Method, Server, and Nuclear Power Plant Spare Part Inspection System for Inspecting Nuclear Power Plant Spare Parts

Abstract

The present application provides a method, a server, and a nuclear power plant spare part inspection system for inspecting nuclear power plant spare parts. The steps of the method include obtaining physical characteristic data of the spare parts to be accepted from a sensor detection device; calculating a deviation of the physical characteristic data to obtain a deviation evaluation result; and sending the deviation evaluation result to a terminal device so that the terminal device outputs the deviation evaluation result. The present invention realizes the efficiency and accuracy of spare part inspection through data collection and deviation evaluation, and effectively solves the cumbersome problems brought by relying on manual operations in the traditional spare part acceptance process.

Description

Technical Field

[0001] This application relates to the field of spare part inspection in nuclear power plants, and particularly to a method, a server, and a nuclear power plant spare part inspection system for inspecting spare parts in nuclear power plants. Background Art

[0002] Measurement is a key step to ensure that spare parts meet the specified requirements and quality standards. Through measurement, the key dimensions, shapes, accuracies, performances, etc. of spare parts can be detected and evaluated. This helps to determine whether the spare parts match the original equipment or design specifications to ensure their correct installation and the normal operation of the equipment. Most traditional measurement-based spare part acceptance relies on manual operations, which are time-consuming, burdensome for personnel, require high skills, there are parts that cannot be measured by traditional acceptance, different measurement personnel lead to different measurement results, and data statistics management is cumbersome, etc. The detection efficiency of the work process is low. Manual operations are used to fill in data, and the final results are prone to errors. Summary of the Invention

[0003] This application provides a method, a server, and a nuclear power plant spare part inspection system for inspecting spare parts in nuclear power plants to solve the problem of cumbersome manual operations in the traditional measurement-based spare part acceptance method.

[0004] The technical solution adopted by this application to solve its technical problems is: providing a method for inspecting spare parts in a nuclear power plant, an application server, and the method includes:

[0005] Step S1: Obtain the physical characteristic data of the spare parts to be accepted from a sensor detection device;

[0006] Step S2: Calculate the deviation of the physical characteristic data to obtain a deviation evaluation result;

[0007] Step S3: Send the deviation evaluation result to a terminal device so that the terminal device outputs the deviation evaluation result.

[0008] In one embodiment, after step S3, it includes:

[0009] Receive a query instruction from the terminal device, and analyze and process the physical characteristic data according to the query instruction to obtain spare part status information;

[0010] Send the spare part status information to the terminal device.

[0011] In one embodiment, after step S3, it further includes:

[0012] Receive a statistical instruction from the terminal device, and perform statistical analysis processing on the physical characteristic data according to the statistical instruction to obtain a statistical analysis result;

[0013] Send the statistical analysis result to the terminal device.

[0014] In one embodiment, after step S3, the following steps are further included:

[0015] Receiving a record export instruction from the terminal device, sorting out data according to the physical characteristic data and / or the deviation evaluation result to obtain intermediate data;

[0016] Performing format conversion processing on the intermediate data to obtain a record file conforming to a preset export format;

[0017] Sending the record file conforming to the preset export format to the terminal device.

[0018] In one embodiment, after step S1, the following steps are included:

[0019] Performing preprocessing on the physical characteristic data;

[0020] Processing the preprocessed physical characteristic data using a preset machine learning algorithm to obtain physically characteristic data after algorithm processing.

[0021] In one embodiment, after step S1, the following steps are further included:

[0022] Performing algorithm evaluation on the preset machine learning algorithm to determine the reliability of the preset machine learning algorithm.

[0023] In one embodiment, the preset machine learning algorithm includes a preset classification learning algorithm: the processing the preprocessed physical characteristic data using a preset machine learning algorithm to obtain physically characteristic data after algorithm processing includes:

[0024] Using the preset classification learning algorithm to classify the preprocessed physical characteristic data to identify the category of spare parts;

[0025] The performing algorithm evaluation on the preset machine learning algorithm to determine the reliability of the preset machine learning algorithm includes:

[0026] Obtaining the actual spare part category;

[0027] Substituting the physical characteristic data into the preset classification learning algorithm to obtain a classification calculation result;

[0028] Calculating an evaluation index through the actual spare part category and the classification calculation result;

[0029] Determining the reliability of the preset classification learning algorithm according to the evaluation index.

