Service area division method and device, and computer device

Abstract

The application relates to the technical field of data processing, and relates to a service area division method and device and computer equipment. The method comprises the following steps: determining an initial service area of a target city, wherein the initial service area comprises a plurality of target areas; acquiring area label data of each target area; for any target area, judging the target area based on the area label data of the target area and a preset threshold to obtain a judgment result of the target area; and determining a service area in the initial service area according to the judgment result of each target area. The method can be used for dividing the service area based on the distribution density of vehicles and orders and dynamically adjusting the range, so that the precision of service area division is improved, and the number of vehicles is evenly distributed.

Description

Technical Field

[0001] This application relates to the field of data processing technology, and in particular to a service area division method, apparatus, and computer equipment. Background Technology

[0002] With the development of transportation technology, shared bicycle technology has emerged. For cities operating shared electric bicycles, how to rationally allocate vehicle resources when the number of electric bicycles is limited has become a relatively important issue.

[0003] Since vehicle demand varies across different areas of a city, rationally dividing service areas can improve vehicle utilization and profitability, while reducing operational costs such as vehicle relocation and battery swapping for maintenance workers. Traditional service area division methods are typically based on road networks, geographical areas, or population density.

[0004] However, the current method of dividing service areas suffers from low precision in service area division and uneven distribution of vehicles. Summary of the Invention

[0005] Therefore, it is necessary to provide a service area division method, apparatus, computer equipment, and computer-readable storage medium to address the aforementioned technical problems, so as to improve the precision of service area division and distribute the number of vehicles more evenly.

[0006] Firstly, this application provides a service area delineation method. The method includes:

[0007] Determine the initial service area of ​​the target city, wherein the initial service area includes multiple target areas;

[0008] Obtain the region label data for each of the target regions;

[0009] For any of the target regions, a judgment is made based on the region label data of the target region and a preset threshold to obtain a judgment result for the target region;

[0010] Based on the judgment results of each target area, the service area in the initial service area is determined.

[0011] In one embodiment, determining the initial service area of ​​the target city includes:

[0012] Obtain a spatial partition map of the target city, which divides the target city into multiple regions;

[0013] For any of the regions, obtain the number of points of interest in the region;

[0014] Based on the number of points of interest in each region, multiple target regions are determined from each region, and the multiple target regions are divided into initial service areas.

[0015] In one embodiment, determining multiple target regions from each of the regions based on the number of points of interest in each region includes:

[0016] Based on the number of points of interest in each region, the regions are arranged in descending order;

[0017] Multiple target regions are determined from each of the regions, and the sum of the number of points of interest in each of the target regions accounts for a preset percentage of the total number of points of interest. The target regions are sequentially connected in the sorted regions.

[0018] In one embodiment, the area label data includes average number of vehicles, number of stationary vehicles, and area vehicle efficiency. The step of acquiring the area label data for each of the target areas includes:

[0019] For any of the target areas, obtain the average number of vehicles in the target area;

[0020] For any of the target areas, obtain the number of stationary vehicles in the target area;

[0021] For any of the target areas, the regional vehicle efficiency of the target area is determined based on the average number of vehicles in the target area, and the regional vehicle efficiency is inversely proportional to the average number of vehicles.

[0022] In one embodiment, obtaining the average number of vehicles in the target area includes:

[0023] The number of vehicles in the target area is obtained every preset time interval;

[0024] The sum of the number of vehicles in the target area obtained within a preset time period shall be taken as the total number of vehicles in the target area.

[0025] Based on the preset time and the total number of vehicles in the target area, the average number of vehicles in the target area is determined.

[0026] In one embodiment, obtaining the number of stationary vehicles in the target area includes:

[0027] For any vehicle in the target area, obtain the order time or vehicle relocation time closest to the current time for that vehicle, and use it as the target time;

[0028] For any vehicle in the target area, the difference between the target time of the vehicle and the current time is used to obtain the stationary duration of the vehicle;

[0029] For any vehicle in the target area, if the vehicle's stationary duration is greater than or equal to the preset time, the vehicle is considered a stationary vehicle.

[0030] The number of stationary vehicles is taken as the number of stationary vehicles in the target area.

[0031] In one embodiment, determining the regional vehicle efficiency of the target area based on the average number of vehicles in the target area includes:

[0032] Obtain the number of orders in the target area within the preset time period, wherein the origin of the vehicle corresponding to each order is located in the target area;

[0033] The regional vehicle efficiency of the target area is obtained based on the number of orders in the target area and the average number of vehicles in the target area within the preset time period. The regional vehicle efficiency of the target area is positively correlated with the number of orders and negatively correlated with the average number of vehicles in the target area.

[0034] In one embodiment, the step of determining the target region based on the region label data and a preset threshold to obtain a determination result for the target region includes:

[0035] Within a preset period, the average number of vehicles in the target area corresponding to each preset time is used as the first label data. The first label data is compared with a first preset threshold to obtain a first judgment result for the target area.

