Intelligent control method and system for super high-rise building construction

Abstract

The invention relates to the technical field of building construction, and discloses an intelligent control method and system for super high-rise building construction, and the method comprises the following steps: S1, carrying out the real-time positioning of a super high-rise building high-altitude operation worker through intelligent safety monitoring equipment which is integrated with a high-precision position sensor and integrates GNSS and UWB technologies, enabling the positioning precision to reach the centimeter level, and enabling the positioning precision to reach the centimeter level; meanwhile, the motion state of a person is sensed cooperatively through acceleration and tilt angle sensors, environmental parameters such as wind speed, temperature and humidity of a working site are monitored through multiple environmental parameter sensors, collected data are transmitted to an edge calculation module for preliminary screening and analysis, and abnormal behavior recognition is conducted through a preset abnormal behavior recognition algorithm; and triggering local early warning within millisecond-level time. Through a high-precision positioning technology, the position of an operator is accurately determined, an optimal rescue path is planned, surrounding rescue workers and equipment are reasonably allocated, the rescue progress is tracked in real time until rescue is completed, and the accident loss is reduced to the maximum extent.

Description

Technical Field

[0001] The present invention relates to the technical field of building construction, and in particular to an intelligent control method and system for super high-rise building construction. Background Art

[0002] During the construction of super-high-rise buildings, the volume of overhead work is enormous and complex. Traditional safety management methods, which rely primarily on manual inspections and empirical judgment, suffer from numerous drawbacks. On the one hand, information is not readily available, making it difficult to detect safety hazards immediately, leading to delays in accident prevention. On the other hand, management efficiency is low, and manual inspections are limited in coverage, preventing real-time, comprehensive monitoring of the entire construction site and potentially leading to regulatory loopholes. Furthermore, traditional methods struggle to provide accurate early warnings of safety hazards and to formulate effective risk prevention and control measures based on real-time data. Consequently, accidents such as falls and impacts from objects frequently occur during construction, seriously threatening the lives of construction workers and impacting project progress and corporate reputation. While the widespread application of information technology and intelligent technology and equipment in construction has provided new approaches for scientific management of construction work, existing technologies for the safety management of overhead work in super-high-rise buildings remain immature and incomplete. A more intelligent and precise intelligent control method and system for super-high-rise building construction is urgently needed to address these issues. Summary of the Invention

[0003] In view of the deficiencies in the prior art, the present invention provides an intelligent control method and system for super high-rise building construction, which solves the problems raised in the above background technology.

[0004] To achieve the above objectives, the present invention is implemented through the following technical solutions: A super high-rise building construction intelligent control method, comprising the following steps:

[0005] S1: Utilizes intelligent safety monitoring equipment that integrates high-precision position sensors and integrates GNSS and UWB technologies to locate workers working at heights in super-high-rise buildings in real time, with centimeter-level accuracy. Acceleration and inclination sensors are used to collaboratively sense the worker's motion status, and multiple environmental parameter sensors are used to monitor wind speed, temperature, humidity, and other environmental parameters at the work site. The collected data is transmitted to the edge computing module for preliminary screening and analysis. Using a preset abnormal behavior recognition algorithm, local warnings are triggered within milliseconds.

[0006] S2: Data collected by intelligent safety monitoring equipment, as well as construction equipment operation data, work site image data, and other monitoring data are transmitted to the smart safety management platform based on big data and cloud computing architecture through wireless communication modules;

[0007] S3: In the smart safety management platform, data mining technology is used to conduct in-depth analysis of massive multi-source heterogeneous data, exploring potential correlations between the data. Machine learning algorithms such as deep neural networks and support vector machines are introduced to build risk prediction models. Based on historical and real-time data, risks during high-altitude operations are dynamically assessed, categorized into low, medium, and high risk levels, and risk warning information is generated.

