CN109978292B

CN109978292B - Intelligent management method and device for store

Info

Publication number: CN109978292B
Application number: CN201711449072.4A
Authority: CN
Inventors: 游海涛; 蔡仲湖; 梁晓阳; 卢孔敏; 李朋轩; 徐建极
Original assignee: Alibaba Group Holding Ltd
Current assignee: Alibaba Group Holding Ltd
Priority date: 2017-12-27
Filing date: 2017-12-27
Publication date: 2023-05-30
Anticipated expiration: 2037-12-27
Also published as: CN109978292A

Abstract

The invention discloses an intelligent management method and device for a store. Wherein the method comprises the following steps: determining at least one store assigned to the user; acquiring store information of at least one store and historical service information of a user on the at least one store; store visit plan information is generated for the user based on the store information and the historical service information to provide the visit plan information to the user. The invention solves the technical problem of low store management efficiency caused by adopting a timing mode to visit stores.

Description

Intelligent management method and device for store

Technical Field

The invention relates to the field of computers, in particular to an intelligent management method and device for a store.

Background

With the continuous development of technology, management of off-line physical stores, such as convenience stores, is increasingly intelligent. Currently, in order to provide better services to users, some suppliers or distributors or third party platforms may visit physical shops selling their goods, such as acquiring sales conditions of the goods, conditions of storefronts, demands for goods in stock, and so on. The physical store, convenience store, etc. are referred to herein as a store.

Conventional store management typically uses a prescribed approach, such as a timed-up approach, to make a visit to a service. For example, the store is serviced by a salesman timing a gate. In addition, the business staff is a scribing area, and needs to cover in a fixed frequency mode, for example, store visits need to be made every day according to a fixed sheet area mode, so that the average distribution of resources is realized. These techniques are also typically provided to the attendant via a simple list of plans, either by pushing messages via a cell phone or by planning a sheet.

The existing visiting processing mode is generally realized by experience or simple regulation, all stores are basically the same visiting service, and in fact, some stores need more services, and some stores do not need frequent services of timing to go on, so that on one hand, stores needing more services are not timely served, and on the other hand, the efficiency of a service staff is also lost.

It can be seen that the current store visit is not good in improving the visit effect, and the simple store management technology obviously cannot meet the requirement of rapid social development. Therefore, it is necessary to provide a better visit processing store management technique.

Aiming at the problem of low store management efficiency caused by the fact that store visit is performed in a timed mode, no effective solution is proposed at present.

Disclosure of Invention

The embodiment of the invention provides an intelligent management method and device for shops, which at least solve the technical problem of low efficiency of shop management caused by performing shop visit in a timing mode.

According to one aspect of the embodiment of the invention, an intelligent management method for a store is provided. The method comprises the following steps: determining at least one store assigned to the user; acquiring store information of at least one store and historical service information of a user on the at least one store; store visit plan information is generated for the user based on the store information and the historical service information to provide the visit plan information to the user.

According to another aspect of the embodiment of the invention, an intelligent management device for a store is also provided. The device comprises: a determining module for determining at least one store assigned to the user; the system comprises an acquisition module, a storage module and a storage module, wherein the acquisition module is used for acquiring store information of at least one store and historical service information of a user on the at least one store; and the generation module is used for generating store visit planning information for the user based on the store information and the historical service information so as to provide the visit planning information for the user.

According to another aspect of the embodiments of the present invention, there is also provided a storage medium. The storage medium includes a stored program, wherein the program, when run, controls a device in which the storage medium resides to perform the steps of: determining at least one store assigned to the user; acquiring store information of at least one store and historical service information of a user on the at least one store; store visit plan information is generated for the user based on the store information and the historical service information to provide the visit plan information to the user.

According to another aspect of an embodiment of the present invention, there is also provided a processor. The processor is used for running a program, wherein the program executes the following steps: determining at least one store assigned to the user; acquiring store information of at least one store and historical service information of a user on the at least one store; store visit plan information is generated for the user based on the store information and the historical service information to provide the visit plan information to the user.

According to another aspect of the embodiment of the invention, a mobile terminal is also provided. The mobile terminal includes: determining at least one store assigned to the user; acquiring store information of at least one store and historical service information of a user on the at least one store; store visit plan information is generated for the user based on the store information and the historical service information to provide the visit plan information to the user.

In an embodiment of the invention, at least one store allocated to a user is determined; acquiring store information of at least one store and historical service information of a user on the at least one store; store visit planning information is generated for a user based on store information and historical service information, so that the visit planning information is provided for the user, the problem of store visit in a timing mode is avoided, the technical effect of improving the efficiency of store management is achieved, and the technical problem of low store management efficiency caused by store visit in a timing mode is solved.

Drawings

The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this application, illustrate embodiments of the invention and together with the description serve to explain the invention and do not constitute a limitation on the invention. In the drawings:

fig. 1 is a schematic diagram of a mobile terminal according to an embodiment of the present invention;

FIG. 2 is a schematic diagram of an intelligent management architecture for a store according to an embodiment of the present invention;

FIG. 3 is a schematic illustration of the structure of a single store scoring model according to an embodiment of the present invention;

fig. 4 is a hardware block diagram of a computer terminal (or mobile device) of a store intelligent management method according to an embodiment of the present invention;

FIG. 5 is a flow chart of a method of intelligent store management according to an embodiment of the present invention;

FIG. 6 is a schematic diagram of an intelligent store management device according to an embodiment of the present invention; and

fig. 7 is a block diagram of another mobile terminal according to an embodiment of the present invention.

Detailed Description

In order that those skilled in the art will better understand the present invention, a technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.

It should be noted that the terms "first," "second," and the like in the description and the claims of the present invention and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the invention described herein may be implemented in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.

Example 1

The embodiment of the invention provides a mobile terminal.

Fig. 1 is a schematic diagram of a mobile terminal according to an embodiment of the present invention. As shown in fig. 1, the mobile terminal 100 includes: a processor 102 and a memory 104.

A processor 102;

a memory 104 coupled to the processor 102 for providing instructions to the processor 102 for processing the following processing steps: determining at least one store assigned to the user; acquiring store information of at least one store and historical service information of a user on the at least one store; store visit plan information is generated for the user based on the store information and the historical service information to provide the visit plan information to the user.

The processor 102 of this embodiment is coupled to the memory 104 for receiving at least one store provided by the memory 104 that is determined to be allocated to the user; acquiring store information of at least one store and historical service information of a user on the at least one store; store visit plan information is generated for the user based on the store information and the historical service information to provide instructions for the user with the visit plan information.

The user of this embodiment may be a dockee, for example, a tempo, a store, and a store, which are different from one user to another, i.e., the service area of each user is different. Different stores have different visit information, for example, some stores need goods-taking service, the user is prompted for goods-taking service, some stores need goods-returning information, and the like. The user can also make a visit to each store, i.e., a gear visit. The user can also make a visit to each store, i.e., a gear visit. The user may have different levels, and the greater the level of the user, the greater the number of stores that can be managed, and the processor 102 determines at least one store to assign to the user.

At least one store of this embodiment has store information and historical service information. After the processor 102 determines at least one store assigned to the user, store information for the at least one store is obtained, as well as historical service information for the at least one store by the user. Optionally, the store information of the store in this embodiment includes at least: commodity sales information, consumer information, wherein the commodity sales information includes commodity sales or sales information, and the consumer information includes consumer consumption information at store, including consumption type, consumption preference, etc., without any limitation. Optionally, the store information is also preferential information, behavior information, geographical location information, etc., where the preferential information may be preferential information of a commodity purchased by a consumer, the behavior information may be store purchase and sales behavior information, consumer transaction behavior information, etc., and the geographical location information may be store geographical location information. The historical service information may be the number of stores served by the user, the coverage of the user's visit, and so on.

The store information and history service information of this embodiment are the basis for generating store visit plan information. After the processor 102 obtains store information for the at least one store and historical service information for the at least one store by the user, store visit plan information is generated for the user based on the store information and the historical service information, the store visit plan information including a store visit route plan, which may be a visit path. Optionally, the user has a visit order when visiting a plurality of stores, and the store visit route plan may be used to indicate the visit order of a part of the stores in the plurality of stores, or to indicate the visit order of all the stores in the plurality of stores, so as to provide the visit plan information to the user.

