Underground building combined surveying and mapping method

Abstract

An underground building joint surveying and mapping method relates to the field of surveying and mapping, and is characterized by comprising the following steps: measuring and investigating data, carrying out data resolving, format conversion, denoising processing, data splicing, coordinate conversion and optimization adjustment processing on point cloud, and carrying out data verification and correction on point cloud data after data acquisition and processing by using a ground penetrating radar. Performing current situation data extraction, generating a live-action three-dimensional model of the underground building, and storing the live-action three-dimensional model in a specified standard permission database; extracting the live-action three-dimensional model of the underground building and the permitted underground building three-dimensional model for data comparison; and outputting the result. The method has the advantages that various types of laser radar equipment can be used for collecting and forming measurement result data in various forms, the real-scene three-dimensional model of the underground building is automatically constructed, the working efficiency is improved, the workload of workers and the influence of human subjective judgment are reduced, and the real-scene three-dimensional model can be used for assisting the workers in quickly positioning problems and facilitating subsequent correction.

Description

Technical Field

[0001] The present invention relates to the field of surveying and mapping, and specifically utilizes mobile, fixed, portable and other types of laser radar equipment to collect data, quickly and accurately measure underground buildings, utilizes point cloud processing technology and multi-source data fusion technology to generate underground building point clouds, and automatically extracts underground building point cloud data to form various forms of measurement results data, and automatically constructs a real-life three-dimensional model of the underground building. By comparing with licensed data, the joint surveying and mapping of underground buildings and planning acceptance and civil air defense acceptance can be quickly and accurately realized, and planning acceptance and civil air defense acceptance reports can be generated, which greatly improves the work efficiency of personnel, reduces the workload of personnel and the influence of human subjective judgment. The method can also utilize the real-life three-dimensional model to realize the display of non-conformity problems, and provide modification suggestions to assist operating personnel in quickly locating problems, so as to facilitate subsequent correction of problems. A method for joint surveying and mapping of underground buildings. Background Art

[0002] With the acceleration of urbanization, the number of underground buildings has gradually increased. The internal functional structure of underground buildings is complex. They are not only used for parking and commercial purposes, but also bear the responsibility of civil defense. The existing acceptance surveying and mapping of underground buildings mostly uses single technical means such as traditional total stations and rangefinders. The workload of data collection, processing and analysis is large, and the collected data is difficult to trace back. It is difficult to obtain the spatial information of underground buildings comprehensively and accurately, and there are deficiencies in data integration and analysis. Various acceptances are processed and analyzed separately, which lacks systematicity and scientificity. Not only are the acceptance problems found not suitable for display, but also the actual situation of underground buildings cannot be intuitively displayed. Therefore, a new type of joint mapping method for underground buildings is urgently needed. It uses advanced technologies such as global navigation satellite systems and laser scanning to realize one-time collection, one-time data processing, and automated data extraction of underground buildings, and realizes the use of real-scene three-dimensional stations to display various acceptance results and problem prompts, thereby realizing high-precision and high-efficiency mapping of underground buildings. Summary of the invention

[0003] In order to overcome the drawbacks of the existing technical solutions, it is possible to use mobile, fixed, portable and other types of laser radar equipment to collect and quickly and accurately measure underground buildings, use point cloud processing technology and multi-source data fusion technology to generate underground building point clouds, and automatically extract underground building point cloud data to form various forms of measurement results data, and automatically construct real-life three-dimensional models of underground buildings. By comparing with licensed data, the joint surveying and planning acceptance and civil air defense acceptance of underground buildings can be quickly and accurately realized, and planning acceptance and civil air defense acceptance reports can be generated, which greatly improves the work efficiency of personnel and reduces the workload of personnel and the influence of human subjective judgment. The method can also use the real-life three-dimensional model to display non-conforming problems and provide modification suggestions to assist operators in quickly locating problems and facilitate subsequent corrections to the problems.

[0004] A method for joint mapping of underground buildings, the method comprising the following steps: data measurement and investigation, data processing, data extraction, verification and comparison, and result output, wherein:

[0005] Data measurement and investigation: Conduct on-site surveys of the area to be measured to check the distribution, size, and approximate location of underground buildings, and collect relevant information on underground buildings in the corresponding area. According to the survey results, choose to set up ground control points at the ground, entrances and exits, and vents of underground buildings, set up underground control points inside underground buildings, conduct control measurements on the ground control points and underground control points respectively, and merge the ground control points and underground control points for network adjustment. Use mobile laser radar to scan and collect the inside of underground buildings, and then use fixed laser radar to scan and collect places inside underground buildings that mobile collection equipment cannot reach. Use portable laser radar to scan and collect small spaces and places that are inconvenient for fixed laser radar scanning. Finally, use high-density electrical methods to explore and collect underground buildings, and investigate and record the relevant attributes of underground buildings;

[0006] Data processing: perform data solution, format conversion, denoising, data splicing, coordinate conversion, and optimization adjustment on the mobile laser radar scanning point cloud, fixed laser radar scanning point cloud, and portable laser radar scanning point cloud, respectively, to obtain the underground building mobile scanning point cloud data, underground building fixed scanning point cloud data, and underground building portable scanning point cloud data, and then perform data fusion processing on various types of underground building point cloud data to obtain the final underground building complete point cloud data, and then use the ground penetrating radar to collect data and process it to verify and correct the point cloud data;

[0007] Data extraction: extract the current data of the final complete point cloud data of underground buildings, generate a real-life 3D model of the underground buildings and store it in the current database, process the paper and digitized permit data, extract the corresponding data and attributes, establish a permit 3D model and set the allowed space before storing it in the designated standard permit database;

[0008] Verification and comparison: extract the real 3D model of the underground building and the 3D model of the permitted underground building from the corresponding database respectively, import the above two models into the comparison module for data comparison, and output the corresponding data according to the comparison results;

[0009] Output results: Extract underground building data from the database, and generate a planning acceptance report for underground buildings, a comparison table of underground building heights, a comparison table of underground building economic indicators, a general plan for underground building planning acceptance, and a cross-sectional view of underground buildings in accordance with the requirements of the planning acceptance report; generate a civil air defense project area surveying and mapping report, a general plan of civil air defense projects, a civil air defense functional zoning area map, an area map of each functional part of the civil air defense project, a civil air defense project area calculation table, and a civil air defense project area comparison table in accordance with the requirements of the civil air defense completion report.

[0010] A method for joint mapping of underground buildings, wherein the specific steps of data processing are:

[0011] Mobile scanning data processing: First, the positioning and attitude system data is solved according to the data collected by a single movement, and then combined with the original data to generate a single-movement point cloud data result. Then, the mobile laser scanning measurement data result is preliminarily corrected through a single-movement global navigation satellite system differential calculation, and then the point cloud is denoised, with a noise removal rate of more than 95%, to obtain a single-movement scanning point cloud data. Then, the mobile scanning point cloud data collected multiple times and the ground control data and underground control data are transformed into coordinates, and the fusion optimization adjustment is performed. When the point cloud accuracy after adjustment meets the acceptance accuracy requirements, the optimization adjustment is stopped to obtain the mobile scanning point cloud data of underground buildings;

[0012] Fixed scanning data processing: convert the format of the data exported from the fixed scanner, use filtering algorithms and human-computer interaction to denoise the clouds of each site, and then automatically stitch them. If automatic stitching is not possible, use manual stitching. Mark the positions of the control points in the preliminary stitched point cloud and import the corresponding control point 3D coordinate data. Perform coordinate transformation and plane and elevation accuracy optimization on the point cloud data to obtain the final underground building fixed scanning point cloud data after meeting the accuracy requirements of the acceptance.

[0013] Portable scanning data processing: format conversion and denoising of portable scanning data are performed, and coordinate transformation and data splicing of portable scanning data are performed using underground control point and splicing control point data. When the point cloud accuracy after splicing adjustment meets the acceptance accuracy requirements, the optimization adjustment is stopped to obtain the portable scanning point cloud data of underground buildings;

[0014] Point cloud data fusion: Different types of underground building point cloud data are color-separated, and corresponding common control points are selected in the overlapping area of ​​the two point cloud data and set as common points. Based on the 3D coordinate values ​​of the known common points, different types of underground building point cloud data are spliced ​​and fused, optimized and smoothed, and then classified and processed to remove point clouds of vehicles, mechanical equipment, etc., to obtain the final complete point cloud data of underground buildings;

[0015] Data verification: The survey line data collected by the high-density electrical method are preprocessed and integrated with interpretation and analysis to obtain the spatial scope of underground buildings, and the spatial scope of underground buildings obtained by the high-density electrical method is compared with the complete point cloud data of underground buildings. If the spatial scope of the underground building to be explored completely includes the point cloud scope of the underground building, the point cloud data of the underground building can be used. If it cannot be completely included, the collected point cloud data and ground penetrating radar data are checked item by item, and data collection is carried out again after the reasons are found until the spatial scope of the underground building to be explored can completely include the point cloud scope of the underground building.

[0016] A method for joint mapping of underground buildings, wherein the specific steps of fixed scanning data processing are:

[0017] Format conversion: export the original scan data from the fixed scanner and convert the format of the original scan data;

[0018] Point cloud denoising: Through calculation, the clouds of each station of fixed scanning are automatically filtered to remove abnormal values ​​in the point cloud. Then, human-computer interaction is used to remove abnormal noise points in the scanned point cloud caused by mirror reflection and other reasons.

[0019] The automatic filtering formula is:

[0020]

[0021] In the formula For p i The unit normal vector, p j The unit normal vector is d represents the distance in the normal direction, is the newly generated point after filtering, w c refers to the weight of the spatial domain, which is is a function of the standard deviation, w s is the feature domain weight, is a function of the standard deviation, where is the distance between the sampling points p i The impact factor, Represents the distance vector of the sampling point at p i The projection pair p on the normal vector i Impact factor;

[0022] Preliminary stitching of point cloud: select common feature points of the two-station scanning data through feature point selection algorithm, establish connecting quadrilaterals, perform preliminary automatic stitching of fixed scanning point cloud data, and manually select common points for some scanning data that cannot be stitched, and perform manual preliminary stitching;

[0023] The implementation steps of the feature point selection algorithm are: perform curvature screening on the point cloud data according to the following formula:

[0024]

[0025] in represents the mean curvature of the point cloud, σ represents the standard deviation of the mean curvature of the point cloud, and k i Represents the average curvature of all points in the point cloud set;

[0026] Setting Thresholds p represents the proportional coefficient. Points with average curvature greater than the threshold are taken as reserved points. Convex and concave points are judged on all reserved points to determine the point cloud features.

[0027] The shape index calculation formula is:

[0028] Among them, k1, k2 are the principal curvatures, S(p)∈[0,1]

[0029] For any point p i The n neighboring points of p are obtained by extreme value determination. i Find the maximum and minimum values ​​of the fitted plane and take the point as the common feature point;

[0030] The maximum value determination formula is:

[0031] S(p i )=max(S(p i1 ),S(p i2 ),...,S(p in ))

[0032] The minimum value determination formula is:

[0033] S(p i )=min(S(p i1 ),S(p i2 ),...,S(pin ))

[0034] Control data setting: Import the coordinates of underground control points in the fixed scan point cloud data after preliminary splicing, and mark the underground control points one by one at the corresponding positions of the point cloud;

[0035] Point cloud precision stitching: The point cloud data is transformed into coordinates and continuously optimized by splicing the plane coordinate data of the control points. The plane accuracy of the point cloud data after precision stitching is evaluated. When it is determined that the plane accuracy requirements for acceptance are met, the optimization precision stitching is stopped. When it is determined that the plane accuracy requirements for acceptance are not met, continuous precision stitching is performed. Then, the elevation data of the underground control points is used to continuously optimize the elevation accuracy of the point cloud data. The optimization is stopped when the elevation accuracy of the point cloud meets the acceptance elevation accuracy requirements, and the final fixed scanning point cloud data of underground buildings is obtained.

[0036] A method for joint mapping of underground buildings, wherein the specific steps of point cloud data fusion are:

[0037] Common point selection and setting: separate the color of underground building mobile scanning point cloud, underground building fixed scanning point cloud, and underground building portable scanning point cloud, and then use the control point data to select the corresponding common control points in the overlapping area of ​​the two point cloud data, and set them as common points;

[0038] Point cloud adjustment fusion: Based on the coordinate values ​​of the set common points, various point cloud data of underground buildings are quickly and automatically spliced ​​and fused, and then optimized adjustment and smooth sampling are performed. When the plane and elevation accuracy of the processed point cloud data meet the acceptance accuracy requirements, the optimization adjustment and smooth sampling are stopped to obtain complete point cloud data of underground buildings;

[0039] Point cloud classification: First, after setting the samples of vehicles and mechanical settings, remove the vehicles and mechanical equipment in the point cloud through deep learning operations, and then use the point cloud classification algorithm to classify the final complete point cloud data of underground buildings to separate the ground point cloud and non-ground point cloud;

[0040] The point cloud classification algorithm formula is as follows:

[0041]

[0042] Where: H′ represents the average elevation of the center point O, H1 represents the elevation of the point at an azimuth of 30 degrees to the upper right of the center point O, H2 represents the elevation of the point at an azimuth of 90 degrees to the right of the center point O, H3 represents the elevation of the point at an azimuth of 150 degrees to the lower right of the center point O, H4 represents the elevation of the point at an azimuth of 210 degrees to the lower left of the center point O, H5 represents the elevation of the point at an azimuth of 270 degrees to the left of the center point O, H6 represents the elevation of the point at an azimuth of 330 degrees to the upper left of the center point O, and H xIndicates the elevation value of the center point X;

[0043] A calculation threshold ω is set for H′. When it is determined that H′<threshold ω, it is determined to be a ground point. When it is determined that H′≥threshold ω, it is determined to be a non-ground point and is eliminated. The point cloud data is traversed in sequence until all point clouds are added to the ground point set, and then the ground point cloud extraction is checked. If there is a missing ground point cloud, the threshold ω is readjusted and recalculated until the complete ground point cloud data is obtained and the judgment is ended. The threshold ω can be set and extracted in layers according to the average elevation of the underground control points of each layer. The remaining point cloud data is the non-ground point data.

[0044] A method for joint mapping of underground buildings, wherein the specific steps of data extraction are:

[0045] Current data extraction: For the final point cloud data of underground buildings, use the point cloud deep learning related algorithm to extract the building outline and parking space, then convert the format of the extracted data and process the topological relationship. After extracting and entering the attribute data and drawing the corresponding plane map, a real-life 3D model of the underground building is generated and stored in the current database;

[0046] License data extraction: For license data in paper archives, the file vectorization method is used to produce license graphic data in a specified format. For license data in digital form, license data in a specified format is produced through data screening, standardization processing, and coordinate transformation. The two types of license data are converted in coordinates and then superimposed to eliminate redundant data. The graphic data of building walls, functional area boundaries, and planned parking spaces are then extracted from the license data, and the topological relationship of the graphic data is processed. The relevant attribute data is entered according to the original license data, and then a connection is established between the graphic data and the attribute data to generate a three-dimensional model of the licensed underground building. According to the issuance requirements and permitted range of the license data, the permitted range space of the license data is set and generated, and the license data after the permitted range space is generated is stored in the specified standard license database.

