Map data processing method and device, equipment, storage medium and program product
Patent Information
- Application Number
- CN202411490119.1
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-10-23
- Publication Date
- 2025-02-14
AI Technical Summary
In the prior art, map job tasks are divided based on fixed area map sizes, resulting in uneven operation time of each map job task, which in turn reduces the overall operation efficiency of generating maps.
By obtaining the data amount of map data of the physical area in the location area to be processed, the number of packets is determined, and the physical area is clustered based on the packet data, the area set is obtained, and a map job task is generated based on the area set.
By introducing the data amount of map data, grouping and generating map job tasks, the efficiency of map generation jobs in the pending location area can be improved and the rationality and balance of job tasks can be ensured.
Smart Images
Figure CN119444921A_ABST
Abstract
Description
Technical Field
[0001] The present application relates to the technical field of map data processing, and in particular to a map data processing method, device, equipment, storage medium and program product. Background Art
[0002] In the process of map making and updating, it is necessary to complete the map operation task of generating maps based on map data.
[0003] In the prior art, a target location area is divided into a plurality of sub-areas based on a fixed area map size, and a map operation task for generating a map corresponding to each sub-area is generated based on map data corresponding to each sub-area.
[0004] However, in the above method, since the map operation elements in each regional map sheet are different, the map operation tasks are evenly divided based on the fixed regional map sheet size, resulting in uneven operation time for each map operation task, which in turn leads to low overall operation efficiency of map generation. Summary of the invention
[0005] The embodiments of the present application provide a method, apparatus, device, storage medium and program product for processing map data, which are used to solve the technical problem that the overall efficiency of map generation is low due to the division of map operation tasks based on a fixed regional map size.
[0006] In a first aspect, an embodiment of the present application provides a method for processing map data, including:
[0007] Acquire map data of physical areas in the location area to be processed; and determine the first number of groups according to the data volume of the map data of each of the physical areas;
[0008] Clustering the physical regions according to the first grouping number to obtain at least one region set; wherein the region set includes at least one physical region;
[0009] A map operation task for generating a map is generated according to the area set.
[0010] In a possible implementation manner, determining the first number of groups according to the amount of map data of each of the physical areas includes:
[0011] The first number of groups is determined according to the data volume of the map data of each of the physical areas and the geometric area corresponding to each of the physical areas; wherein the geometric area is the area of the circumscribed geometric shapes corresponding to all the physical areas.
[0012] In a possible implementation manner, determining the first number of groups according to the amount of map data of each of the physical areas and the geometric area corresponding to each of the physical areas includes:
[0013] Determining the second number of groups according to the geometric areas corresponding to the physical areas and the preset aggregation radius;
[0014] Determining the third number of groups according to the data volume of the map data of each of the physical areas and the preset aggregated data volume;
[0015] The first number of groups is determined according to the second number of groups and the third number of groups.
[0016] In a possible implementation, clustering is performed on each of the physical areas according to the first grouping number to obtain at least one area set, including:
[0017] According to the first number of groups, clustering is performed on the center points corresponding to the physical areas to obtain at least one initial set; wherein the center point is the center point of the geometric plane corresponding to the physical area;
[0018] Each center point in each of the initial sets is adjusted to obtain each of the area sets.
[0019] In a possible implementation manner, adjusting each center point in each of the initial sets to obtain each of the area sets includes:
[0020] Determine the leader point and non-leader point in the initial set; wherein the leader point is the center point at the centroid position in the initial set; the non-leader point is the other center points in the initial set except the leader point;
[0021] Determine a distance set corresponding to the non-leader point; wherein the distance set includes the distance between the non-leader point and each leader point;
[0022] According to the distance set corresponding to the non-leader point, the non-leader point is adjusted to the initial set of neighbor leader points corresponding to the non-leader point to obtain each of the area sets; wherein the neighbor leader point is the leader point with the shortest distance to the non-leader point.
[0023] In a possible implementation, the method further includes:
[0024] Determine at least one target set from each of the area sets; wherein geometric planes corresponding to each physical area in each of the area sets in the target set intersect;
[0025] Each region set in the target set is merged.
[0026] In a possible implementation, the method further includes:
[0027] If it is determined that the data volume of the map data of the area set is greater than the preset aggregate set volume, the step of determining the first grouping number according to the data volume of the map data of each of the physical areas is re-executed.
[0028] In a second aspect, an embodiment of the present application provides a map data processing device, including:
[0029] A determination module, configured to obtain map data of physical areas in the location area to be processed; and determine the first number of groups according to the data volume of the map data of each of the physical areas;
[0030] A processing module, configured to perform clustering processing on each of the physical areas according to the first grouping number to obtain at least one area set; wherein the area set includes at least one physical area;
[0031] A generation module is used to generate a map operation task for generating a map according to the area set.
