CN115049439A

CN115049439A - Data processing method and device

Info

Publication number: CN115049439A
Application number: CN202210801853.XA
Authority: CN
Inventors: 张同心
Original assignee: Beijing Jingdong Tuoxian Technology Co Ltd
Current assignee: Beijing Jingdong Tuoxian Technology Co Ltd
Priority date: 2022-07-08
Filing date: 2022-07-08
Publication date: 2022-09-13

Abstract

The invention discloses a data processing method and device, and relates to the technical field of supply chains. One embodiment of the method comprises: dividing a region to be processed into one or more sub-regions with preset sizes; determining user data corresponding to the sub-area and store data which enter; determining an area demand value corresponding to the sub-area by adopting a supply and demand evaluation model according to the user data and the accessed store data, and determining a target sub-area needing to be accessed to the store based on the area demand value; according to the data of the shops which have entered the shop, obtaining data of shops which do not enter the shop from the data of the whole number of shops corresponding to the target sub-area; and selecting one or more stores to be entered from the stores not to be entered to generate store data to be entered corresponding to the target sub-area. According to the embodiment, the selection efficiency of the target sub-area and the store to be accessed is improved, and the reasonability of the selected target sub-area and the store to be accessed is ensured.

Description

Data processing method and device

Technical Field

The present invention relates to the technical field of supply chains, and in particular, to a data processing method and apparatus.

Background

For an e-commerce platform, the number of stores and the locations of the stores have a decisive role in the quality of service provided, for example, in the field of medicine supply chains, timely and quick delivery of medicines is particularly important, so that the method can be realized by reasonably arranging the stores. It is therefore critical to identify the area where store stays need to be made and the stores in that area that need to stay. At present, the electronic commerce platform mainly depends on business personnel to determine the region where the store is located and the store needing to be located according to personal experience.

In the process of implementing the invention, the inventor finds that at least the following problems exist in the prior art: only according to personal experience, stores and areas for entrance are selected, which is inefficient; in addition, as business personnel are difficult to comprehensively master supply and demand conditions of different areas, the situation that user requirements in the selected area are not matched with store service capacity exists, and the number of the same type of store entering is too large, so that resource waste is caused.

Disclosure of Invention

In view of this, embodiments of the present invention provide a data processing method and apparatus, which can divide a to-be-processed area into sub-areas, evaluate supply and demand conditions of different sub-areas by using an area demand value, and further, on the basis of considering the supply and demand conditions of different areas, do not rely on manual automatic identification of a target sub-area and a to-be-entered store that need to enter the store, thereby improving selection efficiency of the target sub-area and the to-be-entered store, and ensuring reasonability of the selected target sub-area and the to-be-entered store.

To achieve the above object, according to an aspect of an embodiment of the present invention, there is provided a data processing method including:

dividing a region to be processed into one or more sub-regions with preset sizes;

determining user data corresponding to the sub-area and store data which enter;

determining a region demand value corresponding to the sub-region by adopting a supply and demand evaluation model according to the user data and the resident store data, so as to determine a target sub-region needing to enter the resident store based on the region demand value;

according to the data of the shops which have entered the shop, obtaining data of shops which do not enter the shop from the data of the whole number of shops corresponding to the target sub-area;

and selecting one or more stores to be entered from the stores not to be entered to generate store data to be entered corresponding to the target sub-area.

Optionally, an address coding algorithm is adopted to divide the region to be processed into one or more sub-regions with preset sizes.

Optionally, the method further comprises: aggregating the sub-areas with preset sizes to form an aggregated sub-area meeting the size requirement of the entrance area, and selecting a target aggregated sub-area needing entrance to the store from the aggregated sub-area.

Optionally, the determining the user data and the data of the entered store corresponding to the sub-area includes:

recording user search data when a user conducts a search action, wherein the user search data comprises: user identification and user position information;

determining a sub-region corresponding to the user position information according to the user position information so as to determine the number of users performing searching behaviors in the sub-region;

and determining a sub-area corresponding to the accessed store according to the store position information corresponding to the accessed store so as to determine the number of the accessed stores in the sub-area.

Optionally, the user searching data further comprises: search terms employed by the user;

classifying the user searching behaviors according to the searching words to determine service labels corresponding to the user searching behaviors;

and determining the number of users performing searching behaviors under different service tags in the sub-area and the corresponding number of entrance shops according to the service tags.