[0008] S4: Based on 3D modeling and graphics rendering technology, the platform's graphical interface displays the overall picture of the work site, the distribution of workers, the operating status of various construction equipment, and risk areas, allowing managers to conduct remote monitoring and make decisions.

[0009] S5: When a risk warning message appears, based on the warning level and pre-established emergency rescue plan, high-precision positioning technology is used to determine the location of the operator, plan the optimal rescue route, deploy surrounding rescue personnel and equipment to the accident site, and track the rescue progress in real time until the rescue work is completed;

[0010] S6: A safety education, training and simulation drill system developed using virtual reality and augmented reality technologies provides workers with an immersive safety education experience in high-altitude work scenarios, allowing them to conduct simulated high-altitude work operations and accident emergency response drills in a virtual environment. The system provides real-time feedback on operation results and evaluates worker performance to enhance workers' safety awareness and emergency response capabilities.

[0011] Preferably, the intelligent safety monitoring equipment also has an automatic tightening and buffering function. When it detects that the operator has an unexpected falling tendency, it can quickly and automatically tighten to limit the person's falling distance, and use advanced buffering materials and structural design to provide effective buffering force to reduce impact damage to the body.

[0012] Preferably, during the construction of the risk prediction model, principal component analysis is used to reduce the dimensionality of high-dimensional data to improve computational efficiency, and the generalization ability and prediction accuracy of the model are improved by continuously optimizing the model structure and parameters.

[0013] Preferably, in the S5 step, a risk assessment index system is also constructed to determine the key factors and indicators that affect the safety of high-altitude operations, assign reasonable weights to each indicator, analyze and process real-time monitoring data based on the index system, determine the risk warning threshold, and issue a warning signal in a timely manner when the risk level exceeds the warning threshold.

[0014] Preferably, the high-altitude work scenes in the safety education training and simulation drill system are modeled and simulated based on the actual structure and construction process of super high-rise buildings using three-dimensional modeling software and virtual reality engines, constructing realistic building structures, work equipment, safety facilities and other scenes, so that the workers feel as if they are in a real working environment, thereby enhancing the training effect.

[0015] Preferably, it also includes intelligent safety management of the entire life cycle of the super-high-rise building construction process, using building information modeling technology to simulate and analyze building structures and construction processes from the planning and design stage, identifying safety risks in advance and optimizing design plans, achieving real-time monitoring and risk warnings during the construction stage, and continuously monitoring and evaluating the safety status of the building during the operation stage.

[0016] An intelligent control system for super high-rise building construction, comprising:

[0017] Intelligent safety monitoring equipment integrates high-precision position sensors and integrates GNSS and UWB technologies to provide real-time centimeter-level positioning of workers working at height in super-high-rise buildings. It is also equipped with acceleration and inclination sensors to sense the worker's motion status, and multiple environmental parameter sensors to monitor the working site's environmental parameters. The collected data is transmitted to the edge computing module, which triggers local warnings in milliseconds based on a preset abnormal behavior recognition algorithm.

[0018] The smart safety management platform is wirelessly connected to intelligent safety monitoring equipment to receive various monitoring data. It uses data mining technology and machine learning algorithms to build a risk prediction model, dynamically assess and grade the risks of high-altitude operations, and generate risk warning information. It also uses 3D modeling and graphics rendering technology to intuitively display the work site situation.

[0019] The safety protection and emergency rescue system is connected to the smart safety management platform. After receiving risk warning information, it plans rescue routes, deploys rescue resources, and tracks rescue progress based on the warning level and emergency rescue plan, combined with high-precision positioning technology;

[0020] The safety education, training and simulation drill system, based on VR and AR technologies, provides operators with an immersive safety education experience and simulation drill environment for high-altitude work scenarios, and provides real-time feedback on operation evaluation results.

[0021] Preferably, the intelligent safety monitoring equipment also includes an automatically tightening buffer safety belt and an intelligent protective net. The automatically tightening buffer safety belt has a built-in intelligent sensing device, which can quickly and automatically tighten and provide buffering force when it detects the operator's falling tendency. The intelligent protective net is equipped with a pressure sensor and an intelligent control system, which can automatically adjust the tension according to the contact pressure and position.

