Network layout method, device and electronic equipment based on spatial cluster analysis

Abstract

The present invention provides a network layout method, device, and electronic device based on spatial cluster analysis, relating to the field of network layout. The method comprises: determining the types of all target products based on historical sales data, gridding target hotspot areas, determining each subgrid area, calculating the variance value between the grid cluster center and the standard grid cluster center, processing the subgrid area using kernel density analysis to obtain clustered areas; adjusting the sales volume of each target product using the variance value and the clustered area to obtain adjusted sales volume for each target product; determining the order quantity of the target product based on the target POI; determining the fishing net area with the largest order quantity as the target network point; and determining the product configuration quantity for each target product. The present invention can realize store layout site selection, product selection, and product quantity configuration. It uses spatial clustering to evaluate the spatial distribution of store site selection factors, calculates the order quantity of the fishing net area, and provides a more reasonable network layout.

Description

Technical Field

[0001] The present invention relates to the field of network point layout, and in particular to a network point layout method, device and electronic equipment based on spatial cluster analysis. Background Art

[0002] Originating from classical economics, location theory is a theory of spatial selection and the combination of economic activities within that space. Widely applied in various areas, including urban land use, factory site selection, commercial service network establishment, urban systems, and regional planning, location theory has two implications: the spatial selection of economic behavior; the organic combination of economic activities within that space. The former can be called layout location theory, while the latter can be called operational location theory. These two location theories differ in their research procedures and methodologies. Layout location theory, based on the known location subject, analyzes possible spaces suitable for that subject based on its inherent characteristics and then selects the optimal location from these. Operational location theory, in contrast, takes a larger location space and studies the optimal combination of location subjects and their spatial form based on factors such as its geographic characteristics, economic, and social conditions.

[0003] Classic site selection methods consider demographic factors, business district environment, transportation factors, and competitive factors. Demographic factors primarily focus on population mobility and population density, which serve as crucial references for FMCG marketing. Within the business district environment, the number and proximity of commercial points of interest (POIs) are primarily considered, and if scale is available, weights are assigned. Within the transportation factor, recommended stores are located along roads, with higher-ranking roads preferred in areas of equal suitability. Within the competitive factor, excessive and intense competition within a limited area can be extremely costly for retailers. The shortcomings or difficulties of classic site selection methods stem from implementation. For example, data is difficult to obtain statistically. While foot traffic statistics, a demographic factor, can be measured through on-site analysis, this is costly when there are many potential locations. Demographic factors, such as consumption habits and average income, are not directly available. Furthermore, weights are difficult to objectively quantify. Expert ratings can be used to quantify factors like business district environment and transportation, but these results are subject to significant subjective influence.

[0004] However, there is currently no technical solution that can solve the above technical problems, and there is no network layout method, device and electronic equipment based on spatial cluster analysis. Summary of the Invention

[0005] The present invention provides a network layout method, device and electronic equipment based on spatial cluster analysis. The method constructs sales spatial clusters through store sales data, predicts the potential sales of each network in each region, and determines the network layout and the product configuration quantity of the target products in the laid out network to meet the actual needs of users.

[0006] In a first aspect, the present invention provides a network layout method based on spatial cluster analysis, comprising:

[0007] Determine the categories of all target products from the target hotspot area based on historical sales data. The target hotspot area is determined based on each heat map corresponding to all candidate products in the preset area. Each heat map is determined based on all historical sales data of each candidate product in different stores.

[0008] Using a K-means clustering algorithm to process the historical sales data of all target products in the target hotspot area to obtain a standard grid cluster center, gridding the target hotspot area to determine each subgrid area, and for each subgrid area, using a K-means clustering algorithm to process the historical sales data of all target products in the subgrid area to obtain the sales volume of each target product in the grid cluster center, calculating the variance value between the grid cluster center and the standard grid cluster center, and using kernel density analysis to process the subgrid area to obtain the clustering area of ​​each target product;

[0009] Adjusting the sales volume of each target product using the variance value and the clustering area to obtain the adjusted sales volume of each target product in the subgrid area, dividing the subgrid area into all fishing net areas, determining the order quantity of each target product based on the target POI of each fishing net area, traversing each fishing net area in all subgrid areas, and determining the fishing net area with the largest order quantity as the target point;

[0010] The product configuration quantity of each target product in the target outlet is determined based on the adjusted sales volume of each target product corresponding to the target outlet and the other store sales volume of each target product corresponding to other stores in the sub-grid area where the target outlet is located.

[0011] According to the network layout method based on spatial cluster analysis provided by the present invention, the target product is a fast-moving consumer product, and the method of determining the types of all target products based on historical sales data includes:

[0012] Identify all stores with increasing sales in the target hotspot area that have shown an upward trend in sales over the past month.

[0013] Identify the product categories of the three products with the highest sales volume for each of the growth stores across all growth stores.

[0014] Count the cumulative quantity of each product category in all stores with sales growth, and determine the three product categories with the largest cumulative quantity as the categories of all target products.

[0015] According to the network layout method based on spatial cluster analysis provided by the present invention, the K-means clustering algorithm is used to process the historical sales data of all target products in the target hotspot area to obtain the standard grid cluster center, including:

[0016] Constructing a data set based on the historical sales data of all target products in the target hotspot area, wherein the historical sales data includes the sales volume of each store in different historical months;

[0017] A preset number of cluster centers are randomly determined from the data set, the distance from each data point to any cluster center is calculated, and each data point is assigned to the cluster where the nearest cluster center is located. The cluster centers are updated again until a preset number of iterations is reached, and all updated cluster centers are determined as the standard grid cluster centers.

[0018] According to the network layout method based on spatial cluster analysis provided by the present invention, the gridding process of the target hotspot area to determine each sub-grid area includes:

[0019] When it is determined that the store density of the target hotspot area is less than the first preset density, dividing the target hotspot area according to the first preset side length to determine each sub-grid area;

[0020] When it is determined that the store density of the target hotspot area is greater than or equal to the first preset density and less than the second preset density, dividing the target hotspot area according to the second preset side length to determine each sub-grid area;

[0021] When it is determined that the store density of the target hotspot area is greater than or equal to the second preset density, dividing the target hotspot area according to the third preset side length to determine each sub-grid area;

[0022] The first preset side length is greater than the second preset side length, and the second preset side length is greater than the third preset side length.

