Unit fault detection method and device for nuclear power plant, electronic equipment and medium

Abstract

The invention provides a unit fault detection method for a nuclear power plant, and the method comprises the steps: collecting the initial operation data of a target unit in the nuclear power plant and the initial environment data of a target environment region where the target unit is located at each data collection time in a preset time period, performing linear fitting processing on the plurality of initial operation data and the data acquisition time corresponding to each initial operation data to obtain a first function curve, and performing linear fitting processing on the plurality of initial environment data and the data acquisition time corresponding to each initial environment data to obtain a second function curve, and performing linear fitting processing on the initial operation data and the initial environment data to obtain a third function curve, and determining a target fault detection result of the target unit according to the first function curve, and / or the second function curve, and / or the third function curve. The fault detection effect of the unit in the nuclear power plant is effectively improved.

Description

Technical Field

[0001] The present disclosure relates to the technical field of nuclear power plant equipment maintenance management, and in particular to a unit fault detection method, device, electronic equipment and medium for a nuclear power plant. Background Art

[0002] During the operation and maintenance of a nuclear power plant, a failure of a unit in the nuclear power plant (for example, shutdown of the coolant circulation pump, failure of the steam generator, etc.) may cause the entire unit in the nuclear power plant to shut down. In serious cases, it may also cause radiation damage and radioactive contamination to the personnel in the plant. In order to reduce the hazards caused by unit failure, it is necessary to accurately and timely perform fault detection on the units in the nuclear power plant. Summary of the invention

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

[0004] To this end, the purpose of the present disclosure is to propose a unit fault detection method, device, electronic equipment and storage medium for a nuclear power plant, which can combine the operating data of the target unit in the power plant and the environmental data of the environment in which the unit is located, and effectively improve the fault detection effect of the unit in the nuclear power plant.

[0005] To achieve the above-mentioned purpose, a unit fault detection method for a nuclear power plant proposed in an embodiment of the first aspect of the present disclosure includes: collecting initial operating data of a target unit in a nuclear power plant and initial environmental data of a target environmental area where the target unit is located at each data collection time within a preset time period; performing linear fitting processing on multiple initial operating data and the data collection time corresponding to each initial operating data to obtain a first function curve; performing linear fitting processing on multiple initial environmental data and the data collection time corresponding to each initial environmental data to obtain a second function curve; performing linear fitting processing on the initial operating data and the initial environmental data to obtain a third function curve; and determining a target fault detection result of the target unit according to the first function curve, and / or the second function curve, and / or the third function curve.

[0006] To achieve the above-mentioned purpose, a unit fault detection device for a nuclear power plant proposed in an embodiment of the second aspect of the present disclosure includes: an acquisition module, which is used to respectively acquire the initial operation data of the target unit in the nuclear power plant and the initial environmental data of the target environmental area where the target unit is located at each data acquisition time within a preset time period; a first processing module, which is used to perform linear fitting processing on multiple initial operation data and the data acquisition time corresponding to each initial operation data to obtain a first function curve; a second processing module, which is used to perform linear fitting processing on multiple initial environmental data and the data acquisition time corresponding to each initial environmental data to obtain a second function curve; a third processing module, which is used to perform linear fitting processing on the initial operation data and the initial environmental data to obtain a third function curve; a first determination module, which is used to determine the target fault detection result of the target unit according to the first function curve, and / or the second function curve, and / or the third function curve.

[0007] The electronic device proposed in the third aspect embodiment of the present disclosure includes: a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the unit fault detection method for a nuclear power plant proposed in the first aspect embodiment of the present disclosure is implemented.

[0008] The fourth aspect embodiment of the present disclosure proposes a non-temporary computer-readable storage medium, on which a computer program is stored. When the program is executed by a processor, it implements the unit fault detection method for a nuclear power plant proposed in the first aspect embodiment of the present disclosure.

[0009] The fifth aspect embodiment of the present disclosure proposes a computer program product. When the instructions in the computer program product are executed by a processor, the unit fault detection method for a nuclear power plant proposed in the first aspect embodiment of the present disclosure is executed.

[0010] The present disclosure provides a unit fault detection method, device, electronic device and storage medium for a nuclear power plant. Initial operating data of a target unit in the nuclear power plant and initial environmental data of a target environmental area where the target unit is located are collected at each data collection time within a preset time period, and multiple initial operating data and data collection times corresponding to each initial operating data are linearly fitted to obtain a first function curve, multiple initial environmental data and data collection times corresponding to each initial environmental data are linearly fitted to obtain a second function curve, and the initial operating data and initial environmental data are linearly fitted to obtain a third function curve, and a target fault detection result of the target unit is determined based on the first function curve, and / or the second function curve, and / or the third function curve. Thus, the operating data of the target unit in the power plant and the environmental data of the environment where the unit is located can be combined to effectively improve the fault detection effect of the unit in the nuclear power plant.