[0036] Within a preset period, the average number of stationary vehicles in the target area corresponding to each preset time is used as the second label data. The second label data is compared with a second preset threshold to obtain a second judgment result for the target area.

[0037] Within a preset period, the average value of the regional vehicle efficiency of the target area corresponding to each preset time is used as the third label data. The third label data is compared with a third preset threshold to obtain a third judgment result for the target area.

[0038] In one embodiment, determining the service area in the initial service area based on the judgment results of each of the target areas includes:

[0039] For any of the target areas, if the first judgment result for the target area indicates that the first tag data is greater than the first preset threshold, the second judgment result indicates that the second tag data is less than the second preset threshold, and the third judgment result indicates that the third tag data is greater than the third preset threshold, then the target area is designated as a service area.

[0040] Based on each of the service area regions, the service areas in the initial service area are determined.

[0041] Secondly, this application also provides a service area delineation device. The device includes:

[0042] An initial partitioning module is used to determine the initial service area of ​​the target city, wherein the initial service area includes multiple target areas;

[0043] The data acquisition module is used to acquire the region label data of each of the target regions respectively;

[0044] The judgment module is used to make a judgment on any of the target regions based on the region label data of the target region and a preset threshold, and to obtain a judgment result for the target region.

[0045] The service area division module is used to determine the service areas in the initial service area based on the judgment results of each target area.

[0046] In one embodiment, the initial partitioning module is further configured to obtain a spatial partitioning map of the target city, the spatial partitioning map dividing the target city into multiple regions; for any one of the regions, obtain the number of points of interest in the region; and based on the number of points of interest in each region, determine multiple target regions from each region, and partition the multiple target regions into initial service areas.

[0047] In one embodiment, the initial partitioning module is further configured to arrange the regions in descending order based on the number of points of interest in each region; determine multiple target regions from each region, wherein the sum of the number of points of interest in each target region accounts for a preset percentage of the total number of points of interest, and the target regions are sequentially connected in the sorted regions.

[0048] In one embodiment, the area label data includes average number of vehicles, number of stationary vehicles, and area vehicle efficiency. The data acquisition module is further configured to acquire, for any target area, the average number of vehicles in the target area; acquire, for any target area, the number of stationary vehicles in the target area; and, for any target area, determine the area vehicle efficiency based on the average number of vehicles in the target area, wherein the area vehicle efficiency is inversely proportional to the average number of vehicles.

[0049] In one embodiment, the data acquisition module is further configured to acquire the number of vehicles in the target area every preset time interval; to take the sum of the number of vehicles in the target area acquired within the preset time interval as the total number of vehicles in the target area; and to determine the average number of vehicles in the target area based on the preset time interval and the total number of vehicles in the target area.

[0050] In one embodiment, the data acquisition module is further configured to: for any vehicle in the target area, acquire the order time or vehicle relocation time closest to the current time of the vehicle as the target time; for any vehicle in the target area, subtract the target time of the vehicle from the current time to obtain the stationary duration of the vehicle; for any vehicle in the target area, if the stationary duration of the vehicle is greater than or equal to the preset time, designate the vehicle as a stationary vehicle; and use the number of stationary vehicles as the number of stationary vehicles in the target area.

[0051] In one embodiment, the data acquisition module is further configured to acquire the number of orders in the target area within the preset time period, wherein the starting point of the vehicle corresponding to the order is located in the target area; and to obtain the regional vehicle efficiency of the target area based on the number of orders in the target area within the preset time period and the average number of vehicles in the target area, wherein the regional vehicle efficiency of the target area is positively correlated with the number of orders and negatively correlated with the average number of vehicles in the target area.

[0052] In one embodiment, the judgment module is further configured to: within a preset period, use the average number of vehicles in the target area corresponding to each preset time as first label data, and judge the first label data with a first preset threshold to obtain a first judgment result for the target area; within a preset period, use the average number of stationary vehicles in the target area corresponding to each preset time as second label data, and judge the second label data with a second preset threshold to obtain a second judgment result for the target area; within a preset period, use the average regional vehicle efficiency of the target area corresponding to each preset time as third label data, and judge the third label data with a third preset threshold to obtain a third judgment result for the target area.

[0053] In one embodiment, the service area division module is further configured to, for any target area, if a first judgment result for the target area indicates that the first tag data is greater than the first preset threshold, a second judgment result indicates that the second tag data is less than the second preset threshold, and a third judgment result indicates that the third tag data is greater than the third preset threshold, then designate the target area as a service area; and determine the service area in the initial service area based on each of the service area areas.

[0054] Thirdly, this application also provides a computer device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments.

[0055] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps in the above-described method embodiments.