Optionally, the visit plan information may further include customized information of store visits, such as different stores, different visit information, some stores need a goods-taking service, prompt the user for goods-taking service, some store-taking service, provide goods-taking information, etc.

According to the embodiment, abnormal behaviors of the store can be found according to real-time purchasing conditions of the store, and the store can be started in time without being covered in a fixed frequency mode, so that the effect of improving the store management efficiency is achieved.

The processor 102 of this embodiment determines at least one store assigned to the user; acquiring store information of at least one store and historical service information of a user on the at least one store; store visit planning information is generated for a user based on store information and historical service information, so that the visit planning information is provided for the user, the problem of store visit in a timing mode is avoided, the technical effect of improving the efficiency of store management is achieved, and the technical problem of low store management efficiency caused by store visit in a timing mode is solved.

Optionally, the processor 102 of this embodiment is connected to the memory 104, and is configured to receive the store evaluation statistics, the store matching statistics, and the store distance statistics provided by the memory 104 and obtained by each of the at least one store based on the store information and the history service information; and generating instructions of store visit planning information by adopting the store evaluation statistical result, the store proportioning statistical result and the store distance statistical result.

The embodiment obtains the store evaluation statistics of each store in at least one store based on store information and history service information, and the store evaluation statistics are used for indicating the visit frequency of each store in a plurality of stores allocated to the user by a user on a goods-taking platform, so as to realize single-store grading, wherein the goods-taking platform is used for taking goods, and can be a one-stop goods-taking platform, for example, the one-stop goods-taking platform is a retail through platform, and the visit frequency of the store can be the number of times of visiting the store currently managed by each beat in a preset statistics period.

The store proportioning statistics of this embodiment are used to describe the proportional relationship between the number of stores that the order platform has allocated to the user and the number of stores that the order platform has to allocate to the user. The goods-intake platform of the embodiment may allocate stores for the user, including the number of stores allocated, for example, old stores allocated for the user, and may allocate stores to be allocated for the user, for example, new stores allocated for the user, so that the store matching statistics may be new and old store matching statistics. The store matching statistics result of the embodiment may be a ratio between the number of stores allocated by the order platform for the user and the number of stores to be allocated by the order platform for the user, for example, the number of stores allocated by the order platform for the user is C, the number of stores to be allocated by the order platform for the user is D, and the store matching statistics result is C/D.

The store distance statistics of this embodiment are used to describe the distance between every two stores in at least one store. The store distance may be a store distance, which refers to a distance between every two stores. In the case of a plurality of stores, a distance is calculated between every two stores, and the store distance statistics are one statistics for counting the distance between every two stores in at least one store.

The processor 102 of this embodiment generates store visit plan information using the store assessment statistics, store proportioning statistics, and store distance statistics after obtaining store assessment statistics, store proportioning statistics, and store distance statistics for each of at least one store based on the store information and the historical service information.

Optionally, the store information includes at least: the preferential information of the commodity purchased by the consumer, store purchasing and selling behavior information, consumer transaction behavior information and store geographic position information; the history service information includes at least: the number of stores served by the user and the visit coverage rate of the user; the processor 102 of this embodiment is connected to the memory 104, and is configured to receive the store information and the history service information provided by the memory 104, and perform classification integration to determine a purchase decision type of each store, a benefit effect of each store on the goods intake platform and the user, and a growth path of each store; acquiring a store type feature tag and a user type feature tag according to the purchase decision type of each store, the influence of each store on the income of a commodity platform and a user and the growth path of each store; and setting the store type feature tag and the user type feature tag as input data, and inputting the input data into a preset evaluation model to obtain store evaluation statistical results so as to determine a command of the service requirement of each store.

Optionally, the processor 102 collects basic data, which includes at least: the store information comprises preferential information of a commodity purchased by a consumer, store purchase and sales behavior information, consumer transaction behavior information and store geographic position information, optionally, the store information is operation data of each store in a plurality of stores and can comprise store subscription data, store access log data, store transaction data, store red packet/preferential data, store grade data, store address and store distance, wherein the preferential information of the commodity purchased by the consumer can be the store red packet/preferential data, the store purchase and sales behavior information can be the store subscription data, the consumer transaction behavior information can be the store transaction data, and the store geographic position information can be the store address; the history service information comprises the number of stores served by the user and the visit coverage rate of the user, and optionally, the history service information is service data of the user on a plurality of stores, and can comprise data of a shift private store, data of a shift visit and the like, wherein the number of stores served by the user can comprise data of the shift private store, and the visit coverage rate of the user can be the visit coverage rate of the user.

Alternatively, store subscriptions refer to subscriptions between each store and a one-stop type of shipping platform (e.g., retail platform); the store visit log refers to a record generated by visiting a store to an official website of a one-stop type goods-intake platform; store transactions refer to the purchasing behavior of each store for pickup from a one-stop pickup platform; the store red package/preferential data refers to deduction means capable of deducting part of the amount of the incoming goods when each store receives goods from the one-stop type goods incoming platform; store levels refer to different privileges for stores of different levels; store addresses refer to the geographic location of each store; store distance is the distance between every two stores; the data of the private-area store of the racket is that the current management stores of each racket, the stores (namely the service ranges) of each racket are different, the upper limit of the number of stores which can be managed by each racket is different according to the different grades of the racket, and the higher the grade of the racket is, the more stores can be managed; the visiting of the clap refers to the visiting of each store by the clap.

The base data of this embodiment may be used to determine the purchase decision type for each store, the revenue impact of each store on the shipping platform and on the user, and the growth path for each store. After the processor 102 collects the base data, the base data is classified and integrated, such as store subscription data, store access log data, store transaction data, store red/offer data, store level data, store addresses, store distances, gear private store data, gear visit data, and the like, to determine the purchase decision type for each store, the revenue impact of each store on the order platform and the user, and the growth path for each store.

The purchase decision type of this embodiment is used to indicate the type of decision to be taken when conducting a purchase action, the purchase decision type comprising one of: the store autonomous purchasing, the user-driven purchasing and the goods-feeding platform promotion driving purchasing, wherein the store autonomous purchasing can be autonomous periodic purchasing, is the behavior of the store for autonomous purchasing without being influenced by the user, the user-driven purchasing can be the gear-clapping purchasing, is the purchasing behavior carried out under the influence of the user, and the goods-feeding platform promotion driving purchasing can be the activity preferential driving, and is the purchasing behavior carried out under the influence of the goods-feeding platform promotion.

The effect of each store on the income of the income platform and the user in this embodiment may refer to the effect degree of each store on the income of the income platform and the user, and the growth path is used for describing the purchasing process of each store from the income platform, alternatively, the growth path of each store refers to the development history of the purchasing amount of each store in charge of taking a beat from low to high.

After classifying and integrating store information and historical service information, determining the purchase decision type of each store, the influence of each store on the income of a commodity platform and a user and the growth path of each store, acquiring store type feature tags and user type feature tags according to the purchase decision type of each store, the influence of each store on the income of the commodity platform and the user and the growth path of each store. Optionally, the store feature tag of the embodiment includes a resource tag, a behavior tag, a guest tag, a business rule tag, and the like; the user characteristic labels comprise a stock use rate label, a store sales rate label, a subscription duration label and a visit coverage rate label.

Optionally, in the store-like feature tag, the resource-like tag includes a red-pack tag, a brand ticket tag, and the like; the behavior type labels comprise purchase cycle labels, frequently purchased product promotion labels, purchase characteristic labels, after-sales labels and the like, wherein frequently purchased product promotion refers to that if a specific store purchases a specific type of commodity with higher frequency, when the specific store has promotion conditions on a one-stop type commodity feeding platform, the specific store can be visited by a beat to promote the specific store to purchase the commodity, and the purchase characteristic refers to classifying the store according to the purchasing behavior of the store, for example, the store is classified into a store which initiates purchasing behavior when the one-stop type commodity feeding platform has preferential, and a store which initiates purchasing behavior no matter whether the one-stop type commodity feeding platform has preferential or not.

The customer labels in the store type feature labels comprise a reserved store/a laser store/a sleep store label, a new pick store/a new allocation store label, a visit interval label, a competitor label and the like, wherein the reserved store refers to a store which initiates purchasing behavior to a one-stop type goods intake platform at a designated period, the activated store refers to a store which does not initiate purchasing behavior although has signed up with the one-stop type goods intake platform, the sleep store refers to a store which initiates purchasing behavior to the one-stop type goods intake platform previously, but does not initiate purchasing behavior to the one-stop type goods intake platform any more later, the new pick store refers to a store which initiates purchasing behavior to the one-stop type goods intake platform just completes signing, the new allocation store refers to a new pick store which is allocated for a beat and does not belong to the service range of the beat step, and the visit interval value store refers to a time interval of the competitor, and the system mainly comprises a new east passageway.

