Method, device and electronic equipment for generating store data

Abstract

Embodiments of the present application provide a kind of store data generation method, device and electronic equipment.The generation method can include: obtaining the first store data corresponding to store, and the first store data includes identity mark;Determine the historical data corresponding to identity mark;At least one reference store data corresponding to store is determined based on historical data and the first store data;According to at least one reference store data and the first store data, target store data corresponding to store is generated.The technical scheme provided in this embodiment, when publishing new store, can claim a information complete and accurate reference store data, and the automatic filling of data is carried out based on the claimed reference store data, and the store data after automatic filling operation can reduce the problem of artificial cost and opening length caused by audit, simultaneously make the store data generated quality is higher.

Description

Technical Field

[0001] The present invention relates to the field of computer technology, and in particular to a method, device and electronic equipment for generating store data. Background Art

[0002] For object publishing platforms with multiple parties involved, when an object supplier publishes a new store, it is necessary to fill in a lot of relevant information, such as complex information such as name, address, category, brand, etc. This not only gives the object supplier a poor experience, but also makes it easy for key information to be filled in incorrectly when the object supplier fills in the store information. For example, the store address information is too long, the uploaded pictures do not meet the requirements, etc. This not only reduces the user experience, but also reduces the efficiency of store publishing. Summary of the Invention

[0003] In view of this, an embodiment of the present invention provides a method, device and electronic device for generating store data to solve the problem that when users fill in store information, the user experience is likely to be poor or the store review may fail, thereby reducing the store's publishing efficiency.

[0004] In a first aspect, an embodiment of the present invention provides a method for generating store data, comprising:

[0005] Acquire first store data corresponding to the store, where the first store data includes an identity identifier;

[0006] Determining historical data corresponding to the identity identifier;

[0007] Determining at least one reference store data corresponding to the store based on the historical data and the first store data;

[0008] Target store data corresponding to the store is generated based on the at least one reference store data and the first store data.

[0009] In a second aspect, an embodiment of the present invention provides a device for generating store data, including:

[0010] A first acquisition module is used to acquire first store data corresponding to a store, wherein the first store data includes an identity identifier;

[0011] A first determining module, configured to determine historical data corresponding to the identity identifier;

[0012] The first determining module is configured to determine at least one reference store data corresponding to the store based on the historical data and the first store data;

[0013] The first processing module is configured to generate target store data corresponding to the store according to the at least one reference store data and the first store data.

[0014] In a third aspect, an embodiment of the present application provides an electronic device, including a memory and a processor; the memory is configured to store one or more computer instructions, wherein the one or more computer instructions are executed by the processor to implement the method for generating store data in the first aspect.

[0015] In a fourth aspect, an embodiment of the present application provides a computer storage medium for storing a computer program, wherein the computer program is executed by a computer to implement the method for generating store data in the first aspect.

[0016] In a fifth aspect, an embodiment of the present application provides a method for auditing store data, including:

[0017] obtaining store data corresponding to a store to be audited;

[0018] extracting set feature information included in the store data;

[0019] identifying a data source of the set feature information;

[0020] determining an auditing manner corresponding to the store data based on the data source;

[0021] performing data auditing operation on the store data by using the auditing manner, and obtaining an auditing result corresponding to the store to be audited.

[0022] In a sixth aspect, an embodiment of the present application provides an auditing device for store data, including:

[0023] a second obtaining module configured to obtain store data corresponding to a store to be audited;

[0024] a second extracting module configured to extract set feature information included in the store data;

[0025] a second identifying module configured to identify a data source of the set feature information;

[0026] a second determining module configured to determine an auditing manner corresponding to the store data based on the data source;

[0027] a second processing module configured to perform data auditing operation on the store data by using the auditing manner, and obtain an auditing result corresponding to the store to be audited.

[0028] In a seventh aspect, an embodiment of the present application provides an electronic device, comprising: a memory and a processor; the memory is configured to store one or more computer instructions, wherein the one or more computer instructions are executed by the processor to implement the method for auditing store data in the fifth aspect.

[0029] In an eighth aspect, an embodiment of the present application provides a computer storage medium configured to store a computer program, and the computer program causes a computer to implement the method for auditing store data in the fifth aspect when the computer executes the computer program.

[0030] In a ninth aspect, an embodiment of the present application provides a method for recommending store data, comprising:

[0031] obtaining first store data corresponding to a store, wherein the first store data comprises an identity;

[0032] determining historical data corresponding to the identity;

[0033] determining a recommended store list corresponding to the store based on the historical data and the first store data, wherein the recommended store list comprises at least one reference store data.

[0034] In a tenth aspect, an embodiment of the present application provides a device for recommending store data, comprising:

[0035] a third obtaining module configured to obtain first store data corresponding to a store, wherein the first store data comprises an identity;

[0036] a third determining module configured to determine historical data corresponding to the identity;

[0037] a third processing module configured to determine a recommended store list corresponding to the store based on the historical data and the first store data, wherein the recommended store list comprises at least one reference store data.

[0038] In an eleventh aspect, an embodiment of the present application provides an electronic device, comprising: a memory and a processor; the memory is configured to store one or more computer instructions, wherein the one or more computer instructions are executed by the processor to implement the method for recommending store data in the ninth aspect.

[0039] In a twelfth aspect, an embodiment of the present application provides a computer storage medium configured to store a computer program, and the computer program causes a computer to implement the method for recommending store data in the ninth aspect when the computer executes the computer program.

[0040] The technical scheme provided by the embodiment of the application comprises the following steps: obtaining first store data corresponding to a store, determining historical data corresponding to an identity, determining at least one reference store data corresponding to the store based on the historical data and the first store data, and generating target store data corresponding to the store according to the at least one reference store data and the first store data. Therefore, when a new store is published, a reference store data with complete and accurate information that matches the to-be-published store can be claimed. Since the reference store data is a store that has been audited, the data can be automatically filled based on the claimed reference store data. The store data after the automatic filling operation can reduce the labor cost and the store opening time caused by the audit. In addition, since the claimed store information has high quality, the quality and efficiency of the target store data can be effectively guaranteed after the target store data of the store is published. Therefore, the user experience is improved, and the practicability of the method is improved. BRIEF DESCRIPTION OF DRAWINGS

[0041] In order to more clearly illustrate the technical solutions in the embodiments of the application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or the prior art description. Obviously, the drawings in the following description are some embodiments of the application, and other drawings can be obtained by those skilled in the art without creative labor.

[0042] Figure 1 A scene schematic diagram of a store data generation method provided by the embodiment of the application is provided.

[0043] Figure 2 A flowchart of a store data generation method provided by the embodiment of the application is provided.

[0044] Figure 2a A schematic diagram of first store data provided by the embodiment of the application is provided.

[0045] Figure 2b A schematic diagram of historical data provided by the embodiment of the application is provided.

[0046] Figure 2c A schematic diagram of a data filling operation provided by the embodiment of the application is provided.

[0047] Figure 3 A flowchart of determining at least one reference store data corresponding to the store according to the store features provided by the embodiment of the application is provided.

[0048] Figure 4 A flowchart of another store data generation method provided by the embodiment of the application is provided.

[0049] Figure 5 A flowchart of a process of determining at least one reference store data corresponding to the store based on the historical data and the first store data is provided for the embodiment of the present application;

[0050] Figure 6 A flowchart of a process of performing data auditing operation on the target store data is provided for the embodiment of the present application;

[0051] Figure 7 A flowchart of a process of a store data auditing method is provided for the embodiment of the present application;

[0052] Figure 8 A flowchart of a process of a store data recommendation method is provided for the embodiment of the present application;

[0053] Figure 9 A schematic diagram of the principle of a store data generation method is provided for the application embodiment of the present application;

[0054] Figure 10 A structural diagram of a store data generation device is provided for the embodiment of the present application;

[0055] Figure 11 A structural diagram of an electronic device corresponding to the store data generation device provided in the embodiment of the present application is provided; Figure 10

[0056] A structural diagram of a store data auditing device is provided for the embodiment of the present application; Figure 12

[0057] A structural diagram of an electronic device corresponding to the store data auditing device provided in the embodiment of the present application is provided; Figure 13 Figure 12 A structural diagram of a store data recommendation device is provided for the embodiment of the present application;

[0058] Figure 14 A structural diagram of an electronic device corresponding to the store data recommendation device provided in the embodiment of the present application is provided.

[0059] Figure 15 Figure 14 DETAILED DESCRIPTION

[0060] ​​​In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the following will clearly and completely describe the technical solutions in the embodiments of the present application with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some but not all of the embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts should fall into the scope of the present application.

[0061] The terms used in the embodiments of the present application are only for the purpose of describing particular embodiments and are not intended to limit the present application. The singular forms "a", "said" and "the" used in the embodiments of the present application and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise. "Plural" generally includes at least two but does not exclude the case of including at least one.

