A dynamic decision-making method and system for store site selection based on geographic information

Abstract

This application provides a dynamic decision-making method and system for store site selection based on geographic information, relating to the field of geographic information system technology. This application acquires traffic data along main roads and subway lines in a target city, as well as geographic image information of the surrounding business districts of the area to be analyzed within the target city; processes the geographic image information to obtain the physical boundary range and business type distribution information of the surrounding business districts; combines this with traffic data to calculate traffic accessibility parameters; determines preliminary candidate areas within the physical boundary range; and obtains synergy potential parameters based on the traffic accessibility parameters; selects candidate store locations from the preliminary candidate areas based on the traffic accessibility parameters and synergy potential parameters; and evaluates the candidate store locations based on surrounding dynamic factors to determine the final store site selection. This achieves precise, intelligent, and dynamic decision-making for store site selection, improving the scientific nature of site selection and the commercial success rate.

Description

Technical Field

[0001] This application relates to the field of geographic information system technology, and in particular to a dynamic decision-making method and system for store site selection based on geographic information. Background Technology

[0002] In scenarios where retail stores are to be opened in core urban areas or along transportation hubs, site selection decisions need to comprehensively consider the synergistic effects of transportation accessibility and the surrounding commercial ecosystem in order to maximize customer traffic conversion and commercial value. With the rapid changes in urban traffic conditions and commercial layouts, traditional static site selection analysis is no longer sufficient to meet actual needs, and the market urgently needs an intelligent decision-making method that can dynamically respond to fluctuations in traffic flow and the evolution of business district structures.

[0003] Currently, mainstream solutions attempt to collect road traffic and pedestrian flow data through fixed sensor networks and combine it with point-of-interest (POI) information to construct site selection evaluation models based on historical data. However, existing solutions have certain shortcomings. For example, due to a lack of deep image processing capabilities, they cannot accurately identify changes in the commercial district morphology caused by new buildings, temporary construction sites, or functional area adjustments. Furthermore, the coverage blind spots of fixed sensors result in a spatiotemporal lag in the assessment of traffic accessibility. Insufficient modeling of the spatial coupling relationship between traffic flow and commercial activities makes it difficult to quantify the synergistic effect of a specific location and surrounding businesses in terms of spatial distribution. This leads to poor adaptability of site selection recommendations in rapidly changing urban environments, and risks such as insufficient actual traffic-generating capacity of recommended locations or mismatch with the surrounding commercial ecosystem. Summary of the Invention

[0004] The purpose of this application is to provide a dynamic decision-making method and system for store site selection based on geographic information, in order to solve the problems in the existing technology, such as the inability to accurately identify changes in business district form, the time and space lag in the assessment of traffic accessibility, and the poor adaptability of site selection suggestions in rapidly changing urban environments.

[0005] To address the aforementioned technical problems, in a first aspect, this application provides a dynamic decision-making method for store location selection based on geographic information, comprising:

[0006] Acquire traffic data along the main roads and subway lines of the target city, as well as geographic image information of the surrounding business districts of the area to be analyzed in the target city;

[0007] The geographic image information is processed to obtain the physical boundary range and business distribution information of the surrounding business district. Combined with the traffic data, the traffic accessibility parameters are calculated.

[0008] A preliminary candidate area is determined within the physical boundary range. Based on the traffic accessibility parameter, the preliminary candidate area and the business type distribution information are analyzed for synergistic potential to obtain synergistic potential parameters.

[0009] Based on the traffic accessibility parameters and the synergy potential parameters, candidate store locations are selected from the preliminary candidate areas;

[0010] The candidate store locations are evaluated based on surrounding dynamic factors to determine the final store location.

[0011] Optionally, the geographic image information is processed to obtain the physical boundary range and business distribution information of the surrounding business district, and combined with the traffic data, traffic accessibility parameters are calculated, including:

[0012] Feature extraction is performed on the geographic image information to obtain road edge features and building outline features. Then, the road edge features and building outline features are labeled and assigned to obtain labeled road edge features and labeled building outline features.

[0013] Based on the edge points in the marked road edge features that satisfy the first continuity condition and are distributed along the road direction, and the contour points in the marked building contour features that satisfy the second continuity condition and are distributed along the periphery of the building, a set of boundary candidate line segments is formed.

[0014] Identify the candidate line segment groups that meet the conditions in the set of candidate boundary line segments to determine the physical boundary range of the surrounding business district and the coordinates of multiple entrances into the physical boundary range;

[0015] Based on the marked building outline features, the shops within the physical boundary are identified, and the business type of the shops within the range is matched from the preset business type classification rules. The business type includes catering, retail and service.

[0016] The number of shops and their location coordinates for each business type are integrated to form business type distribution information;

[0017] The peak-hour traffic flow, peak-hour pedestrian flow, and peak-hour travel time of the target road segment whose distance from the physical boundary range is less than a preset range threshold are associated with the entrance location coordinates to calculate the traffic accessibility parameters. The traffic accessibility parameters include the average arrival time and the number of route selections corresponding to each entrance location coordinate.

[0018] Optionally, based on the edge points in the identified road edge features that satisfy the first continuity condition and are distributed along the road direction, and the contour points in the identified building contour features that satisfy the second continuity condition and are distributed along the building perimeter, a set of candidate boundary line segments is formed, including:

[0019] The edge points in the marked road edge features that satisfy the first continuity condition and are distributed along the road direction are aggregated to obtain a road edge point set. Each edge point in the road edge point set is vector-combined with its next adjacent edge point to obtain the extension direction vector of each edge point in the road edge point set.

[0020] Two adjacent edge points in the road edge point set whose angle between the extension direction vector of the preceding adjacent edge point and the extension direction vector of the following adjacent edge point is less than a preset direction threshold are combined into a road direction group. The adjacent edge points in each road direction group are connected to obtain the road edge line segment.

[0021] Aggregate the outline points in the marked building outline features that satisfy the second continuity condition and are distributed along the outer perimeter of the building to obtain a building outline point set, and calculate the curvature value of each outline point in the building outline point set.

[0022] Two adjacent contour points whose curvature values ​​in the building contour point set and the curvature values ​​of their next adjacent contour points are both less than a preset curvature threshold are combined into an outer contour group. The adjacent contour points in each outer contour group are connected to obtain the building contour line segment.

[0023] Aggregate all road edge segments and all building outline segments to form a set of candidate boundary segments.

[0024] Optionally, identifying a group of candidate line segments that meet the criteria from the set of candidate boundary line segments to determine the physical boundary of the surrounding business district and the coordinates of multiple entrances into the physical boundary, including:

[0025] Calculate the coordinate distance between the endpoint coordinates and the starting coordinates of any two boundary candidate line segments in the boundary candidate line segment set, and mark the two boundary candidate line segments whose coordinate distance is less than a preset distance threshold as adjacent boundary candidate line segment groups;

[0026] Calculate the included angle between two candidate boundary segments in each adjacent candidate boundary segment group, and mark the adjacent candidate boundary segment groups with included angle values ​​less than a preset angle threshold and segment type identifiers of road edge type as qualified candidate segment groups;

[0027] Connect the candidate line segments at the boundary between groups that meet the conditions, where the endpoint coordinates of the line segments are consistent or the deviation of the endpoint coordinates is less than a preset deviation value, to obtain a closed region. The closed region is then used as the physical boundary of the surrounding business district.

[0028] Identify candidate target boundary segments within the physical boundary range that have passable connection points with the edge segments of the main roads or sidewalks of the target city. Use the midpoint coordinates of each candidate target boundary segment as the entrance coordinates for entering the physical boundary range. The criteria for determining a passable connection point are that the straight-line distance between the endpoint of the candidate boundary segment and the endpoint of the edge segment of the main road or sidewalk is less than a preset connection threshold, and the angle between the extension directions is less than a preset direction threshold.

[0029] Optionally, preliminary candidate areas within the physical boundary are determined. Based on the traffic accessibility parameters, a synergistic potential analysis is performed on the preliminary candidate areas and the business type distribution information to obtain synergistic potential parameters, including:

[0030] The physical boundary range is divided into multiple grid cells. The average distance between the center point of each grid cell and the coordinates of all entrance locations is calculated. Combining the actual traffic scenario and the average arrival time corresponding to each entrance location coordinate, the comprehensive accessibility score of each grid cell is calculated. Grid cells with a comprehensive accessibility score greater than a preset accessibility threshold are marked as preliminary candidate areas.

[0031] From the business type distribution information, select catering shops, retail shops and service shops that are in operation, and calculate the first average distance between each of the preliminary candidate areas and the catering shops, the second average distance between each of the retail shops and the service shops.

[0032] Based on the number of path selections corresponding to the target entrance location coordinates closest to each preliminary candidate area in the traffic accessibility parameters, and combined with the average distance, the first average distance, the second average distance, and the third average distance, the collaborative matching degree of each preliminary candidate area is calculated.

[0033] The collaborative matching degree and comprehensive accessibility score of each preliminary candidate region are weighted and summed to obtain the collaborative potential parameter corresponding to each preliminary candidate region.

[0034] Optionally, based on the traffic accessibility parameter and the synergy potential parameter, candidate store locations are selected from the preliminary candidate areas, including:

[0035] The average arrival time and number of route selections corresponding to the coordinates of the nearest target entrance location in each preliminary candidate area are used as the corresponding associated traffic parameters.

[0036] Based on preset distance and preset flow ranges, the associated traffic parameters of each preliminary candidate area are classified into levels to obtain the traffic parameter levels of each preliminary candidate area.

