A grid index-based vehicle trajectory point space attribution method

Abstract

This invention discloses a method for spatial attribution of vehicle trajectory points based on a grid index, comprising: Step 1, constructing a static grid map based on OSM data; using an equal latitude and longitude division method combined with OpenStreetMap road data to construct a multi-level grid index structure; Step 2, constructing and fusing an elevation information grid; constructing a grid layer with elevation information; Step 3, constructing and fusing an administrative division information grid; constructing a grid layer with administrative division information; Step 4, constructing and fusing a geomorphic information grid; constructing a grid layer with geomorphic information; Step 5, real-time trajectory point processing and matching; receiving vehicle trajectory point data and matching it to the expanded grid road network; Step 6, anomaly handling and optimization; handling anomalies during the matching process and optimizing the matching effect. This invention solves the problems of low efficiency in vehicle trajectory data processing, excessive resource consumption, and insufficient real-time performance in existing technologies.

Description

Technical Field

[0001] This invention relates to the field of intelligent transportation data processing technology, and in particular to a method for spatial attribution of vehicle trajectory points based on grid indexing. Background Technology

[0002] With the rapid development of intelligent transportation systems and the continuous increase in vehicle ownership, urban traffic problems have become increasingly serious, leading to a number of social problems. In order to alleviate urban traffic pressure and improve the traffic environment, real-time analysis and utilization of intelligent transportation data has become an important way to solve urban traffic problems [1]. As a typical spatiotemporal data, vehicle trajectory data integrates time and location information and has the characteristics of frequent location updates and rich information [2]. However, traditional vehicle trajectory data processing methods mainly rely on spatial database technologies, such as PostGIS and Oracle Spatial, which have many bottlenecks when processing massive trajectory data [3].

[0003] First, traditional spatial join query methods consume a lot of CPU resources when processing a large number of trajectory points and complex polygons, and the single-point processing latency is usually in the range of 10-100ms, which is difficult to support real-time scenarios with millisecond-level response [4]. Second, spatial index structures (such as R trees) consume a lot of memory resources during construction and maintenance, and generate high I / O load during querying [3][5]. Finally, in scenarios such as highway monitoring, traditional methods cannot meet the sub-second response requirements due to the need to process trajectory points of tens of thousands of vehicles per second, resulting in data delay backlog [4].

[0004] Specifically, it has the following drawbacks: 1. Traditional vehicle trajectory data processing methods mainly rely on spatial database technologies, such as PostGIS and Oracle Spatial. When processing massive trajectory data, they consume a lot of CPU resources. The single-point processing latency is usually at the level of 10-100ms, which is difficult to support real-time scenarios with millisecond-level response and cannot meet the high real-time requirements of highway monitoring [8][4].

[0005] 2. Existing spatial index structures (such as R trees) require a lot of memory resources to build and maintain, and generate high I / O load during the query process, resulting in low system resource utilization and affecting overall processing efficiency [6][9].

[0006] 3. When dealing with large-scale data, traditional methods have limited optimization space for index structure, which affects query efficiency and overall data processing performance, making it difficult to meet real-time processing requirements [2][9].

[0007] 4. Existing vehicle trajectory data correction methods have limitations when dealing with complex polygons, which affects the accuracy and efficiency of trajectory data and cannot meet the requirements of high-precision real-time analysis

[10] [4].

[0008] 5. Current vehicle trajectory data processing methods lack efficient concurrent processing capabilities when dealing with large-scale real-time data streams, making it difficult to meet the real-time analysis needs of massive data in urban traffic environments [6][9].

[0009] In order to solve these problems, a lightweight, low-latency solution that can meet the real-time trajectory processing requirements is urgently needed. The solution should be able to achieve millisecond-level matching of vehicle trajectory points with administrative region / road information without relying on spatial databases, while supporting real-time processing of tens of thousands of trajectory points per second, reducing memory consumption, and maintaining a high matching accuracy [6]. This can not only improve the efficiency of real-time evidence collection of vehicle violations, but also support important application scenarios such as dynamic route pricing systems and vehicle network security early warning [7].

