Store data processing method, device, computer equipment and storage medium

Abstract

The present application relates to a method, device, computer equipment, storage medium and computer program product for processing store data. The method comprises: screening an abnormal store identification set from the initial store identification set according to the operation data corresponding to each initial store identification in the initial store identification set and the pre-constructed operation evaluation conditions; selecting a target store identification associated with the specified source store identification from at least one candidate store identification according to the index value of the first store index corresponding to the specified source store identification and the index value of the second store index corresponding to each candidate store identification in at least one candidate store identification, wherein the specified source store identification and the candidate store identification are store identifications in the abnormal store identification set; and pushing the virtual resources corresponding to the target store identification to the customer terminal in response to the trigger operation of the customer account for the virtual resources. The use of this method can improve the passenger flow utilization rate.

Description

Technical Field

[0001] The present application relates to the technical field of virtual resource push, and in particular to a method, device, computer equipment, storage medium and computer program product for processing store data. Background Art

[0002] As people's consumption level increases, their shopping desire also increases. Since shopping malls have stores of different formats and more diverse goods, people are more willing to go to shopping malls to buy the goods they need.

[0003] In the prior art, there are many customers going to main stores such as supermarkets in shopping malls. However, among the customers visiting the main stores, many customers choose to leave the mall directly after visiting the main stores, and will not visit other stores in the mall.

[0004] It can be seen that the existing technology cannot effectively utilize the customer flow in the shopping mall, and there is a problem of low customer flow utilization rate. Summary of the invention

[0005] Based on this, it is necessary to provide a method, device, computer equipment, computer-readable storage medium and computer program product for processing store data that can improve customer flow utilization in response to the above technical problems.

[0006] In a first aspect, the present application provides a method for processing store data. The method comprises:

[0007] According to the operation data corresponding to each initial store identification in the initial store identification set and the pre-constructed operation evaluation conditions, the abnormal store identification set is obtained by screening from the initial store identification set; wherein the abnormal store identification is the store identification corresponding to the store with abnormal operation;

[0008] According to the index value of the first store index corresponding to the designated source store identifier and the index value of the second store index corresponding to each candidate store identifier in at least one candidate store identifier, a target store identifier associated with the designated source store identifier is selected from the at least one candidate store identifier; wherein the designated source store identifier and the candidate store identifier are store identifiers in the abnormal store identifier set;

[0009] In response to a trigger operation on a virtual resource by a customer account, the virtual resource corresponding to the target store identifier is pushed to a customer terminal, wherein the customer account is a customer account associated with the designated source store identifier.

[0010] In one embodiment, the operation evaluation conditions include store identification conditions corresponding to a variety of store problems;

[0011] The step of screening the abnormal store identification set from the initial store identification set according to the operation data corresponding to each initial store identification in the initial store identification set and the pre-constructed operation evaluation condition includes:

[0012] Determine the operation data table item corresponding to each initial store identification in the initial store identification set according to the operation data corresponding to each initial store identification and the preset indicator calculation strategy; wherein the operation data table item includes an indicator value of an evaluation indicator for reflecting the store operation status;

[0013] According to the store identification condition corresponding to each store problem, the indicator values ​​of each evaluation indicator contained in the operation data table items corresponding to the initial store identifications are compared and identified;

[0014] In the initial store identification set, a set consisting of initial store identifications whose operation data items satisfy the store identification condition is determined as an abnormal store identification set.

[0015] In one embodiment, selecting a target store identification associated with the specified source store identification from at least one candidate store identification according to an indicator value of a first store indicator corresponding to the specified source store identification and an indicator value of a second store indicator corresponding to each candidate store identification in at least one candidate store identification includes:

[0016] In response to a first indicator query operation triggered by a management account for a source store identification set, an indicator value of a first store indicator corresponding to each source store identification in the source store identification set is displayed through a first indicator display page of the management terminal; wherein the source store identification set is a subset of the abnormal store identification set, the source store identification is a store identification corresponding to a source store, and the first store indicator is used to characterize the joint shopping ability of the source store;

[0017] In response to the management account triggering a second indicator query operation on the first indicator display page for a specified source store identifier in the source store identifier set, the indicator value of the second store indicator corresponding to the candidate store identifier is displayed on the second indicator display page of the management terminal; wherein the specified source store identifier is the store identifier corresponding to the specified source store, the candidate store identifier is the store identifier corresponding to the candidate store, and the second store indicator is used to characterize the customer group similarity between the specified source store and the candidate store;

[0018] In response to a store ID association operation triggered by a management account, a target store ID associated with a specified source store ID is determined, wherein the target store ID is selected from the candidate store IDs.

[0019] In one embodiment, in response to a trigger operation of a customer account on a virtual resource, pushing the virtual resource corresponding to the target store identifier to the customer terminal includes:

[0020] In response to a trigger operation of a customer account on a virtual resource, determining the virtual resource corresponding to the customer account from a set of virtual resources corresponding to the target store identifier according to consumption level information corresponding to the customer account and consumption preference information corresponding to the customer account;

[0021] The virtual resource is pushed to a customer terminal corresponding to the customer account.

[0022] In one embodiment, the method further comprises:

[0023] Obtaining a write-off rate of the virtual resource;

[0024] According to the write-off rate and the target return on investment, the virtual resource set corresponding to the target store identifier is adjusted to obtain an adjusted virtual resource set;

[0025] The virtual resource set is replaced by the adjusted virtual resource set.

[0026] In one embodiment, the method further comprises:

[0027] Acquire a consumption level information set corresponding to a customer identification set and a consumption preference information set corresponding to the customer identification set, wherein the customer identification set is a set of customer identifications associated with the source store;

[0028] According to the consumption level information set and the consumption preference information set, and based on a target return on investment, a virtual resource set corresponding to the target store identifier is determined.

[0029] In one embodiment, the method further comprises:

[0030] Acquire a customer trajectory information set corresponding to a customer identification set, wherein the customer identification set is a set of user identifications associated with the source store;

[0031] Determine the passenger flow volume, passenger flow direction and passenger flow speed according to the customer trajectory information set;

[0032] Constructing a passenger flow route according to the passenger flow volume, passenger flow direction and passenger flow speed;

[0033] According to the customer flow line, a triggering location of the virtual resource is determined, wherein the triggering location is used to instruct the customer account to perform a triggering operation on the virtual resource.

[0034] In one embodiment, the method further comprises:

[0035] Obtaining the delivery amount and redemption rate of the virtual resource corresponding to the triggering location;

[0036] The delivery amount of the virtual resource at the triggering location is adjusted according to the delivery amount and the write-off rate.

