Geographical grid spatial data processing method, terminal device and storage medium

Abstract

The application discloses a geographical grid space data processing method, a terminal device and a computer readable storage medium. The method comprises the following steps: receiving a position information acquisition request, wherein the position information acquisition request at least comprises to-be-measured position latitude and longitude information and a grid level; converting the to-be-measured position latitude and longitude information and the grid level into a grid number based on a preset function; when receiving a region investigation request based on the grid number, acquiring geographical grid space features of a to-be-investigated region; and constructing a region model according to the geographical grid space features and the grid number. The problem that the geographical data obtained finally is inaccurate due to large grid deformation in related grid cutting technology is solved. The effect of improving the accuracy of geographical grid data is achieved.

Description

Technical Field

[0001] This application relates to the field of data processing technology, and in particular to spatial data processing methods, terminal devices, and computer-readable storage media for geographic grids. Background Technology

[0002] Hadoop is a distributed system infrastructure that primarily addresses data storage and data analysis computation, allowing users to develop distributed programs without needing to understand the underlying details of distributed systems.

[0003] When processing data through the Hadoop offline platform, since there is no geographic grid-based processing and analysis solution in the Hadoop series offline platforms, it is necessary to first develop a job outside the offline platform to perform grid processing and analysis, and then input the analyzed data into the offline platform. In some cases, the grid can be divided in Hadoop according to the decimal places of latitude and longitude. This approach results in a grid area difference of about 100 times between each level, and if the data covers the whole country, the grid distortion is large, leading to inaccurate geographic data. Summary of the Invention

[0004] This application provides a spatial data processing method for geographic grids, a terminal device, and a computer-readable storage medium, which solves the problem of large grid deformation leading to inaccurate geographic data in related grid cutting techniques. This achieves the effect of improving the accuracy of geographic grid data.

[0005] This application provides a spatial data processing method for geographic grids, the spatial data processing method for geographic grids includes:

[0006] Receive a location information acquisition request, wherein the location information acquisition request includes at least the latitude and longitude information of the location to be measured and the grid level;

[0007] Based on a preset function, the latitude and longitude information of the location to be measured and the grid level are converted into grid numbers;

[0008] Upon receiving a regional survey request based on the grid number, the geographic grid spatial characteristics of the area to be surveyed are obtained;

[0009] A regional model is constructed based on the spatial characteristics of the geographic grid and the grid number.

[0010] Optionally, the step of obtaining the geographic grid spatial characteristics of the area to be investigated when receiving a regional survey request based on the grid number includes:

[0011] The survey request for the area is parsed to determine the survey radius;

[0012] The area to be investigated is determined based on the survey radius and the grid number.

[0013] Optionally, the step of obtaining the geographic grid spatial characteristics of the area to be investigated when receiving a regional survey request based on the grid number further includes:

[0014] The regional survey request is parsed to determine the content to be investigated;

[0015] The content to be investigated is matched with a tag database to determine the tags;

[0016] In the area to be investigated, geographic grids with the aforementioned labels are selected, and the spatial characteristics of the geographic grids are obtained.

[0017] Optionally, before the step of receiving the location information acquisition request, the following steps are included:

[0018] Collect grid data and generate grid labels based on the relationships between the grid data;

[0019] A label database is constructed based on the grid data and the grid labels.

[0020] Optionally, the step of collecting grid data and generating grid labels based on the correlation between the grid data includes:

[0021] The grid data is associated and integrated according to data type to generate grid sub-labels;

[0022] The grid sub-labels are processed using a preset data processing method to generate the grid labels.

[0023] Optionally, after the step of constructing a label database based on the grid data and the grid labels, the method further includes:

[0024] The grid data is collected periodically or in real-time according to the data type.

[0025] Update the grid labels if the grid data has changed.

[0026] Optionally, the step of obtaining the geographic grid spatial characteristics of the area to be investigated when receiving a regional survey request based on the grid number includes:

[0027] The regional survey request is parsed;

[0028] Based on the analysis results, determine the grid level and user permissions corresponding to the regional survey request;

[0029] Based on the grid level and the user permissions, determine whether to connect to the corresponding data interface.