[0047] A method for joint mapping of underground buildings, wherein the specific steps of extracting the current status data are:

[0048] Building outline extraction: extract non-ground point cloud data, segment the non-ground point cloud according to the set segmentation interval, then automatically extract the wall line, compare and fuse the extracted wall lines and draw the underground building plan; building outline extraction is divided into non-ground extraction, point cloud segmentation, wall line extraction, comparison and fusion, underground building plan production steps, among which:

[0049] Non-ground point extraction: extract non-ground point cloud data from the classified final underground building complete point cloud data;

[0050] Point cloud segmentation: according to the elevation interval of underground buildings, set the point cloud cutting interval β, and cut the corresponding non-ground point cloud data according to the β value;

[0051] Wall line extraction: Automatically extract building wall lines from the cut point cloud. The interior space of the underground building is drawn according to the extracted wall lines, and the outer contour of the underground building is drawn according to the outermost space range of the extracted underground building plus the planned wall thickness;

[0052] Contrast fusion: Compare the outer contour and internal space range of the underground building extracted on the same layer. If the difference is less than or equal to the value of θ, perform wall line fusion to obtain the average contour range, and finally obtain the outer and internal building contours of the underground building. If the difference is greater than the value of θ, retain the outer and internal building contours of the corresponding extracted underground building;

[0053] Production of underground building plan: Draw the plan of underground buildings at different levels according to the outer and inner building outlines of the fused underground buildings;

[0054] Parking space extraction: Extract ground point cloud data, and use deep learning related algorithms to automatically extract parking space information from ground point clouds according to the process of rough extraction of parking space lines, density denoising, intensity extraction, local clustering, clear height verification, and parking space standardization; parking space extraction is divided into ground point extraction, rough extraction of parking spaces, density denoising, intensity extraction, local clustering, clear height verification, and parking space standardization steps, among which:

[0055] Ground point extraction: extract ground point cloud data from the classified final underground building complete point cloud data;

[0056] Rough extraction of parking spaces: Calculate the grayscale difference between adjacent point clouds in the ground point cloud, set the corresponding difference threshold, extract the point clouds with adjacent point differences greater than the threshold, and generate a rough point cloud of parking space lines on the ground;

[0057] Density denoising: Set a dynamic grid for the rough point cloud of the parking space line, set the minimum value of the point cloud density within the dynamic grid, and remove the isolated point clouds in the distance in the rough point cloud of the parking space line;

[0058] Intensity extraction: Set the point cloud intensity range value for the rough point cloud of the parking space line after density denoising, and output the point cloud data within the corresponding intensity value range;

[0059] Local clustering: cluster analysis is performed using the convex hull algorithm, and the distance between the convex hull point and the adjacent ground point and the lower limit of the point cloud intensity difference are set to form the final parking space marking point cloud data;

[0060] Clear height check: merge the parking space marking point cloud data with the final complete point cloud data of the underground building, extract and check the height value within the parking space marking, analyze the lowest height value within the parking space marking one by one, and remove the parking space marking point cloud with the lowest height value less than the Y value. The remaining parking space marking point cloud is the parking space marking point cloud that meets the standards;

[0061] Parking space standardization: establish a standard parking space sample, check the parking space marking point cloud that meets the standard one by one, automatically generate parking space vector graphics, and record parking space attribute information;

[0062] Data format conversion: determine whether the extracted building outlines and parking space vector graphics meet the format requirements. If they do not meet the format requirements, the extracted building outlines and parking space vector graphics are converted into a format that meets the requirements;

[0063] Topological relationship processing: topological processing is performed on the underground building outline, and then topological processing is performed on the parking space vector graphics and the processed building outline;

[0064] Attribute extraction and entry: for the internal space range of underground buildings, automatically extract the internal height information of underground buildings, and calculate the average ground elevation and floor height information of underground buildings at different levels based on the ground point cloud information of underground buildings at different levels. Then, enter the building outline data into the investigated building room usage, room height, location of civil air defense facilities, civil air defense functional zoning, functional facility type, functional facility area, service facility location, number of service facilities, service facility area, elevation of each floor of underground buildings, floor height of each floor of underground buildings, building area, number of parking spaces and other attribute information, and statistically calculate the total area of ​​underground buildings and the area of ​​civil air defense functional zoning;

[0065] Plan drawing: First, draw the plan drawings of each floor of underground buildings according to the planning acceptance standards, and draw the plan drawings of civil air defense functional zoning according to the civil air defense acceptance standards and the civil air defense functional divisions;

[0066] Real-life model production: Based on the generated building's outer contours, internal space range, and extracted height information, a real-life 3D model of the underground building is generated and stored in the current status database.

[0067] A method for joint surveying and mapping of underground buildings, wherein the specific steps of extracting the license data are as follows:

[0068] Format conversion: For license data in paper archives, data scanning is first performed, and then data correction and vectorization are performed to convert it into license graphic data in the specified format. For license data in digital form, data screening is performed to eliminate redundant data, and then standardized processing is performed to convert it into license data in the specified format;

[0069] Coordinate transformation: The two types of license data mentioned above are judged in plane coordinate system and elevation system respectively, and the license data that does not meet the standards are transformed in plane coordinate system and elevation system respectively to form license data whose plane coordinate system and elevation system both meet the coordinate system requirements. Then, the two types of license data are spatially superimposed to eliminate redundant data and generate unique license data that meets the standards;

[0070] Building wall line extraction: filter the permitted data that meet the standards by layer, line shape, color, and width, and extract the building wall line according to the data modeling requirements;

[0071] Functional area extraction: According to the content of the license data, the functional zoning boundaries and planned parking spaces of the civil air defense project are selected. According to the types of different functional zoning, the functional zoning range of the civil air defense project is extracted. According to the planned parking space situation, the planned parking spaces are extracted;

[0072] Topological relationship processing: Process the extracted building wall lines and civil air defense functional partition lines for their own topology and their mutual topological relationship;

[0073] Attribute extraction and entry: according to the original license data, the attribute information of the underground building floor height, the floor elevation of each floor of the underground building, the location of supporting facilities, the nature of the civil air defense function room, the area of ​​the civil air defense function room, the area of ​​the civil air defense function partition, the name of the civil air defense function partition, the level of civil air defense facilities, etc. are entered, and the integrity and logic of the relevant attribute data are checked and modified, and then the connection between the graphic data and the attribute data is established;

[0074] Permit model generation: Generate a 3D model of the permitted underground building based on the building wall line, civil air defense functional zoning and plot range, combined with the extracted attribute data;

[0075] Allowable space setting: According to the allowable range of setback spacing, underground building spacing, underground building size, underground building depth and the requirements for permit data issuance, the allowable range space of the permitted underground building three-dimensional model is set and generated to form the final permitted underground building three-dimensional model data, which is stored in the designated standard permit database.

[0076] A method for joint surveying and mapping of underground buildings, wherein the specific steps of verification and comparison are:

[0077] Data retrieval: retrieve the real-life 3D model of underground buildings from the current status database, and retrieve the final 3D model data of permitted underground buildings from the standard permit database;

[0078] Verification and comparison: spatially superimpose the real-life 3D model of the underground building and the 3D model of the permitted underground building, first perform a planning completion check, store the underground building data that meets the planning completion comparison requirements in the intermediate satisfying database, mark the problem type and problem description for the underground building data that does not meet the planning completion comparison requirements, and store them in the intermediate unsatisfactory database, then perform a civil air defense project judgment on the 3D model data of the permitted underground building, if there is a civil air defense project, perform a civil air defense completion check on the real-life 3D model data of the underground building and the final 3D model data of the permitted underground building, store the real-life 3D model data of the underground building that meets the civil air defense completion comparison requirements in the satisfying database, and mark the problem type and problem description for the real-life 3D model data of the underground building that does not meet the civil air defense completion comparison requirements and store them in the unsatisfactory database; if there is no civil air defense project, store the data in the intermediate satisfying database in the satisfying database, and store the data in the intermediate unsatisfactory database in the unsatisfactory database;

[0079] Feedback results: In the database that does not meet the requirements, the real-life 3D model data of underground buildings is extracted. According to the problem type, the problem model is highlighted in different colors in the real-life 3D model of the underground building, and the corresponding problem content is marked to assist relevant personnel in making judgments and corrections. According to the problem type, the problem report and problem model display diagram are automatically generated according to the planned completion and civil air defense completion types, and a modification opinion report is generated.

[0080] A method for joint surveying and mapping of underground buildings, wherein the specific steps of the verification and comparison are:

[0081] Completion verification of planning: the real-life 3D model data of underground buildings are compared with the final permitted 3D model data of underground buildings in terms of allowed space, area, parking space, function and facilities. All the real-life 3D model data of underground buildings that pass the comparison are stored in the satisfaction database. Corresponding fields are established in the real-life 3D model data of underground buildings that fail any of the comparisons, and the corresponding problems are marked and stored in the problem database.

[0082] Civil defense facility determination: Determine whether there are civil defense facilities in the final three-dimensional model data of the permitted underground building. If there are civil defense facilities, conduct a subsequent civil defense completion check. If there are civil defense facilities, store the data in the intermediate satisfying database into the satisfying database, and store the data in the intermediate unsatisfying database into the unsatisfying database.

[0083] Completion comparison of civil air defense: the real-life 3D model data of underground buildings will be compared with the final permitted 3D model data of underground buildings in terms of allowed space, area, function, facilities, and other comparisons. All the real-life 3D model data of underground buildings that pass the comparison will be stored in the satisfaction database. Corresponding fields will be established in the real-life 3D model data of underground buildings that fail any of the comparisons, and the corresponding problems will be marked and stored in the problem database.

[0084] A method for joint surveying and mapping of underground buildings, wherein the specific steps of the planning completion verification are:

[0085] Allowable space comparison: compare the real-life 3D model data of underground buildings with the final permitted 3D model data of underground buildings for allowable space. If the real-life 3D model of the underground building does not exceed the allowable range space of the final permitted 3D model data of underground buildings in terms of plane position, height and elevation, then conduct a subsequent comparison. If it exceeds the allowable range space of any type, then establish the corresponding problem type and problem description field in the real-life 3D model data of the underground building, record the corresponding problem type and the size of the exceeded space, and then conduct a subsequent comparison.

[0086] Area comparison: compare the real-life 3D model data of underground buildings that have undergone permitted space comparison with the final permitted 3D model data of underground buildings, set area comparison parameters, determine the conformity of the planned area based on the area comparison parameters, and directly conduct subsequent comparison on the real-life 3D model data of underground buildings that meet the judgment results. For the real-life 3D model data of underground buildings that do not meet the judgment results, record the problem type and area inconsistency in the real-life 3D model data, and then conduct subsequent comparison;

[0087] Compare the area of ​​the real 3D model data of underground buildings with the final 3D model data of the permitted underground buildings;

[0088] If 0≤|S 实 -S 许 |<f means that the actual measured building area of ​​the underground building is determined to be consistent with the permitted building area of ​​the underground building;

[0089] If f≤|S 实 -S 许 |That is, it is determined that the actual measured building area of ​​the underground building does not conform to the permitted building area of ​​the underground building;

[0090] Where S 实 is the actual measured building area of ​​underground buildings, S 许 is the permitted building area of ​​underground buildings, f is the area comparison parameter, and if S 许 >300, then 0≤f≤0.01*S 许 , if S许 ≤300, then f=3;

[0091] Parking space comparison: compare the real-life 3D model data of the underground building that has undergone area comparison with the final 3D model data of the permitted underground building, set the parking space comparison parameter β, determine the compliance of the parking space according to the parking space comparison parameter, and directly perform subsequent comparison on the real-life 3D model data of the underground building that meets the judgment result, and perform subsequent comparison after recording the problem type and parking space difference size in the real-life 3D model data of the underground building that does not meet the judgment result;

[0092] Compare the parking spaces between the real-life 3D model data of underground buildings and the final 3D model data of permitted underground buildings;

[0093] If 0≤β, it is determined that the actual measured building area of ​​the underground building is consistent with the permitted building area of ​​the underground building;

[0094] If β<0, it is determined that the actual measured building area of ​​the underground building does not conform to the permitted building area of ​​the underground building;

[0095] Where β = C 实 -C 许 , β is the parking space comparison parameter, C 实 is the number of parking spaces actually measured in underground buildings, C 许 The number of parking spaces permitted for underground buildings;

[0096] Functional comparison: perform a functional comparison between the real-life 3D model data of the underground building that has undergone parking space comparison and the final 3D model data of the permitted underground building. If the supporting facility function type in the real-life 3D model of the underground building is consistent with the supporting facility function type in the final 3D model data of the permitted underground building, a subsequent comparison is performed. If the supporting facility function type in the real-life 3D model of the underground building is inconsistent with the supporting facility function type in the final 3D model data of the permitted underground building, a subsequent comparison is performed after recording the problem type and the inconsistent content of the supporting facility function type in the real-life 3D model data of the underground building;

[0097] Facility comparison: perform facility comparison on the real-life 3D model data of underground buildings that have undergone functional comparison with the final 3D model data of permitted underground buildings, set facility comparison parameters, determine the conformity of the facilities based on the facility comparison parameters, store the real-life 3D model data of underground buildings that meet the determination results in the intermediate satisfaction database, and store the real-life 3D model data of underground buildings that do not meet the determination results in the intermediate non-satisfaction database after recording the problem type and facility difference;

[0098] Conduct facility comparison between the actual 3D model data of underground buildings and the final permitted 3D model data of underground buildings;

[0099] like That is, it is determined that the facility area of ​​category n in the underground building is consistent with the permitted facility area of ​​category n in the underground building permit;

[0100] like That is, it is judged that the facility area of ​​category n in the underground building does not conform to the permitted facility area of ​​category n in the underground building permit;

[0101] in is the facility comparison parameter, V n实 is the actual measured area of ​​n types of facilities in underground buildings, V n许 is the permitted area of ​​type n facilities in underground buildings, and if V n许 >300, then If V n许 ≤300, then

[0102] A method for joint surveying and mapping of underground buildings, wherein the specific steps of the civil air defense completion comparison are:

[0103] Civil defense space comparison: compare the civil defense space range in the real-life 3D model of the underground building with the civil defense space range in the final permitted 3D model of the underground building. If the size, height, and elevation of the civil defense space range in the real-life 3D model of the underground building do not exceed the civil defense space range in the final permitted 3D model of the underground building, conduct a subsequent comparison. If it exceeds the civil defense space range in the final permitted 3D model of the underground building, record the corresponding problem type and the size of the exceeded space before conducting a subsequent comparison.

[0104] Civil defense area comparison: compare the civil defense area in the real-life 3D model of the underground building with the civil defense area in the final permitted 3D model of the underground building, set the civil defense area comparison parameter θ, determine the conformity of the civil defense area in the underground building according to the civil defense area comparison parameter, and directly perform subsequent comparison on the real-life 3D model data of the underground building whose judgment result conforms, and perform subsequent comparison after recording the problem type and civil defense area difference in the real-life 3D model data of the underground building whose judgment result does not conform;

[0105] Compare the civil air defense area between the real-life 3D model data of underground buildings and the final 3D model data of permitted underground buildings;

[0106] If 0≤θ, it is determined that the actual measured civil air defense functional area of ​​the underground building is consistent with the civil air defense functional area of ​​the underground building construction permit;

[0107] If θ<0, it is determined that the actual measured civil air defense functional area of ​​the underground building at the location does not match the civil air defense functional area of ​​the underground building construction permit;

[0108] where θ = F 实 -F 许 , θ is the air defense area comparison parameter, F 实 F is the measured civil air defense functional area of ​​underground buildings. 许 Permitting civil air defense functional areas for underground buildings;

[0109] Civil defense function comparison: compare the real-life 3D model data of the underground building that has undergone civil defense area comparison with the final permitted 3D model data of the underground building for civil defense function. If the civil defense facility function type in the real-life 3D model of the underground building is consistent with the civil defense facility function type in the final permitted 3D model data of the underground building, then conduct a subsequent comparison. If the civil defense facility function type in the real-life 3D model of the underground building is inconsistent with the civil defense facility function type in the final permitted 3D model data of the underground building, then conduct a subsequent comparison after recording the problem type and the inconsistent content of the civil defense facility function type in the real-life 3D model data of the underground building;

[0110] Comparison of civil air defense facilities: the real-life 3D model data of underground buildings that have undergone civil air defense function comparison shall be compared with the final permitted 3D model data of underground buildings for civil air defense facilities. If the civil air defense facility area in the real-life 3D model of the underground building is consistent with the civil air defense facility area type in the final permitted 3D model data of underground buildings, a subsequent comparison shall be conducted. If the civil air defense facility area in the real-life 3D model of the underground building is inconsistent with the civil air defense facility area in the final permitted 3D model data of underground buildings, the problem type and the difference in the civil air defense facility area shall be recorded in the real-life 3D model data of the underground building before a subsequent comparison shall be conducted.

[0111] Other civil defense comparison: The real-life 3D model data of the underground building that has undergone civil defense facility comparison will be compared with the final permitted 3D model data of the underground building for other civil defense comparisons. If the other civil defense facilities in the real-life 3D model of the underground building are consistent with the other civil defense facilities in the final permitted 3D model data of the underground building, the real-life 3D model data of the underground building will be stored in the satisfied database. If the other civil defense facilities in the real-life 3D model of the underground building are inconsistent with the other civil defense facilities in the final permitted 3D model data of the underground building, the problem type and the inconsistency of other civil defense facilities will be recorded in the real-life 3D model data of the underground building and then stored in the unsatisfied database.