[0056] The aforementioned service area delineation method, apparatus, computer equipment, and computer-readable storage medium determine the initial service area of ​​a target city, which includes multiple target areas. They acquire area label data for each target area; for any target area, a judgment is made based on the area label data and a preset threshold to obtain a judgment result for that target area; and based on the judgment results for each target area, service areas are determined within the initial service area. Compared to traditional methods based on road networks or population density, the service area delineation method, apparatus, computer equipment, and computer-readable storage medium provided in this application divide the city into multiple target areas, determine the area label data of the target areas, and determine whether a target area is designated as a service area by comparing the area label data with a preset threshold. This achieves a method for delineating service areas based on vehicle and order distribution density and dynamically adjusting the range, thereby improving the precision of service area delineation and balancing the distribution of vehicle numbers. Attached Figure Description

[0057] Figure 1 This is a flowchart illustrating a service area division method in one embodiment;

[0058] Figure 2 This is a flowchart illustrating step 102 in one embodiment;

[0059] Figure 3 This is a spatial division map of a city in one embodiment;

[0060] Figure 4 This is a flowchart illustrating step 206 in one embodiment;

[0061] Figure 5 This is a flowchart illustrating step 104 in one embodiment;

[0062] Figure 6 This is a flowchart illustrating step 502 in one embodiment;

[0063] Figure 7 This is a flowchart illustrating step 504 in one embodiment;

[0064] Figure 8 This is a flowchart illustrating step 506 in one embodiment;

[0065] Figure 9 This is a flowchart illustrating step 106 in one embodiment;

[0066] Figure 10 This is a flowchart illustrating step 108 in one embodiment;

[0067] Figure 11 This is a structural block diagram of a service area division device in one embodiment;

[0068] Figure 12 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0069] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0070] In one embodiment, such as Figure 1 As shown, a service area partitioning method is provided. This embodiment illustrates the application of this method to a server. It is understood that this method can also be applied to terminals, and further to systems including terminals and servers, and is implemented through interaction between the terminal and the server. In this embodiment, the method includes the following steps:

[0071] Step 102: Determine the initial service area of ​​the target city, which includes multiple target areas.

[0072] The target city is the city where service area division is required. The initial service area is the service area initially divided before any dynamic adjustments are made to the target city.

[0073] Specifically, the target city can be divided into several regions, and the number of POIs (Points of Interest) in each region can be counted. Based on the number of POIs, multiple target regions can be selected from these regions, and these multiple target regions can form the initial service area.

[0074] Step 104: Obtain the region label data for each target region.

[0075] Among these, regional label data can be used to characterize the distribution density of vehicles and orders. Regional label data can include the average number of vehicles, the number of stationary vehicles, and regional vehicle efficiency.

[0076] Specifically, the number of vehicles and orders within each target area can be counted. For any target area, the area label data can be determined based on the number of vehicles and orders within that target area within a preset time period.

[0077] Step 106: For any target area, make a judgment based on the area label data of the target area and a preset threshold to obtain the judgment result for the target area.

[0078] In this embodiment, the preset threshold is not specifically limited and can be set according to actual conditions and needs. The judgment result can be used to characterize whether the average daily number of vehicles, the average daily number of stationary vehicles, and the average daily vehicle efficiency in a target area meet the vehicle scheduling conditions. The vehicle scheduling conditions can be a higher average daily number of vehicles, a lower average daily number of stationary vehicles, and a higher average daily vehicle efficiency.

[0079] Specifically, for any target area, the average number of vehicles, the number of stationary vehicles, and the area vehicle efficiency in the area label data of the target area can be compared with their corresponding preset thresholds to obtain the judgment result of the target area.

[0080] Step 108: Determine the service areas in the initial service area based on the judgment results of each target area.

[0081] Specifically, if the judgment result indicates that the average daily number of vehicles, the average daily number of stationary vehicles, and the average daily vehicle efficiency within a target area meet the vehicle scheduling conditions, then that target area is designated as a service area. Alternatively, if the judgment result indicates that within a target area, there are instances where the average daily number of vehicles and / or the average daily number of stationary vehicles and / or the average daily vehicle efficiency do not meet the vehicle scheduling conditions, then that target area is not designated as a service area. When the judgment results for multiple target areas all indicate that the average daily number of vehicles, the average daily number of stationary vehicles, and the average daily vehicle efficiency within each target area meet the vehicle scheduling conditions, then these multiple target areas collectively constitute a service area.

[0082] In the above-described service area delineation method, an initial service area for the target city is determined, which includes multiple target areas. Regional label data for each target area is acquired. For any target area, a judgment is made based on its regional label data and a preset threshold to obtain a judgment result for that target area. Based on the judgment results for each target area, service areas within the initial service area are determined. Compared to traditional methods based on road network or population density, the service area delineation method provided in this application divides the city into multiple target areas, determines the regional label data for each target area, and determines whether a target area should be designated as a service area by comparing the regional label data with a preset threshold. This method implements a method for delineating service areas based on vehicle and order distribution density and dynamically adjusting the range, thereby improving the precision of service area delineation and balancing the distribution of vehicle numbers.

