Stock violation building investigation method based on mass data

Abstract

A method for investigating buildings with illegal stock based on mass data relates to the technical field of geographic information, and is characterized by comprising the following steps: defining illegal buildings; comparing the data; analyzing and collecting data; making data; judging data; correcting and checking the data; summarizing, analyzing and evaluating; and feeding back reminding. The method has the advantages that the change area of the building is quickly identified by using the orthoimage, and the point cloud data is used for building investigation without on-site investigation, so that the identification and investigation efficiency and speed are greatly improved; the form and position information of the building can be accurately captured through the point cloud data, and compared with traditional two-dimensional image analysis, higher precision and accuracy are achieved; through combination of examination and approval data judgment and management right judgment and verification, higher authority and recognition degree are achieved; carrying out all-around analysis display and passing; and a visualization technology assists decision support.

Description

Technical Field

[0001] The present invention relates to the field of geographic information technology, and specifically utilizes orthophotos to quickly identify building change areas, and utilizes point cloud data to conduct building surveys without the need for field surveys, thereby greatly improving the efficiency and speed of identification and surveys; point cloud data can accurately capture the form and location information of buildings, and has higher precision and accuracy than traditional two-dimensional image analysis; combined with approval data judgment, management authority determination and verification, it has higher authority and recognition; all-round analysis and display are performed and passed; a method for investigating existing illegal buildings based on massive data is provided with decision-making support assisted by visualization technology. Background Art

[0002] At present, with the continuous advancement of urbanization, my country's cities are developing rapidly, but they are also facing the problems of lax construction planning and management, and the proliferation of existing illegal buildings. These illegal buildings have brought multiple hidden dangers to urban development, such as safety risks, environmental pollution, and waste of resources. Therefore, it is particularly important to investigate, rectify and manage existing illegal buildings. In recent years, with the rapid development of drones, satellites and aerial photography technology, we can obtain a large amount of high-resolution image data. These massive image data can provide valuable information for urban planning, environmental monitoring, and building surveys. Among them, the application of existing illegal building survey technology based on point cloud data is becoming more and more extensive, becoming an important tool for urban management and planning. The investigation of existing illegal buildings has traditionally relied on manual sampling, field surveys and other methods, which are time-consuming and labor-intensive, and have limited effects. The investigation of existing illegal buildings based on massive data can effectively solve the problems of traditional investigation methods. Massive data covers information in multiple dimensions such as society, economy, and geography, and can provide high-precision data support to accelerate the progress of investigation work.

[0003] However, there is currently no method that uses orthophotos to quickly identify building change areas, and uses point cloud data to conduct building surveys without the need for field surveys, which greatly improves the efficiency and speed of identification and investigation; point cloud data can accurately capture the shape and location information of buildings, and has higher precision and accuracy than traditional two-dimensional image analysis; combined with approval data judgment, and management authority determination and verification, it has higher authority and recognition; the multi-dimensional analysis results based on survey data can be displayed in three-dimensional form, which can fully understand the situation of illegal buildings, help to formulate more comprehensive and targeted investigation plans, and help decision makers better understand and analyze the problem of existing illegal buildings; by integrating point cloud data with other geographic information data, it can provide decision makers with more detailed and comprehensive visual information, thereby supporting better decision-making and planning management data for the investigation of existing illegal buildings. Summary of the invention

[0004] In order to overcome the drawbacks of existing technical solutions, the use of orthophotos to quickly identify building change areas and the use of point cloud data for building surveys does not require field surveys, greatly improving the efficiency and speed of identification and surveys; point cloud data can accurately capture the shape and location information of buildings, and has higher precision and accuracy than traditional two-dimensional image analysis; combined with approval data judgment, and management authority determination and verification, it has higher authority and recognition; the multi-dimensional analysis results based on survey data can be displayed in three-dimensional form, which can fully understand the situation of illegal buildings, help to formulate more comprehensive and targeted investigation plans, and help decision makers better understand and analyze the problems of existing illegal buildings; by integrating point cloud data with other geographic information data, more detailed and comprehensive visual information can be provided to decision makers, thereby supporting better decision-making and planning management data.

[0005] A method for investigating existing illegal buildings based on massive data, wherein the method comprises the following steps: defining illegal buildings: setting a standard for determining illegal buildings, wherein the standard is set to violate planning approval data within a specified time period; data comparison: according to the time period requirements for defining illegal buildings, obtaining multiple periods of image data corresponding to the time period, performing data correction and data classification on the acquired image data, and making and setting two types of extraction samples, namely, original building changes and newly added buildings, according to the classified data, quickly determining the building change area through computer automatic recognition technology based on convolutional neural network, and summarizing the change area after data re-verification, and superimposing and comparing with the current land use data, Generate the scope of illegal buildings that need to be determined in the end; Data analysis and collection: Analyze the massive amount of existing data within the acquired change area, determine the data range where the above-ground building data and underground building data are missing and the location accuracy does not meet the requirements, collect the above-ground building data within the data range, collect the internal space and external contour of the underground building data, and store the collected above-ground building data and underground building data in the underground building intermediate database and the underground building intermediate database respectively; Data production: Process the paper and digital approval original data to produce standard approval data with rights space, store it in the designated database, and The initial data of the above-ground buildings and the initial data of the underground buildings are collected and obtained, and the above-ground building data and the underground building data are produced and stored in the designated databases respectively; Data judgment: The building data and auxiliary materials are extracted respectively, and the processed illegal buildings are checked and the approval data is judged in the building data, and the subsequent boundary comparison, property comparison, volume ratio comparison, approval data comparison, internal space comparison, color and structure comparison are carried out in turn, and the comparison of all suspected illegal buildings is determined to be completed, and the obtained compliant building data and suspected illegal building data are stored in the corresponding databases respectively; Data correction and verification: The information of the suspected illegal building data is improved by using the existing data to determine the illegal The building management authority generates the stock data of illegal buildings after verification and confirmation, and stores it in the corresponding database; summary analysis and evaluation: retrieve the stock data of illegal buildings and combine it with various auxiliary materials to conduct various special analyses, and generate special analysis reports and special rectification information; feedback reminders: according to the designated areas for rectification of illegal buildings, extract various special analysis result data, rectification suggestion information, and stock data of illegal buildings in the corresponding areas from the rectification information database and the illegal building stock database, regularly collect illegal buildings in the designated area on site, and verify the rectification of illegal buildings, update various special analysis results, generate rectification feedback information, and store and display it on the corresponding terminal.

[0006] A method for investigating existing illegal buildings based on massive data, wherein the specific steps of data comparison are:

[0007] Obtaining multiple images: According to the time period defined by the illegal building, obtain multiple image data corresponding to the time period;

[0008] Image data correction: standardize and judge the multi-phase image data, and perform coordinate transformation, data fusion, projection and correction processing, and format conversion standardization processing on the non-standardized image data according to the corresponding reasons, so as to generate multi-phase orthophoto image data of the same type;

[0009] Sample classification and setting: divide the orthophoto data of multiple periods into regions according to planning areas and building types. According to different regional divisions, extract samples of two categories, original building changes and newly added buildings, are produced respectively. The produced samples are extracted, verified and modeled.

[0010] Building change area detection based on convolutional neural network: According to the corresponding sample settings, the computer automatic recognition technology based on convolutional neural network is used to quickly determine the initial range of the change area of ​​the original building changes and the newly added buildings in the classified orthophotos before and after.

[0011] Data correction: Use multispectral remote sensing image data to verify the preliminary range of the detected change area, remove the misidentified area, supplement the missed change area, and perform geometric normalization on the change area to form the intermediate range of the two types of building change areas of different classifications: original building changes and newly added buildings;

[0012] Data aggregation and overlay optimization: The middle range of the two types of building change areas detected in different categories are aggregated, and compared with the existing land use data, the special land use range in the existing land use is screened, and the special land use range within the detected building change area is eliminated to generate the final change area range that needs to be judged as illegal buildings.

[0013] A method for investigating existing illegal buildings based on massive data, wherein the specific steps of data analysis and collection are:

[0014] Determine the scope of missing data: Analyze the massive amount of existing data within the acquired change area, and judge the existence and location accuracy of the above-ground building data and underground building data. If it is judged that the above-ground building data and underground building data do not exist, record the scope of missing above-ground building data and underground building data. If it is judged that the above-ground building data and underground building data exist, judge the accuracy of the above-ground building data and underground building data respectively. If it is judged that the accuracy of the above-ground building data and underground building data does not meet the requirements, record the scope of the above-ground building data and underground building data not meeting the accuracy requirements. If it is judged that the accuracy of the above-ground building data and underground building data meets the requirements, do not record the relevant scope, and define the scope of missing above-ground building data and underground building data and the scope of the above-ground building data and underground building data not meeting the accuracy requirements in the change area as the scope that needs to be supplemented.

[0015] Supplementary collection of ground building data: Supplementary collection of ground building data is carried out within the scope of supplementary collection to obtain the plane position and height information of the ground building with position accuracy that meets the requirements;

[0016] Underground building data collection: Supplementary collection of underground building data within the scope of supplementary collection. The interior of underground buildings is mainly measured by stationary laser radar scanning, supplemented by handheld laser radar scanning, and the two are combined to collect and measure point cloud data, and draw the internal space plan. The outer contour of underground buildings is collected and measured by seismic wave detection. The internal space and external contour of the underground building are combined by splicing the sampling points with the same name and the obvious feature points on the ground, so as to connect the internal and external contours of the underground space as a whole. Then, the geometric position information of the internal and external contours of the underground building is obtained through the absolute position coordinates of the obtained sampling points and obvious feature points on the ground.

[0017] Data integration and storage: The collected plane position and height information data of the above-ground buildings are defined as the initial data of the above-ground buildings and stored in the intermediate database of the above-ground buildings; the collected outer contour map of the underground buildings and the corresponding internal space data of the underground buildings are defined as the initial data of the underground buildings and stored in the intermediate database of the underground buildings.

[0018] A method for investigating existing illegal buildings based on massive data, wherein the specific steps of collecting underground building data are as follows:

[0019] Sampling point layout: within the scope of supplementary collection, determine and layout the sampling points for seismic wave detection on the ground according to the distribution, size and location characteristics of underground buildings;

[0020] Data collection: Use the GNSS system combined with the total station to digitally measure the ground sampling points and obvious feature points on the ground, use the seismic instrument to transmit signals underground at the sampling points, and record the reflected or refracted waves of the signals;

[0021] Data processing: remove noise from the collected seismic signals, perform seismic data compensation, and analyze the seismic data; perform data conversion and coordinate inverse calculation on the data obtained from the sampling points and obvious feature points on the ground to obtain the absolute position coordinates;

[0022] Establishment of time-distance profile: Based on the acquired seismic data, a time-distance profile is established based on the acquired arrival time and amplitude information, with the horizontal axis representing distance and the vertical axis representing time or depth. The reflection points and the corresponding time distance are marked on the profile.

[0023] Pre-stack data processing: pre-process the data on the time-range section by stacking, filtering, and removing the Doppler effect to generate the final imaging data;

[0024] Imaging and mapping: The imaging algorithm formula is used to invert the final imaging data to obtain the imaging results of the underground building. The specific imaging algorithm calculation formula is as follows:

[0025] Map(x,z)=∫u(x,z,t)d(x,z,t)dt

[0026] Where: Map(x,z) represents the imaging result, which indicates the strength or reflection coefficient of the underground structure at the position x at the underground depth z. u(x,z,t) represents the wave field amplitude reflection coefficient of the seismic wave at the spatial position (x,z) and time t. This parameter reflects the propagation of seismic waves in different underground media. d(x,z,t) represents a parameter of the seismic wave propagation path, which is usually called the attenuation function. It describes the attenuation and scattering of the medium encountered by the seismic wave during the propagation process. dt represents the small increment of the time interval, that is, the differential component.

[0027] Analyze and interpret the depth, size, and location parameters of underground buildings based on the imaging results, draw the outer contour of the underground buildings, and record the corresponding depth information. Combine the absolute position coordinates of the sampling points and obvious feature points on the ground to correct the outer contour of the underground buildings to the corresponding coordinate system;

[0028] Data aggregation and storage: Using the seismic wave sampling points with the same name and obvious ground feature points, the outer contour map of the underground building and the corresponding internal space of the underground building are spliced ​​and aggregated, and stored in the underground building intermediate database.

[0029] A method for investigating existing illegal buildings based on massive data, wherein the specific steps of data production are:

[0030] Approval data production: For paper-based approval data, the approval graphic data is first produced by using the file vectorization method, and then the corresponding attribute information of the approval data is improved according to the file data. For the approval data in digital form, the approval data is produced by means of coordinate transformation, standardization processing, and attribute improvement. The two types of approval data are superimposed, redundant data are eliminated, and the integrity and positivity of the relevant attribute data are checked and modified. Then, a connection is established between the graphic data and the attribute data, and the rights space of the approval data is generated according to the issuance requirements of the approval data. Then, the standard approval data after the rights space is generated is stored in the standard approval database;

[0031] Building data production: pre-process and classify the massive amount of original and supplementary point cloud data of above-ground buildings, and produce contour and height extraction samples according to different types. Automatically classify and extract the contour and height information of above-ground building data through calculation formulas, correct the above-ground building data and improve the attribute information, and then summarize and store it in the above-ground building database; extract the outer contour and internal space location map in the underground building intermediate database, and according to the collected information, produce underground building data to improve the corresponding attribute information, establish a corresponding relationship with the above-ground building data, and then summarize and store the underground building data in the underground building database according to the spatial position; the building data production is divided into two parts: above-ground building data production and underground building data production;

[0032] Data classification storage: The produced standard approval data and construction data are stored in the standard approval database and the construction database respectively.

[0033] A method for investigating existing illegal buildings based on massive data, wherein the specific steps of producing the approval data are as follows:

[0034] Scanning and image processing: Obtain paper archive materials of approval data, scan the archive materials with a scanner, first perform color correction and geometric correction on the scanned images, then perform data matching, data editing, data error correction, projection transformation, edge processing, and image splicing on the scanned images;

[0035] Information conversion and extraction: Stratify the image elements, extract the building-related layers, use the software to automatically extract and manually correct the building graphics of the approval data, and fill in the attribute information of the approval data according to the relevant information of the archives;

[0036] Digital data acquisition: organize and acquire approval data in digital form, and perform coordinate conversion, standardization, and attribute improvement on the acquired approval data in digital form;

[0037] Editing and processing: spatially superimpose the digitized approval data with the approval data produced by archive vectorization. For spatially repeated approval data, remove the data with earlier approval time according to the approval time sequence. Then, check and modify the integrity and positivity of the relevant attribute data of all approval data, establish a connection between the graphic data and the attribute data, and generate the intermediate results of the approval data.

[0038] Rights space generation: According to the issuance requirements of the approval data, the rights space of the intermediate results of the approval data is produced. For approval values ​​that are fixed values, a fixed rights space is generated. For approval values ​​that are interval values, the rights space for approval and construction is generated according to the maximum and minimum intervals of the length, width, and height.

[0039] Approval database: The intermediate results of approval data after generating the rights space are defined as standard approval data and stored in the standard approval database.

[0040] A method for investigating existing illegal buildings based on massive data, wherein the specific steps of producing the building data are:

[0041] Production of ground building data: Preprocess and classify the massive amount of original and supplementary point cloud data of ground buildings, then segment the ground building point cloud data by height type, and classify the building roof types through the curvature calculation formula. Create contour and height extraction samples according to different types, and automatically classify and extract the ground building data contour and height information through the calculation formula. After correcting the ground building data contour and improving the attribute information, generate two-dimensional and three-dimensional ground building data, and summarize and store them in the ground building database;

[0042] Production of underground building data: Extract the outer contour and internal space location map from the underground building intermediate database, and use the corresponding software to produce two- and three-dimensional underground building data based on the collected depth and internal height information, and improve the attribute information of the corresponding two- and three-dimensional underground building data. Use the location information of obvious feature points on the ground obtained during underground building data collection to establish a corresponding relationship with the ground building data. Finally, the two- and three-dimensional underground building data are summarized and stored in the underground building database according to the spatial position.