[0032] In a possible implementation manner, the determining module is specifically configured to:
[0033] The first number of groups is determined according to the data volume of the map data of each of the physical areas and the geometric area corresponding to each of the physical areas; wherein the geometric area is the area of the circumscribed geometric shapes corresponding to all the physical areas.
[0034] In a possible implementation manner, the determining module is specifically configured to:
[0035] Determining the second number of groups according to the rectangular area corresponding to each of the physical areas and a preset aggregation radius;
[0036] Determining the third number of groups according to the data volume of the map data of each of the physical areas and the preset aggregated data volume;
[0037] The first number of groups is determined according to the second number of groups and the third number of groups.
[0038] In a possible implementation manner, the processing module is specifically configured to:
[0039] According to the first number of groups, clustering is performed on the center points corresponding to the physical areas to obtain at least one initial set; wherein the center point is the center point of the geometric plane corresponding to the physical area;
[0040] Each center point in each of the initial sets is adjusted to obtain each of the area sets.
[0041] In a possible implementation manner, the processing module is specifically configured to:
[0042] Determine the leader point and non-leader point in the initial set; wherein the leader point is the center point at the centroid position in the initial set; the non-leader point is the other center points in the initial set except the leader point;
[0043] Determine a distance set corresponding to the non-leader point; wherein the distance set includes the distance between the non-leader point and each leader point;
[0044] According to the distance set corresponding to the non-leader point, the non-leader point is adjusted to the initial set of neighbor leader points corresponding to the non-leader point to obtain each of the area sets; wherein the neighbor leader point is the leader point with the shortest distance to the non-leader point.
[0045] In a possible implementation manner, the processing module is further specifically configured to:
[0046] Determine at least one target set from each of the area sets; wherein geometric planes corresponding to each physical area in each of the area sets in the target set intersect;
[0047] Each region set in the target set is merged.
[0048] In a possible implementation manner, the device is further used for:
[0049] If it is determined that the data volume of the map data of the area set is greater than the preset aggregate set volume, the step of determining the first grouping number according to the data volume of the map data of each of the physical areas is re-executed.
[0050] In a third aspect, an embodiment of the present application provides an electronic device, including: a memory, a processor;
[0051] The memory stores computer-executable instructions;
[0052] The processor executes the computer-executable instructions stored in the memory, so that the processor executes the above first aspect and / or various possible implementations of the first aspect.
[0053] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, in which computer-executable instructions are stored. When the computer-executable instructions are executed by a processor, they are used to implement the first aspect above and / or various possible implementations of the first aspect.
[0054] In a fifth aspect, an embodiment of the present application provides a computer program product, including a computer program, which, when executed by a processor, implements the above first aspect and / or various possible implementation methods of the first aspect.
[0055] The map data processing method, apparatus, device, storage medium and program product provided in the embodiments of the present application determine the number of groups of the location area to be processed by the amount of map data of the physical area in the location area to be processed, and cluster each physical area based on the grouped data to obtain multiple area sets, and generate corresponding map operation tasks for generating maps based on the map data of the physical area in each area set; further, by introducing the information of the amount of map data, the efficiency of map generation operation in the location area to be processed can be improved. BRIEF DESCRIPTION OF THE DRAWINGS
[0056] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.
[0057] Figure 1 A schematic diagram of an application scenario provided for this application;
[0058] Figure 2 A flowchart of a method for processing map data provided in an embodiment of the present application;
[0059] Figure 3 A schematic diagram of geometric data to be processed provided in an embodiment of the present application;
[0060] Figure 4 A schematic diagram of a clustering effect of geometric data provided in an embodiment of the present application;
[0061] Figure 5 A flowchart of another method for processing map data provided in an embodiment of the present application;
[0062] Figure 6 A flowchart of another method for processing map data provided in an embodiment of the present application;
[0063] Figure 7 A schematic diagram of a flow chart of a geometric data processing process provided in an embodiment of the present application;
[0064] Figure 8 A schematic diagram of the structure of a map data processing device provided in an embodiment of the present application;
[0065] Fig. 9 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application.
[0066] The above drawings have shown clear embodiments of the present application, which will be described in more detail later. These drawings and text descriptions are not intended to limit the scope of the present application in any way, but to illustrate the concept of the present application to those skilled in the art by referring to specific embodiments. DETAILED DESCRIPTION
[0067] Here, exemplary embodiments will be described in detail, and examples thereof are shown in the accompanying drawings. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. Instead, they are only examples of devices and methods consistent with some aspects of the present application.
[0068] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use, processing, transmission, provision, disclosure and application of the relevant data comply with relevant laws, regulations and standards, take necessary confidentiality measures, do not violate public order and good morals, and provide corresponding operation entrances for users to choose to authorize or refuse.