[0037] Based on the traffic parameter levels, traffic parameter weights are set for the associated traffic parameters of each preliminary candidate region, and collaborative potential weights are set for the collaborative potential parameters of each preliminary candidate region. Based on the traffic parameter weights and the collaborative potential weights, the comprehensive score of each preliminary candidate region is calculated.

[0038] The comprehensive scores of all preliminary candidate areas are sorted in descending order to obtain a comprehensive score ranking. The preliminary candidate areas with the highest number of candidates in the comprehensive score ranking are marked as candidate store areas.

[0039] Optionally, the candidate store locations are evaluated based on surrounding dynamic factors to determine the final store location, including:

[0040] From the urban planning information of the surrounding dynamic factors, select planning impact information whose spatial distance from the candidate store area is less than the preset planning impact range threshold. The planning impact information includes transportation planning and commercial supporting planning.

[0041] Based on the fluctuation data of shop rents in the surrounding dynamic factors, the rental cycle change rate of each candidate store location is calculated, and the rental cycle change rate is compared with a preset rent threshold to determine the rental stability level corresponding to each candidate store location.

[0042] Based on the surrounding dynamic factors, the population growth rate of the surrounding area is calculated for each candidate store location. The population growth rate is then compared with a preset population threshold to determine the population attractiveness level of each candidate store location.

[0043] The planning impact information is quantified into a planning impact score according to a preset impact level standard. The rental stability level is quantified into a rental impact score according to a preset first level score standard. The population attraction level is quantified into a population impact score according to a preset second level score standard. A first weight is set for the planning impact score, a second weight is set for the rental impact score, and a third weight is set for the population impact score. The evaluation score of each candidate store location is calculated, and the candidate store location with the highest evaluation score is selected as the final store location.

[0044] Secondly, this application provides a dynamic decision-making system for store site selection based on geographic information, including:

[0045] The acquisition module is used to acquire traffic data along the main roads and subway lines of the target city, as well as geographic image information of the surrounding business districts of the area to be analyzed in the target city.

[0046] The processing module is used to process the geographic image information to obtain the physical boundary range and business distribution information of the surrounding business district, and calculate the traffic accessibility parameters by combining the traffic data.

[0047] The analysis module is used to determine preliminary candidate areas within the physical boundary range, and to perform a synergy potential analysis on the preliminary candidate areas and the business distribution information based on the traffic accessibility parameters to obtain synergy potential parameters.

[0048] The selection module is used to select candidate store locations from the preliminary candidate areas based on the traffic accessibility parameters and the collaborative potential parameters.

[0049] The evaluation module is used to assess the location of the candidate store area based on surrounding dynamic factors in order to determine the final store location.

[0050] Thirdly, this application provides an electronic device, comprising:

[0051] Memory, used to store computer programs;

[0052] A processor is configured to execute the computer program to implement the steps of a dynamic decision-making method for store location selection based on geographic information as described in the first aspect above.

[0053] Fourthly, this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, can implement the steps of the dynamic decision-making method for store site selection based on geographic information as described in the first aspect above.

[0054] This application provides a dynamic decision-making method for store site selection based on geographic information. It acquires traffic data along main roads and subway lines in a target city, as well as geographic image information of the surrounding business districts of the area to be analyzed within the target city. The geographic image information is processed to obtain the physical boundary range and business type distribution information of the surrounding business districts. Combined with the traffic data, a traffic accessibility parameter is calculated. Preliminary candidate areas within the physical boundary range are determined. Based on the traffic accessibility parameter, a synergistic potential analysis is performed on the preliminary candidate areas and the business type distribution information to obtain a synergistic potential parameter. Based on the traffic accessibility parameter and the synergistic potential parameter, candidate store locations are selected from the preliminary candidate areas. The candidate store locations are evaluated based on surrounding dynamic factors to determine the final store site selection. By acquiring traffic data along main roads and subway lines in the target city, as well as geographic image information of the surrounding business districts of the area to be analyzed, comprehensive collection of multi-dimensional raw data on the site selection environment is achieved. This approach addresses the challenge of traditional methods in dynamically capturing changes in the actual form of commercial districts, improving the accuracy and timeliness of spatial boundary identification. It enables fine-grained characterization of the internal commercial structure of commercial districts, enhances the quantitative assessment of actual traffic experience, identifies preliminary candidate areas within physical boundaries, and conducts synergistic potential analysis by combining traffic accessibility parameters and business distribution information. This measures the complementarity and agglomeration effect of candidate areas and the surrounding commercial ecosystem in terms of spatial layout, overcoming the limitations of relying solely on independent indicators for evaluation. Ultimately, it improves the scientific rigor and environmental adaptability of site selection decisions.

[0055] Furthermore, by utilizing the outline features of marked buildings to identify shops within the physical boundary area, and determining the business type of each shop according to preset business type classification rules, the system integrates and forms business type distribution information covering catering, retail and service categories. It also correlates the peak-hour traffic flow, pedestrian flow and travel time of target road sections near the physical boundary area in traffic data with the coordinates of each entrance location to calculate traffic accessibility parameters that characterize accessibility efficiency. This solves the problems of lagging business district form recognition and blind spots in traffic accessibility assessment caused by the reliance on periodically updated point of interest data and fixed sensors in existing solutions. Attached Figure Description

[0056] To more clearly illustrate the technical solutions of the embodiments of this application 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 this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0057] Figure 1 A flowchart illustrating a dynamic decision-making method for store site selection based on geographic information, provided in an embodiment of this application;

[0058] Figure 2 A schematic diagram illustrating a specific implementation of a dynamic decision-making method for store site selection based on geographic information, provided in this application embodiment;

[0059] Figure 3 This is a schematic diagram of the structure of a dynamic decision-making system for store location selection based on geographic information, provided in an embodiment of this application. Detailed Implementation

[0060] Addressing the practical need for dynamically balancing accessibility and the synergistic effects of the surrounding commercial ecosystem when selecting store locations in core urban areas or along transportation hubs, this application aims to overcome the bottlenecks of existing mainstream solutions, such as lagging business district morphology recognition, spatiotemporal blind spots in traffic assessment, and insufficient modeling of spatial coupling relationships, which are caused by reliance on fixed sensor networks and periodically updated point-of-interest data. By introducing high-resolution geographic image information and combining it with traffic data, this application enhances the accurate depiction of accessibility efficiency at the entrance level, realizing a shift from static, coarse-grained experience-based judgment to dynamic, fine-grained intelligent decision-making, thereby improving the adaptability and commercial suitability of site selection results in rapidly changing urban environments.

[0061] To enable those skilled in the art to better understand the present application, the present application will be further described in detail below with reference to the accompanying drawings and specific embodiments. Obviously, the described embodiments are merely some embodiments of the present application, and not all embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0062] The core of this application is to provide a dynamic decision-making method for store site selection based on geographic information. A flowchart of one specific implementation method is shown below. Figure 1 As shown, the method includes:

[0063] Step 101: Obtain traffic data along the main roads and subway lines of the target city, as well as geographic image information of the surrounding business districts of the area to be analyzed in the target city.

[0064] In this step, the target city refers to the city where the store location decision-making work will be carried out; the main road refers to the road in the target city that carries the main traffic flow and connects important areas of the city; the subway line refers to the area in the target city through which the subway line passes; traffic data refers to data reflecting the traffic conditions of the main roads and subway lines in the target city; the area to be analyzed refers to the specific area in the target city where it is planned to screen surrounding business districts for store location selection; the surrounding business district refers to the commercial area around the area to be analyzed that has a cluster of shops and can meet certain consumer needs; and geographic image information refers to image-based information that can reflect the geographic characteristics of the surrounding business district.

[0065] In this embodiment of the application, traffic data along the main roads and subway lines of the target city are acquired through data acquisition methods, and geographic image information of the surrounding business districts of the area to be analyzed in the target city is also acquired.

[0066] Step 102: Process the geographic image information to obtain the physical boundary range and business distribution information of the surrounding business district, and calculate the traffic accessibility parameters by combining the traffic data.

[0067] In this step, the physical boundary range refers to the spatial boundary of the surrounding business district determined by processing the geographic image information of the surrounding business district; the business type distribution information refers to the data reflecting the distribution of different types of shops in the surrounding business district; and the traffic accessibility parameter refers to the parameter calculated by combining traffic data, reflecting the ease of access to each entrance of the surrounding business district.

[0068] Step 103: Determine the preliminary candidate areas within the physical boundary range, and perform a collaborative potential analysis on the preliminary candidate areas and the business distribution information based on the traffic accessibility parameters to obtain collaborative potential parameters.

[0069] In this step, the preliminary candidate area refers to the area with certain store location potential selected through comprehensive accessibility scoring within the physical boundary of the surrounding business district; the synergy potential parameter refers to the parameter reflecting the store location potential of the preliminary candidate area, obtained by analyzing the degree of synergy between the preliminary candidate area and the business format distribution information, combined with the comprehensive accessibility score.

[0070] Step 104: Based on the traffic accessibility parameter and the synergy potential parameter, select candidate store locations from the preliminary candidate areas.

[0071] In this step, candidate store location refers to the area selected from the initial candidate areas based on traffic accessibility and synergy potential parameters, which is considered to have greater value for store location selection.

[0072] Step 105: Evaluate the location of the candidate store area based on surrounding dynamic factors to determine the final store location.

[0073] In this step, surrounding dynamic factors refer to the dynamic changes that affect the long-term operation of the candidate store location; final store location refers to the area most suitable for opening a store after evaluating the candidate store locations.

