Site selection analysis method and device, storage medium and electronic device

Abstract

The present disclosure relates to a site selection analysis method and device, a storage medium and an electronic device, the method comprising: determining a plurality of homogeneous geographical areas as candidate geographical areas for a target store, the homogeneous geographical areas being geographical areas with business characteristics similar to those of the candidate geographical areas; determining consumption information of stores of the same type as the target store in the plurality of homogeneous geographical areas during the same historical time period; and determining whether to site the target store in the candidate geographical area based on the consumption information.

Description

Technical Field

[0001] The present disclosure relates to the field of Internet technology, and in particular, to a site selection analysis method and device, a storage medium and an electronic device. Background Art

[0002] The forward warehouse is a warehousing and distribution model in the new retail field. It can be a store with small and medium-sized warehousing and distribution centers, so that the central warehouse at the headquarters only needs to supply goods to each store. After consumers place an order, the goods are shipped from nearby stores instead of from a warehouse far away in the suburbs, thereby improving delivery efficiency.

[0003] At present, the site selection method for stores such as forward warehouses is still in a relatively primitive stage, that is, site selectors determine information such as customer flow, crowd characteristics, etc. around the store's alternative addresses through field visits and other methods, and finally make decisions on the store's location. Summary of the invention

[0004] The purpose of the present disclosure is to provide a site selection analysis method and device, a storage medium and an electronic device to solve the problem that the current store site selection process lacks data support and is more dependent on the experience of site selectors.

[0005] In order to achieve the above objectives, in a first aspect, the present disclosure provides a site selection analysis method, the method comprising:

[0006] Determine multiple homogeneous geographic areas of candidate geographic areas of the target store, wherein the homogeneous geographic areas refer to geographic areas with business characteristics similar to those of the candidate geographic areas;

[0007] For the stores of the same type as the target store in the multiple homogeneous geographical areas, determine the consumption information of the stores of the same type in the same historical time period;

[0008] Determine whether to locate the target store in the candidate geographical area based on the consumption information.

[0009] Optionally, the determining of a plurality of homogeneous geographical areas as candidate geographical areas of the target store comprises:

[0010] Acquire business characteristic data of multiple geographic areas in the same historical time period, wherein the multiple geographic areas include the candidate geographic area;

[0011] Determine a feature vector of each geographic area based on the commercial feature data of the geographic area;

[0012] A homogeneous geographical area of ​​the candidate geographical area is determined from the multiple geographical areas according to the similarity between the feature vectors.

[0013] Optionally, the commercial characteristic data includes at least one of the following information: data used to characterize customer flow portrait characteristics of the geographical area, data used to characterize store type characteristics in the geographical area, data used to characterize revenue characteristics of various stores in the geographical area, and data used to characterize revenue characteristics of stores of the same type as the target store in the geographical area.

[0014] Optionally, determining the feature vector of each geographical area according to the commercial feature data of the geographical area includes:

[0015] For each feature data of each geographical area in the same dimension, the following formula is used for standardization:

[0016]

[0017] Wherein, X* is the standardized value of the feature data, X is the feature data, m is the mean of the feature data of the dimension, s is the standard deviation of the feature data of the dimension, and the feature vector of the geographic area includes the standardized value of the feature data of each dimension;

[0018] The step of determining a homogeneous geographical region of the candidate geographical region from the multiple geographical regions according to the similarity between the feature vectors comprises:

[0019] The similarity d between the feature vectors of each geographic region and the feature vectors of the candidate geographic region is calculated by the following formula 12 :

[0020]

[0021] Among them, x 1k is the kth element value in the feature vector of a certain geographical area, x 2k is the kth element value in the feature vector of the candidate geographic area, n is the dimension of the feature vector, s k is the standard deviation of the eigenvalues ​​of the eigenvectors of each geographic region in the k dimension;

[0022] The N geographical regions having the highest similarity with the feature vectors of the candidate geographical region are regarded as homogeneous geographical regions of the candidate geographical region.

[0023] Optionally, determining the consumption information of the stores of the same type as the target store in the multiple homogeneous geographical areas during the same historical period includes:

[0024] Determine the store entry rate of consumers entering the type of stores, the purchase rate of consumers entering the type of stores, and the average customer spending of consumers in the multiple homogeneous geographic areas during the same historical period, wherein the consumption information includes the store entry rate, the purchase rate, and the average customer spending;

[0025] The determining whether to locate the target store in the candidate geographical area according to the consumption information includes:

[0026] Predicting the total number of second consumers in the candidate geographical area within the future preset time period based on the total number of first consumers in the candidate geographical area within the historical preset time period;

[0027] Predicting the revenue information of the target store in the candidate geographic area according to the total number of the second consumers, the store entry rate, the purchase rate, and the average customer spending; and

[0028] Determine whether to locate the target store in the candidate geographical area according to the revenue information.