Optionally, the supply and demand evaluation model determines the area demand value corresponding to the sub-area based on the number of users performing search activities in the sub-area, the number of users that can be served by a single entrance store, and the number of entrance stores in the sub-area.

Optionally, one or more sub-areas are selected as target sub-areas needing to enter the store according to the sequence of the area demand values corresponding to the sub-areas from high to low.

Optionally, the distance between each store in the total quantity of store data and the store already entered is calculated according to the store location information, so that if the distance is greater than the threshold distance, the store in the total quantity of store data is determined to be the store not entered in the target sub-area.

Alternatively, the distance between each store in the total store data and the store that has entered is calculated based on the latitude of the store, the latitude of the store that has entered the store, and the difference between the latitude of the store and the difference between the longitude of the store in the total store data.

Optionally, determining a brand label to which the non-entry store belongs according to a store name corresponding to the non-entry store;

determining brand demand values corresponding to different brand tags in a target sub-area according to user data corresponding to the brand tags and store entrance data;

and selecting one or more non-entrance stores under the brand label according to the brand demand value to generate store-to-entrance data corresponding to the target sub-area.

To achieve the above object, according to another aspect of an embodiment of the present invention, there is provided a data processing apparatus including: the system comprises a region dividing module, a subregion data determining module, a target subregion determining module, a store data acquisition module which does not enter a store and a store data generating module which waits to enter the store; wherein the content of the first and second substances,

the area dividing module is used for dividing the area to be processed into one or more sub-areas with preset sizes;

the sub-region data determining module is used for determining user data corresponding to the sub-region and store data which enter the store;

the target sub-region determining module is used for determining a region requirement value corresponding to the sub-region by adopting a supply and demand evaluation model according to the user data and the data of the resident stores so as to determine a target sub-region needing to be resident stores based on the region requirement value;

the non-entrance store data acquisition module is used for acquiring non-entrance store data from the full amount of store data corresponding to the target sub-area according to the entered-entrance store data;

and the to-be-entered store data generation module is used for selecting one or more to-be-entered stores from the non-to-be-entered stores to generate the to-be-entered store data corresponding to the target sub-area.

To achieve the above object, according to still another aspect of an embodiment of the present invention, there is provided an electronic device for data processing, including: one or more processors; storage means for storing one or more programs which, when executed by the one or more processors, cause the one or more processors to carry out any of the data processing methods described above.

To achieve the above object, according to still another aspect of embodiments of the present invention, there is provided a computer-readable medium on which a computer program is stored, characterized in that the program, when executed by a processor, implements any one of the data processing methods described above.

One embodiment of the above invention has the following advantages or benefits: by dividing the to-be-processed area into sub-areas and evaluating the supply and demand conditions of different sub-areas by adopting the area demand values, the target sub-areas needing to enter the store and the stores to be entered are not dependent on manual automatic identification on the basis of considering the supply and demand conditions of different areas, the selection efficiency of the target sub-areas and the stores to be entered is improved, and the reasonability of the selected target sub-areas and the stores to be entered is also ensured.

Further effects of the above-mentioned non-conventional alternatives will be described below in connection with the embodiments.

Drawings

The drawings are included to provide a better understanding of the invention and are not to be construed as unduly limiting the invention. Wherein:

fig. 1 is a schematic diagram of a main flow of a data processing method according to an embodiment of the present invention;

FIG. 2 is a schematic diagram of a main flow of another data processing method according to an embodiment of the present invention;

FIG. 3 is a diagram of sub-regions partitioned using a GeoHash algorithm according to an embodiment of the present invention;

FIG. 4 is a schematic diagram of a main flow of a sub-region data determination method according to an embodiment of the present invention;

FIG. 5 is a schematic diagram of a main flow of a non-entrance store data acquisition method according to an embodiment of the present invention;

fig. 6 is a schematic view of a main flow of a to-be-entered store data generation method according to an embodiment of the present invention;

FIG. 7 is a schematic diagram of the main modules of a data processing apparatus according to an embodiment of the present invention;

FIG. 8 is an exemplary system architecture diagram in which embodiments of the present invention may be applied;

fig. 9 is a schematic structural diagram of a computer system suitable for implementing a terminal device or a server according to an embodiment of the present invention.

Detailed Description

Exemplary embodiments of the present invention are described below with reference to the accompanying drawings, in which various details of embodiments of the invention are included to assist understanding, and which are to be considered as merely exemplary. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the invention. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.

