A digital construction method based on 3D mapping by drones

Abstract

The present invention relates to the field of remote sensing mapping technology, specifically a digital construction method based on unmanned aerial vehicle three-dimensional mapping, including the following steps: importing a digital surface model and identifying obstacles, calling identification information to calculate the path avoidance cost to plan the unmanned aerial vehicle path, adjusting the flight altitude in combination with the image clarity requirement, reconstructing the three-dimensional model by analyzing the measurement data, analyzing the actual project progress and size offset, and obtaining the construction project measurement record. In the present invention, through regional obstacle identification, combined with the avoidance cost score calculation of multiple measurement paths, the safety and rationality of route planning are optimized, and the flight altitude and image clarity linkage adjustment method are used to achieve dynamic control of imaging quality, the components of the construction project model, and the identification of construction progress and the detection of size deviation are realized, which effectively improves the progress judgment accuracy and spatial monitoring capability of digital construction, provides a data basis for project management decision-making and improves construction quality.

Description

Technical Field

[0001] The present invention relates to the technical field of remote sensing mapping, and particularly to a digital construction method based on three-dimensional mapping by unmanned aerial vehicles. Background Art

[0002] The technical field of remote sensing mapping includes obtaining surface information using aerospace platforms and performing data processing, analysis, and modeling. The core of this technical field lies in obtaining and expressing the spatial positions and morphological characteristics of targets such as terrain, landforms, and buildings through means such as remote sensing images, lidar, and aerial photography. Remote sensing mapping involves multiple sub-links, including sensor platform mounting, flight path planning, image acquisition and correction, three-dimensional modeling, and result expression. Its systematicness is reflected in aspects such as platform diversification, automated data acquisition, standardized processing procedures, and multi-dimensional result expression, and it is applied in multiple aspects such as geographic information systems, urban and rural construction, resource and environmental monitoring, and engineering surveying.

[0003] Among them, a digital construction method based on three-dimensional mapping by unmanned aerial vehicles refers to using an unmanned aerial vehicle platform as a data acquisition carrier, combining with three-dimensional real-scene modeling of the construction area, to achieve data visualization and model expression throughout the construction process, and to be used for assisting construction planning, progress management, and on-site control. This patent theme mainly covers aspects such as airspace approval at the construction site, landing point layout, obstacle identification, terrain-following flight path design, setting of flight altitude and overlap parameters, etc. By importing DWG drawings or DSM models, terrain analysis and task zoning are completed, images or point cloud data with spatial reference attributes are collected, and later model stitching is achieved through multi-region overlap zone settings, providing three-dimensional basic data support for the digital construction process. The method is based on three-dimensional modeling by unmanned aerial vehicles and runs through stages such as pre-construction preparation, mid-term monitoring, and post-construction re-survey, and is an important component for realizing digital management of construction information.

[0004] In terms of operation logic, traditional three-dimensional mapping by unmanned aerial vehicles and digital construction technologies mainly focus on static modeling or phased data stitching, lacking a fine division of the dynamic structure of regional elevation and an abnormal identification process, and it is difficult to actively construct a continuous terrain risk identification model. In terms of path planning, a preset path or a flight path construction method based on simple obstacle avoidance rules is adopted, ignoring the trade-off analysis between avoidance cost and path burden, resulting in unreasonable path selection. In the data acquisition stage, fixed-height acquisition is mainly used, and the imaging quality and focal length changes of images are not sensed in real time, resulting in blurred images or unstable measurement accuracy. In terms of construction progress judgment, it relies on phased model comparison or manual evaluation methods, lacking an automatic identification mechanism for component-level spatial errors and direction changes, resulting in lagging progress judgment or missed detection of component installation errors, affecting on-site control and post-construction re-survey efficiency, and leading to repeated measurements, cumulative data deviation, and path execution conflicts in actual construction, reducing the data transfer efficiency of digital construction. Summary of the Invention

[0005] To solve the technical problems existing in the prior art, an embodiment of the present invention provides a digital construction method based on UAV three-dimensional surveying and mapping. The technical solution is as follows:

[0006] To achieve the above object, the present invention adopts the following technical solution. A digital construction method based on UAV three-dimensional surveying and mapping includes the following steps:

[0007] S1: Import the regional digital surface model, divide the construction area into multiple grids and identify the elevation peak points. Combine the elevation values at the adjacent positions of the peak points to calculate the elevation offset of the peak points, identify multiple obstacles in the area, and generate obstacle identification information;

[0008] S2: Call the obstacle identification information, extract the spatial position and geometric shape of the obstacles. By analyzing the increased path length, flight height adjustment amount, and path turning angle in the avoidance flight of each UAV path, calculate the avoidance cost score, and obtain the UAV measurement path;

[0009] S3: Obtain the UAV measurement path. According to the acquisition accuracy requirement, combine the real-time image clarity, dynamically adjust the flight height control parameters of the UAV, and collect the image information and lidar point cloud data of the construction area in real time to generate a regional imaging data set;

[0010] S4: According to the regional imaging data set, use the flight height value to analyze the image and lidar point cloud data, reconstruct the construction engineering model of the construction area, compare it with the building information model of the target project, calculate the completion degree of the project in real time and identify the project progress, and generate construction progress information.

[0011] As a further solution of the present invention, the obstacle identification information is specifically an obstacle spatial distribution layer, obstacle geometric attribute data, and obstacle unique identification code. The UAV measurement path includes a path segment start and end coordinate set, a path heading angle sequence, and a path corresponding obstacle avoidance strategy parameter set. The regional imaging data set includes an image resolution label, a point cloud coverage density map, and an image positioning coordinate set. The construction progress information specifically refers to the block completion status, the corresponding time identifier of the process node, and the construction completion ratio.

[0012] As a further solution of the present invention, the step of importing the regional digital surface model, dividing the construction area into multiple grids and identifying the elevation peak points, combining the elevation values at the adjacent positions of the peak points to calculate the elevation offset of the peak points, identifying multiple obstacles in the area, and generating obstacle identification information is specifically as follows:

[0013] S101: Import the digital surface model of the area, divide the construction area into multiple grids, extract the elevation data in each grid, identify the elevation peak points in each grid and record the position coordinates to obtain the peak point coordinate set;

[0014] S102: Based on the peak point coordinate set, combine with the elevation values at adjacent positions, calculate the elevation offset of the peak points in multiple directions to obtain the elevation offset data;

[0015] S103: According to the elevation offset data, use the elevation offset to detect the elevation anomaly positions in the area and identify obstacles, including tower cranes and power towers. By extracting the size and position information of the target obstacles, generate obstacle identification information.