[0083] In one embodiment, such as Figure 2 As shown, in step 102, determining the initial service area of ​​the target city may include:

[0084] Step 202: Obtain the spatial division map of the target city, which divides the target city into multiple regions.

[0085] This can be achieved by dividing the target city into several level 9 H3 geographic index regions according to the Uber H3 standard. Multiple level 9 H3 geographic index regions constitute the spatial map of the target city. The spatial map of the target city can be directly obtained from the corresponding database. For example,... Figure 3 As shown, the hexagons in the figure represent the H3 geographic index area at level 9 (the specific map is not shown; all lines in the figure other than the hexagons representing the area are auxiliary lines of the map).

[0086] Step 204: For any given region, obtain the number of points of interest in that region.

[0087] Points of Interest (POIs) are specific locations or areas on the map, such as shopping malls, restaurants, parks, museums, banks, residential areas, gas stations, and schools. For any given area, the number of POIs within that area can be directly obtained from the map of the target city.

[0088] Step 206: Based on the number of points of interest in each region, determine multiple target regions from each region and divide the multiple target regions into initial service areas.

[0089] Specifically, the target area can be a region that accounts for a preset percentage of the total number of POIs, and these multiple target areas constitute the initial service area.

[0090] In this embodiment, by dividing the target city into multiple regions and dividing the initial service area according to the number of points of interest in each region, the precision of the initial service area division can be improved.

[0091] In one embodiment, such as Figure 4 As shown, in step 206, determining multiple target regions from each region based on the number of points of interest in each region may include:

[0092] Step 402: Arrange the regions in descending order of the number of points of interest in each region.

[0093] Specifically, regions can be sorted and numbered from largest to smallest based on the number of points of interest (POIs) in each region. For example, the sorted regions could be as follows: Region 1: 40 POIs; Region 2: 20 POIs; Region 3: 20 POIs; Region 4: 10 POIs; Region 5: 10 POIs.

[0094] Step 404: Determine multiple target regions from each region. The sum of the number of points of interest in each target region accounts for a preset percentage of the total number of points of interest. The target regions are sequentially connected in the sorted regions.

[0095] Specifically, after sorting the regions, you can start with the region that is first in the order and add the number of points of interest to the next adjacent region until the sum of the number of points of interest in each region is a preset percentage of the total number of points of interest. These regions are then used as target regions.

[0096] For example, the total number of Points of Interest (POIs) is 100, with a preset percentage of 80%. The sorted regions can be as follows: Region 1: 40 POIs; Region 2: 20 POIs; Region 3: 20 POIs; Region 4: 10 POIs; Region 5: 10 POIs. The sum of the POIs in Regions 1, 2, and 3 is 80, representing 80% of the total number of POIs. Therefore, Regions 1, 2, and 3 are designated as target regions. In other words, the regions with the highest percentage of POIs can be designated as the initial service area.

[0097] In this embodiment, by calculating the number of points of interest in each region, the region whose percentage of the number of points of interest reaches a preset percentage is taken as the target region, which can improve the precision of the initial service area division.

[0098] In one embodiment, the area label data includes average number of vehicles, number of stationary vehicles, and area vehicle efficiency, such as... Figure 5 As shown, in step 104, the region label data for each target region is obtained, which may include:

[0099] Step 502: For any target area, obtain the average number of vehicles in the target area.

[0100] Specifically, for any target area, the average number of vehicles in the target area within a preset time period is obtained as the average number of vehicles.

[0101] Step 504: For any target area, obtain the number of stationary vehicles in the target area.

[0102] Specifically, stationary vehicles are those that have not been moved within a preset time period. For any target area, the number of stationary vehicles can be obtained through the location information of vehicles within the target area, or it can be obtained through the order time or relocation time of the vehicle.

[0103] Step 506: For any target area, determine the regional vehicle efficiency based on the average number of vehicles in the target area. The regional vehicle efficiency is inversely proportional to the average number of vehicles.

[0104] Specifically, for any target area, the number of orders in the target area can be obtained, and the area's vehicle efficiency can be obtained by dividing the number of orders by the average number of vehicles. Area vehicle efficiency = Area order volume / Area average number of vehicles.

[0105] In this embodiment, the average number of vehicles, the number of stationary vehicles, and the regional vehicle efficiency are used as the regional label data. This allows the regional label data to characterize the distribution density of vehicles and orders, thereby dividing service areas and improving the precision of service area division.

[0106] In one embodiment, such as Figure 6 As shown, in step 502, obtaining the average number of vehicles in the target area may include:

[0107] Step 602: Obtain the number of vehicles in the target area every preset time interval.

[0108] Specifically, this application embodiment does not impose a specific limitation on the preset duration. Taking a preset duration of 1 hour as an example, the number of vehicles in the target area can be obtained every hour. The location information of the vehicles can be queried. The location information is represented by latitude and longitude. The latitude and longitude are converted into H3 indexes using the H3 function, so the number of vehicles in the target area can be obtained based on the location information corresponding to the vehicles.