[0074] The embodiments of this application solve the problems of existing technologies, such as reliance on static data for site selection, blurred business district boundaries, insufficient coordination between business formats and transportation, and lack of consideration of dynamic factors, thereby improving the accuracy and long-term adaptability of store site selection.

[0075] This application provides a specific embodiment, such as Figure 2 As shown, step 102 involves processing the geographic image information to obtain the physical boundary range and business distribution information of the surrounding business district. Combined with the traffic data, the accessibility parameters are calculated. Specifically, this includes the following steps:

[0076] Step 201: Extract features from the geographic image information to obtain road edge features and building outline features, and assign labels to the road edge features and building outline features to obtain labeled road edge features and labeled building outline features.

[0077] In this step, road edge features refer to feature data extracted from geographic image information that reflects the contour of the road edge; building contour features refer to feature data extracted from geographic image information that reflects the outer contour of the building; tagged road edge features refer to feature data formed after assigning a unique identifier to the road edge features, and the identifier is used to distinguish the road edges of different road segments; tagged building contour features refer to feature data formed after assigning a unique identifier to the building contour features, and the identifier is used to distinguish the contours of different buildings.

[0078] In this embodiment, feature extraction is performed on the acquired geographic image information to separate road edge features reflecting the road edge contour and building contour features reflecting the building's outer contour. The extracted road edge features and building contour features are then assigned identifiers. A unique identifier is assigned to each segment of road edge features and a unique identifier is assigned to each building contour feature. Through this identifier assignment process, road edge features with unique identifiers and building contour features with unique identifiers are obtained. These identifiable features can be used for subsequent precise selection of edge points and contour points in specific areas.

[0079] Step 202: Based on the edge points in the marked road edge features that satisfy the first continuity condition and are distributed along the road direction, and the contour points in the marked building contour features that satisfy the second continuity condition and are distributed along the periphery of the building, a set of candidate boundary line segments is formed.

[0080] In this step, the first continuity condition refers to the preset condition for judging whether the edge points in the road edge features are continuous; the edge points distributed along the road direction refer to the edge points in the road edge features that are arranged along the direction of road extension; the second continuity condition refers to the preset condition for judging whether the contour points in the building outline features are continuous; the contour points distributed along the building perimeter refer to the contour points in the building outline features that are arranged around the outer boundary of the building; the boundary candidate line segment set refers to the set of line segments formed by connecting the edge points and contour points that meet the conditions.

[0081] In this embodiment, edge points distributed along the road direction that meet the first continuity condition and are arranged in the direction of road extension are selected from the marked road edge features. At the same time, contour points distributed around the outer perimeter of the building that meet the second continuity condition and are arranged around the outer boundary of the building are selected from the marked building contour features. The selected edge points distributed along the road direction are grouped by their labels, and adjacent edge points in each group are connected in sequence to form line segments. These line segments are labeled with the line segment type as road edge type. The selected contour points distributed around the outer perimeter of the building are grouped by their labels, and adjacent contour points in each group are connected in sequence to form line segments. These line segments are labeled with the line segment type as building contour type. All the labeled line segments are aggregated to form a set of candidate boundary line segments containing the coordinates of the start point, the coordinates of the end point, and the line segment type label.

[0082] Step 203: Identify the candidate line segment groups that meet the conditions in the set of candidate boundary line segments to determine the physical boundary range of the surrounding business district and the coordinates of multiple entrances into the physical boundary range.

[0083] In this step, the qualified candidate line segment group refers to the combination of adjacent line segments in the boundary candidate line segment set that meets the two conditions of the line segment included angle value being less than the preset angle threshold and the line segment type identifier being the road edge type; the entrance location coordinates refer to the coordinate data used to identify the location of the passage into the physical boundary range of the surrounding business district, which is usually the coordinate of the midpoint of the line segment with the function of passage connection.

[0084] In this embodiment, all line segments in the candidate boundary line segment set are traversed first. The coordinate distance between the endpoint coordinates and the starting coordinates of any two line segments is calculated. Two line segments with a coordinate distance less than a preset distance threshold are marked as adjacent candidate boundary line segment groups. Then, the included angle value between two line segments in each adjacent candidate boundary line segment group is calculated. Adjacent line segment groups with included angle values ​​less than a preset angle threshold and both line segments having the line segment type identifier of road edge are selected and marked as qualified candidate line segment groups. Next, the qualified candidate line segment groups are connected, and the previous qualified candidate line segment is connected to the next qualified candidate line segment. The endpoint coordinates of the line segments in a group are matched with the starting coordinates of the line segments in the next eligible candidate line segment group. All eligible candidate line segment groups are connected sequentially until the endpoint coordinates of the last line segment coincide with the starting coordinates of the first line segment, forming a closed area. This closed area is defined as the physical boundary of the surrounding business district. Finally, the outline line segments of the physical boundary are identified, and the line segments that have passable connection points with the edge line segments of the main road or the edge line segments of the sidewalk in the target city are identified. The midpoint coordinates of these line segments with connection functions are recorded as the entrance position coordinates for entering the physical boundary, resulting in multiple entrance position coordinates.

[0085] Step 204: Based on the marked building outline features, identify the shops within the physical boundary range, and match the business type of the shops within the range from the preset business type classification rules. The business type includes catering, retail and service.

[0086] In this step, "shops within the scope" refers to locations within the physical boundaries of the surrounding business district that have business functions; "preset business type classification rules" refers to pre-defined standards used to classify the types of shop operations; "business type" refers to the categories divided according to the business content of the shop; "food and beverage" refers to the category within the business type that primarily provides food and beverage services; "retail" refers to the category within the business type that primarily sells goods; and "service" refers to the category within the business type that primarily provides services.

[0087] In this embodiment, firstly, based on the outline features of marked buildings, all shops within the physical boundary that have business functions are identified; then, pre-set business type classification rules are retrieved; finally, the business content of each shop within the range is matched with the pre-set business type classification rules. If the business content of a shop meets the definition of catering, it is classified as catering; if it meets the definition of retail, it is classified as retail; if it meets the definition of service, it is classified as service, thereby determining the business type of each shop within the range.

[0088] Step 205: Integrate the number of shops and their location coordinates for each business type to form business type distribution information.

[0089] In this step, the number of shops refers to the specific number of shops within the same business type; the shop location coordinates refer to the coordinate data used to identify the spatial location of each shop within the physical boundary, usually the coordinates of the center point of the shop's entrance / exit.

[0090] In this embodiment, the shops within the scope are first grouped according to their business type, and the number of shops in each group (catering, retail, and service) is counted to obtain the number of catering shops, retail shops, and service shops. Then, the coordinates of the center point of the entrance / exit of each shop within the scope are obtained through the coordinate positioning function of the geographic image information. These coordinates are recorded as shop location coordinates and categorized by business type. Finally, the number of shops corresponding to the same business type and the shop location coordinates of all shops under that business type are integrated to form business type distribution information.

[0091] Step 206: Associate the peak-hour traffic flow, peak-hour pedestrian flow, and peak-hour travel time of the target road segment whose distance from the physical boundary range is less than a preset range threshold with the entrance location coordinates in the traffic data to calculate the traffic accessibility parameters. The traffic accessibility parameters include the average arrival time and the number of route selections corresponding to each entrance location coordinate.

[0092] In this step, the preset range threshold refers to a pre-set distance standard used to filter traffic data related to the physical boundary range; the target road segment refers to the road segment in the traffic data whose distance from the physical boundary range is less than the preset range threshold; peak hour traffic flow refers to the number of vehicles passing through the target road segment per unit time during peak traffic hours; peak hour pedestrian flow refers to the number of pedestrians passing through the target road segment per unit time during peak traffic hours; peak hour travel time refers to the average time required for vehicles or pedestrians to travel / walk from the starting point of the road segment to the ending point of the road segment during peak traffic hours; average arrival time refers to the average time required to reach a certain entrance location coordinate from any location on the target road segment; and the number of route selections refers to the total number of different routes available when reaching a certain entrance location coordinate from any location on the target road segment.

[0093] This application's embodiments address the problems of blurred business district boundaries, disconnect between business types and traffic data, and fragmented data processing in the prior art. It provides accurate and relevant basic data for the initial screening of candidate areas and the analysis of synergistic potential for subsequent store site selection, thereby improving the reliability and relevance of site selection data.

[0094] This application provides a specific embodiment. Step 202 involves forming a set of candidate boundary line segments based on the edge points in the identified road edge features that satisfy the first continuity condition and are distributed along the road direction, and the contour points in the identified building contour features that satisfy the second continuity condition and are distributed along the perimeter of the building. This specifically includes the following steps:

[0095] Step 211: Aggregate the edge points in the marked road edge features that satisfy the first continuity condition and are distributed along the road direction to obtain a road edge point set. Combine each edge point in the road edge point set with its next adjacent edge point to obtain the extension direction vector of each edge point in the road edge point set.

[0096] In this step, the road edge point set refers to the set of points formed by aggregating the edge points that meet the first continuity condition and are distributed along the road direction from the identified road edge features; the next adjacent edge point refers to the edge point that is located after and adjacent to a certain edge point in the road edge point set after it is sorted according to the road direction; vector combination refers to the operation of calculating the coordinates of a certain edge point in the road edge point set with the next adjacent edge point to form a direction vector; the extended direction vector refers to the vector data obtained by vector combination, which reflects the extension direction of the road edge point along the road direction, and is composed of the difference between the coordinates of the two points.