[0029] Optionally, the method further comprises:

[0030] The ratio between the number of consumers who entered the store of the type in the candidate geographical area during the same historical period and the total number of consumers in the candidate geographical area during the same historical period is used as the consumer conversion rate of the store of the type in the candidate geographical area;

[0031] The determining whether to locate the target store in the candidate geographical area according to the consumption information includes:

[0032] Predicting the total number of second consumers in the candidate geographical area within the future preset time period based on the total number of first consumers in the candidate geographical area within the historical preset time period;

[0033] Predicting the revenue information of the target store in the candidate geographic area according to the total number of the second consumers, the store entry rate, the purchase rate, and the average customer spending; and

[0034] Determine whether to locate the target store in the candidate geographical area based on the revenue information and the consumer conversion rate.

[0035] According to a second aspect of an embodiment of the present disclosure, there is provided a site selection analysis device, including:

[0036] A homogeneous region determination module, used to determine a plurality of homogeneous geographical regions of the candidate geographical region of the target store, wherein the homogeneous geographical region refers to a geographical region whose commercial characteristics are similar to those of the candidate geographical region;

[0037] A consumption information determination module, for determining, for the stores of the same type as the target store in the plurality of homogeneous geographical areas, consumption information of the stores of the same type within the same historical period;

[0038] A site selection determination module is used to determine whether to locate the target store in the candidate geographical area based on the consumption information.

[0039] Optionally, the homogeneous region determination module includes:

[0040] A feature acquisition submodule, used to acquire business feature data of multiple geographical areas in the same historical period, wherein the multiple geographical areas include the candidate geographical area;

[0041] A vector determination submodule, used to determine a feature vector of each geographical area according to the commercial feature data of the geographical area;

[0042] The homogeneity determination submodule is used to determine homogeneous geographical areas of the candidate geographical areas from the multiple geographical areas according to the similarity between the feature vectors.

[0043] Optionally, the commercial feature data acquired by the feature acquisition submodule includes at least one of the following information: data used to characterize customer flow portrait characteristics of the geographical area, data used to characterize store type characteristics in the geographical area, data used to characterize revenue characteristics of various stores in the geographical area, and data used to characterize revenue characteristics of stores of the same type as the target store in the geographical area.

[0044] Optionally, the vector determination submodule includes:

[0045] The first calculation module is used to standardize each feature data of each geographical area in the same dimension using the following formula:

[0046]

[0047] Wherein, X* is the standardized value of the feature data, X is the feature data, m is the mean of the feature data of the dimension, s is the standard deviation of the feature data of the dimension, and the feature vector of the geographic area includes the standardized value of the feature data of each dimension;

[0048] The homogeneity determination submodule includes:

[0049] The second calculation module is used to calculate the similarity d between the feature vectors of each geographic region and the feature vectors of the candidate geographic region respectively through the following formula: 12 :

[0050]

[0051] Among them, x 1k is the kth element value in the feature vector of a certain geographical area, x 2k is the kth element value in the feature vector of the candidate geographic area, n is the dimension of the feature vector, s k is the standard deviation of the eigenvalues ​​of the eigenvectors of each geographic region in the k dimension;

[0052] The execution module is used to select N geographical regions with the highest similarity between their feature vectors and the candidate geographical region as homogeneous geographical regions of the candidate geographical region.

[0053] Optionally, the consumption information determination module includes:

[0054] A first determination submodule is used to determine the store entry rate of consumers entering the stores of the type, the purchase rate of consumers entering the stores of the type, and the average customer spending of consumers in the multiple homogeneous geographical areas during the historical period, wherein the consumption information includes the store entry rate, the purchase rate, and the average customer spending;

[0055] The site selection determination module includes:

[0056] A first prediction submodule, configured to predict the total number of second consumers in the candidate geographical area within the preset time period in the future according to the total number of first consumers in the candidate geographical area within the preset time period in history;

[0057] A second prediction submodule is used to predict the revenue information of the target store in the candidate geographical area according to the total number of the second consumers, the store entry rate, the purchase rate and the average customer spending;

[0058] The first address determination submodule is used to determine whether to locate the target store in the candidate geographical area according to the revenue information.

