Intelligent method and system for nuclear power maintenance tools

Abstract

The present application relates to the technical field of tool management, and more particularly to an intelligent method and system for nuclear power maintenance tools, which acquires position information and state information of each maintenance tool in real time through a sensing unit prearranged on each maintenance tool, performs real-time feedback on the position information and state information of each maintenance tool based on a digital twin model of a cloud server, analyzes to obtain a safety limit range of each maintenance tool, and sends corresponding permission instructions to each maintenance tool according to the safety limit range, so as to allocate a use permission within the safety limit range to the maintenance tool.

Description

Technical Field

[0001] This invention relates to the field of tool management technology, and more specifically, to intelligent methods and systems for nuclear power plant maintenance tools. Background Technology

[0002] Nuclear power plants have complex equipment systems involving a large number of precision instruments and key components. Maintenance work requires a high degree of professionalism and accuracy. Different maintenance tasks have different requirements for tools, and the maintenance process usually requires multiple tools to work together. It mainly relies on manual recording and inspection, which can easily lead to information lag and make it difficult to allocate and manage these tools in a reasonable manner to ensure the smooth progress of maintenance work. Summary of the Invention

[0003] In view of this, embodiments of the present invention provide an intelligent method and system for nuclear power plant maintenance tools, so as to achieve automated and rational allocation and management of maintenance tools.

[0004] To achieve the above objectives, the embodiments of the present invention provide the following technical solutions: According to one aspect of the present invention, a method for providing intelligent nuclear power plant maintenance tools includes: The location and status information of each repair tool are collected in real time by sensing units pre-set on each repair tool. The digital twin model based on the cloud server provides real-time feedback on the location and status information of each maintenance tool, and analyzes the results to determine the safety limits of each maintenance tool. Based on the aforementioned safety restrictions, corresponding permission instructions are sent to each repair tool to assign usage permissions within the safety restrictions to the repair tools.

[0005] According to another aspect of the present invention, an intelligent system for providing nuclear power plant maintenance tools includes: The information acquisition module is used to collect the location and status information of each repair tool in real time through the sensing units pre-set on each repair tool; The safety analysis module is used to provide real-time feedback on the location and status information of each maintenance tool based on the digital twin model of the cloud server, and to analyze and obtain the safety limit range of each maintenance tool. The permission sending module is used to send corresponding permission instructions to each repair tool according to the security restriction range, so as to assign the repair tool the usage permission within the security restriction range.

[0006] As can be seen from the above technical solution, the intelligent method for nuclear power plant maintenance tools provided by the present invention has the following beneficial effects: This invention collects location and status information in real time, enabling maintenance personnel to keep abreast of tool dynamics and quickly locate tools. With the help of digital twin model analysis, the safety limit range can be accurately determined, potential risks can be detected in advance, and usage permissions can be allocated based on this range to avoid improper use of tools and reduce the probability of safety accidents. In addition, intelligent management improves maintenance efficiency, reduces the time spent manually searching for tools and judging tool status, and ensures that nuclear power maintenance work is carried out safely, efficiently, and in an orderly manner. Attached Figure Description

[0007] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are merely embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort: Figure 1 A schematic diagram illustrating the steps of an intelligent method for nuclear power plant maintenance tools provided in an embodiment of the present invention; Figure 2 A schematic diagram of the structure of an intelligent system for nuclear power plant maintenance tools provided in an embodiment of the present invention. Detailed Implementation

[0008] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0009] Nuclear power plants have complex equipment systems involving a large number of precision instruments and key components. Maintenance work requires a high degree of professionalism and accuracy. Different maintenance tasks have different requirements for tools, and the maintenance process usually requires multiple tools to work together. It mainly relies on manual recording and inspection, which can easily lead to information lag and make it difficult to allocate and manage these tools in a reasonable manner to ensure the smooth progress of maintenance work.

[0010] In view of this, the present invention provides an intelligent method for nuclear power plant maintenance tools, the steps of which are as follows: Figure 1 As shown, it includes: The first step is to collect the location and status information of each repair tool in real time through the sensing units pre-set on each repair tool.

[0011] Specifically, in the first step of the embodiment provided by this invention, the sensing unit can determine whether the tool is in an active state by detecting the power status of the repair tool, changes in the current of the working circuit, etc. For example, when the tool is powered on and starts working, current flows through the circuit. The sensing unit detects the current signal and can determine that the tool is in an active state. Based on the active state, the monitoring mode of the sensing unit is divided into different levels. If the tool is in an active state, the monitoring mode is adjusted to high-frequency monitoring; if the tool is in an inactive state, it is adjusted to low-frequency monitoring. At the same time, according to different monitoring modes, corresponding information acquisition parameters (such as acquisition frequency, acquisition accuracy, etc.) and information upload parameters (such as upload frequency, upload format, etc.) are deployed.

[0012] More specifically, maintenance tools in different activation states have different information collection needs. Tools in the activation state are participating in maintenance work, and their location and status change more frequently, requiring more timely and accurate information collection and uploading in order to keep track of the tool's usage in real time. Tools in the inactive state are relatively stable, and reducing the frequency of information collection and uploading can save energy consumption of the sensing unit and data transmission resources.

[0013] More specifically, the sensing unit can use various technologies to collect location information, such as GPS positioning, Bluetooth positioning, and RFID positioning. The appropriate positioning technology is selected based on the accuracy requirements of the information collection parameters. For example, GPS positioning can be used for tools requiring high-precision positioning, while Bluetooth or RFID positioning can be used for tools in indoor environments. Sensors detect various status parameters of the maintenance tools, such as temperature, pressure, vibration, and rotation speed. Different types of maintenance tools require different status parameters. The sensing unit selects appropriate sensors for data collection based on the characteristics of the tool and the requirements of the information collection parameters.

[0014] More specifically, accurate location and status information forms the basis for subsequent analysis and management maintenance tools. By specifying the information collection format and parameters, the accuracy, completeness, and consistency of the collected data can be ensured, providing reliable data support for subsequent digital twin model analysis and determination of safety limits.

