Store service range delimiting method and system, computer device and storage medium

Abstract

This invention discloses a method, system, computer equipment, and storage medium for defining the service area of ​​a store. The method includes: acquiring raw data within a target area; preprocessing the raw data to establish a road network dataset and configuring the road network dataset analysis environment; calculating the shortest path for riders to travel from each store to each plot unit along the road network and the delivery time at each time stage; calculating the overall service level index of stores in each plot unit; and calculating the service capacity of the target store for each plot unit based on the target store service level index and the overall service level index of stores in each plot unit, thereby defining the service area of ​​the target store. This invention considers the competitive impact between similar businesses and, in conjunction with the road network dataset, comprehensively considers the impact of residents' ordering time and the actual delivery conditions, making the defined service area closer to the actual situation of food delivery and improving its accuracy.

Description

Technical Field

[0001] This invention relates to a method, system, computer equipment, and storage medium for defining the service area of ​​a store, belonging to the interdisciplinary field of urban planning and computer technology. Background Technology

[0002] The delineation of service areas for various facilities is fundamental to urban planning and design. Traditional delineation methods primarily use accessibility as the main indicator, employing methods such as actual measurement, radius method, buffer zone method (Zhou Aihua et al., 2014), and Thiessen polygon method. Some scholars have proposed the weighted Thiessen polygon method based on actual road networks, using actual distances instead of Euclidean distances and ensuring no redundancy in the calculation of facility service areas (Song Yinghua et al., 2019).

[0003] Existing research suggests that the impact of factors such as facility service capacity and population distribution differences on the service range of facilities should be comprehensively considered. Some scholars have used methods such as constructing P-median models (Chu Jianyu et al., 2015), WVD models (SHI et al., 2019), network analysis (Xu Jinghai et al., 2017), and two-step move search methods (SU et al., 2021) to determine the service range of various facilities. Furthermore, with the development of internet technology, various network computing platform tools are being used in research. For example, Liu Xuefen et al. used spatial analysis tools and agent models to conduct simulations to calculate the service range of emergency shelters.

[0004] The above-mentioned method of dividing service areas by comprehensively considering various factors improves the accuracy of the calculation, but it still has two problems: (1) Although the time to reach the facility is calculated based on the actual road route, in reality, the actual time is extended due to the influence of traffic lights and other vehicles at road intersections during driving or walking. Therefore, it lacks consideration of the impact of multiple scenarios in reality; (2) The calculation of the service level of the facility only considers the service capacity of a single facility and lacks consideration of the competitive impact between similar facilities. Summary of the Invention

[0005] In view of this, the present invention provides a method, system, computer equipment and storage medium for delineating the service area of ​​a store. It takes store density as a starting point, considers the competitive impact between similar businesses, and proposes to divide the area into a hexagonal grid to reduce marginal effects. At the same time, it combines road network datasets to comprehensively consider the impact of residents' ordering time, the delays caused by road intersections during delivery, and the impact of real-world conditions such as fences and walls, so that the delineated service area is closer to the actual situation of food delivery and improves the level of realism.

[0006] The first objective of this invention is to provide a method for defining the service area of ​​a store.

[0007] The second objective of this invention is to provide a system for defining the service scope of a store.

[0008] A third objective of this invention is to provide a computer device.

[0009] A fourth objective of this invention is to provide a storage medium.

[0010] The first objective of this invention can be achieved by adopting the following technical solution:

[0011] A method for defining the service scope of a store, the method comprising:

[0012] Obtain raw data within the target area, including store data, road data, and related data;

[0013] Preprocess the raw data;

[0014] Based on the preprocessed data, a road network dataset is established, and the road network dataset analysis environment is configured.

[0015] Calculate the shortest path for riders to travel from each store along the road network to each plot unit and the delivery time for each time period;

[0016] Based on store data and delivery time, construct a model of the total service level index of stores for each plot unit, and calculate the total service level index of stores for each plot unit.

[0017] The service level index of the target store in each block unit and the total service level index of the store in each block unit are used to calculate the service capacity of the target store in each block unit.

[0018] The service area of ​​the target store is defined based on its service capabilities for each plot of land.

[0019] Furthermore, the store data includes store POIs and monthly sales data for stores providing food delivery services; the road data includes road routes, road names, and road classifications; and the related data includes the location of fences and walls within the area.

[0020] The preprocessing of the raw data specifically includes:

[0021] The region is divided using a hexagonal fishing net, and the region center point feature is generated to obtain the center location of each block unit.

[0022] Extract road intersection data based on road path data;

[0023] Based on the location data of fences and walls within the area, and combined with the field survey, the fences and walls within the area were edited into line elements, projected onto the same coordinate system, and then corrected.

[0024] Furthermore, the step of establishing a road network dataset and configuring the road network dataset analysis environment based on the preprocessed data specifically includes:

[0025] Add road data to generate a road network dataset;

[0026] Import the shop points and plot unit center points into the stop points, add road intersections as cost-increasing point obstacles, add fences and walls as prohibited line obstacles, set driving along the road network in the network analysis layer properties, and set the analysis properties.

