Method and device for evaluating predictive health state of nuclear power equipment, equipment and medium

Abstract

The invention relates to the technical field of nuclear power, in particular to a predictive health state assessment method and device for nuclear power equipment, the equipment and a medium. According to the predictive health state assessment method for the nuclear power equipment, multiple groups of nuclear power measurement data associated with the target nuclear power equipment need to be collected according to multiple detection units configured by the target nuclear power equipment; calculating a branch evaluation confidence coefficient corresponding to the target nuclear power equipment based on each piece of nuclear power measurement data; performing combined fault assessment based on the assessment confidence of each branch to determine a combined assessment confidence corresponding to the target nuclear power equipment; and performing equipment health assessment according to each combination assessment confidence coefficient corresponding to the target nuclear power equipment, and determining health state assessment data of the target nuclear power equipment. Through the method, the health state of the target nuclear power equipment is determined through branch confidence evaluation and combined confidence evaluation, and the accuracy and consistency of health state evaluation are improved.

Description

Technical Field

[0001] This application relates to the field of nuclear power technology, and in particular, to a method and device, equipment, and medium for predictive health status assessment of nuclear power equipment. Background Art

[0002] With the popularization of sensor applications and the development of data analysis technology, the nuclear power industry has developed a technology for evaluating the health status of nuclear power equipment by detecting the physical state data of equipment and using data analysis means to improve the maintenance strategy of nuclear power plants.

[0003] Traditional methods for predictive health status assessment of nuclear power equipment highly rely on the accumulation of manual experience and professional knowledge. Due to the complex and diverse operating states and equipment failure modes of nuclear power equipment, it is often difficult to accurately evaluate the health status of equipment only by manual judgment. In addition, manual judgment is easily affected by subjective factors, resulting in inconsistent and unreliable evaluation results of the health status of nuclear power equipment. Therefore, how to improve the effect of predictive health status assessment of nuclear power equipment has become an urgent problem in the industry. Summary of the Invention

[0004] This application aims to at least solve one of the technical problems existing in the prior art. For this purpose, this application proposes a method and device, equipment, and medium for predictive health status assessment of nuclear power equipment, which can improve the effect of predictive health status assessment of nuclear power equipment.

[0005] The method for predictive health status assessment of nuclear power equipment according to the first aspect embodiment of this application includes:

[0006] Collect multiple sets of nuclear power measurement data associated with the target nuclear power equipment according to multiple detection units configured for the target nuclear power equipment; wherein, each detection unit is used to collect a set of the nuclear power measurement data;

[0007] Calculate the branch evaluation confidence corresponding to the target nuclear power equipment based on each nuclear power measurement data;

[0008] Perform combined fault assessment based on the branch evaluation confidences to determine the combined evaluation confidence corresponding to the target nuclear power equipment;

[0009] Perform equipment health assessment according to the combined evaluation confidences corresponding to the target nuclear power equipment, and determine the health status assessment data of the target nuclear power equipment.

[0010] According to some embodiments of this application, the target nuclear power equipment has multiple equipment failure modes, and the calculating the branch evaluation confidence corresponding to the target nuclear power equipment based on each nuclear power measurement data includes:

[0011] Based on each of the nuclear power measurement data, calculate the branch evaluation confidence corresponding to the target nuclear power equipment under various equipment failure modes;

[0012] Performing a combined fault evaluation based on each of the branch evaluation confidences to determine the combined evaluation confidence corresponding to the target nuclear power equipment, including:

[0013] For each of the equipment failure modes, perform a combined fault evaluation based on the branch evaluation confidences corresponding to the equipment failure mode to determine the combined evaluation confidence corresponding to the target nuclear power equipment under the equipment failure mode.

[0014] According to some embodiments of the present application, the calculating the branch evaluation confidence corresponding to the target nuclear power equipment under various equipment failure modes based on each of the nuclear power measurement data includes:

[0015] In the case where the nuclear power measurement data is discrete type data, determine the fault credibility and fault incredibility corresponding to the nuclear power measurement data under various equipment failure modes;

[0016] For the fault credibility and the fault incredibility corresponding to each of the equipment failure modes, calculate the branch evaluation confidence corresponding to the target nuclear power equipment under various equipment failure modes.

[0017] According to some embodiments of the present application, the determining the fault credibility and fault incredibility corresponding to the nuclear power measurement data under various equipment failure modes includes:

[0018] Obtain the prior fault probability of the target nuclear power equipment under various equipment failure modes; wherein, the prior fault probability is used to characterize the probability of the target nuclear power equipment having a failure in the past;

[0019] For various equipment failure modes, substitute the corresponding prior fault probability and the nuclear power measurement data into a pre-constructed credibility analytical formula for calculation to obtain the fault credibility corresponding to each equipment failure mode;

[0020] For various equipment failure modes, substitute the corresponding prior fault probability and the nuclear power measurement data into a pre-constructed incredibility analytical formula for calculation to obtain the fault incredibility corresponding to each equipment failure mode.

[0021] According to some embodiments of the present application, the method further includes pre-constructing the credibility analytical formula and the incredibility analytical formula, specifically including:

[0022] Determine the fault evidence factors corresponding to various equipment failure modes;

[0023] Obtain the conditional failure probability corresponding to each of the device failure modes; wherein, the conditional failure probability is used to characterize the probability of failure of the target nuclear power equipment in the case where the failure evidence factor appears.

[0024] For each of the device failure modes, construct the credibility analytical formula based on the failure evidence factor and the prior failure probability.

[0025] For each of the device failure modes, construct the incredibility analytical formula based on the failure evidence factor and the prior failure probability.

[0026] According to some embodiments of the present application, for each of the device failure modes, perform combined failure assessment based on the confidence levels evaluated for each branch corresponding to the device failure mode to determine the combined assessment confidence level corresponding to the target nuclear power equipment in the device failure mode, including:

[0027] Obtain the confidence level combination analytical formula pre-constructed for each of the device failure modes.

[0028] For each of the device failure modes, sort the confidence levels evaluated for each branch corresponding to the device failure mode to obtain a branch confidence level sequence.

[0029] Select the target branch evaluation confidence level from the branch confidence level sequence, and substitute the target branch evaluation confidence level into the confidence level combination analytical formula to calculate the combined confidence level, obtaining a candidate combined confidence level.

[0030] In response to the existence of the branch evaluation confidence levels that have not participated in the combined confidence level calculation in the branch confidence level sequence, re-select the target branch evaluation confidence level from the branch evaluation confidence levels that have not participated in the combined confidence level calculation, and substitute the candidate combined confidence level and the target branch evaluation confidence level into the confidence level combination analytical formula to update the candidate combined confidence level.

[0031] After updating the candidate combined confidence level, return to execute in response to the existence of the branch evaluation confidence levels that have not participated in the combined confidence level calculation in the branch confidence level sequence until all the branch evaluation confidence levels in the branch confidence level sequence have participated in the combined confidence level calculation, and determine the latest candidate combined confidence level as the combined assessment confidence level.

[0032] According to some embodiments of the present application, perform equipment health assessment based on the combined assessment confidence levels corresponding to the target nuclear power equipment to determine the health status assessment data of the target nuclear power equipment, including:

[0033] Obtain the device health assessment weights preset for each of the device failure modes;

[0034] Estimate the device health status based on the combined evaluation confidence corresponding to each device failure mode and the device health assessment weight to obtain the health status assessment data of the target nuclear power device.

[0035] According to some embodiments of the present application, calculating the branch evaluation confidence corresponding to the target nuclear power device based on each nuclear power measurement data includes:

[0036] When the nuclear power measurement data is continuous type data, select a target conversion function from a preset plurality of numerical conversion functions for the nuclear power measurement data;

[0037] Perform fitting processing on the nuclear power measurement data through the target conversion function to obtain the branch evaluation confidence corresponding to the target nuclear power device.

[0038] According to some embodiments of the present application, the preset plurality of numerical conversion functions include a high-value interval conversion function, a medium-value interval conversion function, and a low-value interval conversion function. Performing fitting processing on the nuclear power measurement data through the target conversion function to obtain the branch evaluation confidence corresponding to the target nuclear power device includes:

[0039] In response to the target conversion function being the high-value interval conversion function, perform fitting processing on the nuclear power measurement data through the high-value interval conversion function to obtain the branch evaluation confidence corresponding to the target nuclear power device;

[0040] In response to the target conversion function being the medium-value interval conversion function, perform fitting processing on the nuclear power measurement data through the medium-value interval conversion function to obtain the branch evaluation confidence corresponding to the target nuclear power device;

[0041] In response to the target conversion function being the low-value interval conversion function, perform fitting processing on the nuclear power measurement data through the low-value interval conversion function to obtain the branch evaluation confidence corresponding to the target nuclear power device.

[0042] According to the evaluation device of the second aspect embodiment of the present application, it includes:

[0043] A plurality of detection units configured on the target nuclear power device, each detection unit is used to collect a set of the nuclear power measurement data;

[0044] A branch confidence evaluation unit for calculating the branch evaluation confidence corresponding to the target nuclear power device based on each nuclear power measurement data;

[0045] A combined confidence evaluation unit is used to perform combined fault evaluation based on the confidence levels evaluated by each of the branches to determine the combined evaluation confidence level corresponding to the target nuclear power equipment;

[0046] An equipment health evaluation unit is used to perform equipment health evaluation based on each of the combined evaluation confidence levels corresponding to the target nuclear power equipment to determine the health status evaluation data of the target nuclear power equipment.

[0047] In a third aspect, an embodiment of the present application provides an electronic device, including: a memory and a processor, where the memory stores a computer program, and when the processor executes the computer program, it implements the nuclear power equipment predictive health status evaluation method according to any one of the embodiments in the first aspect of the present application.

