update index set to obtain an equipment matching matrix, and performing a hierarchical analysis on the equipment matching matrix to obtain an equipment update priority list; Generate a candidate device data set according to the device update priority list, and perform a multi-criteria decision analysis on the candidate device data set to obtain an optimal replacement device solution; According to the optimal replacement equipment solution, an equipment update cost model is constructed, and a sensitivity analysis is performed on the equipment update cost model to obtain a cost risk assessment report; According to the cost risk assessment report and the equipment update priority list, a phased update plan is formulated, and discrete event simulation is performed on the phased update plan to obtain an update process optimization plan, and an equipment update execution instruction is generated according to the update process optimization plan.

[0011] In combination with the first aspect, in a sixth implementation of the first aspect of the present application, the management system of the nuclear accident emergency compatible decontamination waste liquid pool further includes: an intelligent monitoring and alarm module, and the intelligent monitoring and alarm module is used to: Collecting and preprocessing multi-source sensor data to obtain a multi-dimensional time series data set, and analyzing the multi-dimensional time series data set to obtain a reduced-dimensional feature vector; Constructing a dynamic anomaly detector according to the dimension-reduced feature vector, and performing online learning and parameter update on the dynamic anomaly detector to obtain an adaptive threshold surface; Identify abnormal events on real-time monitoring data according to the adaptive threshold curve to obtain a preliminary abnormal event list, and make a comprehensive assessment of the preliminary abnormal event list to obtain a priority ranking of the abnormal events; Construct an event association map according to the priority ranking of the abnormal events, and perform graph mining and path analysis on the event association map to obtain a potential risk propagation path; Generate a multi-scenario emergency plan according to the potential risk transmission path, and perform optimization analysis on the multi-scenario emergency plan to obtain the optimal emergency plan; A hierarchical alarm instruction is generated according to the optimal emergency plan to obtain an intelligent alarm plan, and the intelligent alarm plan is visualized to obtain a multi-dimensional visualized alarm interface, and a corresponding emergency response mechanism is triggered according to the multi-dimensional visualized alarm interface.

[0012] In combination with the first aspect, in a seventh implementation of the first aspect of the present application, the dual-mode switching control module is specifically used to: Performing fusion analysis on environmental sensor data and external information to obtain a mode switching trigger factor set, and generating a mode switching probability distribution according to the mode switching trigger factor set; Performing a threshold judgment on the mode switching probability distribution to obtain a preliminary switching decision, and calling a preset system state check list according to the preliminary switching decision to obtain a current state matrix; Calculating a mode switching feasibility index according to the current state matrix to obtain a switching risk assessment result, and performing a multi-criteria decision analysis on the switching risk assessment result to obtain a switching instruction; Generate a parameter configuration instruction set according to the switching instruction, and perform parallel processing on the parameter configuration instruction set to obtain a multi-thread execution queue; Initiate a safety lock program according to the multi-threaded execution queue, obtain a system isolation status confirmation signal, and verify the system isolation status confirmation signal to obtain a disinfection and isolation measures execution instruction; According to the disinfection and isolation measures, the execution instructions generate a mode switching process log, obtain a switching process data packet, encrypt and store the switching process data packet, obtain a mode switching history record, and update the human-computer interaction operation interface according to the mode switching history record to display the real-time system status.

[0013] In combination with the first aspect, in an eighth implementation of the first aspect of the present application, the water circulation processing module is specifically used for: Performing real-time multi-parameter monitoring of the influent water quality to obtain a water quality parameter vector, and calculating a comprehensive water quality index based on the water quality parameter vector; According to the water quality comprehensive index and the current mode, calling the corresponding water treatment strategy library to match the optimal water quality treatment plan; According to the optimal water quality treatment scheme, the operating parameters of the water treatment equipment are controlled and real-time treatment effect data are collected. Meanwhile, the real-time treatment effect data is subjected to dynamic time regularization analysis to obtain a water quality treatment efficiency curve; Performing deviation calculation according to the water quality treatment efficiency curve and the preset target value to obtain a treatment deviation matrix, and generating equipment parameter adjustment instructions according to the treatment deviation matrix; Performing real-time adjustment and water quality data detection according to the equipment parameter adjustment instruction to obtain adjusted water quality data, and performing time series prediction on the adjusted water quality data to obtain a water quality trend model; A probability distribution of water quality reaching the standard is generated according to the water quality trend model, and a risk assessment is performed on the probability distribution of water quality reaching the standard to obtain a water quality management report, and optimization suggestions for water quality treatment strategies for the next cycle are generated according to the water quality management report.

[0014] In combination with the first aspect, in a ninth implementation of the first aspect of the present application, the gas processing module is specifically used for: Performing real-time monitoring of multiple gas components in the air to obtain a gas component concentration matrix, and calculating a comprehensive pollution index based on the gas component concentration matrix; selecting an optimal gas treatment solution from a gas treatment strategy database according to the comprehensive pollution index and the current mode; adjusting the operating parameters of the gas processing equipment according to the optimal gas processing scheme and generating a gas processing efficiency curve; Generate a gas parameter adjustment instruction for the gas processing device according to the gas processing efficiency curve, perform real-time adjustment and data collection on the gas processing device according to the gas parameter adjustment instruction to obtain adjusted air quality data, and perform Kalman filtering on the adjusted air quality data to obtain an air quality prediction model; An air quality management report is generated according to the air quality prediction model, and a gas treatment strategy optimization suggestion for the next treatment cycle is generated according to the air quality management report.

[0015] In the technical solution provided by this application, the dual-mode switching control module realizes the intelligent switching between the daily swimming pool and the nuclear accident emergency decontamination mode, greatly improving the utilization rate of the facilities. The water circulation treatment module and the gas treatment module adopt an adaptive processing strategy, which can automatically adjust the processing scheme according to different modes, ensuring the efficient operation of the system in both modes. The assembled equipment management module improves the reliability and response speed of emergency equipment through functions such as rapid deployment, status monitoring and maintenance reminders. The intelligent monitoring and alarm module uses multi-source data fusion and dynamic anomaly detection technology to enhance the risk warning capability of the system. The collaborative management and data sharing mechanism between modules optimizes the operating efficiency and decision-making quality of the overall system. Comprehensive performance testing and update management strategies ensure the long-term stable operation and technological advancement of the system. Through intelligent mode switching, adaptive processing strategies and multi-module collaborative management, seamless conversion between daily and emergency functions is achieved, equipment utilization is improved, nuclear accident response capabilities are enhanced, and resource allocation efficiency is optimized. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0017] Figure 1 This is a schematic diagram of an embodiment of a management system for a nuclear accident emergency compatible decontamination waste liquid pool in an embodiment of the present application. DETAILED DESCRIPTION

[0018] The embodiment of the present application provides a management system for a nuclear accident emergency compatible decontamination waste liquid pool. The terms "first", "second", "third", "fourth", etc. (if any) in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments described here can be implemented in an order other than that illustrated or described here. In addition, the terms "including" or "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0019] For ease of understanding, the specific process of the embodiment of the present application is described below. Figure 1 In the embodiment of the present application, an embodiment of the management system of the nuclear accident emergency compatible decontamination waste liquid pool includes: Dual-mode switching control module 101, water circulation processing module 102 and gas processing module 103; The dual-mode switching control module 101 is used to: automatically identify the current mode through preset trigger conditions, the current mode is the daily swimming pool mode or the nuclear accident emergency decontamination mode; send a switching instruction according to the current mode, and adjust the operating parameters and working status according to the switching instruction; during the mode switching process, start the safety lock program and complete the disinfection and isolation measures; record the time, reason and process of each mode switching; the management personnel manually trigger the mode switching and monitor the system status in real time through the human-computer interactive operation interface; The water circulation treatment module 102 is used to: adopt different treatment schemes according to the current mode. If the current mode is the daily swimming pool mode, a conventional water quality treatment scheme is adopted, which includes filtration, disinfection and pH value adjustment, and constant temperature control is performed through a heat exchanger to maintain a constant water temperature, and water quality parameters are monitored in real time to ensure compliance with swimming pool hygiene standards; if the current mode is the nuclear accident emergency decontamination mode, a preset filter device is used to remove radioactive particles in the water, a neutralizer or flocculant is added, dissolved radioactive substances are treated, and a heat exchanger is used to reduce the temperature to reduce the volume of waste liquid, and the radioactive indicators of the waste liquid are continuously monitored; The gas processing module 103 is used for: if the current mode is the daily swimming pool mode, the indoor environment is maintained by adjusting the temperature and humidity, the irritating gas generated by the volatilization of chlorine in the water is removed, and the air supply is ensured through air circulation; if the current mode is the decontamination waste liquid mode, the particulate air filter and the activated carbon filter are used to remove the radioactive aerosol, create a negative pressure environment, prevent the spread of pollution, monitor the content of radioactive substances in the air in real time, and discharge the multiple filtered gases through a preset dedicated pipeline.

