Multi-dimensional feature fused store site selection recommendation method

Abstract

The invention relates to the field of store site selection recommendation, and discloses a store site selection recommendation method fusing multi-dimensional features, which comprises the following steps: S10, collecting multi-dimensional data of an area where candidate store site selection is located, the multi-dimensional data comprising human traffic, competitive product quantity, price, industry market scale growth rate, area planning trend and consumer group change trend; s20, carrying out preprocessing on the multi-dimensional data, wherein the preprocessing comprises data cleaning and normalization processing; s30, inputting the preprocessed multi-dimensional data into a store site selection recommendation model to obtain a recommendation value of each candidate store site selection; and S40, performing descending sort based on the recommendation value of each candidate store site selection to obtain a store site selection recommendation sequence. According to the method, store site selection recommendation is carried out through multi-dimensional data, multiple key factors influencing store operation are fully considered, and the commercial environment of the candidate region is more comprehensively described, so that more accurate site selection recommendation can be provided for the store, and the site selection accuracy is effectively improved.

Description

Technical Field

[0001] The present invention relates to the field of store location recommendation, and in particular to a store location recommendation method integrating multi-dimensional features. Background Art

[0002] At present, in the process of store site selection, traditional methods often only focus on limited factors such as traffic, number of competing products and price. However, this one-sided site selection method ignores important trend data, such as industry development trends, regional planning trends and consumer population changes.

[0003] Due to the lack of consideration of these trends, many stores face the risk of losses or even closure soon after opening. For example, as cities develop, some popular commercial areas may gradually lose their advantages due to new transportation planning or the transfer of commercial centers, but stores that choose locations based solely on traditional factors such as historical traffic flow fail to adapt to this change in a timely manner. For another example, the proportion and income level of the main consumer groups are constantly changing. If these factors are not considered when selecting a location, the products or services provided by the store may not meet the needs of local consumers, resulting in poor operations. Therefore, it is of great significance to develop a store location recommendation method that can integrate multi-dimensional features. Summary of the invention

[0004] The purpose of the present invention is to provide a store location recommendation method integrating multi-dimensional features to solve the above technical problems.

[0005] The purpose of the present invention can be achieved through the following technical solutions: A store location recommendation method integrating multi-dimensional features comprises the following steps: S10, collecting multi-dimensional data of the area where the candidate store location is located, the multi-dimensional data including: traffic, number of competing products, price, industry market size growth rate, regional planning trend and consumer population change trend; S20, preprocessing the multidimensional data, including data cleaning and normalization; S30, inputting the pre-processed multidimensional data into a store location recommendation model to obtain a recommended value for each candidate store location; S40, after arranging the recommended values ​​of each candidate store location in descending order, a store location recommendation sequence is obtained.

[0006] In a further solution, the expression of the store location recommendation model is: ; In the formula, represents the recommended value of the candidate store location, Indicates Item parameter value, Represents a monitoring cycle, Indicates the start time of a monitoring cycle. Indicates the end time of a monitoring cycle. express The adjustment coefficient corresponding to each parameter is determined based on a comprehensive analysis of historical data and empirical data; Indicates the number of The maximum value of the item parameter, Indicates the number of The minimum value of the item parameter; , corresponding to the flow of people 、Number of competing products ,price , Industry market size growth rate , Regional Planning Trends and consumer demographic trends parameter; It is an environmental impact indicator.

[0007] Further, the price parameter The method to obtain is: Get the rental price of the current candidate store location , and then obtain the average monthly consumer expenditure of residents in the area where the current candidate store location is located ; By formula: Calculate the price parameters ; In the formula, , is a preset scaling factor, determined based on historical data analysis, and ; Get the planning trend parameters of the area The method is: If a new subway line is opened in the area where the current candidate store is located, ,otherwise, ; In the formula, Represents the impact factor, determined based on historical data analysis. Indicates the time interval between the expected opening time of the new subway line and the current time; Get the consumer population change trend parameters The method is: Obtain the proportion F of the consumer group aged 20-40 and the growth rate G of the monthly income level of residents in the area where the current candidate store location is located; Substituting into the formula: Calculate the trend parameters of consumer population changes ; In the formula, , represents the preset weight factor, which is formulated based on historical data and empirical data, and .

[0008] In a further solution, the expression of the environmental impact index is: ; In the formula, Indicates the traffic congestion index of the area where the current candidate store location is located. Indicates the prosperity index of the business district where the current candidate store location is located. represents the coincidence index, , Indicates reference value.

[0009] A further solution is to calculate the traffic congestion index of the area where the current candidate store location is located. The method to obtain is: Get the actual traffic volume Q, actual average vehicle speed V and actual time TC of the area where the current candidate store location is located in a unit time, and substitute them into the following formula: Calculate the traffic congestion index of the area where the current candidate store location is located ; In the formula, , , Represents the weight coefficient, which is determined based on historical data and empirical data. ; Indicates the speed limit value of the area. Indicates the shortest time to pass through the area. It indicates the traffic volume that the area can accommodate at the maximum capacity.

