Nuclear power equipment health record data intelligent acquisition and application method and system

Abstract

The invention discloses a nuclear power equipment health record data intelligent acquisition and application method and system, and the method comprises the steps: S1, determining target equipment, and building a mapping relation between the target equipment and equipment classes, so as to generate an equipment class information library; s2, measuring points of target equipment are obtained, and an equipment measuring point list is generated; s3, collecting original unavailable data of the nuclear power equipment; an equipment measuring point list is obtained, corresponding original state data are collected according to the measuring points of the target equipment, and the original state data comprise switching value data; s4, generating state index data according to the switching value data and a preset first calculation rule, and classifying and summarizing according to the equipment class to which the target equipment belongs so as to generate a first summary table; and S5, classifying and summarizing the original unavailable data to generate a second summary sheet. By implementing the technical scheme of the invention, intelligent collection and application of health record data of multiple power plant equipment can be realized, and the data collection and statistics efficiency is improved.

Description

Technical Field

[0001] This invention relates to the field of nuclear safety supervision and early warning, and in particular to a method and system for intelligent collection and application of health record data of nuclear power equipment. Background Technology

[0002] According to the regulations of the National Nuclear Safety Administration (NNSA), the annual collection, processing, and reporting of health record data for multiple operating units in a group of nuclear power plants is required. Data collection and processing is the core component and constitutes a major part of the workload, especially the collection and processing of data from the Nuclear Power Plant Real-Time Information Monitoring System (KNS). Furthermore, as the NNSA advances the construction of the group plant health record project, it is necessary to access the cyclic operating parameters and real-time operating data of the units from the KNS, which involves the collection of a large amount of analog data. This places higher demands on data collection efficiency and application scenarios.

[0003] Currently, because KNS data is located in physically isolated computer rooms within the three zones of each power plant or on dedicated KNS computers, data collection requires on-site visits to each base, consuming significant manpower. Furthermore, the statistical processing of health record data after collection necessitates manual processing before statistical analysis using Excel formulas. Due to the large number of measurement points, this process is time-consuming, and errors are easily made and difficult to detect. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to provide a method and system for intelligent collection and application of health record data of nuclear power equipment.

[0005] The technical solution adopted by this invention to solve its technical problem is: to construct a method for intelligent collection and application of health record data of nuclear power equipment, the method comprising: S1. Determine the target device and establish a mapping relationship between the target device and the device class to generate a device class information database; S2. Obtain the measurement points of the target device and generate a device measurement point list, the device measurement point list including the name of the target device, the device class to which the target device belongs, and the measurement points of the target device; S3. Collect raw unavailable data of nuclear power equipment; obtain the list of equipment measurement points, and collect corresponding raw status data according to the measurement points of the target equipment, the raw status data including switch quantity data; S4. Generate status index data based on the switch quantity data and the preset first calculation rule, and summarize it according to the equipment category to which the target device belongs to generate a first summary table; S5. Categorize and summarize the original unavailable data to generate a second summary table.

[0006] Further, step S4 includes: S401. Generate corresponding status index data for each target device based on the switch quantity data and the first calculation rule to generate a status summary table. The status index data includes: number of actions and running time. S402. Classify and statistically analyze the target device according to its device class, and summarize the number of actions and running time corresponding to the device class to generate the first summary table.

[0007] Further, the original unavailability data includes the name of the unavailable device and its corresponding operating mode, unavailability category, and duration. Step S5 includes: Based on the system and device list to which the unavailable device belongs, the unavailability category, and the operating mode, the duration of the unavailable device is classified and summarized to generate random unavailability time, planned unavailability time, and required availability time under multiple operating modes corresponding to the device list, and a second summary table is generated.

[0008] Further, the first summary table is obtained, and the status indicator data of the device type is compared with the historical status indicator data to determine whether it is within the preset error range; if it exceeds the range, the data is marked.

[0009] Further, in step S1, determining the target equipment includes: determining the target equipment based on the PSA model, nuclear power plant-related management information, and other nuclear power plant equipment lists.

[0010] Further, in steps S1 and S2, for newly commissioned power plants, the target equipment is determined by model comparison and matching based on the basic event list of the FSAR probability evaluation model, the power plant's production process management system list, and / or the reference power plant list; corresponding measurement points are matched according to the KNS measurement point library to generate the equipment measurement point list.