[0069] It should be noted that the map data processing method, device, equipment, storage medium and program product of the present application can be used in the field of map data processing technology, and can also be used in any field other than map data processing technology. The application field of the map data processing method, device, equipment, storage medium and program product of the present application is not limited.
[0070] Figure 1 A schematic diagram of an application scenario provided by this application. Figure 1 As shown, the specific application scenario of the present application includes an electronic device 101 and multiple operators 102. Based on the electronic device 101, a map operation task for generating a map corresponding to each operator 102 is generated according to the map data of the physical area of the target location area.
[0071] In one example, the electronic device 101 divides the target location area into multiple sub-areas according to a fixed area map size, generates a map operation task for generating a map corresponding to each sub-area based on the map data corresponding to each sub-area, and assigns it to each operator 102 to perform the operation.
[0072] However, in the above method, since the map operation elements in each regional map sheet are different, the map operation tasks are evenly divided based on the fixed regional map sheet size, resulting in uneven operation time for each map operation task, which in turn leads to low overall operation efficiency of map generation.
[0073] In response to the above technical problems, the present application proposes the following technical concept: after obtaining the map data of the physical area in the location area to be processed, the number of groups of the location area to be processed is determined by the data volume of the map data, and each physical area is clustered based on the grouped data to obtain multiple area sets, and the corresponding map operation tasks for generating maps can be generated according to the map data of the physical area in each area set. In this way, the problem existing in the prior art of dividing the map operation tasks based on the fixed area map size, resulting in low overall map generation efficiency, can be solved.
[0074] The technical solution of the present application and how the technical solution of the present application solves the above-mentioned technical problems are described in detail below with specific embodiments. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The embodiments of the present application will be described below in conjunction with the accompanying drawings.
[0075] Figure 2 A flowchart of a method for processing map data provided in an embodiment of the present application is shown as follows: Figure 2 As shown, the method includes:
[0076] 201. Obtain map data of physical areas in the location area to be processed; and determine a first number of groups according to the data volume of the map data of each physical area.
[0077] Exemplarily, the execution subject of this embodiment may be an electronic device, hereinafter referred to as the device. For the location area to be processed, the location area to be processed includes multiple physical areas, that is, each small location area in the location area to be processed, to distinguish it from the location area to be processed, such as the area where multiple cities are located, or the area where lanes are located, or the area where communities are located, etc.; when the location area to be processed needs to be mapped and updated, the device can obtain the geometric plane corresponding to each physical area in the location area to be processed from the preset database, for example, Figure 3 A schematic diagram of geometric data to be processed provided in an embodiment of the present application, such as Figure 3As shown, a batch of geometric data to be processed corresponding to physical areas are included, and each geometric data is geometric data corresponding to the irregular geometric plane where each physical area is located, such as geometric size, geometric coordinates, etc., wherein each geometric data includes map data in the corresponding physical area. The device first determines the data volume of the map data in all physical areas, and determines the number of groups, i.e., the first number of groups, based on the data volume, so as to know how many groups all physical areas need to be divided into.
[0078] For example, the target data volume of map data for each physical area in each group after grouping is determined based on empirical values, and the number of groups can be determined according to the data volume of map data in all physical areas and the target data volume of map data for each physical area in each group after grouping.
[0079] 202. Perform clustering processing on each physical area according to the first grouping number to obtain at least one area set; wherein the area set includes at least one physical area.
[0080] Exemplarily, the device performs clustering processing on each physical area according to the number of groups determined in the aforementioned steps, and performs grouping. For example, a preset spatial clustering algorithm may be used to cluster the geometric data corresponding to all physical areas for the determined number of groups k, and obtain k area sets, each of which includes multiple physical areas.
[0081] For example, Figure 4 A schematic diagram of a clustering effect of geometric data provided in an embodiment of the present application, such as Figure 4 As shown in the figure, after clustering processing, k groups are obtained (different groups have different shades of color in the figure), and each group includes geometric data corresponding to multiple physical areas; furthermore, in the spatial clustering algorithm, not only the factor of spatial distribution is considered, but also the factor of the amount of map data of each physical area is introduced, so that the clustering result of the clustering algorithm, that is, the grouping result is more reasonable.
[0082] 203. Generate a map operation task for generating a map according to the region set.
[0083] Exemplarily, after clustering processing, the device generates a map operation task corresponding to each physical area in the area set according to the physical areas in each area set, so as to generate maps corresponding to each physical area in the area set. Then, each map operation task can be assigned to a corresponding operator, so that each operator can efficiently complete the map generation tasks of all physical areas to be processed, thereby accelerating the map production and update cycle.