[0023] According to the network point layout method based on spatial cluster analysis provided by the present invention, the calculation of the variance value between the grid cluster center and the standard grid cluster center includes:

[0024]

[0025] Among them, M(i) is the variance value, xn(i), yn(i), zn(i) are the spatial coordinates of any grid cluster center, and Xn, Yn, Zn are the spatial coordinates of the standard grid cluster center.

[0026] According to the network layout method based on spatial cluster analysis provided by the present invention, adjusting the sales volume of each target product using the variance value and the clustering area to obtain the adjusted sales volume of each target product in the sub-grid area includes:

[0027] determining a plurality of levels in the sub-grid area according to the aggregation area;

[0028] For each level, the sales volume of each target product is adjusted by adjusting the variance value to obtain the adjusted sales volume of each target product of each level in the sub-grid area.

[0029] According to the network point layout method based on spatial cluster analysis provided by the present invention, determining the order quantity of each target product based on the target POI of each fishing net area includes:

[0030] T=P ij1 *X1+P ij2 *X2+P ij3 *X3

[0031] Among them, T is the order quantity of the target product, P ij1 is the preset factor affected by commercial POI, P ij2 is the preset factor affected by the service POI, P ij3 is the preset factor affected by transportation POI, X1 is the distance to commercial POI, X2 is the distance to service POI, and X3 is the distance to transportation POI.

[0032] According to the network layout method based on spatial cluster analysis provided by the present invention, determining the product configuration quantity of each target product in the target network according to the adjusted sales volume of each target product corresponding to the target network and the other store sales volume of each target product corresponding to other stores in the subgrid area where the target network is located includes:

[0033] For each target product, determine the difference between the adjusted sales volume and the sales volume of other stores, and determine the product configuration quantity of the target product;

[0034] Traverse all target products and determine the product configuration quantity of each target product in the target network.

[0035] In a second aspect, a network layout device based on spatial cluster analysis is provided, comprising:

[0036] a first determining unit, configured to determine the types of all target products from a target hotspot area based on historical sales data, wherein the target hotspot area is determined based on each heat map corresponding to all candidate products in a preset area, and each heat map is determined based on all historical sales data of each candidate product in different stores;

[0037] a processing unit configured to process the historical sales data of all target products in the target hotspot area using a K-means clustering algorithm to obtain a standard grid cluster center, grid-process the target hotspot area, determine each sub-grid area, and for each sub-grid area, process the historical sales data of all target products in the sub-grid area using a K-means clustering algorithm to obtain the sales volume of each target product in the grid cluster center, calculate the variance value between the grid cluster center and the standard grid cluster center, and process the sub-grid area using kernel density analysis to obtain a clustering area for each target product;

[0038] a division unit, the division unit being configured to adjust the sales volume of each target product using the variance value and the clustering area to obtain the adjusted sales volume of each target product in the subgrid area, divide the subgrid area into all fishing net areas, determine the order quantity of each target product based on the target POI of each fishing net area, traverse each fishing net area in all subgrid areas, and determine the fishing net area with the largest order quantity as the target point;

[0039] The second determination unit is used to determine the product configuration quantity of each target product in the target outlet based on the adjusted sales volume of each target product corresponding to the target outlet and the other store sales volume of each target product corresponding to other stores in the sub-grid area where the target outlet is located.

[0040] In a third aspect, an electronic device is provided, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the network layout method based on spatial clustering analysis when executing the program.

[0041] The present invention combines the first law of geography and location theory, that is, it feeds back the impact of factors such as foot traffic, population characteristics, business districts, and traffic conditions on store sales to the spatial location distribution. Then, it constructs sales spatial clustering only through a large amount of store sales data in different locations and different time periods. Based on this, it combines the distribution of various POIs in space to establish a spatial prediction model for store sales, to provide computational support for the layout optimization of marketing outlets. The present invention targets current fast-moving consumer goods, realizes store layout site selection, product selection, and product quantity configuration, uses historical sales data to provide sufficient data sources for network layout site selection, and adopts sales spatial clustering to evaluate the spatial distribution of store location factors. This breaks through the previous problem of needing to collect statistical data such as foot traffic and consumption habits, and being unable to effectively quantify and evaluate the impact of business districts and traffic.

[0042] The present invention calculates the spatial distribution of consumer groups by calculating the cluster centers of different types of goods with large differences in consumption distribution. Then, combined with the division of spatial grids and the calculation of grid variance, the distribution of consumption characteristics is used to determine the quantity demand gap of target products in different areas. Combined with the distribution of various POIs, the order quantity of smaller areas within the sub-grid area, namely the fishing net area, is calculated to provide a more reasonable network layout. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the technical solutions in the present invention or the prior art, a brief introduction is given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0044] Figure 1 It is a schematic diagram of the flow of the network layout method based on spatial cluster analysis provided by the present invention;

[0045] Figure 2 is a thermal map provided by the present invention;

[0046] Figure 3 This is a flow chart of counting the cumulative quantity of each product category in all stores with sales growth, provided by the present invention;

[0047] Figure 4 Schematic diagram of cluster centers provided by the present invention;

[0048] Figure 5 It is a schematic diagram of total sales volume and variance provided by the present invention;

[0049] Figure 6 This is a schematic diagram of the relationship between the grid average sales volume and mean square error provided by the present invention;

[0050] Figure 7 is a schematic diagram of adjusting the sales volume of each target product by adjusting the variance value provided by the present invention;

[0051] Figure 8 is a schematic diagram of determining target network points provided by the present invention;

[0052] Figure 9 It is a structural schematic diagram of a network layout device based on spatial cluster analysis provided by the present invention;

[0053] Figure 10 It is a structural schematic diagram of the electronic device provided by the present invention. DETAILED DESCRIPTION

[0054] To make the objectives, technical solutions, and advantages of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the embodiments described are only some of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0055] Figure 1 : is a schematic flow chart of a network layout method based on spatial clustering analysis provided by the present invention, wherein the network layout method based on spatial clustering analysis comprises:

[0056] Step 101: Determine the categories of all target products from a target hotspot area based on historical sales data. The target hotspot area is determined based on each heat map corresponding to all candidate products in a preset area. Each heat map is determined based on all historical sales data of each candidate product in different stores.

