Map query methods, devices, vehicles, and media based on vehicle-to-everything (V2X) data

Abstract

The application relates to the technical field of Internet of Vehicles, in particular to a map query method and device based on Internet of Vehicles data, a vehicle and a medium, wherein the method comprises the following steps: acquiring at least one map query instruction of a user when a vehicle is in a preset map query working condition; confirming a target map scale and a target map area of the user based on the at least one map query instruction, and matching a current data table corresponding to the target map area according to the target map scale; searching at least one target Internet of Vehicles position data based on the current data table, aggregating the at least one target Internet of Vehicles position data, obtaining summary data, and generating a map query result of the vehicle by using the summary data. According to the application, the different requirements of the user for the detail level of the map can be acquired, and a data table for querying the Internet of Vehicles position data can be obtained, so that the fast retrieval of the massive data resources in the Internet of Vehicles is realized, the map query efficiency is improved, and the application is more practical.

Description

Technical Field

[0001] This application relates to the field of vehicle networking technology, and in particular to a map query method, device, vehicle, and medium based on vehicle networking data. Background Technology

[0002] With the continuous development of vehicle intelligence technology, the application of vehicle networking in practical technology is becoming more and more diverse. The storage of massive geographic location data in vehicle networking can provide the necessary foundation for functions such as real-time reporting of vehicle location.

[0003] In related technologies, the accumulated vehicle network data is massive, making it difficult to achieve fast, second-level query responses. This can be addressed by thinning the massive data to reduce the amount of computational data, or by mapping the location data into a grid and filtering the data based on the network when querying the data.

[0004] However, in related technologies, data thinning may cause data detail distortion, and the operating efficiency of existing grid mapping is affected by encoding rules, making the calculation relatively complex. It cannot break through the computational bottleneck in the process of processing massive data, reducing the query efficiency of massive data, making it difficult to meet the requirements of data quality and the perfection of hierarchical data processing, and affecting the accuracy of query results. These issues urgently need to be addressed. Summary of the Invention

[0005] This application provides a map query method, device, vehicle, and medium based on vehicle network data to solve the problems in related technologies, such as data thinning may cause data detail distortion, and the operation efficiency of existing grid mapping is affected by encoding rules, the calculation is relatively complex, and it is unable to break through the computational bottleneck in the process of processing massive data, which reduces the query efficiency of massive data, makes it difficult to meet the requirements of data quality and the perfection of data hierarchical processing, and affects the accuracy of query results.

[0006] The first aspect of this application provides a map query method based on vehicle network data, comprising the following steps: when the vehicle is in a preset map query condition, obtaining at least one map query instruction from the user; confirming the user's target map scale and target map area based on the at least one map query instruction, matching the current data table corresponding to the target map area according to the target map scale; retrieving at least one target vehicle network location data based on the current data table, aggregating the at least one target vehicle network location data to obtain summary data, and using the summary data to generate the map query result for the vehicle.

[0007] Optionally, in one embodiment of this application, the step of matching the current data table corresponding to the target map area according to the target map ratio includes: determining whether the target map ratio is greater than or equal to a preset ratio threshold; if the target map ratio is greater than or equal to the preset ratio threshold, then determining the detailed data table of the vehicle network location database as the current data table; otherwise, determining the summary data table of the vehicle network database as the current data table.

[0008] Optionally, in one embodiment of this application, before matching the current data table of the target map region according to the target map scale, the method further includes: rounding the longitude and latitude parameters of each vehicle network data in the vehicle network location database using a first preset multiple to obtain a coarse grid code for each vehicle network data; rounding the longitude and latitude parameters of each vehicle network data in the vehicle network location database using a second preset multiple to obtain a fine grid code for each vehicle network data, wherein the first preset multiple is less than the second preset multiple; and constructing the summary data table based on the coarse grid code vehicle network data and the fine grid code vehicle network data.

[0009] Optionally, in one embodiment of this application, retrieving at least one target vehicle network location data based on the current data table includes: if the current data table is the detailed data table, calculating the number range of the target map area according to the target map scale; and locating the at least one target vehicle network location data in the detailed data table according to the number range.