[0112] It can be seen from this that:

[0113] The method in the embodiment of the present invention can utilize mobile, fixed, portable and other types of laser radar equipment to collect data, quickly and accurately measure underground buildings, utilize point cloud processing technology and multi-source data fusion technology to generate underground building point clouds, and automatically extract underground building point cloud data to form various forms of measurement results data, and automatically construct a real-life three-dimensional model of the underground building. By comparing with the licensed data, the joint surveying and mapping of underground buildings and planning acceptance and civil air defense acceptance can be quickly and accurately achieved, and planning acceptance and civil air defense acceptance reports can be generated, greatly improving the work efficiency of personnel, reducing personnel workload and the impact of human subjective judgment. The method can also utilize the real-life three-dimensional model to display non-conforming problems, and provide modification suggestions to assist operators in quickly locating problems and facilitate subsequent corrections to the problems. BRIEF DESCRIPTION OF THE DRAWINGS

[0114] Figure 1 It is a schematic diagram of the overall process of the underground building joint mapping method provided in an embodiment of the present invention;

[0115] Figure 2 A schematic diagram of a data processing flow chart in an embodiment of the present invention;

[0116] Figure 3 Schematic diagram of the fixed scan data processing steps in an embodiment of the present invention;

[0117] Figure 4 Schematic diagram of the point cloud data fusion process in an embodiment of the present invention;

[0118] Figure 5 A schematic diagram of a data extraction process in an embodiment of the present invention;

[0119] Figure 6 It is a schematic diagram of the process of extracting current status data in an embodiment of the present invention;

[0120] Figure 7 This is a flowchart of the licensing data extraction step in an embodiment of the present invention;

[0121] Figure 8 Schematic diagram of the verification and comparison process in an embodiment of the present invention;

[0122] Fig. 9 Schematic diagram of the process flow of the verification and comparison steps in an embodiment of the present invention;

[0123] Fig.10 It is a schematic diagram of the planning completion verification step flow in an embodiment of the present invention;

[0124] Fig.11 Schematic diagram of the process of civil air defense completion comparison steps in an embodiment of the present invention;

[0125] Fig.12 A schematic diagram of a building outline extraction process in an embodiment of the present invention;

[0126] Fig.13 Schematic diagram of the parking space extraction process in an embodiment of the present invention. DETAILED DESCRIPTION

[0127] In order to enable those skilled in the art to better understand the present invention, the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments. The exemplary embodiments and descriptions of the present invention are used to explain the present invention but are not intended to limit the present invention.

[0128] Embodiment 1:

[0129] Figure 1 A schematic diagram of the process flow of the underground building joint mapping method provided in this embodiment is as follows: Figure 1 As shown, a method for joint mapping of underground buildings includes the following steps: data measurement and investigation, data processing, data extraction, verification and comparison, and result output, wherein:

[0130] Data measurement and investigation: Conduct on-site surveys of the area to be measured to check the distribution, size, and approximate location of underground buildings, and collect relevant information on underground buildings in the corresponding area. According to the survey results, choose to set up ground control points at the ground, entrances and exits, and vents of underground buildings, set up underground control points inside underground buildings, conduct control measurements on the ground control points and underground control points respectively, and merge the ground control points and underground control points for network adjustment. Use mobile laser radar to scan and collect the inside of underground buildings, and then use fixed laser radar to scan and collect places inside underground buildings that mobile collection equipment cannot reach. Use portable laser radar to scan and collect small spaces and places that are inconvenient for fixed laser radar scanning. Finally, use high-density electrical methods to explore and collect underground buildings, and investigate and record the relevant attributes of underground buildings;

[0131] Data processing: perform data solution, format conversion, denoising, data splicing, coordinate conversion, and optimization adjustment on the mobile laser radar scanning point cloud, fixed laser radar scanning point cloud, and portable laser radar scanning point cloud, respectively, to obtain the underground building mobile scanning point cloud data, underground building fixed scanning point cloud data, and underground building portable scanning point cloud data, and then perform data fusion processing on various types of underground building point cloud data to obtain the final underground building complete point cloud data, and then use the ground penetrating radar to collect data and process it to verify and correct the point cloud data;

[0132] Data extraction: extract the current data of the final complete point cloud data of underground buildings, generate a real-life 3D model of the underground buildings and store it in the current database, process the paper and digitized permit data, extract the corresponding data and attributes, establish a permit 3D model and set the allowed space before storing it in the designated standard permit database;

[0133] Verification and comparison: extract the real 3D model of the underground building and the 3D model of the permitted underground building from the corresponding database respectively, import the above two models into the comparison module for data comparison, and output the corresponding data according to the comparison results;

[0134] Output results: Extract underground building data from the database, and generate a planning acceptance report for underground buildings, a comparison table of underground building heights, a comparison table of underground building economic indicators, a general plan for underground building planning acceptance, and a cross-sectional view of underground buildings in accordance with the requirements of the planning acceptance report; generate a civil air defense project area surveying and mapping report, a general plan of civil air defense projects, a civil air defense functional zoning area map, an area map of each functional part of the civil air defense project, a civil air defense project area calculation table, and a civil air defense project area comparison table in accordance with the requirements of the civil air defense completion report.

[0135] like Figure 2 As shown, a method for joint mapping of underground buildings, the specific steps of data processing are:

[0136] Mobile scanning data processing: First, the positioning and attitude system data is solved according to the data collected by a single movement, and then combined with the original data to generate a single-movement point cloud data result. Then, the mobile laser scanning measurement data result is preliminarily corrected through a single-movement global navigation satellite system differential calculation, and then the point cloud is denoised, with a noise removal rate of more than 95%, to obtain a single-movement scanning point cloud data. Then, the mobile scanning point cloud data collected multiple times and the ground control data and underground control data are transformed into coordinates, and the fusion optimization adjustment is performed. When the point cloud accuracy after adjustment meets the acceptance accuracy requirements, the optimization adjustment is stopped to obtain the mobile scanning point cloud data of underground buildings;

[0137] Fixed scanning data processing: convert the format of the data exported from the fixed scanner, use filtering algorithms and human-computer interaction to denoise the clouds of each site, and then automatically stitch them. If automatic stitching is not possible, use manual stitching. Mark the positions of the control points in the preliminary stitched point cloud and import the corresponding control point 3D coordinate data. Perform coordinate transformation and plane and elevation accuracy optimization on the point cloud data to obtain the final underground building fixed scanning point cloud data after meeting the accuracy requirements of the acceptance.

[0138] Portable scanning data processing: format conversion and denoising of portable scanning data are performed, and coordinate transformation and data splicing of portable scanning data are performed using underground control point and splicing control point data. When the point cloud accuracy after splicing adjustment meets the acceptance accuracy requirements, the optimization adjustment is stopped to obtain the portable scanning point cloud data of underground buildings;

[0139] Point cloud data fusion: Different types of underground building point cloud data are color-separated, and corresponding common control points are selected in the overlapping area of ​​the two point cloud data and set as common points. Based on the 3D coordinate values ​​of the known common points, different types of underground building point cloud data are spliced ​​and fused, optimized and smoothed, and then classified and processed to remove point clouds of vehicles, mechanical equipment, etc., to obtain the final complete point cloud data of underground buildings;

[0140] Data verification: The survey line data collected by the high-density electrical method are preprocessed and integrated with interpretation and analysis to obtain the spatial scope of underground buildings, and the spatial scope of underground buildings obtained by the high-density electrical method is compared with the complete point cloud data of underground buildings. If the spatial scope of the underground building to be explored completely includes the point cloud scope of the underground building, the point cloud data of the underground building can be used. If it cannot be completely included, the collected point cloud data and ground penetrating radar data are checked item by item, and data collection is carried out again after the reasons are found until the spatial scope of the underground building to be explored can completely include the point cloud scope of the underground building.

[0141] like Figure 3 As shown, a method for joint mapping of underground buildings, the specific steps of fixed scanning data processing are:

[0142] Format conversion: export the original scan data from the fixed scanner and convert the format of the original scan data;

[0143] Point cloud denoising: Through calculation, the clouds of each station of fixed scanning are automatically filtered to remove abnormal values ​​in the point cloud. Then, human-computer interaction is used to remove abnormal noise points in the scanned point cloud caused by mirror reflection and other reasons.

[0144] The automatic filtering formula is:

[0145]

[0146] In the formula For p i The unit normal vector, p j The unit normal vector is d represents the distance in the normal direction, is the newly generated point after filtering, w c refers to the weight of the spatial domain, which is is a function of the standard deviation, ws is the feature domain weight, is a function of the standard deviation, where is the distance between the sampling points p i The impact factor, Represents the distance vector of the sampling point at p i The projection pair p on the normal vector i Impact factor;

[0147] Preliminary stitching of point cloud: select common feature points of the two-station scanning data through feature point selection algorithm, establish connecting quadrilaterals, perform preliminary automatic stitching of fixed scanning point cloud data, and manually select common points for some scanning data that cannot be stitched, and perform manual preliminary stitching;

[0148] The implementation steps of the feature point selection algorithm are: perform curvature screening on the point cloud data according to the following formula:

[0149]

[0150] in represents the mean curvature of the point cloud, σ represents the standard deviation of the mean curvature of the point cloud, and k i Represents the average curvature of all points in the point cloud set;

[0151] Setting Thresholds p represents the proportional coefficient. Points with average curvature greater than the threshold are taken as reserved points. Convex and concave points are judged on all reserved points to determine the point cloud features.

[0152] The shape index calculation formula is:

[0153] Among them, k1, k2 are the principal curvatures, S(p)∈[0,1]

[0154] For any point p i The n neighboring points of p are obtained by extreme value determination. i Find the maximum and minimum values ​​of the fitted plane and take the point as the common feature point;

[0155] The maximum value determination formula is:

[0156] S(p i )=max(S(p i1 ),S(p i2 ),...,S(p in ))

[0157] The minimum value determination formula is:

[0158] S(p i )=min(S(p i1 ),S(pi2 ),...,S(p in ))

[0159] Control data setting: Import the coordinates of underground control points in the fixed scan point cloud data after preliminary splicing, and mark the underground control points one by one at the corresponding positions of the point cloud;

[0160] Point cloud precision stitching: The point cloud data is transformed into coordinates and continuously optimized by splicing the plane coordinate data of the control points. The plane accuracy of the point cloud data after precision stitching is evaluated. When it is determined that the plane accuracy requirements for acceptance are met, the optimization precision stitching is stopped. When it is determined that the plane accuracy requirements for acceptance are not met, continuous precision stitching is performed. Then, the elevation data of the underground control points is used to continuously optimize the elevation accuracy of the point cloud data. The optimization is stopped when the elevation accuracy of the point cloud meets the acceptance elevation accuracy requirements, and the final fixed scanning point cloud data of underground buildings is obtained.

[0161] like Figure 4 As shown, a method for joint mapping of underground buildings, the specific steps of point cloud data fusion are:

[0162] Common point selection and setting: separate the color of underground building mobile scanning point cloud, underground building fixed scanning point cloud, and underground building portable scanning point cloud, and then use the control point data to select the corresponding common control points in the overlapping area of ​​the two point cloud data, and set them as common points;

[0163] Point cloud adjustment fusion: Based on the coordinate values ​​of the set common points, various point cloud data of underground buildings are quickly and automatically spliced ​​and fused, and then optimized adjustment and smooth sampling are performed. When the plane and elevation accuracy of the processed point cloud data meet the acceptance accuracy requirements, the optimization adjustment and smooth sampling are stopped to obtain complete point cloud data of underground buildings;

[0164] Point cloud classification: First, after setting the samples of vehicles and mechanical settings, remove the vehicles and mechanical equipment in the point cloud through deep learning operations, and then use the point cloud classification algorithm to classify the final complete point cloud data of underground buildings to separate the ground point cloud and non-ground point cloud;

[0165] The point cloud classification algorithm formula is as follows:

[0166]

[0167] Where: H′ represents the average elevation of the center point O, H1 represents the elevation of the point at an azimuth of 30 degrees to the upper right of the center point O, H2 represents the elevation of the point at an azimuth of 90 degrees to the right of the center point O, H3 represents the elevation of the point at an azimuth of 150 degrees to the lower right of the center point O, H4 represents the elevation of the point at an azimuth of 210 degrees to the lower left of the center point O, H5 represents the elevation of the point at an azimuth of 270 degrees to the left of the center point O, H6 represents the elevation of the point at an azimuth of 330 degrees to the upper left of the center point O, and H x Indicates the elevation value of the center point X;

[0168] A calculation threshold ω is set for H′. When it is determined that H′<threshold ω, it is determined to be a ground point. When it is determined that H′≥threshold ω, it is determined to be a non-ground point and is eliminated. The point cloud data is traversed in sequence until all point clouds are added to the ground point set, and then the ground point cloud extraction is checked. If there is a missing ground point cloud, the threshold ω is readjusted and recalculated until the complete ground point cloud data is obtained and the judgment is ended. The threshold ω can be set and extracted in layers according to the average elevation of the underground control points of each layer. The remaining point cloud data is the non-ground point data.

[0169] like Figure 5 As shown, a method for joint mapping of underground buildings, the specific steps of data extraction are:

[0170] Current data extraction: For the final point cloud data of underground buildings, use the point cloud deep learning related algorithm to extract the building outline and parking space, then convert the format of the extracted data and process the topological relationship. After extracting and entering the attribute data and drawing the corresponding plane map, a real-life 3D model of the underground building is generated and stored in the current database;

[0171] License data extraction: For license data in paper archives, the file vectorization method is used to produce license graphic data in a specified format. For license data in digital form, license data in a specified format is produced through data screening, standardization processing, and coordinate transformation. The two types of license data are converted in coordinates and then superimposed to eliminate redundant data. The graphic data of building walls, functional area boundaries, and planned parking spaces are then extracted from the license data, and the topological relationship of the graphic data is processed. The relevant attribute data is entered according to the original license data, and then a connection is established between the graphic data and the attribute data to generate a three-dimensional model of the licensed underground building. According to the issuance requirements and permitted range of the license data, the permitted range space of the license data is set and generated, and the license data after the permitted range space is generated is stored in the specified standard license database.

[0172] like Figure 6 As shown, a method for joint mapping of underground buildings, the specific steps of extracting the current status data are:

[0173] Building outline extraction: Extract non-ground point cloud data, segment the non-ground point cloud according to the set segmentation interval, and then automatically extract the wall line. After comparing and fusing the extracted wall lines, draw the underground building plan;

[0174] Parking space extraction: Extract ground point cloud data, and use deep learning-related algorithms to automatically extract parking space information from ground point clouds according to the process of rough extraction of parking space lines, density denoising, intensity extraction, local clustering, clear height verification, and parking space standardization;

[0175] Data format conversion: determine whether the extracted building outlines and parking space vector graphics meet the format requirements. If they do not meet the format requirements, the extracted building outlines and parking space vector graphics are converted into a format that meets the requirements;

[0176] Topological relationship processing: topological processing is performed on the underground building outline, and then topological processing is performed on the parking space vector graphics and the processed building outline;

[0177] Attribute extraction and entry: for the internal space range of underground buildings, automatically extract the internal height information of underground buildings, and calculate the average ground elevation and floor height information of underground buildings at different levels based on the ground point cloud information of underground buildings at different levels. Then, enter the building outline data into the investigated building room usage, room height, location of civil air defense facilities, civil air defense functional zoning, functional facility type, functional facility area, service facility location, number of service facilities, service facility area, elevation of each floor of underground buildings, floor height of each floor of underground buildings, building area, number of parking spaces and other attribute information, and statistically calculate the total area of ​​underground buildings and the area of ​​civil air defense functional zoning;

[0178] Plan drawing: First, draw the plan drawings of each floor of underground buildings according to the planning acceptance standards, and draw the plan drawings of civil air defense functional zoning according to the civil air defense acceptance standards and the civil air defense functional divisions;

[0179] Real-life model production: Based on the generated building's outer contours, internal space range, and extracted height information, a real-life 3D model of the underground building is generated and stored in the current status database.

