Nuclear device fault monitoring method and device, electronic device, and storage medium

Abstract

The application relates to the technical field of nuclear power plant equipment safety, in particular to a nuclear equipment fault monitoring method and device, electronic equipment and a storage medium. According to the nuclear equipment fault monitoring method, a historical monitoring matrix and real-time monitoring data of a target nuclear equipment are first acquired; the historical monitoring matrix is incrementally processed according to the real-time monitoring data to obtain a dynamic memory matrix; an integrated multivariate state estimation model is constructed based on the dynamic memory matrix; the real-time monitoring data is input into the integrated multivariate state estimation model to calculate a state estimation vector corresponding to the integrated multivariate state estimation model; nuclear equipment state evaluation is carried out based on the state estimation vector and the real-time monitoring data to obtain nuclear equipment state information; and in response to the nuclear equipment state information satisfying a preset fault judgment condition, it is determined that the nuclear equipment state information is in a fault state. In this way, the monitoring accuracy of the nuclear equipment can be improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of nuclear power plant equipment safety, in particular to a nuclear equipment fault monitoring method and device, electronic equipment and a storage medium. BACKGROUND

[0002] Nuclear equipment fault prediction and health management is an important content of safe, efficient and reliable operation of nuclear power plants. However, in the long-term service of nuclear equipment, due to factors such as operation errors of nuclear power plant operators, natural environmental disasters, and weak digitalization, nuclear equipment will accelerate performance degradation and the probability of failure will continue to rise under improper use conditions, and in severe cases, it may cause unexpected shutdown for repair of nuclear power plants or even cause major safety accidents such as nuclear leakage.

[0003] Currently, nuclear power plants generally use periodic preventive maintenance measures based on experience and statistical data for nuclear equipment. However, in fact, most nuclear equipment failures are not strongly related to the time of use, and frequent preventive maintenance of nuclear equipment in unnecessary cases will cause over-maintenance to some extent, resulting in increased maintenance costs and reduced unit availability. Therefore, how to improve the monitoring accuracy of nuclear equipment is still a difficult problem to be solved in the industry. SUMMARY

[0004] The present application aims to at least solve one of the technical problems existing in the prior art. To this end, the present application proposes a nuclear equipment fault monitoring method and device, electronic equipment and a storage medium, which can improve the monitoring accuracy of nuclear equipment.

[0005] The nuclear equipment fault monitoring method according to the first aspect of the present application comprises:

[0006] obtaining a historical monitoring matrix and real-time monitoring data of a target nuclear equipment;

[0007] incrementally processing the historical monitoring matrix according to the real-time monitoring data to obtain a dynamic memory matrix;

[0008] constructing an integrated multi-element state estimation model based on the dynamic memory matrix;

[0009] inputting the real-time monitoring data into the integrated multi-element state estimation model to calculate a state estimation vector corresponding to the integrated multi-element state estimation model;

[0010] based on the state estimation vector and the real-time monitoring data, nuclear equipment state evaluation is performed to obtain nuclear equipment state information;

[0011] in response to the nuclear equipment state information satisfying a preset fault judgment condition, it is determined that the nuclear equipment state information is in a fault state.

[0012] According to some embodiments of the present application, the historical monitoring matrix includes multiple historical monitoring vectors, and the incremental processing of the historical monitoring matrix according to the real-time monitoring data to obtain a dynamic memory matrix includes:

[0013] Performing a closeness calculation on the historical monitoring matrix based on the real-time monitoring data to obtain a parameter closeness corresponding to each historical monitoring vector;

[0014] Reordering the historical monitoring vectors in the historical monitoring matrix based on the parameter closeness to obtain a target monitoring matrix;

[0015] Sampling is performed on the target monitoring matrix to obtain the dynamic memory matrix.

[0016] According to some embodiments of the present application, the closeness calculation of the historical monitoring matrix based on the real-time monitoring data to obtain the parameter closeness corresponding to each historical monitoring vector includes:

[0017] Performing standardization processing on the historical monitoring matrix and the real-time monitoring data respectively to obtain a standardized historical matrix corresponding to the historical monitoring matrix and a standardized real-time vector corresponding to the real-time monitoring data;

[0018] Constructing a correlation monitoring matrix based on the real-time monitoring data and each of the historical monitoring vectors;

[0019] The parameter closeness corresponding to each of the historical monitoring vectors is calculated based on the historical monitoring matrix, the real-time monitoring data, and the correlation matrix.

[0020] According to some embodiments of the present application, constructing a correlation monitoring matrix based on the real-time monitoring data and each of the historical monitoring vectors includes:

[0021] Calculate the historical monitoring mean corresponding to each of the historical monitoring vectors;

[0022] Determining each correlation matrix element based on a ratio of the real-time monitoring data to each of the historical monitoring means;

[0023] The correlation matrix elements are integrated into a diagonal matrix to construct the correlation monitoring matrix.

[0024] According to some embodiments of the present application, constructing an integrated multivariate state estimation model based on the dynamic memory matrix includes:

[0025] Performing partition processing on the dynamic memory matrix to obtain a first number of cluster center parameters and a sub-dynamic memory matrix corresponding to each of the cluster center parameters;

[0026] Determining a base model estimation parameter corresponding to each cluster center parameter according to the sub-dynamic memory matrix;

[0027] The first number of the base model estimation parameters and the cluster center parameters corresponding to each of the base model estimation parameters are integrated to obtain the integrated multivariate state estimation model.

[0028] According to some embodiments of the present application, the dynamic memory matrix includes a plurality of historical monitoring vectors obtained by sampling, each of the historical monitoring vectors is configured with a corresponding parameter closeness, and the partitioning process is performed on the dynamic memory matrix to obtain a first number of cluster center parameters and a sub-dynamic memory matrix corresponding to each of the cluster center parameters, including:

[0029] Based on the parameter closeness of each of the historical monitoring vector configurations, configuring a reference value for the dynamic memory matrix;

[0030] The dynamic memory matrix is ​​clustered by neighbor propagation using the reference degree value to partition the dynamic memory matrix to obtain a first number of cluster center parameters and the sub-dynamic memory matrices corresponding to each cluster center parameter.

[0031] According to some embodiments of the present application, inputting the real-time monitoring data into the integrated multivariate state estimation model to calculate a state estimation vector corresponding to the integrated multivariate state estimation model includes:

[0032] Calculating the initial weight corresponding to each cluster center parameter according to the real-time monitoring data and each cluster center parameter;

[0033] Performing weighting processing on the initial weights corresponding to the cluster center parameters to update the initial weights and obtain the base model contribution corresponding to the base model estimation parameters;

[0034] The state estimation vector is calculated based on the estimated parameters of each base model and the base model contribution corresponding to each base model estimated parameter.

[0035] According to some embodiments of the present application, the calculating the state estimation vector according to each of the base model estimation parameters and the base model contribution corresponding to each of the base model estimation parameters includes:

[0036] For each of the base model estimated parameters, multiplying the base model estimated parameter by the corresponding base model contribution to determine the contribution component;

[0037] Summing the first number of the contribution component elements to obtain a first state estimation element;

[0038] Summing the contribution of the first number of base models to obtain a second state estimation element;

[0039] The ratio of the first state estimation element to the second state estimation element is determined as the state estimation vector.

[0040] According to some embodiments of the present application, the performing of nuclear equipment state assessment based on the state estimation vector and the real-time monitoring data to obtain nuclear equipment state information includes:

[0041] Performing residual calculation based on the state estimation vector and the real-time monitoring data to obtain a residual sequence; wherein the residual sequence includes residual values ​​corresponding to each observation time;

[0042] Performing covariance calculation on each residual value in the residual sequence to obtain a first covariance matrix at a target observation moment and a second covariance matrix at a moment immediately before the target observation moment;

[0043] A health index is calculated based on the first covariance matrix and the second covariance matrix to obtain the nuclear equipment status information.

[0044] According to some embodiments of the present application, before determining that the nuclear device status information is in a fault state in response to the nuclear device status information satisfying a preset fault judgment condition, the method further includes:

[0045] Obtaining a warning threshold coefficient and past device status information corresponding to a plurality of observation times before the target observation time;

[0046] Performing threshold calculation based on the warning threshold coefficient and each of the past device status information to obtain a fault determination threshold;

[0047] In response to the core device status information satisfying a preset fault judgment condition, determining that the core device status information is in a fault state includes:

[0048] In response to the core device status information exceeding the fault determination threshold, it is determined that the core device status information meets the fault judgment condition, and the core device status information is determined to be in the fault state.

[0049] According to some embodiments of the present application, the historical monitoring matrix includes multiple historical monitoring vectors. After performing threshold calculation based on the warning threshold coefficient and each of the past device status information to obtain a fault determination threshold, the following further includes:

[0050] In response to the nuclear equipment status information not exceeding the fault determination threshold, incorporating the real-time monitoring data into the historical monitoring matrix;

[0051] After incorporating the real-time monitoring data into the historical monitoring matrix, performing a closeness calculation on the historical monitoring matrix based on the real-time monitoring data to obtain a parameter closeness corresponding to each of the historical monitoring vectors;

[0052] The historical monitoring vectors whose parameter closeness is less than a preset closeness threshold are deleted from the historical monitoring matrix to update the historical monitoring matrix.

