A method for desensitizing real estate geospatial data based on bilinear interpolation model

Abstract

The present invention relates to a method for decrypting real estate geospatial data based on a bilinear interpolation model, comprising the following steps: inputting real estate geospatial data, including boundary point data, boundary line data and boundary surface data after spatialization of real estate houses; selecting and establishing a suitable grid based on the size of a research area; randomly offsetting grid node coordinates within the range of precision requirements; performing grid interpolation to calculate the offset of boundary point data; performing coordinate correction on boundary point data; analyzing the results of desensitized real estate registration data, etc. The present invention can decrypt real estate geospatial data, and well solve the confidentiality and sharing problems of real estate registration data. The decrypted real estate registration data formed is irreversible, and can realize real estate horizontal business sharing, Internet sharing and real estate big data analysis. It can effectively reduce the confidentiality level of real estate registration data, retain the basic characteristics of characterizing the spatiotemporal situation of real estate, and has good application value in practical applications.

Description

Technical Field

[0001] The present invention relates to the technical field of real estate registration information management, and in particular to a real estate geographic space data decryption method based on a bilinear interpolation model. Background Art

[0002] With the development of geographic information technology and economic society, the demand for geographic spatial data in all walks of life has become more and more urgent. Real estate registration data, as multi-dimensional data that records multi-time series, multi-subject related information and archives of the entire city, contains real estate attribute data and geographic spatial data from social, economic, government and other aspects. Urban real estate geographic spatial data is an objective holographic three-dimensional portrayal of the urban development process and an important basis for studying the spatiotemporal change characteristics of urban development. It is of great significance to study the spatiotemporal change characteristics of cities.

[0003] Real estate geospatial data can accurately reflect the exact location, height and other relevant information of the objects, and these object information may involve national defense and military secrets. Therefore, real estate geospatial data is an important basic and strategic resource of the country and has a certain confidentiality. How to meet the social research needs for real estate geospatial data under the premise of ensuring data security is one of the problems currently faced in the study of real estate registration. The confidentiality processing of geospatial data can declassify geospatial data from two aspects: attribute information and control position accuracy. In order to solve the research inconvenience caused by the confidentiality of real estate geospatial data, the present invention proposes a real estate geospatial data declassification method based on a bilinear interpolation model. After declassification, the relevant data and geodetic coordinates of real estate geospatial data can be kept confidential. At the same time, the absolute positioning accuracy of the topographic map is also reduced to the range of military confidentiality requirements, but the spatial relationship and relative accuracy of the real estate geospatial data on the map are guaranteed not to be destroyed, meeting the needs of various research and life construction. The specific approach is as follows: the research area with research data is divided into several grids according to a certain size, and the nodes of each grid are used as conversion common points; the nodes of each grid point are randomly offset according to the method of random variables within a fixed range to obtain the offsets in the X and Y directions of the grid nodes, and the offset coordinates of each grid point are used as the new coordinates after declassification, and a mathematical model of the coordinate difference before and after desensitization is formed according to the principle of bilinear interpolation; the mathematical model of the coordinate offset on the grid node is used to interpolate the coordinate offset on any boundary point in the grid, and the coordinates of the boundary points in the grid are converted according to the offset. Finally, the converted features are converted into boundary line and boundary surface data according to the coding rules and sorting point numbers. This data is the declassified real estate geospatial data. Summary of the invention

[0004] 1. Technical problems solved

[0005] In order to solve the confidentiality problem of real estate geographic spatial data in research, the present invention provides a real estate geographic spatial data decryption method based on a bilinear interpolation model, adopts a grid segmentation method to process geographic spatial data, and adds a random array to the grid points to increase the randomness of the decryption offset, thereby achieving a good decryption effect. The application of the bilinear interpolation method can calculate the offset of any data point in the grid, and the data point is offset according to the offset, so as to achieve the purpose of decrypting the data in the grid.