[0022] Preferably, the intelligent security management platform also includes data conversion middleware and time synchronization modules to solve the problems of inconsistent formats and transmission rates of multi-source heterogeneous data, realize data fusion processing, and ensure data consistency and accuracy.

[0023] Preferably, it also includes a full life cycle intelligent safety management module, which uses BIM technology to simulate and analyze building structures and construction processes during the planning and design stages of super-high-rise buildings, identify safety risks in advance and optimize design plans, and continuously monitor and evaluate building safety conditions during the operation stage to achieve pre-emptive and continuous safety management.

[0024] The present invention provides an intelligent control method and system for super high-rise building construction, which has the following beneficial effects:

[0025] 1. This invention integrates high-precision position sensors and integrates GNSS and UWB technologies into intelligent safety monitoring equipment to perform real-time centimeter-level positioning of high-altitude workers. It also uses acceleration, inclination, and multiple environmental parameter sensors to comprehensively monitor the worker's motion status and the environmental parameters of the work site. Combined with edge computing and a preset abnormal behavior recognition algorithm, it achieves millisecond-level local warnings, enabling timely detection of potential safety risks and rapid responses. Once a risk warning occurs, the system uses high-precision positioning technology to accurately determine the worker's location based on the warning level and emergency rescue plan, plan the optimal rescue path, rationally deploy surrounding rescue personnel and equipment, and track the rescue progress in real time until the rescue is completed, minimizing accident losses and comprehensively improving the efficiency and quality of super-high-rise building construction safety management, building a solid line of defense for the safety of construction workers.

[0026] 2. This invention leverages virtual reality and augmented reality technologies to create an immersive high-altitude work environment for workers, enabling them to gain a deeper understanding of high-altitude work processes, safety regulations, and emergency response methods. The system provides real-time feedback on operational results and evaluates worker performance, helping them promptly correct operational errors and master proper safety techniques. This significantly enhances their safety awareness and emergency response capabilities, fundamentally reducing the risk of accidents caused by improper human operation and fostering a highly qualified, safety-conscious work team for super-high-rise building construction.

[0027] 3. By integrating cutting-edge technologies such as the Internet of Things, big data, cloud computing, and machine learning, it provides a comprehensive and intelligent safety management and control solution for super-high-rise building construction, filling a gap in intelligent safety management technology within the industry. Its innovative technical architecture and functional modules, such as multi-source heterogeneous data fusion processing, machine learning-based risk prediction models, and adaptive safety protection systems, effectively address the problems of information lag, inefficiency, and insufficient risk prevention in traditional super-high-rise building construction safety management. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 This is a flow chart of the intelligent control method of the present invention;

[0029] Figure 2 This is a structural diagram of the intelligent control system of the present invention. DETAILED DESCRIPTION

[0030] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the drawings in the present specification. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0031] Example:

[0032] Please see the attached Figure 1 -Attached Figure 2 The embodiment of the present invention provides an intelligent control method for super high-rise building construction, comprising the following steps:

[0033] S1: Utilizes intelligent safety monitoring equipment that integrates high-precision position sensors and integrates GNSS and UWB technologies to locate workers working at heights in super-high-rise buildings in real time, with centimeter-level accuracy. Acceleration and inclination sensors are used to collaboratively sense the worker's motion status, and multiple environmental parameter sensors are used to monitor wind speed, temperature, humidity, and other environmental parameters at the work site. The collected data is transmitted to the edge computing module for preliminary screening and analysis. Using a preset abnormal behavior recognition algorithm, local warnings are triggered within milliseconds.