[0010] Optionally, in one embodiment of this application, retrieving at least one target vehicle network location data based on the current data table includes: if the current data table is the summary data table, determining the target coding category of the summary data table according to the target map scale; and extracting the at least one target vehicle network location data from the summary data table based on the target coding category.

[0011] A second aspect of this application provides a map query device based on vehicle network data, comprising: an acquisition module, configured to acquire at least one map query instruction from a user when the vehicle is in a preset map query condition; a matching module, configured to confirm the user's target map scale and target map area based on the at least one map query instruction, and match the target map scale with a current data table corresponding to the target map area; and a query module, configured to retrieve at least one target vehicle network location data based on the current data table, aggregate the at least one target vehicle network location data to obtain summary data, and use the summary data to generate a map query result for the vehicle.

[0012] Optionally, in one embodiment of this application, the matching module includes: a judgment unit, configured to judge whether the target map scale is greater than or equal to a preset scale threshold; and a judgment unit, configured to determine that the detailed data table of the vehicle network location database is the current data table when the target map scale is greater than or equal to the preset scale threshold, otherwise, determine that the summary data table of the vehicle network database is the current data table.

[0013] Optionally, in one embodiment of this application, the apparatus further includes: a first rounding module, configured to round the longitude and latitude parameters of each vehicle network data in the vehicle network location database using a first preset multiple before matching the current data table of the target map region according to the target map scale, to obtain a coarse grid code for each vehicle network data; a second rounding module, configured to round the longitude and latitude parameters of each vehicle network data in the vehicle network location database using a second preset multiple, to obtain a fine grid code for each vehicle network data, wherein the first preset multiple is less than the second preset multiple; and a construction module, configured to construct the summary data table based on the vehicle network data with the coarse grid code and the vehicle network data with the fine grid code.

[0014] Optionally, in one embodiment of this application, the query module includes: a calculation unit, configured to calculate the number range of the target map area according to the target map scale when the current data table is the detailed data table; and a positioning unit, configured to locate the at least one target vehicle network location data in the detailed data table according to the number range.

[0015] Optionally, in one embodiment of this application, the query module includes: a confirmation unit, configured to confirm the target coding category of the summary data table according to the target map scale when the current data table is the summary data table; and an extraction unit, configured to extract the at least one target vehicle network location data from the summary data table based on the target coding category.

[0016] A third aspect of this application provides a vehicle, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the map query method based on vehicle network data as described in the above embodiments.

[0017] A fourth aspect of this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the map query method based on vehicle network data as described above.

[0018] A fifth aspect of this application provides a computer program that, when executed, implements the map query method based on vehicle network data as described above.

[0019] This application embodiment can obtain a data table for querying vehicle network location data by acquiring users' different needs for map detail, thereby realizing rapid retrieval of massive data resources in the vehicle network, improving map query efficiency, and making it more practical. This solves the problems in related technologies, such as data thinning potentially causing data detail distortion, and the operational efficiency of existing grid mapping being affected by encoding rules, resulting in complex calculations that cannot overcome computational bottlenecks in the process of processing massive amounts of data, reducing query efficiency, failing to meet the requirements for data quality and hierarchical data processing, and affecting the accuracy of query results.

[0020] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0021] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein:

[0022] Figure 1 This is a flowchart illustrating a map query method based on vehicle network data according to an embodiment of this application;

[0023] Figure 2 This is a schematic diagram illustrating the data storage principle of one embodiment of this application;

[0024] Figure 3 This is a schematic diagram illustrating the principle of query instruction processing according to one embodiment of this application;

[0025] Figure 4 This is a schematic diagram illustrating the principle of map construction in one embodiment of this application;

[0026] Figure 5 This is a schematic diagram of the structure of a map query device based on vehicle network data according to an embodiment of this application;

[0027] Figure 6 This is a structural schematic diagram of a vehicle according to an embodiment of this application. Detailed Implementation

[0028] The embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.

