BIM and unmanned aerial vehicle integrated building construction monitoring method

Abstract

The invention relates to the technical field of building construction, in particular to a BIM and unmanned aerial vehicle integrated building construction monitoring method, which comprises the following steps of S1, deploying an unmanned aerial vehicle at a construction site; s2, the unmanned aerial vehicle is used for collecting construction data when flying on a preset path; s3, performing primary processing and compression on the collected data through an edge calculation unit built in the unmanned aerial vehicle; s4, automatically matching the data transmitted by the unmanned aerial vehicle with a pre-imported BIM model; s5, if it is found that a deviation exists between the construction progress and the BIM model preset plan, an adjustment suggestion is generated according to an analysis result; and S6, correspondingly adjusting the flight path and the data acquisition plan of the unmanned aerial vehicle according to the adjustment suggestion. According to the invention, by integrating the unmanned aerial vehicle and the BIM model and adopting an advanced data synchronization technology, the real-time performance and accuracy of building construction monitoring are remarkably improved, so that project management is more efficient and flexible, and errors and cost are reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of building construction, and in particular to a building construction monitoring method integrating BIM and unmanned aerial vehicle. Background Art

[0002] In modern construction management, it is crucial to monitor the status of the construction site in real time and ensure that the project is carried out according to the predetermined building information model (BIM). Traditional methods usually rely on manual on-site inspections and regular data updates. These methods have significant limitations in processing real-time data and quickly responding to changes on the construction site. With the development of technology, drones have been introduced as an effective data collection tool, providing real-time views and data through aerial photography, but their data integration with BIM models still faces challenges. Existing technologies have failed to fully realize the dynamic synchronization of drone data and BIM models, especially in terms of data processing speed and accuracy, which still needs to be improved.

[0003] In order to solve these problems, the present invention proposes a construction monitoring method integrating BIM and drones. In the prior art, there is a delay in the real-time updating and synchronization of data collected by drones and BIM models, and the data processing efficiency is low, which leads to the fact that construction management decisions cannot be made based on the latest on-site information. In addition, traditional methods face technical obstacles in processing and analyzing large amounts of data collected by drones. How to effectively integrate and utilize these data, and how to ensure the consistency of construction progress with the BIM model are problems that need to be urgently solved in the prior art. Summary of the invention

[0004] Based on the above objectives, the present invention provides a construction monitoring method integrating BIM and drones.

[0005] A construction monitoring method integrating BIM and drones, comprising the following steps:

[0006] S1: Deploy drones at the construction site. The drones use built-in sensor arrays and algorithms to perceive the dynamic changes of the surrounding environment in real time and autonomously plan flight paths to avoid obstacles at the construction site.

[0007] S2: When the drone flies on a preset path, it uses sensors and cameras to collect real-time data on the construction site. The collected data includes three-dimensional images, environmental parameters, and location data of construction materials;

[0008] S3: The collected data is preliminarily processed and compressed by the edge computing unit built into the drone, and transmitted to the server of the construction monitoring center in real time through wireless communication;

[0009] S4: After receiving the data, the server of the construction monitoring center uses a dynamic data synchronization algorithm to automatically match the data transmitted by the drone with the pre-imported BIM model, and maps the actual status of the construction site to the BIM model in real time, realizing real-time update of the BIM model;

[0010] S5: Based on the BIM model update, the construction progress is tracked and evaluated in real time through data association analysis, combining historical data with real-time data. If a deviation is found between the construction progress and the preset plan of the BIM model, adjustment suggestions are generated based on the analysis results;

[0011] S6: Based on the adjustment suggestions, the flight path and data collection plan of the drone are adjusted accordingly, and the on-site construction personnel are notified to make immediate corrections to keep the construction progress consistent with the BIM model.

[0012] Optionally, the S1 specifically includes:

[0013] S11: deploying a ground base station at a predetermined location on the construction site, wherein the base station is equipped with a positioning device and an environmental monitoring sensor for real-time monitoring of the climate, obstacle locations, and movement paths within the construction area;

[0014] S12: The UAV acquires preliminary environmental information and positioning data of the construction site by communicating with the ground base station before taking off, including current climate conditions, spatial locations of known obstacles, and dynamic change data of the construction site;

[0015] S13: After the drone is started, it uses a built-in sensor array to perform a 360-degree full-scale scan to collect dynamic data of the surrounding environment in real time. The sensor array includes a laser radar, an ultrasonic sensor, and a visual sensor;

[0016] S14: The drone autonomously calculates the optimal flight path through a path planning algorithm based on the real-time environmental data obtained from the sensor array and the pre-stored construction site map information, and is used to adjust the flight direction and altitude in real time to avoid obstacles in the construction site;

[0017] S15: During the flight, the drone updates environmental data and replans the path at a high frequency. When a new obstacle or environmental change is detected, the flight control algorithm will update the flight path in real time to ensure that the drone can avoid obstacles and complete the data collection task.

[0018] Optionally, the S2 specifically includes:

[0019] S21: When the drone flies on a preset path, it emits laser pulses through the laser radar it carries, measures the time difference of the laser being reflected from the ground, and uses the time difference data to generate high-resolution 3D point cloud data of the construction site;

[0020] S22: The drone uses the multispectral camera on board to collect high-resolution images of the construction site. The collected image data includes infrared bands and visible light bands. The multispectral images are analyzed through image processing algorithms to obtain environmental parameters such as temperature distribution and humidity status of the construction site.

[0021] S23: Combining the three-dimensional point cloud data generated by the lidar with the image data collected by the multispectral camera, the position of the construction materials at the construction site is accurately located through the data fusion algorithm, and the three-dimensional position coordinates of the construction materials are generated.

