An intelligent auxiliary operation and inspection basic system and method for a hydropower station

Abstract

This invention provides a basic intelligent auxiliary operation and maintenance system and method for hydropower stations. The system includes an operation and maintenance basic data import module, a maintenance operation process database, a 3D visualization engine architecture system, an operation and maintenance operation detection module library, an operation and maintenance operation discrimination module, an operation scoring module, an early warning feedback module, a maintenance data recording module, operation and maintenance data acquisition equipment, and feedback communication equipment. The maintenance operation process database stores standard operation data in the form of spatial relationship coordinates and motion trajectory data of the corresponding process model in 3D space, and includes minimum spatial distance restrictions. The operation and maintenance operation detection module library marks prohibited areas and safe distance spaces in the 3D model according to specifications, generating compliance judgment conditions for each process. The operation scoring module uses a cascaded product scoring formula containing key coefficients of process operations to implement a veto mechanism where any violation of a process results in the overall process score being zero.

Description

Technical Field

[0001] This invention belongs to the field of intelligent operation and maintenance assistance for hydropower stations, and specifically relates to a basic system and method for intelligent auxiliary operation and maintenance of hydropower stations. Background Technology

[0002] In recent years, with social progress and economic development, the average labor cost in China has been continuously increasing, and the country's safety requirements for the operation and maintenance of various hydropower projects have also been continuously raised. Routine operation and maintenance of power stations is a crucial part of hydropower station operation and maintenance management. However, in actual operation, significant safety risks and hidden dangers often arise due to problems such as unsystematic equipment inspection and testing, lax execution of operation and maintenance procedures, difficulties in inspections under special weather conditions, unscientific maintenance plan preparation, inadequate safety supervision and personnel coordination mechanisms, and inconsistent execution of on-site work procedures. Especially in the key operational scenarios of the three types of routine inspection work mentioned above, challenges such as limited working space, numerous environmental interferences, and difficulties in real-time information synchronization are frequently encountered. Traditional manual supervision methods are insufficient for real-time, comprehensive verification and control of inspection points, operating procedures, and equipment status, resulting in blind spots and delays in safety monitoring, seriously affecting the safety and efficiency of operation and maintenance work. Summary of the Invention

[0003] To address the problems existing in the prior art, this invention provides a basic system and method for intelligent auxiliary operation and maintenance of hydropower stations, in order to solve the safety risks and hidden dangers caused by blind spots in manual supervision, lax execution of operation and maintenance procedures, and difficulties in real-time information synchronization during the operation and maintenance of existing hydropower stations.

[0004] This invention is implemented as follows: a basic intelligent auxiliary operation and maintenance system for hydropower stations, comprising: The operation and maintenance basic data import module is used to read and import the engineering BIM model data of the hydropower station. Based on the operation and maintenance operation to be performed, it queries and reads the corresponding operation process model motion trajectory data in the maintenance operation process database. After integration, it sends the data stream to the operation and maintenance operation discrimination module and the operation and maintenance operation detection module library one by one, in units of each operation process. The maintenance operation process database is used to collect and store model motion trajectory data of various operation processes related to the operation and maintenance of hydropower station electrical equipment. Each operation process is stored in the form of spatial relationship coordinates of relevant models under the corresponding process in three-dimensional space and motion trajectory data of each model. The spatial coordinate relationship and motion trajectory stored in each process operation include the minimum spatial distance limit required by each relevant model under the current process in three-dimensional space. A 3D visualization engine architecture system is used to provide 3D visualization data display and interactive operation for various application functional modules; The operation and maintenance detection module library includes multiple expandable operation and maintenance detection modules. Each module is used to read the data stream and call the corresponding supporting algorithm. According to the constraints of each operation and maintenance procedure specification, it annotates the relevant models in the corresponding operation and maintenance operation process and generates compliance judgment conditions for key points of the operation and maintenance process. The operation and maintenance data acquisition equipment is used to receive acquisition commands from the operation and maintenance operation discrimination module, collect spatial movement and operation data of various personnel, tools, and equipment in actual operation and maintenance on site, as well as data from other monitoring systems of the power plant, and then organize and summarize the data by each operation and maintenance operation link and process before sending it to the operation and maintenance operation discrimination module. The operation and maintenance discrimination module is used to compare and judge each item based on the data stream provided by the operation and maintenance basic data import module, by calling the discrimination conditions generated by each module in the operation and maintenance operation detection module library and the actual field data provided by the operation and maintenance data acquisition device. The comparison and discrimination results and corresponding discrimination data are marked on the corresponding model in three-dimensional space, and the data is integrated and sent to the operation scoring module in units of the corresponding operation and maintenance operation process. The operation scoring module is used to score each actual operation and maintenance operation on site based on the judgment results and data imported by the operation and maintenance judgment module, and send the scoring results data of each process and link operation to the early warning feedback module. The early warning feedback module is used to generate prompts and termination commands based on the scoring data of each link and process provided by the operation scoring module, and send them to the feedback communication device and the maintenance data recording module. The feedback communication device is used to receive various prompts or commands sent by the early warning feedback module and broadcast them to the on-site operation and maintenance personnel. The maintenance data recording module is used to receive various commands and complete operation and maintenance data issued by the early warning feedback module, and to organize and record the data in relation to each operation and maintenance link and process.

[0005] Furthermore, the operation and maintenance testing module library includes an operation and maintenance personnel qualification testing module, a routine equipment inspection testing module, and a special weather inspection testing module; The operation and maintenance personnel qualification detection module is used to call the operation and maintenance personnel qualification detection algorithm based on the data flow information and the built-in data tables of various national and industry standard requirements related to hydropower station operation process management, two-ticket management, and personnel qualification management, to generate compliance judgment conditions for the work ticket system detection process, operation ticket system detection process, personnel configuration process, and qualification detection process in the process system management process and personnel qualification configuration process. The equipment routine inspection and detection module is used to call the equipment routine inspection and detection algorithm based on the data stream information and the built-in data tables of various national and industry standards related to equipment routine inspection, and generate compliance judgment conditions for each process under the three links of personnel routine inspection, station equipment routine inspection and dam area routine inspection. The special weather inspection and detection module is used to generate compliance judgment conditions for each process in the three stages of thunderstorm inspection, high wind and high temperature inspection, and heavy fog inspection, based on the data stream information and the built-in data tables of various national and industry standard requirements related to power plant inspection under extreme environments, by calling the special weather inspection and detection algorithm.

[0006] Furthermore, when generating comparison and discrimination results, the operation and maintenance discrimination module first uses the data provided by the operation and maintenance basic data import module and the three-dimensional visualization engine architecture system to generate a three-dimensional model layout scheme for the current hydropower station's operation and maintenance procedures in three-dimensional space. Then, it compares the discrimination conditions generated by each module in the operation and maintenance detection module library with the spatial coordinates, coordinate areas of movement, movement trajectory forms, movement duration, equipment operating status, and environmental factors of each person, equipment, and tool on site collected by the operation and maintenance data acquisition equipment. Based on the degree of difference between the actual on-site operation data and the discrimination conditions, it analyzes and calculates to generate various discrimination results and data.

[0007] A method for intelligent auxiliary operation and maintenance of hydropower stations based on the above system includes the following steps: Step a: Import the BIM model of the hydropower station project using the operation and maintenance basic data import module, call the maintenance operation process database and the operation and maintenance operation detection module database, integrate the data according to each maintenance operation link and process, and provide a three-dimensional visualization interface. Step b: The operation and maintenance operator issues an operation and maintenance start command through the operation and maintenance operation judgment module; Step c: The operation and maintenance judgment module calls the functions of each detection module in the operation and maintenance detection module library, and generates compliance judgment conditions for each operation and maintenance operation link and process based on the current actual operation and maintenance command of the hydropower station, environmental conditions, equipment operating status and related model data. Step d: The operation and maintenance judgment module calls the operation and maintenance data acquisition equipment to collect and organize various status data related to personnel, equipment and tools generated by various real-time operations at the operation and maintenance site, based on the actual operation and maintenance commands of the power plant. Step e: The operation and maintenance discrimination module uses the hydropower station model and process operation flow integrated data provided by the operation and maintenance basic data import module as a basis to compare and analyze the current process discrimination conditions provided by the operation and maintenance operation detection module library and various real-time operation data of the operation and maintenance site provided by the operation and maintenance data acquisition equipment, generate discrimination result data and send it to the operation scoring module. Step f: The operation scoring module calls the operation scoring algorithm to score each operation and maintenance operation, and sends the scoring result data to the early warning feedback module. Step g: The early warning feedback module generates an operation command based on the scoring data and the built-in scoring and processing method correspondence table, and sends a prompt or a termination command to the on-site operation and maintenance personnel through the feedback communication device. Step h: The maintenance data recording module records various commands and complete operation and maintenance data from the early warning feedback module; Step i, repeat steps b to h, to complete the automatic auxiliary inspection of various operation and maintenance operations of the hydropower station and its internal processes in stages, and generate relevant operation and maintenance records.

[0008] Furthermore, the operation and maintenance detection module library mentioned in step c includes an operation and maintenance personnel qualification detection module and a corresponding operation and maintenance personnel qualification detection algorithm. The execution flow of the operation and maintenance personnel qualification detection algorithm is as follows: S1. Receive the operation and maintenance start command and various power plant and operation and maintenance operation information sent by the operation and maintenance operation discrimination module, and start the algorithm process; S2. Read the basic data information of the power plant and the current operation and maintenance process command information. In the operation and maintenance personnel qualification detection module, query the national and industry standard requirements data table of the corresponding operation process and list the two key links of process system management and personnel qualification configuration. S3. By matching and analyzing the data tables according to the specifications, the indicator data list items are matched with each link, and the corresponding indicator data list items are attached to the key link attribute information in the system; the indicator data list items include, according to relevant procedures and specifications, core static data, dynamic process and status data, safety measure data, execution and closed-loop data, and statistical analysis data types; S4. List the work order system inspection process items and operation ticket system inspection process items in the process system management attribute table, and divide the corresponding indicator data list items in step S3 into the corresponding attribute information of each process item through keyword search and matching. S5. List the personnel configuration process items and qualification testing process items in the personnel qualification configuration process attribute table, and divide the corresponding indicator data list items in step S3 into the corresponding attribute information of each process item through keyword search and matching; S6. Based on the data list items contained in the process attributes of each process in steps S4 and S5, generate compliance judgment conditions for the work order system inspection process, operation ticket system inspection process, personnel allocation process, and qualification inspection process. S7. Organize, summarize and summarize the judgment conditions by each link and process. Each judgment condition is attached to the attribute information of each operation process in the data architecture. The data of each operation process is attached to the corresponding operation link. S8. Send the summarized and organized judgment conditions of each link and process to the operation and maintenance judgment module to end the algorithm process.