[0030] Optionally, the step of constructing a regional model based on the spatial features of the geographic grid and the grid number includes:

[0031] Based on actual needs, obtain the corresponding geographic grid spatial features and grid number;

[0032] The region model is constructed based on a preset model construction algorithm.

[0033] In addition, to achieve the above objectives, embodiments of the present invention also provide a terminal device, including a memory, a processor, and a spatial data processing program for a geographic grid stored in the memory and executable on the processor. When the processor executes the spatial data processing program for the geographic grid, it implements the method described above.

[0034] In addition, to achieve the above objectives, embodiments of the present invention also provide a computer-readable storage medium storing a spatial data processing program for a geographic grid, wherein when the spatial data processing program for the geographic grid is executed by a processor, the method described above is implemented.

[0035] One or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages:

[0036] This system uses the Google S2 algorithm to divide the grid. Upon receiving a request for location information including the latitude and longitude of the target location and the grid level, it converts the latitude and longitude information and the grid level into grid numbers based on preset functions. When a regional survey request based on the grid number is received, the system acquires the geographic grid spatial characteristics of the surveyed area and constructs a regional model based on these characteristics and the grid number. The preset functions include functions for latitude and longitude grid conversion, spatial range query, distance calculation, coordinate system conversion, and grid level conversion. This solves the problem of large grid deformation in related grid cutting techniques, which leads to inaccurate geographic data. It effectively improves the accuracy of geographic grid data. Attached Figure Description

[0037] Figure 1 This is a flowchart illustrating an embodiment of the spatial data processing method for geographic grids in this application.

[0038] Figure 2 This is a flowchart illustrating Embodiment 2 of the spatial data processing method for geographic grids in this application;

[0039] Figure 3 This is a schematic diagram of the terminal structure of the hardware operating environment involved in one embodiment of this application. Detailed Implementation

[0040] Currently, there is no processing and analysis solution based on geographic grids within the Hadoop offline platform. Therefore, it is necessary to develop jobs outside the offline platform for grid processing and analysis. In existing grid segmentation techniques, large grid deformations lead to inaccurate final geographic data. To address this issue, this application provides a spatial data processing method for geographic grids. The method divides the grid based on the Google S2 algorithm. Upon receiving a request for location information including the latitude and longitude of the target location and the grid level, the method converts the latitude and longitude information and the grid level into grid numbers based on a preset function. When a regional survey request based on the grid numbers is received, the spatial characteristics of the geographic grid of the surveyed area are obtained. Based on the spatial characteristics of the geographic grid and the grid numbers, a regional model is constructed. This improves the accuracy of geographic grid data.

[0041] To better understand the above technical solutions, exemplary embodiments of this application will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of this application are shown in the drawings, it should be understood that this application can be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of this application and to fully convey the scope of this application to those skilled in the art.

[0042] To better understand the above technical solutions, the following will provide a detailed explanation of the technical solutions in conjunction with the accompanying drawings and specific implementation methods.

[0043] Example 1

[0044] In this embodiment, a spatial data processing method for geographic grids is provided.

[0045] Reference Figure 1 The spatial data processing method for geographic grids in this embodiment includes the following steps:

[0046] Step S100: Receive a location information acquisition request, wherein the location information acquisition request includes at least the latitude and longitude information of the location to be measured and the grid level;

[0047] In this embodiment, the location information retrieval request may include the latitude and longitude information of the location to be measured and the grid level. The latitude and longitude information of the location to be measured refers to the latitude and longitude data of the address to be queried on the map. The grid level refers to the different levels of geographic grids obtained by Google S2, and each grid in the level has a corresponding grid number.

[0048] As an optional implementation, upon receiving a location information acquisition request, the request is parsed to determine the latitude and longitude information of the location to be measured and the desired accuracy level. The grid level is then determined based on the accuracy level.

[0049] For example, after Google S2 divides the grid, different grid levels correspond to different levels of precision. The lower the grid level, the lower the precision level. For instance, the area of ​​each grid cell in a level 16 grid is larger than that of each grid cell in a level 19 grid, and it covers a larger amount of data. However, when obtaining detailed data corresponding to a specific latitude and longitude information G, a higher-level grid should be selected. The higher the grid level, the smaller the area of ​​each grid cell, and the more refined the data within the grid. The grid area between adjacent levels differs by approximately four times. A location information retrieval request can include multiple different grid level requirements. Because the difference in grid area between adjacent levels is small, the transition between different grid levels is relatively smooth.