[0053] According to some embodiments of the present application, the target nuclear equipment is configured with sensor measurement points, and obtaining the historical monitoring matrix and real-time monitoring data of the target nuclear equipment includes:

[0054] Acquire monitoring data sets at multiple observation times; wherein each monitoring data set includes monitoring sensor data collected by each sensor measuring point at one of the observation times;

[0055] Integrating the monitoring sensor data collected at each observation moment into an operation data vector corresponding to the observation moment;

[0056] Determining the operation data vector corresponding to the target observation time as real-time monitoring data;

[0057] The operation data vectors corresponding to multiple observation times before the target observation time are integrated into the historical monitoring matrix.

[0058] According to the second embodiment of the present application, a nuclear equipment fault monitoring device includes:

[0059] Data acquisition module, used to obtain historical monitoring matrix and real-time monitoring data of target nuclear equipment;

[0060] An incremental processing module, configured to perform incremental processing on the historical monitoring matrix according to the real-time monitoring data to obtain a dynamic memory matrix;

[0061] An estimation model construction module, configured to construct an integrated multivariate state estimation model based on the dynamic memory matrix;

[0062] a vector calculation module, configured to input the real-time monitoring data into the integrated multivariate state estimation model to calculate a state estimation vector corresponding to the integrated multivariate state estimation model;

[0063] A state assessment module, configured to perform a nuclear equipment state assessment based on the state estimation vector and the real-time monitoring data to obtain nuclear equipment state information;

[0064] The fault judgment module is used to determine that the nuclear equipment status information is in a fault state in response to the nuclear equipment status information meeting a preset fault judgment condition.

[0065] In a third aspect, an embodiment of the present application provides an electronic device comprising: a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, it implements the nuclear equipment fault monitoring method as described in any one of the embodiments of the first aspect of the present application.

[0066] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, wherein the storage medium stores a program, and the program is executed by a processor to implement a nuclear equipment fault monitoring method as described in any one of the embodiments of the first aspect of the present application.

[0067] The nuclear equipment fault monitoring method, device, electronic device, and storage medium according to the embodiments of the present application have at least the following beneficial effects:

[0068] According to the nuclear equipment fault monitoring method of the embodiment of the present application, it is necessary to first obtain the historical monitoring matrix and real-time monitoring data of the target nuclear equipment; perform incremental processing on the historical monitoring matrix according to the real-time monitoring data to obtain a dynamic memory matrix; construct an integrated multivariate state estimation model based on the dynamic memory matrix; input the real-time monitoring data into the integrated multivariate state estimation model to calculate the state estimation vector corresponding to the integrated multivariate state estimation model; perform nuclear equipment state assessment based on the state estimation vector and the real-time monitoring data to obtain nuclear equipment state information; and in response to the nuclear equipment state information meeting the preset fault judgment condition, determine that the nuclear equipment state information is in a fault state. In this way, the monitoring accuracy of nuclear equipment can be improved.

[0069] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become obvious from the description below, or will be learned through practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the description of the embodiments in conjunction with the following drawings, in which:

[0071] Figure 1 A flowchart of a method for monitoring nuclear equipment failures provided in an embodiment of the present application;

[0072] Figure 2 Another schematic diagram of a process flow of a method for monitoring nuclear equipment failures provided in an embodiment of the present application;

[0073] Figure 3 Another schematic diagram of a process flow of a method for monitoring nuclear equipment failures provided in an embodiment of the present application;

[0074] Figure 4 Another schematic diagram of a process flow of a method for monitoring nuclear equipment failures provided in an embodiment of the present application;

[0075] Figure 5 Another schematic diagram of a process flow of a method for monitoring nuclear equipment failures provided in an embodiment of the present application;

[0076] Figure 6 Another schematic diagram of a process flow of a method for monitoring nuclear equipment failures provided in an embodiment of the present application;

[0077] Figure 7 Another schematic diagram of a process flow of a method for monitoring nuclear equipment failures provided in an embodiment of the present application;

[0078] Figure 8 Another schematic diagram of a process flow of a method for monitoring nuclear equipment failures provided in an embodiment of the present application;

[0079] Figure 9 Another schematic diagram of a process flow of a method for monitoring nuclear equipment failures provided in an embodiment of the present application;

[0080] Figure 10 Another schematic diagram of a process flow of a method for monitoring nuclear equipment failures provided in an embodiment of the present application;

[0081] Figure 11 Another schematic diagram of a process flow of a method for monitoring nuclear equipment failures provided in an embodiment of the present application;

[0082] Figure 12 Another schematic diagram of a process flow of a method for monitoring nuclear equipment failures provided in an embodiment of the present application;

[0083] Figure 13 Schematic diagram of the structure of a nuclear equipment fault monitoring device provided in an embodiment of the present application;

[0084] Figure 14 This is a schematic diagram of the hardware structure of the electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0085] The following describes in detail embodiments of the present application. Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present application and are not to be construed as limiting the present application.

[0086] In the description of this application, "several" means one or more, "many" means more than two, "greater than," "less than," and "exceed" are understood to exclude the number itself, while "above," "below," and "within" are understood to include the number itself. The terms "first" and "second" are used solely to distinguish technical features and are not to be construed as indicating or implying relative importance, or as implicitly specifying the number or order of the technical features indicated.

[0087] In the description of this application, it should be understood that descriptions involving orientations, such as up, down, left, right, front, and back, etc., indicating orientations or positional relationships, are based on the orientations or positional relationships shown in the accompanying drawings. They are only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation. Therefore, they cannot be understood as limitations on this application.

[0088] Throughout this specification, reference to terms such as "one embodiment," "some embodiments," "illustrative embodiments," "examples," "specific examples," or "some examples" means that the specific features, structures, materials, or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0089] In the description of this application, it should be noted that, unless otherwise explicitly defined, terms such as "set," "install," and "connect" 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 based on the specific content of the technical solution. In addition, the identification of specific steps below does not represent a limitation on the order of steps and execution logic. The execution order and execution logic between each step should be understood and inferred with reference to the content described in the embodiments.

[0090] Nuclear equipment failure prediction and health management are crucial components of the safe, efficient, and reliable operation of nuclear power plants. However, over the long term, nuclear equipment can experience accelerated performance degradation and increased failure probability due to factors such as operator errors, natural environmental disasters, and limited digital capabilities. In severe cases, these failures can lead to unplanned nuclear power plant shutdowns for maintenance or even major safety incidents such as nuclear leaks.

[0091] Currently, nuclear power plants generally implement regular preventive maintenance measures for nuclear equipment based on experience and statistical data. However, in reality, the occurrence of most nuclear equipment failures is not strongly correlated with the age of the equipment. Frequent and unnecessary preventive maintenance of nuclear equipment has led to excessive maintenance, increased maintenance costs, and reduced unit availability.

[0092] Therefore, adopting a state-based predictive operation strategy to improve the comprehensiveness, timeliness and accuracy of operating status monitoring of key equipment in nuclear power plants and issuing early warnings before equipment failures occur is of great significance for ensuring the safety and reliability of nuclear power plant operations and reducing the occurrence of nuclear accidents.

[0093] With the rapid development of advanced sensor technology and the Industrial Internet of Things, the real-time operating signals of nuclear power plant equipment can be perceived, managed, and analyzed through sensors or data acquisition terminals, laying the foundation for nuclear equipment performance monitoring and early warning analysis.

[0094] Fault identification methods based on signal processing technology are classic methods for equipment performance monitoring and early warning analysis, including time domain, frequency domain, time-frequency domain, modal decomposition, and various filtering methods (Kalman filtering, blind filtering). These methods extract fault characteristics of monitoring data through deep information mining of equipment operation signals and establish a mapping relationship between fault characteristics and fault modes.

[0095] In recent years, equipment performance monitoring and early warning analysis methods based on multivariate state estimation technology (including principal component analysis, independent component analysis, linear discriminant analysis, and partial least squares analysis) have been widely studied. These methods project the high-dimensional data composed of equipment operation signals into a low-dimensional manifold space, and then mine the correlation information between equipment operation signals.

[0096] However, due to the impact of the radiation environment of nuclear power plants and the high safety requirements of operation, the above methods face some problems in implementation and application:

[0097] First, the high computational complexity makes it difficult to efficiently process large-scale monitoring data, resulting in low timeliness of equipment performance monitoring and an inability to meet the on-site operation requirements of nuclear power plants.

[0098] Secondly, the generalization is poor and it cannot be updated adaptively as the working mechanism of the equipment changes. Therefore, when the external working environment fluctuates, the accuracy of the model warning is greatly reduced.

[0099] The present application aims to solve at least one of the technical problems existing in the prior art. To this end, the present application proposes a nuclear equipment fault monitoring method and apparatus, electronic equipment, and storage medium thereof, which can improve the monitoring accuracy of nuclear equipment.