[0020] Specifically, the environmental sensor data and external information are fused and analyzed, including the aggregation and processing of data from multiple sensors and external information sources, identifying the key factors affecting the system operation mode, and generating a set of mode switching trigger factors, including various parameters from environmental changes to the internal state of the system. The mode switching probability distribution is generated according to the mode switching trigger factor set to evaluate the operation risk and necessity under different modes. The generated mode switching probability distribution is subjected to threshold judgment to determine whether mode switching is required. Through threshold judgment, a preliminary switching decision is obtained. The preset system status check list is called to conduct a comprehensive check on the current system status and generate the current state matrix. The matrix provides the system's operating status information at the current moment. The mode switching feasibility index is calculated based on the current state matrix, which reflects the feasibility and safety of mode switching under current conditions. After obtaining the switching risk assessment result, the necessity and risk of switching are comprehensively evaluated through multi-criteria decision analysis to finally obtain the switching instruction. Based on this instruction, a parameter configuration instruction set is generated, and these instruction sets are parallelized to form a multi-threaded execution queue to ensure the efficient execution of various tasks during the mode switching process. During the actual switching process, the safety lock program is started according to the multi-threaded execution queue to ensure the safety and stability of the system during the mode switching. After verification, the disinfection and isolation measures execution instructions are generated to ensure the safety and pollution-free system environment during the switching process. At the same time, the mode switching process log is generated, the entire switching process is recorded as a data packet, and encrypted storage is performed to form a complete mode switching history record. By updating the human-computer interaction operation interface, the system status is displayed in real time to help managers understand the latest operation of the system.

[0021] The water circulation treatment module is specifically used to monitor the influent water quality in real time with multiple parameters, obtain the water quality parameter vector, and calculate the comprehensive water quality index based on the water quality parameter vector. By real-time monitoring of multiple water quality parameters (such as pH value, temperature, dissolved oxygen, suspended solids content, etc.), comprehensive information on the current influent water quality can be obtained quickly and accurately. A comprehensive water quality index is calculated based on these parameters to evaluate the overall condition of the water quality. According to the comprehensive water quality index and the current mode, the preset water treatment strategy library is called to match the optimal water quality treatment plan. For different operating modes (such as daily swimming pool mode or nuclear accident emergency decontamination mode), the corresponding treatment plans are stored in the strategy library so that the most appropriate treatment method can be adopted in different situations. According to the optimal water quality treatment plan, the operating parameters of the water treatment equipment are controlled, and real-time treatment effect data are collected at the same time. These data include various indicators in the treatment process, such as filtration efficiency, disinfection effect, and the amount of chemical agents used. The real-time treatment effect data is processed using the dynamic time regularization analysis method to obtain the water quality treatment efficiency curve. The curve shows the efficiency changes during the treatment process. According to the water quality treatment efficiency curve and the preset target value, the deviation calculation is performed to obtain the treatment deviation matrix. The treatment deviation matrix reflects the gap between the current treatment effect and the expected target, and is an important basis for adjusting the equipment operation parameters. According to the treatment deviation matrix, the equipment parameter adjustment instructions are generated to guide the equipment to make corresponding adjustments. After the equipment parameters are adjusted, real-time water quality data detection is performed to obtain the adjusted water quality data. These data are processed by the time series prediction method to generate a water quality trend model. The water quality trend model can predict the trend of water quality changes in the future. According to the water quality trend model, the probability distribution of water quality compliance is generated, and risk assessment is performed on it to obtain a water quality management report. The probability distribution of water quality compliance shows the probability of whether the water quality can meet the preset standard in the future. Through risk assessment, potential risk points can be identified and corresponding countermeasures can be proposed. According to the water quality management report, the optimization suggestions for the water quality treatment strategy for the next cycle are generated. The optimization suggestions are based on the treatment effect of the previous cycle and future trend analysis, aiming to further improve the efficiency and stability of water quality treatment. Through closed-loop management, the system can continuously improve water treatment solutions, ensure the safety and compliance of water quality under different modes, and solve the complex technical problems of water quality treatment in nuclear accident emergencies.

[0022] The gas treatment module is specifically used to monitor various gas components in the air in real time, obtain a gas component concentration matrix, and calculate a comprehensive pollution index based on the gas component concentration matrix. Through a high-precision sensor network, the system can continuously monitor various gas components in the air, such as carbon dioxide, carbon monoxide, chlorine, and radioactive aerosols, and summarize these data to form a gas component concentration matrix. Based on the matrix, a comprehensive pollution index is calculated to quantify the overall pollution level of the current air quality. According to the comprehensive pollution index and the current mode, the optimal gas treatment scheme is selected from the gas treatment strategy database. In different modes, such as the daily swimming pool mode and the nuclear accident emergency decontamination mode, corresponding gas treatment strategy libraries are preset. These strategy libraries store the optimal solutions for different pollution conditions and treatment requirements to ensure that pollutants in the air can be effectively treated in various situations. After selecting the optimal gas treatment scheme, the operating parameters of the gas treatment equipment are adjusted according to the scheme, including the working efficiency of the filter, the operating speed of the fan, and the replacement frequency of the activated carbon filter, and a gas treatment efficiency curve is generated at the same time to reflect the actual effect of the current treatment scheme. During the gas treatment process, the gas parameter adjustment instructions of the gas treatment equipment are generated according to the gas treatment efficiency curve. These instructions are used to adjust the operating parameters of the equipment in real time to cope with fluctuations in processing efficiency and changes in air quality. After the equipment parameters are adjusted, the system continues to collect and monitor air quality data, and processes these data to obtain adjusted air quality data. In order to improve the accuracy and reliability of the data, the adjusted air quality data is processed by Kalman filtering. Through this filtering algorithm, noise can be effectively removed, future air quality changes can be predicted, and an air quality prediction model can be generated. Based on the air quality prediction model, an air quality management report is generated. The report records the current air quality status, the operation of the processing equipment, and future air quality forecasts, providing comprehensive information support for managers. Based on the management report, optimization suggestions for the gas processing strategy for the next processing cycle are generated. These optimization suggestions are based on the processing effect and prediction model of the previous cycle, aiming to further improve gas processing efficiency and air quality.

[0023] In a specific embodiment, the management system of the nuclear accident emergency compatible decontamination waste liquid pool further includes: an assembled equipment management module 104; the assembled equipment management module 104 specifically includes: The equipment storage unit 1041 is used to store emergency equipment in the daily swimming pool mode, and the emergency equipment includes: a decontamination water treatment device and a gas treatment device; A rapid deployment unit 1042, for installing emergency equipment after receiving a switching instruction; The device status monitoring unit 1043 is used to regularly check the device status of the emergency device to ensure that the emergency device is in a usable state; The maintenance reminder unit 1044 is used to generate a corresponding target maintenance plan according to the usage and storage time of the emergency equipment; The equipment performance test unit 1045 is used to regularly perform equipment function tests on emergency equipment and obtain equipment function test results; The update management unit 1046 is used to record the update and replacement information of the emergency equipment and ensure that the emergency equipment complies with the nuclear accident response standards based on the update and replacement information.

[0024] Specifically, the equipment storage unit is used to store emergency equipment in the daily swimming pool mode, including the decontamination water treatment device and the gas treatment device. In the daily swimming pool mode, the equipment storage unit is responsible for managing and maintaining the status of the emergency equipment to ensure that these devices are in the best storage state in non-emergency situations, thereby extending their service life and maintaining the reliability of their performance. The unit ensures that the decontamination water treatment device and the gas treatment device are always in good condition through regular inspection and maintenance, so that these devices can be quickly put into use when it is necessary to switch to the nuclear accident emergency decontamination mode.

[0025] The rapid deployment unit is used to install emergency equipment after receiving the switching command. When an emergency occurs, the rapid deployment unit receives and identifies the switching command issued by the system and quickly starts the preset installation process. The unit uses advanced equipment such as automated control and robotic arms to quickly and accurately transfer stored emergency equipment, such as decontamination water treatment devices and gas treatment devices, from the storage unit to the designated work location. The deployment unit is equipped with a sophisticated navigation and positioning system to ensure that the equipment will not be offset or damaged during transportation and installation. At the same time, the rapid deployment unit can automatically connect the power supply, water source and other necessary interfaces according to the specific installation requirements of the equipment to complete all the installation steps of the emergency equipment. The unit is also equipped with a real-time monitoring and feedback system to continuously monitor the equipment status and installation progress during the installation process to ensure that each step is carried out as planned and corrective measures can be taken quickly when problems arise.

[0026] Through high-resolution cameras and image processing technology, the appearance images of the equipment are collected in all directions. The appearance images are analyzed, and the integrity is evaluated by comparing the actual state of the equipment in the image with the preset standard state, and the appearance integrity score is obtained. The score divides the equipment into intact equipment and equipment to be inspected according to the state, forming a corresponding list of intact equipment and a list of equipment to be inspected. A more in-depth inspection of the equipment in the list of equipment to be inspected includes the use of ultrasonic scanning technology to obtain the internal structure data of the equipment. Through ultrasonic scanning, the surface of the equipment is penetrated to capture detailed information of its internal structure, and these data are used to build a three-dimensional structural model of the equipment. By comparing and analyzing the three-dimensional structural model of the equipment with the preset standard structural model, the structural deviation is identified and the structural deviation data is obtained. The deviation data reveals possible wear, deformation or other abnormalities of the equipment. Stress analysis is performed based on the structural deviation data to draw a stress distribution diagram of the equipment. The stress conditions of the equipment at various parts are displayed, and a list of potential fault points is determined by judging the set threshold. In order to verify and further analyze the potential fault points, these equipment are simulated and tested, and their performance parameter sets under different working conditions are collected. The performance parameters obtained from the actual test are compared with the preset standard parameters to calculate the performance attenuation index of the equipment. The index quantifies the degree of decline in equipment performance and reflects the health status and working efficiency of the equipment. The remaining life is predicted based on the performance decay index to obtain a status assessment report of the equipment. The report summarizes the current status of the equipment and predicts its service life in the future. It divides the risk level according to the equipment status and life prediction results, and puts forward corresponding graded maintenance suggestions.

