Maintenance assisting method and system for nuclear power plant

Abstract

The invention discloses a maintenance assisting method and system for a nuclear power plant, and relates to the technical field of nuclear power plants. Performing regional division on the multi-dimensional operation data according to equipment system hierarchies to obtain target maintenance region data and associated region data; performing operation parameter fluctuation characteristic and equipment fault correlation degree analysis on the target maintenance area data and the correlation area data to obtain a risk correlation threshold value; the fault correlation degree between the target maintenance area data and the correlation area data is counted in the current maintenance stage, and the fault correlation degree and a risk correlation threshold value are compared and judged to obtain maintenance risk early warning or continuous analysis information; the effect is that the maintenance auxiliary risk assessment result is more comprehensive and accurate.

Description

Technical Field

[0001] This invention relates to the field of nuclear power plant technology, and more specifically, to a maintenance support method and system for nuclear power plants. Background Technology

[0002] Equipment maintenance is a crucial aspect of ensuring the safe and stable operation of nuclear power plants. Traditional maintenance support methods often assess the failure risk of the target maintenance area in isolation, failing to fully consider the impact of equipment operation in related areas on the target area. They also rarely incorporate factors such as environmental parameter fluctuations and the response efficiency of safety systems. This results in incomplete maintenance risk assessments, failing to provide sufficient and reliable basis for maintenance decisions. Consequently, this affects the efficiency and safety of nuclear power plant maintenance work and may even increase the potential risks of nuclear power plant operation to some extent. Summary of the Invention

[0003] In view of the shortcomings of the existing technology, the purpose of this invention is to provide a maintenance assistance method and system for nuclear power plants.

[0004] To achieve the above objectives, the present invention provides the following technical solution: A maintenance support method for nuclear power plants, comprising the following steps: Acquire multi-dimensional operational data of equipment to be maintained in nuclear power plants, and divide the multi-dimensional operational data into regions according to the equipment system level to obtain target maintenance area data and related area data; Risk correlation thresholds are obtained by analyzing the correlation between operating parameter fluctuation characteristics and equipment failures of data from the target maintenance area and related areas. In the current maintenance phase, the fault correlation degree between the target maintenance area data and related area data is statistically analyzed, and the fault correlation degree is compared with the risk correlation threshold to obtain maintenance risk warning or follow-up analysis information. Based on the analysis information, determine the parameter change dataset of the target maintenance area and related areas affected by the intervention. Based on the parameter change dataset, determine the first fault risk probability of the target maintenance area. Based on the coupling relationship between the first fault risk probability and the maintenance operation complexity, obtain the second target fault risk probability. Determine the first associated failure risk probability of the associated region to which the associated region data belongs, and obtain the second associated failure risk probability based on the first associated failure risk probability and the inter-regional coupling coefficient. The first comprehensive risk value is obtained by judging the risk level of the target maintenance area based on the second target failure risk probability and the second associated failure risk probability. Based on the fluctuation of environmental parameters, the response efficiency of the safety system, and the first comprehensive risk value, an auxiliary maintenance risk assessment is conducted on the target maintenance area to obtain the auxiliary maintenance risk assessment results.

[0005] Preferably, the process involves acquiring multi-dimensional operational data of the nuclear power plant equipment awaiting maintenance, and then dividing this multi-dimensional operational data into target maintenance area data and related area data according to the equipment system hierarchy. This specifically includes the following steps: Acquire multi-dimensional operational data of equipment in a nuclear power plant awaiting maintenance, including temperature, pressure, flow rate, vibration frequency, and electrical parameters; Regional datasets are obtained by dividing multi-dimensional operational data into regions according to the equipment system hierarchy of nuclear power plants. The target maintenance area data is selected from the regional dataset, and the associated area data with operational coupling relationship is filtered out from the regional dataset based on the equipment association relationship corresponding to the target maintenance area data.

[0006] Preferably, a risk correlation threshold is obtained by analyzing the correlation between operating parameter fluctuation characteristics and equipment failures of the target maintenance area data and related area data, specifically including the following steps: During normal historical operation periods, historical target operation parameters and historical associated operation parameters of the target maintenance area and related areas are obtained respectively. The frequency of failures in the target maintenance area and related areas during historical failure periods is statistically analyzed to obtain historical failure frequency data. The influence coefficient of historical parameters is obtained by extracting the fluctuation of historical related operating parameters on the target operating parameters; Extract the historical fault-inducing coefficients of each associated area fault on the target maintenance area fault from the historical fault frequency data; The historical risk reference coefficient is obtained by weighting the historical parameter influence coefficient and the historical failure induction coefficient. During periods of major historical failures, historical anomaly risk coefficients were obtained for the target maintenance area and related areas, respectively. The risk correlation threshold is obtained by calculating the normal fluctuation range of the risk coefficient based on the historical risk reference coefficient and the historical abnormal risk coefficient.

[0007] Preferably, the fault correlation between data from the target maintenance area and data from related areas is statistically analyzed during the current maintenance phase, specifically including the following steps: Real-time operating parameters for collecting data from the target maintenance area and related areas during the current maintenance phase; Calculate the fault correlation degree between the data of each associated area and the data of the target maintenance area.

[0008] Preferably, the fault correlation degree is compared with the risk correlation threshold to determine the maintenance risk warning or follow-up analysis information, which specifically includes the following steps: Areas with a fault correlation degree greater than the risk correlation threshold will be given a maintenance risk warning and marked as high-risk correlation areas. If the fault correlation degree is less than the risk correlation threshold, the difference between the fault correlation degree and the risk correlation threshold is calculated to obtain the risk difference data. If the risk difference data is greater than or equal to the risk difference threshold, it is determined that there is a potential risk in the target maintenance area, and further analysis information is output for the target maintenance area and related areas.

