Full-scene high-altitude operation intelligent monitoring system

Abstract

The present invention discloses a full-scenario intelligent monitoring system for high-altitude operations. The system includes the following steps: using multiple sensors to collect real-time data on the environment, personnel health, equipment operation, and operation paths; generating a safety ecological map through data fusion technology to reflect the safety situation at the operation site in real time; using intelligent algorithms to analyze this data, predict safety risks and generate emergency warnings; dynamically adjusting the order of operation tasks based on task priority, risk level, and personnel health status to optimize resource allocation; during the operation process, the system uses virtual reality and augmented reality technologies to provide real-time safety prompts and operation guidance to operators, enhancing safety awareness and decision-making ability; the system has an automatic emergency response function, can optimize evacuation routes in real time, ensure the safety of operators, and through adaptive learning, can continuously optimize safety management strategies to improve operation efficiency and safety. The present invention aims to improve safety and efficiency in high-risk operation environments.

Description

Technical Field

[0001] The present invention relates to the field of intelligent safety monitoring technology, and in particular to a full-scenario intelligent monitoring system for high-altitude operations. 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 and can often 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 the heart rate and body temperature of workers, and 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 the ability to integrate multi-dimensional data across domains. When multiple safety risks exist at the same time, traditional systems find it difficult 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 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 provide early 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 are often unable to respond in time, or can only passively issue warnings, failing to provide real-time and effective response strategies for operators.

[0005] In the existing technology, when workers work 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 safety information display method 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 information display method cannot effectively guide them to take the best response measures.

[0006] Existing technologies also have defects in their emergency response mechanisms. Although some systems can initiate emergency response procedures after discovering safety risks, such responses are often passive and lack accurate risk assessments. When equipment fails or environmental conditions change significantly, traditional systems may not be able to provide real-time emergency guidance or optimize evacuation routes. 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 a timely manner when danger occurs, thereby affecting the effectiveness of emergency measures. Traditional emergency response systems rely more on post-event rescue and fail to achieve pre-emptive 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 status, 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, resulting in long-term exposure of workers to dangerous environments. 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-scene intelligent monitoring system for high-altitude operations is an urgent problem that technical personnel in this field need to solve. Summary of the Invention

[0009] One purpose of the present invention is to propose a full-scene intelligent monitoring system for high-altitude operations. The present invention monitors the safety of the operation site in real time by integrating multiple sensors, real-time data acquisition and analysis technologies, as well as virtual reality and augmented reality technologies. The system constructs a dynamically updated safety ecological map by integrating environmental data, personnel health status, equipment operating status and other information, uses deep learning algorithms to intelligently identify potential safety risks, and generates 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 equipment, helping operators make quick and safe decisions in complex high-risk environments. This 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 integrating multiple sensors at the work site. These sensors are placed on workers' safety belts. These sensors collect real-time environmental data, worker health data, equipment operating status data, and work path data. Data fusion technology combines the complementarity and real-time feedback of each sensor to generate a dynamically updated work safety ecosystem.

[0012] S2. Based on the operational safety ecosystem map, the system analyzes real-time data through a self-learning algorithm to generate an operational safety evolution model. This model includes intelligent recognition of environmental changes, fluctuations in personnel health, and abnormal equipment status. Based on the safety assessment results, it generates multiple future operational scenarios and their risk warnings, allowing for real-time analysis of safety risks arising during operations and early safety predictions.

[0013] S3. Based on the operational safety evolution model, the system automatically generates a task priority adjustment plan in conjunction with real-time data feedback. It sorts tasks according to their urgency and risk level, and intelligently optimizes task allocation and resource scheduling based on personnel health, equipment operation conditions, and environmental factors. It adjusts the execution order of tasks and personnel load in real time to ensure efficient execution of tasks 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, and remotely commanding workers to evacuate. It also optimizes evacuation routes and safety protection measures based on the workers' real-time location and work environment status to ensure the immediate safety of workers on the job site.