[0027] By mining the equipment usage records, the usage frequency and usage pattern of each device are extracted. A Markov chain model is constructed based on the equipment usage frequency data. The model predicts the intensity and frequency of the equipment in future use and obtains the predicted value of the equipment usage intensity. At the same time, the maintenance reminder unit monitors the storage environment of the emergency equipment through sensors to obtain the environmental parameter set, and calculates the environmental impact factor based on the environmental parameter set to obtain the equipment aging rate. Various sensors arranged in the storage environment monitor parameters such as temperature, humidity, and air quality in real time. These environmental parameters directly affect the aging rate of the equipment. The aging rate of the equipment is evaluated by calculating the combined impact of these parameters. According to the predicted value of equipment usage intensity and the equipment aging rate, a two-factor weighted model is established to obtain the equipment loss index, and the equipment loss index is analyzed by time series to obtain the maintenance demand curve. The two-factor weighted model combines the two key factors of equipment usage intensity and aging rate, and calculates the comprehensive loss of the equipment by weighting. By performing a time series analysis on the equipment loss index, the equipment maintenance demand curve is drawn to show the changing trend of the equipment maintenance demand in the future. According to the maintenance demand curve and the preset maintenance cycle threshold, dynamic programming calculation is performed to obtain the preliminary maintenance time point set, and cluster analysis is performed on the preliminary maintenance time point set to obtain the optimized maintenance time point set. On this basis, according to the optimized maintenance time point set, the historical maintenance records are mined for association rules to obtain the maintenance project association matrix, and the maintenance project association matrix is ​​analyzed to obtain the list of key maintenance projects. Through association rule mining, the correlation between each maintenance project is identified to form a maintenance project association matrix. The analysis results of the matrix generate a list of key maintenance projects, which lists the key projects that must be paid attention to and given priority during the maintenance process. According to the list of key maintenance projects and the optimized maintenance time point set, a multi-objective optimization model is constructed to obtain a preliminary maintenance plan. The multi-objective optimization model comprehensively considers the time, cost and resource allocation of maintenance to form a preliminary maintenance plan. The preliminary maintenance plan is simulated to obtain the reliability evaluation results of the maintenance plan, and the preliminary maintenance plan is adjusted according to the reliability evaluation results of the maintenance plan to finally form a target maintenance plan. Through simulation and evaluation, the feasibility and reliability of the maintenance plan are verified and adjusted to ensure that the final target maintenance plan is optimal and effective.

[0028] A standardized load is applied to each emergency device, and a series of operating parameters are collected through testing, including the temperature, pressure, vibration and current of the device. Based on these data, a device performance benchmark curve is drawn to reflect the operating performance of the device under the standard load. The device performance benchmark curve is subjected to Fourier transform to obtain frequency domain feature data, and wavelet decomposition is performed based on the frequency domain feature data to obtain multi-scale performance indicators. Fourier transform converts time domain data into frequency domain data, from which the characteristics of frequency components are extracted, which can reveal potential problems in the operation of the device. Wavelet decomposition decomposes frequency domain data into performance indicators of different scales, providing a multi-level perspective for device performance analysis. The device performance score is calculated based on the multi-scale performance indicators, and the device performance score is analyzed by time series to build a performance trend prediction model. The performance score is a quantitative indicator based on the comprehensive multi-scale performance indicators, reflecting the overall operating status of the device. Through time series analysis, the trend of device performance changes over time can be captured, so as to establish a performance trend prediction model, which is used to predict the future operating status of the device. A virtual load sequence is generated based on the performance trend prediction model to obtain a simulation test plan, and the simulation test plan is executed on the emergency equipment to obtain simulation test result data. The virtual load sequence simulates the load changes under future use conditions. By executing these simulation test schemes, the system can verify the performance of the equipment in the experimental environment and ensure that the test results are predictive and representative. The performance calculation of the simulation test result data is performed to obtain the key performance factors, and the emergency equipment is classified according to the key performance factors to obtain the equipment performance level. The key performance factors are the core parameters extracted from the simulation test data, which directly reflect the operating efficiency and stability of the equipment. According to these factors, the equipment is classified into performance levels to clarify the performance of the equipment under different loads and operating conditions. The equipment reliability distribution is calculated according to the equipment performance level and performance trend prediction model, and the equipment reliability distribution is updated by Bayesian to obtain the equipment function test results. The equipment reliability distribution is calculated based on the performance level and trend prediction model, reflecting the reliability probability of the equipment in future operation. Through Bayesian updating, the reliability distribution of the equipment is continuously corrected and improved to ensure the accuracy and real-time nature of the prediction results.

[0029] By regularly scanning the nuclear accident response standard database, we ensure that we always have the latest standards and specifications. Based on the latest update information, we build a knowledge graph that shows the relationship between them and forms a comprehensive standard association network. Through graph analysis, we identify the nodes that play a key role in the standard association network. These nodes are usually standards with high requirements for equipment performance and safety. Based on the key standard nodes, we extract the equipment update indicator set, which will serve as an important basis for evaluating the performance of existing equipment. According to the equipment update indicator set, the performance matching of emergency equipment is evaluated to obtain the equipment matching matrix, and the equipment matching matrix is ​​analyzed hierarchically to obtain the equipment update priority list. The performance matching evaluation is to compare the performance of the current equipment with the latest standard requirements to form an equipment matching matrix. Through the hierarchical analysis method, the update priority of each device is determined, and the equipment update priority list is generated, which shows which devices need to be updated first. According to the equipment update priority list, a candidate equipment data set is generated, and a multi-criteria decision analysis is performed on the candidate equipment data set to obtain the optimal replacement equipment solution. A group of candidate equipment is screened out, and a comprehensive analysis is performed based on multiple decision criteria, such as performance, cost, reliability, etc., to determine the optimal replacement equipment solution. Ensure that the equipment selected during the update process not only meets the latest standards, but also has the best cost-effectiveness and performance stability. According to the optimal alternative equipment solution, build an equipment update cost model, and perform sensitivity analysis on the equipment update cost model to obtain a cost risk assessment report. The equipment update cost model takes into account various expenses such as equipment procurement, installation, and commissioning. Through sensitivity analysis, identify the key factors affecting the cost and form a cost risk assessment report. According to the cost risk assessment report and the equipment update priority list, formulate a phased update plan, and perform discrete event simulation on the phased update plan to obtain the update process optimization plan. The phased update plan ensures that the equipment update work can proceed smoothly and will not cause too much impact on the normal operation of the system. Through discrete event simulation, predict and optimize various operations in the update process to ensure the best effect in actual execution. Generate equipment update execution instructions based on the update process optimization plan. These instructions guide the actual equipment update work, from equipment removal, installation to testing and acceptance, to ensure that the entire update process is carried out efficiently and orderly.

[0030] In a specific embodiment, the device status monitoring unit 1043 is specifically used to: Capture appearance images of emergency equipment to obtain equipment appearance images, and perform equipment integrity analysis on the equipment appearance images to obtain appearance integrity scores; Classify emergency equipment according to appearance integrity scores to obtain a list of intact equipment and a list of equipment to be inspected, and perform ultrasonic scanning on the equipment in the list of equipment to be inspected to obtain internal structure data; A three-dimensional model is constructed according to the internal structure data to obtain a three-dimensional structure model of the equipment, and the three-dimensional structure model of the equipment is compared and analyzed with the standard structure model to obtain structure deviation data; Perform stress analysis on emergency equipment based on structural deviation data to obtain a stress distribution diagram, and perform threshold judgment on the stress distribution diagram to obtain a list of potential fault points; Conduct simulated operation tests on emergency equipment according to the list of potential fault points to obtain a performance parameter set, and compare the performance parameter set with preset standard parameters to obtain a performance degradation index; The remaining life of emergency equipment is predicted based on the performance degradation index to obtain an equipment status assessment report, and risk levels are divided based on the equipment status assessment report to obtain graded maintenance recommendations.