[0048] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, where the storage medium stores a program, and when the program is executed by a processor, it implements the nuclear power equipment predictive health status evaluation method according to any one of the embodiments in the first aspect of the present application.

[0049] The nuclear power equipment predictive health status evaluation method, device, equipment, and medium according to the embodiments of the present application at least have the following beneficial effects:

[0050] According to the nuclear power equipment predictive health status evaluation method of the present application, it is necessary to first collect multiple sets of nuclear power measurement data associated with the target nuclear power equipment according to multiple detection units configured for the target nuclear power equipment; wherein, each detection unit is used to collect a set of nuclear power measurement data; based on each nuclear power measurement data, calculate the branch evaluation confidence level corresponding to the target nuclear power equipment; perform combined fault evaluation based on each branch evaluation confidence level to determine the combined evaluation confidence level corresponding to the target nuclear power equipment; perform equipment health evaluation according to each combined evaluation confidence level corresponding to the target nuclear power equipment to determine the health status evaluation data of the target nuclear power equipment. Through this method, the health status evaluation of the target nuclear power equipment no longer solely depends on manual experience and professional knowledge, but through the methods of branch confidence evaluation and combined confidence evaluation, the accuracy and consistency of the health status evaluation are improved, and the effect of the nuclear power equipment predictive health status evaluation is also correspondingly improved. This not only solves the subjectivity problem existing in the traditional method, but also enhances the comprehensiveness and reliability of the evaluation by integrating multi-source data, thereby providing more effective support for the operation safety and maintenance management of nuclear power plants.

[0051] The additional aspects and advantages of the present application will be partially given in the following description, partially become obvious from the following description, or be understood through the practice of the present application. Description of the Drawings

[0052] The above and / or additional aspects and advantages of the present application will become apparent and be readily understood from the description of the embodiments in conjunction with the following drawings, where:

[0053] Figure 1 It is a schematic flowchart of the method for predicting the health state assessment of nuclear power equipment provided by the embodiment of the present application;

[0054] Figure 2 It is another schematic flowchart of the method for predicting the health state assessment of nuclear power equipment provided by the embodiment of the present application;

[0055] Figure 3 It is a schematic flowchart of step S201 in the embodiment of the present application;

[0056] Figure 4 It is a schematic flowchart of pre-constructing the credibility analytical formula and the non-credibility analytical formula in the embodiment of the present application;

[0057] Figure 5 It is a schematic flowchart of step S102 in the embodiment of the present application;

[0058] Figure 6 It is another schematic flowchart of the method for predicting the health state assessment of nuclear power equipment provided by the embodiment of the present application;

[0059] Figure 7 It is another schematic flowchart of the method for predicting the health state assessment of nuclear power equipment provided by the embodiment of the present application;

[0060] Figure 8 It is a schematic flowchart of step S104 in the embodiment of the present application;

[0061] Figure 9 It is a schematic structural diagram of the device for predicting the health state assessment of nuclear power equipment provided by the embodiment of the present application;

[0062] Figure 10 It is a schematic hardware structure diagram of the electronic device provided by the embodiment of the present application. Detailed Description of the Embodiment

[0063] The embodiments of the present application will be described in detail below. The examples of the embodiments are shown in the drawings, where the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions from beginning to end. The embodiments described below by referring to the drawings are exemplary and are only used to explain the present application and should not be construed as a limitation to the present application.

[0064] In the description of this application, "several" means one or more, "multiple" means more than two, and terms such as "greater than", "less than", "exceeding", etc. are understood not to include the corresponding number, while terms such as "above", "below", "within", etc. are understood to include the corresponding number. If there is a description of "first" and "second", it is only for the purpose of distinguishing technical features and cannot be understood as indicating or implying relative importance or implicitly indicating the quantity of the indicated technical features or implicitly indicating the sequence relationship of the indicated technical features.

[0065] In the description of this application, it should be understood that when it comes to orientation descriptions, such as the orientation or positional relationship indicated by "upper", "lower", "left", "right", "front", "rear", etc., it is based on the orientation or positional relationship shown in the drawings. This is only for the convenience of describing this application and simplifying the description, rather than indicating or implying that the device or component referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation to this application.

[0066] In the description of this specification, the description with reference to terms such as "one embodiment", "some embodiments", "schematic embodiments", "examples", "specific examples", or "some examples", etc. means that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of this application. In this specification, the schematic expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in a suitable manner in any one or more embodiments or examples.

[0067] In the description of this application, it should be noted that unless otherwise clearly defined, terms such as "set", "installed", "connected", etc. should be understood in a broad sense, and those skilled in the art can reasonably determine the specific meanings of the above terms in this application in combination with the specific content of the technical solution. In addition, the identification of specific steps hereinafter does not represent a limitation on the step sequence and execution logic. The execution sequence and execution logic between each step should be understood and inferred with reference to the content described in the embodiments.

[0068] With the popularization of sensor applications and the development of data analysis technologies, the nuclear power industry has developed a technology for evaluating the health status of nuclear power equipment by detecting the physical state data of equipment and using data analysis methods, which is used to improve the maintenance strategy of nuclear power plants.

[0069] Traditional predictive health status assessment methods for nuclear power equipment highly rely on the accumulation of manual experience and professional knowledge. Due to the complex and diverse operating states and equipment failure modes of nuclear power equipment, it is often difficult to accurately assess the health status of equipment based solely on manual judgment.

[0070] On the other hand, manual judgment is easily affected by subjective factors, resulting in the evaluation results of the health status of nuclear power equipment being inconsistent and unreliable.

[0071] Furthermore, with the popularization of sensor applications and the development of detection technologies, the amount of detection data for nuclear power equipment has increased significantly. It is difficult for the method of predictive health status assessment of nuclear power equipment mainly based on manual judgment to effectively process and analyze this large amount of data. When facing the comprehensive utilization of multiple detection technologies, integrating data from different sources and conducting comprehensive analysis has become a difficult problem.

[0072] Therefore, how to improve the effectiveness of the predictive health status assessment of nuclear power equipment has become an urgent problem to be solved in the industry.

[0073] This application aims to solve at least one of the technical problems existing in the prior art. For this purpose, this application proposes a method, device, equipment, and medium for predictive health status assessment of nuclear power equipment, which can improve the effectiveness of the predictive health status assessment of nuclear power equipment.

[0074] The following will be further described with reference to the accompanying drawings.

[0075] Referring to Figure 1 , the method for predictive health status assessment of nuclear power equipment according to an embodiment of the present application may include:

[0076] Step S101, collect multiple sets of nuclear power measurement data associated with the target nuclear power equipment according to multiple detection units configured for the target nuclear power equipment; wherein, each detection unit is used to collect a set of nuclear power measurement data;

[0077] Step S102, calculate the branch evaluation confidence corresponding to the target nuclear power equipment based on each nuclear power measurement data;

[0078] Step S103, conduct a combined fault assessment based on each branch evaluation confidence to determine the combined evaluation confidence corresponding to the target nuclear power equipment;

[0079] Step S104, conduct equipment health assessment according to each combined evaluation confidence corresponding to the target nuclear power equipment, and determine the health status assessment data of the target nuclear power equipment.

[0080] According to the method for predicting the health status of nuclear power equipment shown in steps S101 to S104 of the embodiments of the present application, it is necessary to first collect multiple sets of nuclear power measurement data associated with the target nuclear power equipment according to multiple detection units configured for the target nuclear power equipment; wherein, each detection unit is used to collect a set of nuclear power measurement data; based on each nuclear power measurement data, calculate the branch evaluation confidence corresponding to the target nuclear power equipment; based on each branch evaluation confidence, perform a combined fault evaluation to determine the combined evaluation confidence corresponding to the target nuclear power equipment; according to each combined evaluation confidence corresponding to the target nuclear power equipment, perform an equipment health evaluation to determine the health status evaluation data of the target nuclear power equipment. Through this method, the health status evaluation of the target nuclear power equipment no longer solely depends on manual experience and professional knowledge, but rather improves the accuracy and consistency of the health status evaluation through the methods of branch confidence evaluation and combined confidence evaluation, and also correspondingly improves the effect of predicting the health status of nuclear power equipment. This not only solves the subjectivity problem existing in the traditional method, but also enhances the comprehensiveness and reliability of the evaluation by integrating multi-source data, thereby providing more effective support for the operation safety and maintenance management of nuclear power plants.

[0081] In step S101 of some embodiments, according to multiple detection units configured for the target nuclear power equipment, collect multiple sets of nuclear power measurement data associated with the target nuclear power equipment; wherein, each detection unit is used to collect a set of nuclear power measurement data;

[0082] In some embodiments of the present application, in order to comprehensively evaluate the health status of the target nuclear power equipment, multiple dedicated detection units are configured on the target nuclear power equipment, and each detection unit is used to monitor a specific parameter or a performance index during the operation of the target nuclear power equipment. These detection units may include, but are not limited to, temperature sensors, pressure sensors, vibration monitors, radiation detectors, etc., and they are jointly used to collect data closely related to the operation status of the target nuclear power equipment in real time or regularly.

[0083] It should be noted that the data collected by each detection unit is for a specific parameter or a performance index during the operation of the target nuclear power equipment. For example, one detection unit focuses on monitoring the temperature change of a specific component, while another detection unit is used to monitor the vibration mode of some key components. Each detection unit separately collects a set of nuclear power measurement data, thus forming multiple sets of nuclear power measurement data, and each set of nuclear power measurement data represents an aspect of the equipment health status. In this way, it can be ensured that a comprehensive health evaluation of the target nuclear power equipment is carried out from multiple angles and levels.

