Fault monitoring method, device and equipment for electrical equipment of nuclear power plant and storage medium

Abstract

The invention provides a fault monitoring method, device and equipment for electrical equipment in a nuclear power plant and a storage medium, and the method comprises the steps: obtaining simulation operation data of the electrical equipment in the nuclear power plant based on a pre-established simulation model; acquiring actual operation data of the electrical equipment; based on a pre-established fuzzy expert system, performing fuzzy reasoning in combination with the simulation operation data and the actual operation data to obtain a fuzzy reasoning result; and performing defuzzification processing on the fuzzy reasoning result to obtain a fault monitoring result. According to the technical scheme, the operation condition of the electrical equipment of the nuclear power plant can be effectively monitored, so that the maintenance cost is reduced, and safe operation of the nuclear power plant is ensured.

Description

Technical Field

[0001] The present application relates to the field of nuclear power technology, and in particular to a method, device, equipment and storage medium for fault monitoring of electrical equipment in a nuclear power plant. Background Art

[0002] In the related art, the maintenance method of electrical equipment in nuclear power plants is usually to perform regular maintenance at fixed time intervals without considering the actual operating status of the equipment. This maintenance method may lead to unnecessary maintenance when the equipment is in good condition, or failure to perform timely maintenance when the equipment has potential failures, thereby reducing maintenance efficiency and increasing the potential risks of nuclear power plant operation. Summary of the invention

[0003] The present application aims to solve one of the technical problems in the related art at least to some extent.

[0004] In a first aspect, the present application proposes a fault monitoring method for electrical equipment in a nuclear power plant, the method comprising: obtaining simulated operation data of electrical equipment in the nuclear power plant based on a pre-established simulation model; obtaining actual operation data of the electrical equipment; performing fuzzy reasoning on the simulated operation data and the actual operation data based on a pre-established fuzzy expert system to obtain a fuzzy reasoning result; wherein the fuzzy expert system comprises a fuzzy rule base and a fuzzy set, the fuzzy rule base is established based on expert knowledge, the fuzzy rule base comprises at least one fuzzy reasoning rule, each of the fuzzy reasoning rules corresponds to one of the operating conditions, and each of the fuzzy sets corresponds to one of the operating conditions; defuzzifying the fuzzy reasoning result to obtain a fault monitoring result.

[0005] In one implementation, the simulation model is pre-established through the following steps: establishing a device simulation model corresponding to the electrical equipment; establishing an initial simulation model corresponding to the nuclear power plant based on the device simulation model; acquiring initial simulation operation data based on the initial simulation model; acquiring sample operation data of the nuclear power plant; and optimizing and adjusting the initial simulation model based on the sample operation data and the initial simulation operation data to obtain the simulation model.

[0006] In one implementation, the fuzzy expert system established in advance performs fuzzy reasoning in combination with the simulation operation data and the actual operation data to obtain a fuzzy reasoning result, including: comparing the simulation operation data and the actual operation data to obtain difference data; performing fuzzy reasoning based on the fuzzy rule base and the difference data to obtain the fuzzy reasoning result.

[0007] In an optional implementation, the comparing the simulation operation data with the actual operation data includes: performing a dynamic response analysis based on the simulation operation data to obtain first dynamic response data; performing a dynamic response analysis based on the actual operation data to obtain actual dynamic response data; and comparing and analyzing the first dynamic response data with the actual dynamic response data to obtain the difference data.

[0008] In an optional implementation, performing fuzzy reasoning based on the fuzzy rule base and the difference data to obtain the fuzzy reasoning result includes: performing fuzzy processing on the difference data to obtain fuzzy information; respectively obtaining the membership degree corresponding to the fuzzy information and each of the fuzzy sets; determining a target fuzzy reasoning rule from the fuzzy reasoning rules based on the membership degree; performing fuzzy reasoning based on the membership degree and the target fuzzy reasoning rule to obtain the fuzzy reasoning result.

[0009] In one implementation, the method further includes: in response to the fault monitoring result being determined as a fault, obtaining a fault type; generating fault handling information based on the fault type and a preset expert knowledge base; and pushing the fault handling information.