[0010] The existing patents cited above are as follows: [1] CN114265833A, a visualization analysis platform for geographic spatiotemporal features guided by vehicle trajectory; [2] CN113051359A, A method for large-scale trajectory data similarity query based on multi-level index structure, Dalian University of Technology [3] CN112579921A, Trajectory Indexing and Query Method and System Based on Inverted Index and Prefix Tree, Chang'an University [4] CN108510008A, A method for extracting road networks based on the spatial relationship and distribution of floating car trajectory points, China Communications Spatial Information Technology (Beijing) Co., Ltd., +1 [5] CN116108120A, Grid indexing method, trajectory data search method and apparatus for trajectory data, Wuhan University, +1 [6] CN115543998A, A region analysis method based on spatial grid index, Daoshu (Shanghai) Digital Technology Co., Ltd. [7] CN114912689A, A method and system for predicting the destination of oversized vehicles based on map grid index and XGBOOST, Huashe Design Group Co., Ltd. [8] CN114817360A, A method for calibrating trajectory data to improve the timeliness of trajectory-accompanied mining, Guilin University of Electronic Technology [9] CN118779398A, A vehicle trajectory correction method based on the sanitation field, Longma Internet (Fujian) Technology Co., Ltd.

[10] CN114724362A, A method for processing vehicle trajectory data, China Communications Information Technology National Engineering Laboratory Co., Ltd. Regarding the solutions to the problems of low efficiency, excessive resource consumption, and insufficient real-time performance in vehicle trajectory data processing, the aforementioned existing invention patents are partially described below: For example, CN113051359A discloses a large-scale trajectory data similarity query method based on a multi-level index structure. This method preprocesses the original trajectory data and establishes a grid index on the preprocessed trajectory data based on the spatial grid index concept. The trajectory dataset is then divided into grids using the grid index. For each trajectory, feature trajectories are constructed to represent its characteristic information. Start and end indexes are established for the start and end points of each trajectory in the spatial grid, and feature point indexes are built based on the feature trajectory points of each trajectory. However, this method still has room for index structure optimization when processing large-scale data, affecting query efficiency.

[0011] CN114724362A discloses a method for processing vehicle trajectory data. This method collects vehicle trajectory data using an onboard GPS device, preprocesses the data to obtain corrected vehicle trajectory data, associates road network data, traffic element information, and regional POI information with the corrected vehicle trajectory data to obtain geospatial data, performs grid-based tiling on the geospatial data, and matches and merges the tiling with vehicle trajectory points to obtain vehicle trajectory information. However, this method still has room for optimization in correcting vehicle trajectory data, affecting the accuracy and efficiency of the vehicle trajectory data. Summary of the Invention

[0012] Existing technologies suffer from low efficiency in vehicle trajectory data processing, excessive resource consumption, and insufficient real-time performance. Therefore, to address these issues, this invention provides a method for spatial attribution of vehicle trajectory points based on a grid index.

[0013] The specific plan is as follows: A method for spatial attribution of vehicle trajectory points based on grid indexing includes the following steps: Step 1: Constructing a static grid map based on OSM data; using the equal latitude and longitude division method combined with OpenStreetMap road data to construct a multi-level grid index structure; Step 2, Construction and Fusion of Altitude Information Grid: Using the same latitude and longitude grid division method as in Step 1, construct a grid layer with altitude information and fuse it into the road information grid; Step 3: Construction and integration of administrative division information grid; Using the same latitude and longitude grid division method as in Step 1, construct a grid layer with administrative division information and integrate it into the existing grid; Step 4: Construction and fusion of geomorphic information grid; Using the same latitude and longitude grid division method as in Step 1, construct a grid layer with geomorphic information and fuse it into the existing grid; Step 5: Real-time trajectory point processing and matching; Receive vehicle trajectory point data and match it to the expanded grid road network; Step 6, Exception Handling and Optimization: Handle exceptions during the matching process and optimize the matching results.