[0109] Step 604: The sum of the number of vehicles in the target area obtained within the preset time period is taken as the total number of vehicles in the target area.

[0110] Specifically, this application embodiment does not impose a specific limitation on the preset time. Taking a preset duration of 1 hour and a preset time of 1 day as an example, since there are 24 hours in a day, the number of vehicles is acquired 24 times within the preset acquisition time. The sum of the number of vehicles in the target area acquired in these 24 acquisitions is taken as the total number of vehicles in the target area.

[0111] Step 606: Determine the average number of vehicles in the target area based on the preset time and the total number of vehicles in the target area.

[0112] Specifically, the average number of vehicles in the target area can be obtained by dividing the total number of vehicles in the target area by the number of times the vehicle count is collected within a preset time period. For example, if the preset duration is 1 hour and the preset time is 1 day, the average number of vehicles in the target area can be obtained by dividing the total number of vehicles in the target area by 24.

[0113] In this embodiment, by determining the average number of vehicles by summing the number of vehicles in the target area within a preset time period, and using this as one item in the area label data, the precision of service area division can be improved.

[0114] In one embodiment, such as Figure 7 As shown, in step 504, obtaining the number of stationary vehicles in the target area may include:

[0115] Step 702: For any vehicle in the target area, obtain the order time or vehicle relocation time closest to the current time for that vehicle, and use it as the target time.

[0116] Specifically, for any vehicle in the target area, the last order time and relocation time of that vehicle can be obtained, and the time closest to the current time between the order time and the relocation time can be used as the target time.

[0117] Step 704: For any vehicle in the target area, subtract the target time of the vehicle from the current time to obtain the vehicle's stationary duration.

[0118] Specifically, the vehicle's stationary time can be calculated by subtracting the target time from the current time.

[0119] Step 706: For any vehicle in the target area, if the vehicle's stationary duration is greater than or equal to a preset time, the vehicle is designated as a stationary vehicle.

[0120] Specifically, for any vehicle in the target area, if the vehicle remains stationary for a duration greater than or equal to a preset time, it indicates that the vehicle has not moved within the preset time and is considered a stationary vehicle.

[0121] Step 708: Use the number of stationary vehicles as the number of stationary vehicles in the target area.

[0122] Specifically, after selecting stationary vehicles from all vehicles in the target area, the number of stationary vehicles can be counted as the number of stationary vehicles in the target area.

[0123] In this embodiment, the number of stationary vehicles in the target area is obtained by using the vehicle's order time or vehicle relocation time, and this number is used as one of the area label data, which can improve the precision of service area division.

[0124] In one embodiment, as shown in 8, in step 506, determining the regional vehicle efficiency of the target area based on the average number of vehicles in the target area may include:

[0125] Step 802: Obtain the number of orders in the target area within a preset time period, where the starting point of the vehicle corresponding to the order is located in the target area.

[0126] For example, it is possible to obtain all orders originating from region A within one day and count the number of orders.

[0127] Step 804: Based on the number of orders in the target area and the average number of vehicles in the target area within a preset time period, the regional vehicle efficiency of the target area is obtained. The regional vehicle efficiency of the target area is positively correlated with the number of orders and negatively correlated with the average number of vehicles in the target area.

[0128] Specifically, regional vehicle efficiency = regional order volume / average number of vehicles in the region. Taking a preset time of 1 day as an example, you can divide the number of orders in the target region within 1 day by the average number of vehicles in the target region. The result is the regional vehicle efficiency.

[0129] In this embodiment, regional vehicle efficiency is calculated using the number of orders and the average number of vehicles, and this is used as one of the regional labeling data to improve the precision of service area division.

[0130] In one embodiment, such as Figure 9 As shown, in step 106, a judgment is made based on the region label data of the target region and a preset threshold to obtain a judgment result for the target region, which may include:

[0131] Step 902: Within a preset period, the average number of vehicles in the target area corresponding to each preset time is used as the first label data. The first label data is compared with a first preset threshold to obtain a first judgment result for the target area.

[0132] Specifically, this application embodiment does not impose specific limitations on the preset period and the first preset threshold, which can be set according to actual conditions (based on the size of the city and the number of vehicles deployed) and needs. The average number of vehicles in the target area obtained at each preset time within this preset period can be summed and then divided by the total number of times the average number of vehicles was obtained; the resulting average value is used as the first label data. The first label data is compared with the first preset threshold to obtain a first judgment result for the target area. The first judgment result can be used to characterize the distribution density of vehicles within the target area.

[0133] For example, taking a preset period of 3 months and a preset time of 1 day as an example, the average number of vehicles in the target area obtained each day within these 3 months can be added together and then divided by the total number of days. The average value obtained is the average number of vehicles per day in the most recent 3 months, which is used as the first tag data.