[0010] In the second aspect, the present application proposes a fault monitoring device for electrical equipment in a nuclear power plant, the device comprising: a first processing module, used to obtain simulated operation data of electrical equipment in the nuclear power plant based on a pre-established simulation model; an acquisition module, used to obtain actual operation data of the electrical equipment; a fuzzy reasoning module, used to perform fuzzy reasoning based on a pre-established fuzzy expert system in combination with the simulated operation data and the actual operation data to obtain a fuzzy reasoning result; wherein the fuzzy expert system includes a fuzzy rule base and a fuzzy set, the fuzzy rule base is established based on expert knowledge, the fuzzy rule base includes at least one fuzzy reasoning rule, each of the fuzzy reasoning rules corresponds to one of the operating conditions, and each of the fuzzy sets corresponds to one of the operating conditions; a second processing module, used to defuzzify the fuzzy reasoning result to obtain a fault monitoring result.

[0011] In one implementation, the device also includes a third processing module, which is used to: establish an equipment simulation model corresponding to the electrical equipment; establish an initial simulation model corresponding to the nuclear power plant based on the equipment simulation model; obtain initial simulation operation data based on the initial simulation model; obtain sample operation data of the nuclear power plant; and optimize and adjust the initial simulation model based on the sample operation data and the initial simulation operation data to obtain the simulation model.

[0012] In one implementation, the fuzzy reasoning module can be used to: compare the simulation operation data with the actual operation data to obtain difference data; perform fuzzy reasoning based on the fuzzy rule base and the difference data to obtain the fuzzy reasoning result.

[0013] In an optional implementation, the fuzzy reasoning module can be used to: perform dynamic response analysis based on the simulation operation data to obtain first dynamic response data; perform dynamic response analysis based on the actual operation data to obtain actual dynamic response data; compare and analyze the first dynamic response data with the actual dynamic response data to obtain the difference data.

[0014] In an optional implementation, the fuzzy reasoning module can be used to: perform fuzzy processing on the difference data to obtain fuzzy information; respectively obtain the membership degree corresponding to the fuzzy information and each fuzzy set; determine the target fuzzy reasoning rule from the fuzzy reasoning rules based on the membership degree; perform fuzzy reasoning based on the membership degree and the target fuzzy reasoning rule to obtain the fuzzy reasoning result.

[0015] In one implementation, the device further includes a fourth processing module, which is used to: in response to the fault monitoring result being determined as a fault, obtain a fault type; generate fault handling information based on the fault type and a preset expert knowledge base; and push the fault handling information.

[0016] In a third aspect, the present application proposes an electronic device, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the fault monitoring method for electrical equipment of a nuclear power plant as described in the first aspect.

[0017] In a fourth aspect, the present application proposes a computer-readable storage medium for storing instructions, which, when executed, enables the method described in the first aspect to be implemented.

[0018] In a fifth aspect, the present application proposes a computer program product, comprising a computer program, which, when executed by a processor, implements the steps of the fault monitoring method for electrical equipment in a nuclear power plant as described in the first aspect.

[0019] The fault monitoring method, device, equipment and storage medium of nuclear power plant electrical equipment provided by the present application can obtain the simulated operation data of the electrical equipment under different operating conditions based on the pre-established simulation model corresponding to the nuclear power plant electrical equipment, and then compare and analyze the simulated operation data and the actual operation data based on the pre-established fuzzy expert system to obtain the fault monitoring result. It can effectively monitor the operating status of the nuclear power plant electrical equipment, thereby reducing the maintenance cost and ensuring the safe operation of the nuclear power plant.

[0020] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0022] Figure 1 It is a flow chart of a method for monitoring faults of electrical equipment in a nuclear power plant provided in an embodiment of the present application;

[0023] Figure 2 It is a flow chart of another method for fault monitoring of electrical equipment in a nuclear power plant provided in an embodiment of the present application;

[0024] Figure 3 is a schematic diagram of a fault monitoring solution for electrical equipment in a nuclear power plant provided in an embodiment of the present application;

[0025] Figure 4 It is a structural schematic diagram of a fault monitoring device for electrical equipment in a nuclear power plant provided in an embodiment of the present application;

[0026] Figure 5 It is a structural schematic diagram of another fault monitoring device for electrical equipment in a nuclear power plant provided in an embodiment of the present application;

[0027] Figure 6 It is a structural schematic diagram of another fault monitoring device for electrical equipment in a nuclear power plant provided in an embodiment of the present application;

[0028] Figure 7 It is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0029] Embodiments of the present application are described in detail below, and 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 intended to be used to explain the present application, and should not be construed as limiting the present application.