[0037] In a second aspect, the present application also provides a device for processing store data. The device comprises:

[0038] A store identification screening module, used to screen out an abnormal store identification set from the initial store identification set according to the operation data corresponding to each initial store identification in the initial store identification set and the pre-constructed operation evaluation conditions; wherein the abnormal store identification is the store identification corresponding to the store with abnormal operation;

[0039] A store identification selection module, configured to select a target store identification associated with the specified source store identification from at least one candidate store identification according to an indicator value of a first store indicator corresponding to the specified source store identification and an indicator value of a second store indicator corresponding to each candidate store identification in at least one candidate store identification; wherein the specified source store identification and the candidate store identification are store identifications in the abnormal store identification set;

[0040] The virtual resource push module is used to push the virtual resource corresponding to the target store identifier to the customer terminal in response to a trigger operation of a customer account on the virtual resource, wherein the customer account is a customer account associated with the designated source store identifier.

[0041] In a third aspect, the present application further provides a computer device. The computer device includes a memory and a processor, the memory stores a computer program, and the processor implements the following steps when executing the computer program:

[0042] According to the operation data corresponding to each initial store identification in the initial store identification set and the pre-constructed operation evaluation conditions, the abnormal store identification set is obtained by screening from the initial store identification set; wherein the abnormal store identification is the store identification corresponding to the store with abnormal operation;

[0043] According to the index value of the first store index corresponding to the designated source store identifier and the index value of the second store index corresponding to each candidate store identifier in at least one candidate store identifier, a target store identifier associated with the designated source store identifier is selected from the at least one candidate store identifier; wherein the designated source store identifier and the candidate store identifier are store identifiers in the abnormal store identifier set;

[0044] In response to a trigger operation on a virtual resource by a customer account, the virtual resource corresponding to the target store identifier is pushed to a customer terminal, wherein the customer account is a customer account associated with the designated source store identifier.

[0045] In a fourth aspect, the present application further provides a computer-readable storage medium. The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the following steps are implemented:

[0046] According to the operation data corresponding to each initial store identification in the initial store identification set and the pre-constructed operation evaluation conditions, the abnormal store identification set is obtained by screening from the initial store identification set; wherein the abnormal store identification is the store identification corresponding to the store with abnormal operation;

[0047] According to the index value of the first store index corresponding to the designated source store identifier and the index value of the second store index corresponding to each candidate store identifier in at least one candidate store identifier, a target store identifier associated with the designated source store identifier is selected from the at least one candidate store identifier; wherein the designated source store identifier and the candidate store identifier are store identifiers in the abnormal store identifier set;

[0048] In response to a trigger operation on a virtual resource by a customer account, the virtual resource corresponding to the target store identifier is pushed to a customer terminal, wherein the customer account is a customer account associated with the designated source store identifier.

[0049] In a fifth aspect, the present application further provides a computer program product. The computer program product includes a computer program, and when the computer program is executed by a processor, the following steps are implemented:

[0050] According to the operation data corresponding to each initial store identification in the initial store identification set and the pre-constructed operation evaluation conditions, the abnormal store identification set is obtained by screening from the initial store identification set; wherein the abnormal store identification is the store identification corresponding to the store with abnormal operation;

[0051] According to the index value of the first store index corresponding to the designated source store identifier and the index value of the second store index corresponding to each candidate store identifier in at least one candidate store identifier, a target store identifier associated with the designated source store identifier is selected from the at least one candidate store identifier; wherein the designated source store identifier and the candidate store identifier are store identifiers in the abnormal store identifier set;

[0052] In response to a trigger operation on a virtual resource by a customer account, the virtual resource corresponding to the target store identifier is pushed to a customer terminal, wherein the customer account is a customer account associated with the designated source store identifier.

[0053] The above-mentioned store data processing method, device, computer equipment, storage medium and computer program product first determine the set of abnormal store identifications with abnormal operations based on the operation data corresponding to each initial store identification and the pre-constructed operation evaluation conditions, and then select the target store identification associated with the specified source store identification from the candidate store identifications according to the index value of the first store index corresponding to the specified source store identification and the index value of the second store index corresponding to each candidate store identification in at least one candidate store identification in the abnormal store identification set, and finally, when the customer account triggers the operation for the virtual resource, push the virtual resource corresponding to the target store identification to the customer terminal corresponding to the customer account. In other words, the virtual resources (such as electronic coupons) of the target store are pushed to the customers associated with the source store. In this way, customers associated with the source store can be encouraged to go to the target store, and the linkage between the source store and the target store can be increased to increase the probability of customers going to different stores, thereby improving the utilization rate of customer flow. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 An application environment diagram of a method for processing store data in an embodiment;

[0055] Figure 2 It is a flowchart of a method for processing store data in one embodiment;

[0056] Figure 3 A schematic diagram of an interface for dynamic adjustment of virtual resources in one embodiment;

[0057] Figure 4 This is a schematic diagram of an interface of a passenger flow route in an embodiment;

[0058] Figure 5 A schematic diagram of an interface for extracting virtual resources in one embodiment;

[0059] Figure 6 This is a schematic diagram of an interface of a store operation funnel chart in an embodiment;

[0060] Figure 7 is a structural block diagram of a device for processing store data in an embodiment;

[0061] Figure 8 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0062] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0063] The method for processing store data provided in the embodiment of the present application can be applied to Figure 1 In the application environment shown. Among them, the customer terminal 102 and the management terminal 104 communicate with the server 106 through the network. The customer terminal 102 is a terminal used by customers. The management terminal 104 is a terminal used by store managers (such as shopping mall managers). The data storage system can store data that the server 104 needs to process. The data storage system can be integrated on the server 104, or it can be placed on the cloud or other network servers. Among them, the customer terminal 102 and the management terminal 104 can be, but are not limited to, various personal computers, laptops, smart phones, tablets, Internet of Things devices and portable wearable devices. The Internet of Things devices can be smart speakers, smart TVs, smart air conditioners, smart car-mounted devices, etc. Portable wearable devices can be smart watches, smart bracelets, head-mounted devices, etc. The server 104 can be implemented with an independent server or a server cluster consisting of multiple servers.

[0064] In one embodiment, Figure 2 As shown, a method for processing store data is provided, and the method is applied to Figure 1 The server in the example is used to illustrate the following steps:

[0065] Step S202: According to the operation data corresponding to each initial store identification in the initial store identification set and the pre-built operation evaluation conditions, the abnormal store identification set is obtained by screening the initial store identification set.

[0066] Among them, the abnormal store identification is the store identification corresponding to the store with abnormal operation.