[0011] Additional aspects and advantages of the present disclosure will be given in part in the following description and in part will be obvious from the following description or learned through practice of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] The above and / or additional aspects and advantages of the present disclosure will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0013] Figure 1 It is a flow chart of a method for detecting unit failure in a nuclear power plant proposed in an embodiment of the present disclosure;

[0014] Figure 2 is a flow chart of a method for detecting unit failure in a nuclear power plant according to another embodiment of the present disclosure;

[0015] Figure 3 It is a structural schematic diagram of a unit fault detection device for a nuclear power plant proposed in one embodiment of the present disclosure;

[0016] Figure 4 A block diagram of an exemplary electronic device suitable for implementing embodiments of the present disclosure is shown. DETAILED DESCRIPTION

[0017] Embodiments of the present disclosure 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 only used to explain the present disclosure, and are not to be construed as limitations of the present disclosure. On the contrary, the embodiments of the present disclosure include all changes, modifications, and equivalents that fall within the spirit and connotation of the appended claims.

[0018] Figure 1 It is a flow chart of a method for detecting unit faults in a nuclear power plant proposed in one embodiment of the present disclosure.

[0019] It should be noted that the executor of the unit fault detection method for a nuclear power plant in this embodiment is a unit fault detection device for a nuclear power plant, which can be implemented by software and / or hardware. The device can be configured in an electronic device, and the electronic device can include but is not limited to a terminal, a server, etc. For example, the terminal can be a mobile phone, a handheld computer, etc.

[0020] In the embodiments of the present disclosure, see Figure 2 , Figure 2 It is a structural schematic diagram of a wind turbine proposed in an embodiment of the present disclosure. The wind turbine comprises at least: a wind rotor, a main bearing and a wind rotor shaft connecting the wind rotor and the main bearing.

[0021] like Figure 1As shown, the unit fault detection method for a nuclear power plant comprises:

[0022] S101: Initial operation data of a target unit in a nuclear power plant and initial environmental data of a target environmental area where the target unit is located are collected at each data collection time within a preset time period.

[0023] Among them, the preset time period can be a pre-set time period for fault detection of units in a nuclear power plant. The preset time period can be combined with the unit fault detection requirements of the nuclear power plant in the actual business scenario and adaptively configured, and there is no restriction on this.

[0024] The preset time period may include multiple data collection times, and each data collection time may be used to collect relevant data of a target unit in a nuclear power plant.

[0025] The target unit may be a unit in a nuclear power plant for which fault detection is to be performed. The target unit may be, for example, a nuclear reactor, a steam generator, etc. in the nuclear power plant, and there is no limitation to this.

[0026] The operating data of the target unit in the preset time period is the initial operating data, and the initial operating data may be, for example, the operating power of the unit, a primary circuit pressure, etc., and there is no limitation on this.

[0027] Among them, the spatial area within the preset range where the target unit is located can be called the target environment area. The target environment area can be adaptively configured in combination with the unit fault detection requirements of the nuclear power plant in the actual business scenario, and there is no restriction on this.

[0028] Among them, the environmental data of the target environmental area where the target unit is located in the preset time period can be called initial environmental data. The initial environmental area can be, for example, the ambient temperature, the substance content in the environment, etc., which is not limited.

[0029] S102: performing linear fitting processing on a plurality of initial operation data and a data collection time corresponding to each initial operation data to obtain a first function curve.

[0030] In the embodiment of the present disclosure, after respectively collecting the initial operating data of the target unit in the nuclear power plant and the initial environmental data of the target environmental area where the target unit is located at each data collection time within a preset time period, a linear fitting process may be performed on the initial operating data and the data collection time corresponding to each initial operating data to obtain a corresponding first function curve.

[0031] S103: performing linear fitting processing on a plurality of initial environmental data and a data collection time corresponding to each initial environmental data to obtain a second function curve.

[0032] In the embodiment of the present disclosure, after respectively collecting the initial operation data of the target unit in the nuclear power plant and the initial environmental data of the target environmental area where the target unit is located at each data collection time within the preset time period, a linear fitting process may be performed on the multiple initial environmental data and the data collection time corresponding to each initial environmental data to obtain the second function curve accordingly.

[0033] S104: Performing linear fitting processing on the initial operation data and the initial environment data to obtain a third function curve.

[0034] In the embodiment of the present disclosure, after collecting the initial operating data of the target unit in the nuclear power plant and the initial environmental data of the target environmental area where the target unit is located at each data collection time within a preset time period, the initial operating data and the initial environmental data can be linearly fitted to obtain the corresponding third function curve.