[0030] The following describes a method and apparatus for monitoring faults of electrical equipment in a nuclear power plant according to an embodiment of the present application with reference to the accompanying drawings.

[0031] Figure 1 FIG. 1 is a flow chart of a method for monitoring faults of electrical equipment in a nuclear power plant provided in an embodiment of the present application. Figure 1 As shown, the method may include but is not limited to the following steps:

[0032] Step S101: Based on a pre-established simulation model, simulated operation data of electrical equipment in a nuclear power plant is obtained.

[0033] Exemplarily, based on a pre-established overall model of nuclear power plant electrical equipment, according to preset fault types and parameters, the operating status of the electrical system under different fault modes (for example, short circuit, overload, circuit break, generator shutdown, etc.) is simulated to obtain simulated operating data of the nuclear power plant electrical equipment under different operating conditions.

[0034] Step S102: Acquire actual operation data of the electrical equipment.

[0035] Exemplarily, the original operating data of electrical equipment is obtained through the nuclear power plant control system, and the original operating data is preprocessed (for example, data cleaning, denoising, feature extraction, etc.) to eliminate useless information such as noise and outliers, and extract features that are useful for fault prediction and diagnosis as the actual operating data of the electrical equipment.

[0036] Step S103: Based on the pre-established fuzzy expert system, fuzzy reasoning is performed in combination with the simulation operation data and the actual operation data to obtain a fuzzy reasoning result.

[0037] Among them, in an embodiment of the present application, the above-mentioned fuzzy expert system includes a fuzzy rule base and a fuzzy set. The fuzzy rule base is established based on expert knowledge. The fuzzy rule base includes at least one fuzzy reasoning rule, each fuzzy reasoning rule corresponds to an operating condition, and each fuzzy set corresponds to an operating condition.

[0038] Exemplarily, based on a pre-established fuzzy expert system, the simulation operation data and the actual operation data are analyzed, and fuzzy reasoning is performed according to the difference between the simulation operation data and the actual operation data to obtain a fuzzy reasoning result.

[0039] In some embodiments, the value range of each electrical equipment operation data can be defined according to the actual operation status and experience of each electrical equipment, and the operation data can be divided into several fuzzy sets, such as "low", "medium", "high", etc., according to the pre-acquired expert experience and professional knowledge of the operation of nuclear power plant electrical equipment. These fuzzy sets represent the fuzzy description of the operation data under different operation states; and the membership function is defined for each fuzzy set according to the performance characteristics of each electrical equipment.

[0040] Step S104: Defuzzify the fuzzy inference result to obtain a fault monitoring result.

[0041] Exemplarily, a selected defuzzification method (eg, centroid method) is used to defuzzify the fuzzy inference result to obtain a final precise output value, and the fault monitoring result is determined based on the precise output value.

[0042] In an embodiment of the present application, the above-mentioned fault monitoring result may include fault warning information.

[0043] By implementing the embodiments of the present application, it is possible to obtain simulated operation data of electrical equipment under different operating conditions based on the pre-established simulation model corresponding to the electrical equipment of the nuclear power plant, and then compare and analyze the simulated operation data and the actual operation data based on the pre-established fuzzy expert system to obtain fault monitoring results. It is possible to effectively monitor the operating conditions of the electrical equipment of the nuclear power plant, thereby reducing maintenance costs and ensuring the safe operation of the nuclear power plant.

[0044] In some embodiments, a simulation model corresponding to the electrical equipment of a nuclear power plant may be established through the following steps.

[0045] A1: Establish equipment simulation models corresponding to electrical equipment.

[0046] Exemplarily, based on the electrical program simulation software, a device simulation model corresponding to each electrical device in the nuclear power plant is established respectively.