[0084] It is worth adding that the above embodiments are applied to the scene of map making and updating. In addition, the present application can also be applied to the field of regional planning and management. If it is necessary to manage and update the urban data of various facilities, roads, buildings, etc. in the processing location area, such as a certain city, then the urban data processing task is generated according to the regional set, which can help planners to reasonably allocate multiple regional planning and management tasks in the processing location area to ensure the accuracy and timeliness of the urban data in the processing location area; or, the present application can also be applied to environmental monitoring and protection. If it is necessary to regularly collect and analyze the environmental data in the processing location area, then the above clustering method can be used to generate multiple environmental data monitoring tasks for the processing location area according to the regional set, so as to improve the monitoring efficiency and timely discover and solve environmental problems; or, it can also be applied to the field of logistics distribution and warehousing management. Through the above clustering method, multiple logistics distribution tasks and warehousing tasks in the processing location area can be generated, which can optimize the distribution route and warehousing layout and improve the logistics efficiency; or, it can also be applied to other fields that need to process map data, such as traffic planning, disaster emergency response, agricultural resource management, etc., and the map data processing efficiency in these fields can be improved by rationally using the spatial clustering algorithm.
[0085] In the present embodiment, a method for processing map data is provided, wherein the number of groups of the location area to be processed is determined by the amount of map data of the physical area in the location area to be processed, and based on the grouped data, each physical area is clustered to obtain a plurality of area sets, and a corresponding map operation task for generating a map is generated according to the map data of the physical area in each area set; further, by introducing the information of the amount of map data, grouping and generating map operation tasks, the map generation operation efficiency of the location area to be processed can be improved.
[0086] Figure 5 A flowchart of another method for processing map data provided in an embodiment of the present application is shown as follows: Figure 5 As shown, the method includes:
[0087] 301. Obtain map data of a physical area in a location area to be processed.
[0088] Exemplarily, this step may refer to step 201 and will not be described in detail here.
[0089] 302. Determine a first number of groups according to the amount of map data of each physical area and the geometric area corresponding to each physical area; wherein the geometric area is the area of the circumscribed geometric shapes corresponding to all the physical areas.
[0090] Exemplarily, for all physical areas in the location area to be processed, the device first determines the data volume of the map data in all physical areas, and determines the circumscribed geometric figures of the geometric planes corresponding to all physical areas, such as the minimum circumscribed rectangle, or the minimum circumscribed circle, and calculates the geometric area of the circumscribed geometric figure, wherein the minimum circumscribed rectangle can be used to simplify the representation of complex shapes and to determine the boundaries and layout of geometric shapes, thereby achieving more efficient calculations when processing complex geometric shapes; based on preset grouping rules, the data volume and geometric area of the map data in all physical areas are analyzed and processed to obtain the number of groups corresponding to all physical areas, so as to know how many groups all physical areas need to be divided into.
[0091] For example, the amount of map data A of each physical area contained in each group after grouping and the geometric area B of each physical area in each group after grouping are determined based on empirical values. Then, based on a preset algorithm, the amount of map data in all physical areas, the minimum circumscribed rectangular area corresponding to all physical areas, A and B can be calculated to determine the number of groups.
[0092] In one example, step 302 includes the following steps:
[0093] The first step of step 302 is to determine the second number of groups according to the geometric area corresponding to each physical area and the preset aggregation radius.
[0094] The second step of step 302 is to determine the third grouping number according to the data volume of the map data of each physical area and the preset aggregated data volume.
[0095] The third step of step 302 is to determine the first grouping number according to the second grouping number and the third grouping number.
[0096] Exemplarily, the device calls a preset aggregation radius determined based on an empirical value, and based on a preset area division algorithm, calculates and processes the geometric area corresponding to all physical areas and the preset aggregation radius to obtain the number of groups based on area division, that is, the second number of groups; the device then calls the number of data aggregated together determined based on an empirical value, that is, the preset aggregated data volume, and based on a preset quantity division algorithm, calculates and processes the data volume of the map data of all physical areas and the preset aggregated data volume to obtain the number of groups based on quantity division, that is, the third number of groups; and then comprehensively processes the obtained second number of groups and the third number of groups to obtain the final number of groups, that is, the first number of groups. Among them, the area and the number correspond to indicators such as complexity and task size, respectively, so that the map operation tasks corresponding to the clustering results are more in line with actual operation requirements, so as to improve the rationality and balance of task allocation, which can not only avoid excessive concentration of tasks leading to excessive burden on some operators, but also prevent excessive dispersion of tasks leading to waste of resources and inefficiency.
[0097] For example, according to the formula n1=S / (π*r*r), the rectangular area S corresponding to all physical areas and the preset aggregation radius r are calculated and processed to obtain the second grouping number n1; according to the formula n2=n / N, the data volume n of the map data of all physical areas and the preset aggregation data volume N are calculated and processed to obtain the third grouping number n2; according to the formula k=((a*n1)+(b*n2)) / 2, the second grouping number n1 and the third grouping number n2 are calculated and processed to obtain the first grouping number k, wherein a and b are adjustable thresholds, (a+b=1).
