An anomaly response management system and method for nuclear power plants in a cluster mode

Abstract

This invention discloses an anomaly response management system and method for nuclear power plants in a cluster-mode operation, relating to the field of routine maintenance technology for nuclear power plants. It includes a resource optimization and allocation module, comprising a data acquisition unit and a scheduling unit. The data acquisition unit collects operational data and historical maintenance records from each reactor. The scheduling unit predicts the next maintenance time period based on the information from the data acquisition unit and automatically allocates resources according to the prediction results. The maintenance module manages the resource scheduling of each reactor unit and implements the maintenance strategy of the cross-reactor type management module based on the results of the scheduling unit. This invention can solve the cross-reactor type and cross-unit resource coordination problems encountered in the routine management of nuclear power plants in a cluster-mode operation. Through this integrated management approach, nuclear power plants can manage resources more effectively, improve operational efficiency, reduce potential risks, and ensure the safe and stable operation of the nuclear power plant.

Description

Technical Field

[0001] This invention relates to the field of routine maintenance technology for nuclear power plants, and more specifically, to an anomaly response management system and method for nuclear power plants in a cluster mode. Background Technology

[0002] The multi-reactor nuclear power plant anomaly response management system is a management system specifically designed for multi-reactor nuclear power plants. It improves efficiency, safety, and economy by centrally managing and coordinating the operation of multiple reactors.

[0003] In a cluster reactor configuration, a nuclear power plant may contain reactors of different types and generations, which leads to complexities in resource allocation, technical support, and personnel scheduling. For example, different reactors may require different maintenance strategies and spare parts, and resource sharing and optimized configuration may be difficult to achieve in practice. In addition, since each reactor unit may be in a different stage of operation, it affects the daily maintenance of management personnel. Summary of the Invention

[0004] To address the aforementioned problems, this invention provides a nuclear power plant anomaly response management system and method in a cluster reactor mode.

[0005] This invention provides an anomaly response management system for nuclear power plants in a cluster-mode configuration, characterized by comprising:

[0006] The resource optimization and configuration module includes a data acquisition unit and a scheduling unit.

[0007] The data acquisition unit is used to collect the operating data and historical maintenance records of each reactor. The scheduling unit is used to predict the next maintenance time period based on the information from the data acquisition unit, and automatically allocate resources according to the prediction results to meet the needs of each reactor.

[0008] The cross-reactor management module and the maintenance module are provided. The cross-reactor management module is used to formulate maintenance strategies for each reactor based on the data from the data acquisition unit and the prediction results from the scheduling unit, and to transmit the maintenance strategies to the maintenance module during the next maintenance period predicted by the scheduling unit.

[0009] The maintenance module is used to manage the resource scheduling of each reactor unit and implement the maintenance strategy of the cross-reactor management module based on the results of the scheduling unit.

[0010] Preferably, the specific operating mode of the scheduling unit includes:

[0011] The data acquisition unit obtains the operating data and historical maintenance records of each reactor.

[0012] Obtain the most recent maintenance time and historical maintenance interval for each reactor;

[0013] Obtain the cumulative operating time of each reactor;

[0014] According to the formula The average value of the historical maintenance intervals for each reactor was calculated. Where n is the number of times each reactor has undergone historical maintenance. It is the time for the i-th maintenance. It is the first Maintenance time;

[0015] Obtain the difference D between the current operating time and the last maintenance time for each reactor;

[0016] According to the formula The next maintenance time for each reactor can be obtained. The next maintenance time for each reactor will be set. Transmitted to the cross-stacking management module;

[0017] in The degradation rate of each reactor, These are the initial performance characteristics of each reactor. These are the performance thresholds for each reactor.

[0018] Preferably, the resource optimization configuration module further includes an optimization unit, which is used to optimize the prediction results of the scheduling unit based on the historical records and feedback evaluations of the scheduling unit, specifically:

[0019] Obtain the last actual maintenance time and historical predicted maintenance time for each reactor;

[0020] The feedback error coefficient H is obtained by subtracting the historical predicted maintenance time from the previous actual maintenance time of each reactor and then dividing by the previous actual maintenance time.