[0047] A2: Establish the initial simulation model corresponding to the nuclear power plant based on the equipment simulation model.

[0048] Exemplarily, based on the physical characteristics and working principles of different electrical equipment, combined with drawings and data of real units of a nuclear power plant, an initial simulation model is established that can fully simulate the actual operation of the nuclear power plant.

[0049] A3: Obtain initial simulation running data based on the initial simulation model.

[0050] Exemplarily, a simulation run is performed based on the initial simulation model to obtain initial simulation run data

[0051] A4: Obtain sample operating data from nuclear power plants.

[0052] Exemplarily, the actual operating conditions of the nuclear power plant are monitored to obtain sample operating data of the nuclear power plant.

[0053] A5: Based on the sample operation data and the initial simulation operation data, the initial simulation model is optimized and adjusted to obtain a simulation model.

[0054] Exemplarily, the initial simulation operation data is compared and verified with the sample operation data, and the model parameters of the initial simulation model are calibrated and optimized according to the comparison and verification results to obtain a simulation model.

[0055] By implementing the embodiments of the present application, an initial simulation model corresponding to the nuclear power plant can be established based on the device simulation model corresponding to the electrical equipment in the nuclear power plant, and the initial simulation operation data obtained by the initial simulation model can be compared with the sample operation data, so as to calibrate the model parameters of the initial simulation model according to the comparison result and obtain the simulation model. This improves the accuracy and reliability of the simulation operation data, and improves the accuracy of fault monitoring based on the simulation model.

[0056] In some embodiments, the simulated operation data and the actual operation data can be compared based on pre-established fuzzy inference rules. As an example, see Figure 2 , Figure 2 FIG. 1 is a flow chart of another method for monitoring faults of electrical equipment in a nuclear power plant provided in an embodiment of the present application. Figure 2 As shown, the method may include but is not limited to the following steps:

[0057] Step S201: Based on a pre-established simulation model, simulated operation data of electrical equipment in a nuclear power plant is obtained.

[0058] In the embodiments of the present application, step S201 can be implemented in any of the ways in the embodiments of the present application, and the embodiments of the present application do not limit this and will not be described in detail.

[0059] Step S202: Acquire actual operation data of the electrical equipment.

[0060] In the embodiments of the present application, step S202 can be implemented by any of the methods in the embodiments of the present application, and the embodiments of the present application do not limit this and will not be described in detail.

[0061] Step S203: Compare the simulation operation data with the actual operation data to obtain difference data.

[0062] Exemplarily, the simulated operation data corresponding to each electrical device in the nuclear power plant is compared with the actual operation data to obtain the difference data corresponding to each electrical device.

[0063] In some embodiments, the simulated operation data includes simulated operation data under different operating conditions, and the aforementioned comparing the simulated operation data with the actual operation data to obtain difference data may include the following steps:

[0064] B1: Perform dynamic response analysis based on the simulation operation data to obtain first dynamic response data.

[0065] It should be noted that, in the embodiments of the present application, the above-mentioned simulation operation data may be time series data.

[0066] Exemplarily, dynamic response analysis is performed on the simulation operation data corresponding to each operation condition to obtain first dynamic response data.

[0067] B2: Perform dynamic response analysis based on actual operation data to obtain actual dynamic response data.

[0068] Exemplarily, dynamic response analysis is performed on actual operation data corresponding to each electrical device in the nuclear power plant to obtain actual dynamic response data corresponding to each electrical device.

[0069] B3: Compare and analyze the first dynamic response data with the actual dynamic response data to obtain difference data.

[0070] Exemplarily, the first dynamic response data corresponding to each electrical device is compared and analyzed with the actual dynamic response data to obtain difference data between the above data.

[0071] Step S204: Perform fuzzy reasoning based on the fuzzy rule base and the difference data to obtain a fuzzy reasoning result.

[0072] For example, the fuzzification result of the input variable is matched with the rules in the fuzzy rule base, and all matching rules are activated.

[0073] In some embodiments, the fuzzy reasoning based on the fuzzy rule base and the difference data to obtain the fuzzy reasoning result may include the following steps:

[0074] C1: Fuzzy processing is performed on the difference data to obtain fuzzy information.