[0009] Furthermore, the operation and maintenance detection module library mentioned in step c includes a routine equipment inspection and detection module and a corresponding routine equipment inspection and detection algorithm. The execution flow of the routine equipment inspection and detection algorithm is as follows: S1. Receive the operation and maintenance start command and various power plant and operation and maintenance operation information sent by the operation and maintenance operation discrimination module, and start the algorithm process; S2. Read the basic data information of the power station and the current operation and maintenance process command information. In the equipment routine inspection and testing module, query the national and industry standard requirements data table of the corresponding operation process and list the three key links: personnel routine inspection, station equipment routine inspection, and dam area routine inspection. S3. By matching and analyzing the data tables according to the specifications, the indicator data list items are matched with each link, and the corresponding indicator data list items are attached to the key link attribute information in the system. The indicator data list items include, according to relevant regulations and specifications, personnel work status data, personnel action behavior data, tool and equipment movement data, equipment operation status data, dam area hydraulic structure data, and coordinate data of each area of ​​the power station. S4. List the compliance inspection procedures for inspection routes, personnel status, work focus, and inspection environment in the personnel routine inspection process attribute table. Use keyword search and matching to classify the corresponding indicator data list items in step S3 into the corresponding attribute information of each procedure item. S5. List the equipment malfunction detection process item, oil-filled equipment detection process item, rotating equipment detection process item, and equipment overload detection process item in the equipment routine inspection process attribute table within the station. Use keyword search matching to classify the corresponding indicator data list items in step S3 into the corresponding attribute information of each process item. S6. List the hydraulic structure maintenance procedures and the metal structure equipment maintenance procedures in the dam area routine inspection process attribute table. Use keyword search and matching to divide the corresponding indicator data list items in step S3 into the corresponding attribute information of each procedure item. S7. Based on the data list items contained in the process attributes of each process in steps S4, S5, and S6, construct the operation and maintenance process model in three-dimensional space using the model data provided by the operation and maintenance basic data import module, read the coordinate data of each area of ​​the power plant, mark the model space areas that are prohibited from entering under the current process and operation status, as well as the safety distance space that needs to be reserved, generate the compliance judgment conditions of each process, and mark them on the corresponding model in three-dimensional space. S8. Organize, summarize and summarize the judgment conditions by each link and process. Each judgment condition is attached to the attribute information of each operation process in the data architecture. The data of each operation process is attached to the corresponding operation link. S9. Send the summarized and organized judgment conditions of each link and process to the operation and maintenance judgment module to end the algorithm process.

[0010] Furthermore, the operation and maintenance detection module library mentioned in step c includes a special weather inspection and detection module and a corresponding special weather inspection and detection algorithm. The execution flow of the special weather inspection and detection algorithm is as follows: S1. Receive the operation and maintenance start command and various power plant and operation and maintenance operation information sent by the operation and maintenance operation discrimination module, and start the algorithm process; S2. Read the basic data information of the power station and the current operation and maintenance process command information. In the special weather inspection and detection module, query the national and industry standard requirements data table of the corresponding operation process and list the three key links of thunderstorm inspection, strong wind and high temperature inspection, and fog inspection. S3. By matching and analyzing the data tables according to the specifications, the indicator data list items are matched with each link, and the corresponding indicator data list items are attached to the key link attribute information in the system; the indicator data list items include, according to relevant procedures and specifications, the following: calibration data of safe areas for operation and maintenance in extreme weather, data on personnel protection measures in extreme weather, monitoring data of key equipment in extreme weather, and environmental monitoring data in extreme weather. S4. List the compliance testing procedures for thunderstorm inspection protective equipment and the compliance testing procedures for thunderstorm inspection environmental risks in the attribute table of the thunderstorm inspection process. Use keyword search and matching to classify the corresponding indicator data list items in step S3 into the corresponding attribute information of each procedure item. S5. List the compliance testing procedures for windy environment inspection and high temperature equipment inspection in the attribute table of the windy environment inspection and high temperature equipment inspection and compliance testing procedures. Use keyword search and matching to divide the corresponding indicator data list items in step S3 into the corresponding attribute information of each procedure item. S6. List the high-voltage equipment inspection compliance testing procedures in foggy environments and the high-voltage equipment inspection compliance testing procedures in foggy environments in the attribute table of the fog inspection process. Use keyword search and matching to divide the corresponding indicator data list items in step S3 into the corresponding attribute information of each procedure item. S7. Based on the data list items contained in the process attributes of each process in steps S4, S5, and S6, construct the model of each process in three-dimensional space using the model data provided by the operation and maintenance basic data import module. Adjust and generate the compliance logic judgment conditions of the spatial motion trajectory and coordinates of each model under the corresponding process according to the extreme environmental constraints, and mark the corresponding model in three-dimensional space. S8. Organize, summarize and summarize the judgment conditions by each link and process. Each judgment condition is attached to the attribute information of each operation process in the data architecture. The data of each operation process is attached to the corresponding operation link. S9. Send the summarized and organized judgment conditions of each link and process to the operation and maintenance judgment module to end the algorithm process.

[0011] Furthermore, the operation scoring algorithm described in step f is calculated using the following formula:

[0012]

[0013]

[0014] In the formula: The weighted score of the current operation and maintenance step; N is the number of the current operation and maintenance step, and an operation and maintenance process includes N operation and maintenance steps; n is the number of the current process within a certain operation and maintenance step, and an operation and maintenance step includes n processes. The standard parameter for the current process is derived from the discrimination data corresponding to the discrimination results of each process in the operation and maintenance discrimination module. It is calculated based on the absolute value of the difference between the coordinate areas and movement trajectories of relevant equipment, personnel, and tools collected during the actual operation and maintenance process and the discrimination condition constraints of the detection module. The greater the difference... The smaller the value; The discrimination score weight coefficient for the current process is derived from the discrimination basic data of each process provided by the corresponding detection module and algorithm in the operation and maintenance operation discrimination module and operation and maintenance detection module library. This is the key operational coefficient for the current operation and maintenance process, with a value of 0 or 1, and is calculated using a formula. This is the critical coefficient for the current process operation, with a value of 0 or 1. It is used to provide feedback on whether the current process is fully within the compliance range. The data is obtained by comparing and calculating the operation and maintenance judgment module with the actual operation and maintenance data on site. The overall operation process is scored.

[0015] Furthermore, the operation and maintenance data acquisition equipment mentioned in step d includes a positioning base station, a high-definition PTZ camera, positioning tags, and data integration and transmission equipment; wherein: The positioning base station adopts UWB positioning technology and is deployed in various areas of the hydropower station and key equipment rooms. It receives positioning tag signals through the wireless network within the power station and calculates the position and angular coordinate information and movement trajectory information of personnel, equipment and tools related to operation and maintenance based on arrival time, time difference of arrival and arrival angle algorithm. The positioning tag is installed on equipment or tools or carried by maintenance personnel. By installing multiple positioning modules in the same equipment or tool, the position, attitude, and movement trajectory information of electromechanical equipment during operation and maintenance can be obtained. When the positioning module is installed, it is matched with the corresponding position of the equipment model in three-dimensional space to realize the visualization of the installation status.

[0016] 10. The intelligent auxiliary operation and maintenance method for hydropower stations according to claim 4, characterized in that step e further includes calling the personnel action behavior graphic analysis application system, analyzing and judging the actions and behaviors of operation and maintenance personnel by analyzing and judging the images and videos collected on site, generating a judgment result, and then comparing it with the judgment conditions generated by each detection module in the operation and maintenance operation detection module library using the operation and maintenance operation discrimination module function, for the compliance judgment of personnel behavior during operation and maintenance.

[0017] The advantages and technical effects of this invention are as follows: This invention proposes a basic intelligent auxiliary operation and maintenance system, method, and application for hydropower stations that integrates advanced technologies such as digitalization, intelligence, and behavior recognition. This system can provide operation and maintenance personnel with real-time and accurate operation and maintenance assistance and on-site operation guidance, realizing a collaborative management upgrade from "human prevention" to "technology prevention." By constructing a standardized, process-oriented, and visualized basic intelligent auxiliary operation and maintenance system for hydropower stations, the coverage quality and standardization of equipment inspections are improved, and automated monitoring and process early warning are achieved for each daily operation and maintenance operation. This system can effectively reduce safety risks caused by human negligence, operational errors, and lack of supervision. Through intelligent guidance and information collaboration, it can optimize work processes, reduce unnecessary ineffective or repetitive operations, and automatically prompt and assist in the management of non-compliant and risky items in operation and maintenance operations without requiring additional operations from operation and maintenance personnel. At the same time, this system serves as the basic system platform for intelligent auxiliary operation and maintenance of hydropower stations, and new automatic auxiliary inspection and detection function modules can be continuously added according to the actual operation and maintenance needs of subsequent power stations and the development of new technologies, thus improving the system's functionality. This will improve the overall efficiency and reliability of operation and maintenance work, and enhance the long-term stable operation quality and safety management level of hydropower stations. Attached Figure Description

[0018] Figure 1 This is an overall architecture diagram of the intelligent auxiliary operation and maintenance basic system for hydropower stations provided by the present invention; Figure 2 The execution flowchart of the operation and maintenance personnel qualification detection algorithm provided by this invention; Figure 3 The execution flowchart of the equipment routine inspection and detection algorithm provided by the present invention; Figure 4 The execution flowchart of the special weather inspection and detection algorithm provided by this invention. Detailed Implementation

[0019] To make the technical problems solved, the technical solutions, and the beneficial effects of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and are not intended to limit the invention.