[0009] Preferably, the parameter change dataset of the target maintenance area and related areas affected by the intervention is determined based on the continuous analysis information. The first fault risk probability of the target maintenance area is determined based on the parameter change dataset. The second target fault risk probability is obtained based on the coupling relationship between the first fault risk probability and the complexity of the maintenance operation. Specifically, this includes the following steps: Based on the analysis information, the steady-state deviation values ​​of the operating parameters of the target maintenance area and related areas in the current maintenance stage are statistically analyzed, and the mapping relationship between the steady-state deviation values ​​of the operating parameters and maintenance operation intervention is established to determine the parameter change dataset affected by operation intervention within the preset maintenance cycle; By combining the parameter variation dataset, the probability of the first failure risk in the target maintenance area within the preset maintenance cycle is determined; The maintenance operation complexity coefficient is obtained based on the number of maintenance operation steps, operation requirements, and personnel skill matching degree of the maintenance operation task decomposition. The second target failure risk probability is obtained based on the coupling relationship between the first failure risk probability and the maintenance operation complexity.

[0010] Preferably, the first associated failure risk probability of the associated region to which the associated region data belongs is determined, and the second associated failure risk probability is obtained based on the first associated failure risk probability and the inter-regional coupling coefficient, specifically including the following steps: The parameter variation dataset of the associated region and the equipment characteristic parameters are input into the nuclear power plant failure probability model to obtain the first associated failure risk probability. Calculate the inter-regional coupling coefficients between the target maintenance area and each associated area in terms of material flow, energy flow, and information flow based on the topological relationship of the nuclear power plant equipment system; The second associated fault risk probability is obtained by multiplying the first associated fault risk probability by the corresponding inter-regional coupling coefficient.

[0011] Preferably, a maintenance auxiliary risk assessment is conducted on the target maintenance area based on environmental parameter fluctuations, safety system response efficiency, and a first comprehensive risk value to obtain the maintenance auxiliary risk assessment result. This specifically includes the following steps: The fluctuations in the environmental parameters include the temperature, humidity, and radiation dose of the target maintenance area; The fluctuation range of the system's environmental parameters is used to obtain the environmental fluctuation coefficient; The response time and action accuracy of the safety system corresponding to the target maintenance area are tested, and the response efficiency of the safety system is calculated based on the response time and action accuracy. The risk correction coefficient is obtained based on the environmental fluctuation coefficient and the response efficiency of the safety system. The final risk assessment value of the target maintenance area is obtained by multiplying the first comprehensive risk value by the risk correction coefficient. The maintenance auxiliary risk assessment results are determined based on the final risk assessment value and the nuclear power plant maintenance risk level classification standard.

[0012] A maintenance support system for nuclear power plants, comprising: Segmentation Module: Acquire multi-dimensional operational data of equipment to be maintained in nuclear power plants, and divide the multi-dimensional operational data into regions according to the equipment system level to obtain target maintenance area data and related area data; Analysis module: Analyzes the correlation between operating parameter fluctuation characteristics and equipment failures of data from the target maintenance area and related areas to obtain risk correlation thresholds; Comparison module: In the current maintenance phase, it calculates the fault correlation between data of the target maintenance area and data of related areas, compares the fault correlation with the risk correlation threshold, and obtains maintenance risk warning or follow-up analysis information. The first processing module: Based on the continuous analysis information, it determines the parameter change dataset of the target maintenance area and related areas affected by the intervention. Based on the parameter change dataset, it determines the first fault risk probability of the target maintenance area. Based on the coupling relationship between the first fault risk probability and the maintenance operation complexity, it obtains the second target fault risk probability. The second processing module determines the first associated fault risk probability of the associated region to which the associated region data belongs, and obtains the second associated fault risk probability based on the first associated fault risk probability and the inter-regional coupling coefficient. Judgment module: Based on the probability of failure of the second target and the probability of failure of the second associated fault, the risk level of the target maintenance area is determined to obtain the first comprehensive risk value; Assessment module: Based on the fluctuation of environmental parameters, the response efficiency of the safety system, and the first comprehensive risk value, an auxiliary maintenance risk assessment is conducted on the target maintenance area to obtain the auxiliary maintenance risk assessment results.

[0013] An electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the program, implements the aforementioned maintenance assistance method for a nuclear power plant.

[0014] Compared with the prior art, the present invention has the following beneficial effects: This invention acquires multi-dimensional operational data of equipment under maintenance in nuclear power plants and divides it into target maintenance area data and related area data according to equipment system hierarchy. This lays the data foundation for subsequent analysis, allowing maintenance personnel to clearly understand the operational data of different areas and facilitate targeted work. By analyzing the correlation between operational parameter fluctuation characteristics and equipment failure in the target maintenance area data and related area data, a risk correlation threshold is obtained. This provides a quantitative standard for assessing maintenance risks, improving the scientific rigor and accuracy of risk assessment and enabling the identification of potential failure risks. The fault correlation between the target maintenance area data and related area data is statistically analyzed and compared with the risk correlation threshold to determine maintenance risk warnings or further analysis information. If the fault correlation exceeds the threshold, a warning is issued promptly to remind maintenance personnel to take measures; if it is below the threshold, further analysis is conducted to explore potential risks, ensuring the timeliness and effectiveness of maintenance decisions. Based on the second target failure risk probability and the second related failure risk probability, a first comprehensive risk value is obtained, integrating the failure risk information of the target maintenance area and related areas. This achieves a comprehensive assessment of the risk level of the target maintenance area, providing a key basis for subsequent auxiliary risk assessments in maintenance. By combining environmental parameter fluctuations, safety system response efficiency, and the first comprehensive risk value, an auxiliary maintenance risk assessment is conducted on the target maintenance area. This comprehensive consideration of the impact of environmental factors and safety system performance on maintenance risks makes the auxiliary maintenance risk assessment results more comprehensive and accurate, providing more reliable decision support for nuclear power plant maintenance work. Attached Figure Description