[0027] Furthermore, the calculation of the shortest path for riders to travel from each store along the road network to each plot unit and the delivery time at each time stage specifically includes:

[0028] The Floyd algorithm is used to calculate the shortest path for riders to travel from each store to each plot unit along the road network.

[0029] The time period is divided into peak ordering periods and peacetime periods;

[0030] Select the road path layer and assign the electric vehicle speed during peak hours and the electric vehicle speed during normal hours to each road according to the road level.

[0031] The delivery time for each time period is calculated based on the speed of electric vehicles on each road and the length of each road under the shortest path.

[0032] Furthermore, the calculation of the shortest path for riders to travel from each store to each plot unit along the road network based on the Floyd algorithm specifically includes:

[0033] Based on the store location and the center point of the plot unit, establish an adjacency matrix S and a distance matrix D. The element s(i,j) in matrix S is the distance of the shortest path from the takeaway store i to the center point j of the plot. The element d(i,j) in matrix D represents the vertex represented by the value recorded by d(i,j) that the takeaway store i passes through on its way to the center point j of the plot.

[0034] The initial value of the distance matrix D is set to the adjacency matrix S;

[0035] Update matrix D n times;

[0036] During the first update, if s(i,j)>s(i,0)+s(0,j), that is, the straight-line distance between the food delivery shop point i and the center point j of the plot unit is greater than the path distance between the two points through the first node, then update s(i,j) to s(i,0)+s(0,j) and update d(i,j) to d(i,0); otherwise, remain unchanged.

[0037] For each path node k, an update is performed. Check whether s(i,j)>s(i,k-1)+s(k-1,j) is true. If it is true, it proves that the path from i to k and then to j is shorter than the path from i directly to j. Then set s(i,j)=s(i,k-1)+s(k-1,j) and d(i,j)=d(i,k-1).

[0038] During the Floyd algorithm, the labels are continuously modified. When all path nodes k have been traversed, the output s(i,j) is the distance of the shortest path from i to j, and d(i,j) is the vertices traversed by the shortest path from i to j.

[0039] Furthermore, based on store data and delivery time, a model for the overall service level index of stores within a plot of land is constructed, as follows:

[0040]

[0041] Among them, P j α represents the overall service level index of shops in plot unit j. i t represents the monthly sales of store i. ij This represents the delivery time, which is the time it takes for a rider to travel from store i along the road network to plot j using the shortest route.

[0042] The service level index of the target store in each plot unit and the total service level index of the stores in each plot unit are used to calculate the service capability of the target store in each plot unit, as shown in the following formula:

[0043]

[0044] Among them, W j Represents the service capacity of the target store to plot unit j, logα / t j Let α represent the target store service level index for plot unit j, t represent the monthly sales volume of the target store, and t represent the service level index for the target store. j This represents the delivery time, which is the time it takes for a rider to travel from the target store to plot unit j along the road network using the shortest route.

[0045] Furthermore, the step of defining the service scope of the target store based on its service capabilities for each plot of land specifically includes:

[0046] Statistical analysis was conducted on the changing patterns of the target store's service capabilities in various plot units. The percentage of the changing trend of service capabilities was calculated, the number of categories was set, and the inflection point value was identified as the critical value for dividing the target store's service capability tiers.

[0047] Based on the threshold values ​​for classifying the service capabilities of the target stores, the corresponding service ratings are marked in ascending order.

[0048] The second objective of this invention can be achieved by adopting the following technical solution:

[0049] A system for defining the service area of ​​a store, the system comprising:

[0050] The acquisition module is used to acquire raw data within the target area, including store data, road data, and related data.

[0051] The preprocessing module is used to preprocess the raw data;

[0052] The module is used to build a road network dataset based on the preprocessed data and configure the road network dataset analysis environment.

[0053] The first calculation module is used to calculate the shortest path for riders to travel from each store along the road network to each plot unit, as well as the delivery time at each time stage.

[0054] The second calculation module is used to construct a model of the total service level index of the stores in each plot unit based on store data and delivery time, and to calculate the total service level index of the stores in each plot unit.

[0055] The third calculation module is used to calculate the service capability of the target store to each block unit based on the target store service level index of each block unit and the total store service level index of each block unit.

[0056] The delineation module is used to delineate the service area of ​​a target store based on its service capabilities to various plot units.

[0057] The third objective of this invention can be achieved by adopting the following technical solution:

[0058] A computer device includes a processor and a memory for storing a processor-executable program, characterized in that, when the processor executes the program stored in the memory, it implements the above-described method for defining the scope of store services.

[0059] The fourth objective of this invention can be achieved by adopting the following technical solution:

[0060] A storage medium storing a program that, when executed by a processor, implements the above-described method for defining the scope of store services.