[0010] A further solution is to obtain the prosperity index of the business district where the current candidate store location is located. The method is: Get the number of stores N, the average sales volume W and the customer flow in the area where the current candidate store is located , after normalization, it is entered into the following formula: Calculate the prosperity index of the business district where the current candidate store location is located .

[0011] In a further embodiment, the overlap index The expression is: ; In the formula, Indicates each traffic congestion period, Indicates the number of traffic congestion periods in a unit cycle, , Respectively represent The start and end time of each traffic congestion period, , Respectively represent the effective operation start time and end time of the reserved store.

[0012] A further solution is to obtain the traffic congestion period as follows: Divide a unit period into n unit times at equal intervals; The traffic congestion index corresponding to each unit time With preset traffic congestion threshold For comparison, if , the current unit time is marked as a traffic congestion period.

[0013] Beneficial effects of the present invention: (1) The present invention integrates multi-dimensional data such as customer flow, number of competing products, price, industry market size growth rate, regional planning trend and consumer population change trend to recommend store locations. Compared with traditional methods, it fully considers various key factors affecting store operations and more comprehensively depicts the business environment of candidate areas, thereby providing more accurate location recommendations for stores and effectively improving the accuracy of location selection. (2) By incorporating data such as the industry market size growth rate and regional planning trends, the present invention can help stores predict market changes and regional development trends in advance; when the industry is in its rising period, choosing to open stores in areas with great market potential and in line with industry development trends will help stores seize market opportunities and achieve rapid development; when there are positive planning changes in the region, such as the construction of a new subway line, early layout can enable stores to obtain more development dividends in the future; at the same time, paying attention to the changing trends of consumer groups enables stores to better meet the ever-changing needs of consumers, thereby enhancing the stores' adaptability to market changes and their ability to resist risks. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] The present invention will be further described below in conjunction with the accompanying drawings.

[0015] Figure 1 It is a diagram of the method steps of the present invention. DETAILED DESCRIPTION

[0016] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0017] See also Figure 1As shown, the present invention is a store location recommendation method integrating multi-dimensional features, comprising the following steps: S10, collecting multi-dimensional data of the area where the candidate store location is located, the multi-dimensional data including: traffic, number of competing products, price, industry market size growth rate, regional planning trend and consumer population change trend; S20, pre-processing the multi-dimensional data, including data cleaning and normalization processing; data cleaning includes removing noise data, duplicate data and erroneous data from the collected data. For example, for obviously abnormal peaks or valleys in the flow of people data, verification and correction are carried out by comparing with historical data and field research; normalization processing: normalizing data of different dimensions; S30, inputting the pre-processed multidimensional data into a store location recommendation model to obtain a recommended value for each candidate store location; S40, after arranging the recommended values ​​of each candidate store location in descending order, a store location recommendation sequence is obtained.

[0018] The expression of the store location recommendation model is: ; In the formula, represents the recommended value of the candidate store location, Indicates Item parameter value, Represents a monitoring cycle, Indicates the start time of a monitoring cycle. Indicates the end time of a monitoring cycle. express The adjustment coefficient corresponding to each parameter is determined based on a comprehensive analysis of historical data and empirical data, and is used to adjust the importance of each parameter in the model, because different parameters have different importance for different types of stores; Indicates the number of The maximum value of the item parameter, Indicates the number of The minimum value of the item parameter; , corresponding to the flow of people 、Number of competing products ,price , Industry market size growth rate , Regional Planning Trends and consumer demographic trends parameter; It is an environmental impact indicator. It should be noted that the monitoring period corresponding to each parameter is different and can be adaptively adjusted according to the actual situation; These two values ​​are used to measure the fluctuation of the parameters during the monitoring period, reflecting the stability or variation of the factor. Taking the industry market size growth rate as an example, if the maximum and minimum values ​​differ greatly during the monitoring period, it means that the development of the industry is unstable and there may be greater risks or opportunities, which requires special attention when evaluating site selection.

