Nuclear power plant welding information management system

Abstract

The invention provides a nuclear power plant welding information management system. The nuclear power plant welding information management system comprises a welding data management module, a real-time monitoring and early warning module, a welding quality evaluation and optimization module, a system stability and safety guarantee module and a user interaction interface module. The welding data management module is connected with the real-time monitoring and early warning module, the welding quality evaluation and optimization module, the system stability and safety guarantee module and the user interaction interface module. And the system stability and safety guarantee module is connected with the welding data management module, the real-time monitoring and early warning module, the welding quality evaluation and optimization module and the user interaction interface module. Through associated function design of the welding data management module, the real-time monitoring and early warning module, the welding quality evaluation and optimization module and other modules, the problems that an existing welding information management system lacks intelligent and automatic functions, the welding process cannot be monitored in real time, and potential quality problems cannot be found and corrected in time are solved.

Description

Technical Field

[0001] This application belongs to the field of nuclear power plant welding information management technology, specifically relating to a nuclear power plant welding information management system. Background Art

[0002] Nuclear power plants are a crucial component of the energy sector, and their safety and reliability are of paramount importance. Welding, an indispensable part of nuclear power plant construction, directly impacts the overall safety and operational efficiency of the plant. However, current nuclear power plant welding operations still face numerous challenges in terms of information management.

[0003] Nuclear power plant welding operations involve a large amount of data and information, including welding materials, welding processes, welder qualifications, and welding quality inspection reports. This data and information is typically stored in paper form or in fragmented electronic formats, leading to difficulties in information retrieval and compromising data consistency and integrity. Secondly, existing welding information management systems often lack intelligent and automated functions, failing to monitor the welding process in real time and promptly identify and correct potential quality problems. Given the complex welding environment in nuclear power plants, which places high demands on the stability and reliability of welding information management systems, this significantly increases the risks of welding operations and reduces the controllability of welding quality. Summary of the Invention

[0004] In view of this, this application provides a welding information management system for nuclear power plants. Through the design of interconnected functions of various modules such as welding data management module, real-time monitoring and early warning module, and welding quality assessment and optimization module, it aims to solve the technical problem that existing welding information management systems often lack intelligent and automated functions, and are unable to monitor the welding process in real time, and promptly detect and correct potential quality problems.

[0005] This application provides a nuclear power plant welding information management system, which includes a welding data management module, a real-time monitoring and early warning module, a welding quality assessment and optimization module, a system stability and safety assurance module, and a user interface module.

[0006] The welding data management module is connected to the real-time monitoring and early warning module, the welding quality assessment and optimization module, the system stability and security assurance module, and the user interface module, and is used for structured storage, retrieval, and analysis of welding operation-related data.

[0007] The real-time monitoring and early warning module integrates a sensor interface and data analysis software. It uses the sensor interface to receive welding process parameters in real time during the welding process and uses the data analysis software to perform real-time monitoring and anomaly warning. When the welding process parameters are detected to exceed the preset range, the early warning mechanism is automatically triggered to notify relevant personnel for handling.

[0008] The welding quality assessment and optimization module uses machine learning algorithms to predict and evaluate welding quality based on historical welding data stored in the welding data management module and welding process parameters fed back in real time by the real-time monitoring and early warning module, in order to obtain welding quality prediction results. Based on the welding quality prediction results, the module automatically adjusts the welding process parameters and displays the welding quality prediction results through the user interface module.

[0009] The user interface module provides an intuitive and easy-to-use graphical user interface, and supports the input, query, analysis and report generation of welding data.

[0010] The system stability and security assurance module is connected to the welding data management module, real-time monitoring and early warning module, welding quality assessment and optimization module, and user interface module, and is used to provide system stability and security assurance.

[0011] In one specific embodiment of this application, the welding operation-related data is categorized by data type as follows: specific information on welding materials, welding process parameters, welding personnel qualification information, and welding quality inspection reports. Specific information on welding materials includes the type, specifications, and batch number of the welding materials. Welding process parameters include current intensity, voltage, and welding speed.

