Full-scene high-place operation intelligent monitoring system

Abstract

The invention discloses a full-scene high-place operation intelligent monitoring system, and the system comprises the following steps: collecting environment, personnel health, equipment operation and operation path data in real time through a plurality of sensors, generating a safety ecological map through a data fusion technology, and reflecting the safety situation of an operation site in real time; an intelligent algorithm is adopted to analyze the data, safety risks are predicted, emergency early warning is generated, the operation task sequence is dynamically adjusted according to task priorities, risk levels and personnel health conditions, and resource allocation is optimized; in the operation process, the system provides real-time safety prompts and operation guidance for operators through virtual reality and augmented reality technologies, and safety awareness and decision-making ability are improved; the system has an automatic emergency response function, can optimize an evacuation route in real time, guarantees the safety of operators, can continuously optimize a safety management strategy through adaptive learning, and improves the operation efficiency and safety. The invention aims to improve safety and efficiency in high-risk working environments.

Description

Technical Field

[0001] The present invention relates to the field of intelligent safety monitoring technology, and in particular to a full-scenario high-altitude operation intelligent monitoring system. Background Art

[0002] In the current safety management of height operations, existing technologies rely on traditional safety monitoring methods, such as environmental monitoring sensors, personnel positioning equipment, and health monitoring systems. These systems are usually based on a single data source and face many defects and limitations.

[0003] Most traditional monitoring systems are based on a single sensor or device, which can only monitor specific parameters or local operating conditions. Environmental monitoring systems can only detect physical environmental data such as air quality, temperature or humidity. Personnel health monitoring systems are limited to collecting physiological data such as heart rate and body temperature of workers, while equipment operation status monitoring systems can only provide status information of the equipment itself. The limitations of these systems make it impossible to fully reflect the safety status of the work site and lack cross-domain multi-dimensional data fusion capabilities. When multiple safety risks exist at the same time, it is difficult for traditional systems to conduct a comprehensive assessment of these complex safety hazards, resulting in delayed safety precautions and, in some cases, even the inability to identify potential dangers in advance.

[0004] Most of the existing safety monitoring systems lack intelligent analysis capabilities and are unable to achieve in-depth analysis and intelligent decision-making of real-time data. These systems can often only issue warnings based on static rules and lack sufficient response capabilities to dynamic changes in the working environment. If the system only uses simple threshold settings to determine whether the operation is safe, it may ignore changes in complex environmental factors, such as equipment failures, fluctuations in personnel health status, changes in work paths, etc. When sudden or unexpected risks occur at the work site, traditional monitoring systems often cannot respond in time, or can only passively issue warnings, failing to provide real-time and effective response strategies for operators.

[0005] In the prior art, when workers are working at heights, they can usually only rely on some simple safety tips and operating instructions. These tips are mostly presented in the form of text or graphics. Although they can remind workers to pay attention to safety to a certain extent, they lack interactivity and dynamism. In a complex working environment, a single safety tip is difficult to cope with real-time changing risks. When the environment changes rapidly, the health status of workers changes frequently, and equipment may fail, workers may not be able to quickly obtain real-time and specific safety information, which affects their judgment and emergency response. The traditional way of displaying safety information poses a great challenge to the understanding and reaction speed of workers, especially in high-risk high-altitude operations. The risks faced by workers may come from multiple directions, and a single way of displaying information cannot effectively guide them to take the best response measures.

[0006] The emergency response mechanism of existing technologies also has defects. Although some systems can initiate emergency response procedures after discovering safety risks, such responses are often passive and lack accurate assessment of risks. When equipment fails or environmental conditions change significantly, traditional systems may not be able to provide emergency guidance or optimize evacuation routes in real time. Emergency responses often rely on manual judgment and lack intelligent support. Many existing systems cannot provide accurate location and status data of workers at the work site, which makes it difficult for the command center to obtain real-time location information and environmental changes in time when danger occurs, thereby affecting the effectiveness of emergency measures. Traditional emergency response systems rely more on post-event rescue and fail to achieve ex ante safety warnings and automated adjustments.

[0007] Existing technologies generally lack adaptive safety management capabilities. Existing monitoring systems usually use static rules to set safety standards and are unable to automatically adjust work tasks and safety measures based on changes in the work site environment, personnel health, and equipment operation conditions. Existing technologies are unable to cope with complex and dynamic work environments. In high-risk areas, traditional monitoring systems may ignore real-time safety data, causing workers to be exposed to dangerous environments for a long time. These systems usually lack analysis and feedback mechanisms for historical data and are unable to use historical data to guide safety decisions at the work site, further affecting the accuracy and timeliness of work safety management.

[0008] Therefore, how to provide a full-scenario intelligent monitoring system for high-altitude operations is an issue that technical personnel in this field urgently need to solve. Summary of the invention

[0009] One purpose of the present invention is to propose a full-scenario intelligent monitoring system for high-altitude operations. The present invention monitors the safety of the work site in real time by integrating multiple sensors, real-time data collection and analysis technologies, as well as virtual reality and augmented reality technologies. The system builds a dynamically updated safety ecological map by integrating environmental data, personnel health status, equipment operating status and other information, and uses deep learning algorithms to intelligently identify potential safety risks, and generate safety warnings and emergency response plans in real time. The system provides real-time safety prompts and operation guidance to operators through virtual reality and augmented reality devices, helping operators make quick and safe decisions in complex high-risk environments. The invention has the advantages of high precision, strong real-time performance, and intelligent decision-making.

[0010] The full-scenario high-altitude operation intelligent monitoring system according to an embodiment of the present invention includes the following steps:

[0011] S1. Deploy an intelligent safety management system that integrates multiple sensors at the work site, and deploy sensors on the safety belts of workers. The sensors on the safety belts collect environmental data, personnel health status data, equipment working status data and work path data in real time. Based on data fusion technology, combined with the complementarity and real-time feedback of various sensors, a dynamically updated work safety ecological map is generated;

[0012] S2. Based on the operation safety ecological map, the real-time data is analyzed through the autonomous learning algorithm to generate an operation safety evolution model. The model includes intelligent identification of environmental changes, fluctuations in personnel health and abnormal equipment status, and generates multiple future operation scenarios and risk warnings based on the safety assessment results. The safety risks that arise during the operation are deduced in real time, and safety predictions are made in advance;

[0013] S3. Based on the operation safety evolution model, the system combines real-time data feedback to automatically generate a task priority adjustment plan, sorts tasks according to the urgency and risk level of the operation tasks, and intelligently optimizes task allocation and resource scheduling based on personnel health, equipment operation and environmental factors, and adjusts the execution order of operation tasks and personnel load in real time, so that tasks can be executed efficiently according to priority;

[0014] S4. When the system detects potential safety risks and abnormal events through real-time monitoring and analysis, it automatically initiates emergency response procedures, including dynamically adjusting work tasks, activating emergency equipment, remotely commanding workers to evacuate, and optimizing evacuation routes and safety protection measures based on the real-time location of workers and the state of the working environment to ensure the immediate safety of workers on the work site;

[0015] S5. Use virtual reality and augmented reality technologies to overlay real-time safety prompts, operation guidance, and a virtual safety framework of the operating environment in the operator's field of vision, and transmit safety information on the work site to the operator and the command center in real time, thereby optimizing the operator's safety awareness and decision-making ability, so that the operator can always stay in a safe state in high-risk areas and make correct emergency responses;

[0016] S6. The system automatically generates a closed-loop data traceability of the operation process through real-time data collection and historical data feedback, analyzes key events in the operation through machine learning algorithms, continuously optimizes the operation safety management model and emergency response strategy, and forms an adaptive safety management closed-loop system. Based on the two-way feedback of historical data and real-time data, it automatically adjusts and optimizes future operation processes and safety measures.