[0180] like Fig.12 As shown, a method for joint mapping of underground buildings, the specific steps of extracting the building outline are:

[0181] Non-ground point extraction: extract non-ground point cloud data from the classified final underground building complete point cloud data;

[0182] Point cloud segmentation: according to the elevation interval of underground buildings, set the point cloud cutting interval β, and cut the corresponding non-ground point cloud data according to the β value;

[0183] Wall line extraction: Automatically extract building wall lines from the cut point cloud. The interior space of the underground building is drawn according to the extracted wall lines, and the outer contour of the underground building is drawn according to the outermost space range of the extracted underground building plus the planned wall thickness;

[0184] Contrast fusion: Compare the outer contour and internal space range of the underground building extracted on the same layer. If the difference is less than or equal to the value of θ, perform wall line fusion to obtain the average contour range, and finally obtain the outer and internal building contours of the underground building. If the difference is greater than the value of θ, retain the outer and internal building contours of the corresponding extracted underground building;

[0185] Production of underground building plan: Draw the plan of underground buildings at different levels according to the outer and inner building outlines of the fused underground buildings;

[0186] like Fig.13 As shown, a method for joint mapping of underground buildings, the specific steps of extracting parking spaces are:

[0187] Ground point extraction: extract ground point cloud data from the classified final underground building complete point cloud data;

[0188] Rough extraction of parking spaces: Calculate the grayscale difference between adjacent point clouds in the ground point cloud, set the corresponding difference threshold, extract the point clouds with adjacent point differences greater than the threshold, and generate a rough point cloud of parking space lines on the ground;

[0189] Density denoising: Set a dynamic grid for the rough point cloud of the parking space line, set the minimum value of the point cloud density within the dynamic grid, and remove the isolated point clouds in the distance in the rough point cloud of the parking space line;

[0190] Intensity extraction: Set the point cloud intensity range value for the rough point cloud of the parking space line after density denoising, and output the point cloud data within the corresponding intensity value range;

[0191] Local clustering: cluster analysis is performed using the convex hull algorithm, and the distance between the convex hull point and the adjacent ground point and the lower limit of the point cloud intensity difference are set to form the final parking space marking point cloud data;

[0192] Clear height check: merge the parking space marking point cloud data with the final complete point cloud data of the underground building, extract and check the height value within the parking space marking, analyze the lowest height value within the parking space marking one by one, and remove the parking space marking point cloud with the lowest height value less than the Y value. The remaining parking space marking point cloud is the parking space marking point cloud that meets the standards;

[0193] Parking space standardization: Establish standard parking space samples, check the parking space marking point clouds that meet the standards one by one, automatically generate parking space vector graphics, and record parking space attribute information.

[0194] like Figure 7 As shown, a method for joint mapping of underground buildings, the specific steps of extracting the license data are:

[0195] Format conversion: For license data in paper archives, data scanning is first performed, and then data correction and vectorization are performed to convert it into license graphic data in the specified format. For license data in digital form, data screening is performed to eliminate redundant data, and then standardized processing is performed to convert it into license data in the specified format;

[0196] Coordinate transformation: The two types of license data mentioned above are judged in plane coordinate system and elevation system respectively, and the license data that does not meet the standards are transformed in plane coordinate system and elevation system respectively to form license data whose plane coordinate system and elevation system both meet the coordinate system requirements. Then, the two types of license data are spatially superimposed to eliminate redundant data and generate unique license data that meets the standards;

[0197] Building wall line extraction: filter the permitted data that meet the standards by layer, line shape, color, and width, and extract the building wall line according to the data modeling requirements;

[0198] Functional area extraction: According to the content of the license data, the functional zoning boundaries and planned parking spaces of the civil air defense project are selected. According to the types of different functional zoning, the functional zoning range of the civil air defense project is extracted. According to the planned parking space situation, the planned parking spaces are extracted;

[0199] Topological relationship processing: Process the extracted building wall lines and civil air defense functional partition lines for their own topology and their mutual topological relationship;

[0200] Attribute extraction and entry: according to the original license data, the attribute information of the underground building floor height, the floor elevation of each floor of the underground building, the location of supporting facilities, the nature of the civil air defense function room, the area of ​​the civil air defense function room, the area of ​​the civil air defense function partition, the name of the civil air defense function partition, the level of civil air defense facilities, etc. are entered, and the integrity and logic of the relevant attribute data are checked and modified, and then the connection between the graphic data and the attribute data is established;

[0201] Permit model generation: Generate a 3D model of the permitted underground building based on the building wall line, civil air defense functional zoning and plot range, combined with the extracted attribute data;

[0202] Allowable space setting: According to the allowable range of setback spacing, underground building spacing, underground building size, underground building depth and the requirements for permit data issuance, the allowable range space of the permitted underground building three-dimensional model is set and generated to form the final permitted underground building three-dimensional model data, which is stored in the designated standard permit database.

[0203] like Figure 8 As shown, a method for joint surveying and mapping of underground buildings, the specific steps of the verification and comparison are:

[0204] Data retrieval: retrieve the real-life 3D model of underground buildings from the current status database, and retrieve the final 3D model data of permitted underground buildings from the standard permit database;

[0205] Verification and comparison: spatially superimpose the real-life 3D model of the underground building and the 3D model of the permitted underground building, first perform a planning completion check, store the underground building data that meets the planning completion comparison requirements in the intermediate satisfying database, mark the problem type and problem description for the underground building data that does not meet the planning completion comparison requirements, and store them in the intermediate unsatisfactory database, then perform a civil air defense project judgment on the 3D model data of the permitted underground building, if there is a civil air defense project, perform a civil air defense completion check on the real-life 3D model data of the underground building and the final 3D model data of the permitted underground building, store the real-life 3D model data of the underground building that meets the civil air defense completion comparison requirements in the satisfying database, and mark the problem type and problem description for the real-life 3D model data of the underground building that does not meet the civil air defense completion comparison requirements and store them in the unsatisfactory database; if there is no civil air defense project, store the data in the intermediate satisfying database in the satisfying database, and store the data in the intermediate unsatisfactory database in the unsatisfactory database;

[0206] Feedback results: In the database that does not meet the requirements, the real-life 3D model data of underground buildings is extracted. According to the problem type, the problem model is highlighted in different colors in the real-life 3D model of the underground building, and the corresponding problem content is marked to assist relevant personnel in making judgments and corrections. According to the problem type, the problem report and problem model display diagram are automatically generated according to the planned completion and civil air defense completion types, and a modification opinion report is generated.

[0207] like Fig. 9 As shown, a method for joint mapping of underground buildings, the specific steps of the verification and comparison are:

[0208] Completion verification of planning: the real-life 3D model data of underground buildings are compared with the final permitted 3D model data of underground buildings in terms of allowed space, area, parking space, function and facilities. All the real-life 3D model data of underground buildings that pass the comparison are stored in the satisfaction database. Corresponding fields are established in the real-life 3D model data of underground buildings that fail any of the comparisons, and the corresponding problems are marked and stored in the problem database.

[0209] Civil defense facility determination: Determine whether there are civil defense facilities in the final three-dimensional model data of the permitted underground building. If there are civil defense facilities, conduct a subsequent civil defense completion check. If there are civil defense facilities, store the data in the intermediate satisfying database into the satisfying database, and store the data in the intermediate unsatisfying database into the unsatisfying database.

[0210] Completion comparison of civil air defense: the real-life 3D model data of underground buildings will be compared with the final permitted 3D model data of underground buildings in terms of allowed space, area, function, facilities, and other comparisons. All the real-life 3D model data of underground buildings that pass the comparison will be stored in the satisfaction database. Corresponding fields will be established in the real-life 3D model data of underground buildings that fail any of the comparisons, and the corresponding problems will be marked and stored in the problem database.

[0211] like Fig.10 As shown, a method for joint surveying and mapping of underground buildings, the specific steps of the planning completion verification are:

[0212] Allowable space comparison: compare the real-life 3D model data of underground buildings with the final permitted 3D model data of underground buildings for allowable space. If the real-life 3D model of the underground building does not exceed the allowable range space of the final permitted 3D model data of underground buildings in terms of plane position, height and elevation, then conduct a subsequent comparison. If it exceeds the allowable range space of any type, then establish the corresponding problem type and problem description field in the real-life 3D model data of the underground building, record the corresponding problem type and the size of the exceeded space, and then conduct a subsequent comparison.

[0213] Area comparison: compare the real-life 3D model data of underground buildings that have undergone permitted space comparison with the final permitted 3D model data of underground buildings, set area comparison parameters, determine the conformity of the planned area based on the area comparison parameters, and directly conduct subsequent comparison on the real-life 3D model data of underground buildings that meet the judgment results. For the real-life 3D model data of underground buildings that do not meet the judgment results, record the problem type and area inconsistency in the real-life 3D model data, and then conduct subsequent comparison;

[0214] Compare the area of ​​the real 3D model data of underground buildings with the final 3D model data of the permitted underground buildings;

[0215] If 0≤|S 实 -S 许 |<f means that the actual measured building area of ​​the underground building is determined to be consistent with the permitted building area of ​​the underground building;

[0216] If f≤|S 实 -S 许|That is, it is determined that the actual measured building area of ​​the underground building does not conform to the permitted building area of ​​the underground building;

[0217] Where S 实 is the actual measured building area of ​​underground buildings, S 许 is the permitted building area of ​​underground buildings, f is the area comparison parameter, and if S 许 >300, then 0≤f≤0.01*S 许 , if S 许 ≤300, then f=3;

[0218] Parking space comparison: compare the real-life 3D model data of the underground building that has undergone area comparison with the final 3D model data of the permitted underground building, set the parking space comparison parameter β, determine the compliance of the parking space according to the parking space comparison parameter, and directly perform subsequent comparison on the real-life 3D model data of the underground building that meets the judgment result, and perform subsequent comparison after recording the problem type and parking space difference size in the real-life 3D model data of the underground building that does not meet the judgment result;

[0219] Compare the parking spaces between the real-life 3D model data of underground buildings and the final 3D model data of permitted underground buildings;

[0220] If 0≤β, it is determined that the actual measured building area of ​​the underground building is consistent with the permitted building area of ​​the underground building;

[0221] If β<0, it is determined that the actual measured building area of ​​the underground building does not conform to the permitted building area of ​​the underground building;

[0222] Where β = C 实 -C 许 , β is the parking space comparison parameter, C 实 is the number of parking spaces actually measured in underground buildings, C 许 The number of parking spaces permitted for underground buildings;

[0223] Functional comparison: perform a functional comparison between the real-life 3D model data of the underground building that has undergone parking space comparison and the final 3D model data of the permitted underground building. If the supporting facility function type in the real-life 3D model of the underground building is consistent with the supporting facility function type in the final 3D model data of the permitted underground building, a subsequent comparison is performed. If the supporting facility function type in the real-life 3D model of the underground building is inconsistent with the supporting facility function type in the final 3D model data of the permitted underground building, a subsequent comparison is performed after recording the problem type and the inconsistent content of the supporting facility function type in the real-life 3D model data of the underground building;

[0224] Facility comparison: perform facility comparison on the real-life 3D model data of underground buildings that have undergone functional comparison with the final 3D model data of permitted underground buildings, set facility comparison parameters, determine the conformity of the facilities based on the facility comparison parameters, store the real-life 3D model data of underground buildings that meet the determination results in the intermediate satisfaction database, and store the real-life 3D model data of underground buildings that do not meet the determination results in the intermediate non-satisfaction database after recording the problem type and facility difference;

[0225] Conduct facility comparison between the actual 3D model data of underground buildings and the final permitted 3D model data of underground buildings;

[0226] like That is, it is determined that the facility area of ​​category n in the underground building is consistent with the permitted facility area of ​​category n in the underground building permit;

[0227] like That is, it is judged that the facility area of ​​category n in the underground building does not conform to the permitted facility area of ​​category n in the underground building permit;

[0228] in is the facility comparison parameter, V n实 is the actual measured area of ​​n types of facilities in underground buildings, V n许 is the permitted area of ​​type n facilities in underground buildings, and if V n许 >300, then If V n许 ≤300, then

[0229] like Fig.11 As shown, a method for joint mapping of underground buildings, the specific steps of the civil air defense completion comparison are:

[0230] Civil defense space comparison: compare the civil defense space range in the real-life 3D model of the underground building with the civil defense space range in the final permitted 3D model of the underground building. If the size, height, and elevation of the civil defense space range in the real-life 3D model of the underground building do not exceed the civil defense space range in the final permitted 3D model of the underground building, conduct a subsequent comparison. If it exceeds the civil defense space range in the final permitted 3D model of the underground building, record the corresponding problem type and the size of the exceeded space before conducting a subsequent comparison.

[0231] Civil defense area comparison: compare the civil defense area in the real-life 3D model of the underground building with the civil defense area in the final permitted 3D model of the underground building, set the civil defense area comparison parameter θ, determine the conformity of the civil defense area in the underground building according to the civil defense area comparison parameter, and directly perform subsequent comparison on the real-life 3D model data of the underground building whose judgment result conforms, and perform subsequent comparison after recording the problem type and civil defense area difference in the real-life 3D model data of the underground building whose judgment result does not conform;

[0232] Compare the civil air defense area between the real-life 3D model data of underground buildings and the final 3D model data of permitted underground buildings;

[0233] If 0≤θ, it is determined that the actual measured civil air defense functional area of ​​the underground building is consistent with the civil air defense functional area of ​​the underground building construction permit;

[0234] If θ<0, it is determined that the actual measured civil air defense functional area of ​​the underground building at the location does not match the civil air defense functional area of ​​the underground building construction permit;

[0235] where θ = F 实 -F 许 , θ is the air defense area comparison parameter, F 实 F is the measured civil air defense functional area of ​​underground buildings. 许 Permitting civil air defense functional areas for underground buildings;

[0236] Civil defense function comparison: compare the real-life 3D model data of the underground building that has undergone civil defense area comparison with the final permitted 3D model data of the underground building for civil defense function. If the civil defense facility function type in the real-life 3D model of the underground building is consistent with the civil defense facility function type in the final permitted 3D model data of the underground building, then conduct a subsequent comparison. If the civil defense facility function type in the real-life 3D model of the underground building is inconsistent with the civil defense facility function type in the final permitted 3D model data of the underground building, then conduct a subsequent comparison after recording the problem type and the inconsistent content of the civil defense facility function type in the real-life 3D model data of the underground building;

[0237] Comparison of civil air defense facilities: the real-life 3D model data of underground buildings that have undergone civil air defense function comparison shall be compared with the final permitted 3D model data of underground buildings for civil air defense facilities. If the civil air defense facility area in the real-life 3D model of the underground building is consistent with the civil air defense facility area type in the final permitted 3D model data of underground buildings, a subsequent comparison shall be conducted. If the civil air defense facility area in the real-life 3D model of the underground building is inconsistent with the civil air defense facility area in the final permitted 3D model data of underground buildings, the problem type and the difference in the civil air defense facility area shall be recorded in the real-life 3D model data of the underground building before a subsequent comparison shall be conducted.

[0238] Other civil defense comparison: The real-life 3D model data of the underground building that has undergone civil defense facility comparison will be compared with the final permitted 3D model data of the underground building for other civil defense comparisons. If the other civil defense facilities in the real-life 3D model of the underground building are consistent with the other civil defense facilities in the final permitted 3D model data of the underground building, the real-life 3D model data of the underground building will be stored in the satisfied database. If the other civil defense facilities in the real-life 3D model of the underground building are inconsistent with the other civil defense facilities in the final permitted 3D model data of the underground building, the problem type and the inconsistency of other civil defense facilities will be recorded in the real-life 3D model data of the underground building and then stored in the unsatisfied database.