[0016] As a further solution of the present invention, call the obstacle identification information, extract the spatial position and geometric shape of the obstacles, and calculate the avoidance cost score by analyzing the increased path length, flight height adjustment amount, and path turning angle of the avoidance flight in each UAV path. The steps to obtain the UAV measurement path are specifically as follows:

[0017] S201: Call the obstacle identification information, extract the spatial position coordinates, vertical size, and boundary contour points of each obstacle, and identify the positions with spatial overlap in each UAV path according to the spatial envelope range of each obstacle to obtain the path intersection segment data set;

[0018] S202: According to the path intersection segment data set, analyze the path length, flight height adjustment amount, and path turning angle required for the avoidance flight at multiple overlapping positions, calculate the avoidance cost score of each path to obtain the path avoidance cost score set;

[0019] S203: According to the path avoidance cost score set, calculate the priority of multiple UAV paths according to the avoidance cost score, combined with the predicted flight duration, to obtain the UAV measurement path.

[0020] As a further solution of the present invention, the specific formula for calculating the avoidance cost score of each path is:

[0021] ;

[0022] Calculate the comprehensive score value of the path avoidance cost to obtain the path avoidance cost score set;

[0023] Where, is the comprehensive score value of the path avoidance cost for the path numbered , is the unique identification number of the path, is the index number of the intersection segment in the path, is the path The total number of cross segments is the avoidance path length value of the th cross segment in path is the flight altitude adjustment amount required for the th cross segment in path is the actual steering angle value on the th cross segment in path is the average value of the steering angles corresponding to all paths at the th cross segment is the weight coefficient of the path length factor is the weight coefficient of the flight altitude adjustment factor is the weight coefficient of the steering angle deviation factor

[0024] As a further solution of the present invention, the steps of obtaining the measurement path of the unmanned aerial vehicle, dynamically adjusting the flight altitude control parameters of the unmanned aerial vehicle according to the acquisition accuracy requirements and combining the real-time image clarity, and collecting the image information and lidar point cloud data of the construction area to generate a regional imaging data set are specifically as follows:

[0025] S301: Obtain the measurement path of the unmanned aerial vehicle, calculate the focal length parameters required by the image acquisition device according to the regional image acquisition accuracy requirements, and generate focal length - flight altitude comparison data by combining the flight altitude reference values required for the corresponding focal lengths;

[0026] S302: According to the focal length - flight altitude comparison data, evaluate the clarity of the image by extracting the contrast value, edge sharpening degree value and target edge recognition rate of the image in real time during the measurement process, and obtain the image imaging clarity;

[0027] S303: Dynamically update the flight altitude parameters according to the image imaging clarity, record the image acquisition time point and image sequence number, and combine the lidar point cloud data to obtain a regional imaging data set.

[0028] As a further solution of the present invention, the specific formula for evaluating the clarity of the image is:

[0029] ;

[0030] Calculate the image imaging clarity;

[0031] Among them, represents the image imaging clarity, represents the maximum gray value in the image, represents the minimum gray value in the image, is a constant to prevent division by zero is the number of edge pixels, is the gradient magnitude of the th edge pixel, and is the average gradient magnitude of all edge pixels, is the number of regions in the image, represents the target boundary recognition rate of the th region, and is the average target boundary recognition rate of all regions, represents the index of the edge pixels in the image,

[0032] As a further solution of the present invention, according to the regional imaging data set, using the flight altitude value, parsing the image and lidar point cloud data, reconstructing the construction engineering model of the construction area, and comparing it with the building information model of the target project, the steps of calculating the completion degree of the project in real time and identifying the project progress to generate the construction progress information are specifically as follows:

[0033] S401: According to the regional imaging data set, extract the flight altitude value and time stamp corresponding to each frame in the image information, and combine the spatial coordinate information of each measurement point in the lidar point cloud data to perform spatial coordinate registration on the image data and point cloud data to generate a spatial data alignment result;

[0034] S402: Based on the spatial data alignment result, reconstruct the building model in the target construction engineering area, extract the geometric dimension information of the model, and obtain the construction engineering model;

[0035] S403: According to the construction engineering model, call the building information model of the target project and compare it with the actual construction model, calculate the completion degree of the project in multiple grids, identify the project progress and calculate the progress offset difference, and obtain the construction progress information.

[0036] As a further solution of the present invention, the method further includes:

[0037] S5: Call the construction progress information, extract the geometric center point coordinates, spatial extension direction and boundary dimensions of multiple components in the construction engineering model, combine the design position parameters and direction vectors of the corresponding components in the building information model, and calculate the spatial position offset degree of each component by comparing the spatial position differences between geometric center points, the deviation angles between direction vectors, and the coordinate distribution differences between boundary dimensions, and obtain the construction engineering measurement record;

[0038] The construction engineering measurement record includes the actual spatial position set of the components, the component offset angle matrix, and the component size deviation value distribution map.

[0039] As a further solution of the present invention, the steps of calling the construction progress information, extracting the geometric center point coordinates, spatial extension directions, and boundary dimensions of multiple components in the construction engineering model, combining the design position parameters and direction vectors of the corresponding components in the building information model, calculating the spatial position deviation degree of each component by comparing the spatial position differences between geometric center points, the deviation angles between direction vectors, and the coordinate distribution differences between boundary dimensions, and obtaining the construction engineering measurement records are specifically as follows:

[0040] S501: Call the construction progress information, extract the three-dimensional boundary data of the components in the construction engineering model within each grid, and generate a component spatial attribute dataset by extracting the geometric center point coordinates, spatial extension direction vectors, and boundary dimension values of each component;

[0041] S502: According to the component spatial attribute dataset, extract the design coordinates, design direction vectors, and standard boundary dimension data of the corresponding components in the building information model and compare them to obtain component spatial difference data;

[0042] S503: According to the component spatial difference data, analyze the spatial position deviation degree of each component, calculate the deviation level and map it to the construction engineering model to obtain the construction engineering measurement record.

[0043] The beneficial effects brought by the technical solutions provided by the embodiments of the present invention at least include:

[0044] Through regional obstacle recognition, combined with the calculation of the avoidance cost scores of multiple measurement paths, the safety and rationality of the flight path planning are optimized. By using the linkage adjustment method of flight altitude and image clarity, the dynamic control of the imaging quality is realized. For the components of the construction engineering model, the construction progress is recognized and the size deviation is detected, effectively improving the accuracy of the construction progress judgment and the spatial monitoring ability of digital construction, providing a data basis for engineering management decisions and improving the construction quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings 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.