[0062] It should be understood that the term "and / or" used herein is only to describe the association relationship of the associated objects, which means that there can be three relationships, for example, A and / or B, which means that there are three cases of A alone, A and B together, and B alone. In addition, the character " / " in this paper generally represents that the front and rear associated objects are a "or" relationship.

[0063] Depending on the context, the word "if" as used herein can be interpreted as "when" or "upon" or "in response to determining" or "in response to detecting". Similarly, depending on the context, the phrase "if determined" or "if detecting (a stated condition or event)" can be interpreted as "when determined" or "in response to determining" or "when detecting (a stated condition or event)" or "in response to detecting (a stated condition or event)".

[0064] It should also be noted that the terms "comprising", "including", or any other variant thereof are intended to cover non-exclusive inclusion, so that a product or system including a series of elements not only includes those elements, but also includes other elements not explicitly listed or inherent to such product or system. Without more limitations, the element defined by the sentence "including a" does not exclude the presence of other identical elements in the product or system including the element.

[0065] Term explanation:

[0066] Entity: instantiation of an abstract concept, for example: "Xiaoming" is a "person" entity, "the Forbidden City" is a "Point of Interest (POI)" entity.

[0067] Entity alignment: judging whether two entities are the same entity.

[0068] Store matching: determining whether two stores are the same store.

[0069] Store review: determining whether the information of a store is reasonable and legal. Factors such as excessively long name and inappropriate pictures will lead to the store failing to pass the review.

[0070] Store claiming: based on the information input by the object provider (e.g., a merchant), recommending to the object provider an information-complete and accurate store that has passed the review and that is likely to match the store to be published. After the object provider selects a store to claim, the information of the claimed store will be automatically filled in the information of the store to be published.

[0071] Multi-source fusion: combining data from different sources, for example, fusing the POI databases of the Feizhu platform and the POI databases of the Gaode platform into a standard POI database.

[0072] In order to facilitate the understanding of the technical solutions of the present application, the prior art is briefly described as follows:

[0073] In the prior art, for a multi-party parameter object publishing platform, such as a real commodity publishing platform, a service commodity publishing platform, a second-hand commodity publishing platform, a wholesale commodity publishing platform, a retail commodity publishing platform, a group purchase commodity publishing platform, a cross-border commodity publishing platform, etc., when the object provider needs to publish a new store, complex information including name, address, category, brand, etc. needs to be filled in, which results in poor experience of the object provider and reduces the publishing efficiency due to the filling errors of key information (e.g., excessively long address, inappropriate pictures, etc.).

[0074] Specifically, when filling in the data of the store to be published, on the platform side, the information filled in by the object provider when publishing a new store is uncontrollable, so the store to be published needs to pass the review before it can be published, which requires a long time (possibly more than one day) and high labor cost. On the store publishing side, the object provider needs a long time to open a store, and one of the important reasons is that the newly published store of the object provider needs to pass the review, and if the review fails, the store information to be published needs to be modified. Therefore, the object provider may need to fill in the information and pass the review multiple times from the beginning of filling in the store information to the formal publication of the store, which results in poor user experience of opening a store and affects the publishing efficiency of the object provider. On the client side, the poor quality of the information filled in by the object provider easily leads to the problems of incorrect category placement (e.g., a milk tea store being classified as a local specialty snack) and non-standard name (e.g., randomly stacking brand words such as Benz, BMW, and Audi as the store name), which affects the quality of store guide.

[0075] In order to improve the publishing efficiency of the store, the prior art provides a store claiming method. The store claiming method is often used to input information of the object supplier to recommend the store. Thus, a complete and accurate store library is needed. Then, the information (such as name, address, etc.) input by the object supplier is matched with the store in the store library. The matching result is sorted according to the matching degree and recommended to the object supplier for claiming. However, the store claiming method has the following defects:

[0076] 1) The personalized recommendation of the store data cannot be realized. The store is matched only according to the information input by the object supplier. The information is not complete enough, and the accuracy of the store matching is reduced. Thus, some stores that can be claimed are not claimed. Specifically, if different object suppliers input the same information, the recommended store list returned is also the same.

[0077] 2) The store matching operation is performed only according to the field attribute of the store. The attribute and relationship information (such as the superior-inferior relationship between entities) of the store is not fully utilized. Due to insufficient information utilization, the accuracy of the store matching is reduced, and the proportion of the claimed store is affected.

[0078] In order to solve the above technical problems, the embodiment provides a store data generation method. The execution subject of the store data generation method is a store data generation device. The generation device is communicatively connected with a client.

[0079] The client can be any computing device with certain computing capability. The basic structure of the client can include at least one processor. The number of processors depends on the configuration and type of the client. The client can also include a memory, which can be volatile, such as RAM, or non-volatile, such as read-only memory (ROM), flash memory, or both. The memory usually stores an operating system (OS), one or more application programs, and program data, etc. In addition to the processing unit and the memory, the client also includes some basic configurations, such as a network card chip, an IO bus, a display component, and some peripheral devices, etc. Optionally, some peripheral devices can include, for example, a keyboard, a mouse, a stylus, a printer, etc. Other peripheral devices are well known in the art and are not described here. Optionally, the client can be a PC (personal computer) terminal, a handheld terminal (such as a smart phone, a tablet computer), etc.

[0080] The store data generation apparatus refers to a device that can provide computing processing services in a network virtual environment, and generally refers to an apparatus for information planning and data processing using a network. In physical implementation, the store data generation apparatus can be any device that can provide computing services, respond to service requests, and perform processing, such as a cluster server, a general server, a cloud server, a cloud host, a virtual center, etc. The composition of the store data generation apparatus mainly includes a processor, a hard disk, a memory, a system bus, etc., which is similar to the general computer architecture.

[0081] In the above embodiment, the client can be connected to the store data generation apparatus in a network, which can be a wireless or wired network connection. If the client is connected to the store data generation apparatus in a communication connection, the network standard of the mobile network can be any one of 2G (GSM), 2.5G (GPRS), 3G (WCDMA, TD-SCDMA, CDMA2000, UTMS), 4G (LTE), 4G+ (LTE+), WiMax, etc.

[0082] In the embodiment of the present application, the client is configured to obtain the first store data uploaded by the object supplier, and the first store data includes an identity. Of course, in some application scenarios, the first store data can also include at least one of the following: address area information, name information, and the first store data can generally include name information and identity. After obtaining the first store data uploaded by the user, the first store data can be sent to the store data generation apparatus, so that the store data generation apparatus can analyze and process the first store data.

[0083] The store data generation apparatus is configured to receive the first store data uploaded by the client, and then determine the historical data corresponding to the identity. Based on the historical data and the first store data, at least one reference store data corresponding to the store is determined. And according to the at least one reference store data and the first store data, the target store data corresponding to the store is generated. At this time, the obtained target store data is the relatively complete store data corresponding to the store.

[0084] The technical scheme provided by the embodiment provides a store data generation method based on knowledge graph entity alignment technology, which realizes that when an object supplier publishes a new store, a store with complete and accurate information that is relatively matched with the to-be-published store can be claimed. Since the claimed store is a store that has passed the audit, the information of the to-be-published store can be automatically filled based on the claimed store. The store data after the automatic filling operation can effectively reduce the labor cost and store opening time caused by the audit, and the claimed store information has high quality (accurate category, standardized name, etc.). After the data of the store is published, the user experience of the client can be improved.

[0085] The implementation process of the store data generation method, the device, and the electronic equipment provided by the embodiment of the present application will be introduced below in combination with the following method embodiments and the drawings. In addition, the step sequence in each of the following method embodiments is only an example, not a strict limitation.

[0086] Figure 2 A flowchart of a store data generation method provided by the embodiment of the present application is shown in FIG. 1. Figure 2 The embodiment provides a store data generation method, and specifically, the execution subject of the generation method can be a store data generation device. The store data generation device can be implemented as software or a combination of software and hardware. Optionally, the store data generation device can be a platform end. Specifically, the method can include the following steps.

[0087] Step S201: obtaining first store data corresponding to a store, the first store data including an identity.

[0088] Step S202: determining historical data corresponding to the identity.

[0089] Step S203: determining at least one reference store data corresponding to the store based on the historical data and the first store data.

[0090] Step S204: generating target store data corresponding to the store according to the at least one reference store data and the first store data.

[0091] The specific implementation mode and implementation principle of each of the above steps are described in detail as follows.

[0092] Step S201: obtaining first store data corresponding to a store, the first store data including an identity.