Fig. 1 is a schematic diagram of a main flow of a data processing method according to an embodiment of the present invention, and as shown in fig. 1, the data processing method specifically includes the following steps:

step S101, dividing the region to be processed into one or more sub-regions with preset size.

It can be understood that the range that a single store can serve is limited, for example, in an O2O (Online to Offline) scenario, the distribution range of a store is about 3km, and in order to ensure the reasonability of the store parking position and the store parking number, a large area to be processed can be divided into a plurality of sub-areas according to actual business requirements for processing. The preset size corresponding to the sub-region may be actually determined by service requirements, such as 3km, 5km, and the like.

More specifically, an address coding algorithm, such as a GeoHash algorithm, may be used to divide the region to be processed into one or more sub-regions with a preset size, and each sub-region corresponds to a region range. Therefore, the supply and demand conditions in the area can be known more comprehensively and accurately.

And step S102, determining user data corresponding to the sub-area and data of the accessed store.

The user data includes, but is not limited to, user search data, user purchase data, user collection data, and the like, and specifically, the user search data may include a user identifier (such as a user ID and a device identifier), user location information where the user performs a search action, and the like. And the store data refers to store data which is already stored in the operation service platform, and includes but is not limited to store names, store location information, brand information to which stores belong, and the like. On the basis, the matched sub-area can be determined based on the user position information and the store position information, and then the user data corresponding to the sub-area and the store-in data are determined. In the embodiment of the invention, longitude and latitude are adopted to describe the user position information and store position information.

Step S103, according to the user data and the data of the resident stores, a supply and demand evaluation model is adopted to determine a region demand value corresponding to the sub-region, so that a target sub-region needing to be resident stores is determined based on the region demand value.

The supply and demand evaluation model is used for evaluating the matching degree between the user demand and the store supply. Specifically, the supply and demand evaluation model determines the area demand value corresponding to the sub-area based on the number of users who perform search behaviors in the sub-area, the number of users that can be served by a single entrance store, and the number of entrance stores in the sub-area. More specifically, the following supply and demand evaluation model is adopted to determine the area demand value corresponding to the sub-area:

Q＝U-Se*n

wherein Q is a region requirement value corresponding to the sub-region, U is the number of users performing a search action in the sub-region, Se is the number of users that can be served by a single store, which is usually an empirical value, and n is the number of stores in the sub-region.

In this way, the requirement of the sub-area on the store-in can be evaluated by using the area requirement value, and the higher the area requirement value is, the more the corresponding sub-area needs to be stored in the store-in, or the better the store-in effect is. On the basis, one or more sub-areas can be selected as target sub-areas needing to enter the store according to the sequence of the area requirement values corresponding to the sub-areas from high to low. For example, the first 10% of the sub-regions may be selected as the target sub-regions, or a region requirement threshold may be set, and the sub-region is determined as the target sub-region when the region requirement is greater than the region requirement threshold.

It can be understood that, in the embodiment, when determining the target sub-region, only the number of users corresponding to the user search data is taken as an example for description, and in the actual execution process, the number of users corresponding to the user purchase behavior, the number of users corresponding to the user collection behavior, the number of users corresponding to the user browsing behavior, and the like may also be considered to calculate the region requirement value.

And step S104, acquiring data of the un-entered stores from the data of the whole stores corresponding to the target sub-area according to the data of the entered stores.

The total store data refers to all store data located in the sub-area, and includes, but is not limited to, store names and store location information. It can be understood that, in order to ensure the integrity of the data of all stores, the data of all stores generally includes the data of the resident stores, but in this embodiment, the data of the resident stores in the data of all stores needs to be removed to obtain the data of the resident stores, mainly to select the stores to be entered from the non-resident stores.

Step S105, selecting one or more stores to be entered from the stores not to be entered to generate store data to be entered corresponding to the target sub-region.

Specifically, one or more to-be-entered stores may be selected from the non-entered stores based on preset rules. For example, the to-be-parked stores may be selected according to the preset priority of the to-be-parked stores, the historical sales amount corresponding to the to-be-parked stores, and the like, or the to-be-parked stores may be selected based on the distance between the to-be-parked stores and the center position of the sub-area, or the to-be-parked stores may be selected according to the brand to which the to-be-parked stores belong.