[0239] The following is a detailed description of a specific implementation case:

[0240] Embodiment 2:

[0241] Figure 1 A schematic diagram of the process flow of the underground building joint mapping method provided in this embodiment is as follows: Figure 1 As shown, a method for joint mapping of underground buildings includes the following steps: data measurement and investigation, data processing, data extraction, verification and comparison, and result output, wherein:

[0242] Data measurement and investigation: Conduct on-site surveys of the area to be measured to check the distribution, size, and approximate location of underground buildings, and collect relevant information on underground buildings in the corresponding area. According to the survey results, choose to set up ground control points at the ground, entrances and exits, and vents of underground buildings, set up underground control points inside underground buildings, conduct control measurements on the ground control points and underground control points respectively, and merge the ground control points and underground control points for network adjustment. Use mobile laser radar to scan and collect the inside of underground buildings, and then use fixed laser radar to scan and collect places inside underground buildings that mobile collection equipment cannot reach. Use portable laser radar to scan and collect small spaces and places that are inconvenient for fixed laser radar scanning. Finally, use high-density electrical methods to explore and collect underground buildings, and investigate and record the relevant attributes of underground buildings;

[0243] Data processing: perform data solution, format conversion, denoising, data splicing, coordinate conversion, and optimization adjustment on the mobile laser radar scanning point cloud, fixed laser radar scanning point cloud, and portable laser radar scanning point cloud, respectively, to obtain the underground building mobile scanning point cloud data, underground building fixed scanning point cloud data, and underground building portable scanning point cloud data, and then perform data fusion processing on various types of underground building point cloud data to obtain the final underground building complete point cloud data, and then use the ground penetrating radar to collect data and process it to verify and correct the point cloud data;

[0244] Data extraction: extract the current data of the final complete point cloud data of underground buildings, generate a real-life 3D model of the underground buildings and store it in the current database, process the paper and digitized permit data, extract the corresponding data and attributes, establish a permit 3D model and set the allowed space before storing it in the designated standard permit database;

[0245] Verification and comparison: extract the real 3D model of the underground building and the 3D model of the permitted underground building from the corresponding database respectively, import the above two models into the comparison module for data comparison, and output the corresponding data according to the comparison results;

[0246] Output: Extract underground building data from the database, and generate a planning acceptance report for underground buildings, a comparison table of underground building heights, a comparison table of underground building economic indicators, a general plan for underground building planning acceptance, and a cross-sectional view of underground buildings in accordance with the requirements of the planning acceptance report; generate a civil air defense project area surveying and mapping report, a general plan of civil air defense projects, a civil air defense functional zoning area map, an area map of each functional part of the civil air defense project, a civil air defense project area calculation table, and a civil air defense project area comparison table in accordance with the requirements of the civil air defense completion report;

[0247] The specific steps of data measurement and investigation are as follows:

[0248] Preliminary preparation: Conduct on-site surveys of the area to be measured to check the distribution, size, and approximate location of underground buildings, and collect relevant regional plane control points, elevation control points, elevation information and other control measurement data for the underground buildings in the corresponding area, existing surveying and mapping data for underground buildings in the corresponding area, approved planning permits, and design and construction data of planning permits, among which the design and construction data include general plans, floor plans, elevations, sections, area calculation tables, approved civil air defense area approval tables, wartime underground civil air defense design drawings, and wartime underground civil air defense construction drawings;

[0249] Control measurement: According to the survey situation, ground control points are set at the ground, entrances and exits, and ventilation holes of underground buildings, and underground control points are set inside underground buildings. Control measurements are performed on the ground control points and underground control points respectively, and the ground control points and underground control points are combined for network adjustment.

[0250] Data collection and investigation: Use mobile laser radar to scan and collect data inside underground buildings, and then use fixed laser radar to scan and collect data inside underground buildings where mobile laser radar collection equipment cannot reach. Use portable laser radar to scan and collect data in small spaces and places that are inconvenient for national laser radar collection equipment to enter. Finally, use high-density electrical methods to collect data from underground buildings, and investigate and record the location of supporting facilities of underground buildings, the zoning of civil air defense functions, and the functions of civil air defense rooms;

[0251] Among them, the specific steps of control measurement are:

[0252] Control point setting: According to the visibility of the survey area and the surrounding environment, two control points with mutual visibility are set at each ground entrance and exit of the underground building. If there is a vent in the underground building that can see through the underground space, an additional ground control point is required at the vent. Underground control points are set on the ground, columns, and walls of each floor inside the underground building, and a laser radar reflective mark is set at the center of the underground control point. At least one underground control point at the entrance should be able to see through with the ground control point. Underground level control points should be arranged on each floor of the underground building. The underground control points on the ground should be set according to the main direction of the entrance and exit of the underground building, and should not be in a straight line. Splicing control points should be set on the columns of the underground building and the walls of each room door, and the splicing control points should not be in a straight line.

[0253] The surrounding environment includes: the surrounding area should be safe for operation, the distance between the surrounding area and high-power radio transmission sources such as microwave stations, radio transmission towers, substations, etc. should be greater than 200 meters, the distance between the surrounding area and high-voltage transmission lines and microwave channels should be greater than 100 meters, and the height angle of obstacles in the surrounding field of view should be less than 15 degrees;

[0254] Ground control measurement: First, the plane control measurement method of the real-time dynamic network carrier phase difference technology is used to obtain the plane coordinates of the ground control points. Then, the elevation values ​​of the ground control points are obtained by using the leveling measurement method, and finally the three-dimensional coordinate values ​​of the ground control points are obtained.

[0255] Underground control measurement: According to the distribution position of the ground control points and the obtained three-dimensional coordinates, the ground control points are set as known points and the underground control points on the ground are measured by wire connection. The azimuth, vertical angle and distance of the underground control points are measured, and the three-dimensional coordinates of the horizontal and high control points in the underground control points are calculated. Then, the total station is used to determine the three-dimensional coordinates of the splicing control points by the branch wire method. After the measurement of each layer of underground control points is completed, the underground control points of the lower layer should be connected with the underground control points of the upper layer for measurement;

[0256] Control network adjustment: merge the ground control points with the underground control points, first use independent free network adjustment, then use the ground high-level plane starting point for fixed constraint adjustment, and calculate the final three-dimensional coordinates of the ground control points and underground control points;

[0257] The specific steps of data collection and investigation are as follows:

[0258] Mobile LiDAR collection: According to the internal structure of underground buildings, the corresponding driving routes are planned. The collection routes set should cover the internal space of underground buildings and underground control points of underground buildings to the greatest extent. The time of a single mobile LiDAR collection route should be within 10 minutes. After the planned route covers all the collectible spaces inside the underground buildings, the mobile LiDAR scanning equipment is initialized and set up, and the inertial navigation equipment and satellite receiving equipment are turned on. After the equipment is stabilized and initialized and the satellite reaches a fixed solution, data collection starts from the ground entrance and exit, and the single measurement ends when leaving the entrance and exit of the underground building. After the satellite reaches a fixed solution again, the above collection process is repeated according to the planned collection route until all planned route data is collected. During the data collection process, the speed of the mobile device should be 30-40 kilometers per hour, and the speed of the mobile device at the turning point should be 20-30 kilometers per hour.

[0259] Fixed LiDAR collection: For areas not covered by the mobile LiDAR scanning area and the interior of underground buildings, plan a fixed LiDAR scanning route to ensure that the collection area of ​​the collection route overlaps with the collection area of ​​the mobile device. Set up stations for scanning according to the planned route. The distance between each station should be within 30 meters and cover the splicing control points as much as possible. Avoid straight-line layout of the measuring stations and there should be 30% overlap between the two stations. Set up the fixed LiDAR equipment on the cart, fix the cart, and collect data after the equipment is initialized. After the collection is completed, move the cart to the next collection station and repeat the above collection process until all planned scanning areas are covered.

[0260] Portable LiDAR data collection: For small spaces that cannot be covered by fixed LiDAR scans, portable LiDAR devices with SLAM functions are used for data collection. The data collection should overlap with the fixed LiDAR scans, and the data collection should cover at least one stitching control point.

[0261] High-density electrical method acquisition: set up high-density electrical method survey lines with 1-meter electrode spacing on the ground according to the rough range of the underground buildings, and set up high-density electrical method survey lines with 2-meter electrode spacing according to the grid inside the rough range of the underground buildings, and use high-density electrical method instruments to detect and collect underground buildings;

[0262] Property survey: Conduct on-site surveys on the use of rooms in underground buildings, the location of civil air defense facilities, the zoning of civil air defense functions, and the setting of service facilities, and record the survey data on the drawings.

[0263] like Figure 2 As shown, a method for joint mapping of underground buildings, the specific steps of data processing are:

[0264] Mobile scanning data processing: First, the positioning and attitude system data is solved according to the data collected by a single movement, and then combined with the original data to generate a single-movement point cloud data result. Then, the mobile laser scanning measurement data result is preliminarily corrected through a single-movement global navigation satellite system differential calculation, and then the point cloud is denoised, with a noise removal rate of more than 95%, to obtain a single-movement scanning point cloud data. Then, the mobile scanning point cloud data collected multiple times and the ground control data and underground control data are transformed into coordinates, and the fusion optimization adjustment is performed. When the point cloud accuracy after adjustment meets the acceptance accuracy requirements, the optimization adjustment is stopped to obtain the mobile scanning point cloud data of underground buildings;

[0265] Fixed scanning data processing: convert the format of the data exported from the fixed scanner, use filtering algorithms and human-computer interaction to denoise the clouds of each site, and then automatically stitch them. If automatic stitching is not possible, use manual stitching. Mark the positions of the control points in the preliminary stitched point cloud and import the corresponding control point 3D coordinate data. Perform coordinate transformation and plane and elevation accuracy optimization on the point cloud data to obtain the final underground building fixed scanning point cloud data after meeting the accuracy requirements of the acceptance.

[0266] Portable scanning data processing: format conversion and denoising of portable scanning data are performed, and coordinate transformation and data splicing of portable scanning data are performed using underground control point and splicing control point data. When the point cloud accuracy after splicing adjustment meets the acceptance accuracy requirements, the optimization adjustment is stopped to obtain the portable scanning point cloud data of underground buildings;

[0267] Point cloud data fusion: Different types of underground building point cloud data are color-separated, and corresponding common control points are selected in the overlapping area of ​​the two point cloud data and set as common points. Based on the 3D coordinate values ​​of the known common points, different types of underground building point cloud data are spliced ​​and fused, optimized and smoothed, and then classified and processed to remove point clouds of vehicles, mechanical equipment, etc., to obtain the final complete point cloud data of underground buildings;

[0268] Data verification: The survey line data collected by the high-density electrical method are preprocessed and integrated with interpretation and analysis to obtain the spatial scope of underground buildings, and the spatial scope of underground buildings obtained by the high-density electrical method is compared with the complete point cloud data of underground buildings. If the spatial scope of the underground building to be explored completely includes the point cloud scope of the underground building, the point cloud data of the underground building can be used. If it cannot be completely included, the collected point cloud data and ground penetrating radar data are checked item by item, and data collection is carried out again after the reasons are found until the spatial scope of the underground building to be explored can completely include the point cloud scope of the underground building.

[0269] like Figure 3 As shown, a method for joint mapping of underground buildings, the specific steps of fixed scanning data processing are:

[0270] Format conversion: export the original scan data from the fixed scanner and convert the format of the original scan data;

[0271] Point cloud denoising: Through calculation, the clouds of each station of fixed scanning are automatically filtered to remove abnormal values ​​in the point cloud. Then, human-computer interaction is used to remove abnormal noise points in the scanned point cloud caused by mirror reflection and other reasons.

[0272] The automatic filtering formula is:

[0273]

[0274] In the formula For p i The unit normal vector, p j The unit normal vector is d represents the distance in the normal direction, is the newly generated point after filtering, w c refers to the weight of the spatial domain, which is is a function of the standard deviation, w s is the feature domain weight, is a function of the standard deviation, where is the distance between the sampling points p i The impact factor, Represents the distance vector of the sampling point at p i The projection pair p on the normal vector i Impact factor;

[0275] Preliminary stitching of point cloud: select common feature points of the two-station scanning data through feature point selection algorithm, establish connecting quadrilaterals, perform preliminary automatic stitching of fixed scanning point cloud data, and manually select common points for some scanning data that cannot be stitched, and perform manual preliminary stitching;

[0276] The implementation steps of the feature point selection algorithm are: perform curvature screening on the point cloud data according to the following formula:

[0277]

[0278] in represents the mean curvature of the point cloud, σ represents the standard deviation of the mean curvature of the point cloud, and k i Represents the average curvature of all points in the point cloud set;

[0279] Setting Thresholds p represents the proportional coefficient. Points with average curvature greater than the threshold are taken as reserved points. Convex and concave points are judged on all reserved points to determine the point cloud features.

[0280] The shape index calculation formula is:

[0281] Among them, k1, k2 are the principal curvatures, S(p)∈[0,1]

[0282] For any point p i The n neighboring points of p are obtained by extreme value determination. i Find the maximum and minimum values ​​of the fitted plane and take the point as the common feature point;

[0283] The maximum value determination formula is:

[0284] S(p i )=max(S(p i1 ),S(p i2 ),...,S(p in ))

[0285] The minimum value determination formula is:

[0286] S(p i )=min(S(p i1 ),S(p i2 ),...,S(p in ))

[0287] Control data setting: Import the coordinates of underground control points in the fixed scan point cloud data after preliminary splicing, and mark the underground control points one by one at the corresponding positions of the point cloud;

[0288] Point cloud precision stitching: The point cloud data is transformed into coordinates and continuously optimized by splicing the plane coordinate data of the control points. The plane accuracy of the point cloud data after precision stitching is evaluated. When it is determined that the plane accuracy requirements for acceptance are met, the optimization precision stitching is stopped. When it is determined that the plane accuracy requirements for acceptance are not met, continuous precision stitching is performed. Then, the elevation data of the underground control points is used to continuously optimize the elevation accuracy of the point cloud data. The optimization is stopped when the elevation accuracy of the point cloud meets the acceptance elevation accuracy requirements, and the final fixed scanning point cloud data of underground buildings is obtained.

[0289] like Figure 4 As shown, a method for joint mapping of underground buildings, the specific steps of point cloud data fusion are:

[0290] Common point selection and setting: separate the color of underground building mobile scanning point cloud, underground building fixed scanning point cloud, and underground building portable scanning point cloud, and then use the control point data to select the corresponding common control points in the overlapping area of ​​the two point cloud data, and set them as common points;

[0291] Point cloud adjustment fusion: Based on the coordinate values ​​of the set common points, various point cloud data of underground buildings are quickly and automatically spliced ​​and fused, and then optimized adjustment and smooth sampling are performed. When the plane and elevation accuracy of the processed point cloud data meet the acceptance accuracy requirements, the optimization adjustment and smooth sampling are stopped to obtain complete point cloud data of underground buildings;

[0292] Point cloud classification: First, after setting the samples of vehicles and mechanical settings, remove the vehicles and mechanical equipment in the point cloud through deep learning operations, and then use the point cloud classification algorithm to classify the final complete point cloud data of underground buildings to separate the ground point cloud and non-ground point cloud;

[0293] The point cloud classification algorithm formula is as follows:

[0294]

[0295] Where: H′ represents the average elevation of the center point O, H1 represents the elevation of the point at an azimuth of 30 degrees to the upper right of the center point O, H2 represents the elevation of the point at an azimuth of 90 degrees to the right of the center point O, H3 represents the elevation of the point at an azimuth of 150 degrees to the lower right of the center point O, H4 represents the elevation of the point at an azimuth of 210 degrees to the lower left of the center point O, H5 represents the elevation of the point at an azimuth of 270 degrees to the left of the center point O, H6 represents the elevation of the point at an azimuth of 330 degrees to the upper left of the center point O, and H x Indicates the elevation value of the center point X;

[0296] A calculation threshold ω is set for H′. When it is determined that H′<threshold ω, it is determined to be a ground point. When it is determined that H′≥threshold ω, it is determined to be a non-ground point and is eliminated. The point cloud data is traversed in sequence until all point clouds are added to the ground point set, and then the ground point cloud extraction is checked. If there is a missing ground point cloud, the threshold ω is readjusted and recalculated until the complete ground point cloud data is obtained and the judgment is ended. The threshold ω can be set and extracted in layers according to the average elevation of the underground control points of each layer. The remaining point cloud data is the non-ground point data.

[0297] like Figure 5 As shown, a method for joint mapping of underground buildings, the specific steps of data extraction are:

[0298] Current data extraction: For the final point cloud data of underground buildings, use the point cloud deep learning related algorithm to extract the building outline and parking space, then convert the format of the extracted data and process the topological relationship. After extracting and entering the attribute data and drawing the corresponding plane map, a real-life 3D model of the underground building is generated and stored in the current database;

[0299] License data extraction: For license data in paper archives, the file vectorization method is used to produce license graphic data in a specified format. For license data in digital form, license data in a specified format is produced through data screening, standardization processing, and coordinate transformation. The two types of license data are converted in coordinates and then superimposed to eliminate redundant data. The graphic data of building walls, functional area boundaries, and planned parking spaces are then extracted from the license data, and the topological relationship of the graphic data is processed. The relevant attribute data is entered according to the original license data, and then a connection is established between the graphic data and the attribute data to generate a three-dimensional model of the licensed underground building. According to the issuance requirements and permitted range of the license data, the permitted range space of the license data is set and generated, and the license data after the permitted range space is generated is stored in the specified standard license database.