[0015] S5. Utilize virtual reality and augmented reality technologies to overlay real-time safety prompts, operational guidance, and a virtual safety framework of the work environment in the operator's field of view. This transmits safety information from the work site to the operator and the command center in real time, optimizing the operator's safety awareness and decision-making ability, ensuring that operators remain safe in high-risk areas and can respond appropriately to emergencies.

[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 workers' safety belts. Data collected by the sensors on the safety belts is transmitted in real time to the data fusion processing system. The sensors include environmental monitoring sensors, personnel physiological monitoring equipment, equipment operation status sensors, and dynamic path tracking equipment.

[0019] S12. Receive the raw data from each sensor and perform denoising, calibration, and completion on the data. Fusion of multi-sensor data generates a comprehensive safety data set. Joint analysis of the fused multi-dimensional data is performed using Bayesian reasoning technology. The data weights of each sensor are dynamically adjusted based on data quality. The data fusion module implements weighted fusion of data using 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 inverse of the noise variance of all sensors;

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

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

[0027] S15. Through the monitoring terminal and interactive operation interface, the operation safety ecological map is visualized in real time, helping operators and safety managers to 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 operational safety ecological map, analyze the real-time collected data using an autonomous learning algorithm. The data includes environmental data, personnel health status data, equipment operating status data, and operational path data. After data fusion processing, generate a comprehensive safety data set.

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

[0031] S23. The construction process of the operational safety evolution model uses deep learning algorithms to extract key features from real-time data, model the time series characteristics of various data sources, and conduct cross-correlation analysis of environmental, personnel health, and equipment status data to identify the potential impact of various factors on operational safety and generate multiple operational safety evolution scenarios. The system uses adaptive algorithms to dynamically adjust model parameters and continuously optimizes model output based on real-time data feedback to optimize prediction accuracy, adaptability, and real-time performance.

[0032] S24. The Operation Safety Evolution Model uses real-time data feedback to deduce safety risks that may arise during the operation in real time and generates multiple future operation scenarios and corresponding safety risk warnings. The risk warnings include multiple potential risks such as environmental changes, abnormal personnel health, and equipment failures. The generation of risk warnings is evaluated:

[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 operational safety evolution model is continuously optimized and dynamically adjusts prediction parameters based on real-time data feedback, thereby generating the optimal safety prediction plan before each operation and optimizing 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 operational safety evolution model, the system automatically generates a priority adjustment plan for operational tasks based on real-time data feedback. The plan is based on the urgency of the operational tasks, the risk level, the health status of personnel, and the operation status of equipment.

[0039] S32. The system evaluates the urgency of each task based on the task's time requirements, environmental factors, and scheduled completion time. It then performs a safety risk assessment on the tasks based on the risk assessment result R(t) and ranks them by risk level. It dynamically adjusts the priority of tasks based on the health status of personnel, equipment operating conditions, and the operation scenario, prioritizing high-risk tasks, tasks with poor health conditions, and tasks with equipment anomalies. This optimizes safety management during the operation and automatically calculates and adjusts the priority of tasks according to the following formula: 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 health status of personnel in the i-th task, D i (t) is the influencing factor of the equipment operation status of the i-th task, 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 workers, 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 updated 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, and 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 the 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 of workers and environmental conditions, ensuring the immediate safety of workers on the work site;

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

[0050] S43. The system uses an optimization formula to calculate the optimal evacuation path for each worker, taking into account multiple factors including environmental risks, worker 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, the evacuation route of the workers is dynamically adjusted through the path optimization algorithm to avoid high-risk areas in the shortest time possible and minimize the impact of personnel health, equipment failures, 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 workers at time t+Δt, L i (t) is the original evacuation path of the workers at time t, ΔL i (t) is the change in the workers' evacuation path, and λ is the adjustment coefficient;

[0057] S46. Based on real-time monitoring data feedback, dynamically adjust the evacuation path and sequence of workers to cope with changes in the work site environment, fluctuations in personnel health status, 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 work environment in the operator's field of view. The safety information includes environmental data, operator health status, equipment operating status, and work path information, providing real-time safety monitoring information on the work site to help operators understand the safety situation.