[0029] The following description, with reference to the accompanying drawings, outlines a map query method, apparatus, vehicle, and medium based on vehicle network data according to embodiments of this application. Addressing the issues raised in the background section regarding the potential distortion of data details caused by data thinning, the computational complexity of existing grid mapping methods due to encoding rules, and the inability to overcome computational bottlenecks in processing massive amounts of data, this application provides a map query method based on vehicle network data. This method obtains a data table for querying vehicle network location data by acquiring different user requirements for map detail, thereby enabling rapid retrieval of massive data resources in the vehicle network, improving map query efficiency, and enhancing practicality. This solves the problems in related technologies, such as data thinning potentially causing distortion of data details, the computational complexity of existing grid mapping methods due to encoding rules, the inability to overcome computational bottlenecks in processing massive amounts of data, the inability to meet data quality and hierarchical processing requirements, and the impact on the accuracy of query results.

[0030] Specifically, Figure 1 This is a flowchart illustrating a map query method based on vehicle network data provided in an embodiment of this application.

[0031] like Figure 1 As shown, this map query method based on vehicle network data includes the following steps:

[0032] In step S101, when the vehicle is in a preset map query condition, at least one map query instruction from the user is obtained.

[0033] It should be noted that the preset map query conditions can be set by those skilled in the art according to the actual situation, and no specific limitations are made here.

[0034] It is understood that, in the embodiments of this application, the preset map query condition can be the situation where the user opens the map location information query function in the vehicle's infotainment system or navigation system. At least one map query instruction from the user may include the user's touch screen operation, voice command or physical button input, for example, the user opens the map in the vehicle's central control screen and zooms the map scale and moves the map display location by finger to obtain the corresponding location information in the map, including the vehicle network data such as street names, points of interest (POIs), weather updates and traffic conditions that the user needs to obtain.

[0035] In step S102, the user's target map scale and target map area are confirmed based on at least one map query instruction, and the current data table corresponding to the target map area is matched according to the target map scale.

[0036] It is understood that, in the embodiments of this application, the vehicle's infotainment system or navigation system can receive the user's map query command and parse the command to determine the user's target map scale and target map area. For example, by analyzing the user's gestures of zooming and moving the map, or by understanding the user's voice command through speech recognition and natural language processing, the system can determine the map area that the user wants to view and the required level of detail. Then, based on the level of detail of the location information required by the target map scale, a data table corresponding to the target map area that is suitable for the current scale requirement can be confirmed.

[0037] Optionally, in one embodiment of this application, matching the current data table corresponding to the target map area according to the target map ratio includes: determining whether the target map ratio is greater than or equal to a preset ratio threshold; if the target map ratio is greater than or equal to the preset ratio threshold, then determining the detailed data table of the vehicle network location database as the current data table; otherwise, determining the summary data table of the vehicle network database as the current data table.

[0038] It should be noted that the preset ratio threshold can be set by those skilled in the art according to the actual situation, and no specific limitation is made here.

[0039] In actual implementation, the preset ratio threshold is defined based on the level of detail in the map data and the system's performance requirements to determine when to use detailed data tables or summary data tables. Detailed data tables can contain massive amounts of location data from the vehicle network, suitable for situations where users need to view detailed information about a specific area; summary data tables can contain vehicle network data with more generalized location information, suitable for situations where users need to view a larger area or an overview map.

[0040] For example, for instance Figure 2 The diagram illustrates the data storage principle of one embodiment of this application. Detailed data represents the storage of basic vehicle network data, capable of storing massive amounts of detailed data and supporting real-time writing to ensure the real-time nature of map queries. Hive storage is typically used. Hive is a widely used distributed database, and using it as the storage component for detailed data offers advantages such as horizontal scalability and support for massive data storage. Preprocessed data refers to summarized data processed according to certain business rules. It generally refers to a summary data table summarizing location data by business dimensions, such as grid, administrative region of location, or date. Its characteristic is that it summarizes data according to a combination of dimensions, reducing the amount of data processed subsequently and the overhead of network transmission. Hive storage is also used for this purpose.