[0012] In one specific embodiment of this application, the current intensity ranges from 50A to 500A, the voltage ranges from 10V to 50V, and the welding speed ranges from 0.1mm / s to 10mm / s.

[0013] In one specific embodiment of this application, the welding data management module integrates one or more of the following: a welding material traceability system, a welding process knowledge base, a welding heat input calculation function submodule, and a data import function submodule. The welding material traceability system uses QR codes or RFID tags on the welding materials. These QR codes or RFID tags are used to display the full traceability of the welding materials after being scanned by the user. The full traceability of welding materials records the source, processing, storage, and usage of the materials. The welding process knowledge base integrates professional knowledge related to welding processes. This professional knowledge includes welding standards, process guidelines, and frequently asked questions. The welding heat input calculation function submodule performs calculations based on the physical principles of the welding process, including the mathematical relationship between welding heat input and welding current, voltage, welding speed, and welding efficiency. The data import function submodule is used to import data in a manner compatible with multiple data formats. The data import speed in the data import function submodule is no less than 1MB / s.

[0014] In one specific embodiment of this application, the welding data management module stores welding operation-related data using SQL or NoSQL. The data analysis software integrated into the real-time monitoring and early warning module is MATLAB and Python Pandas library data analysis software.

[0015] In one specific embodiment of this application, the real-time monitoring and early warning module also integrates a welding process visualization system. The welding process visualization system uses augmented reality and virtual reality technologies to present a three-dimensional model and data of the welding process to the operator in real time.

[0016] In one specific embodiment of this application, the sensor interface is connected to a high-precision stress-strain sensor. The accuracy of the high-precision stress-strain sensor is not less than 0.1%.

[0017] In one specific embodiment of this application, the welding quality assessment and optimization module integrates a welding defect intelligent identification system, an OpenCV graphics processing library, and an automatic optimization algorithm for welding process parameters. The welding defect intelligent identification system is used to identify defects. The OpenCV graphics processing library is used to measure defect size, locate defects, and identify defect category information through image processing technology. The automatic optimization algorithm for welding process parameters is used to predict and evaluate welding quality based on historical welding data stored in the welding data management module and welding process parameters fed back in real time by the real-time monitoring and early warning module, employing machine learning algorithms to obtain welding quality prediction results; based on the welding quality prediction results, the welding process parameters are automatically adjusted, and the welding quality prediction results are displayed through a user interface module.

[0018] In one specific embodiment of this application, the system stability and security assurance module adopts a redundant architecture design. The redundant architecture design includes redundant configurations at at least one layer in the server, network, and storage.

[0019] In one specific embodiment of this application, the system stability and security assurance module integrates OPC UA and Modbus TCP protocol, AES-256 encryption algorithm and role-based access control.

[0020] The beneficial effects of this technical solution are as follows: By setting up a welding data management module with structured storage, rapid retrieval, and analysis functions, it solves the problems of information retrieval difficulties and data consistency issues caused by traditional paper or decentralized electronic storage formats. Furthermore, by setting up a real-time monitoring and early warning module integrating sensor interfaces and advanced professional data analysis software, it can monitor welding process parameters in real time and automatically trigger an early warning mechanism when an anomaly is detected. This function effectively prevents potential quality problems, reduces operational risks, and improves the controllability of welding quality, especially in the complex environment of nuclear power plants, ensuring the safety and stability of welding operations. The welding quality assessment and optimization module automatically adjusts welding process parameters based on welding quality prediction results, thereby optimizing welding quality using the adjusted parameters. The user interface module intuitively displays the welding quality prediction results to operators, allowing them to clearly understand the welding quality situation and promptly adjust welding process parameters or take other measures to optimize welding quality based on the prediction results. In addition, by setting up a system stability and security assurance module, the welding data management module, real-time monitoring and early warning module, welding quality assessment and optimization module, and user interface module are provided with system stability and security assurance, thereby effectively reducing the risk of welding operations and improving the controllability of welding quality. Attached Figure Description

[0021] Figure 1 The diagram shown is a schematic diagram of a nuclear power plant welding information management system provided in an embodiment of this application.