[0100] The following is a further explanation based on the accompanying drawings:

[0101] Reference Figure 1 The nuclear equipment fault monitoring method according to the embodiment of the present application may include, but is not limited to:

[0102] Step S101, obtaining the historical monitoring matrix and real-time monitoring data of the target nuclear equipment;

[0103] Step S102, performing incremental processing on the historical monitoring matrix according to the real-time monitoring data to obtain a dynamic memory matrix;

[0104] Step S103, constructing an integrated multivariate state estimation model based on the dynamic memory matrix;

[0105] Step S104, inputting the real-time monitoring data into the integrated multivariate state estimation model to calculate a state estimation vector corresponding to the integrated multivariate state estimation model;

[0106] Step S105, evaluating the nuclear equipment status based on the state estimation vector and the real-time monitoring data to obtain nuclear equipment status information;

[0107] Step S106: In response to the nuclear device status information satisfying a preset fault judgment condition, it is determined that the nuclear device status information is in a fault state.

[0108] The nuclear equipment fault monitoring method shown in steps S101 to S106 of the embodiment of the present application requires first obtaining the historical monitoring matrix and real-time monitoring data of the target nuclear equipment; performing incremental processing on the historical monitoring matrix based on the real-time monitoring data to obtain a dynamic memory matrix; constructing an integrated multivariate state estimation model based on the dynamic memory matrix; inputting the real-time monitoring data into the integrated multivariate state estimation model to calculate the state estimation vector corresponding to the integrated multivariate state estimation model; performing nuclear equipment state assessment based on the state estimation vector and the real-time monitoring data to obtain nuclear equipment state information; and determining that the nuclear equipment state information is in a fault state in response to the nuclear equipment state information meeting the preset fault judgment condition. In this way, the embodiment of the present application can realize real-time and accurate monitoring of the nuclear equipment state, effectively solve the technical difficulties of nuclear power plants in equipment monitoring and fault prediction, and provide strong guarantees for the safe, efficient and reliable operation of nuclear power plants.

[0109] In step S101 of some embodiments, the historical monitoring matrix and real-time monitoring data of the target nuclear equipment are first obtained, providing a comprehensive data foundation for the embodiments of the present application. The historical monitoring matrix contains a large amount of historical data of the equipment under normal operation, while the real-time monitoring data reflects the current operating status of the equipment. The combination of these two enables the embodiments of the present application to fully understand the operating history and current status of the equipment.

[0110] Reference Figure 2 According to some embodiments of the present application, the target nuclear equipment is configured with sensor measurement points. Step S101 obtains the historical monitoring matrix and real-time monitoring data of the target nuclear equipment, which may include, but is not limited to:

[0111] Step S201: Acquire monitoring data sets at multiple observation times; wherein each monitoring data set includes monitoring sensor data collected by each sensor point at one observation time;

[0112] Step S202: integrating the monitoring sensor data collected at each observation moment into an operation data vector corresponding to the observation moment;

[0113] Step S203, determining the operation data vector corresponding to the target observation time as real-time monitoring data;

[0114] Step S204 , integrating the operation data vectors corresponding to multiple observation times before the target observation time into a historical monitoring matrix.

[0115] In some embodiments of the present application, the target core equipment is equipped with sensor points that collect real-time information about the equipment's operating status. In step S101, the present embodiment acquires historical monitoring matrices and real-time monitoring data from the target core equipment, providing a data foundation for subsequent analysis and fault prediction. This process may include several sub-steps.

[0116] In step S201 of some embodiments, embodiments of the present application collect monitoring data sets at multiple observation times. Each monitoring data set at each observation time includes monitoring sensor data collected by all sensor points at that time. These data sets provide embodiments of the present application with detailed operating status information for the device at different time points, forming the basis for constructing a historical monitoring matrix and determining real-time monitoring data.

[0117] In step S202 of some embodiments, the present invention integrates the monitoring sensor data collected at each observation time to form an operating data vector corresponding to the observation time. This operating data vector is a mathematical representation of the operating state of the device at a specific time. It contains data from all relevant sensor points and can be used for subsequent state estimation and fault diagnosis.

[0118] In step S203 of some embodiments, the present embodiment determines the operating data vector corresponding to the target observation time as real-time monitoring data. This real-time monitoring data is the current focus of the present embodiment, as it reflects the latest operating status of the device and is crucial for timely detection of changes in device performance.

[0119] In step S204 of some embodiments, the present invention integrates the operational data vectors corresponding to multiple observation times prior to the target observation time into a historical monitoring matrix. This matrix records the changes in the operational status of the device over a period of time and serves as an important data source for trend analysis and state estimation. Using this historical monitoring matrix, the present invention can identify long-term trends and patterns in device operation, providing a basis for predicting the device's future operational status.

[0120] The embodiment of the present application, illustrated by steps S201 to S204, comprehensively collects and integrates equipment monitoring data, providing solid data support for accurate condition assessment and fault prediction. This approach not only improves monitoring accuracy but also enhances the ability to predict future equipment conditions by analyzing historical data, thereby providing strong support for the safe operation of nuclear power plants.

[0121] In step S102 of some embodiments, a dynamic memory matrix is ​​generated by incrementally processing the historical monitoring matrix. This step allows embodiments of the present application to dynamically update and adjust historical data to reflect the latest operating status of the device, thereby resolving the issue of data obsolescence in traditional monitoring methods. The construction of a dynamic memory matrix enables embodiments of the present application to adapt to changes in the operating status of the device, improving the timeliness and accuracy of monitoring.

[0122] In some more specific embodiments, the historical monitoring matrix X his It can be expressed as:

[0123]

[0124] Among them, the historical monitoring matrix X his The element x in ij Indicates that at t i The measurement data of the jth measuring point monitored at all times, x i Indicates that at t i The historical monitoring vector is composed of the measurement data of all the measurement points monitored at all times. n represents the number of monitoring vectors, and m represents the number of measurement points, where: i = 1, ..., n; j = 1, ..., m.

[0125] Reference Figure 3 According to some embodiments of the present application, the historical monitoring matrix includes multiple historical monitoring vectors. Step S102 performs incremental processing on the historical monitoring matrix based on real-time monitoring data to obtain a dynamic memory matrix, which may include, but is not limited to:

[0126] Step S301, performing closeness calculation on the historical monitoring matrix based on the real-time monitoring data to obtain the parameter closeness corresponding to each historical monitoring vector;

[0127] Step S302, reordering the historical monitoring vectors in the historical monitoring matrix based on the parameter closeness to obtain a target monitoring matrix;

[0128] Step S303, sampling the target monitoring matrix to obtain a dynamic memory matrix.

[0129] In some embodiments of the present application, the construction and processing of the historical monitoring matrix are the core part of the nuclear equipment fault monitoring method. The historical monitoring matrix is composed of multiple historical monitoring vectors, and each vector represents the running state of the equipment at different observation times. In step S102, the embodiments of the present application incrementally process the historical monitoring matrix based on real-time monitoring data to obtain a dynamic memory matrix, which includes several key operations.

[0130] In step S301 of some embodiments, the embodiments of the present application calculate the parameter closeness of each historical monitoring vector based on real-time monitoring data. The parameter closeness is an index for measuring the similarity between real-time monitoring data and historical monitoring data. By calculating the parameter closeness of each historical monitoring vector, the embodiments of the present application can evaluate the closeness of real-time data and historical data. The purpose of this step is to identify the historical data that is most similar to the current device state, providing a reference for subsequent monitoring and analysis.

[0131] In step S302 of some embodiments, the embodiments of the present application reorder the historical monitoring vectors in the historical monitoring matrix based on the calculated parameter closeness. The purpose of this operation is to prioritize the historical data that is most similar to the real-time monitoring data, thereby forming a target monitoring matrix. It should be understood that the construction of the target monitoring matrix enables the embodiments of the present application to pay more attention to the data that is most relevant to the current device state when monitoring the target nuclear equipment, improving the relevance and effectiveness of the monitoring.

[0132] In step S303 of some embodiments, the embodiments of the present application sample the target monitoring matrix to obtain a dynamic memory matrix. It should be noted that sampling is a data reduction technique that selects a portion of the most representative historical monitoring vectors from the target monitoring matrix to construct a dynamic memory matrix. This dynamic memory matrix not only contains data that is most relevant to the current device state, but also reduces the size of the data through sampling, improving the efficiency of data processing.

[0133] Through the embodiments provided by steps S301 to S303, the historical monitoring data can be effectively processed and updated, and a dynamic memory matrix that reflects both the current state of the target nuclear equipment and historical continuity can be constructed, improving the accuracy of the monitoring and providing data support for accurate state evaluation and fault prediction.

[0134] Reference Figure 4 According to some embodiments of the present application, step S301 performs a closeness calculation on the historical monitoring matrix based on the real-time monitoring data to obtain the parameter closeness corresponding to each historical monitoring vector, which may include, but is not limited to:

[0135] Step S401, normalizing the historical monitoring matrix and the real-time monitoring data to obtain a normalized historical matrix corresponding to the historical monitoring matrix and a normalized real-time vector corresponding to the real-time monitoring data;

[0136] Step S402: constructing a correlation monitoring matrix based on the real-time monitoring data and each historical monitoring vector;

[0137] Step S403 : Calculate the parameter closeness corresponding to each historical monitoring vector based on the historical monitoring matrix, the real-time monitoring data, and the correlation matrix.

[0138] In some embodiments of the present application, the purpose of calculating the closeness of the historical monitoring matrix is ​​to evaluate the similarity between the real-time monitoring data and the historical data, thereby providing a basis for the condition monitoring and fault prediction of nuclear equipment. This process begins with step S301, which includes several key sub-steps.

