CN112862525A - Shop site selection data determination method and system and electronic equipment - Google Patents
Shop site selection data determination method and system and electronic equipment Download PDFInfo
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Abstract
The embodiment of the application provides a method and a system for determining store site selection data and electronic equipment. Wherein the method comprises the following steps: displaying an interactive interface; acquiring address information corresponding to the store address input by the user through the interactive interface; acquiring multidimensional data generated in the area where the location corresponding to the address information is located; wherein the multidimensional data has relevance with the content of the service provided by the store; analyzing the multidimensional data to obtain store site selection data; wherein the store site selection data reflects an executability of site selection of stores at the location; and displaying the place and the store site data in a correlation mode. The technical scheme provided by the embodiment of the application is applied to various store site selection scenes, can effectively improve the intelligent degree and site selection accuracy of store site selection, can provide objective and scientific site selection guidance for users, and is convenient to implement, high in efficiency and low in cost.
Description
Technical Field
The application relates to the technical field of computers, in particular to a method and a system for determining store site selection data and electronic equipment.
Background
At present, when a merchant selects a site for a merchant in home administration, a restaurant or a retail store, the merchant generally judges and selects a place where the store and the dispensing equipment are properly arranged by an offline data acquisition mode (such as manual customer flow statistics), and the site selection mode has strong subjectivity, needs to input a large amount of manpower and time, has low site selection efficiency and high cost, and has the problems of incomplete data acquisition, low accuracy and the like. Therefore, a more efficient, objective, scientific and accurate site selection scheme is urgently needed.
Disclosure of Invention
In view of the above problems, the present application provides a store site selection data determination method, system and electronic device that solve the above problems, or at least partially solve the above problems.
In one embodiment of the present application, a method for determining store site selection data is provided. The method comprises the following steps:
displaying an interactive interface;
acquiring address information corresponding to the store address input by the user through the interactive interface;
acquiring multidimensional data generated in the area where the location corresponding to the address information is located; wherein the multidimensional data has relevance with the content of the service provided by the store;
analyzing the multidimensional data to obtain store site selection data; wherein the store site selection data reflects an executability of site selection of stores at the location;
and displaying the place and the store site data in a correlation mode.
In one embodiment of the present application, a store site selection data determination system is provided. The system comprises:
the client is used for displaying the interactive interface;
the server is used for acquiring address information corresponding to the store address input by the user through the interactive interface; acquiring multidimensional data generated in the area where the location corresponding to the address information is located; the multidimensional data is associated with the content of the service provided by the store; analyzing the multidimensional data to obtain store site selection data; wherein the store site selection data reflects an executability of site selection of stores at the location; and associating the place with the store address data and sending the place and the store address data to the client so as to display the place and the store address data on an interactive interface of the client.
In one embodiment of the present application, an electronic device is provided. The electronic device includes: a memory and a processor, wherein,
the memory is used for storing programs;
the processor, coupled with the memory, to execute the program stored in the memory to:
displaying an interactive interface;
acquiring address information corresponding to the store address input by the user through the interactive interface;
acquiring multidimensional data generated in the area where the location corresponding to the address information is located; wherein the multidimensional data has relevance with the content of the service provided by the store;
analyzing the multidimensional data to obtain store site selection data; wherein the store site selection data reflects an executability of site selection of stores at the location;
and displaying the place and the store site data in a correlation mode.
According to the technical scheme, on the basis of acquiring the address information corresponding to the store address input by a user through an interactive node and acquiring the multi-dimensional data which is generated in the area where the location corresponding to the address information is located and has relevance with the content of the service provided by the store, the multi-dimensional data is analyzed to obtain the store address data, wherein the store address data reflects the executable degree of the store address in the location, and the location and the store address data are displayed in a relevant mode. The overall scheme is that the site selection data of the stores are obtained by using scientific means and combining multidimensional data information, so that objective and scientific guidance can be provided for site selection of the stores, and the accuracy of the site selection decision result of the stores can be effectively ensured.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required to be utilized in the description of the embodiments or the prior art are briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present application, and other drawings can be obtained according to the drawings without creative efforts for those skilled in the art.