[0050] As an alternative implementation, Google S2 is a geospatial indexing system primarily used for geographic location search, aggregation, and visualization analysis. Google S2 offers implementations in multiple programming languages, such as C++, Java, Go, and Python, to support different development environments and platforms. Developers can use S2 for geospatial analysis and querying operations across various systems and frameworks.

[0051] For example, Google S2 uses a hierarchical indexing structure to divide the Earth's surface into different levels, each level being a square grid. The level numbering, as well as the position and size of each grid cell, are optimized to provide efficient indexing and query performance. To accommodate different application needs, Google S2 can divide the Earth's surface into 30 different levels of grids; the higher the level, the smaller the grid cells and the finer the grid data.

[0052] Step S200: Based on a preset function, convert the latitude and longitude information of the location to be measured and the grid level into a grid number;

[0053] In this embodiment, the preset function refers to a Hive UDF function. Based on the pre-written Hive UDF function, functions such as latitude and longitude grid conversion, spatial range query, distance calculation, coordinate system conversion, and grid level conversion can be implemented. The grid number includes the grid level number and specific location data.

[0054] As an alternative implementation, HiveUDF functions can be written in Java based on the principles of the Google S2 algorithm to generate a function list. In subsequent use, Hive UDF functions can be written and updated to the function list as needed.

[0055] For example, after receiving a location information acquisition request, the latitude and longitude information and grid level are parsed, the latitude and longitude to grid number conversion function is called, and the latitude and longitude information and grid level are passed to the corresponding parameter positions in the function to obtain the grid number corresponding to the location information acquisition request.

[0056] Optionally, updating the function list can be done manually, using automated scripts, through version control systems, or by dynamically loading functions. When the function list is small and infrequently updated, manual updates can be used, allowing users to add, modify, or delete functions manually. When the function list requires frequent updates or depends on external data sources, automated script updates can be used. Scripts are written to read function information from specified data sources or configuration files and automatically update the function list. To better manage the function list and enable synchronous / asynchronous updates, version control systems can be used. The function list is treated as part of the codebase, and a version control system tracks the addition, modification, and deletion of functions. When the programming language or framework used to build the function list provides dynamic function loading functionality, dynamic updates can be used. During program execution, functions are dynamically loaded as needed and added to the function list, allowing for dynamic updates based on runtime conditions and providing greater flexibility and scalability.

[0057] As an alternative implementation, before receiving a location information retrieval request, the latitude and longitude data is processed, corresponding Google S2 grid levels are added, and the data is stored in a NoSQL database such as Elasticsearch. This is done using functions in SQL, making the operation simple. Upon receiving the location information retrieval request, the corresponding function is invoked based on the relevant data to generate grid numbers.

[0058] For example, first install the Google S2 library for your specific programming language; for instance, the s2sphere library can be used with Python. Connect to Elasticsearch or another NoSQL database. Obtain latitude and longitude data and create an S2LatLng object using this data. This object is used for subsequent transformation operations. Use the S2LatLng object to convert the latitude and longitude coordinates to S2 coordinates, generating an S2CellId object containing the grid cell number. Retrieve the grid cell level from the S2CellId object; higher levels correspond to smaller grid cells. Store the latitude and longitude data, the S2 grid cell ID, and the grid level in the corresponding fields of the Elasticsearch or other NoSQL database.

[0059] As another alternative implementation method, in order to facilitate the management and analysis of geographic data, a grid number is generated for each geographic grid.

[0060] For example, to query data F within a specified range surrounding location data A, assuming a 16-level grid is selected. If the specified range surrounding location A occupies 10 grid cells within the 16-level grid, then the grid numbers of these 10 grid cells are obtained. The relevant data for these 10 grid numbers are then linked together in the data table to filter out the data F. Searching for data by grid number results in a smaller data footprint and faster response time.

[0061] Step S300: Upon receiving a regional survey request based on the grid number, obtain the geographic grid spatial characteristics of the area to be surveyed;

[0062] In this embodiment, geographic grid spatial features refer to the data within each geographic grid that can characterize location features. Regional survey requests can be requests requiring specific regional data, such as market research, business decision-making, or urban planning.