[0015] Figure 1 This invention provides a schematic diagram illustrating the steps of a maintenance assistance method for nuclear power plants. Figure 2 This invention provides a schematic diagram of a maintenance support system for nuclear power plants. Figure 3 This is a schematic diagram of the structure of the electronic device provided in an embodiment of the present invention.

[0016] 610. Processor; 620. Communication interface; 630. Memory; 640. Communication bus. Detailed Implementation

[0017] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0018] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0019] Secondly, the term "an embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places throughout this specification does not necessarily refer to the same embodiment, nor is it a single embodiment or an embodiment selectively excluded from other embodiments.

[0020] Reference Figures 1-3 As shown.

[0021] The embodiments further illustrate the maintenance assistance method and system for nuclear power plants proposed in this invention.

[0022] A maintenance support method for nuclear power plants, comprising the following steps: Acquire multi-dimensional operational data of equipment to be maintained in nuclear power plants, and divide the multi-dimensional operational data into regions according to the equipment system level to obtain target maintenance area data and related area data; Risk correlation thresholds are obtained by analyzing the correlation between operating parameter fluctuation characteristics and equipment failures of data from the target maintenance area and related areas. In the current maintenance phase, the fault correlation degree between the target maintenance area data and related area data is statistically analyzed, and the fault correlation degree is compared with the risk correlation threshold to obtain maintenance risk warning or follow-up analysis information. Based on the analysis information, determine the parameter change dataset of the target maintenance area and related areas affected by the intervention. Based on the parameter change dataset, determine the first fault risk probability of the target maintenance area. Based on the coupling relationship between the first fault risk probability and the maintenance operation complexity, obtain the second target fault risk probability. Determine the first associated failure risk probability of the associated region to which the associated region data belongs, and obtain the second associated failure risk probability based on the first associated failure risk probability and the inter-regional coupling coefficient. The first comprehensive risk value is obtained by judging the risk level of the target maintenance area based on the second target failure risk probability and the second associated failure risk probability. Based on the fluctuation of environmental parameters, the response efficiency of the safety system, and the first comprehensive risk value, an auxiliary maintenance risk assessment is conducted on the target maintenance area to obtain the auxiliary maintenance risk assessment results.

[0023] Basic equipment information includes equipment tag number, equipment name, and unit number; local wiring information includes local junction box tag number, terminal block number, and terminal number; associated equipment information includes the associated master equipment tag number and master equipment name. Acquire multi-dimensional operational data of equipment in a nuclear power plant awaiting maintenance, and then divide this multi-dimensional operational data into target maintenance area data and related area data according to the equipment system level. This process includes the following steps: Acquire multi-dimensional operational data of equipment in nuclear power plants awaiting maintenance, including temperature, pressure, flow rate, vibration frequency, and electrical parameters; Regional datasets are obtained by dividing multi-dimensional operational data into regions according to the equipment system hierarchy of nuclear power plants. The target maintenance area data is selected from the regional dataset, and the associated area data with operational coupling relationship is filtered out from the regional dataset based on the equipment association relationship corresponding to the target maintenance area data.

[0024] This application first acquires multi-dimensional operational data of the equipment to be repaired in the nuclear power plant. This multi-dimensional operational data includes temperature, pressure, flow rate, and vibration frequency. Temperature reflects the thermal state of the equipment, pressure relates to the operation of the fluid system, flow rate reflects the amount of medium transported, vibration frequency is used to determine whether the mechanical operation of the equipment is normal, and electrical parameters relate to the power supply and electrical performance of the equipment. These multi-dimensional operational data are divided into regional datasets according to the hierarchical structure of the nuclear power plant's equipment system. Target repair area data is selected from the regional datasets. For example, if a main pump in the nuclear power plant needs repair, the data in the area where the main pump is located is determined as the target repair area data. Then, based on the equipment relationships corresponding to the target repair area data, related regional data with operational coupling relationships are filtered from the regional datasets. Taking the main pump as an example, the main pump has an operational coupling relationship with the valves and pipes of the cooling system; therefore, the data in the areas where the valves and pipes are located are filtered out as related regional data.

[0025] To obtain a risk correlation threshold, the analysis of the correlation between operating parameter fluctuation characteristics and equipment failures is performed on data from the target maintenance area and related areas. This process includes the following steps: During normal historical operation periods, historical target operation parameters and historical associated operation parameters of the target maintenance area and related areas are obtained respectively. The frequency of failures in the target maintenance area and related areas during historical failure periods is statistically analyzed to obtain historical failure frequency data. The influence coefficient of historical parameters is obtained by extracting the fluctuation of historical related operating parameters on the target operating parameters; Extract the historical fault-inducing coefficients of each associated area fault on the target maintenance area fault from the historical fault frequency data; The historical risk reference coefficient is obtained by weighting the historical parameter influence coefficient and the historical failure induction coefficient. During periods of major historical failures, historical anomaly risk coefficients were obtained for the target maintenance area and related areas, respectively. The risk correlation threshold is obtained by calculating the normal fluctuation range of the risk coefficient based on the historical risk reference coefficient and the historical abnormal risk coefficient.