[0097] In this embodiment, firstly, all edge points satisfying the first continuity condition and distributed along the road direction are extracted from the identified road edge features. These edge points are then grouped according to their respective identified road edge features. The edge points within each group are sorted according to the road extension direction and then aggregated to form a set of road edge points for each group. Next, for each edge point in each set of road edge points, its corresponding next-neighbor edge point is determined. Then, a vector combination is performed between each edge point and its corresponding next-neighbor edge point. Taking the edge point as the starting point and the next-neighbor edge point as the ending point, the extension direction vector is obtained through coordinate operations. ,in , Point of edge coordinates , refer to The next adjacent edge point The coordinates are used to obtain the extension direction vectors corresponding to all edge points in each road edge point set.

[0098] For example, extract 20 edge points that meet the first continuity condition and follow the direction of Road C from the road edge feature DL001 of the B business district. After sorting them according to the east-west direction of Road C, aggregate them to form the road edge point set corresponding to DL001, containing 20 points from P1 to P20. Determine that the next adjacent edge point of P1 is P2, and the next adjacent edge points of P2 are P3 to P19, and the next adjacent edge points of P2 are P20. Combine the vectors of P1 and P2. If the coordinates of P1 are (100, 200) and the coordinates of P2 are (105, 200), then the extension direction vector is... =(105-100,200-200)=(5,0), and similarly calculate... to There are a total of 19 extended direction vectors.

[0099] Step 212: Combine two adjacent edge points in the road edge point set whose angle between the extension direction vector of the preceding adjacent edge point and the extension direction vector of the following adjacent edge point is less than a preset direction threshold into a road direction group. Connect the adjacent edge points in each road direction group to obtain the road edge line segment.

[0100] In this step, the preset direction threshold refers to the angle standard set in advance to determine whether adjacent extension direction vectors are consistent; the road direction group refers to the set of two adjacent edge points in the road edge point set where the angle between their extension direction vectors is less than the preset direction threshold; and the road edge segment refers to the line segment formed by connecting two adjacent edge points in the road direction group.

[0101] In this embodiment, a preset direction threshold is first obtained; then, the extended direction vectors of each road edge point set are traversed, and the angle between two adjacent extended direction vectors is calculated. , where the dot product of the pointers, , The modulus of the direction vector is determined; then, two adjacent edge points corresponding to adjacent extension direction vectors with an angle less than a preset direction threshold are selected, and these adjacent edge points are combined into road direction groups; finally, the two adjacent edge points in each road direction group are connected in the order of start point and end point to form line segments, and these line segments are labeled with the line segment type as road edge type to obtain road edge line segments.

[0102] Step 213: Aggregate the outline points in the marked building outline features that satisfy the second continuity condition and are distributed along the outer perimeter of the building to obtain the building outline point set, and calculate the curvature value of each outline point in the building outline point set.

[0103] In this step, the building outline point set refers to the set of points formed by aggregating the outline points that meet the second continuity condition and are distributed along the outer perimeter of the building from the marked building outline features; the curvature value refers to the numerical value that reflects the degree of curvature of the curve at the building outline point, which is calculated by the curve segment formed by the outline point and its adjacent outline points; the adjacent outline point refers to the outline point that is located after and adjacent to a certain outline point in the building outline point set after the outline point is sorted according to the outer perimeter direction of the building.

[0104] In this embodiment, firstly, all contour points that satisfy the second continuity condition and are distributed along the perimeter of the building are extracted from the identifiable building contour features. These contour points are then grouped according to their respective identifiable building contour features. The contour points within each group are sorted clockwise or counterclockwise according to the perimeter of the building and then aggregated to form a set of building contour points for each group. Next, for each contour point in each set of building contour points, its preceding and following adjacent contour points are determined. Finally, the curvature value is calculated based on the three-point group formed by the contour point and its preceding and following adjacent contour points. ,in , Pointing to adjacent contour points coordinates , Refers to the current contour point coordinates , Points adjacent to the outline coordinates The ^ symbol represents the exponentiation operator, indicating that the number after ^ is the exponent of the expression before ^, ultimately yielding the curvature values ​​corresponding to all contour points in each building contour point set.

[0105] For example, extract 16 outline points that meet the second continuity condition and are distributed along the periphery of shopping mall E from the building outline feature JL001 of the B business district. After sorting them in a clockwise direction, aggregate them to form the building outline point set corresponding to JL001, containing 16 points from Q1 to Q16. Determine that the previous adjacent outline point of Q2 is Q1 and the next adjacent outline point is Q3, and the previous adjacent outline point of Q3 is Q2 and the next adjacent outline point is Q4. If the coordinates of Q1 are (200, 150), the coordinates of Q2 are (200, 160), and the coordinates of Q3 are (210, 160), then the curvature value of Q2 is... Similarly, the curvature values ​​of 14 contour points from Q2 to Q15 are calculated. Q1 and Q16 are the first and last points and are not calculated for the time being.

[0106] Step 214: Combine two adjacent contour points whose curvature values ​​in the building contour point set and the curvature values ​​of their next adjacent contour points are both less than a preset curvature threshold into an outer contour group. Connect the adjacent contour points in each outer contour group to obtain the building contour line segment.

[0107] In this step, the preset curvature threshold refers to the pre-set curvature standard used to determine whether adjacent contour points belong to the same smooth contour segment; the outer contour group refers to the set of two adjacent contour points in the building contour point set, whose curvature values ​​and the curvature values ​​of the next adjacent contour points are both less than the preset curvature threshold; the building contour line segment refers to the line segment formed by connecting two adjacent contour points in the outer contour group, including the coordinates of the line segment's starting point, the coordinates of its ending point, and the line segment type identifier.

[0108] First, obtain the preset curvature threshold; then, traverse the contour points in each building contour point set, and check whether the curvature value of the current contour point and the curvature value of its corresponding next adjacent contour point are both less than the preset curvature threshold; if both are satisfied, combine the current contour point and the next adjacent contour point into an outer contour group; finally, connect two adjacent contour points in each outer contour group in the order of start point and end point to form line segments, and label these line segments with the line segment type as the building contour type to obtain the building contour line segments.

[0109] Step 215: Aggregate all road edge segments and all building outline segments to form a set of candidate boundary segments.

[0110] First, collect all road edge segments, including road edge segments corresponding to different road edge features with different identifiers. Then, collect all building outline segments, including building outline segments corresponding to different building outline features with different identifiers. Aggregate these road edge segments and building outline segments, and check whether the start coordinates, end coordinates, and segment type identifier of each segment are complete to ensure that there are no duplicate segments. Finally, integrate all the aggregated segments into a set, which is the boundary candidate segment set.

[0111] This application's embodiments solve the problems of lack of standards in edge point or contour point selection and disordered line segment formation in the prior art. It realizes the standardized generation of boundary candidate line segments, ensuring that the line segments fit the road direction and the outer perimeter of buildings. It provides accurate and orderly basic line segment data for subsequent identification of candidate line segment groups that meet the conditions and determination of physical boundary range, thereby improving the accuracy and standardization of commercial district spatial data processing.

[0112] This application provides a specific embodiment, step 203, identifying the candidate line segment group that meets the conditions in the boundary candidate line segment set, in order to determine the physical boundary range of the surrounding business district and the coordinates of multiple entrance locations into the physical boundary range, specifically including the following steps:

[0113] Step 221: Calculate the coordinate distance between the endpoint coordinates and the starting coordinates of any two boundary candidate line segments in the boundary candidate line segment set, and mark the two boundary candidate line segments whose coordinate distance is less than a preset distance threshold as adjacent boundary candidate line segment groups.

[0114] In this step, the line segment endpoint coordinates refer to the coordinates of the end position of each line segment in the boundary candidate line segment set; the line segment start point coordinates refer to the coordinates of the start position of each line segment in the boundary candidate line segment set; the coordinate distance refers to the spatial distance between the line segment endpoint coordinates and the line segment start point coordinates of two line segments; the preset distance threshold refers to a pre-set distance standard used to determine whether two line segments are adjacent; the adjacent boundary candidate line segment group refers to the set of two line segments in the boundary candidate line segment set whose coordinate distance is less than the preset distance threshold.

[0115] In this embodiment, all candidate boundary segments in the candidate boundary segment set are traversed first. For any two different candidate boundary segments, two sets of coordinate combinations are determined: the endpoint coordinates of one segment and the starting coordinates of the other segment, and the starting coordinates of one segment and the endpoint coordinates of the other segment. Then, the coordinate distance for each set of coordinate combinations is calculated. Here, (x before, y before) is the starting or ending coordinate of one line segment, and (x after, y after) is the ending or starting coordinate of the other line segment. Then, the calculated coordinate distance is compared with a preset distance threshold. If any set of coordinate distances is less than the preset distance threshold, these two boundary candidate line segments are marked as adjacent boundary candidate line segment groups. The above operation is repeated until all two different boundary candidate line segments are traversed to obtain all adjacent boundary candidate line segment groups.

[0116] Step 222: Calculate the included angle between two candidate boundary segments in each adjacent candidate boundary segment group, and mark the adjacent candidate boundary segment groups with included angle values ​​less than a preset angle threshold and whose segment type identifiers are all road edge type as qualified candidate segment groups.

[0117] In this step, the included angle value refers to the angle formed between two candidate boundary line segments in an adjacent candidate boundary line segment group; the preset angle threshold refers to a pre-set angle standard used to determine whether the directions of the two line segments are consistent; the line segment type identifier refers to the mark used to distinguish the types of candidate boundary line segments; the road edge type refers to the type of road edge feature in the line segment type identifier; the qualified candidate line segment group refers to the line segment group in an adjacent candidate boundary line segment group where the included angle value is less than the preset angle threshold and the line segment type identifiers of the two line segments are both road edge type.