[0035] S105: Determine a target fault detection result of the target unit according to the first function curve, and / or the second function curve, and / or the third function curve.

[0036] It is understandable that when a fault occurs in the target unit, the operating data of the target unit will change. Correspondingly, when a fault occurs in the target unit, the environmental data of the target environmental area where the target unit is located will also be affected. That is, the initial environmental data and the initial operating data of a preset time period can be combined to perform fault detection on the target unit, thereby fully leveraging the data value of the target unit-related data and effectively improving the fault detection effect.

[0037] In some embodiments, the target fault detection result of the target unit is determined according to the first function curve, and / or the second function curve, and / or the third function curve. The first function curve, and / or the second function curve, and / or the third function curve can be analyzed to determine the data points that do not fall into the first function curve, and / or the second function curve, and / or the third function curve, and when it is determined that there are data points that do not fall into the first function curve, and / or the second function curve, and / or the third function curve, it is determined that the data point may have an abnormal data change, so that it can be determined that at the data collection time corresponding to the data point, there is a unit fault in the target unit of the nuclear power plant, and there is no limitation on this.

[0038] In other embodiments, the target fault detection result of the target unit is determined according to the first function curve, and / or the second function curve, and / or the third function curve. It is also possible to obtain a first reference function curve corresponding to the first function curve, and / or a second reference function curve corresponding to the second function curve, and / or a third function curve corresponding to the third function curve, and then determine the distance between the first function curve and the first reference function curve, and / or the distance between the second function curve and the second reference function curve, and / or the distance between the third function curve and the third reference function curve, and when at least one of the aforementioned distances is greater than a distance threshold, it is determined that a unit failure has occurred in the target unit within a preset time period, and there is no limitation on this.

[0039] In the disclosed embodiment, initial operation data of a target unit in a nuclear power plant and initial environmental data of a target environmental area in which the target unit is located are collected at each data collection time within a preset time period, and a plurality of initial operation data and a data collection time corresponding to each initial operation data are linearly fitted to obtain a first function curve, and then a plurality of initial environmental data and a data collection time corresponding to each initial environmental data are linearly fitted to obtain a second function curve, and then the initial operation data and the initial environmental data are linearly fitted to obtain a third function curve, and a target fault detection result of the target unit is determined according to the first function curve, and / or the second function curve, and / or the third function curve. Thus, the operation data of the target unit in the power plant and the environmental data of the environment in which the unit is located can be combined to effectively improve the fault detection effect of the unit in the nuclear power plant.

[0040] Figure 2 It is a flow chart of a unit fault detection method for a nuclear power plant proposed in another embodiment of the present disclosure.

[0041] like Figure 2 As shown, the unit fault detection method for a nuclear power plant comprises:

[0042] S201: Initial operation data of a target unit in a nuclear power plant and initial environmental data of a target environmental area where the target unit is located are collected at each data collection time within a preset time period.

[0043] S202: Performing linear fitting processing on a plurality of initial operation data and a data collection time corresponding to each initial operation data to obtain a first function curve.

[0044] S203: Performing linear fitting processing on the multiple initial environmental data and the data collection time corresponding to each initial environmental data to obtain a second function curve.

[0045] S204: Perform linear fitting processing on the initial operation data and the initial environment data to obtain a third function curve.

[0046] The specific description of S201 - S204 can be found in the above embodiment, which will not be repeated here.

[0047] S205: Determine a target fault detection result of the target unit according to the first function curve, and / or the second function curve, and / or the third function curve.

[0048] Optionally, in some embodiments, the target fault detection result of the target unit is determined according to the first function curve, and / or the second function curve, and / or the third function curve, which may be by determining the first slope of the first function curve, and / or the second slope of the second function curve, and / or the third slope of the third function curve, and then determining the fault degradation level of the target unit according to the first slope, and / or the second slope, and / or the third slope, wherein the fault degradation level belongs to the target fault detection result.

[0049] The curve slope of the first function curve is the first slope, the curve slope of the second function curve is the second slope, and the curve slope of the third function curve is the third slope.

[0050] It is understandable that during the operation of a nuclear power plant unit, the operating data and environmental data of the unit may not have any obvious abnormalities (i.e., they have not been lower than the normal threshold, or higher than the normal threshold). In this case, it is impossible to determine that the unit has a fault based on the operating data and environmental data. However, the operating data and environmental data of the unit may continue to decrease until they are lower than the normal threshold, or continue to increase until they are higher than the normal threshold. In this case, the degradation trend of the unit can be determined. Before the unit fails, the unit can be operated and maintained according to its degradation condition, thereby preventing the unit failure from occurring in advance.