[0026] First, focus on the historical operating phases of the nuclear power plant equipment. During normal historical operation, obtain relevant operating parameters for the target maintenance area and related areas. If the target maintenance area is the location of the reactor coolant pumps, the historical target operating parameters include the pump speed and power, which directly reflect the pump's operating status. If the related area is the pipeline area connected to the coolant pumps, the historical related operating parameters include the coolant pressure and flow rate within the pipelines, used to demonstrate the influence of the related area on the target area.

[0027] Historical failure frequency data is generated by statistically analyzing the frequency of failures in the target maintenance area and related areas during historical failure periods. For example, statistics show that there were 10 instances of coolant pump bearing failure and 8 instances of related pipeline shutdowns due to corrosion leaks over the past 5 years. This data can clearly show the frequency of failures.

[0028] The influence coefficient of historical parameters is obtained by extracting the impact of historical correlation operating parameters on the fluctuation of target operating parameters. Assuming that when the coolant flow rate in the pipeline fluctuates by ±10% within the normal range, the speed of the coolant pump will fluctuate by ±5% accordingly, the historical parameter influence coefficient is determined by quantitatively calculating a large amount of data on this fluctuation relationship, thereby measuring the degree of influence of correlation parameter fluctuations on the target parameter.

[0029] Extract the historical fault-inducing coefficient of each associated area's fault on the target maintenance area from historical fault frequency data. For example, if it is found that there were 3 instances of coolant pump overload failure induced by pipe blockage, and the total number of pipe blockage failures was 15, then the historical fault-inducing coefficient of pipe blockage failure on coolant pump overload failure can be calculated as 3 ÷ 15 = 0.2.

[0030] The historical risk reference coefficient is obtained by weighting the historical parameter influence coefficient and the historical failure induction coefficient. Since different parameters and failure induction scenarios have varying degrees of importance in risk assessment, different weights need to be assigned. For example, if failure induction is considered more critical to risk, the historical failure induction coefficient is assigned a weight of 0.6, and the historical parameter influence coefficient is assigned a weight of 0.4. If the historical parameter influence coefficient is 0.3 and the historical failure induction coefficient is 0.2, then the historical risk reference coefficient is 0.3 × 0.4 + 0.2 × 0.6 = 0.24, thus reflecting the proportion of different factors in the risk composition.

[0031] Historical anomaly risk coefficients for the target maintenance area and related areas are obtained during periods of major historical failures. For example, when a major failure such as a severe coolant leak occurs, various parameters of the coolant pump and related piping areas will deviate significantly from the normal range. By analyzing and quantifying the risk situation under such major failure scenarios, the historical anomaly risk coefficient for the coolant pump area is determined to be 0.8, and the historical anomaly risk coefficient for the related piping area is 0.7. These coefficients can reflect the risk characteristics under major failure scenarios.

[0032] The normal fluctuation range of the risk coefficient is calculated based on historical risk reference coefficients and historical abnormal risk coefficients, thus obtaining the risk correlation threshold. For example, combining the historical risk reference coefficient of 0.24 and relevant data on historical abnormal risk coefficients, statistical analysis methods are used to calculate that the normal fluctuation range of the risk coefficient is 0.1-0.3. Therefore, the risk correlation threshold is set to 0.3. The actual statistically obtained fault correlation degree is compared with the risk correlation threshold to determine whether a maintenance risk warning operation needs to be issued, providing a key judgment standard for the maintenance risk assessment of nuclear power plants.

[0033] The current maintenance phase involves calculating the fault correlation between data from the target maintenance area and data from related areas, specifically including the following steps: Real-time operating parameters for collecting data from the target maintenance area and related areas during the current maintenance phase; Calculate the fault correlation degree between the data of each associated area and the data of the target maintenance area.

[0034] During the current maintenance phase, real-time operating parameters of the target maintenance area and related areas of nuclear power plant equipment are collected. Then, the fault correlation between the data from each related area and the data from the target maintenance area is calculated. For example, suppose the target maintenance area is the main pump area of ​​the nuclear reactor, and the related area is the cooling pipe area connected to the main pump. First, the operating parameters of the main pump area, such as rotational speed, power, and vibration amplitude, are acquired in real-time using various sensors and monitoring equipment. Simultaneously, the operating parameters of the coolant pressure, flow rate, and temperature in the cooling pipe area are collected in real-time. Machine learning algorithms are then used to calculate these real-time operating parameters of the main pump area and the cooling pipe area. How will the rotational speed, vibration amplitude, and other parameters of the main pump change when the coolant flow rate in the cooling pipe changes? This allows for the calculation of the fault correlation between the data from the cooling pipe area and the main pump area, determining the degree of correlation in terms of faults and providing a basis for subsequent maintenance risk assessment.

[0035] The comparison between fault correlation and risk correlation threshold is used to determine maintenance risk warnings or follow-up analysis information, which specifically includes the following steps: Areas with a fault correlation degree greater than the risk correlation threshold will be given a maintenance risk warning and marked as high-risk correlation areas. If the fault correlation degree is less than the risk correlation threshold, the difference between the fault correlation degree and the risk correlation threshold is calculated to obtain the risk difference data. If the risk difference data is greater than or equal to the risk difference threshold, it is determined that there is a potential risk in the target maintenance area, and further analysis information is output for the target maintenance area and related areas.

[0036] In nuclear power plant maintenance scenarios, the risk status of maintenance-related areas is determined by comparing fault correlation and risk correlation thresholds.