[0041] Optionally, in one embodiment of this application, before matching the current data table of the target map region according to the target map scale, the method further includes: rounding the longitude and latitude parameters of each vehicle network data in the vehicle network location database using a first preset multiple to obtain a coarse grid code for each vehicle network data; rounding the longitude and latitude parameters of each vehicle network data in the vehicle network location database using a second preset multiple to obtain a fine grid code for each vehicle network data, wherein the first preset multiple is less than the second preset multiple; and constructing a summary data table based on the vehicle network data with coarse grid codes and the vehicle network data with fine grid codes.

[0042] It should be noted that the first preset multiple and the second preset multiple can be set by those skilled in the art according to the actual situation, and no specific limitation is made here.

[0043] In actual execution, the aggregated data can be obtained by gridding the detailed data. Data gridding can be done by dividing the location data into large grids corresponding to a first preset multiple and small grids corresponding to a second preset multiple. The purpose is to request different grid data according to different ranges of scale when the map zoom level changes.

[0044] For example, as shown in Table 1 below, Table 1 is the original coordinate data table without mesh processing.

[0045] Table 1

[0046] Serial Number VIN code Collection time longitude latitude 1 ***********170692 2023-11-23 08:00:00 31.336031 121.365429 2 ***********170692 2023-11-23 08:00:10 31.336481 121.365076 3 ***********170692 2023-11-23 09:56:00 31.399980 121.434432 4 ***********170692 2023-11-23 10:35:09 31.399494 121.434672 5 ***********170692 2023-11-23 11:33:19 30.960482 120.772887

[0047] Four grid attributes can be added to the original data: fine grid codes corresponding to small grid longitude and small grid latitude, and coarse grid codes corresponding to large grid longitude and large grid latitude. The values ​​of these four attributes are calculated based on the original longitude and latitude, resulting in the preprocessed data table after gridding, as shown in Table 2 below.

[0048] Table 2

[0049]

[0050] Specifically, the large grid longitude and large grid latitude can be obtained by multiplying the original 6-digit decimal longitude and 6-digit decimal latitude by 10 (the first preset multiple), and then truncating the integer part as the large grid longitude and large grid latitude. The sum of the number of large grid numbers (vin) is then used to obtain the summary data table of coarse grid codes as shown in Table 3 below.

[0051] Table 3

[0052] date Da Ge Jing Hao Big Grid Latitude Vin number 2023-11-23 313 1214 4 2023-11-23 309 1207 1

[0053] The small grid longitude and small grid latitude can be obtained by multiplying the original 6-digit decimal longitude and 6-digit decimal latitude by 1000 (the second preset multiple), and then truncating the integer part as the small grid longitude and small grid latitude. The sum of the number of small grid numbers (vin) is used to obtain the summary data table of fine grid codes as shown in Table 4 below.

[0054] Table 4

[0055] date Small grid sutra Small Gaussian Vin number 2023-11-23 31336 121365 2 2023-11-23 31399 121434 2 2023-11-23 30960 120772 1

[0056] In step S103, at least one target vehicle network location data is retrieved based on the current data table, and at least one target vehicle network location data is aggregated to obtain summary data, so as to generate vehicle map query results using the summary data.

[0057] It is understood that, in the embodiments of this application, at least one target vehicle network location data can be retrieved from the current data table based on the target map scale and region. This data may include information such as location name, traffic conditions, and points of interest. The location data is then aggregated in real time according to different dimension combinations to obtain a summary result. The summary data is then rendered onto the map to obtain the map query result.

[0058] Specifically, such as Figure 3 The diagram illustrates the principle of query command processing according to one embodiment of this application. Interface processing includes handling service interface transactions throughout the system, such as accepting data requests from front-end applications, maps, and other applications, processing parameters in the requests, handling user authentication, and processing business logic. For different interface requests, the processed data is submitted to the aggregation engine. After receiving the returned data, it is returned to the front-end application according to the protocol. The aggregation engine is used to efficiently aggregate location data on demand. In the front-end map application, as the user operates, such as zooming in, zooming out, and dragging, data from different areas needs to be requested in real time.

