Dynamic unmanned aerial vehicle path planning method, system and equipment based on BIM construction deduction and medium

Abstract

The invention discloses a dynamic unmanned aerial vehicle path planning method, system and device based on BIM construction deduction, and a medium, and relates to the technical field of building construction inspection, and the method comprises the steps: building a BIM construction deduction model, integrating real-time data, generating a deduction sequence of a construction stage, and sensing a construction scene state in real time; developing an intelligent unmanned aerial vehicle path planning system, generating an optimal flight path, and automatically planning an unmanned aerial vehicle inspection path; based on the SLAM technology and multi-sensor fusion, the unmanned aerial vehicle cooperatively controls and dynamically adjusts a path, intelligent obstacle avoidance is verified through deduction, and inspection data is processed in real time; according to the method, the inspection path is dynamically generated and optimized, the method adapts to the real-time change of a construction site, and the inspection efficiency and the coverage rate are improved; a path planning algorithm is adopted, time and obstacle avoidance requirements are balanced, and the accuracy and timeliness of inspection planning are improved; and the SLAM technology and the depth sensor are introduced, so that the safety and reliability of the inspection task are ensured, and flight interruption or collision is avoided.

Description

Technical Field

[0001] The present invention relates to the technical field of building construction inspection, and particularly to a dynamic UAV path planning method, system, device and medium based on BIM construction deduction. Background Art

[0002] In construction management, BIM is used to generate information models of building components and systems, which can be used for schedule planning, resource management, and conflict detection. By integrating BIM with the construction plan, project managers can better understand the construction process, optimize resource allocation, and reduce potential errors and conflicts. However, traditional BIM models are usually created in the design phase and may not reflect the dynamic changes during the construction process. The construction process involves a large number of temporary structures, equipment, and materials, which may not be fully considered in the BIM model. Therefore, path planning based on a static BIM model may not be able to adapt to the actual changes during the construction process.

[0003] On the other hand, the application of UAV technology in the construction field is also developing rapidly. UAVs can be used for various purposes, such as surveying, inspection, monitoring, and mapping. UAV technology can collect data quickly and accurately, reduce manual intervention, and improve safety. In construction management, UAVs can be used to monitor progress, inspect structural integrity, and generate real-time updates of the construction site.

[0004] Although BIM and UAV technology have their respective advantages in the construction field, there are still some deficiencies in the existing technologies that combine BIM and UAV technology for dynamic path planning. Existing UAV path planning usually relies on a static representation of the environment. At a construction site, the environment is dynamic, with workers, equipment, and materials constantly moving. Existing path planning algorithms may not be able to effectively handle these dynamic obstacles, resulting in inaccurate and unsafe path planning.

[0005] In addition, the integration of BIM and UAV data usually requires manual operation or the use of different software tools, which may lead to data inconsistency and low operation efficiency. The lack of automatic integration and update of real-time data makes it difficult to achieve dynamic path planning based on BIM. Summary of the Invention

[0006] In view of the above existing problems, the present invention is proposed.

[0007] Therefore, the technical problems solved by the present invention are as follows: The existing method for planning the inspection path of drones in construction depends on a static BIM construction deduction model, resulting in the inability to dynamically adapt to changes in the construction scenario, low efficiency in synchronizing real-time data with the BIM construction deduction model, insufficient intelligence in path planning and obstacle avoidance decision-making, as well as problems such as how to achieve dynamic coordination between construction deduction and drone paths, how to verify obstacle avoidance strategies through multi-sensor fusion, and how to efficiently integrate the Internet of Things and the BIM system.

[0008] To solve the above technical problems, the present invention provides the following technical solutions: A dynamic drone path planning method based on BIM construction deduction, including constructing a BIM construction deduction model, integrating real-time data, generating a deduction sequence for the construction stage, and real-time sensing of the construction scenario status; developing an intelligent drone path planning system to generate the optimal flight path and automatically plan the drone inspection path; based on SLAM technology and multi-sensor fusion, the drone collaboratively controls and dynamically adjusts the path, and uses deduction to verify intelligent obstacle avoidance and real-time process inspection data; integrating real-time data includes constructing real-time synchronization between the physical state of the construction site and the BIM construction deduction model and establishing a bridge between the BIM construction deduction model and the Internet of Things platform; generating a deduction sequence for the construction stage includes seamlessly integrating the static BIM construction deduction model with the dynamic construction plan to form a spatio-temporal integrated construction deduction system; automatically planning the drone inspection path includes deeply integrating BIM construction information and drone path planning to construct an intelligent inspection path; using deduction to verify intelligent obstacle avoidance includes introducing SLAM real-time mapping and obstacle avoidance mechanisms to real-time control changes in the dynamic construction environment.