Fig. 1a is a schematic flow chart of a store site selection data determining method according to an embodiment of the present application;
FIG. 1b is a schematic diagram of a map displayed on an interactive interface according to an embodiment of the present application;
fig. 2a is a block diagram of a store site selection data determining system according to an embodiment of the present disclosure;
fig. 2b is a schematic diagram of a specific form of the store site data determining system according to an embodiment of the present application;
fig. 3 is a block diagram illustrating a structure of a store selection data determining apparatus according to an embodiment of the present application;
fig. 4 is a block diagram of an electronic device according to an embodiment of the present application.
Detailed Description
In order to make the technical solutions better understood by those skilled in the art, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application.
In some of the flows described in the specification, claims, and above-described figures of the present application, a number of operations are included that occur in a particular order, which operations may be performed out of order or in parallel as they occur herein. The sequence numbers of the operations, e.g., 101, 102, etc., are used merely to distinguish between the various operations, and do not represent any order of execution per se. Additionally, the flows may include more or fewer operations, and the operations may be performed sequentially or in parallel. It should be noted that, the descriptions of "first", "second", etc. in this document are used for distinguishing different messages, devices, modules, etc., and do not represent a sequential order, nor limit the types of "first" and "second" to be different. In the present application, the term "or/and" is only one kind of association relationship describing the associated object, and means that three relationships may exist, for example: a or/and B, which means that A can exist independently, A and B can exist simultaneously, and B can exist independently; the "/" character in this application generally indicates that the objects associated with each other are in an "or" relationship. In addition, the embodiments described below are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
With the rapid development of smart cities, the demand of consumers for merchants is mainly convenience, and the key point of merchants is how to select a store site for better serving the consumers. In the prior art, in the site selection process of a store, a suitable establishment site is often selected for the store by adopting an artificial off-line data acquisition mode. For example, for a home enterprise, in the process of selecting a site for a store to be opened newly, the traditional home enterprise mainly collects multi-dimensional data of some areas in a manual investigation mode, such as information of the number of people in the area, the number of houses in the area, the traffic flow in the area, the counting condition of each intersection in the area, and the like; and then, various scores are carried out on the regions based on the manually collected multidimensional data so as to evaluate the value of opening stores in the regions, thereby providing guidance for store site selection. However, the site selection method has strong subjectivity, low data acquisition efficiency, incomplete data dimensionality, high cost, long time consumption and other problems, so that the evaluation result is not objective, scientific and accurate.
In order to solve the problems of strong subjectivity, time and labor consumption, low efficiency, low accuracy and the like in the shop site selection process in the prior art, the application provides a shop site selection data determination method, in the method, multi-dimensional data of an area is obtained by adopting an AI big data mode, and the multi-dimensional data is analyzed by combining a trained calculation model to obtain the executable degree capable of reflecting the shop site selection in the area, so that efficient, objective, scientific and accurate site selection guidance can be provided for the shop site selection. The method provided in the embodiment of the present application may be applied to a system architecture including a client 201 and a server 202 as shown in fig. 2a or fig. 2b, and the specific work flows and communication interactions between the client 201 and the server 202 will be further described in the following embodiments, for which reference may be made to corresponding contents in the following, which are not described herein again in detail.
Fig. 1a is a schematic flow chart illustrating a store site selection data determining method according to an embodiment of the present application. The execution subject of the method can be an electronic device with a logical operation function, and the electronic device can be a client or a server. The client can be any terminal equipment such as a mobile phone, a tablet personal computer and intelligent wearable equipment; the server may be a local server, a cloud server, a server cluster, a virtual server, and the like, which is not specifically limited in this embodiment. As shown in fig. 1a, the method comprises the steps of:
101. displaying an interactive interface;
102. acquiring address information corresponding to the store address input by the user through the interactive interface;
103. acquiring multidimensional data generated in the area where the location corresponding to the address information is located; wherein the multidimensional data has relevance with the content of the service provided by the store;
104. analyzing the multidimensional data to obtain store site selection data; wherein the store site selection data reflects an executability of site selection of stores at the location;
105. and displaying the place and the store site data in a correlation mode.