[0034] Specifically, to ensure the safety of construction workers working at heights on super-high-rise buildings, this invention utilizes highly advanced intelligent safety monitoring equipment. This equipment integrates high-precision position sensors and combines Global Navigation Satellite System (GNSS) and Ultra-Wideband (UWB) technologies. This combination of technologies enables real-time positioning of workers working at heights with centimeter-level accuracy, providing a powerful guarantee for accurately determining their location in complex on-site environments.

[0035] The device is also equipped with an accelerometer and an inclinometer. These two sensors work together to accurately sense the operator's movements. Whether it's normal walking or climbing, or abnormal movements like falls or collisions, they can all be captured immediately. Furthermore, the device is equipped with a variety of environmental parameter sensors, enabling real-time monitoring of key environmental parameters such as wind speed, temperature, and humidity at the work site.

[0036] All types of data collected by monitoring equipment are transmitted to the edge computing module, where the data undergoes preliminary screening and analysis. Leveraging a pre-set abnormal behavior recognition algorithm, the system can quickly assess the data. Upon detecting abnormal behavior or potential safety risks, it quickly triggers audible and visual alarms, issuing local early warning signals to alert on-site workers and managers to take timely countermeasures, effectively preventing accidents and ensuring the safety and efficiency of the construction process.

[0037] S2: The data collected by the intelligent safety monitoring equipment, as well as various monitoring data such as construction equipment operation data and work site image data, are transmitted to the intelligent safety management platform based on big data and cloud computing architecture through the wireless communication module; the intelligent safety monitoring equipment also has an automatic tightening and buffering function. When it detects that the operator has an unexpected falling trend, it can quickly and automatically tighten to limit the person's falling distance, and use advanced buffering materials and structural design to provide effective buffering force to reduce impact damage to the body.

[0038] Specifically, the intelligent safety monitoring equipment is equipped with an advanced automatic tightening and buffering function, which is designed to deal with possible accidental falls of workers at heights. When the equipment detects that the worker has an unexpected falling trend, it will respond quickly. Through real-time monitoring and analysis of sensor data, once an abnormal motion state (such as sudden acceleration or angle change) is detected, the equipment will immediately start the automatic tightening mechanism. The automatic tightening mechanism will quickly limit the falling distance of the person and prevent the person from continuing to fall and causing more serious injuries. At the same time, the equipment uses advanced buffering materials and structural design to provide effective buffering force at the moment the person falls. This buffering force can significantly reduce the impact damage to the body and protect the safety of the workers.

[0039] S3: In the smart safety management platform, data mining technology is used to conduct in-depth analysis of massive multi-source heterogeneous data, exploring potential correlations between the data. Machine learning algorithms such as deep neural networks and support vector machines are introduced to build risk prediction models. Based on historical and real-time data, risks during high-altitude operations are dynamically assessed, categorized into low, medium, and high risk levels, and risk warning information is generated.

[0040] Specifically, the smart safety management platform has powerful data analysis capabilities and can process massive amounts of multi-source heterogeneous data. These data come from various sources, including but not limited to personnel positioning data, motion status data, environmental monitoring data, construction equipment operation data, and work site image data, and the data types vary, such as structured data, semi-structured data, and unstructured data.

[0041] The platform uses data mining technology to conduct in-depth analysis of this massive amount of data, aiming to uncover potential correlations between the data and identify key factors and risk patterns that may affect construction safety. For example, by analyzing the relationship between personnel movement trajectories and environmental factors (such as wind speed and temperature), it can identify environments where personnel are more likely to engage in unsafe behaviors.

[0042] In addition, the platform has introduced advanced machine learning algorithms such as deep neural networks and support vector machines to build risk prediction models. Deep neural networks excel at processing complex, nonlinear data relationships and can learn potential risk characteristics and patterns from large amounts of historical data. Support vector machines excel in recognizing small samples, nonlinearities, and high-dimensional patterns, and can be used to accurately classify and predict risk levels. By combining historical and real-time data, the model can dynamically assess risks during high-altitude operations, updating and adjusting risk levels in real time, categorizing risks into low, medium, and high levels, and generating corresponding risk warning information to promptly notify managers and operators to take appropriate preventive measures, thereby effectively reducing the probability of safety accidents.