[0022] Optionally, the S23 specifically includes:

[0023] S231: using the three-dimensional point cloud data generated by the laser radar, segmenting the point cloud data by a K-means clustering algorithm, and segmenting the point cloud data into a plurality of regions, wherein each region corresponds to a potential construction material or building structure;

[0024] S232: in the point cloud data after clustering and segmentation, applying a three-dimensional rigid body transformation matrix to align the point cloud data of the laser radar to the coordinate system of the multispectral image data;

[0025] S233: using feature point information in the multispectral image, matching the feature points with the aligned lidar point cloud data through a similarity matching algorithm to determine the specific location of the construction material;

[0026] S234: Fusing the matched data, optimizing and estimating the position of the construction materials through a fusion algorithm based on Kalman filtering, and obtaining the precise three-dimensional position coordinates of the construction materials through the results of Kalman filtering optimization.

[0027] Optionally, the S3 specifically includes:

[0028] S31: After the data collection is completed, the drone uses the built-in edge computing unit to perform preliminary processing on the collected three-dimensional point cloud data, environmental parameters and image data, and the preliminary processing includes data filtering and noise reduction;

[0029] S32: The processed data is compressed by a compression algorithm based on discrete cosine transform;

[0030] S33: The compressed data is transmitted in real time. Specifically, before the transmission starts, the compressed data packet is encrypted first; and the encrypted data packet is transmitted through the LPWAN protocol.

[0031] Optionally, the S4 specifically includes:

[0032] S41: After receiving the data transmitted by the drone, the server of the construction monitoring center will parse the data, store it according to data types, and associate it with the corresponding data structure in the BIM model. The data types include three-dimensional point cloud data, environmental parameters, and image data.

[0033] S42: The server uses the geographic coordinates, material properties, and construction plan information in the pre-imported BIM model to preliminarily match the geographic coordinate information in the drone data with the corresponding position in the BIM model to determine the initial matching conditions;

[0034] S43: The server processes the real-time data collected by the drone through a dynamic data synchronization algorithm to detect and identify the difference between the actual state of the construction site and the expected state in the BIM model, and gradually analyzes the timestamp, spatial position and feature point information of the data to automatically match the drone data with each node of the BIM model and mark the changed or different parts;

[0035] S44: After identifying the differences, the server adjusts the geometric structure, construction material status and environmental parameters in the BIM model in real time according to the construction progress and actual construction status, so that it is consistent with the actual situation on the construction site.

[0036] Optionally, the S42 specifically includes:

[0037] S421: After receiving the geographic coordinate information transmitted by the drone, the server of the construction monitoring center uses a hash mapping algorithm to quickly locate the geographic coordinate data of the drone to the corresponding area of ​​the BIM model;

[0038] S422: For the mapped preliminary matching points, the server uses a spatial similarity algorithm to accurately match the coordinates of the drone and the coordinates of the BIM model. The formula of the spatial similarity algorithm is:

[0039] Among them, S(c u , c b ) is the coordinate c of the drone u With BIM model coordinates c b The spatial similarity score between u ,y u ,z u is the three-dimensional coordinate value of the drone coordinates, xb ,y b ,z b is the three-dimensional coordinate value of the corresponding position in the BIM model, D max is the maximum distance in the BIM model;

[0040] S423: The server selects the matching points with the highest scores according to the calculated spatial similarity scores, and verifies the rationality of the matching points in combination with the material properties and construction plan information in the BIM model. If the matching points are found to be unreasonable during the verification process, the server returns to recalculate the similarity scores until reasonable initial matching conditions are determined.

[0041] Optionally, the S43 specifically includes:

[0042] S431: After receiving the real-time data collected by the drone, the server uses a timestamp synchronization algorithm to time-align the drone data with the expected data in the BIM model. The formula of the timestamp synchronization algorithm is:

[0043] Δt=|t u -t b |, where Δt is the time difference, t u is the timestamp of the drone data, t b is the timestamp of the corresponding node in the BIM model. If Δt is within the set time threshold, the time synchronization is considered successful;

[0044] S432: After time alignment, the server performs spatial position matching between the drone data and the BIM model through a spatial position matching algorithm;

[0045] S433: Use feature point matching algorithm to match feature points in UAV data with feature points in BIM model in detail. Specifically, AKAZE algorithm of feature matching is used. AKAZE algorithm is based on nonlinear diffusion filtering and can effectively detect and describe feature points in complex construction environment.

[0046] S434: Finally, the server performs difference detection on the data after matching the timestamp, spatial position and feature points, and identifies the difference between the actual state of the construction site and the expected state in the BIM model; the difference detection specifically adopts the threshold judgment method, and the formula is: Among them, δ is the maximum difference value detected, is the 3D coordinate of the drone data point, is the three-dimensional coordinate of the corresponding node in the BIM model; when δ exceeds the preset threshold ∈, the server will mark the exceeding data as a difference point.

[0047] Optionally, the S5 specifically includes:

[0048] S51: After the BIM model is updated, the server compares the updated real-time data with the historical data using a data association analysis algorithm; specifically, extracts the historical data set closest to the current construction status from the historical database, and standardizes the historical data to generate a reference benchmark;

[0049] S52: The server uses a dynamic time warping algorithm to match the real-time data with the processed historical data to detect the similarity between the current construction progress and the historical construction progress; the formula of the DTW algorithm is:

[0050] D(i, j) = cost(t, j) + min(D(i-1, j), D(i, j-1), D(i-1, j-1)), where D(i, j) is the matching distance of the current point, and cost(i, j) is the distance between the real-time data point i and the historical data point j. Minimize the distance to achieve the optimal match.