[0017] Optionally, the S1 specifically includes:

[0018] S11. Deploy multiple sensors at the work site. The sensors are deployed on the safety belts of the workers. The data collected by the sensors on the safety belts are transmitted to the data fusion processing system in real time. The sensors include environmental monitoring sensors, personnel physiological monitoring equipment, equipment operation status sensors, and dynamic path tracking equipment.

[0019] S12, receiving the raw data of each sensor, and performing denoising, calibration and completion processing on the data, fusing the multi-sensor data to generate a comprehensive safety data set, and jointly analyzing the fused multi-dimensional data through Bayesian reasoning technology, dynamically adjusting the data weight of each sensor according to the data quality. The data fusion module realizes weighted fusion of data through the following weighted fusion formula:

[0020]

[0021] Among them, X i (t) is the sensor S i The i-th type of data collected at time t, W i (t) is the sensor S i The weighting coefficient at time t;

[0022] Weight coefficient W i (t) is calculated by the following formula:

[0023]

[0024] in, For sensor S i The noise variance at time t is, is the sum of the inverses of all sensor noise variances;

[0025] S13. Based on the generated comprehensive safety data set, a work safety ecological map is constructed. The map includes multiple layers of environmental status, personnel health status, equipment operation status and work path data, which reflects the safety situation of the work site in real time, and provides comprehensive safety situation awareness based on multi-source information in different dimensions, assisting decision makers to promptly discover potential safety hazards;

[0026] S14. The operation safety ecological map uses a dynamic update mechanism to continuously update the safety map in combination with newly collected data and real-time processing results. The updated map can promptly reflect environmental changes, personnel health fluctuations, equipment status abnormalities, and changes in operation paths;

[0027] S15. Through the monitoring terminal and interactive operation interface, the operation safety ecological map is visualized in real time to help operators and safety managers intuitively understand the safety situation of the operation site and obtain safety risk warnings in real time. Operators and managers can make decisions based on the key information provided in the map to optimize the safety management effect of the operation site.

[0028] Optionally, the S2 specifically includes:

[0029] S21. Based on the operation safety ecological map, the real-time collected data is analyzed through an autonomous learning algorithm, wherein the data includes environmental data, personnel health status data, equipment operation status data and operation path data, and a comprehensive safety data set is generated through data fusion processing;

[0030] S22. After obtaining the comprehensive safety data set, construct an operation safety evolution model based on the data set, wherein the model intelligently identifies environmental changes, fluctuations in personnel health, and abnormal equipment status, and predicts potential safety risks in the operation process based on the risk assessment results output by the model;

[0031] S23. The construction process of the operation safety evolution model extracts key features from real-time data through deep learning algorithms, models the time series features of various data sources, and conducts cross-correlation analysis of environmental, personnel health and equipment status data to identify the potential impact of various factors on operation safety and generate multiple operation safety evolution scenarios. The system uses an adaptive algorithm to dynamically adjust model parameters and continuously optimizes model output in combination with real-time data feedback to optimize the accuracy, adaptability and real-time performance of predictions.

[0032] S24. The operation safety evolution model deduce the safety risks that occur during the operation in real time based on real-time data feedback, and generates multiple future operation scenarios and corresponding safety risk warnings. The risk warnings include multiple potential risks such as environmental changes, abnormal health of personnel, and equipment failures, and evaluate the generation of risk warnings:

[0033]

[0034] Where R(t) is the safety risk assessment value at time t, W i (t) is the weight coefficient of the i-th data source, A i (t) is the risk impact value of the i-th data source at time t, and n is the number of factors affecting security risk;

[0035] S25. The operation safety evolution model is continuously optimized and dynamically adjusts the prediction parameters in combination with real-time data feedback, so as to generate the best safety prediction plan before each operation and optimize the timeliness and effectiveness of various safety protection measures during the operation;

[0036] S26. Based on the prediction results of the operation safety evolution model, the system automatically generates targeted safety warnings and issues warning information in a timely manner to provide real-time safety guidance to operators and optimize operation plans.

[0037] Optionally, the S3 specifically includes:

[0038] S31. Based on the operation safety evolution model, the system automatically generates a priority adjustment plan for the operation task according to real-time data feedback, and the plan is based on the urgency of the operation task, the risk level, the health status of the personnel, and the operation status of the equipment;

[0039] S32. The system evaluates the urgency of each task according to the time requirements, environmental factors and scheduled completion time of the task, conducts safety risk assessment on the tasks according to the risk assessment result R(t) and sorts them by risk level, dynamically adjusts the priority of the tasks according to the health status of the personnel, the operating status of the equipment and the operation scenario, gives priority to high-risk tasks, tasks with poor health and abnormal equipment, optimizes the safety management during the operation, and automatically calculates and adjusts the priority of the task according to the following formula P i (t):

[0040] Pi(t)=α·Ei(t)+β·Ri(t)+γ·Hi(t)+δ·Di(t);

[0041] Among them, P i (t) is the priority of the i-th task at time t, E i (t) is the urgency of the i-th task, R i (t) is the risk level of the i-th task, H i (t) is the influencing factor of the personnel health status of the i-th task, D i (t) is the influencing factor of the equipment operation status of the i-th task, and α, β, γ, δ are weighting coefficients;

[0042] S33, the system calculates the result P i (t), sort the job tasks and automatically optimize resource allocation and task execution order. The adjusted priority P i (t) Affects the execution order of tasks, personnel load and resource scheduling;

[0043] S34. The system dynamically adjusts the priority of work tasks by real-time monitoring of the health status of operators, equipment status and environmental changes. The priority of work tasks is optimized over time:

[0044] Pi(t+Δt)=Pi(t)+λ·ΔPi(t);

[0045] Among them, Pi (t+Δt) is the update priority of the task at time t+Δt, P i (t) is the task priority at time t, ΔP i (t) is the change in task priority, λ is the adjustment coefficient;

[0046] S35. Based on the adjusted task priorities and resource scheduling, the system adjusts the execution order of the work tasks, personnel load and task execution plan in real time to optimize work efficiency, and continues to adjust task priorities based on real-time feedback to cope with dynamic factors such as environmental changes, fluctuations in personnel health, and abnormal equipment status.