[0084] In step S102 of some embodiments, based on each nuclear power measurement data, calculate the branch evaluation confidence corresponding to the target nuclear power equipment;

[0085] In some embodiments of the present application, calculating the branch evaluation confidence corresponding to the target nuclear power equipment involves in-depth analysis of the nuclear power measurement data collected by each detection unit. Among them, the branch evaluation confidence is a quantitative evaluation of the correlation between each monitoring parameter or performance index and the equipment health status, which reflects the credibility of the impact of nuclear power measurement data on the equipment health status.

[0086] In some embodiments, for each set of nuclear power measurement data, the relevance between the nuclear power measurement data and the equipment health status can be determined first. In this process, by analyzing historical data, typical data patterns under normal operating conditions can be identified, as well as the changing trends of data when the equipment fails or its performance degrades. Through this analysis, a benchmark or reference range can be established for each nuclear power measurement data to evaluate the abnormality degree of the nuclear power measurement data collected by the detection unit.

[0087] Next, statistical methods, machine learning algorithms or other data analysis techniques are used to process the nuclear power measurement data collected by each detection unit to calculate the branch evaluation confidence. In some cases, during the process of calculating the branch evaluation confidence according to the nuclear power measurement data, normalization processing of the nuclear power measurement data can be included to eliminate the dimensional differences between different monitoring parameters; or specific algorithms can be applied to identify abnormal patterns in the nuclear power measurement data, and these abnormal patterns can be related to the health problems of the equipment. During the calculation process, the uncertainty and error range of the nuclear power measurement data can also be considered. For example, by introducing the concept of confidence interval or error range, the reliability of the measurement data can be evaluated more accurately, so that appropriate adjustments can be made to the possible errors when calculating the branch evaluation confidence.

[0088] In addition, the calculation of the branch evaluation confidence can also involve comprehensive analysis of multiple related nuclear power measurement data. For example, if there is a mutual dependence relationship between two or more nuclear power measurement data, then when calculating the branch evaluation confidence, the interaction between these nuclear power measurement data needs to be considered to more comprehensively reflect the health status of the equipment.

[0089] Through this method, the nuclear power measurement data of each detection unit is converted into a quantitative branch evaluation confidence, which represents the contribution of the nuclear power measurement data to the evaluation of the equipment health status. These branch evaluation confidence values can then be used for combined fault evaluation to determine the overall health status of the target nuclear power equipment. This method improves the accuracy and reliability of the evaluation and provides a basis for the maintenance strategy of the nuclear power plant.

[0090] According to some embodiments of the present application, the target nuclear power equipment has multiple equipment fault modes. Step S102 calculates the branch evaluation confidence corresponding to the target nuclear power equipment based on each nuclear power measurement data, which may include:

[0091] Based on each nuclear power measurement data, calculate the branch evaluation confidence corresponding to the target nuclear power equipment under various equipment failure modes.

[0092] According to some embodiments of the present application, the target nuclear power equipment may have multiple equipment failure modes, which cover various problems that may occur during equipment operation, such as mechanical wear, material fatigue, environmental corrosion, operation errors, etc. To comprehensively evaluate the health status of the nuclear power equipment, it is necessary to conduct a detailed analysis and evaluation for each equipment failure mode. In this process, based on each nuclear power measurement data, calculate the branch evaluation confidence corresponding to the target nuclear power equipment under various equipment failure modes. This means that for each equipment failure mode, it is necessary to analyze the relevant nuclear power measurement data to determine the relationship between these nuclear power measurement data and the likelihood of the equipment failure mode occurring. For example, if a certain equipment failure mode is related to abnormal temperature, then it is necessary to pay attention to the data of the temperature sensor and calculate the probability of failure at the current temperature.

[0093] It should be noted that different methods and techniques can be used to calculate the branch evaluation confidence, which depends on the characteristics of the equipment failure mode and the nature of the nuclear power measurement data. For example, for equipment failure modes based on thresholds, a simple threshold comparison method can be used to calculate the confidence; while for more complex equipment failure modes, statistical analysis, machine learning or artificial intelligence algorithms may be needed to identify abnormal patterns in the data and evaluate the failure risk accordingly.

[0094] In some embodiments, during the process of calculating the branch evaluation confidence corresponding to the target nuclear power equipment under various equipment failure modes, the uncertainty and error range of the nuclear power measurement data can be considered. This involves preprocessing the nuclear power measurement data, such as filtering, normalization or error correction, to improve accuracy and reliability. In addition, dimensionality reduction or feature extraction can also be performed on the nuclear power measurement data to highlight the information most relevant to the equipment failure mode.

[0095] Through this method, a branch evaluation confidence can be calculated for each equipment failure mode, which reflects the likelihood of the occurrence of this equipment failure mode under the condition that the nuclear power measurement data is collected. These branch evaluation confidences can be used for subsequent combined failure evaluation to determine the overall health status of the nuclear power equipment.

[0096] Referring to Figure 2 , according to some embodiments of the present application, based on each nuclear power measurement data, calculating the branch evaluation confidence corresponding to the target nuclear power equipment under various equipment failure modes may include:

[0097] Step S201, when the nuclear power measurement data is discrete type data, determine the fault credibility and fault unbelievability corresponding to the nuclear power measurement data under various equipment fault modes;

[0098] Step S202, for the fault credibility and fault unbelievability corresponding to each equipment fault mode, calculate the branch evaluation confidence corresponding to the target nuclear power equipment under various equipment fault modes.

[0099] In some embodiments of the present application, for the health state assessment of the target nuclear power equipment, especially for the processing of discrete type data in the nuclear power measurement data. Discrete type data refers to those data that can be classified into specific categories or states, such as switch states, fault / non - fault, qualified / unqualified, etc. In the monitoring of the target nuclear power equipment, discrete type data can come from various sensors or monitoring systems, which provide direct information about the equipment state.

[0100] In step S201 of some embodiments, when the nuclear power measurement data is discrete type data, determine the fault credibility and fault unbelievability corresponding to the nuclear power measurement data under various equipment fault modes;

[0101] It should be noted that when processing discrete type nuclear power measurement data, it is first necessary to determine the fault credibility MB and fault unbelievability MD corresponding to these data under various equipment fault modes. This step involves the analysis of discrete type data to identify features or indicators related to specific equipment fault modes. For example, if a certain switch state is associated with a specific equipment fault mode, then when the switch is in a specific state, the fault credibility may increase, while in the opposite state, the fault unbelievability may increase.

[0102] It should be pointed out that the fault credibility MB refers to the degree to which the probability of the equipment failing increases given the discrete type data. The fault unbelievability MD refers to the degree to which the probability of the equipment not failing increases given the discrete type data. These two indicators can help evaluate the confidence of the equipment fault mode under specific discrete type data.

[0103] Refer to Figure 3 , according to some embodiments of the present application, in step S201, determining the fault credibility and fault unbelievability corresponding to the nuclear power measurement data under various equipment fault modes may include:

[0104] Step S301, obtain the prior fault probability of the target nuclear power equipment under various equipment fault modes; wherein, the prior fault probability is used to characterize the probability of the target nuclear power equipment having failed in the past;

[0105] Step S302: For various equipment failure modes, substitute the corresponding prior failure probabilities and nuclear power measurement data into the pre-constructed credibility analytical formula for calculation to obtain the failure credibility corresponding to each equipment failure mode.

[0106] Step S303: For various equipment failure modes, substitute the corresponding prior failure probabilities and nuclear power measurement data into the pre-constructed incredibility analytical formula for calculation to obtain the failure incredibility corresponding to each equipment failure mode.

[0107] In step S301 of some embodiments, obtain the prior failure probabilities of the target nuclear power equipment under various equipment failure modes; wherein, the prior failure probability is used to represent the probability of the target nuclear power equipment having failed in the past.

[0108] It should be noted that determining the failure credibility and failure incredibility corresponding to nuclear power measurement data under various equipment failure modes is a detailed process involving multiple steps. This process begins with obtaining the prior failure probabilities of the target nuclear power equipment under various equipment failure modes, which is achieved by analyzing the historical failure data of the equipment. The prior failure probability is a statistical value that represents the probability of the target nuclear power equipment having had a specific failure in the past and provides a benchmark for evaluating the current equipment state.

[0109] Refer to Figure 4 , according to some embodiments of the present application, the nuclear power equipment predictive health state assessment method of the embodiments of the present application further includes pre-constructing the credibility analytical formula and the incredibility analytical formula, which may specifically include:

[0110] Step S401: Determine the failure evidence factors corresponding to various equipment failure modes.

[0111] Step S402: Obtain the conditional failure probabilities corresponding to each equipment failure mode; wherein, the conditional failure probability is used to represent the probability of the target nuclear power equipment failing when the failure evidence factor appears.

[0112] Step S403: For each equipment failure mode, construct a credibility analytical formula based on the failure evidence factor and the prior failure probability.

[0113] Step S404: For each equipment failure mode, construct an incredibility analytical formula based on the failure evidence factor and the prior failure probability.

[0114] In step S401 of some embodiments, determine the failure evidence factors corresponding to various equipment failure modes.

[0115] It should be noted that in some embodiments of the present application, the core of the nuclear power equipment predictive health status assessment method lies in the pre-construction of the credibility analytical formula and the non-credibility analytical formula. First, it is necessary to determine the fault evidence factors corresponding to various equipment fault modes. These fault evidence factors are key monitoring parameters or key measurement data related to specific fault modes, and they can provide direct or indirect evidence for evaluating the possibility of equipment failure.

[0116] In step S402 of some embodiments, obtain the conditional fault probability corresponding to each equipment fault mode; wherein, the conditional fault probability is used to characterize the probability of the target nuclear power equipment failing when the fault evidence factor appears.

