Nuclear power station equipment life prediction method, system and device, equipment and storage medium

Abstract

The invention discloses a life prediction method, system and device for nuclear power station equipment, the equipment and a storage medium. The method comprises the following steps: acquiring equipment state data of nuclear power station equipment in a nuclear power station; determining a health state evaluation result of the nuclear power station equipment according to the equipment state data; and performing life evaluation based on the historical use data corresponding to the nuclear power station equipment and the health state evaluation result to obtain the predicted residual life of the nuclear power station equipment. According to the method, the health state evaluation result is determined according to the equipment state data of the nuclear power station equipment, the predicted residual life of the nuclear power station equipment is evaluated in combination with the health state evaluation result and the historical use data, and the residual life of the nuclear power station equipment can be accurately predicted.

Description

Technical Field

[0001] Embodiments of the present invention relate to the technical field of nuclear power plant operation management, and in particular to a life prediction method, system, device, equipment, and storage medium for nuclear power plant equipment. Background Art

[0002] With the increasing complexity of nuclear power plant equipment and the continuous improvement of operating requirements, the health management and life prediction of nuclear power plant equipment have become important links in ensuring the safe and efficient operation of nuclear power plants. Accurate prediction of equipment life is also of great significance in extending the equipment service cycle and reducing operation and maintenance costs.

[0003] Traditional equipment maintenance methods often rely on periodic inspections and reactive repairs, making it difficult to capture real-time changes in equipment status. This makes it difficult to provide early warnings of potential equipment failures, impacting the safe operation of nuclear power plants. Faced with the challenges of complex equipment monitoring data and high real-time requirements, accurately managing the health and predicting the lifespan of nuclear power plant equipment is an urgent issue. Summary of the Invention

[0004] The present invention provides a life prediction method, system, device, equipment and storage medium for nuclear power plant equipment to solve the problem in the prior art that health management and life prediction of nuclear power plant equipment cannot be accurately performed.

[0005] According to one aspect of the present invention, a method for predicting the life of nuclear power plant equipment is provided, the method comprising:

[0006] Obtain equipment status data of nuclear power equipment in nuclear power plants;

[0007] Determining a health status assessment result of the nuclear power plant equipment based on the equipment status data;

[0008] A lifespan assessment is performed based on historical usage data corresponding to the nuclear power plant equipment and health status assessment results to obtain a predicted remaining lifespan of the nuclear power plant equipment.

[0009] According to another aspect of the present invention, a life prediction system for nuclear power plant equipment is provided, the system comprising: an equipment monitoring module and an electronic device, the equipment monitoring module being connected to the electronic device;

[0010] The equipment monitoring module is used to detect equipment status data of nuclear power equipment in the nuclear power plant and send the equipment status data to the electronic device;

[0011] The electronic device is used to execute the life prediction method for nuclear power plant equipment described in any embodiment of the present invention.

[0012] According to another aspect of the present invention, a life prediction device for nuclear power plant equipment is provided, the device comprising:

[0013] An acquisition module is used to obtain the equipment status data of the nuclear power plant equipment in the nuclear power plant;

[0014] A health management module, configured to determine a health status assessment result of the nuclear power plant equipment based on the equipment status data;

[0015] The life prediction module is used to perform life evaluation based on the historical usage data and health status evaluation results corresponding to the nuclear power plant equipment to obtain the predicted remaining life of the nuclear power plant equipment.

[0016] According to another aspect of the present invention, there is provided an electronic device, comprising: at least one processor; and

[0017] a memory communicatively connected to the at least one processor; wherein,

[0018] The memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the life prediction method for nuclear power plant equipment described in any embodiment of the present invention.

[0019] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the life prediction method of nuclear power plant equipment described in any embodiment of the present invention when executed.

[0020] Embodiments of the present invention provide a life prediction method, system, apparatus, device, and storage medium for nuclear power plant equipment. The method comprises: obtaining equipment status data of nuclear power plant equipment within a nuclear power plant; determining a health status assessment result of the nuclear power plant equipment based on the equipment status data; and performing a life assessment based on historical usage data corresponding to the nuclear power plant equipment and the health status assessment result to obtain a predicted remaining life of the nuclear power plant equipment. This method accurately predicts the remaining life of the nuclear power plant equipment by determining a health status assessment result based on the equipment status data of the nuclear power plant equipment and combining the health status assessment result with historical usage data to evaluate the predicted remaining life of the nuclear power plant equipment. This improves the safety and operational efficiency of the nuclear power plant equipment and solves the problem in the prior art of being unable to accurately perform health management and life prediction on nuclear power plant equipment.

