Closed-loop tracing method, system and equipment for high-risk operation behavior of power plant and storage medium

Abstract

The invention discloses a power plant high-risk operation behavior closed-loop tracing method, system and device and a storage medium, and relates to the technical field of industrial safety monitoring and management, and the method comprises the steps: obtaining the real-time position and health data of an operator in real time through a UWB positioning technology and an intelligent wearable device, and carrying out the fusion with a three-dimensional digital twinborn model based on AI video analysis, a high-risk operation area is monitored in real time; according to the operation type and the risk factor of the high-risk operation area, an early warning mechanism is triggered in combination with ERP two-ticket information and a standard library; abnormal behaviors and environmental risks in operation are identified based on an early warning mechanism, safety measure contents are automatically configured, and managers are notified to perform timely intervention; according to the method, multi-source data fusion and three-dimensional visual monitoring are achieved, a dynamic risk assessment and grading early warning mechanism is constructed, safety measures are automatically configured, operation behavior closed-loop tracing is supported, and safety is remarkably improved.

Description

Technical Field

[0001] This invention relates to the field of industrial safety monitoring and management technology, specifically to a closed-loop tracing method, system, equipment, and storage medium for high-risk operations in power plants. Background Technology

[0002] With the rapid development of information technology, especially the application of IoT, AI and big data technologies, intelligent safety management has gradually become an important direction for power plant operation safety. Through real-time monitoring, data analysis and automated control technologies, the health status of operators, working environment and operation behavior can be captured in real time in the system and intelligently assessed and processed. The application of these new technologies has significantly improved operation safety and control capabilities, but existing systems still face many challenges, especially in the integration of multi-source data and real-time risk assessment.

[0003] While existing intelligent safety management systems can collect operational data in real time through sensors and monitoring equipment, they still have shortcomings in data fusion, risk assessment, and safety measure configuration. The fusion technology of multi-source data has not yet reached a fully optimized level. Existing systems often rely on a single data source and fail to fully integrate data from the environment, workers, equipment, and behaviors, resulting in limited response speed and decision-making capabilities of the monitoring system. Most current risk assessment models are based on static threshold settings and fail to dynamically adjust according to actual operational conditions and environmental changes. This makes the system less flexible and adaptable in responding to sudden safety incidents. Existing safety measure configurations rely heavily on preset rules and manual intervention, lacking the ability to automatically adjust safety measures based on real-time data and risk assessment results. In high-risk operational scenarios, the system cannot automatically trigger corresponding safety measures based on changes in the environment and personnel behavior, resulting in some high-risk operations not being controlled in a timely and effective manner. These problems significantly reduce the effectiveness of existing technologies in complex and high-risk environments, and still pose considerable safety hazards. Summary of the Invention

[0004] In view of the above-mentioned problems, the present invention is proposed.

[0005] Therefore, the technical problem solved by the present invention is that existing technologies in high-risk operation safety management methods suffer from low data fusion efficiency, insufficient real-time risk assessment accuracy, slow response of early warning mechanisms, and reliance on manual intervention for safety measure configuration.

[0006] To address the aforementioned technical problems, this invention provides the following technical solution: a closed-loop traceability method for high-risk operations in power plants, comprising: acquiring real-time location and health data of workers through UWB positioning technology and smart wearable devices; fusing AI video analysis with a 3D digital twin model to monitor high-risk work areas in real time; triggering an early warning mechanism based on the type of work and risk factors in the high-risk work area, combined with ERP ticket information and a standard library; identifying abnormal behaviors and environmental risks during operations based on the early warning mechanism, automatically configuring safety measures, and notifying management personnel for timely intervention.

[0007] As a preferred embodiment of the closed-loop traceability method for high-risk operations in power plants described in this invention, the integration with the three-dimensional digital twin model includes: dynamically mapping real-time data from the work site to the three-dimensional digital twin model to form a virtual scene; using UWB positioning technology to obtain the real-time location of workers in the work area; collecting physiological data and environmental information of workers through smart wearable devices; connecting to the three-dimensional digital twin platform through a data transmission interface; and updating the personnel location, environmental conditions, and work progress in the virtual scene in real time. AI video analysis performs behavioral recognition on the on-site monitoring video to identify the standardization of workers' operational actions; monitors unsafe behaviors such as personnel lingering or crossing safe zones based on electronic fences; and presents the specific safety status of the work area in the virtual environment.