[0059] Optionally, the device further comprises:

[0060] a conversion rate determination module, configured to use the ratio between the number of consumers who entered the store of the type in the candidate geographical area during the same historical period and the total number of consumers in the candidate geographical area during the same historical period as the consumer conversion rate of the store of the type in the candidate geographical area;

[0061] The site selection determination module includes:

[0062] A third prediction submodule, configured to predict the total number of second consumers in the candidate geographical area within the preset time period in the future according to the total number of first consumers in the candidate geographical area within the preset time period in history;

[0063] A fourth prediction submodule, configured to predict the revenue information of the target store in the candidate geographical area according to the total number of the second consumers, the store entry rate, the purchase rate, and the average customer spending;

[0064] The second address determination submodule is used to determine whether to locate the target store in the candidate geographical area according to the revenue information and the consumer conversion rate.

[0065] According to a third aspect of an embodiment of the present disclosure, a computer-readable storage medium is provided, on which computer program instructions are stored. When the program instructions are executed by a processor, the steps of the site selection analysis method provided in the first aspect of the present disclosure are implemented.

[0066] According to a fourth aspect of an embodiment of the present disclosure, there is provided an electronic device, including:

[0067] a memory having a computer program stored thereon;

[0068] A processor is used to execute the computer program in the memory to implement the steps of the site selection analysis method provided in the first aspect of the present disclosure.

[0069] The above technical solution can at least have the following beneficial effects:

[0070] By determining the candidate geographical areas of the store to be sited, and determining the homogeneous geographical areas of the candidate geographical areas based on the similarity of commercial characteristics, the homogeneous geographical areas can provide a more realistic simulation analysis environment during the site selection analysis. At the same time, the site selection personnel can also obtain the consumption information of the same type of stores as the store to be sited in the homogeneous geographical areas during the same historical period, so as to evaluate the competitiveness and revenue of the store to be sited in the candidate geographical areas based on the consumption information, and then make reference to the site selection decision, and finally improve the rationality of the site selection.

[0071] Other features and advantages of the present disclosure will be described in detail in the following detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0072] The accompanying drawings are used to provide a further understanding of the present disclosure and constitute a part of the specification. Together with the following specific embodiments, they are used to explain the present disclosure but do not constitute a limitation of the present disclosure. In the accompanying drawings:

[0073] Figure 1 The present invention is a flowchart of a site selection analysis method according to an exemplary embodiment.

[0074] Figure 2 The present invention is a flowchart of a site selection analysis method according to an exemplary embodiment.

[0075] Figure 3 The present invention is a flowchart of a site selection analysis method according to an exemplary embodiment.

[0076] Figure 4 The figure is a block diagram of a site selection analysis device according to an exemplary embodiment.

[0077] Figure 5 The figure is a block diagram of a site selection analysis device according to an exemplary embodiment.

[0078] Figure 6 It is a block diagram of another device for site selection analysis according to an exemplary embodiment. DETAILED DESCRIPTION

[0079] The specific embodiments of the present disclosure are described in detail below in conjunction with the accompanying drawings. It should be understood that the specific embodiments described herein are only used to illustrate and explain the present disclosure and are not used to limit the present disclosure. Instead, they are only examples of devices and methods consistent with some aspects of the present disclosure as detailed in the attached claims.

[0080] Before introducing the site selection analysis method and device, storage medium and electronic device provided by the present disclosure, the application scenario of the present disclosure is first introduced. The embodiments provided by the present disclosure can be applied to various store site selection analysis occasions, such as forward warehouse site selection, franchise store site selection, etc.

[0081] Figure 1 is a flow chart of a site selection analysis method according to an exemplary embodiment. Figure 1 As shown, the site selection analysis method includes:

[0082] S11, determining a plurality of homogeneous geographical areas of a candidate geographical area of ​​a target store, wherein the homogeneous geographical area refers to a geographical area having business characteristics similar to those of the candidate geographical area.

[0083] The commercial characteristics may include one or more aspects, for example, the commercial characteristics may include the types of stores included in the candidate geographical area, the distribution ratio of each type of stores, the flow of people, the structure of the flow of people, consumption habits, the types of surrounding facilities, etc. In specific implementation, multiple different geographical areas can be analyzed based on such commercial characteristics, and the geographical areas with high similarity of the commercial characteristics can be determined as homogeneous geographical areas. In other words, the homogeneous geographical areas determined by multi-dimensional commercial characteristics can provide a more realistic simulation analysis environment, thereby providing a more reliable data source for the site selection analysis process.

[0084] S12, for the stores of the same type as the target store in the multiple homogeneous geographical areas, determine the consumption information of the stores of the same type in the same historical time period.

[0085] S13, determining whether to locate the target store in the candidate geographical area according to the consumption information.