[0117] It should be noted that obtaining the conditional fault probability corresponding to each equipment fault mode is the probability of the target nuclear power equipment failing when considering the appearance of the fault evidence factor. Among them, the conditional fault probability is a key parameter in the evaluation process. It combines the influence of the prior fault probability and the fault evidence factor, and more accurately reflects the risk of equipment failure under specific conditions.

[0118] In step S403 of some embodiments, for each equipment fault mode, construct a credibility analytical formula based on the fault evidence factor and the prior fault probability.

[0119] It should be noted that for each equipment fault mode, construct a credibility analytical formula based on the fault evidence factor and the prior fault probability. This credibility analytical formula is a mathematical model used to calculate the equipment fault credibility MB when the given fault evidence factor is considered. This credibility analytical formula can reflect the influence of the fault evidence factor on the possibility of fault occurrence.

[0120] In step S404 of some embodiments, for each equipment fault mode, construct a non-credibility analytical formula based on the fault evidence factor and the prior fault probability.

[0121] It should be noted that for each equipment fault mode, also construct a non-credibility analytical formula based on the fault evidence factor and the prior fault probability. This non-credibility analytical formula is used to calculate the equipment fault non-credibility MD, that is, the possibility that the equipment does not fail when the given fault evidence factor is considered. This non-credibility analytical formula can reflect the influence of the fault evidence factor on the possibility of non-failure.

[0122] The embodiments of the present application shown in steps S401 to S404 provide a quantitative framework for the health state assessment of the target nuclear power equipment through the pre-constructed credibility analysis formula and non-credibility analysis formula. These analysis formulas consider the historical failure data of the target nuclear power equipment. When the detection unit collects nuclear power measurement data, the failure credibility and failure non-credibility corresponding to the nuclear power measurement data under various equipment failure modes can be determined.

[0123] In step S302 of some embodiments, for various equipment failure modes, the corresponding prior failure probability and nuclear power measurement data are substituted into the pre-constructed credibility analysis formula for calculation to obtain the failure credibility corresponding to each equipment failure mode.

[0124] It should be noted that for various equipment failure modes, the corresponding prior failure probability and nuclear power measurement data are substituted into the pre-constructed credibility analysis formula for calculation. This credibility analysis formula is a mathematical model that calculates the failure credibility MB based on the prior failure probability and the current measurement data. This credibility analysis formula considers the correlation between the nuclear power measurement data and the equipment failure mode. Through this calculation process, the failure credibility corresponding to each equipment failure mode can be obtained, and this failure credibility reflects the possibility of the corresponding equipment failure mode occurring under the nuclear power measurement data.

[0125] In step S303 of some embodiments, for various equipment failure modes, the corresponding prior failure probability and nuclear power measurement data are substituted into the pre-constructed non-credibility analysis formula for calculation to obtain the failure non-credibility corresponding to each equipment failure mode.

[0126] It should be noted that for various equipment failure modes, the corresponding prior failure probability and nuclear power measurement data can also be substituted into the pre-constructed non-credibility analysis formula for calculation. The non-credibility analysis formula is another mathematical model that is used to calculate the failure non-credibility MD, and this failure non-credibility reflects the possibility of the equipment failure mode not occurring under the nuclear power measurement data. This non-credibility analysis formula also considers the relationship between the nuclear power measurement data and the equipment failure mode.

[0127] The embodiments of the present application shown in steps S301 to S303 can calculate two key indicators for each equipment failure mode: failure credibility and failure non-credibility. These two indicators provide a comprehensive assessment of the equipment health state. They can be used for the subsequent calculation of the confidence level CF of the branch assessment, further integrating the assessment results of different equipment failure modes to form a quantitative evaluation of the comprehensive health state of the equipment. This method not only improves the accuracy and reliability of the assessment, but also provides a basis for the operation and maintenance of the nuclear power plant, helping to improve the reliability and safety of the target nuclear power equipment.

[0128] In some more specific embodiments, the fault credibility MB refers to a quantitative value of the possibility of a certain nuclear power measurement data for the occurrence of a certain equipment fault mode h, and its value range is between 0 and 1; the fault incredibility MD refers to a quantitative value of the possibility that a certain nuclear power measurement data for a certain equipment fault mode is impossible to occur, and its value range is also between 0 and 1. It is expressed as:

[0129]

[0130] Where:

[0131] p(h) is the prior fault probability, which represents the fault occurrence probability of the equipment fault mode obtained according to historical statistical data.

[0132] p(h|e) is the conditional fault probability of the equipment fault mode in the presence of the fault evidence factor e.

[0133] MB[h, e] is a measure of the increase in the fault credibility of the equipment fault mode h based on the fault evidence factor e. The higher the value, the higher the fault credibility. A value equal to 1 indicates complete certainty about the equipment fault mode h, and a value equal to 0 indicates complete uncertainty about the equipment fault mode h.

[0134] MD[h, e] is a measure of the decrease in the fault credibility of the equipment fault mode h based on the fault evidence factor e. The higher the value, the higher the fault incredibility. A value equal to 0 indicates complete certainty about the equipment fault mode h, and a value equal to 1 indicates complete uncertainty about the equipment fault mode h.

[0135] In step S202 of some embodiments, for the fault credibility and fault incredibility corresponding to each equipment fault mode, the branch evaluation confidence corresponding to the target nuclear power equipment in various equipment fault modes is calculated.

[0136] It should be noted that for the fault credibility and fault incredibility corresponding to each equipment fault mode, the branch evaluation confidence CF corresponding to the target nuclear power equipment in various equipment fault modes is calculated. This calculation process may involve converting MB and MD into CF values. The range of CF values is usually between -1 and 1, where 1 indicates complete certainty that the fault will occur, -1 indicates complete certainty that the fault will not occur, and 0 indicates no confidence in whether the fault will occur.

[0137] In some embodiments, when calculating the confidence level of branch evaluation, the reliability and accuracy of discrete type data need to be considered. This may involve validating discrete type data to ensure that they can truly reflect the state of the device. In addition, the combination of discrete type data and continuous type data may also need to be considered, because discrete type data may need to be used together with continuous type data to provide a more comprehensive assessment of the device health status.

[0138] In some more specific embodiments, the branch evaluation confidence level CF is calculated based on the fault credibility MB and the fault unbelievability MD. Among them, the branch evaluation confidence level CF can be used as a comprehensive measurement standard for the fault credibility MB and the fault unbelievability MD. The branch evaluation confidence level CF is defined by the following formula:

[0139]

[0140] Through this calculation method of discrete branch evaluation confidence level, the health status of some target nuclear power equipment can be accurately evaluated, providing a scientific basis for the maintenance and management of the target nuclear power equipment. This method helps to timely detect potential fault risks during the operation of the equipment, so as to take effective preventive and maintenance measures to ensure the safe and stable operation of the nuclear power plant.

[0141] Through the method shown in steps S201 to S202, a branch evaluation confidence level can be calculated for each device fault mode, and this branch evaluation confidence level reflects the possibility of the occurrence of this device fault mode under the current discrete type data. These branch evaluation confidence levels can be used for subsequent combined fault evaluation to determine the overall health status of the target nuclear power equipment. This evaluation method based on discrete type data provides a detailed perspective for the health evaluation of the target nuclear power equipment, helps to timely detect and prevent various faults, and improves the reliability and safety of the target nuclear power equipment.

[0142] Referring to Figure 5 , according to some embodiments of the present application, step S102 calculates the branch evaluation confidence level corresponding to the target nuclear power equipment based on each nuclear power measurement data, which may include:

[0143] Step S501, in the case where the nuclear power measurement data is continuous type data, select a target conversion function from a preset plurality of numerical conversion functions for the nuclear power measurement data;

[0144] Step S502, perform fitting processing on the nuclear power measurement data through the target conversion function to obtain the branch evaluation confidence level corresponding to the target nuclear power equipment.

[0145] In some embodiments of the present application, for continuous type data of nuclear power measurement data, the process of calculating the branch evaluation confidence corresponding to the target nuclear power equipment involves data conversion and fitting. Continuous type data, such as temperature, pressure, flow rate, etc., usually changes continuously within a certain range, and the analysis of such data requires special processing methods to extract information related to the health status of the equipment.

[0146] In step S501 of some embodiments, when the nuclear power measurement data is continuous type data, a target conversion function is selected from a plurality of preset numerical conversion functions for the nuclear power measurement data;

[0147] It should be noted that first, a target conversion function needs to be selected from a plurality of preset numerical conversion functions. These numerical conversion functions, such as the HIGH function, LOW function, or MODERATE function, are designed to map continuous measurement data to the branch evaluation confidence CF value. The basis for selecting the target conversion function is the characteristics of the nuclear power measurement data and their relationship with the equipment failure mode. For example, if a certain nuclear power measurement data indicates an increased failure risk when exceeding a specific threshold, then the HIGH function may be selected; if the nuclear power measurement data indicates an increased failure risk when below a specific threshold, then the LOW function may be selected; while the MODERATE function is applicable when the failure risk increases when the nuclear power measurement data is near a certain median value.

[0148] In step S502 of some embodiments, the nuclear power measurement data is subjected to fitting processing through the target conversion function to obtain the branch evaluation confidence corresponding to the target nuclear power equipment.

[0149] It should be noted that after the target conversion function is selected, the nuclear power measurement data is then subjected to fitting processing through this target conversion function. During the fitting process, the target conversion function will calculate the corresponding branch evaluation confidence CF based on the value of the nuclear power measurement data. This branch evaluation confidence CF reflects the possibility of the target nuclear power equipment having a failure mode under the nuclear power measurement data. Among them, the fitting processing may involve adjusting the parameters of the target conversion function to ensure that the branch evaluation confidence CF can accurately reflect the relationship between the measurement data and the health status of the equipment.