[0057] Step 102: Process the historical sales data of all target products in the target hotspot area using a K-means clustering algorithm to obtain a standard grid cluster center, grid the target hotspot area, determine each subgrid area, and for each subgrid area, process the historical sales data of all target products in the subgrid area using a K-means clustering algorithm to obtain the sales volume of each target product in the grid cluster center, calculate the variance value between the grid cluster center and the standard grid cluster center, and process the subgrid area using kernel density analysis to obtain the clustering area of ​​each target product;

[0058] Step 103: Using the variance value and the clustered area, adjust the sales volume of each target product to obtain the adjusted sales volume of each target product in the subgrid area. Divide the subgrid area into all fishing net areas. Determine the order quantity of each target product based on the target POI of each fishing net area. Traverse each fishing net area in all subgrid areas and determine the fishing net area with the largest order quantity as the target point.

[0059] Step 104: Determine the product configuration quantity of each target product in the target outlet based on the adjusted sales volume of each target product corresponding to the target outlet and the other store sales volume of each target product corresponding to other stores in the subgrid area where the target outlet is located.

[0060] In step 101, firstly, the historical sales data of all candidate products are collected from the preset area, including sales records of different stores and different time periods. Figure 2 is the thermal map provided by the present invention, such as Figure 2As shown, first select appropriate historical sales data, unify the data format and measurement unit, and then count the sales amount / volume of each product at the store and month granularity, and then form a heat map of each product.

[0061] For each candidate product, a heat map is drawn on the map based on its historical sales data to show the sales popularity of the product in different stores. According to the distribution of the heat map, areas with higher sales popularity are identified as target hot spots. Within the target hot spots, products with growth potential are screened as target products based on factors such as the growth trend of historical sales data, and the types of all target products are determined. The sales distribution of candidate products is intuitively displayed through the drawing of the heat map, which helps to quickly identify hot spots. It is also possible to directly select product types with significantly different sales hot spots by visual inspection. The screened target products have clear positioning characteristics and growth potential, which provides strong support for subsequent spatial clustering analysis and network layout.

[0062] Optionally, the target product is a fast-moving consumer product, and determining the types of all target products based on historical sales data includes:

[0063] Identify all stores with increasing sales in the target hotspot area that have shown an upward trend in sales over the past month.

[0064] Identify the product categories of the three products with the highest sales volume for each of the growth stores across all growth stores.

[0065] Count the cumulative quantity of each product category in all stores with sales growth, and determine the three product categories with the largest cumulative quantity as the categories of all target products.

[0066] Figure 3 : is a flow chart of counting the cumulative quantity of each product category in all stores with increased sales provided by the present invention, such as Figure 3 As shown, the present invention first collects historical sales data of all stores in the target hot spot area in the past period of time (such as the past year or a specific month), and based on the collected data, screens out stores that have shown a sales growth trend in historical months, that is, sales growth stores. By comparing the sales data of each month, stores with continuous sales growth or a significant growth trend can be identified. For each sales growth store, the three products with the highest sales are determined based on the historical sales data, and the screened products are classified according to the product categories they belong to, such as food, beverages, daily necessities, etc. For each product category, the cumulative number of products in this category in all sales growth stores is counted, and based on the statistical results of the cumulative number, the three product categories with the largest cumulative number are selected as the types of all target products.

[0067] In an optional embodiment, the number of stores with increasing and decreasing sales of each product within a certain time range is counted separately to exclude the impact of long holidays on sales fluctuations, such as January and October. The selection criteria for increasing stores are:

[0068]

[0069] Find the stores whose M is greater than 0, that is, the growing stores, where M is the average monthly sales fluctuation compared to January, n is the number of statistical months in the year (usually 12), Si is the sales in the i-th month (excluding January and October), and S1 is the sales in January.

[0070] Among them, the screening criteria for reducing stores are:

[0071]

[0072] Find the stores whose M is less than 0, that is, reduce the number of stores, where M is the average monthly sales fluctuation compared to October, n is the number of statistical months in the year (usually 12), Si is the sales of the i-th month (excluding January and October), and S10 is the sales of October.

[0073] The present invention compares the spatial distribution of stores with increasing sales and stores with decreasing sales of various commodities, selects commodity categories with significant differences, and through combining the screening of stores with increasing sales, the determination of hot-selling product categories, and the statistics of cumulative quantities, can accurately identify the categories of fast-moving consumer goods with the greatest consumer demand and the strongest growth potential in the target hot spot areas. This method is not only data-driven, but also can flexibly adapt to market changes and consumer needs in different regions.

[0074] In step 102, the historical sales data of all target products in the target hotspot area are clustered using the K-means clustering algorithm to obtain the standard grid cluster center. Then, the target hotspot area is gridded to determine each sub-grid area. In each sub-grid area, the historical sales data of the target product are processed again using the K-means clustering algorithm to obtain the sales of the target product in the grid cluster center. The variance value between the grid cluster center and the standard grid cluster center of each sub-grid area is calculated to evaluate the degree of difference between the sub-grid area and the overall sales trend. The kernel density analysis is used to process each sub-grid area to obtain the clustering area of ​​the target product, that is, the sales hotspot or high-density area.

[0075] Optionally, the method of processing the historical sales data of all target products in the target hotspot area using a K-means clustering algorithm to obtain standard grid cluster centers includes:

[0076] Constructing a data set based on the historical sales data of all target products in the target hotspot area, wherein the historical sales data includes the sales volume of each store in different historical months;

[0077] A preset number of cluster centers are randomly determined from the data set, the distance from each data point to any cluster center is calculated, and each data point is assigned to the cluster where the nearest cluster center is located. The cluster centers are updated again until a preset number of iterations is reached, and all updated cluster centers are determined as the standard grid cluster centers.

[0078] Figure 4 It is a schematic diagram of the cluster center provided by the present invention, such as Figure 4 As shown in the figure, historical sales data of all target products are collected from the target hotspot area. These data should include the sales volume of each store in different historical months to ensure the comprehensiveness and accuracy of the data. The collected data are preprocessed, such as removing outliers and filling missing values, to ensure the quality of the data and the accuracy of the analysis; the preprocessed data are organized into a data set suitable for processing by the K-means clustering algorithm.