The business rule labels in the store type feature labels comprise platform task labels, month rhythm labels, forced opening labels and the like, wherein the platform tasks refer to tasks issued by stores initiating purchasing actions to the shifts and specific stores by the one-stop type goods intake platform, if the tasks are completed, the shifts and the specific stores can be rewarded correspondingly, for example, the stores initiating purchasing actions by the one-stop type goods intake platform hope to shift to inform the specific stores to discharge specific goods (such as brand toothpaste) at a designated position, the month rhythm refers to visit sequence of the shifts to each store in the service range, the originally allocated stores are firstly determined from high to low according to the compactness of the one-stop type goods intake platform, and then the stores newly allocated for the shifts are visited by the one-stop type goods intake platform; by strongly opened is meant that if a store managed by one beat does not transact with a one-stop deck for a period of time, that store will be managed by another beat.

Alternatively, in the user class feature tag, the storage capacity usage rate refers to a result calculated from a ratio of the number of stores currently managed by each of the strokes to the upper limit of the number of stores, for example, 100 stores and 80 stores are currently managed by the stroke, after the upper limit of the number of stores that can be managed by the stroke is determined according to the level of each of the strokes, and then the storage capacity usage rate=80 stores/100 stores=80%; the store sales rate refers to the proportion of the number of stores that have undergone transaction actions between one-stop type goods intake platform and the number of stores currently managed by each beat in a preset statistical period, for example, the number of stores currently managed by each beat is 80 stores, and in a statistical period of one month, there are 40 stores and one-stop type goods intake platform that have undergone transaction actions, then the store sales rate=40 stores/80 stores=50%; the contracted time length refers to the contracted time length between each store and a one-stop type goods-entering platform (such as a retail platform); the visit coverage rate refers to the proportion of the number of visited stores in the number of stores currently managed in each beat in a preset statistical period, for example, the number of stores currently managed in each beat in 1 day is A, the number of visited stores in each beat is B, and then A/B is the visit coverage rate of stores.

After acquiring the store feature tag and the user feature tag, the processor 102 sets the store feature tag and the user feature tag as input data, inputs the input data into a preset evaluation model, evaluates the input data through the preset evaluation model, and thus obtains a store evaluation statistical result, so as to instruct a user to visit each store in a plurality of stores distributed to the user by a goods platform, and further determine the service requirement of each store.

Optionally, the preset evaluation model for obtaining the store evaluation statistical result may be any one of a linear regression model, an iterative decision tree model and a deep learning model, or the preset evaluation model is a fused model of at least two of the linear regression model, the iterative decision tree model and the deep learning model. Wherein the linear regression model is a linear function in which some assumptions do not directly relate to the overall distribution form, for example, in regression analysis, it is often assumed that the analysis object can be expressed as some influencing factors; the iterative decision tree model consists of a plurality of decision trees, and the output results of all the trees are accumulated to form a model of a final answer; a deep learning model is a model that forms a more abstract high-level representation attribute category or feature by combining low-level features to discover a distributed feature representation of data. Optionally, the embodiment can perform linear regression fusion, iterative decision tree model fusion, deep learning model fusion or hierarchical fusion on the models, so as to realize comprehensive evaluation on input data.

Optionally, the processor 102 of this embodiment is connected to the memory 104, and configured to receive an upper limit on the number of stores allocated to the user according to the level of the user provided by the memory 104; calculating the number of the stores to be distributed for the user by adopting the upper limit of the number of the stores and the number of the stores of at least one store; and calculating the ratio of the number of the stores to be distributed to obtain the instruction of the store proportioning statistical result.

In this embodiment, when the processor 102 obtains the store distance statistics, a user's level may be obtained, and an upper limit of the number of stores allocated to the user may be determined according to the user's level, where each user's level is different, and the upper limit of the number of stores that each user can manage is also different, and the higher the user's level, the greater the number of stores that can be managed. After the processor 102 determines the upper limit of the number of stores allocated to the user according to the level of the user, the number of stores allocated to the user by the product platform is obtained, the number of stores to be allocated to the user is calculated according to the upper limit of the number of stores and the number of stores of at least one store, for example, the number of stores to be allocated to the user by the product platform is calculated according to the upper limit of the number of stores and the number of stores allocated to the docking person by the product platform, and the difference between the upper limit of the number of stores and the number of stores allocated to the user by the product platform can be determined as the number of stores to be allocated to the user by the product platform. After the processor 102 calculates the number of stores to be allocated to the user by using the upper limit of the number of stores and the number of stores of at least one store, the processor 102 calculates a ratio of the number of stores of at least one store to the number of stores to be allocated to obtain an instruction of a store ratio statistics result, for example, the number of stores allocated to the user by the order platform is a, the number of stores allocated to the user by the order platform is B, and then the a/B is determined as the store ratio statistics result, thereby determining a proportional relationship between the number of stores allocated to the user by the order platform and the number of stores allocated to the user by the order platform.

Optionally, the processor 102 of this embodiment is connected to the memory 104, and is configured to receive the geographical location information provided by the memory 104 and calculate a distance between each two of the at least one store according to the geographical location information of each of the at least one store; and generating a two-dimensional distance relation table by adopting the calculated distance data to obtain an instruction of a store distance statistical result.

In this embodiment, each store assigned to the user by the restocking platform has geographic location information, i.e., store address, indicating the geographic location of each store. When the processor 102 obtains store distance statistics, geographic location information for each of a plurality of stores assigned to the user by the restocking platform is collected. After the processor 102 collects the geographic location information of each of the plurality of stores assigned to the user by the restocking platform, a distance between each two of the at least one store is calculated from the obtained geographic location information of each of the at least one store, i.e., a store distance between each two of the plurality of stores is calculated from the geographic location information. After the processor 102 calculates the distance between each two of the at least one store according to the obtained geographic location information of each of the at least one store, the calculated distance data may be used to generate a two-dimensional distance relationship table, which may be a store distance matrix, to obtain a store distance statistic.

Optionally, the processor 102 of this embodiment is connected to the memory 104, and is configured to receive the first preset weight value corresponding to the store evaluation statistics, the second preset weight value corresponding to the store proportioning statistics, and the third preset weight value corresponding to the store distance statistics from the memory 104; and carrying out weighted summation operation by adopting the product of the store evaluation statistical result and the first preset weight value, the product of the store proportioning statistical result and the second preset weight value and the product of the store distance statistical result and the third preset weight value, and generating store visit planning information so as to instruct a user to visit a visit path of at least one store and an instruction of the service requirement of each store.

In this embodiment, the store evaluation statistic, the store proportioning statistic, and the store distance statistic correspond to different weight values. When the processor 102 generates the store visit plan information using the store evaluation statistic, the store proportioning statistic, and the store distance statistic, the processor 102 obtains the store evaluation statistic, the store proportioning statistic, and a first preset weight value corresponding to the store evaluation statistic, a second preset weight value corresponding to the store proportioning statistic, and a third preset weight value corresponding to the store distance statistic based on the store information and the history service information, wherein the first preset weight value may be used to indicate a relative importance degree of the store evaluation statistic in generating the store visit plan, the second preset weight value may be used to indicate a relative importance degree of the store proportioning statistic in generating the store visit plan, and the third preset weight value may be used to indicate a relative importance degree of the store distance statistic in generating the store visit plan. After the processor 102 obtains a first preset weight value corresponding to the store evaluation statistics, a second preset weight value corresponding to the store proportioning statistics, and a third preset weight value corresponding to the store distance statistics, the processor 102 performs weighted summation operation by using the product of the store evaluation statistics and the first preset weight value, the product of the store proportioning statistics and the second preset weight value, and the product of the store distance statistics and the third preset weight value, thereby generating a store visit route plan to instruct a user to visit paths of at least one store and service requirements of each store. The store emission route planning can be an optimal visit plan, so that store visit planning information is generated through store evaluation statistics results, store proportioning statistics results and store distance statistics results, the problem of store visit in a timing mode is avoided, the technical effect of improving the efficiency of store management is achieved, and the technical problem of low efficiency of store management caused by the fact that the store visit is performed in the timing mode is solved.

