Data verification method, electronic equipment and computer program product

Abstract

The embodiment of the invention discloses a data verification method and device, a computer readable storage medium, electronic equipment and a computer program product. Commodity correctness verification is performed on a commodity database established for a target activity meeting place by an intelligent investment application, and the method comprises the steps that verification configuration information and the commodity database are acquired, and the verification configuration information comprises a plurality of verification dimensions and data types and verification rules required for verification analysis on different verification dimensions; different verification dimensions correspond to different data processing logics used by the intelligent investment application when the commodity database is established; the data processing logic is used for selecting a target commodity from the data source and generating a label of the target commodity under the verification dimension as a to-be-verified label; adding a reference label to the target commodity according to the target data associated with the target commodity under the data type and the marking rule; and according to the verification rule, carrying out information comparison on the to-be-verified label and the reference label associated with the target commodity under the verification dimension to realize commodity correctness verification.

Description

Technical Field

[0001] The present application relates to the technical field of commodity information services, and in particular to a data verification method and device, a computer-readable storage medium, an electronic device, and a computer program product. Background Art

[0002] The product information service system can display and promote products to consumers in a centralized manner during a specific period or for a specific theme by building a venue page. For example, different venue pages can be built around specific dates such as the Spring Festival and Double 11, or around specific themes such as fruit specials and hot pot seasons, and opened to consumers during a specific period of time to purchase products.

[0003] In order to improve the efficiency of venue construction, the product information service system provides an intelligent build-to-order application that can automatically build the venue page using the products in the product pool. This implementation process involves a lot of data processing logic.

[0004] If the data processing logic runs abnormally, the products generated by the venue may not meet the delivery expectations, that is, the products are not exposed in the expected resource positions on the venue page, which will affect the UV value conversion of the venue.

[0005] In order to ensure the correctness of the products generated by the smart tie-in venue, it is necessary to check and verify the data of the products generated by the venue. In one way, the verification personnel can conduct manual spot checks on the venue page to determine whether the products actually exposed on the venue page meet the expected delivery needs; in another way, the data report can be viewed to determine whether there are any abnormalities in the product exposure through the transaction conversion of the venue's main products and other products. The above verification scheme is difficult to implement, and the verification results are inaccurate and have a lag. In other words, the venue page has actually been delivered to consumer users. If the smart tie-in application makes an error in data processing, resulting in the main products or a large number of products not being exposed as expected, it will directly affect the venue's UV value conversion, and then affect the total transaction amount GMV of the goods, and these effects are irreversible.

[0006] How to verify the correctness of the products associated with the event venue automatically built by the smart investment application has become a technical problem that needs to be solved by those skilled in the art. Summary of the invention

[0007] The present application provides a data verification method and device, a computer-readable storage medium, an electronic device, and a computer program product, which can simply and conveniently perform correctness verification on target products associated with a target event venue, and the verification results are more accurate and reliable.

[0008] This application provides the following solutions:

[0009] A data verification method is used to verify the correctness of commodities in a commodity database automatically established by an intelligent investment application for a target event venue, the method comprising:

[0010] The verification configuration information of the target activity venue and the commodity database are obtained, wherein the verification configuration information includes multiple verification dimensions, and data types and verification rules required for verification analysis on different verification dimensions; different verification dimensions correspond to different data processing logics used by the smart betting application when establishing the commodity database, and the data processing logic is used to select a target commodity from a data source and generate a label for the target commodity under the verification dimension, and add the label to the commodity database; wherein the label added to the target commodity by the data processing logic is a label to be verified;

[0011] Obtain target data associated with the target product under the data type, add a label to the target product according to the target data and a labeling rule that has been verified for accuracy, and use the label as a reference label;

[0012] According to the verification rules required for verification analysis of the verification dimension, information comparison is performed on the to-be-verified label and the reference label associated with the target product under the verification dimension, and based on the corresponding matching of the label information associated with the target product under the multiple verification dimensions, the correctness of the target product associated with the target activity venue is judged.

[0013] The verification configuration information is stored in a rule database, and the obtaining of the verification configuration information of the target activity venue includes:

[0014] After determining the multiple verification dimensions that need to be verified and analyzed, dynamically splicing the encoding information of the multiple verification dimensions in the request link for accessing the rule database;

[0015] The data types and verification rules required for verification analysis of the multiple verification dimensions are obtained from the rule database through an access request carrying the coding information of the multiple verification dimensions.

[0016] The verification dimension is defined according to the natural attributes of the product, including at least one of the following items:

[0017] Taking the number of commodities at the venue as the verification dimension, the correctness of the data processing logic for selecting the target commodity from the data source is verified;

[0018] Taking the source of goods as the verification dimension, the correctness of the data processing logic for marking the source of the target goods according to the data source is verified;

[0019] Taking the main intervention products as the verification dimension, the correctness of the data processing logic for labeling the main intervention products for the target products is verified;

[0020] Taking seasonal products as the verification dimension, the correctness of the data processing logic for seasonal product tagging for the target products is verified;

[0021] Taking new products as the verification dimension, the data processing logic for new product labeling of the target product is verified for correctness.

