Unmanned aerial vehicle and BIM collaborative construction site safety early warning system

Abstract

The invention provides an unmanned aerial vehicle and BIM cooperative construction site safety early warning system, and relates to the technical field of building construction safety management, and the system comprises an unmanned aerial vehicle body, a cloud service-based fusion type comprehensive platform and a layered architecture system. The unmanned aerial vehicle body carries a high-definition camera, a thermal imager, a gas detector and a laser radar, is configured with a self-adaptive flight path planning algorithm, and can automatically adjust a path to collect data according to the change of a construction site; according to the invention, the unmanned aerial vehicle body carries a multi-element sensor and is combined with an adaptive algorithm to efficiently collect data; the cloud platform and the layered architecture realize full-process operation of data, and an adaptive protocol ensures stable and safe transmission; the deep learning model is fused with multiple data to accurately identify risks and perform early warning in time; the early warning accuracy, the management convenience and the system stability are improved through the multi-module effect; the user layer function promotes management to be upgraded to active prevention and control, an intelligent and efficient solution is provided, the accident rate is reduced, and the management scientificity and the intelligent level are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of building construction safety management, and in particular to a construction site safety early warning system that collaborates with a drone and BIM. Background Art

[0002] In the construction industry, construction site safety management is crucial, directly impacting the safety of construction workers, project progress, and the company's economic benefits. With the rapid development of the construction industry, construction sites are expanding in size and the construction environment is becoming increasingly complex. Traditional safety management methods are gradually exposing many drawbacks. Therefore, a construction site safety early warning system that combines drones with BIM is designed.

[0003] In the data collection process, existing technologies for construction site safety management currently rely on manpower. Traditional manual inspections are inefficient and have limited coverage. They are unable to quickly and comprehensively obtain construction site information and have obvious lags. Although some technologies use equipment to collect data, they lack adaptive path planning capabilities and are unable to flexibly respond to complex and changing construction site environments. The comprehensiveness and timeliness of data collection are difficult to guarantee. Summary of the Invention

[0004] The present invention relates to a construction site safety early warning system that collaborates with a drone and BIM to solve the technical problems raised in the above-mentioned background technology.

[0005] In a first aspect, the present invention provides a construction site safety warning system that collaborates with drones and BIM, specifically comprising: a drone body, a fusion integrated platform based on cloud services, and a layered architecture system; the drone body is equipped with a high-definition camera, a thermal imager, a gas detector, and a lidar, and is configured with an adaptive flight path planning algorithm, which can automatically adjust the route to collect data according to changes on the construction site; the fusion integrated platform based on cloud services is used to integrate the data collected by the drone body with BIM model information to achieve real-time data processing, storage, and sharing; the layered architecture system includes a data acquisition layer, a data transmission layer, a data processing layer, an application layer, and a user layer, which collaboratively complete data collection, transmission, analysis, and warning functions; the data transmission layer adopts an adaptive data transmission protocol, which can automatically select 5G, Wi-Fi, or satellite communication methods according to network signals and data volume, and cache, compress, and encrypt the data for transmission.

[0006] In at least some embodiments, a risk identification model based on a deep learning algorithm is provided, which integrates the BIM model with the drone body data, analyzes the personnel, equipment, and environmental factors on the construction site in real time, identifies potential risks, and classifies risk levels.

[0007] At least some embodiments also include a real-time warning mechanism. When the risk identification model detects a risk, a warning message is sent via SMS, APP push, or on-site sound and light alarm, including risk details and handling suggestions, and the entire warning process is recorded.

[0008] In at least some embodiments, the BIM model is used to locate construction site personnel in real time, monitor their behavior, analyze their behavior, and implement attendance management, utilizing the drone body for positioning combined with image recognition technology.

[0009] In at least some embodiments, operation data is collected by construction equipment sensors and transmitted to the system platform, which is combined with equipment parameters in the BIM model to evaluate and predict equipment status and optimize equipment scheduling management.