[0061] The present invention has the following advantages over the prior art:

[0062] Based on traditional methods for dividing the service scope of facilities, this invention proposes a method that more closely approximates the actual service level from the perspective of real-world scenarios. It considers the competitive impact between stores, constructs relevant indices to more accurately calculate the service level of stores, uses hexagons to divide the research area to reduce marginal effects, considers the actual delivery route and various scenarios that may be encountered during the delivery process, such as traffic lights, walls, etc., and considers the possibility of differences in rider speed due to congestion during peak ordering periods and other time periods. Attached Figure Description

[0063] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the structures shown in these drawings without creative effort.

[0064] Figure 1 This is a flowchart of the method for defining the service scope of a store according to Embodiment 1 of the present invention.

[0065] Figure 2 This is a schematic diagram showing the distribution of various elements within the target area in Embodiment 2 of the present invention.

[0066] Figure 3 This is a schematic diagram of the shortest delivery path in Embodiment 2 of the present invention.

[0067] Figure 4 This is a schematic diagram of the shortest path distribution for the DELHI TADKA restaurant delivery in Embodiment 2 of the present invention.

[0068] Figure 5 This is a schematic diagram of the total service level index of catering takeaway shops in a regional plot unit according to Embodiment 2 of the present invention.

[0069] Figure 6 This is a schematic diagram of the service area of ​​the DELHI TADKA restaurant in Embodiment 2 of the present invention.

[0070] Figure 7 This is a structural block diagram of the store service scope delineation system of Embodiment 3 of the present invention.

[0071] Figure 8 This is a structural block diagram of the computer device according to Embodiment 4 of the present invention. Detailed Implementation

[0072] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are some embodiments of the present invention, but not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0073] Example 1:

[0074] This embodiment provides a method for defining the service area of ​​a restaurant delivery business. The method uses multi-source data, considers various influencing factors in actual delivery, simulates delivery scenarios at different times, and improves the accuracy of calculating the service level of the restaurant delivery business. The method includes the following steps:

[0075] S101. Obtain the raw data within the target area.

[0076] The raw data includes store data, road data, and related data. Store data includes store POIs and monthly sales data for stores that provide food delivery services. Road data includes road routes, road names, and road classifications. Related data includes the location of fences and walls within the area.

[0077] Specifically, merchants with delivery capabilities were selected from Meituan and Ele.me platforms as food delivery shops, and shop addresses and monthly sales information were crawled. Furthermore, the shop POI coordinate data was obtained by combining the Baidu Map API (Application Program Interface). Road data came from the Open Street Map platform. After downloading, the data was cleaned and filtered, retaining the road name and road level fields. The locations of fences and walls came from Baidu Map.

[0078] S102. Preprocess the raw data.

[0079] S1021. Project store i onto the map based on the store POI coordinate data. The geographic coordinate system is GCS_WGS_1984. Delete stores with duplicate geographic coordinates and abnormal monthly sales values.

[0080] S1022. Use a hexagonal fishing net to divide the area and generate the area center point element to obtain the center position of each block unit.

[0081] Specifically, the study area is selected, and the area is divided using a hexagonal net to reduce marginal effects. Then, the regional center point element is generated. Based on this, the regional center point element j is generated using the element-to-point tool to obtain the coordinates of the center position of each plot and the center position of each plot unit.

[0082] S1023. Extract road intersection data based on road path data.

[0083] Specifically, the road path data was verified using Baidu Maps to ensure the continuity of road intersections. The road network layer elements were selected and exported, causing each road to be interrupted at intersections, and the coordinates of the road intersection points were obtained.

[0084] S1024. Based on the location data of fences and walls within the area and combined with the field survey, edit the fences and walls within the area into line elements, project them onto the same coordinate system, and then perform corrections.

[0085] S103. Based on the preprocessed data, establish a road network dataset and configure the road network dataset analysis environment.

[0086] S1031. Add road data, generate a road network dataset, set connectivity to endpoints, use type as cost, unit as minutes, and define the original values ​​of attributes, selecting fields as the type.

[0087] S1032. Configure the road network dataset analysis environment, import the restaurant delivery shop points and plot unit center points into the stop points; add road intersections as cost-increasing point obstacles, and set the obstacle time to 10 seconds; add fences and walls as prohibited line obstacles, and set the obstacle type to impassable; in the network analysis layer properties, set driving along the road network, set analysis properties, and set delivery time limits and maximum delivery distance limits.

[0088] S104. Calculate the shortest path for riders to travel from each store along the road network to each plot unit and the delivery time for each time period.

[0089] S1041. Calculate the shortest path for riders to travel from each store to each plot unit along the road network based on the Floyd algorithm.

[0090] Based on the store location and the center point of the plot unit, establish an adjacency matrix S and a distance matrix D. The element s(i,j) in matrix S is the distance of the shortest path from the takeaway store i to the center point j of the plot. The element d(i,j) in matrix D represents the vertex represented by the value recorded by d(i,j) that the takeaway store i passes through on its way to the center point j of the plot.