[0037] If the target maintenance area is the main pump area of ​​the nuclear reactor in a nuclear power plant, the associated area is the cooling pipe area connected to it. Assume the pre-set risk association threshold is 0.8, and the pre-set risk difference threshold is 0.05.

[0038] If the calculated fault correlation between the cooling pipe area and the main pump area is 0.9, and since 0.9 is greater than the risk correlation threshold of 0.8, a maintenance risk warning is issued for the cooling pipe area, and this area is marked as a high-risk correlation area. The purpose of this operation is to promptly alert maintenance personnel to the high fault correlation risk in this area, requiring focused attention and priority handling, thereby preventing more serious consequences.

[0039] If the fault correlation degree is less than the risk correlation threshold, for example, a fault correlation degree of 0.7, then the difference between the fault correlation degree and the risk correlation threshold is calculated, i.e., 0.8 - 0.7 = 0.1, resulting in a risk difference value of 0.1. This risk difference value of 0.1 is compared with the risk difference threshold of 0.05. Since 0.1 is greater than 0.05, it is determined that there is a potential risk in the target maintenance area. Further analysis information is then output for the main pump area and the associated cooling pipe area.

[0040] Based on the analysis information, the parameter change dataset of the target maintenance area and related areas affected by the intervention is determined. Based on the parameter change dataset, the first failure risk probability of the target maintenance area is determined. Based on the coupling relationship between the first failure risk probability and the maintenance operation complexity, the second target failure risk probability is obtained. The specific steps include: Based on the analysis information, the steady-state deviation values ​​of the operating parameters of the target maintenance area and related areas in the current maintenance stage are statistically analyzed, and the mapping relationship between the steady-state deviation values ​​of the operating parameters and maintenance operation intervention is established to determine the parameter change dataset affected by operation intervention within the preset maintenance cycle; By combining the parameter variation dataset, the probability of the first failure risk in the target maintenance area within the preset maintenance cycle is determined; The maintenance operation complexity coefficient is obtained based on the number of maintenance operation steps, operation requirements, and personnel skill matching degree of the maintenance operation task decomposition. The second target failure risk probability is obtained based on the coupling relationship between the first failure risk probability and the maintenance operation complexity.

[0041] If the target maintenance area is the turbine area of ​​a nuclear power plant, the associated area is the associated steam pipeline area. Based on the ongoing analysis, the steady-state deviation values ​​of operating parameters in the turbine and steam pipeline areas during the current maintenance phase are statistically analyzed, such as the steady-state deviation of turbine speed and steam pressure in the steam pipeline. A mapping relationship is established between these steady-state deviation values ​​and maintenance interventions. By analyzing past cases of maintenance interventions under similar parameter deviations, the dataset of parameter changes affected by these interventions within the preset maintenance cycle is determined, thereby identifying how the turbine speed and steam pipeline pressure will change after the maintenance operation is implemented.

[0042] The probability of the first failure in the turbine area within a preset maintenance cycle is determined by combining the parameter variation dataset. For example, based on the range and magnitude of turbine speed variation in the parameter variation dataset, and the historical occurrence of turbine failures under similar speed variations, the probability of the first failure is calculated to be 0.2.

[0043] After breaking down the maintenance operation tasks, the number of maintenance operation steps was counted. It was assumed that there were 10 steps in turbine maintenance, including disassembly, inspection, replacement of parts, and assembly. The operation requirements were clarified that some steps need to be performed under specific temperature and pressure conditions and with high precision requirements. If some of the maintenance personnel are not proficient in high-end turbine maintenance technology, the maintenance operation complexity coefficient was found to be 0.6 by referring to the table and considering these factors.

[0044] The second target failure risk probability is obtained by considering the coupling relationship between the first failure risk probability and the maintenance operation complexity. For example, the formula can be set as: Second target failure risk probability = First failure risk probability × (1 + Maintenance operation complexity coefficient). Here, the first failure risk probability is 0.2, and the maintenance operation complexity coefficient is 0.6. Substituting these values ​​into the formula, the second target failure risk probability is calculated to be 0.32.

[0045] Determine the first associated failure risk probability of the associated region to which the associated region data belongs, and obtain the second associated failure risk probability based on the first associated failure risk probability and the inter-regional coupling coefficient. This process includes the following steps: The parameter variation dataset of the associated region and the equipment characteristic parameters are input into the nuclear power plant failure probability model to obtain the first associated failure risk probability. Calculate the inter-regional coupling coefficients between the target maintenance area and each associated area in terms of material flow, energy flow, and information flow based on the topological relationship of the nuclear power plant equipment system; The second associated fault risk probability is obtained by multiplying the first associated fault risk probability by the corresponding inter-regional coupling coefficient.

[0046] The failure risk probability of the associated region is calculated by analyzing the topological relationships of the nuclear power plant's equipment systems and related data. If the target maintenance area is the reactor pressure vessel area of ​​the nuclear power plant, the associated region is the control rod drive mechanism area associated with it. First, the parameter variation dataset of the control rod drive mechanism area (such as control rod displacement variation data and drive current variation data) and equipment characteristic parameters (such as control rod material and rated power of the drive mechanism) are input into the nuclear power plant failure probability model. The nuclear power plant failure probability model is established based on a large amount of historical failure data and equipment operation patterns of nuclear power plants. It can calculate the first associated failure risk probability of the control rod drive mechanism area based on the input parameters. Assuming the obtained first associated failure risk probability is 0.25.