[0093] In the case that a user exists a new store publishing demand, the first store data can be uploaded through the store data generation apparatus, and the first store data can include an identity identifier. It can be understood that the first store data can include not only the identity identifier, but also other types of data, for example, the first store data can further include at least one of the following: address area information, name information. In some examples, the store can refer to a take-out store capable of providing take-out services. In addition, the embodiment does not limit the specific acquisition manner of the first store data corresponding to the store, and a person skilled in the art can acquire the first store data in different implementation manners according to different first store data. For example, in the case that the first store data includes address area information, the address area information can be acquired through a global positioning system (GPS). In the case that the first store data includes name information, the name information can be acquired by detecting an execution operation input by a user to the generation apparatus. Specifically, the name information can be store name information corresponding to the store. The identity identifier information can be acquired by detecting an execution operation input by a user to the generation apparatus. Specifically, the identity identifier information can be user identity identifier information used by the user when performing a new store publishing operation through the store data generation apparatus.

[0094] Of course, a person skilled in the art can also acquire the first store data corresponding to the store in other manners, as long as the accuracy and reliability of acquiring the first store data can be ensured, which will not be described herein.

[0095] Step S202: determining historical data corresponding to the identity identifier.

[0096] After acquiring the identity identifier included in the first store data, the historical data corresponding to the identity identifier can be determined based on the identity identifier. A plurality of standard historical data corresponding to standard identity identifiers is pre-configured. After acquiring the identity identifier, a target standard identity identifier corresponding to the identity identifier is determined, and the standard historical data corresponding to the target standard identity identifier is determined as the historical data corresponding to the identity identifier. Specifically, the historical data can include at least one of the following: historical store data corresponding to the identity identifier, historical behavior data corresponding to the identity identifier. In some examples, the historical behavior data includes at least one of the following: place data corresponding to the identity identifier within a preset time period, and area range data corresponding to a set behavior occurring within a preset time period.

[0097] It should be noted that when there is no corresponding historical store data for some identity, the historical data does not include the historical store data corresponding to the identity, and at this time, the historical data only includes the historical behavior data corresponding to the identity. When some identity corresponds to historical store data, the historical data can include the historical store data corresponding to the identity and the historical behavior data corresponding to the identity.

[0098] Step S203: determining at least one reference store data corresponding to the store based on the historical data and the first store data.

[0099] After obtaining the historical data and the first store data, the historical data and the first store data can be analyzed and processed to determine at least one reference store data corresponding to the store. In some examples, determining at least one reference store data corresponding to the store based on the historical data and the first store data can include: extracting store features corresponding to the store based on the historical data and the first store data; determining at least one reference store data corresponding to the store according to the store features.

[0100] Specifically, after obtaining the historical data and the first store data, a feature extraction operation can be performed on the historical data and the first store data based on a feature extraction algorithm, so that store features corresponding to the store can be obtained. In some examples, the store features can include at least one of the following: store attribute type features, store relationship type features. The above-mentioned store attribute type features can include at least one of the following: name features, address features, brand features, store license features, relative features between names, relative features between addresses, relative features between brands, relative features between store license features; The store relationship type features can include at least one of the following: name address vector, interest point and interest point between the upper and lower relationship, address and address between the upper and lower relationship, address and interest point between the upper and lower relationship, and the direction relationship corresponding to the interest point.

[0101] After obtaining the store features, a store matching operation can be performed based on the store features, so that at least one reference store data corresponding to the store can be determined. It can be understood that the reference store data refers to the store data that matches the store and has passed the audit operation.

[0102] Step S204: generating target store data corresponding to the store according to at least one reference store data and the first store data.

[0103] After obtaining the at least one reference store data and the first store data, the at least one reference store data and the first store data can be analyzed and processed to generate target store data corresponding to the store. In some examples, according to the at least one reference store data and the first store data, the target store data corresponding to the store is generated, including: obtaining a first execution operation input by the user for any reference store data; performing a data filling operation on the first store data based on the reference store data corresponding to the first execution operation, to generate the target store data corresponding to the store.

[0104] For example, in the at least one reference store data includes store a data, store b data, store c data and store d data, after obtaining the above reference store data, the user can input a first execution operation for any one of the above reference store data. The first execution operation can be a data selection operation corresponding to the reference store data, which can be a click operation, a double-click operation, etc. After obtaining the first execution operation input by the user for any reference store data, the reference store data corresponding to the first execution operation can be determined as selected store data. When the selected store data is store c data, the first store data can be filled with data based on the selected store c data, that is, some attribute information included in the store c data is directly filled into the corresponding attribute information position of the store, so as to generate the target store data corresponding to the store, which is the complete store data corresponding to the store. After obtaining the target store data, the store can be audited based on the target store data.

[0105] In other examples, according to the at least one reference store data and the first store data, the target store data corresponding to the store can also be generated, including: when no first execution operation input by the user for any reference store data is detected within a preset time period, the at least one reference store data is ignored; obtaining a second execution operation input by the user for the store; and generating the target store data corresponding to the store based on the second execution operation and the first store data.

[0106] After obtaining at least one reference store data and first store data, if the user confirms that there is a reference store data in at least one reference store data that matches the store, the user can perform a selection operation on the above-mentioned reference store data; when the user confirms that all reference store data in at least one reference store do not match the store, the selection operation may not be performed on any reference store data. When the first execution operation entered by the user for any reference store is not detected within a preset time period (for example, a set 15s, 30s or 45s, 60s, etc.), at least one reference store data may be ignored. At this time, the user can directly perform a manual data filling operation on the store. Specifically, the user can enter a second execution operation (i.e., an operation of manually entering data) for the store. When the second execution operation entered by the user for the store is obtained, the target store data corresponding to the store can be generated based on the second execution operation and the first store data.

[0107] Take the takeaway application scenario as an example, refer to the attached Figure 2a As shown, when the user needs to generate a new store on the food delivery platform, the user can enter the store generation page through the food delivery platform. The user can enter the first store data through the store generation page. The first store data can include name information (for example: Starbucks), address area information (Yuhang District, Hangzhou City, Zhejiang Province) and identity identification (for example: business license information). Specifically, when the identity identification of the store is the business license information, the electronic version of the business license information can be directly uploaded through the preset interface included in the store generation page, thereby completing the identity identification filling operation.

[0108] After obtaining the first store data input by the user, a store search operation can be performed based on the first store data, such as Figure 2b As shown, specifically, a search operation for a reference store can be performed based on the identity identifier included in the first store data, so that reference store data located within a specific area can be found. For example, the reference store data may include: relevant data of Starbucks (Qinchengli Store), the address of which is No. 808, Gudun Road, Hangzhou, etc. It is understandable that the number of reference stores that can be searched can be one or more, and therefore the number of reference store data corresponding to the reference stores is also one or more; when the number of reference stores is multiple, each reference store can correspond to a "select" control, so that the user can independently select a suitable target reference store from the multiple reference stores through the control displayed above.

[0109] It should be noted that the method in this embodiment can achieve that different reference store data can be generated when different users input the same first store data, wherein the different users can be located at the same location or different locations. For example, user A often moves near the West Lake District of Hangzhou City, user B often moves near the Yuhang District of Hangzhou City, and when user A and user B input “Starbucks” at the same time, for user A, reference store data corresponding to user A can be generated, which can include Starbucks (Lingyin Road Store), Starbucks (Lingyin Jibai Store), etc., and for user B, reference store data corresponding to user B can be generated, which can include Starbucks (Haichuanguan Store), Starbucks (Hangzhou Beda Dream Factory Store), etc., thereby effectively achieving that personalized reference store list can be generated for different users, which is conducive to improving the adaptation degree of reference store data.

[0110] After obtaining the reference store data, the store data filling operation can be performed based on the reference store data and the first store data input by the user, thereby generating target store data corresponding to the store. As shown in Figure 2c When the store data filling operation is performed, the main operation includes filling the operating information in the store, wherein the operating information can include operating categories, business hours, contact numbers, overcoming phone numbers, brand IDs, account types, account names, store collection types, electronic voucher information, etc. After generating the target store data, since the target store data can be automatically filled based on the reference store that has been published, the auditing and publishing efficiency of the target store data can be improved. After the target store data is audited, the corresponding store data can be published, so that the user can provide corresponding take-out services through the take-out platform.

[0111] The method for generating store data provided by the embodiment determines at least one reference store data corresponding to the store based on the historical data and the first store data, and generates target store data corresponding to the store according to the at least one reference store data and the first store data, so that when a new store is published, a reference store data with complete and accurate information that matches the to-be-published store can be claimed, and since the reference store data is a store that has been audited, the data can be automatically filled based on the claimed reference store data, the store data after the automatic filling operation can reduce the labor cost and store opening time caused by auditing, in addition, since the claimed store information has high quality, the quality and efficiency of the target store data can be effectively guaranteed after the target store data of the store is published, thereby improving the user experience and improving the practicality of the method.