[0091] The calculation formula is as follows:

[0092]

[0093] in, Let be the shortest path length between shop point i and the center point j of the plot unit, passing through path nodes 1, 2, ..., k.

[0094] The initial value of the distance matrix D is set to the adjacency matrix S, i.e., D (0) =S.

[0095] Update matrix D n times;

[0096] During the first update, if s(i,j)>s(i,0)+s(0,j), that is, the straight-line distance between the food delivery shop point i and the center point j of the plot unit is greater than the path distance between the two points through the first node, then update s(i,j) to s(i,0)+s(0,j) and update d(i,j) to d(i,0); otherwise, remain unchanged.

[0097] For each path node k, an update is performed. Check whether s(i,j)>s(i,k-1)+s(k-1,j) is true. If it is true, it proves that the path from i to k and then to j is shorter than the path from i directly to j. Then set s(i,j)=s(i,k-1)+s(k-1,j) and d(i,j)=d(i,k-1).

[0098] During the Floyd algorithm, the labels are continuously modified. When all path nodes k have been traversed, the output s(i,j) is the distance of the shortest path from i to j, and d(i,j) is the vertices traversed by the shortest path from i to j.

[0099] S1042. Based on the sales distribution in different time periods, the time period is divided into peak ordering period and peacetime period.

[0100] S1043. Select the road path layer, and assign the electric vehicle speed during peak hours and the electric vehicle speed during normal hours to each road according to the road level.

[0101] Specifically, select the road path layer, add "Electric Vehicle Speed ​​(Peak Hour)" and "Electric Vehicle Speed ​​(Peak Hour)" fields, and assign the electric vehicle speed during peak hours and the electric vehicle speed during normal hours to each road based on the road level and the speed limits stipulated in national regulations for electric vehicles and electric bicycles. The specific assignment criteria are as follows:

[0102] The "Safety Technical Specifications for Electric Bicycles" stipulates that the maximum speed of electric bicycles shall not exceed 25 km / h. During peak hours, the speed of electric bicycles on side roads shall not exceed one-third of the maximum speed allowed on that road, which is set at 8 km / h. The speed of electric bicycles on other roads shall be the maximum speed allowed on that road, which is 25 km / h. During normal times, the speed of electric bicycles on all roads shall be the maximum speed allowed on that road, which is 25 km / h.

[0103] S1044. Based on the electric vehicle speeds on each road and the lengths of each road along the shortest path, calculate the delivery time for each time period, as shown in the following formula:

[0104]

[0105] Among them, t ij S represents the delivery time, which is the time it takes for a rider to travel from store i to plot j along the road network using the shortest route. u T represents the length of road u in the shortest path. u This indicates the speed of the electric vehicle traveling on road u.

[0106] S105. Based on store data and delivery time, construct a store total service level index model for each plot unit, and calculate the store total service level index for each plot unit.

[0107] Considering store size and delivery time, a model for the overall service level index of stores within a plot of land is constructed, as follows:

[0108]

[0109] Among them, P j α represents the overall service level index of food delivery shops in plot unit j. i t represents the monthly sales volume of food delivery store i. ij This represents the delivery time, which is the time it takes for a rider to travel from store i along the road network to plot j using the shortest route.

[0110] S106. Calculate the service capacity of the target store to each block unit based on the target store service level index and the total store service level index of each block unit.

[0111] Considering that a higher store density within a region will negatively impact store service capabilities, the proportion of the target store service level index for each plot unit to the total store service level index for that plot unit represents the store's service capability for that plot unit, as shown in the following formula:

[0112]

[0113] Among them, Wj Represents the service capacity of the target store to plot unit j, logα / t j Let α represent the target store service level index for plot unit j, t represent the monthly sales volume of the target store, and t represent the service level index for the target store. j This represents the delivery time, which is the time it takes for a rider to travel from the target store to plot unit j along the road network using the shortest route.

[0114] S107. Determine the service scope of the target store based on its service capabilities for each plot unit.

[0115] S1071. Conduct statistical analysis on the changing patterns of the target store's service capabilities in various plot units, calculate the percentage change trend of service capabilities, and identify the inflection point value as the critical value for dividing the target store's service capability tiers.

[0116] Specifically, to ensure that the range of each class is roughly the same as the number of values ​​it has, and that the changes between intervals are very consistent, the geometric interval grading method is adopted. Based on the geometric series, the class width is defined in arithmetic form to ensure that the range of each class is roughly the same as the number of values ​​it has, and that the changes between intervals are consistent. Five classes are set, and the inflection point value is calculated as the critical value for dividing the service capability circle of catering takeaway stores.

[0117] S1072. Based on the critical value of the target store's service capability tier, label the corresponding service evaluations in ascending order.