[0060] S52. Generate safety alerts for workers at their locations through real-time data analysis, and dynamically update operational guidance and the virtual safety framework 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 at the work site, the health status of the workers, and the operation status of the equipment in real time. It automatically adjusts the safety information in the virtual safety framework based on the real-time location and work path of the workers, so that the workers are always within the safe operating range.

[0062] S54. Operators receive real-time safety warnings, operational guidance, and environmental risk information from the command center through virtual reality and augmented reality devices, enabling them to make real-time safety decisions and responses during operations.

[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 for the operation process through real-time data collection and historical data feedback. It also uses machine learning algorithms to analyze key events in the operation process, cross-analyze historical and real-time data, identify potential safety risks, and dynamically generate operation safety assessment reports.

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

[0067] S64. Based on feedback from historical 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 workers 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 degree 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 optimize the potential safety risks in the operation;

[0071] S66. Through a closed-loop traceability mechanism, continuously monitor key events during the operation process, adjust the operation safety management model based on the feedback of event analysis results, respond to 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] Operational 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: Generates safety risk assessment based on real-time data and predicts potential safety hazards;

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

[0078] Emergency response and optimization module: When a safety risk is detected, it initiates emergency response and optimizes evacuation routes and safety measures;

[0079] Augmented reality and virtual reality safety guidance module: Provides real-time safety prompts and operation guidance to operators through AR and VR technologies;

[0080] Safety monitoring and decision support module: real-time monitoring of work site data to assist 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 operational safety management strategies through machine learning to adapt to changing operational environments;

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

[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 based on 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] This 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 safety of the workers' lives.

[0090] This invention combines virtual reality and augmented reality technologies to superimpose real-time safety prompts, operational guidance, and a virtual safety framework in the operator's field of view, 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 based on their current location and operation path information to ensure their 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, 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 Schematic diagram of the safety guidance display interface based on virtual reality and augmented reality technologies in the present invention. DETAILED DESCRIPTION

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

[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 integrating multiple sensors at the work site. These sensors are placed on workers' safety belts. These sensors collect real-time environmental data, worker health data, equipment operating status data, and work path data. Data fusion technology combines the complementarity and real-time feedback of each sensor to generate a dynamically updated work safety ecosystem.

[0098] S2. Based on the operational safety ecosystem map, the system analyzes real-time data through a self-learning algorithm to generate an operational safety evolution model. This model includes intelligent recognition of environmental changes, fluctuations in personnel health, and abnormal equipment status. Based on the safety assessment results, it generates multiple future operational scenarios and their risk warnings, allowing for real-time analysis of safety risks arising during operations and early safety predictions.

[0099] S3. Based on the operational safety evolution model, the system automatically generates a task priority adjustment plan in conjunction with real-time data feedback. It sorts tasks according to their urgency and risk level, and intelligently optimizes task allocation and resource scheduling based on personnel health, equipment operation conditions, and environmental factors. It adjusts the execution order of tasks and personnel load in real time to ensure efficient execution of tasks 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, and remotely commanding workers to evacuate. It also optimizes evacuation routes and safety protection measures based on the workers' real-time location and work environment status to ensure the immediate safety of workers on the job site.

[0101] S5. Utilize virtual reality and augmented reality technologies to overlay real-time safety prompts, operational guidance, and a virtual safety framework of the work environment in the operator's field of view. This transmits safety information from the work site to the operator and the command center in real time, optimizing the operator's safety awareness and decision-making ability, ensuring that operators remain safe in high-risk areas and can respond appropriately to emergencies.

[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 embodiment, S1 specifically includes:

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

[0105] S12. Receive the raw data from each sensor and perform denoising, calibration, and completion on the data. Fusion of multi-sensor data generates a comprehensive safety data set. Joint analysis of the fused multi-dimensional data is performed using Bayesian reasoning technology. The data weights of each sensor are dynamically adjusted based on data quality. The data fusion module implements weighted fusion of data using 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 inverse of the noise variance of all sensors;

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

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

[0113] S15. Through the monitoring terminal and interactive operation interface, the operation safety ecological map is visualized in real time, helping operators and safety managers to 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 embodiment, S2 specifically includes:

[0115] S21. Based on the operational safety ecological map, analyze the real-time collected data using an autonomous learning algorithm. The data includes environmental data, personnel health status data, equipment operating status data, and operational path data. After data fusion processing, generate a comprehensive safety data set.