[0046] Figure 1 It is a schematic diagram of the working process of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0047] The following will describe the technical solutions in the present invention with reference to the drawings.

[0048] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to give examples, illustrations or explanations. Any embodiment or design described as an "example" in the present invention should not be construed as being more preferred or having more advantages than other embodiments or designs. Rather, the use of the word "example" is intended to present concepts in a specific manner. In addition, in the embodiments of the present invention, the meaning expressed by "and / or" can be both, or either one of the two can be selected.

[0049] In the embodiments of the present invention, "image" and "picture" can sometimes be used interchangeably. It should be noted that when the difference is not emphasized, the meanings they express are the same. "(of)", "corresponding", and "corresponding" can sometimes be used interchangeably. It should be noted that when the difference is not emphasized, the meanings they express are the same.

[0050] In the embodiments of the present invention, sometimes a subscript such as W1 may be written in a non-subscript form such as W1. When the difference is not emphasized, the meanings they express are the same.

[0051] To make the technical problems, technical solutions and advantages to be solved by the present invention clearer, the following will be described in detail with reference to the accompanying drawings and specific embodiments.

[0052] Please refer to Figure 1 , the present invention provides a technical solution, a digital construction method based on three-dimensional mapping by drones, including the following steps:

[0053] S1: Import the regional digital surface model, divide the construction area into multiple grids and identify the elevation peak points, calculate the elevation offset of the peak points by combining the elevation values of the adjacent positions of the peak points, identify multiple obstacles in the area, and generate obstacle identification information;

[0054] S2: Call the obstacle identification information, extract the spatial position and geometric shape of the obstacles, calculate the avoidance cost score by analyzing the increased path length, flight height adjustment amount and path turning angle in each drone path for avoiding flight, and obtain the drone measurement path;

[0055] S3: Obtain the drone measurement path, dynamically adjust the flight height control parameters of the drone according to the acquisition accuracy requirements and in combination with the real-time image clarity, and collect the image information and lidar point cloud data of the construction area in real time to generate a regional imaging data set;

[0056] S4: According to the regional imaging dataset, using the flight altitude value, parse the image and lidar point cloud data, reconstruct the construction engineering model of the construction area, compare it with the building information model of the target project, calculate the completion degree of the project in real time and identify the project progress, and generate construction progress information;

[0057] Call the construction progress information, extract the geometric center point coordinates, spatial extension directions and boundary dimensions of multiple components in the construction engineering model, combine the design position parameters and direction vectors of the corresponding components in the building information model, and calculate the spatial position deviation degree of each component by comparing the spatial position differences between geometric center points, the deviation angles between direction vectors, and the coordinate distribution differences between boundary dimensions, so as to obtain the construction engineering measurement record.

[0058] The obstacle identification information specifically includes the obstacle spatial distribution layer, obstacle geometric attribute data, and obstacle unique identification code. The UAV measurement path includes the start and end coordinate sets of the path segment, the path heading angle sequence, and the path corresponding obstacle avoidance strategy parameter set. The regional imaging dataset includes the image resolution label, the point cloud coverage density map, and the image positioning coordinate set. The construction progress information specifically refers to the block completion status, the time identifier corresponding to the process node, and the construction completion ratio. The construction engineering measurement record includes the actual spatial position set of the components, the component offset angle matrix, and the component size deviation value distribution map.

[0059] Import the regional digital surface model, divide the construction area into multiple grids and identify the elevation peak points, and calculate the elevation offset of the peak points by combining the elevation values of adjacent positions of the peak points. The steps of identifying multiple obstacles in the area and generating obstacle identification information are specifically as follows:

[0060] S101: Import the regional digital surface model, divide the construction area into multiple grids, extract the elevation data in each grid, identify the elevation peak points of each grid and record the position coordinates, and obtain the peak point coordinate set;

[0061] After importing the regional digital surface model, the two-dimensional range of the construction area is divided into 100×100 standard grids according to the rule of equal side length. The side length of each grid unit is set to 5 meters. After the grid division is completed, it is numbered in sequence, and the elevation data extraction operation is performed on each numbered grid. The extraction method is to screen all the elevation point data falling within the range of the grid boundary in the digital surface model, and record the spatial three-dimensional coordinate value of each elevation point. Then, all the elevation points in each grid are sorted by elevation value, and the coordinate point corresponding to the maximum elevation value is selected as the elevation peak point of the current grid. The spatial coordinates of this point are recorded in the peak point coordinate set. The judgment rule for the maximum value in the sorting is the largest of all elevation values ​​in the current grid, and the difference between it and the second largest elevation value in the same grid exceeds 1.2 The threshold is set by the minimum identifiable height of artificial structures in the project site. To avoid incorrect identification of low-undulation disturbances, if the difference is less than 1.2 meters, the current grid does not record the peak point. Through simulation experiments, if the maximum elevation is 47.2 meters and the second largest value is 46.3 meters, the difference is 0.9 meters, and the grid is not recorded as a peak point. If the maximum value is 52.5 meters and the second highest value is 50.8 meters, the difference is 1.7 meters, which exceeds the threshold, then the point is a valid peak point. Continue to traverse all grids to complete the peak extraction operation and obtain a peak point coordinate set with continuous spatial distribution. If the total number of grids is 20,000, 1,275 peak points are finally obtained. The constructed peak point coordinate set consists of the X coordinate, Y coordinate and Z value of each point to form a three-dimensional coordinate array in the form of ,in Indicates the peak point number, is the longitude coordinate, is the latitude coordinate, The array is the elevation value. It is used as the input basis for the next step of elevation offset calculation to obtain the peak point coordinate set.

[0062] S102: Based on the peak point coordinate set and in combination with the elevation values ​​of adjacent positions, the elevation offsets of the peak point in multiple directions are calculated to obtain elevation offset data;

[0063] Based on the extracted peak point coordinate set, the elevation offset calculation operation is performed for each peak point. In this operation, each peak point is taken as the center point, and the center elevation points of the adjacent grids in the eight directions around it are retrieved. The elevation values ​​of the adjacent grids in each direction are differenced with the elevation value of the central peak point. The difference calculation adopts the formula ,in is the elevation value of the current peak point, For the The center point elevation of the adjacent grid in the direction. , the directions are arranged in the order of east, southeast, south, southwest, west, northwest, north, and northeast according to the main azimuth. After calculating the 8-direction differences, record the elevation offset value set of this peak point. This set reflects the undulation degree of this peak point in each direction and can be used for subsequent abnormal area identification and structure protrusion structure judgment. If the difference in a certain direction is greater than 2.5 meters, it is recorded as a strong protrusion direction. This threshold is derived from the average vertical elevation characteristics of artificial structures. Through on-site investigation, the average height of the main structure of the tower crane from the base to the slewing platform is 3.2 meters. Considering the allowable error of DSM accuracy, the lower tolerance limit is set to 2.5 meters. If the elevation of the peak point is 51.6 meters and the elevation of the adjacent grid on the east side is 48.5 meters, then , this direction meets the strong protrusion standard. If the differences in the remaining directions are all lower than 2.5 meters, record that this point only has the protrusion characteristic in the east direction. Finally, form a matrix with the 8-direction offset values of all peak points. The rows of the matrix correspond to the peak point numbers, and the columns correspond to the direction numbers, denoted as , where represents the peak point, represents the direction number. After completing all the calculations, generate the elevation offset data.