[0075] C2: Obtain the membership degree corresponding to the fuzzy information and each fuzzy set respectively.

[0076] Exemplarily, the membership degree of the fuzzy information corresponding to each fuzzy set is obtained based on a preset membership function calculation.

[0077] C3: Determine the target fuzzy inference rule from the fuzzy inference rules based on the membership degree.

[0078] C4: Perform fuzzy reasoning based on membership degree and target fuzzy reasoning rules to obtain fuzzy reasoning results.

[0079] Exemplarily, according to the preset weights of the target fuzzy inference rules and the fuzzy logic operators, a total output fuzzy set is synthesized as the fuzzy inference result.

[0080] Step S205: Defuzzify the fuzzy reasoning result to obtain a fault monitoring result.

[0081] In the embodiments of the present application, step S205 can be implemented by any of the methods in the embodiments of the present application, and the embodiments of the present application do not limit this and will not be described in detail.

[0082] By implementing the embodiments of the present application, fuzzy reasoning can be performed based on the difference data between the simulated operation data and the actual operation data to obtain a fault reasoning result.

[0083] In some embodiments, the method further includes: obtaining a fault type in response to the fault monitoring result that determines that a fault has occurred; generating fault handling information based on the fault type and a preset expert knowledge base; and pushing the fault handling information.

[0084] Exemplarily, in response to the fault monitoring results showing that a fault has occurred in the electrical equipment of a nuclear power plant, the fault type is determined; fault repair suggestions corresponding to the fault type are obtained from a pre-established expert knowledge base as fault handling information, and the fault handling information is pushed to the nuclear power plant operation and maintenance personnel.

[0085] See also Figure 3 , Figure 3 FIG. 1 is a schematic diagram of a fault monitoring solution for electrical equipment in a nuclear power plant provided in an embodiment of the present application. Figure 3 As shown in the figure, the scheme can obtain the actual operation data of the DCS (Distributed Control System) system of a nuclear power plant, and obtain the simulated operation data based on the pre-established full-range electrical simulation model, so as to perform fuzzy reasoning based on the simulated operation data and the actual operation data to predict faults and conduct risk assessment on the nuclear power plant; and obtain corresponding maintenance suggestions based on the preset expert knowledge base when a fault is determined. At the same time, the actual operation data, fault prediction and risk assessment results, maintenance suggestions and other information obtained can be displayed based on the human-computer interaction interface.

[0086] See also Figure 4 , Figure 4 Schematic diagram of a fault monitoring device for electrical equipment in a nuclear power plant provided in an embodiment of the present application. Figure 4 As shown, the device 400 includes: a first processing module 401, which is used to obtain the simulated operation data of the electrical equipment in the nuclear power plant based on a pre-established simulation model; an acquisition module 402, which is used to obtain the actual operation data of the electrical equipment; a fuzzy reasoning module 403, which is used to perform fuzzy reasoning based on a pre-established fuzzy expert system in combination with the simulated operation data and the actual operation data to obtain a fuzzy reasoning result; wherein the fuzzy expert system includes a fuzzy rule base and a fuzzy set, the fuzzy rule base is established based on expert knowledge, the fuzzy rule base includes at least one fuzzy reasoning rule, each fuzzy reasoning rule corresponds to an operating condition, and each fuzzy set corresponds to an operating condition; a second processing module 404, which is used to defuzzify the fuzzy reasoning result to obtain a fault monitoring result.

[0087] In one implementation, the device further includes a third processing module. As an example, see Figure 5 , Figure 5 FIG. 1 is a schematic diagram of the structure of another fault monitoring device for electrical equipment in a nuclear power plant provided in an embodiment of the present application. Figure 5 As shown, the device 500 also includes a third processing module 505: used to: establish a device simulation model corresponding to the electrical device; establish an initial simulation model corresponding to the nuclear power plant based on the device simulation model; obtain initial simulation operation data based on the initial simulation model; obtain sample operation data of the nuclear power plant; optimize and adjust the initial simulation model based on the sample operation data and the initial simulation operation data to obtain a simulation model. Figure 5 Modules 501 to 504 in Figure 4 Modules 401 to 404 in the embodiment have the same structure and function.