[0118] In this embodiment, firstly, for each pair of candidate boundary line segments in each adjacent candidate boundary line segment group, their line segment vectors are extracted; then, the angle between the two line segment vectors is calculated. , where the dot product of the pointers, , These are the magnitudes of the two line segment vectors, respectively; then, the line segment type identifiers of the two line segments in the adjacent boundary candidate line segment group are retrieved, and adjacent boundary candidate line segment groups with included angle values ​​less than a preset angle threshold and both line segment type identifiers of the two line segments are road edge type are selected; the selected line segment groups are marked as candidate line segment groups that meet the conditions, and all candidate line segment groups that meet the conditions are obtained.

[0119] For example, among 22 candidate boundary segments, 15 groups are selected that contain two road edge segments, such as those containing... and Calculate the dot product of the groups. , included angle value Both line segments are road edge types, and they are marked as candidate line segment groups that meet the criteria; then, line segments containing road edges are selected. and The group was used to calculate the included angle value. All of them are road edge types, and are also marked as candidate line segment groups that meet the conditions; a total of 12 candidate line segment groups that meet the conditions were finally obtained.

[0120] Step 223: Connect the candidate line segments at the inter-group boundary in each group that meet the conditions, where the endpoint coordinates are the same or the endpoint coordinate deviation is less than the preset deviation value, to obtain a closed region. The closed region is then used as the physical boundary of the surrounding business district.

[0121] In this step, the endpoint coordinate deviation refers to the distance between the endpoint coordinates of the previous group of line segments and the starting coordinates of the next group of line segments in different groups of eligible candidate line segments; the preset deviation value refers to the pre-set coordinate deviation standard used to determine whether two groups of line segments can be connected; the inter-group boundary candidate line segments refer to the boundary candidate line segments in different groups of eligible candidate line segments; the closed region refers to the closed spatial region formed by connecting the inter-group boundary candidate line segments in sequence until the endpoint coordinates of the last line segment coincide with the starting coordinates of the first line segment.

[0122] In this embodiment, all candidate line segment groups that meet the conditions are first sorted, the last line segment of the first candidate line segment group that meets the conditions is selected, and its endpoint coordinates are recorded; then the first line segment of the second candidate line segment group that meets the conditions is selected, its starting point coordinates are recorded, and the deviation of the endpoint coordinates between the two groups is calculated. Where (x_endpoint before, y_endpoint before) are the coordinates of the endpoint of the previous group of line segments, and (x_startpoint after, y_startpoint after) are the coordinates of the starting point of the next group of line segments. The deviation of the endpoint coordinates is compared with a preset deviation value. If the deviation of the endpoint coordinates is less than the preset deviation value, the two line segments are connected. The candidate line segments of the inter-group boundary of the subsequent candidate line segments that meet the conditions are connected in this way until the endpoint coordinates of the last line segment of the last candidate line segment group that meets the conditions coincide with or the deviation of the starting point coordinates of the first line segment of the first candidate line segment group that meets the conditions is less than the preset deviation value. At this time, all the connected line segments form a closed space, and this closed space is used as the physical boundary of the surrounding business district.

[0123] Step 224: Identify candidate target boundary segments within the physical boundary range that have passable connection points with the edge segments of the main road or sidewalk of the target city. Use the midpoint coordinates of each candidate target boundary segment as the entrance coordinates for entering the physical boundary range. The criteria for determining a passable connection point are that the straight-line distance between the endpoint of the candidate boundary segment and the endpoint of the edge segment of the main road or sidewalk is less than a preset connection threshold, and the included angle of the extension direction is less than a preset direction threshold.

[0124] In this step, the main road edge segment refers to the edge outline segment of the main road in the target city; the sidewalk edge segment refers to the edge outline segment of the sidewalk in the target city; the passable connection point refers to the connection position between the candidate boundary segment of the physical boundary range and the main road edge segment or the sidewalk edge segment, which allows pedestrians and vehicles to pass; the target boundary candidate segment refers to the candidate boundary segment within the physical boundary range that has a passable connection point with the main road edge segment or the sidewalk edge segment; the midpoint coordinate refers to the midpoint coordinate between the starting point coordinate and the ending point coordinate of the target boundary candidate segment; the endpoint refers to the starting point or ending point of the boundary candidate segment, the main road edge segment, or the sidewalk edge segment; the straight-line distance refers to the spatial straight-line distance between the endpoints; the preset connection threshold refers to the pre-set distance standard used to determine whether the endpoints are close; the extension direction angle refers to the angle between the vector of the target boundary candidate segment and the vector of the main road edge segment or the sidewalk edge segment.

[0125] In this embodiment, the edge segments of the main roads and the edge segments of the sidewalks in the target city are first obtained; for each candidate boundary segment within the physical boundary range, the coordinates of its two endpoints are extracted, and the straight-line distance L between each endpoint and all endpoints of the edge segments of the main roads and the sidewalks is calculated. Where (x-end 1, y-end 1) are the endpoint coordinates of the candidate boundary line segment, and (x-end 2, y-end 2) are the endpoint coordinates of the main road edge line segment or the sidewalk edge line segment. At the same time, the angle between the extension direction of the vector of the candidate boundary line segment and the vector of the main road edge line segment or the sidewalk edge line segment is calculated. If the straight-line distance of an endpoint is less than a preset connection threshold and the angle between the extension directions is less than a preset direction threshold, it is determined that there is a passable connection between the candidate boundary line segment and the main road edge line segment or the sidewalk edge line segment, and the candidate boundary line segment is marked as the target boundary candidate line segment. Finally, the midpoint coordinates (x-middle, y-middle) of each target boundary candidate line segment are calculated as [(x-start + x-end) / 2, (y-start + y-end) / 2], where (x-start, y-start) are the starting point coordinates of the target boundary candidate line segment, and (x-end, y-end) are the ending point coordinates of the target boundary candidate line segment. These midpoint coordinates are used as the entrance position coordinates for entering the physical boundary range.

[0126] The embodiments of this application solve the problem of subsequent analysis deviations caused by boundary ambiguity in the prior art.

[0127] This application provides a specific embodiment. Step 103 involves determining preliminary candidate areas within the physical boundary range, and performing a synergistic potential analysis on the preliminary candidate areas and the business distribution information based on the traffic accessibility parameters to obtain synergistic potential parameters. This specifically includes the following steps:

[0128] Step 301: Divide the physical boundary range into multiple grid cells, calculate the average distance between the center point of each grid cell and the coordinates of all entrance locations, combine the actual traffic scenario and the average arrival time corresponding to each entrance location coordinate, calculate the comprehensive accessibility score of each grid cell, and mark the grid cells with a comprehensive accessibility score greater than the preset accessibility threshold as preliminary candidate areas.

[0129] In this step, a grid unit refers to multiple equal-area spatial units into which the physical boundary of the surrounding business district is divided; the center point refers to the geometric center coordinates of each grid unit; the average distance is the value obtained by dividing the sum of the distances from the center point of the grid unit to all entrance coordinates by the total number of entrance coordinates; the actual traffic scenario refers to the actual environmental conditions affecting pedestrians or vehicles' access to the grid unit from the entrance coordinates, such as whether there are road obstacles or pedestrian crossings around the grid unit; the comprehensive accessibility score is a quantitative value reflecting the ease of access to the grid unit, calculated by combining the average distance of the grid unit, the adjustment coefficient of the actual traffic scenario, and the average arrival time corresponding to the entrance coordinates; and the preset accessibility threshold refers to the pre-set comprehensive accessibility scoring standard used to screen grid units with store location potential.

[0130] In this embodiment, the physical boundary of the surrounding business district is first uniformly divided into multiple grid units according to a preset area; then, the center point of each grid unit is determined; next, the distance from the center point of each grid unit to the coordinates of all entrance locations is calculated, and these distances are summed and divided by the total number of entrance location coordinates to obtain the average distance of each grid unit = (sum of distances from the center point to the coordinates of each entrance location) / total number of entrance location coordinates; then, an adjustment coefficient is determined according to the actual traffic scenario, and combined with the average arrival time corresponding to the entrance location coordinates, the comprehensive accessibility score of each grid unit is calculated as (1 / average distance) × adjustment coefficient × (1 / average arrival time) × preset score coefficient; finally, the comprehensive accessibility score of each grid unit is compared with the preset accessibility threshold, and the grid units with a comprehensive accessibility score greater than the preset accessibility threshold are marked as preliminary candidate areas, thus obtaining multiple preliminary candidate areas.

[0131] Step 302: Select catering shops, retail shops and service shops that are in operation from the business distribution information, and calculate the first average distance between each of the preliminary candidate areas and the catering shops, the second average distance between each of the retail shops and the service shops.

[0132] In this step, "operating" refers to shops marked as "catering" in the business type distribution information and currently operating normally; "operating" refers to shops marked as "retail" in the business type distribution information and currently operating normally; "operating" refers to shops marked as "service" in the business type distribution information and currently operating normally; the first average distance is the sum of the distances from each preliminary candidate area to all operating "catering" shops divided by the total number of operating "catering" shops; the second average distance is the sum of the distances from each preliminary candidate area to all operating "retail" shops divided by the total number of operating "retail" shops; and the third average distance is the sum of the distances from each preliminary candidate area to all operating "service" shops divided by the total number of operating "service" shops.