[0051] S53: The server calculates the deviation ΔP between the current construction progress and the preset plan of the BIM model by comparing the matching results of the DTW algorithm. If ΔP exceeds the set threshold, it indicates that there is an obvious deviation between the construction progress and the plan.

[0052] S54: After the deviation is detected, the root cause of the deviation in the construction schedule is analyzed by combining the real-time data, historical data and the construction plan in the BIM model;

[0053] S55: Based on the results of the cause-effect analysis, the server automatically generates adjustment suggestions, including adjusting the priority of construction tasks, reallocating resources, optimizing construction paths, and adjusting construction plans.

[0054] Optionally, the S6 specifically includes:

[0055] S61: After generating the adjustment suggestion, the server replans the flight path of the UAV according to the adjustment content. The server calculates the new flight path based on the latest status and construction progress requirements in the BIM model, and updates the mission plan of the UAV.

[0056] S62: The server sends the updated flight path and data collection plan to the control system of the UAV. After receiving the updated instructions, the UAV automatically adjusts the flight parameters and performs the flight and data collection tasks according to the new path.

[0057] S63: At the same time, the construction management system is used to notify the on-site construction personnel of the adjustment suggestions and the new construction plan. The notification content includes the upcoming drone flight mission, the key areas for data collection, and the construction correction measures required according to the adjustment suggestions.

[0058] Beneficial effects of the present invention:

[0059] The present invention, by integrating drones with BIM models and utilizing an improved data synchronization algorithm, can achieve instant updating and precise matching of construction site data, so that construction managers can make decisions based on the latest on-site conditions, thereby more effectively controlling project quality and progress and reducing errors and costs caused by information delays. In addition, automated data collection and analysis reduces reliance on manual operations and improves work efficiency and safety.

[0060] The present invention ensures the comprehensiveness and representativeness of the data by optimizing the flight path and data collection plan of the drone. At the same time, the efficient data processing and response mechanism on the server side enables any changes on the construction site to be quickly identified and fed back to the BIM model, supporting the needs of rapid response and dynamic adjustment of the construction site. This real-time monitoring and dynamic update mechanism significantly improves the management flexibility and response speed of the project, and provides solid technical support for the smooth execution of the construction project. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only for the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0062] Figure 1 A schematic diagram of a construction monitoring method according to an embodiment of the present invention;

[0063] Figure 2 The figure is a flowchart of mapping the actual status into the BIM model according to an embodiment of the present invention. DETAILED DESCRIPTION

[0064] The present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. At the same time, it is explained here that in order to make the embodiments more detailed, the following embodiments are the best and preferred embodiments, and those skilled in the art may also adopt other alternatives to implement some known technologies; and the accompanying drawings are only for more specific description of the embodiments, and are not intended to specifically limit the present invention.

[0065] It should be noted that the references to "one embodiment", "an embodiment", "an exemplary embodiment", "some embodiments" and the like in the specification indicate that the embodiments described may include specific features, structures or characteristics, but not every embodiment may include the specific features, structures or characteristics. In addition, when a specific feature, structure or characteristic is described in conjunction with an embodiment, it should be within the knowledge of a person skilled in the art to implement such feature, structure or characteristic in conjunction with other embodiments (whether or not explicitly described).

[0066] In general, a term can be understood, at least in part, from its use in context. For example, depending, at least in part, on the context, the term "one or more" as used herein can be used to describe any feature, structure, or characteristic in the singular sense, or can be used to describe a combination of features, structures, or characteristics in the plural sense. Additionally, the term "based on" can be understood as not necessarily intended to convey an exclusive set of factors, but can instead, depending, at least in part, on the context, allow for the presence of other factors that are not necessarily explicitly described.

[0067] like Figure 1-Figure 2 As shown, a construction monitoring method integrating BIM and drones includes the following steps:

[0068] S1: Deploy drones at construction sites. The drones use built-in sensor arrays and algorithms to perceive the dynamic changes in the surrounding environment in real time, autonomously plan flight paths to avoid obstacles at the construction site, and ensure stable flight.

[0069] S2: When the drone flies on a preset path, it uses high-precision sensors and cameras to collect real-time data on the construction site. The collected data includes three-dimensional images, environmental parameters, and location data of construction materials;

[0070] S3: The collected data is preliminarily processed and compressed by the edge computing unit built into the drone, and transmitted to the server of the construction monitoring center in real time through wireless communication, ensuring low latency and high security of data transmission;

[0071] S4: After receiving the data, the server of the construction monitoring center uses a dynamic data synchronization algorithm to automatically match the data transmitted by the drone with the pre-imported BIM model, and maps the actual status of the construction site to the BIM model in real time, realizing real-time update of the BIM model;

[0072] S5: Based on the BIM model update, the construction progress is tracked and evaluated in real time through data association analysis, combining historical data with real-time data. If a deviation is found between the construction progress and the preset plan of the BIM model, adjustment suggestions are generated based on the analysis results;

[0073] S6: Based on the adjustment suggestions, the flight path and data collection plan of the drone are adjusted accordingly, and the on-site construction personnel are notified to make immediate corrections to keep the construction progress consistent with the BIM model.

[0074] S1 specifically includes:

[0075] S11: Deploy ground base stations at predetermined locations on the construction site. The base stations are equipped with high-precision positioning equipment and environmental monitoring sensors to monitor the climate, obstacle locations, and movement paths within the construction area in real time.