[0022] The data types required for verification analysis at different verification dimensions include:

[0023] The verification dimension is the number of commodities in the venue, and the data type includes: identification information of the target commodity;

[0024] The verification dimension is the source of the commodity, and the data type includes: identification information of the data source;

[0025] The verification dimension is the main intervention product, and the data type includes: main intervention product determination logic and / or main intervention product preset identification;

[0026] The verification dimension is the seasonal product, and the data type includes: the seasonal start and end months of the target product;

[0027] The verification dimension is the new product, and the data type includes: the creation time of the target product.

[0028] The verification dimension is defined according to the product exposure conversion situation, including at least one of the following items:

[0029] Taking 0-exposure products as the verification dimension, the correctness of the data processing logic for marking 0-exposure products for the target products is verified;

[0030] Taking the product exposure conversion type as the verification dimension, the correctness of the data processing logic for marking the exposure conversion type of the target product is verified;

[0031] Taking high GMV contribution products as the verification dimension, the correctness of the data processing logic for marking high GMV contribution products for the target products is verified; the GMV contribution ratio of the high GMV contribution products is not less than the preset ratio value;

[0032] Taking the main promoted product as the verification dimension, the correctness of the data processing logic for marking the main promoted product for the target product is verified;

[0033] Taking the commodity stratification level as the verification dimension, the correctness of the data processing logic for stratification level labeling of the target commodity is verified;

[0034] Taking the comprehensive rating of the product as the verification dimension, the correctness of the data processing logic for calculating and marking the comprehensive rating of the target product is verified.

[0035] The data types required for verification analysis at different verification dimensions include:

[0036] The verification dimension is the zero-exposure product, and the data type includes: the number of exposed unique visitors UV of the target product;

[0037] The verification dimension is the product exposure conversion type, and the data type includes: the exposure UV of the target product, the exposure to transaction conversion rate CT-CVR, and the independent visitor UV value;

[0038] The verification dimension is the high GMV contribution product, and the data type includes: the GMV contribution ratio of the target product;

[0039] The verification dimension is the main recommended product, and the data types include: the exposure conversion type of the target product, whether it is a high GMV contribution product, whether it is a main recommended intervention product, and whether it is a 0-exposure product;

[0040] The verification dimension is the product stratification level, and the data type includes: the exposure conversion type, UV value, whether the intervention product is promoted, and whether the product is promoted;

[0041] The verification dimension is the comprehensive score of the product, and the data type includes: the hierarchical level score of the target product, and the UV value ranking score of the product under the stratification.

[0042] Wherein, the verification dimension is the product exposure conversion type, and the product exposure conversion type includes high exposure and high conversion, high exposure and low conversion, low exposure and high conversion, and invalid exposure.

[0043] The reference tag associated with the target product under the verification dimension is obtained in the following manner:

[0044] If the exposure UV of the target product is ≥ the first threshold and the CT-CVR of the target product is ≥ the second threshold; or, the exposure UV of the target product is ≥ the first threshold and the UV value of the target product is ≥ the third threshold, then it is determined that the benchmark tag associated with the target product under the product exposure conversion type is high exposure and high conversion;

[0045] If the exposure UV of the target product is greater than or equal to the first threshold and the CT-CVR of the target product is less than the second threshold; or, the exposure UV of the target product is greater than or equal to the first threshold and the UV value of the target product is less than the third threshold, then it is determined that the benchmark tag associated with the target product under the product exposure conversion type is high exposure and low conversion;

[0046] If the exposure UV of the target product is less than the fourth threshold and the CT-CVR of the target product is greater than or equal to the second threshold; or, the exposure UV of the target product is less than the fourth threshold and the UV value of the target product is greater than or equal to the third threshold, then it is determined that the benchmark tag associated with the target product under the product exposure conversion type is low exposure and high conversion;

[0047] If the exposure UV of the target product is greater than 100 and the CT-CVR of the target product is equal to 0, it is determined that the reference tag associated with the target product under the product exposure conversion type is invalid exposure;

[0048] The first threshold is greater than the fourth threshold.

[0049] Among them, the verification dimension is the main recommended product, then

[0050] The step of adding a label to the target commodity according to the target data and the labeling rules that have been verified for accuracy, and using the label as a reference label, includes:

[0051] The labeling rule is that if the target product satisfies at least one of the following conditions, the reference label associated with the target product under the main product is determined to be a main product; otherwise, the reference label associated with the target product under the main product is determined to be a non-main product:

[0052] The product exposure conversion type of the target product is the high exposure and high conversion or the low exposure and high conversion;

[0053] The target product is a high GMV contribution product;

[0054] The target product is the main intervention product and has 0 exposure.

[0055] Wherein, the intelligent investment application is run in the online environment of the commodity information service system, and the method further includes:

[0056] If the target product passes the correctness check, a target page corresponding to the target event venue is generated based on the tag saved in the product database for the target product, so that the target page is placed in the product information service system to provide shopping services for the target product to consumers.

[0057] The intelligent betting application is run in a test environment of a commodity information service system, and the venue generation capability of the intelligent betting application in the process of application iteration is verified by checking the commodity correctness. The method further includes:

[0058] If the target product passes the correctness check, it is determined that the venue generation capability of the smart betting application has passed the verification, and the smart betting application is published to the online environment of the product information service system to realize automatic venue construction and delivery.