[0014] Further, step 1 includes: Step 101, OSM Road Data Acquisition and Parsing: Obtain road data for the target area from OpenStreetMap, parse road elements including nodes, paths and relationships, and extract road ID, road name, road type and the latitude and longitude sequence of the nodes that make up the road; Step 102, Equal latitude and longitude grid division: The Earth's surface is divided into 5.5m × 5.5m square grids using the equal latitude and longitude division method. Each grid uses the latitude and longitude coordinates of its upper right corner as a unique identifier. The grid ID assignment rule is as follows: the longitude and latitude coordinates of the upper right corner of the grid are encoded in the format of "longitude_latitude" to form a unique grid identifier. Step 103: Road line and grid intersection processing: Connect the nodes in the OSM road data to form road lines, and calculate the intersection relationship between the road lines and the grids; when a road line crosses a grid, assign the attribute information of the road to the grid, and construct an initial static map, with fields including grid ID, road ID, longitude, latitude, road type and road name; Step 104: Grid expansion based on road type: Expand the grid according to the road type, where expressways are expanded to a 7×7 grid set, provincial roads are expanded to a 5×5 grid set, urban roads are expanded to a 3×3 grid set, and other roads remain at a 1×1 grid. Step 105, Mesh Deduplication: The expanded mesh is deduplicated. When the same mesh is marked by multiple road attributes, a unique attribute is determined or a multi-attribute association is established according to preset rules. The preset rules are: attributes with higher road levels are retained first; the priority order is: expressway > provincial road > urban road > other roads. Step 106, Spatial Index Construction: Establish a spatial index for the deduplicated valid grid, including generating Geohash codes, constructing a B+ tree index, and using the upper right corner coordinates of the grid for compressed and optimized storage.

[0015] Further, step 2 includes: Step 201: Acquisition of elevation data. Obtain elevation data of the target area from the SRTM open-source digital elevation model and extract the average elevation value of each grid cell. Step 202: Elevation grid division. Using the same latitude and longitude division method as in Step 102, the Earth's surface is divided into 5.5m × 5.5m square grids, using the same grid ID assignment rules. Step 203: Elevation information fusion. The elevation grid data is fused with the road information grid generated in step 106. An elevation field is added to the original grid attributes to form a comprehensive grid dataset with road and elevation information.

[0016] Further, step 3 includes: Step 301: Obtain administrative division data: Obtain the provincial, municipal, district, and county administrative division boundary data of the target area from the GADM open-source geographic information data source; Step 302: Administrative division grid division. The same latitude and longitude division method as in step 102 is used to divide the Earth's surface into 5.5m × 5.5m square grids, using the same grid ID assignment rules. Step 303: Integrate administrative division information. Integrate the administrative division grid data with the road elevation grid generated in step 203. Add administrative division fields such as province, city, district, and county to the original grid attributes to form a comprehensive grid dataset with road, elevation, and district information.

[0017] Further, step 4 includes: Step 401, Geomorphological Data Acquisition: Acquire plain, hill, and mountain geomorphological type data of the target area from the USGS open-source geographic information data source; Step 402: Terrain grid division. Using the same latitude and longitude division method as in Step 102, the Earth's surface is divided into 5.5m × 5.5m square grids, using the same grid ID assignment rules. Step 403: Geomorphological information fusion. The geomorphological grid data is fused with the road elevation zone grid generated in step 303. A geomorphological type field is added to the original grid attributes to form a comprehensive grid dataset with road, elevation, zone and geomorphological information.