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

[0117] S23. The construction process of the operational safety evolution model uses deep learning algorithms to extract key features from real-time data, model the time series characteristics of various data sources, and conduct cross-correlation analysis of environmental, personnel health, and equipment status data to identify the potential impact of various factors on operational safety and generate multiple operational safety evolution scenarios. The system uses adaptive algorithms to dynamically adjust model parameters and continuously optimizes model output based on real-time data feedback to optimize prediction accuracy, adaptability, and real-time performance.

[0118] S24. The Operation Safety Evolution Model uses real-time data feedback to deduce safety risks that may arise during the operation in real time and generates multiple future operation scenarios and corresponding safety risk warnings. The risk warnings include multiple potential risks such as environmental changes, abnormal personnel health, and equipment failures. The generation of risk warnings is evaluated:

[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 operational safety evolution model is continuously optimized and dynamically adjusts prediction parameters based on real-time data feedback, thereby generating the optimal safety prediction plan before each operation and optimizing 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 embodiment, S3 specifically includes:

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

[0125] S32. The system evaluates the urgency of each task based on the task's time requirements, environmental factors, and scheduled completion time. It then performs a safety risk assessment on the tasks based on the risk assessment result R(t) and ranks them by risk level. It dynamically adjusts the priority of tasks based on the health status of personnel, equipment operating conditions, and the operation scenario, prioritizing high-risk tasks, tasks with poor health conditions, and tasks with equipment anomalies. This optimizes safety management during the operation and automatically calculates and adjusts the priority of tasks according to the following formula: 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 health status of personnel in the i-th task, D i (t) is the influencing factor of the equipment operation status of the i-th task, 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 workers, 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 updated 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, and 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 the 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 embodiment, the 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 of workers and environmental conditions, ensuring the immediate safety of workers on the work site;

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

[0136] S43. The system uses an optimization formula to calculate the optimal evacuation path for each worker, taking into account multiple factors including environmental risks, worker 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, the evacuation route of the workers is dynamically adjusted through the path optimization algorithm to avoid high-risk areas in the shortest time possible and minimize the impact of personnel health, equipment failures, 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 workers at time t+Δt, L i (t) is the original evacuation path of the workers at time t, ΔL i (t) is the change in the workers' evacuation path, and λ is the adjustment coefficient;

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

[0144] In this embodiment, the 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 work environment in the operator's field of view. The safety information includes environmental data, operator health status, equipment operating status, and work path information, providing real-time safety monitoring information on the work site to help operators understand the safety situation.

[0146] S52. Generate safety alerts for workers at their locations through real-time data analysis, and dynamically update operational guidance and the virtual safety framework 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 at the work site, the health status of the workers, and the operation status of the equipment in real time. It automatically adjusts the safety information in the virtual safety framework based on the real-time location and work path of the workers, so that the workers are always within the safe operating range.

[0148] S54. Operators receive real-time safety warnings, operational guidance, and environmental risk information from the command center through virtual reality and augmented reality devices, enabling them to make real-time safety decisions and responses during operations.

[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 embodiment, S6 specifically includes:

[0151] S61. The system automatically generates closed-loop data traceability for the operation process through real-time data collection and historical data feedback. It also uses machine learning algorithms to analyze key events in the operation process, cross-analyze historical and real-time data, identify potential safety risks, and dynamically generate operation safety assessment reports.

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

[0153] S64. Based on feedback from historical 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 workers 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 degree 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 optimize the potential safety risks in the operation;

[0157] S66. Through a closed-loop traceability mechanism, continuously monitor key events during the operation process, adjust the operation safety management model based on the feedback of event analysis results, respond to 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] Operational 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: Generates safety risk assessment based on real-time data and predicts potential safety hazards;

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

[0164] Emergency response and optimization module: When a safety risk is detected, it initiates emergency response and optimizes evacuation routes and safety measures;

[0165] Augmented reality and virtual reality safety guidance module: Provides real-time safety prompts and operation guidance to operators through AR and VR technologies;

[0166] Safety monitoring and decision support module: real-time monitoring of work site data to assist 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 operational safety management strategies through machine learning to adapt to changing operational environments;

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

[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 based on environmental changes and personnel health conditions.