[0112] Figure 3 The process schematic diagram for determining at least one reference store data corresponding to the store according to the store features is provided for the embodiment of the application, and on the basis of the above-mentioned embodiment, the reference Figure 3 The implementation manner for determining at least one reference store data corresponding to the store is provided by the embodiment, and specifically, the implementation manner for determining at least one reference store data corresponding to the store according to the store features in the embodiment can include:

[0113] Step S301: Obtain a standard store library, and the standard store library includes a plurality of standard stores corresponding to different entities and standard store data corresponding to the standard stores.

[0114] Step S302: Determine at least one standard store matched with the store features in the standard store library by using an entity alignment technology.

[0115] Step S303: Determine the standard store data corresponding to the at least one standard store as at least one reference store data corresponding to the store.

[0116] The standard store library is pre-configured, the standard store library includes a plurality of standard stores corresponding to different entities and standard store data corresponding to the standard stores, and specifically, the standard stores corresponding to different entities refer to stores corresponding to different offline entity stores. After the standard store library is configured, the standard store library can be obtained, specifically, the standard store library can be obtained by accessing a preset area, or the standard store library can be sent to the store data generation device by a third-party device, so that the generation device can directly obtain the standard store library.

[0117] After the standard store library is acquired, at least one standard store matching the store features can be determined in the standard store library by using the entity alignment technology. In some examples, determining at least one standard store matching the store features in the standard store library by using the entity alignment technology can include: determining a plurality of intermediate standard stores corresponding to the store based on the address area information and the name information corresponding to the store; and determining at least one target standard store matching the store features in the plurality of intermediate standard stores by using the entity alignment technology.

[0118] Specifically, after the first store data is acquired, the address area information and the name information corresponding to the store can be acquired based on the first store data. After the address area information and the name information are acquired, a first store matching operation can be performed in the standard store library based on the address area information and the name information, so that a plurality of intermediate standard stores corresponding to the store can be obtained. After the plurality of intermediate standard stores are acquired, at least one target standard store matching the store features can be determined in the plurality of intermediate standard stores by using the entity alignment technology. It can be understood that the target standard store is at least part of the intermediate standard stores.

[0119] After the at least one standard store is acquired, the standard store data corresponding to the at least one standard store can be determined as at least one reference store data corresponding to the store, so that the accuracy and reliability of determining the at least one reference store data are effectively ensured.

[0120] In the embodiment, by acquiring the standard store library, the standard store library includes a plurality of standard stores corresponding to different entities and standard store data corresponding to the standard stores. Then, at least one standard store matching the store features is determined in the standard store library by using the entity alignment technology, and the standard store data corresponding to the at least one standard store is determined as at least one reference store data corresponding to the store, so that the accuracy and reliability of determining the reference store data are effectively ensured, and the data filling operation of the first store data based on the reference store data is facilitated, and the target store data corresponding to the store is generated, which is more comprehensive and accurate.

[0121] Figure 4 Another flowchart of a method for generating store data provided by the embodiment of the present application is shown in the figure. Based on the above embodiment, the standard store library is acquired, and the reference store data corresponding to the store is determined by using the entity alignment technology. Figure 4 Before the standard store library is acquired, the embodiment further provides an implementation manner for generating the standard store library. Specifically, the method in the embodiment includes:

[0122] Step S401: acquiring a plurality of store libraries to be integrated.

[0123] Step S402: determine a plurality of store entities and a plurality of store feature data corresponding to a plurality of store libraries.

[0124] Step S403: based on the plurality of store entities, perform a deduplication operation on the plurality of store libraries to obtain an intermediate store library.

[0125] Step S404: adjust all store feature data corresponding to the intermediate store library to a set format to generate a standard store library.

[0126] For different application platforms, different store libraries can correspond to different store libraries. In order to obtain a more comprehensive standard store library, a plurality of store libraries to be integrated can be obtained, and the plurality of store libraries can correspond to different data sources. Specifically, the plurality of store libraries to be integrated can be obtained by accessing data platforms corresponding to different data sources. For example, the plurality of store libraries can include store library a corresponding to a first data platform, store library b corresponding to a second data platform, and store library c corresponding to a third data platform, and the like. Therefore, store library a can be obtained by accessing the first data platform, store library b can be obtained by accessing the second data platform, and store library c can be obtained by accessing the third data platform.

[0127] After obtaining the plurality of store libraries, entity recognition and feature extraction operations can be performed on the plurality of store libraries, and then a plurality of store entities and a plurality of store feature data corresponding to the plurality of store libraries can be determined. Since there can be duplicate data between store libraries corresponding to different data sources, in order to avoid duplicate data in the standard store library and reduce the quality and efficiency of store matching, a deduplication operation can be performed on the plurality of store libraries based on the plurality of store entities, thereby obtaining an intermediate store library. After obtaining the intermediate store library, all store feature data corresponding to the intermediate store library can be adjusted to a set format, thereby generating a standard store library with a set format of store data for statistics.

[0128] For example, the plurality of store libraries can include store library a corresponding to a first data source, store library b corresponding to a second data source, and store library c corresponding to a third data source. Store library a can include store a1, store a2, store a3, and store a4. Store library b can include store b1, store b2, store b3, store b4, and store b5. Store library c can include store c1, store c2, and store c3.

[0129] After the above store library is acquired, entity recognition and feature extraction operations can be performed on the store library, so that a plurality of store entities and a plurality of store feature data corresponding to the plurality of store libraries can be acquired, for example: store a1 corresponds to store entity A, store a2 corresponds to store entity B, store a3 corresponds to store entity C, store a4 corresponds to store entity D, store b1 corresponds to store entity E, store b2 corresponds to store entity B, store b3 corresponds to store entity F, store b4 corresponds to store entity D, store b5 corresponds to store entity G, store c1 corresponds to store entity F, store c2 corresponds to store entity H, and store c3 corresponds to store entity B.

[0130] After the plurality of store entities corresponding to the plurality of store libraries are acquired, the plurality of store libraries can be de-duplicated based on the plurality of store entities to obtain an intermediate store library. Specifically, the intermediate store library can include: store a1, store a2, store a3, store a4, store b1, store b3, store b5, and store c2. Each store in the above intermediate store library corresponds to a different store entity. Store a1 corresponds to store entity A, store a2 corresponds to store entity B, store a3 corresponds to store entity C, store a4 corresponds to store entity D, store b1 corresponds to store entity E, store b3 corresponds to store entity F, store b5 corresponds to store entity G, and store c2 corresponds to store entity H.

[0131] After the intermediate store library is acquired, all store feature data corresponding to the intermediate store library can be adjusted to a set format. It should be noted that the set format can include a data format of up to an attribute feature. When a certain attribute feature is not included in the store feature data corresponding to the intermediate store library, the attribute feature can be filled with set data, for example, data 0 can be filled in the position of the attribute feature.

[0132] In this embodiment, by acquiring a plurality of store libraries to be integrated, a plurality of store entities and a plurality of store feature data corresponding to the plurality of store libraries are determined, and then the plurality of store libraries are de-duplicated based on the plurality of store entities to obtain an intermediate store library, and all store feature data corresponding to the intermediate store library are adjusted to a set format, thereby effectively generating a standard store library that is uniformly in a set format and does not have duplicate data. In this way, when store matching operations are performed based on the standard store library, the quality and efficiency of the store matching operations are effectively guaranteed.

[0133] Figure 5 The flowchart for determining at least one reference store data corresponding to a store based on historical data and first store data is provided in the embodiments of the present application. On the basis of the above embodiments, the reference is made to the accompanying drawings Figure 5As shown, when the historical data includes historical store data corresponding to the identity identifier; the embodiment provides an implementation manner of determining at least one reference store data corresponding to the store, specifically, the determination of at least one reference store data corresponding to the store based on the historical data and the first store data in the embodiment can include:

[0134] Step S501: Obtain the historical store name corresponding to the historical store data.

[0135] Step S502: When the name information matches the historical store name, perform data filling operation on the first store data based on the historical store data to generate second store data.

[0136] Step S503: Determine at least one reference store data corresponding to the store based on the historical data and the second store data.

[0137] Wherein, when the historical data includes historical store data corresponding to the identity identifier, that is, when the user corresponding to the identity identifier has historical store data, the store claiming operation can be performed in combination with the historical store data. Specifically, the historical store name corresponding to the historical store data can be obtained, and then the name information included in the first store data obtained is compared with the historical store name. When the name information matches the historical store name, the first store data can be filled with data based on the historical store data, so as to generate second store data. It can be understood that the second store data can include at least part of the data in the first store data and the historical store data, that is, before the store matching operation, the technical solution in the embodiment can perform the first data filling operation on the first store data based on the historical store data, so as to generate the second store data after the data filling operation.

[0138] After obtaining the historical data and the second store data, the historical data and the second store data can be analyzed and processed to determine at least one reference store data corresponding to the store. Specifically, the implementation process and principle of determining at least one reference store data corresponding to the store in the embodiment are similar to those of determining at least one reference store data corresponding to the store based on the historical data and the first store data in the above embodiment. For details, please refer to the above statements, which will not be repeated here.