[0020] This invention provides a basic intelligent auxiliary operation and maintenance system for hydropower stations, with reference to... Figure 1 It includes modules for importing basic operation and maintenance data for data exchange, a maintenance operation process database, a 3D visualization engine architecture system, an operation and maintenance operation detection module library, an operation and maintenance operation discrimination module, a personnel action behavior graphic analysis application system, an operation scoring module, an early warning feedback module, a maintenance data recording module, operation and maintenance data acquisition equipment, feedback communication equipment, and operation and maintenance personnel qualification detection algorithms, equipment routine inspection detection algorithms, special weather inspection detection algorithms, and operation scoring algorithms to support the functions of each module.

[0021] The following is a detailed introduction to each functional module: 1. Operation and Maintenance Basic Data Import Module The operation and maintenance basic data import module includes a model data import submodule, an operation process import submodule, and a data integration and export submodule. It is used to read and import the engineering BIM model data of the hydropower station, and to query and read the corresponding operation process model movement trajectory from the maintenance operation process database based on the current required operation and maintenance operation. After integration, the aforementioned maintenance operation basic data is exported to the operation and maintenance operation discrimination module and various operation detection modules in the operation and maintenance operation detection module library.

[0022] 1.1 Model Data Import Submodule The model data import submodule is used to convert the complete engineering BIM model of the hydropower station that needs to be operated and maintained into a new format and import it into this module. At the same time, it obtains the BIM information of the hydropower station's building structure and various equipment models, including building geometry information, load information and mechanical structure information; electromechanical equipment model information, electrical and mechanical parameter information and related pipeline information; and calls the 3D visualization engine architecture system for rendering, providing a basic interface for interactive application of the 3D visualized power station operation and maintenance model for subsequent functional modules.

[0023] 1.2 Operation Flow Import Submodule The operation process import submodule queries and reads the motion trajectory data of each model in the corresponding operation process in the maintenance operation process database according to the current operation and maintenance operation to be performed; it calls the 3D visualization engine architecture system function and the model data import submodule to import data, displays the displacement data of personnel, equipment and tools in each stage of each maintenance operation standard process in 3D space and forms model trajectory animation, providing the basis data for the compliance detection of subsequent maintenance operations.

[0024] 1.3 Data Integration and Export Submodule The operation process import submodule is used to integrate and summarize the model data import submodule, the model and motion trajectory data in the operation process import submodule, and the data obtained from the maintenance operation process database. It associates and binds the corresponding operation and maintenance operation process with the corresponding model and various data, and transforms it into a data stream format that is easy for other application modules to read and parse. It sends the data stream one by one to the operation and maintenance operation discrimination module and the operation and maintenance operation detection module library, on a per-operation-process basis, in order to complete the generation of key point discrimination conditions and compliance judgment for each subsequent operation process.

[0025] 2. Maintenance Operation Procedure Database The maintenance operation process database is used to collect and store model motion trajectory data of various operation processes related to the operation and maintenance of electrical equipment in hydropower stations. The maintenance operation process database contains a storage classification structure for multiple operation and maintenance operation processes, such as daily equipment inspection, daily operation, and operation and maintenance under special weather conditions. It can be continuously expanded according to the actual operation and maintenance work undertaken by the system, along with the operation and maintenance operation detection module library. Each major category contains multiple subcategories of daily inspections of various equipment within the station. Each subcategory stores the standard equipment, tools, and personnel operations and spatial relationship data required for each step and process in each operation flow. Each operation and maintenance operation is stored in the form of spatial relationship coordinates of the relevant models under the corresponding process in three-dimensional space and the motion trajectory data of each model. The spatial coordinate relationship and motion trajectory stored for each process operation take into account the minimum distance limit of the module spatial relationship under each operation flow. The minimum limit distance data is the minimum spatial distance required by each relevant model under the current process in three-dimensional space, and safety space is reserved according to various regulations and specifications. When each application module calls the data later, it can be automatically arranged in the three-dimensional model of the power station according to the actual layout, but it cannot be less than the minimum limit distance between the models under each operation flow.

[0026] 3. 3D Visualization Engine Architecture System The described 3D visualization engine architecture system is an application system that can be called by the operation and maintenance basic data import module and its sub-modules. It provides the engine architecture and various rendering functions, offering a basic application interface for a 3D visualized power plant operation and maintenance interactive model for subsequent application modules. The 3D visualization engine architecture system includes: scene management functions for node organization; rendering functions for displaying and rendering the materials and lighting of each model; resource management functions for loading models and setting textures; a perspective system to assist in controlling the perspective of single or multiple cameras; interaction support functions for scene picking and event response; and extended modules such as physics and animation to achieve efficient 3D content presentation and interaction.

[0027] 4. Operation and maintenance testing module library The aforementioned operation and maintenance (O&M) detection module library is an application function module library that can be continuously expanded according to the actual needs of subsequent power station O&M management. For this intelligent auxiliary O&M basic system for hydropower stations, focusing on the daily O&M content, it includes modules for O&M personnel qualification detection, routine equipment inspection, and special weather inspection. Each module also includes data parsing tables of national and industry requirements for the corresponding operation procedures. Each application module within the O&M detection module library first reads the integrated data stream containing the operation procedure models and motion trajectory data from the O&M basic data import module, using it as the basis for generating judgment conditions. Then, through the application functions of each O&M detection module and relevant industry data contained in the library, it calls the supporting algorithms of each module, and according to the constraints of each O&M procedure specification, marks the relevant personnel, equipment, and tool models in the corresponding O&M operation procedures, generating key point judgment conditions for the O&M operation process, for subsequent functional modules to determine the compliance of O&M operations.

[0028] 4.1 Operation and Maintenance Personnel Qualification Testing Module The aforementioned operation and maintenance personnel qualification detection module provides a visual application interface. It uses data stream information integrated by the operation and maintenance basic data import module as its foundational data. The module also includes built-in data tables of various national and industry standards related to hydropower station operation process management, ticket management, and personnel qualification management. The module combines an operation and maintenance personnel qualification detection algorithm to learn and analyze potential process management risks, personnel allocation risks, and ticket management risks during the qualification detection process. It converts key compliance and safety-related points in the qualification detection into judgment conditions that can be restricted by multiple relevant data parameter values ​​and logical operations at each stage. These conditions are then used by the operation and maintenance operation judgment module to make compliance judgments based on the operation and maintenance data collected on-site.

[0029] 4.2 Equipment Routine Inspection and Testing Module The equipment routine inspection and testing module provides a visual application interface. It uses data stream information imported from the operation and maintenance basic data import module as its foundational data. The module also includes built-in data tables related to personnel status, equipment characteristics, and risk avoidance during routine equipment inspections, encompassing various national and industry regulatory requirements. The module combines routine equipment inspection and testing algorithms to learn and analyze potential safety protection and hazard prevention factors in each process of daily equipment inspections. It converts key points related to compliance and safety in equipment inspection operations into relevant models and data for each process, providing judgment conditions that can be limited by multiple parameter values ​​and logical operations. These conditions are then used by the operation and maintenance judgment module to make compliance judgments based on the on-site collected operation and maintenance data.

[0030] 4.3 Special Weather Inspection and Detection Module The special weather inspection and detection module provides a visual application interface. It uses data stream information imported from the operation and maintenance basic data import module as its foundational data. The module also includes built-in data tables of various national and industry standards related to power plant equipment inspection and personnel safety protection under extreme environments. The module combines special weather inspection and detection algorithms to learn and analyze additional personnel and equipment safety risks that may arise in each process of inspection work under extreme environments due to conditions such as high temperatures, strong winds, heavy fog, and thunderstorms. It converts key points related to compliance and safety in special weather inspection operations into relevant models and data for each process, allowing for judgment conditions that can be limited by multiple parameter values ​​and logical operations. These conditions are then used by the operation and maintenance operation judgment module to make compliance judgments based on the operation and maintenance data collected on-site.

[0031] 5. Algorithm for Detecting the Qualifications of Operation and Maintenance Personnel The aforementioned algorithm for detecting the qualifications of operation and maintenance personnel supports and implements the application functions of the module for detecting the qualifications of operation and maintenance personnel, generating compliance judgment conditions for hydropower station work order management, operation order management, personnel qualifications, and configuration procedures. The algorithm first calls data from the module to read the work steps included in the qualification detection workflow. Then, based on the industry and national standard analysis data related to each qualification detection process collected within the module, and according to key operation and maintenance information elements such as power station scale, type of equipment to be inspected, operating status data, and operation and maintenance task data, it generates corresponding indicator data lists and categorizes them into the attribute information of each step and procedure. Next, it reads relevant data on power station operation and maintenance personnel obtained from the operation and maintenance data acquisition equipment from the operation and maintenance operation judgment module, and generates corresponding compliance judgment conditions for each procedure list based on the power station personnel, qualification information, and operation and maintenance requirements involved in this operation and maintenance operation. Finally, the judgment conditions within the module are compiled and summarized and sent to the operation and maintenance operation judgment module for subsequent on-site compliance judgment. (Reference) Figure 2 The algorithm execution flow is as follows: S1. Based on the operation and maintenance personnel qualification detection module receiving the operation and maintenance start command issued by the operation and maintenance operation judgment module and various power plant and operation and maintenance operation information provided by the preceding function modules, the algorithm process begins.

[0032] S2. Read the basic data information of the power plant and the current operation and maintenance process command information. In the operation and maintenance personnel qualification testing module, query the national and industry standard requirements data table of the corresponding operation process, generate the key links in the operation and maintenance personnel qualification testing process, and list the two key links of process system management and personnel qualification configuration.