[0047] Optionally, the S4 specifically includes:

[0048] S41. When the system detects potential safety risks and abnormal events through real-time monitoring and analysis, it automatically initiates emergency response procedures, including dynamically adjusting work tasks, activating emergency equipment, remotely commanding workers to evacuate, and optimizing evacuation routes and safety protection measures based on the real-time location and environmental status of workers, so as to ensure the immediate safety of workers on the work site;

[0049] S42. During the emergency response process, the safety status of the work site is evaluated based on real-time data feedback, potential safety risk areas are generated and the immediate location of the workers is determined. Combined with the environmental data of the work site, the health status data of the workers, the equipment failure information and the work path data, the emergency response plan is dynamically generated, and the evacuation path of the workers is optimized in real time;

[0050] S43. The system uses an optimization formula to calculate the optimal evacuation path for each operator, taking into account multiple factors such as environmental risks, personnel health status, equipment failure information and path length:

[0051]

[0052] Among them, L i (t) is the optimal evacuation path length of the i-th operator at time t, k is the path selection index, m is the total number of path nodes, W j (t) is the weight coefficient of the jth evacuation path node, D j (t) is the distance from the jth path node to the safe area, R j (t) is the environmental risk level of the jth path node, H j (t) is the health risk coefficient of the jth path node, E j (t) is the equipment failure risk coefficient of the jth path node;

[0053] S44. Based on the optimal evacuation path, dynamically adjust the evacuation route of the operators through the path optimization algorithm to avoid high-risk areas in the shortest time and optimize the impact on personnel health, equipment failure and environmental factors on evacuation safety;

[0054] S45. The system automatically updates the evacuation path dynamically based on real-time environmental changes, operator health status and equipment failure information, optimizes the evacuation sequence and personnel load, and dynamically adjusts the evacuation path:

[0055] Li(t+Δt)=Li(t)+λ·ΔLi(t);

[0056] Among them, L i (t+Δt) is the updated evacuation path of the operator at time t+Δt, L i (t) is the original evacuation path of the operator at time t, ΔL i (t) is the change in the evacuation path of the operators, and λ is the adjustment coefficient;

[0057] S46. Based on the data feedback from real-time monitoring, dynamically adjust the evacuation path and sequence of workers to cope with changes in the work site environment, fluctuations in the health status of workers, and uncertainties in equipment failures, to ensure the immediate safety of workers on the work site.

[0058] Optionally, the S5 specifically includes:

[0059] S51. The system uses virtual reality and augmented reality technologies to overlay real-time safety prompts, operation guidance, and a virtual safety framework of the operating environment in the operator's field of vision. The safety information includes environmental data, personnel health status, equipment operating status, and operating path information, providing real-time safety monitoring information on the operating site to help operators understand the safety situation.

[0060] S52. Generate safety tips for workers at their locations through real-time data analysis, and dynamically update operation guidance and virtual safety frameworks based on environmental changes at the work site, optimizing workers’ safety awareness and decision-making capabilities in high-risk areas;

[0061] S53. The system monitors the environmental changes, health status of operators and equipment operation status of the work site in real time, and automatically adjusts the safety information in the virtual safety framework according to the real-time location and work path of the operators, so that the operators are always within the safe operation range;

[0062] S54. Operators receive real-time safety warnings, operation instructions and environmental risk information from the command center through virtual reality and augmented reality devices, enabling operators to make safety decisions and responses in real time during the operation process;

[0063] S55. Based on real-time data feedback, the system intelligently optimizes the safety guidance of workers and dynamically adjusts the safety prompts in the virtual safety framework so that the content of the safety prompts always matches the current working environment and the health status of the workers, thereby optimizing the safety of the work site.

[0064] Optionally, the S6 specifically includes:

[0065] S61. The system automatically generates closed-loop data traceability of the operation process through real-time data collection and historical data feedback, and analyzes key events in the operation process through machine learning algorithms, cross-analyzes historical data and real-time data, identifies potential safety risks, and dynamically generates operation safety assessment reports;

[0066] S63. Based on the operation safety assessment report, the system continuously optimizes the operation safety management model, adjusts the operation safety management strategy, and forms an adaptive safety management closed-loop system. The closed-loop system automatically adjusts the operation process and operation safety measures according to the real-time data and historical data feedback to adapt to the changes in the operation environment;

[0067] S64. Based on the feedback of historical data and real-time data, the system automatically identifies potential risk points in the operation process and generates optimization strategies. The optimization strategies include optimizing the operation process, adjusting safety measures, and optimizing the time that operators are exposed to high-risk areas:

[0068]

[0069] Among them, O(t) represents the optimization degree of the operation at time t, W i (t) is the weight coefficient of the i-th data source, R i (t) is the risk value of the i-th data source at time t, S i (t) is the impact of the i-th data source on operation safety, H i (t) is the health risk value of the i-th data source, P(t) is the priority of the task, and n is the number of factors affecting the optimization of the task;

[0070] S65. The system automatically adjusts various safety measures in the operation process according to the safety optimization degree O(t), and continuously optimizes the execution of the operation process through real-time data feedback, so that the operation process and safety measures always maintain the optimal state in the dynamically changing operation environment, and optimizes the potential safety risks in the operation;

[0071] S66. Through a closed-loop traceability mechanism, key events in the operation process are continuously monitored, and the operation safety management model is adjusted based on the feedback of event analysis results to cope with the ever-changing operating environment and potential risks, and ensure the safety of operators and the working environment.

[0072] Optional modules include:

[0073] Sensor network module: real-time collection of work site environment data, personnel health data, equipment status data and work path data;

[0074] Data fusion and processing module: denoises, calibrates and fuses multi-source data to generate a comprehensive security data set;

[0075] Operation safety ecological map module: integrates various safety data to reflect the safety situation of the operation site in real time;

[0076] Safety risk assessment and prediction module: Generate safety risk assessment based on real-time data and predict potential safety hazards;

[0077] Intelligent task priority adjustment module: dynamically adjust task priorities based on task urgency, risk level and personnel health factors;

[0078] Emergency response and optimization module: When safety risks are detected, emergency response is initiated and evacuation routes and safety measures are optimized;

[0079] Augmented reality and virtual reality safety guidance module: Provide real-time safety tips and operation guidance to operators through AR and VR technology;

[0080] Safety monitoring and decision support module: real-time monitoring of on-site data, assisting operators in making safety decisions;

[0081] Data closed-loop and traceability analysis module: Based on historical and real-time data analysis, continuously optimize the operation safety management model and emergency response strategy;

[0082] System adaptive learning and optimization module: optimizes the operation safety management strategy through machine learning to adapt to the changing operation environment;

[0083] Safety warning and risk control module: Generates real-time safety warnings to help operators respond to risks quickly;

[0084] Job task optimization and resource scheduling module: dynamically adjust the execution order of job tasks and personnel load, and optimize resource scheduling;

[0085] Path optimization and personnel evacuation module: optimize the evacuation path and sequence according to environmental changes and personnel health conditions.

[0086] The beneficial effects of the present invention are:

[0087] By integrating and fusing data from multiple sensors such as environmental monitoring, personnel physiological monitoring, equipment status monitoring and path tracking, the present invention enables the system to perceive various safety information at the work site in real time and dynamically update the work safety ecological map. This map can comprehensively and real-time reflect the safety situation at the work site, greatly improving the ability to discover and evaluate potential safety hazards. Compared with traditional safety management methods, the present invention can effectively integrate multi-source information and adjust safety prompts and operating instructions based on real-time feedback, thereby ensuring that workers can always be in a safe state in complex environments.

[0088] The present invention uses deep learning algorithms to perform intelligent analysis of real-time data, and can accurately identify and predict changes in the work site environment, fluctuations in personnel health, and abnormal equipment status. This innovation enables the system to not only monitor various risks in the operation process in real time, but also predict potential dangers in advance, and through safety warnings, risk assessments, and optimized decision-making plans, help operators make correct safety decisions in a timely manner in high-risk areas. Through this system, operators can minimize the occurrence of human errors and avoid potential safety accidents caused by environmental changes or equipment failures based on real-time safety prompts and operational guidance.

[0089] In terms of emergency management, the system of the present invention has significant advantages. When the system detects potential safety risks or abnormal events, it will automatically start the emergency response program, dynamically adjust the priority of work tasks, optimize resource scheduling, and automatically generate the best evacuation path based on the real-time location of the workers and the status of the working environment. This innovation not only improves the efficiency of emergency response, but also can provide safer and faster evacuation plans in emergency situations, thereby ensuring the life safety of workers.