[0059] The aggregation engine needs to dynamically and efficiently calculate data in real time based on the request dimensions to respond to front-end needs. This module uses the Presto engine, an open-source OLAP query engine based on Facebook's MPP architecture. Presto is a distributed SQL execution engine that can execute large datasets from different data sources, supporting data volumes from GB to PB bytes, and is mainly used to handle sub-second query scenarios. Presto itself does not store data, but it can connect to multiple data sources and supports cascading queries across data sources. Moreover, it is based on in-memory computing, making it fast and highly real-time.

[0060] Furthermore, such as Figure 4The diagram shown illustrates the principle of map construction according to one embodiment of this application. Aggregate perception primarily functions to sense data aggregation resulting from user actions. In the front-end map presentation, it's necessary to recognize any user actions on the map interface, such as zooming in, zooming out, dragging, and clicking, while simultaneously identifying the current grid area. Data requests are used to calculate grid numbers based on the perception results and, combined with other data dimensions selected by the user, retrieve the location data of the corresponding grid number and dimension from the backend.

[0061] Optionally, in one embodiment of this application, retrieving at least one target vehicle network location data based on the current data table includes: if the current data table is a detailed data table, calculating the number range of the target map area according to the target map scale; and locating at least one target vehicle network location data in the detailed data table according to the number range.

[0062] In actual implementation, the numbering range can be the range of the code label for each data point in the summary data table within the target area. By calculating the small grid longitude and latitude corresponding to the fine grid codes or the large grid longitude and latitude corresponding to the coarse grid codes that the current map needs to display, filtering can be performed based on the range defined by the numbering. This allows for pre-screening of the detailed data range, enabling the search for all vehicle network location data within this numbering range in the detailed data table. This facilitates rapid location of detailed vehicle network data, reduces unnecessary data retrieval and processing, and improves query speed.

[0063] Optionally, in one embodiment of this application, retrieving at least one target vehicle network location data based on the current data table includes: if the current data table is a summary data table, determining the target coding category of the summary data table according to the target map scale; and extracting at least one target vehicle network location data from the summary data table based on the target coding category.

[0064] In actual implementation, different granularity levels of summary data tables can be determined according to the target map scale. The target coding category can include coarse grid coding and fine grid coding to adapt to users' different needs for map query refinement, enabling users to quickly obtain key information.

[0065] The map query method based on vehicle network data proposed in this application can obtain a data table for querying vehicle network location data by acquiring different user needs for map detail levels. This enables rapid retrieval of massive data resources in the vehicle network, improves map query efficiency, and enhances practicality. Therefore, it solves the problems in related technologies, such as data thinning potentially causing data detail distortion, and the operational efficiency of existing grid mapping being affected by encoding rules, resulting in complex calculations that cannot overcome computational bottlenecks in the processing of massive data, reducing query efficiency, failing to meet data quality and hierarchical processing requirements, and affecting the accuracy of query results.

[0066] Next, referring to the accompanying drawings, a map query device based on vehicle network data proposed according to an embodiment of this application is described.

[0067] Figure 5 This is a schematic diagram of the structure of a map query device based on vehicle network data according to an embodiment of this application.

[0068] like Figure 5 As shown, the map query device 10 based on vehicle network data includes: an acquisition module 100, a matching module 200, and a query module 300.

[0069] The acquisition module 100 is used to acquire at least one map query instruction from the user when the vehicle is in a preset map query condition.

[0070] The matching module 200 is used to confirm the user's target map scale and target map area based on at least one map query command, and to match the current data table corresponding to the target map area according to the target map scale.

[0071] The query module 300 is used to retrieve at least one target vehicle network location data based on the current data table, aggregate at least one target vehicle network location data to obtain summary data, and use the summary data to generate map query results for vehicles.