[0150] Through the manner shown in steps S501 to S502, the branch evaluation confidence corresponding to the target nuclear power equipment can be obtained. This branch evaluation confidence is obtained through the analysis and conversion of continuous type data. These branch evaluation confidence values can then be used for combined failure assessment to determine the overall health status of the target nuclear power equipment. This method makes the health assessment of the target nuclear power equipment more scientific and accurate, helps to timely discover potential problems of the target nuclear power equipment, take preventive measures, and ensure the safe and stable operation of the nuclear power plant.

[0151] Referring to Figure 6 , according to some embodiments of the present application, the preset multiple numerical conversion functions include a high-value interval conversion function, a median interval conversion function, and a low-value interval conversion function. Fitting the nuclear power measurement data through the target conversion function to obtain the branch evaluation confidence corresponding to the target nuclear power equipment may include:

[0152] Step S601, in response to the target conversion function being the high-value interval conversion function, fitting the nuclear power measurement data through the high-value interval conversion function to obtain the branch evaluation confidence corresponding to the target nuclear power equipment;

[0153] Step S602, in response to the target conversion function being the median interval conversion function, fitting the nuclear power measurement data through the median interval conversion function to obtain the branch evaluation confidence corresponding to the target nuclear power equipment;

[0154] Step S603, in response to the target conversion function being the low-value interval conversion function, fitting the nuclear power measurement data through the low-value interval conversion function to obtain the branch evaluation confidence corresponding to the target nuclear power equipment.

[0155] In some embodiments of the present application, in order to effectively analyze the nuclear power measurement data and calculate the branch evaluation confidence corresponding to the target nuclear power equipment, the embodiments of the present application preset a variety of numerical conversion functions. These numerical conversion functions are specifically designed to process different types of continuous data and convert the continuous data into the branch evaluation confidence that can reflect the health status of the equipment. These numerical conversion functions include a high-value interval conversion function, a median interval conversion function, and a low-value interval conversion function, each corresponding to different data characteristics and equipment failure modes.

[0156] Step S601 of some embodiments, in response to the target conversion function being the high-value interval conversion function, fitting the nuclear power measurement data through the high-value interval conversion function to obtain the branch evaluation confidence corresponding to the target nuclear power equipment;

[0157] It should be noted that when the target conversion function is determined to be the high-value interval conversion function, this function is specifically used to process the data that may indicate equipment failure within the high-value interval. By fitting the nuclear power measurement data through the high-value interval conversion function, the confidence of the target nuclear power equipment failing when the nuclear power measurement data reaches or exceeds a certain preset threshold can be calculated. This method helps to identify the risk points that may cause equipment failure due to too high parameters.

[0158] In step S602 of some embodiments, in response to the target conversion function being the median interval conversion function, the nuclear power measurement data is fitted by the median interval conversion function to obtain the branch evaluation confidence corresponding to the target nuclear power equipment.

[0159] It should be noted that if the target conversion function is the median interval conversion function, it will be used to analyze the measurement data that shows signs of failure within the medium numerical range. This median interval conversion function will fit the nuclear power measurement data to determine the branch evaluation confidence corresponding to the target nuclear power equipment within a specific median interval. This helps to evaluate the health status of the target nuclear power equipment within the normal operating parameter range and helps to evaluate whether there is a potential failure risk due to abnormal parameters for the target nuclear power equipment.

[0160] In step S603 of some embodiments, in response to the target conversion function being the low value interval conversion function, the nuclear power measurement data is fitted by the low value interval conversion function to obtain the branch evaluation confidence corresponding to the target nuclear power equipment.

[0161] It should be noted that when the target conversion function is set to the low value interval conversion function, this function will focus on the data that may indicate equipment failure within the low value interval. By fitting these low value interval conversion functions, the branch evaluation confidence of the target nuclear power equipment failing can be obtained when the nuclear power measurement data is below a certain threshold. This helps to identify the situations that may cause failures due to too low parameters.

[0162] Through the application of these target conversion functions shown in steps S601 to S603, the branch evaluation confidence of the target nuclear power equipment within different numerical intervals can be obtained. These branch evaluation confidences comprehensively reflect the health risks of the target nuclear power equipment in different operating states, providing important reference information for the operation and maintenance of the nuclear power plant. This method not only improves the accuracy of failure assessment but also helps to achieve preventive maintenance, thus ensuring the safe and reliable operation of the target nuclear power equipment.

[0163] In some more specific embodiments, for the case where the nuclear power measurement data is continuous type data and needs to be converted into values in the interval from -1 to 1, three numerical conversion functions, namely the HIGH function, the LOW function, and the MODERATE function, are used to process the nuclear power measurement data and convert the nuclear power measurement data into the branch evaluation confidence CF.

[0164] The HIGH function is specifically used to process data that may indicate equipment failure within a high-value range. When the nuclear power measurement data reaches or exceeds a preset threshold, the HIGH function maps these data points to a CF value close to 1, indicating a high probability of equipment failure. The design of this function takes into account the failure risk of the equipment when the parameters are too high, enabling the embodiments of the present application to identify risk points that may lead to equipment failure due to excessive parameters.

[0165] The LOW function corresponds to the HIGH function and is used to process data that may indicate equipment failure within a low-value range. When the nuclear power measurement data is lower than a preset threshold, the LOW function maps these data points to a CF value close to 1, indicating a high probability of equipment failure. This helps to evaluate the health status of the equipment when the parameters are too low and whether there is a potential failure risk caused by abnormally low parameters.

[0166] The MODERATE function is used to process measurement data that shows signs of failure within a medium numerical range. This function will perform fitting processing on the data to determine the confidence level of equipment failure within a specific median range. The design of the MODERATE function takes into account the health status of the equipment within the normal operating parameter range and whether there is a potential failure risk caused by abnormal parameters.

[0167] By applying these three preset numerical conversion functions, continuous nuclear power measurement data can be converted into branch evaluation confidence levels CF, which provide a quantitative indicator for evaluating the health risks of the target nuclear power equipment under different operating conditions. This method not only improves the accuracy of failure assessment but also helps to achieve preventive maintenance, thereby ensuring the safe and reliable operation of the target nuclear power equipment. In this way, continuous type data can be utilized more effectively to support the operation and maintenance of nuclear power plants.

[0168] In step S103 of some embodiments, combined failure assessment is performed based on each branch evaluation confidence level to determine the combined evaluation confidence level corresponding to the target nuclear power equipment;

[0169] It should be noted that performing combined failure assessment based on each branch evaluation confidence level is a key step, which involves integrating each branch evaluation confidence level to quantify the overall impact of each branch evaluation confidence level on the target nuclear power equipment. This process aims to convert the scattered information collected from different detection units into a comprehensive evaluation result, namely the combined evaluation confidence level, through a systematic method.

[0170] First, the confidence level of each branch evaluation represents the degree of credibility of the impact of nuclear power measurement data on the health status of the equipment. These branch evaluation confidence levels reflect the health or failure risks of the target nuclear power equipment in specific aspects. To conduct a combined fault assessment, it is necessary to determine the mutual relationships and impacts among these branch evaluation confidence levels. This may involve analyzing the correlations between different types of nuclear power measurement data and how they jointly affect the overall performance of the target nuclear power equipment.

[0171] Next, some combination algorithms or models are adopted to fuse the confidence levels of each branch into a combined evaluation confidence level. This process takes into account the importance and influence of each nuclear power measurement data, and the relative importance of each nuclear power measurement data in the overall evaluation can be reflected by assigning different weights. For example, for those nuclear power measurement data that are crucial to the equipment health, higher weights may be needed.

[0172] In some other embodiments, during the process of combined fault assessment, it may also involve handling uncertainties and potential conflicts. If there are contradictions in the evaluation results of different nuclear power measurement data, a reasonable mechanism is needed to resolve these conflicts to ensure that the combined evaluation confidence level can accurately reflect the true state of the equipment. This may include further validating the data or adopting a decision-making method that comprehensively considers all information.

[0173] It should be understood that the combined evaluation confidence level provides a comprehensive perspective for evaluating the overall health status of the target nuclear power equipment. This combined evaluation confidence level can be used to guide maintenance decisions, helping the nuclear power plant management determine when preventive maintenance is needed or take emergency measures when there are equipment failure risks. This method improves the accuracy and reliability of equipment health status assessment and provides support for the safe operation and maintenance management of the nuclear power plant.

[0174] According to some embodiments provided by the present application, step S103 conducts a combined fault assessment based on the confidence levels of each branch to determine the combined evaluation confidence level corresponding to the target nuclear power equipment, which may include:

[0175] For each equipment failure mode, a combined fault assessment is conducted based on the confidence levels of each branch corresponding to the equipment failure mode to determine the combined evaluation confidence level corresponding to the target nuclear power equipment under the equipment failure mode.

[0176] In some embodiments provided by the present application, the combined fault assessment is based on the confidence levels of each branch corresponding to various equipment failure modes to determine the overall health status of the target nuclear power equipment. This process involves comprehensively analyzing multiple confidence levels of each branch collected under different equipment failure modes to form a comprehensive combined evaluation confidence level.

[0177] First, for each equipment failure mode in the embodiments of the present application, all relevant branch evaluation confidence levels need to be combined. These branch evaluation confidence levels reflect the influence of each group of nuclear power measurement data on the equipment health state under various specific equipment failure modes. For example, if there are multiple equipment failure modes in the target nuclear power equipment, such as overheating, abnormal pressure, or excessive vibration, then each equipment failure mode can have corresponding branch evaluation confidence levels.