[0059] Wherein, the method further comprises:

[0060] If the target commodity fails the correctness check, the data processing logic corresponding to the target check dimension is fault located according to the target check dimension associated with the mismatched tag to be checked and the reference tag.

[0061] A data verification device is used to verify the correctness of commodities in a commodity database automatically established by an intelligent investment application for a target event venue, the device comprising:

[0062] A data acquisition unit is used to obtain the verification configuration information of the target activity venue and the commodity database, wherein the verification configuration information includes multiple verification dimensions, and data types and verification rules required for verification analysis on different verification dimensions; different verification dimensions correspond to different data processing logics used by the smart betting application when establishing the commodity database, and the data processing logic is used to select a target commodity from a data source and generate a label for the target commodity under the verification dimension, and add it to the commodity database; wherein the label added to the target commodity by the data processing logic is a label to be verified;

[0063] A reference label adding unit, configured to obtain target data associated with the target commodity under the data type, add a label to the target commodity according to the target data and a labeling rule that has been verified for accuracy, and use the label as a reference label;

[0064] An information comparison unit is used to compare the information of the to-be-verified label and the reference label associated with the target product under the verification dimension according to the verification rules required for verification analysis of the verification dimension, and to judge the correctness of the target product associated with the target activity venue according to the corresponding matching of the label information associated with the target product under the multiple verification dimensions.

[0065] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of any of the methods described above.

[0066] An electronic device, comprising:

[0067] one or more processors; and

[0068] A memory associated with the one or more processors, the memory being used to store program instructions, wherein the program instructions, when read and executed by the one or more processors, execute the steps of any of the aforementioned methods.

[0069] A computer program product comprises a computer program / computer executable instructions, wherein the computer program / computer executable instructions implement the steps of any of the aforementioned methods when executed by a processor in an electronic device.

[0070] According to the specific embodiments provided in this application, this application discloses the following technical effects:

[0071] In an embodiment of the present application, corresponding verification dimensions can be set for different data processing logics of the smart investment application, and the data types and verification rules required for verification analysis of each verification dimension can be configured. In this way, labels can be added to the target products associated with the target event venue based on the target data associated with the target product under the data type and the labeling rules that have passed accuracy verification, and the label can be used as a reference label.

[0072] In addition, a product database established by the smart betting application for the target event venue can also be obtained. The database stores the labels generated by the smart betting application for the target product under each verification dimension through different data processing logics, and uses the labels as the labels to be verified. In this way, according to the verification rules associated with the verification dimension, the labels to be verified and the reference labels associated with the target product under the verification dimension can be compared, and the correctness of the target product associated with the target event venue can be verified based on the information comparison results.

[0073] The implementation process of such data verification is more convenient and is not affected by human factors, which helps to improve the accuracy and reliability of the verification results.

[0074] Of course, any product implementing the present application does not necessarily need to achieve all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0075] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0076] Figure 1 is a flow chart of a data verification method provided in an embodiment of the present application;

[0077] Figure 2 is a schematic diagram of a data verification device provided in an embodiment of the present application;

[0078] Figure 3 It is a schematic diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0079] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments in the present application belong to the scope of protection of this application.

[0080] In order to solve the problems of difficulty in implementing existing verification schemes and low accuracy of verification results, the embodiments of the present application can define multiple verification dimensions according to the natural properties of the goods and the exposure conversion conditions, and configure the data type and verification rules required for verification analysis of each verification dimension. Based on this technology, a data verification scheme is provided that can automatically detect the correctness of the goods generated at the venue in a simple and convenient manner. Compared with the verification results obtained by manual sampling and the verification results indirectly reflected by data reports, the verification results obtained by automatic detection in the embodiments of the present application are more accurate and reliable.

[0081] In the embodiment of the present application, the smart tie-in application can automatically generate a target activity venue according to application requirements, including: establishing a product database for the target activity venue and generating a target page for the target activity venue. Specifically, when automatically establishing a product database, the target product can be selected from the data source through different data processing logics of the smart tie-in application, and labels of the target product under different verification dimensions can be generated and added to the product database. In this way, the target page can be generated later according to the labels saved for different target products in the product database, and the target product can be exposed through the target page.

[0082] As an example, the smart tie-in application of the embodiment of the present application can be run in the online environment of the commodity information service system. After automatically establishing the commodity database, the data verification scheme of the present application is started to perform a correctness verification on the target commodity in the commodity database used to generate the target page, and then the target page is generated after the data verification is passed, and then the target page is put into the commodity information service system to provide shopping services for the target commodity to consumers. In such an application scenario, possible commodity exposure problems can be discovered in time before the target page goes online, avoiding the impact of the abnormal data processing logic of the smart tie-in application on the value conversion of the venue UV (Unique Visitor), and then affecting the venue GMV (Gross Merchandise Volume).

[0083] Alternatively, after the target page is placed in the product information service system, the data verification scheme of this application can be activated to verify the correctness of the products at the target event venue that has been exposed to consumer users. This will help to promptly discover and correct product exposure problems, minimize the impact of abnormal data processing logic of the smart matching application on the UV value conversion of the venue.