Based on the embodiment, the to-be-processed area is divided into the sub-areas, the supply and demand conditions of different sub-areas are evaluated by adopting the area demand values, and then the target sub-areas and the to-be-parked shops which need to be parked in the shop are not manually and automatically identified on the basis of considering the supply and demand conditions of different areas, so that the selection efficiency of the target sub-areas and the to-be-parked shops is improved, and the reasonability of the selected target sub-areas and the to-be-parked shops is guaranteed.

Referring to fig. 2, on the basis of the foregoing embodiment, an embodiment of the present invention provides another data processing method, which includes the following specific steps:

step S201, a GeoHash algorithm is adopted to divide a region to be processed into one or more sub-regions with preset sizes.

Each sub-region obtained by adopting GeoHash algorithm division is a grid and corresponds to a GeoHash code, and the GeoHash code indicates the corresponding size of the sub-region. Specifically, the longitude and latitude corresponding to the position information are described below as an example (31.1932,121.4396).

a) And (6) longitude processing. The longitude has an interval of [ -90,90], which is divided into [ -90,0) and [0,90], and is marked as 1 since the longitude 31.1932 falls within the right interval [0,90 ]. Continue to bifurcate [0,90] into [0,45) and [45,90], since longitude 31.1932 falls within the left interval [0,45), labeled 0. By analogy, longitude can be converted to binary: 101011000101110. the specific treatment process is shown in the following table 1:

TABLE 1 longitude processing

b) And (6) latitude processing. Similar to the longitude process, the idea of dichotomy is used to translate latitude 121.4396 into a binary string: 110101100101101.

c) longitude and latitude are mixed. And recombining the binary character strings corresponding to the longitude and the latitude according to the principle that the longitude places even digits and the latitude places odd digits, wherein the combined character strings are as follows: 111001100111100000110011110110. it should be understood that the present embodiment is only described by using the principle of using longitude to place even digits and using latitude to place odd digits, and longitude may also be used to place longitude to place odd digits and latitude to place even digits in an actual implementation process.

d) The character string is converted. According to the base32 table, the mixed character string is converted into 32-ary system, and the sub-region obtained after conversion is coded into wtw37 q. Specifically, see the sub-regions obtained by dividing with the GeoHash algorithm shown in fig. 3. It can be seen that as the number of encoded bits of the sub-region increases, the accuracy of the sub-region is gradually improved.

Furthermore, due to the fact that the radiation ranges of the stores corresponding to different services are different, the preset sizes of the corresponding sub-areas are also different, for example, in a medical O2O scenario, the average distribution range of a business is 3km, so that the target sub-area and the store to be parked can be determined according to a 3km grid, and the other services are the same. However, the size difference between the sub-regions of each level is large due to the GeoHash algorithm, and as shown in table 2 below, the size corresponding to the sub-region with the coding length of 6 at the GeoHash6 level is 1.22km, and the size corresponding to the sub-region with the coding length of 7 at the GeoHash7 level is 152m, so that the size corresponding to the original GeoHash sub-region may not satisfy the service requirement.

TABLE 2 sub-region sizes corresponding to different GeoHash string lengths

On the basis, the subareas with preset sizes can be polymerized to form the polymerized subarea meeting the size requirement of the entrance area, so that the target polymerized subarea needing to enter the store can be selected from the polymerized subareas. Specifically, for example, the size requirement of the parking area is 300, the GeoHash algorithm may be adopted to divide the area to be processed into sub-areas with a GeoHash coding length of 7 in advance, and the corresponding size is 152 m; then, the sub-regions with the size of 152m are aggregated two by two to generate an aggregated sub-region with the size of 304m, and then a target aggregated sub-region which needs to be accessed to the store and a store to be accessed to the store are determined based on the aggregated sub-regions.

Step S202, determining user data corresponding to the sub-area and store data.

Step S203, determining a region requirement value corresponding to the sub-region by using a supply and demand evaluation model according to the user data and the data of the resident stores, so as to determine a target sub-region needing to be resident stores based on the region requirement value.

Step S204, according to the data of the store which has entered, obtaining data of the store which has not entered from the data of the whole number of stores corresponding to the target sub-area.

Step S205, selecting one or more stores to be entered from the stores not to be entered to generate store data to be entered corresponding to the target sub-region.

Therefore, the GeoHash algorithm is adopted to divide the region to be processed into one or more sub-regions, and the sub-regions are aggregated when the sizes of the sub-regions cannot meet different service requirements, so that the flexibility of the sizes of the sub-regions is ensured, and the applicability of the data processing method is improved; meanwhile, the supply and demand conditions of each area can be comprehensively considered through the evaluation of the supply and demand conditions of the sub-areas, so that the reasonability of the determined target sub-area or target aggregation sub-area and the store to be parked is ensured.

