A method and system for maintenance assistance for a nuclear power plant

Abstract

The application discloses a kind of maintenance auxiliary method and system for nuclear power plant, it is related to nuclear power plant technical field, and its technical solution main points include the following steps: obtaining the multidimensional operation data of nuclear power plant equipment to be maintained, the multidimensional operation data is divided into target maintenance area data and associated area data according to equipment system level;Risk correlation threshold is obtained by analyzing the operation parameter fluctuation characteristics and equipment failure correlation degree of target maintenance area data and associated area data;The failure correlation degree between target maintenance area data and associated area data is counted in the current maintenance stage, and the maintenance risk early warning or follow-up analysis information is obtained by comparing the failure correlation degree with risk correlation threshold;Effect is to make maintenance auxiliary risk assessment result more comprehensive and accurate.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of nuclear power plants, more particularly, it relates to a maintenance assistance method and system for a nuclear power plant. BACKGROUND

[0002] During the operation of a nuclear power plant, equipment maintenance is a key link to ensure its safe and stable operation. The traditional maintenance assistance method for a nuclear power plant often evaluates the fault risk of the target maintenance area in isolation, without fully considering the influence of the operation of the associated area equipment on the target area, and also less considering factors such as environmental parameter fluctuation and safety system response efficiency, so that the maintenance assistance risk assessment is not comprehensive enough, and it cannot provide sufficient and reliable basis for maintenance decision-making, thereby affecting the efficiency and safety of the maintenance work of the nuclear power plant, and even increasing the potential risk of the operation of the nuclear power plant to a certain extent. SUMMARY

[0003] In view of the deficiencies in the prior art, the purpose of the present application is to provide a maintenance assistance method and system for a nuclear power plant.

[0004] To achieve the above-mentioned purpose, the present application provides the following technical solutions:

[0005] A maintenance assistance method for a nuclear power plant, the method comprising the following steps:

[0006] Obtaining multi-dimensional operation data of the equipment to be maintained in the nuclear power plant, and dividing the multi-dimensional operation data according to the equipment system level to obtain target maintenance area data and associated area data;

[0007] Performing operation parameter fluctuation characteristic and equipment failure correlation analysis on the target maintenance area data and the associated area data to obtain a risk correlation threshold;

[0008] Statistically analyzing the failure correlation between the target maintenance area data and the associated area data at the current maintenance stage, comparing the failure correlation with the risk correlation threshold, and obtaining maintenance risk warning or follow-up analysis information;

[0009] According to the follow-up analysis information, judging the parameter variation data set of the target maintenance area and the associated area affected by the intervention influence parameter variation, according to the parameter variation data set, judging the first failure risk probability of the target maintenance area, and according to the coupling relationship between the first failure risk probability and the maintenance operation complexity, obtaining the second target failure risk probability;

[0010] Judging the first associated failure risk probability of the associated area to which the associated area data belongs, and according to the first associated failure risk probability and the inter-regional coupling coefficient, obtaining the second associated failure risk probability;

[0011] The risk degree of the target maintenance area is determined according to the second target fault risk probability and the second associated fault risk probability, and a first comprehensive risk value is obtained;

[0012] The target maintenance area is subjected to maintenance auxiliary risk assessment according to the environmental parameter fluctuation condition, the safety system response efficiency and the first comprehensive risk value, and a maintenance auxiliary risk assessment result is obtained.

[0013] Preferably, multi-dimensional operation data of the nuclear power plant equipment to be maintained are acquired, the multi-dimensional operation data are regionally divided according to the equipment system level to obtain target maintenance area data and associated area data, and the method specifically comprises the following steps:

[0014] The multi-dimensional operation data of the nuclear power plant equipment to be maintained are acquired, wherein the multi-dimensional operation data include temperature, pressure, flow, vibration frequency and electrical parameters;

[0015] The multi-dimensional operation data are regionally divided according to the nuclear power plant equipment system level to obtain a regional data set;

[0016] The target maintenance area data are selected from the regional data set, and the associated area data having an operation coupling relationship are screened from the regional data set according to the equipment association relationship corresponding to the target maintenance area data.

[0017] Preferably, the risk association threshold is obtained by analyzing the operation parameter fluctuation characteristics and the equipment fault association degree of the target maintenance area data and the associated area data, and the method specifically comprises the following steps:

[0018] The historical target operation parameters and the historical associated operation parameters of the target maintenance area and the associated area are respectively acquired during a historical normal operation period;

[0019] The fault occurrence frequencies of the target maintenance area and the associated area are counted during a historical fault period, and historical fault frequency data are obtained;

[0020] The historical parameter influence coefficient is obtained by extracting the fluctuation influence of the historical associated operation parameters on the target operation parameters;

[0021] The historical fault inducing coefficient of each associated area fault on the target maintenance area fault is extracted from the historical fault frequency data;

[0022] The historical risk reference coefficient is obtained by performing weight distribution on the historical parameter influence coefficient and the historical fault inducing coefficient;

[0023] The historical abnormal risk coefficient of the target maintenance area and the associated area is respectively acquired during a historical major fault period;

[0024] The risk association threshold is obtained by calculating the risk coefficient normal fluctuation range according to the historical risk reference coefficient and the historical abnormal risk coefficient.