[0009] As a preferred solution of the dynamic drone path planning method based on BIM construction deduction according to the present invention, wherein: constructing the BIM construction deduction model includes creating a building information model based on BIM software, combining the BIM construction deduction model with the time dimension, and using construction simulation software to generate a deduction sequence for the construction stage to predict construction progress and site changes.

[0010] As a preferred solution of the dynamic drone path planning method based on BIM construction deduction according to the present invention, wherein: integrating real-time data includes constructing a sensor network, real-time synchronizing with the BIM construction deduction model through the Internet of Things platform, and dynamically sensing and updating the construction scenario; based on API plugins, seamlessly integrating the real-time data of the BIM construction deduction model and the construction site sensor network.

[0011] As a preferred solution of the dynamic drone path planning method based on BIM construction deduction according to the present invention, wherein: developing the intelligent drone path planning system includes extracting the spatial layout data and construction stage information of the BIM construction deduction model and converting the spatial layout data and construction stage information into the format required for drone path planning.

[0012] As a preferred solution of the dynamic UAV path planning method based on BIM construction deduction according to the present invention, wherein: the generation of the optimal flight path includes generating the optimal flight path based on an intelligent path planning algorithm according to the construction scenario of the BIM construction deduction model and the real-time data of the construction site sensor network, introducing a heuristic method to optimize the flight path of the UAV, and intelligently generating and adjusting the UAV inspection path.

[0013] As a preferred solution of the dynamic UAV path planning method based on BIM construction deduction according to the present invention, wherein: the collaborative control of the UAV for dynamic path adjustment includes using real-time positioning and SLAM technology to enable the UAV to perform self-positioning and path adjustment during flight.

[0014] As a preferred solution of the dynamic UAV path planning method based on BIM construction deduction according to the present invention, wherein: the use of deduction verification for intelligent obstacle avoidance includes, based on multi-sensor fusion technology, combined with SLAM technology, detecting obstacles ahead in real time, and combining the real-time data of the sensor network to detect and dynamically avoid obstacles in front of the UAV.

[0015] Another object of the present invention is to provide a dynamic UAV path planning system based on BIM construction deduction, which can integrate the BIM construction deduction model, Internet of Things real-time data and intelligent path planning algorithm, solve the problems of the disconnection between the static model and the dynamic construction scenario, low multi-source data collaboration efficiency, and obstacle avoidance strategy relying on manual intervention in the current UAV construction inspection system, realize the dynamic coupling of construction deduction and UAV path, autonomous obstacle avoidance under real-time perception of environmental changes, and the efficient integration of BIM and the Internet of Things platform.

[0016] As a preferred solution of the dynamic UAV path planning system based on BIM construction deduction according to the present invention, wherein: it includes a data fusion module, a path planning module, a UAV control module, and an intelligent analysis module; the data fusion module is used to integrate the BIM construction deduction model and real-time sensor data to construct a dynamically updated construction scenario model; the path planning module is used to generate and optimize the UAV inspection path based on the construction scenario model provided by the data fusion module using intelligent algorithms to adapt to the dynamic changes of the construction site; the UAV control module is used to control the UAV to perform inspections according to the path generated by the path planning module, make dynamic adjustments according to real-time data, collect images, videos and sensor data, and transmit them to the control center in real time; the intelligent analysis module is used to preprocess and format the inspection data collected by the UAV, and conduct comparative analysis with the BIM construction deduction model to identify construction deviations, defects and potential risks, and generate a visual report.

[0017] A computer device includes a memory and a processor. The memory stores a computer program, and the execution of the computer program by the processor realizes the steps of a dynamic UAV path planning method based on BIM construction deduction.

[0018] A computer-readable storage medium stores a computer program thereon. When the computer program is executed by a processor, it realizes the steps of a dynamic UAV path planning method based on BIM construction deduction.