[0139] In step S401 of some embodiments, embodiments of the present application perform normalization on the historical monitoring matrix and real-time monitoring data. Normalization is performed to eliminate dimensional differences between different sensor measurement points, ensuring that all data is compared on the same scale. This process results in a normalized historical monitoring matrix and a normalized real-time monitoring vector. This step is intended to improve data comparability and make the closeness calculation more accurate.

[0140] In step S402 of some embodiments, embodiments of the present application construct a correlation monitoring matrix based on the real-time monitoring data and each historical monitoring vector. This matrix reflects the degree of correlation between the real-time monitoring data and the historical monitoring data and serves as the basis for calculating proximity. The construction of the correlation monitoring matrix may involve various mathematical methods, such as distance metrics and similarity metrics, to quantify the similarity between the real-time data and the historical data.

[0141] Reference Figure 5 According to some embodiments of the present application, step S402 constructs a correlation monitoring matrix based on the real-time monitoring data and each historical monitoring vector, which may include, but is not limited to:

[0142] Step S501, calculating the historical monitoring mean corresponding to each historical monitoring vector;

[0143] Step S502, determining each correlation matrix element based on the ratio of the real-time monitoring data to each historical monitoring mean;

[0144] Step S503 : integrating the elements of each correlation matrix into a diagonal matrix to construct a correlation monitoring matrix.

[0145] In some embodiments of the present application, constructing a correlation monitoring matrix is ​​a key step in implementing a nuclear equipment fault monitoring method. This process, performed in step S402, aims to quantify the correlation between real-time monitoring data and historical monitoring data to more accurately assess the current status of the equipment. The process of constructing the correlation monitoring matrix may include the following steps:

[0146] In step S501 of some embodiments, embodiments of the present application calculate the historical monitoring mean corresponding to each historical monitoring vector. This step involves statistically analyzing the data in the historical monitoring matrix to determine the central tendency of each historical monitoring vector. The calculation of the historical monitoring mean provides a benchmark for subsequent correlation assessments, enabling embodiments of the present application to measure the degree of deviation between real-time data and historical data.

[0147] In step S502 of some embodiments, embodiments of the present application determine each correlation matrix element based on the ratio of the real-time monitoring data to each historical monitoring mean. This step evaluates the similarity between the real-time monitoring data and the historical data by comparing the ratio of the real-time monitoring data to the historical mean. The calculated ratio will serve as an element of the correlation matrix, reflecting the strength of the association between the real-time monitoring data and each historical monitoring vector.

[0148] In step S503 of some embodiments, the embodiments of the present application integrate the elements of each correlation matrix into a diagonal matrix to construct a correlation monitoring matrix. A diagonal matrix is ​​a special matrix in which all elements on the non-diagonal line are zero and only the elements on the diagonal line have values. In the present application, the diagonal elements of the diagonal matrix represent the correlation between the real-time monitoring data and the historical monitoring vector. In this way, the correlation monitoring matrix can concisely and effectively express the correlation between real-time data and historical data.

[0149] The embodiment of the present application, provided through steps S501 to S503, can construct a correlation monitoring matrix that quantifies the correlation between real-time monitoring data and historical monitoring data. This quantitative correlation assessment provides important information for the embodiment of the present application, helping to more accurately assess the current status of the equipment and predict potential failure risks, thereby improving the safety and reliability of nuclear power plant equipment operation.

[0150] In step S403 of some embodiments, the embodiments of the present application calculate the parameter closeness corresponding to each historical monitoring vector based on the historical monitoring matrix, real-time monitoring data, and the correlation monitoring matrix. Parameter closeness is a key metric that reflects the degree of similarity between the real-time monitoring data and each historical monitoring vector. By calculating closeness, the embodiments of the present application can identify the historical data that is most similar to the current device state, which is crucial for understanding the current operating state of the device and predicting its future behavior.

[0151] The embodiment of the present application, provided through steps S401 to S403, can accurately assess the similarity between real-time monitoring data and historical data, providing important data support for subsequent state estimation and fault prediction. This method not only improves monitoring accuracy, but also, through dynamic updates, enables the embodiment of the present application to adapt to changes in equipment operating status, providing solid data support for accurate state assessment and fault prediction. The application of this method is of great significance for improving the safety and reliability of nuclear power plant equipment operation.

[0152] In some more specific embodiments of the present application, the historical monitoring matrix X his and real-time monitoring data x obs Perform standardization processing respectively to obtain the standardized historical matrix corresponding to the historical monitoring matrix Standardized real-time vector corresponding to real-time monitoring data It can be expressed as:

[0153]

[0154] Among them, l n+1 is a column vector whose elements are all 1.

[0155] Further, calculate the normalized real-time observation vector and standardized historical monitoring matrix The parameter closeness r of the monitoring vector in i :

[0156]

[0157] Where Λ represents a diagonal matrix, which is calculated by normalizing the real-time observation vector and standardized historical monitoring matrix The larger the ratio, the higher the monitoring importance of the corresponding measuring point. Its expression is as follows:

[0158]

[0159] Based on the parameter closeness r i Standardized Historical Monitoring Matrix The historical monitoring vectors in ascending order can be obtained by reordering the standardized historical monitoring matrix

[0160] In step S103 of some embodiments, an integrated multivariate state estimation model is constructed based on the dynamic memory matrix. The integrated multivariate state estimation model is an analytical model that integrates multiple base models and can estimate and predict device states from multiple perspectives and levels. This integrated approach improves the accuracy and robustness of state estimation, enabling embodiments of the present application to more accurately capture changing trends in device performance.

[0161] Reference Figure 6 According to some embodiments of the present application, step S103 constructs an integrated multivariate state estimation model based on the dynamic memory matrix, which may include, but is not limited to:

[0162] Step S601, performing partition processing on the dynamic memory matrix to obtain a first number of cluster center parameters and a sub-dynamic memory matrix corresponding to each cluster center parameter;

[0163] Step S602, determining the base model estimation parameters corresponding to each cluster center parameter according to the sub-dynamic memory matrix;

[0164] Step S603 : Integrate the first number of base model estimation parameters and the cluster center parameters corresponding to each base model estimation parameter to obtain an integrated multivariate state estimation model.

[0165] In some embodiments of the present application, constructing an integrated multivariate state estimation model (EMSET) is one of the core steps of the nuclear equipment fault monitoring method. This process aims to improve the accuracy of equipment state estimation by integrating the prediction results of multiple base models. This process is performed in step S103 and may include the following sub-steps.

[0166] In step S601 of some embodiments, the embodiments of the present application perform partitioning processing on the dynamic memory matrix to obtain a certain number of cluster center parameters and sub-dynamic memory matrices corresponding to these cluster center parameters. This step may include cluster analysis techniques, such as the improved proximity propagation (AP) clustering method, which can identify natural groupings or patterns in the data. The cluster center parameters represent the center or typical representative of each subset (or cluster), and the sub-dynamic memory matrix contains data points related to each cluster center parameter. The purpose of this partitioning process is to decompose complex data sets into smaller, more manageable parts, each of which is represented by a cluster center parameter, thereby simplifying subsequent analysis and processing.

[0167] Reference Figure 7According to some embodiments of the present application, the dynamic memory matrix includes a plurality of historical monitoring vectors obtained by sampling, each historical monitoring vector is configured with a corresponding parameter closeness. Step S601 performs partitioning processing on the dynamic memory matrix to obtain a first number of cluster center parameters and a sub-dynamic memory matrix corresponding to each cluster center parameter, which may include, but is not limited to:

[0168] Step S701, configuring a reference value for a dynamic memory matrix based on the closeness of parameters configured for each historical monitoring vector;

[0169] Step S702 , performing neighbor propagation clustering on the dynamic memory matrix by using the reference degree value to partition the dynamic memory matrix to obtain a first number of cluster center parameters and sub-dynamic memory matrices corresponding to each cluster center parameter.

[0170] In some embodiments of the present application, a dynamic memory matrix is ​​constructed and processed to improve the accuracy and efficiency of nuclear equipment fault monitoring. The dynamic memory matrix includes historical monitoring vectors obtained through sampling, each of which is assigned a corresponding parameter proximity. These parameter proximity measures reflect the similarity between the real-time monitoring data and the historical data. In step S601, embodiments of the present application partition the dynamic memory matrix to obtain a certain number of cluster center parameters and sub-dynamic memory matrices corresponding to these cluster center parameters.

[0171] In step S701 of some embodiments, the embodiments of the present application configure a reference value for the dynamic memory matrix based on the parameter proximity of each historical monitoring vector configuration. The reference value is a key parameter for controlling the formation of clusters during the clustering process, which can help determine which data points should be classified into the same category. Based on the parameter proximity of each historical monitoring vector configuration, the reference value is configured for the dynamic memory matrix to ensure that the embodiments of the present application can pay more attention to historical data similar to the real-time monitoring data during the clustering process, thereby improving the accuracy and relevance of the clustering.