[0021] According to the formula The optimized predicted maintenance time for each reactor was obtained. ;

[0022] Optimize the predicted maintenance time for each reactor The data is transmitted to the cross-stack management module.

[0023] Preferably, the cross-heap management module operates as follows:

[0024] First, the collected data from each reactor are analyzed to determine the risk importance of each reactor, and the reactors are divided into low-risk reactor equipment, medium-risk reactor equipment, and high-risk reactor equipment.

[0025] Based on the risk importance of each reactor, a maintenance strategy is selected for each reactor. Specific maintenance strategies include:

[0026] For low-risk reactor equipment, maintenance should be performed at fixed time intervals or operating cycles.

[0027] For medium-risk reactor equipment, potential failures can be predicted and maintenance can be carried out in advance by monitoring the equipment status in real time;

[0028] For high-risk reactor equipment, maintenance time is determined based on the actual operating status and performance data of the equipment;

[0029] For low-risk reactor equipment, the predicted next forecast time for the reactor is used as a fixed maintenance interval for regular maintenance, and no further forecasting is performed at the next forecast time.

[0030] For medium-risk reactor facilities, use As the next maintenance time;

[0031] For high-risk reactor equipment, according to the formula The maintenance time is obtained based on the actual operating status and performance data of the equipment. ,Will This will be used as the next maintenance time.

[0032] Preferably, the specific method for determining the risk importance of each reactor is as follows:

[0033] The equipment unavailability U, failure severity C, and failure frequency F of each reactor were obtained.

[0034] The risk value L of each reactor is the product of its equipment unavailability U, the severity of failure consequences C, and the failure frequency F.

[0035] Based on the calculated risk value L, risks can be categorized into low-risk reactor equipment, medium-risk reactor equipment, and high-risk reactor equipment.

[0036] Preferably, the maintenance module operates as follows:

[0037] Adjust the spare parts inventory at each responsiveness level to be greater than or equal to the predicted spare parts consumption.

[0038] The maintenance module also includes a personnel allocation unit, which recommends the most suitable technical personnel based on the risk importance analyzed by the cross-reactor management module.

[0039] Preferably, the personnel allocation unit operates as follows:

[0040] Obtain the skill level Z of each technician;

[0041] The risk matching coefficient is obtained by dividing each technician's skill level Z by the risk value L of each reactor. The technician with the highest risk matching coefficient is then assigned to the corresponding reactor for maintenance.

[0042] Preferably, the specific method for obtaining the skill level Z of each technician is as follows:

[0043] Establish detailed personal files for each technician;

[0044] The skills of the technicians are assessed to obtain the skill level Z for each technician.

[0045] Preferably, the specific working method of the maintenance module further includes:

[0046] After each maintenance of each reactor, technicians record the details of that maintenance.

[0047] The historical issues of each reactor are categorized based on the recorded information;

[0048] We create detailed personal profiles for each technician to match them with technicians based on the type of problem.

[0049] A method for managing anomaly responses in nuclear power plants under a cluster reactor model is also proposed, including the following steps:

[0050] Step 1: Data Collection: Use the data acquisition unit to collect the operating data and historical maintenance records of each reactor, and analyze the collected data, including the operating status of the equipment, failure rate, maintenance history, etc., to determine the risk level and maintenance requirements of each reactor.

[0051] Step 2: Determine the time period for the next maintenance of each reactor. The cross-reactor management module formulates the maintenance strategy for each reactor based on the prediction results, taking into account the risk level and maintenance requirements of different reactors.

[0052] Step 3: Based on the predicted resource consumption and maintenance strategy, automatically allocate resources to ensure that the needs of each reactor are met within the predetermined maintenance period. Within the maintenance period predicted by the scheduling unit, the maintenance module manages the resource scheduling of each reactor unit according to the maintenance strategy formulated by the cross-reactor management module and executes maintenance activities.