[0019] Advantages of the present invention: The dynamic UAV path planning method based on BIM construction deduction provided by the present invention seamlessly integrates the static BIM construction deduction model and the dynamic construction plan through spatio-temporal integrated modeling, combines SLAM, multi-sensor data and the Internet of Things for real-time synchronization, realizes the dynamic perception and prediction of the construction scene, and significantly improves the adaptability and accuracy of UAV patrol inspection; generates the optimal patrol path based on the BIM construction deduction sequence, and dynamically optimizes it through intelligent algorithms, combines real-time obstacle avoidance and cooperative control, solves the problems of autonomy and safety of path planning in complex construction environments, and reduces the need for manual intervention; efficiently integrates the BIM construction deduction model and Internet of Things data through API, constructs a "physical-digital" real-time mapping, combines an intelligent analysis module to quickly identify construction deviations and risks, realizes the closed-loop control of construction progress, quality and safety, and improves construction management efficiency; organically integrates technologies such as SLAM mapping, heuristic path planning, and multi-sensor fusion to form a complete technical chain covering data collection, path optimization, dynamic obstacle avoidance and result analysis, providing a full-process solution for smart construction sites. Description of the Drawings

[0020] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for description in the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0021] Figure 1 It is the overall flowchart of the dynamic UAV path planning method based on BIM construction deduction provided by the first embodiment of the present invention. Detailed Embodiments

[0022] To make the above objects, features and advantages of the present invention more obvious and understandable, the specific embodiments of the present invention will be described in detail below with reference to the drawings of the specification. Obviously, the described embodiments are some embodiments of the present invention, rather than all embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the scope of protection of the present invention.

[0023] Example 1, refer to Figure 1 , which is an embodiment of the present invention, provides a dynamic UAV path planning method based on BIM construction deduction, including: S1: Build a BIM construction deduction model, integrate real-time data, generate a deduction sequence for the construction stage, and perceive the status of the construction scene in real time.

[0024] Furthermore, building the BIM construction deduction model includes creating a building information model based on BIM software, combining the BIM construction deduction model with the time dimension, and using construction simulation software to generate a deduction sequence for the construction stage to predict construction progress and site changes.

[0025] Moreover, the BIM software includes Autodesk Revit, Navisworks, but is not limited to Autodesk Revit, Navisworks, which analyzes and creates the three-dimensional geometric structure of the building, construction stage plan, key checkpoints, and site layout to build a detailed three-dimensional model; the construction simulation software includes Navisworks Simulate, Synchro, but is not limited to Navisworks Simulate, Synchro, which adds a time dimension to the BIM construction deduction model and transmits the generated deduction sequence and information to the UAV path planning scheme.

[0026] It should be noted that integrating real-time data includes building a sensor network, which is synchronized with the BIM construction deduction model in real time through the Internet of Things platform to dynamically perceive and update the construction scene; based on the API plug-in, seamlessly integrating the real-time data of the BIM construction deduction model with the construction site sensor network.

[0027] It should also be noted that building the sensor network includes arranging cameras, lidar, RFID, temperature and humidity sensors at the construction site to collect environmental data, personnel and equipment positions, and dynamic change information, integrating the sensor data into the data fusion module using the MQTT, Azure IoT Hub Internet of Things platform, binding the sensors to the BIM components using a unified identification rule to enhance the real-time perception of the on-site status, and synchronously updating it with the BIM construction deduction model. The timed refresh of the component attributes in the BIM system is expressed as: ; Among them, represents the component attribute, represents temperature, noise level.

[0028] It should also be noted that by constructing a BIM construction deduction model and combining real-time construction data, the present invention realizes the dynamic perception of the construction site, can reflect the construction progress and the changes in the on-site environment in real time, provides an accurate reference basis for the UAV path planning, overcomes the limitations of traditional methods that rely on static models or preset routes, and improves the timeliness and adaptability of path planning.

[0029] S2: Develop an intelligent UAV path planning system to generate an optimal flight path and automatically plan the UAV inspection path.

[0030] Furthermore, developing an intelligent UAV path planning system includes extracting the spatial layout data and construction stage information of the BIM construction deduction model, and converting the spatial layout data and construction stage information into the format required for UAV path planning.