[0031] Specifically, the appearance images of the emergency equipment are collected, and the appearance images of the equipment are taken from different angles by a high-resolution camera. After the images are obtained, the images are analyzed using image processing algorithms to identify problems such as damage, corrosion, and cracks on the surface of the equipment. The appearance integrity score (ICS) is calculated from the image data. The score can be expressed by the formula: ; in, is the area of ​​the defect region identified in the i-th image, is the total area of ​​the i-th image, and n is the total number of images. The value of ICS is between 0 and 1, and the higher the value, the better the appearance. According to the appearance integrity score ICS, the emergency equipment is classified into intact equipment and equipment to be inspected. Intact equipment enters the intact equipment list and does not require further inspection; equipment to be inspected enters the equipment to be inspected list and requires more in-depth inspection. For the equipment to be inspected, ultrasonic scanning technology is used to obtain its internal structure data. Ultrasonic scanning can identify defects inside the equipment, such as cracks, bubbles, material inhomogeneity, etc. The three-dimensional model of the equipment is constructed using the internal structure data obtained. The three-dimensional model accurately reflects the internal structure of the equipment. The three-dimensional model is compared and analyzed with the standard structure model to identify the structural deviation data. The structural deviation data reflects the difference between the actual structure of the equipment and the standard structure, which may be caused by material fatigue, wear or other reasons. Stress analysis is performed based on the structural deviation data. Stress analysis is implemented by the finite element analysis method to obtain the stress distribution diagram of the equipment. The stress distribution diagram shows the stress conditions of each part of the equipment under working conditions. Threshold judgment is performed based on the stress distribution diagram to identify the areas where stress exceeds the standard, which are the list of potential fault points. The emergency equipment is tested for simulated operation according to the list of potential failure points. The simulated operation test is carried out in a laboratory environment, with a standard workload applied to measure the performance parameter set of the equipment. These parameters include vibration, temperature, pressure, etc., and the performance degradation index of the equipment is calculated by comparing with the preset standard parameters. The performance degradation index can be calculated by the formula: ; in, is the actual measured value of the jth performance parameter, is the standard value of the jth performance parameter, and m is the total number of performance parameters. The higher the value of PDI, the more serious the performance degradation of the equipment. The remaining life of the equipment is predicted according to the performance degradation index. Through statistical analysis and life prediction model, the remaining life of the equipment is obtained, and an equipment status assessment report is generated. The assessment report records the current status, potential problems and remaining life of the equipment. Based on this report, risk level classification is carried out and graded maintenance recommendations are obtained. For example, in the analysis of nuclear accident emergency decontamination pumps, appearance image acquisition and analysis showed that ICS was 0.85, indicating that the appearance of the equipment was good, but further inspection was still required. Ultrasonic scanning found some small cracks inside, and structural deviation data analysis showed that these cracks were in non-critical positions, but needed to be monitored. Stress analysis obtained a stress distribution diagram and found that the stress in the crack area was high, which was included in the list of potential failure points. The performance parameter set was obtained by simulation operation test. After comparison with the standard parameters, the PDI was calculated to be 0.15, indicating that the performance of the equipment was slightly attenuated. Based on PDI and statistical models, the remaining life of the equipment is predicted to be two years, and a detailed inspection is recommended every six months.

[0032] In a specific embodiment, the maintenance reminder unit 1044 is specifically used to: Data mining is performed on the usage records of emergency equipment to obtain the equipment usage frequency data, and a Markov chain model is constructed based on the equipment usage frequency data to obtain the predicted value of equipment usage intensity; Sensors are used to monitor the storage environment of emergency equipment to obtain an environmental parameter set, and environmental impact factors are calculated based on the environmental parameter set to obtain the equipment aging rate; According to the predicted value of equipment usage intensity and equipment aging rate, a two-factor weighted model is established to obtain the equipment loss index. The equipment loss index is then analyzed through time series to obtain the maintenance demand curve. Perform dynamic programming calculations based on the maintenance demand curve and the preset maintenance cycle threshold to obtain a preliminary maintenance time point set, and perform cluster analysis on the preliminary maintenance time point set to obtain an optimized maintenance time point set; According to the optimized maintenance time point set, the historical maintenance records are mined for association rules to obtain the maintenance project association matrix, and the maintenance project association matrix is ​​analyzed to obtain a list of key maintenance projects; According to the list of key maintenance items and the optimized maintenance time point set, a multi-objective optimization model is constructed to obtain a preliminary maintenance plan; the preliminary maintenance plan is simulated to obtain the reliability evaluation results of the maintenance plan, and the preliminary maintenance plan is adjusted according to the reliability evaluation results of the maintenance plan to obtain the target maintenance plan.

[0033] Specifically, by mining the usage records of emergency equipment, we can extract the frequency of equipment usage in each time period. These data record the start time, stop time, operating time, load conditions and other information of the equipment. Suppose there is a nuclear accident emergency decontamination pump, and its usage records show the operation status in different time periods. By analyzing these data, we can calculate the usage frequency of the equipment in each state. The state space can be expressed as ,in Indicates the device’s The Markov chain model assumes that the future state depends only on the current state and has nothing to do with the past state. Define the state transition probability matrix Indicates from the state Transfer to state Probability: ; in Indicates that the device is in state Transfer to state The probability of using the current state vector to predict the equipment usage intensity. The storage environment of the emergency equipment is monitored by sensors to obtain the environmental parameter set , these parameters include temperature, humidity, air quality, vibration, etc. Assume that the weight of each environmental parameter is , the calculation formula of environmental impact factor (EIF) is: ; The equipment aging rate (AR) depends on environmental factors and can be expressed as: ; in Is a constant that represents the relationship coefficient between environmental impact factors and aging rate. A two-factor weighted model is established based on the predicted value of equipment usage intensity and equipment aging rate, and the equipment loss index is calculated by combining equipment usage intensity and equipment aging rate: ; in, and are the weights of equipment usage intensity and equipment aging rate, respectively. By performing time series analysis on the equipment loss index, the equipment loss in the future is predicted, and the maintenance demand curve is obtained, which reflects the maintenance demand of the equipment at different time points in the future. According to the peak value on the maintenance demand curve and the preset maintenance cycle threshold, dynamic planning calculation is performed to determine the preliminary maintenance time point set. These time points indicate the key moments when the equipment needs maintenance in the future. Through cluster analysis, the preliminary maintenance time points are optimized, and similar time points are merged to obtain the optimized maintenance time point set. According to the optimized maintenance time point set, the historical maintenance records are mined for association rules to obtain the maintenance project association matrix, and the maintenance project association matrix is ​​analyzed to obtain the list of key maintenance projects. The historical maintenance records contain a lot of information on equipment maintenance. Through association rule mining, the correlation between different maintenance projects is found to form a maintenance project association matrix. Through the analysis of the association matrix, a list of key maintenance projects that need to be focused on and prioritized during the maintenance process is identified. Based on the list of key maintenance projects and the optimized maintenance time point set, a multi-objective optimization model is constructed to comprehensively consider multiple factors such as time, cost and resource allocation to formulate a preliminary maintenance plan. The multi-objective optimization model aims to balance the needs of all parties and ensure the scientificity and efficiency of the maintenance plan. The preliminary maintenance plan is simulated to predict the performance of the plan in actual execution and identify potential problems and room for improvement. Based on the simulation results, the reliability of the maintenance plan is evaluated. The evaluation results can reveal the potential risks and improvement directions of the plan. Based on the reliability evaluation results of the maintenance plan, the preliminary maintenance plan is adjusted to obtain the target maintenance plan.

[0034] In a specific embodiment, the device performance testing unit 1045 is specifically used to: Conduct standard load tests on emergency equipment to obtain equipment operating parameter sets, and construct equipment performance benchmark curves based on the equipment operating parameter sets; Perform Fourier transform on the equipment performance benchmark curve to obtain frequency domain feature data, and perform wavelet decomposition based on the frequency domain feature data to obtain multi-scale performance indicators; Calculate equipment performance scores based on multi-scale performance indicators, perform time series analysis on equipment performance scores, and build a performance trend prediction model; Generate a virtual load sequence according to the performance trend prediction model to obtain a simulation test plan, and execute the simulation test plan on the emergency equipment to obtain simulation test result data; Perform performance calculation on the simulation test result data to obtain key performance factors, and classify the performance level of emergency equipment according to the key performance factors to obtain the equipment performance level; The equipment reliability distribution is calculated according to the equipment performance level and performance trend prediction model, and the equipment reliability distribution is updated by Bayesian method to obtain the equipment function test results.

[0035] Specifically, by performing a standard load test on the emergency equipment, the equipment operating parameter set is obtained. These operating parameter sets include various performance data of the equipment under different load conditions, such as temperature, pressure, vibration, power, etc. Suppose a nuclear accident emergency decontamination pump is subjected to a standard load test, and its operating parameters under different loads are recorded. These data constitute the equipment operating parameter set. Based on these operating parameters, an equipment performance baseline curve is constructed, which can reflect the performance of the equipment under the standard load. In order to analyze the equipment performance baseline curve, a Fourier transform is performed on it to convert the time domain data into frequency domain feature data. The Fourier transform formula is: ; in, is the frequency domain feature data, It is the equipment performance data in the time domain. is the angular frequency. Through Fourier transform, the characteristic information of the device at different frequencies is extracted. These frequency domain characteristic data reveal the performance of the device at each frequency component, which helps to identify potential problems in the operation of the device. Wavelet decomposition is performed based on the frequency domain characteristic data to obtain multi-scale performance indicators. Wavelet decomposition is a time-frequency analysis method that decomposes the signal into multiple components through wavelet functions of different scales to obtain the characteristics of the signal at different time scales. Multi-scale performance indicators can comprehensively reflect the performance of the device at different time scales. According to the multi-scale performance indicators, the device performance score is calculated. The device performance score is a quantitative evaluation of the comprehensive performance of the device, which can be expressed as: ; in, It is Multi-scale performance indicators, is the corresponding weight, is the number of multi-scale performance indicators. A comprehensive performance score is obtained by weighted summation. Time series analysis is performed on the equipment performance score to build a performance trend prediction model. Time series analysis methods include autoregressive moving average model (ARIMA), exponential smoothing method, etc. These methods can predict the future trend of equipment performance scores. The performance trend prediction model can be expressed as: ; in, is the predicted performance score, is the past performance score, It's the error of the past. and are model parameters, is a constant term. Based on the performance trend prediction model, a virtual load sequence is generated to obtain a simulation test plan. The virtual load sequence is a load change pattern designed according to the predicted performance trend, which is used to simulate the operation of the equipment in future use. The simulation test plan is executed on the emergency equipment to obtain the simulation test result data, which includes the performance of the equipment under the virtual load. The performance calculation is performed on the simulation test result data to obtain the key performance factor. The key performance factor is the core evaluation indicator of the equipment's operating status, such as temperature, vibration frequency, power consumption, etc. under maximum load. According to the key performance factors, the emergency equipment is classified into performance levels to obtain the equipment performance level. The performance level classification can adopt the grade scoring method to divide the equipment into different performance levels according to its performance on the key performance factors, such as excellent, good, general, poor, etc. According to the equipment performance level and the performance trend prediction model, the equipment reliability distribution is calculated. The equipment reliability distribution represents the reliability of the equipment in the future period of time, which is continuously revised through the Bayesian update method. The Bayesian update formula is: ; in, The given data Post Parameters The posterior probability of It’s data At the given parameters The likelihood function under is a parameter The prior probability of It’s data Through Bayesian updating and combining with new test data, the reliability distribution of the equipment is continuously adjusted to obtain more accurate equipment function test results.