[0025] Preferably, the fault correlation degree between the target maintenance area data and the associated area data in the current maintenance stage is calculated, specifically including the following steps:

[0026] Real-time operation parameters of the target maintenance area and the associated area data are collected in the current maintenance stage;

[0027] The fault correlation degree between each associated area data and the target maintenance area data is calculated.

[0028] Preferably, the fault correlation degree is compared with the risk correlation threshold to obtain maintenance risk warning or follow-up analysis information, specifically including the following steps:

[0029] The area with the fault correlation degree greater than the risk correlation threshold is warned of maintenance risk, and the area is marked as a high-risk associated area;

[0030] If the fault correlation degree is less than the risk correlation threshold, a risk difference value data is calculated between the fault correlation degree and the risk correlation threshold;

[0031] If the risk difference value 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.

[0032] Preferably, according to the follow-up analysis information, a parameter variation data set of the target maintenance area and the associated area affected by the intervention influence parameter is judged, a first fault risk probability of the target maintenance area is judged according to the parameter variation data set, and a second target fault risk probability is obtained according to the coupling relationship between the first fault risk probability and the maintenance operation complexity, specifically including the following steps:

[0033] 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 a mapping relationship between the running parameter steady-state deviation value and the maintenance operation intervention is established, and a parameter variation data set affected by the operation intervention in a preset maintenance period is judged;

[0034] The first fault risk probability of the target maintenance area in the preset maintenance period is judged in combination with the parameter variation data set;

[0035] 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;

[0036] The second target fault risk probability is obtained according to the coupling relationship between the first fault risk probability and the maintenance operation complexity.

[0037] Preferably, the first associated failure risk probability of the associated region to which the associated region data belongs is determined, the second associated failure risk probability is obtained according to the first associated failure risk probability and the inter-region coupling coefficient, and the second associated failure risk probability is obtained by multiplying the first associated failure risk probability and the corresponding inter-region coupling coefficient.

[0038] The parameter variation data set of the associated region and the equipment characteristic parameter are input into a nuclear power plant failure probability model to obtain the first associated failure risk probability.

[0039] The inter-region coupling coefficients between the target maintenance region and each associated region in material flow, energy flow and information flow are calculated according to the topological relationship of the nuclear power plant equipment system.

[0040] The first associated failure risk probability and the corresponding inter-region coupling coefficient are multiplied to obtain the second associated failure risk probability.

[0041] Preferably, the target maintenance region is subjected to maintenance auxiliary risk assessment according to the environmental parameter fluctuation, the safety system response efficiency and the first comprehensive risk value, and a maintenance auxiliary risk assessment result is obtained, and the maintenance auxiliary risk assessment result is obtained by the following steps:

[0042] The environmental parameter fluctuation includes temperature, humidity and radiation dose of the target maintenance region.

[0043] The fluctuation amplitude of the environmental parameter fluctuation is obtained to obtain an environmental fluctuation coefficient.

[0044] The response time and the action accuracy of the corresponding safety system of the target maintenance region are tested, and the safety system response efficiency is calculated according to the response time and the action accuracy.

[0045] The risk correction coefficient is obtained according to the environmental fluctuation coefficient and the safety system response efficiency.

[0046] The first comprehensive risk value and the risk correction coefficient are multiplied to obtain the final risk assessment value of the target maintenance region.

[0047] The final risk assessment value and the nuclear power plant maintenance risk grade classification standard are used to determine the maintenance auxiliary risk assessment result.

[0048] A maintenance auxiliary system for a nuclear power plant comprises:

[0049] The division module obtains multi-dimensional operation data of the nuclear power plant equipment to be maintained, divides the multi-dimensional operation data according to the equipment system level to obtain target maintenance region data and associated region data.

[0050] The analysis module analyzes the running parameter fluctuation characteristics and the equipment failure correlation degree of the target maintenance region data and the associated region data to obtain a risk correlation threshold.

[0051] The comparison module compares the fault correlation degree between the target maintenance area data and the associated area data in the current maintenance stage, compares the fault correlation degree with a risk correlation threshold value, and obtains maintenance risk early warning or follow-up analysis information through judgment.

[0052] The first processing module judges 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, judges the first fault risk probability of the target maintenance area according to the parameter variation data set, and obtains the second target fault risk probability according to the coupling relationship between the first fault risk probability and the maintenance operation complexity.

[0053] The second processing module judges the first associated fault risk probability of the associated area to which the associated area data belongs, and obtains the second associated fault risk probability according to the first associated fault risk probability and the inter-area coupling coefficient.