[0031] It should be noted that generating the optimal flight path includes generating the optimal flight path based on an intelligent path planning algorithm according to the construction scenario of the BIM construction deduction model and the real-time data of the construction site sensor network, introducing a heuristic method to optimize the UAV flight path, and intelligently generating and adjusting the UAV inspection path. The heuristic function is expressed as: ; Among them, represents the estimated total cost value from the current UAV flight node to the target node. The smaller the estimated total cost value, the better the path. represents a certain node of the UAV. is the comprehensive environmental risk assessment value when the current UAV is at node , and the normalized value range is from 0 to 1, which is expressed as: ; Among them, represents the risk factor index. represents the total number of risk factors. represents the real-time normalized perception value of the th type of risk factor at node . represents the fusion weight of the construction risk factor in the th section. represents the normalized Euclidean distance between the current node and the target node, which is normalized to 0 to 1 and is expressed as: ; Among them, represents the geometric distance between the current node and the target node. represents the maximum flight distance in the BIM construction deduction model. represents the distance scoring weight parameter, which controls the influence degree of the geometric distance on the total cost. represents the risk scoring weight parameter, which controls the proportion of the safety risk in the total cost. and These two weight parameters can be flexibly configured according to different flight strategies: when in a high-risk construction area, can be set relatively high to give priority to avoiding risks; when the task is highly urgent, is set relatively high to give priority to increasing speed. The system determines whether the current path meets the planning requirements by setting a heuristic threshold :

[0032] If for all nodes of the current path ≤ , the path is considered feasible; if there are consecutive nodes > , the local A* algorithm or intelligent path planning algorithm is called for path replanning.

[0033] It should also be noted that based on the calculation results, an optimal solution for adjusting the UAV inspection path includes path cost clustering analysis: clustering the of each node in the initial planned path to identify the "high-cost sections" in the path (i.e., continuous high intervals), which usually means that the area has a relatively high risk or low flight efficiency; local path reconstruction mechanism: for the identified high-cost sections, the system triggers the local path replanning module (local A* algorithm) to limit the search range between the current position node and the target point to find a feasible low-risk alternative path segment; path optimization strategy (selection of the minimum total cost): comparing the total costs of multiple candidate paths using the total cost comparison function: ; where represents the sum of the total cost values of the UAV flight path, represents the standardized flight time of all flight segments on the path , in seconds, represents the time additional constraint, i.e., the maximum flight time, and the path with the minimum sum of total costs and meeting the constraint conditions is selected as the optimal solution; combination of path memory mechanism and reinforcement learning: the system records the effects and flight performances of each candidate path during the path planning process, and feeds them back to the BIM construction deduction as training samples to gradually form a strategy library for path optimization and achieve adaptive path evolution.

[0034] It should also be noted that the intelligent path planning algorithm includes the intelligent path planning algorithms for implementing the A* algorithm and the Dijkstra algorithm. Based on the BIM construction deduction model, the starting position of the drone is marked as the starting point, the key inspection points that the drone needs to patrol are marked as target points, buildings, equipment, and people are marked as static obstacles as the map information in the algorithm, and the positions of dynamic obstacles in the real-time sensor data are marked as dynamic obstacles and added to the map.

[0035] It should also be noted that introducing heuristic methods to optimize the flight path of the drone includes using genetic algorithms and ant colony algorithms to optimize the flight path of the drone, adopting adaptive crossover rates and mutation rates, adjusting according to the population fitness, and defining the fitness function: ; Among them, represents the total cost of the candidate path , and the smaller the value, the better the path. is the sum of the normalized flight distances of the unit energy consumption of all flight segments of the candidate path , in units of joule-equivalent meters, used to reflect the distance burden generated by the flight path in different energy consumption density regions, and is expressed as: ; Among them, represents the path segment number index, ' represents the path segment number index, represents the Euclidean distance of the th segment of the path, in units of meters, represents the unit distance energy consumption coefficient of the th spatial region. It is set that in the open and empty area ∈[8, 10], in the densely structured area ∈[11, 13], in the high-construction interference area ∈[13, 16], and it is set that in the open and empty area ≥17, in units of joule, represents the normalized flight time of all flight segments on the path ; Among them, represents the path segment number index, ' represents the path segment number index, represents the Euclidean distance of the th segment of the path, in units of meters, represents the estimated flight speed of the drone on the Indicates the The execution delay of the segment task trigger, in seconds, is the construction risk factor of the area through which the path passes, and the sum of the environmental risk intensity assessment values of each node on the path, reflecting the complexity and safety of the construction environment, expressed as: ; in, Indicates the path segment number index, 'Indicates the path segment number index, Indicates the The comprehensive construction risk level score of the section is directly mapped and assigned based on the BIM construction deduction model and construction scene identification. Indicates the The fusion weight of the section construction risk factor is jointly assigned by the BIM construction simulation model and multi-source sensor data. 、 、 These are weight parameters that can be configured as needed or dynamically learned and adjusted. The sum of the three weight parameters can be normalized to 1. They are used to control the weight ratio of each factor during path optimization and provide real-time construction site data for path planning.