Referring to fig. 4, on the basis of the foregoing embodiment, an embodiment of the present invention provides a method for determining sub-region data, which is used to describe the step S102 in detail, and specifically includes the following steps:

step S401, recording user search data when a user conducts a search behavior, wherein the user search data comprises: user identification, user location information.

Step S402, according to the user position information, determining a sub-area corresponding to the user position information so as to determine the number of users performing searching behaviors in the sub-area.

Step S403, determining a sub-area corresponding to the entered store according to the store location information corresponding to the entered store, so as to determine the number of the entered stores located in the sub-area.

Still further, the user search data further includes: search terms employed by the user; classifying the user searching behaviors according to the searching words to determine service labels corresponding to the user searching behaviors; and determining the number of users performing searching behaviors under different service tags in the sub-area and the corresponding number of the entrance stores according to the service tags.

It can be understood that, because different services have different points of interest, for example, medical services pay more attention to search behaviors related to medicines, in order to ensure the reliability of the area requirement value determined by the user search data, it may be considered to respectively process the user search behaviors according to the service types, and then determine stores to be parked which are required by different services.

Specifically, the service tag may be set according to an actual service, and each service tag corresponds to one or more of the following information: item name, item type, keyword.

Based on the method, the service label corresponding to the user searching action can be determined based on the recalled article corresponding to the user searching action; the service label corresponding to the user searching behavior can be determined based on the type of the recalled article corresponding to the user searching behavior; and determining a service label corresponding to the user searching behavior based on the keyword indicated by the searching word adopted by the user.

On the basis, the number of users who search in the region under the service tag and the number of entrance shops can be counted, and then the region required value corresponding to the sub-region under the service tag can be determined by adopting the supply and demand evaluation model, so that the target sub-region and the entrance shop which need to be recruited and correspond to the service tag can be determined according to the region required value, and the rationality of the target sub-region and the entrance shop is further improved.

Referring to fig. 5, on the basis of the foregoing embodiment, an embodiment of the present invention provides a method for acquiring data of an un-resident store, so as to describe step S104 in detail, which includes the following specific steps:

step S501, calculating the distance between each store in the total store data and the store already entered according to the store position information.

Specifically, the distance between each store in the total store data and the store that has entered is calculated based on the latitude of the store, the latitude of the store that has entered the store, and the difference between the latitude of the store and the difference between the longitude of the store in the total store data. More specifically, the distance between each store in the total store data and the store already entered is calculated by using the following distance formula:

where S is the distance between stores, Lat1 is the latitude of the stores in the total store data, Lat2 is the latitude of the store entrance, a is the difference between the latitudes of the stores, and b is the difference between the longitudes of the stores.

Step S502, when the distance is larger than the threshold distance, determining the stores in the total quantity of store data as the non-resident stores in the target sub-area.

As described by taking the threshold distance as 30m as an example, if the distance between the store a which has entered the store and the store B in the total store data is less than 30m, the stores a and B are considered to be the same store, and the store B is marked as the store which has entered the store; if the distances between all stores in the already-entered stores and store B in the full volume data are not less than 30m, the store B is marked as an un-entered store.

Since there may be some difference between longitude or latitude digits of stores in the total store data due to inconsistency of data sources, the matching rate may be low and the data may not be completely cleaned if the data is cleaned by directly matching the location information of the stores already entered with the location information of the stores in the total store data. Therefore, the entered stores in the total store data are cleaned through the distance formula, and the reliability of the obtained data of the un-entered stores is guaranteed.

Referring to fig. 6, on the basis of the foregoing embodiment, an embodiment of the present invention provides a method for generating data of a store to be entered, which is used to describe step S105 in detail, and includes the following specific steps:

step S601, according to the store name corresponding to the store which does not enter the store, determining the brand label of the store which does not enter the store.

Specifically, the brand label and one or more keywords corresponding to the brand label may be configured in advance, and then the brand label corresponding to the store that is not entered may be determined based on the keyword included in the store name. In addition, the brand label corresponding to the non-resident store can be manually confirmed, or the existing brand label corresponding to the non-resident store can be directly used.

Step S602, determining the brand demand values corresponding to different brand labels in the target subarea according to the user data corresponding to the brand labels and the store entrance data.