[0015] More specifically, determine the upload frequency level and security requirement level: Based on the information upload parameters, determine the upload frequency level (e.g., high, medium, low) and security requirement level (e.g., normal security, high security, etc.) of the sensing unit. Use a suitable compression algorithm to compress the collected location and status information to reduce the amount of data and improve data transmission efficiency. For example, lossless compression algorithms (e.g., LZ77, Huffman coding, etc.) can be used to compress the data. According to the security requirement level, retrieve the predetermined encryption format, encrypt the collected information, identify and convert the information features to obtain basic encrypted data, fill the basic encrypted data with obfuscated data to obtain secure encrypted data, and upload the encrypted data to the cloud server.

[0016] More specifically, the location and status information collected by maintenance tools contains a large amount of data. Directly uploading this data would consume a significant amount of network bandwidth and storage resources. Information compression can reduce the amount of data, thereby lowering transmission and storage costs. The location and status information of nuclear power maintenance tools is sensitive information, as it relates to the safety and operation of nuclear power plants. Encrypting this information securely can prevent data from being stolen or tampered with during transmission, ensuring data security and integrity. Furthermore, different encryption methods can be used according to different security requirements, ensuring both security and encryption efficiency.

[0017] The second step involves using a digital twin model on a cloud server to provide real-time feedback on the location and status information of each repair tool, and then analyzing the results to determine the safety limits of each tool.

[0018] Specifically, in the second step of the embodiment provided by this invention, the cloud server continuously receives encrypted information uploaded from the sensing units of each maintenance tool through a network interface, performs decryption and parsing processing, extracts location information and status information, and determines the activation status of the maintenance tool based on specific identifiers in the received information (such as device power status, working mode markers, etc.). For example, if the device power is on and there is a working signal output, it is determined to be in an activated state; if the power is off, it is determined to be in an inactive state. The tools are classified according to the activation status and preset rules of the maintenance task. Activated tools that are actually used at the maintenance site are classified as first-line tools; tools that are activated but not currently in use and are used as backups are classified as second-line backup tools; and inactive tools are classified as tools not involved in maintenance.

[0019] More specifically, maintenance tools with different activation statuses and uses have varying degrees of importance and usage requirements in maintenance tasks. By categorizing them, targeted management and analysis can be conducted. The status of tools used by frontline staff directly affects maintenance progress and quality, requiring close monitoring; backup tools are crucial for handling emergencies and their status also needs to be monitored in real time; tools not used in maintenance can be routinely monitored, and management resources can be allocated appropriately.

[0020] More specifically, the cloud server retrieves corresponding digital twin models from a pre-established digital twin model library based on information such as the type and model of the tools used in the first line and the backup tools in the second line. These digital twin models are virtual mappings of the actual maintenance tools and contain information on the structure, function, performance, and other aspects of the tools. The retrieved digital twin models are combined according to certain logical relationships to form a digital feedback array corresponding to the current maintenance task. This array can simulate the collaborative working relationship and mutual influence between various tools in the maintenance site.

[0021] More specifically, the digital feedback array can integrate and present the location and status information of actual maintenance tools in virtual space. Through this array, the interaction between tools and the overall operating status can be observed more intuitively, providing a comprehensive and accurate virtual environment for subsequent real-time feedback and analysis.

[0022] More specifically, the received location and status information of each maintenance tool is mapped in real time onto the corresponding digital twin model in the digital feedback array. For example, the position coordinates of the actual tool are converted into the position of the digital model in virtual space, and the temperature, pressure and other status parameters of the tool are updated into the corresponding attributes of the digital model. Based on the mapped data, the digital feedback array dynamically adjusts the display and behavior of the digital twin model so that it presents the same operating status and changes as the actual maintenance tool. For example, if the vibration frequency of the actual tool changes, the digital model will also simulate the change in vibration status accordingly.

[0023] More specifically, real-time digital feedback and live simulation allow maintenance managers to understand the actual status of maintenance tools in a virtual environment. Through this visualization, they can more quickly and accurately identify potential problems and anomalies in the tools, and take appropriate measures to deal with them in a timely manner, thereby improving maintenance efficiency and safety.

[0024] More specifically, based on the real-time simulation of the digital feedback array, the operational characteristics of each maintenance tool are identified. For example, by analyzing parameters such as the tool's vibration frequency, temperature changes, and power consumption, a pre-set AI supervision algorithm is used to evaluate these characteristics, resulting in equipment health assessment information for each maintenance tool, such as its healthy, sub-healthy, or potential fault status. Based on the equipment health assessment information of each maintenance tool, combined with the safety standards and requirements for nuclear power maintenance, the safety of each tool's operating parameters within various ranges is assessed. For example, it is analyzed whether the tool's temperature, pressure, and speed parameters under different operating conditions are within safe ranges, and whether the changing trends of these parameters will affect the safe operation of the tool. By combining the equipment health assessment information and the safety analysis results, the safety limits of each maintenance tool are determined, including the upper and lower limits of operating parameters and suitable operating ranges.

[0025] More specifically, the health status of equipment directly affects its safety and reliability during maintenance. By conducting health status assessments and safety analyses of various maintenance tools, potential safety hazards can be identified in advance, allowing for timely prevention and mitigation measures. Determining safety limitations provides a basis for subsequent access control and usage management, ensuring that maintenance tools operate under safe conditions and guaranteeing the smooth progress of nuclear power plant maintenance tasks.

[0026] The third step is to send corresponding permission instructions to each repair tool according to the aforementioned safety restriction range, so as to assign usage permissions within the safety restriction range to the repair tools.

[0027] Specifically, in the third step of the embodiment provided by the present invention, the parameters in the safety limit range (such as the upper limit of temperature, pressure range, speed range, etc.) are compared with the parameters of various preset working modes of the maintenance tool. For example, if the safety limit stipulates that the temperature cannot exceed 60°C, then check whether the temperature parameters of the tool during operation in each working mode meet the requirement. Based on the parameter matching results, all working modes that meet the safety limit range are selected. These working modes are the available working modes of the maintenance tool under the current safety limit.