[0076] S12: Before taking off, the drone communicates with the ground base station to obtain preliminary environmental information and positioning data of the construction site, including current climate conditions, spatial locations of known obstacles, and dynamic change data of the construction site;

[0077] S13: After the drone is started, it uses the built-in sensor array to perform a 360-degree full-scale scan to collect dynamic data of the surrounding environment in real time. The sensor array includes LiDAR, ultrasonic sensors and visual sensors to ensure accurate detection of different types of obstacles;

[0078] S14: Based on the real-time environmental data obtained from the sensor array and the pre-stored construction site map information, the drone autonomously calculates the optimal flight path through the path planning algorithm, which is used to adjust the flight direction and altitude in real time to avoid obstacles in the construction site, and dynamically adjust the flight strategy according to environmental changes;

[0079] The path planning algorithm uses the A* algorithm or the Dijkstra algorithm to calculate the optimal path from the current drone position to the target point. It includes the following steps:

[0080] S141: Define the obstacle area as the impassable area and other areas as the passable area in the construction site map;

[0081] S142: Set the target position to (x goal ,y goal ), the current drone position is (x current ,y current );

[0082] S143: Calculate the cost function f(n) from the current node to the target node. The function includes the path cost g(n) and the heuristic function h(n). The formula is: f(n)=g(n)+h(n), where g(n) is the actual cost from the starting point to the current node, and h(n) is the estimated cost from the current node to the target node. The commonly used heuristic function is Euclidean distance or Manhattan distance.

[0083] S144: According to the value of the cost function f(n), a path with the minimum cost is selected as the flight path of the UAV, and the path is updated in real time to avoid detected obstacles.

[0084] S15: During the flight, the UAV ensures safety through high-frequency environmental data updates and path replanning. Specifically, when new obstacles or environmental changes are detected, the flight control algorithm will instantly update the flight path to ensure that the UAV can avoid obstacles and complete data collection tasks. Through the implementation of the above steps, the UAV can fly autonomously in complex construction environments and effectively avoid obstacles, achieving efficient and stable data collection, which not only improves the accuracy of construction monitoring, but also reduces the need for manual intervention, further optimizing the management and control of the construction process.

[0085] S2 specifically includes:

[0086] S21: When the drone flies on a preset path, it uses the high-precision laser radar (LiDAR) to emit laser pulses, measure the time difference of the laser being reflected from the ground, and use the time difference data to generate high-resolution 3D point cloud data of the construction site. The 3D point cloud data is used to build an accurate 3D model of the construction site;

[0087] S22: The drone uses the multispectral camera on board to collect high-resolution images of the construction site. The collected image data includes infrared bands and visible light bands. The multispectral images are analyzed through image processing algorithms to obtain environmental parameters such as temperature distribution and humidity status of the construction site.

[0088] S23: Combining the three-dimensional point cloud data generated by the lidar with the image data collected by the multispectral camera, the position of the construction materials on the construction site is accurately located through the data fusion algorithm, and the three-dimensional position coordinates of the construction materials are generated to ensure that the position data of the materials are accurately aligned with the position in the BIM model; through the implementation of the above steps, the drone can accurately collect multi-dimensional data of the construction site during the flight, including three-dimensional images, environmental parameters and position data of construction materials, to ensure that the actual situation on the construction site can be synchronized with the BIM model in real time, thereby improving the accuracy and reliability of construction monitoring.

[0089] S23 specifically includes:

[0090] S231: Using the three-dimensional point cloud data generated by the laser radar, the point cloud data is segmented by the K-means clustering algorithm to segment the point cloud data into multiple regions, each of which corresponds to a potential construction material or building structure to identify different objects in the construction site. The objective function of the K-means clustering algorithm is: Among them, J is the objective function, k is the number of clusters, and nj is the number of data points in the jth cluster, x j Belongs to cluster C i The coordinate vector of the jth data point, μ i is the coordinate vector of the center point of the i-th cluster;

[0091] S232: In the point cloud data after clustering and segmentation, a three-dimensional rigid body transformation matrix is ​​applied to align the point cloud data of the laser radar to the coordinate system of the multispectral image data. The three-dimensional rigid body transformation matrix is ​​defined as: Among them, T is the three-dimensional rigid body transformation matrix, R is the rotation matrix, t is the translation vector, 0 is the zero vector, and 1 is the unit scalar. Through this matrix, the spatial alignment of LiDAR data and multispectral image data is achieved to ensure that the two data sources are fused in the same coordinate system;

[0092] S233: Using the feature point information in the multispectral image, the feature points are matched with the aligned LiDAR point cloud data through a similarity matching algorithm to determine the specific location of the construction materials. The similarity matching algorithm uses the Euclidean distance formula to calculate the similarity between the feature points. The formula is: Where d(p, q) is the Euclidean distance between feature points p and q, pi and qi are the coordinate values ​​of feature points p and q in the i-th dimension, and n is the number of dimensions of the coordinates;

[0093] S234: Fusing the matched data, optimizing and estimating the position of the construction materials through a fusion algorithm based on Kalman filtering, and obtaining the precise three-dimensional position coordinates of the construction materials through the optimized results of Kalman filtering; the update formula of Kalman filtering is:

[0094]

[0095] K k =P k-1 H T (HP k-1 H T +R) -1 ,in, is the state estimate at the kth moment, is the state estimate at the k-1th moment, K k is the Kalman gain at the kth moment, z k is the measurement value at the kth moment, H is the measurement matrix, P k-1 is the error covariance matrix at the k-1th moment, and R is the measurement noise covariance matrix;

[0096] Through the implementation of the above steps, the three-dimensional point cloud data generated by LiDAR and the image data collected by the multispectral camera are fused and optimized at multiple levels, which can accurately locate the construction materials on the construction site and generate their three-dimensional position coordinates. This precise positioning method improves the accuracy and reliability of data fusion, thereby enhancing the real-time response capability of the BIM model to the construction site.