The technical solution of the present invention will be illustrated with reference to the preferred embodiments.

Fig. 2 is a schematic diagram of an intelligent management structure of a store according to an embodiment of the present invention. As shown in fig. 2, basic data such as store subscription data, store access log data, store transaction data, store red packet/preferential data, store grade data, data of a private store of a beat, data of a beat visit, store address & distance, store distance and the like can be acquired first, and classified and integrated to determine the purchase decision type of each store, the income influence of each store on a commodity platform and a user and the growth path of each store; and obtaining characteristic labels such as a resource label, a behavior label, a customer label, a business rule label, a stock use rate label, a store sales rate label, a subscription duration label, a visit coverage rate label and the like according to the purchase decision type of each store, the influence of each store on the income platform and the user and the growth path of each store.

Optionally, in the store-like feature tag, the resource-like tag includes a red-pack tag, a brand ticket tag, and the like; behavior class labels include purchase cycle labels, frequent buyer promotion labels, purchase feature labels, after-market labels, and the like.

The customer type labels in the store type feature labels comprise a reserved store/excited store/sleeping store label, a new shop/newly allocated store label, a visit interval label, a competitor label and the like.

Business rule labels in store class feature labels include platform task labels, month rhythm labels, forced open labels and the like.

Optionally, in the signature of the beat, a stock utilization rate is included; dynamic pin rate, subscription duration, visit coverage.

After the feature tag is obtained, the feature tag is set as input data according to a model algorithm, the input data is input into a single-store scoring model, and a single-store scoring result is obtained according to the model algorithm. According to the embodiment, the upper limit of the number of the stores allocated to the user is determined through the new and old store proportioning model according to the grade of the user, the number of the stores to be allocated to the user is calculated by adopting the upper limit of the number of the stores and the number of the stores to be allocated to the user, and the ratio of the number of the stores of the at least one store to the number of the stores to be allocated is calculated to obtain a new and old store proportioning result; the embodiment can also acquire the geographic position information of each store in a plurality of stores distributed to the user by the store distance matrix, calculate the distance between every two stores in at least one store according to the acquired geographic position information of each store in at least one store, and generate a two-dimensional distance relation table by adopting the calculated distance data, thereby obtaining the store distance matrix.

After a single store scoring result, a new and old store proportioning result and a store distance matrix are obtained according to a model algorithm, a first preset weight value corresponding to the single store scoring result, a second preset weight value corresponding to the new and old store proportioning result and a third preset weight value corresponding to the store distance matrix are obtained; and carrying out weighted summation operation by adopting the product of the single store scoring result and the first preset weight value, the product of the new and old store proportioning result and the second preset weight value and the product of the store distance matrix and the third preset weight value, thereby generating a store visit plan.

Fig. 3 is a schematic diagram of the structure of a single store scoring model according to an embodiment of the present invention. As shown in fig. 3, this embodiment includes data introduction and preparation, data analysis, feature engineering, model fusion, evaluation, and optimization.

Optionally, this embodiment may introduce and prepare retail data, high-german data at the time of data introduction & preparation; in the data analysis, the store can be classified according to the data analysis, such as autonomous periodic purchasing, gear emission purchasing, activity preferential driving and the like, wherein the store classification comprises classification according to gear income and classification according to platforms, and further comprises a store growth path, wherein the store growth path refers to the development history of purchasing of each store in charge of gear from one-stop type goods intake platform to high.

Optionally, the feature engineering of this embodiment includes store features, log & trade (purchase), marketing activities, gear visits, gear features.

Optionally, the model of the embodiment may include one of a linear regression model, an iterative decision tree model and a deep learning model, and perform comprehensive evaluation on input data, or may be a model formed by fusing at least two of the linear regression model, the iterative decision tree model and the deep learning model, for example, including fusion of the linear regression model, including fusion of the iterative decision tree model, and the like, and may perform hierarchical fusion on the model, so as to implement comprehensive evaluation on data. And finally, evaluating and optimizing the model, for example, monitoring and analyzing the model, carrying out exception analysis and correction and the like to generate an optimal store visit plan.

According to the embodiment, the shop real-time purchasing behavior can be collected and combined with the distance between shops, so that a visiting plan is made for a salesman, the efficiency of the salesman is improved, the abnormal behavior of the shops can be found through the real-time purchasing condition of the shops, and therefore the shops can be started timely without covering in a fixed frequency mode, and the efficiency of store management is improved.

Example 2

There is also provided, in accordance with an embodiment of the present invention, an embodiment of a store intelligent management method, wherein steps shown in the flowchart of the figures may be performed in a computer system, such as a set of computer executable instructions, and wherein, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in an order other than that shown or described herein.

The method embodiments provided by the embodiments of the present application may be performed in a mobile terminal, a computer terminal, or similar computing device. Fig. 4 is a block diagram of a hardware structure of a computer terminal (or mobile device) of a store intelligent management method according to an embodiment of the present invention. As shown in fig. 4, the computer terminal 40 (or mobile device 40) may include one or more (shown in the figures as 402a, 402b, … …,402 n) processors 402 (the processors 402 may include, but are not limited to, a microprocessor MCU, a programmable logic device FPGA, etc. processing means), a memory 404 for storing data, and a transmission means 406 for communication functions. In addition, the method may further include: a display, an input/output interface (I/O interface), a Universal Serial Bus (USB) port (which may be included as one of the ports of the I/O interface), a network interface, a power supply, and/or a camera. It will be appreciated by those of ordinary skill in the art that the configuration shown in fig. 4 is merely illustrative and is not intended to limit the configuration of the electronic device described above. For example, the computer terminal 40 may also include more or fewer components than shown in FIG. 4, or have a different configuration than shown in FIG. 4.

It should be noted that the one or more processors 402 and/or other data processing circuits described above may be referred to herein generally as "data processing circuits. The data processing circuit may be embodied in whole or in part in software, hardware, firmware, or any other combination. Furthermore, the data processing circuitry may be a single stand-alone processing module, or incorporated, in whole or in part, into any of the other elements in the computer terminal 40 (or mobile device). As referred to in the embodiments of the present application, the data processing circuit acts as a processor control (e.g., selection of the path of the variable resistor termination to interface).

The memory 404 may be used to store software programs and modules of application software, such as program instructions/data storage devices corresponding to the store intelligent management method in the embodiments of the present invention, and the processor 402 executes the software programs and modules stored in the memory 404, thereby executing various functional applications and data processing, that is, implementing the store intelligent management method of application programs. Memory 404 may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, memory 404 may further include memory located remotely from processor 402, which may be connected to computer terminal 40 via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.

The transmission means 406 is used to receive or transmit data via a network. The specific examples of the network described above may include a wireless network provided by a communication provider of the computer terminal 40. In one example, the transmission means 406 comprises a network adapter (Network Interface Controller, NIC) that can be connected to other network devices via a base station to communicate with the internet. In one example, the transmission device 406 may be a Radio Frequency (RF) module for communicating with the internet wirelessly.

It should be noted here that in some alternative embodiments, the computer device (or mobile device) shown in fig. 4 described above may include hardware elements (including circuitry), software elements (including computer code stored on a computer-readable medium), or a combination of both hardware and software elements. It should be noted that fig. 4 is only one example of a specific example, and is intended to illustrate the types of components that may be present in the computer device (or mobile device) described above.

In the above-described operating environment, the present application provides an intelligent management method for a store as shown in fig. 5. It should be noted that, the intelligent management method of the store according to this embodiment may be executed by the mobile terminal according to the embodiment shown in fig. 4.

Fig. 5 is a flowchart of a method of intelligent management of a store according to an embodiment of the present invention. As shown in fig. 5, the method comprises the steps of:

step S502, at least one store assigned to the user is determined.

In the technical solution provided in the above step S502 of the present invention, the user may be a docking person, for example, a partner, and the store may be a store, and the store of each user is different, that is, the service range of each user is different. Different stores have different visit information, for example, some stores need goods-taking service, the user is prompted for goods-taking service, some stores need goods-returning information, and the like. The user can also make a visit to each store, i.e., a gear visit. The user can also make a visit to each store, i.e., a gear visit. The user may have different levels, and the higher the level of the user, the greater the number of stores that can be managed, determining at least one store to assign to the user.