[0064] S103: According to the elevation offset data, use the elevation offset to detect the elevation abnormal positions in the area and identify obstacles, including tower cranes and power towers. By extracting the size and position information of the target obstacles, generate obstacle identification information;

[0065] According to the elevation offset data, sequentially traverse the direction offset matrix of all peak points, and extract the points where the elevation differences in any continuous 3 directions exceed 2.5 meters. Mark these points as candidate points for elevation abnormal areas. Then, extract the number of remaining peak points within the 5×5 grid range around the candidate points. If the number exceeds 5, it is determined as a concentrated elevation fluctuation area and enters the obstacle determination process. Further analyze the distribution density and local maximum elevation value of the elevation peak points in this area. If the local maximum elevation exceeds 3 times the standard deviation of the average elevation in all adjacent grids around, and the number of discontinuous fluctuation directions around is less than 3, identify this area as a candidate area for stable structure obstacles. Perform structure identification operations within this area, extract the vertical span value and horizontal projection area of all abnormal points, compare the structure form of the area where the span value exceeds 10 meters and the area ranges from 4 square meters to 36 square meters with the standard dimensions of the outer contours of target structures such as tower cranes and power towers. Evaluate the similar structure types according to the dimension coincidence rate and boundary fitting degree, construct a structure label record, and establish an obstacle number in combination with the spatial position coordinates. Output the obstacle type and the corresponding coordinates and size information to complete the recording of spatial obstacle characteristics and generate obstacle identification information.

[0066] Call the obstacle identification information, extract the spatial position and geometric shape of the obstacle, and calculate the avoidance cost score by analyzing the increased path length, flight altitude adjustment amount, and path turning angle in each UAV path. The specific steps for obtaining the UAV measurement path are as follows:

[0067] S201: Call the obstacle identification information, extract the spatial position coordinates, vertical dimensions, and boundary contour points of each obstacle, identify the positions with spatial overlap in each UAV path according to the spatial envelope range of each obstacle, and obtain the path intersection segment dataset;

[0068] Call the obstacle identification information. First, extract the spatial position coordinate information of each obstacle, including the longitude and latitude coordinates of the center point and the corresponding elevation value. At the same time, extract the vertical dimension data of the obstacle in the three-dimensional space, record this dimension as the continuous elevation difference from the base to the top of the obstacle, and perform distributed sampling on the boundary contour points of each obstacle. The projected boundary in the horizontal coordinate system forms a closed boundary set. On this basis, construct a three-dimensional envelope body according to the spatial envelope range of each obstacle and calibrate its spatial occupancy volume. Obtain the planned UAV measurement path dataset, extract the sequence of waypoint coordinates of each path, linearly connect all consecutive points in the path segment of the path and construct a flight path segment set. Perform a spatial overlap judgment operation between the obstacle envelope body and the path segment set. The judgment criterion is that there is an intersection point between the path segment and the obstacle envelope surface in any direction, and the intersection point is within the area more than 1 meter below the top of the obstacle, which is regarded as a spatial intersection segment. By traversing each obstacle and all path segments in turn and judging whether the overlap condition is satisfied, record the starting point and ending point numbers of the path segments that meet the conditions and their corresponding path numbers, and generate a dataset with the structure of path number, start index, end index, and intersection coordinates. If there are 36 paths in the survey area and 12 obstacles, 54 intersection segments are obtained after detection, forming a path intersection segment dataset, and obtain the path intersection segment dataset.

[0069] S202: According to the path intersection segment dataset, analyze the path length, flight altitude adjustment amount, and path turning angle required for avoiding flight at multiple overlapping positions, calculate the avoidance cost score of each path, and obtain the path avoidance cost score set;

[0070] The specific formula for calculating the avoidance cost score of each path is:

[0071] ;

[0072] Calculate the comprehensive score value of the path avoidance cost and obtain the path avoidance cost score set;

[0073] Among them, is the comprehensive score value of the path avoidance cost for the path with the path number ​ is the unique identification number of the path, is the index number of the intersection segment in the path, is the path the total number of intersection segments in, is the path in the th avoidance path length value of the intersection segment, is the path in the th flight altitude adjustment amount required for the intersection segment, is the path in the th actual steering angle value on the intersection segment, is at the th average value of the steering angles corresponding to all paths at the intersection segment, is the weight coefficient of the path length factor, is the weight coefficient of the flight altitude adjustment factor, is the weight coefficient of the steering angle deviation factor.

[0074] Formula:

[0075] Detailed explanation of the formula and the derivation process of the formula calculation:

[0076] The formula is used to calculate the comprehensive avoidance cost score of the path numbered on all intersection segments, and is used as the evaluation and sorting basis for the optimal scheduling of the UAV path.

[0077] Parameter meaning and setting value:

[0078] is the current path number, set as path 05;

[0079] is the number of intersection segments detected in path 05, determined by the overlap relationship between the path and the obstacle space envelope, and the result of the spatial registration analysis is 3 segments is the intersection segment index number, from 1 to 3;

[0080] is in path 05 the th avoidance path length of the segment, set to 28.3 meters, is 32.7 meters, is 25.1 meters;

[0081] is the flight altitude adjustment amount of path 05 at the th segment, set to 6.2 meters, is 4.7 meters, is 5.9 meters;

[0082] is the turning angle of path 05 in the section, set to be 34°, to be 48°, to be 27°;

[0083] is the average value of the turning angles of all paths at the intersection section, set , , ;

[0084] , , are the length, flight height, and angle deviation weight coefficient, set to , , ;

[0085] Substitute the parameters into the formula for calculation:

[0086] Cost score of the first section:

[0087] ;

[0088] Cost score of the second section:

[0089] ;

[0090] Cost score of the third section:

[0091] ;

[0092] Sum:

[0093] ;

[0094] The result 50.29 represents the avoidance cost score of path 05 among all intersection sections, considering the avoidance length, flight height adjustment amount, and angle deviation. This value is a key indicator used for sorting in the subsequent path optimization scheduling.