[0043] A method for investigating existing illegal buildings based on massive data, wherein the specific steps of producing the above-ground building data are as follows:

[0044] Data extraction: Extract various point cloud data of above-ground buildings in the changed area from massive existing data and newly collected underground building intermediate databases;

[0045] Data preprocessing: Use fusion algorithms to fuse and register various point clouds including vehicle-mounted, airborne, stationary, backpack, and oblique photography, and then obtain complete and usable ground building point cloud data through noise reduction, rarefaction, and resampling;

[0046] Data classification processing: The average elevation of the center point X is obtained through the calculation formula. The calculation formula used is to calculate the average value by subtracting the elevation of the center point X to the four surrounding directions. The specific calculation formula is as follows:

[0047]

[0048] Where: Z′ represents the average elevation of the center point X, Z1 represents the elevation of the point above the center point X, Z2 represents the elevation of the point below the center point X, Z3 represents the elevation of the point to the left of the center point X, Z4 represents the elevation of the point to the right of the center point X, and Z x Indicates the elevation value of the center point X;

[0049] A calculation threshold ω is set for Z′. When it is determined that Z′ is less than the threshold ω, it is determined to be a ground point. When it is determined that Z′ is greater than or equal to the threshold ω, it is determined to be a non-ground point. The point cloud data is traversed in sequence until all ground point clouds are divided into ground points and non-ground point sets. The determination is completed, and the density clustering method with adaptive parameters is used to divide different ground objects of non-ground points, and the ground building point cloud data containing only building types is selected;

[0050] Data segmentation: refer to the orthophoto data, perform height type segmentation on the ground building point cloud data, and divide the ground building point cloud data into three types of data: high-rise, multi-story, and low-rise according to the size of the ratio of the building shadow length to the building length in the orthophoto. If ξ≥1, the ground building point cloud data is judged to be a high-rise building; if ξ is between 0.5 and 1, the ground building point cloud data is judged to be a multi-story building; if ξ≤0.5, the ground building point cloud data is judged to be a low-rise building;

[0051] Sample setting: According to different segmentation types, the curvature value of each building in high-rise, multi-story, and low-rise buildings is calculated through the curvature calculation formula to determine the roof type of the building. If the curvature is less than the threshold value ψ, the building is judged to be a flat roof building. If the curvature is greater than or equal to the threshold value ψ, the building is judged to be a slope roof building. The curvature calculation formula is as follows: Curvature calculation formula

[0052]

[0053] Where K represents the curvature of the building roof, Z x Indicates the elevation difference of the building roof in the x direction, Z y The elevation difference in the y direction of the building roof;

[0054] First, according to different height types and roof types, the correspondence between the real samples of the main body, balcony, doorway, eaves, and elevator room and the drawn contours is made, and the sample type and normal vector features are annotated. Then, the corresponding negative samples are made, and the real samples are enhanced in the form of rotation, translation, and scaling. According to different roof types, building height information samples are made. Among them, two height extraction samples are set for flat-topped buildings, namely the roof surface and the highest point, and three height extraction samples are set for sloping-topped buildings, namely the slope starting point, the ridge point, and the height average point.

[0055] Automatically extract building information using calculation formulas: According to the classification of different height types and roofs, refer to the sample types of each type, select the machine learning model of the appropriate task for training, and use the training data to optimize the model, input each type of point cloud data to be processed into the corresponding trained model for automatic extraction, and then transform the extracted three-dimensional building points into a two-dimensional plane through projection, use the point cloud geometry algorithm to extract the building contour points, obtain the initial contour, and write the height information into the corresponding main contour; the judgment condition of the point cloud geometry algorithm is: in the point set P, through any two points p i (x i ,y i ), p r (x r ,y r ) Draw a circle with a radius of α and calculate the center of the circle passing through these two points p o (x o ,y o ) coordinates, where p o The coordinate calculation formula is as follows:

[0056]

[0057] In the formula α radius parameter;

[0058] By judging the relationship between the distance from other points to the center of the circle and the radius α, we can determine the distance between the connecting line p and the center of the circle. i , p r Is it a boundary line segment? If the distance from other points to the center of the circle is greater than the radius α, it is judged that there are no other points in the circle, then p is considered i , p r is a boundary point, and its connecting line p i , p r is a boundary segment, otherwise, it is considered that p i , p r Not a boundary point, its connecting line p i , p r It is not a boundary line segment, and then repeat the above steps for the remaining points in the point set P until all the points in the point set P are judged;

[0059] Data optimization: Filter, group, and edge fit the initially extracted underground building outlines, and then correct the ground building outlines to the corresponding coordinate system based on the feature positions of the point cloud data and topographic map information to obtain the final ground building outlines;

[0060] Data production: Automatically generate corresponding two-dimensional and three-dimensional ground building data based on the extracted contour lines and height information of different types of ground buildings, and improve the attribute information of two-dimensional and three-dimensional ground building data including usage nature, building area, volume ratio and building density based on point cloud data and oblique image data;

[0061] Data aggregation and storage: Different types of two- and three-dimensional ground building data are aggregated and stored in the ground building database according to their spatial locations.

[0062] A method for investigating existing illegal buildings based on massive data, wherein the specific steps of data determination are:

[0063] Building data extraction: extracting corresponding ground building data and underground building data from the underground building database and the ground building database respectively;

[0064] Auxiliary data extraction: Extract data including street view data, oblique image data, approved land use data, various restriction boundary data, and processed illegal building data from massive existing data;

[0065] Processed illegal building investigation: Define the names of above-ground building data and underground building data, and arrange them in ascending order according to the verification order as illegal building data① to illegal building data Firstly, spatial comparison analysis is adopted based on the processed illegal building data to automatically check the above-ground building data and underground building data. According to the parameter τ of the graphic area overlap rate of the processed illegal building data, the above-ground building data and the underground building data, a threshold η is set for τ to preliminarily judge whether the above-ground building data and the underground building data are processed illegal buildings. If τ≥threshold η, the illegal buildings are then compared with the above-ground building data and the underground building data in terms of attributes including the number of floors, building area and nature. If the number of floors, building area and nature are determined to be consistent, the above-ground building data and the underground building data are extracted and stored in the compliant building database. If any one of the number of floors, building area and nature is determined to be inconsistent, the above-ground building data and the underground building data are extracted and defined as illegal building data ①. If τ<threshold η, the above-ground building data and the underground building data are extracted and defined as illegal building data ①, and the defined illegal building data ① is subsequently determined.

[0066] Approval judgment: Perform spatial comparison analysis on the illegal building data① and the building data of the standard approval data, set the parameter φ of the spatial overlap rate, set the threshold ρ for φ, if φ≤threshold ρ, define the above-ground building data and underground building data as suspected illegal building data, record the reasons for the violations and extract and store them in the suspected illegal building intermediate database, if φ>threshold ρ, extract the above-ground building data and underground building data as illegal building data②, and conduct subsequent comparison;

[0067] Boundary comparison: Perform boundary comparison on illegal building data ② and various boundary data. If it is determined that illegal building data ② is within the restriction range of various boundary data, the above-ground building data and underground building data within the illegal building data ② within the restriction range are defined as suspected illegal building data, and the reasons for the violation are recorded and extracted and stored in the suspected illegal building data intermediate database; if it is determined that illegal building data ② is not within the restriction range of various boundary data, illegal building data ② is defined as illegal building data ③, and subsequent comparisons are performed in sequence;

[0068] Property comparison: compare the illegal building data ③ with the land use nature of the standard approval data and the land use nature of the construction detailed plan. If it is determined that the illegal building data ③ is different from the land use nature of the standard approval data and the land use nature of the construction detailed plan, the above-ground building data and underground building data in this part of the illegal building data ③ are defined as suspected illegal building data, and the reasons for the violation are recorded and extracted and stored in the suspected illegal building data intermediate library; if it is determined that the land use nature of the illegal building data ③ is the same as the land use nature of the standard approval data and the land use nature of the construction detailed plan, the illegal building data ③ is defined as illegal building data ④ for subsequent comparison;

[0069] Comparison of floor area ratio: Compare the floor area ratio of the illegal building data ④ with that of the standard approval data. If it is determined that the floor area ratio of the illegal building data ④ is different from that of the standard approval data, the above-ground building data and underground building data in the illegal building data ④ with different floor area ratios are defined as suspected illegal building data, and the reasons for the violations are recorded and extracted and stored in the suspected illegal building data intermediate library. If it is determined that the floor area ratio of the illegal building data ④ is the same as that of the standard approval data, the illegal building data ④ is defined as illegal building data ⑤ for subsequent comparison;

[0070] Approval data comparison: determine the type of the approval value of the approval data. If the approval value of the approval data is determined to be an interval value, compare the illegal building data ⑤ with the standard approval data for self-comparison and adjacent spacing comparison. If the approval value of the approval data is determined to be a definite value, compare the illegal building data ⑤ with the standard approval data for plane comparison and height comparison;

[0071] Internal space comparison: determine whether the illegal building data ⑦ contains underground building data. If it is determined that the illegal building data ⑦ contains underground building data, the underground building data in the illegal building data ⑦ is combined with the point cloud data and the approval information of the standard approval data for internal space comparison. If it is determined that the internal space has been demolished or modified, the underground building data in the demolished or modified part of the illegal building data ⑦ is defined as suspected illegal building data, and the reasons for the violation are recorded and extracted and stored in the suspected illegal building data intermediate library. If it is determined that the internal space has not been demolished or modified, the illegal building data ⑦ is defined as compliant above-ground building data and stored in the compliant building database; if it is determined that the illegal building data ⑦ does not contain underground building data, subsequent operations are performed;

[0072] Color and structure comparison: Determine whether the ground building data in the illegal building data ⑦ is located in the newly added building change area. If it is determined that the ground building data in the illegal building data ⑦ is located in the newly added building change area, the ground building data in the illegal building data ⑦ is combined with the oblique image data and compared with the approval information of the standard approval data in color and structure. If it is determined that any one of the color and structure of the building is inconsistent, the inconsistent ground building data in the illegal building data ⑦ is defined as suspected illegal building data, and the cause of the violation is recorded and extracted and stored in the suspected illegal building data intermediate library. If the color and structure of the building are determined to be consistent, the illegal building data ⑦ is defined as compliant ground building data and stored in the compliant building database; if it is determined that the ground building data in the illegal building data ⑦ is not in the newly added building change area, then the subsequent operation is performed;

[0073] Suspected illegal building intermediate database: All suspected illegal building data obtained through the above comparison are stored in the suspected illegal building intermediate database according to spatial location;

[0074] Compliant building database: The above-ground building data and underground building data remaining after the above comparison are defined as compliant building data and stored in the compliant building database.

[0075] A method for investigating existing illegal buildings based on massive data, wherein the specific steps of comparing the approval data are as follows:

[0076] Approval data type determination: extract standard approval data from the standard approval database, determine the type of the approval value of the standard approval data, and if the approval value of the standard approval data is determined to be a fixed value, perform subsequent self-comparison and adjacent spacing comparison; if the approval value of the standard approval data is determined to be an interval value, perform subsequent plane comparison and height comparison;

[0077] Self-comparison: Compare the illegal building data ⑤ with the standard approval data. If it is determined that the illegal building data ⑤ exceeds the rights space of the corresponding standard approval data, the above-ground building data and underground building data in the illegal building data ⑤ that exceed the rights space are defined as suspected illegal building data, and the reasons for the violation are recorded and extracted and stored in the suspected illegal building data intermediate library. If it is determined that the illegal building data ⑤ does not exceed the rights space of the corresponding standard approval data, the illegal building data ⑤ is defined as illegal building data ⑥ for subsequent comparison;

[0078] Adjacent spacing comparison: compare the adjacent spacing of the illegal building data ⑥ with the standard approval data. If it is determined that the illegal building data ⑥ exceeds the range of adjacent spacing required by the approval value of the corresponding standard approval data, the above-ground building data and underground building data in the illegal building data ⑥ that exceed the range of adjacent spacing are defined as suspected illegal building data, and the reasons for the violation are recorded and extracted and stored in the suspected illegal building data intermediate library. If it is determined that the illegal building data ⑥ does not exceed the range of adjacent spacing required by the approval value of the corresponding standard approval data, the illegal building data ⑥ is defined as illegal building data ⑦ for subsequent comparison;

[0079] Plane comparison: Perform a plane comparison of the illegal building data ⑤ with the standard approval data. If it is determined that the illegal building data ⑤ exceeds any one of the plane rights space and adjacent spacing of the corresponding standard approval data, the above-ground building data and underground building data in this part of the illegal building data ⑤ are defined as suspected illegal building data, and the reasons for the violations are recorded and extracted and stored in the suspected illegal building data intermediate library. If it is determined that the illegal building data ⑤ does not exceed the plane rights space and adjacent spacing of the corresponding standard approval data, the illegal building data ⑤ is defined as illegal building data ⑥ for subsequent comparison;

[0080] Height comparison: compare the height of the illegal building data ⑥ with the standard approval data. If it is determined that the illegal building data ⑥ exceeds the height right space of the corresponding standard approval data, the above-ground building data and underground building data in the illegal building data ⑥ that exceed the height right space are defined as suspected illegal building data, and the reasons for the violation are recorded and extracted and stored in the suspected illegal building data intermediate library. If it is determined that the illegal building data ⑥ does not exceed the height right space of the corresponding standard approval data, the illegal building data ⑥ is defined as illegal building data ⑦ for subsequent comparison.

[0081] A method for investigating existing illegal buildings based on massive data, wherein the specific steps of data correction and verification are as follows:

[0082] Improvement of suspected illegal building information: Extract suspected illegal building data from the suspected illegal building intermediate database, and use massive oblique image data, point cloud data, district boundary data, industrial zone boundary data, land price data, land use data, ownership information data, geological conditions information data, supporting facilities data, and green space data to improve the attribute information of suspected illegal buildings, including number, district boundary, industrial park, land price, land use, ownership, geological conditions, green space conditions, and supporting facilities conditions;

[0083] Determination of management authority of suspected illegal buildings: Determine the construction time of suspected illegal building data based on satellite remote sensing image data, and then determine the management authority of suspected illegal buildings based on the issuance time of approval data, the spatial area of ​​suspected illegal building data, and the violation reason of suspected illegal building data; judge all suspected illegal building data in turn until the management authority is defined and the determination is completed;

[0084] Verification and confirmation of suspected illegal buildings: after the information is completed and the management authority is determined, the suspected illegal buildings are verified on site, and after excluding the compliant buildings again, the illegal building inventory data is generated;

[0085] Illegal building stock database: The illegal building stock data is stored in the illegal building stock database.

[0086] A method for investigating existing illegal buildings based on massive data, wherein the specific steps of the summary analysis and evaluation are:

[0087] Retrieval of illegal building data: retrieve illegal building stock data from the illegal building stock database;

[0088] Special analysis: Combine the illegal building stock data with various auxiliary materials to conduct special analysis of zoning, ownership, economy, society and overall categories, generate various special analysis result data and store them in the special analysis database;

[0089] Special analysis report: According to the special analysis results, output the special analysis report containing text, graphics, charts, and 3D models according to the classification;

[0090] Special remediation information: Based on special analysis data, combined with the types of illegal buildings and planning and development goals, remediation plans for illegal buildings in different areas, management powers, numbers of floors, nature, ownership, geological conditions, and types of supporting facilities are formed, and targeted remediation suggestion information is generated for each building and stored in the corresponding remediation information database.

[0091] A method for investigating existing illegal buildings based on massive data, wherein the specific steps of the feedback reminder are:

[0092] Retrieval of special analysis results: According to the designated area for rectification of illegal buildings, various special analysis result data and rectification suggestion information are extracted from the special analysis database;

[0093] On-site collection and verification: According to the designated area for illegal building rectification, the illegal building stock data in the corresponding area is regularly extracted, and the location information of different illegal building stock data is generated and sent to the supervisors. The supervisors can regularly collect oblique images and point cloud data of the corresponding locations by means of drone oblique photography according to the rectification situation;

[0094] Feedback information generation: Automatically extract oblique images and point cloud data, compare and analyze with the stock data of illegal buildings, verify the rectification of illegal buildings, update various special analysis results data, and generate rectification feedback information;

[0095] Display storage: Through a dedicated network, the special analysis results of illegal buildings in different periods and the feedback information of rectification are stored and displayed on the corresponding two-dimensional and three-dimensional terminals according to a fixed period.