Step S504, at least one store assigned to the user is determined.

In the technical solution provided in the above step S504 of the present invention, at least one store has store information and history service information. After determining at least one store assigned to the user, store information for the at least one store is obtained, as well as historical service information for the at least one store by the user. Optionally, the store information of the store in this embodiment includes at least: commodity sales information, consumer information, wherein the commodity sales information includes commodity sales or sales information, and the consumer information includes consumer consumption information at store, including consumption type, consumption preference, etc., without any limitation. Optionally, the store information is also preferential information, behavior information, geographical location information, etc., where the preferential information may be preferential information of a commodity purchased by a consumer, the behavior information may be store purchase and sales behavior information, consumer transaction behavior information, etc., and the geographical location information may be store geographical location information. The historical service information may be the number of stores served by the user, the coverage of the user's visit, and so on.

Step S506, store visit plan information is generated for the user based on the store information and the historical service information to provide the visit plan information to the user.

In the technical solution provided in the above step S506 of the present invention, the store information and the history service information in this embodiment are the basis for generating the store visit plan information. After the store information of at least one store and the historical service information of the user on the at least one store are acquired, store visit planning information is generated for the user based on the store information and the historical service information, wherein the store visit planning information comprises store visit route planning, and the store visit route planning can be a visit path. Optionally, the user has a visit order when visiting a plurality of stores, and the store visit route plan may be used to indicate the visit order of a part of the stores in the plurality of stores, or to indicate the visit order of all the stores in the plurality of stores, so as to provide the visit plan information to the user.

Determining at least one store assigned to the user; acquiring store information of at least one store and historical service information of a user on the at least one store; store visit planning information is generated for a user based on store information and historical service information, so that the visit planning information is provided for the user, the problem of store visit in a timing mode is avoided, the technical effect of improving the efficiency of store management is achieved, and the technical problem of low store management efficiency caused by store visit in a timing mode is solved.

As an alternative embodiment, generating store visit plan information based on store information and historical service information includes: acquiring a store evaluation statistical result, a store proportioning statistical result and a store distance statistical result of each store in at least one store based on store information and historical service information; and generating store visit planning information by adopting the store evaluation statistical result, the store proportioning statistical result and the store distance statistical result.

The embodiment obtains store evaluation statistics, store proportioning statistics, and store distance statistics for each of at least one store based on the store information and the historical service information. The store evaluation statistical result is used for indicating the visit frequency of a user to each store in a plurality of stores distributed to the user by the goods-incoming platform, the store proportion statistical result is used for describing the proportional relation between the quantity of the stores distributed to the user by the goods-incoming platform and the quantity of the stores to be distributed to the user by the goods-incoming platform, and the store distance statistical result is used for describing the distance between every two stores in at least one store.

In this embodiment, instructions for store evaluation statistics, store proportioning statistics, and store distance statistics for each of at least one store are obtained based on the store information and the historical service information. The store evaluation statistics result of the embodiment is used for indicating the visit frequency of the user to each store in a plurality of stores allocated to the user by the goods-intake platform, so that single store scoring is realized. The goods-feeding platform is used for feeding goods and can be a one-stop goods-feeding platform, for example, the one-stop goods-feeding platform is a retail platform; the visit frequency of the stores can be the number of times of visiting the stores currently managed by each beat in a preset statistical period.

After acquiring the store evaluation statistics, the store proportioning statistics, and the store distance statistics for each of the at least one store based on the store information and the historical service information, store visit plan information is generated using the store evaluation statistics, the store proportioning statistics, and the store distance statistics. The store visit plan, i.e., store visit plan, has a visit order when the user visits a plurality of stores, and the store visit plan may be used to indicate the visit order of a portion of the user's stores in the plurality of stores, or to indicate the visit order of all of the user's stores in the plurality of stores.

As an alternative embodiment, the store information includes at least: the preferential information of the commodity purchased by the consumer, store purchasing and selling behavior information, consumer transaction behavior information and store geographic position information; the history service information includes at least: the number of stores served by the user and the visit coverage rate of the user; the acquiring of the store evaluation statistical result based on the store information and the history information comprises: classifying and integrating store information and historical service information, and determining a purchase decision type of each store, the income influence of each store on a goods-intake platform and a user and a growth path of each store, wherein the purchase decision type comprises one of the following steps: independent purchasing of stores, user-driven purchasing and sales promotion driving purchasing of a goods-taking platform, wherein a growing path is used for describing the purchasing amount change process of each store taking goods from the goods-taking platform; acquiring a store type feature tag and a user type feature tag according to the purchase decision type of each store, the influence of each store on the income of a commodity platform and a user and the growth path of each store; setting the store feature tag and the user feature tag as input data, and inputting the input data into a preset evaluation model to obtain store evaluation statistical results so as to determine the service requirement of each store.

Optionally, basic data is collected, where the basic data includes at least: the store information comprises preferential information of a commodity purchased by a consumer, store purchase and sales behavior information, consumer transaction behavior information and store geographic position information, optionally, the store information is operation data of each store in a plurality of stores and can comprise store subscription data, store access log data, store transaction data, store red packet/preferential data, store grade data, store address and store distance, wherein the preferential information of the commodity purchased by the consumer can be the store red packet/preferential data, the store purchase and sales behavior information can be the store subscription data, the consumer transaction behavior information can be the store transaction data, and the store geographic position information can be the store address; the history service information comprises the number of stores served by the user and the visit coverage rate of the user, and optionally, the history service information is service data of the user on a plurality of stores, and can comprise data of a shift private store, data of a shift visit and the like, wherein the number of stores served by the user can comprise data of the shift private store, and the visit coverage rate of the user can be the visit coverage rate of the user.

The base data of this embodiment may be used to determine the purchase decision type for each store, the revenue impact of each store on the shipping platform and on the user, and the growth path for each store. After the basic data is collected, the basic data is classified and integrated, for example, store subscription data, store access log data, store transaction data, store red packet/preferential data, store grade data, store address, store distance, partner private store data, partner visit data and the like are integrated, so that the purchase decision type of each store, the income influence of each store on a goods-intake platform and a user and the growth path of each store are determined.

After the store feature tag and the user feature tag are obtained, the store feature tag and the user feature tag are set as input data, the input data are input into a preset evaluation model, the input data are evaluated through the preset evaluation model, and accordingly store evaluation statistical results are obtained, visit frequency of a user for each store in a plurality of stores distributed to the user through a goods platform is indicated, and then service requirements of each store are determined.

As an optional implementation manner, the preset evaluation model is any one model of a linear regression model, an iterative decision tree model and a deep learning model, or is a model formed by fusing at least two models of the linear regression model, the iterative decision tree model and the deep learning model.

The preset evaluation model for obtaining the store evaluation statistical result in the embodiment may be any one of a linear regression model, an iterative decision tree model and a deep learning model, or the preset evaluation model may be a model formed by fusing at least two models of the linear regression model, the iterative decision tree model and the deep learning model. Wherein the linear regression model is a linear function in which some assumptions do not directly relate to the overall distribution form, for example, in regression analysis, it is often assumed that the analysis object can be expressed as some influencing factors; the iterative decision tree model consists of a plurality of decision trees, and the output results of all the trees are accumulated to form a model of a final answer; a deep learning model is a model that forms a more abstract high-level representation attribute category or feature by combining low-level features to discover a distributed feature representation of data. Optionally, the embodiment can perform linear regression fusion, iterative decision tree model fusion, deep learning model fusion or hierarchical fusion on the models, so as to realize comprehensive evaluation on input data.

As an alternative embodiment, obtaining the store proportioning statistics includes: determining an upper limit of the number of stores allocated to the user according to the grade of the user; calculating the number of the stores to be distributed for the user by adopting the upper limit of the number of the stores and the number of the stores of at least one store; and calculating the ratio of the number of the stores of at least one store to the number of the stores to be distributed to obtain a store proportioning statistical result.