In the foregoing 101, the interactive interface may be an interactive interface provided by the client 201 shown in fig. 2b for a user (e.g., a merchant), and based on the interactive interface, data information interaction may be performed with the client or a server (e.g., the server 202 shown in fig. 2 b) communicatively connected with the client through a corresponding interaction manner (e.g., a mouse, a hand touch, voice, etc.). In the actual site selection process of the store, in order to better leave the store in a layout line in a city or select a suitable site for a store to be newly opened, a user first determines a site corresponding to the site selection of the store, and then collects multidimensional data related to the site by using, for example, a manual investigation method based on the determined site corresponding to the site selection of the store, but the multidimensional data collected by the method lacks comprehensiveness, and for example, traffic data, road conditions and the like are difficult to collect. In this embodiment, the interactive interface may provide an information input entry for a user, the user may input address information corresponding to the store address selection through an interactive manner provided by the interactive interface, and the execution subject in this embodiment may automatically acquire, based on the address information, multi-dimensional data generated in an area where a place corresponding to the address information is located by combining with an AI big data technical means, and the multi-dimensional data has comprehensiveness. In a specific implementation, a map may be displayed on the interactive interface, the map may be, but is not limited to, a national map or a city map, and the address information may be obtained based on a click operation performed by a user on the map.
Based on this, in an implementation technical solution, the step 102 of acquiring address information corresponding to the store address input by the user through the interactive interface may specifically include:
1021. displaying a map on the interactive interface;
1022. and responding to the clicking operation of the user on the map, and acquiring the address information of the clicked position.
In a specific implementation, the map displayed on the interactive interface may be a national map, or may be a city map, and the like, which is not specifically limited herein. Based on the map displayed by the interactive interface, when a user selects an address for a store, the user can click the corresponding position on the map through an interactive mode (such as a mouse and a hand touch) provided by the interactive interface according to the place where the store is to be laid, and the execution main body can automatically acquire the address information of the clicked position after responding to the clicking operation of the user on the map. For example, referring to the map displayed on the interactive interface shown in fig. 1b, when the user wants to lay a home store at the location 10, the user may perform a click operation on the location 10 by using, for example, a mouse, and after the main body responds to the click operation of the user on the location 10, the address information at the location 10 may be acquired, for example, the address information at the location 10 is: zone # # # # # # # town # # # lane.
Here, it should be noted that: in addition to the above-mentioned obtaining of the address information at the location clicked by the user according to the clicking operation on the map, the address information may also be obtained according to other manners, for example, the address information may be obtained according to the address information content input by the user in an input control (e.g., an input box 11 shown in fig. 1 b) provided on the interactive interface, or may also be obtained according to the address information content input by the user through a voice input control (e.g., a microphone control 12 shown in fig. 1 b) provided on the interactive interface, which is not limited in this embodiment.
After address information corresponding to the store address input by the user through the interactive interface is acquired, the area where the place corresponding to the address information is located needs to be further determined, so that multi-dimensional data generated in the area where the place corresponding to the address information is located can be acquired, and the executability of the store address selection in the place is determined based on the multi-dimensional data. That is, further, the step 102 may further include the following steps:
1023. responding to the area frame selection operation of the user on the periphery of the clicked position, and taking the area framed and selected by the user as the area where the place is located;
1024. and forming a regular boundary at the periphery of the clicked position according to a preset region division rule, and taking a region surrounded by the regular boundary as a region where the place is located.
1023, when the user selects the area around the position of the target point, the area selected by the frame may be a regular area, such as a square area, a rectangular area, a triangular area, etc.; or irregular regions such as polygonal irregular regions, etc., which are not specifically limited herein,
In the 1024, the preset area division rule may be determined according to an actual situation, for example, a regular boundary may be formed around the clicked position by taking the clicked position as a center and taking a certain distance (e.g., 50m, 300m, 0.5 km, 1 km) as a radius, so that an area surrounded by the regular boundary is taken as an area where the place is located; for another example, a city, an administrative area, a block, or the like where the clicked location is located may be directly used as the location area, which is not specifically limited in this embodiment.