It will be appreciated that for a store already in the store, its corresponding brand tag is generally known, or can be determined according to the above-described method. On the basis, the number of the resident stores with the same brand label in the target sub-area can be counted, and then the business label is determined based on the actual business corresponding to the brand label, so that the number of the users who perform the searching action under the business label in the target sub-area is counted to serve as the number of the users corresponding to the brand label. Further, the supply and demand relationship evaluation model is adopted, and user data and store entrance data corresponding to the brand labels are used as input to determine the brand demand value in the target sub-area.

Step S603, selecting one or more non-entrance stores under the brand label according to the brand demand value to generate store data to be entered corresponding to the target sub-area.

It will be appreciated that the higher the brand demand value, the more stores under the brand label need to be accessed in the target sub-area, and thus one or more brand labels that need to be accessed in the stores may be determined based on the order of the brand demand value from high to low. On the basis, one or more non-entrance shops can be selected from the brand label based on a preset rule, for example, the entrance shop to be entered is selected according to a preset priority of the brand label, a historical sales amount corresponding to the brand label entrance shop, and the like.

Therefore, the user data and the entrance store data are subdivided according to the brand labels, the brand demand values corresponding to the target sub-areas are calculated, and then the stores in the target sub-areas and under the brand labels needing entrance in the target sub-areas are determined, so that the entrance stores to be determined according to the brands are determined, and the reasonability of the determined target sub-areas and the entrance stores to be determined is further improved.

Referring to fig. 7, on the basis of the above embodiment, an embodiment of the present invention provides a data processing apparatus 700, which includes a region dividing module 701, a sub-region data determining module 702, a target sub-region determining module 703, a non-entrance store data acquiring module 704, and a to-enter store data generating module 705; wherein the content of the first and second substances,

the region dividing module 701 is configured to divide a region to be processed into one or more sub-regions with a preset size;

a sub-region data determining module 702, configured to determine user data and store data corresponding to the sub-region;

a target sub-region determining module 703, configured to determine, according to the user data and the data of the store entering, a region requirement value corresponding to the sub-region by using a supply and demand evaluation model, so as to determine, based on the region requirement value, a target sub-region that needs to enter the store;

the non-entrance store data acquisition module 704 is configured to acquire non-entrance store data from the full amount of store data corresponding to the target sub-area according to the entered-entrance store data;

the to-be-entered store data generating module 705 is configured to select one or more to-be-entered stores from the non-to-be-entered stores to generate to-be-entered store data corresponding to the target sub-region.

In another embodiment of the present invention, the region dividing module 701 is configured to divide the region to be processed into one or more sub-regions with a preset size by using an address coding algorithm. The address coding algorithm preferably used in this embodiment is the GeoHash algorithm.

In another embodiment of the present invention, the region dividing module 701 is further configured to,

aggregating the sub-areas with preset sizes to form an aggregated sub-area meeting the size requirement of the entrance area, and selecting a target aggregated sub-area needing entrance to the store from the aggregated sub-area.

In another embodiment of the present invention, the determining the user data and the data of the entered-store corresponding to the sub-area includes: recording user search data when a user conducts a search action, wherein the user search data comprises: user identification and user position information; determining a sub-region corresponding to the user position information according to the user position information so as to determine the number of users performing searching behaviors in the sub-region; and determining a sub-area corresponding to the resident store according to the store position information corresponding to the resident store so as to determine the number of the resident stores positioned in the sub-area.

In another embodiment of the present invention, the user search data further comprises: search terms employed by the user; classifying the user searching behaviors according to the searching words to determine service labels corresponding to the user searching behaviors; and determining the number of users performing searching behaviors under different service tags in the sub-area and the corresponding number of the entrance stores according to the service tags.

In another embodiment of the invention, the supply and demand evaluation model determines the area demand value corresponding to the sub-area based on the number of users performing search behaviors in the sub-area, the number of users that can be served by a single entrance store and the number of entrance stores in the sub-area. Specifically, the method is used for determining the area demand value corresponding to the sub-area by using the following supply and demand evaluation model:

Q＝U-Se*n

q is the area requirement value corresponding to the sub-area, U is the number of users performing searching in the sub-area, Se is the number of users served by a single entrance store, and n is the number of entrance stores in the sub-area.

In another embodiment of the present invention, the target sub-area determining module 703 is configured to select one or more sub-areas as target sub-areas that need to enter the store according to a sequence from high to low of the area requirement values corresponding to the sub-areas.