[0008] As a preferred embodiment of the closed-loop traceability method for high-risk operations in power plants described in this invention, the step of combining ERP two-ticket information and the standard library includes: based on the types of high-altitude operations and confined space operations, as well as the specific risks of gas leakage, fall injuries, and oxygen deficiency in the working environment, obtaining relevant information on the work area, personnel, tasks, and location based on the ERP two-ticket information; automatically assessing the potential risk factors of the current work area according to the assessment rules; and triggering the corresponding early warning mechanism by combining the safety risk assessment model in the standard library.

[0009] As a preferred embodiment of the closed-loop traceability method for high-risk operations in power plants described in this invention, the assessment rules include: environmental risk assessment, operation type risk assessment, personnel behavior risk assessment, and comprehensive risk assessment; the environmental risk assessment includes acquiring real-time gas concentration, temperature, and humidity data of the work area through environmental monitoring equipment, and conducting assessments using a data fusion model across different dimensions. The risk score is calculated by weighting environmental factors and risk values ​​to dynamically assess the environmental risk of the work area; the operation type risk assessment includes assessing the risk based on the operation type and the spatial structure data of the work area. For operations at height, the assessment is based on the height of the workers and their distance from the safety margin. The risk assessment of personnel behavior includes: real-time analysis of worker behavior using AI video analytics to identify issues such as not wearing safety equipment, exceeding permitted time limits, and non-standard operating procedures; assigning risk weights to each behavior based on historical data and safety standards; and obtaining behavioral risk by the ratio of the duration of the behavior to the maximum permissible time limit, with longer time limits resulting in higher risk scores. The comprehensive risk assessment combines environmental risk, job type risk, and personnel behavior risk, generating a comprehensive risk score through a weighted model. Based on preset risk thresholds, an early warning mechanism is automatically triggered, and corresponding safety measures are taken according to the degree of risk.

[0010] As a preferred embodiment of the closed-loop traceability method for high-risk operation behaviors in power plants described in this invention, the early warning mechanism includes low-level early warning, medium-level early warning, and high-level early warning; the early warning mechanism is represented as follows: , in, The warning levels are indicated as follows: 1 represents a low-level warning, 2 represents a medium-level warning, and 3 represents a high-level warning. This represents the overall risk score. This indicates a preset first warning threshold, used to trigger a low-level warning. This indicates a preset second warning threshold, used to trigger a medium-level warning.

[0011] As a preferred embodiment of the closed-loop traceability method for high-risk operations in power plants according to the present invention, the risk thresholds include: an environmental risk threshold, an operation type risk threshold, and a personnel behavior risk threshold; the formula for calculating the environmental risk threshold is expressed as: , The formula for calculating the risk threshold of the aforementioned job type is expressed as follows: ,

[0012] The formula for calculating the personnel behavior risk threshold is expressed as follows: , in, Indicates the environmental risk threshold. Represents the measured values ​​of environmental factors. This represents the safe maximum value of environmental factors. This represents the environmental weighting coefficient, which is assessed based on historical data, industry standards, and the importance of environmental factors. Indicates the risk threshold for the job type. Indicates the height of the workers. Indicates the maximum safe height of the work area. Indicates the distance between workers and the hazardous area. Indicates the safe working distance. This represents the risk threshold for personnel behavior, set based on the hazards of the work task and historical accident data. Indicates the time the workers stayed. Indicates the maximum allowed stay time. It represents the risk weight of personnel behavior, which is obtained by assessing the impact of violations on operational safety and the frequency of behavioral accidents in historical data.

[0013] As a preferred embodiment of the closed-loop traceability method for high-risk operations in power plants described in this invention, the automatically configured safety measures include: low-level safety measures, medium-level safety measures, and high-level safety measures. The low-level safety measures include automatically configuring basic safety measures when the risk assessment of the work area results in a low-level warning. These measures mainly include monitoring the health of workers, checking environmental monitoring equipment, and wearing basic safety protective equipment. The safety measures are pushed out primarily through SMS or platform notifications to remind workers and supervisors to pay attention to the current environmental conditions. The medium-level safety measures include configuring enhanced pre-operation safety checks, wearing specialized safety equipment, and requiring workers to perform limited-time operations within high-risk areas when the risk assessment of the work area reaches a medium-level warning. The push notifications for safety measures will be sent not only via SMS and platform notifications, but also through multiple channels such as voice broadcasts or video pop-ups to remind workers and monitors to be vigilant and ready for emergency evacuation at any time. The advanced safety measures include configuring a comprehensive emergency response and evacuation plan when the risk assessment of the work area reaches a high warning level, automatically pushing instructions to immediately stop work or evacuate, and instructing workers and managers to take high-intensity safety measures immediately, including but not limited to stopping work, forcibly evacuating the danger zone, and initiating emergency rescue procedures. Workers' smart wearable devices will automatically record work data and transmit it to monitoring personnel in real time, and notify all personnel through SMS, voice broadcasts, and platform notifications. The status of the work area will be monitored in real time to guide personnel to complete the safe evacuation.