[0067] Specifically, the management terminal sends a store diagnosis request to the server. The store diagnosis request carries an initial store identification set. The server responds to the store diagnosis request and determines the operation data table item corresponding to each initial store identification in the initial store identification set according to the operation data corresponding to each initial store identification in the initial store identification set and the preset indicator calculation strategy. The operation data table item contains evaluation indicators and indicator values ​​for reflecting the operation status of the store. Afterwards, the server determines the abnormal store identification based on the pre-constructed operation evaluation conditions and the operation data table items corresponding to each initial store identification, and constructs an abnormal store identification set based on the abnormal store identification.

[0068] Step S204, selecting a target store identification associated with the specified source store identification from at least one candidate store identification according to the indicator value of the first store indicator corresponding to the specified source store identification and the indicator value of the second store indicator corresponding to each candidate store identification in at least one candidate store identification.

[0069] The designated source store identifier and the candidate store identifier are store identifiers in the abnormal store identifier set. Optionally, the designated source store identifier may be a source store identifier input by the management account, such as a source store identifier selected as an option, or a pre-set source store identifier. The source store may refer to a store that meets preset operating conditions, for example, a store whose customer flow is greater than a preset customer flow threshold may be determined as a source store.

[0070] The candidate store identifier is a store identifier corresponding to the candidate store. Optionally, the candidate store identifier may be a store identifier input by the management account, such as a store identifier selected as an option, or a pre-set store identifier.

[0071] Specifically, the management terminal sends a store association request to the server, and the store association request carries a specified source store identifier and at least one candidate store identifier. In response to the store association request, the server selects a target store identifier associated with the specified source store identifier from at least one candidate store identifier according to the index value of the first store index corresponding to the specified source store identifier and the index value of the second store index corresponding to each candidate store identifier in at least one candidate store identifier.

[0072] Step S206: in response to the triggering operation of the customer account on the virtual resource, the virtual resource corresponding to the target store identifier is pushed to the customer terminal.

[0073] The customer account is a customer account associated with the specified source store. The virtual resource may be an electronic coupon, an electronic red envelope, etc. The triggering operation may be a code scanning operation, a click to receive operation, etc.

[0074] Specifically, in one embodiment, the customer account uses the customer terminal to scan the code, triggering a push response of the virtual resource. Therefore, in response to the customer account's scanning operation for the virtual resource, the server pushes the virtual resource corresponding to the target store ID to the customer terminal corresponding to the customer account. In this way, the corresponding customer can be urged to go to the target store to redeem the virtual resource.

[0075] In the above-mentioned method for processing store data, firstly, according to the operation data corresponding to each initial store identification and the pre-constructed operation evaluation conditions, a set of abnormal store identifications with abnormal operations is determined, and then in the set of abnormal store identifications, according to the index value of the first store index corresponding to the specified source store identification and the index value of the second store index corresponding to each candidate store identification in at least one candidate store identification, the target store identification associated with the specified source store identification is selected from the candidate store identifications, and finally, when the customer account triggers the operation for the virtual resources, the virtual resources corresponding to the target store identification are pushed to the customer terminal corresponding to the customer account. In other words, the virtual resources (such as electronic coupons) of the target store are pushed to the customers associated with the source store. In this way, customers associated with the source store can be encouraged to go to the target store, and the linkage between the source store and the target store can be increased to increase the probability of customers going to different stores, thereby improving the utilization rate of customer flow.

[0076] In one embodiment, the operation evaluation conditions include store identification conditions corresponding to various store problems. Based on this, step S202 includes the following steps:

[0077] Step S2022, determining the operation data table item corresponding to each initial store identification in the initial store identification set according to the operation data corresponding to each initial store identification and the preset indicator calculation strategy;

[0078] Step S2024, comparing and judging the index values ​​of the evaluation indexes contained in the operation data table items corresponding to the initial store identifiers according to the store identification conditions corresponding to each store problem;

[0079] Step S2026: In the initial store identification set, a set consisting of initial store identifications whose operation data items meet the store identification condition is determined as an abnormal store identification set.

[0080] Specifically, first, each indicator calculation strategy corresponds to a preset evaluation indicator. The evaluation indicator is calculated from a variety of operating data. The indicator calculation strategy also includes a weight coefficient of the evaluation indicator. The server performs weighted calculation on the operating data corresponding to each initial store identification based on the calculation formula of the indicator in the indicator calculation strategy and the weight coefficient of the evaluation indicator, obtains the indicator value of each preset evaluation indicator corresponding to the indicator calculation strategy, and then constructs the operating data table item corresponding to each initial store identification. Optionally, the operating data table item may include one or more evaluation indicators of a comprehensive index, a conversion index, a stickiness index or a revenue index.

[0081] For example, taking the store conversion index as an example, in the index calculation strategy, the store conversion index is calculated by three operational data: store passing rate, store entry rate, and deep shopping rate. Among them, the store passing rate represents the ratio of the number of people passing through a store (single store) in the target shopping mall to the total number of people entering the target shopping mall during the monitoring period with "days" as the time unit. The store entry rate represents the ratio of the number of people entering a store to the total number of people entering the target shopping mall during the monitoring period with "days" as the time unit. The meaning of deep shopping is the situation where the length of time a customer spends in a store exceeds the preset time threshold. Therefore, the deep shopping rate represents the ratio of the number of deep shopping people entering a store to the total number of people entering the store. Based on this, the server calculates the index according to the index weight coefficient corresponding to each operational data. Among them, the index weight coefficient corresponding to the store passing rate is 0.25, the index weight coefficient corresponding to the store entry rate is 0.5, and the index weight coefficient corresponding to the deep shopping rate is 0.25. The calculation formula of the store conversion index is: store conversion index = 0.25*store passing rate + 0.5 store entry rate + 0.25 in-depth browsing rate. Then, the server substitutes the store passing rate, store entry rate and in-depth browsing rate data in the operation data of each store to obtain the index value of the store conversion index of each store. Similarly, other evaluation indicators can be calculated based on different indicator calculation strategies: store stickiness index, store revenue index, etc., which are not limited in the embodiments of this application.

[0082] Then, an indicator evaluation system is pre-built in the server. The indicator evaluation system includes a variety of store problem scenarios. Each store problem scenario corresponds to a store discrimination condition for evaluating the store. The server compares and discriminates the indicator values ​​contained in the operation data table items of each store according to the store discrimination condition corresponding to each store problem scenario.