[0172] Example 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 is 150 meters high. The construction content includes erecting scaffolding, steel bar binding, 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-scene 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. This data is transmitted to the central control system in real time. After analysis by deep learning algorithms, a dynamically updated operation 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 framework based on the operator's current location and work tasks, and provides real-time safety warnings and evacuation instructions 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 operator, 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 operating efficiency of the construction site. During the two months from May 1 to June 30, 2024, no safety accidents caused by human 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's 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 construction. Through intelligent priority adjustment and resource scheduling, the efficiency of the work process has been improved by 30%. Whenever an equipment failure or safety risk occurs, the system can automatically initiate an emergency response within 10 seconds, and the dispatch personnel will quickly evacuate to a safe area. AR glasses are used to provide the best evacuation route for the workers. Compared with traditional manual emergency response, 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] The data in the table demonstrates the significant advantages of the intelligent safety management system of the present invention in improving operational safety, efficiency, and emergency response capabilities. Under traditional operational management methods, the accident rate is relatively high, with 6 to 7 accidents occurring for every 100 man-hours. This indicates that without effective monitoring and early warning, workers are at high risk. However, with the system of the present invention, the accident rate has been significantly reduced to zero. This zero-accident situation demonstrates the effective safety protection provided by the intelligent safety management system of the present invention at the work site. By collecting and analyzing real-time environmental data, personnel health status, and equipment operation at the work site, the system can promptly identify potential safety hazards, thereby effectively preventing accidents. The system of the present invention also demonstrates significant advantages in safety prediction and early warning. Traditional safety prediction and early warning methods typically rely on manual judgment, resulting in low accuracy and difficulty in timely identifying potential risks. However, the intelligent safety system of the present invention, by combining virtual reality and augmented reality technologies with big data analysis and deep learning algorithms, can achieve a 98% early warning accuracy rate, far exceeding the 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.

[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 embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any technician familiar with the technical field, within the technical scope disclosed by the present invention, who makes equivalent replacements or changes based on the technical solution and inventive concept of the present invention, should be covered by the scope of protection of the present invention.

Claims

1. A method for a full-scene high-altitude operation intelligent monitoring system, characterized in that: The steps include: S1. Deploy an intelligent safety management system integrating multiple sensors at the work site. These sensors are placed on workers' safety belts. These sensors collect real-time environmental data, worker health data, equipment operating status data, and work path data. Data fusion technology combines the complementarity and real-time feedback of each sensor to generate a dynamically updated work safety ecosystem. S2. Based on the operational safety ecosystem map, the system analyzes real-time data through a self-learning algorithm to generate an operational safety evolution model. This model includes intelligent recognition of environmental changes, fluctuations in personnel health, and abnormal equipment status. Based on the safety assessment results, it generates multiple future operational scenarios and their risk warnings, allowing for real-time analysis of safety risks arising during operations and early safety predictions. S3. Based on the operational safety evolution model, the system automatically generates a task priority adjustment plan in conjunction with real-time data feedback. It sorts tasks according to their urgency and risk level, and intelligently optimizes task allocation and resource scheduling based on personnel health, equipment operation conditions, and environmental factors. It adjusts the execution order of tasks and personnel load in real time to ensure efficient execution of tasks 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, and remotely commanding workers to evacuate. It also optimizes evacuation routes and safety protection measures based on the workers' real-time location and work environment status to ensure the immediate safety of workers on the job site. S5. Utilize virtual reality and augmented reality technologies to overlay real-time safety prompts, operational guidance, and a virtual safety framework of the work environment in the operator's field of view. This transmits safety information from the work site to the operator and the command center in real time, optimizing the operator's safety awareness and decision-making ability, ensuring that operators remain safe in high-risk areas and can respond appropriately to emergencies. S6. The system automatically generates a closed-loop data traceability system for the operation process through real-time data collection and historical data feedback. It 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 closed-loop safety management 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. The S2 specifically includes: S21. Based on the operational safety ecological map, analyze the real-time collected data using an autonomous learning algorithm. The data includes environmental data, personnel health status data, equipment operating status data, and operational path data. After data fusion processing, generate a comprehensive safety data set. S22. After obtaining the comprehensive safety data set, construct an operation safety evolution model based on the data set. The model intelligently identifies environmental changes, fluctuations in personnel health, and abnormal equipment status, and predicts potential safety risks during the operation based on the risk assessment results output by the model; S23. The construction process of the operational safety evolution model involves extracting key features from real-time data through deep learning algorithms, modeling the time series characteristics of various data sources, and conducting cross-correlation analysis of environmental, personnel health, and equipment status data to identify the potential impact of various factors on operational safety. Multiple operational safety evolution scenarios are generated. The system uses an adaptive algorithm to dynamically adjust model parameters and continuously optimizes model output based on real-time data feedback to optimize prediction accuracy, adaptability, and real-time performance. S24. The Operation Safety Evolution Model uses real-time data feedback to deduce safety risks that may arise during the operation in real time and generates multiple future operation scenarios and corresponding safety risk warnings. The risk warnings include multiple potential risks such as environmental changes, abnormal personnel health, and equipment failures. The generation of risk warnings is evaluated: 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 operational safety evolution model is continuously optimized and dynamically adjusts prediction parameters based on real-time data feedback, thereby generating the optimal safety prediction plan before each operation and optimizing 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.