[0079] Optionally, a preset number of data points are randomly selected from the data set as initial cluster centers. These initial cluster centers will serve as the starting point of the algorithm iteration. For each data point in the data set, its distance to all cluster centers is calculated, and the data point is assigned to the cluster where the nearest cluster center is located. Based on the data points in each cluster, the cluster center of each cluster is recalculated. The above steps are repeated until the preset number of iterations is reached or the change in the cluster center is less than a certain threshold, to ensure that the algorithm can converge to a stable solution. All updated cluster centers are determined as standard grid cluster centers for subsequent gridding processing and cluster analysis of sub-grid areas.

[0080] By constructing a dataset, randomly determining cluster centers, and iteratively updating them, this method performs K-means cluster analysis on the historical sales data of all target products in a target hotspot area. This method accurately identifies the natural clustering structure of sales data, providing strong support for subsequent gridding and cluster analysis of subgrid areas. Furthermore, by selecting a preset number of iterations and the number of cluster centers, the algorithm's accuracy and efficiency can be flexibly adjusted to accommodate datasets of varying sizes and complexities.

[0081] In another optional embodiment, the k-means clustering algorithm is first used to perform clustering calculations based on the product category-sales of each store to obtain multiple clusters of two-dimensional clusters. Then, the stores are classified into stores with the best sales of different product categories using the silhouette coefficient. The calculation process of the K-means spatial clustering is not described in detail. The silhouette coefficient s of the sample i in the evaluation cluster isi , refer to the following formula:

[0082]

[0083] Among them, a i is the intra-cluster dissimilarity, i.e. the average distance between sample i and each sample in the cluster. i is the out-of-cluster dissimilarity, that is, the average distance from sample i to each sample in other clusters. After 100 k-means clustering iterations, the cluster center is obtained as shown in Figure 4.

[0084] Optionally, determine each subgrid area, and in each subgrid area, again use the K-means clustering algorithm to process the historical sales data of the target product to obtain the sales volume of the target product in the grid cluster center:

[0085] Cluster Center Product Type A Product Type B Product Type C Cluster Center 1 148 114 148 Cluster Center 2 62 34 78 Cluster Center 3 17 12 20

[0086] Optionally, the gridding process of the target hotspot area to determine each sub-grid area includes:

[0087] When it is determined that the store density of the target hotspot area is less than the first preset density, dividing the target hotspot area according to the first preset side length to determine each sub-grid area;

[0088] When it is determined that the store density of the target hotspot area is greater than or equal to the first preset density and less than the second preset density, dividing the target hotspot area according to the second preset side length to determine each sub-grid area;

[0089] When it is determined that the store density of the target hotspot area is greater than or equal to the second preset density, dividing the target hotspot area according to the third preset side length to determine each sub-grid area;

[0090] The first preset side length is greater than the second preset side length, and the second preset side length is greater than the third preset side length.

[0091] Optionally, the present invention first collects the location information of all stores in the target hotspot area to calculate the store density. Based on the location information of the stores, the store density in the target hotspot area is calculated. The store density can be measured by the ratio of the number of stores to the area of ​​the region. If the store density in the target hotspot area is less than the first preset density, it means that the store distribution in the region is relatively sparse. At this time, a larger first preset side length is used to divide the target hotspot area to determine each sub-grid area. The larger side length can reduce the number of sub-grids and improve processing efficiency; if the store density in the target hotspot area is greater than or equal to the first preset density and less than the second preset density, it means that the store distribution in the region is moderate. At this time, a smaller second preset side length is used to divide the target hotspot area to determine each sub-grid area. The moderate side length can ensure that each sub-grid contains an appropriate number of stores, which is convenient for subsequent analysis; if the store density in the target hotspot area is greater than or equal to the second preset density, it means that the store distribution in the region is dense. At this time, a smaller third preset side length is used to divide the target hotspot area to determine each sub-grid area. The smaller side length can divide the area more finely and improve the accuracy of the analysis.

[0092] In an optional embodiment, the density of stores is used as a basis for dividing the grids, and then the variance of each grid is counted against the cluster center of the grids with stores. Generally, a large grid with a side length of 300 meters can be used for city-level stores, a medium grid with a side length of 150 meters can be used for street-level stores, and a small grid with a side length of 70 meters can be used for community-level stores. Then, the sales volume and cluster center of various types of goods in the stores within the grid are counted. By dynamically adjusting the side length of the grid division according to the store density, the flexibility and accuracy of the grid processing can be ensured. In low-density areas, larger side lengths can reduce the amount of calculation; in high-density areas, smaller side lengths can improve the precision of the analysis, thus achieving flexible grid processing of target hotspot areas.

[0093] Figure 5 is a schematic diagram of total sales volume and variance provided by the present invention, wherein the calculation of the variance value between the grid cluster center and the standard grid cluster center includes:

[0094]

[0095] Here, M(i) is the variance value, xn(i), yn(i), and n(i) are the coordinates of the grid cluster centers for each month, and Xn, Yn, and Zn are the coordinates of the standard grid cluster centers. The monthly variance evaluation factor (M) is calculated to measure the difference between each month's cluster center and the standard cluster center. The monthly cluster center coordinates are set to xn(i), yn(i), and zn(i) to assess the degree of difference between the subgrid area and the overall sales trend. The variance calculation provides a basis for evaluating sales differences within subgrid areas, facilitating subsequent sales adjustments and network layout optimization. Sales and variance statistics show that as variance decreases, total sales increase. After a simple linear regression, monthly variance can be used to guide the overall product mix.

[0096] Furthermore, combined with the above formula, the variance M of the i-th grid to the standard cluster center is calculated by referring to the monthly variance statistics. i , the calculation formula is as follows:

[0097]

[0098] Among them, n is the total number of grids, x, y, z are the cluster centers of the three products respectively, x i ,y i ,z i are the cluster centers of the i-th grid respectively.