[0083] Optionally, each store problem scenario included in the pre-built indicator evaluation system includes a combination of multiple store discrimination conditions, as shown in the following Table 1:

[0084]

[0085] Table 1

[0086] The average customer price included in Table 1 refers to the average unit price of consumption of each customer. Sales efficiency per square meter refers to the turnover (or sales) per square meter of business area. The rental-to-sales ratio is the ratio of rent to sales. With respect to the operational evaluation conditions contained in the problem scenarios of each store in Table 1, the server determines the high and low evaluation indicators of each store, and can evaluate the various indicators appearing in the indicator system of a store (i.e., Table 1) by pre-setting indicator thresholds. For example, when the store passing rate of a store is lower than the preset store passing rate threshold, the store passing rate indicator of the store is judged to be a low store passing rate. For another example, when the store entry rate of a store is higher than the preset store entry rate threshold, the store entry rate indicator of the store is judged to be a high store entry rate. Similarly, a high in-depth visit rate means that the store's in-depth visit rate indicator is higher than the preset in-depth visit rate threshold, and so on.

[0087] Finally, for the various store identification conditions contained in a certain store problem scenario, if the operation data table item of a certain initial store identification meets the store identification condition, the server determines the initial store identification as an abnormal store identification. For example, for the indicator value contained in the operation data table item of a certain initial store identification, if the server determines based on the preset indicator threshold that the store has: low store passing rate, high store entry rate, high deep browsing rate and high customer unit price (store identification conditions for insufficient store exposure), then the initial store identification can be determined as an abnormal store identification corresponding to insufficient store exposure. If a store has a high store passing rate, a low store entry rate and a low deep browsing rate (store identification conditions for insufficient store entry conversion), then the initial store identification is determined to be an abnormal store identification corresponding to insufficient store entry conversion.

[0088] Optionally, the server may further mark the abnormal store identifier according to the store problem corresponding to the store identification condition. For example, for the abnormal store identifier corresponding to the determined store underexposure, the abnormal store identifier is marked as having a store underexposure problem.

[0089] In this embodiment, the operation data table items corresponding to the stores are calculated by a preset indicator calculation strategy, and the abnormal store identification is determined through the operation data table items and pre-constructed operation evaluation conditions, thereby improving the accuracy and timeliness of screening abnormal store identifications.

[0090] In one embodiment, after the server marks the abnormal store identifier, the process further includes:

[0091] According to the store problems existing in the abnormal stores, determine the target management strategy corresponding to the store problems of the abnormal stores in the correspondence between the preset store problems and management strategies;

[0092] The target management strategy is used to mark abnormal stores in the form of target event to-do items.

[0093] Specifically, the server obtains a set of pre-stored correspondences between store problems and management policies, and determines the target management policy corresponding to the store problems of the abnormal store based on the correspondence set. Then, the management account manages the store problems existing in the abnormal store by triggering the to-do items of the abnormal event. In response to the triggering operation of the to-do items of the abnormal store, the server outputs a management policy pop-up window, and adds a target management policy to the management policy pop-up window corresponding to the to-do item, so that the policy executor can implement the policy according to the added target management policy. Add the target management policy to the to-do item to prompt the management personnel of the abnormal store to perform the corresponding management operations through the target management policy to carry out the later operation management of the abnormal store. In addition, the server also converts the to-do item with the added target management policy to the done state, indicating that the pending matters of the abnormal store have been processed.

[0094] In one embodiment, step S204 includes the following steps:

[0095] Step S2042, in response to the first indicator query operation triggered by the management account for the source store identification set, displaying the indicator value of the first store indicator corresponding to each source store identification in the source store identification set through the first indicator display page of the management terminal;

[0096] Step S2044, in response to the management account triggering a second indicator query operation for a specified source store identifier in the source store identifier set on the first indicator display page, displaying the indicator value of the second store indicator corresponding to the candidate store identifier on the second indicator display page of the management terminal;

[0097] Step S2046, in response to the store ID association operation triggered by the management account, determining a target store ID associated with the specified source store ID.

[0098] The source store identification set is a subset of the abnormal store identification set. The source store identification is a store identification corresponding to the source store. The first store indicator is used to characterize the associated shopping ability of the source store. The first store indicator includes one or more of the associated shopping depth or the customer flow distribution rate.

[0099] Among them, the designated source store identifier is the store identifier corresponding to the designated source store. The candidate store identifier is the store identifier corresponding to the candidate store. The target store identifier is selected from the candidate store identifiers. The second store indicator is used to characterize the customer base similarity between the designated source store and the candidate store. The second store indicator includes at least one of the number of cross-flow customers entering the store, the number of cross-flow customers who shop in depth, or the store entry gain rate. The number of cross-flow customers entering the store is the total number of customers who have entered both the designated source store and the candidate store. The number of cross-flow customers who shop in depth is the total number of customers who have shopped in depth in both the designated source store and the candidate store. The store entry gain rate is determined based on the joint store entry rate corresponding to the candidate store identifier and the natural store entry rate corresponding to the candidate store identifier.

[0100] Specifically, first, the store manager logs in with a management account on the management terminal, enters the source store identification set on the management terminal after logging in, and triggers the first indicator query operation. In response to the first indicator query operation, the management terminal generates a first indicator query request carrying the source store identification set, and sends the first indicator query request to the server.

[0101] In one embodiment, the server receives the first indicator query request, obtains the total number of all stores entered by the first customer group and the total number of people in the first customer group for each source store identifier in the source store identifier set, and determines the value of the associated shopping depth based on the total number and the total number of people. The first customer group is the customer group that has entered the source store. Optionally, the server divides the total number by the total number of people to obtain the value of the associated shopping depth. Afterwards, the server returns the value of the associated shopping depth corresponding to each source store identifier to the management terminal. The management terminal receives the value of the associated shopping depth corresponding to each source store identifier, and displays the value of the associated shopping depth corresponding to each source store identifier in the first indicator display page.

[0102] In another embodiment, the server receives the first indicator query request, and for each source store identifier in the source store identifier set, obtains the first total number of times the second customer group enters the candidate store and the second total number of times the first customer group enters the source store according to the source store identifier and the candidate store identifier, and determines the value of the passenger flow distribution rate according to the first total number and the second total number. Among them, the first customer group is the customer group that has entered the source store. The second customer group is the customer group that has entered both the source store and the candidate store. Optionally, the server divides the first total number by the first total number of people to obtain the value of the associated shopping depth. Of course, the first total number and the first total number of people can also be input into the associated shopping depth prediction model to obtain the value of the associated shopping depth. Afterwards, the server returns the value of the passenger flow distribution rate corresponding to each source store identifier to the management terminal. The management terminal receives the value of the passenger flow distribution rate corresponding to each source store identifier, and displays the value of the passenger flow distribution rate corresponding to each source store identifier in the first indicator display page.