In another embodiment of the present invention, the non-resident store data obtaining module 704 is configured to calculate a distance between each store in the total quantity of store data and the already resident store according to the store location information, so as to determine that the store in the total quantity of store data is the non-resident store in the target sub-area if the distance is greater than the threshold distance.

In another embodiment of the present invention, the non-resident store data obtaining module 704 is configured to calculate a distance between each store in the total store data and the resident store based on the latitude of the store in the total store data, the latitude of the resident store, a difference between the latitudes of the stores, and a difference between longitudes of the stores. Specifically, the method is used for calculating the distance between each store in the total store data and the store already entered into the store by adopting the following distance formula:

where S is the distance between stores, Lat1 is the latitude of the stores in the total store data, Lat2 is the latitude of the store entrance, a is the difference between the stores 'latitudes, and b is the difference between the stores' longitudes.

In another embodiment of the present invention, the to-be-entered store data generating module 705 is configured to determine, according to a store name corresponding to an un-entered store, a brand tag to which the un-entered store belongs; determining brand demand values corresponding to different brand tags in a target sub-area according to user data corresponding to the brand tags and store entrance data; and selecting one or more non-entrance stores under the brand label according to the brand demand value to generate store data to be entered corresponding to the target sub-area.

An embodiment of the present invention further provides an electronic device for data processing, including: one or more processors; storage means for storing one or more programs which, when executed by the one or more processors, cause the one or more processors to carry out any of the data processing methods described above.

An embodiment of the present invention further provides a computer-readable medium, on which a computer program is stored, where the computer program is configured to implement, when executed by a processor, any one of the data processing methods described above.

Fig. 8 shows an exemplary system architecture 800 of a data processing method or data processing apparatus to which embodiments of the present invention may be applied.

As shown in fig. 8, the system architecture 800 may include

terminal devices

801, 802, 803, a network 804, and a server 805. The network 804 is used to provide a medium for communication links between

terminal devices

801, 802, 803 and a server 805. Network 804 may include various types of connections, such as wire, wireless communication links, or fiber optic cables, to name a few.

A user may use the

terminal devices

801, 802, 803 to interact with a server 805 over a network 804 to receive or send messages or the like. Various communication client applications, such as a shopping application, a web browser application, a search application, etc., may be installed on the

terminal devices

801, 802, 803 to obtain user search data, etc.

The

terminal devices

801, 802, 803 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smart phones, tablet computers, laptop portable computers, desktop computers, and the like.

The server 805 may be a server that provides various services, such as a back-office management server (for example only) that supports shopping-like websites browsed by users using the

terminal devices

801, 802, 803. The background management server may perform processing such as analysis on the received user search data, for example, determine a sub-region corresponding to the user search line according to the user location information indicated by the user search data.

It should be noted that the data processing method provided by the embodiment of the present invention is generally executed by the server 805, and accordingly, the data processing apparatus is generally disposed in the server 805.

It should be understood that the number of terminal devices, networks, and servers in fig. 8 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.

Referring now to FIG. 9, shown is a block diagram of a computer system 900 suitable for use with a terminal device implementing an embodiment of the present invention. The terminal device shown in fig. 9 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present invention.

As shown in fig. 9, the computer system 900 includes a Central Processing Unit (CPU)901 that can perform various appropriate actions and processes in accordance with a program stored in a Read Only Memory (ROM)902 or a program loaded from a storage section 908 into a Random Access Memory (RAM) 903. In the RAM 903, various programs and data necessary for the operation of the system 900 are also stored. The CPU 901, ROM 902, and RAM 903 are connected to each other via a bus 904. An input/output (I/O) interface 905 is also connected to bus 904.

The following components are connected to the I/O interface 905: an input portion 906 including a keyboard, a mouse, and the like; an output section 907 including components such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, and a speaker; a storage portion 908 including a hard disk and the like; and a communication section 909 including a network interface card such as a LAN card, a modem, or the like. The communication section 909 performs communication processing via a network such as the internet. The drive 910 is also connected to the I/O interface 905 as necessary. A removable medium 911 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 910 as necessary, so that a computer program read out therefrom is mounted into the storage section 908 as necessary.

In particular, according to the embodiments of the present disclosure, the processes described above with reference to the flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method illustrated in the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network through the communication section 909, and/or installed from the removable medium 911. The above-described functions defined in the system of the present invention are executed when the computer program is executed by a Central Processing Unit (CPU) 901.