[0090] The present invention combines virtual reality and augmented reality technologies to superimpose real-time safety prompts, operation guidance and a virtual safety framework in the operator's field of vision, thereby optimizing the operator's safety awareness and decision-making ability. When the operator is in a high-risk area, the system can adjust the display content in real time according to its current position and operation path information to ensure its safety and effectiveness throughout the entire operation process. The application of this technology enables operators to not only obtain comprehensive safety information, but also dynamically adjust their operating methods according to the actual environment, thereby enhancing their emergency response capabilities. BRIEF DESCRIPTION OF THE DRAWINGS

[0091] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0092] Figure 1 This is a flow chart of the full-scenario high-altitude operation intelligent monitoring system proposed by the present invention;

[0093] Figure 2 It is a schematic diagram of the work safety ecological map in the present invention;

[0094] Figure 3 It is a schematic diagram of the safety guidance display interface based on virtual reality and augmented reality technology in the present invention. DETAILED DESCRIPTION

[0095] The present invention will now be described in further detail with reference to the accompanying drawings. These drawings are simplified schematic diagrams, which only illustrate the basic structure of the present invention in a schematic manner, and therefore only show the components related to the present invention.

[0096] refer to Figure 1-3 , the full-scenario high-altitude operation intelligent monitoring system includes the following steps:

[0097] S1. Deploy an intelligent safety management system that integrates multiple sensors at the work site, and deploy sensors on the safety belts of workers. The sensors on the safety belts collect environmental data, personnel health status data, equipment working status data and work path data in real time. Based on data fusion technology, combined with the complementarity and real-time feedback of various sensors, a dynamically updated work safety ecological map is generated;

[0098] S2. Based on the operation safety ecological map, the real-time data is analyzed through the autonomous learning algorithm to generate an operation safety evolution model. The model includes intelligent identification of environmental changes, fluctuations in personnel health and abnormal equipment status, and generates multiple future operation scenarios and risk warnings based on the safety assessment results. The safety risks that arise during the operation are deduced in real time, and safety predictions are made in advance;

[0099] S3. Based on the operation safety evolution model, the system combines real-time data feedback to automatically generate a task priority adjustment plan, sorts tasks according to the urgency and risk level of the operation tasks, and intelligently optimizes task allocation and resource scheduling based on personnel health, equipment operation and environmental factors, and adjusts the execution order of operation tasks and personnel load in real time, so that tasks can be executed efficiently according to priority;

[0100] S4. When the system detects potential safety risks and abnormal events through real-time monitoring and analysis, it automatically initiates emergency response procedures, including dynamically adjusting work tasks, activating emergency equipment, remotely commanding workers to evacuate, and optimizing evacuation routes and safety protection measures based on the real-time location of workers and the state of the working environment to ensure the immediate safety of workers on the work site;

[0101] S5. Use virtual reality and augmented reality technologies to overlay real-time safety prompts, operation guidance, and a virtual safety framework of the operating environment in the operator's field of vision, and transmit safety information on the work site to the operator and the command center in real time, thereby optimizing the operator's safety awareness and decision-making ability, so that the operator can always stay in a safe state in high-risk areas and make correct emergency responses;

[0102] S6. The system automatically generates a closed-loop data traceability of the operation process through real-time data collection and historical data feedback, analyzes key events in the operation through machine learning algorithms, continuously optimizes the operation safety management model and emergency response strategy, and forms an adaptive safety management closed-loop system. Based on the two-way feedback of historical data and real-time data, it automatically adjusts and optimizes future operation processes and safety measures.

[0103] In this implementation manner, the S1 specifically includes:

[0104] S11. Deploy multiple sensors at the work site. The sensors are deployed on the safety belts of the workers. The data collected by the sensors on the safety belts are transmitted to the data fusion processing system in real time. The sensors include environmental monitoring sensors, personnel physiological monitoring equipment, equipment operation status sensors, and dynamic path tracking equipment.

[0105] S12, receiving the raw data of each sensor, and performing denoising, calibration and completion processing on the data, fusing the multi-sensor data to generate a comprehensive safety data set, and jointly analyzing the fused multi-dimensional data through Bayesian reasoning technology, dynamically adjusting the data weight of each sensor according to the data quality. The data fusion module realizes weighted fusion of data through the following weighted fusion formula:

[0106]

[0107] Among them, X i (t) is the sensor S i The i-th type of data collected at time t, W i (t) is the sensor S i The weighting coefficient at time t;

[0108] Weight coefficient W i (t) is calculated by the following formula:

[0109]

[0110] in, For sensor S i The noise variance at time t is, is the sum of the inverses of all sensor noise variances;

[0111] S13. Based on the generated comprehensive safety data set, a work safety ecological map is constructed. The map includes multiple layers of environmental status, personnel health status, equipment operation status and work path data, which reflects the safety situation of the work site in real time, and provides comprehensive safety situation awareness based on multi-source information in different dimensions, assisting decision makers to promptly discover potential safety hazards;

[0112] S14. The operation safety ecological map uses a dynamic update mechanism to continuously update the safety map in combination with newly collected data and real-time processing results. The updated map can promptly reflect environmental changes, personnel health fluctuations, equipment status abnormalities, and changes in operation paths;

[0113] S15. Through the monitoring terminal and interactive operation interface, the operation safety ecological map is visualized in real time to help operators and safety managers intuitively understand the safety situation of the operation site and obtain safety risk warnings in real time. Operators and managers can make decisions based on the key information provided in the map to optimize the safety management effect of the operation site.

[0114] In this implementation, S2 specifically includes:

[0115] S21. Based on the operation safety ecological map, the real-time collected data is analyzed through an autonomous learning algorithm, wherein the data includes environmental data, personnel health status data, equipment operation status data and operation path data, and a comprehensive safety data set is generated through data fusion processing;

[0116] S22. After obtaining the comprehensive safety data set, construct an operation safety evolution model based on the data set, wherein the model intelligently identifies environmental changes, fluctuations in personnel health, and abnormal equipment status, and predicts potential safety risks in the operation process based on the risk assessment results output by the model;

[0117] S23. The construction process of the operation safety evolution model extracts key features from real-time data through deep learning algorithms, models the time series features of various data sources, and conducts cross-correlation analysis of environmental, personnel health and equipment status data to identify the potential impact of various factors on operation safety and generate multiple operation safety evolution scenarios. The system uses an adaptive algorithm to dynamically adjust model parameters and continuously optimizes model output in combination with real-time data feedback to optimize the accuracy, adaptability and real-time performance of predictions.

[0118] S24. The operation safety evolution model deduce the safety risks that occur during the operation in real time based on real-time data feedback, and generates multiple future operation scenarios and corresponding safety risk warnings. The risk warnings include multiple potential risks such as environmental changes, abnormal health of personnel, and equipment failures, and evaluate the generation of risk warnings:

[0119]

[0120] Where R(t) is the safety risk assessment value at time t, W i (t) is the weight coefficient of the i-th data source, A i (t) is the risk impact value of the i-th data source at time t, and n is the number of factors affecting security risk;

[0121] S25. The operation safety evolution model is continuously optimized and dynamically adjusts the prediction parameters in combination with real-time data feedback, so as to generate the best safety prediction plan before each operation and optimize the timeliness and effectiveness of various safety protection measures during the operation;

[0122] S26. Based on the prediction results of the operation safety evolution model, the system automatically generates targeted safety warnings and issues warning information in a timely manner to provide real-time safety guidance to operators and optimize operation plans.