[0047] Based on the topology of the nuclear power plant equipment system, the inter-regional coupling coefficient between the reactor pressure vessel region and the control rod drive mechanism region in terms of material flow, energy flow, and information flow was calculated. From the perspective of material flow, the movement of control rods within the reactor pressure vessel affects the reaction of materials within the reactor; the energy transfer of the control rod drive mechanism affects the reactor's energy output; and the position information of the control rods is transmitted to the reactor control system. Considering these factors, the inter-regional coupling coefficient was calculated to be 0.8.

[0048] The probability of the first associated fault risk (0.25) is multiplied by the corresponding inter-regional coupling coefficient (0.8) to obtain the probability of the second associated fault risk (0.25 × 0.8 = 0.2), which reflects the degree of influence of the associated regional fault risk on the target maintenance area.

[0049] Based on environmental parameter fluctuations, safety system response efficiency, and the first comprehensive risk value, a maintenance auxiliary risk assessment is conducted on the target maintenance area to obtain the maintenance auxiliary risk assessment results. This includes the following steps: Environmental parameter fluctuations include temperature, humidity, and radiation dose in the target maintenance area; The fluctuation range of the system's environmental parameters is used to obtain the environmental fluctuation coefficient; The response time and action accuracy of the safety system corresponding to the target maintenance area are tested, and the response efficiency of the safety system is calculated based on the response time and action accuracy. The risk correction coefficient is obtained based on the environmental fluctuation coefficient and the response efficiency of the safety system. The final risk assessment value of the target maintenance area is obtained by multiplying the first comprehensive risk value by the risk correction coefficient. The maintenance auxiliary risk assessment results are determined based on the final risk assessment value and the nuclear power plant maintenance risk level classification standard.

[0050] A maintenance auxiliary risk assessment is conducted on the target maintenance area based on comprehensive environmental parameters and safety system response efficiency. First, the fluctuation of environmental parameters is defined, including temperature, humidity, and radiation dose in the area. Taking a target maintenance area in a nuclear power plant as an example, assuming the normal temperature range is 20-25℃ (weight 0.4); the normal humidity range is 40%-50% (weight 0.3); and the normal radiation dose range is 0-50 μSv / h (weight 0.3). In the current maintenance phase, the actual temperature fluctuation is 22-28℃. The proportion of this fluctuation to the normal range is calculated: the normal range spans 5℃, and the actual fluctuation spans 6℃, resulting in a proportion of 6 ÷ 5 = 1.2. The actual humidity fluctuation is 35%-55%, with a normal range span of 10% and an actual fluctuation span of 20%, resulting in a proportion of 20 ÷ 10 = 2. The actual radiation dose fluctuation is 0-60 μSv / h, with a normal range spanning 50 μSv / h and an actual fluctuation spanning 60 μSv / h, resulting in a proportion of 60 ÷ 50 = 1.2. Then, the fluctuation ratios of each parameter are multiplied by their corresponding weights and summed. That is, temperature is 1.2×0.4=0.48, humidity is 2×0.3=0.6, and radiation dose is 1.2×0.3=0.36. Therefore, the total environmental fluctuation coefficient is 0.48+0.6+0.36=1.44, which comprehensively reflects the degree of impact of the fluctuation range of each environmental parameter on the risk of the maintenance area.

[0051] Taking the fire alarm safety system in a target maintenance area of ​​a nuclear power plant as an example, a simulated fire scenario test is first conducted. A fire is simulated in the target maintenance area, triggering the fire alarm safety system. The time from the generation of the fire signal to the issuance of the alarm command and the activation of relevant fire extinguishing equipment is recorded; this is the response time. Assuming an average response time of 3 seconds after multiple tests, the system's actions in the simulation test are observed, and the number of times it accurately identifies the fire and correctly activates the fire extinguishing equipment is counted. For example, if 95 out of 100 simulation tests result in accurate actions, the accuracy rate is 95 ÷ 100 = 95%. The response efficiency of the safety system is calculated based on the response time and accuracy rate. The formula is: Safety system response efficiency = (1 - percentage of response time exceeding the standard time) × accuracy rate. Assuming the standard response time of this safety system is 2 seconds, and the average response time in this test is 3 seconds, exceeding the standard time by 1 second, the excess percentage is 1 ÷ 2 = 0.5. Therefore, the response efficiency of this fire alarm safety system is (1 - 0.5) × 95% = 47.5%, which is used to measure the response performance of the safety system in the target maintenance area.

[0052] The risk correction coefficient is calculated based on the environmental fluctuation coefficient and the safety system response efficiency. For example, the risk correction coefficient = environmental fluctuation coefficient × (1 - discount rate of safety system response efficiency). This allows us to examine the deviation of response time and action accuracy from ideal conditions in past actual or simulated scenarios. If a target maintenance area of ​​a nuclear power plant has an average response time 10% slower than the design standard and an action accuracy 8% lower than the design value in multiple historical tests, and considering the aging and maintenance status of the safety system, if the system has been in use for a long time and recent maintenance is inadequate, its performance will be affected. The impact of these factors on the safety system response efficiency is quantified. Assuming a 5% discount rate for response time deviation, a 6% discount rate for action accuracy deviation, and an additional 4% discount rate for system aging and insufficient maintenance, the discount rate for the safety system response efficiency is 5% + 6% + 4% = 15%.

[0053] The final risk assessment value for the fuel assembly maintenance area is obtained by multiplying the first comprehensive risk value by a risk correction coefficient. Based on this final risk assessment value and the nuclear power plant's maintenance risk level classification standards, the auxiliary maintenance risk assessment result for the fuel assembly maintenance area is determined. For example, the nuclear power plant's maintenance risk level classification standards stipulate that 0.5-0.6 is a medium risk level, thus providing a basis for maintenance decisions.