[0021] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present invention, nor is it intended to limit the scope of the present invention. Other features of the present invention will become readily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0023] Figure 1 A schematic flow chart of a method for predicting the life of nuclear power plant equipment provided in the first embodiment of the present invention;

[0024] Figure 2 A schematic flow chart of a method for predicting the life of nuclear power plant equipment provided in the second embodiment of the present invention;

[0025] Figure 3 A schematic diagram of the structure of a life prediction system for nuclear power plant equipment provided in a third embodiment of the present invention;

[0026] Figure 4 A schematic structural diagram of a life prediction device for nuclear power plant equipment provided in a fourth embodiment of the present invention;

[0027] Figure 5 Schematic diagram of the structure of an electronic device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0028] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only embodiments of a part of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work should fall within the scope of protection of the present invention. It should be understood that the various steps described in the method implementation mode of the present invention can be performed in different orders and / or in parallel. In addition, the method implementation mode may include additional steps and / or omit the steps shown. The scope of the present invention is not limited in this respect.

[0029] As used herein, the term "including" and its variations are open-ended, i.e., "including but not limited to." The term "based on" means "based, at least in part, on." The term "one embodiment" means "at least one embodiment," the term "another embodiment" means "at least one additional embodiment," and the term "some embodiments" means "at least some embodiments." Other terms are defined in the following description.

[0030] It should be noted that the terms "first," "second," and the like in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or precedence. It should be understood that the numbers used in this manner are interchangeable where appropriate so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, any variations of the terms "including" and "having" are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not necessarily limited to those steps or units that are explicitly listed, but may include other steps or units that are not explicitly listed or that are inherent to these processes, methods, products, or apparatus.

[0031] It should be noted that the modifications of "one" and "multiple" mentioned in the present invention are illustrative rather than restrictive. Those skilled in the art should understand that unless otherwise clearly indicated in the context, it should be understood as "one or more".

[0032] The names of the messages or information exchanged between multiple devices in the embodiments of the present invention are only used for illustrative purposes and are not used to limit the scope of these messages or information.

[0033] The technology published with publication number CN115469643A proposes a method, system, and medium for health management of rotating machinery in a nuclear power plant, including: obtaining monitoring data and DCS data of the rotating machinery; performing data preprocessing on the monitoring data and DCS data; performing feature data extraction on the preprocessed monitoring data and DCS data to generate condition monitoring feature data, fault diagnosis feature data, and fault prediction feature data; performing condition monitoring, fault diagnosis, and fault prediction on the rotating machinery based on the condition monitoring feature data, fault diagnosis feature data, and fault prediction feature data; and determining the health status of the nuclear power plant rotating machinery based on the condition monitoring data, fault diagnosis data, fault prediction data, historical conditions of the rotating machinery, and prior knowledge. This solution addresses the problem of low operation and maintenance management efficiency caused by the poor real-time and incomplete monitoring of rotating machinery in existing technologies. However, this solution is relatively static in the feature extraction and evaluation process, does not fully consider the dynamic changes and real-time adjustments of the key characteristics of the equipment, lacks detailed analysis of key feature anomalies and an automated early warning mechanism, and cannot monitor all nuclear power plant equipment in the nuclear power plant.

[0034] Example 1

[0035] Figure 1A flow chart of a method for predicting the life of nuclear power plant equipment provided in Example 1 of the present invention is provided. The method can be applied to health management of equipment in a nuclear power plant and prediction of the remaining life of the equipment. The method can be executed by a life prediction device for nuclear power plant equipment, wherein the device can be implemented by software and / or hardware and is generally integrated on an electronic device. In this embodiment, the electronic device includes but is not limited to: computers and other devices.

[0036] like Figure 1 As shown, a method for predicting the life of nuclear power plant equipment provided by the first embodiment of the present invention includes the following steps:

[0037] S110 , obtaining equipment status data of nuclear power plant equipment in the nuclear power plant.

[0038] The nuclear power plant equipment may include key equipment such as nuclear island equipment, conventional island equipment, and auxiliary system equipment. The equipment status data may be parameters that characterize the current status of the equipment. The equipment status data may vary for different equipment, and the specific content of the equipment status data is not limited in this embodiment.

[0039] In this embodiment, the equipment status data of the nuclear power plant equipment that needs to be predicted for life in the nuclear power plant can be obtained.

[0040] S120: Determine a health status assessment result of the nuclear power plant equipment according to the equipment status data.

[0041] Among them, the health status assessment results can be used to indicate the current status of nuclear power plant equipment.

[0042] In this embodiment, the equipment status data of the nuclear power plant equipment may be analyzed to determine the health status assessment result of the nuclear power plant equipment.