[0028] More specifically, different working modes will cause maintenance tools to exhibit different operating states and performance characteristics, and their corresponding working parameters will also be different. By analyzing the matching between the safety limit range and the working mode, we can determine the working methods that the tool can adopt under safe conditions, avoid the tool exceeding the safety limit due to inappropriate working modes, and thus ensure the safe operation of the tool and the smooth progress of maintenance tasks.

[0029] More specifically, a set of instruction encoding rules is pre-defined, which corresponds different working modes with specific codes. For example, working mode A corresponds to code "001", working mode B corresponds to code "002", etc. Based on the selected available working modes, the corresponding instruction codes are generated according to the encoding rules. Then, these instruction codes are sent as authorization instructions to the corresponding maintenance tools through wireless communication technologies (such as Wi-Fi, Bluetooth, ZigBee, etc.).

[0030] More specifically, maintenance tools can typically only recognize instruction signals in a specific format. Converting available operating modes into instruction codes ensures that maintenance tools accurately understand and execute authorized instructions. At the same time, adopting unified encoding rules can improve the efficiency of instruction transmission and processing, and facilitate centralized management and control of multiple maintenance tools by the system.

[0031] More specifically, after receiving an authorization command, the maintenance tool parses the command code and identifies the corresponding available operating mode. Based on the parsing results, the maintenance tool's control system automatically adjusts the tool's operating parameters and operating status, switching the operating mode to the available operating mode specified by the authorization command. For example, if the received command code corresponds to operating mode C, the tool will adjust parameters such as motor speed and power output to enter the operating state of operating mode C. After the operating mode switch is completed, the maintenance tool's operating parameters are continuously monitored to ensure that they are always within the safe limits. If the parameters are found to exceed the safe limits, adjustments are made in a timely manner or an alarm is issued.

[0032] More specifically, the ultimate goal of authorization commands is to ensure that maintenance tools operate within safe limits. By adjusting the working mode according to the commands, the tool's operating status can be matched with safety requirements, reducing safety risks caused by abnormal working parameters. At the same time, real-time monitoring of working parameters can promptly detect and correct any deviations that may occur, further ensuring the safe and stable operation of the tool.

[0033] More specifically, considering that maintenance tasks usually require the collaborative work of multiple maintenance tools, a comprehensive analysis and combination of the available working modes of each maintenance tool is conducted. For example, the compatibility and complementarity between the available working modes of different tools are analyzed to find the best combination of modes that can improve maintenance efficiency and quality. Based on the results of the mode combination analysis, combined with the specific requirements and objectives of the maintenance task, usage suggestions for each maintenance tool are generated. These suggestions include the order of tool use, working time, and collaborative methods. The generated usage suggestions are sent to the maintenance personnel's smart terminals (such as mobile data networks, local area networks, etc.) via networks (such as mobile data networks, local area networks, etc.) and displayed on the terminals with an intuitive interface.

[0034] More specifically, maintenance personnel often struggle to understand the specific health status of various maintenance tools during actual operation, making it unclear how to best use each tool under safety limitations. Providing usage suggestions can help maintenance personnel use maintenance tools more scientifically and rationally, improving the efficiency and quality of maintenance work. At the same time, sending suggestions to smart terminals allows maintenance personnel to view and refer to them on-site at any time, providing timely guidance for maintenance work.

[0035] As can be seen from the above technical solution, the intelligent method for nuclear power plant maintenance tools provided by the present invention has the following beneficial effects: Real-time collection of location and status information allows maintenance personnel to keep abreast of tool dynamics and quickly locate tools. With the help of digital twin model analysis, the safety limit range can be accurately determined, potential risks can be detected in advance, and usage permissions can be allocated based on this range to avoid improper use of tools and reduce the probability of safety accidents. In addition, intelligent management improves maintenance efficiency, reduces the time spent manually searching for tools and judging their status, and ensures that nuclear power plant maintenance work is carried out safely, efficiently, and in an orderly manner.

[0036] Furthermore, the step of collecting the location and status information of each repair tool in real time through sensing units pre-installed on each tool includes: S11: Sensing the activation status of each maintenance tool, and adjusting the monitoring mode of the sensing unit pre-set on the maintenance tool according to the activation status, so as to deploy the information acquisition parameters and information upload parameters of the sensing unit. S12: Based on the information acquisition parameters, drive the sensing unit to acquire the position and status information of the maintenance tool in a specified information acquisition form, and obtain the position and status information of the maintenance tool; S13: Based on the information upload parameters, the sensing unit is driven to compress and securely encrypt the collected location and status information, and then upload it to the cloud server.

[0037] Specifically, the sensing unit can determine the activation status by detecting the power circuit status of the repair tool. For example, when the tool is powered on, there is current flowing inside it. The sensing unit uses a current sensor to sense the current signal. If a valid current value is detected, the tool is determined to be in an active state. If the current value is zero, it is in an inactive state. The control system of the repair tool has a software flag bit to indicate its own working status. The sensing unit communicates with the control system and reads the flag bit information to determine the activation status.

[0038] More specifically, if the tool is active, adjust the monitoring mode of the sensing unit to high-frequency monitoring. For information acquisition parameters, increase the acquisition frequency, for example, from once per minute to once per second, while also improving acquisition accuracy. Regarding information upload parameters, accelerate the upload frequency, for example, from uploading data once every 5 minutes to once per minute, and use a more detailed data format for upload. Then, adjust the monitoring mode to low-frequency monitoring. Alternatively, reduce the information acquisition frequency, such as once every 5 minutes, and relax the acquisition accuracy requirements; simultaneously, extend the information upload interval, such as once every 30 minutes, and use a simpler data format to save resources.