[0300] like Figure 6 As shown, a method for joint mapping of underground buildings, the specific steps of extracting the current status data are:

[0301] Building outline extraction: Extract non-ground point cloud data, segment the non-ground point cloud according to the set segmentation interval, and then automatically extract the wall line. After comparing and fusing the extracted wall lines, draw the underground building plan;

[0302] Parking space extraction: Extract ground point cloud data, and use deep learning-related algorithms to automatically extract parking space information from ground point clouds according to the process of rough extraction of parking space lines, density denoising, intensity extraction, local clustering, clear height verification, and parking space standardization;

[0303] Data format conversion: determine whether the extracted building outlines and parking space vector graphics meet the format requirements. If they do not meet the format requirements, the extracted building outlines and parking space vector graphics are converted into a format that meets the requirements;

[0304] Topological relationship processing: topological processing is performed on the underground building outline, and then topological processing is performed on the parking space vector graphics and the processed building outline;

[0305] Attribute extraction and entry: for the internal space range of underground buildings, automatically extract the internal height information of underground buildings, and calculate the average ground elevation and floor height information of underground buildings at different levels based on the ground point cloud information of underground buildings at different levels. Then, enter the building outline data into the investigated building room usage, room height, location of civil air defense facilities, civil air defense functional zoning, functional facility type, functional facility area, service facility location, number of service facilities, service facility area, elevation of each floor of underground buildings, floor height of each floor of underground buildings, building area, number of parking spaces and other attribute information, and statistically calculate the total area of ​​underground buildings and the area of ​​civil air defense functional zoning;

[0306] Plan drawing: First, draw the plan drawings of each floor of underground buildings according to the planning acceptance standards, and draw the plan drawings of civil air defense functional zoning according to the civil air defense acceptance standards and the civil air defense functional divisions;

[0307] Real-life model production: Based on the generated building's outer contours, internal space range, and extracted height information, a real-life 3D model of the underground building is generated and stored in the current status database.

[0308] like Fig.12 As shown, a method for joint mapping of underground buildings, the specific steps of extracting the building outline are:

[0309] Non-ground point extraction: extract non-ground point cloud data from the classified final underground building complete point cloud data;

[0310] Point cloud segmentation: According to the elevation interval of underground buildings, set the point cloud height cutting interval to 20 cm, and cut the corresponding non-ground point cloud data;

[0311] Wall line extraction: Automatically extract building wall lines from the cut point cloud. The interior space of the underground building is drawn according to the extracted wall lines, and the outer contour of the underground building is drawn according to the outermost space range of the extracted underground building plus the planned wall thickness;

[0312] Contrast fusion: Compare the outer contour and internal space range of the underground building extracted on the same layer. If the difference is less than or equal to 10 cm, perform wall line fusion to obtain the average contour range, and finally obtain the outer and internal building contours of the underground building. If the difference is greater than 10 cm, retain the outer and internal building contours of the corresponding extracted underground building;

[0313] Production of underground building plan: Draw the plan of underground buildings at different levels according to the outer and inner building outlines of the fused underground buildings;

[0314] like Fig.13 As shown, a method for joint mapping of underground buildings, the specific steps of extracting parking spaces are:

[0315] Ground point extraction: extract ground point cloud data from the classified final underground building complete point cloud data;

[0316] Rough extraction of parking spaces: Calculate the grayscale difference between adjacent point clouds in the ground point cloud, set the corresponding difference threshold, extract the point clouds with adjacent point differences greater than the threshold, and generate a rough point cloud of parking space lines on the ground;

[0317] Density denoising: Set a dynamic grid for the rough point cloud of the parking space line, set the minimum value of the point cloud density within the dynamic grid, and remove the isolated point clouds in the distance in the rough point cloud of the parking space line;

[0318] Intensity extraction: Set the point cloud intensity range value for the rough point cloud of the parking space line after density denoising, and output the point cloud data within the corresponding intensity value range;

[0319] Local clustering: cluster analysis is performed using the convex hull algorithm, and the distance between the convex hull point and the adjacent ground point and the lower limit of the point cloud intensity difference are set to form the final parking space marking point cloud data;

[0320] Clear height verification: The parking space marking point cloud data is integrated with the final complete point cloud data of the underground building, and the height value within the parking space marking is extracted and verified. For ordinary parking spaces, the minimum height is set to 2.2 meters, and for large vehicle parking spaces, the minimum height is set to 2.5 meters. For barrier-free parking spaces, the minimum height is set to 2.4 meters, and for electric vehicle charging spaces, the minimum height is set to 2.5 meters. For ordinary parking spaces in commercial buildings, public buildings, and high-end residential buildings, the minimum height is set to 3.0 meters, and for large vehicle parking spaces, the minimum height is set to 3.5 meters. The minimum height values ​​within the parking space markings are analyzed one by one, and the parking space marking point clouds with minimum height values ​​less than the minimum height value of the corresponding type of parking spaces are eliminated. The remaining parking space marking point clouds are the parking space marking point clouds that meet the standards;

[0321] Parking space standardization: Use deep learning methods to establish standard parking space samples for small cars, light cars, medium-sized cars, large buses, and large trucks based on point cloud data. Check the parking space marking point clouds that meet the standards one by one, automatically generate vector graphics of the parking space, and record the length, width, parking space type, and minimum clear height information of the parking space.

[0322] like Figure 7 As shown, a method for joint mapping of underground buildings, the specific steps of extracting the license data are:

[0323] Format conversion: For license data in paper archives, data scanning is first performed, and then data correction and vectorization are performed to convert it into license graphic data in the specified format. For license data in digital form, data screening is performed to eliminate redundant data, and then standardized processing is performed to convert it into license data in the specified format;

[0324] Coordinate transformation: The two types of license data mentioned above are judged in plane coordinate system and elevation system respectively, and the license data that does not meet the standards are transformed in plane coordinate system and elevation system respectively to form license data whose plane coordinate system and elevation system both meet the coordinate system requirements. Then, the two types of license data are spatially superimposed to eliminate redundant data and generate unique license data that meets the standards;

[0325] Building wall line extraction: filter the permitted data that meet the standards by layer, line shape, color, and width, and extract the building wall line according to the data modeling requirements;

[0326] Functional area extraction: According to the content of the license data, the functional zoning boundaries and planned parking spaces of the civil air defense project are selected. According to the types of different functional zoning, the functional zoning range of the civil air defense project is extracted. According to the planned parking space situation, the planned parking spaces are extracted;

[0327] Topological relationship processing: the extracted building wall lines and civil air defense functional partition lines are processed for their own topology and the intersection, fine seams, small areas, and node optimization between them;

[0328] Attribute extraction and entry: according to the original license data, the attribute information of the underground building floor height, the floor elevation of each floor of the underground building, the location of supporting facilities, the nature of the civil air defense function room, the area of ​​the civil air defense function room, the area of ​​the civil air defense function partition, the name of the civil air defense function partition, the level of civil air defense facilities, etc. are entered, and the integrity and logic of the relevant attribute data are checked and modified, and then the connection between the graphic data and the attribute data is established;

[0329] Permit model generation: Generate a 3D model of the permitted underground building based on the building wall line, civil air defense functional zoning and plot range, combined with the extracted attribute data;

[0330] Allowable space setting: According to the standards that the allowable range of setback spacing is 1.5 meters, the allowable range of underground building spacing is 1.5 meters; the allowable range of underground building size is ±1.5 meters, and the allowable range of underground building depth is ±1 meter, the allowable range space of the permitted underground building three-dimensional model is set and generated to form the final permitted underground building three-dimensional model data, which is stored in the designated standard permit database.

[0331] like Figure 8 As shown, a method for joint surveying and mapping of underground buildings, the specific steps of the verification and comparison are:

[0332] Data retrieval: retrieve the real-life 3D model of underground buildings from the current status database, and retrieve the final 3D model data of permitted underground buildings from the standard permit database;

[0333] Verification and comparison: spatially superimpose the real-life 3D model of the underground building and the 3D model of the permitted underground building, first perform a planning completion check, store the underground building data that meets the planning completion comparison requirements in the intermediate satisfying database, mark the problem type and problem description for the underground building data that does not meet the planning completion comparison requirements, and store them in the intermediate unsatisfactory database, then perform a civil air defense project judgment on the 3D model data of the permitted underground building, if there is a civil air defense project, perform a civil air defense completion check on the real-life 3D model data of the underground building and the final 3D model data of the permitted underground building, store the real-life 3D model data of the underground building that meets the civil air defense completion comparison requirements in the satisfying database, and mark the problem type and problem description for the real-life 3D model data of the underground building that does not meet the civil air defense completion comparison requirements and store them in the unsatisfactory database; if there is no civil air defense project, store the data in the intermediate satisfying database in the satisfying database, and store the data in the intermediate unsatisfactory database in the unsatisfactory database;

[0334] Feedback results: In the database that does not meet the requirements, the real-life 3D model data of underground buildings is extracted. According to the problem type, the problem model is highlighted in different colors in the real-life 3D model of the underground building, and the corresponding problem content is marked to assist relevant personnel in making judgments and corrections. According to the problem type, the problem report and problem model display diagram are automatically generated according to the planned completion and civil air defense completion types, and a modification opinion report is generated.

[0335] like Fig. 9 As shown, a method for joint mapping of underground buildings, the specific steps of the verification and comparison are:

[0336] Completion verification of planning: the real-life 3D model data of underground buildings are compared with the final permitted 3D model data of underground buildings in terms of allowed space, area, parking space, function and facilities. All the real-life 3D model data of underground buildings that pass the comparison are stored in the satisfaction database. Corresponding fields are established in the real-life 3D model data of underground buildings that fail any of the comparisons, and the corresponding problems are marked and stored in the problem database.

[0337] Civil defense facility determination: Determine whether there are civil defense facilities in the final three-dimensional model data of the permitted underground building. If there are civil defense facilities, conduct a subsequent civil defense completion check. If there are civil defense facilities, store the data in the intermediate satisfying database into the satisfying database, and store the data in the intermediate unsatisfying database into the unsatisfying database.

[0338] Completion comparison of civil air defense: the real-life 3D model data of underground buildings will be compared with the final permitted 3D model data of underground buildings in terms of allowed space, area, function, facilities, and other comparisons. All the real-life 3D model data of underground buildings that pass the comparison will be stored in the satisfaction database. Corresponding fields will be established in the real-life 3D model data of underground buildings that fail any of the comparisons, and the corresponding problems will be marked and stored in the problem database.

[0339] like Fig.10 As shown, a method for joint surveying and mapping of underground buildings, the specific steps of the planning completion verification are:

[0340] Allowable space comparison: compare the real-life 3D model data of underground buildings with the final permitted 3D model data of underground buildings for allowable space. If the real-life 3D model of the underground building does not exceed the allowable range space of the final permitted 3D model data of underground buildings in terms of plane position, height and elevation, then conduct a subsequent comparison. If it exceeds the allowable range space of any type, then establish the corresponding problem type and problem description field in the real-life 3D model data of the underground building, record the corresponding problem type and the size of the exceeded space, and then conduct a subsequent comparison.

[0341] Area comparison: compare the real-life 3D model data of underground buildings that have undergone permitted space comparison with the final permitted 3D model data of underground buildings, set area comparison parameters, determine the conformity of the planned area based on the area comparison parameters, and directly conduct subsequent comparison on the real-life 3D model data of underground buildings that meet the judgment results. For the real-life 3D model data of underground buildings that do not meet the judgment results, record the problem type and area inconsistency in the real-life 3D model data, and then conduct subsequent comparison;

[0342] Compare the area of ​​the real 3D model data of underground buildings with the final 3D model data of the permitted underground buildings;

[0343] If 0≤|S 实 -S 许 |<f means that the actual measured building area of ​​the underground building is determined to be consistent with the permitted building area of ​​the underground building;

[0344] If f≤|S 实 -S 许 |That is, it is determined that the actual measured building area of ​​the underground building does not conform to the permitted building area of ​​the underground building;

[0345] Where S 实 is the actual measured building area of ​​underground buildings, S 许 is the permitted building area of ​​underground buildings, f is the area comparison parameter, and if S 许 >300, then 0≤f≤0.01*S 许 , if S 许 ≤300, then f=3;

[0346] Parking space comparison: compare the real-life 3D model data of the underground building that has undergone area comparison with the final 3D model data of the permitted underground building, set the parking space comparison parameter β, determine the compliance of the parking space according to the parking space comparison parameter, and directly perform subsequent comparison on the real-life 3D model data of the underground building that meets the judgment result, and perform subsequent comparison after recording the problem type and parking space difference size in the real-life 3D model data of the underground building that does not meet the judgment result;

[0347] Compare the parking spaces between the real-life 3D model data of underground buildings and the final 3D model data of permitted underground buildings;

[0348] If 0≤β, it is determined that the actual measured building area of ​​the underground building is consistent with the permitted building area of ​​the underground building;

[0349] If β<0, it is determined that the actual measured building area of ​​the underground building does not conform to the permitted building area of ​​the underground building;

[0350] Where β = C 实 -C 许 , β is the parking space comparison parameter, C 实 is the number of parking spaces actually measured in underground buildings, C 许 The number of parking spaces permitted for underground buildings;

[0351] Functional comparison: perform a functional comparison between the real-life 3D model data of the underground building that has undergone parking space comparison and the final 3D model data of the permitted underground building. If the supporting facility function type in the real-life 3D model of the underground building is consistent with the supporting facility function type in the final 3D model data of the permitted underground building, a subsequent comparison is performed. If the supporting facility function type in the real-life 3D model of the underground building is inconsistent with the supporting facility function type in the final 3D model data of the permitted underground building, a subsequent comparison is performed after recording the problem type and the inconsistent content of the supporting facility function type in the real-life 3D model data of the underground building;

[0352] Facility comparison: perform facility comparison on the real-life 3D model data of underground buildings that have undergone functional comparison with the final 3D model data of permitted underground buildings, set facility comparison parameters, determine the conformity of the facilities based on the facility comparison parameters, store the real-life 3D model data of underground buildings that meet the determination results in the intermediate satisfaction database, and store the real-life 3D model data of underground buildings that do not meet the determination results in the intermediate non-satisfaction database after recording the problem type and facility difference;

[0353] Conduct facility comparison between the actual 3D model data of underground buildings and the final permitted 3D model data of underground buildings;

[0354] like That is, it is determined that the facility area of ​​category n in the underground building is consistent with the permitted facility area of ​​category n in the underground building permit;

[0355] like That is, it is judged that the facility area of ​​category n in the underground building does not conform to the permitted facility area of ​​category n in the underground building permit;

[0356] in is the facility comparison parameter, V n实 is the actual measured area of ​​n types of facilities in underground buildings, V n许 is the permitted area of ​​type n facilities in underground buildings, and if V n许 >300, then If V n许 ≤300, then

[0357] like Fig.11 As shown, a method for joint mapping of underground buildings, the specific steps of the civil air defense completion comparison are:

[0358] Civil defense space comparison: compare the civil defense space range in the real-life 3D model of the underground building with the civil defense space range in the final permitted 3D model of the underground building. If the size, height, and elevation of the civil defense space range in the real-life 3D model of the underground building do not exceed the civil defense space range in the final permitted 3D model of the underground building, conduct a subsequent comparison. If it exceeds the civil defense space range in the final permitted 3D model of the underground building, record the corresponding problem type and the size of the exceeded space before conducting a subsequent comparison.