In this embodiment, when acquiring the store distance statistics, the user's level may be acquired, and the upper limit of the number of stores allocated to the user is determined according to the user's level, where the level of each user is different, the upper limit of the number of stores that each user can manage is also different, and the higher the level of the user, the greater the number of stores that can be managed. After determining the upper limit of the number of stores allocated to the user according to the grade of the user, acquiring the number of stores allocated to the user by the goods-intake platform, calculating the number of stores to be allocated to the user according to the upper limit of the number of stores and the number of stores of at least one store, for example, calculating the number of stores to be allocated to the user by the goods-intake platform according to the upper limit of the number of stores and the number of stores allocated to the butt joint person by the goods-intake platform, and determining the difference between the upper limit of the number of stores and the number of stores allocated to the user by the goods-intake platform as the number of stores to be allocated to the user by the goods-intake platform. After the upper limit of the number of the stores and the number of the stores to be allocated to the user are adopted to calculate the number of the stores to be allocated to the user, calculating the ratio of the number of the stores to be allocated to the number of the stores to obtain an instruction of the store ratio statistical result, for example, the number of the stores allocated to the user by the goods platform is A, the number of the stores allocated to the user by the goods platform is B, the A/B is determined to be the store ratio statistical result, and therefore the proportional relation between the number of the stores allocated to the user by the goods platform and the number of the stores allocated to the user by the goods platform is determined.

As an alternative embodiment, obtaining the store distance statistics includes: calculating a distance between each two of the at least one store according to the geographical position information of each of the at least one store; and generating a two-dimensional distance relation table by adopting the calculated distance data to obtain a store distance statistical result.

In this embodiment, each store assigned to the user by the restocking platform has geographic location information, i.e., store address, indicating the geographic location of each store. And when the store distance statistical result is obtained, collecting geographic position information of each store in a plurality of stores distributed to the user by the goods-intake platform. After the geographic location information of each of the plurality of stores assigned to the user by the order platform is collected, a distance between each two of the at least one store is calculated based on the geographic location information of each of the at least one store, i.e., a store distance between each two of the plurality of stores is calculated based on the geographic location information. After calculating the distance between each two of the at least one store according to the geographical location information of each of the at least one store, the calculated distance data may be used to generate a two-dimensional distance relation table, which may be a store distance matrix, so as to obtain a store distance statistics.

As an alternative embodiment, generating store visit plan information using store assessment statistics, store proportioning statistics, and store distance statistics includes: acquiring a first preset weight value corresponding to a gate store evaluation statistical result, a second preset weight value corresponding to a gate store proportioning statistical result and a third preset weight value corresponding to a gate store distance statistical result; and carrying out weighted summation operation by adopting the product of the store evaluation statistical result and the first preset weight value, the product of the store proportioning statistical result and the second preset weight value and the product of the store distance statistical result and the third preset weight value, and generating store visit planning information so as to indicate a visit path of at least one store and the service requirement of each store.

In this embodiment, the store evaluation statistic, the store proportioning statistic, and the store distance statistic correspond to different weight values. When store visit planning information is generated by adopting store evaluation statistics, store proportioning statistics and store distance statistics, store evaluation statistics, store proportioning statistics and store distance statistics of each store in at least one store are acquired based on store information and historical service information, a first preset weight value corresponding to the store evaluation statistics, a second preset weight value corresponding to the store proportioning statistics and a third preset weight value corresponding to the store distance statistics are used for indicating the relative importance degree of the store evaluation statistics in generating a store visit path planning, and the second preset weight value can be used for indicating the relative importance degree of the store proportioning statistics in generating the store visit path planning. After a first preset weight value corresponding to a store evaluation statistical result, a second preset weight value corresponding to a store proportioning statistical result and a third preset weight value corresponding to a store distance statistical result are obtained, weighted summation operation is carried out by adopting the product of the store evaluation statistical result and the first preset weight value, the product of the store proportioning statistical result and the second preset weight value and the product of the store distance statistical result and the third preset weight value, so that a store visit route planning is generated, the store discharge route planning can be an optimal visit plan, store visit planning information is generated by adopting a store evaluation statistical result, a store proportioning statistical result and a store distance statistical result, the problem of store visit in a timing mode is avoided, the technical effect of improving the store management efficiency is achieved, and the technical problem of low store management efficiency caused by the fact that the store visit is carried out in the timing mode is solved.

It should be noted that, for simplicity of description, the foregoing method embodiments are all described as a series of acts, but it should be understood by those skilled in the art that the present invention is not limited by the order of acts described, as some steps may be performed in other orders or concurrently in accordance with the present invention. Further, those skilled in the art will also appreciate that the embodiments described in the specification are all preferred embodiments, and that the acts and modules referred to are not necessarily required for the present invention.

From the description of the above embodiments, it will be clear to a person skilled in the art that the method according to the above embodiments may be implemented by means of software plus the necessary general hardware platform, but of course also by means of hardware, but in many cases the former is a preferred embodiment. Based on such understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art in the form of a software product stored in a storage medium (e.g. ROM/RAM, magnetic disk, optical disk) comprising instructions for causing a terminal device (which may be a mobile phone, a computer, a server, or a network device, etc.) to perform the method according to the embodiments of the present invention.

Example 3

According to the embodiment of the invention, an intelligent management device for the store is also provided, which is used for implementing the intelligent management method for the store shown in the figure 5. Fig. 6 is a schematic diagram of an intelligent management device for a store according to an embodiment of the present invention. As shown in fig. 6, the apparatus 600 may include: a determining module 601, an acquiring module 602 and a generating module 603.

A determining module 601, configured to determine at least one store allocated to the user.

An acquisition module 602, configured to acquire store information of at least one store, and historical service information of the user for the at least one store.

A generating module 603, configured to generate store visit plan information for the user based on the store information and the history service information, so as to provide the visit plan information to the user.

Optionally, the generating module 603 includes: an acquisition unit 604 for acquiring a store evaluation statistic, a store matching statistic, and a store distance statistic for each of at least one store based on store information and history service information; the generating unit 605 is configured to generate store visit plan information using the store evaluation statistics, the store matching statistics, and the store distance statistics.

Optionally, the store information includes at least: the preferential information of the commodity purchased by the consumer, store purchasing and selling behavior information, consumer transaction behavior information and store geographic position information; the history service information includes at least: the number of stores served by the user and the visit coverage rate of the user; the acquisition unit 604 includes: the analysis subunit 606 is configured to perform classification integration on the store information and the historical service information, and determine a purchase decision type of each store, an effect of each store on the income of the goods platform and the user, and a growth path of each store, where the purchase decision type includes one of the following: independent purchasing of stores, user-driven purchasing and sales promotion driving purchasing of a goods-taking platform, wherein a growing path is used for describing the purchasing amount change process of each store taking goods from the goods-taking platform; a first obtaining subunit 607, configured to obtain a store feature tag and a user feature tag according to a purchase decision type of each store, an effect of each store on a purchase platform and a user, and a growth path of each store; the second obtaining subunit 608 is configured to set the store feature tag and the user feature tag as input data, and input the input data to a preset evaluation model to obtain a store evaluation statistical result, so as to determine a service requirement of each store.

Optionally, the preset evaluation model is any one model of a linear regression model, an iterative decision tree model and a deep learning model, or the preset evaluation model is a model formed by fusing at least two models of the linear regression model, the iterative decision tree model and the deep learning model.

Alternatively, the acquisition unit 604 includes: a determining subunit 609, configured to determine an upper limit of the number of stores allocated to the user according to the level of the user; a first calculating subunit 610, configured to calculate, using the upper limit of the number of stores and the number of stores of the at least one store, a number of stores to be allocated to the user; the third obtaining subunit 611 is configured to calculate a ratio of the number of stores of the at least one store to the number of stores to be allocated, and obtain a store matching statistical result.

Alternatively, the acquisition unit 604 includes: a second calculating subunit 612, configured to calculate a distance between each two stores in the at least one store according to the geographic location information of each store in the at least one store; and the third calculating subunit 613 is configured to generate a two-dimensional distance relation table by using the calculated distance data, so as to obtain a store distance statistical result.

Alternatively, the generating unit 605 includes: a fourth obtaining subunit 614, configured to obtain a first preset weight value corresponding to the shop evaluation statistical result, a second preset weight value corresponding to the shop proportioning statistical result, and a third preset weight value corresponding to the shop distance statistical result; the generating subunit 615 is configured to perform a weighted sum operation by using a product of the store evaluation statistic result and the first preset weight value, a product of the store proportioning statistic result and the second preset weight value, and a product of the store distance statistic result and the third preset weight value, and generate store visit plan information to indicate a user's visit path of at least one store and a service requirement of each store.