[0096] A method for investigating existing illegal buildings based on massive data, wherein planning approval data includes control detailed planning data, planning condition data, national land space planning data, site selection opinion data, construction land planning permit data, construction project planning permit data, rural construction planning permit data, and construction project completion acceptance data; planning approval data includes: land use nature, volume ratio, building area, building size, building height, construction density, surrounding building spacing, color, structure, and shape; image data includes satellite image data, aerial image data, oblique image data, and multi-spectral remote sensing image data; format conversion includes conversion of different types of data (oblique image conversion to orthophoto) and conversion of different formats of the same type of data; planning areas include urban built-up areas and rural areas; building types include high-rise areas, multi-story areas, and low-rise areas; special land use areas include military land and security land; building data includes point cloud data, topographic map data, GIS data, oblique image data, and real-life three-dimensional data; supplementary collection methods include traditional surveying and mapping methods, lidar measurement, and oblique photogrammetry; approval values ​​include: length, width, height, and adjacent spacing volume rate, use nature, building density, and land use scope; the right space form includes: two-dimensional form and three-dimensional form; the right space content includes: length, width, and height; the ground building data includes two-dimensional GIS form and real-scene three-dimensional form; the correspondence between underground building data and ground building data is one-to-one and one-to-many; various types of restriction boundary data include land use red line, permanent basic farmland control line, ecological protection red line, urban development boundary, village construction boundary, flood risk control line, historical and cultural protection line, and various types of urban planning control; district boundary data include: administrative boundary data, management line data, The data includes administrative boundary data, urban planning area boundary data, and township planning area boundary data; the zoning categories include: district boundaries, industrial park boundaries, and ring line boundaries; the ownership categories include: land ownership and building ownership; the economic categories include: project cost, demolition cost, land value, rental price, renovation cost, tax data, and financial data; the social categories include: population, education, medical care, employment, leasing, and transportation; the overall categories include: area, number, number of floors, supporting facilities, nature, greening, and geological conditions; the feedback information includes: early warning information, prompt information for areas exceeding the specified indicators, and target gap information.

[0097] It can be seen from this that:

[0098] The method in the embodiment of the present invention can use orthophotos to quickly identify building change areas, and use point cloud data to conduct building surveys without the need for field surveys, greatly improving the efficiency and speed of identification and investigation; point cloud data can accurately capture the shape and location information of buildings, and has higher precision and accuracy than traditional two-dimensional image analysis; combined with approval data judgment, and management authority judgment and verification, it has higher authority and recognition; the multi-dimensional analysis results based on the survey data can be displayed in three-dimensional form, which can fully understand the situation of illegal buildings, help to formulate more comprehensive and targeted investigation plans, and help decision makers better understand and analyze the problem of existing illegal buildings; by integrating point cloud data with other geographic information data, more detailed and comprehensive visual information can be provided to decision makers, thereby supporting better decision-making and planning management data. BRIEF DESCRIPTION OF THE DRAWINGS

[0099] Figure 1 A schematic diagram of a flow chart of a method for investigating existing illegal buildings based on massive data provided in an embodiment of the present invention;

[0100] Figure 2 A schematic diagram of a data comparison process in an embodiment of the present invention;

[0101] Figure 3 A schematic diagram of a data analysis and collection process in an embodiment of the present invention;

[0102] Figure 4 Schematic diagram of the process of underground building data collection in an embodiment of the present invention;

[0103] Figure 5 A schematic diagram of a data preparation process in an embodiment of the present invention;

[0104] Figure 6 A schematic diagram of the process flow of approval data preparation steps in an embodiment of the present invention;

[0105] Figure 7 A schematic diagram of the steps of making building data in an embodiment of the present invention;

[0106] Figure 8 It is a schematic diagram of the process of preparing ground building data in an embodiment of the present invention;

[0107] Fig. 9 A schematic diagram of a data determination process in an embodiment of the present invention;

[0108] Fig.10 Schematic diagram of the process of comparing approval data in an embodiment of the present invention;

[0109] Fig.11A schematic diagram of a data correction and verification process in an embodiment of the present invention;

[0110] Fig.12 It is a schematic diagram of the process flow of the summary analysis and evaluation steps in an embodiment of the present invention;

[0111] Fig.13 The figure is a schematic diagram of the feedback reminder step flow in an embodiment of the present invention. DETAILED DESCRIPTION

[0112] 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.

[0113] Embodiment 1:

[0114] Figure 1 A flow chart of the method for investigating existing illegal buildings based on massive data provided in this embodiment is shown in FIG. Figure 1As shown, a method for investigating existing illegal buildings based on massive data is provided, wherein the method comprises the following steps: defining illegal buildings: setting a standard for determining illegal buildings, wherein the standard is set to violate planning approval data within a specified time period; data comparison: according to the time period requirements for defining illegal buildings, obtaining multiple periods of image data corresponding to the time period, performing data correction and data classification on the obtained image data, and making and setting two types of extraction samples, namely, original building changes and newly added buildings, according to the classified data; quickly determining the building change area through computer automatic recognition technology based on convolutional neural network, and summarizing the change area after data re-verification, and superimposing and comparing with the current land use data. Yes, generate the scope of illegal buildings that need to be determined in the end; data analysis and collection: analyze the massive amount of existing data within the acquired change area, determine the data range where the above-ground building data and underground building data are missing and the location accuracy does not meet the requirements, collect the above-ground building data within the data range, collect the internal space and external contour of the underground building data, and store the collected above-ground building data and underground building data in the underground building intermediate database and the underground building intermediate database respectively; data production: process the paper and digital approval original data to produce standard approval data with rights space, store it in the designated database, and The initial data of the above-ground buildings and the initial data of the underground buildings obtained by data collection are used to produce the above-ground building data and the underground building data, and they are stored in the designated databases respectively; Data judgment: The building data and auxiliary materials are extracted respectively, and the processed illegal buildings are checked and the approval data is judged in the building data, and the subsequent boundary comparison, property comparison, volume ratio comparison, approval data comparison, internal space comparison, color and structure comparison are carried out in sequence, and all the suspected illegal buildings are judged to be finished. The compliant building data and the suspected illegal building data obtained are stored in the corresponding databases respectively; Data correction and verification: The information of the suspected illegal building data is improved by using the existing data to judge the illegal The management authority of illegal buildings is exercised, and after verification and confirmation, the stock data of illegal buildings is generated and stored in the corresponding database; summary analysis and evaluation: the stock data of illegal buildings is retrieved and combined with various auxiliary materials for various special analyses, and special analysis reports and special rectification information are generated; feedback reminders: according to the designated areas for rectification of illegal buildings, various special analysis result data, rectification suggestion information, and stock data of illegal buildings in the corresponding areas are extracted from the rectification information database and the illegal building stock database, and illegal buildings in the designated areas are regularly collected on-site, and the rectification of illegal buildings is verified, various special analysis results are updated, rectification feedback information is generated, and stored and displayed on the corresponding terminal.

[0115] like Figure 2 As shown, a method for investigating existing illegal buildings based on massive data, wherein the specific steps of data comparison are:

[0116] Obtaining multiple images: According to the time period defined by the illegal building, obtain multiple image data corresponding to the time period;

[0117] Image data correction: standardize and judge the multi-phase image data, and perform coordinate transformation, data fusion, projection and correction processing, and format conversion standardization processing on the non-standardized image data according to the corresponding reasons, so as to generate multi-phase orthophoto image data of the same type;

[0118] Sample classification and setting: divide the orthophoto data of multiple periods into regions according to planning areas and building types. According to different regional divisions, extract samples of two categories, original building changes and newly added buildings, are produced respectively. The produced samples are extracted, verified and modeled.

[0119] Building change area detection based on convolutional neural network: According to the corresponding sample settings, the computer automatic recognition technology based on convolutional neural network is used to quickly determine the initial range of the change area of ​​the original building changes and the newly added buildings in the classified orthophotos before and after.

[0120] Data correction: Use multispectral remote sensing image data to verify the preliminary range of the detected change area, remove the misidentified area, supplement the missed change area, and perform geometric normalization on the change area to form the intermediate range of the two types of building change areas of different classifications: original building changes and newly added buildings;

[0121] Data aggregation and overlay optimization: The middle range of the two types of building change areas detected in different categories are aggregated, and compared with the existing land use data, the special land use range in the existing land use is screened, and the special land use range within the detected building change area is eliminated to generate the final change area range that needs to be judged as illegal buildings.

[0122] like Figure 3 As shown, a method for investigating existing illegal buildings based on massive data, wherein the specific steps of data analysis and collection are:

[0123] Determine the scope of missing data: Analyze the massive amount of existing data within the acquired change area, and judge the existence and location accuracy of the above-ground building data and underground building data. If it is judged that the above-ground building data and underground building data do not exist, record the scope of missing above-ground building data and underground building data. If it is judged that the above-ground building data and underground building data exist, judge the accuracy of the above-ground building data and underground building data respectively. If it is judged that the accuracy of the above-ground building data and underground building data does not meet the requirements, record the scope of the above-ground building data and underground building data not meeting the accuracy requirements. If it is judged that the accuracy of the above-ground building data and underground building data meets the requirements, do not record the relevant scope, and define the scope of missing above-ground building data and underground building data and the scope of the above-ground building data and underground building data not meeting the accuracy requirements in the change area as the scope that needs to be supplemented.

[0124] Supplementary collection of ground building data: Supplementary collection of ground building data is carried out within the scope of supplementary collection to obtain the plane position and height information of the ground building with position accuracy that meets the requirements;

[0125] Underground building data collection: Supplementary collection of underground building data within the scope of supplementary collection. The interior of underground buildings is mainly measured by stationary laser radar scanning, supplemented by handheld laser radar scanning, and the two are combined to collect and measure point cloud data, and draw the internal space plan. The outer contour of underground buildings is collected and measured by seismic wave detection. The internal space and external contour of the underground building are combined by splicing the sampling points with the same name and the obvious feature points on the ground, so as to connect the internal and external contours of the underground space as a whole. Then, the geometric position information of the internal and external contours of the underground building is obtained through the absolute position coordinates of the obtained sampling points and obvious feature points on the ground.

[0126] Data integration and storage: The collected plane position and height information data of the above-ground buildings are defined as the initial data of the above-ground buildings and stored in the intermediate database of the above-ground buildings; the collected outer contour map of the underground buildings and the corresponding internal space data of the underground buildings are defined as the initial data of the underground buildings and stored in the intermediate database of the underground buildings.

[0127] like Figure 4 As shown, a method for investigating existing illegal buildings based on massive data, wherein the specific steps of collecting underground building data are:

[0128] Sampling point layout: within the scope of supplementary collection, determine and layout the sampling points for seismic wave detection on the ground according to the distribution, size and location characteristics of underground buildings;

[0129] Data collection: Use the GNSS system combined with the total station to digitally measure the ground sampling points and obvious feature points on the ground, use the seismic instrument to transmit signals underground at the sampling points, and record the reflected or refracted waves of the signals;

[0130] Data processing: remove noise from the collected seismic signals, perform seismic data compensation, and analyze the seismic data; perform data conversion and coordinate inverse calculation on the data obtained from the sampling points and obvious feature points on the ground to obtain the absolute position coordinates;

[0131] Establishment of time-distance profile: Based on the acquired seismic data, a time-distance profile is established based on the acquired arrival time and amplitude information, with the horizontal axis representing distance and the vertical axis representing time or depth. The reflection points and the corresponding time distance are marked on the profile.

[0132] Pre-stack data processing: pre-process the data on the time-range section by stacking, filtering, and removing the Doppler effect to generate the final imaging data;

[0133] Imaging and mapping: The imaging algorithm formula is used to invert the final imaging data to obtain the imaging results of the underground building. The specific imaging algorithm calculation formula is as follows:

[0134] Map(x,z)=∫u(x,z,t)d(x,z,t)dt

[0135] Where: Map(x,z) represents the imaging result, which indicates the strength or reflection coefficient of the underground structure at the position x at the underground depth z. u(x,z,t) represents the wave field amplitude reflection coefficient of the seismic wave at the spatial position (x,z) and time t. This parameter reflects the propagation of seismic waves in different underground media. d(x,z,t) represents a parameter of the seismic wave propagation path, which is usually called the attenuation function. It describes the attenuation and scattering of the medium encountered by the seismic wave during the propagation process. dt represents the small increment of the time interval, that is, the differential component.

[0136] Analyze and interpret the depth, size, and location parameters of underground buildings based on the imaging results, draw the outer contour of the underground buildings, and record the corresponding depth information. Combine the absolute position coordinates of the sampling points and obvious feature points on the ground to correct the outer contour of the underground buildings to the corresponding coordinate system;

[0137] Data aggregation and storage: Using the seismic wave sampling points with the same name and obvious ground feature points, the outer contour map of the underground building and the corresponding internal space of the underground building are spliced ​​and aggregated, and stored in the underground building intermediate database.

[0138] like Figure 5 As shown, a method for investigating existing illegal buildings based on massive data, wherein the specific steps of data production are:

[0139] Approval data production: For paper-based approval data, the approval graphic data is first produced by using the file vectorization method, and then the corresponding attribute information of the approval data is improved according to the file data. For the approval data in digital form, the approval data is produced by means of coordinate transformation, standardization processing, and attribute improvement. The two types of approval data are superimposed, redundant data are eliminated, and the integrity and positivity of the relevant attribute data are checked and modified. Then, a connection is established between the graphic data and the attribute data, and the rights space of the approval data is generated according to the issuance requirements of the approval data. Then, the standard approval data after the rights space is generated is stored in the standard approval database;

[0140] Building data production: pre-process and classify the massive amount of original and supplementary point cloud data of above-ground buildings, and produce contour and height extraction samples according to different types. Automatically classify and extract the contour and height information of above-ground building data through calculation formulas, correct the above-ground building data and improve the attribute information, and then summarize and store it in the above-ground building database; extract the outer contour and internal space location map in the underground building intermediate database, and according to the collected information, produce underground building data to improve the corresponding attribute information, establish a corresponding relationship with the above-ground building data, and then summarize and store the underground building data in the underground building database according to the spatial position; the building data production is divided into two parts: above-ground building data production and underground building data production;

[0141] Data classification storage: The produced standard approval data and construction data are stored in the standard approval database and the construction database respectively.

[0142] like Figure 6 As shown, a method for investigating existing illegal buildings based on massive data, wherein the specific steps of producing the approval data are:

[0143] Scanning and image processing: Obtain paper archive materials of approval data, scan the archive materials with a scanner, first perform color correction and geometric correction on the scanned images, then perform data matching, data editing, data error correction, projection transformation, edge processing, and image splicing on the scanned images;

[0144] Information conversion and extraction: Stratify the image elements, extract the building-related layers, use the software to automatically extract and manually correct the building graphics of the approval data, and fill in the attribute information of the approval data according to the relevant information of the archives;

[0145] Digital data acquisition: organize and acquire approval data in digital form, and perform coordinate conversion, standardization, and attribute improvement on the acquired approval data in digital form;

[0146] Editing and processing: spatially superimpose the digitized approval data with the approval data produced by archive vectorization. For spatially repeated approval data, remove the data with earlier approval time according to the approval time sequence. Then, check and modify the integrity and positivity of the relevant attribute data of all approval data, establish a connection between the graphic data and the attribute data, and generate the intermediate results of the approval data.

[0147] Rights space generation: According to the issuance requirements of the approval data, the rights space of the intermediate results of the approval data is produced. For approval values ​​that are fixed values, a fixed rights space is generated. For approval values ​​that are interval values, the rights space for approval and construction is generated according to the maximum and minimum intervals of the length, width, and height.

[0148] Approval database: The intermediate results of approval data after generating the rights space are defined as standard approval data and stored in the standard approval database.

[0149] like Figure 7 As shown, a method for investigating existing illegal buildings based on massive data, wherein the specific steps of producing the building data are:

[0150] Production of ground building data: Preprocess and classify the massive amount of original and supplementary point cloud data of ground buildings, then segment the ground building point cloud data by height type, and classify the building roof types through the curvature calculation formula. Create contour and height extraction samples according to different types, and automatically classify and extract the ground building data contour and height information through the calculation formula. After correcting the ground building data contour and improving the attribute information, generate two-dimensional and three-dimensional ground building data, and summarize and store them in the ground building database;

[0151] Production of underground building data: Extract the outer contour and internal space location map from the underground building intermediate database, and use the corresponding software to produce two- and three-dimensional underground building data based on the collected depth and internal height information, and improve the attribute information of the corresponding two- and three-dimensional underground building data. Use the location information of obvious feature points on the ground obtained during underground building data collection to establish a corresponding relationship with the ground building data. Finally, the two- and three-dimensional underground building data are summarized and stored in the underground building database according to the spatial position.