[0051] Among them, the fault degradation level can be used to describe the degree of degradation of the target unit, that is, the possibility that the target unit will degrade to a unit failure. The higher the fault degradation level, the higher the possibility that the target unit will degrade to a unit failure.

[0052] That is to say, in the embodiments of the present disclosure, the first slope of the first function curve, and / or the second slope of the second function curve, and / or the third slope of the third function curve can be determined based on the first function curve, and / or the second function curve, and / or the third function curve obtained by the aforementioned linear fitting, and then, the fault degradation level of the target unit can be determined based on the first slope, and / or the second slope, and / or the third slope.

[0053] Optionally, in some embodiments, the fault degradation level of the target unit is determined based on the first slope, and / or the second slope, and / or the third slope, and a first weight coefficient corresponding to the first slope and a second weight coefficient corresponding to the second slope are obtained, wherein the first weight coefficient is greater than the second weight coefficient, and when the third slope is greater than 0, the first slope and the second slope are weightedly summed based on the first weight coefficient and the second weight coefficient to obtain a first slope sum value, and when the third slope is less than 0, the absolute value of the first slope and the absolute value of the second slope are weightedly summed based on the first weight coefficient and the second weight coefficient to obtain a second slope sum value, and then the fault degradation level of the target unit is determined based on the first slope sum value or the second slope sum value.

[0054] That is to say, in the embodiment of the present disclosure, when the third slope is greater than 0, the first slope and the second slope may be weightedly summed based on the preset first weight coefficient corresponding to the first slope and the second weight coefficient corresponding to the second slope to obtain the first slope sum value, or when the third slope is less than 0, the absolute value of the first slope and the absolute value of the second slope may be weightedly summed based on the preset first weight coefficient corresponding to the first slope and the second weight coefficient corresponding to the second slope to obtain the second slope sum value, and then, the fault degradation level of the target unit may be determined based on the first slope sum value or the second slope sum value.

[0055] Optionally, in some embodiments, the fault degradation level of the target unit is determined based on the first slope sum value or the second slope sum value, and when the first slope sum value is greater than the first slope threshold or the second slope sum value is greater than the first slope threshold, the fault degradation level is determined to be the first degradation level, and when the first slope sum value is greater than the second slope threshold or the second slope sum value is greater than the second slope threshold, the fault degradation level is determined to be the second degradation level.

[0056] The second slope threshold is greater than the first slope threshold, and the degradation degree described by the second degradation level is higher than the degradation degree described by the first degradation level.

[0057] That is to say, compared with when the target unit is at the first degradation level, when the target unit is at the second degradation level, there is a greater probability that a unit failure will occur if the target unit continues to operate.

[0058] S206: Perform frequency domain transformation on the initial operation data to obtain a first frequency domain feature, and perform frequency domain transformation on the initial environment data to obtain a second frequency domain feature.

[0059] The frequency domain transformation may be processed in a manner such as Fourier transformation, wavelet transformation, etc., which is not limited.

[0060] That is to say, in the embodiments of the present disclosure, the initial operation data may be subjected to frequency domain transformation processing to obtain corresponding first frequency domain features, and the initial environment data may be subjected to frequency domain transformation processing to obtain corresponding second frequency domain features.

[0061] In the disclosed embodiment, by performing frequency domain transformation processing on the initial operating data and the initial environmental data, interference features in the initial operating data and the initial environmental data can be eliminated, and key features in the initial operating data and the initial environmental data can be extracted, thereby effectively assisting in improving the accuracy of subsequent unit fault detection.

[0062] S207: Acquire the first reference frequency domain characteristics and the second reference frequency domain characteristics of the target unit.

[0063] Among them, the frequency domain feature of the target unit corresponding to the first frequency domain is the first reference frequency domain feature, and the first frequency domain feature can be the frequency domain feature obtained by performing frequency domain transformation on the operating data of the target unit collected at this time when no unit failure occurs to the target unit. Correspondingly, the frequency domain feature of the target unit corresponding to the second frequency domain is the second reference frequency domain feature, and the second frequency domain feature can be the frequency domain feature obtained by performing frequency domain transformation on the environmental data of the target environmental area where the target unit is located collected at this time when no unit failure occurs to the target unit.

[0064] S208: Determine a first fault detection result of the target unit according to the first frequency domain feature, the second frequency domain feature, the first reference frequency domain feature and the second reference frequency domain feature, wherein the first fault detection result belongs to the target fault detection result.