[0039] More specifically, when activated, the maintenance tool is engaged in actual maintenance work, and its location and status may change at any time. These changes are crucial for maintenance personnel and the system to understand the tool's usage and ensure maintenance safety. Therefore, high-frequency, high-precision information collection and rapid uploading are required to keep track of the tool's dynamics in a timely manner. In the inactive state, the tool is in an idle stage, and its status is relatively stable. It does not require too frequent information collection and uploading. Lowering the monitoring level can effectively reduce the energy consumption of the sensing unit and the network resources occupied by data transmission.

[0040] More specifically, for maintenance tools used in outdoor environments or large spaces, the sensing unit can be equipped with a GPS module. The GPS module receives satellite signals and calculates the tool's precise geographical coordinates (longitude, latitude, and altitude) as location information. When the maintenance tool is used indoors, technologies such as Bluetooth positioning, Wi-Fi positioning, or RFID positioning are used. For example, multiple Bluetooth beacons can be placed indoors, and the sensing unit can calculate its own indoor position by receiving the signal strength of different beacons and using a triangulation algorithm.

[0041] More specifically, various sensors are used to collect physical state information of tools. For example, temperature sensors are used to measure the temperature of key parts of the tool, pressure sensors are used to detect the pressure of hydraulic or pneumatic systems, and vibration sensors are used to monitor the vibration of the tool during operation. By communicating with the control system of the maintenance tool, the working status information of the tool can be obtained, such as the motor speed, the on / off status of switches, and the working mode.

[0042] More specifically, accurate location information helps to understand the distribution of maintenance tools, making it easier for maintenance personnel to quickly find the tools they need. It also helps to monitor whether the tools are within the designated work area. Status information reflects the operating status of the tools, helping to determine whether the tools are working properly and whether there are potential faults. Timely acquisition of this information can provide important basis for subsequent maintenance decisions, tool management and fault warning.

[0043] More specifically, when the data volume is not particularly large and the data accuracy requirement is high, lossless compression algorithms, such as the LZ77 algorithm, are used. This algorithm reduces data storage space by finding repeated strings and replacing the repeated parts with pointers to the positions where they appeared previously. If the data contains some information that does not require high accuracy, lossy compression algorithms, such as the JPEG algorithm (suitable for status information in image data form, if present), can be used. This algorithm achieves a higher compression ratio by removing information that is not sensitive to the human eye.

[0044] More specifically, based on the security requirements determined by the information upload parameters, a suitable symmetric encryption algorithm, such as AES (Advanced Encryption Standard), is selected. The sensing unit and the cloud server share an encryption key beforehand. The sensing unit uses this key to encrypt the compressed data, converting plaintext data into ciphertext data. Alternatively, an asymmetric encryption algorithm, such as RSA, can be used. The cloud server generates a public and private key pair and sends the public key to the sensing unit. The sensing unit uses the public key to encrypt the data. The encrypted data can only be decrypted by the cloud server using the corresponding private key. The sensing unit uploads the encrypted data to the cloud server via a network (such as Wi-Fi, mobile data network, etc.) according to the upload frequency and data format specified in the information upload parameters.

[0045] More specifically, information compression can reduce data size, decrease the time and network bandwidth required for data transmission, and improve upload efficiency. In nuclear power plant maintenance scenarios, data is sensitive and important. Secure encryption can prevent data from being stolen or tampered with during transmission, ensuring data security and integrity, and ensuring that the cloud server receives true and accurate information, providing a reliable foundation for subsequent data analysis and decision-making.

[0046] Furthermore, the step of driving the sensing unit to compress and securely encrypt the collected location and status information based on the information upload parameters includes: S131: Determine the upload frequency level and security requirement level of the sensing unit based on the information upload parameters; S132: Retrieve a predetermined encryption format according to the security requirement level to identify and convert the information features of the collected location information and status information to obtain basic encrypted data; S133: The basic encrypted data is filled with obfuscated data to obtain secure encrypted data.

[0047] Specifically, the information upload parameters usually specify the upload time interval or frequency requirements. Based on these requirements, the upload frequency is divided into different levels, such as high frequency (e.g., once per minute), medium frequency (e.g., once every 5 minutes), and low frequency (e.g., once every 30 minutes). The sensing unit determines its corresponding upload frequency level based on the information upload parameters it receives.

[0048] More specifically, the information upload parameters include descriptions of data security, such as the sensitivity of the data and the security requirements of the usage scenario. Based on these descriptions, security requirements are divided into different levels, such as ordinary security (applicable to general data), higher security (applicable to data containing some sensitive information), and high security (applicable to data involving core secrets or critical security). The sensing unit determines its security requirement level based on the information upload parameters.

[0049] More specifically, different maintenance scenarios and data uses have different requirements for upload frequency and security. Determining the upload frequency level can reasonably allocate network resources and the energy consumption of sensing units, avoiding excessive uploads that would waste resources while ensuring data timeliness. Determining the security requirement level is for selecting appropriate encryption methods to ensure that the security of data during transmission matches the sensitivity of the data, preventing sensitive data from being leaked or tampered with.

[0050] More specifically, based on the determined security requirement level, the appropriate encryption algorithm and key length are selected from a pre-defined encryption format library. For example, for a normal security level, a simpler symmetric encryption algorithm, such as DES (Data Encryption Standard), is selected with a key length of 56 bits; for a higher security level, AES (Advanced Encryption Standard) is selected with a key length of 128 bits; and for a very high security level, an asymmetric encryption algorithm, such as RSA, is used in conjunction with a longer key length.

[0051] More specifically, the collected location and status information is analyzed to identify its data type, format, and characteristics. According to the requirements of the selected encryption format, this information is converted into a form suitable for encryption processing. For example, the location information in text format is converted into binary data for encryption operations. Then, the selected encryption algorithm is used to encrypt the converted data to obtain basic encrypted data.

[0052] More specifically, different security requirements necessitate different levels of encryption protection. By retrieving a predetermined encryption format based on the security requirement level, it can be ensured that the strength of data encryption is commensurate with the sensitivity of the data. The identification and transformation of information features are to enable the data to meet the input requirements of the encryption algorithm, ensuring the smooth progress of the encryption process, thereby transforming the original data into basic encrypted data that is difficult to crack and improving data security.