[0019] The present invention integrates multi-dimensional data such as traffic flow, number of competing products, price, industry market size growth rate, regional planning trend and consumer population change trend to recommend store locations; compared with traditional methods, it fully considers various key factors affecting store operations and more comprehensively depicts the business environment of candidate areas, thereby being able to provide more accurate location recommendations for stores and effectively improve the accuracy of location selection; for example, when evaluating the location of a potential restaurant store, it not only analyzes the current traffic flow and number of surrounding competing products, but also combines the market size growth trend of the local catering industry, whether there is a future transportation construction plan for the region, and the changes in the proportion of key consumer groups, etc., to comprehensively judge the feasibility of the location, greatly reducing the risk of business failure due to improper location selection; At the same time, by incorporating data such as the industry market size growth rate and regional planning trends, the present invention can help stores predict market changes and regional development trends in advance; when the industry is in its rising period, choosing to open stores in areas with great market potential and in line with industry development trends will help stores seize market opportunities and achieve rapid development; when there are positive planning changes in the region, such as the construction of a new subway line, early layout can enable stores to obtain more development dividends in the future; at the same time, paying attention to the changing trends of consumer groups enables stores to better meet the changing needs of consumers, enhancing stores' adaptability to market changes and risk resistance; accurate store site selection recommendations can guide the rational distribution of commercial resources, avoid excessive concentration of resources in certain seemingly popular areas but with limited actual development potential, and reduce resource waste.

[0020] The price parameters The method to obtain is: Get the rental price of the current candidate store location , and then obtain the average monthly consumer expenditure of residents in the area where the current candidate store location is located ; By formula: Calculate the price parameters ; In the formula, , is a preset scaling factor, determined based on historical data analysis, and ; Get the planning trend parameters of the area The method is: If a new subway line is opened in the area where the current candidate store is located, ,otherwise, ; In the formula, Represents the impact factor, determined based on historical data analysis. Indicates the time interval between the expected opening time of the new subway line and the current time; Get the consumer population change trend parameters The method is: Obtain the proportion F of the consumer group aged 20-40 and the growth rate G of the monthly income level of residents in the area where the current candidate store location is located; Substituting into the formula: Calculate the trend parameters of consumer population changes ; In the formula, , represents the preset weight factor, which is formulated based on historical data and empirical data, and .

[0021] Through the above technical solution, the price parameters are given , Regional planning trend parameters , Consumer population change trend parameters The specific method of obtaining it.

[0022] The expression of the environmental impact index is: ; In the formula, Indicates the traffic congestion index of the area where the current candidate store location is located. Indicates the prosperity index of the business district where the current candidate store location is located. represents the coincidence index, , Indicates reference value.

[0023] In the present invention, by formula Calculate the environmental impact index Obviously, traffic congestion indicators Reflects the traffic conditions in the area. Congestion means a large flow of people. Business district prosperity index Reflects the commercial vitality of the area; overlap index It measures the degree of overlap between the store's effective operating time and traffic congestion periods; the environmental impact index combines these aspects to further adjust the final site recommendation value to make the recommendation result more in line with the actual business scenario.

[0024] The traffic congestion index of the area where the current candidate store location is located The method to obtain is: Get the actual traffic volume Q, actual average vehicle speed V and actual time TC of the area where the current candidate store location is located in a unit time, and substitute them into the following formula: Calculate the traffic congestion index of the area where the current candidate store location is located ; In the formula, , , Represents the weight coefficient, which is determined based on historical data and empirical data. ; Indicates the speed limit value of the area. Indicates the shortest time to pass through the area. It indicates the traffic volume that the area can accommodate at the maximum capacity.

[0025] Get the business district prosperity index of the current candidate store location area The method is: Get the number of stores N, the average sales volume W and the customer flow in the area where the current candidate store is located , after normalization, it is entered into the following formula: Calculate the prosperity index of the business district where the current candidate store location is located .

[0026] In the present invention, by , thus integrating the number of stores N, the average store sales W and customer flow The actual traffic volume / the traffic volume that the area can accommodate under the maximum traffic capacity is combined to obtain the prosperity index of the business district where the current candidate store location is located, the number of stores N, the average sales volume W of the stores and the customer flow The larger the value, the more prosperous the business district where the current candidate store is located. The larger the value is, the greater the traffic volume in the area where the current candidate store is located. The more prosperous the business district is, the more prosperous the current candidate store location is.

[0027] The coincidence index The expression is: ; In the formula, Indicates each traffic congestion period, Indicates the number of traffic congestion periods in a unit cycle, , Respectively represent The start and end time of each traffic congestion period, , Respectively represent the effective operation start time and end time of the reserved store.

[0028] The method to obtain traffic congestion period is: Divide a unit period into n unit times at equal intervals; The traffic congestion index corresponding to each unit time With preset traffic congestion threshold For comparison, if , the current unit time is marked as a traffic congestion period.

[0029] In the present invention, The effective operating time reserved for the store is Calculate the overlap duration of each traffic congestion period with the store's effective operating hours; that is, first find the end time (the minimum of the two interval end times) and start time (the maximum of the two interval start times) of the intersection of the two time intervals, and then calculate their difference. If the difference is negative, it means that the two intervals do not overlap, and the overlap duration is 0; then use the formula Add the overlapping time of each traffic congestion period and the store's effective operating time to get the total overlapping time; finally, use the formula The total overlapping time is divided by the total effective operating time of the store to obtain the overlap; through the above technical solution, the overlap between the effective operating time of the store location and the current traffic congestion period of the business district is introduced into the calculation formula of the environmental impact index. Obviously, the larger the overlap index, the higher the recommended value of the area where the store location is located.