[0103] Then, the store manager triggers a second indicator query operation for a specified source store identifier in the source store identifier set in the first indicator display page on the management terminal. In response to the second indicator query operation, the management terminal generates a second indicator query request carrying the specified source store identifier, and sends the second indicator query request to the server. The server receives the second indicator query request, determines the indicator value of the second store indicator corresponding to the candidate store identifier based on the specified source store identifier and the candidate store identifier, and returns the indicator value of the second store indicator corresponding to the candidate store identifier to the terminal. The terminal displays the indicator value of the second store indicator corresponding to the candidate store identifier in the second indicator display page.

[0104] Finally, in response to the store identification association operation triggered by the management account in the second indicator display page, the candidate store identification selected by the management account in the second indicator display page is determined as the target store identification associated with the specified source store identification.

[0105] In this embodiment, by associating indicator values ​​such as the number of cross-customer flows and the store entry rate gain, accurate insight into the correlation between customer groups between stores is obtained, which is conducive to quickly finding stores with high customer similarity and large traffic pools that are suitable for joint marketing.

[0106] In one embodiment, step S206 includes the following steps:

[0107] Step S2062, in response to the triggering operation of the customer account on the virtual resource, according to the consumption level information corresponding to the customer account and the consumption preference information corresponding to the customer account, determining the virtual resource corresponding to the customer account from the virtual resource set corresponding to the target store identifier;

[0108] Step S2064: Push the virtual resources to the customer terminal corresponding to the customer account.

[0109] Among them, consumption level information refers to information that characterizes the customer's consumption level, such as spending power, and consumption preference information refers to information that characterizes the customer's consumption preference.

[0110] Specifically, the server responds to the trigger operation of the customer account on the virtual resources, obtains the consumption level information and consumption preference information corresponding to the customer account, and determines the virtual resources corresponding to the customer account from the virtual resource set corresponding to the target store identifier based on the consumption level information and consumption preference information.

[0111] Optionally, the server obtains historical consumption data corresponding to the customer account (such as consumption times, consumption amount, consumption amount distribution, consumption time period, consumption format category preference, etc.), and inputs the historical consumption data into a pre-trained consumption level prediction model to obtain the consumption level information corresponding to the customer account. The consumption level prediction model can be an XGBoost model or a LightGBM model. Similarly, consumption preference information can also be predicted by the corresponding model.

[0112] In a specific example, the customer's spending power is 500 yuan. He has consumed food and clothing in the past, but has only consumed food and clothing in the past month. So a 600-100 clothing coupon is recommended to him. On the one hand, the high-threshold coupon can increase the customer's average order value; on the other hand, combined with his past consumption behavior, the coupon can be promoted to effectively awaken the customer and realize incremental value.

[0113] In this embodiment, virtual resources corresponding to the customer account are matched according to the consumption level information corresponding to the customer account and the consumption preference information corresponding to the customer account, which is conducive to improving the average customer price and the activity investment return rate.

[0114] In one embodiment, the process of dynamically adjusting the virtual resource set is involved. Based on the above embodiment, the process can be specifically implemented by the following steps:

[0115] Step S212, obtaining the write-off rate of virtual resources;

[0116] Step S214, adjusting the virtual resource set corresponding to the target store identifier according to the write-off rate and the target return on investment to obtain an adjusted virtual resource set;

[0117] Step S216: Use the adjusted virtual resource set to replace the virtual resource set.

[0118] Among them, the write-off rate is the number of virtual resources used divided by the number of virtual resources collected.

[0119] Specifically, the server obtains the write-off rate of the virtual resources, and then adjusts the virtual resource set corresponding to the target store ID according to the write-off rate and the target return on investment to obtain the adjusted virtual resource set. For example, virtual resources with low write-off efficiency and low return on investment are promptly eliminated, and for virtual resources with high write-off, if the consumption is too large, the inventory will be replenished in time. Then, the virtual resource set is replaced with the adjusted virtual resource set. Figure 3 A schematic diagram of an interface for dynamic adjustment of virtual resources in an embodiment.

[0120] In this embodiment, by dynamically adjusting the virtual resource set, virtual resources with low write-offs can be deleted and virtual resources with high write-offs can be retained, thereby achieving dynamic adjustment of inventory.

[0121] In one embodiment, it involves a specific implementation process of determining a virtual resource set corresponding to a target store identifier. Based on the above embodiment, the process can be implemented by the following steps:

[0122] Step S222, obtaining a consumption level information set corresponding to the customer identification set and a consumption preference information set corresponding to the customer identification set;

[0123] Step S224, determining a virtual resource set corresponding to the target store identifier according to the consumption level information set and the consumption preference information set and based on the target return on investment.

[0124] The customer identification set is a set of customer identifications associated with a specified source store. The customer identification associated with a specified source store may be an identification of a customer entering the specified source store, or an identification of a customer who consumes in the specified source store. The consumption level information set is a set of consumption level information corresponding to each customer identification. The consumption preference information set is a set of consumption preference information corresponding to each customer identification.

[0125] Specifically, the server obtains a consumption level information set corresponding to the customer identification set and a consumption preference information set corresponding to the customer identification set, and then determines a virtual resource set corresponding to the target store based on the consumption level information set and the consumption preference information set and the target return on investment, wherein the target return on investment may be an estimated return on investment.

[0126] In this embodiment, based on the consumption level information set and the consumption preference information set and based on the target return on investment, the virtual resource set corresponding to the target store is determined, which is conducive to improving the accuracy of the virtual resource set corresponding to the target store.

[0127] In one embodiment, it involves a specific implementation process of determining an offline triggering location of a virtual resource. Based on the above embodiment, the process can be implemented by the following steps:

[0128] Step S232, obtaining a customer trajectory information set corresponding to the customer identification set;

[0129] Step S234, determining the passenger flow volume, passenger flow direction and passenger flow speed according to the customer trajectory information set;

[0130] Step S236, constructing a passenger flow route according to passenger flow volume, passenger flow direction and passenger flow speed;

[0131] Step S238, determining the triggering location of the virtual resource according to the passenger flow line.

[0132] The customer ID set is a set of customer IDs associated with a specified source store. The trigger location is used to indicate that the customer account performs a trigger operation on a virtual resource. The trigger location is a point, which can be a place where a poster QR code is placed offline, such as a channel entrance or inside a store.