[0036] in, 、 、 Can be configured based on different mission requirements: if flying at low altitude, high navigation accuracy is required, If the task is urgent and the wind speed is high, you can increase If you fly at night, the risk is high and you can increase , compare the fitness values of all paths, if the optimal candidate path ≤ the set fitness threshold, indicating that the path meets the acceptable cost. Otherwise, the number of iterations is increased and the adjustment is made. 、 、 Weight parameters make the algorithm biased towards efficiency or risk aversion.

[0037] It should also be noted that A preferred solution is to apply the calculation results of the method, which specifically includes: in the iterative process, retain the path with the smallest fitness value as the current optimal path. If the fitness threshold is less than or equal to the set one, the path will directly enter the task delivery process; if it is not satisfied, the path re-optimization mechanism will be triggered to perform "local segment replacement" on the path segments with high cost value, and reconstruct the path segments with the help of the surrounding low-cost path points to reduce the total cost. If the construction environment changes drastically and the response demand is fast, the system can enable the "safety weighted fast mode", that is, directly select the path segments that meet the requirements. ≤ In the path of setting the construction risk factor threshold, The shortest one is the optimal. Skip the full evaluation of some cost items to speed up the decision-making.

[0038] The calculation formula of the heuristic method Manhattan distance is expressed as: ; Wherein, represents the Manhattan distance between the current UAV node and the target inspection point, and is used for the heuristic function evaluation in path planning. represents the lateral position of the current UAV in the BIM construction deduction model space. represents the lateral position of the target inspection point in the BIM construction deduction model. represents the longitudinal position of the current UAV in the BIM construction deduction model. represents the longitudinal position of the target inspection point in the BIM construction deduction model.

[0039] It should also be noted that the present invention has developed an intelligent UAV path planning system, which integrates the A* algorithm, Dijkstra algorithm, genetic algorithm, and ant colony algorithm. It can generate efficient and safe flight paths according to the BIM construction deduction model and real-time sensor data, and dynamically adjust to adapt to the changes in the construction site, realizing the intelligence and automation of path planning, and improving the inspection efficiency and accuracy.

[0040] S3: Based on SLAM technology and multi-sensor fusion, the UAV cooperative control dynamically adjusts the path, adopts deduction verification for intelligent obstacle avoidance, and processes inspection data in real time.

[0041] Furthermore, the UAV cooperative control dynamically adjusting the path includes using the instant positioning and SLAM technology to enable the UAV to perform self-positioning and path adjustment during flight.

[0042] It should be noted that adopting deduction verification for intelligent obstacle avoidance includes, based on multi-sensor fusion technology, combining with SLAM technology, detecting the obstacles in front in real time, and combining with the real-time data of the sensor network to detect and dynamically avoid the obstacles in front of the UAV.