[0022] Figure 2 The diagram shown is a real-time monitoring and early warning module in a nuclear power plant welding information management system according to an embodiment of this application.

[0023] Figure 3 The diagram shown is a schematic diagram of a welding quality assessment and optimization module in a nuclear power plant welding information management system according to an embodiment of this application.

[0024] Figure 4 The diagram shown is a schematic diagram of a system stability and security assurance module in a nuclear power plant welding information management system according to an embodiment of this application.

[0025] In the diagram, 100 is the Nuclear Power Plant Welding Information Management System; 110 is the Welding Data Management Module; 111 is the Welding Material Traceability System; 112 is the Welding Process Knowledge Base; 113 is the Welding Heat Input Calculation Function Submodule; 114 is the Data Import Function Submodule; 120 is the Real-time Monitoring and Early Warning Module; 121 is the Sensor Interface; 122 is the Data Analysis Software; 123 is the Welding Process Visualization System; 130 is the Welding Quality Assessment and Optimization Module; 131 is the Welding Defect Intelligent Identification System; 132 is the OpenCV Graphics Processing Library; 133 is the Automatic Optimization Algorithm for Welding Process Parameters; 140 is the System Stability and Security Assurance Module; and 150 is the User Interface Module. Detailed Implementation

[0026] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0027] At least one embodiment of this application provides a nuclear power plant welding information management system. The nuclear power plant welding information management system 100 includes a welding data management module 110, a real-time monitoring and early warning module 120, a welding quality assessment and optimization module 130, a system stability and security assurance module 140, and a user interaction interface module 150.

[0028] The welding data management module 110 is connected to the real-time monitoring and early warning module 120, the welding quality assessment and optimization module 130, the system stability and security assurance module 140, and the user interface module 150, and is used for structured storage, retrieval and analysis of welding operation-related data.

[0029] The real-time monitoring and early warning module 120 integrates a sensor interface 121 and data analysis software 122. It is used to receive welding process parameters in real time during the welding process using the sensor interface 121 and to perform real-time monitoring and anomaly early warning using the data analysis software 122. When the welding process parameters are detected to exceed the preset range, the early warning mechanism is automatically triggered to notify relevant personnel to handle the situation.

[0030] The welding quality assessment and optimization module 130 is used to predict and assess welding quality based on historical welding data stored in the welding data management module 110 and welding process parameters fed back in real time by the real-time monitoring and early warning module 120, and to obtain welding quality prediction results. Based on the welding quality prediction results, the welding process parameters are automatically adjusted, and the welding quality prediction results are displayed through the user interface module 150.

[0031] The user interface module 150 provides an intuitive and easy-to-use graphical user interface and supports the input, query, analysis and report generation of welding data.

[0032] The system stability and security assurance module 140 is connected to the welding data management module 110, the real-time monitoring and early warning module 120, the welding quality assessment and optimization module 130, and the user interface module 150, and is used to provide system stability and security assurance.

[0033] It should be noted that welding process parameters are important indicators for evaluating welding quality and safety. These parameters include, but are not limited to, welding temperature, gas flow rate, and arc stability. The precision of the welding process parameters can be displayed to two decimal places, ensuring data accuracy and readability. The welding quality prediction results can be the predicted probability and strength of defects that may occur during the welding process, and these results can be displayed as a percentage in the user interface module 150.

[0034] The user interface module 150 can be presented as a visual dashboard. The interface displays a quality risk matrix, with porosity defect areas marked as high-risk zones. It also provides optimization suggestions for process parameters, such as increasing the current to the 225A±5A range to reduce porosity, and displays a real-time quality scoring curve. Post-weld ultrasonic testing verified that the error between the prediction and actual results was less than 2%. After operators adjusted the parameters according to the optimization suggestions, the porosity of subsequent welds significantly decreased, confirming the predictive system's effectiveness in improving weld quality control.