[0133] Specifically, the management terminal detects the display operation for the customer flow route triggered by the corresponding management account, and generates a corresponding customer flow route display request. The management terminal sends the customer flow route display request to the server. In response to the customer flow route display request, the server obtains a customer identification set associated with the specified source store, and then obtains a customer trajectory information set corresponding to the customer identification set. Then, the server inputs the customer trajectory information set into a pre-trained model to obtain the customer flow volume, customer flow direction and customer flow speed. Afterwards, the server constructs a customer flow route including the customer flow volume, customer flow direction and customer flow speed, and displays the customer flow route in a preset display mode through the management terminal. Optionally, the customer flow route is displayed in the form of a heat map. Among them, please refer to Figure 4 For passenger flow, based on customer trajectories, the thermal situation of customer flow in the venue is calculated, which is represented by thickness in the heat map; for passenger flow direction, by marking points, such as escalator entrances, turning points, store entrances and other key nodes, the movement of passenger flow in other directions is calculated, and the flow direction is represented by arrows in the heat map; for passenger flow speed, based on customer trajectories, the distance / displacement between two points within a period of time is represented by the time between two points, which is represented by the depth of color in the heat map. Finally, the server determines the triggering location of virtual resources based on the passenger flow line. For example, the server places materials at key points where the target customer group appears.

[0134] In this embodiment, by constructing the passenger flow route through passenger flow volume, passenger flow direction and passenger flow speed, the movement route of any type of customer group in the shopping mall can be more comprehensively portrayed, assisting in designing a smoother customer journey and accurately finding the triggering location of better virtual resources.

[0135] In one embodiment, the process involves dynamically adjusting the delivery amount of virtual resources at the triggering location. Based on the above embodiment, the process can be specifically implemented by the following steps:

[0136] Step S242, obtaining the delivery amount and redemption rate of the virtual resource corresponding to the triggering location;

[0137] Step S244, adjusting the delivery amount of the virtual resource at the triggering location according to the delivery amount and the write-off rate.

[0138] Specifically, when T (virtual resource delivery time) + 1 is reached, the server obtains the delivery amount and verification rate corresponding to the virtual resource at the trigger location, and maps the delivery amount and verification rate to the four quadrants. The four quadrants are divided according to the delivery amount and the verification rate, and each quadrant is associated with a corresponding resource delivery strategy. Then, the server obtains the corresponding resource delivery strategy according to the quadrant where the delivery amount and the verification rate are located, and adjusts the delivery amount of the virtual resource at the trigger location according to the resource delivery strategy. For example, if the quadrant where the delivery amount and the verification rate are located indicates that the delivery amount is low and the verification rate is high, indicating that the quality of the trigger location is high, then the corresponding resource delivery strategy is to increase resource delivery. If the quadrant where the delivery amount and the verification rate are located indicates that the delivery amount is high and the verification rate is low, indicating that the quality of the trigger location is low, then the corresponding resource delivery strategy is to reduce resource delivery to avoid waste.

[0139] In this embodiment, the resource delivery strategy is adjusted in time according to the delivery volume and write-off rate of the virtual resources at the triggering location, which shortens the iteration cycle and helps to improve the utilization rate of the virtual resources, thereby improving the return on marketing investment.

[0140] In one embodiment, it involves a process of guiding customers to a target store through AR navigation technology. Based on the above embodiment, the process can be specifically implemented through the following steps:

[0141] Step S252: In response to the navigation operation of the customer account for the target store logo, AR navigation information to the target store is displayed through the customer terminal.

[0142] Among them, the target store identifier is the store identifier of the target store.

[0143] In this embodiment, AR navigation technology is used to help customers quickly find the target store and increase the fun of the journey, which is conducive to reducing customer loss during the store search process and realizing the distribution of customer flow within the store.

[0144] In one embodiment, in order to increase interactivity, this embodiment provides customers with an interesting activity of extracting virtual resources. Specifically, the method further includes the following steps:

[0145] Step S262, during the AR navigation process, a virtual resource extraction window pops up on the display interface of the customer terminal;

[0146] Step S264, in response to the virtual resource extraction operation triggered by the customer account for the virtual resource extraction window, extracting the virtual resource corresponding to the customer account from the virtual resource set corresponding to the target store identifier;

[0147] Step S266: Send the extracted virtual resources to the customer terminal.

[0148] The virtual resource extraction window is provided with a resource extraction control, which is used to instruct the customer to trigger the virtual resource extraction operation.

[0149] Specifically, during the AR navigation process, a virtual resource extraction window can pop up on the display interface of the customer terminal at an appropriate time, such as when the customer arrives at a designated location. After the customer triggers the resource extraction control in the virtual resource extraction window, the server responds to the virtual resource extraction operation triggered by the customer, extracts the virtual resources corresponding to the customer account from the virtual resource set corresponding to the target store logo, and sends it to the customer terminal. For example, in the form of a secondary lottery, electronic coupons are distributed to the customer's mobile phone through human-computer interaction. Figure 5 A schematic diagram of an interface for extracting virtual resources in an embodiment.

[0150] In this embodiment, interesting interactive games are provided during the entire navigation process to increase the fun of the journey experience.

[0151] In one embodiment, after step S206, the method further includes the following steps:

[0152] In response to the indicator query operation triggered for the store identifier, obtaining the indicator value of the store operation indicator corresponding to the store identifier;

[0153] Determine attribute information of an indicator identifier corresponding to the store operation indicator according to the indicator value of the store operation indicator;

[0154] According to the attribute information of the indicator identifier, a store operation diagram including the indicator identifier is displayed.

[0155] The store ID may be a source store ID or a target store ID.

[0156] In this embodiment, an indicator identifier is used to represent the indicator value of the store operation indicator, and the indicator identifier is displayed in the store operation diagram. The indicator display method is more intuitive and is conducive to improving the visualization effect.

[0157] In one embodiment, the index value of the store operation index includes the index value of the first store operation index and the index value of the second store operation index; and the store operation diagram is a store operation matrix diagram.

[0158] According to the index value of the store operation index, the attribute information of the index identifier corresponding to the store operation index is determined, including:

[0159] According to the indicator value of the first store operation indicator, determine the horizontal axis position of the indicator marker corresponding to the store operation indicator in the store operation matrix diagram, and according to the indicator value of the second store operation indicator, determine the vertical axis position of the indicator marker corresponding to the store operation indicator in the store operation matrix diagram.

[0160] According to the attribute information of the indicator identifier, the store operation diagram containing the indicator identifier is displayed, including:

[0161] According to the horizontal axis position and the vertical axis position, a store operation matrix diagram containing indicator labels is displayed.

[0162] In this embodiment, the position information of the indicator identifier is determined by the indicator value of the store operation indicator, which is conducive to accurately displaying the relevant information of the store operation indicator.