[0011] Furthermore, in step S3, the original state data also includes analog data; in step S4, it also includes: Generate a corresponding trend chart based on the analog data of the target device; And / or determine whether the analog data of the target device exceeds a preset health range value; if it exceeds the range, mark the data and issue an alarm.

[0012] Furthermore, the method also includes: S6. Perform equipment reliability parameter analysis based on the first summary table and the second summary table; the equipment reliability parameter analysis includes: operational failure rate, demand failure probability, and unavailability analysis.

[0013] This invention also constructs an intelligent data acquisition and application system for health records of nuclear power equipment, the system comprising: The device class information module is used to identify target devices and establish a mapping relationship between the target devices and device classes to generate a device class information database. The device measurement point list module is used to obtain the measurement points of the target device and generate a device measurement point list, which includes the name of the target device, the device class to which the target device belongs, and the measurement points of the target device. The data acquisition module is used to collect raw unavailable data from nuclear power equipment; obtain the equipment measurement point list; and collect corresponding raw status data based on the measurement points of the target equipment, the raw status data including switch quantity data. The first application module is used to generate status indicator data based on the switch quantity data and the preset first calculation rule, and to classify and summarize the data according to the equipment category to which the target device belongs, so as to generate a first summary table. The second application module is used to classify and summarize the original unusable data to generate a second summary table.

[0014] Furthermore, the system also includes: The reliability analysis module is used to perform equipment reliability parameter analysis based on the first summary table and the second summary table; the equipment reliability parameter analysis includes: operational failure rate, demand failure probability, and unavailability analysis.

[0015] Implementing this invention has the following beneficial effects: by establishing an equipment information database, generating a list of equipment measurement points for each power plant, intelligently collecting status data and unavailable data of multiple power plant equipment using big data batch processing technology, and performing batch calculations based on equipment categories to obtain relevant equipment reliability statistics, thereby improving collection efficiency and data statistical processing speed, and enhancing calculation accuracy.

[0016] Furthermore, by comparing the statistically analyzed state indicator data with historical state indicator data, reverse verification of the original state data can be achieved, avoiding the use of incorrect data for subsequent statistics and analysis. Attached Figure Description

[0017] The present invention will be further described below with reference to the accompanying drawings and embodiments. In the accompanying drawings: Figure 1 This is a flowchart of a method for intelligent collection and application of health record data of nuclear power equipment in one embodiment of the present invention. Detailed Implementation

[0018] To provide a clearer understanding of the technical features, objectives, and effects of the present invention, specific embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0019] It should be noted that the health record data of nuclear power equipment mainly includes equipment status parameter data and unavailability data. Status parameter data includes switch quantity data and analog quantity data. In this invention, an intelligent acquisition and application system for equipment health record data is developed, utilizing big data batch processing technology to achieve automatic acquisition and intelligent application of health record data from multiple power plant equipment. In this invention, an equipment class refers to a group of equipment with similar process performance, functions, and operating conditions. The equipment class can be the NNSA equipment class and / or the power plant equipment class, which can be selected according to actual business needs. The NNSA equipment class is the classification system established by the National Nuclear Safety Administration of China, while the power plant equipment class is the classification of equipment by each power plant. The power plant equipment class can be divided into parent and child classes; for example, the parent class of the equipment class is electric pumps, and the child class is low-pressure safety injection pumps.

[0020] Figure 1 This is a flowchart of a method for intelligent collection and application of health record data of nuclear power equipment according to one embodiment of the present invention, which includes the following steps: S1. Identify the target device and establish a mapping relationship between the target device and the device class to generate a device class information database; In this step, the status parameter data of nuclear power equipment is statistically analyzed at the equipment category level. Target equipment refers to the equipment objects whose data needs to be collected. A mapping relationship is established between target equipment and equipment categories, that is, the equipment included in each equipment category is clearly defined, including the equipment category's code, function, failure mode, and other necessary information, to generate an equipment category information database. The target equipment contained within each equipment category should conform to the definition of that equipment category and consist of a sufficient number of equipment combinations from which representative real-time data can be collected. Except for equipment specifically referring to a particular system, target equipment should ideally come from multiple systems, but the data must be collectable. It is understood that the equipment category information database contains data from multiple power plants. Due to differences in reactor design, the total number of equipment category classifications varies slightly between different power plants and can be adaptively adjusted according to the characteristics of the power plant. In an optional embodiment, the information in the equipment category information database can be exported as an editable WORD or PDF file based on a preset template. The fields included in this template can also be adjusted according to actual business needs.