[0033] S3. Based on the data tables and information from preceding functional modules required by national and industry standards, in the two key stages of process system management and personnel qualification configuration generated in step S2, the indicator data list items (only data list items, excluding actual power plant operation and maintenance data) are matched with each stage through matching analysis of the standard requirement data tables. The corresponding indicator data list items are then appended to the corresponding key stage attribute information in the system. Relevant data according to relevant regulations and standards includes: ① Core static data It includes basic ticket information, personnel and configuration information, personnel qualification information, work content and location data.

[0034] ② Dynamic process and status data It includes process status, full process timestamp, electronic signature, and approval workflow.

[0035] ③ Safety measures data It includes a list of isolation measures, a list of hazards, requirements for safety briefing records, requirements for hot work, requirements for confined space operations, and requirements for operations at heights.

[0036] ④ Execution and closed-loop data Includes data on attendance and supervision, data on delays and changes, and data on work completion and acceptance. Record data for problems and non-compliance items.

[0037] ⑤ Statistical analysis data It includes data on the current process-related operation list, operation risk level table, team workload, and equipment defect type.

[0038] S4. Based on the data built into this module, list two main categories in the process and system management attribute table: work order system inspection process items and operation ticket system inspection process items. Then, based on the various national and industry standard requirement data tables built into the module, perform keyword search and matching, and assign the corresponding indicator data list items from the process and system management attribute information in step S3 to the corresponding attribute information in the work order system inspection process items and operation ticket system inspection process items (data list items can be repeated in different processes).

[0039] S5. Based on the personnel qualification configuration step attribute table built into this module, list two main categories: personnel configuration process items and qualification testing process items. Then, based on the various national and industry standard requirement data tables built into the module, perform keyword search and matching, and assign the corresponding indicator data list items from the personnel qualification configuration step attribute information in step S3 to the corresponding attribute information of the personnel configuration process items and qualification testing process items (data list items can be repeated in different processes).

[0040] S6. Based on steps S4 and S5, generate compliance judgment conditions for work order system testing, operation ticket system testing, personnel allocation, and qualification testing processes from the data list items included in each process attribute. These conditions are used in the operation and maintenance operation judgment module to compare the information parameters provided by each functional module with the parameters collected or analyzed in real time on site.

[0041] The algorithm first lists the data items to be inspected in each process, generates queries for static and historical data in three categories: core static data, safety measure data, and statistical analysis data. Then, it generates logical judgment conditions for numerical comparison or element discrimination for each category according to relevant standards and specifications. For example, it judges whether the personnel information described in the work order is the same as that of the personnel action behavior graphic analysis application system; and whether the personnel's years of service must be greater than 3 years and whether the personnel qualification information provided by the operation and maintenance data acquisition equipment meets the requirements.

[0042] Then, it reads two dynamic categories of data: dynamic process and status data, and execution and closed-loop data. According to the query cycle designed by the system, it reads the feedback data from the operation and maintenance data acquisition equipment. Based on the actual operation process and the process status added or adjusted on site, it generates logical judgment conditions for numerical comparison or element discrimination based on the standard specification data within the module, such as: whether the timestamps of each operation process link meet the logical requirements, whether the interval duration of each link meets the requirements, and whether the records of each link are complete.

[0043] S7. Organize, summarize, and aggregate the judgment conditions based on each link and process. The organization process is divided into two levels: operation and maintenance operation links and operation and maintenance operation processes. The judgment conditions generated in step S6 must be attached to the attribute information of each operation process in the data architecture, and the data of each operation process should be attached to the corresponding operation link.

[0044] S8. Send the judgment conditions of each link and process summarized in step S7 to the operation and maintenance operation judgment module for subsequent system modules to judge the compliance of each operation and end the operation and maintenance personnel qualification detection algorithm process.

[0045] 6. Equipment routine inspection and detection algorithm The aforementioned routine equipment inspection algorithm supports and implements the application functions of the routine equipment inspection module, generating compliance judgment conditions for each process during routine equipment inspection. First, it calls upon data from the routine equipment inspection module, reading the work steps included in the routine equipment inspection workflow. Then, based on the industry and national standard analysis data related to the routine equipment inspection workflow collected within the module, and according to key operation and maintenance information elements such as power plant scale, type of equipment to be inspected, operating status data, and operation and maintenance task data, it generates corresponding indicator data lists and categorizes them into the attribute information of each step and process. Next, it reads the list of power plant equipment to be inspected, structural model data of each piece of equipment, responsibility and qualification data of operation and maintenance personnel, and key information of this inspection task from the operation and maintenance operation judgment module; and obtains the engineering 3D model and motion trajectory data of each operation process model from the operation and maintenance basic data import module. It generates corresponding compliance judgment conditions for each process list by matching the data list items. Finally, the judgment conditions are compiled and summarized within the module and sent to the operation and maintenance operation judgment module for subsequent on-site compliance judgment. (Reference) Figure 3 The algorithm execution flow is as follows: S1. The routine inspection and detection module receives the operation and maintenance start command issued by the operation and maintenance operation judgment module and various power plant and operation and maintenance operation information provided by the preceding function module, and starts the algorithm process.

[0046] S2. Read the basic data information of the power station and the current operation and maintenance operation process command information. In the equipment routine inspection and testing module, query the national and industry standard requirements data table of the corresponding operation process, generate the key links in the equipment routine inspection and testing operation process, and list the three key links: personnel routine inspection, station equipment routine inspection, and dam area routine inspection.

[0047] S3. Based on the data tables and information from previous functional modules required by national and industry standards, in the three key stages generated in step S2—routine personnel inspection, routine equipment inspection within the station, and routine dam area inspection—the indicator data list items (only data list items, excluding actual power plant operation and maintenance data) are matched with each stage through matching analysis of the data tables required by the standards. The corresponding indicator data list items are then appended to the relevant key stage attribute information in the system. According to relevant regulations and standards, the relevant data includes: ① Personnel work status data: It includes facial feature parameters, gaze direction data, eye movement frequency data, safety equipment identification and analysis information (such as safety helmets, protective clothing, etc.), and physiological signal data collected by smart terminals.

[0048] ②Personnel action and behavior data: It includes data on the coordinates of people and limbs in three-dimensional space, the movement trajectories of people and limbs, and the results of image recognition and analysis of people's movements, obtained through wearable positioning tags.

[0049] ③Motion data of tools and equipment This includes obtaining the coordinates of each piece of equipment and tool in three-dimensional space, the angles and rotational directions of each tool in three-dimensional space, the movement trajectory of movable parts of the equipment in space (such as equipment cabinet doors), and the motion image recognition and analysis results of the equipment and tools through positioning tags that can be affixed to the equipment and tools.

[0050] ④ Equipment operating status data It includes data collected by the power plant's various system equipment operation data, equipment status, equipment main mechanism operation status, equipment monitoring instrument readings, and equipment status image recognition and analysis results such as temperature, damage, defects, and faults.

[0051] ⑤ Data on hydraulic structures in the dam area Includes monitoring data on the main structural properties of the dam area, such as hydraulic structures, displacement, and settlement, as well as dam seepage data. Image recognition and analysis results of cracks, peeling, water seepage, etc.

[0052] ⑥ Coordinate data of each area of ​​the power station The coordinate range of different safe and unsafe areas in the three-dimensional model space corresponding to the power station and each equipment under different operating conditions includes the fixed high-risk area of ​​the hydropower station's energized equipment area, the dynamic danger area of ​​each equipment when in operation, the danger isolation area during equipment maintenance, and the spatial coordinates of various environmentally hazardous areas.

[0053] S4. Based on the data built into this module, list four categories in the personnel routine inspection process attribute table: inspection route compliance inspection process items, personnel status compliance inspection process items, work focus compliance inspection process items, and inspection environment compliance inspection process items. Then, based on the various national and industry standard requirement data tables built into the module, perform keyword search and matching to assign the corresponding indicator data list items from the personnel routine inspection process attribute information in step S3 to the corresponding attribute information of the inspection route compliance inspection process items, personnel status compliance inspection process items, work focus compliance inspection process items, and inspection environment compliance inspection process items (data list items can be repeated in different processes).

[0054] S5. Based on the data built into this module, list four categories of routine equipment inspection procedures in the station's equipment inspection attribute table: equipment malfunction detection, oil-filled equipment inspection, rotating equipment inspection, and equipment overload detection. Then, based on the various national and industry standard requirement data tables built into the module, perform keyword search and matching to assign the corresponding indicator data list items from the routine equipment inspection attribute information in step S3 to the corresponding attribute information of the equipment malfunction detection, oil-filled equipment inspection, rotating equipment inspection, and equipment overload detection procedures (data list items can be repeated in different procedures).

[0055] S6. Based on the data built into this module, list two main categories in the dam area routine inspection process attribute table: hydraulic structure maintenance procedures and dam area metal structure equipment maintenance procedures. Then, based on the various national and industry standard requirement data tables built into the module, perform keyword search and matching to assign the corresponding indicator data list items from the station equipment routine inspection process attribute information in step S3 to the corresponding attribute information of the hydraulic structure maintenance procedure and dam area metal structure equipment maintenance procedure (data list items can be repeated in different procedures).

[0056] S7. Based on the data list items included in the process attributes of steps S4, S5, and S6, generate compliance judgment conditions for 10 processes: inspection route compliance detection, personnel status compliance detection, work focus compliance detection, inspection environment compliance detection, equipment malfunction detection, oil-filled equipment detection, rotating equipment detection, equipment overload detection, hydraulic structure maintenance, and dam area metal structure equipment maintenance. These conditions are then labeled on the corresponding model in 3D space. This is used in the operation and maintenance judgment module to compare the information parameters provided by each functional module with the parameters collected or analyzed in real time on-site.