[0006] 2. Technical solution

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions:

[0008] A method for decrypting real estate geospatial data based on a bilinear interpolation model comprises the following steps:

[0009] (1) Inputting real estate geographic spatial data, the real estate geographic data used for declassification includes the spatialized boundary point data, boundary line data and boundary surface data of the real estate house;

[0010] (2) Select and establish a suitable grid based on the study area;

[0011] 1) Real estate data coding: encode the real estate boundary surface data, that is, classify each figure; connect the boundary line data and boundary surface data by using spatial position, and perform the same classification processing for the subsequent attribute recovery of the corrected boundary point, boundary line, and boundary surface data.

[0012] 2) Grid establishment rule design

[0013] The establishment of grids should comply with the relevant provisions of GB / T17278-2009 "Basic Requirements for Digital Topographic Map Products". The plane accuracy of topographic maps of various scales in basic surveying and mapping results should meet the relevant requirements. When gridding real estate geographic spatial data, it is necessary to ensure the declassification effect of the data and take into account the operation efficiency of the program. Therefore, it is necessary to analyze the method of grid division. If the grid is divided uniformly according to the same size, there will be too many grids for large-scale topographic maps, which will cause the program to run too much and reduce efficiency, while the small-scale topographic maps will be divided into too few grids, resulting in too rough declassification, which will lead to incomplete declassification and easy correction. The grid node coordinates of the original topographic map must be kept confidential, otherwise it is easy to solve the problem inside each small grid according to the specific location of each grid, thereby greatly reducing the difficulty of solving and making the real estate data easy to be corrected. Therefore, when dividing the grid, the scale of the data to be declassified should be determined first, and then the coverage of the data to be declassified should be determined. Then, the grid division interval should be determined according to the plane accuracy requirements of the topographic map in Table 1 and the actual application needs.

[0014] 3) Data storage rule design

[0015] In grid deformation, the data format design of the grid and its corresponding grid points and internal features of the grid needs to be performed twice:

[0016] ① After the real estate geographic spatial data is divided into grids and the grid point coordinates are obtained, it is necessary to design the format of each grid and the four grid points that match it, that is, to design a storage format that can store the grid code and the four grid coordinate points corresponding to this grid; this format can still store the decrypted grid point coordinates after the grid node coordinates are decrypted, and make the two coordinates before and after decryption correspond to each other, and can be called for the calculation of the bilinear interpolation decryption model.

[0017] ② Each grid contains different forms of real estate data, such as boundary points, boundary lines, and boundary surfaces. The attributes of these real estate data of different natures are different. In order to facilitate extraction and calculation, it is necessary to design storage formats for various types of data storage such as points, lines, and surfaces. This requires the establishment of several different storage formats to store different types of data. For boundary point data, three attributes are required: unique ID number, X coordinate, Y coordinate, and elevation, as well as the encoding type of boundary point data and the point number sequence of nodes in the corresponding boundary lines and boundary surfaces; boundary lines must record unique IDs, the number of nodes, and the classification code corresponding to the boundary surface, with storage code and boundary line classification code attributes; after decryption, each real estate data still needs the same format of storage structure to store attributes, so that it is easy to retrieve and write into the decrypted document.

[0018] 4) Generate grid

[0019] According to the above grid establishment rules, a suitable spacing is selected by comprehensively considering the regional scope of the data to be declassified, data distribution and topographic map accuracy requirements.

[0020] (3) Randomly offset the coordinates of each node in the grid and record the offset, including the offset in the X direction and the offset in the Y direction

[0021] When the grid is segmented, a set of random parameters for shifting the grid nodes needs to be generated. When the grid segmentation is completed, the values ​​in the random array are added to the coordinates of each grid node for calculation, so that each grid point is subjected to a certain degree of coordinate offset processing. The offset of the four corner nodes depends on the confidentiality and application requirements of the geospatial data to be declassified. Under these two conditions, the coordinate offset range is set, and a random array is generated within this range to randomly offset the grid nodes. Adjacent grid nodes are offset only once, and the processed grid point coordinate offsets are stored, and each offset forms a one-to-one corresponding storage relationship with its respective grid point. The offset grid is as follows:Figure 2 As shown in the figure, the diamond points represent the offset grid nodes.