[0152] like Figure 8 As shown, a method for investigating existing illegal buildings based on massive data, wherein the specific steps of producing the above-ground building data are as follows;

[0153] Data extraction: Extract various point cloud data of above-ground buildings in the changed area from massive existing data and newly collected underground building intermediate databases;

[0154] Data preprocessing: Use fusion algorithms to fuse and register various point clouds including vehicle-mounted, airborne, stationary, backpack, and oblique photography, and then obtain complete and usable ground building point cloud data through noise reduction, rarefaction, and resampling;

[0155] Data classification processing: The average elevation of the center point X is obtained through the calculation formula. The calculation formula used is to calculate the average value by subtracting the elevation of the center point X to the four surrounding directions. The specific calculation formula is as follows:

[0156]

[0157] Where: Z′ represents the average elevation of the center point X, Z1 represents the elevation of the point above the center point X, Z2 represents the elevation of the point below the center point X, Z3 represents the elevation of the point to the left of the center point X, Z4 represents the elevation of the point to the right of the center point X, and Z x Indicates the elevation value of the center point X;

[0158] A calculation threshold ω is set for Z′. When it is determined that Z′ is less than the threshold ω, it is determined to be a ground point. When it is determined that Z′ is greater than or equal to the threshold ω, it is determined to be a non-ground point. The point cloud data is traversed in sequence until all ground point clouds are divided into ground points and non-ground point sets. The determination is completed, and the density clustering method with adaptive parameters is used to divide different ground objects of non-ground points, and the ground building point cloud data containing only building types is selected;

[0159] Data segmentation: refer to the orthophoto data, perform height type segmentation on the ground building point cloud data, and divide the ground building point cloud data into three types of data: high-rise, multi-story, and low-rise according to the size of the ratio of the building shadow length to the building length in the orthophoto. If ξ≥1, the ground building point cloud data is judged to be a high-rise building; if ξ is between 0.5 and 1, the ground building point cloud data is judged to be a multi-story building; if ξ≤0.5, the ground building point cloud data is judged to be a low-rise building;

[0160] Sample setting: According to different segmentation types, the curvature value of each building in high-rise, multi-story, and low-rise buildings is calculated through the curvature calculation formula to determine the roof type of the building. If the curvature is less than the threshold value ψ, the building is judged to be a flat roof building. If the curvature is greater than or equal to the threshold value ψ, the building is judged to be a slope roof building. The curvature calculation formula is as follows: Curvature calculation formula

[0161]

[0162] Where K represents the curvature of the building roof, Z x Indicates the elevation difference of the building roof in the x direction, Z y The elevation difference in the y direction of the building roof;

[0163] First, according to different height types and roof types, the correspondence between the real samples of the main body, balcony, doorway, eaves, and elevator room and the drawn contours is made, and the sample type and normal vector features are annotated. Then, the corresponding negative samples are made, and the real samples are enhanced in the form of rotation, translation, and scaling. According to different roof types, building height information samples are made. Among them, two height extraction samples are set for flat-topped buildings, namely the roof surface and the highest point, and three height extraction samples are set for sloping-topped buildings, namely the slope starting point, the ridge point, and the height average point.

[0164] Automatically extract building information using calculation formulas: According to the classification of different height types and roofs, refer to the sample types of each type, select the machine learning model of the appropriate task for training, and use the training data to optimize the model, input each type of point cloud data to be processed into the corresponding trained model for automatic extraction, and then transform the extracted three-dimensional building points into a two-dimensional plane through projection, use the point cloud geometry algorithm to extract the building contour points, obtain the initial contour, and write the height information into the corresponding main contour; the judgment condition of the point cloud geometry algorithm is: in the point set P, through any two points p i (x i ,y i ), p r (x r ,y r ) Draw a circle with a radius of α and calculate the center of the circle passing through these two points p o (x o ,y o ) coordinates, where p o The coordinate calculation formula is as follows:

[0165]

[0166] In the formula α radius parameter;

[0167] By judging the relationship between the distance from other points to the center of the circle and the radius α, we can determine the distance between the connecting line p and the center of the circle. i , p r Is it a boundary line segment? If the distance from other points to the center of the circle is greater than the radius α, it is judged that there are no other points in the circle, then p is considered i , p r is a boundary point, and its connecting line p i , p r is a boundary segment, otherwise, it is considered that p i , p r Not a boundary point, its connecting line p i , p r It is not a boundary line segment, and then repeat the above steps for the remaining points in the point set P until all the points in the point set P are judged;

[0168] Data optimization: Filter, group, and edge fit the initially extracted underground building outlines, and then correct the ground building outlines to the corresponding coordinate system based on the feature positions of the point cloud data and topographic map information to obtain the final ground building outlines;

[0169] Data production: Automatically generate corresponding two-dimensional and three-dimensional ground building data based on the extracted contour lines and height information of different types of ground buildings, and improve the attribute information of two-dimensional and three-dimensional ground building data including usage nature, building area, volume ratio and building density based on point cloud data and oblique image data;

[0170] Data aggregation and storage: Different types of two- and three-dimensional ground building data are aggregated and stored in the ground building database according to their spatial locations.

[0171] like Fig. 9 As shown, a method for investigating existing illegal buildings based on massive data, wherein the specific steps of data determination are:

[0172] Building data extraction: extracting corresponding ground building data and underground building data from the underground building database and the ground building database respectively;

[0173] Auxiliary data extraction: Extract data including street view data, oblique image data, approved land use data, various restriction boundary data, and processed illegal building data from massive existing data;

[0174] Processed illegal building investigation: Define the names of above-ground building data and underground building data, and arrange them in ascending order according to the verification order as illegal building data① to illegal building data Firstly, spatial comparison analysis is adopted based on the processed illegal building data to automatically check the above-ground building data and underground building data. According to the parameter τ of the graphic area overlap rate of the processed illegal building data, the above-ground building data and the underground building data, a threshold η is set for τ to preliminarily judge whether the above-ground building data and the underground building data are processed illegal buildings. If τ≥threshold η, the illegal buildings are then compared with the above-ground building data and the underground building data in terms of attributes including the number of floors, building area and nature. If the number of floors, building area and nature are determined to be consistent, the above-ground building data and the underground building data are extracted and stored in the compliant building database. If any one of the number of floors, building area and nature is determined to be inconsistent, the above-ground building data and the underground building data are extracted and defined as illegal building data ①. If τ<threshold η, the above-ground building data and the underground building data are extracted and defined as illegal building data ①, and the defined illegal building data ① is subsequently determined.

[0175] Approval judgment: Perform spatial comparison analysis on the illegal building data① and the building data of the standard approval data, set the parameter φ of the spatial overlap rate, set the threshold ρ for φ, if φ≤threshold ρ, define the above-ground building data and underground building data as suspected illegal building data, record the reasons for the violations and extract and store them in the suspected illegal building intermediate database, if φ>threshold ρ, extract the above-ground building data and underground building data as illegal building data②, and conduct subsequent comparison;

[0176] Boundary comparison: Perform boundary comparison on illegal building data ② and various boundary data. If it is determined that illegal building data ② is within the restriction range of various boundary data, the above-ground building data and underground building data within the illegal building data ② within the restriction range are defined as suspected illegal building data, and the reasons for the violation are recorded and extracted and stored in the suspected illegal building data intermediate database; if it is determined that illegal building data ② is not within the restriction range of various boundary data, illegal building data ② is defined as illegal building data ③, and subsequent comparisons are performed in sequence;

[0177] Property comparison: compare the illegal building data ③ with the land use nature of the standard approval data and the land use nature of the construction detailed plan. If it is determined that the illegal building data ③ is different from the land use nature of the standard approval data and the land use nature of the construction detailed plan, the above-ground building data and underground building data in this part of the illegal building data ③ are defined as suspected illegal building data, and the reasons for the violation are recorded and extracted and stored in the suspected illegal building data intermediate library; if it is determined that the land use nature of the illegal building data ③ is the same as the land use nature of the standard approval data and the land use nature of the construction detailed plan, the illegal building data ③ is defined as illegal building data ④ for subsequent comparison;

[0178] Comparison of floor area ratio: Compare the floor area ratio of the illegal building data ④ with that of the standard approval data. If it is determined that the floor area ratio of the illegal building data ④ is different from that of the standard approval data, the above-ground building data and underground building data in the illegal building data ④ with different floor area ratios are defined as suspected illegal building data, and the reasons for the violations are recorded and extracted and stored in the suspected illegal building data intermediate library. If it is determined that the floor area ratio of the illegal building data ④ is the same as that of the standard approval data, the illegal building data ④ is defined as illegal building data ⑤ for subsequent comparison;

[0179] Approval data comparison: determine the type of the approval value of the approval data. If the approval value of the approval data is determined to be an interval value, compare the illegal building data ⑤ with the standard approval data for self-comparison and adjacent spacing comparison. If the approval value of the approval data is determined to be a definite value, compare the illegal building data ⑤ with the standard approval data for plane comparison and height comparison;

[0180] Internal space comparison: determine whether the illegal building data ⑦ contains underground building data. If it is determined that the illegal building data ⑦ contains underground building data, the underground building data in the illegal building data ⑦ is combined with the point cloud data and the approval information of the standard approval data for internal space comparison. If it is determined that the internal space has been demolished or modified, the underground building data in the demolished or modified part of the illegal building data ⑦ is defined as suspected illegal building data, and the reasons for the violation are recorded and extracted and stored in the suspected illegal building data intermediate library. If it is determined that the internal space has not been demolished or modified, the illegal building data ⑦ is defined as compliant above-ground building data and stored in the compliant building database; if it is determined that the illegal building data ⑦ does not contain underground building data, subsequent operations are performed;

[0181] Color and structure comparison: Determine whether the ground building data in the illegal building data ⑦ is located in the newly added building change area. If it is determined that the ground building data in the illegal building data ⑦ is located in the newly added building change area, the ground building data in the illegal building data ⑦ is combined with the oblique image data and compared with the approval information of the standard approval data in color and structure. If it is determined that any one of the color and structure of the building is inconsistent, the inconsistent ground building data in the illegal building data ⑦ is defined as suspected illegal building data, and the cause of the violation is recorded and extracted and stored in the suspected illegal building data intermediate library. If the color and structure of the building are determined to be consistent, the illegal building data ⑦ is defined as compliant ground building data and stored in the compliant building database; if it is determined that the ground building data in the illegal building data ⑦ is not in the newly added building change area, then the subsequent operation is performed;

[0182] Suspected illegal building intermediate database: All suspected illegal building data obtained through the above comparison are stored in the suspected illegal building intermediate database according to spatial location;

[0183] Compliant building database: The above-ground building data and underground building data remaining after the above comparison are defined as compliant building data and stored in the compliant building database.

[0184] like Fig.10 As shown, a method for investigating existing illegal buildings based on massive data, wherein the specific steps of comparing the approval data are as follows:

[0185] Approval data type determination: extract standard approval data from the standard approval database, determine the type of the approval value of the standard approval data, and if the approval value of the standard approval data is determined to be a fixed value, perform subsequent self-comparison and adjacent spacing comparison; if the approval value of the standard approval data is determined to be an interval value, perform subsequent plane comparison and height comparison;

[0186] Self-comparison: Compare the illegal building data ⑤ with the standard approval data. If it is determined that the illegal building data ⑤ exceeds the rights space of the corresponding standard approval data, the above-ground building data and underground building data in the illegal building data ⑤ that exceed the rights space are defined as suspected illegal building data, and the reasons for the violation are recorded and extracted and stored in the suspected illegal building data intermediate library. If it is determined that the illegal building data ⑤ does not exceed the rights space of the corresponding standard approval data, the illegal building data ⑤ is defined as illegal building data ⑥ for subsequent comparison;

[0187] Adjacent spacing comparison: compare the adjacent spacing of the illegal building data ⑥ with the standard approval data. If it is determined that the illegal building data ⑥ exceeds the range of adjacent spacing required by the approval value of the corresponding standard approval data, the above-ground building data and underground building data in the illegal building data ⑥ that exceed the range of adjacent spacing are defined as suspected illegal building data, and the reasons for the violation are recorded and extracted and stored in the suspected illegal building data intermediate library. If it is determined that the illegal building data ⑥ does not exceed the range of adjacent spacing required by the approval value of the corresponding standard approval data, the illegal building data ⑥ is defined as illegal building data ⑦ for subsequent comparison;

[0188] Plane comparison: Perform a plane comparison of the illegal building data ⑤ with the standard approval data. If it is determined that the illegal building data ⑤ exceeds any one of the plane rights space and adjacent spacing of the corresponding standard approval data, the above-ground building data and underground building data in this part of the illegal building data ⑤ are defined as suspected illegal building data, and the reasons for the violations are recorded and extracted and stored in the suspected illegal building data intermediate library. If it is determined that the illegal building data ⑤ does not exceed the plane rights space and adjacent spacing of the corresponding standard approval data, the illegal building data ⑤ is defined as illegal building data ⑥ for subsequent comparison;

[0189] Height comparison: compare the height of the illegal building data ⑥ with the standard approval data. If it is determined that the illegal building data ⑥ exceeds the height right space of the corresponding standard approval data, the above-ground building data and underground building data in the illegal building data ⑥ that exceed the height right space are defined as suspected illegal building data, and the reasons for the violation are recorded and extracted and stored in the suspected illegal building data intermediate library. If it is determined that the illegal building data ⑥ does not exceed the height right space of the corresponding standard approval data, the illegal building data ⑥ is defined as illegal building data ⑦ for subsequent comparison.

[0190] like Fig.11 As shown, a method for investigating existing illegal buildings based on massive data, wherein the specific steps of the data correction and verification are:

[0191] Improvement of suspected illegal building information: Extract suspected illegal building data from the suspected illegal building intermediate database, and use massive oblique image data, point cloud data, district boundary data, industrial zone boundary data, land price data, land use data, ownership information data, geological conditions information data, supporting facilities data, and green space data to improve the attribute information of suspected illegal buildings, including number, district boundary, industrial park, land price, land use, ownership, geological conditions, green space conditions, and supporting facilities conditions;

[0192] Determination of management authority of suspected illegal buildings: Determine the construction time of suspected illegal building data based on satellite remote sensing image data, and then determine the management authority of suspected illegal buildings based on the issuance time of approval data, the spatial area of ​​suspected illegal building data, and the violation reason of suspected illegal building data; judge all suspected illegal building data in turn until the management authority is defined and the determination is completed;

[0193] Verification and confirmation of suspected illegal buildings: after the information is completed and the management authority is determined, the suspected illegal buildings are verified on site, and after excluding the compliant buildings again, the illegal building inventory data is generated;

[0194] Illegal building stock database: The illegal building stock data is stored in the illegal building stock database.

[0195] like Fig.12 As shown, a method for investigating existing illegal buildings based on massive data, wherein the specific steps of the summary analysis and evaluation are:

[0196] Retrieval of illegal building data: retrieve illegal building stock data from the illegal building stock database;

[0197] Special analysis: Combine the illegal building stock data with various auxiliary materials to conduct special analysis of zoning, ownership, economy, society and overall categories, generate various special analysis result data and store them in the special analysis database;

[0198] Special analysis report: According to the special analysis results, output the special analysis report containing text, graphics, charts, and 3D models according to the classification;

[0199] Special remediation information: Based on special analysis data, combined with the types of illegal buildings and planning and development goals, remediation plans for illegal buildings in different areas, management powers, numbers of floors, nature, ownership, geological conditions, and types of supporting facilities are formed, and targeted remediation suggestion information is generated for each building and stored in the corresponding remediation information database.