[0010] In at least some embodiments, a data fusion module is configured to match and fuse the drone data with the BIM model based on a spatiotemporal consistency algorithm to eliminate errors and improve the accuracy of safety warnings.

[0011] In at least some embodiments, a three-dimensional visualization module is provided to construct a three-dimensional dynamic model of the construction site based on the BIM model and real-time data, intuitively display the safety status, personnel and equipment distribution, and support multi-perspective switching and data query.

[0012] In at least some embodiments, a system self-diagnosis and maintenance module is provided that can monitor the operating status of each system module in real time, automatically detect problems such as data transmission anomalies and algorithm operation failures, locate the fault point through an intelligent diagnostic algorithm, generate maintenance suggestions, support remote online repair and upgrades, and ensure stable system operation.

[0013] In at least some embodiments, the user layer is equipped with a personalized interactive interface that supports user customization of warning thresholds, display parameters, and data presentation methods.

[0014] In at least some embodiments, the user layer integrates an intelligent decision-making assistance module, which automatically generates risk prevention and control strategy recommendations based on historical risk data and real-time monitoring information using big data analysis and machine learning algorithms; at the same time, it supports users to upload construction plans and resource allocation data, and through simulation and deduction functions, predicts the safety risk probability under different construction plans, provides a quantitative basis for construction management decisions, and realizes the upgrade from safety warning to active safety management.

[0015] The present invention provides a construction site safety early warning system that combines drones with BIM, which has the following beneficial effects:

[0016] In this invention, through the fusion of multiple technologies and the integration of functions, the safety management level and efficiency of the construction site have been significantly improved. The drone is equipped with multiple sensors such as high-definition cameras and thermal imagers, and combined with an adaptive flight path planning algorithm, it can quickly, comprehensively and flexibly collect construction site data, making up for the limitations and lags of traditional manual inspections. The integrated comprehensive platform and layered architecture system based on cloud services realizes the efficient operation of the entire process from data collection, transmission, processing to application. The adaptive data transmission protocol ensures the stable and secure transmission of data in complex network environments.

[0017] In addition, in the present invention, the risk identification model based on deep learning algorithms deeply integrates BIM models and drone data, which can analyze construction site personnel, equipment, and environmental factors in real time and accurately, quickly identify potential risks and classify them, and cooperate with the real-time early warning mechanism to timely push risk details and treatment suggestions through multiple channels, greatly improving the timeliness and effectiveness of risk prevention and control. The combination of BIM models and drone positioning and image recognition technology not only realizes real-time positioning of personnel, behavior monitoring and attendance management, but also combines construction equipment sensor data to evaluate and predict equipment status, optimize scheduling management, and effectively reduce safety hazards caused by human errors and equipment failures.

[0018] In addition, in the present invention, the data fusion module eliminates data errors based on the spatiotemporal consistency algorithm to improve the accuracy of safety warnings. The three-dimensional visualization module intuitively displays the dynamics of the construction site, making it easier for managers to grasp the overall situation in real time. The system self-diagnosis and maintenance module ensures stable operation of the system and reduces management blind spots caused by faults. In addition, the personalized interactive interface of the user layer meets the customized needs of different users. The intelligent decision-making assistance module generates risk prevention and control strategies based on big data analysis and machine learning, and predicts the risks of construction plans through simulation and deduction, promoting the upgrade of construction site management from passive warning to active prevention and control, providing a comprehensive, intelligent and efficient solution for construction safety management, effectively reducing the incidence of construction safety accidents, and improving the scientificity and intelligence level of engineering management. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings of the embodiments are briefly introduced below.

[0020] The drawings described below only relate to some embodiments of the present invention, but are not intended to limit the present invention.

[0021] In the attached figure:

[0022] Figure 1 The overall system architecture diagram of the present invention is shown.

[0023] Figure 2 The diagram shows the architecture of the data collection layer of the present invention.

[0024] Figure 3 The diagram shows the architecture of the data transmission layer of the present invention.