[0014] Another objective of this invention is to provide a closed-loop traceability system for high-risk operations in power plants. This system solves the problems of data silos and response delays in current safety management technologies by intelligently integrating and monitoring multi-source data in real time. By combining the health data of operators, work environment information and three-dimensional digital twin models in real time, it improves the risk assessment and early warning capabilities during the operation process, thereby effectively making up for the lack of real-time dynamic adjustment and intelligent decision-making in existing technologies.

[0015] As a preferred embodiment of the closed-loop traceability system for high-risk operations in power plants described in this invention, the system includes: a real-time monitoring module, a risk assessment module, and a safety measures module. The real-time monitoring module uses UWB positioning technology and smart wearable devices to acquire the real-time location and health data of workers, and dynamically maps and fuses this data based on AI video analysis and a 3D digital twin model. The risk assessment module combines ERP ticket information and a standard library to assess potential risk factors in the current work area using a weighted model, considering multiple dimensions such as work type, environmental risk, and personnel behavior. It automatically triggers low-, medium-, or high-level warnings based on set risk thresholds. The safety measures module automatically configures low-, medium-, and high-level safety measures based on the risk assessment results, pushes corresponding safety measures to workers and managers, and dynamically adjusts the method of pushing safety measures according to the warning level.

[0016] Another object of the present invention is to provide a closed-loop traceability device for high-risk operations in power plants, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program as a step in implementing a closed-loop traceability method for high-risk operations in power plants.

[0017] Another object of the present invention is to provide a closed-loop traceability storage medium for high-risk operation behavior in power plants, wherein a computer program is stored thereon, and when the computer program is executed by a processor, the steps of the closed-loop traceability method for high-risk operation behavior in power plants are implemented.

[0018] The beneficial effects of this invention are as follows: The closed-loop traceability method for high-risk operations in power plants provided by this invention acquires the health data and environmental information of operators through multi-source data fusion and real-time monitoring, and integrates it with a three-dimensional digital twin model in real time. This avoids the problem that traditional monitoring methods cannot accurately capture the details of high-risk operation sites, and improves the accuracy of real-time monitoring and data integration of the work environment. By combining ERP ticket information and a standard library, the system can automatically assess the potential risks in the work area and trigger different levels of early warnings based on preset thresholds. This solves the limitations of traditional safety management that relies on manual judgment and statically set thresholds, and improves the automation of risk assessment and the response speed and accuracy of the early warning mechanism. Based on the comprehensive risk assessment, safety measures are automatically configured and notified to operators and managers through multiple channels, ensuring that emergency response measures can be taken in a timely manner in high-risk environments. This overcomes the problems of delay and error in manual operation, thus achieving better results in terms of automated configuration of safety measures, timeliness of emergency response, and comprehensiveness of safety assurance. Attached Figure Description

[0019] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 This is an overall flowchart of the closed-loop traceability method for high-risk operation behaviors in power plants provided in Embodiment 1 of the present invention.

[0021] Figure 2 This is a schematic diagram of the detection zone setting for the closed-loop traceability method for high-risk operation behaviors in power plants provided in Embodiment 1 of the present invention. Detailed Implementation

[0022] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0023] Example 1, referring to Figure 1-2 As an embodiment of the present invention, a closed-loop traceability method for high-risk operation behaviors in power plants is provided, the method comprising the following steps: S1: Through UWB positioning technology 100, smart wearable device 101 and AI video analysis 102, real-time health data and work environment information of workers are obtained and integrated with the three-dimensional digital twin model 103 to monitor high-risk work areas in real time.

[0024] It should be noted that the integration with the three-dimensional digital twin model 103 includes dynamically mapping the real-time data of the work site with the three-dimensional digital twin model 103 to form a virtual scene, using UWB positioning technology 100 to obtain the real-time location of the workers in the work area, collecting the workers' physiological data and environmental information through the smart wearable device 101, and connecting with the three-dimensional digital twin platform through the data transmission interface to update the personnel's location, environmental conditions, and work progress in the virtual scene in real time.

[0025] AI video analytics 102 performs behavior recognition on on-site monitoring videos, identifies the standardization of workers' operations, monitors unsafe behaviors such as lingering or crossing safe zones based on electronic fences, and presents the specific safety status of the work area in a virtual environment.