[0021] In some optional embodiments, determining the target equipment in step S1 includes: identifying the target equipment based on the PSA model, relevant management information of the nuclear power plant, and other nuclear power plant equipment lists. Specifically, the target equipment is the equipment object whose data needs to be collected; in equipment reliability analysis, not all equipment is the data collection object. In this embodiment of the invention, a relevant equipment list is obtained based on the basic events modeled in the nuclear power plant PSA model; the target equipment is determined by referring to other similar nuclear power plant equipment lists and supplementing them based on the nuclear power plant Production Process Management System (SAP) list, flowcharts, electrical single-line diagrams, Operation and Maintenance Manual (EOM), and System Manual (SDM).

[0022] S2. Obtain the measurement points of the target device and generate a device measurement point list. The device measurement point list includes the name of the target device, the device class to which the target device belongs, and the measurement points of the target device. In this step, each target device may include one or more measuring points. The name of the measuring point corresponding to the target device is obtained, and the mapping relationship between the target device and the device class from step S1 is linked to automatically generate an equipment measuring point list that includes at least the name of the target device, the device class to which the target device belongs, and the measuring points of the target device. Different power plant units correspond to different equipment measuring point lists. The equipment measuring point list can be saved as a CSV file in the format of power plant code + unit number and stored in a specified folder. For example, the equipment measuring point list file for unit A is saved as FCG1.CSV file and placed in the folder resources / FCG / FCG1, and the equipment measuring point list file for unit B is saved as FCG2.CSV file and placed in the folder resources / FCG / FCG2.

[0023] In some optional embodiments, the equipment measurement point list may also include one or more of the following fields: NNSA equipment class to which the target equipment belongs, business area, supplier, failure mode, description, and full names of measurement points for Unit 1 and Unit 2. The equipment measurement point list can be exported as an editable WORD or PDF file based on a preset template, and the fields included in this template can be adjusted according to actual business needs. It is understood that the equipment measurement point list can be adjusted according to actual changes, such as adjustments to the actual measurement points of the equipment. There is a correlation between the equipment class information database and the equipment measurement point list; when relevant information in the equipment class information database changes, the equipment measurement point list is automatically updated synchronously, or it can be manually added or updated.

[0024] By selecting target equipment, establishing a mapping relationship between target equipment and equipment class, and obtaining the measurement points of the target equipment, a list of equipment measurement points is finally generated. This eliminates the need for manual information acquisition and the creation of the equipment measurement point list, reducing human error and improving work efficiency.

[0025] In some optional embodiments, in steps S1 and S2, for newly commissioned power plants, target equipment is determined through model comparison and matching based on the basic event list of the FSAR probability evaluation model, the power plant's production process management system list, and / or a reference power plant list; corresponding measurement points are matched according to the KNS measurement point library to generate an equipment measurement point list. Specifically, the process of establishing an intelligent model and using this model to compare and determine the equipment type information library and the equipment measurement point list may include the following steps: 1. Preprocess the basic event list for FSAR probability evaluation, identify basic events including equipment information, remove duplicates, and form a preliminary screening list; 2. The initial screening list containing equipment information is intelligently compared with the list in the nuclear power plant production process management system (SAP). If the equipment matches, it can be highlighted and a process list is formed. If the SAP list does not match, the difference can be marked in red and compared with the historical database. If it still does not match, manual judgment is required to confirm that the equipment does not exist and delete it.

[0026] 3. Using similar power plants as reference power plants, establish a mapping relationship between the process list and the equipment list database of the reference power plant (including the target equipment of the reference power plant). Utilize rule matching and semantic understanding to incorporate the equipment in the process list as target equipment into the new power plant equipment list database.

[0027] 4. For equipment that exists in the reference power plant but is not in the process inventory, compare it with the SAP inventory. Equipment that matches the SAP inventory will also be included as target equipment in the new power plant equipment inventory library.

[0028] 5. By comparing with the reference power plant database, select equipment categories and classify target equipment in the new power plant equipment list database to generate an equipment category information database.

[0029] 6. Based on the target device, compare it with the KNS measurement point library, and automatically match the related device measurement point names through semantic similarity to generate a device measurement point list.

[0030] 7. Perform a one-time verification of equipment type, target equipment, and equipment measurement point name. If there are discrepancies or missing measurement points, issue an alert so that manual intervention can be performed to correct them.