[0118] Specifically, based on the critical values ​​for dividing the service capability tiers of the target stores, they are labeled in ascending order as very poor, poor, average, good, and excellent.

[0119] The service capabilities of each parcel unit are divided according to the above classification criteria, and then visualized in two-dimensional diagrams and output in vector form.

[0120] It should be noted that although the method operations of the above embodiments are described in a specific order, this does not require or imply that these operations must be performed in that specific order, or that all the operations shown must be performed to achieve the desired result. On the contrary, the described steps may be performed in a different order. Additionally or alternatively, certain steps may be omitted, multiple steps may be combined into one step, and / or one step may be broken down into multiple steps.

[0121] Example 2:

[0122] like Figures 2-6 As shown, in order to verify the implementation effect of the store service scope delineation method in the above implementation case 1, this embodiment collects a section of Wushan Road and nearby streets in Tianhe District, Guangzhou City as an application example for verification.

[0123] Wushan Road is located in Wushan Subdistrict, Tianhe District, Guangzhou, with a high density of restaurants and food delivery shops nearby. The surrounding area also has a high degree of mixed-use development, a complex population structure, and a high demand for food delivery. To explore the service capacity of restaurants and food delivery shops around Wushan Road and provide data support for their site selection, a rectangular area was selected from the section of Wushan Road from South China Normal University to South China University of Technology and its surrounding areas, including Jiayi Garden Community, Yaoyi City Commercial Center, and Guangdong Academy of Agricultural Sciences, among other spaces. The service capacity level of the DELHI TADKA restaurant within this area was precisely calculated, and its service area was defined. The specific implementation can be achieved through the following steps:

[0124] S201. Obtain the raw data within the target area.

[0125] The raw data in this embodiment includes location data of restaurants and takeout shops near Wushan Road, roads and fences, walls, and building outline vector data;

[0126] Merchants with delivery capabilities were selected from Meituan and Ele.me platforms as food delivery outlets, and their addresses and monthly sales information were crawled. Furthermore, the POI coordinates of the outlets were obtained by combining the Baidu Map API (Application Program Interface). Road data was obtained from the Open Street Map platform. After downloading, the data was cleaned and filtered, retaining the road name and road grade fields. The locations of fences and walls were obtained from Baidu Map.

[0127] S202. Preprocess the raw data.

[0128] S2021. Project store i onto the map based on the store POI coordinate data. The geographic coordinate system is GCS_WGS_1984. Delete stores with duplicate geographic coordinates and abnormal monthly sales values.

[0129] S2022. Select the study area, use the subdivision tool to divide the area into a hexagonal net with a size of 200 square meters, and then use the feature conversion tool to generate the center point feature j of the area and obtain the coordinates of the center position of each plot.

[0130] S2023. In conjunction with Baidu Maps, the road path data is verified to ensure that roads intersect and are continuous. Further, road network layer elements are selected and exported, causing roads to be interrupted at intersections, and the coordinates of these intersection points are obtained.

[0131] S2024. Based on the location data of fences and walls within the area and combined with the field survey, edit the fences and walls within the area into line elements, project them onto the same coordinate system, and then make corrections.

[0132] S203. Based on the preprocessed data, establish a road network dataset and configure the road network dataset analysis environment.

[0133] S2031. Add road data to generate a road network dataset, set connectivity to endpoints, use type as cost, unit as minutes, and define the original values ​​of attributes, selecting fields as the type.

[0134] S2032. Configure the road network dataset analysis environment. Import the restaurant delivery points and plot unit center points into the stop points; add road intersections as cost-increasing point obstacles and set the obstacle time to 10 seconds; add fences and walls as prohibited line obstacles and set the obstacle type to impassable; in the network analysis layer properties, set driving along the road network, set analysis properties, and set delivery time limits and maximum delivery distance limits.

[0135] S204. Calculate the shortest path for riders to travel from each store along the road network to each plot unit and the delivery time for each time period.

[0136] S2041. Based on the Floyd algorithm, calculate the shortest path for riders to travel from various food delivery shops to various plot units along the road network. The calculation process is as follows:

[0137] Based on the locations of food delivery shops and the center point of the unit, establish an adjacency matrix S and a distance matrix D. The element s(i,j) in matrix S is the distance of the shortest path from food delivery shop i to the center point j of the unit. The element d(i,j) in matrix D represents the vertex represented by the value recorded in d(i,j) that food delivery shop i passes through on its way to the center point j of the unit.

[0138] The calculation formula is as follows:

[0139]

[0140] in, Let i be the shortest path length between the food delivery shop point i and the center point j of the plot unit, passing through path nodes 1, 2, ..., k.

[0141] The initial value of the distance matrix D is set to the adjacency matrix S, i.e., D (0) =S.

[0142] Update matrix D n times;

[0143] During the first update, if s(i,j)>s(i,0)+s(0,j), that is, the straight-line distance between shop point i and the center point j of the plot unit is greater than the path distance between the two points through the first node, then update s(i,j) to s(i,0)+s(0,j) and update d(i,j) to d(i,0); otherwise, remain unchanged.