[0036] In a specific embodiment, the update management unit 1046 is specifically used to: Regularly scan the nuclear accident response standard database to obtain the latest standard update information, and build a knowledge graph based on the latest standard update information to obtain a standard association network; Conduct graph theory analysis on the standard association network to obtain key standard nodes, and extract the device update indicator set based on the key standard nodes; According to the equipment update index set, the performance matching degree of the emergency equipment is evaluated to obtain the equipment matching degree matrix, and the equipment matching degree matrix is ​​subjected to hierarchical analysis to obtain the equipment update priority list; Generate a candidate equipment data set based on the equipment update priority list, and perform multi-criteria decision analysis on the candidate equipment data set to obtain the optimal replacement equipment solution; According to the optimal replacement equipment plan, build an equipment update cost model, conduct sensitivity analysis on the equipment update cost model, and obtain a cost risk assessment report; According to the cost risk assessment report and the equipment update priority list, a phased update plan is formulated, and a discrete event simulation is performed on the phased update plan to obtain an update process optimization plan, and an equipment update execution instruction is generated according to the update process optimization plan.

[0037] Specifically, the latest standard update information is obtained by regularly scanning the nuclear accident response standard database. These update information may include new safety regulations, technical requirements, operating specifications, etc. By parsing the standard update information, key information is extracted and a knowledge graph is constructed. A knowledge graph is a graph structure used to represent knowledge and its relationships, which can show the associations between different standards. Through the knowledge graph, a standard association network is formed, which can reveal the interdependence and influence between standards. Graph theory analysis is performed on the standard association network to determine the key standard nodes in the network. Key standard nodes refer to those standards that have a significant impact on equipment performance and safety. These nodes usually have high degree centrality or betweenness centrality in the network. Through graph theory analysis, these key standard nodes are identified, and based on this, the equipment update indicator set is extracted. Based on the extracted equipment update indicator set, the performance matching of the emergency equipment is evaluated. The performance matching evaluation aims to determine how well the existing equipment meets the latest standard requirements. Assume that there is Device update indicators and emergency equipment, build an equipment matching matrix , where each element Indicates The device in The matching degree of each indicator is calculated. The matching degree between the equipment and the standard is quantified by calculating the equipment matching matrix. The equipment matching matrix is ​​subjected to hierarchical analysis to obtain the equipment update priority list. The analytic hierarchy process is a multi-criteria decision-making method that calculates the weight and priority of each factor by constructing a hierarchical model and performing pairwise comparisons. The specific steps include constructing a hierarchical model, constructing a judgment matrix, calculating a weight vector, and performing a consistency test. Through the analytic hierarchy process, the matching degree of the equipment is sorted to determine which equipment needs to be updated first. According to the equipment update priority list, a candidate equipment data set is generated, and a multi-criteria decision analysis is performed on the candidate equipment data set to obtain the optimal replacement equipment solution. Multi-criteria decision analysis methods include weighted sum method, weighted product method, analytic hierarchy process, etc. Through multi-criteria decision analysis, multiple factors (such as performance, cost, reliability, etc.) are comprehensively considered to select the optimal replacement equipment. According to the optimal replacement equipment solution, an equipment update cost model is constructed. The equipment update cost model includes equipment procurement cost, installation cost, commissioning cost, maintenance cost, etc. Assume that there is cost factors, the total cost It can be expressed as: ; in, Indicates Cost factors, Indicates The weights of the cost factors are calculated. The total cost of equipment update is estimated by calculating the equipment update cost model. A sensitivity analysis is performed on the equipment update cost model to obtain a cost risk assessment report. The sensitivity analysis aims to evaluate the impact of each cost factor on the total cost and identify the key cost factors. By adjusting the value range of each factor, the change in total cost is analyzed to assess the cost risk. According to the cost risk assessment report and the equipment update priority list, a phased update plan is formulated. The phased update plan aims to implement equipment updates in stages to reduce one-time investment and operational risks. When formulating the update plan, the equipment update priority, cost factors and implementation time are comprehensively considered. Discrete event simulation is performed on the phased update plan to obtain the update process optimization plan. Discrete event simulation is a simulation method that evaluates the actual effect and optimization plan of the update plan by simulating the occurrence of events at different time points. According to the update process optimization plan, the equipment update execution instructions are generated. The equipment update execution instructions include specific implementation steps, time arrangements, responsibility allocation, etc., to guide the actual operation of equipment update.

[0038] In a specific embodiment, the management system of the nuclear accident emergency compatible decontamination waste liquid pool further includes: an intelligent monitoring and alarm module 105, which is used to: Collect and preprocess multi-source sensor data to obtain a multi-dimensional time series data set, and analyze the multi-dimensional time series data set to obtain a reduced-dimensional feature vector; A dynamic anomaly detector is constructed based on the reduced-dimensional feature vector, and online learning and parameter update are performed on the dynamic anomaly detector to obtain an adaptive threshold surface. According to the adaptive threshold curve, abnormal events are identified on the real-time monitoring data to obtain a preliminary abnormal event list, and the preliminary abnormal event list is comprehensively evaluated to obtain the priority ranking of the abnormal events; Construct an event correlation map based on the priority ranking of abnormal events, and perform graph mining and path analysis on the event correlation map to obtain potential risk propagation paths; Generate multi-scenario emergency response plans based on potential risk transmission paths, and optimize the multi-scenario emergency response plans to obtain the optimal emergency response plan; According to the optimal emergency plan, a hierarchical alarm instruction is generated to obtain an intelligent alarm plan, and the intelligent alarm plan is visualized to obtain a multi-dimensional visual alarm interface. According to the multi-dimensional visual alarm interface, a corresponding emergency response mechanism is triggered.

[0039] Specifically, a multi-dimensional time series data set is obtained by collecting and preprocessing multi-source sensor data. Multi-source sensor data includes temperature, pressure, vibration, humidity, etc. These data can be represented as a multi-dimensional time series matrix , where each column represents the time series data of a sensor, and each row is the data record at a different time point. Sensors and A point in time, then The dimension is . Analyze multidimensional time series data sets and use dimensionality reduction techniques such as principal component analysis to extract feature vectors. Through dimensionality reduction, high-dimensional data is projected into a low-dimensional space to obtain reduced-dimensional feature vectors The goal of principal component analysis is to find a linear transformation that maximizes the variance of the projected data in the low-dimensional space. The specific formula is: ; in, is the original data matrix, is the matrix containing the principal components, is the feature vector after dimensionality reduction. A dynamic anomaly detector is constructed based on the reduced dimensionality feature vector. The core of the dynamic anomaly detector is to detect abnormal patterns in time series data. In order to improve the accuracy and adaptability of detection, an online learning algorithm is used to train the detector and update the parameters to obtain an adaptive threshold surface. The adaptive threshold surface is a dynamically adjusted boundary used to determine whether the current monitoring data is abnormal. Assuming that a support vector machine is used for anomaly detection, the threshold surface can be expressed as: ; in, is the weight vector, is the input feature vector, is the bias term. Through online learning algorithms, we continuously adjust and , so that the detector can adapt to new data patterns. According to the adaptive threshold surface, abnormal events are identified on the real-time monitoring data to obtain a preliminary abnormal event list. The preliminary abnormal event list contains all data points detected as abnormal. Comprehensively judge the abnormal events, and consider factors such as the severity of the event and the scope of impact to obtain the priority ranking of the abnormal events. According to the priority ranking of the abnormal events, an event association graph is constructed. The event association graph is a directed graph in which nodes represent abnormal events and edges represent the association between events. Through graph mining and path analysis, potential risk propagation paths are identified. Graph mining techniques include community detection and frequent subgraph mining, which can be used to discover potential connections between events. Based on the potential risk propagation paths, multi-scenario emergency plans are generated. Multi-scenario emergency plans are emergency response measures designed for different risk scenarios. In order to optimize these plans, optimization algorithms are used for optimization analysis to obtain the optimal emergency plan. The optimization goal can be to minimize response time, maximize resource utilization, etc. Assuming that a genetic algorithm is used for optimization, the objective function can be expressed as: ; in, It is The cost of an emergency plan, is the number of emergency plans. According to the optimal emergency plan, a hierarchical alarm instruction is generated to obtain an intelligent alarm plan. The intelligent alarm plan includes alarm information of different levels and issues corresponding alarms for events of different severity. In order to improve the visualization effect of the alarm, the intelligent alarm plan is visualized to generate a multi-dimensional visual alarm interface. The multi-dimensional visual alarm interface can display the real-time status, correlation and emergency response measures of abnormal events.