[0030] For example, there are three candidate store locations in the current city, A, B, and C. The most suitable store address is selected for a chain coffee shop; as shown in the following table:

[0031] As can be seen from the above table, the recommended order for choosing the most suitable store location for a chain coffee shop is: A, C, B.

[0032] It should be noted that the calculation formulas and various parameters involved in the calculation in the present invention have been dimensionally processed in advance, and the process of dimensionless processing is well known in the industry and will not be described here.

[0033] The above is a detailed description of an embodiment of the present invention, but the content is only a preferred embodiment of the present invention and cannot be considered to limit the scope of implementation of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the patent coverage of the present invention.

Claims

1. A store location recommendation method integrating multi-dimensional features, characterized in that: The steps include: S10, collecting multi-dimensional data of the area where the candidate store location is located, the multi-dimensional data including: traffic, number of competing products, price, industry market size growth rate, regional planning trend and consumer population change trend; S20, preprocessing the multidimensional data, including data cleaning and normalization; S30, inputting the pre-processed multidimensional data into a store location recommendation model to obtain a recommended value for each candidate store location; S40, after arranging the recommended values ​​of each candidate store location in descending order, a store location recommendation sequence is obtained.

2. The store location recommendation method integrating multi-dimensional features according to claim 1 is characterized in that: The expression of the store location recommendation model is: ； In the formula, represents the recommended value of the candidate store location, Indicates Item parameter value, Represents a monitoring cycle, Indicates the start time of a monitoring cycle. Indicates the end time of a monitoring cycle. express The adjustment coefficient corresponding to the item parameter; Indicates the number of The maximum value of the item parameter, Indicates the number of The minimum value of the item parameter; , corresponding to the flow of people 、Number of competing products ,price 、Industry market size growth rate , Regional Planning Trends and consumer demographic trends parameter; It is an environmental impact indicator.

3. The store location recommendation method integrating multi-dimensional features according to claim 2 is characterized in that: The price parameters The method to obtain is: Get the rental price of the current candidate store location , and then obtain the average monthly consumer expenditure of residents in the area where the current candidate store location is located ; By formula: Calculate the price parameters ; In the formula, , is the preset scale factor, and ; Get the planning trend parameters of the area The method is: If a new subway line is opened in the area where the current candidate store is located, ,otherwise, ; In the formula, represents the impact factor, Indicates the time interval between the expected opening time of the new subway line and the current time; Get the consumer population change trend parameters The method is: Obtain the proportion F of the consumer group aged 20-40 and the growth rate G of the monthly income level of residents in the area where the current candidate store location is located; Substituting into the formula: Calculate the trend parameters of consumer population changes ; In the formula, , represents the preset weight factor, and .

4. The store location recommendation method integrating multi-dimensional features according to claim 2 is characterized in that: The expression of the environmental impact index is: ; In the formula, Indicates the traffic congestion index of the area where the current candidate store location is located. Indicates the prosperity index of the business district where the current candidate store location is located. represents the coincidence index, , Indicates reference value.

5. The store location recommendation method integrating multi-dimensional features according to claim 4 is characterized in that: The traffic congestion index of the area where the current candidate store location is located The method to obtain is: Get the actual traffic volume Q, actual average vehicle speed V and actual time TC of the area where the current candidate store location is located in a unit time, and substitute them into the following formula: Calculate the traffic congestion index of the area where the current candidate store location is located ; In the formula, , , represents the weight coefficient, ; Indicates the speed limit value of the area. Indicates the shortest time to pass through the area. It indicates the traffic volume that the area can accommodate at the maximum capacity.

6. The store location recommendation method integrating multi-dimensional features according to claim 5 is characterized in that: Get the business district prosperity index of the current candidate store location area The method is: Get the number of stores N, the average sales volume W and the customer flow in the area where the current candidate store is located , after normalization, it is entered into the following formula: Calculate the prosperity index of the business district where the current candidate store location is located .

7. The store location recommendation method integrating multi-dimensional features according to claim 4 is characterized in that: The coincidence index The expression is: ; In the formula, Indicates each traffic congestion period, Indicates the number of traffic congestion periods in a unit cycle, , Respectively represent The start and end time of each traffic congestion period, , Respectively represent the effective operation start time and end time of the reserved store.

8. The store location recommendation method integrating multi-dimensional features according to claim 7 is characterized in that: The method to obtain traffic congestion period is: Divide a unit period into n unit times at equal intervals; The traffic congestion index corresponding to each unit time With preset traffic congestion threshold For comparison, if , the current unit time is marked as a traffic congestion period.

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