[0043] S4: Based on 3D modeling and graphics rendering technology, the platform's graphical interface displays the overall picture of the work site, the distribution of workers, the operating status of various construction equipment, and risk areas, allowing managers to conduct remote monitoring and make decisions.

[0044] S5: When a risk warning message appears, the location of the operator is determined based on the warning level and pre-established emergency rescue plans, combined with high-precision positioning technology. The optimal rescue route is planned, surrounding rescue personnel and equipment are deployed to the accident site, and the rescue progress is tracked in real time until the rescue work is completed. In the process of constructing the risk prediction model, principal component analysis is used to reduce the dimensionality of high-dimensional data to improve computational efficiency. By continuously optimizing the model structure and parameters, the model's generalization ability and prediction accuracy are improved. A risk assessment indicator system is also constructed to identify the key factors and indicators that affect the safety of high-altitude operations. A reasonable weight is assigned to each indicator. Based on this indicator system, real-time monitoring data is analyzed and processed to determine the risk warning threshold. When the risk level exceeds the warning threshold, a warning signal is issued in a timely manner.

[0045] Specifically, when the smart safety management platform generates a risk warning, it initiates the corresponding emergency rescue procedures based on the warning level (low, medium, or high) and pre-defined emergency rescue plans. Using high-precision positioning technology to precisely locate the operator's location, the system quickly plans the optimal rescue route from their current position to the accident site.

[0046] When constructing a risk prediction model, principal component analysis (PCA) is used to reduce the dimensionality of the data because the original monitoring data is often high-dimensional, which increases the computational complexity and may introduce noise.

[0047] When modeling the reduced-dimensional data, machine learning algorithms such as deep neural networks (DNNs) and support vector machines (SVMs) are introduced. DNNs learn complex nonlinear relationships in data through a multi-layer neural network structure. Its basic unit is neurons, and the connection weights between neurons are optimized using the back-propagation algorithm. The formula used is as follows:

[0048]

[0049] Among them, w is the normal vector of the classification hyperplane, b is the bias term, C is the penalty parameter, ξ i It is a slack variable that allows a certain degree of misclassification. By optimizing the above objective function, the optimal classification hyperplane is found to achieve classification prediction of risks.

[0050] In order to improve the generalization ability and prediction accuracy of the model, the model structure and parameters will be continuously optimized. For example, the DNN model can be evaluated and optimized by adjusting hyperparameters such as the number of network layers, the number of neurons, and the activation function, and using methods such as cross-validation; the SVM model can be optimized by selecting a suitable kernel function (such as linear kernel, polynomial kernel, radial basis kernel, etc.), adjusting the penalty parameter C, etc. In addition, a risk assessment index system has been constructed to comprehensively determine the various key factors (such as personnel position deviation, motion acceleration, wind speed, equipment operating status, etc.) and indicators that affect the safety of high-altitude operations. For each indicator, a reasonable weight is assigned according to the degree of its impact on the safety risk (the weight can be determined by expert scoring, hierarchical analysis method, etc.). Based on this indicator system, the real-time monitoring data is weighted and analyzed, and the current risk level is calculated through a comprehensive risk assessment formula:

[0051]

[0052] Among them, R represents the comprehensive risk level, w i is the weight of the i-th risk indicator, x i It is the normalized value of the real-time monitoring value of the i-th risk indicator. According to the pre-set risk warning threshold (such as the low risk threshold Rlow , medium risk threshold R mid , high risk threshold R high ), when the comprehensive risk level R exceeds the corresponding warning threshold, a warning signal is issued in time to remind relevant personnel to take corresponding measures.