[0084] As another example, the smart tie-in application of the embodiment of the present application can be run in the test environment of the commodity information service system. During the application iteration process, the venue generation capability of the smart tie-in application is verified through the data verification scheme of the present application. In other words, the correctness of the commodities is verified for the commodity database automatically established by the iterative smart tie-in application for the target activity venue, so as to verify the venue generation capability of the iterative smart tie-in application and determine whether to put the iterative smart tie-in application into the online environment of the commodity information service system.

[0085] Specifically, if the target product associated with the target activity venue passes the correctness check, it can be determined that its venue generation capability verification has passed, and it can be published to the online environment of the product information service system to provide venue automatic construction and delivery services.

[0086] Ensure that the venue generation capability of the smart pairing application is not affected by application iterations, promptly discover abnormal data processing logic that occurs during the iteration of the smart pairing application, and perform logic corrections. This can ensure that when the iterated smart pairing application is released to the online environment, there will be no abnormal product exposure problems due to data processing errors.

[0087] In the embodiment of the present application, the online environment is also the production environment, which is the environment in which the commodity information service system formally provides services to consumer users; the test environment may include a pre-release environment and / or a secure production environment (SPE environment), which can simulate the online environment for testing and verification before the application is released.

[0088] Specifically, the smart tie-in application can be run in a pre-release environment or an SPE environment, and published to an online environment when its venue generation capability is verified; alternatively, the smart tie-in application can be double-verified in a pre-release environment and an SPE environment, and published to an online environment after the double verification is passed.

[0089] It can be understood that the application scenarios of the above data verification solutions can solve the verification lag problem existing in the existing verification solutions to a certain extent.

[0090] The implementation process of the data verification method of the embodiment of the present application is explained below with reference to specific examples. Figure 1 The flow chart shown may include:

[0091] S101: Obtain verification configuration information of the target activity venue and the commodity database, wherein the verification configuration information includes multiple verification dimensions, as well as data types and verification rules required for verification analysis on different verification dimensions; different verification dimensions correspond to different data processing logics used by the smart matching application when establishing the commodity database, and the data processing logic is used to select a target commodity from a data source and generate a label for the target commodity under the verification dimension, and add the label to the commodity database; wherein the label added to the target commodity by the data processing logic is a label to be verified.

[0092] In an embodiment of the present application, multiple verification dimensions can be defined based on the natural properties of the goods and the exposure conversion status, and the correctness of the target goods associated with the target activity venue can be verified. If the correctness verification of the target goods passes, it can be determined that the smart tie-in application automatically establishes a product database for the target activity venue, and problems such as abnormal product exposure will not occur.

[0093] Optionally, in order to reduce the consumption of local memory by the verification configuration information, the verification configuration information may be stored in a rule database and obtained from the rule database when needed.

[0094] As an example, the data types and verification rules associated with the verification dimensions can be obtained from the rule database by means of dynamic SQL splicing. In this way, personalized product correctness verification can be performed based on different verification dimensions according to actual application needs, which helps to improve the flexibility of data verification. For example, when the correctness verification requirements are high, it can be determined that verification analysis is required for all verification dimensions, and the data types and verification rules associated with all verification dimensions can be requested from the rule database by dynamically splicing all verification dimension codes; or, data types and verification rules associated with some verification dimensions can be requested from the rule database according to the focus of the verification.

[0095] Specifically, after determining the multiple verification dimensions that need to be verified and analyzed, the encoding information of the multiple verification dimensions can be dynamically spliced ​​in the request link for accessing the rule database; and then, through the access request carrying the encoding information of the multiple verification dimensions, the data type and verification rules required for the verification analysis of the multiple verification dimensions can be obtained from the rule database.

[0096] As an example, the rule database can provide the data type and verification rule associated with the requested verification dimension by returning an SQL statement, so that by running the SQL statement, the data required for rule verification can be obtained, and the rule verification can be automatically performed.

[0097] In the embodiment of the present application, the verification dimension can be defined according to the natural attributes of the product and / or the exposure conversion status of the product, which is illustrated below with examples.

[0098] The verification dimension defined according to the natural attributes of the goods can be reflected in at least one of the following items: the number of goods at the venue, the source of goods, the main intervention products, seasonal goods, and new products.

[0099] Among them, taking the number of commodities at the venue as the verification dimension, the data processing logic for selecting target commodities from the data source can be verified for correctness; taking the source of commodities as the verification dimension, the data processing logic for marking the source of target commodities according to the data source can be verified for correctness; taking the main intervention products as the verification dimension, the data processing logic for marking the main intervention products for target commodities can be verified for correctness; taking seasonal commodities as the verification dimension, the data processing logic for marking seasonal commodities for target commodities can be verified for correctness; taking new products as the verification dimension, the data processing logic for marking new products for target commodities can be verified for correctness.

[0100] The verification dimension defined according to the product exposure conversion situation can be reflected in at least one of the following items: 0-exposure products, product exposure conversion types, high GMV contribution products, main products, product stratification levels, and comprehensive product scores.