[0031] By comparing the above models, a list of equipment measurement points for newly commissioned power plants can be automatically generated. The list of basic events of the FSAR probability evaluation model, the list of the power plant's production process management system, and / or the list of reference power plants are used as the basis, which makes the selection of target equipment reliable, reduces repetitive manual labor, and improves efficiency.

[0032] S3. Collect raw unavailable data; obtain a list of device measurement points, and collect the corresponding raw status data based on the measurement points of the target device. The raw status data includes switch quantity data. In this step, unavailability data of nuclear power equipment is automatically and in real-time collected from an unavailability database; this raw unavailability data includes the name of the unavailable equipment, its corresponding operating mode, the duration of unavailability, and the unavailability category. For status parameter data, a list of equipment measurement points selected or uploaded by the user is obtained. This data is then connected to a database containing status parameter data for multiple plants, such as the Egret database, via an interface. Measurement point values ​​corresponding to the measurement points in the equipment measurement point list are collected from the database at a set frequency (e.g., every two minutes); this raw status data is generated from these measurement points. The collection time range can include the period from equipment commissioning to the time of collection, but must avoid early equipment failures; alternatively, the range can be set by the user. The collected data does not include equipment failures caused by human error or failures of support systems outside the equipment boundary. Besides obtaining the equipment measurement point list to collect data, the measurement point name of a single measurement point can also be manually entered to collect data for that point. Preferably, a retry mechanism is set up for the acquisition of status parameter data. During the acquisition process, if a certain measurement point cannot be obtained from the database, it will be re-acquired at set intervals. If the data still cannot be obtained after more than a set number of retries (e.g., three), this measurement point will be stored in the error measurement point list, generating error list information for users to view, so as to verify whether there are any abnormalities such as missing or incorrect measurement points. The acquisition of raw unavailable data and raw status data is not sequential. After the data acquisition is completed, a file containing the raw unavailable data of each target device and a file containing the raw status data can be generated.

[0033] S4. Generate status index data based on the switch quantity data and the preset first calculation rule, and summarize it according to the equipment category to which the target equipment belongs to generate a first summary table; In this step, as an application scenario of the collected data, big data batch processing technology is used to automatically calculate and generate corresponding status index data for each target device containing switch data based on its switch data according to a preset first calculation rule. The status index data is the feature data required for reliability analysis. According to the mapping relationship between target devices and device classes, the data is categorized and summarized according to the device class to which the target device belongs, to generate a status data table corresponding to all device classes, i.e., the first summary table.

[0034] In some alternative embodiments, step S4 includes: S401. Generate corresponding status indicator data for each target device based on the switch quantity data and the first calculation rule to generate a status summary table. The status indicator data includes: number of actions and running time. Specifically, in this step, the status indicator data includes the number of actions and the running time. Using big data batch processing technology, for each target device containing switch data, the number of actions of the target device is obtained by calculating the number of times the status changes from 1 (start) to 0 (stop) in the switch data. The running time of the target device is obtained by calculating the duration from when the status changes to 1 (start) until the status changes to 0 (stop) for the first time. These are then summarized to generate a status summary table. In some optional embodiments, the status summary table may include other necessary fields such as NNSA classification and measurement point description.

[0035] S402. Classify and statistically analyze the target equipment according to its equipment category, and summarize the number of actions and running time corresponding to the equipment category to generate the first summary table.

[0036] Specifically, in this step, based on the mapping relationship between target devices and device classes, the actions and running times of all target devices included in each device class are summed to obtain the action count and running time corresponding to each device class, and a first summary table is generated. In some optional embodiments, the action count and running time can be further summarized according to NNSA device classification.

[0037] S5. Categorize and summarize the original unavailable data to generate a second summary table.

[0038] In this step, as an application scenario of the collected data, the original unusable data is classified and summarized according to the set classification rules to generate a second summary table. It should be understood that steps S4 and S5 have no sequential relationship.