[0178] Next, the embodiments of the present application can perform combined fault evaluation based on these branch evaluation confidence levels. This process involves using some algorithms or models to integrate each branch evaluation confidence level into a combined evaluation confidence level. This combined evaluation confidence level will reflect the health state of the equipment corresponding to a specific equipment failure mode, considering the comprehensive influence of each group of nuclear power measurement data.

[0179] In some embodiments, when performing combined fault evaluation, the mutual relationships and influences between different nuclear power measurement data can be considered. For example, certain nuclear power measurement data may have a greater impact on the health state of the target nuclear power equipment, so higher weights may need to be assigned in the combined evaluation. In addition, the embodiments of the present application can also consider the interactions between nuclear power measurement data and how they jointly affect the health state of the equipment.

[0180] Through this method, the embodiments of the present application can determine a combined evaluation confidence level for each equipment failure mode. This combined evaluation confidence level provides a quantitative indicator for evaluating the health risk of the target nuclear power equipment corresponding to a specific equipment failure mode.

[0181] Refer to Figure 7 , according to some embodiments of the present application, for each equipment failure mode, combined fault evaluation is performed based on the branch evaluation confidence levels corresponding to the equipment failure mode to determine the combined evaluation confidence level corresponding to the target nuclear power equipment in the equipment failure mode, which may include:

[0182] Step S701, obtain the confidence level combination analysis formula pre-constructed for each equipment failure mode;

[0183] Step S702, for each equipment failure mode, sort the branch evaluation confidence levels corresponding to the equipment failure mode to obtain a branch confidence level sequence;

[0184] Step S703, select the target branch evaluation confidence level from the branch confidence level sequence, and substitute the target branch evaluation confidence level into the confidence level combination analysis formula for combined confidence level calculation to obtain a candidate combined confidence level;

[0185] Step S704, in response to the existence of branch evaluation confidence degrees in the branch confidence degree sequence that have not participated in the combined confidence degree calculation, reselect a target branch evaluation confidence degree from the branch evaluation confidence degrees that have not participated in the combined confidence degree calculation, and substitute the candidate combined confidence degree and the target branch evaluation confidence degree into the confidence degree combination analytical formula to update the candidate combined confidence degree;

[0186] Step S705, after updating the candidate combined confidence degree, return to execute in response to the existence of branch evaluation confidence degrees in the branch confidence degree sequence that have not participated in the combined confidence degree calculation until all the branch evaluation confidence degrees in the branch confidence degree sequence have participated in the combined confidence degree calculation, and determine the latest candidate combined confidence degree as the combined evaluation confidence degree.

[0187] In some embodiments of the present application, for each equipment failure mode, the process of combined failure evaluation is used to integrate various branch evaluation confidence degrees CF and determine the combined evaluation confidence degree of the target nuclear power equipment under a specific equipment failure mode.

[0188] In step S701 of some embodiments, obtain the confidence degree combination analytical formula pre-constructed for each equipment failure mode;

[0189] It should be noted that this process first involves obtaining the confidence degree combination analytical formulas pre-constructed for each equipment failure mode. These confidence degree combination analytical formulas are mathematical models used to describe how different branch evaluation confidence degrees jointly affect the health state of the target nuclear power equipment regarding a specific equipment failure mode. It should be pointed out that each confidence degree combination analytical formula corresponds to one equipment failure mode.

[0190] In step S702 of some embodiments, for each equipment failure mode, sort the corresponding branch evaluation confidence degrees under the equipment failure mode to obtain a branch confidence degree sequence;

[0191] It should be noted that for each equipment failure mode, sort the corresponding branch evaluation confidence degrees to form a branch confidence degree sequence. This branch confidence degree sequence helps to identify which nuclear power measurement parameters have the greatest impact on the health state of the target nuclear power equipment, thus providing a clear perspective for subsequent combined failure evaluation.

[0192] In step S703 of some embodiments, select a target branch evaluation confidence degree from the branch confidence degree sequence, and substitute the target branch evaluation confidence degree into the confidence degree combination analytical formula to calculate the combined confidence degree, obtaining a candidate combined confidence degree;

[0193] It should be noted that the target branch evaluation confidence is selected from the branch confidence sequence, and is substituted into the confidence combination analytical formula for combined confidence calculation to obtain the candidate combined confidence. This candidate combined confidence is the preliminary result of the combined confidence calculation, which reflects the comprehensive impact of the currently selected branch evaluation confidence on the health status of the device.

[0194] In step S704 of some embodiments, in response to the existence of branch evaluation confidences in the branch confidence sequence that have not participated in the combined confidence calculation, a target branch evaluation confidence is reselected from the branch evaluation confidences that have not participated in the combined confidence calculation, and the candidate combined confidence and the target branch evaluation confidence are jointly substituted into the confidence combination analytical formula to update the candidate combined confidence;

[0195] It should be noted that if there are still branch evaluation confidences in the branch confidence sequence that have not participated in the combined confidence calculation, the embodiments of the present application will reselect a target branch evaluation confidence from these unparticipated branch evaluation confidences, and jointly substitute the current candidate combined confidence and the newly selected target branch evaluation confidence into the confidence combination analytical formula to obtain a new candidate combined confidence to replace the current candidate combined confidence.

[0196] In step S705 of some embodiments, after updating the candidate combined confidence, return to execute in response to the existence of branch evaluation confidences in the branch confidence sequence that have not participated in the combined confidence calculation until all the branch evaluation confidences in the branch confidence sequence have participated in the combined confidence calculation, and determine the latest candidate combined confidence as the combined evaluation confidence.

[0197] It should be noted that the process of updating the candidate combined confidence continues until all the branch evaluation confidences in the branch confidence sequence have participated in the combined confidence calculation. When all the branch evaluation confidences have been considered and integrated into the combined evaluation confidence, the latest candidate combined confidence is determined as the combined evaluation confidence. This combined evaluation confidence provides a comprehensive health status assessment, which synthesizes the impacts of all nuclear power measurement parameters matching the device failure mode, and provides a scientific basis for the maintenance and management of the target nuclear power equipment.

[0198] It should be understood that this method of steps S701 to S705 not only helps to improve the accuracy of fault assessment, but also helps to achieve preventive maintenance and ensure the safe and stable operation of the nuclear power plant. Through this systematic combined fault assessment, continuous measurement data can be more effectively utilized to provide scientific decision-making support for the operation and maintenance of the nuclear power plant.

[0199] In some more specific embodiments, combined confidence calculations can be performed for each of multiple device failure modes. Among them, the combined confidence calculation strengthens two values with the same conclusion, while two values with opposite conclusions reduce the certainty of a certain result (shifting the certainty factor towards 0). The pre-constructed confidence combination analytical formula can be expressed as:

[0200]

[0201] Where A and B represent two values participating in the combined confidence calculation.

[0202] For each device failure mode, sort the branch evaluation confidences corresponding to the device failure mode to obtain a branch confidence sequence, denoted as X 1 , X 2 ,..., X N . The combined confidence calculation process of one embodiment is as follows:

[0203] Select the target branch evaluation confidence from the branch confidence sequence, such as X 1 , X 2 . Take X 1 as A and X 2 as B and substitute them into the confidence combination analytical formula CF 组合 (A, B) to perform combined confidence calculation, and obtain the candidate combined confidence, denoted as:

[0204]

[0205] In response to the branch confidence sequence X 1 , X 2 ,..., X N If there are branch evaluation confidences that have not participated in the combined confidence calculation, re-select the target branch evaluation confidence from the branch evaluation confidences that have not participated in the combined confidence calculation, such as X 3 .

[0206] Furthermore, substitute the candidate combined confidence and the target branch evaluation confidence X 3 into the confidence combination analytical formula together to update the candidate combined confidence, denoted as:

[0207]

[0208] After updating the candidate combined confidence, return to execute in response to the branch confidence sequence X 1 , X 2 ,..., X NThere is a branch evaluation confidence that has not yet participated in the combined confidence calculation. Select a target branch evaluation confidence from the branch evaluation confidences that have not yet participated in the combined confidence calculation.

[0209] In this way, gradually incorporate the influence of the new branch evaluation confidence during the process of updating the candidate combined confidence, expressed as:

[0210]

[0211] where i ∈ {1, 2,..., N}.

[0212] Until all the branch evaluation confidences in the branch confidence sequence have participated in the combined confidence calculation, obtain the latest candidate combined confidence, expressed as:

[0213]

[0214] Finally, the latest candidate combined confidence is determined as the combined evaluation confidence.

[0215] It should be understood that during the combined confidence calculation process of the above embodiments, if two branch evaluation confidences are consistent, they will strengthen the candidate combined confidence. For example, if two branch evaluation confidences both indicate that a given transformer may have "aged", then the candidate combined confidence indicates a greater certainty that the target nuclear power device has actually aged. However, if one branch evaluation confidence indicates that the transformer is aged and the other branch evaluation confidence indicates that the transformer is available, then the combination of the two will bring greater uncertainty to the candidate combined confidence.

[0216] It should be noted that there are various ways to calculate the combined confidence, not limited to the above examples.

[0217] In step S104 of some embodiments, perform equipment health assessment based on the combined evaluation confidences corresponding to the target nuclear power equipment, and determine the health status assessment data of the target nuclear power equipment.

[0218] It should be noted that performing equipment health assessment based on the combined evaluation confidences corresponding to the target nuclear power equipment is directly related to the formulation of nuclear power equipment maintenance and operation strategies. The combined evaluation confidence is obtained through comprehensive analysis and calculation of each branch evaluation confidence. These branch evaluation confidences reflect the credibility of the influence of different nuclear power measurement data on the equipment health status, and the combined evaluation confidence is used to quantify the overall influence of each branch evaluation confidence on the target nuclear power equipment.