[0065] After performing frequency domain transformation on the initial operation data to obtain the first frequency domain feature, performing frequency domain transformation on the initial environment data to obtain the second frequency domain feature, and obtaining the first reference frequency domain feature and the second reference frequency domain feature of the target unit, the embodiment of the present disclosure can determine the first fault detection result of the target unit based on the first frequency domain feature, the second frequency domain feature, the first reference frequency domain feature and the second reference frequency domain feature, wherein the first fault detection result belongs to the target fault detection result.

[0066] Optionally, in some embodiments, the first fault detection result of the target unit is determined based on the first frequency domain feature, the second frequency domain feature, the first reference frequency domain feature and the second reference frequency domain feature. The first similarity value between the first frequency domain feature and the first reference frequency domain feature can be determined, and the second similarity value between the second frequency domain feature and the second reference frequency domain feature can be determined. The first similarity value and the second similarity value are weightedly summed to obtain a target similarity value. When the target similarity value is greater than a similarity threshold, it is determined that the first fault detection result is that the target unit fails within a preset time period. When the target similarity value is less than or equal to the similarity threshold, it is determined that the first fault detection result is that the target unit does not fail within the preset time period.

[0067] Among them, the first similarity value between the first frequency domain feature and the first reference frequency domain feature can be, for example, the cosine similarity between the first frequency domain feature and the first reference frequency domain feature, the Euclidean distance between the first frequency domain feature and the first reference frequency domain feature, etc., without limitation.

[0068] Among them, the second similarity value between the second frequency domain feature and the second reference frequency domain feature can be, for example, the cosine similarity between the second frequency domain feature and the second reference frequency domain feature, the Euclidean distance between the second frequency domain feature and the second reference frequency domain feature, etc., without limitation.

[0069] In the embodiment of the present disclosure, based on a preset weight coefficient (the preset weight coefficient can be adaptively set in combination with the importance of environmental data and operating data in the unit fault detection process in actual business scenarios, and there is no restriction on this), the first similarity value and the second similarity value are weighted and summed to obtain a target similarity value, and the target similarity value is compared with a preset similarity threshold, and when the target similarity value is greater than the similarity threshold, it is determined that the first fault detection result is that the target unit has failed within a preset time period, and when the target similarity value is less than or equal to the similarity threshold, it is determined that the first fault detection result is that the target unit has not failed within the preset time period.

[0070] In the embodiment of the present disclosure, initial operation data of a target unit in a nuclear power plant and initial environmental data of a target environmental area where the target unit is located are collected at each data collection time within a preset time period, and a plurality of initial operation data and a data collection time corresponding to each initial operation data are linearly fitted to obtain a first function curve, and then a plurality of initial environmental data and a data collection time corresponding to each initial environmental data are linearly fitted to obtain a second function curve, and then the initial operation data and the initial environmental data are linearly fitted to obtain a third function curve, and a target fault detection result of the target unit is determined according to the first function curve, and / or the second function curve, and / or the third function curve, so that a first function curve can be determined. The degradation trend of the unit is determined. Before the unit fails, the unit is operated and maintained according to the degradation condition of the unit, so as to prevent the unit failure in advance. The initial operation data is transformed in the frequency domain to obtain the first frequency domain feature, and the initial environmental data is transformed in the frequency domain to obtain the second frequency domain feature. After obtaining the first reference frequency domain feature and the second reference frequency domain feature of the target unit, the first fault detection result of the target unit can be determined according to the first frequency domain feature, the second frequency domain feature, the first reference frequency domain feature and the second reference frequency domain feature, so as to eliminate the interference features in the initial operation data and the initial environmental data, and help extract the key features in the initial operation data and the initial environmental data, effectively improving the fault detection effect of the unit in the nuclear power plant.

[0071] Figure 3 It is a structural schematic diagram of a unit fault detection device for a nuclear power plant proposed in one embodiment of the present disclosure.

[0072] like Figure 3 As shown, the unit fault detection device 30 for a nuclear power plant comprises:

[0073] The acquisition module 301 is used to respectively acquire the initial operation data of the target unit in the nuclear power plant and the initial environmental data of the target environmental area where the target unit is located at each data acquisition time within a preset time period;

[0074] A first processing module 302 is used to perform linear fitting processing on a plurality of initial operation data and a data collection time corresponding to each initial operation data to obtain a first function curve;

[0075] The second processing module 303 is used to perform linear fitting processing on the multiple initial environmental data and the data collection time corresponding to each initial environmental data to obtain a second function curve;

[0076] The third processing module 304 is used to perform linear fitting processing on the initial operation data and the initial environment data to obtain a third function curve;

[0077] The first determination module 305 is used to determine the target fault detection result of the target unit according to the first function curve, and / or the second function curve, and / or the third function curve.