[0039] In some optional embodiments, the original unavailability data includes unavailable equipment and its corresponding operating mode, unavailability category, and duration. Step S5 includes: classifying and summarizing the duration of unavailable equipment according to the system and equipment column to which the unavailable equipment belongs, the unavailability category, and the operating mode, generating random unavailability time, planned unavailability time, and required availability time under multiple operating modes corresponding to the equipment column, and summarizing them to generate a second summary table. Specifically, the original unavailability data includes three unavailability categories and their corresponding unavailability durations: random unavailability time, planned unavailability time, and required availability time, as well as the operating mode. For units using French technical specifications, the operating modes include: reactor power operation mode (RP), evaporator cooling normal shutdown mode (NS / SG), RRA cooling normal shutdown mode (NS / RRA), and maintenance shutdown mode (MCS); for units using Chinese technical specifications, the operating modes include: mode 1, mode 3, mode 4, and mode 5. The statistics on unavailable data are categorized by equipment column. Based on the system and equipment column to which the unavailable equipment belongs, the unavailable time for each type of operation mode is summed to obtain the random unavailable time, planned unavailable time, and required available time for each equipment column in each system under multiple operation modes. These are then summarized to generate a second summary table. The systems include safety-critical systems such as auxiliary water supply / emergency water supply systems, containment sprinkler / containment waste heat removal systems, emergency diesel generator sets, low-pressure safety injection systems, high-pressure safety injection systems, equipment cooling water systems, and important plant water systems. Multiple operation modes can include one or more of the above operation modes, selected for calculation based on statistical needs.

[0040] By classifying and statistically processing the collected data, statistical results of status data at the device category level and unavailable data at the device column level can be obtained quickly in batches, improving calculation speed and accuracy while reducing manual operations. Furthermore, integrating status data and unavailable data into a single system facilitates further reliability analysis.

[0041] In some optional embodiments, the method further includes: obtaining a first summary table, comparing the status indicator data of the device class with historical status indicator data, and determining whether it is within a preset error range; if it exceeds the range, marking the data. Specifically, historical status indicator data corresponding to the device class is obtained, an allowable data error range is set, for example, 0.5%-1.5%, the difference between the current status indicator data of the device class in the first summary table and the corresponding historical status indicator data is calculated, and if it exceeds the allowable error range, it indicates that the data fluctuation is large and there may be an anomaly in the original data, so the status indicator data is marked. By comparing and judging, it is possible to monitor and identify whether there are errors in the original status data, such as abnormalities in the collection of original status data or abnormalities in the collection of hardware devices, etc. If there are errors, corresponding corrections can be made, thereby improving data accuracy and avoiding the use of incorrect original status data for analysis.

[0042] In some optional embodiments, in step S3, the original state data further includes analog quantity data; in step S4, it further includes: generating a corresponding trend chart based on the analog quantity data of the target device; and / or determining whether the analog quantity data of the target device exceeds a preset health range value; if it exceeds the range, marking the data and issuing an alarm. Specifically, as another application scenario for data collection, the analog quantity data can be used to monitor relevant parameters of the device. The analog quantity data consists of continuously monitored parameters such as temperature, pressure, and flow rate of the device. Based on the analog quantity data of the target device, a corresponding trend chart is generated. A health range value for the analog quantity data can also be set. If the collected analog quantity data exceeds this range value, it indicates that the device may be abnormal. The data is marked and an alarm is issued to remind the user to confirm and handle the issue, thus realizing batch collection and monitoring of device analog quantity data.

[0043] In some optional embodiments, the method further includes: S6. Performing equipment reliability parameter analysis based on the first summary table and the second summary table; the equipment reliability parameter analysis includes: operational failure rate, demand failure probability, and unavailability analysis. Specifically, as another application scenario of the collected data, in this step, the first summary table and the second summary table are further applied, and the equipment reliability parameter analysis mainly includes three types: operational failure rate (λ), demand failure probability (P), and unavailability. Operational failure rate refers to the number of times the equipment fails per unit time during operation; demand failure probability refers to the probability that the equipment fails when demand occurs (a state change occurs); unavailability refers to the probability that the equipment is in an unavailable state due to testing or maintenance. Among them, failures during operation can be divided into failure modes such as operational failure, running failure, internal leakage, and external leakage, while failures during demand can be divided into failure modes such as startup failure, refusal to open, and refusal to close. For the operational failure rate, the number of operational failures of the target equipment is obtained from a third-party database to obtain the number of operational failures of the corresponding equipment class, and the running time of the relevant equipment class in the first summary table is obtained to calculate the operational failure rate. For the probability of requirement failure, the number of requirement failures for the target device is obtained from a third-party database to obtain the number of requirement failures for the corresponding device class. The number of actions for the relevant device class in the first summary table is also obtained, i.e., the number of requirement actions, to calculate the probability of requirement failure. For unavailability, data from the second summary table is obtained to calculate the random unavailability, planned unavailability, and total unavailability for each device column.