[0022] (4) Perform bilinear interpolation on the offset grid to obtain the offset data of the real estate object;

[0023] 1) Formula derivation

[0024] ①Linear interpolation formula

[0025] Suppose the coordinates of two points A(x1,y1) and B(x2,y2) on line AB are known. Find the vertical coordinate y of point C between points A and B whose horizontal coordinate is x.

[0026]

[0027] It is deduced that:

[0028]

[0029] ② Bilinear interpolation formula

[0030] First, interpolate twice in the y direction to obtain the values ​​z5 and z6 corresponding to R1(x1,y) and R2(x2,y), that is:

[0031]

[0032] Then interpolate once in the x direction to get the z corresponding to P(x,y), that is:

[0033]

[0034] Substitute z5 and z6 and simplify to get:

[0035]

[0036] 2) Grid interpolation

[0037] According to the 4-6 grid interpolation formula, the values ​​of z1, z2, z3, and z4 are used as the offsets of the four nodes of the grid. Then the z value is the offset of any point to be determined in the grid, and x and y are the coordinate values ​​of the point to be de-encrypted in the grid. For the convenience of observation and calculation, the offset is divided into the offset in the x direction and the offset in the y direction. The offset of the real estate geospatial data features in the grid also includes the offset ΔX in the x direction and the offset ΔY in the y direction. The formula is expressed as follows. According to the offset of the features, the offset coordinates of the features can be calculated, that is, the de-encrypted real estate data geospatial coordinates.

[0038]

[0039] Where ΔX and ΔY represent the offset of the object to be declassified in the grid in the X direction and the Y direction, x and y are the coordinate values ​​of the object point to be declassified in the grid, T 1x 、T 2X 、T 3X 、T 4X is the random offset of the four nodes in the x direction of the grid, T 1y 、T 2y 、T 3y 、T 4y is the random offset of the four nodes in the y direction of the grid. (x1,y1), (x2,y1), (x1,y2), (x2,y2) are the coordinate points of the grid nodes before random offset.

[0040] 3) The desensitized boundary points are spliced ​​to form desensitized real estate geographic data, which requires not only the boundary point data after the spatialization of the house location, but also the boundary line data and boundary surface data. Therefore, after the boundary points of the land feature are de-encrypted, the boundary line data and boundary surface data formed after conversion according to the boundary point data coding and point number sorting rules are required.

[0041] (5) Analyze the results of desensitizing the geospatial data of real estate registration;

[0042] 1) Deformation analysis

[0043] By comparing the real estate geospatial data before and after desensitization, it can be found that the real estate geospatial data has a certain positional offset after desensitization, but the overall shape has not changed significantly, the relative positions between boundary points, boundary lines and boundary surfaces remain unchanged, the topological rules between different boundary surfaces remain unchanged, and the data integrity and continuity are good.

[0044] 2) Coordinate offset analysis

[0045] By comparing the coordinates of the real estate boundary point data before and after declassification, it was found that displacement occurred in both the X and Y directions. The displacement was within a controllable range, and the corresponding geographic error met the requirements of geospatial data confidentiality and practical application.

[0046] 3) Irreversibility analysis

[0047] Since the offset of the grid node is a random array, even if the decryption process is known, the original data cannot be restored. Therefore, such a decryption process is irreversible.