[0030] In one embodiment, the evaluation index includes accuracy rate and recall rate.

[0031] The present application also provides a server, including a processor and a memory storing a computer program, where the processor implements the steps of the method for inspecting spare parts of a nuclear power plant as described above when executing the computer program.

[0032] The present application also provides a nuclear power plant spare part inspection system, including:

[0033] A sensor detection device;

[0034] A terminal device;

[0035] The server as described above.

[0036] Implementing the present application has the following beneficial effects: The present application provides a method for inspecting spare parts of a nuclear power plant, a server, and a nuclear power plant spare part inspection system. The steps of the method include obtaining physical characteristic data of the spare parts to be inspected from the sensor detection device; calculating deviations from the physical characteristic data to obtain a deviation evaluation result; and sending the deviation evaluation result to the terminal device so that the terminal device outputs the deviation evaluation result. The present invention realizes the efficiency and accuracy of spare part inspection through data collection and deviation evaluation. It effectively solves the cumbersome problems brought by relying on manual operations in the traditional spare part acceptance process. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] The present application will be further described below in conjunction with the drawings and embodiments. In the drawings:

[0038] Figure 1 is a flowchart of a method for inspecting spare parts of a nuclear power plant according to the present application;

[0039] Figure 2 is a histogram of the physical characteristic data of the spare parts according to the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0040] The present application will be further described in detail below in conjunction with the drawings through specific embodiments. Similar elements in different embodiments are labeled with related similar element numbers. In the following embodiments, many details are described to make the present application better understood. However, those skilled in the art can easily recognize that some of the features can be omitted in different situations, or can be replaced by other elements, materials, and methods. In some cases, some operations related to the present application are not shown or described in the specification to avoid the core part of the present application being overwhelmed by excessive description. For those skilled in the art, it is not necessary to describe these related operations in detail, and they can fully understand the related operations based on the description in the specification and the general technical knowledge in the art.

[0041] As Figure 1 shown, Figure 1This is a schematic flow diagram of a method for inspecting spare parts in a nuclear power plant in this application.

[0042] This application provides a method for inspecting spare parts in a nuclear power plant, and an application server. The method includes:

[0043] Step S1: Obtain the physical characteristic data of the spare parts to be inspected from the sensor detection equipment;

[0044] In this step, it should be noted that the physical characteristic data of the spare parts to be inspected is obtained from the sensor detection equipment. The sensor detection equipment includes but is not limited to high-precision measuring instruments, non-destructive testing equipment, etc., which can detect the key dimensions, shapes, accuracies, performances, etc. of the spare parts. For example, the size data of the spare parts is obtained through a laser rangefinder, and the internal defects of the spare parts are detected through an ultrasonic flaw detector.

[0045] In the digital intelligent inspection process, the physical characteristic data of the spare parts to be inspected is obtained relying on the sensor detection equipment. These equipment cover various types such as high-precision measuring instruments and non-destructive testing, and can comprehensively detect key indicators such as the key dimensions, shapes, accuracies, and performances of the spare parts. For example, a laser rangefinder can accurately measure the size of the spare parts, while an ultrasonic flaw detector can effectively detect potential internal defects of the spare parts. As the core component of this process, the performance of the sensor is crucial for ensuring the accuracy and reliability of the inspection data. Currently, widely used sensors include photoelectric sensors, pressure sensors, and temperature sensors, etc., and there are also new types of sensors such as fiber optic sensors, microwave sensors, and image sensors.

[0046] Step S2: Perform deviation calculation on the physical characteristic data to obtain a deviation evaluation result;

[0047] In this step, it should be noted that the obtained physical characteristic data is subjected to deviation calculation to obtain a deviation evaluation result. The deviation calculation is carried out through preset algorithms and models, and the actual measurement data is compared with the design specifications or standard values to calculate the deviation value. For example, for a spare part with a designed size of 100 mm, the actual measured size is 100.5 mm, and the deviation value is 0.5 mm. The deviation evaluation result includes information such as the magnitude, direction of the deviation value, and whether it exceeds the allowable range.