[0099] In step 103, sales volume adjustments are made for the target product in each subgrid area based on the variance value and clustering area. Sales volume adjustments can be made based on factors such as sales trends and market demand. Each subgrid area is further divided into multiple fishing net areas, each with similar sales volume characteristics and trends. Based on the target POIs (such as commercial facilities and population density) and adjusted sales volume of each fishing net area, the order quantity of each target product is calculated. All fishing net areas are traversed, and the fishing net area with the largest order volume is selected as the target point.

[0100] Optionally, adjusting the sales volume of each target product using the variance value and the clustering area to obtain the adjusted sales volume of each target product in the subgrid area includes:

[0101] determining a plurality of levels in the sub-grid area according to the aggregation area;

[0102] For each level, the sales volume of each target product is adjusted by adjusting the variance value to obtain the adjusted sales volume of each target product of each level in the sub-grid area.

[0103] Figure 6 This is a schematic diagram of the relationship between the grid average sales volume and mean square error provided by the present invention. Figure 7This is a schematic diagram of adjusting the sales volume of each target product by adjusting the variance value, as provided by the present invention. The relationship between grid sales volume and variance indicates that sales volume is inversely proportional to the variance within a specific interval. The variance M is converted to M_new = (1 / M). A larger M_new indicates a smaller variance and a closer distance to the standard ratio center. Kernel density analysis is performed on this data to extract and divide regions with spatially clustered M_new into multiple levels, around which sales volume can be increased.

[0104] First, it is necessary to identify areas with significant sales concentration characteristics within the sub-grid area, namely, concentration areas. These can be determined by analyzing various factors such as sales data, customer flow, and geographic location. Based on the characteristics of the concentration areas, such as size, sales intensity, and customer distribution, the sub-grid area is divided into multiple levels. The higher the level, the higher the degree of sales concentration in the area, and the sales volume of the target product is likely to be greater. For each level, the variance value of the sales volume of each target product in the level is calculated. The variance value reflects the degree of dispersion of the sales distribution. The larger the variance, the more uneven the sales distribution. Based on the size of the variance value, the sales volume of each target product in each level is adjusted. Specifically, if the variance value is large, it means that the sales distribution is uneven, and it may be necessary to balance the sales volume through promotions, increased inventory, etc.; if the variance value is small, it means that the sales distribution is relatively uniform, and it may not be necessary to make large-scale sales adjustments. After the above adjustments, the adjusted sales volume of each target product in each level of the sub-grid area is obtained. The adjusted sales volume more accurately reflects the actual sales volume of each target product in the area. By adjusting sales volume according to variance values ​​and hierarchical structures, the present invention can more flexibly cope with sales volume differences in different regions, thereby improving the accuracy of predictions and the effectiveness of strategies.

[0105] Strategy 1: For each tier, adjust the M_new of stores with M_new below 8.1, 5.6, or 2.9 to 8.1 / 5.6 / 2.9, effectively reducing the gap with the standard cluster center. Then, using the previous formula to calculate the theoretical sales volume, we assign a matching ratio. The following table shows the sales improvement achieved with this strategy.

[0106] Strategy 2: For each layer, further increase its M_new by 10-15% based on 2.1 or 3.6 or 5.9 to get closer to the center of the ratio. Then, query the theoretical sales volume and ratio according to the formula:

[0107] Store level Actual average sales volume Strategy 1 average sales volume Strategy 2 Average Sales First floor 604 628 655 Second floor 415 438 457 Third floor 308 325 364 Improvement rate 0 5.10％ 13.50％

[0108] Optionally, determining the order quantity of each target product according to the target POI of each fishing net area includes:

[0109] T=P ij1 *X1+P ij2 *X2+Pij3 *X3

[0110] Among them, T is the order quantity of the target product, P ij1 is the preset factor affected by commercial POI, P ij2 is the preset factor affected by the service POI, P ij3 is the preset factor affected by transportation POI, X1 is the distance to commercial POI, X2 is the distance to service POI, and X3 is the distance to transportation POI.

[0111] Different from the general site selection that models the entire area, in order to improve the accuracy of the optimized site selection, the present invention further divides the grid into different fishing net (BUFFER) areas according to regional segmentation (BUFFER), and establishes unique models for different areas; at the same time, the selection of BUFFER also ensures the maximum difference between regions and has the best effect. There are many categories of POI, with commercial, service, and transportation POIs as the key considerations; in this way, the commercial, service, and transportation attractions in each fishing net M are PM1, PM2, and PM3 respectively, and the user's purchase choice is inversely proportional to the distance from the POI. According to the law of gravity, to be precise, it is inversely proportional to the square of the distance, set as D. Then, for any product store numbered i in a circular surface, the factor affected by the surrounding POI is P ij .

[0112] According to the above formula:

[0113] serial number Commercial POI distance Traffic POI distance Service POI distance Order quantity Sales 0 112.1 0 109.2 3330 550107 1 123.7 0 108.2 2945 522719 4 118.5 0 118.3 2615 411444 6 129.9 0 105.6 2165 372491

[0114] Calculate the distance from the center of each fishing net to the surrounding POIs, and use the formula to calculate the theoretical sales volume to obtain the fishing net with the maximum sales volume. This means that it is reasonable to set up stores in these fishing nets, and their product order quantities can be roughly predicted.

[0115] In step 104, other-store sales data of each target product in other stores in the subgrid area where the target outlet is located is collected. For each target product, the difference between its adjusted sales at the target outlet and its sales at other stores is calculated. The product configuration quantity of each target product in the target outlet is determined based on factors such as the size of the difference and market demand. In an optional embodiment, determining the product configuration quantity of each target product in the target outlet based on the adjusted sales of each target product corresponding to the target outlet and the other-store sales of each target product corresponding to other stores in the subgrid area where the target outlet is located includes:

[0116] For each target product, determine the difference between the adjusted sales volume and the sales volume of other stores, and determine the product configuration quantity of the target product;

[0117] Traverse all target products and determine the product configuration quantity of each target product in the target network.