[0025] Figure 4 The diagram shows the architecture of the data processing layer of the present invention.

[0026] Figure 5 The architecture diagram of the application layer of the present invention is shown.

[0027] Figure 6 The diagram shows the architecture of the user layer of the present invention.

[0028] Figure 7 The figure shows a schematic structural diagram of the drone body of the present invention.

[0029] Figure 8 The figure shows a schematic structural diagram of the bottom portion of the drone body of the present invention.

[0030] Figure 9 The present invention shows Figure 8 Schematic diagram of the enlarged structure at point A in the middle.

[0031] Reference Signs List

[0032] 1. Drone body; 11. High-definition camera; 12. Thermal imager; 13. Gas detector; 14. LiDAR. DETAILED DESCRIPTION

[0033] To make the purpose, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions of the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the described embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0034] Please refer to Figures 1 to 9 :Example 1:

[0035] The present invention proposes a construction site safety early warning system that collaborates with drones and BIM, comprising: a drone body 1, a fusion integrated platform based on cloud services, and a layered architecture system; the drone body 1 is equipped with a high-definition camera 11, a thermal imager 12, a gas detector 13, and a lidar 14, and is configured with an adaptive flight path planning algorithm, which can automatically adjust the route to collect data according to changes on the construction site; the fusion integrated platform based on cloud services is used to integrate the data collected by the drone body 1 with BIM model information to realize real-time data processing, storage, and sharing; the layered architecture system includes a data acquisition layer, a data transmission layer, a data processing layer, an application layer, and a user layer, which collaboratively complete data collection, transmission, analysis, and early warning functions; the data transmission layer adopts an adaptive data transmission protocol, which can automatically select 5G, Wi-Fi, or satellite communication methods according to network signals and data volume, and cache, compress, and encrypt the data for transmission.

[0036] In the present invention, a risk identification model based on a deep learning algorithm is provided, which integrates the BIM model and the data of the drone body 1 to analyze the personnel, equipment, and environmental factors of the construction site in real time, identify potential risks and classify risk levels. Its function is as follows: the risk identification model based on the deep learning algorithm, by integrating the BIM model and the data collected by the drone body 1, can analyze the personnel, equipment, and environmental factors of the construction site in real time. Compared with traditional manual inspections, it greatly improves the timeliness of risk identification, can quickly discover potential risks and prevent them from expanding. At the same time, with the powerful data analysis capabilities of the deep learning algorithm, it accurately captures problems such as personnel violations, abnormal equipment conditions, and environmental safety hazards, significantly enhancing the accuracy of risk identification and reducing the probability of misjudgment and missed judgment. The model can also classify the identified potential risks, making it easier for managers to rationally allocate resources according to the level of risk, give priority to high-risk issues, and improve the efficiency of safety management resource utilization. In addition, clear risk identification and classification provide accurate information for the real-time early warning mechanism and lay the foundation for the intelligent decision-making assistance module to generate risk prevention and control strategy recommendation plans, promoting the development of construction site safety management in a scientific and intelligent direction.

[0037] The present invention also includes a real-time early warning mechanism. When the risk identification model detects a risk, an early warning message is sent through text messages, APP push, on-site sound and light alarms, including risk details and handling suggestions, and the entire early warning process is recorded. Its function is: the real-time early warning mechanism sends early warning information simultaneously through multiple channels such as text messages, APP push, on-site sound and light alarms, etc., which can quickly reach different groups such as management personnel and on-site workers, so that they can receive risk warnings in the first time no matter where they are, and gain valuable time for timely response measures, effectively reducing the probability of accidents. At the same time, the risk details and handling suggestions covered in the early warning information can help relevant personnel quickly grasp the risk situation and clarify the correct response methods, avoid improper handling due to panic or lack of guidance, and significantly improve the accuracy and effectiveness of risk handling. In addition, the recording of the entire early warning process facilitates the subsequent review and analysis of risk events, helps to discover weak links in the safety management process, and then optimizes the risk identification model and early warning mechanism, promotes the continuous improvement of the safety management level of the construction site, and realizes the transformation from passive response to active optimization.