[0048] Step S3: Send the deviation evaluation result to the terminal device so that the terminal device outputs the deviation evaluation result.

[0049] In this step, it should be noted that the deviation evaluation result is sent to the terminal device, which can be a computer, a tablet computer, a smart phone, etc. After receiving the deviation evaluation result, the terminal device outputs the result in the form of a graphical interface or a report, so that the inspectors can intuitively understand the quality status of the spare parts. For example, the terminal device can display detailed information such as the size deviation, shape deviation, and performance deviation of the spare parts, and mark whether they meet the acceptance criteria.

[0050] The inspection method for spare parts of nuclear power plants in this application significantly improves the inspection efficiency by automatically acquiring and processing physical property data, and reduces the time and workload of manual operations. At the same time, this method avoids the errors that may occur during manual operations and data filling, thereby improving the accuracy of inspection results. In addition, this method can acquire the physical property data of spare parts in real time, and perform deviation calculation and evaluation in a timely manner to ensure that the quality of spare parts is always under control. By centrally managing and storing inspection data on the server, this method also improves the convenience and reliability of data management, facilitating data query, statistics, and analysis. Finally, this method ensures that the spare parts meet the design specifications and quality standards, thereby enhancing the safety and reliability of nuclear power plant equipment.

[0051] In one embodiment, after step S3, it includes:

[0052] Receiving a query instruction from the terminal device, and analyzing and processing the physical property data according to the query instruction to obtain the spare part status information;

[0053] Sending the spare part status information to the terminal device.

[0054] It should be noted that the query instruction can include detailed information query of specific spare parts, overall quality assessment of a batch of spare parts, etc. The server further analyzes and processes the stored physical property data according to the query instruction to generate the spare part status information. For example, statistical information such as the average deviation value, maximum deviation value, and minimum deviation value of the spare parts can be calculated, or the quality of the spare parts can be graded and evaluated. After that, the spare part status information is sent to the terminal device. After receiving the spare part status information, the terminal device outputs the result in the form of a graphical interface or a report, so that the inspectors can comprehensively understand the status of the spare parts. For example, the terminal device can display a detailed quality report of the spare parts, including statistical analysis results of various physical property data, quality assessment levels and other information. In addition, the measurement results and their main statistical value information (such as average value, σ, 3σ, 6σ, Ca, Cp, Cpk, etc.) will be automatically recorded and archived, and the operator can select different screening conditions to extract historical records, so as to achieve comprehensive monitoring and management of the quality of spare parts.

[0055] In one embodiment, after step S3, it further includes:

[0056] Receive a statistical instruction from a terminal device, and perform statistical analysis processing on the physical characteristic data according to the statistical instruction to obtain a statistical analysis result;

[0057] Send the statistical analysis result to the terminal device.

[0058] Such as Figure 2 , it should be noted that the statistical instruction may include detailed statistical analysis of specific spare parts, overall quality statistical evaluation of a batch of spare parts, etc. The server further performs statistical analysis processing on the stored physical characteristic data according to the statistical instruction to generate a statistical analysis result. For example, statistical information such as the average deviation value, maximum deviation value, minimum deviation value, σ (standard deviation), 3σ (triple standard deviation), 6σ (six-fold standard deviation), process capability indices: Ca (process performance index), Cp (process capability index), Cpk (process capability index) of the spare parts can be calculated, or the quality of the spare parts can be classified and evaluated. After that, the statistical analysis result is sent to the terminal device. After receiving the statistical analysis result, the terminal device outputs the result in the form of a graphical interface or a report so that the inspection personnel can comprehensively understand the quality status of the spare parts. For example, the terminal device can display a detailed quality report of the spare parts, including the statistical analysis results of various physical characteristic data, quality evaluation grades and other information. In addition, statistical analysis plays an important role in controlling the production process and improving product quality. Through SPC (Statistical Process Control) analysis, using statistical methods for quality diagnosis and analysis can monitor the changing trend of product quality, play a preventive role in the production process, reduce the waste caused by post-inspection, and thus achieve the control of the production process and the improvement of product quality. A histogram can reflect the fluctuation state and distribution of product quality, transmit information about the process quality status, and be used to judge and predict product quality and unqualified rate. A trend chart monitors the anomalies of production equipment and production process through the regular changing trend of measured values, such as the monotonic change and periodic change of measured values.