In 103, based on the determined area where the location corresponding to the address information is located, the execution subject in this embodiment may automatically and directly obtain the multidimensional data generated in the area where the location corresponding to the address information is located from the internet by using an AI big data technology; the multidimensional data has relevance to the content of the store-and-door service, and specifically, the multidimensional data may include, but is not limited to, at least part of the following data: the number of users placing orders in the area, the number of orders completed in the area, the number of orders not completed in the area, the number of competitive store in the area, the number of personnel capable of providing services corresponding to stores in the area, the types of services capable of being provided in the area, the number of personnel capable of providing various services in the area, the number of population in the area and the density of houses in the area. The competitive store in the above description may be defined by the layout of the existing stores, for example, the competitive store may be defined by the distance between stores, the number of service categories provided by stores, and the like; the number of outstanding orders in the area may be a running order number, that is, a number of orders cancelled by the user.
In 104, the multidimensional data may be analyzed by using a pre-trained calculation model, so as to obtain store address data capable of reflecting the executable degree of the store address at the location. Specifically, the multidimensional data may be used as an input of the pre-trained calculation model, the calculation model is executed, and the calculation model performs comprehensive analysis on the multidimensional data, so that store site selection data can be obtained quickly. That is, in step 104, "analyzing the multidimensional data to obtain store site selection data" is an achievable technical solution:
1041. acquiring a calculation model;
1042. and analyzing the multidimensional data by using the calculation model to obtain the shop site selection data.
In 1041, the calculation model is a machine learning model and is obtained by training a machine model to be trained based on a large number of training samples, where the training samples may be generated according to historical multidimensional data generated in an area where a plurality of existing stores are located and Key Performance Indicator (KPI) data of each existing store, and the KPI is a measurable metric value used for measuring business Performance of the stores, and is usually evaluated in a period of time, for example, a store may use monthly revenue amount as the KPI. In general, the KPI of an office is affected by various factors, for example, for an office, the KPI of an office is affected by various factors such as the number of users who make orders in the area where the office is located, the number of people who can provide services corresponding to the office, the number of competitive stores, the number of population, the density of houses, the number of completed orders, the number of incomplete orders, and the like. The training of the machine model to be trained is carried out by acquiring a large amount of historical multidimensional data generated in the area of the existing store in different time periods and KPI data corresponding to the historical multidimensional data, so that a high-precision calculation model is obtained.
Based on the above, the calculation model in step 1041 can be obtained by the following training modes:
s10, acquiring historical multidimensional data generated in the areas where a plurality of existing stores are located and key performance index KPI data of each existing store;
s11, generating a training sample according to historical multidimensional data generated in the areas where the existing stores are located and key performance indicator KPI data of the existing stores;
and S13, training the machine learning model by using the training samples to obtain the calculation model.
In specific implementation, after obtaining historical multidimensional data generated in areas where a plurality of existing stores are located and key performance indicator KPI data of each existing store, preprocessing the historical multidimensional data generated in the areas where the plurality of existing stores are located and the key performance indicator KPI data of each existing store, and taking the preprocessed historical multidimensional data generated in the areas where the plurality of existing stores are located and the key performance indicator KPI data of each existing store as training samples; the preprocessing operation can be filtering processing, noise point removing and the like, and the preprocessing operation is used for removing abnormal data so as to ensure that a calculation model with higher accuracy is trained.
It should be noted here that the trained calculation model can be continuously adjusted according to the update of the training sample, so as to ensure the real-time performance of the training model, thereby being beneficial to the accuracy of shop site selection.
In 1042, since the calculation model of this embodiment is obtained based on a large number of training samples, and the training samples have diversity, and thus have high accuracy, when the calculation model is used to analyze the multidimensional data generated in the area where the location corresponding to the address information is located, the accurate shop location data can be obtained. Wherein the store site selection data reflects an executability of site selection of stores at the location; for example, referring to fig. 1b, analyzing the multidimensional data generated in the area by computational modeling can obtain that the store address data of the place 10 is 0.8, and the higher the store address data is, the more valuable the place is.
In the above 105, the obtained store site selection data and the corresponding location are displayed in an associated manner on the interactive interface, so that an analysis result is visually presented to the user, and store site selection guidance is conveniently, intuitively and scientifically provided for the user.