[0086] It should be understood that when the candidate geographical area is determined to be homogeneous with a certain geographical area through commercial characteristics, the operating status of the target store in the candidate area will also be similar to the operating status of the same type of store in the homogeneous geographical area. In other words, the status of the target store in the candidate geographical area can be evaluated by the consumption information of the same type of store in the homogeneous geographical area during the same historical period, and finally, whether to locate the target store in the candidate geographical area can be determined based on the evaluation result.

[0087] By adopting the above method, the following technical effects can be achieved:

[0088] By determining the candidate geographical areas of the store to be sited, and determining the homogeneous geographical areas of the candidate geographical areas based on the similarity of commercial characteristics, the homogeneous geographical areas can provide a more realistic simulation analysis environment during the site selection analysis. At the same time, the site selection personnel can also obtain the consumption information of the same type of stores as the store to be sited in the homogeneous geographical areas during the same historical period, so as to evaluate the competitiveness and revenue of the store to be sited in the candidate geographical areas based on the consumption information, and then make reference to the site selection decision, and finally improve the rationality of the site selection.

[0089] Figure 2 is a flow chart of a site selection analysis method according to an exemplary embodiment. Figure 2 , the method comprising:

[0090] S21, obtaining business feature data of multiple geographical areas in the same historical period, wherein the multiple geographical areas include the candidate geographical area.

[0091] S22, determining a feature vector of each geographical area according to the commercial feature data of the geographical area.

[0092] In the specific implementation, different types of data can be set as components of corresponding feature vectors according to the commercial feature data. For example, the commercial feature data of the target geographical area includes the name of the geographical area, the type of the geographical area, the type of merchants, and the proportion of each merchant type. When determining the feature vector of the target geographical area, the name of the geographical area, the type of the geographical area, the type of merchants, and the proportion of each merchant type can be used as a component of the feature vector, thereby obtaining a feature vector.

[0093] S23: Determine a homogeneous geographical area of ​​the candidate geographical areas from the multiple geographical areas according to the similarity between the feature vectors.

[0094] S24, for the stores of the same type as the target store in the multiple homogeneous geographical areas, determine the consumption information of the stores of the same type in the same historical time period.

[0095] S25, determining whether to locate the target store in the candidate geographical area according to the consumption information.

[0096] In this way, by obtaining the business feature data of multiple geographical areas in the same historical period, and determining the feature vector corresponding to each geographical area according to the business feature data of the geographical area, the complex data volume can be simplified into the feature vector. Finally, by calculating the similarity between the feature vectors, the homogeneous geographical area of ​​the candidate geographical area can be determined from the multiple geographical areas.

[0097] In a possible embodiment, the commercial characteristic data described in step S21 includes at least one of the following information: data used to characterize the customer flow portrait characteristics of the geographical area, data used to characterize the store type characteristics in the geographical area, data used to characterize the revenue characteristics of various stores in the geographical area, and data used to characterize the revenue characteristics of stores of the same type as the target store in the geographical area.

[0098] That is to say, when evaluating the similarity between geographical areas, one or more of the data such as customer flow profiles, store types, and various store revenues of the geographical area can be combined to construct a feature vector of the geographical area, so that more homogeneous geographical areas that meet the requirements can be accurately obtained through the feature vector, providing a basis for subsequent site selection analysis steps.

[0099] Still Figure 2 To illustrate, in another possible implementation, step S22 includes:

[0100] For each feature data of each geographical area in the same dimension, the following formula is used for standardization:

[0101]

[0102] Among them, X* is the standardized value of the feature data, X is the feature data, m is the mean of the feature data of this dimension, s is the standard deviation of the feature data of this dimension, and the feature vector of the geographic area includes the standardized value of the feature data of each dimension.

[0103] The step S23 comprises:

[0104] The similarity d between the feature vectors of each geographic region and the feature vectors of the candidate geographic region is calculated by the following formula 12 :

[0105]

[0106] Among them, x 1k is the kth element value in the feature vector of a certain geographical area, x 2k is the kth element value in the feature vector of the candidate geographic area, n is the dimension of the feature vector, s k is the standard deviation of the eigenvalues ​​of the eigenvectors of each geographic region in the k dimension;

[0107] The N geographical regions having the highest similarity with the feature vectors of the candidate geographical region are regarded as homogeneous geographical regions of the candidate geographical region.

[0108] In this way, even when the dimensions of the feature vectors corresponding to different geographical areas are different, the site selection analysis method can still be used to calculate the similarity between different geographical areas and candidate geographical areas through feature vectors, thereby determining the homogeneous geographical areas of the candidate geographical areas based on the results of the calculation, and finally providing data support for site selection decisions based on the operating status of the same type of stores in the homogeneous geographical areas.