[0123] In this implementation, S3 specifically includes:

[0124] S31. Based on the operation safety evolution model, the system automatically generates a priority adjustment plan for the operation task according to real-time data feedback, and the plan is based on the urgency of the operation task, the risk level, the health status of the personnel, and the operation status of the equipment;

[0125] S32. The system evaluates the urgency of each task according to the time requirements, environmental factors and scheduled completion time of the task, conducts safety risk assessment on the tasks according to the risk assessment result R(t) and sorts them by risk level, dynamically adjusts the priority of the tasks according to the health status of the personnel, the operating status of the equipment and the operation scenario, gives priority to high-risk tasks, tasks with poor health and abnormal equipment, optimizes the safety management during the operation, and automatically calculates and adjusts the priority of the task according to the following formula P i (t):

[0126] Pi(t)=α·Ei(t)+β·Ri(t)+γ·Hi(t)+δ·Di(t);

[0127] Among them, P i (t) is the priority of the i-th task at time t, E i (t) is the urgency of the i-th task, R i (t) is the risk level of the i-th task, H i (t) is the influencing factor of the personnel health status of the i-th task, D i (t) is the influencing factor of the equipment operation status of the i-th task, and α, β, γ, δ are weighting coefficients;

[0128] S33, the system calculates the result P i(t), sort the job tasks and automatically optimize resource allocation and task execution order. The adjusted priority P i (t) Affects the execution order of tasks, personnel load and resource scheduling;

[0129] S34. The system dynamically adjusts the priority of work tasks by real-time monitoring of the health status of operators, equipment status and environmental changes. The priority of work tasks is optimized over time:

[0130] Pi(t+Δt)=Pi(t)+λ·ΔPi(t);

[0131] Among them, P i (t+Δt) is the update priority of the task at time t+Δt, P i (t) is the task priority at time t, ΔP i (t) is the change in task priority, λ is the adjustment coefficient;

[0132] S35. Based on the adjusted task priorities and resource scheduling, the system adjusts the execution order of the work tasks, personnel load and task execution plan in real time to optimize work efficiency, and continues to adjust task priorities based on real-time feedback to cope with dynamic factors such as environmental changes, fluctuations in personnel health, and abnormal equipment status.

[0133] In this implementation, S4 specifically includes:

[0134] S41. When the system detects potential safety risks and abnormal events through real-time monitoring and analysis, it automatically initiates emergency response procedures, including dynamically adjusting work tasks, activating emergency equipment, remotely commanding workers to evacuate, and optimizing evacuation routes and safety protection measures based on the real-time location and environmental status of workers, so as to ensure the immediate safety of workers on the work site;

[0135] S42. During the emergency response process, the safety status of the work site is evaluated based on real-time data feedback, potential safety risk areas are generated and the immediate location of the workers is determined. Combined with the environmental data of the work site, the health status data of the workers, the equipment failure information and the work path data, the emergency response plan is dynamically generated, and the evacuation path of the workers is optimized in real time;

[0136] S43. The system uses an optimization formula to calculate the optimal evacuation path for each operator, taking into account multiple factors such as environmental risks, personnel health status, equipment failure information and path length:

[0137]

[0138] Among them, L i(t) is the optimal evacuation path length of the i-th operator at time t, k is the path selection index, m is the total number of path nodes, W j (t) is the weight coefficient of the jth evacuation path node, D j (t) is the distance from the jth path node to the safe area, R j (t) is the environmental risk level of the jth path node, H j (t) is the health risk coefficient of the jth path node, E j (t) is the equipment failure risk coefficient of the jth path node;

[0139] S44. Based on the optimal evacuation path, dynamically adjust the evacuation route of the operators through the path optimization algorithm to avoid high-risk areas in the shortest time and optimize the impact on personnel health, equipment failure and environmental factors on evacuation safety;

[0140] S45. The system automatically updates the evacuation path dynamically based on real-time environmental changes, operator health status and equipment failure information, optimizes the evacuation sequence and personnel load, and dynamically adjusts the evacuation path:

[0141] Li(t+Δt)=Li(t)+λ·ΔLi(t);

[0142] Among them, L i (t+Δt) is the updated evacuation path of the operator at time t+Δt, L i (t) is the original evacuation path of the operator at time t, ΔL i (t) is the change in the evacuation path of the operators, and λ is the adjustment coefficient;

[0143] S46. Based on the data feedback from real-time monitoring, dynamically adjust the evacuation path and sequence of workers to cope with changes in the work site environment, fluctuations in the health status of workers, and uncertainties in equipment failures, to ensure the immediate safety of workers on the work site.

[0144] In this implementation manner, S5 specifically includes:

[0145] S51. The system uses virtual reality and augmented reality technologies to overlay real-time safety prompts, operation guidance, and a virtual safety framework of the operating environment in the operator's field of vision. The safety information includes environmental data, personnel health status, equipment operating status, and operating path information, providing real-time safety monitoring information on the operating site to help operators understand the safety situation.

[0146] S52. Generate safety tips for workers at their locations through real-time data analysis, and dynamically update operation guidance and virtual safety frameworks based on environmental changes at the work site, optimizing workers’ safety awareness and decision-making capabilities in high-risk areas;

[0147] S53. The system monitors the environmental changes, health status of operators and equipment operation status of the work site in real time, and automatically adjusts the safety information in the virtual safety framework according to the real-time location and work path of the operators, so that the operators are always within the safe operation range;

[0148] S54. Operators receive real-time safety warnings, operation instructions and environmental risk information from the command center through virtual reality and augmented reality devices, enabling operators to make safety decisions and responses in real time during the operation process;

[0149] S55. Based on real-time data feedback, the system intelligently optimizes the safety guidance of workers and dynamically adjusts the safety prompts in the virtual safety framework so that the content of the safety prompts always matches the current working environment and the health status of the workers, thereby optimizing the safety of the work site.

[0150] In this implementation manner, S6 specifically includes:

[0151] S61. The system automatically generates closed-loop data traceability of the operation process through real-time data collection and historical data feedback, and analyzes key events in the operation process through machine learning algorithms, cross-analyzes historical data and real-time data, identifies potential safety risks, and dynamically generates operation safety assessment reports;

[0152] S63. Based on the operation safety assessment report, the system continuously optimizes the operation safety management model, adjusts the operation safety management strategy, and forms an adaptive safety management closed-loop system. The closed-loop system automatically adjusts the operation process and operation safety measures according to the real-time data and historical data feedback to adapt to the changes in the operation environment;

[0153] S64. Based on the feedback of historical data and real-time data, the system automatically identifies potential risk points in the operation process and generates optimization strategies. The optimization strategies include optimizing the operation process, adjusting safety measures, and optimizing the time that operators are exposed to high-risk areas:

[0154]

[0155] Among them, O(t) represents the optimization degree of the operation at time t, W i (t) is the weight coefficient of the i-th data source, R i (t) is the risk value of the i-th data source at time t, S i (t) is the impact of the i-th data source on operation safety, H i (t) is the health risk value of the i-th data source, P(t) is the priority of the task, and n is the number of factors affecting the optimization of the task;

[0156] S65. The system automatically adjusts various safety measures in the operation process according to the safety optimization degree O(t), and continuously optimizes the execution of the operation process through real-time data feedback, so that the operation process and safety measures always maintain the optimal state in the dynamically changing operation environment, and optimizes the potential safety risks in the operation;

[0157] S66. Through a closed-loop traceability mechanism, key events in the operation process are continuously monitored, and the operation safety management model is adjusted based on the feedback of event analysis results to cope with the ever-changing operating environment and potential risks, and ensure the safety of operators and the working environment.

