CN110675177A - Store site selection method and device - Google Patents
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Abstract
The invention provides a method and a device for selecting a store site, wherein the method comprises the following steps: extracting store demand characteristics of each block according to a target store to be addressed, inputting the store demand characteristics into a pre-trained store addressing demand model, and acquiring a target store demand degree of each block; extracting store competition characteristics of each block according to a target store, inputting the store competition characteristics into a pre-trained store site selection competition model, and acquiring the target store competition degree of each block; calculating the address matching degree of each block according to the target store demand degree of each block and the target store competition degree of each block; and determining the address selection block of the target store according to the address selection matching degree of each block. Therefore, the site selection block of the store is determined by combining the demand degree and the competition degree, and the site selection efficiency and accuracy of the store are improved.
Description
Technical Field
The invention relates to the technical field of electronic maps, in particular to a method and a device for selecting a site of a store.
Background
With the development of smart cities, the demand of consumers for merchants is mainly convenience, and the key point of merchants is how to select addresses for stores in order to better serve the consumers. In the prior art, aiming at store site selection, various scores of offline positions can be manually collected and summarized through an empirical formula, some more key strong features are defined for certain types of stores, and then the two more strong features are relied on more to guide site selection. However, the site selection method has low efficiency and incomplete results, so that the site selection of stores in the prior art has low efficiency and is not accurate enough.
Disclosure of Invention
The present invention is directed to solving, at least to some extent, one of the technical problems in the related art.
Therefore, a first objective of the present invention is to provide a method for selecting an address of a store, so as to determine an address block of the store by combining a demand degree and a competition degree, thereby improving efficiency and accuracy of address selection of the store.
A second object of the present invention is to provide an apparatus for site selection in an store.
A third object of the invention is to propose a computer device.
A fourth object of the invention is to propose a non-transitory computer-readable storage medium.
A fifth object of the invention is to propose a computer program product.
In order to achieve the above object, an embodiment of a first aspect of the present invention provides a method for selecting an address of an store, including:
extracting store demand characteristics of each block according to a target store to be addressed, inputting the store demand characteristics into a pre-trained store addressing demand model, and acquiring a target store demand degree of each block;
extracting store competition characteristics of each block according to the target stores, inputting the store competition characteristics into a pre-trained store site selection competition model, and acquiring the target store competition degree of each block;
calculating the address matching degree of each block according to the target store demand degree of each block and the target store competition degree of each block;
and determining the address selection block of the target store according to the address selection matching degree of each block.
According to the store site selection method provided by the embodiment of the invention, the store demand characteristics of each block are extracted according to the target store to be site selected, the store demand characteristics are input into the pre-trained store site selection demand model to obtain the target store demand degree of each block, the store competition characteristics of each block are extracted according to the target store, the store competition degrees of each block are input into the pre-trained store site selection competition model to obtain the target store competition degree of each block, so that the site selection matching degree of each block is calculated according to the target store demand degree of each block and the target store competition degree of each block, and finally, the site selection block of the target store is determined according to the site selection matching degree of each block. Therefore, the site selection block of the store is determined by combining the demand degree and the competition degree, and the site selection efficiency and accuracy of the store are improved.
In order to achieve the above object, a second embodiment of the present invention provides an apparatus for selecting an address of a store, including:
the first acquisition module is used for extracting store demand characteristics of each block according to a target store to be addressed, inputting the store demand characteristics into a pre-trained store addressing demand model, and acquiring the target store demand of each block;
the second acquisition module is used for extracting store competition characteristics of each block according to the target stores, inputting the store competition characteristics into a pre-trained store site selection competition model and acquiring the target store competition degree of each block;
the calculating module is used for calculating the address selection matching degree of each block according to the target store demand degree of each block and the target store competition degree of each block;
and the determining module is used for determining the address selection blocks of the target store according to the address selection matching degree of each block.