[0095] S203: According to the set of path avoidance cost scores, based on the avoidance cost scores, combined with the predicted flight duration, calculate the priorities of multiple UAV paths to obtain the UAV measurement paths;

[0096] According to the path avoidance cost score set, extract the cost scoring results of all paths, and calculate the flight duration by combining the flight distance of each path with the set flight speed. The calculation method of the flight duration is to divide the total path length by the flight speed, and the unit is unified to seconds. Set the drone flight speed to 8 meters per second. If the path length is 620 meters, the flight duration is 77.5 seconds. Normalize the cost score and flight duration of each path. The normalization value uses the interval mapping formula: , where is the original value, are the minimum and maximum values among all paths respectively. Let the two normalized values of the avoidance cost and flight duration be and respectively. Finally, combine the two weighted to obtain the path priority index, and the weights are 0.6 and 0.4 respectively. If the of a certain path, , then the priority value is . Arrange all paths in ascending order according to the priority value, extract the numbers of the top several paths and record their waypoint index sequences, and combine them to form the final drone measurement path to obtain the drone measurement path.

[0097] Obtain the drone measurement path. According to the acquisition accuracy requirements and combined with the real-time image clarity, dynamically adjust the flight height control parameters of the drone, and the steps of collecting the image information and lidar point cloud data of the construction area in real time to generate the regional imaging data set are as follows:

[0098] S301: Obtain the drone measurement path. According to the regional image acquisition accuracy requirements, calculate the focal length parameters required by the image acquisition device, and combine the flight height reference value corresponding to the focal length to generate the focal length - flight height comparison data;

[0099] Obtain the drone measurement path. According to the regional image acquisition accuracy requirements, first extract the segment coordinate sequence and its corresponding number of waypoints in each drone path, calculate the horizontal distance between waypoints for each segment, and combine the sensor size of the imaging device, the pixel size, the vertical distance between the flight platform and the ground. According to the standard spatial resolution formula: , inversely deduce the focal length parameter , where is the ground resolution, with the unit of centimeters per pixel, is the lens focal length, with the unit of millimeters. Set the target resolution to 3 cm / pixel, the pixel size to 4.2 μm, and substitute the initial flight height value of 90 meters for calculation, then the focal length is In millimeters, to ensure imaging clarity, multiple sets of flight altitude values are taken at different heights and substituted into the above formula respectively to obtain the corresponding required focal length values, constructing an altitude-focal length mapping table. Each set of flight altitude and its corresponding focal length value recorded in the table are organized into data pairs , and arranged in ascending order of flight altitude to support subsequent dynamic adjustment operations. For a typical working area, there are 5 common heights, namely 70 meters, 80 meters, 90 meters, 100 meters, and 110 meters. The required focal lengths calculated are 1.05 mm, 1.2 mm, 1.35 mm, 1.5 mm, and 1.65 mm respectively, and the corresponding records are taken as a set of control items to obtain the contrast data of focal length and flight altitude

[0100] S302: According to the contrast data of focal length and flight altitude, by extracting the contrast value, edge sharpening degree value, and target edge recognition rate of the image in real time during the measurement process, evaluate the clarity of the image to obtain the imaging clarity of the image

[0101] The specific formula for evaluating the clarity of the image is

[0102] ;

[0103] Calculate the imaging clarity of the image

[0104] Among them, represents the imaging clarity of the image, represents the maximum gray value in the image, represents the minimum gray value in the image, is a constant to prevent division by zero, is the number of edge pixel points, is the gradient amplitude of the th edge pixel, is the average gradient amplitude of all edge pixels, is the number of regions in the image, represents the target boundary recognition rate of the th region, is the average target boundary recognition rate of all regions, represents the index of edge pixels in the image,

[0105] Formula:

[0106] ;

[0107] Detailed explanation of the formula and the derivation process of formula calculation:

[0108] This formula is used to calculate the imaging clarity of the image , and the result is used to evaluate the quality of the image. The formula consists of three parts, namely the grayscale contrast part, the edge sharpening part, and the edge recognizability part.

[0109] Parameter meaning and set values:

[0110] is the maximum grayscale value of the image, set to 200, which reflects the maximum value of the image brightness;

[0111] is the minimum grayscale value of the image, set to 50, which reflects the minimum value of the image brightness;

[0112] is a constant to prevent division by zero, set to 0.01, which is used to prevent division by zero problems in calculations;

[0113] is the number of edge pixels, set to 5000, is the gradient magnitude of the th edge pixel, representing the intensity of the edge part in the image, is the average gradient magnitude of all edge pixels, set to 110, representing the average intensity of all edge pixels, set

[0114] is the number of regions in the image, set to 10, indicating that the image is divided into 10 regions, is the target boundary recognition rate of the th region, representing the proportion of the boundary of this region that is correctly recognized, is the average boundary recognition rate of all regions, set to 0.80, representing the average boundary recognition rate of all regions, set

[0115] Substitute the parameters into the formula for calculation:

[0116] Substitute the above parameters into the formula to calculate the sharpness of the image:

[0117] ;

[0118] ;

[0119] Result indicates the comprehensive sharpness of the image. The image has a high contrast, good edge sharpening effect, and high target boundary recognition rate, indicating that the image quality is good and suitable for further analysis and modeling.

[0120] S303: Dynamically update the flight altitude parameter according to the image imaging clarity, record the image acquisition time point and the image sequence number, and combine the lidar point cloud data to obtain the regional imaging dataset;

[0121] Dynamically compare the evaluation results of each image according to the image imaging clarity data, record the flight altitude corresponding to the images that fail the clarity evaluation into the regulation queue, and correct the altitude of the flight segments to which these images belong. The correction value is set according to the missing items of the clarity index, and the altitude adjustment ratio is set to 5% step of adjacent altitude groups in the focal length comparison table. If the shooting altitude of the current image is 90 meters and the recognition rate index is lower than the set threshold, the flight altitude is corrected to meters, and mark the corresponding flight time point and image number information. At the same time, record the point cloud data frame number of the lidar system at the corresponding time, synchronize the image acquisition and the laser point cloud with the same time stamp as the main key, and combine and construct a regional information record including the image frame number, shooting time, flight altitude, and point cloud frame number. Summarize the synchronization sequences of images and point clouds in all operation segments to generate a full-time series dataset of the measurement area and obtain the regional imaging dataset.