[0053] S6: Developed using virtual reality and augmented reality technologies, the safety education, training, and simulation drill system provides workers with an immersive safety education experience in high-altitude work scenarios. This allows them to conduct simulated high-altitude work operations and emergency response drills in a virtual environment. The system provides real-time feedback on operational results and performance evaluations to enhance workers' safety awareness and emergency response capabilities. The high-altitude work scenarios in the safety education, training, and simulation drill system utilize 3D modeling software and a virtual reality engine to model and simulate the actual structure and construction processes of super-high-rise buildings. This creates realistic scenes of building structures, operating equipment, and safety facilities, allowing workers to immerse themselves in a realistic work environment and enhance training effectiveness.

[0054] It also includes intelligent safety management of the entire life cycle of super-high-rise building construction, using building information modeling technology to simulate and analyze building structures and construction processes from the planning and design stage, identifying safety risks in advance and optimizing design plans, achieving real-time monitoring and risk warnings during the construction stage, and continuously monitoring and evaluating the safety status of the building during the operation stage.

[0055] An intelligent control system for super high-rise building construction, comprising:

[0056] Intelligent safety monitoring equipment integrates high-precision position sensors and integrates GNSS and UWB technologies to provide real-time centimeter-level positioning of workers working at height in super-high-rise buildings. It is also equipped with acceleration and inclination sensors to sense the worker's motion status, and multiple environmental parameter sensors to monitor the working site's environmental parameters. The collected data is transmitted to the edge computing module, which triggers local warnings in milliseconds based on a preset abnormal behavior recognition algorithm.

[0057] Specifically, it integrates high-precision positioning sensors, integrating GNSS and UWB. GNSS can provide wide-area positioning information in open outdoor areas, while UWB leverages its short-range, high-precision positioning capabilities to achieve centimeter-level positioning accuracy in areas blocked by satellite signals, such as inside buildings. The combination of these two ensures real-time and accurate positioning of personnel working at heights. Furthermore, the device is equipped with accelerometers and inclinometers to collaboratively sense the movement of personnel. These sensors can keenly capture normal walking and climbing, as well as unusual movements such as falls and collisions, keeping track of personnel movements at all times.

[0058] The edge computing module performs preliminary screening and analysis of data at the edge, close to the data source. With the help of a preset abnormal behavior recognition algorithm, it can determine whether the operator's behavior is abnormal or whether there is a safety hazard in a very short time (milliseconds). If an abnormal situation is found, it will quickly trigger local early warning signals such as sound and light alarms to promptly alert operators and managers, so as to achieve rapid response and effective prevention of safety accidents.

[0059] The smart safety management platform is wirelessly connected to intelligent safety monitoring equipment to receive various monitoring data. It uses data mining technology and machine learning algorithms to build a risk prediction model, dynamically assess and grade the risks of high-altitude operations, and generate risk warning information. It also uses 3D modeling and graphics rendering technology to intuitively display the work site situation.

[0060] The safety protection and emergency rescue system is connected to the smart safety management platform. After receiving risk warning information, it plans rescue routes, deploys rescue resources, and tracks rescue progress based on the warning level and emergency rescue plan, combined with high-precision positioning technology;

[0061] The safety education, training and simulation drill system, based on VR and AR technologies, provides operators with an immersive safety education experience and simulation drill environment for high-altitude work scenarios, and provides real-time feedback on operation evaluation results.

[0062] Intelligent safety monitoring equipment also includes automatically tightening safety belts and intelligent safety nets. The automatic safety belts feature built-in intelligent sensors that automatically tighten and provide cushioning force when they detect a worker's falling tendency. The intelligent safety nets are equipped with pressure sensors and an intelligent control system that automatically adjusts tension based on contact pressure and position. The intelligent safety management platform also includes data conversion middleware and time synchronization modules to address the issues of inconsistent formats and transmission rates for heterogeneous data from multiple sources, enabling data fusion processing and ensuring data consistency and accuracy.