[0139] In this embodiment, by acquiring the historical store name corresponding to the historical store data, when the name information matches the historical store name, the first store data is filled with data based on the historical store data to generate the second store data, and then at least one reference store data corresponding to the store is determined based on the historical data and the second store data, thereby effectively ensuring the accuracy and reliability of determining at least one reference store data.

[0140] On the basis of any one of the above embodiments, after generating the target store data corresponding to the store, the method in this embodiment can further include: performing a data auditing operation on the target store data to obtain an auditing result corresponding to the store.

[0141] After obtaining the target store data, a data auditing operation can be performed based on the target store data, thereby obtaining an auditing result corresponding to the store. It can be understood that when the target store data meets the set requirements, a result of passing the audit corresponding to the store can be obtained; when the target store data does not meet the set requirements, a result of failing the audit corresponding to the store can be obtained.

[0142] Figure 6 A flowchart of the data auditing operation on the target store data provided by the embodiments of the present application is shown. On the basis of the above embodiments, with reference to the accompanying drawings, Figure 6 The embodiment provides an implementation manner of the data auditing operation on the target store data. Specifically, the data auditing operation on the target store data in this embodiment can include:

[0143] Step S601: identifying a data source corresponding to set data in the target store data.

[0144] Step S602: when the data source is reference store data, performing a data auditing operation on the target store data by using a first auditing manner.

[0145] Step S603: when the data source is non-reference store data, performing a data auditing operation on the target store data by using a second auditing manner, wherein the time length corresponding to the second auditing manner is greater than the time length corresponding to the first auditing manner.

[0146] The target store data can include multiple data. When performing a data auditing operation on the store, set data to be audited can be acquired, that is, a part of attribute data included in the target store data can be audited. In different application scenarios, the set data included in the target store data can refer to different attribute features.

[0147] After obtaining the target store data, the target store data can be analyzed and processed to identify the data source corresponding to the set data in the target store data. Specifically, the data source corresponding to the set data in the target store data can include reference store data and non-reference store data, wherein the non-reference store data can include manually input data and data obtained after editing the reference store data.

[0148] When the data source is reference store data, the target store data can be audited using a first auditing method. When the data source is non-reference store data, the target store data can be audited using a second auditing method, wherein the time length corresponding to the second auditing method is greater than the time length corresponding to the first auditing method. Thus, different auditing methods can be used to audit target store data with different data sources. Specifically, when the data source of the set data in the obtained target store data is from reference store data that has passed the audit, a shorter auditing method can be used to audit the target store data. When the data source of the set data in the obtained target store data is not from reference store data that has passed the audit, a normal auditing method can be used to audit the target store data. This effectively improves the flexibility and reliability of data auditing.

[0149] Based on any one of the above embodiments, after generating the target store data corresponding to the store, the method in this embodiment can further include: obtaining an execution operation input by a user for the target store data; and performing editing operation on the target store data based on the execution operation to generate edited target store data.

[0150] After obtaining the target store data, in order to enable the target store data to meet the individual needs of users, the user can perform editing operation on the target store data. Specifically, after obtaining the execution operation input by the user for the target store data, data editing operation can be performed on the target store data based on the execution operation, such as adding, deleting, modifying, etc. After editing the target store data based on the execution operation, the target store data after editing can be generated, which can meet the individual needs of different users.

[0151] In this embodiment, by obtaining the execution operation input by the user for the target store data, and then performing editing operation on the target store data based on the execution operation to generate edited target store data, the target store data is effectively edited, and target store data meeting different needs can be generated, further improving the flexibility and reliability of the use of target store data.

[0152] Figure 7 A flowchart of a store data auditing method provided by an embodiment of the present application is shown in FIG. 7. As shown in FIG. 7, the embodiment provides a store data auditing method. Specifically, the execution subject of the auditing method can be a store data auditing device. The store data auditing device can be implemented as software or a combination of software and hardware. Optionally, the store data auditing device can be a platform end. Specifically, the method can include the following steps. Figure 7

[0153] Step S701: Obtain store data corresponding to a store to be audited.

[0154] Step S702: Extract set feature information included in the store data.

[0155] Step S703: Identify the data source of the set feature information.

[0156] Step S704: Determine an auditing method corresponding to the store data based on the data source.

[0157] Step S705: Perform a data auditing operation on the store data using the auditing method to obtain an auditing result corresponding to the store to be audited.

[0158] When there is a data auditing requirement for the store to be audited, the store data corresponding to the store to be audited can be obtained. The store data is relatively complete store data corresponding to the store to be audited. Specifically, the embodiment does not limit the manner of obtaining the store data corresponding to the store to be audited. A person skilled in the art can set the manner according to a specific application scenario and application requirement. For example, the store data corresponding to the store to be audited can be stored in a preset area. The store data corresponding to the store to be audited can be obtained by accessing the preset area. Alternatively, the store data corresponding to the store to be audited can be sent to the store data auditing device by a third device, so that the auditing device can directly obtain the store data corresponding to the store to be audited.

[0159] After the store data is obtained, a feature extraction operation can be performed on the store data, so that the set feature information included in the store data can be extracted. The set feature information can be part of the feature information configured in the store data. After the set feature information is obtained, a data source identification operation can be performed on the set feature information, so that the data source of the set feature information can be identified. The data source can include reference store data and non-reference store data.

[0160] ​After the data source of the set feature information is identified, the audit mode corresponding to the store data can be determined based on the data source. Specifically, based on the data source, the audit mode corresponding to the store data can be determined, which can include: when the data source is reference store data, the first audit mode is used to perform data audit operation on the store data, wherein the reference store data corresponds to the store that has passed the audit; when the data source is non-reference store data, the second audit mode is used to perform data audit on the store data, wherein the non-reference store data corresponds to the store that has not passed the audit or the store that has not been audited, and the time length corresponding to the second audit mode is greater than the time length corresponding to the first audit mode.

[0161] After the audit mode corresponding to the store data is determined, the store data can be audited by using the audit mode, so as to obtain the audit result corresponding to the store to be audited. Specifically, the implementation mode of the above step S705 in the embodiment is similar to the implementation mode of the steps S601-S603 in the above embodiment, and specific reference can be made to the above description, which will not be repeated here.

[0162] The store data auditing method provided by the embodiment includes obtaining the store data corresponding to the store to be audited, extracting the set feature information included in the store data, identifying the data source of the set feature information, determining the audit mode corresponding to the store data based on the data source, and then performing data audit operation on the store data by using the audit mode to obtain the audit result corresponding to the store to be audited. The different audit modes can be used for auditing the target store data of different data sources, that is, when the data source of the set data in the obtained target store data is from the reference store data that has passed the audit, the target store data can be audited by using the shorter-time audit mode; when the data source of the set data in the obtained target store data is not from the reference store data that has passed the audit, the target store data can be audited by using the normal audit mode, which effectively improves the flexibility and reliability of the data audit method.

[0163] Figure 8 A flowchart of a store data recommendation method provided by the embodiment of the application is shown in FIG. 8. Figure 8 The embodiment provides a store data recommendation method, and specifically, the execution subject of the recommendation method can be a store data recommendation device. The store data recommendation device can be implemented as software or a combination of software and hardware. Specifically, the method can include the following steps.

[0164] In step S801, first store data corresponding to a store is obtained, and the first store data includes an identity identifier.

[0165] Step S802: determining the historical data corresponding to the identity identifier.

[0166] Step S803: determining the recommended store list corresponding to the store based on the historical data and the first store data, the recommended store list including at least one reference store data.

[0167] Wherein, after obtaining the first store data corresponding to the store, the historical data corresponding to the identity identifier in the first store data can be determined, and the historical data can include at least one of the following: historical store data corresponding to the identity identifier, historical behavior data corresponding to the identity identifier, and the historical behavior data can include at least one of the following: location data corresponding to the identity identifier within a preset time period, and area range data corresponding to the set behavior within the preset time period.

[0168] After obtaining the historical data and the first store data, the historical data and the first store data can be analyzed and processed to determine the recommended store list corresponding to the store, and the recommended store list includes at least one reference store data. It can be understood that the reference store data included in the recommended store list matches the store, and the generated recommended store list is determined in combination with the historical data corresponding to the identity identifier. Therefore, even if the same first store data corresponding to the store is obtained from different users, different recommended store lists can be generated for different users due to different historical data corresponding to the user identifier information. This is beneficial to meet the individual needs of users.