[0097] S3 specifically includes:

[0098] S31: After the data collection is completed, the drone uses the built-in edge computing unit to perform preliminary processing on the collected 3D point cloud data, environmental parameters and image data. The preliminary processing includes data filtering and noise reduction. Specifically, the noise data is first removed by the median filtering algorithm. The calculation formula of the median filtering algorithm is: y(i) = median(x(ij), ..., x(i), x(i+j)). Among them, y(i) is the filtering result of the i-th data point, x(i) is the i-th data point, j is the size of the filtering window, and median is a function for calculating the median of a set of values. The expression is:

[0099]

[0100] Where n is the number of values, a1, a2, …, a n The values ​​are arranged in ascending order; median filtering is used to remove abnormal noise points to ensure data quality;

[0101] S32: The processed data is compressed by a compression algorithm based on discrete cosine transform (DCT);

[0102] The main steps of the compression algorithm include:

[0103] S321: Divide the data into blocks, divide the processed data into multiple small blocks, and perform compression processing on each block;

[0104] S322: Apply the DCT algorithm to each data block. The calculation formula of DCT is:

[0105] Among them, C(u) is the transformed coefficient, α(u) is the normalization coefficient, x(i) is the original data value, N is the size of the data block, and u is the frequency index. The original data is converted from the time domain to the frequency domain through DCT transformation to achieve data compression;

[0106] S33: The compressed data is transmitted in real time. Specifically, before the transmission begins, the compressed data packet is encrypted first. The Advanced Encryption Standard (AES) is used for encryption. The encryption formula is: C=E(K, P), where C is the encrypted ciphertext, K is the encryption key, and P is the original data packet. AES encryption is used to ensure the security of data during transmission. The encrypted data packet is transmitted through the LPWAN protocol. The LPWAN protocol has the characteristics of low power consumption and long-distance transmission. It is suitable for real-time communication between drones and construction monitoring centers to ensure the timeliness and reliability of data.

[0107] S4 specifically includes:

[0108] S41: After receiving the data transmitted by the drone, the server of the construction monitoring center will parse the data, store it according to the data type, and associate it with the corresponding data structure in the BIM model. The data type includes three-dimensional point cloud data, environmental parameters and image data;

[0109] S42: The server uses the geographic coordinates, material properties, and construction plan information in the pre-imported BIM model to preliminarily match the geographic coordinate information in the drone data with the corresponding position in the BIM model to determine the initial matching conditions;

[0110] S43: The server processes the real-time data collected by the drone through a dynamic data synchronization algorithm to detect and identify the difference between the actual state of the construction site and the expected state in the BIM model, and gradually analyzes the timestamp, spatial position and feature point information of the data to automatically match the drone data with each node of the BIM model and mark the changed or different parts;

[0111] S44: After identifying the differences, the server adjusts the geometric structure, construction material status and environmental parameters in the BIM model in real time according to the construction progress and actual construction status, so that it is consistent with the actual situation on the construction site; through the implementation of the above steps, the server of the construction monitoring center can efficiently and automatically match the data transmitted by the drone with the pre-imported BIM model, and map the actual status of the construction site to the BIM model in real time, so as to realize the real-time update of the BIM model. This method ensures the dynamic accuracy of the BIM model and provides more reliable data support for the management of construction projects.

[0112] The initial conditions for matching determined in S42 specifically include:

[0113] S421: After receiving the geographic coordinate information transmitted by the drone, the server of the construction monitoring center uses a hash mapping algorithm to quickly locate the geographic coordinate data of the drone to the corresponding area of ​​the BIM model. The formula of the hash mapping algorithm is: Among them, H(c u ) is the hash value after mapping, x u ,y u is the geographic coordinates collected by the drone, x min ,x max ,y min ,y max are the minimum and maximum coordinate ranges in the BIM model, respectively. m and n are the grid sizes of the BIM model. Through hash mapping, the coordinates of the drone can be quickly mapped to the grid area of ​​the BIM model.

[0114] S422: For the mapped preliminary matching points, the server uses a spatial similarity algorithm to accurately match the coordinates of the drone and the coordinates of the BIM model. The formula of the spatial similarity algorithm is:

[0115] Among them, S(c u , c b ) is the coordinate c of the drone u With BIM model coordinates c b The spatial similarity score between u ,y u ,z u is the three-dimensional coordinate value of the drone coordinates, x b ,y b ,z b is the three-dimensional coordinate value of the corresponding position in the BIM model, D max is the maximum distance in the BIM model, and the closest matching point is determined by calculating the similarity score;

[0116] S423: The server selects the matching points with the highest scores according to the calculated spatial similarity scores, and verifies the rationality of the matching points in combination with the material properties and construction plan information in the BIM model. If the matching points are found to be unreasonable during the verification process, the server returns to recalculate the similarity scores until a reasonable initial matching condition is determined. The matching points finally determined are used as the initial conditions, and subsequent data matching and synchronization are carried out based on this initial condition to ensure accurate matching and mapping between the drone data and the BIM model. Through the implementation of the above steps, the server can effectively utilize the geographic coordinates, material properties and construction plan information in the BIM model, combined with the geographic coordinate data of the drone, to perform accurate preliminary matching, thereby providing a reliable foundation for subsequent real-time updating of the BIM model and data synchronization.