[0048] 3. Beneficial effects

[0049] The beneficial effects of the present invention are as follows: a method for decrypting real estate geographic spatial data based on a bilinear interpolation model adopts a grid segmentation method to process geographic spatial data, and adding a random offset to the grid points increases the randomness of the decryption offset, making the data decryption irreversible. The use of the bilinear interpolation method ensures the local detailed fitting of the data and the continuity of the entire map, and the map accuracy after decryption can be manually controlled, and the topological consistency of the data between and within the layers is also guaranteed, which has good application value in practical applications. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 It is a flow chart of the present invention;

[0051] Figure 2 The boundary point and boundary line data of the present invention;

[0052] Figure 3 The data of the boundary points and boundary surfaces of the present invention;

[0053] Figure 4 It is the grid diagram of the present invention;

[0054] Figure 5 It is the grid offset map of the present invention;

[0055] Figure 6 It is a schematic diagram of bilinear interpolation of the present invention;

[0056] Figure 7 This is a comparison diagram of the real estate geographic data before and after decryption of the present invention;

[0057] Figure 8 Schematic diagram of linear interpolation of the present invention

[0058] Fig. 9 Schematic diagram of bilinear interpolation of the present invention DETAILED DESCRIPTION

[0059] In order to make the implementation purpose, technical solutions and advantages of the present invention clearer, the technical solutions in the implementation of the present invention will be clearly and completely described below in conjunction with the drawings in the implementation of the present invention. Obviously, the described implementation is a part of the implementation of the present invention, not all the implementations. Based on the implementation of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0060] Combined with the diagram process Figure 1 A method for decrypting real estate geospatial data based on a bilinear interpolation model comprises the following steps:

[0061] 1. Input real estate geospatial data. The real estate geospatial data used for decryption includes the spatialized boundary point data, boundary line data and boundary surface data of real estate houses. The boundary surface data is the land parcel data in the cadastral data, the boundary line data is the boundary line of the land parcel, and the boundary point data refers to the turning point or corner point of the boundary line. The specific data format is shown in Figure 2 and Figure 3 ;

[0062] 2. Select and establish a suitable grid based on the study area.

[0063] (1) Real estate data coding: Encode the real estate boundary surface data, that is, classify each figure; connect the boundary line data and boundary surface data using spatial position attributes, and perform the same classification processing for the subsequent attribute recovery of the corrected boundary point, boundary line, and boundary surface data.

[0064] (2) Grid establishment rule design

[0065] According to the surveying and mapping management work in the "Regulations on the Scope of State Secrets in Surveying and Mapping Management Work" formulated by the National Bureau of Surveying, Mapping and Geoinformation and the State Bureau of Secrets, the confidentiality level of topographic maps is related to the size of their scale, but the confidentiality level does not change with the trend of changes in their scale. According to this confidentiality regulation, we can know that the factors that affect the confidentiality level between different scale stereotyped maps are the accuracy of the topographic map, the different elements and scopes involved, that is, the accuracy of topographic map surveying, the level of detail of the map content and the size of the map area. According to the relevant provisions in GB / T17278-2009 "Basic Requirements for Digital Topographic Map Products", the plane accuracy requirements for topographic maps of various scales in basic surveying and mapping results are as follows:

[0066] Table 1 Plane accuracy requirements for topographic maps of various scales of basic surveying and mapping results

[0067]

[0068] From the table above, we can know that the plane accuracy of flat and hilly land in 1:500∽1:2000 scale topographic maps is no more than 0.6mm, and the plane accuracy of mountain and high mountain land is no more than 0.8mm. According to this ratio, it can be deduced that the plane accuracy of the two different terrains of the 1:500 scale topographic map is 0.3 meters and 0.4 meters respectively, and the plane accuracy of the 1:1000 scale topographic map is 0.6 meters and 0.8 meters respectively; while the plane accuracy of the 1:2000 scale topographic map in flat land, hilly land and mountain and high mountain land is 1.2 meters and 1.6 meters respectively. It can also be calculated that the plane accuracy of the two different terrains of the 1:5000 scale topographic map is 2.5 meters and 3.75 meters respectively.