[0063] As an alternative implementation, geographic grid spatial characteristics can be characterized by creating grid profiles. A grid profile involves dividing a geographic area into grids and collecting and analyzing data such as population, consumption behavior, and interests within each grid cell to depict the demographic characteristics and behavioral trends of the region. Grid profiles can help understand information such as population distribution, consumption habits, and interests.

[0064] For example, after establishing geographic grid spatial features, different geographic regions can be compared and analyzed, and data can be correlated. Understanding the characteristics and differences of each region provides data support for decisions such as marketing, business positioning, and urban planning. For instance, retailers can use geographic grid spatial features to understand consumer characteristics in different regions to better position stores and formulate marketing strategies, while urban planners can use geographic grid spatial features to understand population distribution and needs, and optimize public facilities and transportation planning.

[0065] As an alternative implementation, to protect data security, upon receiving a regional survey request, it is necessary to first verify whether the user who issued the request has the necessary permissions to obtain the corresponding data. If the user does not have the relevant permissions, they will be unable to access the corresponding API interface and obtain the relevant grid data.

[0066] For example, since different levels of grid have different data granularity, the data corresponding to the finer-grained small grid is more accurate and involves more private data. Therefore, only users with high privileges can access the data of the fine-grained grid.

[0067] As another optional implementation, when a regional survey request based on grid number is received, the regional survey request is parsed to determine the survey radius, and the area to be surveyed is determined based on the survey radius and grid number.

[0068] For example, upon receiving a regional survey request, the area to be surveyed is determined using the grid corresponding to the grid number as the center and the survey radius. After determining the area to be surveyed, the user can adjust the grid level according to actual needs.

[0069] As another optional implementation, each grid has a corresponding label to facilitate the extraction of grid content. Upon receiving a regional survey request, the request is parsed to determine the content to be surveyed. The content to be surveyed is matched with the grid label database to determine the labels. Then, geographic grids with the labels are selected from the region to be surveyed, thereby obtaining the geographic grid spatial characteristics of these geographic grids.

[0070] For example, when the label is a population density label, each label reflects a different population density level. When the label is a per capita income label, each label reflects a different income level. The corresponding label is extracted based on the different levels.

[0071] Step S400: Construct a regional model based on the spatial characteristics of the geographic grid and the grid number.

[0072] In this embodiment, after generating the geographic grid spatial features and grid number, a corresponding regional model is constructed based on the regional survey request.

[0073] As an alternative implementation, the region model can be a statistical model, a machine learning model, or other type of model used to describe and predict the relationships and characteristics between different grids. The model can be constructed using methods such as regression clustering, cluster analysis, or spatial interpolation.

[0074] For example, after retrieving geographic grid spatial features and grid numbers based on a regional survey request, models for predicting population growth trends and evaluating marketing product prices can be constructed. After constructing the model, methods such as cross-validation or error analysis can be used to validate and evaluate the model.

[0075] In this embodiment, a geographic grid is constructed. Upon receiving a location information retrieval request, the grid number corresponding to the request is determined based on a preset function. Subsequent data queries can be performed based on the grid number, resulting in faster response times. When a regional survey request based on the grid number is received, the geographic grid spatial characteristics of the area to be surveyed are obtained. Based on the geographic grid spatial characteristics and the grid number, a regional model is constructed. Because the grid is segmented using Google S2 segmentation technology, switching between different grid levels is smoother. Furthermore, by employing Hive UDF functions, users can write functions according to their actual needs, enabling them to quickly obtain accurate geographic data.

[0076] Example 2

[0077] Based on Embodiment 1, another embodiment of this application is proposed, with reference to... Figure 2 Before the step of receiving a location information acquisition request, the following steps are included:

[0078] Step S001: Collect grid data and generate grid labels based on the correlation between the grid data;

[0079] In this embodiment, when constructing a geographic grid, data from various points on the map needs to be collected first. The collected data is then merged to generate grid data. Next, grid labels are generated based on the relationships between the grid data. Grid labels are used to represent the characteristics of each geographic grid. These relationships can include spatial relationships, attribute relationships, temporal relationships, network management relationships, and hierarchical relationships.