[0144] For each path node k, an update is performed. Check if s(i,j)>s(i,k-1)+s(k-1,j) is true. If it is true, it proves that the path from i to k and then to j is shorter than the path from i directly to j. Then set s(i,j)=s(i,k-1)+s(k-1,j) and d(i,j)=d(i,k-1).

[0145] During the Floyd algorithm, the labels are continuously modified. When all path nodes k have been traversed, the output s(i,j) is the distance of the shortest path from i to j, and d(i,j) is the vertices traversed by the shortest path from i to j.

[0146] S2042. Divide the time into periods, setting 11:00-13:00 and 4:00-7:00 as peak ordering periods, and the rest of the time as normal periods.

[0147] S2043. Select the road path layer and add the fields "Electric Vehicle Speed ​​(Peak Time)" and "Electric Vehicle Speed ​​(Peak Time)". Based on the road level and the speed limits stipulated in national regulations for electric vehicles and electric bicycles, assign the electric vehicle speeds during peak hours and during normal times to each road. The specific assignment standards are as follows:

[0148] The "Safety Technical Specifications for Electric Bicycles" stipulates that the maximum speed of electric bicycles shall not exceed 25 km / h. During peak hours, the speed of electric bicycles on side roads shall not exceed one-third of the maximum speed allowed on that road, which is set at 8 km / h. The speed of electric bicycles on other roads shall be the maximum speed allowed on that road, which is 25 km / h. During normal times, the speed of electric bicycles on all roads shall be the maximum speed allowed on that road, which is 25 km / h.

[0149] S2044. Based on the electric vehicle speeds on each road and the lengths of each road along the shortest path, calculate the delivery time for each time period, as shown in the following formula:

[0150]

[0151] Among them, t ij S represents the delivery time, which is the time it takes for a rider to travel from store i to plot j along the road network using the shortest route. u T represents the length of road u in the shortest path. u This indicates the speed of the electric vehicle traveling on road u.

[0152] S205. Based on store data and delivery time, construct a store total service level index model for each plot unit, and calculate the store total service level index for each plot unit.

[0153] Considering store size and delivery time, a model for the overall service level index of stores within a plot of land is constructed, as follows:

[0154]

[0155] Among them, P j α represents the overall service level index of food delivery shops in plot unit j. i t represents the monthly sales volume of food delivery store i. ij This represents the delivery time, which is the time it takes for a rider to travel from store i along the road network to plot j using the shortest route.

[0156] S206. Calculate the service capacity of the target store to each block unit based on the target store service level index and the total store service level index of each block unit.

[0157] Considering that a higher store density within a region will negatively impact store service capabilities, the proportion of the target store service level index for each plot unit to the total store service level index for that plot unit represents the store's service capability for that plot unit, as shown in the following formula:

[0158]

[0159] Among them, W j Represents the service capacity of the target store to plot unit j, logα / t j Let α represent the target store service level index for plot unit j, t represent the monthly sales volume of the target store, and t represent the service level index for the target store. j This represents the delivery time, which is the time it takes for a rider to travel from the target store to plot unit j along the road network using the shortest route.

[0160] S207. Determine the service scope of the target store based on its service capabilities for each plot of land.

[0161] S2071. Conduct statistical analysis on the changing patterns of service level values ​​in various plots, calculate the percentage change trend of service capability level values, identify the inflection point values, and use them as the critical values ​​for dividing the service capability tiers of catering takeaway shops.

[0162] To ensure that the range of each class is roughly the same as the number of values ​​it has, and that the changes between intervals are very consistent, the geometric interval grading method is adopted. Based on the geometric series, the class width is defined in arithmetic form. It is set to 5 classes, and the inflection point value is calculated, which is the critical value for dividing the service capability circle of DELHI TADKA restaurant.

[0163] S2072. According to the numerical order, label them in ascending order as very poor, poor, average, good, and very good.

[0164] The service level values ​​of unit plots are divided according to the above classification criteria, and then visualized in two-dimensional diagrams and output in vector form.

[0165] Example 3:

[0166] like Figure 7 As shown, this embodiment provides a store service area delineation system. The system includes an acquisition module 701, a preprocessing module 702, an establishment module 703, a first calculation module 704, a second calculation module 705, a third calculation module 706, and a delineation module 707. The specific functions of each module are as follows:

[0167] The acquisition module 701 is used to acquire raw data within the target area, including store data, road data, and related data;

[0168] Preprocessing module 702 is used to preprocess the raw data;

[0169] Module 703 is used to build a road network dataset based on the preprocessed data and configure the road network dataset analysis environment.

[0170] The first calculation module 704 is used to calculate the shortest path for riders to travel from each store along the road network to each plot unit and the delivery time at each time stage.