[0053] More specifically, based on the security requirement level and data characteristics, a certain amount of random data is generated as obfuscated data. The length and format of the obfuscated data can be adjusted according to the specific situation. Generally speaking, the higher the security requirement level, the longer the obfuscated data. The generated obfuscated data is inserted into the basic encrypted data to disrupt the original structure and characteristics of the data. This can be done by random insertion, insertion at fixed intervals, or other methods. For example, a piece of obfuscated data is inserted every certain number of bytes to make the encrypted data more complex and difficult to analyze.

[0054] More specifically, even if the basic encrypted data has been encrypted, attackers can try to crack the encryption by analyzing certain features or patterns of the data. By filling in obfuscated data, the complexity and randomness of the data can be further increased, masking the true characteristics and structure of the data, making it more difficult for attackers to obtain useful information from the encrypted data, thereby improving the security of the data during transmission and storage, and obtaining secure encrypted data with a higher level of security.

[0055] Furthermore, the steps involved in using a cloud-based digital twin model to provide real-time feedback on the location and status information of each repair tool, and analyzing this information to determine the safety limits for each tool, include: S21: Based on the location and status information uploaded by each repair tool through the cloud server, determine the activation status of each repair tool in this repair task, and classify each repair tool into first-line tools, second-line backup tools, and tools not involved in the repair based on the activation status. S22: Schedule the digital twin models of the first-line tools and the second-line backup tools to form a digital feedback array corresponding to this maintenance task; S23: Based on the digital feedback array, the received position information and status information are given real-time digital feedback so that the digital feedback content of the digital feedback array is switched to the real-time simulation form of each corresponding maintenance tool. S24: Based on the real-time simulation of the digital feedback array, assess the health status of each maintenance tool to perform a safety analysis on each maintenance tool and obtain the safety limit range of each maintenance tool.

[0056] Specifically, the cloud server receives encrypted and compressed data uploaded by the sensor units of each maintenance tool through a network interface, decrypts and decompresses the data, and extracts location information (such as latitude and longitude, indoor coordinates, etc.) and status information (such as temperature, pressure, operating status indicators, etc.). The activation status of the maintenance tool is determined based on specific indicators in the status information. For example, if the status information contains indicators such as device power on or working mode activated, the tool is determined to be in an activated state; otherwise, it is in an inactive state.

[0057] More specifically, tools are classified according to their activation status and the preset rules for maintenance tasks. Tools that are currently being used on-site and are in an activated state are classified as first-line tools; tools that are activated but not currently in use and are kept as emergency reserves are classified as second-line backup tools; and tools that are not activated are classified as tools not being used for maintenance.

[0058] More specifically, maintenance tools with different activation states and uses have different importance and management needs in maintenance tasks. By classifying them, we can focus on monitoring the tools used on the front line, keep abreast of their operating status, and ensure the smooth progress of maintenance work; maintain real-time status monitoring of backup tools so that they can be quickly put into use when needed; and conduct routine management of tools not involved in maintenance, so as to rationally allocate management resources and improve management efficiency.

[0059] More specifically, the cloud server retrieves the corresponding virtual models from a pre-built digital twin model library based on information such as the model and specifications of the tools used in the first line and the backup tools in the second line. These digital twin models are precise digital mappings of the actual maintenance tools and contain detailed information such as the tools' physical structure, working principle, and performance parameters.

[0060] More specifically, the retrieved digital twin models are combined according to certain logical relationships to form a digital feedback array corresponding to the current maintenance task. This array can simulate the spatial positional relationship, collaborative working relationship, and mutual influence between various tools in the maintenance site.

[0061] More specifically, the digital feedback array can fully present the overall operation of the first-line tools and the second-line backup tools in virtual space. Through this array, the status and interaction of each tool in the maintenance task can be observed intuitively, providing a comprehensive and accurate virtual environment for subsequent real-time feedback and analysis, which helps to manage and optimize the maintenance process more efficiently.

[0062] More specifically, the received location and status information of each maintenance tool is mapped in real time onto the corresponding digital twin model in the digital feedback array. For example, the latitude and longitude coordinates of the actual tool are converted into the location coordinates of the digital model in virtual space, and the temperature, pressure and other status parameters of the tool are updated into the corresponding attributes of the digital model. Based on the mapped data, the digital feedback array dynamically adjusts the display and behavior of the digital twin model so that it presents the same operating status and changes as the actual maintenance tool. For example, if the vibration frequency of the actual tool changes, the digital model will also simulate the change in vibration status accordingly.

[0063] More specifically, real-time digital feedback and live simulation allow maintenance managers to intuitively understand the actual operating status of maintenance tools in a virtual environment. Through this visualization method, they can promptly identify any abnormalities or potential problems with the tools, take preventative measures to address them, avoid the occurrence and escalation of faults, and improve the safety and efficiency of maintenance work.

[0064] More specifically, by using preset evaluation algorithms and rules, the tool status reflected by each digital twin model in the digital feedback array is analyzed. For example, by monitoring the changing trends of parameters such as temperature, pressure, and vibration of the tool and comparing them with the parameter range under normal operating conditions, the health status of the tool can be judged. The health status can be divided into different levels such as good, sub-healthy, potential for failure, and failure.

[0065] More specifically, based on the equipment health status assessment results and in conjunction with the safety standards and specifications for nuclear power plant maintenance, a safety analysis is conducted on each maintenance tool. Factors considered include the impact of tool failure on maintenance personnel, equipment, and the entire nuclear power system, and the safety risks of the tools under different operating conditions are assessed.

[0066] More specifically, based on the results of comprehensive health status assessment and safety analysis, safety limits are determined for each maintenance tool. These safety limits include the upper and lower limits of operating parameters (such as upper temperature limit, pressure range, speed range, etc.), environmental conditions (such as humidity range, altitude limit, etc.), and operating procedure requirements.