According to the technical scheme provided by the embodiment, on the basis of acquiring the address information corresponding to the store address, which is input by a user through an interactive node, and acquiring the multidimensional data which is generated in the area where the location corresponding to the address information is located and has relevance with the content of the service provided by the store, the store address data is obtained by analyzing the multidimensional data, wherein the store address data reflects the executable degree of the store address at the location, and the location and the store address data are displayed in a relevant mode. The overall scheme is that the site selection data of the stores are obtained by using scientific means and combining multidimensional data information, so that objective and scientific guidance can be provided for site selection of the stores, and the accuracy of the site selection decision result of the stores can be effectively ensured.
Further, the method provided by the present embodiment may further include:
106. displaying a map on the interactive interface;
107. acquiring attribute characteristics of a plurality of areas within the range of the map displayed;
108. generating corresponding thermodynamic diagrams on the map according to the attribute characteristics of the plurality of areas;
wherein the attribute features include: the density of the houses in the area, the number of the competitive store in the area and the service type distribution data provided by the store in the area.
In specific implementation, the attribute features of a plurality of areas in the map displayed range are acquired by an AI big data technology, so that the efficiency and accuracy of acquiring the attribute features can be improved, and the cost of acquiring the attribute features can be reduced. It should be noted that: the attribute characteristics of the area may include, in addition to the above-mentioned features of the density of houses in the area, the number of competitive store in the area, and the distribution data of service types provided by store in the area, other attribute characteristics, such as the number of population in the area, the traffic volume in the area, the road condition in the area, and the like, which is not limited in this embodiment. In addition, after the acquired map shows the attribute features of the plurality of areas within the range, clustering processing can be performed on the attribute features of the plurality of areas, so that a corresponding thermodynamic diagram is generated on the map according to a clustering result. Namely: the aforementioned 108 "generating a corresponding thermodynamic diagram on the map according to the attribute features of the plurality of areas" may include the following steps:
1081. clustering the attribute characteristics of the plurality of regions;
1082. and generating a corresponding thermodynamic diagram on the map according to the clustering result.
In specific implementation, the existing clustering algorithm, such as K-means and mean shift clustering algorithm, may be used to perform clustering processing on the longitude and latitude attribute characteristics of the multiple regions, where the clustering processing is not specifically limited, and the specific clustering processing process is the same as that in the prior art. And the thermodynamic diagrams generated on the map based on the clustering result can be particularly shown by the color shades in fig. 1 b.
The clustering results corresponding to the attribute features of the areas are displayed and processed on the map in a thermodynamic diagram mode, so that the user can intuitively feel the conditions of the demand degree, the number of competitive store shops and the like of each area on the whole, and the brand exposure rate is increased. In addition, the user can also perform a delineation operation on the displayed thermodynamic diagram for an area range, and the embodiment mainly responds to the situation that after the user performs the delineation operation on the thermodynamic diagram for an area range, the attribute characteristics (such as the number of competitive stores and the density of rooms in the area) of the area range can be displayed in detail. For example, with continued reference to FIG. 1b, after a user performs a delineation operation on a thermodynamic diagram for an area 20, the attribute features associated with the area 20 may be presented in the presentation area 21 shown in FIG. 1 b.
It should be noted that, when a user selects an address for a store, the user may first determine a location corresponding to the address of the store based on the thermodynamic diagram exhibited by the embodiment in a macroscopic manner, and then specifically calculate the address data (i.e., the execution degree) of the store where the address of the store is located in the location through the related content provided by the embodiment, so as to specifically grasp whether the address of the store has a value in the location, thereby further improving the accuracy of the address of the store.
The method provided by the embodiment can be applied to any application scenario in which store site selection data needs to be determined, where the type of the store may be, but is not limited to, a home administration type, a catering type, an express delivery type, a hairdressing and beauty type, a sports and fitness type, a hotel type, an education type, a retail goods type, a real estate type, an information consulting service type, a travel industry type, and the like, and this embodiment is not particularly limited thereto.