[0134] Step 904: Within a preset period, the average number of stationary vehicles in the target area corresponding to each preset time is used as the second label data. The second label data is compared with the second preset threshold to obtain the second judgment result for the target area.

[0135] Specifically, this application embodiment does not impose a specific limitation on the second preset threshold, which can be set according to actual conditions (based on the size of the city and the number of vehicles deployed) and needs. The number of stationary vehicles in the target area obtained at each preset time within this preset period can be summed, then divided by the total number of times stationary vehicles were obtained, and the average value obtained is used as the second label data. The second label data is compared with the second preset threshold to obtain a second judgment result for the target area. The second judgment result can be used to characterize the distribution density of orders within the target area.

[0136] For example, with a preset period of 3 months and a preset time of 1 day, the number of stationary vehicles in the target area obtained each day within these 3 months can be added together and then divided by the total number of days. The average value obtained is the average number of stationary vehicles per day for the most recent 3 months, which can be used as the second tag data.

[0137] Step 906: Within a preset period, the average value of the regional vehicle efficiency of the target area corresponding to each preset time is used as the third label data. The third label data is compared with the third preset threshold to obtain the third judgment result for the target area.

[0138] Specifically, this application embodiment does not impose a specific limitation on the third preset threshold, which can be set according to actual conditions (based on the size of the city and the number of vehicles deployed) and needs. The regional vehicle efficiency data of the target area obtained at each preset time within this preset period can be summed, then divided by the total number of times regional vehicle efficiency is obtained, and the average value is used as the third label data. The third label data is compared with the third preset threshold to obtain the third judgment result for the target area. The third judgment result can be used to characterize the distribution density of vehicles and orders within the target area.

[0139] For example, with a preset period of 3 months and a preset time of 1 day, the regional vehicle efficiency of the target area obtained each day within these 3 months can be added together and then divided by the total number of days. The average value obtained is the average daily vehicle efficiency within the most recent 3 months, which can be used as the third tag data.

[0140] In this embodiment, first label data, second label data, and third label data are obtained through regional label data, and then corresponding judgment results are obtained respectively. Service areas are divided according to the judgment results, which improves the precision of service area division and balances vehicle distribution.

[0141] In one embodiment, such as Figure 10 As shown, in step 108, determining the service areas in the initial service area based on the judgment results of each target area may include:

[0142] Step 1002: For any target area, if the first judgment result for the target area indicates that the first label data is greater than the first preset threshold, the second judgment result indicates that the second label data is less than the second preset threshold, and the third judgment result indicates that the third label data is greater than the third preset threshold, then the target area is designated as a service area.

[0143] Specifically, if a target area is designated as a service area when the first judgment result indicates that the first label data is greater than a first preset threshold, the second judgment result indicates that the second label data is less than a second preset threshold, and the third judgment result indicates that the third label data is greater than a third preset threshold; or, if a target area is designated as a service area when the first judgment result indicates that the first label data is less than or equal to the first preset threshold, and / or the second judgment result indicates that the second label data is greater than or equal to the second preset threshold, and / or the third judgment result indicates that the third label data is less than or equal to the third preset threshold, then the target area is not designated as a service area. For example, the first preset threshold is 10, the second preset threshold is 10, and the third preset threshold is 1. A target area is designated as a service area if it simultaneously meets the following conditions: an average daily number of vehicles > 10 in the past 3 months, an average daily 24-hour stationary vehicle count < 10 in the past 3 months, and an average daily vehicle efficiency > 1 in the past 3 months.

[0144] Step 1004: Based on each service area, determine the service areas within the initial service area.

[0145] Specifically, the various service areas within the initial service area can be combined to form a single service area. The above steps can be repeated at preset intervals to dynamically adjust the service area's boundaries.

[0146] To facilitate further understanding, this application provides a most complete embodiment. This application embodiment provides a service area division method, the specific steps of which are as follows: (1) Initial service area division. According to the Huber H3 standard, the city is divided into several level 9 H3 geographic index areas, and the number of POIs in each area is counted. Wherein, POI refers to a specific location or position on the map, such as shopping malls, restaurants, parks, museums, banks, residential areas, gas stations, schools, etc. The area with the highest proportion of POIs is designated as the initial service area. (2) Area label construction. Area labels are used to measure the popularity of the area, evaluate the rationality of the service area division, and provide a reference for the dynamic adjustment of the service area range. It includes the following information: (a) Average number of vehicles in the area (b) Number of vehicles that do not move in the area in 24 hours (c) Area vehicle efficiency; Area vehicle efficiency = Area order volume / Average number of vehicles in the area (3) Area evaluation and service area adjustment. The following dimensions of information are statistically analyzed for the region, and regions that simultaneously meet the following conditions are selected: (a) the average daily number of vehicles in the past 3 months is >10; (b) the average daily number of stationary vehicles in the past 3 months is <10; (c) the average daily vehicle efficiency in the past 3 months is >1. Regions that simultaneously meet the above conditions are divided into the final service area range. (4) Dynamic adjustment of service areas. Steps 2-3 above are recalculated every three months to dynamically adjust the service area range. This application proposes a service area division method based on vehicle and order distribution density to divide service areas and dynamically adjust the range, thereby improving the precision of service area division and balancing the distribution of vehicle numbers.