[0067] More specifically, the health status of equipment is directly related to its safety and reliability during maintenance. By assessing the health status of equipment and conducting safety analysis, we can gain a comprehensive understanding of the operating conditions and potential risks of each maintenance tool. Determining the safety limits can provide clear operational guidance for maintenance personnel, ensuring that tools operate under safe conditions, avoiding safety accidents caused by tool failure or improper operation, and guaranteeing the smooth progress of nuclear power maintenance work and the safe and stable operation of the nuclear power system.

[0068] Furthermore, the steps of assessing the health status of each maintenance tool based on the real-time simulation of the digital feedback array, and conducting a safety analysis of each maintenance tool to obtain the safety limits of each maintenance tool, include: S241: Based on the real-time simulation of the digital feedback array, the equipment operation performance characteristics of each maintenance tool are identified, and the equipment health status is evaluated based on the identification results using a preset AI supervision algorithm to obtain the equipment health assessment information of each maintenance tool. S242: Based on the equipment health assessment information of each maintenance tool, conduct a safety assessment of the working parameters of each maintenance tool within a certain range to obtain the safety limit range of each maintenance tool.

[0069] Specifically, data related to the operating performance of each maintenance tool is extracted from the real-time simulation of the digital feedback array. For example, for power tools, parameters such as current, voltage, speed, vibration frequency, and temperature are extracted; for hydraulic tools, parameters such as pressure and flow rate are extracted. The extracted data is analyzed to identify features that reflect the operating status of the equipment. For example, by analyzing the variation pattern of vibration frequency, it can be determined whether there is an imbalance or looseness problem in the equipment; by observing the rising trend of temperature, the heat dissipation of the equipment can be evaluated.

[0070] More specifically, based on the type and characteristics of the maintenance tools, select appropriate AI supervision algorithms, such as neural network algorithms, decision tree algorithms, support vector machine algorithms, etc. Before using the algorithm for evaluation, train the algorithm model using historical data. Historical data includes various parameter data when the equipment is running normally and when it malfunctions. Through training, the model learns the relationship between the health status of the equipment and its operating parameters.

[0071] More specifically, the identified equipment operation characteristics are input into a trained AI supervised algorithm model. The model evaluates the health status of the equipment based on preset rules and learned knowledge, and outputs evaluation results, such as different levels like healthy, sub-healthy, potential for failure, and failure, and gives corresponding evaluation scores or confidence levels.

[0072] More specifically, equipment operational characteristics are a direct indicator of equipment health status. By identifying these characteristics, the complex operating states of equipment can be transformed into specific, analyzable parameters, providing a foundation for subsequent health assessments. AI-supervised algorithms possess powerful data analysis and pattern recognition capabilities, enabling them to process large amounts of operational data and uncover potential patterns and anomalies. Utilizing pre-set AI-supervised algorithms for health status assessments can improve the accuracy and objectivity of the evaluation, avoid the subjectivity and limitations of human judgment, and promptly identify potential equipment problems.

[0073] More specifically, based on the type and purpose of the maintenance tools, determine the safety-related working parameter evaluation indicators, such as temperature, pressure, speed, and torque. Refer to the equipment's design specifications, industry standards, and past operating experience to establish corresponding safety standards and threshold ranges for each evaluation indicator. For example, for a certain motor, the temperature range for normal operation is specified as 30℃ - 60℃, and exceeding 60℃ may pose a safety risk.

[0074] More specifically, the equipment health assessment information of each maintenance tool is compared and analyzed with the assessment standards. If the equipment health assessment information shows that the equipment is in a healthy state, the safe range of working parameters can be appropriately relaxed; if the equipment has potential faults or has already malfunctioned, the working parameters need to be strictly limited, or even the equipment should be stopped.

[0075] More specifically, taking into account the safety assessment results of various evaluation indicators, a safety limit range is determined for each maintenance tool. The safety limit range can be expressed in specific numerical ranges, such as a temperature range of 20℃ - 55℃, a pressure range of 1 - 5MPa, etc., or it can be described in words, such as "Under the current healthy condition, the speed shall not exceed 80% of the rated speed".

[0076] More specifically, equipment health assessment information is only a comprehensive evaluation of the overall condition of the equipment, while the safety of operating parameters is directly related to the reliability and stability of the equipment during operation. By conducting a safety assessment of operating parameters, we can gain a more specific understanding of the equipment's safety status under different parameters. Determining the safety limit range can provide clear operating guidance for maintenance personnel, ensuring that the equipment operates within a safe parameter range and avoiding equipment damage, malfunctions, or even safety accidents caused by operating parameters exceeding the safe range. At the same time, dynamically adjusting the safety limit range according to the equipment's health status can maximize the equipment's performance while ensuring safety.

[0077] Furthermore, the step of sending corresponding permission instructions to each repair tool according to the aforementioned security restriction range, in order to assign usage permissions within the security restriction range to the repair tools, includes: S31: Analyze the working modes of the maintenance tool according to the safety limit range to obtain the available working modes of the maintenance tool that match the safety limit range; S32: Generate corresponding instruction codes based on the available working modes, and send them to the maintenance tools as authorization instructions; S33: Adjust the working mode of the maintenance tool according to the permission instruction so that the working parameters of the maintenance tool are within the safe limit range.

[0078] Specifically, maintenance tools typically have multiple preset operating modes, each corresponding to different combinations of operating parameters. A detailed analysis of all operating modes of the maintenance tool is conducted, identifying the key operating parameters for each mode, such as speed, power, and pressure. Each parameter within the safety limits is compared with the parameters of each operating mode. For example, if the safety limits stipulate that the temperature should not exceed 60℃ and the power should be between 200 and 500 watts, then it is necessary to check whether the temperature and power of the tool under each operating mode are within these ranges. Based on the parameter matching results, all operating modes that meet the safety limits are selected. These modes are the usable operating modes of the maintenance tool under the current safety limits.