[0038] In the present invention, the BIM model is used to locate and monitor the behavior of construction site personnel in real time, analyze personnel behavior and implement attendance management, and use the drone body 1 for positioning combined with image recognition technology. Its function is: by combining the BIM model with drone positioning and image recognition technology, real-time positioning and behavior monitoring of construction site personnel can be implemented, personnel dynamics can be accurately grasped, illegal operations, leaving the post and other phenomena can be detected in time, personnel work behavior can be effectively standardized, and safety risks caused by human factors can be reduced. At the same time, personnel behavior can be automatically analyzed and attendance management can be implemented, replacing traditional manual attendance, reducing statistical errors and management costs, and relying on real-time and accurate attendance data to help managers reasonably arrange construction tasks and ensure project progress. In addition, in emergencies, the real-time positioning function can help managers quickly lock the location of personnel, shorten the rescue response time, and improve the level of personnel safety protection. Through continuous behavior monitoring, a standardized and orderly working environment can be created to prevent the occurrence of safety accidents from the root, and comprehensively improve the scientificity and safety of construction site personnel management.

[0039] In the present invention, operation data is collected by construction equipment sensors and transmitted to the system platform, which is combined with the equipment parameters in the BIM model to evaluate and predict the equipment status, and optimize equipment scheduling and management. Its function is: with the help of construction equipment sensors, operation data is collected in real time and transmitted to the system platform, and then combined with the equipment parameters in the BIM model, the current status of the equipment can be accurately and comprehensively evaluated, and abnormal conditions in the operation of the equipment can be detected in time, thereby avoiding sudden equipment failures that interfere with the construction progress. On this basis, through predictive analysis of the equipment status, the use, maintenance and deployment of the equipment can be planned in advance, and equipment resources can be reasonably allocated to prevent the equipment from being idle or overused, thereby promoting the efficient flow of equipment between different construction tasks, thereby optimizing the construction process and significantly improving the overall construction efficiency.

[0040] In the present invention, a data fusion module is configured to match and fuse the drone data with the BIM model based on the spatiotemporal consistency algorithm to eliminate errors and improve the accuracy of safety warnings. Its function is: the data fusion module uses the spatiotemporal consistency algorithm to accurately match and fuse the data collected by the drone with the BIM model, effectively eliminating data errors caused by differences in data sources and collection time and space, ensuring that the construction site data is true and reliable, and laying a solid data foundation for subsequent analysis. On this basis, the system can more accurately analyze factors such as personnel, equipment, and environment, significantly improve the accuracy of safety warnings, greatly reduce false alarms and missed alarms, make warning information more in line with reality, and help managers quickly and effectively prevent safety accidents. At the same time, reliable data fusion ensures the stable operation of various functional modules of the system such as the risk identification model and the intelligent decision-making assistance module, making analysis and judgment and strategy generation more scientific, and enhancing the reliability and practicality of the entire safety warning system in complex construction site environments.

[0041] The present invention provides a three-dimensional visualization module that constructs a three-dimensional dynamic model of the construction site based on the BIM model and real-time data, intuitively displays the safety status, personnel and equipment distribution, and supports multi-perspective switching and data query. Its function is as follows: the three-dimensional visualization module constructs a three-dimensional dynamic model of the construction site based on the BIM model and real-time data, and displays the safety status, personnel and equipment distribution in an intuitive and three-dimensional manner. Compared with traditional two-dimensional drawings or text reports, it allows managers to grasp the actual situation on site more quickly and comprehensively, effectively avoiding management errors caused by information misunderstanding deviations. Its multi-perspective switching and data query functions support managers to observe the construction site in depth from different angles and obtain detailed information, which helps to quickly discover potential risks and management loopholes, provide strong support for the scientific formulation of construction plans, allocation of resources, and risk prevention and control, and significantly improve the accuracy and timeliness of decision-making. In addition, with the help of this module, managers can remotely monitor the construction progress, personnel operations and equipment operating status in real time without having to visit the site in person, which not only greatly improves management efficiency and reduces management costs, but also facilitates multi-party collaborative communication, ensuring the efficient and orderly progress of construction site management.

