3MXB three-dimensional model Beidou gridding implementation method

Abstract

The invention provides a 3MXB three-dimensional model Beidou gridding implementation method, belongs to the field of public safety, converts a 3MXB three-dimensional model into a Beidou grid, and provides building information support for air route safety detection in an unmanned aerial vehicle air route early warning scene. The earth surface is divided into a series of grids, and each grid is endowed with a unique code. The spatial information is divided into Beidou grids, so that the efficiency of geographic information processing is improved, the privacy and security of data are enhanced, and the method can also be used in the field of unmanned aerial vehicle management.

Description

Technical Field

[0001] This invention relates to the field of public safety, and in particular to a method for implementing BeiDou gridding in a 3MXB three-dimensional model. Background Technology

[0002] The BeiDou Navigation Satellite System (BDS) is a global satellite navigation system independently developed by my country and is one of the four major global satellite navigation systems. It possesses multiple functions, including basic positioning and navigation. In China, the BDS system, through its ground-based augmentation system, can provide longitude positioning accuracy of 2 cm (horizontal) and 5 cm (high altitude), offering high-precision positioning services for UAV flight path planning and detection. The BeiDou grid code is a discretized, multi-scale regional location identification system developed based on the GeoSOT (Geospatial Grid Theory) theory. It divides the Earth's space from the Earth's center to a distance of 60,000 kilometers into a multi-scale, nested three-dimensional grid group, with a grid accuracy of up to 1.5 cm, covering fine-grained needs from global to local levels. Each grid is assigned a globally unique one-dimensional integer code, enabling rapid retrieval and association through binary or decimal formats. Its three-dimensional coding adds a height dimension code (12 bits) to the two-dimensional code, supporting three-dimensional identification of above-ground, underground, and airspace locations. Furthermore, its grid can be subdivided as needed (e.g., from kilometer-level to centimeter-level), achieving seamless association between regional locations and physical objects. BeiDou Grid provides an efficient and accurate solution for detecting drone flight path conflicts through digital and grid-based airspace management.

[0003] Point clouds, as the core data structure for 3D spatial perception, consist of a massive number of discrete points. Each point not only contains precise 3D coordinates (X, Y, Z) but can also be augmented with multimodal attributes such as color, reflection intensity, and normal vectors, thereby achieving micron-level precise description of the geometric features of an object's surface. By discretizing a continuous 3D model into a point cloud, not only can the spatial topological relationships of the model be completely preserved digitally, but also high-precision coordinate calculation and geometric deformation analysis can be achieved based on spatial feature parameters such as point distances and normal vector angles. Furthermore, combined with the stereo partitioning theory of BeiDou grid codes, building point clouds can be automatically mapped to multi-scale grid cells, generating globally unique spatiotemporal codes. This provides a standardized spatial index for the integration of Building Information Modeling (BIM) and Geographic Information Systems (GIS), significantly improving the efficiency of organizing, retrieving, and associating 3D data in smart city management.

[0004] Automation technology, as a key area of ​​modern science and technology, plays an irreplaceable role in improving production efficiency, optimizing quality of life, and driving overall social progress. With technological iteration and the continuous expansion of application scenarios, its value will become even more prominent. In drone flight path conflict monitoring, automation technology can automatically detect collisions between generated drone flight paths and surrounding buildings or drones, improving the accuracy of conflict detection, reducing accuracy errors caused by manual identification, minimizing manual intervention, and significantly reducing labor costs. Simultaneously, relying on real-time data processing and intelligent decision-making capabilities, the system can significantly shorten response time, reduce waiting and delays, and build a proactive protection system based on risk prediction, comprehensively improving the safety and stability of site operations. Summary of the Invention

[0005] To address the aforementioned technical challenges, this invention provides a method for implementing BeiDou gridding of 3MXB three-dimensional models. This method can convert specific types of 3D model data into universal BeiDou grid code information, improving the versatility of building data, enhancing the accuracy of UAV flight path conflict detection, reducing errors caused by manual identification, and visualizing conflict alarms between flight paths and buildings. It offers new ideas and methods for UAV flight path planning and management, possessing enormous potential and development space.