[0169] The store data recommendation method provided by the embodiment can obtain first store data corresponding to the store, the first store data including at least one of the following: address area information, name information and identity identifier, then determine the historical data corresponding to the identity identifier, and determine the recommended store list corresponding to the store based on the historical data and the first store data. Thus, the recommended store list corresponding to the store is effectively generated in combination with the historical data, which can be used to help users perform data filling operations on the store, thereby improving the quality and efficiency of data filling. In addition, the method can generate different recommended store lists for different users, which is beneficial to meet the individual needs of users and further improve the flexibility and reliability of the method.

[0170] In specific applications, referring to the accompanying drawings Figure 9 The application embodiment provides a store data generation method, which can use the historical data of the object provider and the knowledge graph entity alignment technology to perform store claiming operations. Specifically, the method can include the following steps:

[0171] Step 1: obtaining first store data corresponding to the store input by the user.

[0172] The first store data can include store name information input by the user and user identity information, and the address area information included in the first store data can be determined by automatic positioning by a positioning system, for example, the address area information can include province, city and district.

[0173] Step 2: based on the identity, detecting whether there is historical store data corresponding to the identity.

[0174] The historical store data can refer to offline stores corresponding to the same user, and the offline stores can correspond to store brand information, store category information, business license or business permit information.

[0175] Step 3: when there is historical store data corresponding to the identity, the first data filling operation can be performed based on the historical store data; when there is no historical store data corresponding to the identity, the first data filling operation is not required based on the historical store data.

[0176] After obtaining the historical store data, target historical store data corresponding to the store name information can be determined, and data completion operation is performed based on the target historical store data. Specifically, a pre-trained text vectorization model can be used to perform feature extraction operation on the historical store data, so as to obtain historical name keywords and name / address vectors corresponding to the historical store data, and then based on the extracted historical name keywords and name / address vectors, information completion operation is performed, so as to generate first intermediate store data.

[0177] Step 4: determining historical behavior data corresponding to the identity.

[0178] The historical behavior data includes at least one of the following: place data corresponding to the identity within a preset time period, and region range data corresponding to the set behavior within the preset time period, for example: the place where the current user is located, the POI frequently visited offline, etc.

[0179] Step 5: based on the historical behavior data and the first intermediate store data, extracting store features corresponding to the store.

[0180] The store features can include at least one of the following: store attribute features and store relationship features. The store attribute features include at least one of the following: name features, address features, brand features, store license features, relative features between names, relative features between addresses, relative features between brands, and relative features between store license features. The store relationship features include at least one of the following: name-address vectors, hierarchical relationships between interest points and interest points, hierarchical relationships between addresses and addresses, hierarchical relationships between addresses and interest points, and orientation relationships corresponding to interest points.

[0181] It can be understood that the store attribute features and the store relationship features not only include the above-mentioned examples, but also can be configured by a person skilled in the art according to specific application requirements. For example, the store attribute features can include string similarity information of names, maximum common string length between any two names, whether POIs contained in address information are consistent, whether house numbers are the same, whether brands are consistent, whether business license numbers are consistent, and the like. The access relationship corresponding to the interest point included in the store relationship features can be whether located near the interest point, and the like.

[0182] It should be noted that, for a user of the network platform with a shorter application time, the store attribute features and the store relationship features included in the store features are relatively simple, and for a user of the network platform with a longer application time, the store attribute features and the store relationship features included in the store features are relatively rich.

[0183] Step 6: generating a standard store library.

[0184] A store library is constructed by using a multi-source fusion technology. Store data of different sources is processed into store data with the same format and containing key information by ontology alignment and relationship extraction, and then the store data is merged and de-duplicated by using entity alignment technology to generate a standard store library with complete and accurate information.

[0185] Step 7: performing a store matching operation in the standard store library based on the store features to obtain at least one reference store data corresponding to the store.

[0186] After obtaining the store features, the first store recall operation can be performed based on the name features and address area in the store features, so as to obtain the similarity ranking between each reference store and the name vector of the store located in the address area, and obtain a first store set including a first number of reference stores based on the similarity ranking, where the first number can be 20, 30, 40 or 50, etc. After recalling the first set of stores ranked first, reference store attribute features such as keywords and address areas of the reference stores can be extracted. Based on the reference store attribute features and the store attribute features of the stores included in the store features, the similarity between the reference stores and the stores can be calculated in parallel through the entity alignment model. In this way, a second store recall operation can be performed from the first store set to obtain a second store set including a second number of reference stores, where the second number can be 10, 15, 5 or 20, etc. For example, 5 reference stores with the highest similarity can be obtained. Through two store recall operations, the recall rate can be effectively guaranteed while reducing the calculation amount of the entity alignment model. The second store set can then be recommended to the user for store claiming. After the user claims a store, the information of the store will be automatically filled with the store to be released.

[0187] Step 8: Perform data filling operations on the store based on at least one reference store data to obtain target store data corresponding to the store.

[0188] The store data generation method provided by this application embodiment combines the user's background information and user input information in the current ecology, thereby realizing user personalized recommendations, and combines the knowledge graph entity alignment technology to realize store recommendation and claiming operations, thereby ensuring the quality and efficiency of store data generation; specifically, when a user publishes a store, it can combine the user's historical information and user input information in the current ecology to extract richer store features that are more relevant to the current user (including not only entity attribute features, but also entity relationship features), and realize more accurate personalized store recommendation operations based on store features, and then perform data completion operations on the store data based on the recommended reference stores, thereby realizing not only recommending different reference store data to different users; in addition, this embodiment regards store data as multi-attribute entities, and then uses the entity alignment method to complete the similarity calculation between stores, thereby recommending stores with higher matching degrees and easy to be claimed to users, further improving the quality and efficiency of store data generation.

[0189] Figure 10 A schematic diagram of a device for generating store data according to an embodiment of the present invention; Figure 10 As shown, this embodiment provides a device for generating store data, which can execute Figure 2The generation method of the store data is shown. Specifically, the store data generation device can include

[0190] The first acquisition module 11 is configured to acquire first store data corresponding to the store. The first store data can include an identity. It can be understood that the first store data can include at least one of the following: address area information, name information, and identity.

[0191] The first determination module 12 is configured to determine historical data corresponding to the identity.

[0192] The first determination module 12 is configured to determine at least one reference store data corresponding to the store based on the historical data and the first store data.

[0193] The first processing module 13 is configured to generate target store data corresponding to the store according to the at least one reference store data and the first store data.

[0194] In some examples, the historical data includes at least one of the following: historical store data corresponding to the identity, historical behavior data corresponding to the identity.

[0195] In some examples, the historical behavior data includes at least one of the following: location data corresponding to the identity within a preset time period, and area range data corresponding to a set behavior within a preset time period.

[0196] In some examples, when the first determination module 12 determines at least one reference store data corresponding to the store based on the historical data and the first store data, the first determination module 12 is configured to perform the following: based on the historical data and the first store data, extracting store features corresponding to the store; and determining at least one reference store data corresponding to the store according to the store features.

[0197] In some examples, the store features include at least one of the following: store attribute features, and store relationship features.

[0198] In some examples, the store attribute features include at least one of the following: name features, address features, brand features, store permission features, relative features between names, relative features between addresses, relative features between brands, and relative features between store permission features.

[0199] The store relationship features include at least one of the following: name address vectors, hierarchical relationships between interest points and interest points, hierarchical relationships between addresses and addresses, hierarchical relationships between addresses and interest points, and orientation relationships corresponding to interest points.

[0200] In some examples, when the first determining module 12 determines the at least one reference store data corresponding to the store according to the store features, the first determining module 12 is configured to perform the following: obtaining a standard store library, the standard store library including a plurality of standard stores corresponding to different entities and standard store data corresponding to the standard stores; determining at least one standard store matching the store features in the standard store library by using an entity alignment technology; and determining the standard store data corresponding to the at least one standard store as the at least one reference store data corresponding to the store.

[0201] In some examples, when the first determining module 12 determines at least one standard store matching the store features in the standard store library by using the entity alignment technology, the first determining module 12 is configured to perform the following: determining a plurality of intermediate standard stores corresponding to the store in the standard store library based on the address area information and the name information corresponding to the store; and determining at least one target standard store matching the store features in the plurality of intermediate standard stores by using the entity alignment technology.

[0202] In some examples, before obtaining the standard store library, the first obtaining module 11, the first determining module 12 and the first processing module 13 in the embodiment can perform the following steps:

[0203] The first obtaining module 11 is configured to obtain a plurality of store libraries to be integrated.

[0204] The first determining module 12 is configured to determine a plurality of store entities and a plurality of store feature data corresponding to the plurality of store libraries.

[0205] The first processing module 13 is configured to perform a deduplication operation on the plurality of store libraries based on the plurality of store entities to obtain an intermediate store library; and adjust all store feature data corresponding to the intermediate store library to a set format to generate a standard store library.

[0206] In some examples, when the first processing module 13 generates the target store data corresponding to the store according to the at least one reference store data and the first store data, the first processing module 13 is configured to perform the following: obtaining a first execution operation input by a user for any reference store data; and performing a data filling operation on the first store data based on the reference store data corresponding to the first execution operation to generate the target store data corresponding to the store.