[0117] S43 specifically includes:

[0118] S431: After receiving the real-time data collected by the drone, the server uses a timestamp synchronization algorithm to time-align the drone data with the expected data in the BIM model. The formula of the timestamp synchronization algorithm is:

[0119] Δt=|t u -t b |, where Δt is the time difference, t u is the timestamp of the drone data, t b is the timestamp of the corresponding node in the BIM model. If Δt is within the set time threshold, the time synchronization is considered successful;

[0120] S432: After time alignment, the server performs spatial position matching between the drone data and the BIM model through a spatial position matching algorithm. The spatial position matching algorithm is calculated based on the least squares method, and the formula is: in, is the three-dimensional coordinate of the i-th drone data point, is the three-dimensional coordinate of the corresponding node in the BIM model, N is the number of matching points, and the server determines the spatial position matching relationship between the drone data and the BIM model by minimizing the square error;

[0121] S433: Use feature point matching algorithm to match feature points in UAV data with feature points in BIM model in detail. Specifically, the AKAZE (accelerated KAZE feature) algorithm of feature matching is used. The AKAZE algorithm is based on nonlinear diffusion filtering and can effectively detect and describe feature points in complex construction environments.

[0122] The specific matching process includes the following steps:

[0123] S4331: Detect feature points on the data collected by the drone and extract multi-scale features using nonlinear diffusion filtering;

[0124] S4332: Perform the same feature point detection and extraction on the expected data in the BIM model;

[0125] S4333: Use Hamming distance to calculate the similarity between binary descriptors, the formula is:

[0126] Among them, d HAM (p, q) is the Hamming distance between feature descriptors p and q, p j ,q j is the jth bit of the binary feature descriptor, M is the length of the descriptor, and the feature point pairs with high similarity are matched by calculating the Hamming distance.

[0127] S434: Finally, the server performs difference detection on the data after matching the timestamp, spatial position and feature points, and identifies the difference between the actual state of the construction site and the expected state in the BIM model; the difference detection specifically adopts the threshold judgment method, and the formula is: Among them, δ is the maximum difference value detected, is the 3D coordinate of the drone data point, is the three-dimensional coordinate of the corresponding node in the BIM model; when δ exceeds the preset threshold ∈, the server will mark the exceeding data as a difference point and record it in the change log of the BIM model for subsequent adjustment and update; through the implementation of the above steps, the server can accurately detect and identify the difference between the actual state of the construction site and the expected state in the BIM model through the dynamic data synchronization algorithm, and automatically match the drone data with each node of the BIM model by gradually analyzing the timestamp, spatial position and feature point information of the data, so as to ensure the real-time update and accurate synchronization of the BIM model.

[0128] S5 specifically includes:

[0129] S51: After the BIM model is updated, the server compares the updated real-time data with the historical data using a data association analysis algorithm; specifically, extracts the historical data set closest to the current construction status from the historical database, and standardizes the historical data to generate a reference benchmark;

[0130] S52: The server uses a dynamic time warping (DTW) algorithm to match the real-time data with the processed historical data to detect the similarity between the current construction progress and the historical construction progress; the formula of the DTW algorithm is:

[0131] D(i,j)=cost(i,j)+min(D(i-1,j),D(i,j-1),D(i-1,j-1)),

[0132] Where D(i, j) is the matching distance of the current point, cost(i, j) is the distance between the real-time data point i and the historical data point j, and the distance is minimized to achieve the optimal match;

[0133] S53: The server calculates the deviation ΔP between the current construction progress and the preset plan of the BIM model by comparing the matching results of the DTW algorithm. The deviation formula is: Among them, ΔP is the construction progress deviation, P real For real-time construction progress, P planned The planned progress preset for the BIM model; if ΔP exceeds the set threshold, it indicates that there is a significant deviation between the construction progress and the plan;

[0134] S54: After the deviation is detected, the root cause of the deviation in the construction schedule is analyzed by combining the real-time data, historical data and the construction plan in the BIM model;

[0135] The specific analysis uses the Root Cause Analysis (RCA) algorithm, and the steps are as follows:

[0136] S541: Compare the actual completion time of critical path tasks with the planned time to identify delayed or advanced tasks;

[0137] S542: Analyze the impact of resource bottlenecks, equipment failures or external environmental factors on the construction progress in combination with resource allocation information;

[0138] S543: Comprehensively analyze nonlinear factors in the construction process, such as cross-operation and construction site restrictions, to determine the main factors affecting the construction progress.

[0139] S55: Based on the results of the causal analysis, the server automatically generates adjustment suggestions, including adjusting the priority of construction tasks, reallocating resources, optimizing construction paths, and adjusting construction plans; these suggestions are fed back to project managers through the construction management system so that corrective measures can be taken in a timely manner during the construction process to ensure that the construction progress is consistent with the preset plan in the BIM model; through the implementation of the above steps, the server can combine historical data with real-time data to accurately track and evaluate the construction progress, and when progress deviations are found, it can generate reasonable adjustment suggestions based on in-depth causal analysis, thereby effectively supporting the management and decision-making of construction projects.

[0140] S6 specifically includes:

[0141] S61: After generating the adjustment suggestion, the server replans the flight path of the drone according to the adjustment content. The server calculates the new flight path based on the latest status and construction progress requirements in the BIM model, and updates the mission plan of the drone to ensure that the drone can effectively cover the key areas of the construction site on the new path and collect the required data;

[0142] S62: The server sends the updated flight path and data collection plan to the control system of the drone. After receiving the updated instructions, the drone automatically adjusts the flight parameters and performs the flight and data collection tasks according to the new path, ensuring that the collected data can reflect the actual status of the current construction site.