[0040] In a specific embodiment, the dual-mode switching control module 101 is specifically used for: Perform fusion analysis on environmental sensor data and external information to obtain a set of mode switching trigger factors, and generate a mode switching probability distribution based on the set of mode switching trigger factors; Perform threshold judgment on the mode switching probability distribution to obtain a preliminary switching decision, and call a preset system status check list according to the preliminary switching decision to obtain a current state matrix; Calculate the mode switching feasibility index according to the current state matrix to obtain the switching risk assessment result, and perform multi-criteria decision analysis on the switching risk assessment result to obtain the switching instruction; Generate a parameter configuration instruction set according to the switching instruction, and perform parallel processing on the parameter configuration instruction set to obtain a multi-thread execution queue; Starting a safety lock program according to a multi-threaded execution queue, obtaining a system isolation status confirmation signal, verifying the system isolation status confirmation signal, and obtaining disinfection and isolation measures execution instructions; According to the disinfection and isolation measures, the instructions are executed to generate a mode switching process log, obtain a switching process data packet, encrypt and store the switching process data packet, obtain a mode switching history record, and update the human-computer interaction operation interface according to the mode switching history record to display the real-time system status.

[0041] Specifically, the mode switching trigger factor set is obtained by fusing and analyzing the environmental sensor data and external information. The environmental sensor data includes temperature, humidity, pressure, gas concentration, etc., while the external information may include weather forecasts, equipment operation logs, emergency event notifications, etc. By fusing and analyzing these data, the key factors that affect the system mode switching are extracted. These factors form the mode switching trigger factor set, which is expressed as . Generate the mode switching probability distribution according to the mode switching trigger factor set. The mode switching probability distribution represents the possibility of the system switching to different modes under the current conditions. Assuming there are two modes: normal mode and emergency mode, the probability distribution of mode switching is calculated by the Bayesian network model. The Bayesian network is a probabilistic graph model that calculates the conditional probability to obtain the switching probability under different modes. The mode switching probability distribution is expressed as ,in Indicates the mode, Represents the trigger factor set. The mode switching probability distribution is subjected to threshold judgment to obtain a preliminary switching decision. Threshold judgment is to compare the switching probability with the preset threshold. If the switching probability exceeds the threshold, the mode switching is triggered. Assume that the threshold is , the preliminary switching decision can be expressed as ; in, Indicates the preliminary switching decision, 1 indicates triggering mode switching, and 0 indicates not triggering. According to the preliminary switching decision, call the preset system status check list to obtain the current state matrix. The system status check list includes the operating status, resource usage, security status, etc. of the device. Through the check list, the current state matrix of the system is obtained, which is expressed as According to the current state matrix , calculate the feasibility index of mode switching. Feasibility index Indicates the feasibility of system mode switching under the current state. The calculation formula of the feasibility index can be expressed as: ; in, is a mapping function, which can usually be in the form of a linear combination or other functions. For example, if Include state variables , we can define for: ; in, is the weight coefficient of each state variable, which is used to adjust the influence of each state variable on the feasibility index. After obtaining the switching risk assessment result, a multi-criteria decision analysis is performed on it to obtain the switching instruction. The multi-criteria decision analysis includes analyzing multiple criteria such as system safety, efficiency, and cost, comprehensively evaluating the switching risk, and generating the final switching instruction. The decision model can be expressed as: ; in, Indicates a switch instruction. Indicates the cost, Indicates efficiency, Is a multi-criteria decision function. Function It can be in the form of a weighted sum: ; in, , and is the weight coefficient of each criterion, which is used to balance the impact of different criteria on the decision. According to the switching instruction, a parameter configuration instruction set is generated, and the parameter configuration instruction set is parallelized to obtain a multi-threaded execution queue. The parameter configuration instruction set includes instructions such as device configuration, resource allocation, and safety measures. Through parallel processing, the execution speed of instructions can be accelerated and the response efficiency of the system can be improved. According to the multi-threaded execution queue, the safety lock program is started to obtain a system isolation status confirmation signal. The safety lock program is used to ensure the security of the system during the mode switching process, and prevent safety accidents during the switching process by locking key devices and resources. The system isolation status confirmation signal is verified to obtain the disinfection and isolation measures execution instruction. The verification process includes checking the locking status of each device and resource to ensure that all safety measures are in place. According to the disinfection and isolation measures execution instruction, a mode switching process log is generated to obtain a switching process data packet. The mode switching process log records all operations and state changes during the entire switching process. The switching process data packet is encrypted and stored to obtain a mode switching history record. Encrypted storage can ensure the security and integrity of data and prevent unauthorized access and tampering. According to the mode switching history, the human-computer interaction interface is updated to display the real-time system status. The operation interface includes the current system status, mode switching process, warning and prompt information, etc.

[0042] In a specific embodiment, the water circulation processing module 102 is specifically used for: Conduct multi-parameter real-time monitoring of the influent water quality to obtain a water quality parameter vector, and calculate a comprehensive water quality index based on the water quality parameter vector; According to the comprehensive water quality index and current mode, call the corresponding water treatment strategy library to match the optimal water quality treatment plan; According to the optimal water quality treatment plan, the operating parameters of the water treatment equipment are controlled and real-time treatment effect data is collected. At the same time, dynamic time regularization analysis is performed on the real-time treatment effect data to obtain the water quality treatment efficiency curve; Deviation calculation is performed according to the water quality treatment efficiency curve and the preset target value to obtain a treatment deviation matrix, and equipment parameter adjustment instructions are generated according to the treatment deviation matrix; According to the equipment parameter adjustment instructions, real-time adjustment and water quality data detection are performed to obtain the adjusted water quality data, and the adjusted water quality data is predicted in time series to obtain a water quality trend model; The probability distribution of water quality reaching the standard is generated according to the water quality trend model, and a risk assessment is performed on the probability distribution of water quality reaching the standard to obtain a water quality management report, and optimization suggestions for the water quality treatment strategy for the next cycle are generated according to the water quality management report.

[0043] Specifically, the water quality of the influent is monitored in real time with multiple parameters to obtain a water quality parameter vector. These parameters include pH value, dissolved oxygen, turbidity, temperature, ammonia nitrogen concentration, etc. Assuming there are n monitoring parameters, these parameters are represented as a vector ,in Represents the real-time monitoring value of the i-th parameter. According to the water quality parameter vector , calculate the comprehensive water quality index. The comprehensive water quality index is an indicator for comprehensively evaluating water quality conditions, calculated by weighted average method. Assume that the weight of each parameter is , comprehensive water quality index It can be expressed as: ; in, is the weight of the i-th parameter, reflecting the influence of this parameter on the comprehensive evaluation of water quality. And the current mode, call the corresponding water treatment strategy library to match the optimal water quality treatment plan. The current mode can be daily operation mode or emergency treatment mode. According to different modes, the treatment plans stored in the water treatment strategy library are also different. By searching the strategy library, the optimal water quality treatment plan under the current conditions is matched. According to the optimal water quality treatment plan, the operating parameters of the water treatment equipment are controlled, and the treatment effect data is collected in real time. The real-time treatment effect data includes the treated water quality parameters, treatment efficiency, etc. Assume that the treatment effect data is ,in represents the real-time value of the effect parameter. At the same time, the real-time processing effect data is subjected to dynamic time warping analysis to obtain the water quality treatment efficiency curve. Dynamic time warping analysis is an algorithm for measuring the similarity of time series. By aligning the time series, the best matching path can be found. Deviation calculation is performed based on the water quality treatment efficiency curve and the preset target value to obtain the processing deviation matrix, which represents the difference between the treatment effect and the target value. According to the processing deviation matrix, the equipment parameter adjustment instruction is generated. The adjustment instruction is used to adjust the operating parameters of the water treatment equipment to reduce the processing deviation and improve the treatment effect. According to the equipment parameter adjustment instruction, real-time adjustment and water quality data detection are performed to obtain the adjusted water quality data. The adjusted water quality data includes the real-time monitoring values ​​of each water quality parameter after adjustment. The adjusted water quality data is subjected to time series prediction to obtain the water quality trend model. Time series prediction can use methods such as ARIMA model and LSTM neural network to predict the changing trend of water quality parameters in the future. According to the water quality trend model, the water quality compliance probability distribution is generated. The water quality compliance probability distribution represents the probability that the water quality parameters meet the standard requirements at different time points in the future. Through Monte Carlo simulation and other methods, the probability of reaching the standard at each time point in the future is calculated. The probability distribution of water quality reaching the standard is risk assessed to obtain a water quality management report. Risk assessment includes analyzing the changing trend of the probability of reaching the standard and identifying potential water quality problems. The water quality management report summarizes the current water quality status, predicted water quality change trend and possible risks. Based on the water quality management report, optimization suggestions for the water quality treatment strategy for the next cycle are generated. Optimization suggestions include adjusting the operating parameters of the water treatment equipment, improving the treatment process, etc., to improve the water quality treatment effect and ensure that the water quality continues to meet the standard.