[0063] It also includes a full-life cycle intelligent safety management module, which uses BIM technology to simulate and analyze building structures and construction processes during the planning and design stages of super-high-rise buildings, identify safety risks in advance and optimize design plans, and continuously monitor and evaluate building safety status during the operation stage to achieve pre-emptive and continuous safety management.

[0064] Specifically, during the planning and design phase, this module fully utilizes BIM (Building Information Modeling) technology. BIM technology can create a virtual model that includes detailed information such as the building structure and construction process. Through this model, various aspects of the construction process of super-high-rise buildings can be simulated and analyzed, and potential safety risks can be identified in advance. For example, by simulating the flow of personnel, equipment operation, and material transportation during the construction process, potential risk areas such as collisions and falls can be discovered. Based on these analysis results, the design team can make targeted optimizations in the design plan, such as adjusting the construction sequence and adding safety protection facilities, thereby reducing safety risks during the construction process at the source.

[0065] During the operational phase, this module enables continuous monitoring and assessment of the building's safety status. By deploying various sensors throughout the building (such as structural health monitoring sensors and environmental sensors), it collects real-time building usage data, such as structural deformation, stress conditions, and ambient temperature and humidity. This data is transmitted to the intelligent safety management platform for analysis, enabling timely identification of potential safety hazards that may arise during the building's use, such as structural fatigue and material aging. Once an anomaly is detected, the system can promptly issue an early warning, alerting relevant management personnel to take maintenance or repair measures to ensure the building's safety throughout its service life.

[0066] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. An intelligent control method for super high-rise building construction, characterized in that: The following steps are involved: S1: Utilizes intelligent safety monitoring equipment that integrates high-precision position sensors and integrates GNSS and UWB technologies to locate workers working at heights in super-high-rise buildings in real time, with centimeter-level accuracy. Acceleration and inclination sensors are used to collaboratively sense the worker's motion status, and multiple environmental parameter sensors are used to monitor wind speed, temperature, humidity, and other environmental parameters at the work site. The collected data is transmitted to the edge computing module for preliminary screening and analysis. Using a preset abnormal behavior recognition algorithm, local warnings are triggered within milliseconds. S2: Data collected by intelligent safety monitoring equipment, as well as construction equipment operation data, work site image data, and other monitoring data are transmitted to the smart safety management platform based on big data and cloud computing architecture through wireless communication modules; S3: In the smart safety management platform, data mining technology is used to conduct in-depth analysis of massive multi-source heterogeneous data, exploring potential correlations between the data. Machine learning algorithms such as deep neural networks and support vector machines are introduced to build risk prediction models. Based on historical and real-time data, risks during high-altitude operations are dynamically assessed, categorized into low, medium, and high risk levels, and risk warning information is generated. S4: Based on 3D modeling and graphics rendering technology, the platform's graphical interface displays the overall picture of the work site, the distribution of workers, the operating status of various construction equipment, and risk areas, allowing managers to conduct remote monitoring and make decisions. S5: When a risk warning message appears, based on the warning level and pre-established emergency rescue plan, high-precision positioning technology is used to determine the location of the operator, plan the optimal rescue route, deploy surrounding rescue personnel and equipment to the accident site, and track the rescue progress in real time until the rescue work is completed; S6: A safety education, training and simulation drill system developed using virtual reality and augmented reality technologies provides workers with an immersive safety education experience in high-altitude work scenarios, allowing them to conduct simulated high-altitude work operations and accident emergency response drills in a virtual environment. The system provides real-time feedback on operation results and evaluates worker performance to enhance workers' safety awareness and emergency response capabilities.

2. The intelligent control method for super high-rise building construction according to claim 1 is characterized in that: The intelligent safety monitoring equipment also has an automatic tightening and buffering function. When it detects that the operator is showing an unexpected falling trend, it can quickly and automatically tighten to limit the person's falling distance. It also uses advanced buffering materials and structural design to provide effective buffering force to reduce impact damage to the body.