[0035] According to the technical solution provided in this application embodiment, by setting the welding data management module 110 to have structured storage, rapid retrieval, and analysis functions, the difficulties in information retrieval and data consistency problems caused by traditional paper or decentralized electronic format storage are solved. Furthermore, by setting the real-time monitoring and early warning module 120 to integrate sensor interfaces and advanced professional data analysis software, welding process parameters during the welding process can be monitored in real time, and an early warning mechanism can be automatically triggered when an anomaly is detected. This function effectively prevents potential quality problems, reduces operational risks, and improves the controllability of welding quality, especially in the complex environment of nuclear power plants, ensuring the safety and stability of welding operations. The welding quality assessment and optimization module 130 automatically adjusts welding process parameters based on welding quality prediction results, thereby optimizing welding quality using the adjusted welding process parameters. The user interface module 150 displays the welding quality prediction results to the operator in an intuitive way, allowing the operator to intuitively understand the welding quality situation and adjust welding process parameters or take other measures to optimize welding quality in a timely manner based on the welding quality prediction results. In addition, by setting up a system stability and security assurance module 140, the welding data management module 110, the real-time monitoring and early warning module 120, the welding quality assessment and optimization module 130, and the user interface module 150 are provided with system stability and security assurance, thereby effectively reducing the risk of welding operations and improving the controllability of welding quality.

[0036] Welding operation-related data can be divided into historical welding data and welding data collected and analyzed in real time by the real-time monitoring and early warning module 120, based on their real-time nature.

[0037] The data related to welding operations can be divided into the following categories based on the generation method: input data entered by the user in the user interaction interface module 150, data collected directly received by the real-time monitoring and early warning module 120, and analysis data after the welding data management module 110, the real-time monitoring and early warning module 120, and the welding quality assessment and optimization module 130 analyze the input data and collected data.

[0038] Welding operation-related data can be categorized by data type, including specific information on welding materials, welding process parameters (or welding process parameters), welder qualification information, and welding quality inspection reports. Specific information on welding materials includes the type, specifications, and batch number of the welding materials. Welding process parameters include current intensity, voltage, and welding speed. Thus, the welding data management module 110 not only records specific information on welding materials but also records detailed welding process parameters, achieving intelligent management of the entire welding operation data chain and ensuring the consistency and integrity of the data across the entire welding operation chain.

[0039] In some embodiments, the current intensity ranges from 50A to 500A, the voltage ranges from 10V to 50V, and the welding speed ranges from 0.1mm / s to 10mm / s. Thus, the current intensity is strictly limited to the range of 50A to 500A, the voltage is strictly limited to the range of 10V to 50V, and the welding speed is strictly limited to the range of 0.1mm / s to 10mm / s, ensuring the accuracy and traceability of the data.

[0040] As the core foundation of the system, the welding data management module 110 achieves structured storage and management of welding operation data across the entire chain through a precisely designed database architecture. The welding data management module 110 only needs to be able to perform structured storage, retrieval, and analysis of welding operation-related data; therefore, this application embodiment does not specifically limit the composition of the welding data management module 110. The composition of the welding data management module 110 will be illustrated below with specific examples.

[0041] In at least one embodiment of this application, the welding data management module 110 integrates a welding material traceability system 111. The welding material traceability system 111 is equipped with a QR code or RFID tag on the welding material. The QR code or RFID tag is used to display the full traceability of the welding material after being scanned by a user. The full traceability of the welding material records the source, processing, storage, and usage of the material. Thus, users can achieve full traceability of the welding material by scanning the QR code or RFID tag on the welding material.