According to the store site selection device provided by the embodiment of the invention, the store demand characteristics of each block are extracted according to the target store to be site selected, the store demand characteristics are input into the pre-trained store site selection demand model to obtain the target store demand degree of each block, the store competition characteristics of each block are extracted according to the target store, the store competition degree of each block is input into the pre-trained store site selection competition model to obtain the target store competition degree of each block, the site selection matching degree of each block is calculated according to the target store demand degree of each block and the target store competition degree of each block, and finally the site selection block of the target store is determined according to the site selection matching degree of each block. Therefore, the site selection block of the store is determined by combining the demand degree and the competition degree, and the site selection efficiency and accuracy of the store are improved.
To achieve the above object, a third embodiment of the present invention provides a computer device, including: a processor; a memory for storing the processor-executable instructions; the processor reads the executable program code stored in the memory to run a program corresponding to the executable program code, so as to execute the store site selection method described in the embodiment of the first aspect.
In order to achieve the above object, a fourth aspect of the present invention provides a non-transitory computer-readable storage medium having a computer program stored thereon, wherein the computer program is configured to implement a store site selection method according to an embodiment of the first aspect of the present invention when executed by a processor.
To achieve the above object, a fifth embodiment of the present invention provides a computer program product, wherein when being executed by an instruction processor, the computer program product implements the store site selection method according to the first embodiment of the present invention.
Additional aspects and advantages of the invention will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the invention.
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The foregoing and/or additional aspects and advantages of the present invention will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings of which:
fig. 1 is a schematic flow chart of a store site selection method according to an embodiment of the present invention;
fig. 2 is a schematic flow chart of a store site selection method according to a second embodiment of the present invention;
fig. 3 is a schematic structural diagram of an apparatus for selecting an address of a store according to an embodiment of the present invention; and
FIG. 4 illustrates a block diagram of an exemplary computer device suitable for use in implementing embodiments of the present application.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the drawings are illustrative and intended to be illustrative of the invention and are not to be construed as limiting the invention.
A store site selection method and apparatus according to an embodiment of the present invention will be described below with reference to the accompanying drawings.
Fig. 1 is a schematic flow chart of a store site selection method according to an embodiment of the present invention.
As shown in fig. 1, the store site selection method includes the following steps:
Generally, in order to select a suitable area for a brand or a channel to be better located in a target store in a city layout line or a newly opened target store, the target store is of a different type and has different concerns about user needs, peripheral competition, and the like in the area. Therefore, in order to facilitate the site selection of stores, the target area, for example, a city, an administrative district, or the like, needs to be divided into blocks in advance.
It should be noted that the target area may be divided into a plurality of blocks according to a preset division algorithm (e.g., GeoHash algorithm), each of the divided blocks may be uniform or non-uniform, and generally, in order to improve accuracy of store address selection, the target area is uniformly divided into a plurality of blocks.
Specifically, the store demand characteristics may be basic attribute characteristics of each block, such as a geographic location characteristic, a point of interest distribution characteristic, and the like, or target attribute characteristics related to the target store, such as a user behavior characteristic of a resident group to the target store, a user behavior characteristic of a visiting group to the target store, and the like, and one or more attribute characteristics may be selected as the store demand characteristics according to the selection of actual application demands.
Therefore, after the needed store requirement characteristics are determined, extraction can be performed in a corresponding manner, for example, as follows:
as an example, the basic attribute feature of each block is directly extracted as the store demand feature of each block.
As another example, the basic attribute feature of each block and the target attribute feature of each block related to the target store are extracted, and the basic attribute feature of each block and the target attribute feature related to the target store are fused to generate the store demand feature of each block.
Therefore, the target store demand degree of each block can be obtained by inputting the store demand characteristics into the pre-trained store site selection demand model. Namely, the store demand characteristics are taken as input and processed through the store site selection demand model, so that the target store demand degree can be output to be 0.25 for example.