[0109] Figure 3 is a flow chart of a site selection analysis method according to an exemplary embodiment. Figure 3 , the method comprising:

[0110] S31, determining a plurality of homogeneous geographical areas of a candidate geographical area of ​​a target store, wherein the homogeneous geographical area refers to a geographical area having business characteristics similar to those of the candidate geographical area.

[0111] S32, determining the store entry rate of consumers entering the type of stores, the purchase rate of consumers entering the type of stores, and the average order value of consumers' purchases in the multiple homogeneous geographical areas during the same historical period, wherein the consumption information includes the store entry rate, the purchase rate, and the average order value.

[0112] When there are multiple homogeneous geographical areas, the store entry rate, purchase rate and average customer spending may be the average values ​​of the store entry rate, purchase rate and average customer spending of the multiple homogeneous geographical areas.

[0113] S33: predicting the total number of second consumers in the candidate geographical area within the preset time period in the future according to the total number of first consumers in the candidate geographical area within the preset time period in history.

[0114] For example, the total number of the second consumers may be predicted in the following manner:

[0115] F t+1 =(a+(b-1))*y t +(1-a)*F t .

[0116] Among them, F t+1 is the predicted value of the number of consumers in the t+1 period, y t is the actual value of the number of consumers in the tth period, F t is the predicted value of the number of consumers in the tth time period, a is the smoothing coefficient (0≤a≤1), and b is the time coefficient. It should be understood that the total number of consumers in the candidate geographic area may be associated with the time period, that is, the number of consumers in different time periods also changes (for example, seasonal factors cause changes in the number of consumers in the candidate geographic area, etc.). The time coefficient reflects the relationship between the number of consumers in a certain time period and the total average number of consumers. When the time coefficient is greater than 1, it means that the number of consumers in this time period is often higher than the total average; when the time coefficient is less than 1, it means that the number of consumers in this time period is often lower than the total average; when the time index is approximately equal to 1, it means that the total number of consumers in the candidate geographic area has no obvious time effect.

[0117] For example, when determining the time coefficient of a target quarter last year, the average monthly number of consumers in the target quarter may be divided by the average monthly number of consumers last year.

[0118] That is, the formula can use the preset time period as a calculation unit, and can predict the total number of consumers in the next time period based on the total number of consumers in the previous time period.

[0119] S34, predicting the revenue information of the target store in the candidate geographical area according to the total number of the second consumers, the store entry rate, the purchase rate and the average customer spending.

[0120] The revenue of the target store in the candidate geographical area = the total number of second consumers * store entry rate * purchase rate * average customer spending.

[0121] S35: Determine whether to locate the target store in the candidate geographical area according to the revenue information.

[0122] In this way, by predicting the total number of consumers in the future preset time period of the candidate geographical area, and obtaining the consumer store entry rate, store purchase conversion rate and customer unit price of the corresponding stores of the same type from the historical consumption data of the homogeneous geographical areas of the candidate geographical area, the revenue information of the target store in the candidate geographical area can be predicted to facilitate the decision-making of the site selection personnel. Compared with the site selection method in the related art, the analysis result is more reliable and more conducive to helping the site selection personnel make reasonable decisions.

[0123] In a possible implementation, the method further includes: taking the ratio between the number of consumers who enter the store of the type in the candidate geographical area during the same historical period and the total number of consumers in the candidate geographical area during the same historical period as the consumer conversion rate of the store of the type in the candidate geographical area;

[0124] The determining whether to locate the target store in the candidate geographical area according to the consumption information includes:

[0125] Predicting the total number of second consumers in the candidate geographical area within the future preset time period based on the total number of first consumers in the candidate geographical area within the historical preset time period;

[0126] Predicting the revenue information of the target store in the candidate geographic area according to the total number of the second consumers, the store entry rate, the purchase rate, and the average customer spending; and

[0127] Determine whether to locate the target store in the candidate geographical area based on the revenue information and the consumer conversion rate.

[0128] That is to say, when conducting site selection analysis, the above implementation will not only consider the revenue of the target store in the candidate geographical area, but also consider the brand conversion rate of the target store in the candidate geographical area, thereby increasing the dimension of site selection analysis and further improving the reliability of the analysis results.