[0079] More specifically, different working modes will cause maintenance tools to exhibit different operating states and performance characteristics, and their corresponding working parameters will also be different. By analyzing the matching between the safety limit range and the working mode, we can determine the working methods that the tool can adopt under safe conditions, avoid the tool exceeding the safety limit due to inappropriate working modes, and thus ensure the safe operation of the tool and the smooth progress of maintenance tasks.

[0080] More specifically, a set of instruction encoding rules is pre-defined, which associates different working modes with specific codes. For example, working mode A corresponds to code "001", working mode B corresponds to code "002", etc. The encoding rules must ensure uniqueness and readability to facilitate system recognition and tool parsing. Based on the selected available working modes, the corresponding instruction codes are generated according to the encoding rules. If the available working modes are working mode C and working mode D, then the corresponding codes "003" and "004" are generated. The generated instruction codes are sent as authorized instructions to the maintenance tools through wireless communication technologies (such as Wi-Fi, Bluetooth, ZigBee, etc.). Before sending, the instructions need to be encrypted and verified to ensure the security and accuracy of instruction transmission.

[0081] More specifically, maintenance tools can typically only recognize instruction signals in a specific format. Converting available operating modes into instruction codes ensures that maintenance tools accurately understand and execute authorized instructions. At the same time, adopting unified encoding rules can improve the efficiency of instruction transmission and processing, and facilitate centralized management and control of multiple maintenance tools by the system.

[0082] More specifically, after receiving an authorization command, the maintenance tool parses the command code to identify the corresponding available operating mode. The tool's internal control system then converts the command code into specific operating mode information according to preset encoding rules. Based on the parsing results, the control system automatically adjusts the tool's operating parameters and status, switching the operating mode to the available mode specified by the authorization command. For example, if the received command code corresponds to operating mode E, the tool will adjust parameters such as motor speed, power output, and pressure to enter operating mode E. After the operating mode switch is complete, the tool's operating parameters are continuously monitored to ensure they remain within safe limits. Built-in sensors collect operating parameters in real time and compare them with the safe limits. If parameters are found to exceed the safe limits, adjustments are made promptly or an alarm is issued.

[0083] More specifically, the ultimate goal of authorization commands is to ensure that maintenance tools operate within safe limits. By adjusting the working mode according to the commands, the tool's operating status can be matched with safety requirements, reducing safety risks caused by abnormal working parameters. At the same time, real-time monitoring of working parameters can promptly detect and correct any deviations that may occur, further ensuring the safe and stable operation of the tool.

[0084] Furthermore, it also includes: after determining the available working modes of each of the maintenance tools, combining the available working modes of each of the maintenance tools to analyze and generate usage suggestions for each of the maintenance tools in this maintenance task, and sending them to the maintenance personnel's smart terminal for display.

[0085] Specifically, collect information on the available operating modes of each repair tool, including key parameters (such as power, speed, torque, temperature range, etc.) for each operating mode, applicable scenarios (such as different types of repair operations, different material handling, etc.), and switching conditions between operating modes. At the same time, obtain detailed information on this repair task, such as repair objectives, repair steps, time requirements, quality standards, etc.

[0086] More specifically, when faced with complex repair tasks and multiple working modes, maintenance personnel may find it difficult to quickly determine the best way to use tools. By providing usage suggestions, maintenance personnel can be helped to quickly select the appropriate working mode and operation sequence, reducing the time spent on trial and error, thereby improving the overall efficiency of maintenance work.

[0087] More specifically, consider the collaborative working relationship between various repair tools, and analyze how the available working modes of different tools can be combined to improve repair efficiency and quality. For example, when performing a repair task involving mechanical assembly and electrical connection, analyze how the available working modes of electric screwdrivers and multimeters can be matched to ensure that mechanical assembly and electrical testing can be smoothly connected.

[0088] More specifically, the available operating modes of each tool are matched with the various steps of the maintenance task. This involves determining which combination of operating modes is best suited for completing a specific maintenance task. For example, for steps requiring precise drilling, the compatibility of different speed and torque modes of the drilling tool with that step is analyzed.

[0089] More specifically, considering the rational use of resources, such as energy consumption and tool wear, we should select a combination of working modes that can minimize resource consumption and maximize tool life while meeting the requirements of maintenance tasks. For example, we should prioritize working modes with lower power consumption but still able to complete the task in order to reduce energy consumption.

[0090] More specifically, reasonable combinations of working modes and operational recommendations can ensure that maintenance tools operate in optimal condition, making maintenance operations more precise and standardized. This helps improve maintenance quality, reduce maintenance errors and quality problems caused by improper operation or incorrect selection of working modes, and ensure the safe and stable operation of nuclear power equipment.

[0091] More specifically, based on the results of the pattern analysis, specific usage suggestions are generated for each maintenance tool. These suggestions should include the order of tool use, selection of working mode, and precautions. For example, it is suggested to first use an electric wrench to tighten the bolt in mode A, and then use a torque wrench in mode B for precise calibration. It also reminds users to pay attention to temperature changes when using a certain tool to avoid overheating due to prolonged continuous operation. The usage suggestions should be organized into a clear and easy-to-understand text format, with necessary charts or diagrams to help maintenance personnel better understand them.

[0092] More specifically, the rational use of resources is crucial in nuclear power plant maintenance. The usage recommendations take into account resource optimization factors, which can guide maintenance personnel to choose working modes with low energy consumption and minimal tool wear, thereby reducing maintenance costs, extending tool life, and achieving sustainable resource utilization.

[0093] More specifically, the generated usage suggestions are sent to the maintenance personnel's smart terminals (such as mobile phones, tablets, etc.) via wireless communication technologies (such as Wi-Fi, Bluetooth, mobile data networks, etc.). A dedicated application is developed on the smart terminal or a message push function is used to ensure that the maintenance personnel can receive the usage suggestions in a timely manner and display them in an intuitive interface.