It should be noted that the computer readable medium shown in the present invention can be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present invention, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In the present invention, however, a computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic cable, RF, etc., or any suitable combination of the foregoing.

The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

The modules described in the embodiments of the present invention may be implemented by software or hardware. The described modules may also be provided in a processor, which may be described as: a processor comprises a region dividing module, a sub-region data determining module, a target sub-region determining module, a non-entrance store data acquiring module and a to-entrance store data generating module. The names of these modules do not in some cases constitute a limitation on the module itself, and for example, the region dividing module may also be described as a "module for dividing a region to be processed into one or more sub-regions having a preset size".

As another aspect, an embodiment of the present invention further provides a computer-readable medium, where the computer-readable medium may be included in the apparatus described in the foregoing embodiment; or may be separate and not incorporated into the device. The computer readable medium carries one or more programs which, when executed by a device, cause the device to comprise: dividing a region to be processed into one or more sub-regions with preset sizes; determining user data corresponding to the sub-area and store data which enter; determining a region demand value corresponding to the sub-region by adopting a supply and demand evaluation model according to the user data and the resident store data, so as to determine a target sub-region needing to enter the resident store based on the region demand value; according to the data of the shops which have entered the shop, obtaining data of shops which do not enter the shop from the data of the whole number of shops corresponding to the target sub-area; and selecting one or more stores to be entered from the stores not to be entered to generate store data to be entered corresponding to the target sub-area.

According to the technical scheme of the embodiment of the invention, the to-be-processed area is divided into the sub-areas, and the supply and demand conditions of different sub-areas are evaluated by adopting the area demand values, so that the target sub-area and the to-be-parked store which need to be parked are not manually and automatically identified on the basis of considering the supply and demand conditions of different areas, the selection efficiency of the target sub-area and the to-be-parked store is improved, and the rationality of the selected target sub-area and the to-be-parked store is ensured.

The above-described embodiments should not be construed as limiting the scope of the invention. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions can occur, depending on design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A data processing method, comprising:

determining user data corresponding to the sub-area and store data which enter;

one or more to-be-entered stores are selected from the non-entered stores to generate to-be-entered store data corresponding to the target sub-area.

2. The data processing method of claim 1,

and dividing the region to be processed into one or more sub-regions with preset sizes by adopting an address coding algorithm.

3. The data processing method of claim 2, further comprising:

4. The data processing method of claim 1, wherein the determining the user data and the store data corresponding to the sub-area comprises:

and determining a sub-area corresponding to the resident store according to the store position information corresponding to the resident store so as to determine the number of the resident stores positioned in the sub-area.

5. The data processing method of claim 4,

the user search data further comprises: search terms employed by the user;

and determining the number of users performing searching behaviors under different service tags in the sub-area and the corresponding number of the entrance stores according to the service tags.

6. The data processing method according to claim 4 or 5,

the supply and demand evaluation model determines the area demand value corresponding to the sub-area based on the number of users who search in the sub-area, the number of users which can be served by a single entrance store and the number of entrance stores in the sub-area.

7. The data processing method of claim 6,

and selecting one or more sub-areas as target sub-areas needing to enter the store according to the sequence of the area demand values corresponding to the sub-areas from high to low.

8. The data processing method of claim 1,

and calculating the distance between each store in the total quantity store data and the store already in the store according to the store position information, and determining the store in the total quantity store data as the store not in the target subregion when the distance is greater than the threshold distance.

9. The data processing method of claim 8,

and calculating the distance between each store in the total store data and the store which has already entered based on the latitude of the stores, the latitude of the store which has entered the store, the difference between the latitudes of the stores and the longitude of the stores in the total store data and the difference between the longitudes of the stores.

10. The data processing method of claim 9,

determining a brand label of the non-resident store according to the store name corresponding to the non-resident store;

and selecting one or more non-entrance stores under the brand label according to the brand demand value to generate store data to be entered corresponding to the target sub-area.

11. A data processing apparatus, characterized by comprising: the system comprises a region dividing module, a subregion data determining module, a target subregion determining module, a store data acquisition module which does not enter a store and a store data generating module which waits to enter the store; wherein the content of the first and second substances,

12. An electronic device for data processing, comprising:

one or more processors;

a storage device for storing one or more programs,

the one or more programs, when executed by the one or more processors, cause the one or more processors to implement the method of any of claims 1-10.

13. A computer-readable medium, on which a computer program is stored, which, when being executed by a processor, carries out the method according to any one of claims 1-10.