[0043] It should be noted that detecting and dynamically avoiding the obstacles in front of the UAV includes the UAV running SLAM through lidar or binocular cameras, constructing a construction site map in real time, and at the same time performing image recognition, calibrating and classifying the detected obstacles. Introduce a dynamic interactive obstacle avoidance evaluation model. The set of obstacles perceived by the current UAV during the map construction process can be a set composed of multiple obstacle point clouds and contours. The dynamic interactive obstacle avoidance evaluation model is expressed as: ; Wherein, represents the function value of the risk degree of the obstacles faced by the current environment. Indicates the current candidate path of the UAV. Indicates the number of obstacles. Indicates the current number of obstacles. Indicates the distance measurement from the UAV to the th obstacle. Indicates the probability that the th obstacle has dynamic characteristics. If the obstacle shows a continuous movement trend, a value greater than 0.7 is assigned; if it is in a static state, a value of 0 is assigned. Indicates the th obstacle has been marked in the current BIM construction deduction model, then it is 1; otherwise, it is 0. Indicates the intersection probability within the time window. If the intersection time point is close, it is 1; if the obstacle is in the direction away from the path, it is 0. , , , Indicates dynamically adjustable parameters, which are optimized and assigned through inspection historical data. Indicates a constant value.

[0044] It should also be noted that real-time processing of inspection data includes the UAV transmitting inspection videos and images to the control center in real time through wireless networks, receiving data using 4G / 5G, Wi-Fi high-bandwidth wireless communication protocols, automatically comparing with the BIM construction deduction model, and using computer vision image processing algorithms to identify construction deviations, defects, and potential risks; the data analysis results are used to generate an automatic inspection report, including the problems found, the inspection path map, and a summary of the construction status, and feedback is generated based on the analysis result report to update the BIM construction deduction model or adjust the sensor configuration, and a three-dimensional display of the construction status, flight path, and problem area is provided for construction management personnel to reference.

[0045] It should also be noted that the present invention realizes the real-time positioning and dynamic obstacle avoidance of the UAV through SLAM technology and sensors, transmits inspection data to the control center in real time through wireless networks, automatically identifies construction deviations, defects, and potential risks using image processing algorithms and computer vision technology, and generates an inspection report, realizing the full-process automation from data collection to feedback, improving the inspection efficiency and the accuracy of data analysis, and providing an intelligent solution for construction management.

[0046] Embodiment 2 is an embodiment of the present invention, which provides a dynamic UAV path planning method based on BIM construction deduction. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.

[0047] To verify the practicality and innovation of the dynamic UAV path planning method based on BIM construction deduction, 6 groups of experiments with typical construction stages and scenarios are designed, covering daytime construction, dense structure areas, and areas with frequent operation dynamics. The experiments respectively compare the actual performance of the traditional static path UAV inspection method and the solution of the present invention. The BIM construction deduction model used is constructed by Autodesk Revit and Navisworks, integrates the construction stage deduction sequence, and forms a dynamic construction state perception network by synchronizing construction data in real time with sensors deployed at the construction site through the MQTT Internet of Things platform.

[0048] First, based on the BIM construction deduction model, the geometric structure, component status, and construction interference information of the current stage are extracted, the spatial layout point cloud data is exported, and it is structured into a three-dimensional topological map acceptable to the path planning algorithm. In terms of path generation, the present invention combines the A* algorithm, ant colony optimization algorithm, and construction risk cost function to automatically calculate the optimal flight path of the UAV from the starting point to the inspection target, and adjusts the cost model in real time according to the construction risk factors to realize dynamic adaptation and optimization of the path.

[0049] During the flight, the UAV performs high-precision real-time positioning and mapping based on the SLAM technology. By fusing visual images and lidar data, it identifies obstacles in front of the current path, dynamically evaluates the risk level of the obstacles, and accordingly calls the obstacle avoidance function to perform path replanning. If there is a high-risk trend in the current path, the path update result is synchronously written back to the BIM construction deduction model to perform real-time adjustment and feedback on the construction state space.

[0050] After the flight is completed, the UAV transmits the image and point cloud data back to the control platform through the wireless communication network. The platform calls the image processing algorithm to automatically compare with the BIM component model, identifies construction deviations, defects, and risk factors, and automatically generates an inspection report to realize the closed-loop automation from path perception to feedback decision-making. A total of 6 groups of scenario data are collected in the entire experiment, and multi-dimensional indicators such as flight path length, task duration, obstacle recognition rate, path update frequency, inspection coverage integrity, and report output delay are statistically analyzed.