[0044] In a specific embodiment, the gas processing module 103 is specifically used for: Monitor multiple gas components in the air in real time to obtain a gas component concentration matrix, and calculate a comprehensive pollution index based on the gas component concentration matrix; Select the best gas treatment plan from the gas treatment strategy database based on the comprehensive pollution index and current mode; Adjusting the operating parameters of the gas processing equipment according to the optimal gas processing scheme and generating a gas processing efficiency curve; Generate a gas parameter adjustment instruction for the gas processing equipment according to the gas processing efficiency curve, and perform real-time adjustment and data collection on the gas processing equipment according to the gas parameter adjustment instruction to obtain adjusted air quality data, and perform Kalman filtering on the adjusted air quality data to obtain an air quality prediction model; Generate an air quality management report based on the air quality prediction model, and generate gas treatment strategy optimization suggestions for the next treatment cycle based on the air quality management report.

[0045] Specifically, by real-time monitoring of multiple gas components in the air, a gas component concentration matrix is ​​obtained. The monitored gas components include carbon dioxide (CO2), carbon monoxide (CO), nitrogen oxides (NOx), hydrogen sulfide (H2S), etc. Assume that The concentration of each gas component is The measurements can be expressed as a gas component concentration matrix ,in Indicates The gas composition is The concentration at the time of measurement ; According to the gas component concentration matrix , calculate the comprehensive pollution index. The comprehensive pollution index is an indicator that reflects the degree of air pollution and can be calculated by weighted average method. Assume that the weight of each gas component is , comprehensive pollution index It can be expressed as: ; in, For the The weight of a gas component reflects the impact of the gas component on air pollution. and the current mode, select the optimal gas treatment solution from the gas treatment strategy database. The current mode can be the daily operation mode or the emergency treatment mode. According to different modes, the treatment solutions stored in the gas treatment strategy library are also different. By searching the strategy library, the optimal gas treatment solution under the current conditions is matched. According to the optimal gas treatment solution, the operating parameters of the gas treatment equipment are adjusted, and the treatment effect data is collected in real time. The treatment effect data includes the concentration of the treated gas components, the treatment efficiency, etc. Assume that the treatment effect data is ,in Indicates The treatment effect of various gas components. The gas treatment efficiency curve is generated through the treatment effect data. The gas treatment efficiency curve reflects the treatment efficiency of the gas treatment equipment under different conditions and can be obtained through the curve fitting method. Assume that the treatment efficiency curve is ,in is a time variable. According to the gas processing efficiency curve , generate gas parameter adjustment instructions for gas processing equipment. The adjustment instructions are used to optimize the operating parameters of the gas processing equipment to improve the processing efficiency. The gas processing equipment is adjusted in real time, and data is collected to obtain the adjusted air quality data. The adjusted air quality data is processed by Kalman filtering to obtain an air quality prediction model. Kalman filtering is an adaptive filtering method that obtains the change trend of air quality by filtering and predicting real-time data. The air quality prediction model can be expressed as: ; in, is the predicted value of air quality at the next moment, is the state transfer matrix, is the control input matrix, is the current control input, The air quality management report is generated based on the air quality prediction model. The management report includes the current air quality status, the predicted air quality change trend and the possible pollution source analysis. By analyzing the management report, optimization suggestions are provided for the next processing cycle. For example, suppose that air quality needs to be monitored and processed in an industrial park. By real-time monitoring of multiple gas components in the air, the concentrations of CO2, CO, NOx, H2S and other gases are recorded to form a gas component concentration matrix. . Calculate the comprehensive pollution index based on the weighted average method , assuming the weights are

[0046] ; According to the comprehensive pollution index and current mode (daily operation mode), select the optimal gas treatment solution from the gas treatment strategy library, adjust the operating parameters of the gas treatment equipment, and collect treatment effect data in real time . Generate a gas treatment efficiency curve by processing effect data. Assume that the treatment efficiency curve is ,in and is the fitting parameter. According to the gas processing efficiency curve , generate gas parameter adjustment instructions for gas processing equipment, adjust gas processing equipment in real time, and collect data to obtain adjusted air quality data The adjusted air quality data is processed by Kalman filtering to obtain the air quality prediction model. Assume that the state transfer matrix is the identity matrix, controlling the input matrix is a zero matrix, process noise is Gaussian white noise, the air quality prediction model is: ; Based on the air quality prediction model, an air quality management report is generated, including the current air quality status, predicted air quality change trends, and possible pollution source analysis. By analyzing the management report, optimization suggestions for the gas treatment strategy for the next treatment cycle are generated. For example, by analyzing the air quality management report, it can be found that the NOx concentration is high during certain periods, which may be related to specific industrial activities. Optimization suggestions include strengthening the operation of NOx treatment equipment during these periods and adjusting the schedule of related industrial activities to reduce NOx emissions.

[0047] In the embodiment of the present application, the dual-mode switching control module realizes the intelligent switching between the daily swimming pool and the nuclear accident emergency decontamination mode, greatly improving the utilization rate of the facilities. The water circulation treatment module and the gas treatment module adopt an adaptive processing strategy, which can automatically adjust the processing scheme according to different modes, ensuring the efficient operation of the system in both modes. The assembled equipment management module improves the reliability and response speed of emergency equipment through functions such as rapid deployment, status monitoring and maintenance reminders. The intelligent monitoring and alarm module uses multi-source data fusion and dynamic anomaly detection technology to enhance the risk warning capability of the system. The collaborative management and data sharing mechanism between modules optimizes the operating efficiency and decision-making quality of the overall system. Comprehensive performance testing and update management strategies ensure the long-term stable operation and technological advancement of the system. Through intelligent mode switching, adaptive processing strategies and multi-module collaborative management, seamless conversion between daily and emergency functions is achieved, equipment utilization is improved, nuclear accident response capabilities are enhanced, and resource allocation efficiency is optimized.

[0048] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described systems, systems and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0049] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, etc., various media that can store program codes.

[0050] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A management system for a nuclear accident emergency compatible decontamination waste liquid pool, characterized in that: The system comprises: a dual-mode switching control module, a water circulation processing module and a gas processing module; The dual-mode switching control module is used to: automatically identify the current mode through preset trigger conditions, the current mode being the daily swimming pool mode or the nuclear accident emergency decontamination mode; send a switching instruction according to the current mode, and adjust the operating parameters and working status according to the switching instruction; during the mode switching process, start the safety lock program and complete the disinfection and isolation measures; record the time, reason and process of each mode switching; the management personnel manually trigger the mode switching and monitor the system status in real time through the human-computer interactive operation interface; The water circulation treatment module is used to: adopt different treatment schemes according to the current mode. If the current mode is the daily swimming pool mode, a conventional water quality treatment scheme is adopted, and the conventional water quality treatment scheme includes filtration, disinfection and pH value adjustment, and constant temperature control is performed through a heat exchanger to maintain a constant water temperature, and water quality parameters are monitored in real time to ensure compliance with swimming pool hygiene standards; if the current mode is the nuclear accident emergency decontamination mode, a preset filtration device is used to remove radioactive particles in the water, a neutralizer or flocculant is added, dissolved radioactive substances are treated, and the heat exchanger is used to reduce the temperature to reduce the volume of waste liquid, and the radioactive indicators of the waste liquid are continuously monitored; The gas processing module is used for: if the current mode is the daily swimming pool mode, the indoor environment is maintained by adjusting the temperature and humidity, the irritating gas generated by the volatilization of chlorine in the water is removed, and the air supply is ensured through air circulation; if the current mode is the decontamination waste liquid mode, the particulate air filter and the activated carbon filter are used to remove radioactive aerosols, create a negative pressure environment, prevent the spread of pollution, monitor the content of radioactive substances in the air in real time, and discharge the multiple filtered gases through a preset dedicated pipeline.

2. The management system of the nuclear accident emergency compatible decontamination waste liquid pool according to claim 1 is characterized in that: The management system of the nuclear accident emergency compatible decontamination waste liquid pool also includes: an assembled equipment management module; the assembled equipment management module specifically includes: An equipment storage unit is used to store emergency equipment in the daily swimming pool mode, wherein the emergency equipment includes: a decontamination water treatment device and a gas treatment device; A rapid deployment unit, configured to install the emergency equipment after receiving the switching instruction; An equipment status monitoring unit, used for regularly checking the equipment status of the emergency equipment to ensure that the emergency equipment is in a usable state; A maintenance reminder unit, used to generate a corresponding target maintenance plan according to the usage and storage time of the emergency equipment; An equipment performance testing unit, used to regularly perform equipment function tests on the emergency equipment and obtain equipment function test results; The update management unit is used to record the update information of the emergency equipment and ensure that the emergency equipment complies with the nuclear accident response standards based on the update information.