[0026] It should also be noted that by pre-calibrating the coordinate system of the three-dimensional digital twin model 103 in high-risk work areas, the spatial coordinates of UWB positioning base stations, on-site monitoring cameras, and environmental monitoring points in the three-dimensional digital twin model 103 are bound one-to-one, and the real-time data of the work site is dynamically mapped to the three-dimensional digital twin model 103 to form a virtual scene that can reflect the actual work scene.

[0027] During the operation, UWB positioning technology 100 is used to obtain the real-time location coordinates and identity of each worker in the work area. The worker's location coordinates are mapped to the coordinate system of the three-dimensional digital twin model 103 according to the preset coordinate transformation relationship. The physiological data and work environment information of the workers are collected by the smart wearable device 101. After the physiological data and work environment information are associated with the corresponding worker identity and timestamp, they are sent to the three-dimensional digital twin platform through the data transmission interface. The three-dimensional digital twin platform updates the personnel position, environmental conditions and work progress frame by frame in the virtual scene.

[0028] For reference Figure 2As shown, AI video analysis 102 performs behavior recognition on the operation actions of workers based on on-site monitoring video. The recognized behavior type, the degree of standardization of the action, and the corresponding spatial location information are associated with the installation location information and shooting field of view information of the camera in the 3D digital twin model 103, and converted into behavior annotation data in the virtual scene. Based on the electronic fence, high-risk operation areas, detection areas 104, inspection-free areas, and prohibited areas are predefined in the 3D digital twin model 103. By comparing the position trajectory of the workers mapped to the virtual scene with the range of the electronic fence, the system monitors the workers' stay time in the electronic fence area, the path information of crossing the safety area, and the corresponding behavior recognition results. Unsafe behaviors such as personnel staying and crossing the safety area are superimposed on the virtual operation environment in the form of primitives, markers, or color changes. The consistency information of personnel position distribution, environmental status, and behavior status in the high-risk operation area is displayed in real time on the 3D digital twin platform interface, realizing real-time monitoring and behavior closed-loop traceability of the high-risk operation area.

[0029] S2: Based on the operation type and risk factors of high-risk operation areas, combined with ERP two-ticket information 201 and standard library, trigger the early warning mechanism 202.

[0030] It should be noted that, in conjunction with the ERP two-ticket information 201 and the standard library, based on the types of high-altitude operations and confined space operations, as well as the specific risks of gas leakage, fall injuries, and oxygen deficiency in the working environment, the relevant information on the work area, personnel, tasks, and location is obtained from the ERP two-ticket information 201. According to the assessment rules, the potential risk factors of the current work area are automatically assessed, and combined with the safety risk assessment model in the standard library, the corresponding early warning mechanism 202 is triggered.

[0031] The assessment rules include environmental risk assessment, work type risk assessment, personnel behavior risk assessment, and comprehensive risk assessment. Environmental risk assessment involves acquiring real-time gas concentration, temperature, and humidity data of the work area using environmental monitoring equipment (200), and then using a data fusion model across different dimensions for evaluation. Risk scores are calculated by weighting environmental factors and risk values ​​to dynamically assess the environmental risk of the work area. Work type risk assessment combines the work type and spatial structure data of the work area. For work at height, height risk factors are obtained based on the height of the workers and their distance from the safety boundary. Personnel behavior risk assessment... The assessment includes real-time analysis of worker behavior using AI video analytics 102 to identify issues such as not wearing safety equipment, exceeding permitted time limits, and non-standard operating procedures. Based on historical data and safety standards, risk weights are assigned to each behavior, and behavioral risk is obtained by the ratio of the duration of the behavior to the maximum permissible time of stay; the longer the time of stay, the higher the behavioral risk score. The comprehensive risk assessment includes combining environmental risk, job type risk, and personnel behavior risk, generating a comprehensive risk score through a weighted model, and automatically triggering an early warning mechanism 202 based on preset risk thresholds, taking corresponding safety measures according to the degree of risk.

[0032] The early warning mechanism 202 includes low-level early warning, medium-level early warning, and high-level early warning.

[0033] Among them, early warning mechanism 202 is represented as: , in, The warning levels are indicated as follows: 1 represents a low-level warning, 2 represents a medium-level warning, and 3 represents a high-level warning. This represents the overall risk score. This indicates a preset first warning threshold, used to trigger a low-level warning. This indicates a preset second warning threshold, used to trigger a medium-level warning.

[0034] Risk thresholds include environmental risk thresholds, job type risk thresholds, and personnel behavior risk thresholds.