[0101] Among them, taking 0-exposure products as the verification dimension, the data processing logic for marking 0-exposure products for the target products can be verified for correctness; taking product exposure conversion type as the verification dimension, the data processing logic for marking exposure conversion type for the target products can be verified for correctness; taking high-GMV contribution products as the verification dimension, the data processing logic for marking high-GMV contribution products for the target products can be verified for correctness; taking main promoted products as the verification dimension, the data processing logic for marking main promoted products for the target products can be verified for correctness; taking product stratification level as the verification dimension, the data processing logic for marking stratification level for the target products can be verified for correctness; taking product comprehensive score as the verification dimension, the data processing logic for calculating and marking the target products for comprehensive score can be verified for correctness.

[0102] For specific examples and explanations of the verification dimensions, please refer to the following introduction and will not be described in detail here.

[0103] In addition, the embodiments of the present application also provide labeling rules for correctly labeling target products. Based on the labeling rules that have been verified for accuracy, the labels added to the target products can be used as benchmark labels to verify the correctness of the labels added to the target products in the process of establishing a product database by the smart tie-in application.

[0104] Optionally, the labeling rules can also be stored in the rule database and requested from the rule database by dynamic SQL splicing. That is, the rule database can store the data types, verification rules and labeling rules required for verification analysis on different verification dimensions.

[0105] For specific examples of marking rules, please refer to the following introduction, which will not be described in detail here.

[0106] In addition, for the verification rules associated with the verification dimension, the embodiment of the present application can configure the verification rules according to at least one of the following rule types:

[0107] 1. According to the negation logic, the rule verification is performed by counting the number of target products whose labels to be verified do not match the reference labels;

[0108] 2. The reference tag includes at least two preset enumeration values, and the rule verification is performed by comparing the tag to be verified with the at least two enumeration values; that is, the tag to be verified is one of the at least two enumeration values;

[0109] 3. The reference tag is a preset value or a preset value range, and the rule verification is performed by comparing the tag to be verified with the preset value, or comparing the tag to be verified with the preset value range;

[0110] 4. The tag to be verified cannot be null or empty.

[0111] For specific examples of verification rules, please refer to the following introduction, which will not be described in detail here.

[0112] It should be noted that when performing data verification on the target products associated with the target activity venue, in addition to obtaining the verification configuration information of the target activity venue, it is also necessary to obtain the product database established for the target activity venue. The product database stores the labels generated by the smart betting application for the target products under multiple verification dimensions when establishing the product database. The label can be used as the label to be verified in the embodiment of the present application.

[0113] For example, for the target product A, the tags to be verified under the verification dimensions listed above are: from product pool A and not in product pool B, main intervention product, seasonal product, not new product, not 0 exposure product, low exposure and high conversion, high GMV contribution product, main product, stratification level L1, comprehensive score S A .

[0114] S102: Obtain target data associated with the target product under the data type, add a label to the target product according to the target data and a labeling rule that has passed accuracy verification, and use the label as a reference label.

[0115] In the embodiment of the present application, for the same verification dimension, the reference label and the label to be verified associated with the target product under the verification dimension can be obtained respectively. The reference label is obtained in the process of executing the data verification of the present application according to the real target data and the labeling rules under the data type associated with the verification dimension of the target product.

[0116] S103: According to the verification rules required for verification analysis of the verification dimension, information comparison is performed on the to-be-verified label and the reference label associated with the target product under the verification dimension, and according to the corresponding matching of the label information associated with the target product under the multiple verification dimensions, the correctness of the target product associated with the target event venue is judged.

[0117] After obtaining the reference tag associated with the target product under the verification dimension and reading the tag to be verified associated with the target product under the verification dimension from the product database, the two can be compared according to the verification rule associated with the verification dimension, and the correctness of the target product associated with the target event venue can be judged according to the corresponding matching of the tag information associated with the target product under multiple verification dimensions. For example, when the tag information associated with the target product under multiple verification dimensions all corresponds to each other, it can be determined that the target product has passed the correctness verification.

[0118] The following is an example of how to implement data verification in different verification dimensions.

[0119] 1. The verification dimension is the number of products at the venue

[0120] In the embodiment of the present application, the data processing logic for selecting target products from the data source can be checked for correctness from the perspective of the quantity of products.

[0121] In this example, the data type may include: the product ID of the target product, the corresponding reference tag is the expected product delivery quantity of the target activity venue, and the tag to be verified is the actual quantity of the target product associated with the target activity venue.

[0122] The verification rule can be reflected as follows: the actual quantity of the target product is consistent with the expected quantity of the product. That is, the reference tag is a preset value, and the rule verification is performed by comparing the tag to be verified with the preset value.

[0123] Specifically, the product ID of the target product can be read from the product database of the target event venue, and the actual quantity of the target product can be obtained by statistics. If the actual quantity of the target product is consistent with the expected quantity of the product, then it can be determined that the rule verification of the number of venue products has passed. Otherwise, the rule verification of the number of venue products has failed, indicating that the data processing logic for selecting the target product from the data source is abnormal, resulting in an error in the number of selected target products.

[0124] 2. Verification dimension is product source

[0125] In actual applications, the target products may come from different product pools, so from the perspective of the data source, the data processing logic for marking the source of the target products according to the data source can be verified for correctness.