2. The method of the full-scene high-altitude operation intelligent monitoring system according to claim 1 is characterized in that: Said S1 specifically includes: S11. Deploy multiple sensors at the work site. The sensors are deployed on the workers' safety belts. Data collected by the sensors on the safety belts is transmitted in real time to the data fusion processing system. The sensors include environmental monitoring sensors, personnel physiological monitoring equipment, equipment operation status sensors, and dynamic path tracking equipment. S12. Receive the raw data from each sensor and perform denoising, calibration, and completion on the data. Fusion of multi-sensor data generates a comprehensive safety data set. Joint analysis of the fused multi-dimensional data is performed using Bayesian reasoning technology. The data weights of each sensor are dynamically adjusted based on data quality. The data fusion module implements weighted fusion of data using 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: in, For sensor S i The noise variance at time t is, is the sum of the inverse of the noise variance of all sensors; S13. Based on the generated comprehensive safety data set, a safety ecological map of the operation is constructed. The map includes multiple layers of environmental status, personnel health status, equipment operation status, and operation path data. It reflects the safety situation of the operation site in real time and provides comprehensive safety situation awareness based on multi-source information from different dimensions, assisting decision makers in timely identifying potential safety hazards. S14. The operational safety ecological map utilizes a dynamic update mechanism, combining newly collected data with real-time processing results to continuously update the safety map. The updated map can promptly reflect environmental changes, fluctuations in personnel health, abnormal equipment status, and changes in operational paths. S15. Through the monitoring terminal and interactive operation interface, the operation safety ecological map is visualized in real time, helping operators and safety managers to 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-scene height operation intelligent monitoring system according to claim 1 is characterized in that: The S3 specifically includes: S31. Based on the operational safety evolution model, the system automatically generates a priority adjustment plan for operational tasks based on real-time data feedback. The plan is based on the urgency of the operational tasks, the risk level, the health status of personnel, and the operating status of equipment. S32. The system evaluates the urgency of each task based on the task's time requirements, environmental factors, and scheduled completion time. It then performs a safety risk assessment on the tasks based on the risk assessment result R(t) and ranks them by risk level. It dynamically adjusts the priority of tasks based on the health status of personnel, equipment operating conditions, and the operation scenario, prioritizing high-risk tasks, tasks with poor health conditions, and tasks with equipment anomalies. This optimizes safety management during the operation and automatically calculates and adjusts the priority of tasks according to the following formula: i (t): P i (t)=α·E i (t)+β·R i (t)+γ·H i (t)+δ·D i (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 health status of personnel in the i-th task, D i (t) is the influencing factor of the equipment operation status of the i-th task, α, β, γ, δ are weighting coefficients; 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; S34. The system dynamically adjusts the priority of work tasks by real-time monitoring of the health status of workers, equipment status, and environmental changes. The priority of work tasks is optimized over time: P i (t+Δt)=P i (t)+λ·ΔP i (t); Among them, P i (t+Δt) is the updated 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, and 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 the task priorities based on real-time feedback to cope with dynamic factors such as environmental changes, fluctuations in personnel health, and abnormal equipment status.