Fig. 2a and 2b are schematic structural diagrams illustrating a store site data determination system according to an embodiment of the present application. As shown in fig. 2a, the system specifically includes:
the client 201 is used for displaying an interactive interface;
the server 202 is used for acquiring address information corresponding to the store address input by the user through the interactive interface; acquiring multidimensional data generated in the area where the location corresponding to the address information is located; the multidimensional data is associated with the content of the service provided by the store; analyzing the multidimensional data to obtain store site selection data; wherein the store site selection data reflects an executability of site selection of stores at the location; and associating the place with the store address data and sending the place and the store address data to the client so as to display the place and the store address data on an interactive interface of the client.
In a specific implementation, referring to fig. 2b, the client 201 may be a device capable of interacting with a user and having a communication function. The implementation form of the client 201 may be different in different application scenarios. For example, in some scenarios, client 201 may be: a mobile phone, a tablet computer, a Personal Digital Assistant (PDA), a desktop computer, a notebook computer, an intelligent wearable device (such as an intelligent glasses, an intelligent watch, etc.), etc., which is not limited in this embodiment.
The server 202 includes a device capable of performing data processing and having a communication function. In some embodiments, the server device 202 may be implemented as a conventional server, a cloud host, a virtual center, or other devices, which is not limited in this embodiment. The Cloud server is a computer set based on Cloud Computing, that is, the Cloud server is composed of a large number of hosts or network servers based on Cloud Computing (Cloud Computing), wherein the Cloud Computing is one of distributed Computing and is a super virtual computer composed of a group of loosely coupled computers.
In the store site selection data determination system of this embodiment, the data interaction process between the client 201 and the server 202 can be implemented based on the communication connection relationship established between the client 201 and the server 202. The specific communication connection mode may depend on the actual application scenario.
In some exemplary embodiments, the client 201 and the server 202 may communicate with each other in a wired communication manner and a wireless communication manner. The WIreless communication mode includes short-distance communication modes such as bluetooth, ZigBee, infrared, WiFi (WIreless-Fidelity), long-distance WIreless communication modes such as LORA, and WIreless communication mode based on a mobile network. When the mobile network is connected through communication, the network format of the mobile network may be any one of 3d (gsm), 3G (WCDMA, TD-SCDMA, CDMA2000, UTMS), 4G (LTE), 4G + (LTE +), 5G, WiMax, and the like.
Further, the client 201 is further configured to display a map on the interactive interface; accordingly, the number of the first and second electrodes,
the server 202 is further configured to obtain attribute features of a plurality of areas within the displayed range of the map; generating corresponding thermodynamic diagrams on the map according to the attribute characteristics of the plurality of areas;
wherein the attribute features include: the density of the houses in the area, the number of the competitive store in the area and the service type distribution data provided by the store in the area.
Here, it should be noted that: for the content of each step in the store address data determining system provided in this embodiment, which is not described in detail in the foregoing embodiments, reference may be made to the corresponding content in each embodiment, and details are not described here again. In addition, the store site selection data determining system provided in this embodiment may further include, in addition to the above steps, some or all of the other steps in the above embodiments, which may be specifically referred to corresponding contents in the above embodiments, and details are not described here.
Fig. 3 is a block diagram illustrating a structure of a store site data determination apparatus according to an embodiment of the present application. As shown in fig. 3, the apparatus specifically includes:
the first display module 301 is used for displaying an interactive interface;
a first obtaining module 302, configured to obtain address information corresponding to a store address input by a user through the interactive interface;
a second obtaining module 303, configured to obtain multidimensional data generated in an area where a location corresponding to the address information is located; wherein the multidimensional data has relevance with the content of the service provided by the store;
the analysis module 304 is configured to analyze the multidimensional data to obtain store site selection data; wherein the store site selection data reflects an executability of site selection of stores at the location;
and a second display module 305, configured to associate and display the location with the store address data.