[0200] like Fig.13 As shown, a method for investigating existing illegal buildings based on massive data, wherein the specific steps of the feedback reminder are:

[0201] Retrieval of special analysis results: According to the designated area for rectification of illegal buildings, various special analysis result data and rectification suggestion information are extracted from the special analysis database;

[0202] On-site collection and verification: According to the designated area for illegal building rectification, the illegal building stock data in the corresponding area is regularly extracted, and the location information of different illegal building stock data is generated and sent to the supervisors. The supervisors can regularly collect oblique images and point cloud data of the corresponding locations by means of drone oblique photography according to the rectification situation;

[0203] Feedback information generation: Automatically extract oblique images and point cloud data, compare and analyze with the stock data of illegal buildings, verify the rectification of illegal buildings, update various special analysis results data, and generate rectification feedback information;

[0204] Display storage: Through a dedicated network, the special analysis results of illegal buildings in different periods and the feedback information of rectification are stored and displayed on the corresponding two-dimensional and three-dimensional terminals according to a fixed period.

[0205] A method for investigating existing illegal buildings based on massive data. In a specific implementation case, planning approval data includes control detailed planning data, planning condition data, national land space planning data, site selection opinion data, construction land planning permit data, construction project planning permit data, rural construction planning permit data, and construction project completion acceptance data; planning approval data includes: land use nature, volume ratio, building area, building size, building height, construction density, surrounding building spacing, color, structure, and shape; image data includes satellite image data, aerial image data, oblique image data, and multi-spectral remote sensing image data; format conversion includes conversion of different types of data (oblique image conversion to orthophoto) and conversion of different formats of the same type of data; planning areas include urban built-up areas and rural areas; building types include high-rise areas, multi-story areas, and low-rise areas; special land use areas include military land and security land; building data includes point cloud data, topographic map data, GIS data, oblique image data, and real-life three-dimensional data; supplementary collection methods include traditional surveying and mapping methods, lidar measurement, and oblique photogrammetry; approval values ​​include: length, width, height, and adjacent spacing Volume ratio, use nature, building density, land use scope; the right space form includes: two-dimensional form, three-dimensional form; the right space content includes: length, width, height; the ground building data includes two-dimensional GIS form, real-scene three-dimensional form; the correspondence between underground building data and ground building data is one-to-one and one-to-many; various restriction boundary data include land red line, permanent basic farmland control line, ecological protection red line, urban development boundary, village construction boundary, flood risk control line, historical and cultural protection line, various urban planning controls; district boundary data include: administrative boundary data, Management boundary data, urban planning area boundary data, township planning area boundary data; zoning categories include: district boundaries, industrial park boundaries, ring line boundaries; ownership categories include: land ownership, building ownership; economic categories include: project cost, demolition cost, land value, rental price, renovation cost, tax data, financial data; social categories include: population, education, medical care, employment, leasing, transportation; overall categories include: area, number, number of floors, supporting facilities, nature, greening, geological conditions; feedback information includes: early warning information, prompt information of areas exceeding specified indicators, and target gap information.

[0206] The following is an example of a specific implementation:

[0207] Embodiment 2:

[0208] Figure 1 A flow chart of the method for investigating existing illegal buildings based on massive data provided in this embodiment is shown in FIG. Figure 1As shown, a method for investigating existing illegal buildings based on massive data is provided, wherein the method comprises the following steps: defining illegal buildings: setting a standard for determining illegal buildings, wherein the standard is set to violate planning approval data within a specified time period; data comparison: according to the time period requirements for defining illegal buildings, obtaining multiple periods of image data corresponding to the time period, performing data correction and data classification on the obtained image data, and making and setting two types of extraction samples, namely, original building changes and newly added buildings, according to the classified data; quickly determining the building change area through computer automatic recognition technology based on convolutional neural network, and summarizing the change area after data re-verification, and superimposing and comparing with the current land use data. Yes, generate the scope of illegal buildings that need to be determined in the end; data analysis and collection: analyze the massive amount of existing data within the acquired change area, determine the data range where the above-ground building data and underground building data are missing and the location accuracy does not meet the requirements, collect the above-ground building data within the data range, collect the internal space and external contour of the underground building data, and store the collected above-ground building data and underground building data in the underground building intermediate database and the underground building intermediate database respectively; data production: process the paper and digital approval original data to produce standard approval data with rights space, store it in the designated database, and The initial data of the above-ground buildings and the initial data of the underground buildings obtained by data collection are used to produce the above-ground building data and the underground building data, and they are stored in the designated databases respectively; Data judgment: The building data and auxiliary materials are extracted respectively, and the processed illegal buildings are checked and the approval data is judged in the building data, and the subsequent boundary comparison, property comparison, volume ratio comparison, approval data comparison, internal space comparison, color and structure comparison are carried out in sequence, and all the suspected illegal buildings are judged to be finished. The compliant building data and the suspected illegal building data obtained are stored in the corresponding databases respectively; Data correction and verification: The information of the suspected illegal building data is improved by using the existing data to judge the illegal The management authority of illegal buildings is exercised, and after verification and confirmation, the stock data of illegal buildings is generated and stored in the corresponding database; summary analysis and evaluation: the stock data of illegal buildings is retrieved and combined with various auxiliary materials for various special analyses, and special analysis reports and special rectification information are generated; feedback reminders: according to the designated areas for rectification of illegal buildings, various special analysis result data, rectification suggestion information, and stock data of illegal buildings in the corresponding areas are extracted from the rectification information database and the illegal building stock database, and illegal buildings in the designated areas are regularly collected on-site, and the rectification of illegal buildings is verified, various special analysis results are updated, rectification feedback information is generated, and stored and displayed on the corresponding terminal.

[0209] like Figure 2 As shown, a method for investigating existing illegal buildings based on massive data, wherein the specific steps of data comparison are:

[0210] Obtaining multiple images: According to the time period defined by the illegal building, obtain multiple image data corresponding to the time period;

[0211] Image data correction: standardize and judge the multi-phase image data, and perform coordinate transformation, data fusion, projection and correction processing, and format conversion standardization processing on the non-standardized image data according to the corresponding reasons, so as to generate multi-phase orthophoto image data of the same type;

[0212] Sample classification and setting: divide the orthophoto data of multiple periods into regions according to planning areas and building types. According to different regional divisions, extract samples of two categories, original building changes and newly added buildings, are produced respectively. The produced samples are extracted, verified and modeled.

[0213] Building change area detection based on convolutional neural network: According to the corresponding sample settings, the computer automatic recognition technology based on convolutional neural network is used to quickly determine the initial range of the change area of ​​the original building changes and the newly added buildings in the classified orthophotos before and after.

[0214] Data correction: Use multispectral remote sensing image data to verify the preliminary range of the detected change area, remove the misidentified area, supplement the missed change area, and perform geometric normalization on the change area to form the intermediate range of the two types of building change areas of different classifications: original building changes and newly added buildings;

[0215] Data aggregation and overlay optimization: The middle range of the two types of building change areas detected in different categories are aggregated, and compared with the existing land use data, the special land use range in the existing land use is screened, and the special land use range within the detected building change area is eliminated to generate the final change area range that needs to be judged as illegal buildings.

[0216] like Figure 3 As shown, a method for investigating existing illegal buildings based on massive data, wherein the specific steps of data analysis and collection are:

[0217] Determine the scope of missing data: Analyze the massive amount of existing data within the acquired change area, and judge the existence and location accuracy of the above-ground building data and underground building data. If it is judged that the above-ground building data and underground building data do not exist, record the scope of missing above-ground building data and underground building data. If it is judged that the above-ground building data and underground building data exist, judge the accuracy of the above-ground building data and underground building data respectively. If it is judged that the accuracy of the above-ground building data and underground building data does not meet the requirements, record the scope of the above-ground building data and underground building data not meeting the accuracy requirements. If it is judged that the accuracy of the above-ground building data and underground building data meets the requirements, do not record the relevant scope, and define the scope of missing above-ground building data and underground building data and the scope of the above-ground building data and underground building data not meeting the accuracy requirements in the change area as the scope that needs to be supplemented.

[0218] Supplementary collection of ground building data: Supplementary collection of ground building data is carried out within the scope of supplementary collection to obtain the plane position and height information of the ground building with position accuracy that meets the requirements;

[0219] Underground building data collection: Supplementary collection of underground building data within the scope of supplementary collection. The interior of underground buildings is mainly measured by stationary laser radar scanning, supplemented by handheld laser radar scanning, and the two are combined to collect and measure point cloud data, and draw the internal space plan. The outer contour of underground buildings is collected and measured by seismic wave detection. The internal space and external contour of the underground building are combined by splicing the sampling points with the same name and the obvious feature points on the ground, so as to connect the internal and external contours of the underground space as a whole. Then, the geometric position information of the internal and external contours of the underground building is obtained through the absolute position coordinates of the obtained sampling points and obvious feature points on the ground.

[0220] Data integration and storage: The collected plane position and height information data of the above-ground buildings are defined as the initial data of the above-ground buildings and stored in the intermediate database of the above-ground buildings; the collected outer contour map of the underground buildings and the corresponding internal space data of the underground buildings are defined as the initial data of the underground buildings and stored in the intermediate database of the underground buildings.

[0221] like Figure 4 As shown, a method for investigating existing illegal buildings based on massive data, wherein the specific steps of collecting underground building data are:

[0222] Sampling point layout: within the scope of supplementary collection, determine and layout the sampling points for seismic wave detection on the ground according to the distribution, size and location characteristics of underground buildings;

[0223] Data collection: Use the GNSS system combined with the total station to digitally measure the ground sampling points and obvious feature points on the ground, use the seismic instrument to transmit signals underground at the sampling points, and record the reflected or refracted waves of the signals;

[0224] Data processing: remove noise from the collected seismic signals, perform seismic data compensation, and analyze the seismic data; perform data conversion and coordinate inverse calculation on the data obtained from the sampling points and obvious feature points on the ground to obtain the absolute position coordinates;

[0225] Establishment of time-distance profile: Based on the acquired seismic data, a time-distance profile is established based on the acquired arrival time and amplitude information, with the horizontal axis representing distance and the vertical axis representing time or depth. The reflection points and the corresponding time distance are marked on the profile.

[0226] Pre-stack data processing: pre-process the data on the time-range section by stacking, filtering, and removing the Doppler effect to generate the final imaging data;

[0227] Imaging and mapping: The imaging algorithm formula is used to invert the final imaging data to obtain the imaging results of the underground building. The specific imaging algorithm calculation formula is as follows:

[0228] Map(x,z)=∫u(x,z,t)d(x,z,t)dt

[0229] Where: Map(x,z) represents the imaging result, which indicates the strength or reflection coefficient of the underground structure at the position x at the underground depth z. u(x,z,t) represents the wave field amplitude reflection coefficient of the seismic wave at the spatial position (x,z) and time t. This parameter reflects the propagation of seismic waves in different underground media. d(x,z,t) represents a parameter of the seismic wave propagation path, which is usually called the attenuation function. It describes the attenuation and scattering of the medium encountered by the seismic wave during the propagation process. dt represents the small increment of the time interval, that is, the differential component.

[0230] Analyze and interpret the depth, size, and location parameters of underground buildings based on the imaging results, draw the outer contour of the underground buildings, and record the corresponding depth information. Combine the absolute position coordinates of the sampling points and obvious feature points on the ground to correct the outer contour of the underground buildings to the corresponding coordinate system;

[0231] Data aggregation and storage: Using the seismic wave sampling points with the same name and obvious ground feature points, the outer contour map of the underground building and the corresponding internal space of the underground building are spliced ​​and aggregated, and stored in the underground building intermediate database.

[0232] like Figure 5 As shown, a method for investigating existing illegal buildings based on massive data, wherein the specific steps of data production are:

[0233] Approval data production: For paper-based approval data, the approval graphic data is first produced by using the file vectorization method, and then the corresponding attribute information of the approval data is improved according to the file data. For the approval data in digital form, the approval data is produced by means of coordinate transformation, standardization processing, and attribute improvement. The two types of approval data are superimposed, redundant data are eliminated, and the integrity and positivity of the relevant attribute data are checked and modified. Then, a connection is established between the graphic data and the attribute data, and the rights space of the approval data is generated according to the issuance requirements of the approval data. Then, the standard approval data after the rights space is generated is stored in the standard approval database;

[0234] Building data production: pre-process and classify the massive amount of original and supplementary point cloud data of above-ground buildings, and produce contour and height extraction samples according to different types. Automatically classify and extract the contour and height information of above-ground building data through calculation formulas, correct the above-ground building data and improve the attribute information, and then summarize and store it in the above-ground building database; extract the outer contour and internal space location map in the underground building intermediate database, and according to the collected information, produce underground building data to improve the corresponding attribute information, establish a corresponding relationship with the above-ground building data, and then summarize and store the underground building data in the underground building database according to the spatial position; the building data production is divided into two parts: above-ground building data production and underground building data production;

[0235] Data classification storage: The produced standard approval data and construction data are stored in the standard approval database and the construction database respectively.

[0236] like Figure 6 As shown, a method for investigating existing illegal buildings based on massive data, wherein the specific steps of producing the approval data are:

[0237] Scanning and image processing: Obtain paper archive materials of approval data, scan the archive materials with a scanner, first perform color correction and geometric correction on the scanned images, then perform data matching, data editing, data error correction, projection transformation, edge processing, and image splicing on the scanned images;

[0238] Information conversion and extraction: Stratify the image elements, extract the building-related layers, use the software to automatically extract and manually correct the building graphics of the approval data, and fill in the attribute information of the approval data according to the relevant information of the archives;

[0239] Digital data acquisition: organize and acquire approval data in digital form, and perform coordinate conversion, standardization, and attribute improvement on the acquired approval data in digital form;

[0240] Editing and processing: spatially superimpose the digitized approval data with the approval data produced by archive vectorization. For spatially repeated approval data, remove the data with earlier approval time according to the approval time sequence. Then, check and modify the integrity and positivity of the relevant attribute data of all approval data, establish a connection between the graphic data and the attribute data, and generate the intermediate results of the approval data.

[0241] Rights space generation: According to the issuance requirements of the approval data, the rights space of the intermediate results of the approval data is produced. For approval values ​​that are fixed values, a fixed rights space is generated. For approval values ​​that are interval values, the rights space for approval and construction is generated according to the maximum and minimum intervals of the length, width, and height.

[0242] Approval database: The intermediate results of approval data after generating the rights space are defined as standard approval data and stored in the standard approval database.

[0243] like Figure 7 As shown, a method for investigating existing illegal buildings based on massive data, wherein the specific steps of producing the building data are:

[0244] Production of ground building data: Preprocess and classify the massive amount of original and supplementary point cloud data of ground buildings, then segment the ground building point cloud data by height type, and classify the building roof types through the curvature calculation formula. Create contour and height extraction samples according to different types, and automatically classify and extract the ground building data contour and height information through the calculation formula. After correcting the ground building data contour and improving the attribute information, generate two-dimensional and three-dimensional ground building data, and summarize and store them in the ground building database;

[0245] Production of underground building data: Extract the outer contour and internal space location map from the underground building intermediate database, and use the corresponding software to produce two- and three-dimensional underground building data based on the collected depth and internal height information, and improve the attribute information of the corresponding two- and three-dimensional underground building data. Use the location information of obvious feature points on the ground obtained during underground building data collection to establish a corresponding relationship with the ground building data. Finally, the two- and three-dimensional underground building data are summarized and stored in the underground building database according to the spatial position.