[0163] In one embodiment, the index value of the store operation index also includes the index value of the third store operation index.

[0164] According to the index value of the store operation index, the attribute information of the index identifier corresponding to the store operation index is determined, and also includes:

[0165] According to the indicator value of the third store operation indicator, the size information of the indicator identifier corresponding to the store operation indicator in the store operation matrix diagram is determined.

[0166] According to the horizontal and vertical axis positions, a store operation matrix diagram containing indicator labels is displayed, including:

[0167] According to the horizontal axis position, vertical axis position and size information, a store operation matrix diagram containing indicator labels is displayed.

[0168] In this embodiment, the size information of the indicator identifier is determined by the indicator value of the store operation indicator, which is conducive to accurately displaying the relevant information of the store operation indicator and assisting users to quickly determine the difference in the indicator value of the store operation indicator.

[0169] In one embodiment, in response to an indicator query operation under a specified evaluation dimension triggered for a store identifier, an indicator value of a store operation indicator under the specified evaluation dimension corresponding to the store identifier is obtained.

[0170] Among them, the designated evaluation dimensions include one or more of the store conversion dimension, the store customer attraction dimension, the store operation dimension or the store contribution dimension.

[0171] Regarding the store conversion dimension, the first store operation indicator is the store entry rate, the second store operation indicator is the deep browsing rate, and the third store operation indicator is the number of customers entering the store;

[0172] Alternatively, for the store conversion dimension, the first store operation indicator is the store entry rate, the second store operation indicator is the store turnover rate, and the third store operation indicator is the number of customers entering the store;

[0173] Alternatively, for the store customer attraction dimension, the first store operation indicator is the store customer flow per square meter, the second store operation indicator is the store customer flow per square meter, and the third store operation indicator is the number of store customers;

[0174] Or, for the store customer attraction dimension, the first store operation indicator is customer attraction, the second store operation indicator is customer retention, and the third store operation indicator is the number of customers entering the store;

[0175] Alternatively, for the store operation dimension, the first store operation indicator is sales per square meter, the second store operation indicator is rental per square meter, and the third store operation indicator is the number of customers entering the store;

[0176] Alternatively, for the store contribution dimension, the first store operation indicator is the cross-traffic efficiency per square meter, and the second store operation indicator is the sales efficiency per square meter;

[0177] Alternatively, for the store contribution dimension, the first store operation indicator is contribution power, and the second store operation indicator is sales per square meter.

[0178] In one embodiment, the calculation method of the store operation index is shown in Table 2.

[0179]

[0180]

[0181]

[0182] Table 2

[0183] In one embodiment, there are multiple store operation indicators; and the store operation chart is a store operation funnel chart.

[0184] According to the index value of the store operation index, the attribute information of the index identifier corresponding to the store operation index is determined, including:

[0185] Among the multiple store operation indicators, for each store operation indicator, according to the indicator value of the store operation indicator, the size information of the indicator identifier corresponding to the store operation indicator in the store operation funnel chart is determined.

[0186] According to the attribute information of the indicator identifier, the store operation diagram containing the indicator identifier is displayed, including:

[0187] According to the size information of multiple indicator identifiers in the store operation funnel chart, a store operation funnel chart containing multiple indicator identifiers is displayed.

[0188] In this embodiment, a store operation funnel chart is used to display a store operation funnel model constructed by indicator identifiers corresponding to multiple store operation indicators, which can effectively evaluate traffic conversion efficiency and achieve business benchmarking.

[0189] In one embodiment, Figure 6 As shown, store operation indicators include at least two of reach, customer attraction, customer retention or sales efficiency per square meter.

[0190] In response to the indicator query operation triggered for the store identifier, the indicator value of the store operation indicator corresponding to the store identifier is obtained, including:

[0191] In response to the indicator query operation triggered for the store identifier, an indicator query request is generated, wherein the indicator query request carries the store identifier;

[0192] Send the indicator query request to the server;

[0193] Receiving at least two values ​​of reach, customer attraction, customer retention, or sales efficiency per square meter returned by the server in response to the indicator query request;

[0194] Among them, the value of reach is determined based on the number of customers passing through the store, the store area and the average customer spending corresponding to the store logo; or, the value of customer attraction is determined based on the number of customers entering the store, the store area and the average customer spending corresponding to the store logo; or, the value of customer retention is determined based on the number of customers who visit the store in depth, the store area and the average customer spending corresponding to the store logo; or, the value of sales efficiency per square meter is determined based on the number of orders, the store area and the average customer spending.

[0195] In this embodiment, by constructing a store operation funnel chart and adopting normalized indicators: reach, customer attraction, customer retention and sales efficiency per square meter, the "incomparability" problem affected by time trends, area factors and brand level factors can be reduced, and the real "operation benchmarking" of offline shopping centers can be achieved, which becomes a powerful tool for operating effect evaluation and business review.

[0196] In one embodiment, reach = (number of customers passing through the store / store area) * average customer spending. Meaning: If all customers passing through the store are converted, how much sales efficiency per square meter should the store achieve.

[0197] Customer attraction = (number of customers entering the store / store area) * average customer spending. Meaning: If all customers entering the store are converted, how much sales per square meter should the store achieve.

[0198] Customer retention power = (number of in-depth shoppers / store area) * store average customer spending. Meaning: If all in-depth shoppers are converted, how much sales per square meter should the store achieve.

[0199] Sales efficiency per square meter = (number of customers who place orders / store area) * average order value per customer. Meaning: actual sales efficiency per square meter of the store.

[0200] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0201] Based on the same inventive concept, the embodiment of the present application also provides a store data processing device for implementing the store data processing method involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in the one or more store data processing device embodiments provided below can refer to the limitations of the store data processing method above, and will not be repeated here.

[0202] In one embodiment, Figure 7 As shown, a store data processing device is provided, comprising:

[0203] The store identification screening module 302 is used to screen the initial store identification set to obtain an abnormal store identification set according to the operation data corresponding to each initial store identification in the initial store identification set and the pre-constructed operation evaluation conditions; wherein the abnormal store identification is the store identification corresponding to the store with abnormal operation;

[0204] The store identification selection module 304 is used to select a target store identification associated with the specified source store identification from at least one candidate store identification according to the indicator value of the first store indicator corresponding to the specified source store identification and the indicator value of the second store indicator corresponding to each candidate store identification in at least one candidate store identification; wherein the specified source store identification and the candidate store identification are store identifications in the abnormal store identification set;

[0205] The virtual resource push module 306 is used to push the virtual resource corresponding to the target store ID to the customer terminal in response to a trigger operation on the virtual resource by a customer account, wherein the customer account is a customer account associated with the specified source store ID.