[0080] As an optional implementation, when constructing grid labels, the collected grid data is associated and integrated according to data type to generate grid sub-labels. Then, the grid sub-labels are processed using a preset data processing method to generate the grid labels.

[0081] For example, grid data can be associated and integrated based on data type, which may include data with different substantive content, such as geographic location data, population data, and economic data. Data of the same type can be associated and integrated according to certain rules. For instance, the average economic indicator for each grid can be calculated as one sub-label, and the population density for each grid can be calculated as another sub-label. Each sub-label represents a feature or attribute of the grid, and the number and type of sub-labels generated can be selected as needed. After generating grid sub-labels, the values ​​of multiple sub-labels can be weighted and summed to obtain a comprehensive grid label; alternatively, the values ​​of multiple sub-labels can be averaged to obtain an average label representing the grid characteristics. A grid label can be a specific numerical value or a discrete category, and can be divided into different categories according to preset thresholds or classification rules.

[0082] As an alternative implementation, grid labels can be generated by associating grid data spatially, by attribute, temporally, by network, or by hierarchy. Different associations can be explored and analyzed using various analytical methods and techniques, enabling better geographic decision analysis. Grid labels are then established by linking and integrating data with existing relationships.

[0083] For example, in spatial association, adjacent grids are usually spatially adjacent and have a spatial proximity relationship. This association can be used for spatial analysis and spatial pattern recognition, such as studying the spatial distribution and clustering of geographical phenomena.

[0084] Regarding attribute relationships, data in a geographic grid typically includes multiple attributes, and these attributes are correlated. For example, the population of different grids may be related to their economic indicators, or the land use type of different grids may be related to their ecological and environmental indicators. These relationships can be explored and analyzed using statistical analysis and data mining methods.

[0085] For temporal correlations, geographic grid data typically has a time dimension, and there may be correlations between grid data at different points in time. For example, temperature data at different points in time may show seasonal variations, or population data at different points in time may show population growth trends. Such correlations can be used for time series analysis and trend forecasting.

[0086] In network relationships, within certain geographic grids, grids may be connected to each other, forming a grid structure. For example, in a traffic grid, road grids are connected through other road grids. This relationship can be used for network analysis and route planning, such as studying shortest and optimal paths.

[0087] In terms of hierarchical relationships, geographic grid data typically exhibits multiple levels of correlation. For example, a country can be divided into grids at the provincial, municipal, and county levels. These relationships can be used for spatial analysis and multi-scale modeling, such as studying the interactions and correlations between grids at different levels.

[0088] Based on actual needs, different relationships can be established to generate different grid labels. When a data acquisition request or related data query request is received, the corresponding grid label can be extracted to obtain the grid data with relationships. Data decision-making and data analysis can be carried out based on the associated grid data to improve efficiency.

[0089] Step S002: Construct a label database based on the grid data and the grid labels.

[0090] As an optional implementation, when constructing the tag database, the structure of the tag database is first defined, including the unique identifier of the grid, the attribute fields of the grid, and the tag fields of the grid.

[0091] For example, when creating the database, a database management system such as MySQL, Qracle, or SQLite is used to create database tables and set the data types of the fields. Data import tools or scripts are then used to import the collected grid data into the database tables, and finally, grid label data is inserted into the database tables to generate a label database.

[0092] As another alternative implementation, after creating the tag database, grid data is collected periodically or in real time according to the data type, and the grid data that has changed is updated to the grid tags and synchronized to the tag database.

[0093] For example, some data in a geographic grid may not change frequently. To streamline the data processing workflow, the data can be categorized based on its frequency of change. Data with high change frequency can be collected in real time, while data with low change frequency can be collected periodically. For instance, population density data, which does not change frequently, can be collected periodically; road condition data, which changes frequently, can be collected in real time.

[0094] As an alternative implementation, when building a tag database, one can first define the tag types required by the database, then collect data related to those tag types, and design the database architecture based on the collected tag data. According to the database architecture, database tables are created, and finally, the collected tag data is imported into the database tables. To facilitate access to the tag database, an access interface also needs to be defined so that users can search and filter data using various conditions and tags. During the use of the tag database, it is also necessary to regularly update and manage the tag database to ensure the addition of new data and the updating of existing data.