[0057] The algorithm first lists the data items to be inspected in each process. Using the power plant equipment, personnel, tools, and building structure models provided by the operation and maintenance basic data import module, it constructs each operation and maintenance process model in 3D space. It then reads the coordinate data of each area of ​​the power plant and uses industry and national standards to analyze the data, marking the prohibited areas and required safety distances for each model under the current process and operating state. For example, the required safety distances differ depending on whether the equipment is operating normally, malfunctioning, damaged, or burning. Furthermore, the algorithm analyzes whether the spatial state, angle, and integrity of the models in each process meet the process requirements. For example, it uses positioning tags to determine whether operation and maintenance personnel are standing or fallen, whether maintenance tools are left in the work area, whether equipment or tools are used correctly, and whether equipment is tilted or misaligned. In summary, the algorithm generates corresponding compliance logic judgment conditions for the model's spatial movement trajectory and coordinates based on the regional restrictions of each model under each process.

[0058] The algorithm then analyzes and calculates the data items to be detected in each process, combined with industry and national standards, and lists the monitoring items, personnel characteristic parameter arrays, implementation direction vector parameters, blink frequency data, and intelligent terminal biosignal acquisition data parameter requirements. Based on each data item, the algorithm generates logical judgment conditions for numerical comparison or element discrimination.

[0059] Meanwhile, the algorithm analyzes and calculates the data list items to be tested in each process, combined with industry and national standards, and lists the required range of parameters such as equipment readings, temperature, damage, defects, and faults when each piece of equipment is running normally in the current process, as well as the integrity requirements of the overall appearance and key mechanisms. Based on the data, it generates logical judgment conditions for numerical comparison or element discrimination.

[0060] Simultaneously, based on the data list items to be detected in each process, combined with industry and national standards for data analysis, the algorithm analyzes and calculates, and lists the corresponding operations and behaviors that operation and maintenance personnel need to perform after the abnormal monitoring data of the building and structure, as well as the observed displacement, settlement, cracks, spalling, and seepage, are detected under the daily observation and maintenance process of hydraulic structures. According to the risk avoidance and response handling methods required by the rules and regulations, the algorithm generates logical judgment conditions for numerical comparison or element discrimination based on various data.

[0061] S8. Organize, summarize, and aggregate the judgment conditions based on each link and process. The organization process is divided into two levels: operation and maintenance operation links and operation and maintenance operation processes. The judgment conditions generated in step S7 must be attached to the attribute information of each operation process in the data architecture, and the data of each operation process should be attached to the corresponding operation link.

[0062] S9. Send the judgment conditions of each link and process summarized in step S8 to the operation and maintenance judgment module for subsequent system modules to judge the compliance of each operation and end the routine equipment inspection and detection algorithm process.

[0063] 7. Special Weather Inspection and Detection Algorithm The special weather inspection and detection algorithm is used to support and implement the application functions of the special weather inspection and detection module, generating compliance judgment conditions for each procedure of the inspection work under extreme conditions. The algorithm first calls the data within the special weather inspection and detection module, reading the work steps included in the special weather inspection and detection workflow. Then, based on the industry and national standard analysis data related to the hydropower station inspection process under special weather conditions collected in the module, and according to key operation and maintenance information elements such as power station scale, type of equipment to be inspected, operating status data, and operation and maintenance task data, it generates corresponding indicator data lists and categorizes them into the attribute information of each step and procedure. Next, it reads the list of power station equipment to be inspected, the structural model data of each piece of equipment, the responsibility and qualification data of operation and maintenance personnel, and the key information of this inspection task from the operation and maintenance operation judgment module; and obtains the engineering 3D model and the motion trajectory data of each operation process model from the operation and maintenance basic data import module. It generates the corresponding compliance judgment conditions for each procedure list by matching the data list items. Finally, the judgment conditions are compiled and summarized within the module and sent to the operation and maintenance operation judgment module for subsequent on-site compliance judgment. refer to Figure 4 The algorithm execution flow is as follows: S1. Based on the special weather inspection and detection module, receive the operation and maintenance start command issued by the operation and maintenance operation judgment module and various power plant and operation and maintenance operation information provided by the preceding function module, and start the algorithm flow.

[0064] S2. Read the basic data information of the power station and the current operation and maintenance operation process command information. In the special weather inspection and testing module, query the national and industry standard requirements data table of the corresponding operation process, generate the key links in the special weather inspection and testing operation process, and list the three key links of thunderstorm inspection, strong wind and high temperature inspection, and heavy fog inspection.

[0065] S3. Based on the data tables and information from previous functional modules required by national and industry standards, the three key stages—thunderstorm inspection, high wind and high temperature inspection, and heavy fog inspection—generated in step S2, are matched with the indicator data list items (only data list items, excluding actual power plant operation and maintenance data) through matching analysis of the standard requirement data tables. The corresponding indicator data list items are then appended to the key stage attribute information in the system. Relevant data according to relevant regulations and standards includes: ① Calibration data for safe operation and maintenance areas during extreme weather This includes calibration and modification data for spatial safety distances of main transformers, GIS equipment, surge arresters and other lightning-related equipment, outdoor outgoing line equipment and conductors, and various insulation equipment under extreme weather conditions, in each operational process.

[0066] ② Data on personnel protection measures under extreme weather conditions This includes information on the identification and analysis of wearing insulated boots and rainproof clothing for outdoor work in thunderstorms; information on wearing goggles and windproof helmets for outdoor work in windy weather; information on the identification and analysis of anti-fog headlamps and reflective vests for work in foggy weather; and information on the identification and analysis of breathable protective clothing and body temperature detection for work in high-temperature weather.

[0067] ③ Monitoring data of key equipment under extreme weather conditions This includes monitoring data items such as the number of surge arrester operations and leakage current, grounding grid potential, and leakage current data on the surface of equipment insulators during thunderstorms; monitoring data items such as vibration amplitude, structural stress, and conductor wind deflection value of outdoor equipment (such as circuit breakers and instrument transformers) during windy weather; monitoring data items such as transformer / reactor oil temperature and winding temperature, important cable joint temperature, switch cabinet internal temperature, and cooling system efficiency data during high-temperature weather; and monitoring data items such as salt density / ash density value, leakage current increase, and porcelain insulator surface wettability of outdoor insulation equipment during foggy weather.

[0068] ④ Extreme weather and environmental monitoring data It includes real-time and forecast monitoring data items for extreme weather conditions, such as lightning location, wind speed / direction, ambient temperature and humidity, visibility, and rainfall; and identification and analysis information for cameras and sensors monitoring outdoor equipment discharge arcs (thunderstorms / fog), equipment swaying (strong winds), equipment overheating (high temperatures) under thermal imaging, and on-site visibility (fog).

[0069] S4. Based on the data built into this module, list two main categories in the thunderstorm inspection process attribute table: "Thunderstorm Inspection Protective Equipment Compliance Testing Procedure" and "Thunderstorm Inspection Environmental Risk Compliance Testing Procedure." Then, based on the various national and industry standard requirement data tables built into the module, perform keyword search and matching to assign the corresponding indicator data list items from the thunderstorm inspection process attribute information in step S3 to the corresponding attribute information of the "Thunderstorm Inspection Protective Equipment Compliance Testing Procedure" and "Thunderstorm Inspection Environmental Risk Compliance Testing Procedure" (data list items can be repeated in different procedures).

[0070] S5. Based on the data built into this module, list two main categories in the attribute table for the high-wind and high-temperature inspection process: "High-wind environment inspection compliance testing procedures" and "High-temperature environment equipment inspection compliance testing procedures." Then, based on the various national and industry standard requirement data tables built into the module, perform keyword search and matching to assign the corresponding indicator data list items from the high-wind and high-temperature inspection process attribute information in step S3 to the corresponding attribute information of the "High-wind environment inspection compliance testing procedures" and "High-temperature environment equipment inspection compliance testing procedures" (data list items can be repeated in different processes).

[0071] S6. Based on the data built into this module, the attribute table for the fog inspection process lists two main categories: "High-voltage equipment inspection compliance testing procedures in foggy environments" and "Fog environment inspection compliance testing procedures." Then, based on the various national and industry standard requirement data tables built into the module, keyword search and matching are performed to assign the corresponding indicator data list items from the wind and high temperature inspection process attribute information in step S3 to the corresponding attribute information of the "High-voltage equipment inspection compliance testing procedures in foggy environments" and "Fog environment inspection compliance testing procedures" categories (data list items can be repeated in different processes).

[0072] S7. Based on the data list items included in the process attributes of steps S4, S5, and S6, generate compliance judgment conditions for six processes: thunderstorm inspection and protection equipment compliance testing, thunderstorm inspection and environmental risk compliance testing, high wind environment inspection and compliance testing, high temperature environment equipment inspection and compliance testing, foggy environment high voltage equipment inspection and compliance testing, and foggy environment inspection and compliance testing. Mark these conditions on the corresponding model in 3D space. These conditions are used in the operation and maintenance judgment module to compare the information parameters provided by each functional module with the parameters collected or analyzed in real time on-site.

[0073] The algorithm first lists the data items to be inspected in each process. Using the power plant equipment, personnel, tools, and building structure models provided by the operation and maintenance basic data import module, it constructs each operation and maintenance process model in three-dimensional space. It then reads the coordinate data of each area of ​​the power plant and uses industry and national standards to analyze the data, marking the model space areas prohibited from entering under extreme weather conditions and the adjustment values ​​for the safety distance space that needs to be reserved. For example: adjusting the safety distance for personnel working on outdoor lines during thunderstorms and heavy fog; adjusting the distance from rock walls, trees, and other environmental hazards during strong winds; and adjusting the safety distance for overheated equipment under high temperatures. In summary, the algorithm will adjust and generate compliance logic judgment conditions for the spatial movement trajectory and coordinates of each model under the corresponding process based on the extreme environmental restrictions.

[0074] The algorithm then analyzes and calculates the data requirements of the data to be tested in each process, combined with the industry and national standards, and lists the parameter requirements for the data collection of protective equipment, tools, devices and clothing for workers under different extreme weather conditions. Based on the data, it generates logical judgment conditions for numerical comparison or element discrimination.

[0075] The algorithm then analyzes and calculates the data requirements of each process, based on the data list items to be tested, industry and national standards, and lists the amplitude, temperature, insulation capacity, wind deflection parameter requirements, overall appearance and integrity requirements of key equipment and parts when the equipment is running under different extreme weather conditions. Based on the data, it generates logical judgment conditions for numerical comparison or element discrimination.