[0053] Beneficial effects: It can solve the resource coordination problems encountered by nuclear power plants in the daily management of reactor types and units under the group reactor mode. Through this integrated management approach, nuclear power plants can manage resources more effectively, improve operational efficiency, reduce potential risks, and ensure the safe and stable operation of nuclear power plants. Attached Figure Description

[0054] Figure 1 This is a flowchart of the management system of the present invention;

[0055] Figure 2 This is a method diagram of the present invention. Detailed Implementation

[0056] Application scenarios: In the cluster reactor mode, nuclear power plants may contain reactors of different types and generations, which leads to complexity in resource allocation, technical support and personnel scheduling. For example, different reactors may require different maintenance strategies and spare parts, and resource sharing and optimized configuration may be difficult to achieve in actual operation. In addition, since each reactor unit may be in a different stage of operation, it affects the daily maintenance of managers.

[0057] like Figure 1 As shown: A nuclear power plant anomaly response management system in a cluster reactor mode, comprising:

[0058] The resource optimization and configuration module includes a data acquisition unit and a scheduling unit.

[0059] The data acquisition unit is used to collect the operating data and historical maintenance records of each reactor. The scheduling unit is used to predict the next maintenance time period based on the information from the data acquisition unit, and automatically allocate resources according to the prediction results to meet the needs of each reactor.

[0060] The cross-reactor management module and the maintenance module are provided. The cross-reactor management module is used to formulate maintenance strategies for each reactor based on the data from the data acquisition unit and the prediction results from the scheduling unit, and to transmit the maintenance strategies to the maintenance module during the next maintenance period predicted by the scheduling unit.

[0061] The maintenance module is used to manage the resource scheduling of each reactor unit and implement the maintenance strategy of the cross-reactor type management module based on the results of the scheduling unit. This can solve the resource coordination problems encountered by nuclear power plants in the daily management of reactor clusters. Through this integrated management approach, nuclear power plants can manage resources more effectively, improve operational efficiency, reduce potential risks, and ensure the safe and stable operation of the nuclear power plant.

[0062] As an optional embodiment, the specific operating mode of the scheduling unit includes:

[0063] The data acquisition unit obtains the operating data and historical maintenance records of each reactor. It should be noted that the operating data includes key parameters such as temperature, pressure, and flow rate, while the historical maintenance records include information such as maintenance activities, fault records, repair time, and replaced parts.

[0064] Obtain the most recent maintenance time and historical maintenance interval for each reactor;

[0065] Obtain the cumulative operating time of each reactor; the cumulative operating time refers to the total time from the start of equipment operation to the current moment. This indicator reflects the actual working time of the equipment since it was put into use, including all operating cycles, regardless of whether the equipment is operating under normal load or under different operating conditions.

[0066] According to the formula The average value of the historical maintenance intervals for each reactor was calculated. Where n is the number of times each reactor has undergone historical maintenance. It is the time for the i-th maintenance. It is the first Maintenance time;

[0067] The difference D between the current operating time and the last maintenance time of each reactor is obtained; it should be noted that this is obtained by subtracting the time of the most recent maintenance from the cumulative operating time of the equipment.

[0068] According to the formula The next maintenance time for each reactor can be obtained. The next maintenance time for each reactor will be set. Transmitted to the cross-stacking management module;

[0069] in The degradation rate of each reactor, These are the initial performance characteristics of each reactor. These are the performance thresholds for each reactor. It should be noted that initial performance usually refers to the performance indicators of a reactor in a brand-new state, such as power output, efficiency, neutron flux density, etc. These data can be obtained through the testing and commissioning process during reactor startup. Initial performance can also be determined through design parameters and data provided by the manufacturer. These data are usually based on the reactor's design and expected performance, and the performance threshold is the expected performance.

[0070] The degradation rate refers to the rate at which reactor performance declines over time. This is typically related to factors such as material aging, fuel consumption, and radiation damage. By periodically monitoring and analyzing operational data, the changing trends of performance parameters over time can be observed, thereby estimating the degradation rate. For example, the degradation rate can be estimated by monitoring the decrease in reactor power output over time.

[0071] It should also be noted that, in this embodiment, The calculated predicted time is based on historical maintenance intervals and adjusted using current operational differences. Predicted maintenance time based on performance degradation model: By combining these two factors, the shortest time period is selected as the next predicted maintenance time.

[0072] This method allows for more accurate prediction of the next maintenance time, taking into account the equipment's cumulative uptime and performance degradation. It is suitable for situations where equipment performance significantly degrades over time, providing better guidance for maintenance decisions.