According to the technical scheme provided by the embodiment, on the basis of acquiring the address information corresponding to the store address, which is input by a user through an interactive node, and acquiring the multidimensional data which is generated in the area where the location corresponding to the address information is located and has relevance with the content of the service provided by the store, the store address data is obtained by analyzing the multidimensional data, wherein the store address data reflects the executable degree of the store address at the location, and the location and the store address data are displayed in a relevant mode. The overall scheme is that the site selection data of the stores are obtained by using scientific means and combining multidimensional data information, so that objective and scientific guidance can be provided for site selection of the stores, and the accuracy of the site selection decision result of the stores can be effectively ensured.
Further, the first obtaining module 302, when configured to obtain address information corresponding to the store address input by the user through the interactive interface, is specifically configured to: displaying a map on the interactive interface; and responding to the clicking operation of the user on the map, and acquiring the address information of the clicked position.
Further, the apparatus provided in this embodiment further includes:
the response module is used for responding to the area frame selection operation of the user on the periphery of the clicked position, and taking the area framed and selected by the user as the area where the place is located;
and the forming module is used for forming a rule boundary at the periphery of the clicked position according to a preset region division rule and taking the region surrounded by the rule boundary as the region of the place.
Further, the multidimensional data includes at least part of the following data:
the number of users placing orders in the area, the number of orders completed in the area, the number of orders not completed in the area, the number of competitive store in the area, the number of personnel capable of providing services corresponding to stores in the area, the types of services capable of being provided in the area, the number of personnel capable of providing various services in the area, the number of population in the area and the density of houses in the area.
Further, the analysis module 304, when configured to analyze the multidimensional data to obtain store site selection data, is specifically configured to: acquiring a calculation model; and analyzing the multidimensional data by using the calculation model to obtain the shop site selection data.
Further, the calculation model is a machine learning model; accordingly, the number of the first and second electrodes,
the apparatus provided in this embodiment further includes:
the third acquisition module is used for acquiring historical multidimensional data which are not generated in areas where a plurality of existing stores are located and key performance indicator KPI data of each existing store to generate a training sample;
and the training module is used for training the machine learning model by using the training samples to obtain the calculation model.
Further, the apparatus provided in this embodiment further includes:
the display module is used for displaying a map on the interactive interface;
the fourth acquisition module is used for acquiring attribute characteristics of a plurality of areas within the range of the map displayed;
the generating module is used for generating corresponding thermodynamic diagrams on the map according to the attribute characteristics of the plurality of areas;
wherein the attribute features include: the density of the houses in the area, the number of the competitive store in the area and the service type distribution data provided by the store in the area.
Here, it should be noted that: the device for determining store site selection data provided in this embodiment may execute the method for determining store site selection data described in the embodiment shown in fig. 1a, and the implementation principle and technical effect thereof are not described again. The specific implementation manner of the operation performed by each module or unit in the store addressing data determination device in the above embodiment has been described in detail in the embodiment related to the method, and will not be described in detail here.
Fig. 4 shows a schematic structural diagram of an electronic device according to an embodiment of the present application. As shown in fig. 4, the electronic apparatus includes: a memory 401 and a processor 402. The memory 401 may be configured to store other various data to support operations on the sensors. Examples of such data include instructions for any application or method operating on the sensor. The memory 401 may be implemented by any type or combination of volatile or non-volatile memory devices such as Static Random Access Memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic or optical disks.
The processor 402, coupled to the memory 401, is configured to execute the program stored in the memory 401 to:
displaying an interactive interface;
acquiring address information corresponding to the store address input by the user through the interactive interface;
acquiring multidimensional data generated in the area where the location corresponding to the address information is located; wherein the multidimensional data has relevance with the content of the service provided by the store;
analyzing the multidimensional data to obtain store site selection data; wherein the store site selection data reflects an executability of site selection of stores at the location;
and displaying the place and the store site data in a correlation mode.
When the processor 402 executes the program in the memory 401, in addition to the above functions, other functions may be implemented, which may be specifically referred to the description of the foregoing embodiments.
Further, as shown in fig. 4, the electronic device further includes: communication components 403, display 404, power components 405, and audio components 406, among other components. Only some of the components are schematically shown in fig. 4, and the electronic device is not meant to include only the components shown in fig. 4.
Accordingly, embodiments of the present application also provide a computer-readable storage medium storing a computer program, where the computer program can implement the steps or functions of the store address data determination method provided in the foregoing embodiments when executed by a computer.