[0069] When gridding real estate geospatial data, it is necessary to ensure the decryption effect of the data and take into account the operation efficiency of the program. Therefore, it is necessary to analyze the method of grid division. If the grid is divided uniformly according to the same size, there will be too many grids for large-scale topographic maps, which will cause the program to run too heavily and reduce efficiency, while too few grids for small-scale topographic maps will cause the decryption to be too rough, resulting in incomplete decryption and easy correction. The grid node coordinates of the original topographic map must be kept confidential, otherwise it is easy to solve inside each small grid according to the specific location of each grid, thereby greatly reducing the difficulty of solving and making the topographic map easy to be corrected. Therefore, when gridding, the scale of the data to be decrypted should be determined first, and then the coverage of the data to be decrypted should be determined. Then, the grid division interval should be determined according to the topographic map plane accuracy requirements and actual application requirements in Table 1.

[0070] (3) Data storage rule design

[0071] In grid deformation, the data format design of the grid and its corresponding grid points and internal features of the grid needs to be performed twice:

[0072] 1) After the real estate geographic spatial data is divided into grids and the grid point coordinates are obtained, it is necessary to design the format of each grid and the four grid points that match it, that is, to design a storage format that can store the grid code and the four grid coordinate points corresponding to this grid; this format can still store the decrypted grid point coordinates after the grid node coordinates are decrypted, and make the two coordinates before and after decryption correspond to each other, and can be called to perform calculations on the bilinear interpolation decryption model.

[0073] 2) Each grid contains different forms of real estate data, such as boundary points, boundary lines, and boundary surfaces. The attributes of these real estate data of different natures are different. In order to facilitate extraction and calculation, it is necessary to design storage formats for various types of data storage such as points, lines, and surfaces. This requires the establishment of several different storage formats to store different types of data in a centralized manner. For boundary point data, three attributes are required: unique ID number, X coordinate, Y coordinate, and elevation, as well as the encoding type of boundary point data and the point number sequence of the nodes in the corresponding boundary lines and boundary surfaces; the boundary line must record the unique ID, the number of nodes, and the classification code corresponding to the boundary surface, with storage code and boundary surface classification code attributes; after decryption, each real estate data still needs the same format of storage structure to store attributes, so that it is easy to retrieve and write into the decrypted document.

[0074] (4) Generate grid

[0075] According to the above grid establishment rules, comprehensively consider the regional scope and data distribution of the data to be declassified, select an appropriate spacing, and establish a grid such as Figure 4As shown, the dots in the grid represent the nodes at the four corners of the grid.

[0076] 3. Randomly offset the coordinates of each node in the grid and record the offset, including the offset in the X direction and the offset in the Y direction;

[0077] When the grid is segmented, a set of random parameters for shifting the grid nodes needs to be generated. When the grid segmentation is completed, the values ​​in the random array are added to the coordinates of each grid node for calculation, so that each grid point is subjected to a certain degree of coordinate offset processing. The offset of the four corner nodes depends on the confidentiality and application requirements of the geospatial data to be declassified. Under these two conditions, the coordinate offset range is set, and a random array is generated within this range to randomly offset the grid nodes. Adjacent grid nodes are offset only once, and the processed grid point coordinate offsets are stored, and each offset forms a one-to-one corresponding storage relationship with its respective grid point. The offset grid is as follows: Figure 5 As shown in the figure, the diamond points represent the offset grid nodes.

[0078] 4. Perform bilinear interpolation on the offset grid to obtain the offset data of the real estate boundary point;

[0079] (1) Formula derivation

[0080] 1) Linear interpolation formula

[0081] like Figure 8 As shown, suppose the coordinates of two points A(x1,y1) and B(x2,y2) on straight line AB are known, find the vertical coordinate y of point C between points A and B with horizontal axis coordinate x.