[0018] Further, step 5 includes: Step 501, Receiving Track Point Data: Receive track point data uploaded by the vehicle, including vehicle ID, timestamp, longitude and latitude information; Step 502, Track Point Grid Matching: Calculate the reference grid where the track point is located based on its latitude and longitude, search for the extended area containing this reference grid in the expanded grid set, and return the corresponding road attribute information; Step 503, Attribute Information Binding: Bind the trajectory points to the matching grid road information to generate trajectory point attribution results containing vehicle ID, timestamp, grid ID and road information.

[0019] Further, step 6 includes: Step 601, Boundary Case Handling: For trajectory points falling on grid boundaries, Manhattan distance is used to determine the nearest grid to improve the matching accuracy of boundary points; Step 602, Cache query mechanism: For unmatched trajectory points, start the second-level cache query, set the cache time threshold and capacity limit, and use the LRU algorithm for cache management; Step 603, Anomaly Recording and Analysis: Record anomalies such as mismatches and cache timeouts, and statistically analyze the frequency and latency of anomalies to provide data support for system optimization.

[0020] The beneficial effects of this invention are as follows: 1. This invention uses the equal latitude and longitude division method to divide the Earth's surface into 5.5m×5.5m square grids, and through spatial attribute labeling and grid compression optimization, it significantly reduces the number of effective grids, reduces the memory consumption of spatial index construction and maintenance, and solves the problem of excessive resource consumption in traditional methods; 2. This invention achieves rapid matching and spatial attribution of vehicle trajectory points through a real-time trajectory processing flow based on static grid index. The processing latency of a single point can be controlled within 0.8ms, improving the computational efficiency by 31 times and meeting the high real-time requirements of highway monitoring, etc. 3. This invention employs Geohash encoding and B+ tree indexing technology to achieve efficient retrieval and positioning in two-dimensional space, avoiding the complex calculations of traditional spatial join queries and improving query efficiency; 4. This invention ensures the accuracy of trajectory point matching through upper right corner coordinate representation and grid boundary processing mechanism, with a matching accuracy rate of up to 79.5%, effectively improving the accuracy of real-time evidence collection of vehicle violations; 5. This invention supports real-time processing of tens of thousands of trajectory points per second, meeting the real-time analysis needs of massive data in urban traffic environments and significantly improving the overall processing efficiency of the system. 6. This invention reduces the system's dependence on hardware resources and lowers the system deployment cost through optimized design of static grid construction and real-time processing flow, while improving the system's stability and scalability. Attached Figure Description

[0021] Figure 1 This is a flowchart of the method of the present invention. Detailed Implementation

[0022] The present invention will be further illustrated below with reference to the accompanying drawings and specific embodiments. It should be understood that the following specific embodiments are for illustrative purposes only and are not intended to limit the scope of the invention.

[0023] As shown in the figure, this invention provides a method for spatial attribution of vehicle trajectory points based on grid indexing, including the following steps: Step 1: Constructing a static grid map based on OSM data. A multi-level grid index structure is constructed by combining equal latitude and longitude subdivision with OpenStreetMap road data. Step 1 includes: Step 101: OSM Road Data Acquisition and Parsing: Obtain road data for the target area from OpenStreetMap, parse road elements including nodes, paths, and relationships, and extract road IDs, road names, road types, and the latitude and longitude sequences of the nodes that make up the road.

[0024] Step 102: Isotropic Grid Division: The Earth's surface is divided into 5.5m × 5.5m square grids using the isotropic grid division method. Each grid uses its upper right corner latitude and longitude coordinates as a unique identifier. The grid ID assignment rule is as follows: the longitude and latitude coordinates of the upper right corner of the grid are encoded in the format "longitude_latitude" to form a unique grid identifier. For example, if the upper right corner coordinates of a grid are (116.350050°, 39.980050°), then the generated grid ID will be "116.350050_39.980050".