The above-described embodiments of the apparatus are merely illustrative, and the units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware. With this understanding in mind, the above-described technical solutions may be embodied in the form of a software product, which can be stored in a computer-readable storage medium such as ROM/RAM, magnetic disk, optical disk, etc., and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the methods described in the embodiments or some parts of the embodiments.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present application, and not to limit the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions in the embodiments of the present application.
Claims (10)
1. A method for determining store site selection data is characterized by comprising the following steps:
displaying an interactive interface;
acquiring address information corresponding to the store address input by the user through the interactive interface;
acquiring multidimensional data generated in the area where the location corresponding to the address information is located; wherein the multidimensional data has relevance with the content of the service provided by the store;
analyzing the multidimensional data to obtain store site selection data; wherein the store site selection data reflects an executability of site selection of stores at the location;
and displaying the place and the store site data in a correlation mode.
2. The method of claim 1, wherein obtaining address information corresponding to the store site entered by the user via the interactive interface comprises:
displaying a map on the interactive interface;
and responding to the clicking operation of the user on the map, and acquiring the address information of the clicked position.
3. The method of claim 2, further comprising:
responding to the area frame selection operation of the user on the periphery of the clicked position, and taking the area framed and selected by the user as the area where the place is located;
and forming a rule boundary at the periphery of the clicked position according to a preset region division rule, and taking a region surrounded by the rule boundary as a region where the place is located.
4. The method of any one of claims 1 to 3, wherein the multidimensional data comprises at least part of:
the number of users placing orders in the area, the number of orders completed in the area, the number of orders not completed in the area, the number of competitive store in the area, the number of personnel capable of providing services corresponding to stores in the area, the types of services capable of being provided in the area, the number of personnel capable of providing various services in the area, the number of population in the area and the density of houses in the area.
5. The method of claim 4, wherein analyzing the multidimensional data to derive store location data comprises:
acquiring a calculation model;
and analyzing the multidimensional data by using the calculation model to obtain the shop site selection data.
6. The method of claim 5, wherein the computational model is a machine learning model; and
acquiring historical multidimensional data generated in an area where a plurality of existing stores are located and key performance index KPI data of each existing store to generate a training sample;
and training the machine learning model by using the training samples to obtain the calculation model.
7. The method of claim 1, further comprising:
displaying a map on the interactive interface;
acquiring attribute characteristics of a plurality of areas within the range of the map displayed;
generating corresponding thermodynamic diagrams on the map according to the attribute characteristics of the plurality of areas;
wherein the attribute features include: the density of the houses in the area, the number of the competitive store in the area and the service type distribution data provided by the store in the area.
8. An store site selection data determination system, comprising:
the client is used for displaying the interactive interface;
the server is used for acquiring address information corresponding to the store address input by the user through the interactive interface; acquiring multidimensional data generated in the area where the location corresponding to the address information is located; the multidimensional data is associated with the content of the service provided by the store; analyzing the multidimensional data to obtain store site selection data; wherein the store site selection data reflects an executability of site selection of stores at the location; and associating the place with the store address data and sending the place and the store address data to the client so as to display the place and the store address data on an interactive interface of the client.
9. The system of claim 8,
the client is also used for displaying a map on the interactive interface;
the server is further used for acquiring attribute features of a plurality of areas within the displayed range of the map; generating corresponding thermodynamic diagrams on the map according to the attribute characteristics of the plurality of areas;
wherein the attribute features include: the density of the houses in the area, the number of the competitive store in the area and the service type distribution data provided by the store in the area.
10. An electronic device, comprising: a memory and a processor, wherein,
the memory is used for storing programs;
the processor, coupled with the memory, to execute the program stored in the memory to:
displaying an interactive interface;
acquiring address information corresponding to the store address input by the user through the interactive interface;
acquiring multidimensional data generated in the area where the location corresponding to the address information is located; wherein the multidimensional data has relevance with the content of the service provided by the store;
analyzing the multidimensional data to obtain store site selection data; wherein the store site selection data reflects an executability of site selection of stores at the location;
and displaying the place and the store site data in a correlation mode.
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