The store site selection demand model is trained in advance, and in order to improve the accuracy of an output result, the number and diversity of samples need to be improved generally, so that the result processed by the store site selection demand model can be effective. Therefore, the store site selection demand model can be trained in a proper mode according to the actual application requirements, for example, the store site selection demand model can be trained by taking the block with the store as a positive sample and the block without the store as a negative sample, and the store site selection demand model can also be trained by taking the block before sales ranking as a positive sample and the block after sales ranking as a negative sample for chain stores.
It should be noted that the trained store site selection demand model can be continuously adjusted according to the update of the sample, so that the real-time performance of the model is ensured, and the site selection accuracy is facilitated.
And 102, extracting store competition characteristics of each block according to the target stores, inputting the store competition characteristics into a pre-trained store site selection competition model, and acquiring the target store competition degree of each block.
Specifically, taking the target stores as an example, some competitive brands are selected as expedition stores (for example, dame is the current store, and some competitive brands such as happy family and beauty are selected), competition is defined by the layout of the existing stores, and competition can be defined by the distance between stores, the number of interest points of stores, and the like. The store competition feature may be a number of competing stores, etc.
Specifically, after the required store competition characteristics are determined, obtaining can be performed in a corresponding manner, as an example, a current store related to the target store in each block is obtained, and a reference range is determined according to the reference position and a preset reference distance by taking the position of the current store as a reference position; and acquiring the number of competitive stores corresponding to the current store in the reference range as the store competition characteristic.
Therefore, the target store competition degree of each block can be obtained by inputting the store competition characteristics into the pre-trained store site selection competition model. Namely, the store competition characteristic is taken as an input and is processed by the store site selection competition model, so that the target store competition degree can be output to be 0.5 for example.
The shop site selection competition model is trained in advance, and in order to improve the accuracy of output results, the number and diversity of samples need to be improved so as to ensure that the results processed by the shop site selection competition model can be effective. Therefore, an appropriate mode can be selected to train the store site selection demand model according to the actual application demand, for example, the store site selection competition model can be processed by uniformly processing the same brand and similar brands as the same sample, or giving different weights according to the similarity of stores.
It should be noted that the trained shop site selection competition model can be continuously adjusted according to the update of the sample, so that the real-time performance of the model is ensured, and the site selection accuracy is facilitated.
And 103, calculating the address matching degree of each block according to the target store demand degree of each block and the target store competition degree of each block.
And step 104, determining the address selection blocks of the target store according to the address selection matching degree of each block.
Specifically, there are various ways of obtaining the target store demand degree of each block and the target store competition degree of each block to calculate the address matching degree of each block, and the address matching degree of each block may be selected and set according to actual application requirements, for example, as follows:
in the first example, the target store demand degree and the target store competition degree are directly superposed to determine the site selection matching degree.
In the second example, the site selection is performed by giving a weight with a higher demand degree to the target store and giving a weight with a lower competitive degree to the target store, which focuses more on the location selection of the store by the demand degree, so as to calculate the site selection matching degree.
Therefore, the address blocks of the target store are determined according to the address matching degree of each block.
According to the store site selection method provided by the embodiment of the invention, the store demand characteristics of each block are extracted according to the target store to be site selected, the store demand characteristics are input into the pre-trained store site selection demand model to obtain the target store demand degree of each block, the store competition characteristics of each block are extracted according to the target store, the store competition degrees of each block are input into the pre-trained store site selection competition model to obtain the target store competition degree of each block, so that the site selection matching degree of each block is calculated according to the target store demand degree of each block and the target store competition degree of each block, and finally, the site selection block of the target store is determined according to the site selection matching degree of each block. Therefore, the site selection block of the store is determined by combining the demand degree and the competition degree, and the site selection efficiency and accuracy of the store are improved.
To clearly illustrate the above embodiment, this embodiment provides a schematic flow chart of a store site selection method, and fig. 2 is a schematic flow chart of a store site selection method provided by a second embodiment of the present invention.
As shown in fig. 2, the store site selection method may include the steps of:
It should be noted that the target area may be divided into a plurality of blocks according to a preset division algorithm (e.g., GeoHash algorithm), each of the divided blocks may be uniform or non-uniform, and generally, in order to improve accuracy of store address selection, the target area is uniformly divided into a plurality of blocks.