[0171] The second calculation module 705 is used to construct a total service level index model for the plot unit based on the store data and delivery time, and to calculate the total service level index of the store for each plot unit.

[0172] The third calculation module 706 is used to calculate the service capability of the target store to each block unit based on the target store service level index of each block unit and the total store service level index of each block unit.

[0173] The delineation module 707 is used to delineate the service range of a target store based on its service capabilities to various plot units.

[0174] It should be noted that the system provided in this embodiment is only an example of the above-described division of functional modules. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure can be divided into different functional modules to complete all or part of the functions described above.

[0175] It is understood that the terms "first," "second," etc., used in the above system can be used to describe various modules, but these modules are not limited by these terms. These terms are only used to distinguish the first module from another module. For example, without departing from the scope of the invention, the first computing module can be referred to as the second computing module, and similarly, the second computing module can be referred to as the first computing module. Both the first computing module and the second computing module are computing modules, but they are not the same computing module.

[0176] Example 4:

[0177] This embodiment provides a computer device, which may be a computer, such as... Figure 8 As shown, it includes a processor 802, a memory, an input device 803, a display 804, and a network interface 805 connected via a system bus 801. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium 806 and internal memory 807. The non-volatile storage medium 806 stores an operating system, computer programs, and a database. The internal memory 807 provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. When the processor 802 executes the computer programs stored in the memory, it implements the store service scope delineation method of Embodiment 1 above, as follows:

[0178] Obtain raw data within the target area, including store data, road data, and related data;

[0179] Preprocess the raw data;

[0180] Based on the preprocessed data, a road network dataset is established, and the road network dataset analysis environment is configured.

[0181] Calculate the shortest path for riders to travel from each store along the road network to each plot unit and the delivery time for each time period;

[0182] Based on store data and delivery time, construct a model of the total service level index of stores for each plot unit, and calculate the total service level index of stores for each plot unit.

[0183] The service level index of the target store in each block unit and the total service level index of the store in each block unit are used to calculate the service capacity of the target store in each block unit.

[0184] The service area of ​​the target store is defined based on its service capabilities for each plot of land.

[0185] Example 5:

[0186] This embodiment provides a storage medium, which is a computer-readable storage medium, storing a computer program. When the computer program is executed by a processor, it implements the store service scope delineation method of Embodiment 1 above, as follows:

[0187] Obtain raw data within the target area, including store data, road data, and related data;

[0188] Preprocess the raw data;

[0189] Based on the preprocessed data, a road network dataset is established, and the road network dataset analysis environment is configured.

[0190] Calculate the shortest path for riders to travel from each store along the road network to each plot unit and the delivery time for each time period;

[0191] Based on store data and delivery time, construct a model of the total service level index of stores for each plot unit, and calculate the total service level index of stores for each plot unit.

[0192] The service level index of the target store in each block unit and the total service level index of the store in each block unit are used to calculate the service capacity of the target store in each block unit.

[0193] The service area of ​​the target store is defined based on its service capabilities for each plot of land.

[0194] It should be noted that the computer-readable storage medium in this embodiment can be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. The computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof.

[0195] In this embodiment, the computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in connection with an instruction execution system, apparatus, or device. In this embodiment, the computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying a computer-readable program. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. The computer-readable signal medium can also be any computer-readable storage medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The computer program contained on the computer-readable storage medium can be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination thereof.

[0196] The computer-readable storage medium described above can be used to write computer programs for executing this embodiment in one or more programming languages ​​or combinations thereof. These programming languages ​​include object-oriented programming languages—such as Java, Python, and C++—and conventional procedural programming languages—such as C or similar programming languages. The program can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0197] In summary, this invention utilizes a multi-source open platform to acquire data, processes and calculates the acquired data, simulates real-world scenarios, constructs an index model to calculate the service capacity of a target store to various plot units, and statistically analyzes the numerical variation patterns to delineate the service area of ​​the food delivery store. This method helps improve the scientific rigor of government departments' commercial planning. Compared with existing technologies, this invention has stronger operability and higher accuracy, and also provides a completely new approach to the layout and site selection of food delivery stores.

[0198] The above description is merely a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope disclosed in the present invention, based on the technical solution and inventive concept of the present invention, shall fall within the scope of protection of the present invention.