[0043] S130: Perform life assessment based on the historical usage data and health status assessment results corresponding to the nuclear power plant equipment to obtain the predicted remaining life of the nuclear power plant equipment.

[0044] The historical usage data may be the historical usage of nuclear power plant equipment, and the predicted remaining life may be the predicted number of years the equipment can still be used.

[0045] In this embodiment, a lifespan assessment may be performed based on historical usage data and health status assessment results corresponding to the nuclear power plant equipment to obtain a predicted remaining lifespan of the nuclear power plant equipment.

[0046] A first embodiment of the present invention provides a life prediction method for nuclear power plant equipment, comprising: obtaining equipment status data of nuclear power plant equipment within a nuclear power plant; determining a health status assessment result of the nuclear power plant equipment based on the equipment status data; and performing a life assessment based on historical usage data corresponding to the nuclear power plant equipment and the health status assessment result to obtain a predicted remaining life of the nuclear power plant equipment. This method accurately predicts the remaining life of the nuclear power plant equipment by determining a health status assessment result based on the equipment status data of the nuclear power plant equipment and combining the health status assessment result with historical usage data to evaluate the predicted remaining life of the nuclear power plant equipment, thereby improving the safety and operating efficiency of the nuclear power plant equipment and resolving the problem in the prior art of being unable to accurately perform health management and life prediction on nuclear power plant equipment.

[0047] Based on the above embodiment, a modified embodiment of the above embodiment is proposed. It should be noted that, in order to simplify the description, only the differences from the above embodiment are described in the modified embodiment.

[0048] In one embodiment, the health status assessment result includes a health status and a fault type; accordingly, determining the health status assessment result of the nuclear power plant equipment based on the equipment status data includes:

[0049] Obtain equipment status data with timestamps for each nuclear power plant equipment;

[0050] Extracting key features that can indicate the operating status of nuclear power plant equipment from the equipment status data with timestamps;

[0051] Performing a health assessment based on the key features to obtain the health status of the nuclear power plant equipment;

[0052] The fault type of the nuclear power plant equipment is identified based on the health status and the fault mode library.

[0053] Key features may be features related to the health and lifespan of nuclear power plant equipment within all equipment status data. A health status may include healthy or unhealthy, or may include different health levels. A fault type may be the type of fault occurring in nuclear power plant equipment. A fault pattern library may include key feature change patterns corresponding to different fault types. A key feature change pattern may refer to changes in a key feature before and after a specific fault occurs in nuclear power plant equipment.

[0054] In this embodiment, equipment status data with timestamps can be obtained, and key features that can indicate the operating status of nuclear power plant equipment can be extracted from all equipment status data with timestamps. Health assessment is performed based on the key features to obtain the health status of the nuclear power plant equipment. The fault type of the nuclear power plant equipment can be identified through the health status and fault mode library.

[0055] In one embodiment, performing health assessment based on the key features to obtain the health status of the nuclear power plant equipment includes:

[0056] Based on the feature anomaly judgment model, all key features are quantitatively judged for anomalies to obtain the anomaly judgment value of each key feature;

[0057] Determining a comprehensive abnormality probability at each time point within the operation evaluation cycle based on the operation evaluation cycle of the nuclear power plant equipment and the abnormality determination value of each key feature;

[0058] Determining a health index of the nuclear power plant equipment based on the comprehensive abnormality probability and abnormal conditions of key features at each time point;

[0059] The health status of the nuclear power plant equipment is obtained based on the health index and the health index threshold.

[0060] The feature anomaly determination model may be a pre-trained model that can output an anomaly determination value for the device. Quantitative anomaly determination may refer to the process of analyzing data, behavior, or phenomena using quantitative methods and specific standards to determine whether they deviate from a normal range or pattern. The anomaly determination value may be a numerical value indicating the degree of anomaly in the device. The operation evaluation cycle may be the time interval for regularly conducting a comprehensive evaluation of the health status of the device during operation. The comprehensive anomaly probability may be the probability of an anomaly in the device throughout the operation evaluation cycle. The abnormality of a key feature may refer to an adjustment factor set based on the number of key feature anomalies during the operation evaluation cycle.

[0061] In this embodiment, for each nuclear power plant equipment, all key features of the nuclear power plant equipment can be quantitatively abnormally judged through the feature abnormality judgment model to obtain the abnormality judgment value of each key feature. Through the operation evaluation cycle of the nuclear power plant equipment and the abnormality judgment value of each key feature, the comprehensive abnormality probability at each time point within the operation evaluation cycle can be determined. Based on the comprehensive abnormality probability at each time point and the abnormal conditions of the key features, the health index of the nuclear power plant equipment can be determined, and the health status of the nuclear power plant equipment can be obtained according to the health index and the health index threshold.