Here, it should be noted that the determining module 601, the obtaining module 602, and the generating module 603 correspond to steps S502 to S506 in embodiment 2, and the two modules are the same as the examples and application scenarios implemented by the corresponding steps, but are not limited to the disclosure of the first embodiment. It should be noted that the above-described module may be operated as a part of the apparatus in the computer terminal 40 provided in embodiment 2.

According to the embodiment, the determining module 601 determines at least one store allocated to the user, the acquiring module 602 acquires store information of the at least one store and historical service information of the user on the at least one store, and the generating module 603 generates store visit planning information for the user based on the store information and the historical service information to provide the visit planning information for the user, so that the problem of store visit in a timing mode is avoided, the technical effect of improving the efficiency of store management is achieved, and the technical problem of low efficiency of store management caused by store visit in a timing mode is solved.

Example 3

Embodiments of the present invention may provide a computer terminal, which may be any one of a group of computer terminals. Alternatively, in the present embodiment, the above-described computer terminal may be replaced with a terminal device such as a mobile terminal.

Alternatively, in this embodiment, the above-mentioned computer terminal may be located in at least one network device among a plurality of network devices of the computer network.

In this embodiment, the computer terminal may execute the program code of the following steps in the intelligent management method of the store of the application program: determining at least one store assigned to the user; acquiring store information of at least one store and historical service information of a user on the at least one store; store visit plan information is generated for the user based on the store information and the historical service information to provide the visit plan information to the user.

Alternatively, fig. 7 is a block diagram of another mobile terminal according to an embodiment of the present invention. As shown in fig. 7, the mobile terminal a may include: one or more (only one is shown) processors 702, memory 704, and transmission means 706.

The memory may be used to store software programs and modules, such as program instructions/modules corresponding to the store intelligent management method and apparatus in the embodiments of the present invention, and the processor executes the software programs and modules stored in the memory, thereby executing various functional applications and data processing, that is, implementing the store intelligent management method. The memory may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory may further include memory remotely located with respect to the processor, which may be connected to mobile terminal a via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.

The processor may call the information and the application program stored in the memory through the transmission device to perform the following steps: determining at least one store assigned to the user; acquiring store information of at least one store and historical service information of a user on the at least one store; store visit plan information is generated for the user based on the store information and the historical service information to provide the visit plan information to the user.

Optionally, the above processor may further execute program code for: acquiring a store evaluation statistical result, a store proportioning statistical result and a store distance statistical result of each store in at least one store based on store information and historical service information; and generating store visit planning information by adopting the store evaluation statistical result, the store proportioning statistical result and the store distance statistical result.

Optionally, the above processor may further execute program code for: classifying and integrating store information and historical service information, and determining a purchase decision type of each store, the income influence of each store on a goods-intake platform and a user and a growth path of each store, wherein the purchase decision type comprises one of the following steps: independent purchasing of stores, user-driven purchasing and sales promotion driving purchasing of a goods-taking platform, wherein a growing path is used for describing the purchasing amount change process of each store taking goods from the goods-taking platform; acquiring a store type feature tag and a user type feature tag according to the purchase decision type of each store, the influence of each store on the income of a commodity platform and a user and the growth path of each store; setting the store feature tag and the user feature tag as input data, and inputting the input data into a preset evaluation model to obtain store evaluation statistical results so as to determine the service requirement of each store.

Optionally, the above processor may further execute program code for: determining an upper limit of the number of stores allocated to the user according to the grade of the user; calculating the number of the stores to be distributed for the user by adopting the upper limit of the number of the stores and the number of the stores of at least one store; and calculating the ratio of the number of the stores of at least one store to the number of the stores to be distributed to obtain a store proportioning statistical result.

Optionally, the above processor may further execute program code for: calculating a distance between each two of the at least one store according to the geographical position information of each of the at least one store; and generating a two-dimensional distance relation table by adopting the calculated distance data to obtain a store distance statistical result.

Optionally, the above processor may further execute program code for: acquiring a first preset weight value corresponding to a gate store evaluation statistical result, a second preset weight value corresponding to a gate store proportioning statistical result and a third preset weight value corresponding to a gate store distance statistical result; and carrying out weighted summation operation by adopting the product of the store evaluation statistical result and the first preset weight value, the product of the store proportioning statistical result and the second preset weight value and the product of the store distance statistical result and the third preset weight value, and generating store visit planning information so as to indicate a visit path of at least one store and the service requirement of each store.

By adopting the embodiment of the invention, an intelligent management scheme of a store is provided. Determining at least one store assigned to the user; acquiring store information of at least one store and historical service information of a user on the at least one store; store visit planning information is generated for a user based on store information and historical service information, so that the visit planning information is provided for the user, the problem of store visit in a timing mode is avoided, the technical effect of improving store management efficiency is achieved, and the technical problem of low store management efficiency caused by store visit in a timing mode is solved.

It will be appreciated by those skilled in the art that the structure shown in fig. 7 is only illustrative, and the mobile terminal a may be a smart phone (such as an Android phone, an iOS phone, etc.), a tablet computer, a palm computer, a mobile internet device (Mobile Internet Devices, MID), a PAD, etc. Fig. 7 is not limited to the structure of the electronic device. For example, mobile terminal a may also include more or fewer components (e.g., network interfaces, display devices, etc.) than shown in fig. 7, or have a different configuration than shown in fig. 7.

Those of ordinary skill in the art will appreciate that all or part of the steps in the various methods of the above embodiments may be implemented by a program for instructing a terminal device to execute in association with hardware, the program may be stored in a computer readable storage medium, and the storage medium may include: flash disk, read-Only Memory (ROM), random-access Memory (Random Access Memory, RAM), magnetic or optical disk, and the like.

Example 4

The embodiment of the invention also provides a storage medium. Alternatively, in this embodiment, the storage medium may be used to store the program code executed by the store intelligent management method provided in embodiment 2.

Alternatively, in this embodiment, the storage medium may be located in any one of the computer terminals in the computer terminal group in the computer network, or in any one of the mobile terminals in the mobile terminal group.

Alternatively, in the present embodiment, the storage medium is configured to store program code for performing the steps of: determining at least one store assigned to the user; acquiring store information of at least one store and historical service information of a user on the at least one store; store visit plan information is generated for the user based on the store information and the historical service information to provide the visit plan information to the user.

Alternatively, in the present embodiment, the storage medium is configured to store program code for performing the steps of: acquiring a store evaluation statistical result, a store proportioning statistical result and a store distance statistical result of each store in at least one store based on store information and historical service information; and generating store visit planning information by adopting the store evaluation statistical result, the store proportioning statistical result and the store distance statistical result.

Optionally, the storage medium is further arranged to store program code for performing the steps of: classifying and integrating store information and historical service information, and determining a purchase decision type of each store, the income influence of each store on a goods-intake platform and a user and a growth path of each store, wherein the purchase decision type comprises one of the following steps: independent purchasing of stores, user-driven purchasing and sales promotion driving purchasing of a goods-taking platform, wherein a growing path is used for describing the purchasing amount change process of each store taking goods from the goods-taking platform; acquiring a store type feature tag and a user type feature tag according to the purchase decision type of each store, the influence of each store on the income of a commodity platform and a user and the growth path of each store; setting the store feature tag and the user feature tag as input data, and inputting the input data into a preset evaluation model to obtain store evaluation statistical results so as to determine the service requirement of each store.

Optionally, the storage medium is further arranged to store program code for performing the steps of: determining an upper limit of the number of stores allocated to the user according to the grade of the user; calculating the number of the stores to be distributed for the user by adopting the upper limit of the number of the stores and the number of the stores of at least one store; and calculating the ratio of the number of the stores of at least one store to the number of the stores to be distributed to obtain a store proportioning statistical result.

Optionally, the storage medium is further arranged to store program code for performing the steps of: calculating a distance between each two of the at least one store according to the geographical position information of each of the at least one store; and generating a two-dimensional distance relation table by adopting the calculated distance data to obtain a store distance statistical result.

Optionally, the storage medium is further arranged to store program code for performing the steps of: acquiring a first preset weight value corresponding to a gate store evaluation statistical result, a second preset weight value corresponding to a gate store proportioning statistical result and a third preset weight value corresponding to a gate store distance statistical result; and carrying out weighted summation operation by adopting the product of the store evaluation statistical result and the first preset weight value, the product of the store proportioning statistical result and the second preset weight value and the product of the store distance statistical result and the third preset weight value, and generating store visit planning information so as to indicate a visit path of at least one store and the service requirement of each store.