[0147] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0148] Based on the same inventive concept, this application also provides a service area division apparatus for implementing the service area division method described above. The solution provided by this apparatus is similar to the implementation scheme described in the above method; therefore, the specific limitations in one or more service area division apparatus embodiments provided below can be found in the limitations of the service area division method described above, and will not be repeated here.

[0149] In one embodiment, such as Figure 11 As shown, a service area division device 1100 is provided, including: an initial division module 1102, a data acquisition module 1104, a judgment module 1106, and a service area division module 1108, wherein:

[0150] The initial division module 1102 is used to determine the initial service area of ​​the target city, which includes multiple target areas.

[0151] The data acquisition module 1104 is used to acquire the region label data of each target region.

[0152] The judgment module 1106 is used to make a judgment on any target area based on the area label data of the target area and a preset threshold, and obtain a judgment result for the target area.

[0153] The service area division module 1108 is used to determine the service areas in the initial service area based on the judgment results of each target area.

[0154] The service area division device provided in this application divides a city into multiple target areas, determines the area label data of the target areas, and determines whether the target area is classified as a service area by comparing the area label data with a preset threshold. This realizes a method of dividing service areas based on vehicle and order distribution density and dynamically adjusting the range, so as to improve the precision of service area division and evenly distribute the number of vehicles.

[0155] In one embodiment, the initial partitioning module 1102 is further configured to obtain a spatial partitioning map of the target city, which divides the target city into multiple regions; for any region, obtain the number of points of interest in the region; and based on the number of points of interest in each region, determine multiple target regions from each region and partition the multiple target regions into initial service areas.

[0156] In one embodiment, the initial segmentation module 1102 is further configured to arrange the regions in descending order based on the number of points of interest in each region; determine multiple target regions from each region, wherein the sum of the number of points of interest in each target region accounts for a preset percentage of the total number of points of interest, and the target regions are sequentially connected in the sorted regions.

[0157] In one embodiment, the area label data includes the average number of vehicles, the number of stationary vehicles, and the area vehicle efficiency. The data acquisition module 1104 is also used to acquire the average number of vehicles in any target area; acquire the number of stationary vehicles in any target area; and determine the area vehicle efficiency of any target area based on the average number of vehicles in the target area, wherein the area vehicle efficiency is inversely proportional to the average number of vehicles.

[0158] In one embodiment, the data acquisition module 1104 is further configured to acquire the number of vehicles in the target area once every preset time interval; take the sum of the number of vehicles in the target area acquired within the preset time interval as the total number of vehicles in the target area; and determine the average number of vehicles in the target area based on the preset time interval and the total number of vehicles in the target area.

[0159] In one embodiment, the data acquisition module 1104 is further configured to: acquire, for any vehicle in the target area, the order time or vehicle relocation time closest to the current time of the vehicle, as the target time; for any vehicle in the target area, subtract the target time of the vehicle from the current time to obtain the stationary duration of the vehicle; for any vehicle in the target area, if the stationary duration of the vehicle is greater than or equal to a preset time, classify the vehicle as a stationary vehicle; and use the number of stationary vehicles as the number of stationary vehicles in the target area.

[0160] In one embodiment, the data acquisition module 1104 is further configured to acquire the number of orders in a target area within a preset time period, wherein the starting point of the vehicle corresponding to the order is located in the target area; and to obtain the regional vehicle efficiency of the target area based on the number of orders in the target area within the preset time period and the average number of vehicles in the target area, wherein the regional vehicle efficiency of the target area is positively correlated with the number of orders and negatively correlated with the average number of vehicles in the target area.

[0161] In one embodiment, the judgment module 1106 is further configured to: within a preset period, use the average number of vehicles in the target area corresponding to each preset time as the first label data, and judge the first label data with a first preset threshold to obtain a first judgment result for the target area; within a preset period, use the average number of stationary vehicles in the target area corresponding to each preset time as the second label data, and judge the second label data with a second preset threshold to obtain a second judgment result for the target area; within a preset period, use the average regional vehicle efficiency of the target area corresponding to each preset time as the third label data, and judge the third label data with a third preset threshold to obtain a third judgment result for the target area.

[0162] In one embodiment, the service area division module 1108 is further configured to, for any target area, if the first judgment result for the target area indicates that the first label data is greater than the first preset threshold, the second judgment result indicates that the second label data is less than the second preset threshold, and the third judgment result indicates that the third label data is greater than the third preset threshold, designate the target area as a service area; and determine the service area in the initial service area based on each service area.