[0072] Optionally, in one embodiment of this application, the matching module 200 includes:

[0073] The judgment unit is used to determine whether the target map scale is greater than or equal to a preset scale threshold.

[0074] The determination unit is used to determine the detailed data table of the vehicle network location database as the current data table when the target map scale is greater than or equal to a preset scale threshold; otherwise, it determines the summary data table of the vehicle network database as the current data table.

[0075] Optionally, in one embodiment of this application, the device 10 further includes:

[0076] The first rounding module is used to round the longitude and latitude parameters of each vehicle network data in the vehicle network location database by a first preset multiple before matching the current data table of the target map area according to the target map scale, so as to obtain the coarse grid code of each vehicle network data.

[0077] The second rounding module is used to round the longitude and latitude parameters of each vehicle network data in the vehicle network location database using a second preset multiple, so as to obtain the fine grid code of each vehicle network data. The first preset multiple is less than the second preset multiple.

[0078] The module is used to build summary data tables based on coarse-grid encoded vehicle-to-everything (V2X) data and fine-grid encoded V2X data.

[0079] Optionally, in one embodiment of this application, the query module 300 includes:

[0080] The calculation unit is used to calculate the number range of the target map area based on the target map scale, when the current data table is a detailed data table.

[0081] The positioning unit is used to locate at least one target vehicle network location data in the detailed data table according to the number range.

[0082] Optionally, in one embodiment of this application, the query module 300 includes:

[0083] The confirmation unit is used to confirm the target coding category of the summary data table based on the target map scale when the current data table is a summary data table.

[0084] The extraction unit is used to extract at least one target vehicle network location data from the summary data table based on the target coding category.

[0085] It should be noted that the foregoing explanation of the map query method embodiment based on vehicle network data also applies to the map query device based on vehicle network data in this embodiment, and will not be repeated here.

[0086] The map query device based on vehicle network data proposed in this application can obtain a data table for querying vehicle network location data by acquiring different user needs for map detail levels. This enables rapid retrieval of massive data resources in the vehicle network, improves map query efficiency, and enhances practicality. Therefore, it solves the problems in related technologies, such as data thinning potentially causing data detail distortion, and the operational efficiency of existing grid mapping being affected by encoding rules, resulting in complex calculations that cannot overcome computational bottlenecks in the processing of massive data, reducing query efficiency, failing to meet data quality and hierarchical processing requirements, and affecting the accuracy of query results.

[0087] Figure 6 A schematic diagram of the structure of a vehicle provided in an embodiment of this application. The vehicle may include:

[0088] The memory 601, the processor 602, and the computer program stored on the memory 601 and capable of running on the processor 602.

[0089] When the processor 602 executes the program, it implements the map query method based on vehicle network data provided in the above embodiments.

[0090] Furthermore, the vehicle also includes:

[0091] Communication interface 603 is used for communication between memory 601 and processor 602.

[0092] The memory 601 is used to store computer programs that can run on the processor 602.

[0093] The memory 601 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk storage device.

[0094] If the memory 601, processor 602, and communication interface 603 are implemented independently, then the communication interface 603, memory 601, and processor 602 can be interconnected via a bus to complete communication between them. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. The bus can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 6 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0095] Optionally, in a specific implementation, if the memory 601, processor 602, and communication interface 603 are integrated on a single chip, then the memory 601, processor 602, and communication interface 603 can communicate with each other through an internal interface.

[0096] The processor 602 may be a central processing unit (CPU), an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of this application.

[0097] This embodiment also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the map query method based on vehicle network data as described above.

[0098] This embodiment also provides a computer program that, when executed, implements the map query method based on vehicle network data as described above.

[0099] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0100] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0101] Any process or method described in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or N executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0102] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0103] It should be understood that the various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, the N steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0104] Those skilled in the art will understand that all or part of the steps of the methods described in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, it includes one or a combination of the steps of the method embodiments.

[0105] Furthermore, the functional units in the various embodiments of this application can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0106] The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc. Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of this application.