Claims

1. A method for defining the service scope of a store, characterized in that, The method includes: Obtain raw data within the target area. The raw data includes store data, road data, and related data. The store data includes store POIs and monthly sales data for stores providing food delivery services. The road data includes road routes, road names, and road classifications. The related data includes the location of fences and walls within the area. Preprocess the raw data; Based on the preprocessed data, a road network dataset is established; Calculate the shortest path for riders to travel from each store along the road network to each plot unit and the delivery time for each time period; Based on store data and delivery time, construct a model of the total service level index of stores for each plot unit, and calculate the total service level index of stores for each plot unit. The service level index of the target store in each block unit and the total service level index of the store in each block unit are used to calculate the service capacity of the target store in each block unit. The service area of ​​the target store is defined based on its service capabilities for each plot of land. The preprocessing of the raw data specifically includes: The region is divided using a hexagonal fishing net, and the region center point feature is generated to obtain the center location of each block unit. Extract road intersection data based on road path data; Based on the location data of fences and walls within the area, and combined with the field survey, the fences and walls within the area were edited into line elements, projected onto the same coordinate system, and then corrected. The process of establishing a road network dataset based on the preprocessed data specifically includes: Add road data to generate a road network dataset; Import the store points and plot unit center points into the stop points, add road intersections as cost-increasing point obstacles, add fences and walls as prohibition line obstacles, set driving along the road network in the network analysis layer properties, and set the analysis properties. The calculation of the shortest path for riders to travel from each store along the road network to each plot unit and the delivery time at each time stage specifically includes: The Floyd algorithm is used to calculate the shortest path for riders to travel from each store to each plot unit along the road network. The time period is divided into peak ordering periods and peacetime periods; Select the road path layer and assign the electric vehicle speed during peak hours and the electric vehicle speed during normal hours to each road according to the road level. The delivery time for each time period is calculated based on the speed of electric vehicles on each road and the length of each road under the shortest path. The process of defining the service scope of a target store based on its service capabilities for various plot units specifically includes: Statistical analysis was conducted on the changing patterns of the target store's service capabilities in various plot units. The percentage of the changing trend of service capabilities was calculated, the number of categories was set, and the inflection point value was identified as the critical value for dividing the target store's service capability tiers. Based on the critical value of the target store's service capability tier, the corresponding service evaluations are marked in ascending order; The calculation of the shortest path for riders to travel from each store to each plot of land based on the Floyd algorithm specifically includes: Based on the store location and the center point of the plot unit, establish an adjacency matrix S and a distance matrix D. The element s(i,j) in matrix S is the distance of the shortest path from the takeaway store i to the center point j of the plot. The element d(i,j) in matrix D represents the vertex represented by the value recorded by d(i,j) that the takeaway store i passes through on its way to the center point j of the plot. The initial value of the distance matrix D is set to the adjacency matrix S; Update matrix D n times; During the first update, if s(i,j) > s(i,0) + s(0,j), that is, the straight-line distance between the restaurant / takeout shop point i and the center point j of the plot unit is greater than the path distance between the two points through the first node, then update s(i,j) to s(i,0) + s(0,j) and update d(i,j) to d(i,0); otherwise, leave it unchanged. For each path node k, an update is performed. Check if s(i,j) > s(i,k-1) + s(k-1,j) is true. If it is true, it proves that the path from i to k and then to j is shorter than the path from i directly to j. Then set s(i,j) = s(i,k-1) + s(k-1,j) and d(i,j) = d(i,k-1). During the Floyd algorithm, the labels are continuously modified. When all path nodes k have been traversed, the output s(i,j) is the distance of the shortest path from i to j, and d(i,j) is the vertices traversed by the shortest path from i to j.

2. The method for defining the service scope of a store according to claim 1, characterized in that, The model for the overall service level index of stores within a plot of land, based on store data and delivery time, is as follows: ； in, This represents the overall service level index of shops in plot unit j. This represents the monthly sales volume of store i. This represents the delivery time, which is the time it takes for a rider to travel from store i along the road network to plot j using the shortest route. The service level index of the target store in each plot unit and the total service level index of the stores in each plot unit are used to calculate the service capability of the target store in each plot unit, as shown in the following formula: ； in, This indicates the service capacity of the target store for plot unit j. This represents the target store service level index for plot unit j. This indicates the target store's monthly sales volume. This represents the delivery time, which is the time it takes for a rider to travel from the target store to plot unit j along the road network using the shortest route.

3. A system for defining the service area of ​​a store, used to implement the method for defining the service area of ​​a store as described in any one of claims 1-2, characterized in that, The system includes: The acquisition module is used to acquire raw data within the target area, including store data, road data, and related data. The preprocessing module is used to preprocess the raw data; A module is established to build a road network dataset based on the preprocessed data. The first calculation module is used to calculate the shortest path for riders to travel from each store along the road network to each plot unit, as well as the delivery time at each time stage. The second calculation module is used to construct a model of the total service level index of the stores in each plot unit based on store data and delivery time, and to calculate the total service level index of the stores in each plot unit. The third calculation module is used to calculate the service capability of the target store to each block unit based on the target store service level index of each block unit and the total store service level index of each block unit. The delineation module is used to delineate the service area of ​​a target store based on its service capabilities to various plot units.

4. A computer device, comprising a processor and a memory for storing a processor-executable program, characterized in that, When the processor executes the program stored in the memory, it implements the store service scope delineation method according to any one of claims 1-2.

5. A storage medium storing a program, characterized in that, When the program is executed by the processor, it implements the store service scope delineation method according to any one of claims 1-2.

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