[0133] In this embodiment, all operating food and beverage shops, retail shops, and service shops are first selected from the business type distribution information. Then, for each preliminary candidate area, the distance from that area to each operating food and beverage shop is calculated. These distances are summed and divided by the total number of operating food and beverage shops to obtain the first average distance of the preliminary candidate area. Similarly, the sum of the distances from the preliminary candidate area to all operating retail shops is calculated and divided by the total number of corresponding shops to obtain the second average distance. The sum of the distances from the preliminary candidate area to all operating service shops is calculated and divided by the total number of corresponding shops to obtain the third average distance. The above operations are repeated to obtain the first average distance, second average distance, and third average distance for each preliminary candidate area.

[0134] Step 303: Based on the number of path selections corresponding to the coordinates of the target entrance location closest to each preliminary candidate area in the traffic accessibility parameters, and combined with the average distance, the first average distance, the second average distance, and the third average distance, calculate the collaborative matching degree of each preliminary candidate area.

[0135] In this step, the coordinates of the nearest target entrance location refer to the coordinates of the entrance location that is closest to the center point of each preliminary candidate area; the collaborative matching degree refers to the quantitative value that reflects the degree of matching between the preliminary candidate area and the surrounding business and traffic conditions, calculated by combining the number of path selections, average distance, first average distance, second average distance and third average distance corresponding to the coordinates of the nearest target entrance location.

[0136] In this embodiment, the distance from the center point of each preliminary candidate area to the coordinates of all entrance locations is first calculated, and the coordinates of the entrance location with the smallest distance are determined as the coordinates of the nearest target entrance location for that preliminary candidate area. Then, the number of path selections corresponding to the nearest target entrance location coordinates is extracted from the traffic accessibility parameters. Then, the collaborative matching degree of each preliminary candidate area is calculated by combining the average distance, the first average distance, the second average distance, and the third average distance according to the preset weights: (number of path selections × path weight) + (1 / average distance × distance weight 1) + (1 / first average distance × distance weight 2) + (1 / second average distance × distance weight 3) + (1 / third average distance × distance weight 4), where the path weight, distance weight 1, distance weight 2, distance weight 3, and distance weight 4 are preset weight values, and the sum of each weight value is 1. The above operation is repeated to obtain the collaborative matching degree of each preliminary candidate area.

[0137] Step 304: The collaborative matching degree and the comprehensive accessibility score of each preliminary candidate region are weighted and summed to obtain the collaborative potential parameter corresponding to each preliminary candidate region.

[0138] In this embodiment, preset weights are first set for the synergy matching degree and comprehensive accessibility score of each preliminary candidate area; then, the synergy matching degree of each preliminary candidate area is multiplied by its corresponding weight, and the comprehensive accessibility score of the area is multiplied by its corresponding weight, and a weighted sum is calculated to obtain the synergy potential parameter = (synergy matching degree × synergy weight) + (comprehensive accessibility score × accessibility weight); the above operation is repeated to obtain the synergy potential parameter corresponding to each preliminary candidate area, which is used to select candidate store area locations from the preliminary candidate areas.

[0139] This application's embodiments address the shortcomings of existing technologies that only consider traffic or business type when selecting store locations, ignoring the synergistic relationship between the two and the actual impact on traffic flow. It achieves precise synergistic analysis of traffic conditions and business type distribution, providing a quantitative and realistic basis for the selection of candidate store locations, and improving the rationality of the initial candidate area screening and the scientific nature of the location decision.

[0140] This application provides a specific embodiment. Step 104, selecting candidate store locations from the preliminary candidate areas based on the traffic accessibility parameters and the collaborative potential parameters, specifically includes the following steps:

[0141] Step 401: Use the average arrival time and number of route selections corresponding to the coordinates of the nearest target entrance location for each preliminary candidate area as the corresponding associated traffic parameters.

[0142] In this step, the associated traffic parameters refer to the combined data of the average arrival time and the number of route selections corresponding to the coordinates of the nearest target entrance location for each preliminary candidate area.

[0143] In this embodiment, for each preliminary candidate area, the coordinates of the nearest target entrance are first determined; then, the average arrival time and the number of route selections corresponding to the coordinates of the nearest target entrance are extracted from the traffic accessibility parameters; the extracted average arrival time and the number of route selections are integrated as the associated traffic parameters corresponding to the preliminary candidate area, ensuring that each preliminary candidate area has a unique associated traffic parameter, providing a data basis for subsequent level classification.

[0144] Step 402: Based on the preset distance range and preset flow range, classify the associated traffic parameters of each preliminary candidate area into levels to obtain the traffic parameter levels of each preliminary candidate area.

[0145] In this step, the preset distance interval refers to a pre-set time interval used to divide the average arrival time; the preset flow interval refers to a pre-set quantity interval used to divide the number of route selections; and the traffic parameter level refers to the level of the associated traffic parameters determined by combining the preset distance interval to which the average arrival time belongs and the preset flow interval to which the number of route selections belongs.

[0146] In this embodiment, a preset distance interval and a preset traffic flow interval are first determined. The preset distance interval is divided into short distance interval, medium distance interval, and long distance interval, and the preset traffic flow interval is divided into high traffic flow interval, medium traffic flow interval, and low traffic flow interval. Then, for the associated traffic parameters of each preliminary candidate area, it is determined which preset distance interval its average arrival time belongs to, and which preset traffic flow interval its number of route selections belongs to. The traffic parameter level is determined based on the two determination results. The above operation is repeated to obtain the traffic parameter level of each preliminary candidate area.

[0147] Step 403: Based on the traffic parameter level, set traffic parameter weights for the associated traffic parameters of each preliminary candidate area, and set collaborative potential weights for the collaborative potential parameters of each preliminary candidate area. Based on the traffic parameter weights and the collaborative potential weights, calculate the comprehensive score of each preliminary candidate area.

[0148] In this step, traffic parameter weight refers to the numerical value set for associated traffic parameters according to the traffic parameter level, used to quantify the degree of influence of associated traffic parameters on the comprehensive score; collaborative potential weight refers to the numerical value set for collaborative potential parameters, used to quantify the degree of influence of collaborative potential parameters on the comprehensive score; the comprehensive score refers to the quantitative value calculated by multiplying the traffic parameter weight by the quantitative value of the associated traffic parameter, and adding the product of the collaborative potential weight by the collaborative potential parameter.

[0149] In this embodiment, firstly, the corresponding traffic parameter weights are set according to the traffic parameter level; then, the collaborative potential weights are set for the collaborative potential parameters, and the sum of the traffic parameter weights and collaborative potential weights for each preliminary candidate area is the preset total weight value; next, the associated traffic parameters are quantified; finally, the comprehensive score is calculated as (quantified value of associated traffic parameters × traffic parameter weight) + (collaborative potential parameter × collaborative potential weight); the above operations are repeated to obtain the comprehensive score for each preliminary candidate area.

[0150] Step 404: Sort all preliminary candidate areas in descending order of their comprehensive scores to obtain a comprehensive score ranking, and mark the preliminary candidate areas with the highest number of candidates in the comprehensive score ranking as candidate store areas.

[0151] In this step, the overall score ranking refers to the ranking sequence formed by arranging the overall scores of all preliminary candidate areas in descending order; the preset candidate number threshold refers to the pre-set standard used to determine how many preliminary candidate areas with high overall score rankings should be selected as candidate store locations.

[0152] In this embodiment, the comprehensive scores of all preliminary candidate areas are first arranged in descending order to form a comprehensive score ranking; then a preset candidate number threshold is determined; preliminary candidate areas with the highest rankings above the preset candidate number threshold are selected from the comprehensive score ranking; these selected preliminary candidate areas are marked as candidate store area locations, thus completing the screening from preliminary candidate areas to candidate store area locations.

[0153] This application's embodiments address the shortcomings of existing technologies where traffic parameters and collaborative potential parameters lack hierarchical correlation, and screening relies solely on a single value without priority. It achieves hierarchical weighting of traffic conditions and collaborative potential, making the screening of candidate store locations more aligned with actual site selection needs, and improving the accuracy and priority differentiation of the screening results.

[0154] This application provides a specific embodiment. Step 105 involves evaluating the location of the candidate store area based on surrounding dynamic factors to determine the final store location, specifically including the following steps:

[0155] Step 501: Select planning impact information from the urban planning information of the surrounding dynamic factors, where the spatial distance to the candidate store area is less than the preset planning impact range threshold. The planning impact information includes transportation planning and commercial supporting planning.

[0156] In this step, urban planning information refers to information reflecting the construction plans for roads, commercial facilities, etc., in the target city within a certain period of time; spatial distance refers to the straight-line distance from the center point of the candidate store area to the construction area involved in the urban planning information; the preset planning impact range threshold refers to the distance standard set in advance to determine whether the urban planning information has an impact on the candidate store area; planning impact information refers to the part of the urban planning information where the spatial distance to the candidate store area is less than the preset planning impact range threshold; transportation planning refers to the content of the planning impact information involving the construction or expansion of roads or the adjustment of public transportation routes; and commercial supporting planning refers to the content of the planning impact information involving the construction or renovation of commercial facilities such as shopping malls and supermarkets.

[0157] In this embodiment, urban planning information is first extracted from surrounding dynamic factors to identify the coordinates of the construction areas involved in the transportation planning and commercial supporting planning included therein; then, the coordinates of the center point of each candidate store location are determined, and the spatial distance from the center point to the coordinates of each construction area is calculated; the calculated spatial distance is compared with a preset planning influence range threshold, and urban planning information corresponding to construction areas whose spatial distance is less than the preset planning influence range threshold is selected; these selected urban planning information are determined as the planning influence information corresponding to each candidate store location, ensuring that the planning influence information and the candidate store location are actually related.