[0025] Step 103: Road Line and Grid Intersection Processing: Connect the nodes in the OSM road data to form road lines, and calculate the intersection relationship between the road lines and the grid. When a road line crosses a grid, assign the road's attribute information to that grid to construct an initial static map. The fields include grid ID, road ID, longitude, latitude, road type, and road name. An example of the constructed initial static map is shown in the table below: Table 1. Example of initial static map representation

[0026] Step 104: Grid Expansion Based on Road Type: Expand the grid according to road type. Highways are expanded to a 7×7 grid set, provincial roads to a 5×5 grid set, urban roads to a 3×3 grid set, and other roads remain at a 1×1 grid. Specific expansion rules are as follows: (1) The motorway is expanded into a 7-row × 7-column grid set; (2) The provincial trunk is expanded into a grid set of 5 rows × 5 columns; (3) Urban roads (primary, secondary) are expanded into a 3-row × 3-column grid set; (4) Other roads (tertiary, residential) should maintain a 1×1 grid.

[0027] Step 105: Mesh Deduplication: The expanded mesh is deduplicated. When the same mesh is marked by multiple road attributes, a unique attribute is determined or a multi-attribute association is established according to preset rules. The preset rule is: attributes with higher road levels are retained first. The priority order is: Expressway > Provincial Highway > Urban Road > Other Roads.

[0028] Step 106, Spatial Index Construction: Establish a spatial index for the deduplicated valid grid, including generating Geohash codes, constructing a B+ tree index, and using the upper right corner coordinates of the grid for compressed and optimized storage.

[0029] Step 2: Construction and Fusion of Altitude Information Grid. Using the same latitude-longitude grid division method as in Step 1, a grid layer with altitude information is constructed and fused into the road information grid. Step 2 includes: Step 201: Elevation Data Acquisition: Obtain elevation data of the target area from open-source digital elevation models (such as SRTM, ASTER GDEM) and extract the average elevation value of each grid cell.

[0030] Step 202, Elevation Grid Generation: Using the same latitude and longitude grid generation method as in Step 102, the Earth's surface is divided into 5.5m × 5.5m square grids, using the same grid ID assignment rules. The generated elevation grid is represented as follows: Table 2. Examples of elevation grid representation

[0031] Step 203, Elevation Information Fusion: The elevation grid data is fused with the road information grid generated in Step 106. An elevation field is added to the original grid attributes to form a comprehensive grid dataset with road and elevation information. An example of the fused comprehensive grid is shown below: Table 3. Examples of Integrated Altitude Grid Representation

[0032] Step 3: Construction and Integration of Administrative Division Information Grid. Using the same latitude and longitude grid division method as in Step 1, a grid layer with administrative division information is constructed and integrated into the existing grid. Step 3 includes: Step 301: Obtain administrative division data: Obtain the administrative division boundary data of the target area, such as province, city, district, and county, from open-source geographic information data sources (such as Natural Earth and GADM).

[0033] Step 302, Administrative Division Grid Division: Using the same latitude and longitude division method as in Step 102, the Earth's surface is divided into 5.5m × 5.5m square grids, using the same grid ID assignment rules.

[0034] Step 303: Administrative Division Information Fusion: The administrative division grid data is fused with the road elevation grid generated in Step 203. Administrative division fields such as province, city, district, and county are added to the original grid attributes to form a comprehensive grid dataset with road, elevation, and district information. An example of the fused comprehensive grid is shown below: Table 4. Examples of Integrated Grid Representation for Integrated Districts

[0035] Step 4: Construction and Fusion of Geomorphological Information Grid. Using the same latitude and longitude grid division method as in Step 1, a grid layer with geomorphological information is constructed and fused into the existing grid. Step 4 includes: Step 401: Geomorphological data acquisition: Obtain geomorphological data such as plains, hills, and mountains of the target area from open-source geographic information data sources (such as OpenStreetMap terrain labels and USGS geomorphological data).

[0036] Step 402, Topographic grid division: Using the same latitude and longitude division method as in Step 102, the Earth's surface is divided into 5.5m × 5.5m square grids, using the same grid ID assignment rules.