[0054] The judgment module judges the risk degree of the target maintenance area according to the second target fault risk probability and the second associated fault risk probability, and obtains the first comprehensive risk value.

[0055] The evaluation module performs maintenance auxiliary risk evaluation on the target maintenance area according to the environmental parameter fluctuation, the safety system response efficiency and the first comprehensive risk value, and obtains a maintenance auxiliary risk evaluation result.

[0056] An electronic device comprises a memory, a processor and a computer program stored in the memory and executable on the processor, and the processor implements the maintenance auxiliary method for nuclear power plants when executing the program.

[0057] Compared with the prior art, the present application has the following beneficial effects:

[0058] The application lays a data foundation for subsequent analysis by obtaining multi-dimensional operation data of the equipment to be maintained in the nuclear power plant, and dividing the target maintenance area data and the associated area data according to the equipment system level, so that the maintenance personnel can clearly understand the operation data of different areas and facilitate targeted work. The risk correlation threshold is obtained by analyzing the operation parameter fluctuation characteristics and the equipment failure correlation degree of the target maintenance area data and the associated area data, so that the judgment of maintenance risk has a quantitative standard, and the scientificity and accuracy of risk judgment are improved, so that potential failure risks can be identified. The failure correlation degree between the target maintenance area data and the associated area data is calculated, and compared with the risk correlation threshold to judge the maintenance risk warning or further analysis information. If the failure correlation degree exceeds the threshold, the warning is sent in time to remind the maintenance personnel to take measures; if it is lower than the threshold, the potential risk is further explored through the further analysis information to ensure the timeliness and effectiveness of the maintenance decision. The first comprehensive risk value is obtained according to the second target failure risk probability and the second associated failure risk probability, which integrates the failure risk information of the target maintenance area and the associated area, realizes the comprehensive evaluation of the risk degree of the target maintenance area, and provides a key basis for subsequent maintenance auxiliary risk evaluation. The maintenance auxiliary risk evaluation of the target maintenance area is combined with the environmental parameter fluctuation, the safety system response efficiency and the first comprehensive risk value, the influence of environmental factors and safety system performance on maintenance risk is comprehensively considered, the maintenance auxiliary risk evaluation result is more comprehensive and accurate, and more reliable decision support is provided for the maintenance work of the nuclear power plant. BRIEF DESCRIPTION OF DRAWINGS

[0059] Fig. 1 A step schematic diagram of a maintenance auxiliary method for a nuclear power plant is provided for the application;

[0060] Fig. 2 A module schematic diagram of a maintenance auxiliary system for a nuclear power plant is provided for the application;

[0061] Fig. 3 A structure schematic diagram of an electronic device provided by an embodiment of the application.

[0062] 610, processor; 620, communication interface; 630, memory; 640, communication bus. DETAILED DESCRIPTION

[0063] In order to make the above-mentioned purposes, features and advantages of the application more obvious and easy to understand, the specific embodiments of the application will be described in detail below with reference to the drawings.

[0064] In the following description, many specific details are set forth in order to provide a thorough understanding of the application, but the application can also be implemented in other ways different from the description, and those skilled in the art can make similar generalizations without departing from the concept of the application, therefore the application is not limited to the specific embodiments disclosed below.

[0065] Second, the term "one embodiment" or "an embodiment" as may be used herein means a specific implementation, or example, that can include features that are, but are not required to be, included in at least one implementation of the present disclosure. This use of "in one embodiment" or "an embodiment" is simply used to provide a concrete example of features included in at least one implementation of the present disclosure. It can be used in the description of a feature, structure, or characteristic as being included in one or more implementations of the disclosure, and each variation thereof is a candidate for implementation. Failure to describe such features, structures, or characteristics in "in one embodiment" or "an embodiment" should not be regarded as preventing such features, structures, or characteristics from being claimed.

[0066] Referring to Figs. 1-3 as shown.

[0067] Embodiments further illustrate a method and system for maintenance assistance of a nuclear power plant proposed by the present disclosure.

[0068] A method for maintenance assistance of a nuclear power plant, the method comprising the steps of:

[0069] Obtaining multi-dimensional operation data of equipment to be maintained in the nuclear power plant, and dividing the multi-dimensional operation data according to an equipment system hierarchy to obtain target maintenance area data and associated area data;

[0070] Performing operation parameter fluctuation characteristic and equipment failure correlation degree analysis on the target maintenance area data and the associated area data to obtain a risk correlation threshold;

[0071] Statistically analyzing the failure correlation degree between the target maintenance area data and the associated area data in a current maintenance stage, comparing the failure correlation degree with the risk correlation threshold, and determining maintenance risk early warning or follow-up analysis information;

[0072] According to the follow-up analysis information, determining a parameter variation data set of a parameter variation of the target maintenance area and the associated area affected by intervention, determining a first failure risk probability of the target maintenance area according to the parameter variation data set, and determining a second target failure risk probability according to a coupling relationship between the first failure risk probability and a maintenance operation complexity;

[0073] Determining a first associated failure risk probability of the associated area to which the associated area data belongs, and determining a second associated failure risk probability according to the first associated failure risk probability and an inter-area coupling coefficient;

[0074] According to the second target failure risk probability and the second associated failure risk probability, determining a risk degree of the target maintenance area to obtain a first comprehensive risk value;

[0075] According to an environmental parameter fluctuation, a safety system response efficiency, and the first comprehensive risk value, performing maintenance assistance risk assessment on the target maintenance area to obtain a maintenance assistance risk assessment result.