[0172] In step S702 of some embodiments, the embodiments of the present application perform neighbor propagation clustering on the dynamic memory matrix through reference values. Neighbor propagation clustering is a clustering algorithm based on graph theory, which forms clusters by iteratively transferring similarity information between data points. In the present application, neighbor propagation clustering uses configured reference values ​​to guide the clustering process, so that historical monitoring vectors similar to real-time monitoring data are more likely to be classified into the same cluster. The result of this step is to obtain a certain number of cluster center parameters, each cluster center parameter represents the center or typical representative of a class of data, and sub-dynamic memory matrices corresponding to these cluster center parameters, which contain historical monitoring data similar to each cluster center parameter.

[0173] The embodiment of the present application, illustrated by steps S701 to S702, can effectively partition the dynamic memory matrix into multiple related subsets, each of which has a certain similarity with the real-time monitoring data. This partitioning process not only simplifies the complexity of the data but also makes subsequent state estimation and fault prediction more accurate and efficient. The application of this method is of great significance for improving the safety and reliability of nuclear power plant equipment operation, because it ensures that the embodiment of the present application pays more attention to the data most relevant to the current equipment status, thereby providing more accurate fault warnings and status assessments.

[0174] In step S602 of some embodiments, the embodiments of the present application determine the base model estimation parameters corresponding to each cluster center parameter based on each sub-dynamic memory matrix. This step involves selecting or constructing a most suitable base model for each subset, where the base model can accurately capture and describe the characteristics and dynamics of the data in the subset. The base model estimation parameters are key parameters corresponding to the base model, which are used to characterize the corresponding output parameters after the base model captures and describes the characteristics and dynamics of the data in the subset.

[0175] In step S603 of some embodiments, the present invention integrates all base model estimation parameters and their corresponding cluster center parameters to obtain a final integrated multivariate state estimation model. This integrated multivariate state estimation model incorporates the characteristics of all base models and can estimate and predict device states from multiple perspectives and levels. In this way, the integrated multivariate state estimation model can provide more comprehensive and accurate device state information than a single model, thereby providing strong support for fault monitoring and health management of nuclear equipment.

[0176] The embodiment of the present application shown through steps S601 to S603 can construct an integrated multivariate state estimation model. This integrated multivariate state estimation model improves the estimation accuracy of the nuclear equipment state by integrating the prediction results of multiple base models, providing a reliable basis for achieving accurate state assessment and fault prediction.

[0177] In some specific embodiments of the present application, a fixed sampling interval δ can be selected according to a preset value n to reorder the standardized historical monitoring matrix Take samples.

[0178] Select the sampled historical monitoring vector to construct the dynamic memory matrix D dyn , in order to reduce the dynamic memory matrix D under the premise of ensuring the diversity of historical monitoring vector distribution dyn scale.

[0179] According to the parameter proximity, the negative value of the parameter proximity is assigned to the reference value of the affinity propagation clustering (Affinity Propagation, AP) to control the number of cluster centers and complete the improvement of the affinity propagation clustering algorithm.

[0180] Furthermore, the improved neighbor propagation clustering algorithm is used to transform the dynamic memory matrix D dyn Perform partitioning to obtain l cluster centers and corresponding clusters, and construct a sub-dynamic memory matrix for each cluster:

[0181] AP(D dyn )=[c1,L,c k ,L,c l ,sD dyn1 ,K,sD dynk ,K,sD dynl ]

[0182] Among them, AP(·) is the improved neighbor propagation clustering algorithm, c k is the kth cluster center, sD dynk is the kth sub-dynamic memory matrix, where l is the number of clusters, i.e., the first number, k=1,…,l.

[0183] Furthermore, the base model is trained according to the sub-dynamic memory matrix, which is expressed as:

[0184]

[0185] in, It is the output result of the k-th base model MSETk, that is, the base model estimation parameters corresponding to the k-th base model.

[0186] Furthermore, the output result of the base model MSETk is and the cluster center c k Arranged in column direction, it constitutes the integrated multivariate state estimation model EMSET.

[0187] In some embodiments, step S104 involves inputting real-time monitoring data into an integrated multivariate state estimation model to calculate a state estimation vector. This vector represents the best estimate of the current state of the equipment and provides an important basis for subsequent state assessment. By comparing the state estimation vector with the real-time monitoring data, the method can accurately assess the state of the nuclear equipment in step S105 and obtain equipment status information.

[0188] Reference Figure 8 According to some embodiments of the present application, step S104 inputs the real-time monitoring data into the integrated multivariate state estimation model to calculate the state estimation vector corresponding to the integrated multivariate state estimation model, which may include, but is not limited to:

[0189] Step S801, calculating the initial weight corresponding to each cluster center parameter based on the real-time monitoring data and the parameters of each cluster center;

[0190] Step S802: performing weighting processing on the initial weights corresponding to the parameters of each cluster center to update the initial weights and obtain the contribution of each base model estimation parameter to the base model;

[0191] Step S803 : Calculate and obtain a state estimation vector based on the estimated parameters of each base model and the base model contribution corresponding to the estimated parameters of each base model.

[0192] In some embodiments of the present application, real-time monitoring data is input into an integrated multivariate state estimation model to calculate the corresponding state estimation vector. This process is a key step in achieving nuclear equipment fault monitoring. The state estimation vector can provide an accurate estimate of the current operating state of the equipment, providing an important basis for subsequent state assessment and fault prediction. In step S104, this calculation process includes several key operations:

[0193] In step S801 of some embodiments, the embodiments of the present application calculate the initial weights corresponding to the parameters of each cluster center based on the real-time monitoring data and the parameters of each cluster center. It should be noted that the initial weights reflect the similarity between the real-time monitoring data and the parameters of each cluster center, and are the starting point for determining the importance of each base model in the integrated multivariate state estimation model. By calculating the initial weights, the embodiments of the present application can identify the base model that is most relevant to the real-time monitoring data, providing a basis for subsequent weight adjustment and state estimation.

[0194] In step S802 of some embodiments, the embodiments of the present application perform weighted processing on the initial weights corresponding to the parameters of each cluster center to update the initial weights and obtain the base model contribution corresponding to the estimated parameters of each base model. Weighted processing is a method of adjusting weights that takes into account the performance of the base model on different data subsets and the relationship between the base models. The base model contribution corresponding to the estimated parameters of each base model is obtained through weighted processing, so that the contribution of each base model to the final state estimation can be more accurately evaluated through the base model contribution, thereby improving the accuracy and reliability of the state estimation.

[0195] In step S803 of some embodiments, the present invention calculates a state estimation vector based on the estimated parameters of each base model and the corresponding base model contribution. This step involves taking a weighted average of each base model estimated parameter according to its corresponding base model contribution to form the final state estimation vector. The state estimation vector integrates information from all base models, providing a comprehensive estimate of the current operating state of the equipment and providing an important basis for nuclear equipment state assessment and fault prediction.

[0196] Reference Figure 9 According to some embodiments of the present application, step S803 calculates the state estimation vector based on the estimated parameters of each base model and the base model contribution corresponding to each base model estimated parameter, which may include, but is not limited to:

[0197] Step S901: for each base model estimated parameter, the product of the base model estimated parameter and the corresponding base model contribution is determined as a contribution component;

[0198] Step S902, summing the first number of contribution components to obtain a first state estimation element;

[0199] Step S903, summing the contribution of the first number of base models to obtain a second state estimation element;

[0200] Step S904: Determine the ratio of the first state estimation element to the second state estimation element as a state estimation vector.

[0201] In some embodiments of the present application, the process of calculating the state estimate vector is a key component of the integrated multivariate state estimation model. It involves combining the estimation results of each base model with its contribution to produce a comprehensive device state estimate. In step S803, this process includes several key operations:

[0202] In step S901 of some embodiments, the present invention multiplies each base model estimated parameter by its corresponding base model contribution to determine a contribution component. This step aims to quantify the contribution of each base model to the overall state estimate. The product reflects the weight of the base model estimate in the final state vector.

[0203] In step S902 of some embodiments, the present invention sums the contributing elements of all base models to obtain a first state estimate element. This summation operation actually integrates the information of all base models to form a comprehensive estimate value, which represents one aspect of the device state.

[0204] In step S903 of some embodiments, the present invention sums the contributions of all base models to obtain a second state estimation element. This summation operation provides an indicator of the total contribution of all base models, which helps to consider the relative importance of the base models in the final state estimation.

[0205] In step S904 of some embodiments, the present invention determines the ratio of the first state estimation element to the second state estimation element as a state estimation vector. This ratio operation is a normalization process, so that the state estimation vector can more accurately reflect the actual state of the device.

[0206] Through steps S901 to S904 shown in the embodiment of the present application, a comprehensive state estimation vector can be calculated. This vector not only takes into account the estimation results of each base model, but also considers the degree of their contribution to the final estimate. This method improves the accuracy and reliability of state estimation and provides important information for the state assessment and fault prediction of target nuclear equipment. This method of calculating the state estimation vector enables the embodiment of the present application to more accurately capture the performance changes of the equipment, thereby providing strong support for the safe operation of nuclear power plants.

[0207] Through steps S801 to S803 shown in the embodiment of this application, real-time monitoring data can be effectively input into the integrated multivariate state estimation model to calculate a state estimation vector that accurately reflects the current state of the device. This method not only improves the accuracy of state estimation, but also makes the state estimation more comprehensive and reliable by considering the contributions between the base models.