[0143] S63: At the same time, the construction management system is used to notify the on-site construction personnel of the adjustment suggestions and the new construction plan. The notification content includes the upcoming drone flight mission, key data collection areas, and construction correction measures that need to be taken according to the adjustment suggestions. The notification can be conveyed in real time through the on-site mobile terminal or display screen to ensure that the construction personnel can timely understand the adjustment content and perform corresponding operations. After receiving the notification, the construction personnel will make on-site corrections according to the guidance provided by the server, and coordinate with the drone when necessary to ensure that the operations on the construction site are consistent with the updated content in the BIM model, and feedback the correction results to the server in real time for further analysis and verification. Through the implementation of the above steps, the server can effectively adjust the flight path and data collection plan of the drone according to the adjustment suggestions, and ensure that the construction personnel can obtain the adjustment information and make corrections in time, thereby ensuring that the construction progress is consistent with the BIM model and improving the efficiency and accuracy of construction management.

[0144] The present invention covers any substitution, modification, equivalent method and scheme made on the essence and scope of the present invention. In order to make the public have a thorough understanding of the present invention, specific details are described in detail in the following preferred embodiments of the present invention, but those skilled in the art can fully understand the present invention without the description of these details. In addition, in order to avoid unnecessary confusion about the essence of the present invention, well-known methods, processes, procedures, components and circuits are not described in detail.

[0145] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A construction monitoring method integrating BIM and drones, characterized in that: The following steps are involved: S1: Deploy drones at the construction site. The drones use built-in sensor arrays and algorithms to perceive the dynamic changes of the surrounding environment in real time and autonomously plan flight paths to avoid obstacles at the construction site. S2: When the drone flies on a preset path, it uses sensors and cameras to collect real-time data on the construction site. The collected data includes three-dimensional images, environmental parameters, and location data of construction materials; S3: The collected data is preliminarily processed and compressed by the edge computing unit built into the drone, and transmitted to the server of the construction monitoring center in real time through wireless communication; S4: After receiving the data, the server of the construction monitoring center uses a dynamic data synchronization algorithm to automatically match the data transmitted by the drone with the pre-imported BIM model, and maps the actual status of the construction site to the BIM model in real time, thus realizing real-time update of the BIM model; S5: Based on the BIM model update, the construction progress is tracked and evaluated in real time through data association analysis, combining historical data with real-time data. If a deviation is found between the construction progress and the preset plan of the BIM model, adjustment suggestions are generated based on the analysis results; S6: Based on the adjustment suggestions, the flight path and data collection plan of the drone are adjusted accordingly, and the on-site construction personnel are notified to make immediate corrections to keep the construction progress consistent with the BIM model.

2. A construction monitoring method integrating BIM and UAV according to claim 1, characterized in that: The S1 specifically includes: S11: deploying a ground base station at a predetermined location on the construction site, wherein the base station is equipped with a positioning device and an environmental monitoring sensor for real-time monitoring of the climate, obstacle locations, and movement paths within the construction area; S12: The UAV acquires preliminary environmental information and positioning data of the construction site by communicating with the ground base station before taking off, including current climate conditions, spatial locations of known obstacles, and dynamic change data of the construction site; S13: After the drone is started, it uses a built-in sensor array to perform a 360-degree full-scale scan to collect dynamic data of the surrounding environment in real time. The sensor array includes a laser radar, an ultrasonic sensor, and a visual sensor; S14: The drone autonomously calculates the optimal flight path through a path planning algorithm based on the real-time environmental data obtained from the sensor array and the pre-stored construction site map information, and is used to adjust the flight direction and altitude in real time to avoid obstacles in the construction site; S15: During the flight, the drone updates environmental data and replans the path at a high frequency. When a new obstacle or environmental change is detected, the flight control algorithm will instantly update the flight path to ensure that the drone can avoid obstacles and complete the data collection task.

3. The method for monitoring construction integrating BIM and UAV according to claim 2 is characterized in that: The S2 specifically includes: S21: When the drone flies on a preset path, it emits laser pulses through the laser radar it carries, measures the time difference of the laser being reflected from the ground, and uses the time difference data to generate high-resolution 3D point cloud data of the construction site; S22: The drone uses the multispectral camera on board to collect high-resolution images of the construction site. The collected image data includes infrared bands and visible light bands. The multispectral images are analyzed through image processing algorithms to obtain environmental parameters such as temperature distribution and humidity status of the construction site. S23: Combining the three-dimensional point cloud data generated by the lidar with the image data collected by the multispectral camera, the position of the construction materials at the construction site is accurately located through the data fusion algorithm, and the three-dimensional position coordinates of the construction materials are generated.

4. The method for monitoring construction integrating BIM and UAV according to claim 1, characterized in that: The S23 specifically includes: S231: using the three-dimensional point cloud data generated by the laser radar, segmenting the point cloud data by a K-means clustering algorithm, and segmenting the point cloud data into a plurality of regions, wherein each region corresponds to a potential construction material or building structure; S232: in the point cloud data after clustering and segmentation, applying a three-dimensional rigid body transformation matrix to align the point cloud data of the laser radar to the coordinate system of the multispectral image data; S233: using feature point information in the multispectral image, matching the feature points with the aligned lidar point cloud data through a similarity matching algorithm to determine the specific location of the construction material; S234: Fusing the matched data, optimizing and estimating the position of the construction materials through a fusion algorithm based on Kalman filtering, and obtaining the precise three-dimensional position coordinates of the construction materials through the results of Kalman filtering optimization.

5. The method for monitoring construction integrating BIM and UAV according to claim 1, characterized in that: The S3 specifically includes: S31: After the data collection is completed, the drone uses the built-in edge computing unit to perform preliminary processing on the collected three-dimensional point cloud data, environmental parameters and image data, and the preliminary processing includes data filtering and noise reduction; S32: The processed data is compressed by a compression algorithm based on discrete cosine transform; S33: The compressed data is transmitted in real time. Specifically, before the transmission starts, the compressed data packet is encrypted first; and the encrypted data packet is transmitted through the LPWAN protocol.