[0006] This invention can effectively convert 3MXB format models into BeiDou grids, thereby providing accurate and unique building information in flight path conflict detection scenarios.

[0007] The technical solution of this invention is:

[0008] A method for BeiDou gridding of a 3MXB three-dimensional model is proposed. The method involves converting the model into point cloud data to obtain information between points, obtaining GPS coordinate information of all points through standard point registration, and converting the GPS coordinate information into BeiDou grid code, thereby realizing the BeiDou gridding of the three-dimensional model.

[0009] Furthermore,

[0010] include:

[0011] Convert 3MXB format 3D models to 3D point clouds;

[0012] Obtain the GPS coordinate information of all 3D point cloud points;

[0013] The 3D model generates the BeiDou grid code.

[0014] Furthermore,

[0015] The Poisson disk sampling method is used to sample the 3D model to generate point cloud points.

[0016] The specific steps for Poisson disk sampling are as follows:

[0017] 1) Determine each triangular face as a sampling area, and confirm the minimum distance r according to the Beidou grid code encoding level, where r is the minimum distance maintained between two points;

[0018] 2) Randomly select a starting point within the sampling area and add it to the result set;

[0019] 3) For each point in the result set, randomly generate several candidate points within a ring-shaped region with a radius of [r, 2r] around it; check whether the distance between each candidate point and all points in the existing result set is greater than r; if so, add the candidate point to the candidate pool; otherwise, discard the candidate point.

[0020] 4) Randomly select a point from the candidate pool as the new result point and remove it from the candidate pool; add the newly selected result point to the result set and repeat step 3).

[0021] 5) The algorithm terminates when the candidate pool is empty and no new points satisfying the conditions can be generated.

[0022] Furthermore,

[0023] The generated point cloud data is a series of discrete points in space, which represents the data structure of buildings in three-dimensional space.

[0024] Based on the coordinates of the point, calculate the interpolation between the point and other points in each dimension;

[0025] Then, the GPS coordinates of several landmark points in the point cloud were collected and generated. Based on the basic information of the point cloud, the GPS coordinate information corresponding to all points was calculated.

[0026] After obtaining the GPS coordinates of all points, the BeiDou grid code of each point is calculated. For the BeiDou grid code at the city building level, a level 7 coding level is selected. The latitude and longitude of the region are divided into a 7.73m × 7.73m grid according to the level 7 coding level. A unique code is assigned to each grid. Based on the given latitude and longitude, the grid where the point cloud point is located is determined, and the corresponding code is returned.

[0027] The first-level grid is divided according to the 1:1,000,000 map sheet in GB / T13989-2012, with a unit size of 6°×4°, corresponding to 4 bits. The first bit, with a value of N or S, represents the Northern and Southern Hemispheres of the Earth's surface, respectively. The second to fourth bits identify the first-level grid. Among them, the second and third bits identify the longitude grid, encoded with 01-60; the fourth bit identifies the latitude grid, with latitude divided into Northern and Southern Hemispheres according to AV encoding; the first bit can be determined by the sign of the latitude.

[0028] The second-level grid is formed by dividing the first-level 6°×4° grid into 12×8 sub-grids based on latitude and longitude, corresponding to a 30′×30′ grid, approximately equal to the 55.66km×55.66km grid at the Earth's equator. This corresponds to the fifth and sixth code bits; the fifth code bit identifies the longitude grid, encoded using 0-B; the sixth code bit identifies the latitude grid, encoded using 0-7.

[0029] The third-level grid is formed by dividing the second-level grid into 2×3 equal parts according to latitude and longitude. This corresponds to a 15′×10′ grid on a 1:50000 map sheet, approximately equal to the 27.83km-18.55km grid at the Earth's equator. Its corresponding seventh bit is encoded using a Z-sequence encoding from 0 to 5, with the Z-coding direction related to the hemisphere where the third-level grid is located.