[0219] In some embodiments, when performing equipment health assessment based on the combined evaluation confidence levels corresponding to the target nuclear power equipment, the combined evaluation confidence levels can first be compared with preset health state thresholds. These health state thresholds are determined based on the design standards, historical performance data, and industry best practices of nuclear power equipment, and are used to distinguish different health states of the equipment. For example, a high confidence level may indicate that the equipment is in good health, while a low confidence level may indicate that the equipment has potential failure risks.

[0220] In addition, based on the specific values of the combined evaluation confidence levels, the health state assessment data of the target nuclear power equipment can be determined. These health state assessment data can include key indicators such as the health level of the equipment, the failure probability, and the remaining service life. These health state assessment data provide a quantitative reference for the operation and maintenance of nuclear power plants, enabling managers to more accurately grasp the health status of the equipment.

[0221] Furthermore, equipment health assessment may also involve the analysis of the changing trend of the equipment health state. By tracking the change of the combined evaluation confidence level over time, the health trend of the equipment can be predicted, so as to take preventive maintenance measures in advance and avoid potential failures and accidents.

[0222] In some more specific embodiments, during the assessment process, factors such as the operating environment, maintenance history, and load conditions of the target nuclear power equipment can also be considered to ensure the comprehensiveness and accuracy of the assessment results. These factors may affect the health state of the equipment, so they need to be comprehensively considered during the assessment.

[0223] After performing equipment health assessment based on the combined evaluation confidence levels corresponding to the target nuclear power equipment in step S104 to determine the health state assessment data of the target nuclear power equipment, corresponding maintenance and operation strategies can be formulated according to the results of the equipment health assessment. For example, for equipment in good health state, a regular maintenance plan can be adopted; while for equipment in poor health state, more frequent inspections and maintenance may be required, or key components may be considered for replacement.

[0224] It should be understood that performing equipment health assessment based on the combined evaluation confidence levels corresponding to the target nuclear power equipment not only improves the accuracy and reliability of the health state assessment data, but also provides support for the safe operation and maintenance management of nuclear power plants. This data-driven assessment method makes the maintenance of nuclear power equipment more scientific and efficient, helps to extend the service life of the equipment, and reduces the operation risks.

[0225] Referring to Figure 8 , according to some embodiments of the present application, step S104 of performing equipment health assessment based on the combined evaluation confidence levels corresponding to the target nuclear power equipment to determine the health state assessment data of the target nuclear power equipment may include:

[0226] Step S801: Obtain the device health assessment weights preset for each device failure mode.

[0227] Step S802: Estimate the device health status based on the combined assessment confidence corresponding to each device failure mode and the device health assessment weights, and obtain the health status assessment data of the target nuclear power equipment.

[0228] In some embodiments of the present application, the process of health assessment of the target nuclear power equipment involves using the preset device health assessment weights and the combined assessment confidence CF corresponding to each device failure mode. The purpose of this process is to determine the comprehensive health status of the nuclear power equipment for effective maintenance and management.

[0229] In step S801 of some embodiments, obtain the device health assessment weights preset for each device failure mode.

[0230] It should be noted that the device health assessment weights preset for each device failure mode are obtained. These device health assessment weights are allocated according to the importance of the device failure mode to the overall device health. For example, certain device failure modes are crucial for the safe operation of the device, so their device health assessment weights in the health assessment will be higher. Among them, the allocation of the device health assessment weights can be based on various aspects such as historical failure data, the design characteristics of the device, and the degree of influence of the failure mode on the device performance.

[0231] In step S802 of some embodiments, estimate the device health status based on the combined assessment confidence corresponding to each device failure mode and the device health assessment weights, and obtain the health status assessment data of the target nuclear power equipment.

[0232] It should be noted that the device health status is estimated based on the combined assessment confidence corresponding to each device failure mode and the device health assessment weights. This step involves multiplying the combined assessment confidence of each device failure mode by its corresponding device health assessment weight, and then summing up these weighted confidence values to obtain the overall health status assessment data of the target nuclear power equipment. This health status assessment data reflects the comprehensive health level of the target nuclear power equipment under all considered device failure modes.

[0233] Through the method shown in steps S801 to S802, a quantified device health status assessment data can be obtained, which comprehensively considers the impacts of all relevant device failure modes. This health status assessment data can be used to guide the operation and maintenance decisions of nuclear power plants, helping managers identify device failure modes that require priority attention and handling, thereby improving the reliability and safety of the target nuclear power equipment. For example, if the combined assessment confidence of a certain device failure mode is relatively high and its device health assessment weight is also high, it may indicate that the target nuclear power equipment has a relatively high risk under this device failure mode, and preventive or maintenance measures need to be taken immediately. In this way, not only the accuracy of failure assessment is improved, but also preventive maintenance is facilitated, ensuring the safe and stable operation of nuclear power plants. Through this device health assessment based on device health assessment weights, continuous measurement data can be more effectively utilized to provide scientific decision-making support for the operation and maintenance of nuclear power plants.

[0234] In some more specific embodiments, in order to comprehensively evaluate the health status of the target nuclear power equipment, especially for a specific device failure mode, multiple detection units are usually used to collect nuclear power measurement data. The categories corresponding to these nuclear power measurement data can include intelligent monitoring, ultrasonic detection, vibration analysis, lubrication condition assessment, infrared thermal imaging, and motor diagnosis, etc., each providing nuclear power measurement data on the device operating state from different angles and levels, and then obtaining the combined assessment confidence corresponding to the device failure mode.

[0235] It should be understood that the sensitivity and accuracy of each detection technology may vary, so each combined assessment confidence will be assigned a different device health assessment weight to reflect its importance and reliability in judging the device health status.

[0236] For example, the combined assessment confidence corresponding to intelligent monitoring may be assigned a weight of 0.5, the combined assessment confidence corresponding to ultrasonic detection is 0.6, the combined assessment confidence corresponding to vibration and lubrication is 0.9, the combined assessment confidence corresponding to infrared thermal imaging is 1, and the combined assessment confidence corresponding to motor diagnosis is 0.8.

[0237] Taking intelligent monitoring as an example, if the corresponding combined assessment confidence is 0.9, indicating that the target nuclear power equipment is about to have such a failure, according to the preset health assessment weight, this combined assessment confidence needs to be multiplied by 0.5 to get 0.45. This weighted value can be used to represent that when only relying on intelligent monitoring, the likelihood of the target nuclear power equipment failing is evaluated as medium (Grade B, status to be concerned about).

[0238] When multiple combined evaluation confidence levels are simultaneously applied to the evaluation of the health status of a device, each combined evaluation confidence level needs to be multiplied by its corresponding device health evaluation weight, and then the weighted values are integrated according to some algorithms to obtain the health status evaluation data of the target nuclear power device. In this way, a more comprehensive and accurate evaluation of the device health status can be achieved. In this manner, the potential failure risks of the target nuclear power device can be more effectively identified, providing a basis for maintenance decisions and ensuring the safety and reliability of the operation of the target nuclear power device.

[0239] Referring to Figure 9 , the predictive health status evaluation device for nuclear power equipment according to an embodiment of the present application may include:

[0240] A plurality of detection units 901 configured on the target nuclear power device, each detection unit is used to collect a set of nuclear power measurement data;

[0241] A branch confidence evaluation unit 902 for calculating the branch evaluation confidence corresponding to the target nuclear power device based on each nuclear power measurement data;

[0242] A combined confidence evaluation unit 903 for performing combined fault evaluation based on the branch evaluation confidences to determine the combined evaluation confidence corresponding to the target nuclear power device;

[0243] A device health evaluation unit 904 for performing device health evaluation based on the combined evaluation confidences corresponding to the target nuclear power device to determine the health status evaluation data of the target nuclear power device.

[0244] It can be seen that the content in the above embodiments of the predictive health status evaluation method for nuclear power equipment is applicable to the embodiments of this predictive health status evaluation device for nuclear power equipment. The functions specifically implemented in the embodiments of this predictive health status evaluation device for nuclear power equipment are the same as those in the above embodiments of the predictive health status evaluation method for nuclear power equipment, and the beneficial effects achieved are also the same as those in the above embodiments of the predictive health status evaluation method for nuclear power equipment.

[0245] Referring to Figure 10 , Figure 10 Schematically shows the hardware structure of an electronic device in another embodiment. The electronic device may include:

[0246] A processor 1001, which can be implemented in a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, etc., and is used to execute relevant programs to implement the technical solutions provided in the embodiments of the present application;

[0247] The memory 1002 can be implemented in the form of a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM), etc. The memory 1002 can store an operating system and other application programs. When implementing the technical solutions provided in the embodiments of this specification through software or firmware, the relevant program codes are stored in the memory 1002, and the processor 1001 is called to execute the predictive health status assessment method for nuclear power equipment in the embodiments of this application;

[0248] The input / output interface 1003 is used to implement information input and output;

[0249] The communication interface 1004 is used to implement communication and interaction between this device and other devices. It can achieve communication through wired means (such as USB, network cable, etc.), or can also achieve communication through wireless means (such as mobile network, WI FI, Bluetooth, etc.);

[0250] The bus 1005 transmits information between various components of the device (such as the processor 1001, the memory 1002, the input / output interface 1003, and the communication interface 1004);

[0251] Among them, the processor 1001, the memory 1002, the input / output interface 1003, and the communication interface 1004 achieve communication connections with each other inside the device through the bus 1005.

[0252] The embodiments of this application also provide a computer program product, which includes a computer program. The processor of the computer device reads and executes this computer program, so that the computer device executes to implement the above-mentioned predictive health status assessment method for nuclear power equipment.