[0122] According to the regional imaging dataset, use the flight altitude value to analyze the image and lidar point cloud data, reconstruct the construction engineering model of the construction area, and compare it with the building information model of the target project. The steps of calculating the completion degree of the project in real time and identifying the project progress to generate the construction progress information are as follows:

[0123] S401: According to the regional imaging dataset, extract the flight altitude value and time mark corresponding to each frame in the image information, and combine the spatial coordinate information of each measurement point in the lidar point cloud data to perform spatial coordinate registration on the image data and the point cloud data to generate the spatial data alignment result;

[0124] According to the regional imaging dataset, first extract the flight altitude data corresponding to each frame of the image, record the acquisition time mark information of each frame of the image, and convert the pixel coordinates of each frame of the image into ground coordinates. The conversion process is to call the flight altitude value and the focal length , and use the interior orientation formula , , where is the pixel coordinate value of a point in the image coordinate system. In an actual case, if the pixel coordinates of a point in the image are (1200, 1500), the focal length mm, and the flight altitude meters, then the corresponding ground coordinates are mm, mm, that is, (80 meters, 100 meters). Extract the spatial coordinates of each measurement point from the lidar point cloud data, including the longitude, latitude, and elevation coordinates of each measurement point in the point cloud data, and record them respectively as , perform a spatial alignment operation on the ground coordinates obtained by image coordinate transformation and the lidar point cloud measurement points. Specifically, compare the point coordinates after image coordinate transformation with the point cloud data measurement point coordinates, and adopt the minimum distance matching principle, that is, using the calculated Euclidean distance value as an index. If the distance is less than the set spatial registration threshold of 2 meters, it is considered a successfully registered point. Traverse all image frame pixel coordinates and point cloud positions in sequence for registration matching operations. In an actual example, the coordinates of a certain point cloud point are (81 meters, 99 meters, 90.5 meters), and the calculated distance is meters, which is less than the threshold of 2 meters and is recorded as a successfully registered point. After completing the registration of all images and point clouds, record the image serial number of each group of successfully registered points and the corresponding point cloud measurement point index to obtain the spatial data alignment result.

[0125] S402: Based on the spatial data alignment result, reconstruct the building model in the target construction project area, and extract the geometric dimension information of the model to obtain the construction project model;

[0126] Based on the spatial data alignment result, unify the point cloud data point sets of successfully registered points into the same spatial coordinate system, call all point cloud data, calculate the surface coordinate set of the area where each building target is located, and reconstruct the three-dimensional shape of the outer surface of the target building through the point cloud data. By extracting the three-dimensional boundary points of each part of the building, use the spatial coordinate solution method to obtain the geometric dimensions of the building components, including length, width, and height. Specifically, perform a search and matching operation on the farthest point pairs along the three-axis directions of the boundary point set of each component. The search process performs a difference operation between the maximum and minimum values of the X, Y, and Z coordinates of the component boundary points. For example, the maximum value of the X coordinate of the component point cloud in a certain construction area is 115 meters and the minimum value is 100 meters, then the length dimension of the component in the X direction is 15 meters. Determine the dimensions of the component in the Y and Z directions in the same way. Assume that the coordinate difference in the Y direction is 12 meters and the coordinate difference in the Z direction is 8 meters, then the three-dimensional geometric dimensions of the component are obtained as length 15 meters, width 12 meters, and height 8 meters. Measure the dimensions of all components in the area in sequence, summarize and record the spatial positions and geometric dimension parameters of all components in the area, and establish a complete set of regional building structure information to obtain the construction project model.

[0127] S403: According to the construction project model, call the building information model of the target project and compare it with the actual construction model, calculate the project completion degree within multiple grids, identify the project progress, and calculate the progress offset difference to obtain the construction progress information;

[0128] According to the construction engineering model, extract the spatial positions and corresponding geometric dimension data of each component in the model. Call the target engineering building information model that has completed the design, and compare the actual construction model with the design model at the component level according to the spatial coordinates. Set the grid cell size to 5 meters × 5 meters, and use the actual construction volume of the construction model within each grid cell and the target volume of the design model for comparison, and calculate the project completion degree as , for example, if the volume of the design model of a certain grid cell is 200 cubic meters and the measured volume of the construction model is 160 cubic meters, then the completion degree of this grid cell is . If the completion degree is less than the set completion degree benchmark value of 90%, it is determined as a unit with slow construction progress. Separate records and marks are made for all slow grid cells. By calculating the difference between the actual completion degree and the design completion degree within all grid cells, that is, calculating the progress deviation difference as , the difference of the above grid cell is cubic meters, that is, the deviation difference is negative, indicating that the construction progress lags behind by 40 cubic meters. Summarize the completion degrees and their deviation differences of all grid cells to establish a complete construction area progress information set and obtain the construction progress information.

[0129] Call the construction progress information, extract the geometric center point coordinates, spatial extension directions, and boundary dimensions of multiple components in the construction engineering model, and combine the design position parameters and direction vectors of the corresponding components in the building information model. By comparing the spatial position differences between geometric center points, the deviation angles between direction vectors, and the coordinate distribution differences between boundary dimensions, calculate the spatial position deviation degree of each component. The specific steps for obtaining the construction engineering measurement record are as follows:

[0130] S501: Call the construction progress information, extract the three-dimensional boundary data of the components in the construction engineering model within each grid, and generate a component spatial attribute data set by extracting the geometric center point coordinates, spatial extension direction vectors, and boundary dimension values of each component;

[0131] Call the construction progress information, sequentially read the component units that have completed construction within each spatial grid, extract the point cloud boundary point set formed by them in the three-dimensional model, extract the farthest point pairs along the X, Y, and Z directions respectively for each component boundary point set, and perform difference operations on their coordinates to determine the boundary dimension values of the component in the three-axis directions. Suppose the maximum value of a certain component in the X-axis direction is 102.4 meters and the minimum value is 98.6 meters, then the X-direction dimension is meters. At the same time, calculate the arithmetic mean of all boundary point coordinates from the boundary point set to obtain the geometric center point coordinates. For example, if the X coordinates of the boundary points of a certain component are 98.6, 99.1, 100.4, and 102.4 in sequence, then the center point X coordinate is In meters, further based on the connection relationships of each boundary point in space, the principal component analysis method is used to obtain the direction of the maximum direction projection vector as the spatial extension direction vector. Suppose the extension vector is (0.87, 0.49, 0), indicating that the component extends in the northeast direction along the XY plane. After calculating the geometric center point coordinates, spatial extension direction vectors, and boundary dimensions of all components, they are structurally summarized according to the component numbers to form a structure data set with the component number as the primary key, and a component spatial attribute data set is generated.