[0158] In this embodiment, the following modules are included:

[0159] Sensor network module: real-time collection of work site environment data, personnel health data, equipment status data and work path data;

[0160] Data fusion and processing module: denoises, calibrates and fuses multi-source data to generate a comprehensive security data set;

[0161] Operation safety ecological map module: integrates various safety data to reflect the safety situation of the operation site in real time;

[0162] Safety risk assessment and prediction module: Generate safety risk assessment based on real-time data and predict potential safety hazards;

[0163] Intelligent task priority adjustment module: dynamically adjust task priorities based on task urgency, risk level and personnel health factors;

[0164] Emergency response and optimization module: When safety risks are detected, emergency response is initiated and evacuation routes and safety measures are optimized;

[0165] Augmented reality and virtual reality safety guidance module: Provide real-time safety tips and operation guidance to operators through AR and VR technology;

[0166] Safety monitoring and decision support module: real-time monitoring of on-site data, assisting operators in making safety decisions;

[0167] Data closed-loop and traceability analysis module: Based on historical and real-time data analysis, continuously optimize the operation safety management model and emergency response strategy;

[0168] System adaptive learning and optimization module: optimizes the operation safety management strategy through machine learning to adapt to the changing operation environment;

[0169] Safety warning and risk control module: Generates real-time safety warnings to help operators respond to risks quickly;

[0170] Job task optimization and resource scheduling module: dynamically adjust the execution order of job tasks and personnel load, and optimize resource scheduling;

[0171] Path optimization and personnel evacuation module: optimize the evacuation path and sequence according to environmental changes and personnel health conditions.

[0172] Embodiment 1:

[0173] In order to verify the feasibility of the present invention in implementation, the present invention is applied to a certain construction site, with the aim of solving the problems existing in traditional high-altitude operations, such as lagging safety management, untimely response, and lack of effective safety guidance for workers. The construction project is located at a high-rise building site in the city center. There are about 100 construction workers and the building height is 150 meters. The construction content includes erecting scaffolding, steel bar tying and concrete pouring. Due to the complex high-rise construction environment and high safety risks, the construction site often faces problems such as workers working in high-risk areas for too long, equipment failures not being discovered in time, and unclear operation paths. Traditional manual safety management methods cannot effectively cope with these challenges. The project party introduced the full-scenario high-altitude operation intelligent monitoring system of the present invention.

[0174] In actual applications, the system deploys a variety of sensors on site to collect real-time environmental data, personnel health status data, equipment operation status data and operation path data of the work site, and generates a comprehensive safety data set through data fusion technology. These data are transmitted to the central control system in real time. After analysis by deep learning algorithms, a dynamically updated work safety ecological map is generated. In this map, information such as environmental changes, personnel health status, and equipment operation status is updated in real time, allowing safety managers to intuitively understand the safety situation at the work site and make timely decisions based on real-time data.

[0175] The system also uses virtual reality and augmented reality technologies to provide real-time safety tips and operational guidance to operators. In the AR glasses worn by each operator, the system dynamically displays a virtual safety frame based on the operator's current location and work tasks, and provides real-time safety warnings and evacuation guidance in high-risk areas. The system can also monitor the operating status of the equipment and the health status of the operators in real time. When equipment failure or personnel abnormality is detected, the system will automatically adjust the priority of the work task and promptly initiate the emergency response procedure. The emergency response includes automatically scheduling the tasks of the operators, activating emergency equipment, and providing the best evacuation route.

[0176] In actual application, the system's intelligent safety monitoring and guidance significantly improved the safety and operation efficiency of the construction site. During the two months from May 1 to June 30, 2024, no safety accidents caused by personnel negligence or equipment failure occurred at the construction site. Compared with traditional management methods, the accident rate was reduced by 40%. Through real-time monitoring, the system also discovered and warned of 12 health abnormalities of the workers. All abnormal personnel were successfully evacuated within 5 minutes after the system warning, avoiding the occurrence of safety accidents.

[0177] The system's optimization and adjustment of work tasks have also greatly improved construction efficiency. When the system detects a tower crane equipment failure, it can quickly adjust the task priority and dispatch relevant workers to other areas, thereby ensuring the smooth progress of the construction. Through intelligent priority adjustment and resource scheduling, the efficiency of the work process has been improved by 30%. Whenever there is an equipment failure or safety risk, the system can automatically initiate an emergency response within 10 seconds, and the dispatchers will quickly evacuate to a safe area. AR glasses will be used to provide the best evacuation route for the workers. Compared with traditional manual emergency responses, the response time is greatly shortened, ensuring the timely and safe evacuation of personnel.

[0178] Table 1 Data comparison of intelligent monitoring system for high-altitude operations

[0179]

[0180] According to the data in the table, it can be seen that the intelligent safety management system of the present invention has significant advantages in improving operation safety, operation efficiency and emergency response capabilities. Under the traditional operation management method, the accident rate is relatively high, with 6 to 7 accidents occurring in every 100 man-times of operation, which indicates that without effective monitoring and early warning, the operators are at high risk. After using the system of the present invention, the accident rate is greatly reduced to 0. The occurrence of zero accidents indicates the efficient safety protection role of the intelligent safety management system of the present invention at the operation site. The system can timely identify potential safety hazards by real-time collection and analysis of environmental data, personnel health status and equipment operation status at the operation site, thereby effectively preventing accidents. The system of the present invention also shows its significant advantages in safety prediction and early warning. Safety prediction and early warning in traditional operation methods usually rely on manual judgment, with low accuracy, and it is difficult to detect potential risks in time. The intelligent safety system of the present invention can achieve 98% early warning accuracy through virtual reality and augmented reality technology, combined with big data analysis and deep learning algorithms, which is much higher than the early warning accuracy of traditional methods. By transmitting safety information from the work site to the workers and the command center in real time, the workers can respond quickly and make correct safety decisions, greatly reducing the probability of accidents caused by emergencies. In terms of work efficiency, the intelligent safety management system of the present invention can effectively optimize the execution sequence of work tasks and personnel load, improving work efficiency by about 30%; in terms of emergency response, the system can complete the calculation and adjustment of the optimal evacuation path within 6 seconds, which greatly improves the emergency response speed and evacuation safety compared to the traditional method that may take tens of seconds or even minutes of reaction time.

[0181] To sum up, the advantages of the intelligent safety management system of the present invention in terms of operational safety, efficiency and emergency response can not only effectively reduce the occurrence of accidents, but also optimize operational processes and resource scheduling, and improve overall operational efficiency. In dangerous and high-risk operating environments, the system can identify potential risks in real time and make accurate warnings, providing precise safety guidance for operators, making the work site safer and more efficient.