[0042] In the present invention, a system self-diagnosis and maintenance module is provided, which can monitor the operating status of each module of the system in real time, automatically detect data transmission anomalies, algorithm operation failures and other problems, locate the fault point through intelligent diagnosis algorithm, generate maintenance suggestions, support remote online repair and upgrade, and ensure the stable operation of the system. Its role is: the system self-diagnosis and maintenance module can keenly capture data transmission anomalies, algorithm operation failures and other problems by monitoring the operating status of each module of the system in real time, and issue early warnings at the embryonic stage of the fault, avoiding system paralysis or functional failure due to failure to handle the fault in time, thereby ensuring the continuous and stable operation of the construction site safety early warning system, laying a solid technical foundation for construction safety management, and at the same time, with the help of intelligent The module can accurately locate the fault point and narrow the investigation scope to specific sub-modules or code segments. Compared with traditional manual investigation, it greatly shortens the time spent on fault diagnosis, helps maintenance personnel quickly grasp the core of the fault, and significantly improves the efficiency of fault diagnosis. In addition, the maintenance suggestions automatically generated by the module provide clear guidance for maintenance work. Combined with the remote online repair and upgrade functions, common problems can be solved without on-site personnel. This not only reduces maintenance costs, but also completes system maintenance and update iterations without interfering with normal operations at the construction site, ensuring that the construction progress is not affected. At the same time, it enables the system to flexibly adapt to the ever-changing construction environment and management needs, and comprehensively improves the system operation and maintenance efficiency.

[0043] In the second embodiment, based on the first embodiment, the user layer is equipped with a personalized interactive interface that supports user customization of warning thresholds, display parameters, and data display methods. Its function is as follows: the personalized interactive interface equipped with the user layer supports user customization of warning thresholds, display parameters, and data display methods, which can effectively meet the diverse needs of users in different positions and with different management focuses, allowing them to flexibly set system parameters that match their personal management habits and business priorities based on their actual work, accurately filter key information, avoid interference from invalid information, and achieve differentiated and refined construction site safety management. At the same time, users can quickly adjust the warning threshold to make system warnings more in line with the actual risk status of the project and obtain effective warning information in a timely manner. With the help of customized display parameters and data display methods, data can be viewed in the most intuitive and convenient form, quickly grasping the dynamics of the construction site, and then making scientific decisions quickly, significantly improving the response speed and execution efficiency of management work. In addition, the interface allows users to customize it independently, fully respecting user operating habits and preferences, greatly reducing the time cost of users adapting to the system, improving the convenience and comfort of use, and enhancing users' recognition and dependence on the system, effectively promoting the efficient application of the system in construction site safety management.

[0044] Example 3. Based on Example 1 and Example 2, the user layer integrates an intelligent decision-making assistance module. Based on historical risk data and real-time monitoring information, it uses big data analysis and machine learning algorithms to automatically generate risk prevention and control strategy recommendations. At the same time, it supports users to upload construction plans and resource allocation data. Through simulation and deduction functions, it predicts the safety risk probability under different construction plans, provides a quantitative basis for construction management decisions, and realizes the upgrade from safety warning to active safety management. Its role is: the user layer integrates an intelligent decision-making assistance module. With the help of historical risk data and real-time monitoring information, it uses big data analysis and machine learning algorithms to deeply analyze the laws and trends behind the data, and automatically generate accurate risk prevention and control strategy recommendations, which completely changes the traditional model of decision-making that relies on experience, and establishes construction management decisions on objective Based on the solid foundation of data and scientific analysis, the accuracy and reliability of decision-making are greatly improved. At the same time, the module supports users to upload construction plans and resource allocation data, and uses simulation and deduction functions to quantitatively predict the probability of safety risks under different construction plans, helping managers to evaluate the feasibility and potential risks of the plans in advance, and timely adjust and optimize construction plans and resource allocation, reducing the possibility of safety accidents from the source, and ensuring the safe and efficient progress of construction projects. In addition, the intelligent decision-making assistance module provides a quantitative basis for construction management, promotes the transformation of safety management from simple early warning to active prevention and control, and encourages managers to shift from passive response to risks to active planning of prevention and control measures, improve the construction site safety management system, and realize the leapfrog upgrade of construction safety management from passive response to active intervention, and comprehensively improve the overall safety management level of the project.