4. The method of the full-scene height 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 workers' real-time location and environmental conditions 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 assessed based on real-time data feedback, potential safety risk areas are generated, and the immediate location of workers is determined. Combined with the work site's environmental data, worker health status data, equipment failure information, and work path data, an emergency response plan is dynamically generated, and evacuation routes are optimized in real time. S43. The system uses an optimization formula to calculate the optimal evacuation path for each worker, taking into account multiple factors including environmental risks, worker 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, the evacuation route of the workers is dynamically adjusted through the path optimization algorithm to avoid high-risk areas in the shortest time possible and minimize the impact of personnel health, equipment failures, 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: L i (t+Δt)=L i (t)+λ·ΔL i (t); Among them, L i (t+Δt) is the updated evacuation path of the workers at time t+Δt, L i (t) is the original evacuation path of the workers at time t, ΔL i (t) is the change in the workers' evacuation path, and λ is the adjustment coefficient; S46. Based on real-time monitoring data feedback, dynamically adjust the evacuation path and sequence of workers to cope with changes in the work site environment, fluctuations in personnel health status, and uncertainties in equipment failures, to ensure the immediate safety of workers on the work site.

5. The method of the full-scene height 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 work environment in the operator's field of view. The safety information includes environmental data, operator health status, equipment operating status, and work path information, providing real-time safety monitoring information on the work site to help operators understand the safety situation. S52. Generate safety alerts for workers at their locations through real-time data analysis, and dynamically update operational guidance and the virtual safety framework 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 at the work site, the health status of the workers, and the operation status of the equipment in real time. It automatically adjusts the safety information in the virtual safety framework based on the real-time location and work path of the workers, so that the workers are always within the safe operating range. S54. Operators receive real-time safety warnings, operational guidance, and environmental risk information from the command center through virtual reality and augmented reality devices, enabling them to make real-time safety decisions and responses during operations. 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.

6. The method of the full-scene height 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 for the operation process through real-time data collection and historical data feedback. It also uses machine learning algorithms to analyze key events in the operation process, cross-analyze historical and real-time data, identify potential safety risks, and dynamically generate operation safety assessment reports. S62. Based on the work safety assessment report, the system continuously optimizes the work safety management model and adjusts the work safety management strategy to form an adaptive safety management closed-loop system. The closed-loop system automatically adjusts work processes and work safety measures based on real-time and historical data feedback to adapt to changes in the work environment. S63. Based on feedback from historical 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 workers 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 degree of the task; S64. The system automatically adjusts various safety measures in the operation process according to the operation 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 optimize the potential safety risks in the operation; S65. Through a closed-loop traceability mechanism, continuously monitor key events during the operation process, adjust the operation safety management model based on the feedback of event analysis results, respond to the ever-changing operating environment and potential risks, and ensure the safety of operators and the working environment.

7. The full-scenario high-altitude operation intelligent monitoring system according to claim 1 is characterized in that: 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; Operational 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: Generates safety risk assessment based on real-time data and predicts potential safety hazards; Intelligent task priority adjustment module: dynamically adjusts task priorities based on task urgency, risk level, and personnel health factors; Emergency response and optimization module: When a safety risk is detected, it initiates emergency response and optimizes evacuation routes and safety measures; Augmented reality and virtual reality safety guidance module: Provides real-time safety prompts and operation guidance to operators through AR and VR technologies; Safety monitoring and decision support module: real-time monitoring of work site data to assist 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 operational safety management strategies through machine learning to adapt to changing operational environments; Safety warning and risk control module: Generates real-time safety warnings to help operators quickly respond to risks; 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 based on environmental changes and personnel health conditions.

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