[0073] As an optional embodiment: the resource optimization configuration module further includes an optimization unit, which is used to optimize the prediction results of the scheduling unit based on the historical records and feedback evaluations of the scheduling unit, specifically:

[0074] Obtain the last actual maintenance time and historical predicted maintenance time for each reactor;

[0075] The feedback error coefficient H is obtained by subtracting the historical predicted maintenance time from the previous actual maintenance time of each reactor and then dividing by the previous actual maintenance time.

[0076] According to the formula The optimized predicted maintenance time for each reactor was obtained. ;

[0077] Optimize the predicted maintenance time for each reactor The data is transmitted to the cross-stack management module.

[0078] For example, nuclear power plants have different types of reactors, including VVER-1000, M310, and VVER-1200.

[0079] In this cluster reactor mode, the maintenance time prediction can be optimized using the above method. First, collect the operating data and historical maintenance records of each reactor, calculate the feedback error coefficient H, then adjust the predicted maintenance time according to the feedback error coefficient H, and finally use the adjusted parameters to predict the next maintenance time, and update and optimize the prediction results through the effect of actual maintenance activities.

[0080] This method allows nuclear power plants to more accurately predict maintenance times, reduce the risk of unexpected downtime, and improve operational efficiency and safety.

[0081] As an optional embodiment, the specific operation of the cross-heap management module is as follows:

[0082] First, the collected data from each reactor is analyzed to determine the risk importance of each reactor, and the reactors are divided into low-risk, medium-risk, and high-risk reactors. It should be noted that in this embodiment, the collected data from each reactor is obtained through the data acquisition unit, which specifically includes historical maintenance records, operating data, and predicted resource consumption.

[0083] Based on the risk importance of each reactor, a maintenance strategy is selected for each reactor. Specific maintenance strategies include:

[0084] For low-risk reactor equipment, maintenance is performed at fixed time intervals or operating cycles; this method is simple and easy to implement, but may lead to over-maintenance or under-maintenance.

[0085] For medium-risk reactor equipment, real-time monitoring of equipment status can predict potential failures and enable proactive maintenance; this approach can reduce unforeseen downtime and optimize maintenance plans.

[0086] For high-risk reactor equipment, maintenance time is determined based on the actual operating status and performance data of the equipment; this method can maximize the uptime of the equipment and minimize unnecessary maintenance tasks.

[0087] For low-risk reactor equipment, the predicted next forecast time for the reactor is used as a fixed maintenance interval for regular maintenance, and no further forecasting is performed at the next forecast time; this can reduce the workload of the scheduling unit.

[0088] For medium-risk reactor facilities, use This will serve as the next maintenance time; it should be noted that the optimized maintenance time for each reactor can also be used to predict the maintenance time. ;

[0089] For high-risk reactor equipment, according to the formula The maintenance time is obtained based on the actual operating status and performance data of the equipment. ,Will This will be used as the next maintenance time.

[0090] For example, a nuclear power plant has three different types of reactors, A, B, and C; by analyzing the data, we found that:

[0091] Reactor A has a low failure rate and low maintenance costs, therefore it is classified as low-risk.

[0092] Reactor B has a moderate failure rate and maintenance costs, and is classified as a medium-risk reactor.

[0093] Reactor C is classified as high-risk due to its older design, high failure rate, and high maintenance costs.

[0094] For the low-risk reactor A, we choose to perform maintenance once at fixed time intervals;

[0095] For reactor B, which is of medium risk, we use real-time monitoring of equipment status, predict potential failures through predictive analytics tools, and perform maintenance before failures occur.

[0096] For high-risk reactor C, we determine the maintenance time based on the actual operating status and performance data of the equipment, which may require more frequent inspections and maintenance.

[0097] In this way, different daily management strategies can be implemented for different types of reactors, which avoids wasting manpower and ensures maintenance efficiency.

[0098] As an optional embodiment, the specific method for determining the risk importance of each reactor is as follows:

[0099] The equipment unavailability U, failure severity C, and failure frequency F of each reactor were obtained.

[0100] The risk value L of each reactor is the product of its equipment unavailability U, the severity of failure consequences C, and the failure frequency F.

[0101] Based on the calculated risk value L, risks can be categorized into low-risk reactor equipment, medium-risk reactor equipment, and high-risk reactor equipment.