The foregoing embodiment numbers of the present invention are merely for the purpose of description, and do not represent the advantages or disadvantages of the embodiments.

In the foregoing embodiments of the present invention, the descriptions of the embodiments are emphasized, and for a portion of this disclosure that is not described in detail in this embodiment, reference is made to the related descriptions of other embodiments.

In the several embodiments provided in the present application, it should be understood that the disclosed technology content may be implemented in other manners. The above-described embodiments of the apparatus are merely exemplary, and the division of the units, such as the division of the units, is merely a logical function division, and may be implemented in another manner, for example, multiple units or components may be combined or may be integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be through some interfaces, units or modules, or may be in electrical or other forms.

The units described as separate units may or may not be physically separate, and units shown 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 units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

In addition, each functional unit in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.

The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied essentially or in part or all of the technical solution or in part in the form of a software product stored in a storage medium, including instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a removable hard disk, a magnetic disk, or an optical disk, or other various media capable of storing program codes.

The foregoing is merely a preferred embodiment of the present invention and it should be noted that modifications and adaptations to those skilled in the art may be made without departing from the principles of the present invention, which are intended to be comprehended within the scope of the present invention.

Claims

1. An intelligent management method for a store, comprising the steps of:

determining at least one store assigned to the user;

acquiring store information of the at least one store and historical service information of the user on the at least one store;

based on the store information and the historical service information, obtaining a store evaluation statistical result, a store proportion statistical result and a store distance statistical result of each store in the at least one store, wherein the store evaluation statistical result is used for indicating the visit frequency of a user to each store in a plurality of stores distributed to the user by a goods platform, and the store proportion statistical result is used for describing the proportional relation between the quantity of the stores distributed to the user by the goods platform and the quantity of the stores to be distributed to the user by the goods platform;

and generating store visit planning information by adopting the store evaluation statistical result, the store proportioning statistical result and the store distance statistical result.

2. The method of claim 1, wherein the store information comprises at least: the preferential information of the commodity purchased by the consumer, store purchasing and selling behavior information, consumer transaction behavior information and store geographic position information; the history service information includes at least: the number of stores served by the user and the visit coverage rate of the user; the acquiring the store evaluation statistical result based on the store information and the historical service information comprises:

classifying and integrating the store information and the historical service information, and determining a purchase decision type of each store, the income influence of each store on a commodity platform and the user and a growth path of each store, wherein the purchase decision type comprises one of the following steps: the store autonomous purchasing, the user driven purchasing and the commodity-in platform promotion driven purchasing, wherein the growth path is used for describing the purchasing amount change process of each store commodity in the commodity-in platform;

acquiring a store type feature tag and a user type feature tag according to the purchase decision type of each store, the influence of each store on the income of the goods platform and the user and the growth path of each store;

And setting the store feature tag and the user feature tag as input data, and inputting the input data into a preset evaluation model to obtain the store evaluation statistical result so as to determine the service requirement of each store.

3. The method according to claim 2, wherein the preset evaluation model is any one of a linear regression model, an iterative decision tree model and a deep learning model, or the preset evaluation model is a model formed by fusing at least two of a linear regression model, an iterative decision tree model and a deep learning model.

4. The method of claim 1, wherein obtaining the store proportioning statistic comprises:

determining an upper limit of the number of stores allocated to the user according to the grade of the user;

calculating the number of the stores to be distributed for the user by adopting the upper limit of the number of the stores and the number of the stores of the at least one store;

and calculating the ratio of the number of the stores of the at least one store to the number of the stores to be distributed to obtain the store proportioning statistical result.

5. The method of claim 2, wherein obtaining the store distance statistic comprises:

Calculating a distance between each two stores in the at least one store according to the geographic position information of each store in the at least one store;

and generating a two-dimensional distance relation table by adopting the calculated distance data to obtain the store distance statistical result.

6. The method of claim 1, wherein generating the store visit plan information using the store assessment statistics, the store proportioning statistics, and the store distance statistics comprises:

acquiring a first preset weight value corresponding to the store evaluation statistical result, a second preset weight value corresponding to the store proportioning statistical result and a third preset weight value corresponding to the store distance statistical result;

and carrying out weighted summation operation by adopting the product of the store evaluation statistical result and the first preset weight value, the product of the store proportioning statistical result and the second preset weight value and the product of the store distance statistical result and the third preset weight value, and generating store visit planning information so as to indicate a visit path of the user to at least one store and service requirements of each store.

7. An intelligent management device for a store, comprising:

a determining module for determining at least one store assigned to the user;

the acquisition module is used for acquiring store information of the at least one store and historical service information of the user on the at least one store;

the generating module is used for acquiring a store evaluation statistical result, a store proportioning statistical result and a store distance statistical result of each store in the at least one store based on the store information and the historical service information, wherein the store evaluation statistical result is used for indicating the visit frequency of a user to each store in a plurality of stores allocated to the user by the goods platform, and the store proportioning statistical result is used for describing the proportional relation between the quantity of the stores allocated to the user by the goods platform and the quantity of the stores to be allocated to the user by the goods platform;

the generation module is also used for generating store visit planning information by adopting the store evaluation statistical result, the store proportioning statistical result and the store distance statistical result.

8. The apparatus of claim 7, wherein the store information comprises at least: the preferential information of the commodity purchased by the consumer, store purchasing and selling behavior information, consumer transaction behavior information and store geographic position information; the history service information includes at least: the number of stores served by the user and the visit coverage rate of the user; the generation module comprises:

The analysis subunit is used for classifying and integrating the store information and the historical service information, and determining a purchase decision type of each store, the income influence of each store on a commodity delivery platform and the user and a growth path of each store, wherein the purchase decision type comprises one of the following steps: the store autonomous purchasing, the user driven purchasing and the commodity-in platform promotion driven purchasing, wherein the growth path is used for describing the purchasing amount change process of each store commodity in the commodity-in platform;

the first acquisition subunit is used for acquiring store type feature tags and user type feature tags according to the purchase decision type of each store, the influence of each store on the income of the goods platform and the user and the growth path of each store;

the second obtaining subunit is configured to set the store feature tag and the user feature tag as input data, and input the input data to a preset evaluation model to obtain the store evaluation statistical result, so as to determine a service requirement of each store.

9. The apparatus of claim 8, wherein the predetermined evaluation model is any one of a linear regression model, an iterative decision tree model, and a deep learning model, or wherein the predetermined evaluation model is a fused model of at least two of a linear regression model, an iterative decision tree model, and a deep learning model.

10. The apparatus of claim 7, wherein the generating module comprises:

a determining subunit, configured to determine an upper limit of a number of stores allocated to the user according to the level of the user;

the first calculating subunit is used for calculating the number of the stores to be distributed for the user by adopting the upper limit of the number of the stores and the number of the stores of the at least one store;

and the third acquisition subunit is used for calculating the ratio of the number of the stores of the at least one store to the number of the stores to be distributed to obtain the store proportioning statistical result.

11. The apparatus of claim 8, wherein the generating module comprises:

a second calculating subunit, configured to calculate a distance between each two stores in the at least one store according to the geographic location information of each store in the at least one store;

and the third calculation subunit is used for generating a two-dimensional distance relation table by adopting the calculated distance data to obtain the store distance statistical result.

12. The apparatus of claim 7, wherein the generating module comprises:

a fourth obtaining subunit, configured to obtain a first preset weight value corresponding to the store evaluation statistical result, a second preset weight value corresponding to the store proportioning statistical result, and a third preset weight value corresponding to the store distance statistical result;

And the generation subunit is used for carrying out weighted summation operation on the product of the store evaluation statistical result and the first preset weight value, the product of the store proportioning statistical result and the second preset weight value and the product of the store distance statistical result and the third preset weight value to generate store visit planning information so as to indicate the visit path of the user to at least one store and the service requirement of each store.

13. A storage medium comprising a stored program, wherein the program, when run, controls a device on which the storage medium resides to perform the steps of:

determining at least one store assigned to the user;

14. A processor for running a program, wherein the program when run performs the steps of:

determining at least one store assigned to the user;

15. A mobile terminal, comprising:

a processor; and

a memory, coupled to the processor, for providing instructions to the processor to process the following processing steps:

determining at least one store assigned to the user;