[0253] Terms such as "first", "second", "third", "fourth", etc. (if any) in the specification of this disclosure and the above-mentioned drawings are used to distinguish similar objects, and do not necessarily need to be used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged under appropriate circumstances, so that the embodiments of this disclosure described here can be implemented in an order other than those illustrated or described here. In addition, the terms "include" and "contain" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products, or devices.

[0254] It should be understood that in this disclosure, "at least one (item)" means one or more, and "a plurality" means two or more. "And / or" is used to describe the association relationship of associated objects, indicating that there can be three relationships. For example, "A and / or B" can mean: only A exists, only B exists, and both A and B exist at the same time. Here, A and B can be singular or plural. The character " / " generally indicates that the associated objects before and after are in an "or" relationship. "At least one (one)" or similar expressions below refer to any combination of these items, including any combination of single items (one) or plural items (ones). For example, at least one (one) of a, b, or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or multiple.

[0255] It should be understood that in the description of the embodiments of this application, the meaning of a plurality (or multiple items) is more than two. Understandings such as greater than, less than, exceeding, etc. do not include the present number, and understandings such as above, below, within, etc. include the present number.

[0256] In several embodiments provided in this disclosure, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of units is only a logical function division. In actual implementation, there can be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed coupling or direct coupling or communication connection between each other can be through some interfaces, and the indirect coupling or communication connection of devices or units can be in electrical, mechanical, or other forms.

[0257] The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they can be located in one place, or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0258] In addition, the functional units in each embodiment of this disclosure can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above-mentioned integrated units can be implemented in the form of hardware or in the form of software functional units.

[0259] When the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present disclosure, in essence, or the part that contributes to the prior art, or all or part of this 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 for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods of various embodiments of the present disclosure. The aforementioned storage medium may include: various media that can store program codes, such as USB flash drives, mobile hard disks, read-only memories (ROM), random access memories (RAM), magnetic disks, or optical discs.

[0260] It should also be understood that the various embodiments provided in the embodiments of the present application can be combined arbitrarily to achieve different technical effects.

[0261] The above is a specific description of the embodiments of the present disclosure, but the present disclosure is not limited to the above embodiments. Those skilled in the art can also make various equivalent deformations or substitutions without departing from the spirit of the present disclosure, and these equivalent deformations or substitutions are all included within the scope defined by the claims of the present disclosure.

Claims

1. A method for predictive health status assessment of nuclear power equipment, characterized in that: include: According to the multiple detection units configured on the target nuclear power equipment, multiple groups of nuclear power measurement data associated with the target nuclear power equipment are collected; wherein each of the detection units is used to collect a group of the nuclear power measurement data; Based on each of the nuclear power measurement data, calculating the branch assessment confidence corresponding to the target nuclear power equipment; Performing a combined fault assessment based on each of the branch assessment confidences to determine a combined assessment confidence corresponding to the target nuclear power equipment; An equipment health assessment is performed according to each of the combined assessment confidences corresponding to the target nuclear power equipment to determine health status assessment data of the target nuclear power equipment.

2. The method according to claim 1, characterized in that The target nuclear power equipment has multiple equipment failure modes, and the branch evaluation confidence corresponding to the target nuclear power equipment is calculated based on each of the nuclear power measurement data, including: Based on each of the nuclear power measurement data, calculating the branch assessment confidence corresponding to the target nuclear power equipment under various equipment failure modes; The performing of combined fault assessment based on the assessment confidence of each branch to determine the combined assessment confidence corresponding to the target nuclear power equipment includes: For each of the equipment failure modes, a combined fault assessment is performed based on the branch assessment confidences corresponding to the equipment failure mode to determine the combined assessment confidence corresponding to the target nuclear power equipment under the equipment failure mode.

3. The method according to claim 2, characterized in that The step of calculating the branch evaluation confidence corresponding to each of the nuclear power measurement data under various equipment failure modes of the target nuclear power equipment includes: In the case where the nuclear power measurement data is discrete type data, determining the fault credibility and fault unreliability corresponding to the nuclear power measurement data under various equipment failure modes; For the fault credibility and the fault unreliability corresponding to each of the equipment failure modes, the branch evaluation confidence corresponding to the target nuclear power equipment under various equipment failure modes is calculated.

4. The method according to claim 3, characterized in that: Determining the fault credibility and fault unreliability corresponding to the nuclear power measurement data under various equipment failure modes includes: Obtaining a priori failure probability of the target nuclear power equipment under various equipment failure modes; wherein the a priori failure probability is used to characterize the probability of past failure of the target nuclear power equipment; For each of the equipment failure modes, the corresponding priori failure probability and the nuclear power measurement data are substituted into a pre-constructed credibility analytical formula for calculation to obtain the failure credibility corresponding to each of the equipment failure modes; For each of the equipment failure modes, the corresponding a priori failure probability and the nuclear power measurement data are substituted into the pre-constructed unreliability analytical formula for calculation to obtain the failure unreliability corresponding to each of the equipment failure modes.

5. The method according to claim 4, characterized in that The method further includes pre-constructing the credibility analytical formula and the unreliability analytical formula, specifically including: Determine the failure evidence factors corresponding to various equipment failure modes; Obtaining a conditional failure probability corresponding to each of the equipment failure modes; wherein the conditional failure probability is used to characterize the probability of failure of the target nuclear power equipment when the failure evidence factor occurs; For each of the equipment failure modes, constructing the credibility analytical formula based on the failure evidence factor and the priori failure probability; For each of the equipment failure modes, the unreliability analytical expression is constructed based on the failure evidence factor and the priori failure probability.

6. The method according to claim 2, characterized in that The step of performing a combined fault assessment based on each of the branch assessment confidences corresponding to the equipment failure mode for each of the equipment failure modes to determine the combined assessment confidence corresponding to the target nuclear power equipment under the equipment failure mode includes: Obtaining a pre-constructed confidence combination analytical expression for each of the equipment failure modes; For each of the equipment failure modes, sorting the branch evaluation confidences corresponding to the equipment failure mode to obtain a branch confidence sequence; Selecting a target branch evaluation confidence from the branch confidence sequence, and substituting the target branch evaluation confidence into the confidence combination analytical expression to perform combination confidence calculation to obtain a candidate combination confidence; In response to the branch evaluation confidence that has not yet participated in the combined confidence calculation in the branch confidence sequence, reselecting the target branch evaluation confidence from the branch evaluation confidence that has not yet participated in the combined confidence calculation, and substituting the candidate combined confidence and the target branch evaluation confidence into the confidence combination analytical expression to update the candidate combined confidence; After updating the candidate combination confidence, return to execute in response to the existence of the branch evaluation confidence in the branch confidence sequence that has not yet participated in the combination confidence calculation, until all the branch evaluation confidences in the branch confidence sequence have participated in the combination confidence calculation, and determine the latest candidate combination confidence as the combination evaluation confidence.

7. The method according to claim 2, characterized in that Performing equipment health assessment according to each of the combined assessment confidences corresponding to the target nuclear power equipment to determine health status assessment data of the target nuclear power equipment includes: Obtaining a preset equipment health assessment weight for each of the equipment failure modes; The health status of the equipment is estimated according to the combined assessment confidence and the equipment health assessment weight corresponding to each of the equipment failure modes, so as to obtain the health status assessment data of the target nuclear power equipment.

8. The method according to any one of claims 1 to 7, characterized in that: The calculating the branch assessment confidence corresponding to the target nuclear power equipment based on each of the nuclear power measurement data includes: In the case where the nuclear power measurement data is continuous type data, selecting a target conversion function from a plurality of preset numerical conversion functions for the nuclear power measurement data; The nuclear power measurement data is fitted through the target conversion function to obtain the branch evaluation confidence corresponding to the target nuclear power equipment.

9. The method according to claim 8, characterized in that The preset multiple numerical conversion functions include a high value interval conversion function, a median value interval conversion function and a low value interval conversion function. The nuclear power measurement data is fitted by the target conversion function to obtain the branch evaluation confidence corresponding to the target nuclear power equipment, including: In response to the target conversion function being the high-value interval conversion function, fitting the nuclear power measurement data through the high-value interval conversion function to obtain the branch evaluation confidence corresponding to the target nuclear power equipment; In response to the target conversion function being the median interval conversion function, fitting the nuclear power measurement data through the median interval conversion function to obtain the branch evaluation confidence corresponding to the target nuclear power equipment; In response to the target conversion function being the low-value interval conversion function, the nuclear power measurement data is fitted by means of the low-value interval conversion function to obtain the branch evaluation confidence corresponding to the target nuclear power equipment.

10. An evaluation device, characterized in that include: A plurality of detection units configured at the target nuclear power equipment, each of the detection units being used to collect a set of the nuclear power measurement data; A branch confidence evaluation unit, used to calculate the branch evaluation confidence corresponding to the target nuclear power equipment based on each of the nuclear power measurement data; A combined confidence evaluation unit, used to perform a combined fault evaluation based on the evaluation confidences of each branch, so as to determine the combined evaluation confidence corresponding to the target nuclear power equipment; The equipment health assessment unit is used to perform equipment health assessment according to the confidence levels of the combined assessments corresponding to the target nuclear power equipment, and determine health status assessment data of the target nuclear power equipment.

11. An electronic device, characterized in that: include: A memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the method for predictive health status assessment of nuclear power equipment as described in any one of claims 1 to 9 is implemented.

12. A computer-readable storage medium, characterized in that: The storage medium stores a program, and the program is executed by a processor to implement the predictive health status assessment method for nuclear power equipment as described in any one of claims 1 to 9.

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