[0042] In at least one embodiment of this application, the welding data management module 110 further integrates a welding process knowledge base 112. The welding process knowledge base 112 integrates professional knowledge related to welding processes. This professional knowledge includes welding standards, process guidelines, and frequently asked questions (FAQs). This provides operators with a convenient way to query and learn.

[0043] In at least one embodiment of this application, the welding data management module 110 further integrates a welding heat input calculation function submodule 113. The welding heat input calculation function submodule 113 is used to perform calculations based on the physical principles of the welding process, including the mathematical relationship between welding heat input and welding current, voltage, welding speed, and welding efficiency. This achieves accurate calculation of the welding heat input.

[0044] In at least one embodiment of this application, the welding data management module 110 further integrates a data import function submodule 114, which is used to import data in multiple formats. The data import speed in the data import function submodule 114 is no less than 1MB / s. This enables the nuclear power plant welding information management system 100 to support data exchange and integration with other systems, and provides a powerful data import function, ensuring fast and accurate data transmission.

[0045] It should be noted that various data formats are supported, including CSV, Excel, and JSON.

[0046] In at least one embodiment of this application, the welding data management module 110 stores welding operation-related data in SQL or NoSQL format. This ensures data integrity and accuracy, greatly improving the efficiency of welding operation management.

[0047] The data analysis software integrated into the real-time monitoring and early warning module 120 only needs to be capable of real-time monitoring and anomaly warning. Therefore, this application embodiment does not specifically limit the type of data analysis software. For example, in some embodiments, the data analysis software integrated into the real-time monitoring and early warning module 120 is MATLAB and Python Pandas library data analysis software. In this way, the MATLAB and Python Pandas library data analysis software embedded in the real-time monitoring and early warning module 120 can be used to perform in-depth processing and analysis of these welding process parameters. Once an anomaly in the welding process parameters is detected, such as temperature exceeding the safe range or insufficient gas flow, the system will immediately trigger an early warning mechanism, sending an alarm to relevant personnel through preset communication channels, and taking timely measures to avoid potential safety risks and quality problems.

[0048] In at least one embodiment of this application, the real-time monitoring and early warning module 120 further integrates a welding process visualization system 123. The welding process visualization system 123 utilizes augmented reality (AR) and virtual reality (VR) technologies to present a three-dimensional model and data of the welding process to the operator in real time. Thus, by utilizing AR and VR technologies, the operator can view welding process parameters and welding quality prediction results in real time.

[0049] In at least one embodiment of this application, the sensor interface 121 is connected to a high-precision stress-strain sensor. The high-precision stress-strain sensor has an accuracy of not less than 0.1%. This enables the real-time monitoring and early warning module 120 to monitor welding stress and deformation. The real-time monitoring and early warning module 120 can receive the stress distribution and deformation during the welding process monitored by the high-precision stress-strain sensor in real time through the sensor interface 121. The high-precision stress-strain sensor can capture minute stress changes and deformation trends during the welding process, providing important data support for the evaluation and optimization of welding quality.

[0050] In at least one embodiment of this application, the welding quality assessment and optimization module 130 integrates a welding defect intelligent identification system 131, an OpenCV graphics processing library 132, and a welding process parameter automatic optimization algorithm 133. The welding defect intelligent identification system 131 is used to identify defects. The OpenCV graphics processing library 132 is used to measure defect size, defect location, and identify defect category information through image processing technology. The welding process parameter automatic optimization algorithm 133 is used to predict and evaluate welding quality based on historical welding data stored in the welding data management module 110 and welding process parameters fed back in real time by the real-time monitoring and early warning module 120, using machine learning algorithms to obtain welding quality prediction results; based on the welding quality prediction results, it automatically adjusts the welding process parameters and displays the welding quality prediction results through the user interface module 150.