[0158] Step 502: Based on the shop rent fluctuation data in the surrounding dynamic factors, calculate the rent cycle change rate of each candidate store location, and compare the rent cycle change rate with the preset rent threshold to determine the rent stability level corresponding to each candidate store location.

[0159] In this step, the shop rent fluctuation data refers to the data reflecting the changes in shop rents within a certain range around the candidate store location in different periods; the rent cycle change rate refers to the change in shop rents around the candidate store location in the current period compared to the previous period, expressed as a percentage; the preset rent threshold refers to the pre-set change rate standard used to judge rent stability; and the rent stability level refers to the level of rent stability of the candidate store location determined based on the comparison between the rent cycle change rate and the preset rent threshold.

[0160] In this embodiment, shop rent fluctuation data is first extracted from surrounding dynamic factors to determine the previous and current period rents of shops surrounding each candidate store location; the rent cycle change rate for each candidate store location is calculated as (current period average rent - previous period average rent) / previous period average rent × 100%; the calculated rent cycle change rate is compared with a preset rent threshold; and the rent stability level corresponding to each candidate store location is determined based on the comparison results.

[0161] Step 503: Based on the surrounding dynamic factors of the change in the permanent resident population, calculate the surrounding population growth rate of each candidate store location, and compare the surrounding population growth rate with a preset population threshold to determine the population attractiveness level corresponding to each candidate store location.

[0162] In this step, the surrounding permanent resident population change data refers to data reflecting the changes in the number of permanent residents within a certain range around the candidate store location in different periods; the surrounding population growth rate refers to the increase in the number of permanent residents around the candidate store location in the current period compared to the previous period, expressed as a percentage; the preset population threshold refers to a pre-set growth rate standard used to judge population attractiveness; and the population attractiveness level refers to the level of the candidate store location's ability to attract population, determined based on the comparison between the surrounding population growth rate and the preset population threshold.

[0163] In this embodiment, the data on changes in the surrounding permanent resident population are first extracted from the surrounding dynamic factors to determine the number of permanent residents in the previous period and the number of permanent residents in the current period around each candidate store location; the population growth rate of the surrounding area for each candidate store location is calculated as (number of permanent residents in the current period - number of permanent residents in the previous period) / number of permanent residents in the previous period × 100%; the calculated population growth rate is compared with a preset population threshold; and the population attractiveness level corresponding to each candidate store location is determined based on the comparison results.

[0164] Step 504: Quantify the planning impact information into a planning impact score according to a preset impact level standard, quantify the rent stability level into a rent impact score according to a preset first level score standard, quantify the population attraction level into a population impact score according to a preset second level score standard, set a first weight for the planning impact score, a second weight for the rent impact score, and a third weight for the population impact score, so as to calculate the evaluation score of each candidate store location, and take the candidate store location with the highest evaluation score as the final store location.

[0165] In this step, the preset impact level standard refers to the pre-set standard used to quantify the planning impact information into a planning impact score; the planning impact score refers to the value obtained by quantifying the planning impact information according to the preset impact level standard; the preset first-level score standard refers to the pre-set standard used to quantify the rent stability level into a rent impact score; the rent impact score refers to the value obtained by quantifying the rent stability level according to the preset first-level score standard; the preset second-level score standard refers to the pre-set standard used to quantify the population attractiveness level into a population impact score; the population impact score refers to the value obtained by quantifying the population attractiveness level according to the preset second-level score standard; the first weight refers to the value set for the planning impact score; the second weight refers to the value set for the rent impact score; the third weight refers to the value set for the population impact score; the evaluation score refers to the value obtained by multiplying the planning impact score, rent impact score, and population impact score by their respective weights and then summing them; the evaluation score with the largest value refers to the evaluation score with the highest value among all candidate store location evaluation scores.

[0166] In this embodiment, the planning impact information is first quantified according to a preset impact level standard to obtain the planning impact score of each candidate store location; then, the rent stability level is quantified according to a preset first-level scoring standard to obtain the rent impact score; the population attraction level is quantified according to a preset second-level scoring standard to obtain the population impact score; a first weight is set for the planning impact score, a second weight is set for the rent impact score, and a third weight is set for the population impact score, and the sum of the three weights is a preset total value; the evaluation score of each candidate store location is calculated as (planning impact score × first weight) + (rent impact score × second weight) + (population impact score × third weight); the evaluation score with the largest value is selected from all evaluation scores, and the candidate store location corresponding to this score is determined as the final store location.

[0167] This application's embodiments address the shortcomings of existing technologies in store site selection, which ignore dynamic factors and lack quantitative and comprehensive evaluation of dynamic factors. It achieves accurate quantification and collaborative evaluation of dynamic factors, ensuring that the final store site selection has long-term adaptability and operational advantages.

[0168] Figure 3 This application provides a schematic diagram of a specific implementation of a geographic information-based dynamic decision-making system for store site selection, with reference to... Figure 3 The system may include:

[0169] The acquisition module 21 is used to acquire traffic data along the main roads and subway lines of the target city, as well as geographic image information of the surrounding business districts of the area to be analyzed in the target city.

[0170] The processing module 22 is used to process the geographic image information to obtain the physical boundary range and business distribution information of the surrounding business district, and calculate the traffic accessibility parameters by combining the traffic data.

[0171] Analysis module 23 is used to determine preliminary candidate areas within the physical boundary range, and to perform synergy potential analysis on the preliminary candidate areas and the business distribution information based on the traffic accessibility parameters to obtain synergy potential parameters.

[0172] Selection module 24 is used to select candidate store locations from the preliminary candidate areas based on the traffic accessibility parameters and the collaborative potential parameters.

[0173] Evaluation module 25 is used to evaluate the location of the candidate store area based on surrounding dynamic factors in order to determine the final store location.

[0174] This application provides an embodiment of a dynamic decision-making system for store site selection based on geographic information, which is used to implement the aforementioned dynamic decision-making method for store site selection based on geographic information. Therefore, the specific implementation of the dynamic decision-making system for store site selection based on geographic information can be found in the embodiment section of the dynamic decision-making method for store site selection based on geographic information above. The specific implementation can be referred to the description of the corresponding embodiments, and will not be repeated here.

[0175] This application also provides an electronic device, including: a memory for storing a computer program; and a processor for executing the computer program to implement the steps of the above-described dynamic decision-making method for store site selection based on geographic information.

[0176] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of any of the above-described methods for dynamic decision-making on store location based on geographic information.

[0177] In one exemplary embodiment, the aforementioned computer-readable storage medium may include, but is not limited to, various media capable of storing computer programs, such as USB flash drives, read-only memory, random access memory, portable hard drives, magnetic disks, or optical disks.

[0178] Embodiments of the present invention also provide a computer program product, which includes a computer program that, when executed by a processor, implements the steps in any of the above embodiments of the dynamic decision-making method for store site selection based on geographic information.

[0179] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0180] The foregoing has provided a detailed description of a dynamic decision-making method and system for store site selection based on geographic information, as provided in this application. Specific examples have been used to illustrate the principles and implementation methods of this application. The descriptions of the embodiments above are merely for the purpose of helping to understand the method and its core ideas. It should be noted that those skilled in the art can make various improvements and modifications to this application without departing from its principles, and these improvements and modifications also fall within the protection scope of this application.

Claims

1. A dynamic decision-making method for store site selection based on geographic information, characterized in that, include: Acquire traffic data along the main roads and subway lines of the target city, as well as geographic image information of the surrounding business districts of the area to be analyzed in the target city; The geographic image information is processed to obtain the physical boundary range and business distribution information of the surrounding business district. Combined with the traffic data, the traffic accessibility parameters are calculated. A preliminary candidate area is determined within the physical boundary range. Based on the traffic accessibility parameter, the preliminary candidate area and the business type distribution information are analyzed for synergistic potential to obtain synergistic potential parameters. Based on the traffic accessibility parameters and the synergy potential parameters, candidate store locations are selected from the preliminary candidate areas; The candidate store locations are evaluated based on surrounding dynamic factors to determine the final store location; A preliminary candidate area is determined within the physical boundary range. Based on the traffic accessibility parameter, a synergistic potential analysis is performed on the preliminary candidate area and the business type distribution information to obtain synergistic potential parameters, including: The physical boundary range is divided into multiple grid cells. The average distance between the center point of each grid cell and the coordinates of all entrance locations is calculated. Combining the actual traffic scenario and the average arrival time corresponding to each entrance location coordinate, the comprehensive accessibility score of each grid cell is calculated. Grid cells with a comprehensive accessibility score greater than a preset accessibility threshold are marked as preliminary candidate areas. From the business type distribution information, select catering shops, retail shops and service shops that are in operation, and calculate the first average distance between each of the preliminary candidate areas and the catering shops, the second average distance between each of the retail shops and the service shops. Based on the number of path selections corresponding to the target entrance location coordinates closest to each preliminary candidate area in the traffic accessibility parameters, and combined with the average distance, the first average distance, the second average distance, and the third average distance, the collaborative matching degree of each preliminary candidate area is calculated. The collaborative matching degree and the comprehensive accessibility score of each preliminary candidate region are weighted and summed to obtain the collaborative potential parameter corresponding to each preliminary candidate region. The geographic image information is processed to obtain the physical boundary range and business distribution information of the surrounding business district. Combined with the traffic data, traffic accessibility parameters are calculated, including: Feature extraction is performed on the geographic image information to obtain road edge features and building outline features. Then, the road edge features and building outline features are labeled and assigned to obtain labeled road edge features and labeled building outline features. Based on the edge points in the marked road edge features that satisfy the first continuity condition and are distributed along the road direction, and the contour points in the marked building contour features that satisfy the second continuity condition and are distributed along the periphery of the building, a set of boundary candidate line segments is formed. Identify the candidate line segment groups that meet the conditions in the set of candidate boundary line segments to determine the physical boundary range of the surrounding business district and the coordinates of multiple entrances into the physical boundary range; Based on the marked building outline features, the shops within the physical boundary are identified, and the business type of the shops within the range is matched from the preset business type classification rules. The business type includes catering, retail and service. The number of shops and their location coordinates for each business type are integrated to form business type distribution information; The peak-hour traffic flow, peak-hour pedestrian flow, and peak-hour travel time of the target road segment whose distance from the physical boundary range is less than a preset range threshold are associated with the entrance location coordinates to calculate the traffic accessibility parameters. The traffic accessibility parameters include the average arrival time and the number of route selections corresponding to each entrance location coordinate.