6. The method for monitoring construction integrating BIM and UAV according to claim 1, characterized in that: The S4 specifically includes: S41: After receiving the data transmitted by the drone, the server of the construction monitoring center will parse the data, store it according to data types, and associate it with the corresponding data structure in the BIM model. The data types include three-dimensional point cloud data, environmental parameters, and image data. S42: The server uses the geographic coordinates, material properties, and construction plan information in the pre-imported BIM model to preliminarily match the geographic coordinate information in the drone data with the corresponding position in the BIM model to determine the initial matching conditions; S43: The server processes the real-time data collected by the drone through a dynamic data synchronization algorithm to detect and identify the difference between the actual state of the construction site and the expected state in the BIM model, and gradually analyzes the timestamp, spatial position and feature point information of the data to automatically match the drone data with each node of the BIM model and mark the changed or different parts; S44: After identifying the differences, the server adjusts the geometric structure, construction material status and environmental parameters in the BIM model in real time according to the construction progress and actual construction status, so that it is consistent with the actual situation on the construction site.

7. The method for monitoring construction integrating BIM and UAV according to claim 6, characterized in that: The S42 specifically includes: S421: After receiving the geographic coordinate information transmitted by the drone, the server of the construction monitoring center uses a hash mapping algorithm to quickly locate the geographic coordinate data of the drone to the corresponding area of ​​the BIM model; S422: For the mapped preliminary matching points, the server uses a spatial similarity algorithm to accurately match the coordinates of the drone and the coordinates of the BIM model. The formula of the spatial similarity algorithm is: Among them, S(c u , c b ) is the coordinate of the drone c u With BIM model coordinates c b The spatial similarity score between u ,y u ,z u is the three-dimensional coordinate value of the drone coordinates, x b ,y b ,z b is the three-dimensional coordinate value of the corresponding position in the BIM model, D max is the maximum distance in the BIM model; S423: The server selects the matching points with the highest scores according to the calculated spatial similarity scores, and verifies the rationality of the matching points in combination with the material properties and construction plan information in the BIM model. If the matching points are found to be unreasonable during the verification process, the server returns to recalculate the similarity scores until reasonable initial matching conditions are determined.

8. The method for monitoring construction integrating BIM and UAV according to claim 7, characterized in that: The S43 specifically includes: S431: After receiving the real-time data collected by the drone, the server uses a timestamp synchronization algorithm to time-align the drone data with the expected data in the BIM model. The formula of the timestamp synchronization algorithm is: Δt=|t u -t b |, where Δt is the time difference, t u is the timestamp of the drone data, t b is the timestamp of the corresponding node in the BIM model. If Δt is within the set time threshold, the time synchronization is considered successful; S432: After time alignment, the server performs spatial position matching between the drone data and the BIM model through a spatial position matching algorithm; S433: Use feature point matching algorithm to match feature points in UAV data with feature points in BIM model in detail. Specifically, AKAZE algorithm of feature matching is used. AKAZE algorithm is based on nonlinear diffusion filtering and can effectively detect and describe feature points in complex construction environment. S434: Finally, the server performs difference detection on the data after matching the timestamp, spatial position and feature points, and identifies the difference between the actual state of the construction site and the expected state in the BIM model; the difference detection specifically adopts the threshold judgment method, and the formula is: Among them, δ is the maximum difference value detected, is the 3D coordinate of the drone data point, is the three-dimensional coordinate of the corresponding node in the BIM model; when δ exceeds the preset threshold ∈, the server will mark the exceeding data as a difference point.

9. The method for monitoring construction integrating BIM and UAV according to claim 1, characterized in that: The S5 specifically includes: S51: After the BIM model is updated, the server compares the updated real-time data with the historical data using a data association analysis algorithm; specifically, extracts the historical data set closest to the current construction status from the historical database, and standardizes the historical data to generate a reference benchmark; S52: The server uses a dynamic time warping algorithm to match the real-time data with the processed historical data to detect the similarity between the current construction progress and the historical construction progress; the formula of the DTW algorithm is: D(i, j) = cost(i, j) + min(D(i-1, j), D(i, j-1), D(i-1, j-1)), where D(i, j) is the matching distance of the current point, and cost(i, j) is the distance between the real-time data point i and the historical data point j. Minimize the distance to achieve the optimal match. S53: The server calculates the deviation ΔP between the current construction progress and the preset plan of the BIM model by comparing the matching results of the DTW algorithm. If ΔP exceeds the set threshold, it indicates that there is an obvious deviation between the construction progress and the plan. S54: After the deviation is detected, the root cause of the deviation in the construction schedule is analyzed by combining the real-time data, historical data and the construction plan in the BIM model; S55: Based on the results of the cause-effect analysis, the server automatically generates adjustment suggestions, including adjusting the priority of construction tasks, reallocating resources, optimizing construction paths, and adjusting construction plans.

10. The method for monitoring construction integrating BIM and UAV according to claim 1, characterized in that: The S6 specifically includes: S61: After generating the adjustment suggestion, the server replans the flight path of the UAV according to the adjustment content. The server calculates the new flight path based on the latest status and construction progress requirements in the BIM model, and updates the mission plan of the UAV. S62: The server sends the updated flight path and data collection plan to the control system of the UAV. After receiving the updated instructions, the UAV automatically adjusts the flight parameters and performs the flight and data collection tasks according to the new path. S63: At the same time, the construction management system is used to notify the on-site construction personnel of the adjustment suggestions and the new construction plan. The notification content includes the upcoming drone flight mission, the key areas for data collection, and the construction correction measures required according to the adjustment suggestions.

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