[0030] The fourth-level grid is formed by dividing the third-level grid into 15×10 fourth-level grids according to latitude and longitude, which is equivalent to a 1.85km×1.85km grid at the Earth's equator. It corresponds to the eighth and ninth level code elements. The eighth code element identifies the longitude direction grid and is encoded with 0-E; the ninth code element identifies the latitude direction grid and is encoded with 0-9.

[0031] The fifth-level grid is formed by dividing the fourth-level grid into 15×15 grids according to latitude and longitude, which is equivalent to a 123.69m×123.69m grid at the Earth's equator. The tenth and eleventh bits are encoded using 0-E, and the encoding direction is the same as that of the fourth-level grid.

[0032] The sixth-level grid is formed by dividing the fifth-level grid into 2×2 sixth-level grids according to latitude and longitude, which is equivalent to a 61.84m×61.84m grid at the Earth's equator, corresponding to the twelfth bit; the encoding order adopts 0-3 encoding according to Z order, and the Z encoding direction is related to the hemisphere where the sixth-level grid is located.

[0033] The seventh-level grid is formed by dividing the sixth-level grid into 8×8 grids according to latitude and longitude, which is equivalent to a 7.73m×7.73m grid at the Earth's equator. The corresponding thirteenth and fourteenth bits are encoded using 0-7, and the encoding direction is related to the hemisphere where the grid is located.

[0034] The beneficial effects of this invention are

[0035] First, the 3MXB 3D model is converted into a point cloud, and then all the BeiDou grid codes of the point cloud points are calculated. This yields all the BeiDou grid codes on the surface of the building model. Rendering the BeiDou grid codes according to the 7-level encoding size reveals that this invention converts all the BeiDou grid codes on the surface of the 3D model. Detailed Implementation

[0036] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below. Obviously, the described embodiments are some embodiments of the present invention, but not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0037] This invention proposes a method for converting 3D models of 3MXB data type into BeiDou grid encoding, including converting 3D models into point clouds.

[0038] A 3D model in 3MXB format is composed of triangular faces, each face consisting of three vertices.

[0039] The Poisson disk sampling method is used to sample the 3D model to generate a point cloud. This ensures that the point cloud is dense enough, thus guaranteeing the integrity of the building's surface information. The specific steps of Poisson disk sampling are as follows:

[0040] (1) Determine each triangular face as a sampling area, and confirm the minimum distance r according to the Beidou grid code encoding level, where r is the minimum distance maintained between two points.

[0041] (2) Randomly select a starting point within the sampling area and add it to the result set.

[0042] (3) For each point in the result set, randomly generate several candidate points within a ring-shaped region with a radius of [r, 2r]. Check whether the distance between each candidate point and all points in the existing result set is greater than r. If so, add the candidate point to the candidate pool; otherwise, discard the candidate point.

[0043] (4) Randomly select a point from the candidate pool as the new result point and remove it from the candidate pool. Add the newly selected result point to the result set and repeat step 3.

[0044] (5) The algorithm ends when the candidate pool is empty and no new points that meet the conditions can be generated.

[0045] The generated point cloud data is a series of discrete points in space, representing the data structure of buildings in three-dimensional space. Its basic data components are:

[0046]

[0047] Where x, y, z are the positions of the point in space, and N x N y N z Let be the normal vector at that point. This represents the distance from this point to other points.

[0048] Based on the coordinates of a point, the interpolation Δ between that point and other points in various dimensions can be calculated. x ,Δ y ,Δ z The calculation formula is as follows:

[0049]

[0050] Where X1, Y1, Z1 are the coordinates of point P1, and X2, Y2, Z2 are the coordinates of point P2.

[0051] Then, the GPS coordinates of several landmark points in the point cloud were collected and generated. Based on the basic information of the point cloud, the GPS coordinate information corresponding to all points was calculated.

[0052] After obtaining the GPS coordinates of all points, the BeiDou grid code of each point is calculated. For the BeiDou grid code at the city building level, a level 7 coding level is selected. The latitude and longitude of the region are divided into a 7.73m × 7.73m grid according to the level 7 coding level. A unique code is assigned to each grid. Based on the given latitude and longitude, the grid where the point cloud point is located is determined, and the corresponding code is returned.