[0132] S502: According to the component spatial attribute data set, extract the design coordinates, design direction vectors, and standard boundary dimension data of the corresponding components in the building information model and compare them to obtain the component spatial difference data;

[0133] According to the component spatial attribute data set, read the design component parameters corresponding to it in the building information model one by one, compare the design coordinates and actual center point coordinates of each component with the same number, and perform three-dimensional space difference calculation. The position difference calculation formula is: , where are the center point coordinates of the component in the actual model respectively, is the center point coordinate of the component in the design model. If the actual component center point is (100.1, 36.7, 4.8) and the design center point is (100.0, 36.6, 4.9), then the position difference is meters, and the judgment of spatial direction difference is carried out. If the included angle is greater than 15 degrees, it is regarded as a direction deviation. In addition, compare the length, width, and height differences of the boundary dimensions. If the single-axis dimension difference exceeds 10% of the design dimension, it is marked as a component with abnormal dimensions. After comparing the positions, directions, and boundary dimensions of all components, output the difference value records of each component in the three types of dimensions to obtain the component spatial difference data.

[0134] S503: According to the component spatial difference data, analyze the spatial position offset degree of each component, calculate the offset level and map it to the construction engineering model to obtain the construction engineering measurement record;

[0135] According to the component space difference data, extract the difference value ranges of each component in three dimensions: position, direction, and boundary dimension. Set the threshold for dividing the offset levels. The position offset level is set as follows: level 1 for 0 - 0.1 meters, level 2 for 0.1 - 0.3 meters, and level 3 for greater than 0.3 meters. The direction offset level is divided into level 1 for 0 - 5 degrees, level 2 for 5 - 15 degrees, and level 3 for greater than 15 degrees. The dimension offset level is that the relative error of 0 - 5% is level 1, 5% - 10% is level 2, and greater than 10% is level 3. Assign level labels according to the interval where the difference value is located. Make a combined judgment on the level values of each component in the three types of indicators, and use the maximum offset level as the overall offset level of the component. For example, if the position offset of a component is 0.28 meters (level 2), the direction offset is 17 degrees (level 3), and the dimension offset is 4% (level 1), then the offset level of this component is level 3. Correlate the offset level with the component number one by one, and mark its corresponding spatial coordinate position and level information in the construction model to generate an offset level distribution layer for all components in the area, and obtain the construction project measurement records.

[0136] The above embodiments can be implemented in whole or in part by software, hardware (such as circuits), firmware, or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When loading or executing the computer instructions or computer programs on a computer, the processes or functions according to the embodiments of the present invention are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center by wired (such as infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that the computer can access or a data storage device such as a server or data center that contains one or more sets of available media. The available media can be magnetic media (such as floppy disks, hard disks, magnetic tapes), optical media (such as DVDs), or semiconductor media. The semiconductor media can be a solid-state drive.

[0137] It should be understood that the term "and / or" in this article is merely a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone. Here, A and B can be singular or plural. In addition, the character " / " in this article generally represents an "or" relationship between the associated objects before and after, but it may also represent an "and / or" relationship, which can be specifically understood by referring to the context before and after.

[0138] In the present invention, "at least one" means one or more, and "a plurality of" means two or more. "At least one of the following" or a similar expression means any combination of these items, including any combination of single item or plural items. For example, at least one of a, b, or c can represent: a, b, c, a - b, a - c, b - c, or a - b - c, where a, b, and c can be single or plural.

[0139] It should be understood that in various embodiments of the present invention, the magnitudes of the serial numbers of the above - mentioned processes do not imply the sequence of execution. The execution sequence of each process should be determined by its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present invention.

[0140] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods for each specific application to implement the described functions, but such implementation should not be considered as exceeding the scope of the present invention.

[0141] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the above - described devices, apparatuses, and units can refer to the corresponding processes in the foregoing method embodiments, and will not be elaborated herein.

[0142] In several embodiments provided by the present invention, it should be understood that the disclosed devices, apparatuses, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is only a logical function division. In actual implementation, there can be other division methods. For example, multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed. Another point is that the displayed or discussed coupling, direct coupling, or communication connection to each other can be through some interfaces. The indirect coupling or communication connection of the devices or units can be in an electrical, mechanical, or other form.

[0143] The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they can be located in one place, or can be distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0144] In addition, in each embodiment of the present invention, each functional unit may be integrated into one processing unit, may exist physically alone for each unit, or two or more units may be integrated into one unit.

[0145] If the above-mentioned function is implemented in the form of a software functional unit and sold or used as an independent product, it may be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of the technical solution, may be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs that can store program codes.

[0146] The above is only the specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention can easily think of changes or substitutions, which should all be covered by the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims

1. A digital construction method based on three-dimensional mapping by drones, characterized in that, The method includes: S1: Import the regional digital surface model, divide the construction area into multiple grids, identify the elevation peak points, calculate the elevation offset of the peak points in combination with the elevation values of adjacent positions of the peak points, identify multiple obstacles in the area, and generate obstacle identification information; S2: Call the obstacle identification information, extract the spatial position and geometric shape of the obstacles, calculate the avoidance cost score by analyzing the increased path length, flight height adjustment amount, and path turning angle in the avoidance flight of each UAV path, and obtain the UAV measurement path; S3: Obtain the UAV measurement path, dynamically adjust the flight height control parameters of the UAV according to the acquisition accuracy requirements and in combination with the real-time image clarity, and collect the image information and lidar point cloud data of the construction area in real time to generate a regional imaging data set; S4: According to the regional imaging data set, use the flight height value to analyze the image and lidar point cloud data, reconstruct the construction engineering model of the construction area, compare it with the building information model of the target project, calculate the completion degree of the project in real time and identify the project progress, and generate construction progress information; The steps of importing the regional digital surface model, dividing the construction area into multiple grids, identifying the elevation peak points, calculating the elevation offset of the peak points in combination with the elevation values of adjacent positions of the peak points, identifying multiple obstacles in the area, and generating obstacle identification information are specifically as follows: S101: Import the regional digital surface model, divide the construction area into multiple grids, extract the elevation data in each grid, identify the elevation peak points of each grid and record the position coordinates, and obtain the peak point coordinate set; S102: Based on the peak point coordinate set, calculate the elevation offset of the peak points in multiple directions in combination with the elevation values of adjacent positions to obtain the elevation offset data; S103: According to the elevation offset data, use the elevation offset to detect the elevation abnormal positions in the area and identify obstacles, including tower cranes and electric towers, and generate obstacle identification information by extracting the size and position information of the target obstacles; The steps of calling the obstacle identification information, extracting the spatial position and geometric shape of the obstacles, calculating the avoidance cost score by analyzing the increased path length, flight height adjustment amount, and path turning angle in the avoidance flight of each UAV path, and obtaining the UAV measurement path are specifically as follows: S201: Call the obstacle identification information, extract the spatial position coordinates, vertical size, and boundary contour points of each obstacle, identify the positions with spatial overlap in each UAV path according to the spatial envelope range of each obstacle, and obtain the path intersection segment data set; S202: According to the path intersection segment data set, analyze the path length, flight height adjustment amount, and path turning angle required for avoidance flight at multiple overlapping positions, calculate the avoidance cost score of each path, and obtain the path avoidance cost score set; S203: According to the path avoidance cost score set, calculate the priority of multiple UAV paths according to the avoidance cost score in combination with the predicted flight duration, and obtain the UAV measurement path.