[0035] The formula for calculating the environmental risk threshold is as follows: , The formula for calculating the risk threshold for job type is as follows: , The formula for calculating the risk threshold of personnel behavior is as follows: , in, Indicates the environmental risk threshold. Represents the measured values ​​of environmental factors. This represents the safe maximum value of environmental factors. This represents the environmental weighting coefficient, which is assessed based on historical data, industry standards, and the importance of environmental factors. Indicates the risk threshold for the job type. Indicates the height of the workers. Indicates the maximum safe height of the work area. Indicates the distance between workers and the hazardous area. Indicates the safe working distance. This represents the risk threshold for personnel behavior, set based on the hazards of the work task and historical accident data. Indicates the time the workers stayed. Indicates the maximum allowed stay time. It represents the risk weight of personnel behavior, which is obtained by assessing the impact of violations on operational safety and the frequency of behavioral accidents in historical data.

[0036] It should also be noted that, combining the high-altitude operation process and the confined space operation process, based on the operation type and risk factors of high-risk operation areas, and combined with the ERP two-ticket information 201 and the standard library, the early warning mechanism 202 is triggered. The system first reads fields such as operation ticket type, operation ticket number, operator, operation location, and operation time from the ERP two-ticket information 201, matches the operation ticket type with the preset high-altitude operation standard library or confined space operation standard library in the standard library, and obtains the operation type entry to which the current operation belongs. After the matching is completed, the system sends an SMS notification to the target operator according to the operation process, prompting them to pick up wearable devices and monitoring devices such as smart helmets, smart bracelets, smart safety belts, and mobile surveillance balls corresponding to the operation type from the smart tool and equipment cabinet. During the picking process, the unique identifier of the device is bound to the operator's identity information and operation ticket number by scanning the code or swiping the certificate, ensuring that the health data and operation environment data collected later can correspond one-to-one with the specific operation ticket and operator.

[0037] During the pre-operation monitoring phase, in the high-altitude operation process, the system uses AI video analysis 102 to identify the workers and their attire, and judges the wearing status of smart helmets, smart bracelets, and smart safety belts. At the same time, it combines the personnel identification results to confirm the consistency between the workers and the two tickets 201 in the ERP system. In the confined space operation process, based on the completion of the identification of workers and their attire and the identification of personnel, the system calls the hazardous gas factor collection equipment through the preset detection items in the confined space operation standard library to collect real-time detection indicators such as oxygen content, carbon monoxide concentration, carbon dioxide concentration, and electrochemical toxic and harmful gas concentration. The detection values ​​are used as environmental factor measurement values ​​in the environmental risk assessment, and are correlated with the corresponding safe maximum values ​​and environmental weight coefficients stored in the standard library to obtain the environmental risk threshold. Health data such as blood pressure, blood oxygen, and heart rate collected by smart bracelets are classified as health factors, and pre-operation health baseline data are formed through the health evaluation rules in the standard library.

[0038] During the monitoring phase of the operation, in the high-altitude operation process, the system uses video footage from the mobile control ball and on-site monitoring cameras to perform high-altitude range identification and safety rope identification through AI video analysis 102. The operating height of the operator in the three-dimensional digital twin model 103 and the horizontal distance from the safety boundary are used as input parameters for the risk assessment of the operation type. The parameters are then normalized with the maximum safe height and safe distance in the standard library to obtain the risk threshold of the operation type.

[0039] In confined space operations, hazardous gas monitoring equipment continuously outputs real-time concentration values ​​of oxygen, carbon monoxide, carbon dioxide, and electrochemically toxic and hazardous gases. The system updates these values ​​based on the environmental safety ranges in the confined space operation standard library, reflecting the trend of exceeding limits as a dynamic change in environmental risk thresholds. In both types of operations, smart bracelets continuously collect data on the time workers spend in high-risk work areas. Combining this with the maximum permissible dwell time preset in the standard library and the risk weights of different unsafe behaviors, the system calculates the personnel behavior risk thresholds. Unsafe behaviors include not wearing smart safety belts as required, staying at the edge of high-altitude work areas, exceeding the prescribed number of personnel or the prescribed work duration in confined spaces, etc. Behavioral tags are jointly provided by AI video analysis 102 and electronic fence monitoring results.

[0040] During the work completion monitoring phase, both high-altitude and confined space work processes collect on-site technical briefing photos, worker photos, work protection photos, and completed site photos at work completion monitoring nodes. The image data and corresponding work ticket numbers are uploaded to the supervisor management platform as historical data for behavioral risk assessment, used for statistical updates of personnel behavioral risk weights in the standard library. The system inputs environmental risk thresholds, work type risk thresholds, and personnel behavioral risk thresholds into the comprehensive risk assessment model to obtain a comprehensive risk score, which is compared with the pre-set risk thresholds in the standard library to obtain the warning level.