[0207] In some examples, when the first processing module 13 generates the target store data corresponding to the store according to the at least one reference store data and the first store data, the first processing module 13 is configured to perform the following: when no first execution operation input by the user for any reference store data is detected within a preset time period, the at least one reference store data is ignored; the second execution operation input by the user for the store is obtained; and the target store data corresponding to the store is generated based on the second execution operation and the first store data.

[0208] In some examples, when the historical data includes historical store data corresponding to the identity, and the first determination module 12 determines the at least one reference store data corresponding to the store based on the historical data and the first store data, the first determination module 12 is configured to perform the following: the historical store name corresponding to the historical store data is obtained; when the name information matches the historical store name, the first store data is subjected to a data filling operation based on the historical store data to generate second store data; and the at least one reference store data corresponding to the store is determined based on the historical data and the second store data.

[0209] In some examples, after the target store data corresponding to the store is generated, the first processing module 13 in the embodiment is configured to perform the following: the target store data is subjected to a data auditing operation to obtain an auditing result corresponding to the store.

[0210] In some examples, when the first processing module 13 subjects the target store data to the data auditing operation, the first processing module 13 is configured to perform the following: the data source corresponding to the set data in the target store data is identified; when the data source is the reference store data, the target store data is subjected to the data auditing operation by using the first auditing method; and when the data source is the non-reference store data, the target store data is subjected to the data auditing by using the second auditing method, wherein the time length corresponding to the second auditing method is greater than the time length corresponding to the first auditing method.

[0211] In some examples, after the target store data corresponding to the store is generated, the first obtaining module 11 and the first processing module 13 in the embodiment are respectively configured to perform the following steps:

[0212] The first obtaining module 11 is configured to obtain an execution operation input by the user for the target store data.

[0213] The first processing module 13 is configured to perform an editing operation on the target store data based on the execution operation to generate edited target store data.

[0214] Figure 10 The apparatus shown in the figure can perform Figures 1-6 、 Figure 9The method of the embodiment shown, the part of the embodiment not described in detail, can refer to the related description of the embodiment shown Figures 1-6 , Figure 9 The execution process and technical effects of the technical solution are described in the embodiment shown Figures 1-6 , Figure 9 , and will not be repeated here.

[0215] In one possible design, Figure 10 The structure of the store data generation apparatus shown can be implemented as an electronic device, which can be a mobile phone, a tablet computer, a server, a client, or various devices. As shown in Figure 11 , the electronic device can include a first processor 21 and a first memory 22. The first memory 22 is configured to store a program supporting the electronic device to execute the store data generation method in the embodiments shown Figures 1-6 , Figure 9 The first processor 21 is configured to execute the program stored in the first memory 22.

[0216] The program includes one or more computer instructions, and the one or more computer instructions can implement the following steps when executed by the first processor 21:

[0217] Obtain first store data corresponding to the store, and the first store data can include an identity identifier. It can be understood that the first store data can include at least one of the following: address area information, name information, and an identity identifier.

[0218] Determine historical data corresponding to the identity identifier;

[0219] Determine at least one reference store data corresponding to the store based on the historical data and the first store data;

[0220] Generate target store data corresponding to the store according to the at least one reference store data and the first store data.

[0221] The structure of the electronic device can further include a first communication interface 23 for communication between the electronic device and other devices or communication networks.

[0222] Further, the first processor 21 is further configured to execute all or part of the steps in the embodiments shown Figures 1-6 , Figure 9 .

[0223] In addition, the embodiment of the application provides a computer storage medium for storing computer software instructions for an electronic device, which includes a program involved in the store data generation method in the method embodiments shown Figures 1-6 , Figure 9 .

[0224] Figure 12 A structural schematic diagram of a store data auditing device provided by an embodiment of the present application is shown in FIG. 1. The embodiment provides a store data auditing device, which can execute the store data auditing method shown in FIG. 2. Specifically, the store data auditing device can include the following modules. Figure 12 Figure 7

[0225] A second obtaining module 31 is configured to obtain store data corresponding to a store to be audited.

[0226] A second extracting module 32 is configured to extract set feature information included in the store data.

[0227] A second identifying module 33 is configured to identify a data source of the set feature information.

[0228] A second determining module 34 is configured to determine an auditing method corresponding to the store data based on the data source.

[0229] A second processing module 35 is configured to perform a data auditing operation on the store data by using the auditing method, and obtain an auditing result corresponding to the store to be audited.

[0230] In some examples, when the second determining module 34 determines the auditing method corresponding to the store data based on the data source, the second determining module 34 is configured to perform the following operations: when the data source is reference store data corresponding to a store that has passed the auditing, a first auditing method is used to perform the data auditing operation on the store data; and when the data source is non-reference store data corresponding to a store that has not passed the auditing or a store that has not been audited, a second auditing method is used to perform the data auditing operation on the store data, and a time length corresponding to the second auditing method is greater than a time length corresponding to the first auditing method.

[0231] Figure 12 The device shown in FIG. 1 can execute the method of the embodiment shown in FIG. 2. Figure 7 、 Figure 9 The embodiment not described in detail can refer to the related description of the embodiment shown in FIG. 1 and FIG. 2. The execution process and technical effects of the technical solution can refer to the description of the embodiment shown in FIG. 1 and FIG. 2, which will not be described here. Figure 7 、 Figure 9 The embodiment not described in detail can refer to the related description of the embodiment shown in FIG. 1 and FIG. 2. The execution process and technical effects of the technical solution can refer to the description of the embodiment shown in FIG. 1 and FIG. 2, which will not be described here. Figure 7 、 Figure 9 The embodiment not described in detail can refer to the related description of the embodiment shown in FIG. 1 and FIG. 2. The execution process and technical effects of the technical solution can refer to the description of the embodiment shown in FIG. 1 and FIG. 2, which will not be described here.

[0232] In one possible design, the structure of the store data auditing device shown in FIG. 1 can be implemented as an electronic device, which can be a mobile phone, a tablet computer, a server, a client, or various devices. As shown in FIG. 3, the electronic device can include a processor 10, a memory 20, and a communication interface 30. Figure 12 、 Figure 13 In one possible design, the structure of the store data auditing device shown in FIG. 1 can be implemented as an electronic device, which can be a mobile phone, a tablet computer, a server, a client, or various devices. As shown in FIG. 3, the electronic device can include a processor 10, a memory 20, and a communication interface 30.​As shown, the electronic device may include: a second processor 41 and a second memory 42. The second memory 42 is used to store the data that supports the electronic device to execute the above Figure 7 、 Figure 9 In the embodiment shown, the second processor 41 is configured to execute the program stored in the second memory 42 .

[0233] The program includes one or more computer instructions, wherein when the one or more computer instructions are executed by the second processor 41, the following steps can be implemented:

[0234] Obtain store data corresponding to the store to be reviewed;

[0235] Extracting set feature information included in store data;

[0236] Identify the data source of the set feature information;

[0237] Based on the data source, determine the audit method corresponding to the store data;

[0238] Use the audit method to perform data audit operations on store data and obtain audit results corresponding to the store to be audited.

[0239] The structure of the electronic device may further include a second communication interface 43 for the electronic device to communicate with other devices or a communication network.

[0240] Furthermore, the second processor 41 is also used to execute the aforementioned Figure 7 、 Figure 9 All or part of the steps in the illustrated embodiments.

[0241] In addition, an embodiment of the present invention provides a computer storage medium for storing computer software instructions used by electronic devices, which includes instructions for executing the above Figure 7 、 Figure 9 The procedures involved in the store data audit method in the method embodiment shown.

[0242] Figure 14 A schematic diagram of a store data recommendation device according to an embodiment of the present invention; Figure 14 As shown, this embodiment provides a store data recommendation device, which can perform Figure 8 The method for recommending store data shown in the figure, specifically, the device for recommending store data may include:

[0243] A third acquisition module 51 is configured to acquire first store data corresponding to a store, where the first store data may include an identity identifier. It is understood that the first store data may include not only the identity identifier but also at least one of the following: address area information, name information, and the identity identifier.

[0244] A third determining module 52 is used to determine historical data corresponding to the identity identifier;

[0245] The third processing module 53 is used to determine a recommended store list corresponding to the store based on the historical data and the first store data, where the recommended store list includes at least one reference store data.

[0246] Figure 14 The device shown can perform Figures 8-9 For the method of the embodiment shown in FIG. 1 , reference may be made to the description of the part not described in detail in the embodiment. Figures 8-9 The implementation process and technical effects of this technical solution can be found in Figures 8-9 The description in the illustrated embodiment will not be repeated here.