[0076] The device basic information includes device position number, device name and unit number; the device local wiring information includes local wiring box position number, terminal row number and terminal number; the associated device information includes main device position number and main device name associated with the device;

[0077] Obtaining multi-dimensional operation data of a nuclear power plant device to be maintained, and dividing the multi-dimensional operation data according to a device system level to obtain target maintenance region data and associated region data, specifically including the following steps:

[0078] Obtaining multi-dimensional operation data of a nuclear power plant device to be maintained, wherein the multi-dimensional operation data includes temperature, pressure, flow, vibration frequency and electrical parameters;

[0079] Dividing the multi-dimensional operation data according to a nuclear power plant device system level to obtain a region data set;

[0080] Selecting target maintenance region data from the region data set, and filtering associated region data having a running coupling relationship from the region data set according to a device association relationship corresponding to the target maintenance region data.

[0081] The application first obtains multi-dimensional operation data of a nuclear power plant device to be maintained, which includes temperature, pressure, flow and vibration frequency. Temperature can reflect the thermal state of the device, pressure is related to the running condition of the fluid system, flow reflects the medium conveying capacity, vibration frequency is used to determine whether the mechanical operation of the device is normal, and electrical parameters are related to the power supply and electrical performance of the device. The multi-dimensional operation data is divided according to the hierarchical structure of the nuclear power plant device system to form a region data set. Target maintenance region data is selected from the region data set, for example, a main pump of the nuclear power plant is to be maintained, and the data of the region where the main pump is located is determined as the target maintenance region data. Then, associated region data having a running coupling relationship is filtered from the region data set according to a device association relationship corresponding to the target maintenance region data. Taking the main pump as an example, the main pump has a running coupling relationship with valves and pipelines of the cooling system, and the data of the regions where the valves and pipelines are located is filtered out as the associated region data.

[0082] Analyzing the running parameter fluctuation characteristics and the device fault association degree of the target maintenance region data and the associated region data to obtain a risk association threshold, specifically including the following steps:

[0083] Obtaining historical target operation parameters and historical associated operation parameters of the target maintenance region and the associated region during a historical normal running period;

[0084] Statistically obtaining the fault occurrence frequency of the target maintenance region and the associated region during a historical fault period to obtain historical fault frequency data;

[0085] extracting the influence of the historical associated operation parameters on the fluctuation of the target operation parameter to obtain a historical parameter influence coefficient;

[0086] extracting the historical failure induction coefficient of each associated region failure on the target maintenance region failure from the historical failure frequency data;

[0087] weighting the historical parameter influence coefficient and the historical failure induction coefficient to obtain a historical risk reference coefficient;

[0088] obtaining the historical abnormal risk coefficient of the target maintenance region and the associated region during the historical major failure period;

[0089] calculating the risk coefficient normal fluctuation range according to the historical risk reference coefficient and the historical abnormal risk coefficient to obtain a risk correlation threshold.

[0090] First, focus on the historical operation stage of the nuclear power plant equipment. During the historical normal operation period, obtain the relevant operation parameters of the target maintenance region and the associated region. If the target maintenance region is the region where the nuclear reactor coolant pump is located, the historical target operation parameters include the speed and power of the coolant pump, which directly reflect the operating state of the coolant pump. If the associated region is the pipeline region connected to the coolant pump, the historical associated operation parameters include the pressure and flow of the coolant in the pipeline, which are used to reflect the influence factors of the associated region on the target region.

[0091] During the historical failure period, count the failure occurrence frequency of the target maintenance region and the associated region to form historical failure frequency data. For example, the number of times of bearing damage failure of the coolant pump in the past 5 years is 10, and the number of times of pipeline failure due to corrosion leakage is 8. These data can intuitively present the frequency of failure occurrence.

[0092] Extract the influence of the historical associated operation parameters on the fluctuation of the target operation parameter to obtain a historical parameter influence coefficient. Suppose that when the coolant flow in the pipeline fluctuates ±10% within the normal range, the speed of the coolant pump will fluctuate ±5%. Through quantitative calculation of a large number of such fluctuation data, the historical parameter influence coefficient is determined to measure the degree of influence of the fluctuation of the associated parameter on the target parameter.