[0059] In one embodiment, after step S3, it further includes:

[0060] Receive a record export instruction from the terminal device, organize the data according to the physical characteristic data and / or deviation evaluation result to obtain intermediate data;

[0061] Perform format conversion processing on the intermediate data to obtain a record file that conforms to a preset export format;

[0062] Send the record file that conforms to the preset export format to the terminal device.

[0063] In one embodiment, the record management function includes: measurement records can be imported and exported, users can select the report form, and users are supported to set report templates. Record files can be saved in formats such as pdf and csv, and print reports can be quickly output with one key.

[0064] In one embodiment, after step S1, it includes:

[0065] Preprocess the physical property data;

[0066] Use a preset machine learning algorithm to process the preprocessed physical property data to obtain the physically characterized data after algorithm processing.

[0067] In a specific embodiment, the obtained physical property data is preprocessed to remove interference factors such as noise and outliers, and improve the quality and reliability of the data. The preprocessing methods include but are not limited to data cleaning, data smoothing, data normalization, etc. For example, outliers are removed through data cleaning, data fluctuations are reduced through data smoothing, and data is scaled to a specific range through data normalization. Use a preset machine learning algorithm to process the preprocessed physical property data to obtain the physically characterized data after algorithm processing. The machine learning algorithms can include but are not limited to support vector machine (SVM), decision tree, random forest, neural network, etc. For example, the data is classified through the support vector machine algorithm, and regression analysis is performed on the data through the random forest algorithm, so as to obtain more accurate physical property data.

[0068] Calculate the deviation of the physically characterized data after algorithm processing to obtain a deviation evaluation result. The deviation calculation is based on a preset algorithm and model, and the actual measurement data is compared with the design specification or standard value to calculate the deviation value. The deviation evaluation result includes information such as the magnitude, direction of the deviation value, and whether it exceeds the allowable range. For example, for a spare part with a designed size of 100 mm, the actual measured size is 100.5 mm, and the deviation value is 0.5 mm.

[0069] Send the deviation evaluation result. Send the deviation evaluation result to terminal devices such as computers, tablets, smart phones, etc. After receiving the result, the terminal device outputs it through a graphical interface or report form so that the inspection personnel can intuitively understand the quality status of the spare parts. For example, the terminal device can display detailed information such as the size deviation, shape deviation, and performance deviation of the spare parts, and mark whether it meets the acceptance standard.

[0070] In one embodiment, after step S1, it further includes:

[0071] Conduct an algorithm evaluation on the preset machine learning algorithm to determine the reliability of the preset machine learning algorithm.

[0072] It should be noted that the preset machine learning algorithm is evaluated to determine its reliability. The algorithm evaluation is carried out through preset evaluation metrics and methods. For example, through methods such as cross-validation, accuracy evaluation, recall rate evaluation, etc., the performance and accuracy of the algorithm in processing physical property data are evaluated. The evaluation results will be used to determine whether the algorithm is suitable for subsequent physical property data processing.

[0073] In one embodiment, the preset machine learning algorithm includes a preset classification learning algorithm: using the preset machine learning algorithm to process the preprocessed physical property data, the obtained physical property data after algorithm processing includes:

[0074] Using the preset classification learning algorithm to classify the preprocessed physical property data, so as to identify the category of spare parts;

[0075] Evaluating the preset machine learning algorithm to determine the reliability of the preset machine learning algorithm includes:

[0076] Obtaining the actual spare part category;

[0077] Substituting the physical property data into the preset classification learning algorithm to obtain a classification calculation result;

[0078] Calculating the evaluation metric through the actual spare part category and the classification calculation result;

[0079] Determining the reliability of the preset classification learning algorithm according to the evaluation metric.

[0080] In a specific embodiment, the preset machine learning algorithm is used to process the preprocessed physical property data to obtain the physical property data after algorithm processing. Specifically, first, the preset classification learning algorithm is used to classify the preprocessed physical property data, so as to identify the category of spare parts. For example, the SVM (Support Vector Machine algorithm) can be used to classify the data to determine the type of spare parts.