[0163] Each module in the aforementioned service area division device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the operations corresponding to each module.

[0164] In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 12 As shown, the computer device includes a processor, memory, and a network interface connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The database stores city maps, vehicle data, and order data. The network interface communicates with external terminals via a network connection. When executed by the processor, the computer program implements a service area partitioning method.

[0165] Those skilled in the art will understand that Figure 12 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0166] In one embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments.

[0167] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored, which, when executed by a processor, implements the steps in the above-described method embodiments. 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.

[0168] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0169] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0170] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A service area delineation method, characterized in that, The method includes: Determine the initial service area of ​​the target city, wherein the initial service area includes multiple target areas; Each target region's region label data is acquired; the region label data includes the average number of vehicles, the number of stationary vehicles, and the region's vehicle efficiency. For any of the target regions, a judgment is made based on the region label data of the target region and a preset threshold to obtain a judgment result for the target region; Based on the judgment results of each target area, the service area in the initial service area is determined; The step of obtaining the region label data for each of the target regions includes: For any of the target areas, the average number of vehicles in the target area within a preset time period is obtained as the average number of vehicles. For any of the target areas, obtain the number of stationary vehicles in the target area; For any of the target areas, the regional vehicle efficiency of the target area is determined based on the average number of vehicles in the target area, and the regional vehicle efficiency is inversely proportional to the average number of vehicles. The judgment based on the region label data of the target region and a preset threshold to obtain a judgment result for the target region includes: Within a preset period, the average number of vehicles in the target area corresponding to each preset time is used as the first label data. The first label data is compared with a first preset threshold to obtain a first judgment result for the target area. Within a preset period, the average number of stationary vehicles in the target area corresponding to each preset time is used as the second label data. The second label data is compared with a second preset threshold to obtain a second judgment result for the target area. Within a preset period, the average value of the regional vehicle efficiency of the target area corresponding to each preset time is used as the third label data. The third label data is compared with a third preset threshold to obtain a third judgment result for the target area. The step of determining the service area in the initial service area based on the judgment results of each target area includes: For any of the target areas, if the first judgment result for the target area indicates that the first tag data is greater than the first preset threshold, the second judgment result indicates that the second tag data is less than the second preset threshold, and the third judgment result indicates that the third tag data is greater than the third preset threshold, then the target area is designated as a service area. Based on each of the service area regions, the service areas in the initial service area are determined.

2. The method according to claim 1, characterized in that, The determination of the initial service area of ​​the target city includes: Obtain a spatial partition map of the target city, which divides the target city into multiple regions; For any of the regions, obtain the number of points of interest in the region; Based on the number of points of interest in each region, multiple target regions are determined from each region, and the multiple target regions are divided into initial service areas.

3. The method according to claim 2, characterized in that, The step of determining multiple target regions from each of the regions based on the number of points of interest in each region includes: Based on the number of points of interest in each region, the regions are arranged in descending order; Multiple target regions are determined from each of the regions, and the sum of the number of points of interest in each of the target regions accounts for a preset percentage of the total number of points of interest. The target regions are sequentially connected in the sorted regions.

4. The method according to claim 1, characterized in that, The step of determining the regional vehicle efficiency of any target area based on the average number of vehicles in the target area includes: For any of the target areas, the number of orders in the target area is obtained, and the number of orders is divided by the average number of vehicles to obtain the regional vehicle efficiency of the target area.

5. The method according to claim 1, characterized in that, Obtaining the average number of vehicles in the target area includes: The number of vehicles in the target area is obtained every preset time interval; The sum of the number of vehicles in the target area obtained within a preset time period shall be taken as the total number of vehicles in the target area. Based on the preset time and the total number of vehicles in the target area, the average number of vehicles in the target area is determined.

6. The method according to claim 5, characterized in that, The acquisition of the number of stationary vehicles in the target area includes: For any vehicle in the target area, obtain the order time or vehicle relocation time closest to the current time for that vehicle, and use it as the target time; For any vehicle in the target area, the difference between the target time of the vehicle and the current time is used to obtain the duration of the vehicle's stationary position. For any vehicle in the target area, if the vehicle's stationary duration is greater than or equal to the preset time, the vehicle is considered a stationary vehicle. The number of stationary vehicles is taken as the number of stationary vehicles in the target area.

7. The method according to claim 5, characterized in that, Determining the regional vehicle efficiency of the target area based on the average number of vehicles in the target area includes: Obtain the number of orders in the target area within the preset time period, wherein the origin of the vehicle corresponding to each order is located in the target area; The regional vehicle efficiency of the target area is obtained based on the number of orders in the target area and the average number of vehicles in the target area within the preset time period. The regional vehicle efficiency of the target area is positively correlated with the number of orders and negatively correlated with the average number of vehicles in the target area.

8. A service area delineation device, characterized in that, The apparatus is used to perform the steps of the method according to any one of claims 1 to 7.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 7.

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