[0041] S3: Based on the early warning mechanism 202, identify abnormal behaviors and environmental risks in the operation, automatically configure safety measures 300 and notify management personnel to intervene in a timely manner.

[0042] It should be noted that the automatic configuration of security measures 300 includes low-level security measures, medium-level security measures, and high-level security measures.

[0043] Basic safety measures include automatically configuring basic safety measures when a low-level warning is issued by the risk assessment of the work area. These measures mainly include health monitoring of workers, inspection of environmental monitoring equipment 200, and wearing of basic safety protective equipment. Safety measures 300 are pushed out mainly through SMS or platform notifications to remind workers and supervisors to pay attention to the current environmental conditions.

[0044] Intermediate safety measures include strengthening pre-operation safety checks, wearing dedicated safety equipment, and requiring workers to work in high-risk areas for a limited time when the risk assessment of the work area reaches the intermediate warning level. Safety measures 300 push notifications will not only be sent via SMS and platform notifications, but will also remind workers through multiple channels such as voice broadcasts or video pop-ups. Workers and supervisors are required to be vigilant and ready to evacuate in case of emergency.

[0045] Advanced safety measures include configuring a comprehensive emergency response and evacuation plan when the risk assessment of the work area reaches a high warning level, automatically pushing instructions to immediately stop work or evacuate, and instructing workers and managers to immediately take high-intensity safety measures, including but not limited to stopping work, forcibly evacuating the danger zone, and initiating emergency rescue procedures. The smart wearable devices 101 worn by workers will automatically record work data and transmit it to monitoring personnel in real time, notify all personnel through SMS, voice broadcasts, and platform notifications, and monitor the status of the work area in real time to guide personnel to complete the safe evacuation.

[0046] It should also be noted that after completing the comprehensive calculation of environmental risk, work type risk, and personnel behavior risk, the early warning mechanism 202 compares the comprehensive risk score with the first and second early warning thresholds pre-set in the standard library, classifying the current work into low-level, medium-level, and high-level early warning. The early warning level, along with the corresponding work order number, work type (including work at height, confined space work, etc.), and identified risk factors, are sent to the safety measure configuration system. The safety measure configuration system uses the early warning level, work type, and risk factors as search criteria to retrieve the corresponding low-level, medium-level, or high-level safety measures from the standard library. The system parses the items into a safety measure content list 300, consisting of personnel health monitoring, environmental monitoring equipment 200 inspection, wearing of basic or special safety protective equipment, work time and work area restrictions, emergency evacuation and rescue procedures, etc., and establishes a link with the current work order.

[0047] When a low-level warning is issued, the system only provides reminders based on the requirements of low-level safety measures, such as continuous monitoring of the health data of the workers, confirmation of the integrity of the on-site environmental monitoring equipment 200, and the wearing of basic protective equipment such as safety helmets and work clothes. These reminders are sent to the workers' terminals and the supervisor's management platform via SMS and platform notifications, respectively, and a low-level risk mark is made for the work area in the three-dimensional digital twin model 103.

[0048] When a medium-level warning is issued, the system, while implementing basic safety measures, further conducts mandatory checks on the integrity and wearing status of specialized safety equipment such as smart safety belts and gas detectors based on predefined medium-level safety measures in the high-altitude operation standard library or confined space operation standard library. It also restricts the duration of entry into high-risk areas and the number of people present at the same time, generating a safety measure content 300 that includes mandatory inspection items and restrictive conditions. This content is simultaneously distributed through multiple channels such as SMS, platform notifications, and voice broadcasts. The restricted areas and restricted time periods are clearly marked in the three-dimensional digital twin model 103 for monitoring personnel to verify on the management platform.

[0049] When a high-level warning is detected, the safety measures configuration module calls upon emergency response safety measures from the standard library to generate an emergency response and evacuation plan for the current operation. This plan includes an immediate stop order, personnel evacuation routes, assembly points, emergency rescue contact information, and continuous environmental monitoring requirements. The system simultaneously distributes this plan to both operators and managers via platform notifications, SMS messages, and voice broadcasts. The relevant work area is marked as prohibited in the 3D digital twin platform. At the same time, high-frequency collection of ultra-wideband positioning trajectories and health data from smart wearable devices is initiated, uploading real-time location and vital signs to the guardian management platform for tracking the progress and status of personnel evacuation throughout the entire process. The generation time of the safety measures content list, the recipients of the notifications, the confirmation records of the managers, and the on-site execution feedback, along with the corresponding work tickets, are archived together.