[0078] In some embodiments of the present disclosure, the unit fault detection device 30 for a nuclear power plant further includes:

[0079] A fourth processing module is used to perform a frequency domain transformation on the initial operation data to obtain a first frequency domain feature, and to perform a frequency domain transformation on the initial environment data to obtain a second frequency domain feature;

[0080] An acquisition module, used to acquire a first reference frequency domain feature and a second reference frequency domain feature of a target unit;

[0081] The second determination module is used to determine a first fault detection result of the target unit according to the first frequency domain feature, the second frequency domain feature, the first reference frequency domain feature and the second reference frequency domain feature, wherein the first fault detection result belongs to the target fault detection result.

[0082] In some embodiments of the present disclosure, the second determining module is further configured to:

[0083] Determine a first similarity value between the first frequency domain feature and the first reference frequency domain feature, and determine a second similarity value between the second frequency domain feature and the second reference frequency domain feature;

[0084] Performing weighted sum processing on the first similarity value and the second similarity value to obtain a target similarity value;

[0085] If the target similarity value is greater than the similarity threshold, determining that the first fault detection result is that the target unit fails within a preset time period;

[0086] If the target similarity value is less than or equal to the similarity threshold, it is determined that the first fault detection result is that the target unit has not failed in a preset time period.

[0087] In some embodiments of the present disclosure, the first determining module 305 is further configured to:

[0088] Determining a first slope of the first function curve, and / or a second slope of the second function curve, and / or a third slope of the third function curve;

[0089] The fault degradation level of the target unit is determined according to the first slope, and / or the second slope, and / or the third slope, wherein the fault degradation level belongs to the target fault detection result.

[0090] In some embodiments of the present disclosure, the second determining module 305 is further configured to:

[0091] Obtaining a first weight coefficient corresponding to the first slope and a second weight coefficient corresponding to the second slope, wherein the first weight coefficient is greater than the second weight coefficient;

[0092] If the third slope is greater than 0, performing weighted sum processing on the first slope and the second slope based on the first weight coefficient and the second weight coefficient to obtain a first slope sum value;

[0093] If the third slope is less than 0, based on the first weight coefficient and the second weight coefficient, the absolute value of the first slope and the absolute value of the second slope are weighted and summed to obtain a second slope sum value;

[0094] The fault degradation level of the target unit is determined according to the first slope and value or the second slope and value.

[0095] In some embodiments of the present disclosure, the second determining module 305 is further configured to:

[0096] If the first slope sum value is greater than the first slope threshold, or the second slope sum value is greater than the first slope threshold, determining that the fault degradation level is the first degradation level;

[0097] If the first slope sum value is greater than the second slope threshold, or the second slope sum value is greater than the second slope threshold, it is determined that the fault degradation level is the second degradation level, wherein the second slope threshold is greater than the first slope threshold, and the degradation degree described by the second degradation level is higher than the degradation degree described by the first degradation level.

[0098] It should be noted that the aforementioned explanation of the unit fault detection method for a nuclear power plant is also applicable to the unit fault detection device for a nuclear power plant of this embodiment, and will not be repeated here.

[0099] In the disclosed embodiment, initial operation data of a target unit in a nuclear power plant and initial environmental data of a target environmental area in which the target unit is located are collected at each data collection time within a preset time period, and a plurality of initial operation data and a data collection time corresponding to each initial operation data are linearly fitted to obtain a first function curve, and then a plurality of initial environmental data and a data collection time corresponding to each initial environmental data are linearly fitted to obtain a second function curve, and then the initial operation data and the initial environmental data are linearly fitted to obtain a third function curve, and a target fault detection result of the target unit is determined according to the first function curve, and / or the second function curve, and / or the third function curve. Thus, the operation data of the target unit in the power plant and the environmental data of the environment in which the unit is located can be combined to effectively improve the fault detection effect of the unit in the nuclear power plant.

[0100] Figure 4A block diagram of an exemplary electronic device suitable for implementing embodiments of the present disclosure is shown. Figure 4 The electronic device 12 shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present disclosure.

[0101] like Figure 4 As shown, the electronic device 12 is in the form of a general purpose computing device. The components of the electronic device 12 may include, but are not limited to: one or more processors or processing units 16, a system memory 28, and a bus 18 that connects various system components (including the system memory 28 and the processing unit 16).

[0102] The bus 18 represents one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, a graphics acceleration port, a processor or a local bus using any of a variety of bus structures. For example, these architectures include but are not limited to Industry Standard Architecture (ISA) bus, Micro Channel Architecture (MAC) bus, Enhanced ISA bus, Video Electronics Standards Association (VESA) local bus and Peripheral Component Interconnection (PCI) bus.