[0062] For example, the health of nuclear power plant equipment can be assessed through the following steps:

[0063] 1) Establish a feature anomaly determination model based on key feature settings, and use the feature anomaly determination model to perform quantitative anomaly determination on all key features to obtain the anomaly determination value of each key feature.

[0064] Assume that the total number of key features is n, and the specific value f of the i-th key feature is i , set its standard operating range [f min ,f max ], the standard operating range is the expected value range of the key feature under normal working conditions of nuclear power plant equipment; the abnormal judgment value of the key feature is calculated by the following formula:

[0065]

[0066] Among them, P i is the abnormality judgment value of the i-th key feature; f min is the minimum value of the key feature within the standard operating range, f max It is the maximum value of the key characteristic within the standard operating range.

[0067] 2) Set an operation evaluation cycle, which is the time interval for regularly conducting comprehensive evaluations of the equipment health status during equipment operation. At each time point within the operation evaluation cycle, the comprehensive abnormality probability at that time point is obtained by combining the abnormality judgment value of each key feature at that time point:

[0068]

[0069] Among them, P is the comprehensive abnormal probability at a certain time point; ω i is the normalized weight coefficient of the i-th key feature, which can be obtained through the sensitivity of the key feature to equipment health and historical experience.

[0070] 3) During each operation evaluation cycle, the health index of the equipment is calculated by combining the comprehensive abnormal probability and abnormal conditions of key features at all time points in the cycle. The specific calculation method of the health index is as follows:

[0071]

[0072] Among them, H is the health index of nuclear power plant equipment in a certain operation evaluation cycle, is the average comprehensive abnormal probability during the operation evaluation period; ΔP is the incremental coefficient of the comprehensive abnormal probability during the operation evaluation period; γ is the adjustment factor set based on the number of key feature abnormalities during the operation evaluation period;

[0073] in, satisfy:

[0074]

[0075] Where P(t) is the comprehensive abnormal probability at the tth time point in the operation evaluation cycle, and m is the total number of time points in the operation evaluation cycle. The larger the value of t, the closer the specific time of this time point is to the current time.

[0076] Among them, ΔP satisfies:

[0077]

[0078] Among them, γ satisfies:

[0079]

[0080]

[0081] Where q is the number of key features of persistent anomalies, I() is the indicator function, when the conditions in () are met, I() is 1, otherwise it is 0; P i (t) is the abnormality judgment value of key feature i at the tth time point.

[0082] 4) Set a health index threshold, which is used to determine whether the equipment is in a healthy state. When the health index of a nuclear power plant device exceeds the health index threshold during a certain operation assessment cycle, an early warning mechanism is triggered to alert users that there may be potential risks or abnormal conditions in the nuclear power plant equipment.

[0083] This embodiment can also identify the specific fault type of nuclear power plant equipment through the fault mode library, which contains key feature change patterns corresponding to different fault types; when the health index of a device exceeds the health index threshold, the key feature changes actually monitored are matched with the key feature change patterns in the fault mode library to identify the specific fault type of the device.

[0084] This embodiment extracts key features used to describe the operating status of equipment based on the collected real-time operating status data of nuclear power plant equipment, and quantifies abnormality judgments based on the key features, thereby accurately identifying changes in the equipment operating status and potential abnormalities at various time points. By combining the comprehensive abnormality probability and key feature changes within the operation evaluation cycle, the health index is calculated to evaluate the equipment health status, thereby achieving timely early warning and optimized maintenance, and improving the safety and reliability of equipment operation.

[0085] Example 2

[0086] Figure 2 This is a flow chart of a method for predicting the life of nuclear power plant equipment provided by the second embodiment of the present invention. This second embodiment is optimized based on the above embodiments. For details not yet fully described in this embodiment, please refer to the first embodiment.

[0087] like Figure 2 As shown, a method for predicting the life of nuclear power plant equipment provided by the second embodiment of the present invention includes the following steps:

[0088] S210: Obtain equipment status data of nuclear power plant equipment in the nuclear power plant.

[0089] S220: Determine a health status assessment result of the nuclear power plant equipment according to the equipment status data.

[0090] S230: Acquire historical usage data of the nuclear power plant equipment, where the historical usage data includes accumulated usage time and maintenance record information of the nuclear power plant equipment.

[0091] The maintenance record information may include a preset maintenance frequency and an actual maintenance frequency. The preset maintenance frequency may be a pre-planned frequency for maintaining nuclear power plant equipment, and the actual maintenance frequency may be a frequency for maintaining nuclear power plant equipment in actual work.

[0092] In this embodiment, historical usage data of nuclear power plant equipment may be obtained, and the historical usage data may include accumulated usage time and maintenance record information of the nuclear power plant equipment.