[0126] In this example, the data type may include: identification information of the data source, that is, identification information of the commodity pool providing the target commodity; correspondingly, the benchmark label is the designated commodity pool providing the target commodity, that is, the correct source information; the label to be verified is the marked source information of the target commodity in the commodity database.

[0127] The verification rule can be embodied as follows: the target product's labeled source information matches the correct source information. That is, the reference tag includes at least two preset enumeration values, and the rule verification is performed by comparing the tag to be verified with at least two enumeration values. It can be understood that the target product's labeled source information cannot be null or empty. If it is null or empty, it can be determined that the target product's labeled source information does not match the correct source information.

[0128] Specifically, for the same target product, the marked source information of the target product can be read from the product database of the target event venue, and the correct source information of the target product can be determined based on the relationship between the product pool and the product. The two are compared. If the marked source information of all target products matches the correct source information, it can be determined that the rule verification of the product source has passed; otherwise, it can be determined that the rule verification of the product source has failed, indicating that the data processing logic for marking the source of the target product is abnormal.

[0129] For example, the designated commodity pool includes: commodity pool A and commodity pool B. In one way, two commodity sources can be determined, which are marked as: 11-commodity pool A, 12-commodity pool B; in another way, three commodity sources can be determined, which are marked as: 21-in commodity pool A and in commodity pool B, 22-in commodity pool A and not in commodity pool B, 23-in commodity pool B and not in commodity pool A.

[0130] Corresponding to the example of target product A in the above text, it comes from product pool A and is not in product pool B, that is, the tag to be verified is 22. The tag to be verified can be covered by the marking values ​​21, 22, and 23 representing the correct source information. Therefore, it can be determined that the target product A has passed the rule verification under the product source.

[0131] Optionally, in addition to comparing the label to be verified with the enumeration values ​​of the three correct source information, the benchmark label of the target product A can be further obtained based on the products included in product pool A and product pool B, and when the label to be verified is the same as the benchmark label, it is determined that the rule verification of the target product A under the product source has passed.

[0132] 3. Verification dimension is the main intervention product

[0133] In actual applications, commodities can be classified according to usage requirements. For example, commodity classification can be reflected in whether it is a main intervention product, that is, a commodity that can be recommended by priority through pre-configuration. From the perspective of the accuracy of the classification of main intervention products, the correctness of the data processing logic for marking the main intervention products for the target commodities can be verified.

[0134] In this example, the data type may include: main intervention product determination logic, and / or main intervention product preset identification. Correspondingly, the benchmark label is the correct classification information of the target product under the main intervention product, and the label to be verified is the marked classification information of the target product under the main intervention product.

[0135] The verification rule can be reflected as follows: the labeled classification information of the target product under the main intervention product matches the correct classification information.

[0136] In this example, you can add a benchmark label under the main intervention product to the target product according to the following labeling rules:

[0137] Labeling rule 1: If the main intervention product judgment logic and the main intervention product preset identification are configured, then when the target product meets the main intervention product judgment logic and has the main intervention product preset identification, the correct classification information of the target product under the main intervention product is determined to be the main intervention product, and the corresponding benchmark label of the main intervention product can be set to 1; otherwise, it can be determined that the correct classification information of the target product under the main intervention product is not the main intervention product, and the corresponding benchmark label of the main intervention product can be set to 0.

[0138] Labeling rule 2: If the main intervention product judgment logic is configured and the main intervention product preset logo is not configured, then when the target product meets the main intervention product judgment logic, it is determined that the correct classification information of the target product under the main intervention product is the main intervention product, and the corresponding benchmark label of the main intervention product can be set to 1; otherwise, it can be determined that the correct classification information of the target product under the main intervention product is not the main intervention product, and the corresponding benchmark label of the main intervention product can be set to 0.

[0139] Labeling rule 3: If the main intervention product judgment logic is not configured and the main intervention product preset mark is configured, then when the target product has the main intervention product preset mark, it is determined that the correct classification information of the target product under the main intervention product is the main intervention product, and the corresponding benchmark label of the main intervention product can be set to 1; otherwise, it can be determined that the correct classification information of the target product under the main intervention product is not the main intervention product, and the corresponding benchmark label of the main intervention product can be set to 0.

[0140] For example, for the target event venue, if both the main intervention product determination logic and the main intervention product preset logo are pre-configured, the main intervention product can be identified according to labeling rule 1.

[0141] If N1 main intervention products (where N1≥1) are identified from the target products, that is, the baseline label of these N1 target products under the main intervention product label is 1, then the labels to be verified of these N1 target products under the main intervention product label can be read from the product database of the target event venue. If all the labels to be verified are 1, that is, the marked classification information is consistent with the correct classification information, then it can be determined that the rule verification of the main intervention product has passed, that is, the classification of the main intervention product is accurate; if there is a product with a label to be verified of 0 among the N1 target products, it can be determined that the rule verification of the main intervention product has failed.

[0142] As an example, the above verification rules can be implemented through negation logic. For example, the verification rules are specifically reflected as follows: for target products classified as main intervention products, the number of products with a to-be-verified label of 0 under the main intervention product label can be counted from the product database of the target event venue. If the number of products is 0, it can be determined that the rule verification of the main intervention product has passed; if the number of products is greater than 0, it can be determined that the rule verification of the main intervention product has failed.