[0051] Table 1 Comparison table of intelligent obstacle avoidance for BIM UAV path planning

[0052] As shown in Table 1, the present invention shows advantages in both path efficiency and planning intelligence. In terms of path length and flight time, compared with the traditional static planning method, the average path is shortened by about 23%, and the flight time is compressed by more than 20%. Especially in the dense structure area, good flight efficiency can still be maintained, indicating that the heuristic function and component risk cost model have good cooperative control capabilities during the path planning process.

[0053] In terms of obstacle recognition ability, the multi-source perception fusion strategy introduced in the present invention effectively improves the dynamic recognition accuracy rate, with the average recognition rate increased to over 94%. In contrast, the traditional method is limited by the static layer and single visual sensor, and the accuracy rate remains at about 80%. The experimental scenarios include crowded pedestrian areas and mechanical intersection areas, both of which verify the support of the dynamic consistency mechanism between the SLAM map and the BIM construction deduction model for the accuracy rate of the obstacle avoidance strategy.

[0054] In summary, by constructing the BIM construction deduction model, the present invention integrates the intelligent path optimization, dynamic obstacle avoidance modeling, SLAM collaborative control and visual intelligent recognition mechanisms, realizing a full-process closed-loop dynamic path planning system from construction state perception, path calculation, obstacle warning to data feedback. It overcomes the limitations of the traditional method, such as rigid path, lagging feedback and insufficient recognition accuracy, demonstrating obvious novelty and practicality.

[0055] Embodiment 3, an embodiment of the present invention, provides a dynamic UAV path planning system based on BIM construction deduction, including a data fusion module, a path planning module, a UAV control module and an intelligent analysis module.

[0056] Among them: The data fusion module includes a BIM construction deduction module, a sensor data acquisition module, a data preprocessing module and a data synchronization module.

[0057] It should be noted that the BIM construction deduction module is used to import the BIM construction deduction model into the system and extract the building structure, construction progress and key inspection points; the sensor data acquisition module is used to receive real-time data from sensors deployed at the construction site, including environmental parameters, personnel and equipment positions, and dynamic changes; the data preprocessing module is used to clean, format and fuse the collected BIM construction deduction model data and sensor data; the data synchronization module is used to synchronize the preprocessed data to the database and update the BIM construction deduction model in real time to ensure that path planning and intelligent analysis are always based on the latest construction state information.

[0058] It should also be noted that the data fusion module is used to execute sequentially, through deduction, acquisition, preprocessing, and finally synchronize to the path planning module. The data fusion module provides a data basis for the path planning module and the intelligent analysis module.

[0059] The path planning module includes a map construction module, a path generation algorithm module, a path optimization module and a dynamic adjustment module.

[0060] It should be noted that the map construction module is used to construct a three-dimensional environmental map based on the data transmitted by the data fusion module; the path generation algorithm module is used to implement intelligent path planning algorithms such as the A* algorithm and the Dijkstra algorithm, and generate the initial inspection path of the UAV according to the map information; the path optimization module is used to optimize the initial path using genetic algorithms and ant colony algorithms; the dynamic adjustment module, based on sensor data and SLAM technology, monitors the changes at the construction site in real time and dynamically adjusts the flight path of the UAV.

[0061] It should also be noted that the path planning module generates the inspection path of the UAV according to the information provided by the data fusion module and provides path planning for the UAV control module.

[0062] The UAV control module includes a UAV communication module, a UAV control module, and a UAV scheduling module.

[0063] It should be noted that the UAV communication module is used to establish a wireless communication connection with the UAV, send control instructions, and receive flight status information; the UAV control module is used to control the flight direction, speed, and altitude of the UAV according to the flight path and control instructions generated by the path planning module; the UAV scheduling module is used to manage the collaborative work of multiple UAVs, avoid path conflicts, and achieve automatic switching and scheduling of UAVs.

[0064] It should also be noted that the UAV communication module is responsible for establishing a connection with the UAV, the UAV control module controls the flight of the UAV according to the instructions of the path planning module, and the UAV scheduling module manages the collaborative work of multiple UAVs.

[0065] The intelligent analysis module includes a target detection module, a data analysis module, and a report generation module.

[0066] It should be noted that the target detection module is used to identify buildings, equipment, and personnel in the image based on computer vision technology, and extract the location and feature information of the target; the data analysis module is used to compare the identified target information with the BIM construction deduction model to analyze construction deviations, quality problems, and safety hazards; the report generation module is used to generate an inspection report based on the analysis results for the reference of construction management personnel.