[0088] In one implementation, the fuzzy reasoning module 403 can be used to: compare the simulation operation data with the actual operation data to obtain difference data; perform fuzzy reasoning based on the fuzzy rule base and the difference data to obtain a fuzzy reasoning result.

[0089] In an optional implementation, the fuzzy reasoning module 403 can be used to: perform dynamic response analysis based on simulation operation data to obtain first dynamic response data; perform dynamic response analysis based on actual operation data to obtain actual dynamic response data; compare and analyze the first dynamic response data with the actual dynamic response data to obtain difference data.

[0090] In an optional implementation, the fuzzy reasoning module 403 can be used to: perform fuzzy processing on the difference data to obtain fuzzy information; obtain the membership degree corresponding to the fuzzy information and each fuzzy set respectively; determine the target fuzzy reasoning rule from the fuzzy reasoning rules based on the membership degree; perform fuzzy reasoning based on the membership degree and the target fuzzy reasoning rule to obtain the fuzzy reasoning result.

[0091] In one implementation, the device further includes a fourth processing module. As an example, see Figure 6 , Figure 6 FIG. 1 is a schematic diagram of the structure of another fault monitoring device for electrical equipment in a nuclear power plant provided in an embodiment of the present application. Figure 6 As shown, the device 600 further includes a fourth processing module 605, which is used to: obtain the fault type in response to the fault monitoring result that a fault occurs; generate fault processing information based on the fault type and a preset expert knowledge base; and push the fault processing information. Figure 6 Modules 601 to 604 in Figure 4 Modules 401 to 404 in the embodiment have the same structure and function.

[0092] Through the device of the embodiment of the present application, based on the pre-established simulation model corresponding to the electrical equipment of the nuclear power plant, the simulated operation data of the electrical equipment under different operating conditions can be obtained, and then based on the pre-established fuzzy expert system, the simulated operation data and the actual operation data can be compared and analyzed to obtain the fault monitoring result. The operating status of the electrical equipment of the nuclear power plant can be effectively monitored, thereby reducing maintenance costs and ensuring the safe operation of the nuclear power plant.

[0093] It should be noted that the above explanation of the embodiment of the method for monitoring faults of electrical equipment in a nuclear power plant is also applicable to the apparatus for monitoring faults of electrical equipment in a nuclear power plant in this embodiment, and will not be described in detail here.

[0094] In order to implement the above embodiment, the present application also proposes an electronic device. Figure 7 , Figure 7 Schematic diagram of the structure of the electronic device provided in the embodiment of the present application. Figure 7 As shown, the electronic device 700 includes: a processor 701, and a memory 702 communicatively connected to the processor 701; the memory 702 stores computer-executable instructions; the processor 701 executes the computer-executable instructions stored in the memory to implement the method provided in the aforementioned embodiment.

[0095] In order to implement the above embodiments, the present application also proposes a computer-readable storage medium, in which computer-executable instructions are stored. When the computer-executable instructions are executed by a processor, they are used to implement the methods provided by the above embodiments.

[0096] In order to implement the above embodiments, the present application also proposes a computer program product, including a computer program, which implements the methods provided by the above embodiments when executed by a processor.

[0097] In the description of this application, unless otherwise specified, “ / ” means or, for example, A / B can mean A or B; “and / or” in this article is merely a way to describe the association relationship of associated objects, indicating that there can be three relationships, for example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone.

[0098] In the description of the aforementioned embodiments, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. 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 schematic representations 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 one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0099] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In the description of this application, the meaning of "plurality" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0100] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, fragment or portion of code comprising one or more executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present application belong.

[0101] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute the instructions), or in combination with these instruction execution systems, devices or apparatuses. For the purpose of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in combination with these instruction execution systems, devices or apparatuses. More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting or processing in other suitable ways if necessary, and then stored in a computer memory.