[0053] The origin of the BeiDou 2D grid is located at the intersection of the equatorial plane and the Prime Meridian, covering non-polar regions of the Earth's surface (88°S to 88°N). The first-level grid is divided according to the 1:1,000,000 map sheet in GB / T13989-2012, with a unit size of 6° × 4°, corresponding to 4 code bits. The first code bit, with a value of N or S, represents the Northern and Southern Hemispheres of the Earth's surface, respectively. The second to fourth code bits identify the first-level grid. Among them, the second and third code bits identify the longitude grid, encoded using 01-60; the fourth code bit identifies the latitude grid, with latitude divided into Northern and Southern Hemispheres according to AV encoding. The sign of the first code bit can be used to determine the grid's orientation.

[0054] The second-level grid is formed by dividing the first-level 6°×4° grid into 12×8 equal sub-grids based on latitude and longitude, corresponding to a 30′×30′ grid, approximately equal to the 55.66km×55.66km grid at the Earth's equator. This corresponds to the fifth and sixth code bits. The fifth code bit identifies the longitude grid, encoded using 0-B; the sixth code bit identifies the latitude grid, encoded using 0-7.

[0055] The third-level grid is formed by dividing the second-level grid into 2×3 equal parts according to latitude and longitude. This corresponds to a 15′×10′ grid on a 1:50,000 map sheet, approximately equal to the 27.83km-18.55km grid at the Earth's equator. Its corresponding seventh bit is encoded using a Z-sequence encoding from 0 to 5, with the Z-coding direction related to the hemisphere where the third-level grid is located.

[0056] The fourth-level grid is formed by dividing the third-level grid into 15 × 10 fourth-level grids according to latitude and longitude, approximately equivalent to a 1.85 km × 1.85 km grid at the Earth's equator. These correspond to the eighth and ninth level code elements. The eighth code element identifies the longitude direction grid and is encoded using 0-E; the ninth code element identifies the latitude direction grid and is encoded using 0-9.

[0057] The fifth-level grid is formed by dividing the fourth-level grid into 15×15 grids according to latitude and longitude, which is approximately equal to the 123.69m×123.69m grid at the Earth's equator. The tenth and eleventh bits are encoded using 0-E, and the encoding direction is the same as that of the fourth-level grid.

[0058] The sixth-level grid is formed by dividing the fifth-level grid into 2×2 equal parts according to latitude and longitude, approximately equivalent to a 61.84m×61.84m grid at the Earth's equator, corresponding to the twelfth bit. The encoding order follows the Z-sequence using 0-3 encoding, with the Z-encoding direction related to the hemisphere where the sixth-level grid is located.

[0059] The seventh-level grid is formed by dividing the sixth-level grid into 8×8 grids according to latitude and longitude, which is approximately equal to a 7.73m×7.73m grid at the Earth's equator. The corresponding thirteenth and fourteenth bits are encoded using 0-7, and the encoding direction is related to the hemisphere where the grid is located.

[0060] The above description is merely a preferred embodiment of the present invention and is used only to illustrate the technical solution of the present invention, and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention are included within the scope of protection of the present invention.

Claims

1. A method for implementing BeiDou gridding in a 3MXB three-dimensional model, characterized in that, The model is converted into point cloud data to obtain information between points. The GPS coordinate information of all points is obtained through standard point registration. The GPS coordinate information is then converted into BeiDou grid code, thereby realizing the BeiDou gridification of the 3D model.

2. The method according to claim 1, characterized in that, include: Convert 3MXB format 3D models to 3D point clouds; Obtain the GPS coordinate information of all 3D point cloud points; The 3D model generates the BeiDou grid code.

3. The method according to claim 2, characterized in that, The Poisson disk sampling method is used to sample the 3D model to generate point cloud points.