Claims

1. A dynamic UAV path planning method based on BIM construction deduction, characterized in that Including: Construct a BIM construction deduction model, integrate real-time data, generate a deduction sequence in the construction stage, and perceive the status of the construction scene in real time; Develop an intelligent UAV path planning system, generate the optimal flight path, and automatically plan the UAV inspection path; Based on SLAM technology and multi-sensor fusion, the UAV cooperative control dynamically adjusts the path, uses deduction to verify intelligent obstacle avoidance, and processes inspection data in real time; Integrating real-time data includes the real-time synchronization of the physical state of the construction site and the BIM construction deduction model, and establishing a bridge between the BIM construction deduction model and the Internet of Things platform; Generating a deduction sequence in the construction stage includes seamlessly integrating the static BIM construction deduction model with the dynamic construction plan to form a spatio-temporal integrated construction deduction system; Automatically planning the UAV inspection path includes deeply integrating the BIM construction information and the UAV path planning to construct an intelligent inspection path; Using deduction to verify intelligent obstacle avoidance includes introducing the SLAM real-time mapping and obstacle avoidance mechanism to control the changes in the dynamic construction environment in real time.

2. The dynamic UAV path planning method based on BIM construction deduction according to claim 1, characterized in that: The construction of the BIM construction deduction model includes, Create a building information model based on BIM software, combine the BIM construction deduction model with the time dimension, and use construction simulation software to generate a deduction sequence in the construction stage to predict the construction progress and site changes.

3. The dynamic UAV path planning method based on BIM construction deduction according to claim 2, wherein: The integration of real-time data includes, Construct a sensor network, synchronize it with the BIM construction deduction model in real time through the Internet of Things platform, and dynamically perceive and update the construction scene; Based on the API plugin, seamlessly integrate the real-time data of the BIM construction deduction model and the construction site sensor network.

4. The dynamic UAV path planning method based on BIM construction deduction according to claim 3, characterized in that: The development of the intelligent UAV path planning system includes, Extract the spatial layout data and construction stage information of the BIM construction deduction model, and convert the spatial layout data and construction stage information into the format required for UAV path planning.

5. The dynamic UAV path planning method based on BIM construction deduction according to claim 4, characterized in that: The generation of the optimal flight path includes, Based on the intelligent path planning algorithm, generate the optimal flight path according to the construction scene of the BIM construction deduction model and the real-time data of the construction site sensor network, introduce heuristic methods to optimize the flight path of the UAV, and intelligently generate and adjust the UAV inspection path.

6. The dynamic UAV path planning method based on BIM construction deduction according to claim 5, wherein: The UAV cooperative control for dynamic path adjustment includes, Adopt the instant positioning and SLAM technology to enable the UAV to perform self-positioning and path adjustment during flight.

7. The dynamic UAV path planning method based on BIM construction deduction according to claim 6, characterized in that: The use of deduction to verify intelligent obstacle avoidance includes, Based on the multi-sensor fusion technology, combined with the SLAM technology, detect the obstacles ahead in real time, and combine the real-time data of the sensor network to detect and dynamically avoid the obstacles in front of the UAV.

8. A dynamic UAV path planning system based on BIM construction deduction, characterized in that: Including a data fusion module, a path planning module, a UAV control module, and an intelligent analysis module; The data fusion module is used to integrate the BIM construction deduction model and the real-time sensor data to construct a dynamically updated construction scene model; The path planning module is used to generate and optimize the UAV inspection path based on the construction scene model provided by the data fusion module using intelligent algorithms to adapt to the dynamic changes of the construction site; The drone control module is used to control the drone to perform inspection according to the path generated by the path planning module, dynamically adjust according to real-time data, collect images, videos and sensor data, and transmit them to the control center in real time; The intelligent analysis module is used to preprocess and format the inspection data collected by the drone, compare and analyze it with the BIM construction deduction model, identify construction deviations, defects and potential risks, and generate a visual report.

9. A computer device, comprising a memory and a processor, the memory storing a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the dynamic drone path planning method based on BIM construction deduction according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the dynamic drone path planning method based on BIM construction deduction according to any one of claims 1 to 7.

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