[0076] Meanwhile, the algorithm analyzes the data list items to be tested in each process, combined with industry and national standards, and lists whether there are landslides, fallen trees, unstable towers and line structures, and whether visibility meets the operation requirements when the power station is under extreme weather conditions. It also generates logical judgment conditions for numerical comparison or element discrimination based on the data.

[0077] S8. Organize, summarize, and aggregate the judgment conditions based on each link and process. The organization process is divided into two levels: operation and maintenance operation links and operation and maintenance operation processes. The judgment conditions generated in step S7 must be attached to the attribute information of each operation process in the data architecture, and the data of each operation process should be attached to the corresponding operation link.

[0078] S9. Send the judgment conditions of each link and process summarized in step S8 to the operation and maintenance judgment module for subsequent system modules to judge the compliance of each operation, and end the special weather inspection and detection algorithm process.

[0079] 8. Personnel Motion and Behavior Graphical Analysis Application System The personnel action and behavior graphic analysis application system is an application function system that can be called by the operation and maintenance judgment module. It is used to analyze and judge the actions and behaviors of personnel during operation and maintenance by analyzing and judging images and videos collected on site. After generating the judgment result, it compares it with the judgment conditions generated by each detection module in the operation and maintenance detection module library to determine the compliance of personnel behavior during operation and maintenance.

[0080] 9. Operation and maintenance data acquisition equipment The operation and maintenance data acquisition equipment includes a positioning base station, a high-definition PTZ camera, positioning tags, and data integration and transmission equipment. It receives acquisition commands from the operation and maintenance judgment module, collects spatial movement and operational data of various personnel, tools, and equipment during actual on-site operation and maintenance, as well as data from other monitoring systems of the power plant, and organizes and summarizes the data by each operation and maintenance step and procedure before sending it to the operation and maintenance judgment module to complete the compliance judgment of each operation.

[0081] 9.1 Positioning Base Station The positioning base station uses UWB positioning technology to locate the coordinates of on-site operation and maintenance personnel, equipment, and tools. Positioning base stations are deployed in various areas of the hydropower station and in key equipment rooms. They receive positioning tag signals through the wireless network within the power station and calculate the position and angle of personnel, equipment, and tools related to operation and maintenance based on arrival time, time difference of arrival, and angle of arrival algorithms, thereby obtaining corresponding coordinate information and movement trajectory information.

[0082] 9.2 HD PTZ Camera The high-definition cameras are deployed in areas and rooms related to operation and maintenance work to acquire high-definition images related to operation and maintenance operations and the surrounding environment. They provide real-time image data of personnel, equipment, and environmental factors, serving as foundational data for functions such as operation and maintenance status, equipment status, and hazard warnings. If the power station already possesses compliant power station video monitoring equipment and systems, the corresponding data can be directly connected to the intelligent auxiliary operation and maintenance system for hydropower stations described in this invention for video data retrieval.

[0083] 9.3 Location Labels The positioning tag can be installed on equipment, tools, or carried by maintenance personnel. By installing multiple positioning modules in the same equipment or tool, information such as the position, attitude, and movement trajectory of electromechanical equipment during operation and maintenance can be obtained. At the same time, the positioning module will be matched with the corresponding position of the equipment model in three-dimensional space to visualize the installation status and collect and monitor installation data.

[0084] 9.4 Data Integration and Transmission Equipment The data integration and transmission equipment is used to summarize and organize the field data acquired by the aforementioned operation and maintenance data acquisition equipment, as well as the real-time data from the existing monitoring systems of the hydropower station. It can access monitoring data from various existing power station systems, including: online monitoring data of equipment operating status, environmental and air quality monitoring data of the work area, power station safety monitoring data, tool management data, personnel management system data, video surveillance system data, and facial recognition data. The data integration and transmission equipment can organize and summarize the above data by each operation and maintenance step and procedure, and then send it to the operation and maintenance operation judgment module to complete the compliance judgment of each operation.

[0085] 10. Operation and Maintenance Judgment Module The operation and maintenance discrimination module is based on the data stream sent one by one according to the corresponding operation process provided by the operation and maintenance basic data import module. It compares and judges the data by calling the discrimination conditions generated by the module in the operation and maintenance operation detection module library and the actual field data provided by the operation and maintenance data acquisition device.

[0086] The operation and maintenance judgment module first uses the data provided by the operation and maintenance basic data import module and the 3D visualization engine architecture system. It then uses the engine's spatial judgment and analysis capabilities, combined with the minimum spatial distance requirements of each process link, to generate a 3D model layout plan of the building structure, personnel, tools, and equipment positions under the current operation and maintenance procedures of the hydropower station in 3D space.

[0087] Subsequently, upon receiving an order from the operation and maintenance personnel, the operation and maintenance operation discrimination module will compare each element in the operation and maintenance operation detection module library with the actual discrimination conditions generated by each module according to the relevant national and industry standards, process and method requirements, and management requirements in three-dimensional space. This comparison will be performed on the spatial coordinates, coordinate area of ​​movement, movement trajectory form, movement duration, equipment operating status, and environmental status of each person, equipment, and tool on site collected by the operation and maintenance data acquisition equipment.

[0088] Subsequently, the operation and maintenance judgment module analyzes and calculates various judgment results and data based on the degree of difference between the actual on-site operation data and the judgment conditions provided by the detection module. The judgment results and corresponding judgment data are then marked on the corresponding model in three-dimensional space. Finally, the above data is integrated according to the corresponding operation and maintenance process and sent to the operation scoring module.

[0089] 11. Operational Scoring Module The operation scoring module, based on the data imported by the operation and maintenance operation discrimination module, calls the operation scoring algorithm to score each actual operation and maintenance operation on site. It then sends the scoring results of each process and step to the early warning feedback module. The data imported by the operation and maintenance operation discrimination module includes discrimination results and data already marked in the corresponding 3D model of the operation and maintenance scenario. The discrimination data includes standard parameters for each process calculated based on the degree of matching between the actual operation and the discrimination conditions, as well as the discrimination scoring weight coefficient and the key operation coefficient for the current process.

[0090] 12. Operational scoring algorithm The operation scoring algorithm supports and implements the application functions of the operation scoring module. It calculates the score for the current operation and maintenance step based on the judgment results and data of each operation and maintenance process. Then, based on the scores of each operation and maintenance step, it calculates the score for a higher-level operation and maintenance process. The calculation formula is as follows:

[0091]

[0092]

[0093] In the formula The weighted score of the current operation and maintenance step; N is the number of the current operation and maintenance step, and an operation and maintenance process includes N operation and maintenance steps; n is the number of the current process within a certain operation and maintenance step, and an operation and maintenance step includes n processes. These are the standard parameters for the current process; This refers to the weighting coefficient for the current process's discrimination score. This refers to the operational criticality coefficient in the current operation and maintenance process; This is the criticality coefficient for the current process operation; The overall operation process is scored.

[0094] in The value is used to represent the degree of standardization in the current operation and maintenance process. The value is derived from the discrimination data corresponding to the discrimination results of each process in the operation and maintenance discrimination module. Specifically, it is determined by calculating the absolute value of the difference between the coordinate area and motion trajectory of the relevant equipment, personnel, and tools collected during the actual operation and maintenance process of the current process and the discrimination condition constraints of the detection module. The greater the difference, the higher the value. The smaller the corresponding value; The value is used to represent the criticality and importance of the current process in the corresponding operation and maintenance process, and at the same time reflects the sensitivity of the current process to the operation compliance requirements. The value comes from the basic data for each process discrimination provided by the corresponding detection modules and algorithms in the operation and maintenance operation discrimination module and operation and maintenance detection module library. This indicates the compliance of each key operation point under the current process. The value is 0 or 1, and it is used to reflect whether the current process is completely within the compliance range. The data comes from the operation and maintenance judgment module and the actual operation and maintenance data on site. This indicates the compliance of each key operational point in the current operation and maintenance process. The value is 0 or 1, and it is used to reflect whether the current operation and maintenance process is fully within the scope of compliance. It is calculated by a formula. The value is obtained by multiplying the weighted scores of each link in the operation and maintenance process.

[0095] 13 Early Warning Feedback Module The early warning feedback module generates prompts and termination commands based on the scoring data for each stage and process provided by the operation scoring module, and sends them to the feedback communication device and the maintenance data recording module. The early warning feedback module has a built-in table mapping scoring and processing methods for each stage and process. This table includes the prompt commands to be sent for different score ranges and reasons for deductions for each stage and process. This table also specifies that when the score for a stage or process falls below a certain standard value, the module will issue a command to terminate operation privileges. The module generates various operation commands based on the scoring and processing method mapping table and sends them to the feedback communication device to communicate with on-site operation and maintenance personnel. It also sends the various commands generated during the process and the corresponding operation data to the maintenance data recording module for recording.

[0096] 14. Feedback communication equipment The feedback communication equipment includes portable earphones, area broadcasting equipment, and smart mobile devices. It is used to send various operation prompts or commands generated by the early warning feedback module to on-site operation and maintenance personnel, and provides portable devices for communication with these personnel.

[0097] 14.1 Portable Headphones The portable headset can be worn by maintenance personnel to receive various operation prompts or commands generated by the early warning feedback module.

[0098] 14.2 Area Broadcasting Equipment The regional broadcasting equipment is installed in important areas and rooms of the hydropower station to inform operation and maintenance personnel who are not carrying portable headphones. At the same time, in the event of an operation and maintenance accident, a major operational error, or a significant change in the external environment, it sends corresponding warning messages to all operation and maintenance personnel in the corresponding area.

[0099] 14.3 Smart Mobile Devices The aforementioned intelligent mobile device, carried by maintenance personnel, has a built-in mobile app for the hydropower station's intelligent auxiliary operation and maintenance system. This app is used to receive and respond to interactive operations by maintenance personnel, such as querying maintenance tasks and processes, checking maintenance records, and accessing maintenance information. Simultaneously, the mobile app can also receive and respond to various operation commands sent by the early warning feedback module.