4. The method according to claim 3, characterized in that, The specific steps for Poisson disk sampling are as follows: 1) Determine each triangular face as a sampling area, and confirm the minimum distance r according to the Beidou grid code encoding level, where r is the minimum distance maintained between two points; 2) Randomly select a starting point within the sampling area and add it to the result set; 3) For each point in the result set, randomly generate several candidate points within a ring-shaped region with a radius of [r, 2r] around it; check whether the distance between each candidate point and all points in the existing result set is greater than r; if so, add the candidate point to the candidate pool; otherwise, discard the candidate point. 4) Randomly select a point from the candidate pool as the new result point and remove it from the candidate pool; add the newly selected result point to the result set and repeat step 3). 5) The algorithm terminates when the candidate pool is empty and no new points satisfying the conditions can be generated.

5. The method according to claim 4, characterized in that, The generated point cloud data is a series of discrete points in space, which represents the data structure of buildings in three-dimensional space.

6. The method according to claim 5, characterized in that, Based on the coordinates of the point, calculate the interpolation between the point and other points in each dimension; Then, the GPS coordinates of several landmark points in the point cloud were collected and generated. Based on the basic information of the point cloud, the GPS coordinate information corresponding to all points was calculated.

7. The method according to claim 6, characterized in that, After obtaining the GPS coordinates of all points, the BeiDou grid code of each point is calculated. For the BeiDou grid code at the city building level, a level 7 coding level is selected. The latitude and longitude of the region are divided into a 7.73m × 7.73m grid according to the level 7 coding level. A unique code is assigned to each grid. Based on the given latitude and longitude, the grid where the point cloud point is located is determined, and the corresponding code is returned.

8. The method according to claim 7, characterized in that, The first-level grid is divided according to the 1:1,000,000 map sheet in GB / T13989-2012, with a unit size of 6°×4°, corresponding to 4 bits; the first bit, with a value of N or S, represents the Northern Hemisphere and Southern Hemisphere of the Earth's surface, respectively; the second to fourth bits identify the first-level grid; among them, the second and third bits identify the longitude direction grid, encoded with 01-60; The fourth code element identifies the latitude direction grid, with latitude divided into the Northern and Southern Hemispheres according to AV encoding; The first code element can be determined based on the sign of the latitude; The second-level grid is formed by dividing the first-level 6°×4° grid into 12×8 sub-grids based on latitude and longitude, corresponding to a 30′×30′ grid, approximately equal to the 55.66km×55.66km grid at the Earth's equator. This corresponds to the fifth and sixth code bits; the fifth code bit identifies the longitude grid, encoded using 0-B; the sixth code bit identifies the latitude grid, encoded using 0-7. The third-level grid is formed by dividing the second-level grid into 2×3 equal parts according to latitude and longitude. This corresponds to a 15′×10′ grid on a 1:50000 map sheet, approximately equal to the 27.83km-18.55km grid at the Earth's equator. Its corresponding seventh bit is encoded using a Z-sequence encoding from 0 to 5, with the Z-coding direction related to the hemisphere where the third-level grid is located. The fourth-level grid is formed by dividing the third-level grid into 15×10 fourth-level grids according to latitude and longitude, which is equivalent to a 1.85km×1.85km grid at the Earth's equator. It corresponds to the eighth and ninth level code elements. The eighth code element identifies the longitude direction grid and is encoded with 0-E; the ninth code element identifies the latitude direction grid and is encoded with 0-9. The fifth-level grid is formed by dividing the fourth-level grid into 15×15 grids according to latitude and longitude, which is equivalent to a 123.69m×123.69m grid at the Earth's equator. The tenth and eleventh bits are encoded using 0-E, and the encoding direction is the same as that of the fourth-level grid. The sixth-level grid is formed by dividing the fifth-level grid into 2×2 sixth-level grids according to latitude and longitude, which is equivalent to a 61.84m×61.84m grid at the Earth's equator, corresponding to the twelfth bit; the encoding order adopts 0-3 encoding according to Z-order, and the Z encoding direction is related to the hemisphere where the sixth-level grid is located; The seventh-level grid is formed by dividing the sixth-level grid into 8×8 grids according to latitude and longitude, which is equivalent to a 7.73m×7.73m grid at the Earth's equator. The corresponding thirteenth and fourteenth bits are encoded using 0-7, and the encoding direction is related to the hemisphere where the grid is located.

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