[0182] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A method for a full-scenario high-altitude operation intelligent monitoring system, characterized in that: The steps include: S1. Deploy an intelligent safety management system that integrates multiple sensors at the work site, and deploy sensors on the safety belts of workers. The sensors on the safety belts collect environmental data, personnel health status data, equipment working status data and work path data in real time. Based on data fusion technology, combined with the complementarity and real-time feedback of various sensors, a dynamically updated work safety ecological map is generated; S2. Based on the operation safety ecological map, the real-time data is analyzed through the autonomous learning algorithm to generate an operation safety evolution model. The model includes intelligent identification of environmental changes, fluctuations in personnel health and abnormal equipment status, and generates multiple future operation scenarios and risk warnings based on the safety assessment results. The safety risks that arise during the operation are deduced in real time, and safety predictions are made in advance; S3. Based on the operation safety evolution model, the system combines real-time data feedback to automatically generate a task priority adjustment plan, sorts tasks according to the urgency and risk level of the operation tasks, and intelligently optimizes task allocation and resource scheduling based on personnel health, equipment operation and environmental factors, and adjusts the execution order of operation tasks and personnel load in real time, so that tasks can be executed efficiently according to priority; S4. When the system detects potential safety risks and abnormal events through real-time monitoring and analysis, it automatically initiates emergency response procedures, including dynamically adjusting work tasks, activating emergency equipment, remotely commanding workers to evacuate, and optimizing evacuation routes and safety protection measures based on the real-time location of workers and the state of the working environment to ensure the immediate safety of workers on the work site; S5. Use virtual reality and augmented reality technologies to overlay real-time safety prompts, operation guidance, and a virtual safety framework of the operating environment in the operator's field of vision, and transmit safety information on the work site to the operator and the command center in real time, thereby optimizing the operator's safety awareness and decision-making ability, so that the operator can always stay in a safe state in high-risk areas and make correct emergency responses; S6. The system automatically generates a closed-loop data traceability of the operation process through real-time data collection and historical data feedback, analyzes key events in the operation through machine learning algorithms, continuously optimizes the operation safety management model and emergency response strategy, and forms an adaptive safety management closed-loop system. Based on the two-way feedback of historical data and real-time data, it automatically adjusts and optimizes future operation processes and safety measures.

2. The method of the full-scenario high-altitude operation intelligent monitoring system according to claim 1 is characterized in that: The S1 specifically includes: S11. Deploy multiple sensors at the work site. The sensors are deployed on the safety belts of the workers. The data collected by the sensors on the safety belts are transmitted to the data fusion processing system in real time. The sensors include environmental monitoring sensors, personnel physiological monitoring equipment, equipment operation status sensors, and dynamic path tracking equipment. S12, receiving the raw data of each sensor, and performing denoising, calibration and completion processing on the data, fusing the multi-sensor data to generate a comprehensive safety data set, and jointly analyzing the fused multi-dimensional data through Bayesian reasoning technology, dynamically adjusting the data weight of each sensor according to the data quality. The data fusion module realizes weighted fusion of data through the following weighted fusion formula: Among them, X i (t) is the sensor S i The i-th type of data collected at time t, W i (t) is the sensor S i The weighting coefficient at time t; Weight coefficient W i (t) is calculated by the following formula: Among them, σ i 2 (t) is the sensor S i The noise variance at time t is, is the sum of the inverses of all sensor noise variances; S13. Based on the generated comprehensive safety data set, a work safety ecological map is constructed. The map includes multiple layers of environmental status, personnel health status, equipment operation status and work path data, which reflects the safety situation of the work site in real time, and provides comprehensive safety situation awareness based on multi-source information in different dimensions, assisting decision makers to promptly discover potential safety hazards; S14. The operation safety ecological map uses a dynamic update mechanism to continuously update the safety map in combination with newly collected data and real-time processing results. The updated map can promptly reflect environmental changes, personnel health fluctuations, equipment status abnormalities, and changes in operation paths; S15. Through the monitoring terminal and interactive operation interface, the operation safety ecological map is visualized in real time to help operators and safety managers intuitively understand the safety situation of the operation site and obtain safety risk warnings in real time. Operators and managers can make decisions based on the key information provided in the map to optimize the safety management effect of the operation site.

3. The method of the full-scenario high-altitude operation intelligent monitoring system according to claim 1 is characterized in that: The S2 specifically includes: S21. Based on the operation safety ecological map, the real-time collected data is analyzed through an autonomous learning algorithm, wherein the data includes environmental data, personnel health status data, equipment operation status data and operation path data, and a comprehensive safety data set is generated through data fusion processing; S22. After obtaining the comprehensive safety data set, construct an operation safety evolution model based on the data set, wherein the model intelligently identifies environmental changes, fluctuations in personnel health, and abnormal equipment status, and predicts potential safety risks in the operation process based on the risk assessment results output by the model; S23. The construction process of the operation safety evolution model extracts key features from real-time data through deep learning algorithms, models the time series features of various data sources, and conducts cross-correlation analysis of environmental, personnel health and equipment status data to identify the potential impact of various factors on operation safety and generate multiple operation safety evolution scenarios. The system uses an adaptive algorithm to dynamically adjust model parameters and continuously optimizes model output in combination with real-time data feedback to optimize the accuracy, adaptability and real-time performance of predictions. S24. The operation safety evolution model deduce the safety risks that occur during the operation in real time based on real-time data feedback, and generates multiple future operation scenarios and corresponding safety risk warnings. The risk warnings include multiple potential risks such as environmental changes, abnormal health of personnel, and equipment failures, and evaluate the generation of risk warnings: Where R(t) is the safety risk assessment value at time t, W i (t) is the weight coefficient of the i-th data source, A i (t) is the risk impact value of the i-th data source at time t, and n is the number of factors affecting security risk; S25. The operation safety evolution model is continuously optimized and dynamically adjusts the prediction parameters in combination with real-time data feedback, so as to generate the best safety prediction plan before each operation and optimize the timeliness and effectiveness of various safety protection measures during the operation; S26. Based on the prediction results of the operation safety evolution model, the system automatically generates targeted safety warnings and issues warning information in a timely manner to provide real-time safety guidance to operators and optimize operation plans.

4. The method of the full-scenario high-altitude operation intelligent monitoring system according to claim 1 is characterized in that: The S3 specifically includes: S31. Based on the operation safety evolution model, the system automatically generates a priority adjustment plan for the operation task according to real-time data feedback, and the plan is based on the urgency of the operation task, the risk level, the health status of the personnel, and the operation status of the equipment; S32. The system evaluates the urgency of each task according to the time requirements, environmental factors and scheduled completion time of the task, conducts safety risk assessment on the tasks according to the risk assessment result R(t) and sorts them by risk level, dynamically adjusts the priority of the tasks according to the health status of the personnel, the operating status of the equipment and the operation scenario, gives priority to high-risk tasks, tasks with poor health and abnormal equipment, optimizes the safety management during the operation, and automatically calculates and adjusts the priority of the task according to the following formula P i (t): Pi(t)=α·Ei(t)+β·Ri(t)+γ·Hi(t)+δ·Di(t); Among them, P i (t) is the priority of the i-th task at time t, E i (t) is the urgency of the i-th task, R i (t) is the risk level of the i-th task, H i (t) is the influencing factor of the personnel health status of the i-th task, D i (t) is the influencing factor of the equipment operation status of the i-th task, and α, β, γ, δ are weighting coefficients; S33, the system calculates the result P i (t), sort the tasks and automatically optimize resource allocation and task execution order. The adjusted priority P i (t) Affects the execution order of tasks, personnel load and resource scheduling; S34. The system dynamically adjusts the priority of work tasks by real-time monitoring of the health status of operators, equipment status and environmental changes. The priority of work tasks is optimized over time: Pi(t+Δt)=Pi(t)+λ·ΔPi(t); Among them, P i (t+Δt) is the update priority of the task at time t+Δt, P i (t) is the task priority at time t, ΔP i (t) is the change in task priority, λ is the adjustment coefficient; S35. Based on the adjusted task priorities and resource scheduling, the system adjusts the execution order of the work tasks, personnel load and task execution plan in real time to optimize work efficiency, and continues to adjust task priorities based on real-time feedback to cope with dynamic factors such as environmental changes, fluctuations in personnel health, and abnormal equipment status.