[0098] 303. Perform clustering processing on each physical area according to the first grouping number to obtain at least one area set; wherein the area set includes at least one physical area.
[0099] Exemplarily, this step may refer to step 202 and will not be described in detail here.
[0100] 304. Generate a map operation task for generating a map according to the region set.
[0101] Exemplarily, this step may refer to step 203 and will not be described in detail here.
[0102] In this embodiment, on the basis of the above embodiment, the number of groups of the location area to be processed is determined by the amount of map data of the physical area in the location area to be processed and the area of the corresponding circumscribed geometric shape, and based on the grouping data, each physical area is clustered to obtain multiple area sets, and the corresponding map operation task for generating a map can be generated according to the map data of the physical area in each area set; further, by introducing the two information of the amount of map data and the area corresponding to the physical area, the map operation tasks are grouped and generated, thereby improving the map generation efficiency of the location area to be processed.
[0103] Figure 6 A flowchart of another method for processing map data provided in an embodiment of the present application is shown as follows: Figure 6 As shown, the method includes:
[0104] 401. Obtain map data of physical areas in the location area to be processed; and determine a first grouping number according to the data volume of the map data of each physical area.
[0105] Exemplarily, this step may refer to step 201 and will not be described in detail here.
[0106] 402. Perform clustering processing on the center points corresponding to each physical area according to the first grouping number to obtain at least one initial set; wherein the center point is the center point of the geometric plane corresponding to the physical area.
[0107] Exemplarily, for each geometric plane corresponding to each physical area input, the device first determines the center point of the geometric plane corresponding to each physical area, determines the first number of groups based on the above steps, and based on a preset spatial clustering algorithm such as the k-means algorithm or other spatial clustering algorithms, clusters each center point according to the first number of groups k, and obtains k initial sets, each of which includes at least one center point.
[0108] For example, according to the geometric data processing process, it is divided into multiple modules, which is convenient for independent development and maintenance of each module, and also convenient for the expansion and upgrading of the algorithm. Figure 7 A schematic diagram of a geometric data processing process provided in an embodiment of the present application is shown in FIG. Figure 7 As shown, after the data is input, the geometric plane corresponding to each physical area is traversed, and the center point of each geometric plane is obtained through the preset spatial function. After the grouping number k value is determined, based on the k-means clustering analysis algorithm, the first grouping number k value is selected, and k center points are randomly selected as the initial cluster centers for clustering processing. This is repeated until the cluster center no longer changes or the preset number of iterations is reached, and k initial sets are obtained.
[0109] 403. Adjust each center point in each initial set to obtain each regional set.
[0110] Exemplarily, after obtaining the initial set, in order to further improve the rationality of the grouping of each physical area, the device adjusts each center point in all the initial sets to obtain each area set. For example, the number of initial sets k is kept unchanged, and according to the number of center points in each initial set, some center points in the initial set with a large number are adjusted to the initial set with a small number to obtain the corresponding k adjusted initial sets, and the physical area corresponding to each center point in the k adjusted initial sets is organized into k area sets.
[0111] In one example, step 403 includes the following steps:
[0112] The first step of step 403 is to determine the leader point and non-leader points in the initial set; wherein the leader point is the center point at the centroid position in the initial set; and the non-leader point is the other center points in the initial set except the leader point.
[0113] The second step of step 403 is to determine the distance set corresponding to the non-leader point; wherein the distance set includes the distance between the non-leader point and each leader point.
[0114] The third step of step 403 is to adjust the non-leader point to the initial set of neighbor leader points corresponding to the non-leader point according to the distance set corresponding to the non-leader point, so as to obtain each area set; wherein the neighbor leader point is the leader point with the shortest distance to the non-leader point.
[0115] Exemplarily, for each initial set, the device determines the center point at the centroid position in the initial set as the leader point of the initial set, and determines the other center points in the initial set except the leader point as non-leader points. For each non-leader point, the distance between the non-leader point and each leader point is calculated through the spatial distance in the coordinate system to obtain the distance set corresponding to the non-leader point; according to the distance set corresponding to the non-leader point, the leader point with the closest distance to the non-leader point is determined as the neighbor leader point corresponding to the non-leader point, and the non-leader point is adjusted and moved to the initial set where the neighbor leader point corresponding to the non-leader point is located, thereby completing the adjustment grouping process, obtaining k adjusted initial sets, and organizing the physical areas corresponding to each center point in the k adjusted initial sets into k area sets. Furthermore, by clustering the center points corresponding to physical areas with similar geographical locations and properties into new clusters to obtain new groups, operators can reduce the moving distance and switching cost when performing the map making tasks corresponding to the new groups, thereby significantly improving the work efficiency.