[0082]

[0083] It is deduced that:

[0084]

[0085] Formula 4-2 is the linear interpolation formula

[0086] 2) Bilinear interpolation formula

[0087] like Fig. 9 As shown, first interpolate twice in the y direction to obtain the values ​​z5 and z6 corresponding to R1(x1,y) and R2(x2,y), that is:

[0088]

[0089] Then interpolate once in the x direction to get the z corresponding to P(x,y), that is:

[0090]

[0091] Substituting 4-3 and 4-4 into 4-5 and simplifying, we obtain:

[0092]

[0093] (2) Grid interpolation

[0094] According to the 4-6 grid interpolation formula, the values ​​of z1, z2, z3, and z4 are used as the offsets of the four nodes of the grid. Then the z value is the offset of any point to be determined in the grid, and x and y are the coordinate values ​​of the boundary points to be de-encrypted in the grid. For the convenience of observation and calculation, the offset is divided into the offset in the x direction and the offset in the y direction. The offset of the boundary point of the real estate geospatial data in the grid also includes the offset ΔX in the x direction and the offset ΔY in the y direction. The formula is expressed as follows. According to the offset of the point, the coordinates of the boundary point after the offset can be calculated, that is, the geospatial coordinates of the real estate data after de-sensitization. The schematic diagram of the grid interpolation is shown as follows. Figure 6 shown.

[0095]

[0096]

[0097] Where ΔX and ΔY represent the offset of the boundary point to be declassified in the grid in the X direction and the Y direction, x and y are the coordinate values ​​of the boundary point to be declassified in the grid, T 1X 、T 2X 、T 3X 、T 4X is the random offset of the four nodes in the x direction of the grid, T 1y 、T 2y 、T 3y 、T 4y is the random offset of the four nodes in the y direction of the grid. (x1,y1), (x2,y1), (x1,y2), (x2,y2) are the coordinate points of the grid nodes before random offset.

[0098] (3) The desensitized boundary points are spliced ​​to form desensitized real estate geographic data, which requires not only the boundary point data after the spatialization of the house location, but also the boundary line data and boundary surface data. Therefore, after the boundary points of the land feature are de-encrypted, the boundary line data and boundary surface data formed after conversion according to the boundary point data coding and point number sorting rules are required.

[0099] 5. If Figure 7 The figure shows the comparison of real estate geographic data before and after decryption, and analyzes the results of decrypting the real estate registration geospatial data;

[0100] (1) Deformation analysis

[0101] By comparing the real estate geospatial data before and after desensitization, it can be found that the real estate geospatial data has a certain positional offset after desensitization, but the overall shape has not changed significantly, the relative positions between boundary points, boundary lines and boundary surfaces remain unchanged, the topological rules between different boundary surfaces remain unchanged, and the data integrity and continuity are good.

[0102] (2) Coordinate offset analysis

[0103] By comparing the coordinates of the real estate boundary point data before and after declassification, it was found that displacement occurred in both the X and Y directions. The displacement was within a controllable range, and the corresponding geographic error met the requirements of geospatial data confidentiality and practical application.

[0104] (3) Irreversibility analysis

[0105] Since the offset of the grid node is a random array, even if the decryption process is known, the original data cannot be restored. Therefore, such a decryption process is irreversible.

[0106] In summary, the present invention is a method for decrypting real estate geographic spatial data based on a bilinear interpolation model. The decryption of geographic spatial data should comply with relevant specifications and standards. The grid segmentation method is used to process geographic spatial data. The addition of random arrays to grid points increases the randomness of the decryption offset, making the data decryption irreversible. The use of the bilinear interpolation method ensures the local detailed fitting of the data and the continuity of the entire map. The map accuracy after decryption can be manually controlled, and the topological consistency of the data between and within layers is also guaranteed. It has good application value in practical applications. The decrypted real estate registration data formed can realize real estate horizontal business sharing, Internet sharing and real estate big data analysis. It is widely used, effectively reducing the confidentiality level of real estate registration data and retaining the basic characteristics of the real estate spatiotemporal situation type. It can be used for urban spatiotemporal feature research and has good application value in practical applications.

[0107] The above are only preferred embodiments of the present invention, and the protection scope of the present invention is not limited to the above embodiments. All technical solutions under the concept of the present invention belong to the protection scope of the present invention. It should be pointed out that for ordinary technicians in this technical field, some improvements and modifications without departing from the principle of the present invention should be regarded as the protection scope of the present invention.