3. The intelligent control method for super high-rise building construction according to claim 1, characterized in that: In the process of constructing the risk prediction model, principal component analysis is used to reduce the dimensionality of high-dimensional data to improve computational efficiency, and the generalization ability and prediction accuracy of the model are improved by continuously optimizing the model structure and parameters.

4. The intelligent control method for super high-rise building construction according to claim 1, characterized in that: In the S5 step, a risk assessment index system is also constructed to determine the key factors and indicators that affect the safety of high-altitude operations, assign reasonable weights to each indicator, analyze and process real-time monitoring data based on the index system, determine the risk warning threshold, and issue a warning signal in a timely manner when the risk level exceeds the warning threshold.

5. The intelligent control method for super high-rise building construction according to claim 1 is characterized in that: The high-altitude work scenes in the safety education training and simulation drill system are modeled and simulated based on the actual structure and construction process of super high-rise buildings using three-dimensional modeling software and virtual reality engines, constructing realistic building structures, work equipment, safety facilities and other scenes, making the workers feel as if they are in a real working environment, thereby enhancing the training effect.

6. The intelligent control method for super high-rise building construction according to claim 1, characterized in that: It also includes intelligent safety management of the entire life cycle of super-high-rise building construction, using building information modeling technology to simulate and analyze building structures and construction processes from the planning and design stage, identifying safety risks in advance and optimizing design plans, achieving real-time monitoring and risk warnings during the construction stage, and continuously monitoring and evaluating the safety status of the building during the operation stage.

7. An intelligent control system for super high-rise building construction, according to the intelligent control method for super high-rise building construction according to any one of claims 1 to 6, characterized in that: include: Intelligent safety monitoring equipment integrates high-precision position sensors and integrates GNSS and UWB technologies to provide real-time centimeter-level positioning of workers working at height in super-high-rise buildings. It is also equipped with acceleration and inclination sensors to sense the worker's motion status, and multiple environmental parameter sensors to monitor the working site's environmental parameters. The collected data is transmitted to the edge computing module, which triggers local warnings in milliseconds based on a preset abnormal behavior recognition algorithm. The smart safety management platform is wirelessly connected to intelligent safety monitoring equipment to receive various monitoring data. It uses data mining technology and machine learning algorithms to build a risk prediction model, dynamically assess and grade the risks of high-altitude operations, and generate risk warning information. It also uses 3D modeling and graphics rendering technology to intuitively display the work site situation. The safety protection and emergency rescue system is connected to the smart safety management platform. After receiving risk warning information, it plans rescue routes, deploys rescue resources, and tracks rescue progress based on the warning level and emergency rescue plan, combined with high-precision positioning technology; The safety education, training and simulation drill system, based on VR and AR technologies, provides operators with an immersive safety education experience and simulation drill environment for high-altitude work scenarios, and provides real-time feedback on operation evaluation results.

8. The intelligent control system for super high-rise building construction according to claim 7, characterized in that: The intelligent safety monitoring equipment also includes an automatically tightening buffer safety belt and an intelligent protective net. The automatically tightening buffer safety belt has a built-in intelligent sensing device, which can quickly and automatically tighten and provide buffering force when it detects the operator's falling tendency. The intelligent protective net is equipped with a pressure sensor and an intelligent control system, which can automatically adjust the tension according to the contact pressure and position.

9. The intelligent control system for super high-rise building construction according to claim 7, characterized in that: The intelligent security management platform also includes data conversion middleware and time synchronization modules, which are used to solve the problems of inconsistent formats and transmission rates of multi-source heterogeneous data, realize data fusion processing, and ensure data consistency and accuracy.

10. The intelligent control system for super high-rise building construction according to claim 7, characterized in that: It also includes a full-life cycle intelligent safety management module, which uses BIM technology to simulate and analyze building structures and construction processes during the planning and design stages of super-high-rise buildings, identify safety risks in advance and optimize design plans, and continuously monitor and evaluate building safety status during the operation stage to achieve pre-emptive and continuous safety management.

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