[0045] The working principle of the present invention is as follows: in the data collection link, the drone body 1 is equipped with a high-definition camera 11, a thermal imager 12, a gas detector 13 and a laser radar 14, and is configured with an adaptive flight path planning algorithm. When the drone body 1 flies at the construction site, the algorithm automatically adjusts the flight path according to changes in the on-site environment to ensure that the high-definition camera 11, the thermal imager 12, the gas detector 13 and the laser radar 14 can comprehensively and dynamically collect multi-dimensional data such as personnel, equipment, and environment at the construction site. The collected data is processed by the data transmission layer, which adopts an adaptive data transmission protocol. The protocol can monitor the network signal strength and data volume in real time and automatically select 5G, Wi-Fi or satellite communication. The data is cached and compressed to improve transmission efficiency, and encrypted to ensure data security. The collected data is stably transmitted to a fusion integrated platform based on cloud services. At the data processing layer, the data fusion module uses a spatiotemporal consistency algorithm to accurately match and fuse the data collected by the drone body 1 with the BIM model to eliminate data errors caused by different data sources and spatiotemporal differences in collection. The fused data is input into a risk identification model based on a deep learning algorithm. The model analyzes personnel, equipment, and environmental factors on the construction site in real time, identifies potential risks, and divides them into risk levels. When the risk identification model detects a risk, the real-time warning mechanism is immediately activated and pushed via SMS, APP, and on-site notification. Early warning information containing risk details and treatment suggestions is sent through multiple channels such as on-site sound and light alarms, and the entire early warning process is fully recorded. At the same time, the BIM model combines the positioning and image recognition technology of the drone body 1 to achieve real-time positioning, behavior monitoring and attendance management of construction site personnel. After the operating data collected by the construction equipment sensors is transmitted to the system platform, the equipment status is evaluated and predicted in combination with the equipment parameters in the BIM model, and equipment scheduling management is optimized. The system's 3D visualization module builds a 3D dynamic model of the construction site based on the BIM model and real-time data, which intuitively displays the safety status, personnel and equipment distribution, supports multi-view switching and data query, and is convenient for managers to grasp the on-site situation. The system self-diagnosis and maintenance model The block monitors the operating status of each module in real time, automatically detects data transmission anomalies, algorithm operation failures and other problems, locates the fault point through intelligent diagnosis algorithm, generates maintenance suggestions and supports remote online repair and upgrade, to ensure stable operation of the system. At the user level, the personalized interactive interface supports users to customize warning thresholds, display parameters and data display methods to meet the management needs of different users. The intelligent decision-making assistance module is based on historical risk data and real-time monitoring information, uses big data analysis and machine learning algorithms to automatically generate risk prevention and control strategy recommendations, and through simulation and deduction functions, predicts the safety risk probability under different construction plans, provides a quantitative basis for construction management decisions, and realizes the upgrade from safety warning to active safety management.

[0046] In this article, there are several points to note:

[0047] 1. The drawings of the embodiments of the present invention only relate to the structures related to the embodiments of the present invention. Other structures may refer to conventional designs.

[0048] 2. In the absence of conflict, the embodiments of the present invention and the features therein may be combined with each other to form new embodiments.