[0054] A maintenance support system for nuclear power plants, comprising: Segmentation Module: Acquire multi-dimensional operational data of equipment to be maintained in nuclear power plants, and divide the multi-dimensional operational data into regions according to the equipment system level to obtain target maintenance area data and related area data; Analysis module: Analyzes the correlation between operating parameter fluctuation characteristics and equipment failures of data from the target maintenance area and related areas to obtain risk correlation thresholds; Comparison module: In the current maintenance phase, it calculates the fault correlation between data of the target maintenance area and data of related areas, compares the fault correlation with the risk correlation threshold, and obtains maintenance risk warning or follow-up analysis information. The first processing module: Based on the continuous analysis information, it determines the parameter change dataset of the target maintenance area and related areas affected by the intervention. Based on the parameter change dataset, it determines the first fault risk probability of the target maintenance area. Based on the coupling relationship between the first fault risk probability and the maintenance operation complexity, it obtains the second target fault risk probability. The second processing module determines the first associated fault risk probability of the associated region to which the associated region data belongs, and obtains the second associated fault risk probability based on the first associated fault risk probability and the inter-regional coupling coefficient. Judgment module: Based on the probability of failure of the second target and the probability of failure of the second associated fault, the risk level of the target maintenance area is determined to obtain the first comprehensive risk value; Assessment module: Based on the fluctuation of environmental parameters, the response efficiency of the safety system, and the first comprehensive risk value, an auxiliary maintenance risk assessment is conducted on the target maintenance area to obtain the auxiliary maintenance risk assessment results.

[0055] An electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement a maintenance assistance method for a nuclear power plant.

[0056] like Figure 3 As shown, the electronic device may include a processor 610, a communication interface 620, a memory 630, and a communication bus 640, wherein the processor 610, the communication interface 620, and the memory 630 communicate with each other via the communication bus 640. The processor 610 can call logical instructions in the memory 630 to execute a maintenance assistance method for nuclear power plants.

[0057] Furthermore, the logical instructions in the aforementioned memory 630 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory, random access memory, magnetic disks, or optical disks.

[0058] On the other hand, the present invention also provides a computer program product, the computer program product including a computer program that can be stored on a non-transitory computer-readable storage medium, and when the computer program is executed by a processor, the computer is able to execute a maintenance assistance method for a nuclear power plant.

[0059] In another aspect, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, is implemented to perform a maintenance assistance method for a nuclear power plant.

[0060] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0061] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0062] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A maintenance support method for a nuclear power plant, characterized by, The method comprises the following steps: Obtain multi-dimensional operation data of the nuclear power plant equipment to be maintained, and divide the multi-dimensional operation data according to the equipment system level to obtain target maintenance area data and associated area data; Analyze the running parameter fluctuation characteristics and the equipment failure correlation degree of the target maintenance area data and the associated area data to obtain a risk correlation threshold; Statistically analyze the failure correlation degree between the target maintenance area data and the associated area data in the current maintenance stage, compare the failure correlation degree with the risk correlation threshold, and determine the maintenance risk warning or follow-up analysis information; Determine the parameter variation data set of the target maintenance area and the associated area affected by the intervention influence parameter variation according to the follow-up analysis information, determine the first failure risk probability of the target maintenance area according to the parameter variation data set, and determine the second target failure risk probability according to the coupling relationship between the first failure risk probability and the maintenance operation complexity; Determine the first associated failure risk probability of the associated area to which the associated area data belongs, and determine the second associated failure risk probability according to the first associated failure risk probability and the inter-regional coupling coefficient; Determine the risk degree of the target maintenance area according to the second target failure risk probability and the second associated failure risk probability to obtain a first comprehensive risk value; According to the environmental parameter fluctuation, the safety system response efficiency and the first comprehensive risk value, the target maintenance area is maintained to assist in risk assessment, and a maintenance auxiliary risk assessment result is obtained.

2. The maintenance support method for a nuclear power plant according to Claim 1, characterized by, Obtain multi-dimensional operation data of the nuclear power plant equipment to be maintained, and divide the multi-dimensional operation data according to the equipment system level to obtain target maintenance area data and associated area data, which comprises the following steps: Obtain multi-dimensional operation data of the nuclear power plant equipment to be maintained, wherein the multi-dimensional operation data includes temperature, pressure, flow, vibration frequency and electrical parameters; Divide the multi-dimensional operation data according to the nuclear power plant equipment system level to obtain a region data set; Select the target maintenance area data from the region data set, and filter out the associated area data with running coupling relationship from the region data set according to the device correlation relationship corresponding to the target maintenance area data.

3. The maintenance support method for a nuclear power plant according to Claim 2, characterized by, Analyze the running parameter fluctuation characteristics and the equipment failure correlation degree of the target maintenance area data and the associated area data to obtain a risk correlation threshold, which comprises the following steps: Obtain the historical target operation parameters and the historical associated operation parameters of the target maintenance area and the associated area respectively in the historical normal operation period; Statistically analyze the failure occurrence frequency of the target maintenance area and the associated area in the historical failure period to obtain historical failure frequency data; Extract the fluctuation influence of the historical associated operation parameters on the target operation parameters to obtain historical parameter influence coefficients; Extract the historical failure induction coefficients of each associated area failure on the target maintenance area failure from the historical failure frequency data; Distribute the historical parameter influence coefficients and the historical failure induction coefficients to obtain historical risk reference coefficients; Obtain the historical abnormal risk coefficients of the target maintenance area and the associated area respectively in the historical major failure period; Calculate the risk coefficient normal fluctuation range according to the historical risk reference coefficients and the historical abnormal risk coefficients to obtain the risk correlation threshold.