[0246] like Figure 8 As shown, a method for investigating existing illegal buildings based on massive data, wherein the specific steps of producing the above-ground building data are as follows;

[0247] Data extraction: Extract various point cloud data of above-ground buildings in the changed area from massive existing data and newly collected underground building intermediate databases;

[0248] Data preprocessing: Use fusion algorithms to fuse and register various point clouds including vehicle-mounted, airborne, stationary, backpack, and oblique photography, and then obtain complete and usable ground building point cloud data through noise reduction, rarefaction, and resampling;

[0249] Data classification processing: The average elevation of the center point X is obtained through the calculation formula. The calculation formula used is to calculate the average value by subtracting the elevation of the center point X to the four surrounding directions. The specific calculation formula is as follows:

[0250]

[0251] Where: Z′ represents the average elevation of the center point X, Z1 represents the elevation of the point above the center point X, Z2 represents the elevation of the point below the center point X, Z3 represents the elevation of the point to the left of the center point X, Z4 represents the elevation of the point to the right of the center point X, and Z x Indicates the elevation value of the center point X;

[0252] When it is determined that Z′<1.5, it is determined to be a ground point, and when it is determined that Z′≥1.5, it is determined to be a non-ground point. The point cloud data is traversed in sequence until all ground point clouds are divided into ground points and non-ground point sets. The determination is completed, and the density clustering method with adaptive parameters is used to divide different ground objects of non-ground points, and the ground building point cloud data containing only building types is selected;

[0253] Data segmentation: refer to the orthophoto data, perform height type segmentation on the ground building point cloud data, and divide the ground building point cloud data into three types of data: high-rise, multi-story, and low-rise according to the size of the ratio of the building shadow length to the building length in the orthophoto. If ξ≥1, the ground building point cloud data is judged to be a high-rise building; if ξ is between 0.5 and 1, the ground building point cloud data is judged to be a multi-story building; if ξ≤0.5, the ground building point cloud data is judged to be a low-rise building;

[0254] Sample settings: According to different segmentation types, the curvature calculation formula is used to calculate the curvature value of each building in high-rise, multi-story, and low-rise buildings to determine the roof type of the building. The curvature calculation formula is as follows: Curvature calculation formula

[0255]

[0256] Where K represents the curvature of the building roof, Z x Indicates the elevation difference of the building roof in the x direction, Z y The elevation difference in the y direction of the building roof;

[0257] If the curvature of the building roof K is less than 0.1, the building is judged to be a flat roof building. If the curvature of the building roof K is greater than or equal to 0.1, the building is judged to be a sloping roof building. Flat roof buildings can be further distinguished. For relatively flat flat roofs, the curvature K value of the building roof is between 0.01 and 0.05. For slightly inclined flat roofs, the curvature K value of the building roof is between 0.05 and 0.1.

[0258] First, according to different height types and roof types, the correspondence between the real samples of the main body, balcony, doorway, eaves, and elevator room and the drawn contours is made, and the sample type and normal vector features are annotated. Then, the corresponding negative samples are made, and the real samples are enhanced in the form of rotation, translation, and scaling. According to different roof types, building height information samples are made. Among them, two height extraction samples are set for flat-topped buildings, namely the roof surface and the highest point, and three height extraction samples are set for sloping-topped buildings, namely the slope starting point, the ridge point, and the height average point.

[0259] Automatically extract building information using calculation formulas: For point cloud data in multi-story and high-rise building areas, refer to the sample type and use the point cloud model feature extraction algorithm of normal vector region clustering segmentation combined with density clustering algorithm for training and optimization extraction. For point cloud data in low-rise building areas, refer to the sample type and use the point cloud model feature recognition algorithm based on region segmentation for training and optimization extraction. Use the training data to optimize the corresponding model, and then use the cross-validation method of the two to evaluate the accuracy and robustness of the model. Input each type of point cloud data to be processed into the corresponding trained model for automatic extraction, and then transform the extracted three-dimensional building points into a two-dimensional plane through projection. Use the point cloud geometry algorithm to extract building contour points to obtain the initial contour, and write the height information into the corresponding main contour. The judgment condition of the point cloud geometry algorithm is: in the point set P, through any two points p i (x i ,y i ), p r (x r ,y r ) Draw a circle with a radius of α and calculate the center of the circle passing through these two points p o (x o ,y o ) coordinates, where p o The coordinate calculation formula is as follows:

[0260]

[0261] In the formula α radius parameter;

[0262] By judging the relationship between the distance from other points to the center of the circle and the radius α, we can determine the distance between the connecting line p and the center of the circle. i , pr Is it a boundary line segment? If the distance from other points to the center of the circle is greater than the radius α, it is judged that there are no other points in the circle, then p is considered i , p r is a boundary point, and its connecting line p i , p r is a boundary segment, otherwise, it is considered that p i , p r Not a boundary point, its connecting line p i , p r It is not a boundary line segment, and then repeat the above steps for the remaining points in the point set P until all the points in the point set P are judged;

[0263] Data optimization: Filter, group, and edge fit the initially extracted underground building outlines, and then correct the ground building outlines to the corresponding coordinate system based on the feature positions of the point cloud data and topographic map information to obtain the final ground building outlines;

[0264] Data production: Automatically generate corresponding two-dimensional and three-dimensional ground building data based on the extracted contour lines and height information of different types of ground buildings, and improve the attribute information of two-dimensional and three-dimensional ground building data including usage nature, building area, volume ratio and building density based on point cloud data and oblique image data;

[0265] Data aggregation and storage: Different types of two- and three-dimensional ground building data are aggregated and stored in the ground building database according to their spatial locations.

[0266] like Fig. 9 As shown, a method for investigating existing illegal buildings based on massive data, wherein the specific steps of data determination are:

[0267] Building data extraction: extracting corresponding ground building data and underground building data from the underground building database and the ground building database respectively;

[0268] Auxiliary data extraction: Extract data including street view data, oblique image data, approved land use data, various restriction boundary data, and processed illegal building data from massive existing data;

[0269] Processed illegal building investigation: Define the names of above-ground building data and underground building data, and arrange them in ascending order according to the verification order as illegal building data① to illegal building data First, based on the processed illegal building data, a spatial comparison analysis method is used to automatically check the ground building data and underground building data. According to the parameter τ of the graphic area overlap rate of the processed illegal building data and the ground building data and the underground building data, a threshold η of τ is set to 0.95, and a preliminary judgment is made as to whether the ground building data and the underground building data are processed illegal buildings. If τ≥0.95, the illegal buildings are compared with the ground building data and the underground building data in terms of attributes including the number of floors, building area, and nature. If the number of floors, building area, and nature are determined to be consistent, the ground building data and the underground building data of this part are extracted and stored in the compliant building database. If any of the number of floors, building area, and nature are determined to be inconsistent, the ground building data and the underground building data of this part are extracted and defined as illegal building data①. If τ<0.95, the ground building data and the underground building data are extracted and defined as illegal building data①, and the defined illegal building data① is subsequently determined;

[0270] Approval judgment: Perform spatial comparison analysis on the illegal building data ① and the building data of the standard approval data, set the parameter φ of the spatial overlap rate, set the threshold ρ of φ to 0.1, if φ≤0.1, define the above-ground building data and underground building data as suspected illegal building data, record the reasons for the violations and extract and store them in the suspected illegal building intermediate database, if φ>0.1, extract the above-ground building data and underground building data as illegal building data ②, and conduct subsequent comparison;

[0271] Boundary comparison: Perform boundary comparison on illegal building data ② and various boundary data. If it is determined that illegal building data ② is within the restriction range of various boundary data, the above-ground building data and underground building data within the illegal building data ② within the restriction range are defined as suspected illegal building data, and the reasons for the violation are recorded and extracted and stored in the suspected illegal building data intermediate database; if it is determined that illegal building data ② is not within the restriction range of various boundary data, illegal building data ② is defined as illegal building data ③, and subsequent comparisons are performed in sequence;

[0272] Property comparison: compare the illegal building data ③ with the land use nature of the standard approval data and the land use nature of the construction detailed plan. If it is determined that the illegal building data ③ is different from the land use nature of the standard approval data and the land use nature of the construction detailed plan, the above-ground building data and underground building data in this part of the illegal building data ③ are defined as suspected illegal building data, and the reasons for the violation are recorded and extracted and stored in the suspected illegal building data intermediate library; if it is determined that the land use nature of the illegal building data ③ is the same as the land use nature of the standard approval data and the land use nature of the construction detailed plan, the illegal building data ③ is defined as illegal building data ④ for subsequent comparison;

[0273] Comparison of floor area ratio: Compare the floor area ratio of the illegal building data ④ with that of the standard approval data. If it is determined that the floor area ratio of the illegal building data ④ is different from that of the standard approval data, the above-ground building data and underground building data in the illegal building data ④ with different floor area ratios are defined as suspected illegal building data, and the reasons for the violations are recorded and extracted and stored in the suspected illegal building data intermediate library. If it is determined that the floor area ratio of the illegal building data ④ is the same as that of the standard approval data, the illegal building data ④ is defined as illegal building data ⑤ for subsequent comparison;

[0274] Approval data comparison: determine the type of the approval value of the approval data. If the approval value of the approval data is determined to be an interval value, compare the illegal building data ⑤ with the standard approval data for self-comparison and adjacent spacing comparison. If the approval value of the approval data is determined to be a definite value, compare the illegal building data ⑤ with the standard approval data for plane comparison and height comparison;

[0275] Internal space comparison: determine whether the illegal building data ⑦ contains underground building data. If it is determined that the illegal building data ⑦ contains underground building data, the underground building data in the illegal building data ⑦ is combined with the point cloud data and the approval information of the standard approval data for internal space comparison. If it is determined that the internal space has been demolished or modified, the underground building data in the demolished or modified part of the illegal building data ⑦ is defined as suspected illegal building data, and the reasons for the violation are recorded and extracted and stored in the suspected illegal building data intermediate library. If it is determined that the internal space has not been demolished or modified, the illegal building data ⑦ is defined as compliant above-ground building data and stored in the compliant building database; if it is determined that the illegal building data ⑦ does not contain underground building data, subsequent operations are performed;

[0276] Color and structure comparison: Determine whether the ground building data in the illegal building data ⑦ is located in the newly added building change area. If it is determined that the ground building data in the illegal building data ⑦ is located in the newly added building change area, the ground building data in the illegal building data ⑦ is combined with the oblique image data and compared with the approval information of the standard approval data in color and structure. If it is determined that any one of the color and structure of the building is inconsistent, the inconsistent ground building data in the illegal building data ⑦ is defined as suspected illegal building data, and the cause of the violation is recorded and extracted and stored in the suspected illegal building data intermediate library. If the color and structure of the building are determined to be consistent, the illegal building data ⑦ is defined as compliant ground building data and stored in the compliant building database; if it is determined that the ground building data in the illegal building data ⑦ is not in the newly added building change area, then the subsequent operation is performed;

[0277] Suspected illegal building intermediate database: All suspected illegal building data obtained through the above comparison are stored in the suspected illegal building intermediate database according to spatial location;

[0278] Compliant building database: The above-ground building data and underground building data remaining after the above comparison are defined as compliant building data and stored in the compliant building database.

[0279] like Fig.10 As shown, a method for investigating existing illegal buildings based on massive data, wherein the specific steps of comparing the approval data are as follows:

[0280] Approval data type determination: extract standard approval data from the standard approval database, determine the type of the approval value of the standard approval data, and if the approval value of the standard approval data is determined to be a fixed value, perform subsequent self-comparison and adjacent spacing comparison; if the approval value of the standard approval data is determined to be an interval value, perform subsequent plane comparison and height comparison;

[0281] Self-comparison: Compare the illegal building data ⑤ with the standard approval data. If it is determined that the illegal building data ⑤ exceeds the rights space of the corresponding standard approval data, the above-ground building data and underground building data in the illegal building data ⑤ that exceed the rights space are defined as suspected illegal building data, and the reasons for the violation are recorded and extracted and stored in the suspected illegal building data intermediate library. If it is determined that the illegal building data ⑤ does not exceed the rights space of the corresponding standard approval data, the illegal building data ⑤ is defined as illegal building data ⑥ for subsequent comparison;

[0282] Adjacent spacing comparison: compare the adjacent spacing of the illegal building data ⑥ with the standard approval data. If it is determined that the illegal building data ⑥ exceeds the range of adjacent spacing required by the approval value of the corresponding standard approval data, the above-ground building data and underground building data in the illegal building data ⑥ that exceed the range of adjacent spacing are defined as suspected illegal building data, and the reasons for the violation are recorded and extracted and stored in the suspected illegal building data intermediate library. If it is determined that the illegal building data ⑥ does not exceed the range of adjacent spacing required by the approval value of the corresponding standard approval data, the illegal building data ⑥ is defined as illegal building data ⑦ for subsequent comparison;

[0283] Plane comparison: Perform a plane comparison of the illegal building data ⑤ with the standard approval data. If it is determined that the illegal building data ⑤ exceeds any one of the plane rights space and adjacent spacing of the corresponding standard approval data, the above-ground building data and underground building data in this part of the illegal building data ⑤ are defined as suspected illegal building data, and the reasons for the violations are recorded and extracted and stored in the suspected illegal building data intermediate library. If it is determined that the illegal building data ⑤ does not exceed the plane rights space and adjacent spacing of the corresponding standard approval data, the illegal building data ⑤ is defined as illegal building data ⑥ for subsequent comparison;

[0284] Height comparison: compare the height of the illegal building data ⑥ with the standard approval data. If it is determined that the illegal building data ⑥ exceeds the height right space of the corresponding standard approval data, the above-ground building data and underground building data in the illegal building data ⑥ that exceed the height right space are defined as suspected illegal building data, and the reasons for the violation are recorded and extracted and stored in the suspected illegal building data intermediate library. If it is determined that the illegal building data ⑥ does not exceed the height right space of the corresponding standard approval data, the illegal building data ⑥ is defined as illegal building data ⑦ for subsequent comparison.

[0285] like Fig.11 As shown, a method for investigating existing illegal buildings based on massive data, wherein the specific steps of the data correction and verification are:

[0286] Improvement of suspected illegal building information: Extract suspected illegal building data from the suspected illegal building intermediate database, and use massive oblique image data, point cloud data, district boundary data, industrial zone boundary data, land price data, land use data, ownership information data, geological conditions information data, supporting facilities data, and green space data to improve the attribute information of suspected illegal buildings, including number, district boundary, industrial park, land price, land use, ownership, geological conditions, green space conditions, and supporting facilities conditions;

[0287] Determination of management authority of suspected illegal buildings: Determine the construction time of suspected illegal building data based on satellite remote sensing image data, and then determine the management authority of suspected illegal buildings based on the issuance time of approval data, the spatial area of ​​suspected illegal building data, and the cause of violation of suspected illegal building data. When it is determined that the suspected illegal building data is in the newly added building change area within the urban planning area, and the violation reason is: failure to obtain the statutory approval certificate or failure to construct in accordance with the statutory approval certificate, the management authority of this type of suspected illegal building data is defined as the planning administrative department; when it is determined that the suspected illegal building data is in the newly added building change area within the township and village planning area, and the violation reason is: failure to obtain the statutory approval certificate or failure to construct in accordance with the statutory approval certificate, the management authority of this type of suspected illegal building data is defined as the township people's government; when it is determined that the suspected illegal building data is within the original building change, and the violation reason is: failure to obtain the statutory approval certificate, the management authority of this type of suspected illegal building data is defined as the urban management comprehensive law enforcement department, and all suspected illegal building data are judged in turn until the management authority is defined and the determination is completed;

[0288] Verification and confirmation of suspected illegal buildings: after the information is completed and the management authority is determined, the suspected illegal buildings are verified on site, and after excluding the compliant buildings again, the illegal building inventory data is generated;

[0289] Illegal building stock database: The illegal building stock data is stored in the illegal building stock database.

[0290] like Fig.12 As shown, a method for investigating existing illegal buildings based on massive data, wherein the specific steps of the summary analysis and evaluation are:

[0291] Retrieval of illegal building data: retrieve illegal building stock data from the illegal building stock database;

[0292] Special analysis: Combine the illegal building stock data with various auxiliary materials to conduct special analysis of zoning, ownership, economy, society and overall categories, generate various special analysis result data and store them in the special analysis database;

[0293] Special analysis report: According to the special analysis results, output the special analysis report containing text, graphics, charts, and 3D models according to the classification;

[0294] Special remediation information: Based on special analysis data, combined with the types of illegal buildings and planning and development goals, remediation plans for illegal buildings in different areas, management powers, numbers of floors, nature, ownership, geological conditions, and types of supporting facilities are formed, and targeted remediation suggestion information is generated for each building and stored in the corresponding remediation information database.

[0295] like Fig.13 As shown, a method for investigating existing illegal buildings based on massive data, wherein the specific steps of the feedback reminder are:

[0296] Retrieval of special analysis results: According to the designated area for rectification of illegal buildings, various special analysis result data and rectification suggestion information are extracted from the special analysis database;

[0297] On-site collection and verification: According to the designated area for illegal building rectification, the illegal building stock data in the corresponding area is regularly extracted, and the location information of different illegal building stock data is generated and sent to the supervisors. The supervisors can regularly collect oblique images and point cloud data of the corresponding locations by means of drone oblique photography according to the rectification situation;

[0298] Feedback information generation: Automatically extract oblique images and point cloud data, compare and analyze with the stock data of illegal buildings, verify the rectification of illegal buildings, update various special analysis results data, and generate rectification feedback information;

[0299] Display storage: Through a dedicated network, the special analysis results of illegal buildings in different periods and the feedback information of rectification are stored and displayed on the corresponding two-dimensional and three-dimensional terminals according to a fixed period.