The basic attribute characteristics of each block can include but are not limited to one or more of geographical location characteristics, interest point distribution characteristics, resident people portrait characteristics and visited people portrait characteristics.
Specifically, a large number of location features represented on the internet can be utilized, and for example, a satellite map and a map of each block are represented by a CNN (Convolutional Neural Network) model such as VGG-16, ResNet, and the like, and the intermediate layers thereof are used as multidimensional features (for example, 1024 dimensions) as geographic location features.
Specifically, the types of various POIs (points of interest) and the number of different POIs under each block are used as the distribution characteristics of the points of interest.
Specifically, the user portrait characteristics of the people resident under each block, i.e., the people at home and at work within each block, include gender, age group, income level, and the like, and interest tags of the people, and the like, as the resident portrait characteristics of the block.
Specifically, the user profile characteristics of the visiting person in the next period of time for each tile include gender, age, income level, etc. And interest tags of people as the interview people portrait features of the block.
The target attribute characteristics of each block related to the target store may include, but are not limited to, one or more of user behavior characteristics of resident people to the target store and/or user behavior characteristics of visiting people to the target store.
Specifically, the online behavior characteristics of the resident crowd under each block, such as the search number related to the client brand, are used as the user behavior characteristics of the resident crowd to the target store.
Specifically, the online behavior feature of each block of the visiting crowd, such as the search number related to the client brand, is used as the user behavior feature of the visiting crowd to the target store.
And 204, fusing the basic attribute characteristics of each block and the target attribute characteristics related to the target store to generate store demand characteristics of each block, inputting the store demand characteristics into a pre-trained store address selection demand model, and acquiring the target store demand of each block.
In step 205, the current store associated with the target store in each block is obtained.
And step 206, determining a reference range according to the reference position and a preset reference distance by taking the position of the current store as the reference position.
And step 207, acquiring the number of competitive stores corresponding to the current store in the reference range as store competitive characteristics of each block, inputting the competitive characteristics into a pre-trained store address selection competitive model, and acquiring the target store competitive degree of each block.
Specifically, taking the current store as an example, some competitive brands are selected as expedition stores (for example, dame is the current store, and some competitive brands such as happy family and beauty are selected), competition is defined by the layout of the existing stores, and competition can be defined by the distance between stores, the number of interest points of the stores, and the like.
More specifically, from each current store i, the number of competing stores (not including the current store) that appear within a distance x is defined as comp (x, i), and the number of all stores that appear within the distance x is total (x, i). The argument (x, i) is defined as total (x, i)/comp (x, i). Because of the existence of each target store, comp (x, i) is always not 0. And then, taking the Average value of all the stores i of the same type as Average (x, i), as a store competition characteristic of the stores within the distance x, inputting the Average value into a pre-trained store address competition model, and acquiring the target store competition degree of each block.
It should be noted that the competitive definition may be adjusted. For example, the number of other interest points is not considered but only the distance.
And step 208, acquiring a first weight corresponding to the demand degree of the target store and a second weight corresponding to the competition degree of the target store according to the type of the target store.
Specifically, because different types of stores and different customers have different attention to competition, a parameter may be set to balance the weight between competition and demand, that is, the target store demand degree of each block and the target store competition degree of each block are given different weights to recalculate the address matching degree of each block.
For example, the demand degree is paid more attention, so that the first weight corresponding to the target store demand degree of each block is 0.8, and the second weight corresponding to the target store competition degree of each block is 0.2, so as to calculate the address selection matching degree of each block; the competition degree is paid more attention, so that the first weight corresponding to the target store requirement degree of each block is 0.7, the second weight corresponding to the target store competition degree of each block is 0.3, the site selection matching degree of each block and the like can be calculated and adjusted according to requirements, the independence of site selection of stores is further improved, and the site selection result of the stores meets the requirements of customers.