[0037] Step 403, Geomorphological Information Fusion: The geomorphological grid data is fused with the road elevation and zoning grid generated in Step 303. A geomorphological type field is added to the original grid attributes to form a comprehensive grid dataset with road, elevation, zoning, and geomorphological information. An example of the fused comprehensive grid is shown below: Table 5. Examples of Integrated Altitude Grid Representation

[0038] Step 5: Real-time trajectory point processing and matching. This involves receiving vehicle trajectory point data and matching it to the expanded grid road network. Step 5 includes: Step 501, Receiving Track Point Data: Receive track point data uploaded by the vehicle, including vehicle ID, timestamp, longitude and latitude information.

[0039] Step 502, Track Point Grid Matching: Calculate the reference grid where the track point is located based on its latitude and longitude, search for the extended area containing this reference grid in the expanded grid set, and return the corresponding road attribute information.

[0040] Step 503, Attribute Information Binding: Bind the trajectory points to the matching grid road information to generate trajectory point attribution results containing vehicle ID, timestamp, grid ID and road information.

[0041] Step 6, Anomaly Handling and Optimization: Handling anomalies during the matching process and optimizing the matching results. Step 6 includes: Step 601, Boundary Case Handling: For trajectory points that fall on the grid boundary, Manhattan distance is used to determine the nearest grid to improve the matching accuracy of boundary points.

[0042] Step 602, Cache Query Mechanism: For unmatched trajectory points, initiate a second-level cache query, set a cache time threshold and capacity limit, and use the LRU algorithm for cache management.

[0043] Step 603, Anomaly Recording and Analysis: Record anomalies such as mismatches and cache timeouts, and statistically analyze the frequency and latency of anomalies to provide data support for system optimization.

[0044] The above embodiments are merely typical illustrative methods of the present invention, and the scope of protection of the present invention is not limited thereto. All equivalent substitutions and improvements made under the concept of the present invention should fall within the scope of protection. It should be emphasized that any modifications or minor adjustments made by those skilled in the art without departing from the basic principles of the present invention should also be considered within the scope of protection of the present invention.

Claims

1. A grid index based vehicle trajectory point space attribution method, characterized in that, Includes the following steps: Step 1: Constructing a static grid map based on OSM data; A multi-level grid index structure was constructed by combining the equal latitude and longitude division method with OpenStreetMap road data; Step 2: Construction and integration of altitude information grid; Using the same latitude and longitude grid division method as in step 1, a grid layer with elevation information is constructed and integrated into the road information grid; Step 3: Construction and integration of administrative division information grid; Using the same latitude and longitude grid division method as in Step 1, construct a grid layer with administrative division information and integrate it into the existing grid; Step 4: Construction and fusion of geomorphological information grid; Using the same latitude and longitude grid division method as in step 1, a grid layer with geomorphological information is constructed and then integrated into the existing grid; Step 5: Real-time trajectory point processing and matching; Receive vehicle trajectory point data and match it to the expanded grid road network; Step 6, Exception Handling and Optimization; Handling exceptions during the matching process and optimizing matching results; Step 1 specifically includes: Step 101, OSM Road Data Acquisition and Parsing: Obtain road data for the target area from OpenStreetMap, parse road elements including nodes, paths and relationships, and extract road ID, road name, road type and the latitude and longitude sequence of the nodes that make up the road; Step 102, Equal latitude and longitude grid division: The Earth's surface is divided into 5.5m × 5.5m square grids using the equal latitude and longitude division method. Each grid uses the latitude and longitude coordinates of its upper right corner as a unique identifier. The grid ID assignment rule is as follows: the longitude and latitude coordinates of the upper right corner of the grid are encoded in the format of "longitude_latitude" to form a unique grid identifier. Step 103: Road line and grid intersection processing: Connect the nodes in the OSM road data to form road lines, and calculate the intersection relationship between the road lines and the grids; when a road line crosses a grid, assign the attribute information of the road to the grid, and construct an initial static map, with fields including grid ID, road ID, longitude, latitude, road type and road name; Step 104: Grid expansion based on road type: Expand the grid according to the road type, where expressways are expanded to a 7×7 grid set, provincial roads are expanded to a 5×5 grid set, urban roads are expanded to a 3×3 grid set, and other roads remain at a 1×1 grid. Step 105, Mesh Deduplication: The expanded mesh is deduplicated. When the same mesh is marked by multiple road attributes, a unique attribute is determined or a multi-attribute association is established according to preset rules. The preset rules are: attributes with higher road levels are retained first; the priority order is: expressway > provincial road > urban road > other roads. Step 106, Spatial Index Construction: Establish a spatial index for the deduplicated valid grid, including generating Geohash encoding, constructing a B+ tree index, and using the upper right corner coordinates of the grid for compressed and optimized storage; Step 2 includes: Step 201: Acquisition of elevation data. Obtain elevation data of the target area from the SRTM open-source digital elevation model and extract the average elevation value of each grid cell. Step 202: Elevation grid division. Using the same latitude and longitude division method as in Step 102, the Earth's surface is divided into 5.5m × 5.5m square grids, using the same grid ID assignment rules. Step 203: Elevation information fusion. The elevation grid data is fused with the road information grid generated in step 106. An elevation field is added to the original grid attributes to form a comprehensive grid dataset with road and elevation information.