[0103] The electronic device 12 typically includes a variety of computer system readable media. These media can be any available media that can be accessed by the electronic device 12, including volatile and non-volatile media, removable and non-removable media.

[0104] The memory 28 may include computer system readable media in the form of volatile memory, such as random access memory (RAM) 30 and / or cache memory 32. The electronic device 12 may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, the storage system 34 may be used to read and write non-removable, non-volatile magnetic media ( Figure 4 Not shown, often called a "hard drive").

[0105] although Figure 4Not shown in the figure, a disk drive for reading and writing a removable non-volatile disk (e.g., a "floppy disk"), and an optical disk drive for reading and writing a removable non-volatile optical disk (e.g., a compact disc read only memory (hereinafter referred to as: CD-ROM), a digital versatile disc read only memory (hereinafter referred to as: DVD-ROM) or other optical media) may be provided. In these cases, each drive may be connected to the bus 18 via one or more data medium interfaces. The memory 28 may include at least one program product having a set (e.g., at least one) of program modules that are configured to perform the functions of the various embodiments of the present disclosure.

[0106] A program / utility 40 having a set (at least one) of program modules 42 may be stored, for example, in the memory 28, such program modules 42 including but not limited to an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment. The program modules 42 generally perform the functions and / or methods of the embodiments described in the present disclosure.

[0107] The electronic device 12 may also communicate with one or more external devices 14 (e.g., keyboard, pointing device, display 24, etc.), may also communicate with one or more devices that enable the human body to interact with the electronic device 12, and / or communicate with any device that enables the electronic device 12 to communicate with one or more other computing devices (e.g., network card, modem, etc.). Such communication may be performed through an input / output (I / O) interface 22. In addition, the electronic device 12 may also communicate with one or more networks (e.g., a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) through a network adapter 20. As shown, the network adapter 20 communicates with other modules of the electronic device 12 through a bus 18. It should be understood that, although not shown in the figure, other hardware and / or software modules may be used in conjunction with the electronic device 12, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0108] The processing unit 16 executes various functional applications and data processing by running the programs stored in the system memory 28, such as implementing the unit fault detection method for a nuclear power plant mentioned in the above-mentioned embodiment.

[0109] In order to implement the above embodiments, the present disclosure also proposes a non-temporary computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the unit fault detection method for a nuclear power plant proposed in the above embodiments of the present disclosure is implemented.

[0110] In order to implement the above embodiments, the present disclosure further proposes a computer program product. When the instruction processor in the computer program product is executed, the unit fault detection method for a nuclear power plant proposed in the above embodiments of the present disclosure is executed.

[0111] Those skilled in the art will readily appreciate other embodiments of the present disclosure after considering the specification and practicing the invention disclosed herein. The present disclosure is intended to cover any variations, uses, or adaptations of the present disclosure that follow the general principles of the present disclosure and include common knowledge or customary techniques in the art that are not disclosed in the present disclosure. The description and examples are to be considered exemplary only, and the true scope and spirit of the present disclosure are indicated by the following claims.

[0112] It should be understood that the present disclosure is not limited to the exact structures that have been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.

[0113] It should be noted that, in the description of the present disclosure, the terms "first", "second", etc. are only used for descriptive purposes and cannot be understood as indicating or implying relative importance. In addition, in the description of the present disclosure, unless otherwise specified, the meaning of "plurality" is two or more.

[0114] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code that includes one or more executable instructions for implementing the steps of a specific logical function or process, and the scope of the preferred embodiments of the present disclosure 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 those skilled in the art to which the embodiments of the present disclosure belong.

[0115] It should be understood that the various parts of the present disclosure can be implemented in hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in 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.

[0116] 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.

[0117] In addition, each functional unit in each embodiment of the present disclosure 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.

[0118] The storage medium mentioned above can be a read-only memory, a magnetic disk or an optical disk, etc.

[0119] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", 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 disclosure. In this specification, the schematic representation of the above terms does 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.

[0120] Although the embodiments of the present disclosure have been shown and described above, it is to be understood that the above embodiments are exemplary and are not to be construed as limitations of the present disclosure. A person skilled in the art may change, modify, replace and vary the above embodiments within the scope of the present disclosure.

Claims

1. A method for detecting unit faults in a nuclear power plant, characterized in that: The method comprises: At each data collection time within a preset time period, initial operation data of a target unit in a nuclear power plant and initial environmental data of a target environmental area where the target unit is located are collected respectively; Performing linear fitting processing on the plurality of initial operation data and the data collection time corresponding to each of the initial operation data to obtain a first function curve; Performing linear fitting processing on the plurality of initial environmental data and the data collection time corresponding to each of the initial environmental data to obtain a second function curve; Performing linear fitting processing on the initial operation data and the initial environment data to obtain a third function curve; The target fault detection result of the target unit is determined according to the first function curve, and / or the second function curve, and / or the third function curve.