The shop location method of the embodiment of the invention comprises the steps of dividing a target area into a plurality of blocks according to a preset division algorithm, extracting basic attribute characteristics of each block, extracting target attribute characteristics of each block related to a target shop, fusing the basic attribute characteristics of each block and the target attribute characteristics related to the target shop, generating the shop demand characteristics of each block, inputting the shop demand characteristics into a pre-trained shop location demand model to obtain the target shop demand of each block, simultaneously obtaining a current shop related to the target shop in each block, determining a reference range according to the reference position and a preset reference distance by taking the position of the current shop as a reference position, obtaining the number of competitive shops corresponding to the current shop in the reference range as the shop competitive characteristics of each block, inputting the competitive shop competitive features into the pre-trained shop location competitive model to obtain the target shop competitive degree of each block, therefore, a first weight corresponding to the demand degree of the target store and a second weight corresponding to the competition degree of the target store are obtained according to the type of the target store, the address selection matching degree of each block is calculated according to the demand degree and the first weight of the target store of each block and the competition degree and the second weight of the target store of each block, and finally the address selection block of the target store is determined according to the address selection matching degree of each block. Therefore, the site selection block of the store is determined by combining the demand degree and the competition degree, and the site selection efficiency and accuracy of the store are improved. In addition, the weight can be adjusted according to the specific requirements of demand and competition, and the site selection demand and efficiency of stores are further met.
In order to implement the embodiment, the invention further provides an address selecting device for the store.
Fig. 3 is a schematic structural diagram of an apparatus for selecting an address of a store according to an embodiment of the present invention.
As shown in fig. 3, the store site selection apparatus includes: a first obtaining module 310, a second obtaining module 320, a calculating module 330, and a determining module 340.
The first obtaining module 310 is configured to extract store demand characteristics of each block according to a target store to be addressed, and input the store demand characteristics into a pre-trained store addressing demand model to obtain a target store demand degree of each block.
It should be noted that the target area may be divided into a plurality of blocks according to a preset division algorithm (e.g., GeoHash algorithm), each of the divided blocks may be uniform or non-uniform, and generally, in order to improve accuracy of store address selection, the target area is uniformly divided into a plurality of blocks.
The first obtaining module 310 is specifically configured to extract a basic attribute feature of each block and extract a target attribute feature of each block related to a target store, fuse the basic attribute feature of each block and the target attribute feature related to the target store, generate a store demand feature of each block, and input the store demand feature into a pre-trained store site selection demand model to obtain a target store demand degree of each block.
The second obtaining module 320 is configured to extract store competition characteristics of each block according to the target store, and input the store competition characteristics into a pre-trained store site selection competition model to obtain the target store competition degree of each block.
The second obtaining module 320 is specifically configured to obtain a current store related to the target store in each block, determine a reference range according to the reference position and a preset reference distance by using the position of the current store as the reference position, obtain the number of competitive stores corresponding to the current store in the reference range as a store competitive feature of each block, and input the number of competitive stores to a pre-trained store address selection competitive model to obtain the target store competitive strength of each block.
The calculating module 330 is configured to calculate the address matching degree of each block according to the target store demand degree of each block and the target store competition degree of each block.
The calculating module 330 is specifically configured to obtain a first weight corresponding to the demand degree of the target store and a second weight corresponding to the competition degree of the target store according to the type of the target store, and calculate the address matching degree of each block according to the demand degree and the first weight of the target store of each block and the competition degree and the second weight of the target store of each block.
And the determining module 340 is configured to determine the address blocks of the target store according to the address matching degree of each block.
According to the store site selection device provided by the embodiment of the invention, the store demand characteristics of each block are extracted according to the target store to be site selected, the store demand characteristics are input into the pre-trained store site selection demand model to obtain the target store demand degree of each block, the store competition characteristics of each block are extracted according to the target store, the store competition degree of each block is input into the pre-trained store site selection competition model to obtain the target store competition degree of each block, the site selection matching degree of each block is calculated according to the target store demand degree of each block and the target store competition degree of each block, and finally the site selection block of the target store is determined according to the site selection matching degree of each block. Therefore, the site selection block of the store is determined by combining the demand degree and the competition degree, and the site selection efficiency and accuracy of the store are improved.