[0359] Civil defense area comparison: compare the civil defense area in the real-life 3D model of the underground building with the civil defense area in the final permitted 3D model of the underground building, set the civil defense area comparison parameter θ, determine the conformity of the civil defense area in the underground building according to the civil defense area comparison parameter, and directly perform subsequent comparison on the real-life 3D model data of the underground building whose judgment result conforms, and perform subsequent comparison after recording the problem type and civil defense area difference in the real-life 3D model data of the underground building whose judgment result does not conform;

[0360] Compare the civil air defense area between the real-life 3D model data of underground buildings and the final 3D model data of permitted underground buildings;

[0361] If 0≤θ, it is determined that the actual measured civil air defense functional area of ​​the underground building is consistent with the civil air defense functional area of ​​the underground building construction permit;

[0362] If θ<0, it is determined that the actual measured civil air defense functional area of ​​the underground building at the location does not match the civil air defense functional area of ​​the underground building construction permit;

[0363] where θ = F 实 -F 许 , θ is the air defense area comparison parameter, F 实 F is the measured civil air defense functional area of ​​underground buildings. 许 Permitting civil air defense functional areas for underground buildings;

[0364] Civil defense function comparison: compare the real-life 3D model data of the underground building that has undergone civil defense area comparison with the final licensed 3D model data of the underground building for civil defense function. If all types of the structural part, the external passage part, the mouth part, the auxiliary part, the use part, the shielding part, and the shaft part in the real-life 3D model of the underground building are consistent with all types of the structural part, the external passage part, the mouth part, the auxiliary part, the use part, the shielding part, and the shaft part in the final licensed 3D model data of the underground building, then conduct a subsequent comparison. If any type of the structural part, the external passage part, the mouth part, the auxiliary part, the use part, the shielding part, and the shaft part in the real-life 3D model of the underground building is inconsistent with any type of the structural part, the external passage part, the mouth part, the auxiliary part, the use part, the shielding part, and the shaft part in the final licensed 3D model data of the underground building, then conduct a subsequent comparison after recording the problem type and the inconsistent content of the civil defense facility function type in the real-life 3D model data of the underground building;

[0365] Comparison of civil air defense facilities: the real-life 3D model data of underground buildings that have undergone civil air defense function comparison shall be compared with the final 3D model data of permitted underground buildings for civil air defense facilities. If the areas of the structural part, the external passage part, the mouth part, the auxiliary part, the use part, the shielding part, and the shaft part in the real-life 3D model of the underground building are consistent with the areas of the structural part, the external passage part, the mouth part, the auxiliary part, the use part, the shielding part, and the shaft part in the final 3D model data of permitted underground buildings, then subsequent comparison shall be carried out. If any area of ​​the structural part, the external passage part, the mouth part, the auxiliary part, the use part, the shielding part, and the shaft part in the real-life 3D model of the underground building is inconsistent with the areas of the structural part, the external passage part, the mouth part, the auxiliary part, the use part, the shielding part, and the shaft part in the final 3D model data of permitted underground buildings, then subsequent comparison shall be carried out after recording the problem type and the difference in the area of ​​civil air defense facilities in the real-life 3D model data of the underground building;

[0366] Other air defense comparisons: compare the real-life 3D model data of the underground buildings that have undergone civil defense facility comparison with the final permitted 3D model data of the underground buildings in terms of civil defense protection level, defense category, usable area, and building area. If other air defense facilities in the real-life 3D model of the underground building are consistent with the civil defense protection level, defense category, usable area, and building area of ​​the final permitted 3D model data of the underground building, the real-life 3D model data of the underground building shall be stored in the satisfied database. If the civil defense protection level, defense category, usable area, and building area in the real-life 3D model of the underground building are inconsistent with any of the civil defense protection level, defense category, usable area, and building area of ​​the final permitted 3D model data of the underground building, the problem type and inconsistency shall be recorded in the real-life 3D model data of the underground building and stored in the unsatisfied database.

[0367] The foregoing method descriptions and flow charts are provided only as illustrative examples and are not intended to require or imply that the above operations or steps of various aspects must be performed in the order given. As will be appreciated by those skilled in the art, the order of the boxes in the foregoing aspects may be performed in any order. Words such as "thereafter," "then," "next," and the like are not intended to limit the order of operations or steps; these words are only used to guide the reader through the description of the method. In addition, any singular reference to a claim element, for example, the use of the article "a," "an," or "the" is not to be construed as limiting the element to the singular.

[0368] The various illustrative logic blocks, modules, circuits, and algorithm steps described in conjunction with the aspects disclosed herein can all be implemented as electronic hardware, computer software, or a combination thereof. In order to clearly represent the interchangeability between hardware and software, the various illustrative components, blocks, modules, circuits, and steps are generally described above around their functions. Whether such functions are implemented as hardware or software depends on the specific application and the design constraints imposed on the entire method. A skilled person can implement the described functions in an alternative manner for each specific application, but such implementation decisions should not be interpreted as causing a departure from the scope of protection of the present invention.

[0369] The method in the embodiment of the present invention utilizes mobile, fixed, portable and other types of laser radar equipment to collect data, quickly and accurately measure underground buildings, utilizes point cloud processing technology and multi-source data fusion technology to generate underground building point clouds, and automatically extracts underground building point cloud data to form various forms of measurement results data, and automatically constructs a real-life three-dimensional model of the underground building. By comparing with the licensed data, the joint surveying and mapping of underground buildings and planning acceptance and civil air defense acceptance can be quickly and accurately achieved, and planning acceptance and civil air defense acceptance reports can be generated, greatly improving the work efficiency of personnel, reducing the workload of personnel and the influence of human subjective judgment. The method can also utilize the real-life three-dimensional model to display non-conforming problems, and provide modification suggestions to assist operators in quickly locating problems and facilitate subsequent corrections to the problems.

[0370] The foregoing description of the disclosed aspects is provided to enable any person skilled in the art to implement or use the present invention. Various modifications to these aspects will be apparent to those skilled in the art, and the general principles defined herein may also be applied to other embodiments without departing from the spirit and scope of the present invention. Therefore, the present invention is not intended to be limited to the aspects provided herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for joint mapping of underground buildings, characterized in that The method includes the following steps: data measurement and investigation, data processing, data extraction, verification and comparison, and output of results, among which: Data measurement and investigation: Conduct on-site surveys of the area to be measured to check the distribution, size, and approximate location of underground buildings, and collect relevant information on underground buildings in the corresponding area. According to the survey results, choose to set up ground control points at the ground, entrances and exits, and vents of underground buildings, set up underground control points inside underground buildings, conduct control measurements on the ground control points and underground control points respectively, and merge the ground control points and underground control points for network adjustment. Use mobile laser radar to scan and collect the inside of underground buildings, and then use fixed laser radar to scan and collect places inside underground buildings that mobile collection equipment cannot reach. Use portable laser radar to scan and collect small spaces and places that are inconvenient for fixed laser radar scanning. Finally, use high-density electrical methods to explore and collect underground buildings, and investigate and record the relevant attributes of underground buildings; Data processing: perform data solution, format conversion, denoising, data splicing, coordinate conversion, and optimization adjustment on the mobile laser radar scanning point cloud, fixed laser radar scanning point cloud, and portable laser radar scanning point cloud, respectively, to obtain the underground building mobile scanning point cloud data, underground building fixed scanning point cloud data, and underground building portable scanning point cloud data, and then perform data fusion processing on various types of underground building point cloud data to obtain the final underground building complete point cloud data, and then use the ground penetrating radar to collect data and process it to verify and correct the point cloud data; Data extraction: extract the current data of the final complete point cloud data of underground buildings, generate a real-life 3D model of the underground buildings and store it in the current database, process the paper and digitized permit data, extract the corresponding data and attributes, establish a permit 3D model and set the allowed space before storing it in the designated standard permit database; Verification and comparison: extract the real 3D model of the underground building and the 3D model of the permitted underground building from the corresponding database respectively, import the above two models into the comparison module for data comparison, and output the corresponding data according to the comparison results; Output results: Extract underground building data from the database, and generate a planning acceptance report for underground buildings, a comparison table of underground building heights, a comparison table of underground building economic indicators, a general plan for underground building planning acceptance, and a cross-sectional view of underground buildings in accordance with the requirements of the planning acceptance report; generate a civil air defense project area surveying and mapping report, a general plan of civil air defense projects, a civil air defense functional zoning area map, an area map of each functional part of the civil air defense project, a civil air defense project area calculation table, and a civil air defense project area comparison table in accordance with the requirements of the civil air defense completion report.

2. A method for joint mapping of underground buildings according to claim 1, characterized in that: The specific steps of the data processing are: Mobile scanning data processing: First, the positioning and attitude system data is solved according to the data collected by a single movement, and then combined with the original data to generate a single-movement point cloud data result. Then, the mobile laser scanning measurement data result is preliminarily corrected through a single-movement global navigation satellite system differential calculation, and then the point cloud is denoised, with a noise removal rate of more than 95%, to obtain a single-movement scanning point cloud data. Then, the mobile scanning point cloud data collected multiple times and the ground control data and underground control data are transformed into coordinates, and the fusion optimization adjustment is performed. When the point cloud accuracy after adjustment meets the acceptance accuracy requirements, the optimization adjustment is stopped to obtain the mobile scanning point cloud data of underground buildings; Fixed scanning data processing: convert the format of the data exported from the fixed scanner, use filtering algorithms and human-computer interaction to denoise the clouds of each site, and then automatically stitch them. If automatic stitching is not possible, use manual stitching. Mark the positions of the control points in the preliminary stitched point cloud and import the corresponding control point 3D coordinate data. Perform coordinate transformation and plane and elevation accuracy optimization on the point cloud data to obtain the final underground building fixed scanning point cloud data after meeting the accuracy requirements of the acceptance. Portable scanning data processing: format conversion and denoising of portable scanning data are performed, and coordinate transformation and data splicing of portable scanning data are performed using underground control point and splicing control point data. When the point cloud accuracy after splicing adjustment meets the acceptance accuracy requirements, the optimization adjustment is stopped to obtain the portable scanning point cloud data of underground buildings; Point cloud data fusion: Different types of underground building point cloud data are color-separated, and corresponding common control points are selected in the overlapping area of ​​the two point cloud data and set as common points. Based on the 3D coordinate values ​​of the known common points, different types of underground building point cloud data are spliced ​​and fused, optimized and smoothed, and then classified and processed to remove point clouds of vehicles, mechanical equipment, etc., to obtain the final complete point cloud data of underground buildings; Data verification: The survey line data collected by the high-density electrical method are preprocessed and integrated with interpretation and analysis to obtain the spatial scope of underground buildings, and the spatial scope of underground buildings obtained by the high-density electrical method is compared with the complete point cloud data of underground buildings. If the spatial scope of the underground building to be explored completely includes the point cloud scope of the underground building, the point cloud data of the underground building can be used. If it cannot be completely included, the collected point cloud data and ground penetrating radar data are checked item by item, and data collection is carried out again after the reasons are found until the spatial scope of the underground building to be explored can completely include the point cloud scope of the underground building.

3. A method for joint mapping of underground buildings according to claim 2, characterized in that: The specific steps of the fixed scan data processing are: Format conversion: export the original scan data from the fixed scanner and convert the format of the original scan data; Point cloud denoising: Through calculation, the clouds of each station of fixed scanning are automatically filtered to remove abnormal values ​​in the point cloud. Then, human-computer interaction is used to remove abnormal noise points in the scanned point cloud caused by mirror reflection and other reasons. The automatic filtering formula is: In the formula For p i The unit normal vector, p j The unit normal vector is d represents the distance in the normal direction, is the newly generated point after filtering, w c refers to the weight of the spatial domain, which is is a function of the standard deviation, w s is the feature domain weight, is a function of the standard deviation, where is the distance between the sampling points p i The impact factor, Represents the distance vector of the sampling point at p i The projection pair p on the normal vector i Impact factor; Preliminary stitching of point cloud: select common feature points of the two-station scanning data through feature point selection algorithm, establish connecting quadrilaterals, perform preliminary automatic stitching of fixed scanning point cloud data, and manually select common points for some scanning data that cannot be stitched, and perform manual preliminary stitching; The implementation steps of the feature point selection algorithm are: perform curvature screening on the point cloud data according to the following formula: in represents the mean curvature of the point cloud, σ represents the standard deviation of the mean curvature of the point cloud, and k i Represents the average curvature of all points in the point cloud set; Setting Thresholds p represents the proportional coefficient. Points with average curvature greater than the threshold are taken as reserved points. Convex and concave points are judged on all reserved points to determine the point cloud features. in The shape index calculation formula is: Among them, k1, k2 are the principal curvatures, S(p)∈[0,1] For any point p i The n neighboring points of p are obtained by extreme value determination. i Find the maximum and minimum values ​​of the fitted plane and take the point as the common feature point; The maximum value determination formula is: S(p i )=max(S(p i1 ),S(p i2 ),...,S(p in )) The minimum value determination formula is: S(p i )=min(S(p i1 ),S(p i2 ),...,S(p in )) Control data setting: Import the coordinates of underground control points in the fixed scan point cloud data after preliminary splicing, and mark the underground control points one by one at the corresponding positions of the point cloud; Point cloud precision stitching: The point cloud data is transformed into coordinates and continuously optimized by splicing the plane coordinate data of the control points. The plane accuracy of the point cloud data after precision stitching is evaluated. When it is determined that the plane accuracy requirements for acceptance are met, the optimization precision stitching is stopped. When it is determined that the plane accuracy requirements for acceptance are not met, continuous precision stitching is performed. Then, the elevation data of the underground control points is used to continuously optimize the elevation accuracy of the point cloud data. The optimization is stopped when the elevation accuracy of the point cloud meets the acceptance elevation accuracy requirements, and the final fixed scanning point cloud data of underground buildings is obtained.

4. A method for joint mapping of underground buildings according to claim 2, characterized in that: The specific steps of point cloud data fusion are: Common point selection and setting: separate the color of underground building mobile scanning point cloud, underground building fixed scanning point cloud, and underground building portable scanning point cloud, and then use the control point data to select the corresponding common control points in the overlapping area of ​​the two point cloud data, and set them as common points; Point cloud adjustment fusion: Based on the coordinate values ​​of the set common points, various point cloud data of underground buildings are quickly and automatically spliced ​​and fused, and then optimized adjustment and smooth sampling are performed. When the plane and elevation accuracy of the processed point cloud data meet the acceptance accuracy requirements, the optimization adjustment and smooth sampling are stopped to obtain complete point cloud data of underground buildings; Point cloud classification: First, after setting the samples of vehicles and mechanical settings, remove the vehicles and mechanical equipment in the point cloud through deep learning operations, and then use the point cloud classification algorithm to classify the final complete point cloud data of underground buildings to separate the ground point cloud and non-ground point cloud; The point cloud classification algorithm formula is as follows: Where: H′ represents the average elevation of the center point O, H1 represents the elevation of the point at an azimuth of 30 degrees to the upper right of the center point O, H2 represents the elevation of the point at an azimuth of 90 degrees to the right of the center point O, H3 represents the elevation of the point at an azimuth of 150 degrees to the lower right of the center point O, H4 represents the elevation of the point at an azimuth of 210 degrees to the lower left of the center point O, H5 represents the elevation of the point at an azimuth of 270 degrees to the left of the center point O, H6 represents the elevation of the point at an azimuth of 330 degrees to the upper left of the center point O, and H x Indicates the elevation value of the center point X; A calculation threshold ω is set for H′. When it is determined that H′<threshold ω, it is determined to be a ground point. When it is determined that H′≥threshold ω, it is determined to be a non-ground point and is eliminated. The point cloud data is traversed in sequence until all point clouds are added to the ground point set, and then the ground point cloud extraction is checked. If there is a missing ground point cloud, the threshold ω is readjusted and recalculated until the complete ground point cloud data is obtained and the judgment is ended. The threshold ω can be set and extracted in layers according to the average elevation of the underground control points of each layer. The remaining point cloud data is the non-ground point data.

5. The underground building joint mapping method according to claim 1, characterized in that: The specific steps of data extraction are: Current data extraction: For the final point cloud data of underground buildings, use the point cloud deep learning related algorithm to extract the building outline and parking space, then convert the format of the extracted data and process the topological relationship. After extracting and entering the attribute data and drawing the corresponding plane map, a real-life 3D model of the underground building is generated and stored in the current database; License data extraction: For license data in paper archives, use the archive vectorization method to produce license graphic data in a specified format. For license data in digital form, use data screening, standardization, and coordinate transformation to produce license data in a specified format. The two types of license data are converted into coordinates and then superimposed to eliminate redundant data. The graphic data of building walls, functional area boundaries, and planned parking spaces are then extracted from the license data, and the topological relationship of the graphic data is processed. The relevant attribute data is entered according to the original license data, and then a connection is established between the graphic data and the attribute data to generate a three-dimensional model of the licensed underground building. According to the issuance requirements and permitted range of the license data, the permitted range space of the license data is set and generated, and the license data after the permitted range space is generated is stored in the specified standard license database.