[0208] According to some more specific embodiments of this application, based on standardized real-time monitoring data and the cluster center parameters c1,L,c k ,L,c l , calculate the initial weight w corresponding to each cluster center parameter k , expressed as:

[0209]

[0210] The initial weight w corresponding to each cluster center parameter k Perform weighted processing to update the initial weights and obtain the contribution w of each base model estimation parameter to the base model k ′, expressed as:

[0211]

[0212] Estimate parameters based on each basic model And the base model contribution w′ corresponding to the estimated parameters of each base model k , calculate the state estimation vector x est , expressed as:

[0213]

[0214] in, To contribute elements, is the first state estimation element, is the second state estimation element.

[0215] In step S105 of some embodiments, when the nuclear equipment status information meets the preset fault judgment conditions, the embodiments of the present application can determine that the equipment is in a fault state and issue a timely warning. This state-based monitoring and warning mechanism can not only reduce unnecessary preventive maintenance and lower maintenance costs, but also improve unit availability. More importantly, it can promptly detect potential fault risks and avoid major safety accidents such as unexpected shutdowns and nuclear leaks.

[0216] Reference Figure 10 According to some embodiments of the present application, step S105 performs nuclear equipment status assessment based on the state estimation vector and real-time monitoring data to obtain nuclear equipment status information, which may include, but is not limited to:

[0217] Step S1001, performing residual calculation based on the state estimation vector and the real-time monitoring data to obtain a residual sequence; wherein the residual sequence includes the residual value corresponding to each observation time;

[0218] Step S1002, performing covariance calculation on each residual value in the residual sequence to obtain a first covariance matrix at the target observation moment and a second covariance matrix at the moment immediately before the target observation moment;

[0219] Step S1003 , calculating the health index based on the first covariance matrix and the second covariance matrix to obtain nuclear equipment status information.

[0220] In some embodiments of the present application, nuclear equipment status assessment is a key step that determines the current status of the equipment based on the state estimation vector and real-time monitoring data. This process, performed in step S105, includes several key operations designed to assess the health of the equipment by analyzing deviations and changes in the data.

[0221] In step S1001 of some embodiments, the present application performs residual calculations based on the state estimate vector and real-time monitoring data to obtain a residual sequence. Residuals refer to the difference between the real-time monitoring data and the state estimate vector. The residual sequence includes residual values ​​corresponding to each observation moment. This step aims to identify deviations between the actual operating state of the device and the state predicted by the model. The residual sequence can reveal fluctuations in device performance and potential anomalies.

[0222] In step S1002 of some embodiments, embodiments of the present application perform covariance calculations on each residual value in the residual sequence to obtain a first covariance matrix for the target observation moment and a second covariance matrix for the moment immediately preceding the target observation moment. A covariance matrix is ​​a statistical tool used to describe the correlation between residual values ​​and can reflect the pattern and trend of changes in device status. By calculating the covariance matrix at different moments, embodiments of the present application can assess the stability and consistency of the device status.

[0223] In step S1003 of some embodiments, the health index is calculated based on the first covariance matrix and the second covariance matrix, and the nuclear equipment state information is obtained. The nuclear equipment state information is a comprehensive index that combines the information in the covariance matrix, and is used to quantify the health state of the equipment. By comparing the covariance matrices at different times, the nuclear equipment state information can reveal the long-term trend of the equipment state, and provide support for the maintenance and fault prevention of the target nuclear equipment.

[0224] Through steps S1001 to S1003 provided by the embodiments of the present application, in-depth state evaluation can be performed based on the state estimation vector and real-time monitoring data. This method not only can identify the current state of the equipment, but also can predict the future performance of the equipment by analyzing the residual error and covariance, thereby providing strong support for the safe operation of the nuclear power plant. The application of this state evaluation method enables the embodiments of the present application to more accurately capture the performance changes of the equipment and timely discover potential fault risks.

[0225] According to some more specific embodiments of the present application, the residual error calculation is performed according to the state estimation vector x est and the normalized real-time monitoring data , which is expressed as:

[0226]

[0227] wherein e obs is the residual error value at observation time t obs .

[0228] Further, according to the residual error value e obs , a residual error sequence E obs is constructed, which is expressed as:

[0229] E obs = [e obs-N+1 , K, e obs-s+1 ,..., e obs ]

[0230] wherein E obs is the residual error sequence at observation time t obs , e obs-s+1 is the residual error value at observation time t obs-s+1 , and N is the length of the residual error sequence, s = 1, …, N.

[0231] Further, the observation time t obs is determined as the target observation time, and the covariance calculation is performed on each residual error value in the residual error sequence E obs , and the first covariance matrix C obs at the target observation time t obs is obtained.

[0232] It should be noted that, near the observation time t obs The covariance matrix of the continuous residual values ​​is calculated as the first covariance matrix C obs , expressed as;

[0233]

[0234] Similarly, we get the target observation time t obs The previous moment t obs-1 The second covariance matrix C obs-1 .

[0235] Furthermore, based on the first covariance matrix C obs and the second covariance matrix C obs-1 Calculate the health index and obtain the nuclear equipment status information h obs , expressed as:

[0236]

[0237] Among them, h obs is the target observation time t obs The health index of the core device is the status information of the core device. ||.||1 is the L1 norm.

[0238] Reference Figure 11 According to some embodiments of the present application, in step S106, in response to the nuclear equipment status information meeting the preset fault judgment condition, before determining that the nuclear equipment status information is in a fault state, the following steps may also be included, but are not limited to:

[0239] Step S1101, obtaining a warning threshold coefficient and past device status information corresponding to multiple observation times before the target observation time;

[0240] Step S1102 , performing threshold calculation based on the warning threshold coefficient and each past device status information to obtain a fault determination threshold.

[0241] In some embodiments of the present application, before determining whether nuclear equipment status information is in a fault state, a series of preprocessing steps are performed to ensure the accuracy and reliability of the fault determination. These steps include obtaining a warning threshold coefficient and past equipment status information corresponding to multiple observation times prior to the target observation time, and performing threshold calculation based on this information to obtain a fault determination threshold.

[0242] In some embodiments, step S1101 involves obtaining a warning threshold coefficient and historical device status information. It should be noted that the warning threshold coefficient is used to adjust the sensitivity of fault determination and can be set based on the specific operating conditions and historical fault data of the device. Historical device status information provides information about the device's operating status prior to the target observation time. This information is crucial for understanding changes in the target nuclear device's status and identifying potential failure modes.

[0243] In step S1102 of some embodiments, the present application performs a threshold calculation based on the warning threshold coefficient and historical device status information to obtain a fault determination threshold. This calculation process may involve statistical analysis and machine learning techniques to determine a reasonable threshold that can distinguish between normal operating conditions and potential fault conditions of the device. The setting of the fault determination threshold must take into account the operating characteristics and safety requirements of the target nuclear equipment to ensure that a warning can be issued in a timely manner before the target nuclear equipment fails.

[0244] In subsequent steps, this embodiment of the present application can compare the nuclear equipment status information with the fault determination threshold to determine whether the equipment is in a fault state. If the nuclear equipment status information meets the preset fault determination conditions, that is, exceeds the fault determination threshold, this embodiment of the present application will determine that the nuclear equipment status information is in a fault state and take appropriate measures. This method not only improves the accuracy of fault determination but also enhances the predictive and preventive maintenance capabilities of this embodiment of the present application by considering the equipment's historical operating data.

[0245] By obtaining the early warning threshold coefficient and historical equipment status information and calculating the fault determination threshold value through steps S1101 to S1102 in the embodiment of this application, the nuclear equipment fault monitoring method of the embodiment of this application can provide timely early warnings before equipment failure occurs, thereby providing strong support for the safe operation of nuclear power plants. This data-driven fault determination method enables the embodiment of this application to more accurately capture equipment performance changes, promptly identify potential fault risks, and avoid major safety accidents such as unexpected shutdowns and nuclear leaks.

[0246] According to some more specific embodiments, the warning threshold coefficient and the past device status information corresponding to multiple observation times before the target observation time are obtained, and the threshold is calculated based on the warning threshold coefficient and each past device status information to obtain the fault judgment threshold, which can be expressed as:

[0247]

[0248] Among them, λ obs is the observation time t obs The fault judgment threshold is α. a is the early warning threshold coefficient, which can be determined based on the on-site equipment operation experience.

[0249] Reference Figure 12 According to some embodiments of the present application, the historical monitoring matrix includes multiple historical monitoring vectors. After the threshold value is calculated based on the warning threshold coefficient and the past device status information in step S1102 to obtain the fault determination threshold value, the following may also be included, but are not limited to:

[0250] Step S1201 , in response to the nuclear equipment status information not exceeding the fault determination threshold, incorporating the real-time monitoring data into the historical monitoring matrix;

[0251] Step S1202: After incorporating the real-time monitoring data into the historical monitoring matrix, a closeness calculation is performed on the historical monitoring matrix based on the real-time monitoring data to obtain a parameter closeness corresponding to each historical monitoring vector;

[0252] Step S1203 : The historical monitoring vectors whose parameter closeness is less than a preset closeness threshold are deleted from the historical monitoring matrix to update the historical monitoring matrix.