[0095] For example, the tag type can be product, article, image, or other entity type. Multiple tag types can be defined according to actual needs, and attributes can be determined for each type. After determining the tag type, tag data is collected using data collection tools, ensuring data accuracy and consistency, and the collected data is categorized according to the defined tag types. For the categorized tag data, a database architecture is designed based on the proposed table and field structure. Appropriate fields and constraints are added to each table to ensure data integrity and consistency, generating the database tables. The collected tag data is imported into the database tables according to the agreed format and structure, and necessary data cleaning and validation are performed. When using the tag database, to improve query and response speed, the tag database can be optimized as needed. The quality and performance of the database can be monitored and improved in real time or periodically. For example, when memory usage exceeds a preset percentage, reducing the running speed of the tag database, historical data can be cleared to free up memory space.

[0096] In this embodiment, grid labels are established to represent the characteristics of each grid, which facilitates data querying and improves data query efficiency.

[0097] Example 3

[0098] In this application embodiment, a spatial data processing device for geographic grids is proposed.

[0099] Reference Figure 3 , Figure 3 This is a schematic diagram of the terminal structure of the hardware operating environment involved in one embodiment of this application.

[0100] like Figure 3 As shown, the control terminal may include: a processor 1001, such as a CPU, a network interface 1003, a memory 1004, and a communication bus 1002. The communication bus 1002 is used to enable communication between these components. The network interface 1003 may optionally include a standard wired interface or a wireless interface (such as a Wi-Fi interface). The memory 1004 may be high-speed RAM or stable non-volatile memory, such as a disk drive. Alternatively, the memory 1004 may be a storage device independent of the aforementioned processor 1001.

[0101] Those skilled in the art will understand that Figure 3 The terminal structure shown does not constitute a limitation on the terminal and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0102] like Figure 3As shown, the memory 1004, which serves as a computer storage medium, may include an operating system, a network communication module, and a spatial data processing program for a geographic grid.

[0103] exist Figure 3 In the hardware structure of the spatial data processing device for the geographic grid shown, the processor 1001 can call the spatial data processing program for the geographic grid stored in the memory 1004 and perform the following operations:

[0104] Receive a location information acquisition request, wherein the location information acquisition request includes at least the latitude and longitude information of the location to be measured and the grid level;

[0105] Based on a preset function, the latitude and longitude information of the location to be measured and the grid level are converted into grid numbers;

[0106] Upon receiving a regional survey request based on the grid number, the geographic grid spatial characteristics of the area to be surveyed are obtained;

[0107] A regional model is constructed based on the spatial characteristics of the geographic grid and the grid number.

[0108] Optionally, the processor 1001 may call the spatial data processing program for the geographic grid stored in the memory 1004, and further perform the following operations:

[0109] The survey request for the area is parsed to determine the survey radius;

[0110] The area to be investigated is determined based on the survey radius and the grid number.

[0111] Optionally, the processor 1001 may call the spatial data processing program for the geographic grid stored in the memory 1004, and further perform the following operations:

[0112] The regional survey request is parsed to determine the content to be investigated;

[0113] The content to be investigated is matched with a tag database to determine the tags;

[0114] In the area to be investigated, geographic grids with the aforementioned labels are selected, and the spatial characteristics of the geographic grids are obtained.

[0115] Optionally, the processor 1001 may call the spatial data processing program for the geographic grid stored in the memory 1004, and further perform the following operations:

[0116] Collect grid data and generate grid labels based on the relationships between the grid data;

[0117] A label database is constructed based on the grid data and the grid labels.

[0118] Optionally, the processor 1001 may call the spatial data processing program for the geographic grid stored in the memory 1004, and further perform the following operations:

[0119] The grid data is associated and integrated according to data type to generate grid sub-labels;

[0120] The grid sub-labels are processed using a preset data processing method to generate the grid labels.

[0121] Optionally, the processor 1001 may call the spatial data processing program for the geographic grid stored in the memory 1004, and further perform the following operations:

[0122] The grid data is collected periodically or in real-time according to the data type.

[0123] Update the grid labels if the grid data has changed.