[0093] Extract the historical failure induction coefficient of each associated region failure on the target maintenance region failure from the historical failure frequency data. For example, it is found that the coolant pump has been overloaded 3 times due to the pipeline blockage failure in the past, and the pipeline blockage failure has occurred a total of 15 times. Therefore, the historical failure induction coefficient of the pipeline blockage failure on the coolant pump overload failure can be calculated as 3 ÷ 15 = 0.2.

[0094] The historical risk reference coefficient is obtained by weight distribution of the historical parameter influence coefficient and the historical failure inducing coefficient. Different weights are needed to be distributed due to the difference in the importance of different parameter influence and failure inducing in risk assessment. For example, if the failure inducing is considered to be more critical to the risk, the historical failure inducing coefficient is given a weight of 0.6, and the historical parameter influence coefficient is given a weight of 0.4. If the historical parameter influence coefficient is 0.3, and the historical failure inducing coefficient is 0.2, then the historical risk reference coefficient is 0.3*0.4+0.2*0.6=0.24, which reflects the proportion of different factors in the risk composition.

[0095] The historical abnormal risk coefficient of the target maintenance area and the associated area is obtained during the period of historical major failure. For example, when a serious coolant leakage occurs, the parameters of the coolant pump and the related pipeline area will deviate greatly from the normal range. Through the analysis and quantification of the risk situation under such a major failure scenario, the historical abnormal risk coefficient of the coolant pump area is determined to be 0.8, and the historical abnormal risk coefficient of the associated pipeline area is determined to be 0.7. These coefficients can reflect the risk characteristics under the major failure scenario.

[0096] The normal fluctuation range of the risk coefficient is calculated according to the historical risk reference coefficient and the historical abnormal risk coefficient, so as to obtain the risk correlation threshold. For example, combined with the historical risk reference coefficient 0.24 and the related data of the historical abnormal risk coefficient, the normal fluctuation range of the risk coefficient is calculated to be 0.1-0.3 by statistical analysis method, and the risk correlation threshold is set to be 0.3. The actual statistical failure correlation degree is compared with the risk correlation threshold, so as to judge whether the maintenance risk warning operation needs to be issued, and to provide a key judgment standard for the maintenance risk assessment of the nuclear power plant.

[0097] The failure correlation degree between the target maintenance area data and the associated area data is calculated in the current maintenance stage, which includes the following steps:

[0098] The real-time running parameters of the target maintenance area and the associated area data are collected in real time in the current maintenance stage.

[0099] The failure correlation degree between each associated area data and the target maintenance area data is calculated.

[0100] In the current maintenance stage, real-time operation parameters of the target maintenance area and the associated area data of the nuclear power plant equipment are collected in real time, and then the fault correlation degree between the associated area data and the target maintenance area data is calculated. For example, assuming that the target maintenance area is the main pump area of the nuclear reactor, and the associated area is the cooling pipeline area connected with the main pump. First, the rotation speed, power, and vibration amplitude of the main pump area are obtained in real time through various sensors and monitoring equipment, and the coolant pressure, flow rate, and temperature of the cooling pipeline area are also obtained in real time. The real-time operation parameters of the main pump area and the cooling pipeline area are calculated using a machine learning algorithm. When the coolant flow rate in the cooling pipeline changes, the rotation speed and vibration amplitude of the main pump will change accordingly, and the fault correlation degree between the cooling pipeline area data and the main pump area data is calculated to determine the degree of correlation between the two in terms of faults, thereby providing a basis for subsequent maintenance risk assessment.

[0101] The fault correlation degree is compared with the risk correlation threshold to obtain maintenance risk warning or follow-up analysis information, including the following steps:

[0102] If the fault correlation degree is greater than the risk correlation threshold, a maintenance risk warning is given for the area corresponding to the risk correlation threshold, and the area is marked as a high-risk associated area.

[0103] 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 risk difference data.

[0104] 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.

[0105] In the maintenance scenario of the nuclear power plant, the risk status of the maintenance related area is determined by comparing the fault correlation degree and the risk correlation threshold.

[0106] If the target maintenance area is the main pump area of the nuclear reactor of the nuclear power plant, and the associated area is the cooling pipeline area connected with it. Assuming that the pre-set risk correlation threshold is 0.8, and the set risk difference threshold is 0.05.

[0107] If the calculated fault correlation degree between the cooling pipeline area and the main pump area is 0.9, since 0.9 is greater than the risk correlation threshold 0.8, a maintenance risk warning is given for the cooling pipeline area, and the cooling pipeline area is marked as a high-risk associated area. The purpose of this operation is to timely remind the maintenance personnel that there is a high fault correlation risk in this area, which needs to be paid attention to and prioritized, so as to prevent more serious consequences.