[0118] Figure 8 This is a schematic diagram of determining target outlets provided by the present invention. The order volumes corresponding to the two fishing net numbers in areas 34 and 44 in the figure are the highest, which are around areas 34 and 44 in the figure; this is reflected on the map as follows: (the fishing net in area 34 in the figure already contains a store with an order volume of 3330, indicating that the demonstration result is feasible) The first four fishing nets are: areas 34, 44, 36, and 66 in the figure. The optimization measure is to open three marketing outlets in areas 36, 44, and 66 in the figure. Referring to the map, it is found that area 66 in the figure is not near the road network and is therefore eliminated. The conclusion is to open stores in areas 36 and 44 in the figure.

[0119] The network layout method based on spatial cluster analysis of the present invention realizes in-depth analysis of target product sales data, accurate identification of spatial clusters, reasonable optimization of sales adjustments and scientific planning of network layout through a series of specific implementation plans and technical means, thereby improving the accuracy and effectiveness of network layout. According to location theory, factors affecting store site selection include surrounding fixed population, floating population, population structure, consumption habits, income situation, etc., which are in turn affected by transportation, residential areas, etc. These data are either difficult to objectively quantify or the cost of statistical acquisition is very high. The present invention combines the relevant theories of classical economics and geographic information science, constructs sales spatial clusters through store marketing data to predict the potential sales of each network in each region, and calculates the optimization of network layout.

[0120] The present invention is based on the combination of geographic information science and classical economics, and adopts the sales spatial clustering method to construct an analysis model, which can effectively avoid the above two adverse effects and directly use more objective and easier to obtain historical sales data of outlets as the basis for analysis.

[0121] As another optional embodiment of the present invention, the network layout optimization calculation is mainly divided into three steps: First, spatial cluster centers are calculated. Based on the sales data of different products at each network, multiple product categories with the largest possible spatial distribution differences in sales are selected. Based on the spatial relationships, cluster centers of each spatial cluster with different product ratios are calculated. Because different demographics, consumption habits, and income levels are reflected in store sales, sales values / volumes of different product categories can vary significantly. Therefore, the cluster centers of spatial clusters with different ratios objectively reflect the actual spatial distribution of specific consumer groups. Next, spatial grids are divided, and sales areas are divided according to the distribution density of sales network points. The variance between the average sales value of each grid and the average sales value of the cluster centers is then calculated to infer the difference between the current product ratio and the ideal ratio within this grid area. By adjusting the distribution of product ratios within the grid, the sales potential of the region can be further explored. Since the distribution of specific consumer groups is similar only within a certain range, the spatial grid division can better quantify these consumption characteristics. The cluster center is the most ideal distribution area of ​​consumption characteristics, and the variance from the cluster center is the gap from this ideal distribution. By narrowing this gap (not a completely linear relationship), we can further fit the distribution of consumption characteristics and transform potential consumer demand; finally, the location optimization within the grid is aimed at the store location within the grid, which needs to be further optimized in combination with the specific distribution of various POIs (commercial, transportation, etc.). First, further divide the grid into smaller fishing nets. Based on the public POI data, calculate its anti-gravity impact factor on the center of each fishing net, including the drainage factor of commercial and transportation POIs and the competition factor of similar stores. Then, based on the multivariate regression of the sales sample, estimate the fishing net with the highest expected sales, and combine field investigations to determine the store location.

[0122] Based on the above technical solution, the present invention solves the problem of difficult site selection for fast-moving consumer goods stores. In view of the large amount of population data collection and statistical requirements required by the classic site selection algorithm, as well as the inability to objectively quantify the impact of business districts and traffic, the present invention only needs to collect and organize the historical sales data of each store in the network (classified by commodity type), and then combine it with the public POI data to carry out site selection calculations, without spending a lot of time and investing high costs to collect third-party data; exclude the influence of expert subjective factors in the site selection analysis and calculation as much as possible, and obtain alternative addresses with smaller alternative areas and higher accuracy through reasonable and objective calculations; through the constructed spatial clustering and calculation of grid variance (with the variance of the cluster center), it can further guide the reasonable distribution of goods within the grid area to tap the sales potential. In addition, the calculation results of the present invention can also be combined for management optimization such as sales forecasting and abnormal perception warning.

[0123] Figure 9 It is a structural schematic diagram of the network layout device based on spatial clustering analysis provided by the present invention, and the network layout device based on spatial clustering analysis includes a first determination unit 1, which is used to determine the types of all target products from the target hot spot area based on historical sales data. The target hot spot area is determined in a preset area based on each heat map corresponding to all candidate products. Each heat map is determined based on all historical sales data of each candidate product in different stores. The working principle of the first determination unit 1 can refer to the aforementioned step 101 and will not be repeated here.

[0124] The network layout device based on spatial clustering analysis also includes a processing unit 2, which is used to use the K-means clustering algorithm to process the historical sales data of all target products in the target hot spot area to obtain a standard grid cluster center, grid-process the target hot spot area, determine each sub-grid area, and for each sub-grid area, use the K-means clustering algorithm to process the historical sales data of all target products in the sub-grid area to obtain the sales of each target product in the grid cluster center, calculate the variance value between the grid cluster center and the standard grid cluster center, use kernel density analysis to process the sub-grid area to obtain the clustering area of ​​each target product. The working principle of the processing unit 2 can refer to the aforementioned step 102 and will not be repeated here.

[0125] The network layout device based on spatial clustering analysis also includes a division unit 3, which is used to adjust the sales volume of each target product using the variance value and the clustering area to obtain the adjusted sales volume of each target product in the sub-grid area, divide the sub-grid area into all fishing net areas, determine the order quantity of each target product according to the target POI of each fishing net area, traverse each fishing net area in all sub-grid areas, and determine the fishing net area with the largest order quantity as the target network point. The working principle of the division unit 3 can refer to the aforementioned step 103 and will not be repeated here.

[0126] The network layout device based on spatial clustering analysis also includes a second determination unit 4, which is used to determine the product configuration quantity of each target product in the target network according to the adjusted sales volume of each target product corresponding to the target network and the other store sales volume of each target product corresponding to other stores in the sub-grid area where the target network is located. The working principle of the second determination unit 4 can be referred to the aforementioned step 104 and will not be repeated here.