[0129] Figure 4is a block diagram of a site selection analysis device according to an exemplary embodiment. Figure 4 As shown, the site selection analysis device 400 includes:

[0130] A homogeneous region determination module 401 is used to determine a plurality of homogeneous geographical regions of the candidate geographical region of the target store, wherein the homogeneous geographical region refers to a geographical region whose commercial characteristics are similar to those of the candidate geographical region;

[0131] The consumption information determination module 402 is used to determine the consumption information of the stores of the same type as the target store in the multiple homogeneous geographical areas during the same historical period;

[0132] The site selection determination module 403 is used to determine whether to locate the target store in the candidate geographical area according to the consumption information.

[0133] By adopting the above device, the following technical effects can be achieved:

[0134] By determining the candidate geographical areas of the store to be sited, and determining the homogeneous geographical areas of the candidate geographical areas according to the similarity of business characteristics through the homogeneous area determination module, the homogeneous geographical areas can provide a more realistic simulation analysis environment during the site selection analysis. At the same time, the site selection personnel can also obtain the consumption information of the same type of stores as the store to be sited in the homogeneous geographical areas during the same historical period, so as to evaluate the competitiveness and benefits of the store to be sited in the candidate geographical areas based on the consumption information, and then make reference to the site selection decision, and finally improve the rationality of the site selection.

[0135] Optionally, refer to Figure 5 A block diagram of a site selection analysis device is shown, such as Figure 5 As shown, the site selection analysis device 400 is Figure 4 On the basis of, the homogeneous region determination module 401 includes:

[0136] The feature acquisition submodule 4011 is used to acquire business feature data of multiple geographic areas in the same historical period, wherein the multiple geographic areas include the candidate geographic area;

[0137] A vector determination submodule 4012, configured to determine a feature vector of each geographic region according to the commercial feature data of the geographic region;

[0138] The homogeneity determination submodule 4013 is used to determine homogeneous geographical areas among the candidate geographical areas from the multiple geographical areas according to the similarity between the feature vectors.

[0139] In this way, by obtaining the business feature data of multiple geographical areas in the same historical period, and determining the feature vector corresponding to each geographical area according to the business feature data of the geographical area, the complex data volume can be simplified into the feature vector. Finally, by calculating the similarity between the feature vectors, the homogeneous geographical area of ​​the candidate geographical area can be determined from the multiple geographical areas.

[0140] Optionally, the commercial feature data acquired by the feature acquisition submodule includes at least one of the following information: data used to characterize customer flow portrait characteristics of the geographical area, data used to characterize store type characteristics in the geographical area, data used to characterize revenue characteristics of various stores in the geographical area, and data used to characterize revenue characteristics of stores of the same type as the target store in the geographical area.

[0141] Optionally, the vector determination submodule includes:

[0142] The first calculation module is used to standardize each feature data of each geographical area in the same dimension using the following formula:

[0143]

[0144] Wherein, X* is the standardized value of the feature data, X is the feature data, m is the mean of the feature data of the dimension, s is the standard deviation of the feature data of the dimension, and the feature vector of the geographic area includes the standardized value of the feature data of each dimension;

[0145] The homogeneity determination submodule comprises:

[0146] The second calculation module is used to calculate the similarity d between the feature vectors of each geographic region and the feature vectors of the candidate geographic region respectively through the following formula: 12 :

[0147]

[0148] Among them, x 1k is the kth element value in the feature vector of a certain geographical area, x 2k is the kth element value in the feature vector of the candidate geographic area, n is the dimension of the feature vector, s k is the standard deviation of the eigenvalues ​​of the eigenvectors of each geographic region in the k dimension;

[0149] The execution module is used to select N geographical regions with the highest similarity between their feature vectors and the candidate geographical region as homogeneous geographical regions of the candidate geographical region.

[0150] Optionally, the consumption information determination module includes:

[0151] A first determination submodule is used to determine the store entry rate of consumers entering the stores of the type, the purchase rate of consumers entering the stores of the type, and the average customer spending of consumers in the multiple homogeneous geographical areas during the historical period, wherein the consumption information includes the store entry rate, the purchase rate, and the average customer spending;

[0152] The site selection determination module includes:

[0153] A first prediction submodule, configured to predict the total number of second consumers in the candidate geographical area within the preset time period in the future according to the total number of first consumers in the candidate geographical area within the preset time period in history;

[0154] A second prediction submodule is used to predict the revenue information of the target store in the candidate geographical area according to the total number of the second consumers, the store entry rate, the purchase rate and the average customer spending;

[0155] The first address determination submodule is used to determine whether to locate the target store in the candidate geographical area according to the revenue information.