[0108] If the fault correlation degree is less than the risk correlation threshold, such as the fault correlation degree is 0.7, at this time, the difference between the fault correlation degree and the risk correlation threshold is calculated, that is, 0.8-0.7=0.1, and the risk difference data is obtained as 0.1. Compare the risk difference data 0.1 with the risk difference threshold 0.05, because 0.1 is greater than 0.05, it is determined that the target maintenance area exists potential risk. Output the follow-up analysis information for the main pump area and the associated cooling pipeline area.

[0109] 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 judged, the first fault risk probability of the target maintenance area is judged according to the parameter variation data set, and the second target fault risk probability is obtained according to the coupling relationship between the first fault risk probability and the maintenance operation complexity. Specifically, the following steps are included:

[0110] 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 counted, 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 judged;

[0111] The first fault risk probability of the target maintenance area in the preset maintenance period is judged in combination with the parameter variation data set;

[0112] 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;

[0113] The second target fault risk probability is obtained according to the coupling relationship between the first fault risk probability and the maintenance operation complexity.

[0114] If the target maintenance area is the turbine area of the nuclear power plant, and the associated area is the steam pipeline area associated with it. According to the follow-up analysis information, the running parameter steady-state deviation value of the turbine area and the steam pipeline area in the current maintenance stage is counted, such as the steady-state deviation value of the turbine speed and the steady-state deviation value of the steam pressure in the steam pipeline. The mapping relationship between these running parameter steady-state deviation values and the maintenance operation intervention is established, the parameter variation data set affected by these maintenance operation interventions in the preset maintenance period is judged by judging the maintenance operation intervention under the condition of similar parameter deviation in the past, so as to determine how the parameters of turbine speed and steam pipeline pressure will change after the maintenance operation is implemented.

[0115] The first fault risk probability of the turbine area in the preset maintenance period is judged in combination with the parameter variation data set. For example, according to the variation range and amplitude of the turbine speed in the parameter variation data set, and the fault condition of the turbine under the condition of similar speed variation in the history, the first fault risk probability is calculated as 0.2.

[0116] The number of maintenance operation steps is counted after the maintenance operation task is decomposed, assuming that there are 10 steps such as disassembly, inspection, replacement of parts, and assembly in steam turbine maintenance; it is clear that some steps need to be performed in a specific temperature and pressure environment, and the accuracy requirement is very high; if some of the personnel performing the maintenance are not familiar enough with the high-end maintenance technology of the steam turbine, and considering these factors, the maintenance operation complexity coefficient is obtained by table lookup and comparison, which is 0.6.

[0117] The second target failure risk probability is obtained according to the coupling relationship between the first failure risk probability and the maintenance operation complexity. For example, the formula is 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. The second target failure risk probability is calculated to be 0.32 by substituting the values into the formula.

[0118] The first associated failure risk probability of the associated region data belonging to the associated region is judged, and the second associated failure risk probability is obtained according to the first associated failure risk probability and the inter-regional coupling coefficient, which specifically includes the following steps:

[0119] The parameter variation data set and the equipment characteristic parameter of the associated region are input into the nuclear power plant failure probability model to obtain the first associated failure risk probability;

[0120] The inter-regional coupling coefficient between the target maintenance region and each associated region in the material flow, energy flow and information flow is calculated according to the topological relationship of the nuclear power plant equipment system;

[0121] The first associated failure risk probability is multiplied by the corresponding inter-regional coupling coefficient to obtain the second associated failure risk probability.

[0122] The failure risk probability of the associated region is calculated through the topological relationship of the nuclear power plant equipment system and the relevant data of the associated region. If the target maintenance region is the reactor pressure vessel region of the nuclear power plant, and the associated region is the control rod drive mechanism region associated with it. First, the parameter variation data set of the control rod drive mechanism region (such as displacement variation data of the control rod, variation data of the drive current) and the equipment characteristic parameter (such as the material of the control rod, the 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 nuclear power plant historical failure data and equipment operation rules, and can calculate the first associated failure risk probability of the control rod drive mechanism region according to the input parameters. Assuming that the first associated failure risk probability obtained is 0.25.

[0123] According to the nuclear power plant equipment system topology relationship, the inter-regional coupling coefficient between the reactor pressure vessel region and the control rod drive mechanism region in the material flow, energy flow and information flow is calculated. From the material flow, the movement of the control rod in the reactor pressure vessel will affect the reaction of the material in the reactor; the energy transfer of the control rod drive mechanism will affect the energy output of the reactor; the position information of the control rod will be transmitted to the reactor control system. The inter-regional coupling coefficient is 0.8 calculated by comprehensively considering these factors.

[0124] The first associated failure risk probability 0.25 is multiplied by the corresponding inter-regional coupling coefficient 0.8 to obtain the second associated failure risk probability 0.25x0.8=0.2, which reflects the influence degree of the associated region failure risk on the target maintenance region.