[0050] Example 2, an embodiment of the present invention, provides a closed-loop traceability system for high-risk operations in power plants, including a real-time monitoring module, a risk assessment module, and a safety measures module.

[0051] The real-time monitoring module is used to obtain the real-time location and health data of the workers through UWB positioning technology 100 and smart wearable device 101, and to perform dynamic mapping and fusion based on AI video analysis 102 and three-dimensional digital twin model 103.

[0052] The risk assessment module combines ERP two-ticket information 201 and standard library to assess potential risk factors in the current work area based on multiple dimensions such as work type, environmental risk, and personnel behavior, using a weighted model. It automatically triggers low-level, medium-level, or high-level warnings based on the set risk thresholds.

[0053] The safety measures module is used to automatically configure low-level, medium-level, and high-level safety measures based on risk assessment results, push the corresponding safety measures content to operators and managers, and dynamically adjust the way safety measures are pushed according to the warning level.

[0054] This embodiment also provides a computer device, including a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, it implements a closed-loop traceability system for high-risk operation behaviors in power plants as proposed in the above embodiment.

[0055] This embodiment also provides a computer-readable storage medium storing a computer program thereon. When the computer program is executed by a processor, it implements a closed-loop traceability system for high-risk operation behaviors in power plants as proposed in the above embodiment.

[0056] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, essentially, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0057] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-including system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0058] More specific examples of computer-readable media (a non-exhaustive list) include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0059] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0060] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A closed-loop traceability method for high-risk operations in power plants, characterized in that, include: By using UWB positioning technology and smart wearable devices to obtain the real-time location and health data of workers, and by integrating AI video analysis with 3D digital twin models, high-risk work areas can be monitored in real time. Based on the type of work and risk factors in high-risk work areas, combined with ERP ticket information and standard library, an early warning mechanism is triggered. Based on the early warning mechanism, abnormal behaviors and environmental risks in the operation are identified, safety measures are automatically configured, and management personnel are notified to intervene in a timely manner.

2. The closed-loop traceability method for high-risk operation behaviors in power plants as described in claim 1, characterized in that: The fusion with the three-dimensional digital twin model includes, By dynamically mapping real-time data from the work site to a 3D digital twin model, a virtual scene is formed. UWB positioning technology is used to obtain the real-time location of workers in the work area. Smart wearable devices are used to collect the physiological data and environmental information of workers. The data is then connected to the 3D digital twin platform through a data transmission interface to update the personnel location, environmental conditions, and work progress in the virtual scene in real time. AI video analytics performs behavioral recognition on on-site monitoring videos, identifying the standardization of workers' operational actions. Based on electronic fences, it monitors unsafe behaviors such as personnel lingering or crossing safe zones, presenting the specific safety status of the work area in a virtual environment.

3. The closed-loop traceability method for high-risk operation behaviors in power plants as described in claim 1 or 2, characterized in that: The combination of ERP two-ticket information and standard library includes, Based on the types of work at height and confined space operations, as well as the specific risks of gas leaks, falls from heights, and oxygen deficiency in the work environment, relevant information on the work area, personnel, tasks, and location is obtained from the ERP two-ticket information. The potential risk factors of the current work area are automatically assessed according to the assessment rules, and the corresponding early warning mechanism is triggered by combining the safety risk assessment model in the standard library.

4. The closed-loop traceability method for high-risk operation behaviors in power plants as described in claim 3, characterized in that: The assessment rules include environmental risk assessment, work type risk assessment, personnel behavior risk assessment, and comprehensive risk assessment. The environmental risk assessment includes acquiring real-time data on gas concentration, temperature, and humidity in the work area through environmental monitoring equipment, and conducting an assessment by combining data fusion models from different dimensions. The risk score is calculated by weighting environmental factors and risk values ​​to dynamically assess the environmental risk of the work area. The risk assessment for the type of work includes assessment based on the spatial structure data of the work type and the work area. For work at height, a height risk factor is obtained based on the height of the workers and their distance from the safety boundary. The personnel behavior risk assessment includes real-time analysis of the behavior of workers through AI video analysis to identify problems such as not wearing safety equipment, staying for too long, and non-standard operation. Based on historical data and safety standards, risk weights are assigned to each behavior. The behavior risk is obtained by the ratio of the duration of the behavior to the maximum permissible stay time. The longer the stay time, the higher the behavior risk score. The comprehensive risk assessment includes combining environmental risks, work type risks, and personnel behavior risks, generating a comprehensive risk score through a weighted model, automatically triggering an early warning mechanism based on a preset risk threshold, and taking corresponding safety measures according to the degree of risk.