[0049] The above are only specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any technician familiar with this technical field can easily think of changes or replacements within the technical scope disclosed by the present invention, which should be covered by the scope of protection of the present invention.

Claims

1. A construction site safety warning system that uses drones and BIM in collaboration, including: A drone body (1), a fusion integrated platform based on cloud services, and a layered architecture system; characterized in that the drone body (1) is equipped with a high-definition camera (11), a thermal imager (12), a gas detector (13), and a laser radar (14), and is configured with an adaptive flight path planning algorithm, which can automatically adjust the route to collect data according to changes in the construction site; the fusion integrated platform based on cloud services is used to integrate the data collected by the drone body (1) with BIM model information to achieve real-time data processing, storage, and sharing; the layered architecture system includes a data acquisition layer, a data transmission layer, a data processing layer, an application layer, and a user layer, which collaboratively complete data collection, transmission, analysis, and early warning functions; the data transmission layer adopts an adaptive data transmission protocol, which can automatically select 5G, Wi-Fi, or satellite communication methods according to network signals and data volume, and cache, compress, and encrypt the data for transmission.

2. The construction site safety early warning system based on the collaboration of drone and BIM according to claim 1 is characterized in that: A risk identification model based on a deep learning algorithm is provided, which integrates the BIM model with the drone body (1) data, analyzes the personnel, equipment, and environmental factors on the construction site in real time, identifies potential risks, and divides the risk levels.

3. The construction site safety early warning system based on the collaboration of UAV and BIM according to claim 2 is characterized in that: It also includes a real-time early warning mechanism. When the risk identification model detects a risk, it sends early warning information via SMS, APP push, and on-site sound and light alarms, including risk details and handling suggestions, and records the entire early warning process.

4. The construction site safety early warning system based on the collaboration of UAV and BIM according to claim 2 is characterized in that: The BIM model is used for real-time positioning and behavior monitoring of construction site personnel, analyzing personnel behavior and implementing attendance management, and the drone body (1) is used for positioning combined with image recognition technology.

5. The construction site safety early warning system based on the collaboration of UAV and BIM according to claim 4 is characterized in that: Operation data is collected through construction equipment sensors and transmitted to the system platform. Combined with equipment parameters in the BIM model, equipment status is evaluated and predicted, and equipment scheduling management is optimized.

6. The construction site safety warning system based on the collaboration of UAV and BIM according to claim 5 is characterized in that: A data fusion module is configured to match and fuse the drone data with the BIM model based on a spatiotemporal consistency algorithm to eliminate errors and improve the accuracy of safety warnings.

7. The construction site safety early warning system based on the collaboration of UAV and BIM according to claim 6 is characterized in that: It is equipped with a three-dimensional visualization module, which builds a three-dimensional dynamic model of the construction site based on the BIM model and real-time data, intuitively displays the safety status, personnel and equipment distribution, and supports multi-perspective switching and data query.

8. The construction site safety warning system based on the collaboration of UAV and BIM according to claim 7 is characterized in that: It is equipped with a system self-diagnosis and maintenance module, which can monitor the operating status of each system module in real time, automatically detect data transmission anomalies, algorithm operation failures and other problems, locate the fault point through intelligent diagnosis algorithm, generate maintenance suggestions, support remote online repair and upgrade, and ensure stable system operation.

9. The construction site safety early warning system based on the collaboration of UAV and BIM according to claim 1, characterized in that: The user layer is equipped with a personalized interactive interface, which supports users to customize warning thresholds, display parameters and data display methods.

10. The construction site safety warning system based on the collaboration of UAV and BIM according to claim 9 is characterized in that: The user layer integrates an intelligent decision-making assistance module, which automatically generates risk prevention and control strategy recommendations based on historical risk data and real-time monitoring information, using big data analysis and machine learning algorithms. It also supports users to upload construction plans and resource allocation data, and through simulation and deduction functions, predicts the safety risk probability under different construction plans, providing a quantitative basis for construction management decisions, and realizing the upgrade from safety warning to active safety management.

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