[0093] S240. Determine the life loss factor of the nuclear power plant equipment based on the preset maintenance frequency, actual maintenance frequency, and the total number of operation evaluation cycles and abnormal operation cycles of the nuclear power plant equipment in the maintenance record information.

[0094] The total number of operation evaluation cycles may be the total number of operation evaluation cycles included in the cumulative usage time of the device, and the number of abnormal operation cycles may be the number of operation evaluation cycles with abnormalities in the total number of all operation evaluation cycles.

[0095] In this embodiment, the life loss factor of the nuclear power plant equipment can be determined based on the preset maintenance frequency, actual maintenance frequency, total number of operation evaluation cycles and number of abnormal operation cycles of the nuclear power plant equipment in the maintenance record information.

[0096] S250: Determine the predicted remaining life of the nuclear power plant equipment based on the preset service life, the accumulated usage time, and the life loss factor of the nuclear power plant equipment.

[0097] In this embodiment, the predicted remaining life of the nuclear power plant equipment can be determined based on the preset service life, accumulated usage time, and life loss factor of the nuclear power plant equipment.

[0098] In one embodiment, determining the predicted remaining life of the nuclear power plant equipment based on the preset service life, the accumulated usage time, and the life loss factor of the nuclear power plant equipment includes:

[0099] The predicted remaining service life (RUL) of nuclear power plant equipment is determined by the following formula:

[0100]

[0101] Among them, T design is the preset service life of nuclear power plant equipment, T actual is the cumulative usage time of nuclear power plant equipment, s is the life loss factor, which represents the impact of the health status of nuclear power plant equipment during its usage time on its service life, and μ is the adjustment coefficient for s.

[0102] In this embodiment, historical usage data of nuclear power plant equipment can be obtained, and the service life of the nuclear power plant equipment can be predicted by combining the evaluation results of the health status of the nuclear power plant equipment and the historical usage data. For example, the service life of the nuclear power plant equipment can be predicted using the following formula:

[0103]

[0104] Among them, RUL is the predicted remaining service life of nuclear power plant equipment, T design is the preset service life of nuclear power plant equipment, T actual is the cumulative usage time of the nuclear power plant equipment, s is the life loss factor, which represents the impact of the health status of the nuclear power plant equipment during the usage time on the service life, and μ is the adjustment coefficient for s. μ can be set through pre-experimentation, for example, μ can be set to 0-10.

[0105] Among them, the life loss factor s satisfies:

[0106]

[0107] Among them, N thresh is the number of abnormal operation cycles, that is, the number of times the health index of the nuclear power plant equipment exceeds the health index threshold during the cumulative use time, N total The total number of operation assessment cycles is the total number of operation assessment cycles within the cumulative use time of nuclear power plant equipment; design The preset maintenance frequency is the time interval at which nuclear power plant equipment should be maintained according to design specifications or manufacturer recommendations; actual is the actual maintenance frequency, that is, the average maintenance time interval experienced by the equipment in actual operation; ∈ is a minimum value greater than 0.

[0108] A second embodiment of the present invention provides a life prediction method for nuclear power plant equipment, which specifically performs a life assessment based on the historical usage data and health status assessment results corresponding to the nuclear power plant equipment to obtain the predicted remaining life of the nuclear power plant equipment, including: obtaining the historical usage data of the nuclear power plant equipment, the historical usage data including the cumulative usage time and maintenance record information of the nuclear power plant equipment; determining the life loss factor of the nuclear power plant equipment based on the preset maintenance frequency, actual maintenance frequency, and the total number of operation assessment cycles and abnormal operation cycles of the nuclear power plant equipment in the maintenance record information; and determining the predicted remaining life of the nuclear power plant equipment based on the preset service life, cumulative usage time, and the life loss factor of the nuclear power plant equipment. By combining the health status assessment results of the nuclear power plant equipment during its usage time with the historical usage data, this method takes into account the actual impact of the health status and maintenance frequency of the nuclear power plant equipment on the equipment life, thereby improving the accuracy of life prediction. It can also optimize maintenance strategies based on the prediction results to extend the effective service life of the equipment.

[0109] In one embodiment, the method further comprises:

[0110] Based on the health status assessment results and the predicted remaining life of the nuclear power plant equipment, an equipment operation report of the nuclear power plant equipment is generated.

[0111] Among them, the equipment operation report may include a health status report and a life prediction report. The health status report may include the health index, comprehensive abnormality probability, abnormal conditions of key characteristics and fault types of the current nuclear power plant equipment. The life prediction report may include the currently predicted remaining service life of the equipment.