[0102] It should be noted that a threshold range for a risk value L can be set. If the risk value L is less than this range, it is considered low risk; if the risk value L is within this threshold range, it is considered medium risk; and if the risk value L is greater than this threshold range, it is considered high risk. The specific threshold range is set by the staff and can refer to historical averages.

[0103] It should also be noted that equipment unavailability U reflects the degree of impact on the system after equipment failure. It can be estimated using the equipment failure rate and repair time.

[0104] The severity of failure consequences (C) indicates the severity of the consequences that equipment failure may result in, and can be scored based on the impact of equipment failure on personnel safety, the environment, and production.

[0105] The failure frequency F is the frequency of equipment failure, which can be estimated using historical failure data;

[0106] In this embodiment, the specific scoring criteria are as follows:

[0107] Equipment unavailability U:

[0108] 0.00-0.10: Very high availability

[0109] 0.11-0.25: High Availability

[0110] 0.26-0.50: Medium availability

[0111] 0.51-0.75: Low availability

[0112] 0.76-1.00: Very low availability

[0113] Severity of failure consequences C:

[0114] 1: Minor consequences

[0115] 2: Moderate consequences

[0116] 3: Serious consequences

[0117] 4: Very serious consequences

[0118] 5: Catastrophic consequences

[0119] Failure frequency F:

[0120] 1: Very low (less than 0.1 times per year)

[0121] 2: Low (0.1 to 1 time per year)

[0122] 3: Moderate (1 to 10 times per year)

[0123] 4: High (10 to 100 times per year)

[0124] 5: Very high (more than 100 times per year).

[0125] As an optional embodiment, the maintenance module operates as follows:

[0126] Adjust the spare parts inventory at each responsiveness level to be greater than or equal to the predicted spare parts consumption.

[0127] The maintenance module also includes a personnel allocation unit, which recommends the most suitable technical personnel based on the risk importance analyzed by the cross-reactor management module.

[0128] As an optional embodiment: the specific working method of the personnel allocation unit is as follows:

[0129] Obtain the skill level Z of each technician;

[0130] A risk matching coefficient is obtained by dividing each technician's skill level Z by the risk value L of each reactor. The technician with the highest risk matching coefficient is then assigned to the corresponding reactor for maintenance. It should be noted that each technician can only be assigned to one reactor.

[0131] As an optional embodiment: the specific method for obtaining the skill level Z of each technician is as follows:

[0132] Establish detailed personal files for each technician; including but not limited to:

[0133] Certifications: Record the professional qualifications and certifications of technical personnel, such as electrician's certificate, welder's certificate, etc.; Work experience: Record the work experience of technical personnel, including past projects, responsibilities and achievements; Training records: Record the training courses attended by technical personnel, training dates and training results.

[0134] The skills of the technicians are assessed to obtain the skill level Z for each technician; including:

[0135] The professional skills of technical personnel are assessed through written tests, practical skills assessments, and on-site Q&A sessions.

[0136] Project management capability assessment: This assesses the technical personnel's capabilities in project planning, organization and coordination, and resource management.

[0137] The professional skills test score U and project management ability assessment score E of each technician are obtained. Weights are designed for the two scores, and the skill level Z of each technician is obtained by weighted summation. The sum of the two score weights is 1. In this embodiment, they can be 0.293 and 0.707, respectively.

[0138] As an optional embodiment, the specific operation of the maintenance module further includes:

[0139] After each maintenance of each reactor, technicians record the details of that maintenance.

[0140] The historical issues of each reactor are categorized based on the recorded information;

[0141] We create detailed personal profiles for each technician to match them with technicians based on the type of problem.

[0142] In this embodiment, specifically:

[0143] Create a skills matrix listing the names of all technicians and their key skills. These skills should include, but are not limited to, mechanical repair, electrical engineering, control systems, and radiation protection. Use a rating system of 1 to 5 to rate each technician's ability in each skill.

[0144] Establish a historical problem database to record the maintenance history of each reactor, including problem type, frequency of occurrence, solution, and relevant technical personnel;

[0145] Based on information from the problem database, assess the skills required to solve specific problems. For example, if a problem is related to electrical systems, technicians with high electrical engineering skill scores will be given priority.