[0044] The failure rate and demand failure probability can be calculated using classical estimation and Bayesian estimation. Classical estimation assumes the parameter being estimated is an unknown constant, typically based on specific data collected from the nuclear power plant. Bayesian estimation, on the other hand, assumes the parameter being estimated is a random variable, relying on past experience and specific nuclear power plant data. Directly using general data from similar nuclear power plants may not reflect certain characteristics of a specific plant, and directly using data from a specific plant suffers from insufficient equipment failure data. For most equipment, the number of failures is low or even nonexistent, and the parameters obtained from classical estimation often differ from reality, making it impossible to use reliability data specific to a particular nuclear power plant. Using Bayesian estimation, combined with prior data and specific data, is the most suitable reliability data processing method. In this embodiment of the invention, classical and Bayesian estimation parameters for different failure modes under different equipment classes are calculated, and suggestions for parameter selection are provided. In particular, for newly commissioned power plant units, the new units have a short commercial operation time and a small number of data samples. The calculation results of classical estimation parameters have high uncertainty. Therefore, general data or data from reference power plants should be used as the parameters estimated by Bayesian estimation based on prior data.

[0045] The following example illustrates the intelligent data collection and application method for nuclear power equipment health records according to the present invention. In the equipment information database, a summary table of basic equipment information for a power plant (Table 1 below) and a summary table of detailed equipment information for a power plant (Table 2 below) can be generated. The equipment samples represent the target equipment of the power plant, and fields such as structural characteristics and functional descriptions can be adjusted and filled in according to actual conditions.

[0046] Table 1 Table 2 Table 3 below shows an example of a list of equipment measurement points that includes NNSA equipment class information and power plant equipment class-subclass information. It should include at least the name of the target equipment, the equipment class to which the target equipment belongs, and the measurement points of the target equipment. It may also include other required fields. PSA, MR, MSPI, PSR, and fire protection are business domains.

[0047] Table 3 Table 4 below shows the first summary table generated by collecting the corresponding raw status data from the KNS measurement point library based on the measurement points of the target equipment in the equipment measurement point list, and summarizing the data according to the power plant equipment category and subcategory. Understandably, summarizing and summarizing according to NNSA equipment categories can also be performed as needed.

[0048] Table 4 Table 5 below shows a second summary table generated from the collected raw unavailable data, categorized by device column, containing only operating mode 1. Understandably, a second summary table containing multiple operating modes can also be generated.

[0049] Table 5 Table 6 below shows the analysis results of step S6 on the operational failure rate and demand failure probability in a specific application example, including classical estimation results and Bayesian estimation results. Table 7 below shows the analysis results of unavailability in step S6 in a specific application example.

[0050] Table 6 Table 7 The intelligent data acquisition and application system for nuclear power equipment health records of the present invention can be used to execute the intelligent data acquisition and application method for nuclear power equipment health records of the above embodiments. The intelligent data acquisition and application system for nuclear power equipment health records of this embodiment includes: The device information module is used to identify target devices and establish a mapping relationship between target devices and device classes in order to generate a device class information database. The Equipment Measurement Point List module is used to obtain the measurement points of the target equipment and generate an equipment measurement point list. The equipment measurement point list includes the name of the target equipment, the equipment class to which the target equipment belongs, and the measurement points of the target equipment. The data acquisition module is used to collect raw, unavailable data from nuclear power equipment; obtain a list of equipment measurement points; and collect corresponding raw status data based on the measurement points of the target equipment. The raw status data includes switch quantity data. The first application module is used to generate status indicator data based on switch quantity data and preset first calculation rules, and to summarize the data according to the equipment category to which the target equipment belongs, so as to generate a first summary table. The second application module is used to classify and summarize the original unusable data to generate a second summary table.

[0051] In some alternative embodiments, the system further includes: The reliability analysis module is used to perform equipment reliability parameter analysis based on the first summary table and the second summary table. The equipment reliability parameter analysis includes: operational failure rate, demand failure probability, and unavailability analysis.

[0052] It is understood that the above embodiments only illustrate preferred embodiments of the present invention, and their descriptions are relatively specific and detailed, but they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can freely combine the above technical features without departing from the concept of the present invention, and can also make several modifications and improvements, all of which fall within the protection scope of the present invention. Therefore, all equivalent transformations and modifications made with respect to the scope of the claims of the present invention should fall within the scope of the claims of the present invention.