[0112] Example 3

[0113] Figure 3 This is a structural schematic diagram of a life prediction system for nuclear power plant equipment provided in Example 3 of the present invention. The system can be used to manage the health of equipment in a nuclear power plant and predict the remaining life of the equipment, wherein the electronic equipment in the system can execute the life prediction method for nuclear power plant equipment.

[0114] like Figure 3 As shown, the system includes: a device monitoring module 20 and an electronic device 10, wherein the device monitoring module 20 is connected to the electronic device 10;

[0115] The equipment monitoring module 20 is used to detect equipment status data of nuclear power equipment in the nuclear power plant and send the equipment status data to the electronic device 10;

[0116] The electronic device 10 is used to execute the life prediction method of nuclear power plant equipment described in any one of the embodiments of the present invention.

[0117] An embodiment of the present invention provides a life prediction system for nuclear power plant equipment, which has functional modules and beneficial effects corresponding to the execution method.

[0118] In one embodiment, the equipment monitoring module 20 includes a sensor array unit and a data sorting and transmission unit connected to the sensor array unit, wherein the sensor array unit includes a plurality of monitoring sensors installed at different locations on the nuclear power plant equipment in the nuclear power plant;

[0119] The sensor array unit is used to obtain the equipment status data of each nuclear power plant equipment in the nuclear power plant in real time through the monitoring sensor, and send the equipment status data to the data sorting and transmission unit;

[0120] The data sorting and transmission unit is used to clean, filter and compress the device status data, and send the processed device status data to the electronic device 10.

[0121] Example 4

[0122] Figure 4 This is a structural schematic diagram of a life prediction device for nuclear power plant equipment provided in Example 4 of the present invention. The device can be used to manage the health of equipment in a nuclear power plant and predict the remaining life of the equipment. The device can be implemented by software and / or hardware and is generally integrated into an electronic device.

[0123] like Figure 4 As shown, the device includes:

[0124] An acquisition module 410 is used to acquire device status data of nuclear power plant devices in a nuclear power plant;

[0125] A health management module 420 is configured to determine a health status assessment result of the nuclear power plant equipment based on the equipment status data;

[0126] The life prediction module 430 is used to perform life evaluation based on the historical usage data and health status evaluation results corresponding to the nuclear power plant equipment to obtain the predicted remaining life of the nuclear power plant equipment.

[0127] This embodiment provides a life prediction device for nuclear power plant equipment, comprising: an acquisition module for acquiring equipment status data of nuclear power plant equipment within a nuclear power plant; a health management module for determining a health status assessment result of the nuclear power plant equipment based on the equipment status data; and a life prediction module for performing a life assessment based on historical usage data corresponding to the nuclear power plant equipment and the health status assessment result, thereby obtaining a predicted remaining life of the nuclear power plant equipment. By determining the health status assessment result based on the equipment status data of the nuclear power plant equipment and combining the health status assessment result with the historical usage data to evaluate the predicted remaining life of the nuclear power plant equipment, the remaining life of the nuclear power plant equipment can be accurately predicted, thereby improving the safety and operating efficiency of the nuclear power plant equipment, and resolving the problem in the prior art of being unable to accurately perform health management and life prediction on nuclear power plant equipment.

[0128] Furthermore, the health status assessment result includes the health status and the fault type; accordingly, the health management module 420 includes a data acquisition unit, a feature extraction unit, a model evaluation unit, and an abnormality identification unit, wherein the feature extraction unit is connected to the data acquisition unit and the model evaluation unit, respectively, and the model evaluation unit is connected to the abnormality identification unit;

[0129] The data acquisition unit is used to acquire the device status data with timestamps of each nuclear power plant device and send the device status data with timestamps to the feature extraction unit;

[0130] The feature extraction unit is configured to extract key features that can indicate the operating status of nuclear power plant equipment from the equipment status data with timestamps, and send the key features to the model evaluation unit;

[0131] The model evaluation unit is configured to perform a health evaluation based on the key features to obtain a health status of the nuclear power plant equipment and send the health status to the abnormality identification unit;

[0132] The abnormality identification unit is used to identify the fault type of the nuclear power plant equipment based on the health status and the fault mode library.

[0133] Furthermore, the model evaluation unit is specifically used to:

[0134] Based on the feature anomaly judgment model, all key features are quantitatively judged for anomalies to obtain the anomaly judgment value of each key feature;

[0135] Determining a comprehensive abnormality probability at each time point within the operation evaluation cycle based on the operation evaluation cycle of the nuclear power plant equipment and the abnormality determination value of each key feature;

[0136] Determining a health index of the nuclear power plant equipment based on the comprehensive abnormality probability and abnormal conditions of key features at each time point;

[0137] The health status of the nuclear power plant equipment is obtained based on the health index and the health index threshold.