[0051] For example, the welding quality assessment and optimization module 130 initiates the welding quality prediction process. The real-time monitoring and early warning module 120 continuously collects welding process parameters through high-precision sensors, including current intensity of 215 amps, voltage of 23.5 volts, and welding speed of 1.2 mm / s. Simultaneously, the intelligent welding defect identification system 131 acquires the X-ray non-destructive testing image of the current weld. The intelligent welding defect identification system 131 performs correlation analysis with the above real-time parameters and 50 sets of historical process parameters and corresponding inspection reports of welds of the same material stored in the welding data management module 110. The OpenCV graphics processing library 132 performs grayscale processing on the X-ray image and uses an edge detection algorithm to identify the defect contour. The automatic optimization algorithm for welding process parameters 133 loads a pre-trained support vector machine classification model. The input feature vector includes the real-time process parameters of current intensity of 215 amps, voltage of 23.5 volts, and welding speed of 1.2 mm / s, the defect feature parameter of pore diameter, and the average impact toughness of the deposited metal of the same batch of welding materials in the historical correlation data. After the model is calculated, the output is the welding quality prediction result, which may include the probability of porosity defects, the probability of non-fusion risk, and the overall weld quality score.

[0052] In the above embodiments, during the identification process, the welding quality assessment and optimization module 130 not only provides defect category information but also possesses more precise defect location and size measurement functions. Through image processing technologies, such as edge detection and contour extraction, the welding quality assessment and optimization module 130 can accurately measure the size of defects (accuracy not less than 0.1 mm), providing an important basis for subsequent repair work. Furthermore, the welding quality assessment and optimization module 130 also integrates an automatic optimization algorithm for welding process parameters. This algorithm, based on historical welding data and real-time feedback, uses machine learning or reinforcement learning techniques to dynamically adjust advanced parameters such as welding current waveform (adjustment range within ±10%) and gas ratio (adjustment accuracy to 0.1%), achieving continuous optimization of welding quality.

[0053] The system stability and safety assurance module 140 can adopt a high-availability architecture design to ensure the stable operation of the system in the complex environment of a nuclear power plant. The design of the system stability and safety assurance module 140 is illustrated below with specific embodiments.

[0054] In at least one embodiment of this application, the system stability and security assurance module 140 adopts a redundant architecture design. The redundant architecture design includes, but is not limited to, redundant configurations at at least one layer in the server, network, and storage. This ensures the stable operation of the system.

[0055] In at least one embodiment of this application, the system stability and security assurance module 140 integrates OPC UA and Modbus TCP protocols. This enables seamless communication and data sharing with other automation systems, improving the system's integration and compatibility.

[0056] In at least one embodiment of this application, the system stability and security assurance module 140 integrates the AES-256 encryption algorithm and role-based access control (RBAC). Thus, the application of the AES-256 encryption algorithm and role-based access control (RBAC) further ensures the security of data during transmission and storage.

[0057] It should be noted that the combination of the technical features in the embodiments of this application is not limited to the combination methods described in the embodiments of this application or the combination methods described in specific embodiments. All technical features described in this application can be freely combined or combined in any way, unless they contradict each other.

[0058] As indicated in this application and claims, unless the context clearly indicates otherwise, the words "a," "an," and / or "the" do not specifically refer to the singular and may also include the plural. Generally speaking, the term "comprising" only indicates that it includes the explicitly identified steps and elements, which do not constitute an exclusive list, and the method or apparatus may also include other steps or elements.