2. The method according to claim 1, characterized in that The method further comprises: Performing a frequency domain transformation on the initial operation data to obtain a first frequency domain feature, and performing a frequency domain transformation on the initial environment data to obtain a second frequency domain feature; Acquire a first reference frequency domain feature and a second reference frequency domain feature of the target unit; A first fault detection result of the target unit is determined according to the first frequency domain feature, the second frequency domain feature, the first reference frequency domain feature, and the second reference frequency domain feature, wherein the first fault detection result belongs to the target fault detection result.

3. The method according to claim 2, characterized in that The determining, according to the first frequency domain feature, the second frequency domain feature, the first reference frequency domain feature, and the second reference frequency domain feature, a first fault detection result of the target unit includes: Determine a first similarity value between the first frequency domain feature and the first reference frequency domain feature, and determine a second similarity value between the second frequency domain feature and the second reference frequency domain feature; Performing weighted sum processing on the first similarity value and the second similarity value to obtain a target similarity value; If the target similarity value is greater than the similarity threshold, determining that the first fault detection result is that the target unit fails within the preset time period; If the target similarity value is less than or equal to the similarity threshold, it is determined that the first fault detection result is that the target unit has not failed in the preset time period.

4. The method according to claim 1, characterized in that Determining the target fault detection result of the target unit according to the first function curve, and / or the second function curve, and / or the third function curve includes: Determining a first slope of the first function curve, and / or a second slope of the second function curve, and / or a third slope of the third function curve; The fault degradation level of the target unit is determined according to the first slope, and / or the second slope, and / or the third slope, wherein the fault degradation level belongs to the target fault detection result.

5. The method according to claim 4, characterized in that Determining the fault degradation level of the target unit according to the first slope, and / or the second slope, and / or the third slope includes: Acquire a first weight coefficient corresponding to the first slope, and a second weight coefficient corresponding to the second slope, wherein the first weight coefficient is greater than the second weight coefficient; If the third slope is greater than 0, performing weighted sum processing on the first slope and the second slope based on the first weight coefficient and the second weight coefficient to obtain a first slope sum value; If the third slope is less than 0, performing weighted sum processing on the absolute value of the first slope and the absolute value of the second slope based on the first weight coefficient and the second weight coefficient to obtain a second slope sum value; The fault degradation level of the target unit is determined according to the first slope and value, or the second slope and value.

6. The method according to claim 5, characterized in that Determining the fault degradation level of the target unit according to the first slope and value, or the second slope and value, includes: If the first slope sum value is greater than a first slope threshold, or the second slope sum value is greater than the first slope threshold, determining that the fault degradation level is a first degradation level; If the first slope sum value is greater than a second slope threshold, or the second slope sum value is greater than the second slope threshold, it is determined that the fault degradation level is a second degradation level, wherein the second slope threshold is greater than the first slope threshold, and the degree of degradation described by the second degradation level is higher than the degree of degradation described by the first degradation level.

7. A unit fault detection device for a nuclear power plant, characterized in that: The device comprises: A collection module, used to collect initial operation data of a target unit in a nuclear power plant and initial environmental data of a target environmental area where the target unit is located at each data collection time within a preset time period; A first processing module, configured to perform linear fitting processing on the plurality of initial operation data and the data collection time corresponding to each of the initial operation data, so as to obtain a first function curve; A second processing module, used for performing linear fitting processing on the plurality of initial environmental data and the data collection time corresponding to each of the initial environmental data, so as to obtain a second function curve; A third processing module, used for performing linear fitting processing on the initial operation data and the initial environment data to obtain a third function curve; The first determination module is used to determine the target fault detection result of the target unit according to the first function curve, and / or the second function curve, and / or the third function curve.

8. The device according to claim 7, characterized in that The device also includes: a fourth processing module, configured to perform a frequency domain transformation on the initial operation data to obtain a first frequency domain feature, and perform a frequency domain transformation on the initial environment data to obtain a second frequency domain feature; An acquisition module, used for acquiring a first reference frequency domain feature and a second reference frequency domain feature of the target unit; The second determination module is used to determine a first fault detection result of the target unit according to the first frequency domain feature, the second frequency domain feature, the first reference frequency domain feature and the second reference frequency domain feature, wherein the first fault detection result belongs to the target fault detection result.

9. An electronic device, characterized in that: include: at least one processor; as well as a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 6.

10. A non-transitory computer-readable storage medium storing computer instructions, characterized in that: in, The computer instructions are used to cause the computer to execute the method according to any one of claims 1 to 6.

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