[0206] Each module in the above-mentioned store data processing device can be implemented in whole or in part by software, hardware or a combination thereof. Each of the above-mentioned modules can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute operations corresponding to each of the above modules.

[0207] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Figure 8 As shown. The computer device includes a processor, a memory and a network interface connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a method for processing store data is implemented.

[0208] Those skilled in the art will understand that Figure 8 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0209] In one embodiment, a computer device is further provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above-mentioned method embodiments when executing the computer program.

[0210] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0211] In one embodiment, a computer program product is provided, including a computer program, which implements the steps in the above method embodiments when executed by a processor.

[0212] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties.

[0213] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but are not limited to this.

[0214] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0215] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A method for processing store data, characterized in that: The method comprises: According to the operation data corresponding to each initial store identification in the initial store identification set and the preset indicator calculation strategy, the operation data table item corresponding to each initial store identification is determined; wherein the operation data table item contains the indicator value of the evaluation indicator used to reflect the store operation status; according to the store discrimination condition corresponding to each store problem, the indicator value of each evaluation indicator contained in the operation data table item corresponding to each initial store identification is compared and discriminated; in the initial store identification set, the set consisting of the initial store identifications whose operation data table items meet the store discrimination condition is determined as the abnormal store identification set; wherein the abnormal store identification is the store identification corresponding to the store with abnormal operation; In response to a first indicator query operation triggered by the management account for the source store identification set, the indicator value of the first store indicator corresponding to each source store identification in the source store identification set is displayed through a first indicator display page of the management terminal; In response to the management account triggering a second indicator query operation for a specified source store identifier in the source store identifier set on the first indicator display page, displaying the indicator value of the second store indicator corresponding to the candidate store identifier on the second indicator display page of the management terminal; In response to a store identification association operation triggered by a management account, determining a target store identification associated with a specified source store identification, wherein the target store identification is selected from candidate store identifications; the specified source store identification and the candidate store identification are store identifications in the abnormal store identification set; In response to a trigger operation on a virtual resource by a customer account, the virtual resource corresponding to the target store identifier is pushed to a customer terminal, wherein the customer account is a customer account associated with the designated source store identifier.

2. The method according to claim 1, characterized in that The source store identification set is a subset of the abnormal store identification set, the source store identification is a store identification corresponding to a source store, and the first store indicator is used to characterize the joint shopping ability of the source store; The designated source store identifier is the store identifier corresponding to the designated source store, the candidate store identifier is the store identifier corresponding to the candidate store, and the second store indicator is used to characterize the customer group similarity between the designated source store and the candidate store.

3. The method according to claim 1, characterized in that The step of pushing the virtual resource corresponding to the target store identifier to the customer terminal in response to the triggering operation of the customer account on the virtual resource includes: In response to a trigger operation of a customer account on a virtual resource, determining the virtual resource corresponding to the customer account from a set of virtual resources corresponding to the target store identifier according to consumption level information corresponding to the customer account and consumption preference information corresponding to the customer account; The virtual resource is pushed to a customer terminal corresponding to the customer account.

4. The method according to claim 3, characterized in that The method further comprises: Obtaining a write-off rate of the virtual resource; According to the write-off rate and the target return on investment, the virtual resource set corresponding to the target store identifier is adjusted to obtain an adjusted virtual resource set; The virtual resource set is replaced by the adjusted virtual resource set.

5. The method according to claim 3 or 4, characterized in that: The method further comprises: Acquire a consumption level information set corresponding to a customer identification set and a consumption preference information set corresponding to the customer identification set, wherein the customer identification set is a set of customer identifications associated with the designated source store; According to the consumption level information set and the consumption preference information set, and based on a target return on investment, a virtual resource set corresponding to the target store identifier is determined.

6. The method according to claim 1, characterized in that The method further comprises: Acquire a customer trajectory information set corresponding to a customer identification set, wherein the customer identification set is a set of customer identifications associated with the specified source store; Determine the passenger flow volume, passenger flow direction and passenger flow speed according to the customer trajectory information set; Constructing a passenger flow route according to the passenger flow volume, passenger flow direction and passenger flow speed; According to the customer flow line, a triggering location of the virtual resource is determined, wherein the triggering location is used to instruct the customer account to perform a triggering operation on the virtual resource.

7. The method according to claim 6, characterized in that The method further comprises: Obtaining the delivery amount and redemption rate of the virtual resource corresponding to the triggering location; The delivery amount of the virtual resource at the triggering location is adjusted according to the delivery amount and the write-off rate.

8. A store data processing device, characterized in that: The device comprises: A store identification screening module is used to determine the operation data table items corresponding to each initial store identification in the initial store identification set according to the operation data corresponding to each initial store identification and the preset indicator calculation strategy; wherein the operation data table items contain the indicator values ​​of the evaluation indicators used to reflect the store operation status; according to the store discrimination conditions corresponding to each store problem, the indicator values ​​of the evaluation indicators contained in the operation data table items corresponding to the initial store identifications are compared and discriminated; in the initial store identification set, the set consisting of the initial store identifications whose operation data table items meet the store discrimination conditions is determined as the abnormal store identification set; wherein the abnormal store identification is the store identification corresponding to the store with abnormal operation; A store identification selection module, for displaying the indicator value of the first store indicator corresponding to each source store identification in the source store identification set through the first indicator display page of the management terminal in response to a first indicator query operation triggered by the management account for a source store identification set; for displaying the indicator value of the second store indicator corresponding to the candidate store identification through the second indicator display page of the management terminal in response to a second indicator query operation triggered by the management account for a specified source store identification in the source store identification set in the first indicator display page; for determining the target store identification associated with the specified source store identification in response to the store identification association operation triggered by the management account, wherein the target store identification is selected from the candidate store identifications; the specified source store identification and the candidate store identification are store identifications in the abnormal store identification set; The virtual resource push module is used to push the virtual resource corresponding to the target store identifier to the customer terminal in response to a trigger operation of a customer account on the virtual resource, wherein the customer account is a customer account associated with the designated source store identifier.

9. The device according to claim 8, characterized in that The source store identification set is a subset of the abnormal store identification set, the source store identification is a store identification corresponding to a source store, and the first store indicator is used to characterize the joint shopping ability of the source store; The designated source store identifier is the store identifier corresponding to the designated source store, the candidate store identifier is the store identifier corresponding to the candidate store, and the second store indicator is used to characterize the customer group similarity between the designated source store and the candidate store.

10. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

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