[0156] Optionally, the device further comprises:

[0157] a conversion rate determination module, configured to use the ratio between the number of consumers who entered the store of the type in the candidate geographical area during the same historical period and the total number of consumers in the candidate geographical area during the same historical period as the consumer conversion rate of the store of the type in the candidate geographical area;

[0158] The site selection determination module includes:

[0159] A third prediction submodule, configured to predict the total number of second consumers in the candidate geographical area within the preset time period in the future according to the total number of first consumers in the candidate geographical area within the preset time period in history;

[0160] A fourth prediction submodule, configured to predict the revenue information of the target store in the candidate geographical area according to the total number of the second consumers, the store entry rate, the purchase rate, and the average customer spending;

[0161] The second address determination submodule is used to determine whether to locate the target store in the candidate geographical area according to the revenue information and the consumer conversion rate.

[0162] Regarding the device in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated here.

[0163] The present disclosure also provides a computer-readable storage medium having computer program instructions stored thereon, and when the program instructions are executed by a processor, the steps of the site selection analysis method provided by the present disclosure are implemented.

[0164] The present disclosure also provides an electronic device, comprising:

[0165] a memory having a computer program stored thereon;

[0166] A processor is used to execute the computer program in the memory to implement the steps of the site selection analysis method provided by the present disclosure.

[0167] Figure 6 6 is a block diagram of an electronic device 600 according to an exemplary embodiment. For example, the electronic device 600 may be provided as a server. Figure 6 The electronic device 600 includes a processor 622, which may be one or more, and a memory 632 for storing a computer program executable by the processor 622. The computer program stored in the memory 632 may include one or more modules, each corresponding to a set of instructions. In addition, the processor 622 may be configured to execute the computer program to perform the above-mentioned site selection analysis method.

[0168] In addition, the electronic device 600 may further include a power supply component 626 and a communication component 650, wherein the power supply component 626 may be configured to perform power management of the electronic device 600, and the communication component 650 may be configured to implement communication, such as wired or wireless communication, of the electronic device 600. In addition, the electronic device 600 may further include an input / output (I / O) interface 658. The electronic device 600 may operate based on an operating system stored in the memory 632, such as Windows Server™, Mac OSX™, Unix™, Linux™, etc.

[0169] In another exemplary embodiment, a computer-readable storage medium including program instructions is also provided, and when the program instructions are executed by a processor, the steps of the above-mentioned site selection analysis method are implemented. For example, the computer-readable storage medium can be the above-mentioned memory 632 including program instructions, and the above-mentioned program instructions can be executed by the processor 622 of the electronic device 600 to complete the above-mentioned site selection analysis method.

[0170] In another exemplary embodiment, a computer program product is also provided. The computer program product includes a computer program that can be executed by a programmable device. The computer program has a code portion for executing the above-mentioned site selection analysis method when executed by the programmable device.

[0171] Those skilled in the art will readily appreciate other embodiments of the present disclosure after considering the specification and practicing the present disclosure. This application is intended to cover any variations, uses or adaptations of the present disclosure, which follow the general principles of the present disclosure and include common knowledge or customary technical means in the art that are not disclosed in the present disclosure. The specification and examples are to be considered as exemplary only, and the true scope and spirit of the present disclosure are indicated by the following claims.

[0172] It should be understood that the present disclosure is not limited to the exact structures that have been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.

Claims

1. A site selection analysis method, characterized in that: The method comprises: Determine multiple homogeneous geographic areas of candidate geographic areas of the target store, wherein the homogeneous geographic areas refer to geographic areas with business characteristics similar to those of the candidate geographic areas; For the stores of the same type as the target store in the multiple homogeneous geographical areas, determine the consumption information of the stores of the same type in the same historical time period; Determining whether to locate the target store in the candidate geographical area according to the consumption information; The multiple homogeneous geographical areas of the candidate geographical areas for determining the target store include: Acquire business characteristic data of multiple geographic areas in the same historical time period, wherein the multiple geographic areas include the candidate geographic area; Determine a feature vector of each geographic area based on the commercial feature data of the geographic area; determining a homogeneous geographical area of ​​the candidate geographical area from the plurality of geographical areas according to the similarity between the feature vectors; The step of determining the feature vector of each geographical area according to the commercial feature data of the geographical area includes: For each feature data of each geographical area in the same dimension, the following formula is used for standardization: ； Wherein, X* is the standardized value of the feature data, X is the feature data, m is the mean of the feature data of the dimension, s is the standard deviation of the feature data of the dimension, and the feature vector of the geographic area includes the standardized value of the feature data of each dimension; The step of determining a homogeneous geographical region of the candidate geographical region from the multiple geographical regions according to the similarity between the feature vectors comprises: The similarity d between the feature vectors of each geographic region and the feature vectors of the candidate geographic region is calculated by the following formula 12 : ； Among them, x 1k is the kth element value in the feature vector of a certain geographical area, x 2k is the kth element value in the feature vector of the candidate geographic area, n is the dimension of the feature vector, s k is the standard deviation of the eigenvalues ​​of the eigenvectors of each geographic region in the k dimension; The N geographical regions having the highest similarity with the feature vectors of the candidate geographical region are regarded as homogeneous geographical regions of the candidate geographical region.