It should be noted that the foregoing explanation on the embodiment of the store site selection method is also applicable to the store site selection apparatus of this embodiment, and is not repeated here.
In order to implement the foregoing embodiment, the present invention further provides a computer device, including: a processor, and a memory for storing processor-executable instructions.
Wherein the processor runs a program corresponding to the executable program code by reading the executable program code stored in the memory, so as to implement the store site selection method proposed by the foregoing embodiment of the present invention.
To achieve the above embodiments, the present invention also proposes a non-transitory computer-readable storage medium, in which instructions are executed by a processor to enable the processor to execute the store site selection method proposed by the foregoing embodiments of the present invention.
In order to implement the foregoing embodiments, the present invention further provides a computer program product, wherein when the instructions in the computer program product are executed by a processor, the method for selecting an address of a store, which implements the method for selecting an address of a store according to the foregoing embodiments of the present invention, is executed.
FIG. 4 illustrates a block diagram of an exemplary computer device suitable for use in implementing embodiments of the present application. The computer device 12 shown in fig. 4 is only an example and should not bring any limitation to the function and scope of use of the embodiments of the present application.
As shown in FIG. 4, computer device 12 is in the form of a general purpose computing device. The components of computer device 12 may include, but are not limited to: one or more processors or processing units 16, a system memory 28, and a bus 18 that couples various system components including the system memory 28 and the processing unit 16.
A program/utility 40 having a set (at least one) of program modules 42 may be stored, for example, in memory 28, such program modules 42 including, but not limited to, an operating system, one or more application programs, other program modules, and program data, each of which examples or some combination thereof may comprise an implementation of a network environment. Program modules 42 generally perform the functions and/or methodologies of the embodiments described herein.
The computer device 12 may also communicate with one or more external devices 14 (e.g., keyboard, pointing device, display 24, etc.), with one or more devices that enable a user to interact with the computer system/server 12, and/or with any devices (e.g., network card, modem, etc.) that enable the computer system/server 12 to communicate with one or more other computing devices. Such communication may be through an input/output (I/O) interface 22. Moreover, computer device 12 may also communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public Network such as the Internet) via Network adapter 20. As shown, network adapter 20 communicates with the other modules of computer device 12 via bus 18. It should be understood that although not shown in the figures, other hardware and/or software modules may be used in conjunction with computer device 12, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, among others.
The processing unit 16 executes various functional applications and data processing, such as implementing the store addressing method mentioned in the foregoing embodiments, by running a program stored in the system memory 28.
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.
Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present invention, "a plurality" means at least two, e.g., two, three, etc., unless specifically limited otherwise.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing steps of a custom logic function or process, and alternate implementations are included within the scope of the preferred embodiment of the present invention in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the present invention.
The logic and/or steps represented in the flowcharts or otherwise described herein, e.g., an ordered listing of executable instructions that can be considered to implement logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium could even be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, via for instance optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner if necessary, and then stored in a computer memory.
It should be understood that portions of the present invention may be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, the various steps or methods may be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. If implemented in hardware, as in another embodiment, any one or combination of the following techniques, which are known in the art, may be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application specific integrated circuit having an appropriate combinational logic gate circuit, a Programmable Gate Array (PGA), a Field Programmable Gate Array (FPGA), or the like.
It will be understood by those skilled in the art that all or part of the steps carried by the method for implementing the above embodiments may be implemented by hardware related to instructions of a program, which may be stored in a computer readable storage medium, and when the program is executed, the program includes one or a combination of the steps of the method embodiments.
In addition, functional units in the embodiments of the present invention may be integrated into one processing module, or each unit may exist alone physically, or two or more units are integrated into one module. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode. The integrated module, if implemented in the form of a software functional module and sold or used as a stand-alone product, may also be stored in a computer readable storage medium.