5. The method of the full-scenario high-altitude operation intelligent monitoring system according to claim 1 is characterized in that: The S4 specifically includes: S41. When the system detects potential safety risks and abnormal events through real-time monitoring and analysis, it automatically initiates emergency response procedures, including dynamically adjusting work tasks, activating emergency equipment, remotely commanding workers to evacuate, and optimizing evacuation routes and safety protection measures based on the real-time location and environmental status of workers, so as to ensure the immediate safety of workers on the work site; S42. During the emergency response process, the safety status of the work site is evaluated based on real-time data feedback, potential safety risk areas are generated and the immediate location of the workers is determined. Combined with the environmental data of the work site, the health status data of the workers, the equipment failure information and the work path data, the emergency response plan is dynamically generated, and the evacuation path of the workers is optimized in real time; S43. The system uses an optimization formula to calculate the optimal evacuation path for each operator, taking into account multiple factors such as environmental risks, personnel health status, equipment failure information and path length: Among them, L i (t) is the optimal evacuation path length of the i-th operator at time t, k is the path selection index, m is the total number of path nodes, W j (t) is the weight coefficient of the jth evacuation path node, D j (t) is the distance from the jth path node to the safe area, R j (t) is the environmental risk level of the jth path node, H j (t) is the health risk coefficient of the jth path node, E j (t) is the equipment failure risk coefficient of the jth path node; S44. Based on the optimal evacuation path, dynamically adjust the evacuation route of the operators through the path optimization algorithm to avoid high-risk areas in the shortest time and optimize the impact on personnel health, equipment failure and environmental factors on evacuation safety; S45. The system automatically updates the evacuation path dynamically based on real-time environmental changes, operator health status and equipment failure information, optimizes the evacuation sequence and personnel load, and dynamically adjusts the evacuation path: Li(t+Δt)=Li(t)+λ·ΔLi(t); Among them, L i (t+Δt) is the updated evacuation path of the operator at time t+Δt, L i (t) is the original evacuation path of the operator at time t, ΔL i (t) is the change in the evacuation path of the operators, and λ is the adjustment coefficient; S46. Based on the data feedback from real-time monitoring, dynamically adjust the evacuation path and sequence of workers to cope with changes in the work site environment, fluctuations in the health status of workers, and uncertainties in equipment failures, to ensure the immediate safety of workers on the work site.

6. The method of the full-scenario high-altitude operation intelligent monitoring system according to claim 1 is characterized in that: The S5 specifically includes: S51. The system uses virtual reality and augmented reality technologies to overlay real-time safety prompts, operation guidance, and a virtual safety framework of the operating environment in the operator's field of vision. The safety information includes environmental data, personnel health status, equipment operating status, and operating path information, providing real-time safety monitoring information on the operating site to help operators understand the safety situation. S52. Generate safety tips for workers at their locations through real-time data analysis, and dynamically update operation guidance and virtual safety frameworks based on environmental changes at the work site, optimizing workers’ safety awareness and decision-making capabilities in high-risk areas; S53. The system monitors the environmental changes, health status of operators and equipment operation status of the work site in real time, and automatically adjusts the safety information in the virtual safety framework according to the real-time location and work path of the operators, so that the operators are always within the safe operation range; S54. Operators receive real-time safety warnings, operation instructions and environmental risk information from the command center through virtual reality and augmented reality devices, enabling operators to make safety decisions and responses in real time during the operation process; S55. Based on real-time data feedback, the system intelligently optimizes the safety guidance of workers and dynamically adjusts the safety prompts in the virtual safety framework so that the content of the safety prompts always matches the current working environment and the health status of the workers, thereby optimizing the safety of the work site.

7. The method of the full-scenario high-altitude operation intelligent monitoring system according to claim 1 is characterized in that: The S6 specifically includes: S61. The system automatically generates closed-loop data traceability of the operation process through real-time data collection and historical data feedback, and analyzes key events in the operation process through machine learning algorithms, cross-analyzes historical data and real-time data, identifies potential safety risks, and dynamically generates operation safety assessment reports; S63. Based on the operation safety assessment report, the system continuously optimizes the operation safety management model, adjusts the operation safety management strategy, and forms an adaptive safety management closed-loop system. The closed-loop system automatically adjusts the operation process and operation safety measures according to the real-time data and historical data feedback to adapt to the changes in the operation environment; S64. Based on the feedback of historical data and real-time data, the system automatically identifies potential risk points in the operation process and generates optimization strategies. The optimization strategies include optimizing the operation process, adjusting safety measures, and optimizing the time that operators are exposed to high-risk areas: Among them, O(t) represents the optimization degree of the operation at time t, W i (t) is the weight coefficient of the i-th data source, R i (t) is the risk value of the i-th data source at time t, S i (t) is the impact of the i-th data source on operation safety, H i (t) is the health risk value of the i-th data source, P(t) is the priority of the task, and n is the number of factors affecting the optimization of the task; S65. The system automatically adjusts various safety measures in the operation process according to the safety optimization degree O(t), and continuously optimizes the execution of the operation process through real-time data feedback, so that the operation process and safety measures always maintain the optimal state in the dynamically changing operation environment, and optimizes the potential safety risks in the operation; S66. Through a closed-loop traceability mechanism, key events in the operation process are continuously monitored, and the operation safety management model is adjusted based on the feedback of event analysis results to cope with the ever-changing operating environment and potential risks, and ensure the safety of operators and the working environment.

8. The full-scenario high-altitude operation intelligent monitoring system is characterized by: Includes the following modules: Sensor network module: real-time collection of work site environment data, personnel health data, equipment status data and work path data; Data fusion and processing module: denoises, calibrates and fuses multi-source data to generate a comprehensive security data set; Operation safety ecological map module: integrates various safety data to reflect the safety situation of the operation site in real time; Safety risk assessment and prediction module: Generate safety risk assessment based on real-time data and predict potential safety hazards; Intelligent task priority adjustment module: dynamically adjust task priorities based on task urgency, risk level and personnel health factors; Emergency response and optimization module: When safety risks are detected, emergency response is initiated and evacuation routes and safety measures are optimized; Augmented reality and virtual reality safety guidance module: Provide real-time safety tips and operation guidance to operators through AR and VR technology; Safety monitoring and decision support module: real-time monitoring of on-site data, assisting operators in making safety decisions; Data closed loop and traceability analysis module: Based on historical and real-time data analysis, continuously optimize the operation safety management model and emergency response strategy; System adaptive learning and optimization module: optimizes the operation safety management strategy through machine learning to adapt to the changing operating environment; Safety warning and risk control module: Generates real-time safety warnings to help operators respond to risks quickly; Job task optimization and resource scheduling module: dynamically adjust the execution order of job tasks and personnel load, and optimize resource scheduling; Path optimization and personnel evacuation module: optimize the evacuation path and sequence according to environmental changes and personnel health conditions.

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