[0143] That is to say, for the target products classified as the main intervention products, their labels to be verified should all be 1. If the number of products with the label to be verified as 0 is greater than 0, it means that the data processing logic for labeling the target products as the main intervention products is abnormal.

[0144] Optionally, for target products classified as non-main intervention products, you can also refer to the verification rules implemented by the inversion logic above, and implement rule verification of these products under the main intervention products by counting the number of products with the to-be-verified label of 1. The specific implementation process can be found above, and no examples will be given here.

[0145] In addition, the verification rule that the tag to be verified cannot be null or empty can be combined to verify the data processing logic. In other words, whether it is a target product classified as a main intervention product or a target product classified as a non-main intervention product, if the tag to be verified under the main intervention product label is null or empty, it can be determined that the actual classification information of the target product under the main intervention product does not match the labeled classification information.

[0146] In this way, comprehensive data verification can be achieved for all target products, which will help to further improve the accuracy and reliability of data verification results.

[0147] 4. Check the dimensions for seasonal products

[0148] For another example, the classification of goods can be reflected in whether they are seasonal goods, that is, seasonal goods. From the perspective of the accuracy of seasonal goods classification, the correctness of the data processing logic for seasonal goods labeling for target goods can be verified.

[0149] In this example, the data types may include: the seasonal start month and the seasonal end month of the target product. Correspondingly, the benchmark label is the correct classification information of the target product under seasonal products, and the label to be verified is the marked classification information of the target product under seasonal products.

[0150] The verification rule can be reflected as follows: the labeled classification information of the target product under the seasonal product matches the correct classification information.

[0151] In this example, you can add a base label under seasonal products to the target product according to the following labeling rules:

[0152] The seasonal start month and seasonal end month of the target commodity are obtained respectively. If the seasonal start and end months of the target commodity include a future preset time period, such as the next 30 days, it can be determined that the correct classification information of the target commodity under seasonal commodities is a seasonal commodity, and the corresponding benchmark label of the seasonal commodity can be set to 1; otherwise, it can be determined that the correct classification information of the target commodity under seasonal commodities is a non-seasonal commodity, and the corresponding benchmark label of the seasonal commodity can be set to 0.

[0153] If N2 seasonal products (where N2 ≥ 1) are identified from the target products, that is, the reference label of these N2 target products under the seasonal product label is 1, then the labels to be verified under the seasonal product label of these N2 target products can be read from the product database of the target event venue. If the labels to be verified are all 1, that is, the marked classification information is consistent with the correct classification information, then it can be determined that the rule verification of the seasonal products has passed, that is, the classification of the seasonal products is accurate; if there is a product with a label to be verified of 0 among the N2 target products, it can be determined that the rule verification of the seasonal products has failed.

[0154] As an example, the above verification rules can be implemented through negation logic. For example, the verification rules are specifically reflected as follows: for target products classified as seasonal products, the number of products with a to-be-verified label of 0 under the seasonal product label can be counted from the product database of the target event venue. If the number of products is 0, it can be determined that the rule verification of the seasonal products has passed; if the number of products is greater than 0, it can be determined that the rule verification of the seasonal products has failed.

[0155] That is to say, for target products classified as seasonal products, their labels to be verified should all be 1. If the number of products with labels to be verified of 0 is greater than 0, it means that the data processing logic for seasonal product labeling of the target products is abnormal.

[0156] 5. Verify dimensions are new

[0157] For another example, the classification of a product can be reflected as whether it is a new product. From the perspective of the accuracy of the new product classification, the correctness of the data processing logic for marking new products for the target product can be verified.

[0158] In this example, the data type may include: the creation time of the target product, and correspondingly, the reference label is the correct classification information of the target product under the new product, and the label to be verified is the marked classification information of the target product under the new product.

[0159] The verification rule can be reflected as follows: the labeled classification information of the target product under the new product matches the correct classification information.

[0160] In this example, you can add a benchmark label under New Products to the target product according to the following labeling rules:

[0161] Get the new product creation time of the target product. If the new product creation time of the target product has not exceeded the preset time, for example, less than 30 days, it can be determined that the correct classification information of the target product under the new product is a new product, and the corresponding benchmark label of the new product can be set to 1; otherwise, it can be determined that the correct classification information of the target product under the new product is not a new product, and the corresponding benchmark label of the new product can be set to 0.

[0162] If N3 new products are identified from the target products (where N3 ≥ 1), that is, the reference label of these N3 target products under the new product label is 1, then the labels to be verified of these N3 target products under the new product label can be read from the product database of the target event venue. If the labels to be verified are all 1, that is, the marked classification information is consistent with the correct classification information, then it can be determined that the rule verification of the new product has passed, that is, the classification of the new product is accurate; if there is a product with a label to be verified of 0 among the N3 products, it can be determined that the rule verification of the new product has failed.

[0163] As an example, the above verification rules can be implemented through negation logic. For example, the verification rules are specifically reflected as follows: for target products classified as new products, the number of products with a to-be-verified label of 0 under the new product label can be counted from the product database of the target event venue. If the number of products is 0, it can be determined that the rule verification of the new product has passed; if the number of products is greater than 0, it can be determined that the rule verification of the new product has failed.