[0247] In one possible design, Figure 14 The structure of the store data recommendation device shown can be implemented as an electronic device, which can be a mobile phone, tablet computer, server, client and other devices. Figure 15 As shown, the electronic device may include: a third processor 61 and a third memory 62. The third memory 62 is used to store the data that supports the electronic device to execute the above Figures 8-9 In the embodiment shown, the third processor 61 is configured to execute the program stored in the third memory 62 .

[0248] The program includes one or more computer instructions, wherein when the one or more computer instructions are executed by the third processor 61, the following steps can be implemented:

[0249] Acquire first store data corresponding to the store, where the first store data may include an identity identifier; it is understood that the first store data may include not only the identity identifier but also at least one of the following: address area information, name information, and the identity identifier;

[0250] Determining historical data corresponding to the identity identifier;

[0251] Based on the historical data and the first store data, a recommended store list corresponding to the store is determined, where the recommended store list includes at least one reference store data.

[0252] The structure of the electronic device may further include a third communication interface 63 for the electronic device to communicate with other devices or a communication network.

[0253] Further, the third processor 61 is further configured to execute the foregoing Figures 8-9 all or part of the steps in the embodiments shown.

[0254] In addition, the embodiments of the present application provide a computer storage medium for storing computer software instructions for an electronic device, which contains programs for executing the foregoing Figures 8-9 the programs involved in the recommendation method of the store data in the method embodiments shown.

[0255] The device embodiments described above are only schematic, wherein the units shown as separate components can or can not be physically separate, and the components shown as units can or can not be physical units, i.e., can be located in one place, or can be distributed on multiple network units. Part or all of the modules can be selected according to actual needs to achieve the purpose of the present embodiment. Those skilled in the art can understand and implement without creative labor.

[0256] From the above description of the embodiments, those skilled in the art can clearly understand that the embodiments can be realized by means of the general hardware platform as necessary, and of course can also be realized by means of hardware and software combination. Based on such understanding, the above technical solutions can be embodied in the form of computer products, and the present application can be in the form of computer program products implemented on one or more computer usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer usable program code.

[0257] The present application is described with reference to flowcharts and / or block diagrams of the method, device (system), and computer program product according to the embodiments of the present application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and the combination of the flows and / or blocks in the flowcharts and / or block diagrams can be realized by computer program instructions. These computer program instructions can be provided to a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable network-connected devices to produce a machine, so that the instructions executed by the computer or other programmable network-connected devices produce a machine that implements the functions specified in the flowcharts and / or block diagrams. Figure One one flow or multiple flows and / or blocks Figure One the device that implements the functions specified in one block or multiple blocks.

[0258] These computer program instructions can also be stored in a computer- readable memory that can direct a computer or other programmable network Figure One Figure One

[0259] These computer program instructions can also be loaded onto a computer or other programmable network Figure One Figure One

[0260] In a typical configuration, a computing device includes one or more processors (CPUs), input / output interfaces, network interfaces, and memory.

[0261] The memory can include non-persistent memory and / or volatile memory, such as random access memory (RAM) and / or cache memory, and non-volatile memory, such as read-only memory (ROM), EPROM, and / or flash memory. The memory is an example of computer-readable media.

[0262] Computer-readable media includes permanent and non-permanent, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disc read-only memory (CD-ROM), digital versatile disc (DVD), or other optical storage, magnetic cassettes, magnetic tapes, magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information accessible to a computing device. According to the definition herein, computer-readable media does not include transitory media, such as modulated data signals and carrier waves.

[0263] ​​​​It should be pointed out finally that the above embodiments are only used to illustrate the technical solutions of the present application, but not to limit the same; and although the present application has been described in detail with reference to the foregoing embodiments, it should be appreciated by those skilled in the art that the technical solutions recorded in the foregoing embodiments can be modified, or some technical features thereof can be replaced equivalently; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for generating store data, characterized in that: include: Acquire first store data corresponding to the store, where the first store data includes an identity identifier; Determining historical data corresponding to the identity identifier; Extracting store features corresponding to the store based on the historical data and the first store data, the store features including store attribute features and store relationship features; Determining at least one reference store data corresponding to the store according to the store characteristics; Target store data corresponding to the store is generated based on the at least one reference store data and the first store data.

2. The method according to claim 1, characterized in that Determining at least one reference store data corresponding to the store according to the store characteristics includes: Obtain a standard store library, wherein the standard store library includes a plurality of standard stores corresponding to different entities and standard store data corresponding to the standard stores; Using entity alignment technology, determining at least one standard store in the standard store library that matches the store characteristics; The standard store data corresponding to the at least one standard store is determined as at least one reference store data corresponding to the store.

3. The method according to claim 2, characterized in that Determining at least one standard store matching the store characteristics in the standard store library by using entity alignment technology includes: Determining a plurality of intermediate standard stores corresponding to the store in the standard store library based on the address area information and name information corresponding to the store; By using entity alignment technology, at least one target standard store that matches the store characteristics is determined from the multiple intermediate standard stores.

4. The method according to claim 2, characterized in that Before obtaining the standard store library, the method further includes: Obtain multiple store libraries to be integrated; Determining a plurality of store entities and a plurality of store feature data corresponding to the plurality of store libraries; Performing a deduplication operation on the multiple store databases based on the multiple store entities to obtain an intermediate store database; All store feature data corresponding to the intermediate store library are adjusted to a set format to generate the standard store library.

5. The method according to claim 1, wherein Generating target store data corresponding to the store according to the at least one reference store data and the first store data includes: Obtaining the first execution operation entered by the user for any reference store data; A data filling operation is performed on the first store data based on the reference store data corresponding to the first execution operation to generate target store data corresponding to the store.

6. The method according to claim 5, characterized in that Generating target store data corresponding to the store based on the at least one reference store data and the first store data further includes: If no first execution operation input by the user for any reference store data is detected within a preset time period, the at least one reference store data is ignored; Obtain the second execution operation entered by the user for the store; Based on the second execution operation and the first store data, target store data corresponding to the store is generated.

7. The method according to claim 1, characterized in that When the historical data includes historical store data corresponding to the identity identifier; determining at least one reference store data corresponding to the store based on the historical data and the first store data includes: Obtain the historical store name corresponding to the historical store data; When the name information corresponding to the store matches the historical store name, performing a data filling operation on the first store data based on the historical store data to generate second store data; At least one reference store data corresponding to the store is determined based on the historical data and the second store data.

8. The method according to any one of claims 1 to 7, characterized in that After generating target store data corresponding to the store, the method further includes: Perform a data audit operation on the target store data to obtain an audit result corresponding to the store.

9. The method according to claim 8, characterized in that Perform data review operations on the target store data, including: Identifying a data source corresponding to the set data in the target store data; When the data source is reference store data, a data audit operation is performed on the target store data using a first audit method; When the data source is non-reference store data, the target store data is audited using a second audit method, wherein the duration corresponding to the second audit method is greater than the duration corresponding to the first audit method.

10. The method according to any one of claims 1 to 7, characterized in that After generating target store data corresponding to the store, the method further includes: Obtaining an execution operation input by the user for the target store data; An editing operation is performed on the target store data based on the execution operation to generate the edited target store data.

11. A method for recommending store data, characterized in that: include: Acquire first store data corresponding to the store, the first store data including: an identity identifier; Determining historical data corresponding to the identity identifier; Extracting store features corresponding to the store based on the historical data and the first store data, the store features including store attribute features and store relationship features; According to the store characteristics, a recommended store list corresponding to the store is determined, and the recommended store list includes at least one reference store data.

12. A device for generating store data, characterized in that: include: A first acquisition module is used to acquire first store data corresponding to a store, wherein the first store data includes an identity identifier; A first determining module, configured to determine historical data corresponding to the identity identifier; The first determining module is configured to extract store features corresponding to the store based on the historical data and the first store data, wherein the store features include store attribute features and store relationship features; Determining at least one reference store data corresponding to the store according to the store characteristics; The first processing module is configured to generate target store data corresponding to the store based on the at least one reference store data and the first store data.

13. An electronic device, characterized in that: include: memory and processor; The memory is used to store one or more computer instructions, wherein the one or more computer instructions, when executed by the processor, implement the method for generating store data according to any one of claims 1 to 10.

14. A store data recommendation device, characterized in that: include: A third acquisition module is used to acquire first store data corresponding to the store, wherein the first store data includes an identity identifier; A third determining module, configured to determine historical data corresponding to the identity identifier; The third processing module is used to extract store features corresponding to the store based on the historical data and the first store data, and the store features include store attribute features and store relationship features; according to the store features, determine a recommended store list corresponding to the store, and the recommended store list includes at least one reference store data.

15. An electronic device, characterized in that: include: memory and processor; The memory is used to store one or more computer instructions, wherein the one or more computer instructions, when executed by the processor, implement the store data recommendation method according to claim 11.

Images