2. The method according to claim 1, characterized in that The commercial characteristic data includes at least one of the following information: data used to characterize the customer flow portrait characteristics of the geographical area, data used to characterize the store type characteristics in the geographical area, data used to characterize the revenue characteristics of various stores in the geographical area, and data used to characterize the revenue characteristics of stores of the same type as the target store in the geographical area.

3. The method according to claim 1, characterized in that The step of determining, for the stores of the same type as the target store in the plurality of homogeneous geographical areas, consumption information of the stores of the same type in the same historical time period includes: Determine the store entry rate of consumers entering the type of stores, the purchase rate of consumers entering the type of stores, and the average customer spending of consumers in the multiple homogeneous geographic areas during the same historical period, wherein the consumption information includes the store entry rate, the purchase rate, and the average customer spending; The determining whether to locate the target store in the candidate geographical area according to the consumption information includes: Predicting the total number of second consumers in the candidate geographical area within the future preset time period based on the total number of first consumers in the candidate geographical area within the historical preset time period; Predicting the revenue information of the target store in the candidate geographic area according to the total number of the second consumers, the store entry rate, the purchase rate, and the average customer spending; and Determine whether to locate the target store in the candidate geographical area according to the revenue information.

4. The method according to any one of claims 1 to 3, characterized in that The method further comprises: The ratio between the number of consumers who entered the store of the type in the candidate geographical area during the same historical period and the total number of consumers in the candidate geographical area during the same historical period is used as the consumer conversion rate of the store of the type in the candidate geographical area; The determining whether to locate the target store in the candidate geographical area according to the consumption information includes: Predicting the total number of second consumers in the candidate geographical area within the future preset time period based on the total number of first consumers in the candidate geographical area within the historical preset time period; Predicting the revenue information of the target store in the candidate geographic area according to the total number of the second consumers, the store entry rate, the purchase rate, and the average customer spending; and Determine whether to locate the target store in the candidate geographical area based on the revenue information and the consumer conversion rate.

5. A site selection analysis device, characterized in that: The device comprises: A homogeneous region determination module, used to determine a plurality of homogeneous geographical regions of a candidate geographical region of a target store, wherein the homogeneous geographical region refers to a geographical region whose commercial characteristics are similar to those of the candidate geographical region; A consumption information determination module, for determining, for the stores of the same type as the target store in the plurality of homogeneous geographical areas, consumption information of the stores of the same type within the same historical period; A site selection determination module, used to determine whether to locate the target store in the candidate geographical area according to the consumption information; Wherein, the homogeneous region determination module includes: A feature acquisition submodule, used to acquire business feature data of multiple geographical areas in the same historical period, wherein the multiple geographical areas include the candidate geographical area; A vector determination submodule, used to determine a feature vector of each geographical area according to the commercial feature data of the geographical area; A homogeneity determination submodule, configured to determine a homogeneous geographical region of the candidate geographical region from the multiple geographical regions according to the similarity between the feature vectors; The step of determining the feature vector of each geographical area according to the commercial feature data of the geographical area includes: For each feature data of each geographical area in the same dimension, the following formula is used for standardization: ； Wherein, X* is the standardized value of the feature data, X is the feature data, m is the mean of the feature data of the dimension, s is the standard deviation of the feature data of the dimension, and the feature vector of the geographic area includes the standardized value of the feature data of each dimension; The step of determining a homogeneous geographical region of the candidate geographical region from the multiple geographical regions according to the similarity between the feature vectors comprises: The similarity d between the feature vectors of each geographic region and the feature vectors of the candidate geographic region is calculated by the following formula 12 : ； Among them, x 1k is the kth element value in the feature vector of a certain geographical area, x 2k is the kth element value in the feature vector of the candidate geographic area, n is the dimension of the feature vector, s k is the standard deviation of the eigenvalues ​​of the eigenvectors of each geographic region in the k dimension; The N geographical regions having the highest similarity with the feature vectors of the candidate geographical region are regarded as homogeneous geographical regions of the candidate geographical region.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the method described in any one of claims 1 to 4 are implemented.

7. An electronic device, characterized in that: include: a memory having a computer program stored thereon; A processor, configured to execute the computer program in the memory to implement the steps of the method according to any one of claims 1 to 4.

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