[0125] According to the environmental parameter fluctuation, the safety system response efficiency and the first comprehensive risk value, the maintenance auxiliary risk assessment of the target maintenance region is carried out, and the maintenance auxiliary risk assessment result is obtained, which specifically includes the following steps:

[0126] The environmental parameter fluctuation includes the temperature, humidity and radiation dose of the target maintenance region;

[0127] The fluctuation range of the environmental parameter fluctuation is obtained to obtain the environmental fluctuation coefficient;

[0128] The response time and action accuracy of the safety system corresponding to the target maintenance region are tested, and the safety system response efficiency is calculated according to the response time and action accuracy;

[0129] The risk correction coefficient is obtained according to the environmental fluctuation coefficient and the safety system response efficiency;

[0130] The first comprehensive risk value is multiplied by the risk correction coefficient to obtain the final risk assessment value of the target maintenance region;

[0131] The final risk assessment value and the nuclear power plant maintenance risk grade division standard are used to determine the maintenance auxiliary risk assessment result.

[0132] The environmental parameters and the safety system response efficiency are comprehensively considered to evaluate the maintenance auxiliary risk of the target maintenance area. Firstly, the fluctuation of the environmental parameters including the temperature, humidity and radiation dose of the target maintenance area is determined. For example, the normal range of the temperature is 20-25℃, the weight is 0.4; the normal range of the humidity is 40%-50%, the weight is 0.3; and the normal range of the radiation dose is 0-50μSv / h, the weight is 0.3. In the current maintenance stage, the actual fluctuation range of the temperature is 22-28℃, the normal range span is 5℃, the actual fluctuation span is 6℃, the ratio is 6÷5=1.2; the actual fluctuation range of the humidity is 35%-55%, the normal range span is 10%, the actual fluctuation span is 20%, the ratio is 20÷10=2; and the actual fluctuation range of the radiation dose is 0-60μSv / h, the normal range span is 50μSv / h, the actual fluctuation span is 60μSv / h, the ratio is 60÷50=1.2. Then, the fluctuation ratio of each parameter is multiplied by the corresponding weight and summed, i.e. the temperature is 1.2×0.4=0.48, the humidity is 2×0.3=0.6, and the radiation dose is 1.2×0.3=0.36. Therefore, the total fluctuation coefficient is 0.48+0.6+0.36=1.44, which comprehensively reflects the influence of the fluctuation amplitude of each environmental parameter on the risk of the maintenance area.

[0133] For example, the fire alarm safety system of the target maintenance area of the nuclear power plant is taken as an example. Firstly, the simulation fire scene test is carried out, the fire is simulated in the target maintenance area, the fire alarm safety system is triggered, the time from the generation of the fire signal to the issuance of the alarm instruction and the start of the related fire extinguishing equipment is recorded, which is the response time. Assuming that the average response time is 3 seconds after multiple tests. Then, the action of the safety system in the simulation test is observed, and the number of times of accurate identification of the fire and correct start of the fire extinguishing equipment is counted, for example, 95 times of accurate action are obtained in 100 times of simulation test, and the action accuracy is 95÷100=95%. The response efficiency of the safety system is calculated according to the response time and the action accuracy, and the calculation formula is safety system response efficiency=(1-proportion of response time exceeding standard time)×action accuracy. Assuming that the standard response time of the safety system is 2 seconds, the average response time is 3 seconds, the standard time is exceeded by 1 second, and the exceeding proportion is 1÷2=0.5. Therefore, the response efficiency of the 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.

[0134] The risk correction coefficient is calculated according to the environmental fluctuation coefficient and the response efficiency of the safety system, for example, the risk correction coefficient = environmental fluctuation coefficient * (1-discount rate of the response efficiency of the safety system), and the deviation of the response time and the action accuracy in the ideal state in the past actual or simulated scenarios is viewed. If the average response time of a target maintenance area of a nuclear power plant is 10% slower than the design standard in multiple historical tests, and the action accuracy is 8% lower than the design value. Considering the aging degree and maintenance situation of the safety system, if the system has been in use for a long time and the recent maintenance is not up to standard, it will affect its performance, and the influence degree of these factors on the response efficiency of the safety system is quantified and valued. Assuming that the discount rate caused by the deviation of the response time is 5%, the discount rate caused by the deviation of the action accuracy is 6%, and the additional discount rate caused by the aging and insufficient maintenance of the system is 4%, then the discount rate of the response efficiency of the safety system is 5%+6%+4%=15%.

[0135] The first comprehensive risk value is multiplied by the risk correction coefficient to obtain the final risk assessment value of the fuel assembly maintenance area. According to the final risk assessment value and the nuclear power plant maintenance risk grade division standard, the maintenance auxiliary risk assessment result of the fuel assembly maintenance area is determined, for example, the nuclear power plant maintenance risk grade division standard stipulates that 0.5-0.6 is the medium risk grade, thereby providing a basis for maintenance decision.