Claims

1. A method for desensitizing real estate geospatial data based on a bilinear interpolation model, characterized in that: The following steps are involved: Step 1: Input the real estate geospatial data, including the spatialized boundary point data, boundary line data and boundary surface data of the real estate house; select and establish a suitable grid based on the size of the study area; Step 2: Randomly offset the coordinates of each node in the grid and record the offset, including the offset in the X direction and the offset in the Y direction; use the bilinear interpolation method, the four nodes of the grid and the random offset, and the boundary points in each grid to derive the calculation model of the real estate boundary point offset; Bilinear interpolation is performed on the offset grid to obtain the offset data of the real estate boundary points, including: (1) Formula derivation: a calculation model for the offset of real estate boundary points based on bilinear interpolation is derived. Among them, ΔX and ΔY represent the offset of the object to be desensitized in the grid in the X direction and the Y direction respectively, and x and y are the coordinate values ​​of the object point to be desensitized in the grid respectively. 1X , T 2X , T 3X , T 4X is the random offset of the four nodes in the X direction, T 1y , T 2y , T 3y , T 4y is the random offset of the four nodes in the Y direction of the grid, (x1,y1), (x2,y1), (x1,y2), (x2,y2) are the original coordinates of the grid nodes before random offset; (2) Grid interpolation, obtaining the offset of the real estate boundary point after displacement, including the offset ΔX in the X direction and the offset ΔY in the Y direction, and calculating the coordinates of the boundary point after displacement based on the offset of the boundary point, that is, the geospatial coordinates of the real estate data after desensitization; (3) The desensitized boundary points are spliced ​​to form desensitized real estate geographic data, which requires not only the boundary point data after the spatialization of the house location, but also the boundary line data and boundary surface data. Therefore, after the boundary points of the land feature are desensitized, the boundary line data and boundary surface data formed after conversion according to the boundary point data coding and point number sorting rules are required; (4) According to the classification coding of the boundary point, boundary line and boundary surface data, the attributes are connected with the original data, so as to transfer the attributes of the data; Step 3: Analyze the results of desensitizing the real estate registration geospatial data.

2. According to claim 1, a method for desensitizing real estate geospatial data based on a bilinear interpolation model is characterized in that In step 1, based on the input spatialized boundary points, boundary lines, and boundary surface data of real estate houses, the study area where the data is located is obtained, and the row and column widths of the grid are determined according to the confidentiality level and the size of the study area using the coordinates of the corner points of the study area. A grid that meets the scale and coverage of the data to be desensitized and takes into account the plane accuracy requirements of the topographic map and actual application needs is established, including: (1) Real estate coding: Encode the input boundary points, boundary lines, and boundary surfaces. First, classify each boundary surface and set a classification code. Then, use the spatial position to connect the boundary line data and boundary surface data, and perform the same classification processing. (2) Design of grid establishment rules: Grids should be established in accordance with the relevant provisions of GB / T17278-2009 "Basic Requirements for Digital Topographic Map Products". The plane accuracy of topographic maps of various scales in basic surveying and mapping results should meet the relevant requirements. When gridding real estate geographic spatial data, it is necessary to ensure the desensitization effect of the data and also take into account the computational efficiency of the program; (3) Data storage rule design: Design a storage format for storing the grid code and the four grid coordinate points corresponding to this grid. After the grid node coordinates are desensitized, the format is still used to store the desensitized grid point coordinates, and the two coordinates before and after desensitization correspond to each other, which can be called to perform calculations on the bilinear interpolation desensitization model.

3. The method for desensitizing real estate geospatial data based on a bilinear interpolation model according to claim 1, characterized in that In step three, the results of desensitizing the real estate registration geospatial data are analyzed, including: (1) Conduct deformation analysis on the desensitized results of real estate registration geospatial data; (2) Coordinate offset analysis is performed on the offset of the desensitized results of the real estate registration geospatial data; (3) Conduct irreversibility analysis on the results of desensitizing the geospatial data of real estate registration.

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