2. The digital construction method based on three-dimensional mapping by drone according to claim 1, wherein The obstacle identification information specifically includes an obstacle spatial distribution layer, obstacle geometric attribute data, and an obstacle unique identification code. The UAV measurement path includes a set of start and end coordinates of path segments, a sequence of path heading angles, and a set of obstacle avoidance strategy parameters corresponding to the path. The regional imaging data set includes an image resolution label, a point cloud coverage density map, and a set of image positioning coordinates. The construction progress information specifically refers to the completion status of blocks, the time identifiers corresponding to process nodes, and the construction completion ratio.

3. The digital construction method based on three-dimensional mapping by unmanned aerial vehicle according to claim 1, wherein The specific formula for calculating the avoidance cost score of each path is as follows: ； Calculate the comprehensive score value of the path avoidance cost and obtain a set of path avoidance cost scores; Among them, is the comprehensive score value of the path avoidance cost for the path with path number . is the unique identification number of the path, is the index number of the intersection segment in the path, is the path The total number of intersection segments in, is the path In the The avoidance path length value of the th intersection segment, is the path In the The required flight altitude adjustment amount for the th intersection segment, is the path In the The actual steering angle value on the th intersection segment, is at the The average value of the steering angles corresponding to all paths at the th intersection segment, is the weight coefficient of the path length factor, is the weight coefficient of the flight altitude adjustment factor, is the weight coefficient of the steering angle deviation factor.

4. The digital construction method based on 3D mapping by unmanned aerial vehicle according to claim 3, wherein, The steps of obtaining the UAV measurement path, dynamically adjusting the flight height control parameters of the UAV according to the acquisition accuracy requirements and in combination with the real-time image clarity, and collecting the image information and lidar point cloud data of the construction area in real time to generate a regional imaging data set are specifically as follows: S301: Obtain the UAV measurement path, calculate the focal length parameters required by the image acquisition device according to the regional image acquisition accuracy requirements, and generate focal length flight height comparison data in combination with the flight height reference value required by the corresponding focal length; S302: According to the focal length flight height comparison data, evaluate the clarity of the image by extracting the contrast value, edge sharpening degree value, and target edge recognition rate of the image in real time during the measurement process to obtain the image imaging clarity; S303: Dynamically update the flight height parameters according to the image imaging clarity, record the image acquisition time point and the image sequence number, and obtain a regional imaging data set in combination with the lidar point cloud data.

5. The digital construction method based on 3D mapping by unmanned aerial vehicle according to claim 4, characterized in that, The specific formula for evaluating the clarity of the image is as follows: ； Calculate the image imaging clarity; in, Represents the image clarity, Represents the maximum gray value in the image. Represents the minimum gray value in the image. To prevent division by zero, is the number of edge pixels, For the The gradient magnitude of edge pixels, is the average gradient magnitude of all edge pixels, is the number of regions in the image, Representative The target boundary recognition rate of the region is is the average target boundary recognition rate of all regions, represents the index of edge pixels in the image, Represents the index of the region in the image.

6. The digital construction method based on three-dimensional mapping by unmanned aerial vehicle according to claim 4, wherein The steps of parsing the image and lidar point cloud data using the flight height value according to the regional imaging data set, reconstructing the construction project model of the construction area, comparing it with the building information model of the target project, calculating the completion degree of the project in real time and identifying the project progress, and generating construction progress information are specifically as follows: S401: According to the regional imaging data set, extract the flight height value and time stamp corresponding to each frame in the image information, and perform spatial coordinate registration on the image data and the point cloud data by combining the spatial coordinate information of each measurement point in the lidar point cloud data to generate a spatial data alignment result; S402: Based on the spatial data alignment result, reconstruct the building model in the target construction project area and extract the geometric dimension information of the model to obtain the construction project model; S403: According to the construction project model, call the building information model of the target project and compare it with the actual construction model, calculate the completion degree of the project in multiple grids, identify the project progress and calculate the progress offset difference to obtain the construction progress information.

7. The digital construction method based on 3D mapping by unmanned aerial vehicle according to claim 1, characterized in that The method further includes: S5: Call the construction progress information, extract the geometric center point coordinates, spatial extension directions, and boundary dimensions of multiple components in the construction project model, combine the design position parameters and direction vectors of the corresponding components in the building information model, and calculate the spatial position deviation degree of each component by comparing the spatial position differences between geometric center points, the deviation angles between direction vectors, and the coordinate distribution differences between boundary dimensions, so as to obtain the construction project measurement record; The construction project measurement record includes the actual spatial position set of components, the component deviation angle matrix, and the component size deviation value distribution map.

8. The digital construction method based on three-dimensional mapping by unmanned aerial vehicle according to claim 7, wherein The steps of calling the construction progress information, extracting the geometric center point coordinates, spatial extension directions, and boundary dimensions of multiple components in the construction project model, combining the design position parameters and direction vectors of the corresponding components in the building information model, and calculating the spatial position deviation degree of each component by comparing the spatial position differences between geometric center points, the deviation angles between direction vectors, and the coordinate distribution differences between boundary dimensions, so as to obtain the construction project measurement record are specifically as follows: S501: Call the construction progress information, extract the three-dimensional boundary data of the components in the construction project model within each grid, and generate a component spatial attribute data set by extracting the geometric center point coordinates, spatial extension direction vectors, and boundary dimension values of each component; S502: According to the component spatial attribute data set, extract the design coordinates, design direction vectors, and standard boundary dimension data of the corresponding components in the building information model and make comparisons to obtain component spatial difference data; S503: According to the component spatial difference data, analyze the spatial position deviation degree of each component, calculate the deviation level and map it to the construction project model to obtain the construction project measurement record.

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