[0127] The present invention combines the first law of geography and location theory, that is, it feeds back the impact of factors such as foot traffic, population characteristics, business districts, and traffic conditions on store sales to the spatial location distribution. Then, it constructs sales spatial clustering only through a large amount of store sales data in different locations and different time periods. Based on this, it combines the distribution of various POIs in space to establish a spatial prediction model for store sales, to provide computational support for the layout optimization of marketing outlets. The present invention targets current fast-moving consumer goods, realizes store layout site selection, product selection, and product quantity configuration, uses historical sales data to provide sufficient data sources for network layout site selection, and adopts sales spatial clustering to evaluate the spatial distribution of store location factors. This breaks through the previous problem of needing to collect statistical data such as foot traffic and consumption habits, and being unable to effectively quantify and evaluate the impact of business districts and traffic.

[0128] The present invention calculates the spatial distribution of consumer groups by calculating the cluster centers of different types of goods with large differences in consumption distribution. Then, combined with the division of spatial grids and the calculation of grid variance, the distribution of consumption characteristics is used to determine the quantity demand gap of target products in different areas. Combined with the distribution of various POIs, the order quantity of smaller areas within the sub-grid area, namely the fishing net area, is calculated to provide a more reasonable network layout.

[0129] Figure 10 Schematic diagram of the structure of the electronic device provided by the present invention. Figure 10As shown, the electronic device may include: a processor 110, a communication interface 120, a memory 130 and a communication bus 140, wherein the processor 110, the communication interface 120 and the memory 130 communicate with each other via the communication bus 140. The processor 110 may call the logic instructions in the memory 130 to execute a network layout method based on spatial clustering analysis, the method comprising: determining the types of all target products in a target hotspot area according to historical sales data, wherein the target hotspot area is determined in a preset area according to each heat map corresponding to all candidate products, and each heat map is determined according to all historical sales data of each candidate product in different stores; using the K-means clustering algorithm to process the historical sales data of all target products in the target hotspot area to obtain a standard grid cluster center, gridding the target hotspot area to determine each sub-grid area, and for each sub-grid area, using the K-means clustering algorithm to process the historical sales data of all target products in the sub-grid area to obtain the standard grid cluster center of each target product. , calculate the sales volume of each target product, calculate the variance value between the grid cluster center and the standard grid cluster center, use kernel density analysis to process the sub-grid area, and obtain the clustering area of ​​each target product; use the variance value and the clustering area to adjust the sales volume of each target product to obtain the adjusted sales volume of each target product in the sub-grid area, divide the sub-grid area into all fishing net areas, determine the order quantity of each target product according to the target POI of each fishing net area, traverse each fishing net area in all sub-grid areas, and determine the fishing net area with the largest order quantity as the target network point; determine the product configuration quantity of each target product in the target network point according to the adjusted sales volume of each target product corresponding to the target network point and the other store sales volume of each target product corresponding to other stores in the sub-grid area where the target network point is located.

[0130] In addition, the logic instructions in the above-mentioned memory 130 can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when sold or used as an independent product. Based on this understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art or the part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0131] On the other hand, the present invention also provides a computer program product, which includes a computer program, which can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute a network layout method based on spatial clustering analysis provided by the above methods, the method including: determining the types of all target products from the target hotspot area based on historical sales data, the target hotspot area is determined in a preset area based on each heat map corresponding to all candidate products, and each heat map is determined based on all historical sales data of each candidate product in different stores; using the K-means clustering algorithm to process the historical sales data of all target products in the target hotspot area to obtain a standard grid cluster center, gridding the target hotspot area, determining each sub-grid area, and for each sub-grid area, using the K-means clustering algorithm to process the sub-grid area. The historical sales data of all target products in the grid area are used to obtain the sales volume of each target product in the grid cluster center, the variance value between the grid cluster center and the standard grid cluster center is calculated, and the sub-grid area is processed by kernel density analysis to obtain the clustering area of ​​each target product; the sales volume of each target product is adjusted using the variance value and the clustering area to obtain the adjusted sales volume of each target product in the sub-grid area, and the sub-grid area is divided into all fishing net areas. The order quantity of each target product is determined according to the target POI of each fishing net area, and each fishing net area in all sub-grid areas is traversed to determine the fishing net area with the largest order quantity as the target network point; the product configuration quantity of each target product in the target network point is determined according to the adjusted sales volume of each target product corresponding to the target network point and the other store sales volume of each target product corresponding to other stores in the sub-grid area where the target network point is located.

[0132] On the other hand, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, is implemented to execute the network layout method based on spatial clustering analysis provided by the above-mentioned methods, the method comprising: determining the types of all target products from a target hotspot area based on historical sales data, the target hotspot area being determined in a preset area based on each heat map corresponding to all candidate products, and each heat map being determined based on all historical sales data of each candidate product in different stores; processing the historical sales data of all target products in the target hotspot area using a K-means clustering algorithm to obtain a standard grid cluster center, gridding the target hotspot area, determining each sub-grid area, and for each sub-grid area, processing the historical sales data of all target products in the sub-grid area using a K-means clustering algorithm. The sales volume data of each target product in the grid cluster center is obtained, the variance value between the grid cluster center and the standard grid cluster center is calculated, and the sub-grid area is processed by kernel density analysis to obtain the clustering area of ​​each target product; the sales volume of each target product is adjusted by using the variance value and the clustering area to obtain the adjusted sales volume of each target product in the sub-grid area, and the sub-grid area is divided into all fishing net areas, and the order quantity of each target product is determined according to the target POI of each fishing net area. Each fishing net area in all sub-grid areas is traversed, and the fishing net area with the largest order quantity is determined as the target network point; the product configuration quantity of each target product in the target network point is determined according to the adjusted sales volume of each target product corresponding to the target network point and the other store sales volume of each target product corresponding to other stores in the sub-grid area where the target network point is located.

[0133] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, i.e., they may be located in one location or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of the present embodiment. Persons of ordinary skill in the art will be able to understand and implement the present invention without inventive effort.

[0134] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a necessary general hardware platform, or of course, by hardware. Based on this understanding, the essence of the above technical solution or the part that contributes to the existing technology can be embodied in the form of a software product. The computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, an optical disk, etc., and includes a number of instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or certain parts of the embodiments.