[0253] In some embodiments of this application, maintaining and updating the historical monitoring matrix is ​​an important component of the nuclear equipment fault monitoring method. After completing the threshold calculation and obtaining the fault determination threshold, embodiments of this application determine how to process the real-time monitoring data and the historical monitoring matrix based on whether the nuclear equipment status information exceeds the fault determination threshold.

[0254] In step S1201 of some embodiments, if the nuclear equipment status information does not exceed the fault determination threshold, embodiments of the present application incorporate the real-time monitoring data into the historical monitoring matrix. This step aims to continuously enrich the historical data set to better capture long-term trends and patterns in equipment operation. The incorporation of real-time monitoring data enables the historical monitoring matrix to reflect the latest equipment operating status, providing the most up-to-date information for subsequent status estimation and fault prediction.

[0255] In step S1202 of some embodiments, after incorporating the real-time monitoring data into the historical monitoring matrix, embodiments of the present application perform a closeness calculation on the historical monitoring matrix based on the real-time monitoring data to obtain a parameter closeness corresponding to each historical monitoring vector. Parameter closeness is a measure of the degree of similarity between the historical monitoring vector and the real-time monitoring data, and it helps identify which historical data is most relevant to the current device status.

[0256] In step S1203 of some embodiments, the present application deletes historical monitoring vectors whose parameter proximity is less than a preset proximity threshold from the historical monitoring matrix to update the historical monitoring matrix. This step ensures that the data in the historical monitoring matrix is ​​highly relevant to the current equipment status, thereby preventing noise and irrelevant data from interfering with the monitoring results. By deleting irrelevant data, the historical monitoring matrix maintains its accuracy and effectiveness, providing more reliable data support for the condition assessment and fault prediction of the target nuclear equipment.

[0257] Through steps S1201 to S1203 provided in this embodiment of the present application, not only can the historical monitoring matrix be updated in a timely manner to ensure that it reflects the latest operating status of the equipment, but the quality of the historical monitoring matrix can also be improved through closeness calculation and data screening, thereby providing more accurate and reliable monitoring results for the safe operation of the nuclear power plant. The application of this method enables this embodiment of the present application to more effectively identify and predict equipment failures, avoiding the occurrence of major safety accidents such as unexpected shutdowns and nuclear leaks.

[0258] In step S106 of some embodiments, when the nuclear equipment status information meets the preset fault judgment conditions, the embodiments of the present application can determine that the equipment is in a fault state and issue a timely warning. This state-based monitoring and warning mechanism can not only reduce unnecessary preventive maintenance and lower maintenance costs, but also improve unit availability. More importantly, it can promptly detect potential fault risks and avoid major safety accidents such as unexpected shutdowns and nuclear leaks.

[0259] According to some embodiments provided herein, step S106, in response to the nuclear device status information satisfying a preset fault judgment condition, determining that the nuclear device status information is in a fault state, may include, but is not limited to:

[0260] In response to the nuclear device status information exceeding the fault determination threshold, it is determined that the nuclear device status information meets the fault determination condition, and the nuclear device status information is determined to be in a fault state.

[0261] It should be noted that the embodiments of the present application require comparing different nuclear equipment status information with a preset fault determination threshold to determine whether the target nuclear equipment meets the fault determination criteria. When the nuclear equipment status information exceeds the fault determination threshold, the embodiments of the present application determine that the nuclear equipment status information meets the fault determination criteria and designates the nuclear equipment status information as a fault. It should be understood that the key to this process lies in the setting of the fault determination threshold, which is calculated based on historical monitoring data, real-time monitoring data, and possible early warning threshold coefficients. The fault determination threshold is a critical value used to distinguish between normal operation and fault conditions of the equipment. When the actual status information of the nuclear equipment exceeds this threshold, it means that the equipment may be experiencing performance degradation or abnormal conditions, which may indicate a potential failure or performance degradation. By comparing the nuclear equipment status information with the fault determination threshold and determining that the equipment is in a fault state when the threshold is exceeded, the nuclear equipment fault monitoring method of the embodiments of the present application can provide an effective fault detection and response mechanism for nuclear power plants. This method not only improves the accuracy of fault detection but also enhances the nuclear power plant's ability to prevent and respond to equipment failures, thereby ensuring the safe and reliable operation of the nuclear power plant.

[0262] In some specific embodiments, after nuclear equipment status information is determined to be in a fault state, embodiments of the present application can take appropriate measures, such as issuing an alarm, notifying maintenance personnel, or automatically triggering preventive maintenance procedures. Such a response mechanism is crucial to ensuring the safe operation of nuclear power plants because it can promptly detect and address issues that may affect equipment safety, thereby preventing the occurrence and escalation of accidents.

[0263] In some more specific embodiments, the present invention can further analyze the nature and cause of the fault to enable more precise repair or adjustment measures. For example, historical monitoring data can be reviewed, operating conditions before and after the fault are verified, and possible failure modes can be identified. In this way, nuclear power plants can not only respond to current faults but also learn from them, improve future monitoring and maintenance strategies, and enhance overall equipment health management.

[0264] Reference Figure 13 The nuclear equipment fault monitoring device according to the second embodiment of the present application may include, but is not limited to:

[0265] Data acquisition module 1301, used to obtain historical monitoring matrix and real-time monitoring data of target nuclear equipment;

[0266] Incremental processing module 1302, for performing incremental processing on the historical monitoring matrix according to the real-time monitoring data to obtain a dynamic memory matrix;

[0267] The estimation model construction module 1303 is configured to construct an integrated multi-state estimation model based on the dynamic memory matrix;

[0268] The vector calculation module 1304 is configured to input the real-time monitoring data into the integrated multi-state estimation model to calculate a state estimation vector corresponding to the integrated multi-state estimation model;

[0269] The state evaluation module 1305 is configured to evaluate the state of the nuclear equipment based on the state estimation vector and the real-time monitoring data to obtain nuclear equipment state information.

[0270] The fault judgment module 1306 is configured to determine that the nuclear equipment state information is in a fault state in response to the nuclear equipment state information satisfying a preset fault judgment condition.

[0271] It can be seen that the contents in the above nuclear equipment fault monitoring method embodiments are all applicable to the embodiments of the nuclear equipment fault monitoring device. The nuclear equipment fault monitoring device embodiments specifically implement the functions of the above nuclear equipment fault monitoring method embodiments, and achieve the same beneficial effects as the above nuclear equipment fault monitoring method embodiments.

[0272] Referring to Figure 14 , Figure 14 The electronic device of another embodiment is illustrated, and the electronic device includes:

[0273] The processor 1401 can be implemented in a general-purpose CPU (Central Processing Unit), a microprocessor, an ASIC (Application Specific Integrated Circuit), or one or more integrated circuits, and is configured to execute related programs to implement the technical solutions provided by the embodiments of the present application.

[0274] The memory 1402 can be implemented in the form of a ROM (Read Only Memory), a static storage device, a dynamic storage device, or a RAM (Random Access Memory). The memory 1402 can store an operating system and other application programs. When the technical solutions provided by the embodiments of the present application are implemented by software or firmware, the related program codes are stored in the memory 1402 and are called and executed by the processor 1401 to implement the nuclear equipment fault monitoring method of the embodiments of the present application.

[0275] The input / output interface 1403 is configured to realize information input and output.

[0276] Communication interface 1404, used to implement communication interaction between this device and other devices, which can be achieved through wired means (such as USB, network cable, etc.) or wireless means (such as mobile network, WiFi, Bluetooth, etc.);

[0277] Bus 1405 , which transmits information between various components of the device (e.g., processor 1401 , memory 1402 , input / output interface 1403 , and communication interface 1404 );

[0278] The processor 1401 , the memory 1402 , the input / output interface 1403 and the communication interface 1404 are connected to each other in communication within the device via a bus 1405 .

[0279] The embodiment of the present application further provides a computer program product, which includes a computer program. A processor of a computer device reads and executes the computer program, so that the computer device executes and implements the above-mentioned nuclear equipment fault monitoring method.

[0280] The terms "first," "second," "third," "fourth," and the like (if any) in the specification of the present disclosure and the accompanying drawings are used to distinguish similar objects and are not necessarily used to describe a particular order or precedence. It should be understood that the terms used in this manner are interchangeable where appropriate, so that the embodiments of the present disclosure described herein, for example, can be implemented in orders other than those illustrated or described herein. In addition, the terms "comprises" and "comprising," and any variations thereof, are intended to cover non-exclusive inclusions, e.g., a process, method, system, product, or apparatus comprising a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such process, method, product, or apparatus.

[0281] It should be understood that in the present disclosure, "at least one (item)" refers to one or more, and "plurality" refers to two or more. "And / or" is used to describe the association relationship of associated objects, indicating that three relationships may exist. For example, "A and / or B" can represent: only A exists, only B exists, and A and B exist at the same time, where A and B can be singular or plural. The character " / " generally indicates that the previous and next associated objects are in an "or" relationship. "At least one of the following items" or similar expressions refers to any combination of these items, and may include, but is not limited to, any combination of single items or plural items. For example, at least one of a, b or c can represent: 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.