[0094] More specifically, the operational precautions included in the usage recommendations can remind maintenance personnel to pay attention to the safe use requirements of the tools and avoid safety accidents caused by improper operation. Especially in fields with extremely high safety requirements such as nuclear power plant maintenance, timely and accurate usage recommendations can provide strong protection for the personal safety of maintenance personnel and the safety of equipment.

[0095] Based on the technical content of the intelligent method for nuclear power plant maintenance tools described in the above-disclosed embodiments, the present invention provides an intelligent system for nuclear power plant maintenance tools, the structure of which is as follows: Figure 2 A method for implementing intelligent nuclear power plant maintenance tools according to any one of the first aspects, comprising: The information acquisition module is used to collect the location and status information of each repair tool in real time through the sensing units pre-set on each repair tool; The safety analysis module is used to provide real-time feedback on the location and status information of each maintenance tool based on the digital twin model of the cloud server, and to analyze and obtain the safety limit range of each maintenance tool. The permission sending module is used to send corresponding permission instructions to each repair tool according to the security restriction range, so as to assign the repair tool the usage permission within the security restriction range.

[0096] In this embodiment, the specific implementation of each module in the above system embodiment is described in the above method embodiment, and will not be repeated here.

[0097] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other.

[0098] The steps of the methods or algorithms described in conjunction with the embodiments disclosed herein can be implemented directly by hardware, a software module executed by a processor, or a combination of both. The software module can be located in random access memory (RAM), main memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium known in the art.

[0099] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. An intelligent method for nuclear power plant maintenance tools, characterized in that, include: The location and status information of each repair tool are collected in real time by sensing units pre-set on each repair tool. The digital twin model based on the cloud server provides real-time feedback on the location and status information of each maintenance tool, and analyzes the results to determine the safety limits of each maintenance tool. Based on the aforementioned safety restrictions, corresponding permission instructions are sent to each repair tool to assign usage permissions within the safety restrictions to the repair tools.

2. The intelligent method for nuclear power plant maintenance tools as described in claim 1, characterized in that, The steps of collecting the location and status information of each repair tool in real time through sensing units pre-installed on each repair tool include: The system senses the activation status of each maintenance tool and adjusts the monitoring mode of the sensing unit pre-set on the maintenance tool according to the activation status, so as to deploy the information acquisition parameters and information upload parameters of the sensing unit. Based on the information acquisition parameters, the sensing unit is driven to acquire the location and status information of the maintenance tool in a specified information acquisition format, thereby obtaining the location and status information of the maintenance tool. Based on the information upload parameters, the sensing unit compresses and securely encrypts the collected location and status information, and uploads it to the cloud server.

3. The intelligent method for nuclear power plant maintenance tools as described in claim 2, characterized in that, The steps for driving the sensing unit to compress and securely encrypt the acquired location and status information based on the information upload parameters include: The upload frequency level and security requirement level of the sensing unit are determined based on the information upload parameters. According to the security requirement level, a predetermined encryption format is retrieved to identify and convert the information features of the collected location and status information to obtain basic encrypted data. The basic encrypted data is filled with obfuscated data to obtain secure encrypted data.

4. The intelligent method for nuclear power plant maintenance tools as described in claim 1, characterized in that, The steps involved in using a cloud-based digital twin model to provide real-time feedback on the location and status information of each repair tool, and analyzing this information to determine the safety limits for each tool, include: Based on the location and status information uploaded by each repair tool by the cloud server, the activation status of each repair tool in this repair task is determined, and the repair tools are divided into first-line tools, second-line backup tools, and tools not involved in the repair according to the activation status. The digital twin models of the first-line tools and the second-line backup tools are used to form a digital feedback array corresponding to this maintenance task; Based on the digital feedback array, the received position information and status information are fed back in real time, so that the digital feedback content of the digital feedback array is switched to the real-time simulation form of each corresponding maintenance tool. The health status of each maintenance tool is assessed based on the real-time simulation of the digital feedback array, and a safety analysis is performed on each maintenance tool to obtain the safety limit range of each maintenance tool.

5. The intelligent method for nuclear power plant maintenance tools as described in claim 4, characterized in that, The steps for assessing the health status of each maintenance tool based on a real-time simulation of the digital feedback array, and for conducting a safety analysis of each maintenance tool to determine its safety limits, include: Based on the real-time simulation of the digital feedback array, the equipment operation performance characteristics of each maintenance tool are identified, and the equipment health status is evaluated based on the identification results using a preset AI supervision algorithm, thereby obtaining equipment health assessment information for each maintenance tool. Based on the equipment health assessment information of each maintenance tool, a safety assessment of the operating parameters of each maintenance tool is conducted to determine the safety limits of each maintenance tool.

6. The intelligent method for nuclear power plant maintenance tools as described in claim 1, characterized in that, The steps of sending corresponding permission instructions to each repair tool according to the aforementioned security restrictions to assign usage permissions within the security restrictions to the repair tools include: The working modes of the maintenance tool are analyzed according to the safety limit range to obtain the available working modes of the maintenance tool that match the safety limit range; Based on the available working modes, corresponding instruction codes are generated to be sent to the maintenance tools as authorization instructions; The operating mode of the repair tool is adjusted according to the permission instructions, so that the operating parameters of the repair tool are within the safe limit range.

7. The intelligent method for nuclear power plant maintenance tools as described in claim 6, characterized in that, Also includes: After determining the available working modes of each of the aforementioned maintenance tools, the available working modes of each maintenance tool are combined to analyze and generate usage suggestions for each of the aforementioned maintenance tools in this maintenance task, and then sent to the maintenance personnel's smart terminal for display.

8. An intelligent system for nuclear power plant maintenance tools, characterized in that: include: The information acquisition module is used to collect the location and status information of each repair tool in real time through the sensing units pre-set on each repair tool; The safety analysis module is used to provide real-time feedback on the location and status information of each maintenance tool based on the digital twin model of the cloud server, and to analyze and obtain the safety limit range of each maintenance tool. The permission sending module is used to send corresponding permission instructions to each repair tool according to the security restriction range, so as to assign the repair tool the usage permission within the security restriction range.

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