[0135] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A network layout method based on spatial cluster analysis, characterized in that: include: Determine the categories of all target products from the target hotspot area based on historical sales data. The target hotspot area is determined based on each heat map corresponding to all candidate products in the preset area. Each heat map is determined based on all historical sales data of each candidate product in different stores. Using a K-means clustering algorithm to process the historical sales data of all target products in the target hotspot area to obtain a standard grid cluster center, gridding the target hotspot area to determine each subgrid area, and for each subgrid area, using a K-means clustering algorithm to process the historical sales data of all target products in the subgrid area to obtain the sales volume of each target product in the grid cluster center, calculating the variance value between the grid cluster center and the standard grid cluster center, and using kernel density analysis to process the subgrid area to obtain the clustering area of ​​each target product; Adjusting the sales volume of each target product using the variance value and the clustering area to obtain the adjusted sales volume of each target product in the subgrid area, dividing the subgrid area into all fishing net areas, determining the order quantity of each target product based on the target POI of each fishing net area, traversing each fishing net area in all subgrid areas, and determining the fishing net area with the largest order quantity as the target point; The product configuration quantity of each target product in the target outlet is determined based on the adjusted sales volume of each target product corresponding to the target outlet and the other store sales volume of each target product corresponding to other stores in the sub-grid area where the target outlet is located.

2. The network layout method based on spatial cluster analysis according to claim 1, characterized in that: The target product is a fast-moving consumer product. The types of all target products determined based on historical sales data include: Identify all stores with increasing sales in the target hotspot area that have shown an upward trend in sales over the past month. Identify the product categories of the three products with the highest sales volume for each of the growth stores across all growth stores. Count the cumulative quantity of each product category in all stores with sales growth, and determine the three product categories with the largest cumulative quantity as the categories of all target products.

3. The network layout method based on spatial cluster analysis according to claim 1, characterized in that: The K-means clustering algorithm is used to process the historical sales data of all target products in the target hotspot area to obtain the standard grid cluster center, including: Constructing a data set based on the historical sales data of all target products in the target hotspot area, wherein the historical sales data includes the sales volume of each store in different historical months; A preset number of cluster centers are randomly determined from the data set, the distance from each data point to any cluster center is calculated, and each data point is assigned to the cluster where the nearest cluster center is located. The cluster centers are updated again until a preset number of iterations is reached, and all updated cluster centers are determined as the standard grid cluster centers.

4. The network layout method based on spatial cluster analysis according to claim 1, characterized in that: The gridding process of the target hotspot area to determine each sub-grid area includes: When it is determined that the store density of the target hotspot area is less than the first preset density, dividing the target hotspot area according to the first preset side length to determine each sub-grid area; When it is determined that the store density of the target hotspot area is greater than or equal to the first preset density and less than the second preset density, dividing the target hotspot area according to the second preset side length to determine each sub-grid area; When it is determined that the store density of the target hotspot area is greater than or equal to the second preset density, dividing the target hotspot area according to the third preset side length to determine each sub-grid area; The first preset side length is greater than the second preset side length, and the second preset side length is greater than the third preset side length.

5. The network layout method based on spatial cluster analysis according to claim 1, characterized in that: The calculating the variance value between the grid cluster center and the standard grid cluster center includes: Among them, M(i) is the variance value, xn(i), yn(i), zn(i) are the spatial coordinates of any grid cluster center, and Xn, Yn, Zn are the spatial coordinates of the standard grid cluster center.

6. The network layout method based on spatial cluster analysis according to claim 1, characterized in that: The adjusting the sales volume of each target product by using the variance value and the clustering area to obtain the adjusted sales volume of each target product in the subgrid area includes: determining a plurality of levels in the sub-grid area according to the aggregation area; For each level, the sales volume of each target product is adjusted by adjusting the variance value to obtain the adjusted sales volume of each target product of each level in the sub-grid area.

7. The network layout method based on spatial cluster analysis according to claim 1, characterized in that: Determining the order quantity of each target product according to the target POI of each fishing net area includes: T=P ij1 *X1+P ij2 *X2+P ij3 *X3 Among them, T is the order quantity of the target product, P ij1 is the preset factor affected by commercial POI, P ij2 is the preset factor affected by the service POI, P ij3 is the preset factor affected by transportation POI, X1 is the distance to commercial POI, X2 is the distance to service POI, and X3 is the distance to transportation POI.

8. The network layout method based on spatial cluster analysis according to claim 1, characterized in that: Determining the product configuration quantity of each target product in the target outlet based on the adjusted sales volume of each target product corresponding to the target outlet and the other store sales volume of each target product corresponding to other stores in the subgrid area where the target outlet is located includes: For each target product, determine the difference between the adjusted sales volume and the sales volume of other stores, and determine the product configuration quantity of the target product; Traverse all target products and determine the product configuration quantity of each target product in the target network.

9. A network layout device based on spatial cluster analysis, characterized in that: include: a first determining unit, configured to determine the types of all target products from a target hotspot area based on historical sales data, wherein the target hotspot area is determined based on each heat map corresponding to all candidate products in a preset area, and each heat map is determined based on all historical sales data of each candidate product in different stores; a processing unit configured to process the historical sales data of all target products in the target hotspot area using a K-means clustering algorithm to obtain a standard grid cluster center, grid-process the target hotspot area, determine each sub-grid area, and for each sub-grid area, process the historical sales data of all target products in the sub-grid area using a K-means clustering algorithm to obtain the sales volume of each target product in the grid cluster center, calculate the variance value between the grid cluster center and the standard grid cluster center, and process the sub-grid area using kernel density analysis to obtain a clustering area for each target product; a division unit, the division unit being configured to adjust the sales volume of each target product using the variance value and the clustering area to obtain the adjusted sales volume of each target product in the subgrid area, divide the subgrid area into all fishing net areas, determine the order quantity of each target product based on the target POI of each fishing net area, traverse each fishing net area in all subgrid areas, and determine the fishing net area with the largest order quantity as the target point; The second determination unit is used to determine the product configuration quantity of each target product in the target outlet based on the adjusted sales volume of each target product corresponding to the target outlet and the other store sales volume of each target product corresponding to other stores in the sub-grid area where the target outlet is located.

10. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the network layout method based on spatial clustering analysis as claimed in any one of claims 1 to 8 is implemented.

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