[0146] Match the technicians' skill scores with the skills required for the problem, and select the most suitable technicians;

[0147] For example, suppose a nuclear power plant has three reactors: reactor A, B, and C. Data analysis reveals that reactor A mainly suffers from equipment failure, reactor B has performance degradation issues, and reactor C faces safety hazards. Based on these issues, the following steps are performed:

[0148] Reactor A: Based on the type of equipment failure, select technicians with experience in mechanical failure repair for maintenance;

[0149] Reactor B: In response to the performance degradation, select technicians with relevant performance assessment and optimization experience to conduct inspections and adjustments;

[0150] Reactor C: For potential safety hazards, deploy technical personnel with safety management and emergency response capabilities to conduct a comprehensive inspection.

[0151] This invention also proposes a method for anomaly response management in nuclear power plants under a cluster reactor model, comprising the following steps:

[0152] Step 1: Data Collection: Use the data acquisition unit to collect the operating data and historical maintenance records of each reactor, and analyze the collected data, including the operating status of the equipment, failure rate, maintenance history, etc., to determine the risk level and maintenance requirements of each reactor.

[0153] Step 2: Determine the time period for the next maintenance of each reactor. The cross-reactor management module formulates the maintenance strategy for each reactor based on the prediction results, taking into account the risk level and maintenance requirements of different reactors.

[0154] Step 3: Based on the predicted resource consumption and maintenance strategy, automatically allocate resources to ensure that the needs of each reactor are met within the predetermined maintenance period. Within the maintenance period predicted by the scheduling unit, the maintenance module manages the resource scheduling of each reactor unit and executes maintenance activities according to the maintenance strategy formulated by the cross-reactor management module.

[0155] Working principle

[0156] It can solve the resource coordination problems encountered by nuclear power plants in the daily management of reactor types and units under the cluster mode. Through this integrated management approach, nuclear power plants can manage resources more effectively, improve operational efficiency, reduce potential risks, and ensure the safe and stable operation of nuclear power plants.

[0157] The above are merely preferred embodiments of the present invention. The scope of protection of the present invention is not limited to the above embodiments. All technical solutions falling within the scope of the present invention's concept are within the scope of protection of the present invention. It should be noted that for those skilled in the art, any improvements and modifications made without departing from the principles of the present invention should also be considered within the scope of protection of this template.

Claims

1. A nuclear power plant anomaly response management system in a cluster reactor mode, characterized in that, include: The resource optimization and configuration module includes a data acquisition unit and a scheduling unit. The data acquisition unit is used to collect the operating data and historical maintenance records of each reactor. The scheduling unit is used to predict the next maintenance time period based on the information from the data acquisition unit, and automatically allocate resources according to the prediction results to meet the needs of each reactor. The cross-reactor management module and the maintenance module are provided. The cross-reactor management module is used to formulate maintenance strategies for each reactor based on the data from the data acquisition unit and the prediction results from the scheduling unit, and to transmit the maintenance strategies to the maintenance module during the next maintenance period predicted by the scheduling unit. The maintenance module is used to manage the resource scheduling of each reactor unit and implement the maintenance strategy of the cross-reactor management module based on the results of the scheduling unit. The specific operating mode of the scheduling unit includes: The data acquisition unit obtains the operating data and historical maintenance records of each reactor. Obtain the most recent maintenance time and historical maintenance interval for each reactor; Obtain the cumulative operating time of each reactor; According to the formula The average value of the historical maintenance intervals for each reactor was calculated. Where n is the number of times each reactor has undergone historical maintenance. It is the time for the i-th maintenance. It is the first Maintenance time; Obtain the difference D between the current operating time and the last maintenance time for each reactor; According to the formula The next maintenance time for each reactor can be obtained. The next maintenance time for each reactor will be determined. Transmitted to the cross-stack management module; in The degradation rate of each reactor, These are the initial performance characteristics of each reactor. These are the performance thresholds for each reactor; The resource optimization and configuration module further includes an optimization unit, which is used to optimize the prediction results of the scheduling unit based on the historical records and feedback evaluations of the scheduling unit, specifically: Obtain the last actual maintenance time and historical predicted maintenance time for each reactor; The feedback error coefficient H is obtained by subtracting the historical predicted maintenance time from the previous actual maintenance time of each reactor and then dividing by the previous actual maintenance time. According to the formula The optimized predicted maintenance time for each reactor was obtained. ; Optimize the predicted maintenance time for each reactor Transmitted to the cross-stack management module; The specific working method of the cross-heap management module is as follows: First, the collected data from each reactor are analyzed to determine the risk importance of each reactor, and the reactors are divided into low-risk reactor equipment, medium-risk reactor equipment, and high-risk reactor equipment. Based on the risk importance of each reactor, a maintenance strategy is selected for each reactor. Specific maintenance strategies include: For low-risk reactor equipment, maintenance should be performed at fixed time intervals or operating cycles. For medium-risk reactor equipment, potential failures can be predicted and maintenance can be carried out in advance by monitoring the equipment status in real time; For high-risk reactor equipment, maintenance time is determined based on the actual operating status and performance data of the equipment; For low-risk reactor equipment, the predicted next forecast time for the reactor is used as a fixed maintenance interval for regular maintenance, and no further forecasting is performed at the next forecast time. For medium-risk reactor facilities, use As the next maintenance time; For high-risk reactor equipment, according to the formula The maintenance time is obtained based on the actual operating status and performance data of the equipment. ,Will This will be used as the next maintenance time.