4. The maintenance support method for a nuclear power plant according to Claim 3, characterized by, In the current maintenance stage, the fault correlation degree between the target maintenance area data and the associated area data is calculated, specifically including the following steps: In the current maintenance stage, the real-time running parameters of the target maintenance area and the associated area data are collected in real time; The fault correlation degree between each associated area data and the target maintenance area data is calculated.

5. The maintenance support method for a nuclear power plant according to Claim 4, characterized by, The fault correlation degree is compared with the risk correlation threshold to determine the maintenance risk warning or follow-up analysis information, specifically including the following steps: If the fault correlation degree is greater than the risk correlation threshold, the area is warned of maintenance risk, and the area is marked as a high-risk correlation area; If the fault correlation degree is less than the risk correlation threshold, the difference between the fault correlation degree and the risk correlation threshold is calculated to obtain the risk difference data; If the risk difference data is greater than or equal to the risk difference threshold, it is determined that the target maintenance area has potential risks, and follow-up analysis information is output for the target maintenance area and the associated area.

6. The maintenance support method for a nuclear power plant according to Claim 5, characterized by, According to the follow-up analysis information, the parameter variation data set of the target maintenance area and the associated area affected by the intervention influence parameter is determined, the first fault risk probability of the target maintenance area is determined according to the parameter variation data set, and the second target fault risk probability is determined according to the coupling relationship between the first fault risk probability and the maintenance operation complexity, specifically including the following steps: According to the follow-up analysis information, the running parameter steady-state deviation value of the target maintenance area and the associated area in the current maintenance stage is calculated, and the mapping relationship between the running parameter steady-state deviation value and the maintenance operation intervention is established, and the parameter variation data set affected by the operation intervention in the preset maintenance period is determined; The first fault risk probability of the target maintenance area in the preset maintenance period is determined in combination with the parameter variation data set; The maintenance operation complexity coefficient is obtained according to the number of maintenance operation steps, operation requirements, and personnel skill matching degree of the maintenance operation task decomposition; The second target fault risk probability is obtained according to the coupling relationship between the first fault risk probability and the maintenance operation complexity.

7. The maintenance support method for a nuclear power plant according to Claim 6, characterized by, The first associated fault risk probability of the associated area data belonging to the associated area is determined, and the second associated fault risk probability is determined according to the first associated fault risk probability and the inter-regional coupling coefficient, specifically including the following steps: The parameter variation data set and the equipment characteristic parameter of the associated area are input into the nuclear power plant fault probability model to obtain the first associated fault risk probability; The inter-regional coupling coefficient between the target maintenance area and each associated area in the material flow, energy flow and information flow is calculated according to the topological relationship of the nuclear power plant equipment system; The first associated fault risk probability is multiplied by the corresponding inter-regional coupling coefficient to obtain the second associated fault risk probability.

8. The maintenance support method for a nuclear power plant according to Claim 7, characterized by, The target maintenance area is assisted in risk assessment according to the environmental parameter fluctuation, the safety system response efficiency and the first comprehensive risk value, and the maintenance auxiliary risk assessment result is obtained, specifically including the following steps: The environmental parameter fluctuation includes the temperature, humidity and radiation dose of the target maintenance area; The fluctuation amplitude of the environmental parameter fluctuation is obtained to obtain the environmental fluctuation coefficient; The response time and action accuracy of the safety system corresponding to the target maintenance area are tested, and the safety system response efficiency is calculated according to the response time and action accuracy; According to the environmental fluctuation coefficient and the safety system response efficiency, a risk correction coefficient is obtained; The first comprehensive risk value is multiplied by the risk correction coefficient to obtain a final risk assessment value of the target maintenance area; According to the final risk assessment value and the nuclear power plant maintenance risk grade classification standard, a maintenance auxiliary risk assessment result is determined.

9. A maintenance assistance system for a nuclear power plant, applied to the maintenance assistance method for a nuclear power plant according to any one of claims 1 to 8, characterized in that, Comprise: The division module: obtain multi-dimensional operation data of the nuclear power plant equipment to be maintained, divide the multi-dimensional operation data according to the equipment system level to obtain target maintenance area data and associated area data; The analysis module: the operation parameter fluctuation characteristics and the equipment failure correlation degree of the target maintenance area data and the associated area data are analyzed to obtain the risk correlation threshold; The comparison module: the failure correlation degree between the target maintenance area data and the associated area data is counted in the current maintenance stage, the failure correlation degree is compared with the risk correlation threshold to determine the maintenance risk warning or the continuation analysis information; The first processing module: according to the continuation analysis information, the parameter variation data set of the target maintenance area and the associated area affected by the intervention influence parameter variation is determined, the first failure risk probability of the target maintenance area is determined according to the parameter variation data set, and the second target failure risk probability is obtained according to the coupling relationship between the first failure risk probability and the maintenance operation complexity; The second processing module: determine the first associated failure risk probability of the associated area data belonging to the associated area, and obtain the second associated failure risk probability according to the first associated failure risk probability and the inter-area coupling coefficient; The judgment module: according to the second target failure risk probability and the second associated failure risk probability, the risk degree of the target maintenance area is determined to obtain the first comprehensive risk value; The evaluation module: according to the environmental parameter fluctuation, the safety system response efficiency and the first comprehensive risk value, the target maintenance area is subjected to maintenance auxiliary risk assessment to obtain the maintenance auxiliary risk assessment result.

10. An electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, The processor executes the program to realize the maintenance auxiliary method for the nuclear power plant according to any one of claims 1 to 8.

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