[0300] A method for investigating existing illegal buildings based on massive data. In a specific implementation case, planning approval data includes control detailed planning data, planning condition data, national land space planning data, site selection opinion data, construction land planning permit data, construction project planning permit data, rural construction planning permit data, and construction project completion acceptance data; planning approval data includes: land use nature, volume ratio, building area, building size, building height, construction density, surrounding building spacing, color, structure, and shape; image data includes satellite image data, aerial image data, oblique image data, and multi-spectral remote sensing image data; format conversion includes conversion of different types of data (oblique image conversion to orthophoto) and conversion of different formats of the same type of data; planning areas include urban built-up areas and rural areas; building types include high-rise areas, multi-story areas, and low-rise areas; special land use areas include military land and security land; building data includes point cloud data, topographic map data, GIS data, oblique image data, and real-life three-dimensional data; supplementary collection methods include traditional surveying and mapping methods, lidar measurement, and oblique photogrammetry; approval values ​​include: length, width, height, and adjacent spacing Volume ratio, use nature, building density, land use scope; the right space form includes: two-dimensional form, three-dimensional form; the right space content includes: length, width, height; the ground building data includes two-dimensional GIS form, real-scene three-dimensional form; the correspondence between underground building data and ground building data is one-to-one and one-to-many; various restriction boundary data include land red line, permanent basic farmland control line, ecological protection red line, urban development boundary, village construction boundary, flood risk control line, historical and cultural protection line, various urban planning controls; district boundary data include: administrative boundary data, Management boundary data, urban planning area boundary data, township planning area boundary data; zoning categories include: district boundaries, industrial park boundaries, ring line boundaries; ownership categories include: land ownership, building ownership; economic categories include: project cost, demolition cost, land value, rental price, renovation cost, tax data, financial data; social categories include: population, education, medical care, employment, leasing, transportation; overall categories include: area, number, number of floors, supporting facilities, nature, greening, geological conditions; feedback information includes: early warning information, prompt information of areas exceeding specified indicators, and target gap information.

[0301] 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.

[0302] 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.

[0303] The method in the embodiment of the present invention uses orthophotos to quickly identify building change areas, and uses point cloud data to conduct building surveys without the need for field surveys, which greatly improves the efficiency and speed of identification and investigation; point cloud data can accurately capture the shape and location information of buildings, and has higher precision and accuracy than traditional two-dimensional image analysis; combined with approval data judgment, and management authority judgment and verification, it has higher authority and recognition; the multi-dimensional analysis results based on the survey data can be displayed in three-dimensional form, which can fully understand the situation of illegal buildings, help to formulate more comprehensive and targeted investigation plans, and help decision makers better understand and analyze the problem of existing illegal buildings; by integrating point cloud data with other geographic information data, more detailed and comprehensive visual information can be provided to decision makers, thereby supporting better decision-making and planning management data.

[0304] 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 investigating existing illegal buildings based on massive data, characterized in that The method comprises the following steps: defining illegal buildings: setting a standard for determining illegal buildings, wherein the standard is set to violate planning approval data within a specified time period; data comparison: obtaining multiple periods of image data corresponding to the time period according to the time period requirements for defining illegal buildings, performing data correction and data classification on the obtained image data, and making and setting two types of extraction samples, namely, original building changes and newly added buildings, according to the classified data, quickly determining the building change area through computer automatic recognition technology based on convolutional neural network, and summarizing the change area after data re-inspection, and superimposing and comparing with the current land use data to generate the final range of illegal buildings to be determined; data analysis and collection: analyzing a large amount of existing data within the acquired change area, respectively determining the data range where the above-ground building data and underground building data are missing and the location accuracy does not meet the requirements, supplementing the above-ground building data within the data range, and collecting the underground building data. The data is collected for internal space and external contours, and the collected ground building data and underground building data are stored in the underground building intermediate database and the underground building intermediate database respectively; Data production: Process the paper and digital approval original data to produce standard approval data with rights space, which are stored in the designated database. According to the massive existing data and the collected initial data of ground buildings and underground buildings, the ground building data and underground building data are produced and stored in the designated database respectively; Data judgment: Extract building data and auxiliary materials respectively, conduct the processed illegal building investigation and approval data judgment in the building data, and carry out subsequent boundary comparison, property comparison, volume ratio comparison, approval data comparison, internal space comparison, color and structure comparison in turn, and judge that all suspected illegal building data comparisons are completed, and store the obtained compliant building data and suspected illegal building data in the corresponding databases respectively; Data correction and verification: Use existing data to improve the information of suspected illegal building data, determine the management authority of illegal buildings, generate illegal building inventory data after verification and confirmation, and store it in the corresponding database; Summary analysis and evaluation: Retrieve illegal building inventory data and combine it with various auxiliary materials to conduct various special analyses, and generate special analysis reports and special rectification information; Feedback reminder: According to the designated area for illegal building rectification, various special analysis result data, rectification suggestion information, and illegal building stock data of the corresponding area are extracted from the rectification information database and the illegal building stock database. Regular on-site collection of illegal buildings in the designated area is carried out, and the rectification status of illegal buildings is verified. Various special analysis results are updated, rectification feedback information is generated, and stored and displayed on the corresponding terminal.

2. The method for investigating existing illegal buildings based on massive data according to claim 1 is characterized by: The specific steps of the data comparison are: Obtaining multiple images: According to the time period defined by the illegal building, obtain multiple image data corresponding to the time period; Image data correction: standardize and judge the multi-phase image data, and perform coordinate transformation, data fusion, projection and correction processing, and format conversion standardization processing on the non-standardized image data according to the corresponding reasons, so as to generate multi-phase orthophoto image data of the same type; Sample classification and setting: divide the orthophoto data of multiple periods into regions according to planning areas and building types. According to different regional divisions, extract samples of two categories, original building changes and newly added buildings, are produced respectively. The produced samples are extracted, verified and modeled. Building change area detection based on convolutional neural network: According to the corresponding sample settings, the computer automatic recognition technology based on convolutional neural network is used to quickly determine the initial range of the change area of ​​the original building changes and the newly added buildings in the classified orthophotos before and after. Data correction: Use multispectral remote sensing image data to verify the preliminary range of the detected change area, remove the misidentified area, supplement the missed change area, and perform geometric normalization on the change area to form the intermediate range of the two types of building change areas of different classifications: original building changes and newly added buildings; Data aggregation and overlay optimization: The middle range of the two types of building change areas detected in different categories are aggregated, and compared with the existing land use data, the special land use range in the existing land use is screened, and the special land use range within the detected building change area is eliminated to generate the final change area range that needs to be judged as illegal buildings.

3. The method for investigating existing illegal buildings based on massive data according to claim 1 is characterized by: The specific steps of data analysis and collection are: Determine the scope of missing data: Analyze the massive amount of existing data within the acquired change area, and judge the existence and location accuracy of the above-ground building data and underground building data. If it is judged that the above-ground building data and underground building data do not exist, record the scope of missing above-ground building data and underground building data. If it is judged that the above-ground building data and underground building data exist, judge the accuracy of the above-ground building data and underground building data respectively. If it is judged that the accuracy of the above-ground building data and underground building data does not meet the requirements, record the scope of the above-ground building data and underground building data not meeting the accuracy requirements. If it is judged that the accuracy of the above-ground building data and underground building data meets the requirements, do not record the relevant scope, and define the scope of missing above-ground building data and underground building data and the scope of the above-ground building data and underground building data not meeting the accuracy requirements in the change area as the scope that needs to be supplemented. Supplementary collection of ground building data: Supplementary collection of ground building data is carried out within the scope of supplementary collection to obtain the plane position and height information of the ground building with position accuracy that meets the requirements; Underground building data collection: Supplementary collection of underground building data within the scope of supplementary collection. The interior of underground buildings is mainly measured by stationary laser radar scanning, supplemented by handheld laser radar scanning, and the two are combined to collect and measure point cloud data, and draw the internal space plan. The outer contour of underground buildings is collected and measured by seismic wave detection. The internal space and external contour of the underground building are combined by splicing the sampling points with the same name and the obvious feature points on the ground, so as to connect the internal and external contours of the underground space as a whole. Then, the geometric position information of the internal and external contours of the underground building is obtained through the absolute position coordinates of the obtained sampling points and obvious feature points on the ground. Data integration and storage: The collected plane position and height information data of the above-ground buildings are defined as the initial data of the above-ground buildings and stored in the intermediate database of the above-ground buildings; the collected outer contour map of the underground buildings and the corresponding internal space data of the underground buildings are defined as the initial data of the underground buildings and stored in the intermediate database of the underground buildings.

4. The method for investigating existing illegal buildings based on massive data according to claim 3 is characterized by: The specific steps of underground building data collection are: Sampling point layout: within the scope of supplementary collection, determine and layout the sampling points for seismic wave detection on the ground according to the distribution, size and location characteristics of underground buildings; Data collection: Use the GNSS system combined with the total station to digitally measure the ground sampling points and obvious feature points on the ground, use the seismic instrument to transmit signals underground at the sampling points, and record the reflected or refracted waves of the signals; Data processing: remove noise from the collected seismic signals, perform seismic data compensation, and analyze and process the seismic data; Perform data conversion and coordinate inverse calculation on the data obtained from the sampling points and obvious feature points on the ground to obtain the absolute position coordinates; Establishment of time-distance profile: Based on the acquired seismic data, a time-distance profile is established based on the acquired arrival time and amplitude information, with the horizontal axis representing distance and the vertical axis representing time or depth. The reflection points and the corresponding time distance are marked on the profile. Pre-stack data processing: pre-process the data on the time-range section by stacking, filtering, and removing the Doppler effect to generate the final imaging data; Imaging and mapping: The imaging algorithm formula is used to invert the final imaging data to obtain the imaging results of the underground building. The specific imaging algorithm calculation formula is as follows: Map(x,z)=∫u(x,z,t)d(x,z,t)dt Where: Map(x,z) represents the imaging result, which indicates the strength or reflection coefficient of the underground structure at the position x at the underground depth z. u(x,z,t) represents the wave field amplitude reflection coefficient of the seismic wave at the spatial position (x,z) and time t. This parameter reflects the propagation of seismic waves in different underground media. d(x,z,t) represents a parameter of the seismic wave propagation path, which is usually called the attenuation function. It describes the attenuation and scattering of the medium encountered by the seismic wave during the propagation process. dt represents the small increment of the time interval, that is, the differential component. Analyze and interpret the depth, size, and location parameters of underground buildings based on the imaging results, draw the outer contour of the underground buildings, and record the corresponding depth information. Combine the absolute position coordinates of the sampling points and obvious feature points on the ground to correct the outer contour of the underground buildings to the corresponding coordinate system; Data aggregation and storage: Using the seismic wave sampling points with the same name and obvious ground feature points, the outer contour map of the underground building and the corresponding internal space of the underground building are spliced ​​and aggregated, and stored in the underground building intermediate database.

5. The method for investigating existing illegal buildings based on massive data according to claim 1 is characterized by: The specific steps of data production are: Approval data production: For paper-based approval data, the approval graphic data is first produced by using the file vectorization method, and then the corresponding attribute information of the approval data is improved according to the file data. For the approval data in digital form, the approval data is produced by means of coordinate transformation, standardization processing, and attribute improvement. The two types of approval data are superimposed, redundant data are eliminated, and the integrity and positivity of the relevant attribute data are checked and modified. Then, a connection is established between the graphic data and the attribute data, and the rights space of the approval data is generated according to the issuance requirements of the approval data. Then, the standard approval data after the rights space is generated is stored in the standard approval database; Building data production: pre-process and classify the massive amount of original and supplementary point cloud data of above-ground buildings, and produce contour and height extraction samples according to different types. Automatically classify and extract the contour and height information of above-ground building data through calculation formulas, correct the above-ground building data and improve the attribute information, and then summarize and store it in the above-ground building database; extract the outer contour and internal space location map from the underground building intermediate database, and according to the collected information, produce underground building data and improve the corresponding attribute information, establish a corresponding relationship with the above-ground building data, and then summarize and store the underground building data in the underground building database according to the spatial position; The building data production is divided into two parts: above-ground building data production and underground building data production; Data classification storage: The produced standard approval data and construction data are stored in the standard approval database and the construction database respectively.

6. The method for investigating existing illegal buildings based on massive data according to claim 5 is characterized by: The specific steps of preparing the approval data are as follows: Scanning and image processing: Obtain paper archive materials of approval data, scan the archive materials with a scanner, first perform color correction and geometric correction on the scanned images, then perform data matching, data editing, data error correction, projection transformation, edge processing, and image splicing on the scanned images; Information conversion and extraction: Stratify the image elements, extract the building-related layers, use the software to automatically extract and manually correct the building graphics of the approval data, and fill in the attribute information of the approval data according to the relevant information of the archives; Digital data acquisition: organize and acquire approval data in digital form, and perform coordinate conversion, standardization, and attribute improvement on the acquired approval data in digital form; Editing and processing: spatially superimpose the digitized approval data with the approval data produced by archive vectorization. For spatially repeated approval data, remove the data with earlier approval time according to the approval time sequence. Then, check and modify the integrity and positivity of the relevant attribute data of all approval data, establish a connection between the graphic data and the attribute data, and generate the intermediate results of the approval data. Rights space generation: According to the issuance requirements of the approval data, the rights space of the intermediate results of the approval data is produced. For approval values ​​that are fixed values, a fixed rights space is generated. For approval values ​​that are interval values, the rights space for approval and construction is generated according to the maximum and minimum intervals of the length, width, and height. Approval database: The intermediate results of approval data after generating the rights space are defined as standard approval data and stored in the standard approval database.

7. The method for investigating existing illegal buildings based on massive data according to claim 5 is characterized by: The specific steps of producing the building data are as follows: Production of ground building data: Preprocess and classify the massive amount of original and supplementary point cloud data of ground buildings, then segment the ground building point cloud data by height type, and classify the building roof types through the curvature calculation formula. Create contour and height extraction samples according to different types, and automatically classify and extract the ground building data contour and height information through the calculation formula. After correcting the ground building data contour and improving the attribute information, generate two-dimensional and three-dimensional ground building data, and summarize and store them in the ground building database; Production of underground building data: Extract the outer contour and internal space location map from the underground building intermediate database, and use the corresponding software to produce two- and three-dimensional underground building data based on the collected depth and internal height information, and improve the attribute information of the corresponding two- and three-dimensional underground building data. Use the location information of obvious feature points on the ground obtained during underground building data collection to establish a corresponding relationship with the ground building data. Finally, the two- and three-dimensional underground building data are summarized and stored in the underground building database according to the spatial position.