[0282] It should be understood that in the description of the embodiments of the present application, multiple (or multiple items) means more than two, greater than, less than, exceed, etc. are understood to exclude the number itself, and above, below, within, etc. are understood to include the number itself.

[0283] In the several embodiments provided in the present 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 schematic. For example, the division of units is only a logical function division. In actual implementation, there may be other division methods, such as 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 mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0284] Units described as separate components may or may not be physically separate, and components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0285] In addition, the functional units in the various embodiments of the present disclosure may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0286] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present disclosure is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium and may include, but is not limited to, a number of instructions for 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 various embodiments of the present disclosure. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, and other media that can store program code.

[0287] It should also be understood that the various implementation methods provided in the embodiments of the present application can be combined arbitrarily to achieve different technical effects.

[0288] The above is a specific description of the implementation methods of the present disclosure, but the present disclosure is not limited to the above implementation methods. Those skilled in the art can make various equivalent modifications or substitutions without violating the spirit of the present disclosure. These equivalent modifications or substitutions are all included in the scope defined by the claims of the present disclosure.

Claims

1. A method for monitoring nuclear equipment failure, characterized in that: include: Obtain historical monitoring matrix and real-time monitoring data of target nuclear equipment; Incrementally processing the historical monitoring matrix according to the real-time monitoring data to obtain a dynamic memory matrix; wherein the dynamic memory matrix includes a plurality of historical monitoring vectors obtained by sampling, each of the historical monitoring vectors being configured with a corresponding parameter closeness; Based on the parameter closeness of each of the historical monitoring vector configurations, configuring a reference value for the dynamic memory matrix; Performing neighbor propagation clustering on the dynamic memory matrix using the reference degree value to partition the dynamic memory matrix to obtain a first number of cluster center parameters and sub-dynamic memory matrices corresponding to each of the cluster center parameters; Determining a base model estimation parameter corresponding to each cluster center parameter according to the sub-dynamic memory matrix; Integrating a first number of the base model estimation parameters and the cluster center parameters corresponding to each of the base model estimation parameters to obtain an integrated multivariate state estimation model; Inputting the real-time monitoring data into the integrated multivariate state estimation model to calculate a state estimation vector corresponding to the integrated multivariate state estimation model; Performing nuclear equipment status assessment based on the state estimation vector and the real-time monitoring data to obtain nuclear equipment status information; In response to the core device status information satisfying a preset fault judgment condition, it is determined that the core device status information is in a fault state.

2. The method according to claim 1, characterized in that The historical monitoring matrix includes a plurality of historical monitoring vectors. The historical monitoring matrix is ​​incrementally processed according to the real-time monitoring data to obtain a dynamic memory matrix, including: Performing a closeness calculation on the historical monitoring matrix based on the real-time monitoring data to obtain a parameter closeness corresponding to each of the historical monitoring vectors; Reordering the historical monitoring vectors in the historical monitoring matrix based on the parameter closeness to obtain a target monitoring matrix; Sampling is performed on the target monitoring matrix to obtain the dynamic memory matrix.

3. The method according to claim 2, characterized in that The performing closeness calculation on the historical monitoring matrix based on the real-time monitoring data to obtain the parameter closeness corresponding to each historical monitoring vector includes: Performing standardization processing on the historical monitoring matrix and the real-time monitoring data respectively to obtain a standardized historical matrix corresponding to the historical monitoring matrix and a standardized real-time vector corresponding to the real-time monitoring data; Constructing a correlation monitoring matrix based on the real-time monitoring data and each of the historical monitoring vectors; The parameter closeness corresponding to each of the historical monitoring vectors is calculated based on the historical monitoring matrix, the real-time monitoring data, and the correlation monitoring matrix.

4. The method according to claim 3, characterized in that The constructing of a correlation monitoring matrix based on the real-time monitoring data and each of the historical monitoring vectors includes: Calculate the historical monitoring mean corresponding to each of the historical monitoring vectors; Determining each correlation matrix element based on a ratio of the real-time monitoring data to each of the historical monitoring means; The correlation matrix elements are integrated into a diagonal matrix to construct the correlation monitoring matrix.

5. The method according to claim 1, wherein Inputting the real-time monitoring data into the integrated multivariate state estimation model to calculate a state estimation vector corresponding to the integrated multivariate state estimation model includes: Calculating the initial weight corresponding to each cluster center parameter according to the real-time monitoring data and each cluster center parameter; Performing weighting processing on the initial weights corresponding to the cluster center parameters to update the initial weights and obtain the base model contribution corresponding to the base model estimation parameters; The state estimation vector is calculated based on the estimated parameters of each base model and the base model contribution corresponding to each base model estimated parameter.

6. The method according to claim 5, characterized in that The calculating the state estimation vector according to each of the base model estimation parameters and the base model contribution corresponding to each of the base model estimation parameters includes: For each of the base model estimated parameters, multiplying the base model estimated parameter by the corresponding base model contribution to determine the contribution component; Summing the first number of the contribution component elements to obtain a first state estimation element; Summing the contribution of the first number of base models to obtain a second state estimation element; The ratio of the first state estimation element to the second state estimation element is determined as the state estimation vector.

7. The method according to claim 1, characterized in that The nuclear equipment status assessment based on the state estimation vector and the real-time monitoring data to obtain nuclear equipment status information includes: Performing residual calculation based on the state estimation vector and the real-time monitoring data to obtain a residual sequence; wherein the residual sequence includes residual values ​​corresponding to each observation time; Performing covariance calculation on each residual value in the residual sequence to obtain a first covariance matrix at a target observation moment and a second covariance matrix at a moment immediately before the target observation moment; A health index is calculated based on the first covariance matrix and the second covariance matrix to obtain the nuclear equipment status information.

8. The method according to claim 7, characterized in that Before determining that the nuclear device status information is in a fault state in response to the nuclear device status information satisfying a preset fault judgment condition, the method further includes: Obtaining a warning threshold coefficient and past device status information corresponding to a plurality of observation times before the target observation time; Performing threshold calculation based on the warning threshold coefficient and each of the past device status information to obtain a fault determination threshold; In response to the core device status information satisfying a preset fault judgment condition, determining that the core device status information is in a fault state includes: In response to the core device status information exceeding the fault determination threshold, it is determined that the core device status information meets the fault judgment condition, and the core device status information is determined to be in the fault state.

9. The method according to claim 8, characterized in that The historical monitoring matrix includes a plurality of historical monitoring vectors. After the threshold value is calculated based on the early warning threshold coefficient and each of the past device status information to obtain the fault judgment threshold value, the following further includes: In response to the nuclear equipment status information not exceeding the fault determination threshold, incorporating the real-time monitoring data into the historical monitoring matrix; After incorporating the real-time monitoring data into the historical monitoring matrix, performing a closeness calculation on the historical monitoring matrix based on the real-time monitoring data to obtain a parameter closeness corresponding to each of the historical monitoring vectors; The historical monitoring vectors whose parameter closeness is less than a preset closeness threshold are deleted from the historical monitoring matrix to update the historical monitoring matrix.

10. The method according to claim 1, characterized in that The target nuclear equipment is configured with sensor measurement points, and obtaining the historical monitoring matrix and real-time monitoring data of the target nuclear equipment includes: Acquire monitoring data sets at multiple observation times; wherein each monitoring data set includes monitoring sensor data collected by each sensor measuring point at one of the observation times; Integrating the monitoring sensor data collected at each observation moment into an operation data vector corresponding to the observation moment; Determining the operation data vector corresponding to the target observation time as real-time monitoring data; The operation data vectors corresponding to multiple observation times before the target observation time are integrated into the historical monitoring matrix.

11. A nuclear equipment fault monitoring device, characterized in that: include: Data acquisition module, used to obtain historical monitoring matrix and real-time monitoring data of target nuclear equipment; an incremental processing module, configured to perform incremental processing on the historical monitoring matrix according to the real-time monitoring data to obtain a dynamic memory matrix; wherein the dynamic memory matrix includes a plurality of historical monitoring vectors obtained by sampling, each of the historical monitoring vectors being configured with a corresponding parameter closeness; an estimation model construction module, configured to configure a reference value for the dynamic memory matrix based on the parameter proximity configured for each of the historical monitoring vectors, perform neighbor propagation clustering on the dynamic memory matrix using the reference value to partition the dynamic memory matrix, obtain a first number of cluster center parameters, and sub-dynamic memory matrices corresponding to each of the cluster center parameters, determine a base model estimation parameter corresponding to each of the cluster center parameters based on the sub-dynamic memory matrices, and integrate the first number of base model estimation parameters and the cluster center parameters corresponding to each of the base model estimation parameters to obtain an integrated multivariate state estimation model; a vector calculation module, configured to input the real-time monitoring data into the integrated multivariate state estimation model to calculate a state estimation vector corresponding to the integrated multivariate state estimation model; A state assessment module, configured to perform a nuclear equipment state assessment based on the state estimation vector and the real-time monitoring data to obtain nuclear equipment state information; The fault judgment module is used to determine that the nuclear equipment status information is in a fault state in response to the nuclear equipment status information meeting a preset fault judgment condition.

12. 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 monitoring nuclear equipment failure according to any one of claims 1 to 10 is implemented.

13. 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 nuclear equipment fault monitoring method according to any one of claims 1 to 10.

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