2. The method according to claim 1, characterized in that, Based on the edge points in the marked road edge features that satisfy the first continuity condition and are distributed along the road direction, and the contour points in the marked building contour features that satisfy the second continuity condition and are distributed along the perimeter of the building, a set of candidate boundary line segments is formed, including: The edge points in the marked road edge features that satisfy the first continuity condition and are distributed along the road direction are aggregated to obtain a road edge point set. Each edge point in the road edge point set is vector-combined with its next adjacent edge point to obtain the extension direction vector of each edge point in the road edge point set. Two adjacent edge points in the road edge point set whose angle between the extension direction vector of the preceding adjacent edge point and the extension direction vector of the following adjacent edge point is less than a preset direction threshold are combined into a road direction group. The adjacent edge points in each road direction group are connected to obtain the road edge line segment. Aggregate the outline points in the marked building outline features that satisfy the second continuity condition and are distributed along the outer perimeter of the building to obtain a building outline point set, and calculate the curvature value of each outline point in the building outline point set. Two adjacent contour points whose curvature values ​​in the building contour point set and the curvature values ​​of their next adjacent contour points are both less than a preset curvature threshold are combined into an outer contour group. The adjacent contour points in each outer contour group are connected to obtain the building contour line segment. Aggregate all road edge segments and all building outline segments to form a set of candidate boundary segments.

3. The method according to claim 1, characterized in that, Identify candidate line segment groups that meet the criteria in the set of candidate boundary line segments to determine the physical boundary range of the surrounding business district and the coordinates of multiple entrances into the physical boundary range, including: Calculate the coordinate distance between the endpoint coordinates and the starting coordinates of any two boundary candidate line segments in the boundary candidate line segment set, and mark the two boundary candidate line segments whose coordinate distance is less than a preset distance threshold as adjacent boundary candidate line segment groups; Calculate the included angle between two candidate boundary segments in each adjacent candidate boundary segment group, and mark the adjacent candidate boundary segment groups with included angle values ​​less than a preset angle threshold and segment type identifiers of road edge type as qualified candidate segment groups; Connect the candidate line segments at the boundary between groups that meet the conditions, where the endpoint coordinates of the line segments are consistent or the deviation of the endpoint coordinates is less than a preset deviation value, to obtain a closed region. The closed region is then used as the physical boundary of the surrounding business district. Identify candidate target boundary segments within the physical boundary range that have passable connection points with the edge segments of the main roads or sidewalks of the target city. Use the midpoint coordinates of each candidate target boundary segment as the entrance coordinates for entering the physical boundary range. The criteria for determining a passable connection point are that the straight-line distance between the endpoint of the candidate boundary segment and the endpoint of the edge segment of the main road or sidewalk is less than a preset connection threshold, and the angle between the extension directions is less than a preset direction threshold.

4. The method according to claim 1, characterized in that, Based on the traffic accessibility parameters and the synergy potential parameters, candidate store locations are selected from the preliminary candidate areas, including: The average arrival time and number of route selections corresponding to the coordinates of the nearest target entrance location in each preliminary candidate area are used as the corresponding associated traffic parameters. Based on preset distance and preset flow ranges, the associated traffic parameters of each preliminary candidate area are classified into levels to obtain the traffic parameter levels of each preliminary candidate area. Based on the traffic parameter levels, traffic parameter weights are set for the associated traffic parameters of each preliminary candidate region, and collaborative potential weights are set for the collaborative potential parameters of each preliminary candidate region. Based on the traffic parameter weights and the collaborative potential weights, the comprehensive score of each preliminary candidate region is calculated. The comprehensive scores of all preliminary candidate areas are sorted in descending order to obtain a comprehensive score ranking. The preliminary candidate areas with the highest number of candidates in the comprehensive score ranking are marked as candidate store areas.

5. The method according to claim 1, characterized in that, The candidate store locations are evaluated based on surrounding dynamic factors to determine the final store location, including: From the urban planning information of the surrounding dynamic factors, select planning impact information whose spatial distance from the candidate store area is less than the preset planning impact range threshold. The planning impact information includes transportation planning and commercial supporting planning. Based on the fluctuation data of shop rents in the surrounding dynamic factors, the rental cycle change rate of each candidate store location is calculated, and the rental cycle change rate is compared with a preset rent threshold to determine the rental stability level corresponding to each candidate store location. Based on the surrounding dynamic factors, the population growth rate of the surrounding area is calculated for each candidate store location. The population growth rate is then compared with a preset population threshold to determine the population attractiveness level of each candidate store location. The planning impact information is quantified into a planning impact score according to a preset impact level standard. The rental stability level is quantified into a rental impact score according to a preset first level score standard. The population attraction level is quantified into a population impact score according to a preset second level score standard. A first weight is set for the planning impact score, a second weight is set for the rental impact score, and a third weight is set for the population impact score. The evaluation score of each candidate store location is calculated, and the candidate store location with the highest evaluation score is selected as the final store location.

6. A dynamic decision-making system for store site selection based on geographic information, characterized in that, include: The acquisition module is used to acquire traffic data along the main roads and subway lines of the target city, as well as geographic image information of the surrounding business districts of the area to be analyzed in the target city. The processing module processes the geographic image information to obtain the physical boundary range and business distribution information of the surrounding business district, and calculates traffic accessibility parameters based on the traffic data. Specifically, the processing module extracts features from the geographic image information to obtain road edge features and building outline features, and assigns labels to these features to obtain labeled road edge features and labeled building outline features. Based on edge points in the labeled road edge features that satisfy a first continuity condition and are distributed along the road direction, and outline points in the labeled building outline features that satisfy a second continuity condition and are distributed along the perimeter of the buildings, a set of boundary candidate line segments is formed. The module then identifies candidate line segment groups that meet the conditions in the boundary candidate line segment set to determine the physical boundaries of the surrounding business district. The system identifies the physical boundary range and the coordinates of multiple entrances into the physical boundary range; based on the marked building outline features, it identifies shops within the physical boundary range and matches the business type of the shops within the range according to preset business type classification rules. The business type includes catering, retail, and service. The number of shops and their corresponding location coordinates for each business type are integrated to form business type distribution information. The system associates the peak-hour traffic flow, peak-hour pedestrian flow, and peak-hour travel time of target road segments whose distance from the physical boundary range is less than a preset range threshold with the entrance location coordinates to calculate traffic accessibility parameters. The traffic accessibility parameters include the average arrival time and the number of route selections corresponding to each entrance location coordinate. The analysis module is used to determine preliminary candidate areas within the physical boundary range, and to perform a synergy potential analysis on the preliminary candidate areas and the business type distribution information based on the traffic accessibility parameters to obtain synergy potential parameters. Specifically, the analysis module is used to determine preliminary candidate areas within the physical boundary range, and to perform a synergy potential analysis on the preliminary candidate areas and the business type distribution information based on the traffic accessibility parameters to obtain synergy potential parameters, including: The physical boundary range is divided into multiple grid cells. The average distance between the center point of each grid cell and the coordinates of all entrance locations is calculated. Combining the actual traffic scenario and the average arrival time corresponding to each entrance location coordinate, the comprehensive accessibility score of each grid cell is calculated. Grid cells with a comprehensive accessibility score greater than a preset accessibility threshold are marked as preliminary candidate areas. From the business type distribution information, select catering shops, retail shops and service shops that are in operation, and calculate the first average distance between each of the preliminary candidate areas and the catering shops, the second average distance between each of the retail shops and the service shops. Based on the number of path selections corresponding to the target entrance location coordinates closest to each preliminary candidate area in the traffic accessibility parameters, and combined with the average distance, the first average distance, the second average distance, and the third average distance, the collaborative matching degree of each preliminary candidate area is calculated. The collaborative matching degree and the comprehensive accessibility score of each preliminary candidate region are weighted and summed to obtain the collaborative potential parameter corresponding to each preliminary candidate region. The selection module is used to select candidate store locations from the preliminary candidate areas based on the traffic accessibility parameters and the collaborative potential parameters. The evaluation module is used to assess the location of the candidate store area based on surrounding dynamic factors in order to determine the final store location.

7. An electronic device, characterized in that, include: Memory, used to store computer programs; A processor, configured to execute the computer program to implement the steps of a dynamic decision-making method for store location selection based on geographic information as described in any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, enables a dynamic decision-making method for store location selection based on geographic information as described in any one of claims 1 to 5.

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