3. The management system of the nuclear accident emergency compatible decontamination waste liquid pool according to claim 2 is characterized in that: The device status monitoring unit is specifically used for: Capturing appearance images of the emergency equipment to obtain equipment appearance images, and performing equipment integrity analysis on the equipment appearance images to obtain an appearance integrity score; Classifying the emergency equipment according to the appearance integrity score to obtain a list of intact equipment and a list of equipment to be inspected, and performing ultrasonic scanning on the equipment in the list of equipment to be inspected to obtain internal structure data; Constructing a three-dimensional model according to the internal structure data to obtain a three-dimensional structure model of the device, and performing comparative analysis between the three-dimensional structure model of the device and a standard structure model to obtain structure deviation data; Performing stress analysis on the emergency equipment according to the structural deviation data to obtain a stress distribution diagram, and performing threshold judgment on the stress distribution diagram to obtain a list of potential fault points; Performing a simulated operation test on the emergency equipment according to the potential failure point list to obtain a performance parameter set, and comparing the performance parameter set with preset standard parameters to obtain a performance degradation index; The remaining life of the emergency equipment is predicted according to the performance degradation index to obtain an equipment status assessment report, and risk levels are divided according to the equipment status assessment report to obtain graded maintenance suggestions.

4. The management system of the nuclear accident emergency compatible decontamination waste liquid pool according to claim 2 is characterized in that: The maintenance reminder unit is specifically used for: Performing data mining on the usage records of the emergency equipment to obtain equipment usage frequency data, and constructing a Markov chain model based on the equipment usage frequency data to obtain a predicted value of equipment usage intensity; Performing sensor monitoring on the storage environment of the emergency equipment to obtain an environmental parameter set, and calculating an environmental impact factor based on the environmental parameter set to obtain an equipment aging rate; A two-factor weighted model is established according to the predicted value of the equipment usage intensity and the equipment aging rate to obtain an equipment loss index, and a time series analysis is performed on the equipment loss index to obtain a maintenance demand curve; Performing dynamic programming calculation according to the maintenance demand curve and the preset maintenance cycle threshold to obtain a preliminary maintenance time point set, and performing cluster analysis on the preliminary maintenance time point set to obtain an optimized maintenance time point set; According to the optimized maintenance time point set, association rule mining is performed on historical maintenance records to obtain a maintenance item association matrix, and the maintenance item association matrix is ​​analyzed to obtain a key maintenance item list; According to the key maintenance item list and the optimized maintenance time point set, a multi-objective optimization model is constructed to obtain a preliminary maintenance plan; The preliminary maintenance plan is simulated to obtain a reliability evaluation result of the maintenance plan, and the preliminary maintenance plan is adjusted according to the reliability evaluation result of the maintenance plan to obtain a target maintenance plan.

5. The management system of the nuclear accident emergency compatible decontamination waste liquid pool according to claim 2 is characterized in that: The equipment performance testing unit is specifically used for: Performing a standard load test on the emergency equipment to obtain an equipment operation parameter set, and constructing an equipment performance benchmark curve based on the equipment operation parameter set; Performing Fourier transformation on the equipment performance benchmark curve to obtain frequency domain characteristic data, and performing wavelet decomposition based on the frequency domain characteristic data to obtain multi-scale performance indicators; Calculating equipment performance scores according to the multi-scale performance indicators, and performing time series analysis on the equipment performance scores to construct a performance trend prediction model; Generate a virtual load sequence according to the performance trend prediction model to obtain a simulation test plan, and execute the simulation test plan on the emergency equipment to obtain simulation test result data; Performing performance calculation on the simulation test result data to obtain key performance factors, and classifying the emergency equipment into performance levels according to the key performance factors to obtain equipment performance levels; The equipment reliability distribution is calculated according to the equipment performance level and the performance trend prediction model, and the equipment reliability distribution is updated by Bayesian method to obtain equipment function test results.

6. The management system of the nuclear accident emergency compatible decontamination waste liquid pool according to claim 2 is characterized in that: The update management unit is specifically used for: Regularly scan the nuclear accident response standard database to obtain the latest standard update information, and construct a knowledge graph based on the latest standard update information to obtain a standard association network; Performing graph theory analysis on the standard association network to obtain key standard nodes, and extracting a device update indicator set based on the key standard nodes; Performing a performance matching evaluation on the emergency equipment according to the equipment update index set to obtain an equipment matching matrix, and performing a hierarchical analysis on the equipment matching matrix to obtain an equipment update priority list; Generate a candidate device data set according to the device update priority list, and perform a multi-criteria decision analysis on the candidate device data set to obtain an optimal replacement device solution; According to the optimal replacement equipment solution, an equipment update cost model is constructed, and a sensitivity analysis is performed on the equipment update cost model to obtain a cost risk assessment report; According to the cost risk assessment report and the equipment update priority list, a phased update plan is formulated, and discrete event simulation is performed on the phased update plan to obtain an update process optimization plan, and an equipment update execution instruction is generated according to the update process optimization plan.

7. The management system of the nuclear accident emergency compatible decontamination waste liquid pool according to claim 1 is characterized in that: The management system of the nuclear accident emergency compatible decontamination waste liquid pool also includes: an intelligent monitoring and alarm module, which is used to: Collecting and preprocessing multi-source sensor data to obtain a multi-dimensional time series data set, and analyzing the multi-dimensional time series data set to obtain a reduced-dimensional feature vector; Constructing a dynamic anomaly detector according to the dimension-reduced feature vector, and performing online learning and parameter update on the dynamic anomaly detector to obtain an adaptive threshold surface; Identify abnormal events on real-time monitoring data according to the adaptive threshold curve to obtain a preliminary abnormal event list, and make a comprehensive assessment of the preliminary abnormal event list to obtain a priority ranking of the abnormal events; Construct an event association map according to the priority ranking of the abnormal events, and perform graph mining and path analysis on the event association map to obtain a potential risk propagation path; Generate a multi-scenario emergency plan according to the potential risk transmission path, and perform optimization analysis on the multi-scenario emergency plan to obtain the optimal emergency plan; A hierarchical alarm instruction is generated according to the optimal emergency plan to obtain an intelligent alarm plan, and the intelligent alarm plan is visualized to obtain a multi-dimensional visualized alarm interface, and a corresponding emergency response mechanism is triggered according to the multi-dimensional visualized alarm interface.

8. The management system of the nuclear accident emergency compatible decontamination waste liquid pool according to claim 1 is characterized in that: The dual-mode switching control module is specifically used for: Performing fusion analysis on environmental sensor data and external information to obtain a mode switching trigger factor set, and generating a mode switching probability distribution according to the mode switching trigger factor set; Performing a threshold judgment on the mode switching probability distribution to obtain a preliminary switching decision, and calling a preset system state check list according to the preliminary switching decision to obtain a current state matrix; Calculating a mode switching feasibility index according to the current state matrix to obtain a switching risk assessment result, and performing a multi-criteria decision analysis on the switching risk assessment result to obtain a switching instruction; Generate a parameter configuration instruction set according to the switching instruction, and perform parallel processing on the parameter configuration instruction set to obtain a multi-thread execution queue; Initiate a safety lock program according to the multi-threaded execution queue, obtain a system isolation status confirmation signal, and verify the system isolation status confirmation signal to obtain a disinfection and isolation measures execution instruction; According to the disinfection and isolation measures, the execution instructions generate a mode switching process log, obtain a switching process data packet, encrypt and store the switching process data packet, obtain a mode switching history record, and update the human-computer interaction operation interface according to the mode switching history record to display the real-time system status.

9. The management system of the nuclear accident emergency compatible decontamination waste liquid pool according to claim 1, characterized in that: The water circulation processing module is specifically used for: Performing real-time multi-parameter monitoring of the influent water quality to obtain a water quality parameter vector, and calculating a comprehensive water quality index based on the water quality parameter vector; According to the water quality comprehensive index and the current mode, calling the corresponding water treatment strategy library to match the optimal water quality treatment plan; According to the optimal water quality treatment scheme, the operating parameters of the water treatment equipment are controlled and real-time treatment effect data are collected. Meanwhile, the real-time treatment effect data is subjected to dynamic time regularization analysis to obtain a water quality treatment efficiency curve; Performing deviation calculation according to the water quality treatment efficiency curve and the preset target value to obtain a treatment deviation matrix, and generating equipment parameter adjustment instructions according to the treatment deviation matrix; Performing real-time adjustment and water quality data detection according to the equipment parameter adjustment instruction to obtain adjusted water quality data, and performing time series prediction on the adjusted water quality data to obtain a water quality trend model; A probability distribution of water quality reaching the standard is generated according to the water quality trend model, and a risk assessment is performed on the probability distribution of water quality reaching the standard to obtain a water quality management report, and optimization suggestions for water quality treatment strategies for the next cycle are generated according to the water quality management report.

10. The management system of the nuclear accident emergency compatible decontamination waste liquid pool according to claim 1, characterized in that: The gas processing module is specifically used for: Performing real-time monitoring of multiple gas components in the air to obtain a gas component concentration matrix, and calculating a comprehensive pollution index based on the gas component concentration matrix; selecting an optimal gas treatment solution from a gas treatment strategy database according to the comprehensive pollution index and the current mode; adjusting the operating parameters of the gas processing equipment according to the optimal gas processing scheme and generating a gas processing efficiency curve; Generate a gas parameter adjustment instruction for the gas processing device according to the gas processing efficiency curve, perform real-time adjustment and data collection on the gas processing device according to the gas parameter adjustment instruction to obtain adjusted air quality data, and perform Kalman filtering on the adjusted air quality data to obtain an air quality prediction model; An air quality management report is generated according to the air quality prediction model, and a gas treatment strategy optimization suggestion for the next treatment cycle is generated according to the air quality management report.

Images