6. A method for joint mapping of underground buildings according to claim 5, characterized in that: The specific steps of extracting the status data are as follows: Building outline extraction: Extract non-ground point cloud data, segment the non-ground point cloud according to the set segmentation interval, and then automatically extract the wall line. After comparing and fusing the extracted wall lines, draw the underground building plan; Building outline extraction is divided into non-ground extraction, point cloud segmentation, wall line extraction, contrast fusion, and underground building floor plan production steps, among which: Non-ground point extraction: extract non-ground point cloud data from the classified final underground building complete point cloud data; Point cloud segmentation: according to the elevation interval of underground buildings, set the point cloud cutting interval β, and cut the corresponding non-ground point cloud data according to the β value; Wall line extraction: Automatically extract building wall lines from the cut point cloud. The interior space of the underground building is drawn according to the extracted wall lines, and the outer contour of the underground building is drawn according to the outermost space range of the extracted underground building plus the planned wall thickness; Contrast fusion: Compare the outer contour and internal space range of the underground building extracted on the same layer. If the difference is less than or equal to the value of θ, perform wall line fusion to obtain the average contour range, and finally obtain the outer and internal building contours of the underground building. If the difference is greater than the value of θ, retain the outer and internal building contours of the corresponding extracted underground building; Production of underground building plan: Draw the plan of underground buildings at different levels according to the outer and inner building outlines of the fused underground buildings; Parking space extraction: Extract ground point cloud data, and use deep learning related algorithms to automatically extract parking space information from ground point clouds according to the process of rough extraction of parking space lines, density denoising, intensity extraction, local clustering, clear height verification, and parking space standardization; parking space extraction is divided into ground point extraction, rough extraction of parking spaces, density denoising, intensity extraction, local clustering, clear height verification, and parking space standardization steps, among which: Ground point extraction: extract ground point cloud data from the classified final underground building complete point cloud data; Rough extraction of parking spaces: Calculate the grayscale difference between adjacent point clouds in the ground point cloud, set the corresponding difference threshold, extract the point clouds with adjacent point differences greater than the threshold, and generate a rough point cloud of parking space lines on the ground; Density denoising: Set a dynamic grid for the rough point cloud of the parking space line, set the minimum value of the point cloud density within the dynamic grid, and remove the isolated point clouds in the distance in the rough point cloud of the parking space line; Intensity extraction: Set the point cloud intensity range value for the rough point cloud of the parking space line after density denoising, and output the point cloud data within the corresponding intensity value range; Local clustering: cluster analysis is performed using the convex hull algorithm, and the distance between the convex hull point and the adjacent ground point and the lower limit of the point cloud intensity difference are set to form the final parking space marking point cloud data; Clear height check: merge the parking space marking point cloud data with the final complete point cloud data of the underground building, extract and check the height value within the parking space marking, analyze the lowest height value within the parking space marking one by one, and remove the parking space marking point cloud with the lowest height value less than the Y value. The remaining parking space marking point cloud is the parking space marking point cloud that meets the standards; Parking space standardization: establish a standard parking space sample, check the parking space marking point cloud that meets the standard one by one, automatically generate parking space vector graphics, and record parking space attribute information; Data format conversion: determine whether the extracted building outlines and parking space vector graphics meet the format requirements. If they do not meet the format requirements, the extracted building outlines and parking space vector graphics are converted into a format that meets the requirements; Topological relationship processing: topological processing is performed on the underground building outline, and then topological processing is performed on the parking space vector graphics and the processed building outline; Attribute extraction and entry: for the internal space range of underground buildings, automatically extract the internal height information of underground buildings, and calculate the average ground elevation and floor height information of underground buildings at different levels based on the ground point cloud information of underground buildings at different levels. Then, enter the building outline data into the investigated building room usage, room height, location of civil air defense facilities, civil air defense functional zoning, functional facility type, functional facility area, service facility location, number of service facilities, service facility area, elevation of each floor of underground buildings, floor height of each floor of underground buildings, building area, number of parking spaces and other attribute information, and statistically calculate the total area of ​​underground buildings and the area of ​​civil air defense functional zoning; Plan drawing: First, draw the plan drawings of each floor of underground buildings according to the planning acceptance standards, and draw the plan drawings of civil air defense functional zoning according to the civil air defense acceptance standards and the civil air defense functional divisions; Real-life model production: Based on the generated building's outer contours, internal space range, and extracted height information, a real-life 3D model of the underground building is generated and stored in the current status database.

7. The underground building joint mapping method according to claim 5, characterized in that: The specific steps of extracting the license data are as follows: Format conversion: For license data in paper archives, data scanning is first performed, and then data correction and vectorization are performed to convert it into license graphic data in the specified format. For license data in digital form, data screening is performed to eliminate redundant data, and then standardized processing is performed to convert it into license data in the specified format; Coordinate transformation: The two types of license data mentioned above are judged in plane coordinate system and elevation system respectively, and the license data that does not meet the standards are transformed in plane coordinate system and elevation system respectively to form license data whose plane coordinate system and elevation system both meet the coordinate system requirements. Then, the two types of license data are spatially superimposed to eliminate redundant data and generate unique license data that meets the standards; Building wall line extraction: filter the permitted data that meet the standards by layer, line shape, color, and width, and extract the building wall line according to the data modeling requirements; Functional area extraction: According to the content of the license data, the functional zoning boundaries and planned parking spaces of the civil air defense project are selected. According to the types of different functional zoning, the functional zoning range of the civil air defense project is extracted. According to the planned parking space situation, the planned parking spaces are extracted; Topological relationship processing: Process the extracted building wall lines and civil air defense functional partition lines for their own topology and their mutual topological relationship; Attribute extraction and entry: according to the original license data, the attribute information of the underground building floor height, the floor elevation of each floor of the underground building, the location of supporting facilities, the nature of the civil air defense function room, the area of ​​the civil air defense function room, the area of ​​the civil air defense function partition, the name of the civil air defense function partition, the level of civil air defense facilities, etc. are entered, and the integrity and logic of the relevant attribute data are checked and modified, and then the connection between the graphic data and the attribute data is established; Permit model generation: Generate a 3D model of the permitted underground building based on the building wall line, civil air defense functional zoning and plot range, combined with the extracted attribute data; Allowable space setting: According to the allowable range of setback spacing, underground building spacing, underground building size, underground building depth and the requirements for permit data issuance, the allowable range space of the permitted underground building three-dimensional model is set and generated to form the final permitted underground building three-dimensional model data, which is stored in the designated standard permit database.

8. The underground building joint mapping method according to claim 1, characterized in that: The specific steps of the verification and comparison are: Data retrieval: retrieve the real-life 3D model of underground buildings from the current status database, and retrieve the final 3D model data of permitted underground buildings from the standard permit database; Verification and comparison: spatially superimpose the real-life 3D model of the underground building and the 3D model of the permitted underground building, first perform a planning completion check, store the underground building data that meets the planning completion comparison requirements in the intermediate satisfying database, mark the problem type and problem description for the underground building data that does not meet the planning completion comparison requirements, and store them in the intermediate unsatisfactory database, then perform a civil air defense project judgment on the 3D model data of the permitted underground building, if there is a civil air defense project, perform a civil air defense completion check on the real-life 3D model data of the underground building and the final 3D model data of the permitted underground building, store the real-life 3D model data of the underground building that meets the civil air defense completion comparison requirements in the satisfying database, and mark the problem type and problem description for the real-life 3D model data of the underground building that does not meet the civil air defense completion comparison requirements and store them in the unsatisfactory database; if there is no civil air defense project, store the data in the intermediate satisfying database in the satisfying database, and store the data in the intermediate unsatisfactory database in the unsatisfactory database; Feedback results: In the database that does not meet the requirements, the real-life 3D model data of underground buildings is extracted. According to the problem type, the problem model is highlighted in different colors in the real-life 3D model of the underground building, and the corresponding problem content is marked to assist relevant personnel in making judgments and corrections. According to the problem type, the problem report and problem model display diagram are automatically generated according to the planned completion and civil air defense completion types, and a modification opinion report is generated.

9. A method for joint mapping of underground buildings according to claim 8, characterized in that: The specific steps of the verification and comparison are: Completion verification of planning: the real-life 3D model data of underground buildings are compared with the final permitted 3D model data of underground buildings in terms of allowed space, area, parking space, function and facilities. All the real-life 3D model data of underground buildings that pass the comparison are stored in the satisfaction database. Corresponding fields are established in the real-life 3D model data of underground buildings that fail any of the comparisons, and the corresponding problems are marked and stored in the problem database. Civil defense facility determination: Determine whether there are civil defense facilities in the final three-dimensional model data of the permitted underground building. If there are civil defense facilities, conduct a subsequent civil defense completion check. If there are civil defense facilities, store the data in the intermediate satisfying database into the satisfying database, and store the data in the intermediate unsatisfying database into the unsatisfying database. Completion comparison of civil air defense: the real-life 3D model data of underground buildings will be compared with the final permitted 3D model data of underground buildings in terms of allowed space, area, function, facilities, and other comparisons. All the real-life 3D model data of underground buildings that pass the comparison will be stored in the satisfaction database. Corresponding fields will be established in the real-life 3D model data of underground buildings that fail any of the comparisons, and the corresponding problems will be marked and stored in the problem database.

10. The underground building joint mapping method according to claim 9, characterized in that: The specific steps of the planning completion check are as follows: Allowable space comparison: compare the real-life 3D model data of underground buildings with the final permitted 3D model data of underground buildings for allowable space. If the real-life 3D model of the underground building does not exceed the allowable range space of the final permitted 3D model data of underground buildings in terms of plane position, height and elevation, then conduct a subsequent comparison. If it exceeds the allowable range space of any type, then establish the corresponding problem type and problem description field in the real-life 3D model data of the underground building, record the corresponding problem type and the size of the exceeded space, and then conduct a subsequent comparison. Area comparison: compare the real-life 3D model data of underground buildings that have undergone permitted space comparison with the final permitted 3D model data of underground buildings, set area comparison parameters, determine the conformity of the planned area based on the area comparison parameters, and directly conduct subsequent comparison on the real-life 3D model data of underground buildings that meet the judgment results. For the real-life 3D model data of underground buildings that do not meet the judgment results, record the problem type and area inconsistency in the real-life 3D model data, and then conduct subsequent comparison; Compare the area of ​​the real 3D model data of underground buildings with the final 3D model data of the permitted underground buildings; If 0≤|S 实 -S 许 |<f means that the actual measured building area of ​​the underground building is determined to be consistent with the permitted building area of ​​the underground building; If f≤|S 实 -S 许 |That is, it is determined that the actual measured building area of ​​the underground building does not conform to the permitted building area of ​​the underground building; Where S 实 is the actual measured building area of ​​underground buildings, S 许 is the permitted building area of ​​underground buildings, f is the area comparison parameter, and if S 许 >300, then 0≤f≤0.01*S 许 , if S 许 ≤300, then f=3; Parking space comparison: compare the real-life 3D model data of the underground building that has undergone area comparison with the final 3D model data of the permitted underground building, set the parking space comparison parameter β, determine the compliance of the parking space according to the parking space comparison parameter, and directly perform subsequent comparison on the real-life 3D model data of the underground building that meets the judgment result, and perform subsequent comparison after recording the problem type and parking space difference size in the real-life 3D model data of the underground building that does not meet the judgment result; Compare the parking spaces between the real-life 3D model data of underground buildings and the final 3D model data of permitted underground buildings; If 0≤β, it is determined that the actual measured building area of ​​the underground building is consistent with the permitted building area of ​​the underground building; If β<0, it is determined that the actual measured building area of ​​the underground building does not conform to the permitted building area of ​​the underground building; Where β = C 实 -C 许 , β is the parking space comparison parameter, C 实 is the number of parking spaces actually measured in underground buildings, C 许 The number of parking spaces permitted for underground buildings; Functional comparison: perform a functional comparison between the real-life 3D model data of the underground building that has undergone parking space comparison and the final 3D model data of the permitted underground building. If the supporting facility function type in the real-life 3D model of the underground building is consistent with the supporting facility function type in the final 3D model data of the permitted underground building, a subsequent comparison is performed. If the supporting facility function type in the real-life 3D model of the underground building is inconsistent with the supporting facility function type in the final 3D model data of the permitted underground building, a subsequent comparison is performed after recording the problem type and the inconsistent content of the supporting facility function type in the real-life 3D model data of the underground building; Facility comparison: perform facility comparison on the real-life 3D model data of underground buildings that have undergone functional comparison with the final 3D model data of permitted underground buildings, set facility comparison parameters, determine the conformity of the facilities based on the facility comparison parameters, store the real-life 3D model data of underground buildings that meet the determination results in the intermediate satisfaction database, and store the real-life 3D model data of underground buildings that do not meet the determination results in the intermediate non-satisfaction database after recording the problem type and facility difference; Conduct facility comparison between the actual 3D model data of underground buildings and the final 3D model data of permitted underground buildings; like That is, it is determined that the facility area of ​​category n in the underground building is consistent with the permitted facility area of ​​category n in the underground building permit; like That is, it is judged that the facility area of ​​category n in the underground building does not conform to the permitted facility area of ​​category n in the underground building permit; in is the facility comparison parameter, V n实 is the actual measured area of ​​n types of facilities in underground buildings, V n许 is the permitted area of ​​type n facilities in underground buildings, and if V n许 >300, then If V n许 ≤300, then 11. The underground building joint mapping method according to claim 9, characterized in that: The specific steps of the civil air defense completion comparison are as follows: Civil defense space comparison: compare the civil defense space range in the real-life 3D model of the underground building with the civil defense space range in the final permitted 3D model of the underground building. If the size, height, and elevation of the civil defense space range in the real-life 3D model of the underground building do not exceed the civil defense space range in the final permitted 3D model of the underground building, conduct a subsequent comparison. If it exceeds the civil defense space range in the final permitted 3D model of the underground building, record the corresponding problem type and the size of the exceeded space before conducting a subsequent comparison. Civil defense area comparison: compare the civil defense area in the real-life 3D model of the underground building with the civil defense area in the final permitted 3D model of the underground building, set the civil defense area comparison parameter θ, determine the conformity of the civil defense area in the underground building according to the civil defense area comparison parameter, and directly perform subsequent comparison on the real-life 3D model data of the underground building whose judgment result conforms, and perform subsequent comparison after recording the problem type and civil defense area difference in the real-life 3D model data of the underground building whose judgment result does not conform; Compare the civil air defense area between the real-life 3D model data of underground buildings and the final 3D model data of permitted underground buildings; If 0≤θ, it is determined that the actual measured civil air defense functional area of ​​the underground building is consistent with the civil air defense functional area of ​​the underground building construction permit; If θ<0, it is determined that the actual measured civil air defense functional area of ​​the underground building at the location does not match the civil air defense functional area of ​​the underground building construction permit; where θ = F 实 -F 许 , θ is the air defense area comparison parameter, F 实 F is the measured civil air defense functional area of ​​underground buildings. 许 Permitting civil air defense functional areas for underground buildings; Civil defense function comparison: compare the real-life 3D model data of the underground building that has undergone civil defense area comparison with the final permitted 3D model data of the underground building for civil defense function. If the civil defense facility function type in the real-life 3D model of the underground building is consistent with the civil defense facility function type in the final permitted 3D model data of the underground building, then conduct a subsequent comparison. If the civil defense facility function type in the real-life 3D model of the underground building is inconsistent with the civil defense facility function type in the final permitted 3D model data of the underground building, then conduct a subsequent comparison after recording the problem type and the inconsistent content of the civil defense facility function type in the real-life 3D model data of the underground building; Comparison of civil air defense facilities: the real-life 3D model data of underground buildings that have undergone civil air defense function comparison shall be compared with the final 3D model data of permitted underground buildings for civil air defense facilities. If the civil air defense facility area in the real-life 3D model of the underground building is consistent with the civil air defense facility area type in the final 3D model data of permitted underground buildings, a subsequent comparison shall be conducted. If the civil air defense facility area in the real-life 3D model of the underground building is inconsistent with the civil air defense facility area in the final 3D model data of permitted underground buildings, the problem type and the difference in the civil air defense facility area shall be recorded in the real-life 3D model data of the underground building before a subsequent comparison shall be conducted. Other civil defense comparison: The real-life 3D model data of the underground building that has undergone civil defense facility comparison will be compared with the final permitted 3D model data of the underground building for other civil defense comparisons. If the other civil defense facilities in the real-life 3D model of the underground building are consistent with the other civil defense facilities in the final permitted 3D model data of the underground building, the real-life 3D model data of the underground building will be stored in the satisfied database. If the other civil defense facilities in the real-life 3D model of the underground building are inconsistent with the other civil defense facilities in the final permitted 3D model data of the underground building, the problem type and the inconsistency of other civil defense facilities will be recorded in the real-life 3D model data of the underground building and then stored in the unsatisfied database.

Images