[0138] Furthermore, the life prediction module 430 includes a health information acquisition unit, a history information acquisition unit, and a life prediction unit, wherein the history information acquisition unit is connected to the health information acquisition unit and the life prediction unit respectively;

[0139] The health information acquisition unit is configured to acquire the health status assessment result sent by the health management module and send the health status assessment result to the lifespan prediction unit;

[0140] The historical information acquisition unit is used to acquire historical usage data of the nuclear power plant equipment and send the historical usage data to the life prediction unit; the historical usage data includes the accumulated usage time and maintenance record information of the nuclear power plant equipment;

[0141] The life prediction unit is used to determine the life loss factor of the nuclear power plant equipment based on the preset maintenance frequency, actual maintenance frequency, total number of operation evaluation cycles and number of abnormal operation cycles of the nuclear power plant equipment in the maintenance record information; and determine the predicted remaining life of the nuclear power plant equipment based on the preset service life, accumulated usage time and the life loss factor of the nuclear power plant equipment.

[0142] Furthermore, determining the predicted remaining life of the nuclear power plant equipment based on the preset service life, the accumulated usage time, and the life loss factor of the nuclear power plant equipment includes:

[0143] The predicted remaining service life (RUL) of nuclear power plant equipment is determined by the following formula:

[0144]

[0145] Among them, T design is the preset service life of nuclear power plant equipment, T actual is the cumulative usage time of nuclear power plant equipment, s is the life loss factor, which represents the impact of the health status of nuclear power plant equipment during its usage time on its service life, and μ is the adjustment coefficient for s.

[0146] Furthermore, the device further includes a report generation module, which is connected to the health management module 420 and the lifespan prediction module 430 respectively;

[0147] The health management module 420 is further configured to send the health status assessment results of the nuclear power plant equipment to the report generation module;

[0148] The life prediction module 430 is further configured to send the predicted remaining life of the nuclear power plant equipment to the report generation module;

[0149] The report generation module is used to generate an equipment operation report of the nuclear power plant equipment based on the health status assessment results and the predicted remaining life of the nuclear power plant equipment.

[0150] The above-mentioned life prediction device for nuclear power plant equipment can execute the life prediction method for nuclear power plant equipment provided by any embodiment of the present invention, and has functional modules and beneficial effects corresponding to the execution method.

[0151] Example 5

[0152] Figure 5 A schematic diagram of the structure of an electronic device 10 that can be used to implement an embodiment of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or claimed herein.

[0153] like Figure 5 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., which is communicatively connected to the at least one processor 11. The memory stores a computer program that can be executed by the at least one processor. The processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 into the random access memory (RAM) 13. Various programs and data required for the operation of the electronic device 10 can also be stored in the RAM 13. The processor 11, ROM 12, and RAM 13 are connected to each other via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0154] Multiple components in the electronic device 10 are connected to the I / O interface 15, including an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a magnetic disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0155] The processor 11 can be any general-purpose and / or specialized processing component with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various specialized artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any other suitable processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as the life prediction method for nuclear power plant equipment.

[0156] In some embodiments, the life prediction method for nuclear power plant equipment can be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed on the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the life prediction method for nuclear power plant equipment described above can be performed. Alternatively, in other embodiments, the processor 11 can be configured to execute the life prediction method for nuclear power plant equipment in any other appropriate manner (for example, by means of firmware).

[0157] Various embodiments of the systems and techniques described herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), system-on-chip systems (SOCs), programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include being implemented in one or more computer programs that are executable and / or interpreted on a programmable system that includes at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0158] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when the computer program is executed by the processor, the functions / operations specified in the flowcharts and / or block diagrams are implemented. The computer program may be executed entirely on the machine, partially on the machine, as a stand-alone software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0159] In the context of the present invention, computer-readable storage media can be tangible media that can contain or store a computer program for use with an instruction execution system, device or equipment or used in combination with an instruction execution system, device or equipment. Computer-readable storage media can include but are not limited to electronic, magnetic, optical, electromagnetic, infrared or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, computer-readable storage media can be machine-readable signal media. More specific examples of machine-readable storage media can include electrical connections based on one or more lines, portable computer disks, hard disks, random access memories (RAM), read-only memories (ROM), erasable programmable read-only memories (EPROM or flash memory), optical fibers, portable compact disk read-only memories (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0160] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0161] The systems and techniques described herein can be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0162] A computing system may include clients and servers. The clients and servers are typically remote from each other and typically interact via a communication network. This client-server relationship arises through computer programs running on the respective computers, creating a client-server relationship. The server may be a cloud server, also known as a cloud computing server or cloud host. This server is a hosting product within the cloud computing service ecosystem that addresses the management difficulties and limited scalability of traditional physical hosting and VPS services.