The storage medium mentioned above may be a read-only memory, a magnetic or optical disk, etc. Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention, and that variations, modifications, substitutions and alterations can be made to the above embodiments by those of ordinary skill in the art within the scope of the present invention.
Claims (10)
1. A store site selection method is characterized by comprising the following steps:
extracting store demand characteristics of each block according to a target store to be addressed, inputting the store demand characteristics into a pre-trained store addressing demand model, and acquiring a target store demand degree of each block;
extracting store competition characteristics of each block according to the target stores, inputting the store competition characteristics into a pre-trained store site selection competition model, and acquiring the target store competition degree of each block;
calculating the address matching degree of each block according to the target store demand degree of each block and the target store competition degree of each block;
and determining the address selection block of the target store according to the address selection matching degree of each block.
2. The method of claim 1, further comprising:
and dividing the target area into a plurality of blocks according to a preset division algorithm.
3. The method of claim 1, wherein the extracting store demand characteristics for each block from the target stores to be addressed comprises:
extracting basic attribute features of each block;
extracting target attribute characteristics of each block related to the target store;
and fusing the basic attribute characteristics of each block with the target attribute characteristics related to the target store to generate store demand characteristics of each block.
4. The method of claim 3,
the basic attribute features include: one or more of the characteristics of the geographic position, the distribution characteristics of interest points, the image characteristics of resident people and the image characteristics of visiting people are combined;
the target attribute features associated with the target store include: the user behavior characteristics of the frequent visitor group to the target store and/or the user behavior characteristics of the visiting visitor group to the target store.
5. The method of claim 1, wherein said extracting store competition features for each block from the target stores comprises:
acquiring a current store related to the target store in each block;
determining a reference range according to the reference position and a preset reference distance by taking the position of the current store as the reference position;
and acquiring the number of competitive stores corresponding to the current store in the reference range.
6. The method according to any one of claims 1 to 5, wherein the calculating the address matching degree of each block according to the target store demand degree of each block and the target store competition degree of each block comprises:
acquiring a first weight corresponding to the demand degree of the target store and a second weight corresponding to the competition degree of the target store according to the type of the target store;
and calculating the address matching degree of each block according to the target store demand degree and the first weight of each block, and the target store competition degree and the second weight of each block.
7. An apparatus for site selection in an store, the apparatus comprising:
the first acquisition module is used for extracting store demand characteristics of each block according to a target store to be addressed, inputting the store demand characteristics into a pre-trained store addressing demand model, and acquiring the target store demand of each block;
the second acquisition module is used for extracting store competition characteristics of each block according to the target stores, inputting the store competition characteristics into a pre-trained store site selection competition model and acquiring the target store competition degree of each block;
the calculating module is used for calculating the address selection matching degree of each block according to the target store demand degree of each block and the target store competition degree of each block;
and the determining module is used for determining the address selection blocks of the target store according to the address selection matching degree of each block.
8. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor when executing the program effecting store location as claimed in any one of claims 1 to 6.
9. A non-transitory computer readable storage medium having stored thereon a computer program, wherein the program, when executed by a processor, implements the store addressing method of any of claims 1-6.
10. A computer program product, characterized in that instructions in the computer program product, when executed by a processor, perform the store addressing method according to any of claims 1-6.
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| CN201810718995.3A CN110675177A (en) | 2018-07-03 | 2018-07-03 | Store site selection method and device |
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| CN201810718995.3A CN110675177A (en) | 2018-07-03 | 2018-07-03 | Store site selection method and device |
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| CN112785233A (en) * | 2021-01-28 | 2021-05-11 | 湖南汽车工程职业学院 | Automobile part distribution center site selection method based on block chain technology |
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| CN113947425A (en) * | 2021-09-27 | 2022-01-18 | 上海维智卓新信息科技有限公司 | Shop address determining method and device |
| CN114820039A (en) * | 2022-04-01 | 2022-07-29 | 上海维智卓新信息科技有限公司 | Method and device for selecting commercial store site |
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