5. The closed-loop traceability method for high-risk operation behaviors in power plants as described in any one of claims 1, 2, and 4, characterized in that: The early warning mechanism includes low-level early warning, medium-level early warning, and high-level early warning; The early warning mechanism is represented as: ， in, The warning levels are indicated as follows: 1 represents a low-level warning, 2 represents a medium-level warning, and 3 represents a high-level warning. This represents the overall risk score. This indicates a preset first warning threshold, used to trigger a low-level warning. This indicates a preset second warning threshold, used to trigger a medium-level warning.

6. The closed-loop traceability method for high-risk operation behaviors in power plants as described in claim 5, characterized in that: The risk thresholds include environmental risk thresholds, job type risk thresholds, and personnel behavior risk thresholds; The formula for calculating the environmental risk threshold is expressed as follows: ， The formula for calculating the risk threshold of the aforementioned job type is expressed as follows: ， The formula for calculating the personnel behavior risk threshold is expressed as follows: ， in, Indicates the environmental risk threshold. Represents the measured values ​​of environmental factors. This represents the safe maximum value of environmental factors. This represents the environmental weighting coefficient, which is assessed based on historical data, industry standards, and the importance of environmental factors. Indicates the risk threshold for the job type. Indicates the height of the workers. Indicates the maximum safe height of the work area. Indicates the distance between workers and the hazardous area. Indicates the safe working distance. This represents the risk threshold for personnel behavior, set based on the hazards of the work task and historical accident data. Indicates the time the workers stayed. Indicates the maximum allowed stay time. It represents the risk weight of personnel behavior, which is obtained by assessing the impact of violations on operational safety and the frequency of behavioral accidents in historical data.

7. The closed-loop traceability method for high-risk operation behaviors in power plants as described in any one of claims 1, 2, 4, and 6, characterized in that: The automatically configured security measures include low-level security measures, medium-level security measures, and high-level security measures. The basic safety measures include automatically configuring basic safety measures when the risk assessment of the work area results in a low-level warning. These measures mainly include health monitoring of workers, inspection of environmental monitoring equipment, and wearing of basic safety protective equipment. The safety measures are pushed out mainly through SMS or platform notifications to remind workers and supervisors to pay attention to the current environmental conditions. The intermediate safety measures include, when the risk assessment of the work area reaches the intermediate warning level, strengthening the pre-work safety inspection, wearing special safety equipment, and requiring workers to work in high-risk areas for a limited time. The safety measures are not only pushed through SMS and platform notifications, but also through multiple channels such as voice broadcasts or video pop-ups to remind workers. Workers and supervisors are required to be vigilant and ready to evacuate in case of emergency. The advanced safety measures include configuring a comprehensive emergency response and evacuation plan when the risk assessment of the work area reaches a high warning level, automatically pushing out instructions to immediately stop work or evacuate, and instructing workers and managers to immediately take high-intensity safety measures, including but not limited to stopping work, forcibly evacuating the danger zone, and initiating emergency rescue procedures. Workers' smart wearable devices will automatically record work data and transmit it to monitoring personnel in real time, notify all personnel through SMS, voice broadcasts, and platform notifications, and monitor the status of the work area in real time to guide personnel to complete a safe evacuation.

8. A closed-loop traceability system for high-risk operations in power plants, employing the closed-loop traceability method for high-risk operations in power plants as described in any one of claims 1 to 7, characterized in that: Includes a real-time monitoring module, a risk assessment module, and a security measures module; The real-time monitoring module is used to obtain the real-time location and health data of the workers through UWB positioning technology and smart wearable devices, and to perform dynamic mapping and fusion based on AI video analysis and three-dimensional digital twin models; The risk assessment module is used to combine ERP two-ticket information and standard library, and assess potential risk factors in the current work area through a weighted model based on multiple dimensions such as work type, environmental risk, and personnel behavior. It automatically triggers low-level, medium-level, or high-level warnings based on the set risk threshold. The safety measures module is used to automatically configure low-level, medium-level, and high-level safety measures based on risk assessment results, push corresponding safety measures content to operators and managers, and dynamically adjust the way safety measures are pushed according to the warning level.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the closed-loop tracing method for high-risk operation behavior in power plants as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the closed-loop tracing method for high-risk operation behavior in power plants as described in any one of claims 1 to 7.

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