2. The nuclear power plant anomaly response management system in a cluster reactor mode according to claim 1, characterized in that, The specific method for determining the risk importance of each reactor is as follows: The equipment unavailability U, failure severity C, and failure frequency F of each reactor were obtained. The risk value L of each reactor is the product of its equipment unavailability U, the severity of failure consequences C, and the failure frequency F. Based on the calculated risk value L, risks can be categorized into low-risk reactor equipment, medium-risk reactor equipment, and high-risk reactor equipment.

3. The nuclear power plant anomaly response management system in a cluster reactor mode according to claim 1, characterized in that, The maintenance module operates as follows: Adjust the spare parts inventory of each reactor to be greater than or equal to the predicted spare parts consumption. The maintenance module also includes a personnel allocation unit, which recommends the most suitable technical personnel based on the risk importance analyzed by the cross-reactor management module.

4. The nuclear power plant anomaly response management system in a cluster reactor mode according to claim 3, characterized in that, The specific working method of the personnel allocation unit is as follows: Obtain the skill level Z of each technician; The risk matching coefficient is obtained by dividing each technician's skill level Z by the risk value L of each reactor. The technician with the highest risk matching coefficient is then assigned to the corresponding reactor for maintenance.

5. A nuclear power plant anomaly response management system in a cluster reactor mode according to claim 4, characterized in that, The specific method for obtaining the skill level Z of each technician is as follows: Establish detailed personal files for each technician; The skills of the technicians are assessed to obtain the skill level Z for each technician.

6. The nuclear power plant anomaly response management system in a cluster reactor mode according to claim 5, characterized in that, The specific working method of the maintenance module also includes: After each maintenance of each reactor, technicians record the details of that maintenance. The historical issues of each reactor are categorized based on the recorded information; We create detailed personal profiles for each technician to match them with technicians based on the type of problem.

7. A method for managing anomaly response in a nuclear power plant under a cluster reactor mode, applicable to the anomaly response management system for a nuclear power plant under a cluster reactor mode as described in any one of claims 1 to 6, characterized in that, Includes the following steps: Step 1: Data Collection: Use the data acquisition unit to collect the operating data and historical maintenance records of each reactor, and analyze the collected data, including the operating status of the equipment, failure rate, and maintenance history, to determine the risk level and maintenance requirements of each reactor; Step 2: Determine the time period for the next maintenance of each reactor. The cross-reactor management module formulates the maintenance strategy for each reactor based on the prediction results, taking into account the risk level and maintenance requirements of different reactors. Step 3: Based on the predicted resource consumption and maintenance strategy, automatically allocate resources to ensure that the needs of each reactor are met within the predetermined maintenance period. Within the maintenance period predicted by the scheduling unit, the maintenance module manages the resource scheduling of each reactor unit and executes maintenance activities according to the maintenance strategy formulated by the cross-reactor management module.

Images