Claims

1. A method for intelligent collection and application of health record data from nuclear power equipment, characterized in that, include: S1. Determine the target device and establish a mapping relationship between the target device and the device class to generate a device class information database; S2. Obtain the measurement points of the target device and generate a device measurement point list, the device measurement point list including the name of the target device, the device class to which the target device belongs, and the measurement points of the target device; S3. Collect raw unavailable data of nuclear power equipment; obtain the list of equipment measurement points, and collect the corresponding raw status data according to the measurement points of the target equipment, the raw status data including switch quantity data; S4. Generate status index data based on the switch quantity data and the preset first calculation rule, and summarize it according to the equipment category to which the target device belongs to generate a first summary table; S5. Categorize and summarize the original unavailable data to generate a second summary table.

2. The method for intelligent collection and application of health record data of nuclear power equipment according to claim 1, characterized in that, Step S4 includes: S401. Generate corresponding status index data for each target device based on the switch quantity data and the first calculation rule to generate a status summary table. The status index data includes: number of actions and running time. S402. Classify and statistically analyze the target device according to its device class, and summarize the number of actions and running time corresponding to the device class to generate the first summary table.

3. The method for intelligent collection and application of health record data of nuclear power equipment according to claim 1, characterized in that, The original unavailability data includes the name of the unavailable device and its corresponding operating mode, unavailability category, and duration. Step S5 includes: Based on the system and device list to which the unavailable device belongs, the unavailability category, and the operating mode, the duration of the unavailable device is classified and summarized to generate random unavailability time, planned unavailability time, and required availability time under multiple operating modes corresponding to the device list, and a second summary table is generated.

4. The method for intelligent collection and application of health record data of nuclear power equipment according to claim 1, characterized in that, The method also includes: Obtain the first summary table, compare the status indicator data of the device type with the historical status indicator data, and determine whether it is within the preset error range; if it exceeds the range, mark the data.

5. The method for intelligent collection and application of health record data of nuclear power equipment according to claim 1, characterized in that, In step S1, determining the target equipment includes: determining the target equipment based on the PSA model, nuclear power plant-related management information, and other nuclear power plant equipment lists.

6. The method for intelligent collection and application of health record data of nuclear power equipment according to claim 1, characterized in that, In steps S1 and S2, for newly commissioned power plants, the target equipment is determined by model comparison and matching based on the basic event list of the FSAR probability evaluation model, the power plant's production process management system list, and / or the reference power plant list. The corresponding measurement points are matched according to the KNS measurement point library to generate the equipment measurement point list.

7. The method for intelligent collection and application of health record data of nuclear power equipment according to claim 1, characterized in that, In step S3, the original state data further includes analog data; in step S4, it further includes: Generate a corresponding trend chart based on the analog data of the target device; And / or determine whether the analog data of the target device exceeds a preset health range value; if it exceeds the range, mark the data and issue an alarm.

8. The method for intelligent collection and application of health record data of nuclear power equipment according to claim 1, characterized in that, The method also includes: S6. Perform equipment reliability parameter analysis based on the first summary table and the second summary table; the equipment reliability parameter analysis includes: operational failure rate, demand failure probability, and unavailability analysis.

9. A smart data acquisition and application system for health records of nuclear power equipment, characterized in that, include: The device information module is used to identify target devices and establish a mapping relationship between the target devices and device classes to generate a device information database. The device measurement point list module is used to obtain the measurement points of the target device and generate a device measurement point list, which includes the name of the target device, the device class to which the target device belongs, and the measurement points of the target device. The data acquisition module is used to collect raw, unavailable data from nuclear power equipment. Obtain the list of device measurement points, and collect the corresponding raw status data based on the measurement points of the target device. The raw status data includes switch quantity data. The first application module is used to generate status indicator data based on the switch quantity data and the preset first calculation rule, and to classify and summarize the data according to the equipment category to which the target device belongs, so as to generate a first summary table. The second application module is used to classify and summarize the original unusable data to generate a second summary table.

10. The intelligent data acquisition and application system for nuclear power equipment health records according to claim 9, characterized in that, The system also includes: The reliability analysis module is used to perform equipment reliability parameter analysis based on the first summary table and the second summary table; the equipment reliability parameter analysis includes: operational failure rate, demand failure probability, and unavailability analysis.

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