8. The method for investigating existing illegal buildings based on massive data according to claim 7 is characterized by: The specific steps of producing the above-ground building data are: Data extraction: Extract various point cloud data of above-ground buildings in the changed area from massive existing data and newly collected underground building intermediate databases; Data preprocessing: Use fusion algorithms to fuse and register various point clouds including vehicle-mounted, airborne, stationary, backpack, and oblique photography, and then obtain complete and usable ground building point cloud data through noise reduction, rarefaction, and resampling; Data classification processing: The average elevation of the center point X is obtained through the calculation formula. The calculation formula used is to calculate the average value by subtracting the elevation of the center point X to the four surrounding directions. The specific calculation formula is as follows: Where: Z′ represents the average elevation of the center point X, Z1 represents the elevation of the point above the center point X, Z2 represents the elevation of the point below the center point X, Z3 represents the elevation of the point to the left of the center point X, Z4 represents the elevation of the point to the right of the center point X, and Z x Indicates the elevation value of the center point X; A calculation threshold ω is set for Z′. When it is determined that Z′ is less than the threshold ω, it is determined to be a ground point. When it is determined that Z′ is greater than or equal to the threshold ω, it is determined to be a non-ground point. The point cloud data is traversed in sequence until all ground point clouds are divided into ground points and non-ground point sets. The determination is completed, and the density clustering method with adaptive parameters is used to divide different ground objects of non-ground points, and the ground building point cloud data containing only building types is selected; Data segmentation: refer to the orthophoto data, perform height type segmentation on the ground building point cloud data, and divide the ground building point cloud data into three types of data: high-rise, multi-story, and low-rise according to the size of the ratio of the building shadow length to the building length in the orthophoto. If ξ≥1, the ground building point cloud data is judged to be a high-rise building; if ξ is between 0.5 and 1, the ground building point cloud data is judged to be a multi-story building; if ξ≤0.5, the ground building point cloud data is judged to be a low-rise building; Sample setting: According to different segmentation types, the curvature value of each building in high-rise, multi-story, and low-rise buildings is calculated through the curvature calculation formula to determine the roof type of the building. If the curvature is less than the threshold value ψ, the building is judged to be a flat roof building. If the curvature is greater than or equal to the threshold value ψ, the building is judged to be a slope roof building. The curvature calculation formula is as follows: Curvature calculation formula Where K represents the curvature of the building roof, Z x Indicates the elevation difference of the building roof in the x direction, Z y The elevation difference in the y direction of the building roof; First, according to different height types and roof types, the correspondence between the real samples of the main body, balcony, doorway, eaves, and elevator room and the drawn contours is made, and the sample type and normal vector features are annotated. Then, the corresponding negative samples are made, and the real samples are enhanced in the form of rotation, translation, and scaling. According to different roof types, building height information samples are made. Among them, two height extraction samples are set for flat-topped buildings, namely the roof surface and the highest point, and three height extraction samples are set for sloping-topped buildings, namely the slope starting point, the ridge point, and the height average point. Automatically extract building information using calculation formulas: According to the classification of different height types and roofs, refer to the sample types of each type, select the machine learning model of the appropriate task for training, and use the training data to optimize the model, input each type of point cloud data to be processed into the corresponding trained model for automatic extraction, and then transform the extracted three-dimensional building points into a two-dimensional plane through projection, use the point cloud geometry algorithm to extract the building contour points, obtain the initial contour, and write the height information into the corresponding main contour; the judgment condition of the point cloud geometry algorithm is: in the point set P, through any two points p i (x i ,y i ), p r (x r ,y r ) Draw a circle with a radius of α and calculate the center of the circle passing through these two points p o (x o ,y o ) coordinates, where p o The coordinate calculation formula is as follows: In the formula α radius parameter; By judging the relationship between the distance from other points to the center of the circle and the radius α, we can determine the distance between the connecting line p and the center of the circle. i , p r Is it a boundary line segment? If the distance from other points to the center of the circle is greater than the radius α, it is judged that there are no other points in the circle, then p is considered i , p r is a boundary point, and its connecting line p i , p r is a boundary segment, otherwise, it is considered that p i , p r Not a boundary point, its connecting line p i , p r It is not a boundary line segment, and then repeat the above steps for the remaining points in the point set P until all the points in the point set P are judged; Data optimization: Filter, group, and edge fit the initially extracted underground building outlines, and then correct the ground building outlines to the corresponding coordinate system based on the feature positions of the point cloud data and topographic map information to obtain the final ground building outlines; Data production: Automatically generate corresponding two-dimensional and three-dimensional ground building data based on the extracted contour lines and height information of different types of ground buildings, and improve the attribute information of two-dimensional and three-dimensional ground building data including usage nature, building area, volume ratio and building density based on point cloud data and oblique image data; Data aggregation and storage: Different types of two- and three-dimensional ground building data are aggregated and stored in the ground building database according to their spatial locations.

9. The method for investigating existing illegal buildings based on massive data according to claim 1 is characterized by: The specific steps of data determination are: Building data extraction: extracting corresponding ground building data and underground building data from the underground building database and the ground building database respectively; Auxiliary data extraction: Extract data including street view data, oblique image data, approved land use data, various restriction boundary data, and processed illegal building data from massive existing data; Processed illegal building investigation: Define the names of above-ground building data and underground building data, and arrange them in ascending order according to the verification order as illegal building data① to illegal building data Firstly, spatial comparison analysis is adopted based on the processed illegal building data to automatically check the above-ground building data and underground building data. According to the parameter τ of the graphic area overlap rate of the processed illegal building data, the above-ground building data and the underground building data, a threshold η is set for τ to preliminarily judge whether the above-ground building data and the underground building data are processed illegal buildings. If τ≥threshold η, the illegal buildings are then compared with the above-ground building data and the underground building data in terms of attributes including the number of floors, building area and nature. If the number of floors, building area and nature are determined to be consistent, the above-ground building data and the underground building data are extracted and stored in the compliant building database. If any one of the number of floors, building area and nature is determined to be inconsistent, the above-ground building data and the underground building data are extracted and defined as illegal building data ①. If τ<threshold η, the above-ground building data and the underground building data are extracted and defined as illegal building data ①, and the defined illegal building data ① is subsequently determined. Approval judgment: Perform spatial comparison analysis on the illegal building data① and the building data of the standard approval data, set the parameter φ of the spatial overlap rate, set the threshold ρ for φ, if φ≤threshold ρ, define the above-ground building data and underground building data as suspected illegal building data, record the reasons for the violations and extract and store them in the suspected illegal building intermediate database, if φ>threshold ρ, extract the above-ground building data and underground building data as illegal building data②, and conduct subsequent comparison; Boundary comparison: Perform boundary comparison on illegal building data ② and various boundary data. If it is determined that illegal building data ② is within the restriction range of various boundary data, the above-ground building data and underground building data within the illegal building data ② within the restriction range are defined as suspected illegal building data, and the reasons for the violation are recorded and extracted and stored in the suspected illegal building data intermediate database; if it is determined that illegal building data ② is not within the restriction range of various boundary data, illegal building data ② is defined as illegal building data ③, and subsequent comparisons are performed in sequence; Property comparison: compare the illegal building data ③ with the land use nature of the standard approval data and the land use nature of the construction detailed plan. If it is determined that the illegal building data ③ is different from the land use nature of the standard approval data and the land use nature of the construction detailed plan, the above-ground building data and underground building data in this part of the illegal building data ③ are defined as suspected illegal building data, and the reasons for the violation are recorded and extracted and stored in the suspected illegal building data intermediate library; if it is determined that the land use nature of the illegal building data ③ is the same as the land use nature of the standard approval data and the land use nature of the construction detailed plan, the illegal building data ③ is defined as illegal building data ④ for subsequent comparison; Comparison of floor area ratio: Compare the floor area ratio of the illegal building data ④ with that of the standard approval data. If it is determined that the floor area ratio of the illegal building data ④ is different from that of the standard approval data, the above-ground building data and underground building data in the illegal building data ④ with different floor area ratios are defined as suspected illegal building data, and the reasons for the violations are recorded and extracted and stored in the suspected illegal building data intermediate library. If it is determined that the floor area ratio of the illegal building data ④ is the same as that of the standard approval data, the illegal building data ④ is defined as illegal building data ⑤ for subsequent comparison; Approval data comparison: determine the type of the approval value of the approval data. If the approval value of the approval data is determined to be an interval value, compare the illegal building data ⑤ with the standard approval data for self-comparison and adjacent spacing comparison. If the approval value of the approval data is determined to be a definite value, compare the illegal building data ⑤ with the standard approval data for plane comparison and height comparison; Internal space comparison: Determine whether the illegal building data ⑦ contains underground building data. If it is determined that the illegal building data ⑦ contains underground building data, the underground building data in the illegal building data ⑦ is combined with the point cloud data and the approval information of the standard approval data for internal space comparison. If it is determined that the internal space has been demolished or modified, the underground building data in the demolished or modified illegal building data ⑦ is defined as suspected illegal building data, and the reasons for the violation are recorded and extracted and stored in the suspected illegal building data intermediate library. If it is determined that the internal space has not been demolished or modified, the illegal building data ⑦ is defined as compliant ground building data and stored in the compliant building database; If it is determined that the illegal building data ⑦ does not include underground building data, then proceed with the subsequent operation; Color and structure comparison: Determine whether the ground building data in the illegal building data ⑦ is located in the newly added building change area. If it is determined that the ground building data in the illegal building data ⑦ is located in the newly added building change area, the ground building data in the illegal building data ⑦ is combined with the oblique image data and compared with the approval information of the standard approval data in color and structure. If it is determined that any one of the color and structure of the building is inconsistent, the inconsistent ground building data in the illegal building data ⑦ is defined as suspected illegal building data, and the cause of the violation is recorded and extracted and stored in the suspected illegal building data intermediate library. If it is determined that the color and structure of the building are consistent, the illegal building data ⑦ is defined as compliant ground building data and stored in the compliant building database; If it is determined that the above-ground building data in the illegal building data ⑦ is not within the newly added building change area, then proceed with the subsequent operation; Suspected illegal building intermediate database: All suspected illegal building data obtained through the above comparison are stored in the suspected illegal building intermediate database according to spatial location; Compliant building database: The above-ground building data and underground building data remaining after the above comparison are defined as compliant building data and stored in the compliant building database.

10. The method for investigating existing illegal buildings based on massive data according to claim 9 is characterized by: The specific steps of the approval data comparison are: Approval data type determination: extract standard approval data from the standard approval database, determine the type of the approval value of the standard approval data, and if the approval value of the standard approval data is determined to be a fixed value, perform subsequent self-comparison and adjacent spacing comparison; if the approval value of the standard approval data is determined to be an interval value, perform subsequent plane comparison and height comparison; Self-comparison: Compare the illegal building data ⑤ with the standard approval data. If it is determined that the illegal building data ⑤ exceeds the rights space of the corresponding standard approval data, the above-ground building data and underground building data in the illegal building data ⑤ that exceed the rights space are defined as suspected illegal building data, and the reasons for the violation are recorded and extracted and stored in the suspected illegal building data intermediate library. If it is determined that the illegal building data ⑤ does not exceed the rights space of the corresponding standard approval data, the illegal building data ⑤ is defined as illegal building data ⑥ for subsequent comparison; Adjacent spacing comparison: compare the adjacent spacing of the illegal building data ⑥ with the standard approval data. If it is determined that the illegal building data ⑥ exceeds the range of adjacent spacing required by the approval value of the corresponding standard approval data, the above-ground building data and underground building data in the illegal building data ⑥ that exceed the range of adjacent spacing are defined as suspected illegal building data, and the reasons for the violation are recorded and extracted and stored in the suspected illegal building data intermediate library. If it is determined that the illegal building data ⑥ does not exceed the range of adjacent spacing required by the approval value of the corresponding standard approval data, the illegal building data ⑥ is defined as illegal building data ⑦ for subsequent comparison; Plane comparison: Perform a plane comparison of the illegal building data ⑤ with the standard approval data. If it is determined that the illegal building data ⑤ exceeds any one of the plane rights space and adjacent spacing of the corresponding standard approval data, the above-ground building data and underground building data in this part of the illegal building data ⑤ are defined as suspected illegal building data, and the reasons for the violations are recorded and extracted and stored in the suspected illegal building data intermediate library. If it is determined that the illegal building data ⑤ does not exceed the plane rights space and adjacent spacing of the corresponding standard approval data, the illegal building data ⑤ is defined as illegal building data ⑥ for subsequent comparison; Height comparison: compare the height of the illegal building data ⑥ with the standard approval data. If it is determined that the illegal building data ⑥ exceeds the height right space of the corresponding standard approval data, the above-ground building data and underground building data in the illegal building data ⑥ that exceed the height right space are defined as suspected illegal building data, and the reasons for the violation are recorded and extracted and stored in the suspected illegal building data intermediate library. If it is determined that the illegal building data ⑥ does not exceed the height right space of the corresponding standard approval data, the illegal building data ⑥ is defined as illegal building data ⑦ for subsequent comparison.

11. The method for investigating existing illegal buildings based on massive data according to claim 1 is characterized by: The specific steps of the data correction verification are: Improvement of suspected illegal building information: Extract suspected illegal building data from the suspected illegal building intermediate database, and use massive oblique image data, point cloud data, district boundary data, industrial zone boundary data, land price data, land use data, ownership information data, geological conditions information data, supporting facilities data, and green space data to improve the attribute information of suspected illegal buildings, including number, district boundary, industrial park, land price, land use, ownership, geological conditions, green space conditions, and supporting facilities conditions; Determination of management authority of suspected illegal buildings: Determine the construction time of suspected illegal buildings based on satellite remote sensing image data, and then determine the management authority of suspected illegal buildings based on the issuance time of approval data, the spatial area of ​​suspected illegal buildings, and the reasons for violation of suspected illegal buildings; All suspected illegal building data are judged in turn until the management authority is defined and the judgment is completed; Verification and confirmation of suspected illegal buildings: after the information is completed and the management authority is determined, the suspected illegal buildings are verified on site, and after excluding the compliant buildings again, the illegal building inventory data is generated; Illegal building stock database: The illegal building stock data is stored in the illegal building stock database.

12. The method for investigating existing illegal buildings based on massive data according to claim 1 is characterized by: The specific steps of the summary analysis and evaluation are: Retrieval of illegal building data: retrieve illegal building stock data from the illegal building stock database; Special analysis: Combine the illegal building stock data with various auxiliary materials to conduct special analysis of zoning, ownership, economy, society and overall categories, generate various special analysis result data and store them in the special analysis database; Special analysis report: According to the special analysis results, output the special analysis report containing text, graphics, charts, and 3D models according to the classification; Special remediation information: Based on special analysis data, combined with the types of illegal buildings and planning and development goals, remediation plans for illegal buildings in different areas, management powers, numbers of floors, nature, ownership, geological conditions, and types of supporting facilities are formed, and targeted remediation suggestion information is generated for each building and stored in the corresponding remediation information database.

13. The method for investigating existing illegal buildings based on massive data according to claim 1 is characterized by: The specific steps of the feedback reminder are: Retrieval of special analysis results: According to the designated area for rectification of illegal buildings, various special analysis result data and rectification suggestion information are extracted from the special analysis database; On-site collection and verification: According to the designated area for illegal building rectification, the illegal building stock data in the corresponding area is regularly extracted, and the location information of different illegal building stock data is generated and sent to the supervisors. The supervisors can regularly collect oblique images and point cloud data of the corresponding locations by means of drone oblique photography according to the rectification situation; Feedback information generation: Automatically extract oblique images and point cloud data, compare and analyze with the stock data of illegal buildings, verify the rectification of illegal buildings, update various special analysis results data, and generate rectification feedback information; Display storage: Through a dedicated network, the special analysis results of illegal buildings in different periods and the feedback information of rectification are stored and displayed on the corresponding two-dimensional and three-dimensional terminals according to a fixed period.

14. A method for investigating existing illegal buildings based on massive data according to any one of claims 1 to 13, characterized in that: Planning approval data includes control detailed planning data, planning conditions data, national land space planning data, site selection opinion data, construction land planning permit data, construction project planning permit data, rural construction planning permit data, and construction project completion acceptance data; The planning approval data include: land use nature, floor area ratio, building area, building size, building height, construction density, surrounding building spacing, color, structure, and shape; image data include satellite image data, aerial image data, oblique image data, and multi-spectral remote sensing image data; format conversion includes conversion of different types of data (oblique image conversion to orthophoto) and conversion of different formats of the same type of data; the planning area includes urban built-up areas and rural areas; building types include high-rise areas, multi-story areas, and low-rise areas; special land use areas include military land and security land; building data include point cloud data, topographic map data, GIS data, oblique image data, and real-life three-dimensional data; supplementary collection methods include traditional surveying and mapping methods, lidar measurement, and oblique photogrammetry; approval values ​​include: length, width, height, adjacent spacing floor area ratio, use nature, building density, and land use scope; right space shape The formats include: two-dimensional format and three-dimensional format; the content of the right space includes: length, width, and height; the ground building data includes two-dimensional GIS format and real-scene three-dimensional format; the correspondence between underground building data and ground building data is one-to-one and one-to-many; various types of restriction boundary data include land use red line, permanent basic farmland control line, ecological protection red line, urban development boundary, village construction boundary, flood risk control line, historical and cultural protection line, and various types of urban planning control; district boundary data include: administrative boundary data, management boundary data, urban planning area boundary data, and township planning area boundary data; zoning categories include: district boundaries, industrial park boundaries, and ring line boundaries; ownership categories include: land ownership and building ownership; economic categories include: project cost, demolition cost, land value, rental price, renovation cost, tax data, and financial data; social categories include: population, education, medical care, employment, leasing, and transportation; The overall categories include: area, number, number of floors, supporting facilities, nature, greening, and geological conditions; feedback information includes: early warning information, prompt information for areas exceeding specified indicators, and target gap information.

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