[0116] For example, for three initial sets O, initial set Q, and initial set P, the initial set O includes the leader point O1, non-leader points O2, and O3, the initial set Q includes the leader point Q1, non-leader points Q2, and Q3, and the initial set P includes the leader point P1, non-leader points P2, and P3. The distance between each non-leader point and each target leader point corresponding to the non-leader point is calculated respectively through the spatial distance in the coordinate system, and multiple distance sets are obtained, including: O2O1, O2Q1, and O2 P1 in the distance set corresponding to O2, O3 O1, O3Q1, and O3 P1 in the distance set corresponding to O3, Q2 O1, Q2Q1, and Q2 P1 in the distance set corresponding to Q2, Q3O1, Q3Q1, and Q3 P1 in the distance set corresponding to Q3, P2O1, P2Q1, and P2P1 in the distance set corresponding to P2, and P3O1, P3Q1, and P3P1 in the distance set corresponding to P3 Q1, P3P1, and then, based on the shortest distance, determine that the neighbor leader point corresponding to O2 is O1, the neighbor leader point corresponding to O3 is O1, the neighbor leader point corresponding to Q2 is O1, the neighbor leader point corresponding to Q3 is O1, the neighbor leader point corresponding to P2 is P1, and the neighbor leader point corresponding to P3 is P1, then each non-leader point is adjusted and moved to the initial set where the neighbor leader point corresponding to the non-leader point is located, and obtain three new initial sets O`, initial set Q`, and initial set P`, among which the initial set O` includes O1, O2, O3, Q2, and Q3, the initial set Q` includes Q1, and the initial set P` includes P1, P2, and P3, and then obtain the corresponding three area sets.
[0117] In one example, step 403 further includes the following steps:
[0118] Step 1: determining at least one target set from each area set; wherein geometric planes corresponding to each physical area in each area set in the target set intersect.
[0119] Step 2: Merge each region set in the target set.
[0120] For example, in combination Figure 7In the aforementioned process of adjusting the grouping, after obtaining k area sets, each area set can be traversed to determine the entire geometric plane corresponding to each physical area in each area set, and to determine whether the corresponding geometric planes intersect between the two area sets. If there is an intersection of geometric planes, the area set where the corresponding physical area with the geometric plane intersection is located is determined as the area set in the target set; and the area sets in each target set are merged to obtain a new grouping set for the subsequent map operation task generation process. Furthermore, by further grouping similar Gorgo physical areas into one group, the rationality of grouping can be improved, and then the rationality of task allocation of the corresponding map production tasks after grouping can be improved, which is helpful to improve work efficiency and work quality, reduce waste of manpower and material resources, and further reduce costs.
[0121] 404. If the data volume of the map data of the determined area set is greater than the preset aggregate set volume, re-execute the step of determining the first grouping number according to the data volume of the map data of each physical area.
[0122] For example, in combination Figure 7 After the above grouping steps, the device determines the data volume of the map data of each physical area in each determined area set. If it is determined that the data volume is greater than the preset aggregate set volume, it means that the map generation task corresponding to the area set has exceeded the limit, and it is necessary to readjust the preset parameters related to the data volume level, such as the preset aggregate set volume, to re-determine the new number of groups based on the data volume of the map data of all physical areas, and then re-cluster and process to obtain new groups until the data volume of the map data in each new group is less than the new preset aggregate set volume. In this way, the rationality and balance of the distribution of map generation tasks are ensured.
[0123] 405. Generate a map operation task for generating a map according to the region set.
[0124] Exemplarily, based on the physical areas in each area set, a map operation task corresponding to each physical area in the area set is generated to generate maps corresponding to each physical area in the area set. Then, each map operation task can be assigned to a corresponding operator so that each operator can efficiently complete the map generation tasks of all physical areas to be processed, thereby accelerating the map production and update cycle. In addition, when the operator performs the map operation task, the completion efficiency of the map operation task is monitored and counted to correct the task radius and data volume, and the parameter feedback aggregation algorithm is continuously improved to obtain a more reasonable task allocation result. Furthermore, when the operator's ability changes, a new map generation task is added, or an old map generation task is completed, cluster analysis and map generation task allocation can be re-performed to adapt to new needs.
[0125] In this embodiment, on the basis of the above embodiment, after the number of groups is determined by introducing information such as the amount of data and the corresponding area of the physical area, the groups after clustering processing are adjusted and merged to obtain a new area set to obtain a more reasonable grouping result, thereby improving the rationality of the allocation of map work tasks corresponding to map generation and further improving the overall map generation efficiency.