[0100] 15. Maintenance Data Recording Module The maintenance data recording module receives various commands and complete operation and maintenance data from the early warning feedback module for recording and analysis. Simultaneously, this module organizes and correlates the collected operation commands and corresponding operation data by each operation and maintenance link and process. The operation data includes on-site operation and maintenance data, judgment conditions, judgment results, judgment data, scoring data, operation commands, and personnel feedback response data provided by preceding modules in each link and process. Furthermore, it allows for 3D visualization data querying based on a model.

[0101] The following example, using the daily operation and maintenance work of a hydropower station, illustrates the implementation method of the intelligent auxiliary operation and maintenance system for hydropower stations of this invention: Step 1: Before the actual application of the project, complete the development and debugging of the basic intelligent auxiliary operation and maintenance system for hydropower stations. During the development process, relevant operation and maintenance operation detection data items and parsing data need to be entered into the maintenance operation process database and operation and maintenance operation detection module library according to the requirements of various national and industry standards. The system's module functions are then used to convert the data into a list of judgment conditions to be compared with the data collected from the field operation.

[0102] Step 2: Deploy and install this intelligent auxiliary operation and maintenance basic system for hydropower stations on the data center or control center server of the hydropower station. Simultaneously, deploy operation and maintenance data acquisition equipment, including positioning base stations, high-definition PTZ cameras, positioning tags, and data monitoring and transmission equipment, in relevant areas of each hydropower station's operation and maintenance work. Test the performance of each device and the flow and effectiveness of command and data exchange with this system. If a relevant system already exists at the hydropower station, the corresponding data can be directly entered into this system for use.

[0103] Step 3: Import the hydropower station project BIM model into the server using the operation and maintenance basic data import module. The model includes the hydropower station structure and buildings, equipment of various specialties and systems, various operation and maintenance tools and standard operation and maintenance personnel models and corresponding BIM data. Within this module, the maintenance operation process database and operation and maintenance operation testing module database are called, and the data is integrated according to each maintenance operation link and process, and a three-dimensional visualization interface is provided.

[0104] Step 4: After the operation and maintenance personnel issue commands through the operation and maintenance operation judgment module via the system interface, this set of intelligent auxiliary operation and maintenance basic system for hydropower stations begins to provide auxiliary guidance and early warning for on-site operation and maintenance work.

[0105] Step 5: The operation and maintenance judgment module calls the functions of each detection module in the operation and maintenance detection module library, and generates the judgment conditions for each operation and maintenance operation link and process based on the current actual operation and maintenance command of the hydropower station, environmental conditions, equipment operating status and related model data.

[0106] Step 6: The operation and maintenance judgment module calls the operation and maintenance data acquisition equipment to collect and organize various status data related to personnel, equipment, and tools generated by various real-time operations at the power plant, based on the actual operation and maintenance commands of the power plant.

[0107] Step 7: In the operation and maintenance operation discrimination module, based on the hydropower station model and process operation flow integrated data provided by the operation and maintenance basic data import module, compare and analyze the current process discrimination conditions provided by the operation and maintenance operation detection module library and various real-time operation data of the operation and maintenance site provided by the operation and maintenance data acquisition equipment, and send the generated result data to the operation scoring module.

[0108] Step 8: After the operation scoring module scores the operation, it publishes the relevant data to the early warning feedback module and provides on-site operation and maintenance personnel with prompts on various operation details and feedback on operation commands through the feedback communication device.

[0109] Step 9: The maintenance data recording module will record various commands and complete operation and maintenance data from the early warning feedback module.

[0110] Step 10: Repeat steps 4 to 10 above to complete the automatic auxiliary inspection of various operation and maintenance operations of the hydropower station and its internal processes, and generate relevant operation and maintenance records.

[0111] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A smart auxiliary operation and inspection basic system for a hydropower station, characterized in that, include: The operation and maintenance basic data import module is used to read and import the engineering BIM model data of the hydropower station. Based on the operation and maintenance operation to be performed, it queries and reads the corresponding operation process model motion trajectory data in the maintenance operation process database. After integration, it sends the data stream to the operation and maintenance operation discrimination module and the operation and maintenance operation detection module library one by one, in units of each operation process. The maintenance operation process database is used to collect and store model motion trajectory data of various operation processes related to the operation and maintenance of hydropower station electrical equipment. Each operation process is stored in the form of spatial relationship coordinates of relevant models under the corresponding process in three-dimensional space and motion trajectory data of each model. The spatial coordinate relationship and motion trajectory stored in each process operation include the minimum spatial distance limit required by each relevant model under the current process in three-dimensional space. A 3D visualization engine architecture system is used to provide 3D visualization data display and interactive operation for various application functional modules; The operation and maintenance detection module library includes multiple expandable operation and maintenance detection modules. Each module is used to read the data stream and call the corresponding supporting algorithm. According to the constraints of each operation and maintenance procedure specification, it annotates the relevant models in the corresponding operation and maintenance operation process and generates compliance judgment conditions for key points of the operation and maintenance process. The operation and maintenance data acquisition equipment is used to receive acquisition commands from the operation and maintenance operation discrimination module, collect spatial movement and operation data of various personnel, tools, and equipment in actual operation and maintenance on site, as well as data from other monitoring systems of the power plant, and then organize and summarize the data by each operation and maintenance operation link and process before sending it to the operation and maintenance operation discrimination module. The operation and maintenance discrimination module is used to compare and judge each item based on the data stream provided by the operation and maintenance basic data import module, by calling the discrimination conditions generated by each module in the operation and maintenance operation detection module library and the actual field data provided by the operation and maintenance data acquisition device. The comparison and discrimination results and corresponding discrimination data are marked on the corresponding model in three-dimensional space, and the data is integrated and sent to the operation scoring module in units of the corresponding operation and maintenance operation process. The operation scoring module is used to score each actual operation and maintenance operation on site based on the judgment results and data imported by the operation and maintenance judgment module, and send the scoring results data of each process and link operation to the early warning feedback module. The early warning feedback module is used to generate prompts and termination commands based on the scoring data of each link and process provided by the operation scoring module, and send them to the feedback communication device and the maintenance data recording module. The feedback communication device is used to receive various prompts or commands sent by the early warning feedback module and broadcast them to the on-site operation and maintenance personnel. The maintenance data recording module is used to receive various commands and complete operation and maintenance data issued by the early warning feedback module, and to organize and record the data in relation to each operation and maintenance link and process.

2. The intelligent auxiliary operation and inspection basic system of the hydropower station according to claim 1, characterized in that, The operation and maintenance testing module library includes an operation and maintenance personnel qualification testing module, a routine equipment inspection testing module, and a special weather inspection testing module. The operation and maintenance personnel qualification detection module is used to call the operation and maintenance personnel qualification detection algorithm based on the data flow information and the built-in data tables of various national and industry standard requirements related to hydropower station operation process management, two-ticket management, and personnel qualification management, to generate compliance judgment conditions for the work ticket system detection process, operation ticket system detection process, personnel configuration process, and qualification detection process in the process system management process and personnel qualification configuration process. The equipment routine inspection and detection module is used to call the equipment routine inspection and detection algorithm based on the data stream information and the built-in data tables of various national and industry standards related to equipment routine inspection, and generate compliance judgment conditions for each process under the three links of personnel routine inspection, station equipment routine inspection and dam area routine inspection. The special weather inspection and detection module is used to generate compliance judgment conditions for each process in the three stages of thunderstorm inspection, high wind and high temperature inspection, and heavy fog inspection, based on the data stream information and the built-in data tables of various national and industry standard requirements related to power plant inspection under extreme environments, by calling the special weather inspection and detection algorithm.

3. The intelligent auxiliary operation and maintenance basic system for hydropower stations according to claim 1, characterized in that, When generating comparison and discrimination results, the operation and maintenance discrimination module first uses the data provided by the operation and maintenance basic data import module and the 3D visualization engine architecture system to generate a 3D model layout scheme for the current operation and maintenance process of the hydropower station in 3D space. Then, it compares the discrimination conditions generated by each module in the operation and maintenance detection module library with the spatial coordinates, coordinate areas of movement, movement trajectory forms, movement duration, equipment operating status, and environmental factors of each person, equipment, and tool on site collected by the operation and maintenance data acquisition equipment. Based on the degree of difference between the actual on-site operation data and the discrimination conditions, it analyzes and calculates to generate various discrimination results and data.

4. A method for intelligent auxiliary operation and maintenance of a hydropower station based on the system described in claim 1, characterized in that, Includes the following steps: Step a: Import the BIM model of the hydropower station project using the operation and maintenance basic data import module, call the maintenance operation process database and the operation and maintenance operation detection module database, integrate the data according to each maintenance operation link and process, and provide a three-dimensional visualization interface. Step b: The operation and maintenance operator issues an operation and maintenance start command through the operation and maintenance operation judgment module; Step c: The operation and maintenance judgment module calls the functions of each detection module in the operation and maintenance detection module library, and generates compliance judgment conditions for each operation and maintenance operation link and process based on the current actual operation and maintenance command of the hydropower station, environmental conditions, equipment operating status and related model data. Step d: The operation and maintenance judgment module calls the operation and maintenance data acquisition equipment to collect and organize various status data related to personnel, equipment and tools generated by various real-time operations at the operation and maintenance site, based on the actual operation and maintenance commands of the power plant. Step e: The operation and maintenance discrimination module uses the hydropower station model and process operation flow integrated data provided by the operation and maintenance basic data import module as a basis to compare and analyze the current process discrimination conditions provided by the operation and maintenance operation detection module library and various real-time operation data of the operation and maintenance site provided by the operation and maintenance data acquisition equipment, generate discrimination result data and send it to the operation scoring module. Step f: The operation scoring module calls the operation scoring algorithm to score each operation and maintenance operation, and sends the scoring result data to the early warning feedback module. Step g: The early warning feedback module generates an operation command based on the scoring data and the built-in scoring and processing method correspondence table, and sends a prompt or a termination command to the on-site operation and maintenance personnel through the feedback communication device. Step h: The maintenance data recording module records various commands and complete operation and maintenance data from the early warning feedback module; Step i, repeat steps b to h, to complete the automatic auxiliary inspection of various operation and maintenance operations of the hydropower station and its internal processes in stages, and generate relevant operation and maintenance records.