[0102] It should be understood that the various parts of the present application can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0103] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0104] In addition, each functional unit in each embodiment of the present application may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0105] The storage medium mentioned above may be a read-only memory, a magnetic disk or an optical disk, etc. Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limiting the present application. A person of ordinary skill in the art may change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. A method for monitoring faults of electrical equipment in a nuclear power plant, characterized in that: include: Based on the pre-established simulation model, obtain the simulation operation data of the electrical equipment in the nuclear power plant; Acquiring actual operation data of the electrical equipment; Based on a pre-established fuzzy expert system, fuzzy reasoning is performed in combination with the simulation operation data and the actual operation data to obtain a fuzzy reasoning result; wherein the fuzzy expert system includes a fuzzy rule base and a fuzzy set, the fuzzy rule base is established based on expert knowledge, the fuzzy rule base includes at least one fuzzy reasoning rule, each of the fuzzy reasoning rules corresponds to one of the operation conditions, and each of the fuzzy sets corresponds to one of the operation conditions; The fuzzy inference result is defuzzified to obtain a fault monitoring result.

2. The method according to claim 1, characterized in that The simulation model is pre-established by the following steps: Establishing a device simulation model corresponding to the electrical device; Establishing an initial simulation model corresponding to the nuclear power plant based on the equipment simulation model; Acquiring initial simulation operation data based on the initial simulation model; obtaining sample operating data of the nuclear power plant; Based on the sample operation data and the initial simulation operation data, the initial simulation model is optimized and adjusted to obtain the simulation model.

3. The method according to claim 1, characterized in that The fuzzy expert system established in advance performs fuzzy reasoning in combination with the simulation operation data and the actual operation data to obtain a fuzzy reasoning result, including: Comparing the simulation operation data with the actual operation data to obtain difference data; Fuzzy reasoning is performed based on the fuzzy rule base and the difference data to obtain the fuzzy reasoning result.

4. The method according to claim 3, characterized in that The comparing the simulation operation data with the actual operation data includes: Performing dynamic response analysis based on the simulation operation data to obtain first dynamic response data; Performing dynamic response analysis based on the actual operation data to obtain actual dynamic response data; The first dynamic response data is compared and analyzed with the actual dynamic response data to obtain the difference data.

5. The method according to claim 3, characterized in that The performing fuzzy reasoning based on the fuzzy rule base and the difference data to obtain the fuzzy reasoning result includes: Performing fuzzy processing on the difference data to obtain fuzzy information; Respectively obtaining the membership degree corresponding to the fuzzy information and each of the fuzzy sets; determining a target fuzzy inference rule from the fuzzy inference rules based on the membership degree; Fuzzy reasoning is performed based on the membership degree and the target fuzzy reasoning rule to obtain the fuzzy reasoning result.

6. The method according to claim 1, characterized in that The method further comprises: In response to the fault monitoring result being determined as a fault occurring, obtaining a fault type; Generate fault handling information based on the fault type and a preset expert knowledge base; Push the fault handling information.

7. A fault monitoring device for electrical equipment in a nuclear power plant, characterized in that: include: A first processing module is used to obtain simulation operation data of electrical equipment in a nuclear power plant based on a pre-established simulation model; An acquisition module, used for acquiring actual operation data of the electrical equipment; A fuzzy reasoning module, used for performing fuzzy reasoning based on a pre-established fuzzy expert system in combination with the simulation operation data and the actual operation data to obtain a fuzzy reasoning result; wherein the fuzzy expert system includes a fuzzy rule base and a fuzzy set, the fuzzy rule base is established based on expert knowledge, the fuzzy rule base includes at least one fuzzy reasoning rule, each of the fuzzy reasoning rules corresponds to one of the operation conditions, and each of the fuzzy sets corresponds to one of the operation conditions; The second processing module is used to perform defuzzification processing on the fuzzy reasoning result to obtain a fault monitoring result.

8. An electronic device, characterized in that: include: A processor, and a memory communicatively connected to the processor; The memory stores computer-executable instructions; The processor executes the computer-executable instructions stored in the memory to implement the method according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, and when the computer-executable instructions are executed by a processor, they are used to implement the method according to any one of claims 1 to 6.

10. A computer program product, characterized in that The method comprises a computer program, which implements the method according to any one of claims 1 to 6 when being executed by a processor.

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