Claims

1. A map query method based on Internet of Vehicles data, characterized in that, The method comprises the following steps: In the case that the vehicle is in a preset map query working condition, at least one map query instruction of a user is acquired; The target map scale and target map area of the user are determined based on the at least one map query instruction, and a current data table corresponding to the target map area is matched according to the target map scale; The matching of the current data table corresponding to the target map area according to the target map scale comprises: It is judged whether the target map scale is greater than or equal to a preset scale threshold; If the target map scale is greater than or equal to the preset scale threshold, it is determined that a detailed data table of an Internet of Vehicles location database is the current data table, otherwise, it is determined that a summary data table of the Internet of Vehicles location database is the current data table; Before the matching of the current data table corresponding to the target map area according to the target map scale, the method further comprises: The longitude parameter and the latitude parameter of each Internet of Vehicles data in the Internet of Vehicles location database are respectively rounded off by using a first preset multiple to obtain a coarse grid code of each Internet of Vehicles data; The longitude parameter and the latitude parameter of each Internet of Vehicles data in the Internet of Vehicles location database are respectively rounded off by using a second preset multiple to obtain a fine grid code of each Internet of Vehicles data, wherein the first preset multiple is less than the second preset multiple; The summary data table is constructed based on the coarse grid coded Internet of Vehicles data and the fine grid coded Internet of Vehicles data; At least one target Internet of Vehicles location data is retrieved based on the current data table, the at least one target Internet of Vehicles location data is aggregated to obtain summary data, and a map query result of the vehicle is generated by using the summary data.

2. The method of claim 1, wherein, The retrieval of the at least one target Internet of Vehicles location data based on the current data table comprises: In the case that the current data table is the detailed data table, the number range of the target map area is calculated according to the target map scale; The at least one target Internet of Vehicles location data is located in the detailed data table according to the number range.

3. The method of claim 1, wherein, The retrieval of the at least one target Internet of Vehicles location data based on the current data table comprises: In the case that the current data table is the summary data table, the target coding category of the summary data table is determined according to the target map scale; The at least one target Internet of Vehicles location data is extracted in the summary data table based on the target coding category. 4.A map query device based on Internet of Vehicles data, characterized in that, The method comprises: An acquisition module is configured to acquire at least one map query instruction of a user in the case that a vehicle is in a preset map query working condition; A matching module is configured to determine a target map scale and a target map area of the user based on the at least one map query instruction, and match a current data table corresponding to the target map area according to the target map scale; The matching module comprises: A judging unit is configured to judge whether the target map scale is greater than or equal to a preset scale threshold; The determination unit is configured to determine that the detailed data table of the vehicle networking position database is the current data table when the target map scale is greater than or equal to the preset scale threshold, and otherwise, determine that the summary data table of the vehicle networking position database is the current data table; Further comprising: A first rounding module is configured to round the longitude parameter and the latitude parameter of each vehicle networking data in the vehicle networking position database by a first preset multiple before matching the current data table of the target map region according to the target map scale, to obtain coarse grid encoding of each vehicle networking data; A second rounding module is configured to round the longitude parameter and the latitude parameter of each vehicle networking data in the vehicle networking position database by a second preset multiple to obtain fine grid encoding of each vehicle networking data, wherein the first preset multiple is less than the second preset multiple; A construction module is configured to construct the summary data table based on the coarse grid encoding of the vehicle networking data and the fine grid encoding of the vehicle networking data; A query module is configured to search at least one target vehicle networking position data based on the current data table, aggregate the at least one target vehicle networking position data to obtain summary data, and generate a map query result of the vehicle by using the summary data.

5. A vehicle characterized by comprising: Comprise: A memory, a processor and a computer program stored on the memory and executable on the processor, the processor executes the program to implement the map query method based on vehicle networking data according to any one of claims 1-3.

6. A computer readable storage medium having stored thereon a computer program, characterized in that, The program is executed by the processor to implement the map query method based on vehicle networking data according to any one of claims 1-3.

Images