[0163] It should be understood that the various forms of the processes shown above can be used to reorder, add, or delete steps. For example, the steps described in the present invention can be performed in parallel, sequentially, or in a different order, as long as the desired results of the technical solution of the present invention can be achieved. This is not limited herein.

[0164] The above specific embodiments do not limit the scope of protection of the present invention. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions may be made based on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention are intended to be included within the scope of protection of the present invention.

Claims

1. A method for predicting the life of nuclear power plant equipment, characterized in that: The method comprises: Obtain equipment status data of nuclear power equipment in nuclear power plants; Determining a health status assessment result of the nuclear power plant equipment based on the equipment status data; A lifespan assessment is performed based on the historical usage data and health status assessment results corresponding to the nuclear power plant equipment to obtain a predicted remaining lifespan of the nuclear power plant equipment.

2. The method according to claim 1, characterized in that The health status assessment result includes the health status and the fault type; accordingly, determining the health status assessment result of the nuclear power plant equipment based on the equipment status data includes: Obtain equipment status data with timestamps for each nuclear power plant equipment; Extracting key features that can indicate the operating status of nuclear power plant equipment from the equipment status data with timestamps; Performing a health assessment based on the key features to obtain the health status of the nuclear power plant equipment; The fault type of the nuclear power plant equipment is identified based on the health status and the fault mode library.

3. The method according to claim 2, characterized in that The health assessment based on the key features to obtain the health status of the nuclear power plant equipment includes: Based on the feature anomaly judgment model, all key features are quantitatively judged for anomalies to obtain the anomaly judgment value of each key feature; Determining a comprehensive abnormality probability at each time point within the operation evaluation cycle based on the operation evaluation cycle of the nuclear power plant equipment and the abnormality determination value of each key feature; Determining a health index of the nuclear power plant equipment based on the comprehensive abnormality probability and abnormal conditions of key features at each time point; The health status of the nuclear power plant equipment is obtained based on the health index and the health index threshold.

4. The method according to claim 1, wherein The performing of life assessment based on the historical usage data and health status assessment results corresponding to the nuclear power plant equipment to obtain the predicted remaining life of the nuclear power plant equipment includes: Acquiring historical usage data of the nuclear power plant equipment, the historical usage data including cumulative usage time and maintenance record information of the nuclear power plant equipment; Determining a life loss factor of the nuclear power plant equipment based on a preset maintenance frequency, an actual maintenance frequency, and a total number of operation evaluation cycles and a number of abnormal operation cycles of the nuclear power plant equipment in the maintenance record information; The predicted remaining life of the nuclear power plant equipment is determined based on the preset service life, the accumulated usage time and the life loss factor of the nuclear power plant equipment.

5. The method according to claim 4, characterized in that The determining of the predicted remaining life of the nuclear power plant equipment based on the preset service life, the accumulated usage time, and the life loss factor of the nuclear power plant equipment includes: The predicted remaining service life (RUL) of nuclear power plant equipment is determined by the following formula: Among them, T design is the preset service life of nuclear power plant equipment, T actual is the cumulative usage time of nuclear power plant equipment, s is the life loss factor, which represents the impact of the health status of nuclear power plant equipment during its usage time on its service life, and μ is the adjustment coefficient for s.

6. The method according to claim 1, characterized in that The method further comprises: Based on the health status assessment results and the predicted remaining life of the nuclear power plant equipment, an equipment operation report of the nuclear power plant equipment is generated.

7. A life prediction system for nuclear power plant equipment, characterized in that: The system includes: a device monitoring module and an electronic device, wherein the device monitoring module is connected to the electronic device; The equipment monitoring module is used to detect equipment status data of nuclear power equipment in the nuclear power plant and send the equipment status data to the electronic device; The electronic device is used to execute the life prediction method for nuclear power plant equipment as described in any one of claims 1-6.

8. A life prediction device for nuclear power plant equipment, characterized in that: The device comprises: An acquisition module is used to obtain the equipment status data of the nuclear power plant equipment in the nuclear power plant; A health management module, configured to determine a health status assessment result of the nuclear power plant equipment based on the equipment status data; The life prediction module is used to perform life evaluation based on the historical usage data and health status evaluation results corresponding to the nuclear power plant equipment to obtain the predicted remaining life of the nuclear power plant equipment.

9. An electronic device, characterized in that: The device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the life prediction method for nuclear power plant equipment according to any one of claims 1-6.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the life prediction method for nuclear power plant equipment according to any one of claims 1 to 6 when executed.

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