2. The method of claim 1, wherein, Step 3 includes: Step 301: Obtain administrative division data: Obtain the provincial, municipal, district, and county administrative division boundary data of the target area from the GADM open-source geographic information data source; Step 302: Administrative division grid division. The same latitude and longitude division method as in step 102 is used to divide the Earth's surface into 5.5m × 5.5m square grids, using the same grid ID assignment rules. Step 303: Integrate administrative division information. Integrate the administrative division grid data with the road elevation grid generated in step 203. Add provincial, municipal, district, and county administrative division fields to the original grid attributes to form a comprehensive grid dataset with road, elevation, and district information.

3. The method of claim 1, wherein, Step 4 includes: Step 401, Geomorphological Data Acquisition: Acquire plain, hill, and mountain geomorphological type data of the target area from the USGS open-source geographic information data source; Step 402: Terrain grid division. Using the same latitude and longitude division method as in Step 102, the Earth's surface is divided into 5.5m × 5.5m square grids, using the same grid ID assignment rules. Step 403: Geomorphological information fusion. The geomorphological grid data is fused with the road elevation zone grid generated in step 303. A geomorphological type field is added to the original grid attributes to form a comprehensive grid dataset with road, elevation, zone and geomorphological information.

4. The method of claim 1, wherein, Step 5 includes: Step 501, Receiving Track Point Data: Receive track point data uploaded by the vehicle, including vehicle ID, timestamp, longitude and latitude information; Step 502, Track Point Grid Matching: Calculate the reference grid where the track point is located based on its latitude and longitude, search for the extended area containing this reference grid in the extended grid set, and return the corresponding road attribute information; Step 503, Attribute Information Binding: Bind the trajectory points to the matching grid road information to generate trajectory point attribution results containing vehicle ID, timestamp, grid ID and road information.

5. The method of claim 1, wherein, Step 6 includes: Step 601, Boundary Case Handling: For trajectory points falling on grid boundaries, Manhattan distance is used to determine the nearest grid to improve the matching accuracy of boundary points; Step 602, Cache query mechanism: For unmatched trajectory points, initiate a second-level cache query, set a cache time threshold and capacity limit, and use the LRU algorithm for cache management; Step 603, Anomaly Recording and Analysis: Record mismatch and cache timeout anomalies, and statistically analyze the frequency and latency of anomalies to provide data support for system optimization.

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