[0081] To ensure the reliability of the preset machine learning algorithm, it needs to be evaluated. The evaluation process includes the following steps: First, obtain the actual spare part category, which can be obtained through manual annotation or obtaining the standard data of spare parts from the manufacturer. Second, substitute the preprocessed physical property data into the preset classification learning algorithm to obtain a classification calculation result; then, calculate the evaluation metrics, such as accuracy rate, recall rate, F1 score, etc., by comparing the actual spare part category with the classification calculation result; finally, determine the reliability of the preset classification learning algorithm according to these evaluation metrics. If the evaluation metrics show that the performance and reliability of the algorithm meet the requirements, it can be considered that the algorithm is applicable to the identification of spare part categories.

[0082] In one embodiment, the evaluation metrics include accuracy rate and recall rate.

[0083] It should be noted that the evaluation indicators include one or more of accuracy, recall, F1 score, and precision.

[0084] In one embodiment, before the acceptance of the machine learning algorithm, a data set containing representative samples needs to be collected and prepared to fully reflect the application effect of the algorithm in the actual scenario. The data set should be of high quality and fully annotated to facilitate the evaluation of the algorithm performance. For different tasks, corresponding performance evaluation indicators are used, such as accuracy, precision, recall, F1-score (F1 score) for classification tasks, and mean square error (MSE) or mean absolute error (MAE) for regression tasks. Among them, F1-score is an indicator used to measure the performance of classification models, especially widely used in binary classification problems. Its Chinese meaning is "F1 score" or "F1 score". In order to reduce the deviation of the evaluation results, cross-validation can be used to divide the data set into multiple subsets, repeat training and testing, and finally take the average result to effectively evaluate the generalization ability of the algorithm. In addition, comparing the algorithm to be evaluated with the existing benchmark algorithm can more objectively evaluate its advantages and disadvantages. After completing the algorithm verification experiment, the results need to be analyzed and interpreted to clarify the advantages and disadvantages of the algorithm and the room for improvement. For example, manual imagers rely on the operator's proficiency to perform learning tasks by manually taking points; fully automatic imagers use software to identify the edge of the product and automatically take points. Each time, the measurement needs to be fixed, and the software completes the learning task; the intelligent automatic measurement and acceptance device uses a camera to take pictures, and the software captures pixels to identify the edge of the product and calculates the number of pixels. The product can be placed in any direction to complete the learning task. By comparing the measurement data collected by different machines, the same product can be measured multiple times at fixed positions and different positions to evaluate indicators such as accuracy, recall rate and F1-score. In addition, the multiple measurement data of different products at fixed positions and different positions collected by different machines can calculate the mean square error or mean absolute error to evaluate the stability of machine measurement. Among the collected measurement data, some size data can be selected for cross-validation to avoid excessive verification due to too much product data. The benchmark algorithm comparison is based on the true value of the product. After the sensor equipment obtains the spare parts data, it first uses the basic algorithm comparison to understand the deviation between the data and the true value, and then uses cross-validation to evaluate the measurement stability of the equipment. Finally, the basic algorithm comparison and cross-validation data are summarized to evaluate the reliability and stability of the product data.

[0085] The present application also provides a server, including a processor and a memory storing a computer program, and the processor implements the steps of the above-mentioned method for nuclear power plant spare parts inspection when executing the computer program.

[0086] The present application also provides a nuclear power plant spare parts inspection system, comprising:

[0087] Sensor detection equipment;

[0088] Terminal device;

[0089] The above-mentioned server.

[0090] Implementing this application has the following advantages:

[0091] 1. Spare part acceptance efficiency: The intelligent digital acceptance device can quickly obtain measurement results, usually only taking a few seconds or even less time. Measuring with a vernier caliper is relatively slower.

[0092] 2. Spare part acceptance automation and intelligence: The intelligent digital acceptance device usually has the characteristics of automation and intelligence, can automatically identify, measure and analyze the characteristics of objects, reducing the errors and cumbersome nature of manual operations.

[0093] 3. Non-contact measurement: Using non-contact measurement technology will not cause damage or deformation to the object being measured.

[0094] 4. High precision and accuracy: The measurement precision and accuracy of the intelligent digital acceptance device are higher than those of traditional acceptance tools, and can provide more accurate measurement data.

[0095] 5. Data processing and recording: Equipped with data processing and recording functions, it can display measurement results in real time and generate detailed measurement reports, facilitating data analysis and storage.