[0059] The above description is merely a preferred embodiment of this application and is not intended to limit this application. Any modifications or equivalent substitutions made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A nuclear power plant welding information management system, characterized in that, It includes a welding data management module, a real-time monitoring and early warning module, a welding quality assessment and optimization module, a system stability and security assurance module, and a user interface module. Among them, the welding data management module is connected to the real-time monitoring and early warning module, the welding quality assessment and optimization module, the system stability and security assurance module, and the user interaction interface module, and is used for structured storage, retrieval and analysis of welding operation-related data; The real-time monitoring and early warning module integrates a sensor interface and data analysis software. It uses the sensor interface to receive welding process parameters in real time during the welding process and uses the data analysis software to perform real-time monitoring and anomaly early warning. When the welding process parameters are detected to exceed the preset range, the early warning mechanism is automatically triggered to notify relevant personnel for handling. The welding quality assessment and optimization module is used to predict and assess welding quality based on historical welding data stored in the welding data management module and welding process parameters fed back in real time by the real-time monitoring and early warning module, using machine learning algorithms to obtain welding quality prediction results; based on the welding quality prediction results, the welding process parameters are automatically adjusted, and the welding quality prediction results are displayed through the user interface module. The user interface module provides an intuitive and easy-to-use graphical user interface and supports the input, query, analysis and report generation of welding data. The system stability and security assurance module is connected to the welding data management module, real-time monitoring and early warning module, welding quality assessment and optimization module, and user interface module, and is used to provide system stability and security assurance.

2. The nuclear power plant welding information management system according to claim 1, characterized in that, The data related to welding operations are categorized by data type, including specific information on welding materials, welding process parameters, welding personnel qualification information, and welding quality inspection reports. Specific information on welding materials includes the type, specifications, and batch number of the welding materials, while welding process parameters include current intensity, voltage, and welding speed.

3. The nuclear power plant welding information management system according to claim 2, characterized in that, The current intensity range is 50A to 500A, the voltage range is 10V to 50V, and the welding speed range is 0.1mm / s to 10mm / s.

4. The nuclear power plant welding information management system according to claim 1, characterized in that, The welding data management module integrates one or more of the following: a welding material traceability system, a welding process knowledge base, a welding heat input calculation function submodule, and a data import function submodule. The welding material traceability system uses QR codes or RFID tags on welding materials, which, when scanned by the user, display the entire traceability process of the welding materials, recording their origin, processing, storage, and usage. The welding process knowledge base integrates professional knowledge related to welding processes, including welding standards, process guidelines, and frequently asked questions. The welding heat input calculation function submodule performs calculations based on the physical principles of the welding process, including the mathematical relationships between welding heat input, welding current, voltage, welding speed, and welding efficiency. The data import function submodule is compatible with importing multiple data formats, with a data import speed of at least 1MB / s.

5. A nuclear power plant welding information management system according to claim 1, characterized in that, The welding data management module stores welding operation-related data in SQL or NoSQL, and / or the real-time monitoring and early warning module integrates data analysis software using MATLAB and Python Pandas libraries.

6. The nuclear power plant welding information management system according to claim 1, characterized in that, The real-time monitoring and early warning module also integrates a welding process visualization system, which uses augmented reality and virtual reality technologies to present the three-dimensional model and data of the welding process to the operators in real time.

7. A nuclear power plant welding information management system according to claim 1, characterized in that, The sensor interface connects to a high-precision stress-strain sensor, which has an accuracy of no less than 0.1%.

8. A nuclear power plant welding information management system according to claim 1, characterized in that, The welding quality assessment and optimization module integrates a welding defect intelligent identification system, an OpenCV graphics processing library, and an automatic optimization algorithm for welding process parameters. The welding defect intelligent identification system is used to identify defects; the OpenCV graphics processing library is used to measure defect size, locate defects, and identify defect category information through image processing technology; the automatic optimization algorithm for welding process parameters uses machine learning algorithms to predict and evaluate welding quality based on historical welding data stored in the welding data management module and welding process parameters fed back in real time by the real-time monitoring and early warning module, in order to obtain welding quality prediction results; based on the welding quality prediction results, the welding process parameters are automatically adjusted, and the welding quality prediction results are displayed through the user interface module.

9. A nuclear power plant welding information management system according to any one of claims 1 to 8, characterized in that, The system stability and security assurance module adopts a redundant architecture design, which includes redundant configurations at least one layer in the server, network, and storage.

10. A nuclear power plant welding information management system according to any one of claims 1 to 8, characterized in that, The system stability and security assurance module integrates OPC UA and Modbus TCP protocol, AES-256 encryption algorithm, and role-based access control.

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