[0126] Figure 8 A structural diagram of a map data processing device provided in an embodiment of the present application is shown in FIG. Figure 8 As shown, the device comprises:
[0127] The determination module 501 is used to obtain the map data of the physical area in the location area to be processed; and determine the first grouping number according to the data volume of the map data of each physical area;
[0128] The processing module 502 is used to perform clustering processing on each physical area according to the first grouping number to obtain at least one area set; wherein the area set includes at least one physical area;
[0129] The generation module 503 is used to generate a map operation task for generating a map according to the area set.
[0130] In a possible implementation, the determination module 501 is specifically used to determine the first number of groups according to the data volume of the map data of each physical area and the geometric area corresponding to each physical area; wherein the geometric area is the area of the circumscribed geometric shape corresponding to all physical areas.
[0131] In a possible implementation, the determination module 501 is specifically used to: determine the second number of groups based on the geometric area corresponding to each physical area and a preset aggregation radius; determine the third number of groups based on the amount of map data of each physical area and a preset aggregation data amount; determine the first number of groups based on the second number of groups and the third number of groups.
[0132] In one possible implementation, the processing module 502 is specifically used to: cluster the center points corresponding to each physical area according to the first grouping number to obtain at least one initial set; wherein the center point is the center point of the geometric plane corresponding to the physical area; and adjust each center point in each initial set to obtain each area set.
[0133] In one possible implementation, the processing module 502 is specifically used to: determine the leader point and non-leader points in the initial set; wherein the leader point is the center point at the centroid position in the initial set; the non-leader point is the other center point in the initial set except the leader point; determine the distance set corresponding to the non-leader point; wherein the distance set includes the distance between the non-leader point and each leader point; according to the distance set corresponding to the non-leader point, adjust the non-leader point to the initial set where the neighbor leader point corresponding to the non-leader point is located, so as to obtain each area set; wherein the neighbor leader point is the leader point that is closest to the non-leader point.
[0134] In a possible implementation, the processing module 502 is further specifically used to: determine at least one target set from each area set; wherein the geometric planes corresponding to each physical area in each area set in the target set intersect; and merge each area set in the target set.
[0135] In a possible implementation, the device is further used for: if the data volume of the map data of the determined area set is greater than the preset aggregate set volume, re-execute the step of determining the first grouping number according to the data volume of the map data of each physical area.
[0136] The device of this embodiment can execute the technical solution in the above method. Its specific implementation process and technical principles are the same and will not be repeated here.
[0137] It should be noted that it should be understood that the division of the various modules of the above device is only a division of logical functions. In actual implementation, they can be fully or partially integrated into one physical entity, or they can be physically separated. And these modules can all be implemented in the form of software called by processing elements; they can also be all implemented in the form of hardware; some modules can also be implemented in the form of software called by processing elements, and some modules can be implemented in the form of hardware. Each module can be a separately established processing element, or it can be integrated in a chip of the above device. In addition, it can also be stored in the memory of the above device in the form of program code, and called and executed by a processing element of the above device. The functions of each module. In addition, all or part of these modules can be integrated together, or they can be implemented independently. The processing element here can be an integrated circuit with signal processing capabilities. In the implementation process, each step of the above method or each module above can be completed by an integrated logic circuit of hardware in the processor element or instructions in the form of software.
[0138] Fig. 9 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application is shown in FIG. Fig. 9 As shown, the electronic device includes: a memory 601 and a processor 602; the memory 601 is a memory for storing instructions executable by the processor 602.
[0139] The processor 602 is configured to execute the method provided in the above embodiment.
[0140] The electronic device further includes a receiver 603 and a transmitter 604. The receiver 603 is used to receive instructions and data sent by other devices, and the transmitter 604 is used to send instructions and data to external devices.
[0141] The specific implementation process of the processor can be found in the above method embodiment, and its implementation principle and technical effect are similar, so this embodiment will not be repeated here.
[0142] In the above embodiments, it should be understood that the processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), etc. The general-purpose processor may be a microprocessor or any conventional processor. The steps of the method disclosed in the invention may be directly implemented as being executed by a hardware processor, or may be executed by a combination of hardware and software modules in the processor.
[0143] The memory may include a high-speed memory (Random Access Memory, referred to as RAM), and may also include a non-volatile memory (Non-volatile Memory, referred to as NVM), such as at least one disk memory.
[0144] An embodiment of the present application also provides a chip for executing instructions, which is used to execute the technical solution of the processing method in the above embodiment.
[0145] The embodiment of the present application also provides a computer-readable storage medium, in which computer-executable instructions are stored. When the computer-executable instructions are executed on a computer, the computer executes the technical solution of the processing method of the above embodiment.
[0146] The above-mentioned readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory, electrically erasable programmable read-only memory, erasable programmable read-only memory, programmable read-only memory, read-only memory, magnetic memory, flash memory, magnetic disk or optical disk. The readable storage medium can be any available medium that can be accessed by a general or special-purpose computer.