5. The intelligent auxiliary operation and maintenance method for hydropower stations according to claim 4, characterized in that, The operation and maintenance detection module library mentioned in step c includes an operation and maintenance personnel qualification detection module and a corresponding operation and maintenance personnel qualification detection algorithm. The execution flow of the operation and maintenance personnel qualification detection algorithm is as follows: S1. Receive the operation and maintenance start command and various power plant and operation and maintenance operation information sent by the operation and maintenance operation discrimination module, and start the algorithm process; S2. Read the basic data information of the power plant and the current operation and maintenance process command information. In the operation and maintenance personnel qualification detection module, query the national and industry standard requirements data table of the corresponding operation process and list the two key links of process system management and personnel qualification configuration. S3. By matching and analyzing the data tables according to the specifications, the indicator data list items are matched with each link, and the corresponding indicator data list items are attached to the key link attribute information in the system; the indicator data list items include, according to relevant procedures and specifications, core static data, dynamic process and status data, safety measure data, execution and closed-loop data, and statistical analysis data types; S4. List the work order system inspection process items and operation ticket system inspection process items in the process system management attribute table, and divide the corresponding indicator data list items in step S3 into the corresponding attribute information of each process item through keyword search and matching. S5. List the personnel configuration process items and qualification testing process items in the personnel qualification configuration process attribute table, and divide the corresponding indicator data list items in step S3 into the corresponding attribute information of each process item through keyword search and matching; S6. Based on the data list items contained in the process attributes of each process in steps S4 and S5, generate compliance judgment conditions for the work order system inspection process, operation ticket system inspection process, personnel allocation process, and qualification inspection process. S7. Organize, summarize and summarize the judgment conditions by each link and process. Each judgment condition is attached to the attribute information of each operation process in the data architecture. The data of each operation process is attached to the corresponding operation link. S8. Send the summarized and organized judgment conditions of each link and process to the operation and maintenance judgment module to end the algorithm process.

6. The intelligent auxiliary operation and maintenance method for hydropower stations according to claim 4, characterized in that, The operation and maintenance detection module library mentioned in step c includes a routine equipment inspection detection module and a corresponding routine equipment inspection detection algorithm. The execution flow of the routine equipment inspection detection algorithm is as follows: S1. Receive the operation and maintenance start command and various power plant and operation and maintenance operation information sent by the operation and maintenance operation discrimination module, and start the algorithm process; S2. Read the basic data information of the power station and the current operation and maintenance process command information. In the equipment routine inspection and testing module, query the national and industry standard requirements data table of the corresponding operation process and list the three key links: personnel routine inspection, station equipment routine inspection, and dam area routine inspection. S3. By matching and analyzing the data tables according to the specifications, the indicator data list items are matched with each link, and the corresponding indicator data list items are attached to the key link attribute information in the system. The indicator data list items include, according to relevant regulations and specifications, personnel work status data, personnel action behavior data, tool and equipment movement data, equipment operation status data, dam area hydraulic structure data, and coordinate data of each area of ​​the power station. S4. List the compliance inspection procedures for inspection routes, personnel status, work focus, and inspection environment in the personnel routine inspection process attribute table. Use keyword search and matching to classify the corresponding indicator data list items in step S3 into the corresponding attribute information of each procedure item. S5. List the equipment malfunction detection process item, oil-filled equipment detection process item, rotating equipment detection process item, and equipment overload detection process item in the equipment routine inspection process attribute table within the station. Use keyword search matching to classify the corresponding indicator data list items in step S3 into the corresponding attribute information of each process item. S6. List the hydraulic structure maintenance procedures and the metal structure equipment maintenance procedures in the dam area routine inspection process attribute table. Use keyword search and matching to divide the corresponding indicator data list items in step S3 into the corresponding attribute information of each procedure item. S7. Based on the data list items contained in the process attributes of each process in steps S4, S5, and S6, construct the operation and maintenance process model in three-dimensional space using the model data provided by the operation and maintenance basic data import module, read the coordinate data of each area of ​​the power plant, mark the model space areas that are prohibited from entering under the current process and operation status, as well as the safety distance space that needs to be reserved, generate the compliance judgment conditions of each process, and mark them on the corresponding model in three-dimensional space. S8. Organize, summarize and summarize the judgment conditions by each link and process. Each judgment condition is attached to the attribute information of each operation process in the data architecture. The data of each operation process is attached to the corresponding operation link. S9. Send the summarized and organized judgment conditions of each link and process to the operation and maintenance judgment module to end the algorithm process.

7. The intelligent auxiliary operation and maintenance method for hydropower stations according to claim 4, characterized in that, The operation and maintenance detection module library mentioned in step c includes a special weather inspection and detection module and a corresponding special weather inspection and detection algorithm. The execution flow of the special weather inspection and detection algorithm is as follows: S1. Receive the operation and maintenance start command and various power plant and operation and maintenance operation information sent by the operation and maintenance operation discrimination module, and start the algorithm process; S2. Read the basic data information of the power station and the current operation and maintenance process command information. In the special weather inspection and detection module, query the national and industry standard requirements data table of the corresponding operation process and list the three key links of thunderstorm inspection, strong wind and high temperature inspection, and fog inspection. S3. By matching and analyzing the data tables according to the specifications, the indicator data list items are matched with each link, and the corresponding indicator data list items are attached to the key link attribute information in the system; the indicator data list items include, according to relevant procedures and specifications, the following: calibration data of safe areas for operation and maintenance in extreme weather, data on personnel protection measures in extreme weather, monitoring data of key equipment in extreme weather, and environmental monitoring data in extreme weather. S4. List the compliance testing procedures for thunderstorm inspection protective equipment and the compliance testing procedures for thunderstorm inspection environmental risks in the attribute table of the thunderstorm inspection process. Use keyword search and matching to classify the corresponding indicator data list items in step S3 into the corresponding attribute information of each procedure item. S5. List the compliance testing procedures for windy environment inspection and high temperature equipment inspection in the attribute table of the windy environment inspection and high temperature equipment inspection and compliance testing procedures. Use keyword search and matching to divide the corresponding indicator data list items in step S3 into the corresponding attribute information of each procedure item. S6. List the high-voltage equipment inspection compliance testing procedures in foggy environments and the high-voltage equipment inspection compliance testing procedures in foggy environments in the attribute table of the fog inspection process. Use keyword search and matching to divide the corresponding indicator data list items in step S3 into the corresponding attribute information of each procedure item. S7. Based on the data list items contained in the process attributes of each process in steps S4, S5, and S6, construct the model of each process in three-dimensional space using the model data provided by the operation and maintenance basic data import module. Adjust and generate the compliance logic judgment conditions of the spatial motion trajectory and coordinates of each model under the corresponding process according to the extreme environmental constraints, and mark the corresponding model in three-dimensional space. S8. Organize, summarize and summarize the judgment conditions by each link and process. Each judgment condition is attached to the attribute information of each operation process in the data architecture. The data of each operation process is attached to the corresponding operation link. S9. Send the summarized and organized judgment conditions of each link and process to the operation and maintenance judgment module to end the algorithm process.

8. The intelligent auxiliary operation and maintenance method for hydropower stations according to claim 4, characterized in that, The operation scoring algorithm described in step f is calculated using the following formula: In the formula: The weighted score of the current operation and maintenance step; N is the number of the current operation and maintenance step, and an operation and maintenance process includes N operation and maintenance steps; n is the number of the current process within a certain operation and maintenance step, and an operation and maintenance step includes n processes. The standard parameter for the current process is derived from the discrimination data corresponding to the discrimination results of each process in the operation and maintenance discrimination module. It is calculated based on the absolute value of the difference between the coordinate areas and movement trajectories of relevant equipment, personnel, and tools collected during the actual operation and maintenance process and the discrimination condition constraints of the detection module. The greater the difference... The smaller the value; The discrimination score weight coefficient for the current process is derived from the discrimination basic data of each process provided by the corresponding detection module and algorithm in the operation and maintenance operation discrimination module and operation and maintenance detection module library. This is the key operational coefficient for the current operation and maintenance process, with a value of 0 or 1, and is calculated using a formula. This is the critical coefficient for the current process operation, with a value of 0 or 1. It is used to provide feedback on whether the current process is fully within the compliance range. The data is obtained by comparing and calculating the operation and maintenance judgment module with the actual operation and maintenance data on site. The overall operation process is scored.

9. The intelligent auxiliary operation and maintenance method for hydropower stations according to claim 4, characterized in that, The operation and maintenance data acquisition equipment mentioned in step d includes a positioning base station, a high-definition PTZ camera, positioning tags, and data integration and transmission equipment; wherein: The positioning base station adopts UWB positioning technology and is deployed in various areas of the hydropower station and key equipment rooms. It receives positioning tag signals through the wireless network within the power station and calculates the position and angular coordinate information and movement trajectory information of personnel, equipment and tools related to operation and maintenance based on arrival time, time difference of arrival and arrival angle algorithm. The positioning tag is installed on equipment or tools or carried by maintenance personnel. By installing multiple positioning modules in the same equipment or tool, the position, attitude, and movement trajectory information of electromechanical equipment during operation and maintenance can be obtained. When the positioning module is installed, it is matched with the corresponding position of the equipment model in three-dimensional space to realize the visualization of the installation status.

10. The intelligent auxiliary operation and maintenance method for hydropower stations according to claim 4, characterized in that, Step e also includes calling the personnel action behavior graphic analysis application system, which analyzes and judges the images and videos collected on site to analyze and judge the actions and behaviors of operation and maintenance personnel. After generating the judgment result, it compares the judgment result with the judgment conditions generated by each detection module in the operation and maintenance detection module library, and uses it to judge the compliance of personnel behavior during operation and maintenance.

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