[0096] 6. Multifunctional measurement: The intelligent digital acceptance device can measure a variety of parameters, such as size, shape, angle, position, etc., while the functions of traditional acceptance tools are relatively single.

[0097] 7. Adaptability and flexibility: The intelligent digital acceptance device can adapt to the measurement of objects with different shapes and sizes, and has a wider applicability and flexibility.

[0098] The purpose of the present invention is to solve the above problems, improve the efficiency of spare part acceptance, reduce the errors and cumbersome nature of manual operations, use non-contact measurement and acceptance technology that will not cause damage or deformation to the object being measured, can provide more accurate measurement data, is equipped with data processing and recording functions, can display measurement results in real time and generate detailed measurement reports, facilitating data analysis and storage. The present invention relies on advanced technologies such as the Internet / Big Data / Cloud Computing, etc., to achieve visual one-key acceptance, can quickly and accurately identify items and process data. Secondly, using high-precision sensors and algorithms to ensure the accuracy of spare part information, reduce human errors, accelerate the efficiency of spare part acceptance, improve the quality of spare part acceptance, reduce labor costs, and enhance the operational safety and economy of power plants.

Claims

1. A method for inspecting spare parts of a nuclear power plant, using a server, characterized in that: The method comprises: Step S1: obtaining physical property data of the spare parts to be inspected from the sensor detection equipment; Step S2: performing deviation calculation on the physical property data to obtain a deviation evaluation result; Step S3: sending the deviation evaluation result to the terminal device so that the terminal device outputs the deviation evaluation result; wherein the deviation evaluation result is a size deviation evaluation result; The step S1 then includes: Preprocessing the physical property data; Using a preset machine learning algorithm to process the preprocessed physical property data to obtain algorithm-processed physical property data; The preset machine learning algorithm includes a preset classification learning algorithm: the physical property data processed by the preset machine learning algorithm is processed to obtain the physical property data processed by the algorithm, including: The pre-processed physical property data is classified using the preset classification learning algorithm, thereby identifying the category of the spare parts.

2. The method for inspecting spare parts of a nuclear power plant according to claim 1, characterized in that: The step S3 then includes: receiving a query instruction from the terminal device, and analyzing and processing the physical characteristic data according to the query instruction to obtain spare part status information; The spare part status information is sent to the terminal device.

3. The method for inspecting spare parts of a nuclear power plant according to claim 1, characterized in that: After step S3, the following steps are also included: receiving a statistical instruction from the terminal device, and performing statistical analysis on the physical characteristic data according to the statistical instruction to obtain a statistical analysis result; Send the statistical analysis result to the terminal device.

4. The method for inspecting spare parts of a nuclear power plant according to claim 1, characterized in that: After step S3, the following steps are also included: receiving a record export instruction from the terminal device, and performing data sorting according to the physical property data and / or the deviation evaluation result to obtain intermediate data; Performing format conversion processing on the intermediate data to obtain a record file that complies with a preset export format; Sending the record file that complies with the preset export format to the terminal device.

5. The method for inspecting spare parts of a nuclear power plant according to claim 4, characterized in that: After step S1, the following steps are also included: An algorithm evaluation is performed on the preset machine learning algorithm to determine the reliability of the preset machine learning algorithm.

6. The method for inspecting spare parts of a nuclear power plant according to claim 5, characterized in that: The performing algorithm evaluation on the preset machine learning algorithm to determine the reliability of the preset machine learning algorithm includes: Get the actual spare parts category; Substituting the physical property data into the preset classification learning algorithm to obtain a classification calculation result; Calculate the evaluation index by using the actual spare parts category and the classification calculation result; The reliability of the preset classification learning algorithm is determined according to the evaluation index.

7. The method for inspecting spare parts of a nuclear power plant according to claim 6, characterized in that: The evaluation indicators include precision and recall.

8. A server comprising a processor and a memory storing a computer program, characterized in that: The processor implements the steps of the method for inspecting spare parts of a nuclear power plant as described in any one of claims 1 to 7 when executing the computer program.

9. A nuclear power plant spare parts inspection system, characterized in that: include: Sensor detection equipment; Terminal equipment; The server of claim 8.

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