[0136] A maintenance auxiliary system for a nuclear power plant comprises:

[0137] The division module obtains multi-dimensional operation data of the nuclear power plant equipment to be maintained, and divides the multi-dimensional operation data according to the equipment system level to obtain target maintenance area data and associated area data;

[0138] The analysis module analyzes the running parameter fluctuation characteristics and the fault correlation degree of the target maintenance area data and the associated area data to obtain a risk correlation threshold;

[0139] The comparison module compares the fault correlation degree between the target maintenance area data and the associated area data in the current maintenance stage, compares the fault correlation degree with the risk correlation threshold, and judges to obtain maintenance risk warning or follow-up analysis information;

[0140] The first processing module judges 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, judges the first fault risk probability of the target maintenance area according to the parameter variation data set, and obtains the second target fault risk probability according to the coupling relationship between the first fault risk probability and the maintenance operation complexity;

[0141] The second processing module judges the first associated fault risk probability of the associated area to which the associated area data belongs, and obtains the second associated fault risk probability according to the first associated fault risk probability and the inter-regional coupling coefficient.

[0142] The judging module judges the risk degree of the target maintenance area according to the second target fault risk probability and the second associated fault risk probability to obtain a first comprehensive risk value;

[0143] The evaluation module evaluates the maintenance auxiliary risk of the target maintenance area according to the environmental parameter fluctuation, the safety system response efficiency and the first comprehensive risk value to obtain a maintenance auxiliary risk evaluation result.

[0144] An electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements a maintenance assistance method for a nuclear power plant when executing the program.

[0145] As shown in Fig. 3 The electronic device can 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 through the communication bus 640. The processor 610 can invoke the logical instructions in the memory 630 to execute a maintenance assistance method for a nuclear power plant.

[0146] In addition, the logical instructions in the memory 630 described above can be implemented in the form of a software function unit and sold or used as an independent product, which can be stored in a computer-readable storage medium. Based on such understanding, the technical solutions of the present application essentially or the part that contributes to the prior art or part of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium, and includes several instructions to make a computer device (which can be a personal computer, a server, or a network device, etc.) execute all or part of the steps of the methods described in the embodiments of the present application. The aforementioned storage medium includes: a U disk, a mobile hard disk, a read-only memory, a random access memory, a magnetic disk or an optical disk, and various media that can store program codes.

[0147] On the other hand, the present application also provides a computer program product, which includes a computer program, the computer program can be stored on a non-transitory computer readable storage medium, and the computer program is executed by a processor, and the computer can execute a maintenance assistance method for a nuclear power plant.

[0148] In yet another aspect, the present application also provides a non-transitory computer readable storage medium having a computer program stored thereon, and the computer program is executed by a processor to implement a maintenance assistance method for a nuclear power plant.

[0149] The device embodiments described above are merely illustrative, wherein the units described as separate components can or can not be physically separate, and the components displayed as units can or can not be physical units, i.e., can be located in one place, or can be distributed to multiple network units. Part or all of the modules can be selected to achieve the purposes of the embodiments according to actual needs. Those skilled in the art can understand and implement without creative labor.

[0150] Through the description of the above embodiments, those skilled in the art can clearly understand that the embodiments can be realized by means of software and the necessary general hardware platform, and of course can also be realized by hardware. Based on such understanding, the above technical solutions can be embodied in the form of a software product, which can be stored in a computer readable storage medium, such as a ROM / RAM, a magnetic disk, an optical disk, etc., and includes a number of instructions to make a computer device (which can be a personal computer, a server, or a network device, etc.) execute the methods described in each embodiment or some parts of the embodiments.

[0151] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present application, and not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: it can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement for part of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A maintenance auxiliary method for nuclear power plants, characterized in that, The method includes 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.

2. The maintenance assistance method for nuclear power plants according to claim 1, characterized in that, 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 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.

3. A maintenance auxiliary method for nuclear power plants according to claim 2, characterized in that, 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.

4. A maintenance assistance method for nuclear power plants according to claim 3, characterized in that, 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.

5. A maintenance auxiliary method for nuclear power plants according to claim 4, characterized in that, 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.

6. A maintenance auxiliary method for nuclear power plants according to claim 5, characterized in that, 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. Specifically, the following steps are included: Based on the analysis of information, the steady-state deviation values ​​of operating parameters of the target maintenance area and related areas in the current maintenance stage are statistically analyzed, and a mapping relationship between the steady-state deviation values ​​of 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.

7. A maintenance assistance method for nuclear power plants according to claim 6, characterized in that, 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.

8. A maintenance auxiliary method for nuclear power plants according to claim 7, characterized in that, 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: 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.

9. A maintenance support system for a nuclear power plant, applied to the maintenance support method for a nuclear power plant as described in any one of claims 1 to 8, characterized in that, include: 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: Performs correlation analysis on the fluctuation characteristics of operating parameters 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.

10. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements a maintenance assistance method for a nuclear power plant as described in any one of claims 1 to 8.

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