[0124] Optionally, the processor 1001 may call the spatial data processing program for the geographic grid stored in the memory 1004, and further perform the following operations:

[0125] The regional survey request is parsed;

[0126] Based on the analysis results, determine the grid level and user permissions corresponding to the regional survey request;

[0127] Based on the grid level and the user permissions, determine whether to connect to the corresponding data interface.

[0128] Optionally, the processor 1001 may call the spatial data processing program for the geographic grid stored in the memory 1004, and further perform the following operations:

[0129] Based on actual needs, obtain the corresponding geographic grid spatial features and grid number;

[0130] The region model is constructed based on a preset model construction algorithm.

[0131] In addition, to achieve the above objectives, embodiments of the present invention also provide a terminal device, including a memory, a processor, and a spatial data processing program for a geographic grid stored in the memory and executable on the processor. When the processor executes the spatial data processing program for the geographic grid, it implements the spatial data processing method for the geographic grid as described above.

[0132] In addition, to achieve the above objectives, embodiments of the present invention also provide a computer-readable storage medium storing a spatial data processing program for a geographic grid. When the spatial data processing program for the geographic grid is executed by a processor, it implements the spatial data processing method for the geographic grid as described above.

[0133] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0134] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0135] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0136] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0137] It should be noted that any reference signs placed between parentheses in the claims should not be construed as limiting the claims. The word "comprising" does not exclude the presence of components or steps not listed in the claims. The word "a" or "an" preceding a component does not exclude the presence of a plurality of such components. This application can be implemented by means of hardware comprising several different components and by means of a suitably programmed computer. In a unit claim enumerating several means, several of these means may be embodied by the same item of hardware. The use of the words first, second, and third, etc., does not indicate any order. These words can be interpreted as names.

[0138] Although preferred embodiments of this application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this application.

[0139] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of the invention. Therefore, if these modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include these modifications and variations.

Claims

1. A spatial data processing method for geographic grids, characterized in that, The spatial data processing method for the geographic grid includes: Collect grid data, and associate and integrate the grid data according to its data type, space, attribute, time or level to generate grid sub-labels; The grid sub-labels are processed using a preset data processing method to generate grid labels; Based on the grid data and the grid labels, construct a label database; Receive a location information acquisition request, wherein the location information acquisition request includes at least the latitude and longitude information of the location to be measured and the grid level; Based on a preset function, the latitude and longitude information of the location to be measured and the grid level are converted into grid numbers; Upon receiving a regional survey request based on the grid number, the geographic grid spatial characteristics of the area to be surveyed are obtained, including: parsing the regional survey request, determining the survey radius and the content to be surveyed, determining the area to be surveyed based on the survey radius and the grid number, matching the content to be surveyed with a tag database to determine tags, filtering out geographic grids with the tags in the area to be surveyed, and obtaining the geographic grid spatial characteristics of the geographic grids as the geographic grid spatial characteristics of the area to be surveyed; A regional model is constructed based on the geographical grid spatial characteristics of the area to be investigated and the grid number.

2. The spatial data processing method for geographic grids as described in claim 1, characterized in that, After the step of constructing a tag database based on the grid data and the grid labels, the following steps are included: The grid data is collected periodically or in real-time according to the data type. Update the grid labels if the grid data has changed.

3. The spatial data processing method for geographic grids as described in claim 1, characterized in that, The step of obtaining the geographic grid spatial characteristics of the area to be investigated when receiving a regional survey request based on the grid number includes: The regional survey request is parsed; Based on the analysis results, determine the grid level and user permissions corresponding to the regional survey request; Based on the grid level and the user permissions, determine whether to connect to the corresponding data interface.

4. The spatial data processing method for geographic grids as described in claim 1, characterized in that, The step of constructing a regional model based on the geographic grid spatial characteristics of the area to be investigated and the grid number includes: Based on actual needs, obtain the corresponding geographic grid spatial features and grid number; The region model is constructed based on a preset model construction algorithm.

5. A terminal device, characterized in that, The method includes a memory, a processor, and a spatial data processing program for a geographic grid stored in the memory and executable on the processor. When the processor executes the spatial data processing program for the geographic grid, it implements the method according to any one of claims 1-4.

6. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a spatial data processing program for a geographic grid, which, when executed by a processor, implements the method described in any one of claims 1-4.

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