[0147] An exemplary readable storage medium is coupled to a processor so that the processor can read information from the readable storage medium and write information to the readable storage medium. Of course, the readable storage medium can also be an integral part of the processor. The processor and the readable storage medium can be located in a dedicated integrated circuit. Of course, the processor and the readable storage medium can also exist in a device as discrete components.
[0148] An embodiment of the present application also provides a computer program product, which includes a computer program stored in a computer-readable storage medium. At least one processor can read the computer program from the computer-readable storage medium. When at least one processor executes the computer program, the technical solution of the processing method in the above embodiment can be implemented.
[0149] The division of units is only a logical function division, and there may be other divisions in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interface, device or unit, which can be electrical, mechanical or other forms.
[0150] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
[0151] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.
[0152] If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods of various embodiments of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory, random access memory, disk or optical disk, etc., various media that can store program codes.
[0153] Those skilled in the art will appreciate that all or part of the steps of implementing the above-mentioned method embodiments can be completed by hardware associated with program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, the steps of the above-mentioned method embodiments are executed.
[0154] Finally, it should be noted that those skilled in the art will readily conceive of other embodiments of the present invention after considering the specification and practicing the invention disclosed herein. The present invention is intended to cover any variations, uses or adaptations of the present invention, which follow the general principles of the present invention and include common knowledge or customary technical means in the art not disclosed by the present invention, are not limited to the precise structure described above and shown in the drawings, and may be modified and changed in various ways without departing from the scope thereof. The scope of the present invention is limited only by the appended claims.
Claims
1. A method for processing map data, characterized in that: include: Obtaining map data of a physical area in the location area to be processed; and determining the first number of groups according to the amount of map data of each of the physical areas; Clustering the physical regions according to the first grouping number to obtain at least one region set; wherein the region set includes at least one physical region; A map operation task for generating a map is generated according to the area set.
2. The method according to claim 1, characterized in that: Determining the first number of groups according to the amount of map data of each of the physical areas includes: The first number of groups is determined according to the data volume of the map data of each of the physical areas and the geometric area corresponding to each of the physical areas; wherein the geometric area is the area of the circumscribed geometric shapes corresponding to all the physical areas.
3. The method according to claim 2, characterized in that Determining the first number of groups according to the amount of map data of each of the physical areas and the geometric area corresponding to each of the physical areas includes: Determining the second number of groups according to the geometric areas corresponding to the physical areas and the preset aggregation radius; Determining the third number of groups according to the data volume of the map data of each of the physical areas and the preset aggregated data volume; The first number of groups is determined according to the second number of groups and the third number of groups.
4. The method according to claim 1, characterized in that: Clustering the physical areas according to the first grouping number to obtain at least one area set includes: According to the first grouping number, clustering is performed on the center points corresponding to each of the physical areas to obtain at least one initial set; wherein the center point is the center point of the geometric plane corresponding to the physical area; Each center point in each of the initial sets is adjusted to obtain each of the area sets.
5. The method according to claim 4, characterized in that Each center point in each of the initial sets is adjusted to obtain each of the area sets, including: Determine the leader point and non-leader point in the initial set; wherein the leader point is the center point at the centroid position in the initial set; the non-leader point is the other center points in the initial set except the leader point; Determine a distance set corresponding to the non-leader point; wherein the distance set includes the distance between the non-leader point and each leader point; According to the distance set corresponding to the non-leader point, the non-leader point is adjusted to the initial set of neighbor leader points corresponding to the non-leader point to obtain each of the area sets; wherein the neighbor leader point is the leader point with the shortest distance to the non-leader point.
6. The method according to any one of claims 1 to 5, characterized in that The method further comprises: Determine at least one target set from each of the area sets; wherein geometric planes corresponding to each physical area in each of the area sets in the target set intersect; Each region set in the target set is merged.
7. The method according to any one of claims 1 to 5, characterized in that The method further comprises: If it is determined that the data volume of the map data of the area set is greater than the preset aggregate set volume, the step of determining the first grouping number according to the data volume of the map data of each of the physical areas is re-executed.
8. A map data processing device, characterized in that: include: A determination module, used to obtain map data of a physical area in a location area to be processed; and determining the first number of groups according to the amount of map data of each of the physical areas; A processing module, configured to perform clustering processing on each of the physical areas according to the first grouping number to obtain at least one area set; wherein the area set includes at least one physical area; A generation module is used to generate a map operation task for generating a map according to the area set.
9. An electronic device, characterized in that: include: Memory, processor; The memory stores computer-executable instructions; The processor executes the computer-executable instructions stored in the memory, so that the processor performs the method according to any one of claims 1 to 7.
10. A computer-readable storage medium / computer program product, characterized in that: The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the method as described in any one of claims 1-7; and / or, the computer program product includes a computer program, which, when executed by a processor, implements the method as described in any one of claims 1-7.