[0164] That is to say, for target products classified as new products, their labels to be verified should all be 1. If the number of products with labels to be verified of 0 is greater than 0, it means that the data processing logic for new product labeling of the target products is abnormal.

[0165] In practical applications, verification dimensions can also be divided from the perspective of B-end labeling. For example, the main intervention products, seasonal products, and new products in the above examples all belong to the verification dimensions determined under B-end labeling.

[0166] 6. Check the dimension to see if the product is exposed to zero

[0167] For another example, product classification can be reflected as whether it is a 0-exposure product, that is, a product that is not displayed to consumers for viewing. From the perspective of the accuracy of the 0-exposure product classification, the correctness of the data processing logic for marking the target product with 0-exposure products can be verified.

[0168] In this example, the data type may include: the exposure UV of the target product, and the corresponding benchmark label is the correct classification information of the target product under the 0 exposure product, and the label to be verified is the marked classification information of the target product under the 0 exposure product. Among them, the exposure UV refers to the number of times the product is exposed to independent visitors. When the product is viewed once by a consumer user through the display page, it is recorded as one exposure UV.

[0169] The verification rule can be reflected as follows: the labeled classification information of the target product under the 0-exposure product matches the correct classification information.

[0170] In this example, you can add a benchmark label under the 0-exposure product to the target product according to the following labeling rules:

[0171] Obtain the exposure UV of the target product. If the exposure UV of the target product = 0, it can be determined that the correct classification information of the target product under the 0-exposure product is a 0-exposure product, and the corresponding benchmark label of the 0-exposure product can be set to 1; otherwise, it can be determined that the correct classification information of the target product under the 0-exposure product is a non-0-exposure product, and the corresponding benchmark label of the 0-exposure product can be set to 0.

[0172] If N4 0-exposure products (where N4 ≥ 1) are identified from the target products, that is, the reference label of these N4 target products under the 0-exposure product label is 1, then the labels to be verified of these N4 target products under the 0-exposure product label can be read from the product database of the target activity venue. If the labels to be verified are all 1, that is, the marked classification information is consistent with the correct classification information, then it can be determined that the rule verification of the 0-exposure product has passed, that is, the classification of the 0-exposure product is accurate; if there is a product with a label to be verified of 0 among the N4 products, it can be determined that the rule verification of the 0-exposure product has failed.

[0173] As an example, the above verification rules can be implemented through negation logic. For example, the verification rules are specifically reflected as follows: for target products classified as 0-exposure products, the number of products with a to-be-verified label of 0 under the 0-exposure product label can be counted from the product database of the target event venue. If the number of products is 0, it can be determined that the rule verification of the 0-exposure product has passed; if the number of products is greater than 0, it can be determined that the rule verification of the 0-exposure product has failed.

[0174] That is to say, for the target products classified as 0-exposure products, their to-be-verified labels should all be 1. If the number of products with the to-be-verified label of 0 is greater than 0, it means that the data processing logic for marking the target products with 0-exposure products is abnormal.

[0175] 7. Verify the dimension as product exposure conversion type

[0176] In actual applications, the exposure conversion of products can be classified according to usage requirements. For example, the exposure conversion types of products can at least include: high exposure and high conversion, high exposure and low conversion, low exposure and high conversion, and invalid exposure. From the perspective of exposure conversion classification accuracy, the data processing logic for marking the exposure conversion type of the target product can be verified for correctness.

[0177] In this example, the data types may include: exposure UV of the target product, exposure to transaction conversion rate CT-CVR, and UV value. Correspondingly, the benchmark label is the correct exposure conversion type of the target product, and the label to be verified is the marked exposure conversion type of the target product. Among them, CT-CVR is the conversion from the exposure of the product to the consumer user to the consumer user's final purchase, which can be calculated by dividing the number of completed orders by the exposure. UV value is the average value generated by each independent visitor, which can be calculated by dividing the total transaction amount by the number of independent visitors.

[0178] The verification rule can be reflected as follows: the labeled exposure conversion type of the target product matches the correct exposure conversion type.

[0179] In this example, you can add a benchmark label under the product exposure conversion type to the target product according to the following labeling rules:

[0180] If the exposure UV of the target product is ≥ the first threshold and the CT-CVR of the target product is ≥ the second threshold; or, if the exposure UV of the target product is ≥ the first threshold and the UV value of the target product is ≥ the third threshold, then the correct exposure conversion type of the target product can be determined to be a high-exposure and high-conversion product, and the exposure conversion type benchmark label can be set to high-exposure and high-conversion;

[0181] If the exposure UV of the target product is ≥ the first threshold and the CT-CVR of the target product is < the second threshold; or, if the exposure UV of the target product is ≥ the first threshold and the UV value of the target product is < the third threshold, then it can be determined that the correct exposure conversion type of the target product is a high-exposure, low-conversion product, and the exposure conversion type benchmark label can be set to high-exposure, low-conversion;

[0182] If the exposure UV of the target product is less than the fourth threshold and the CT-CVR of the target product is greater than or equal to the second threshold; or, if the exposure UV of the target product is less than the fourth threshold and the UV value of the target product is greater than or equal to the third threshold, then it can be determined that the correct exposure conversion type of the target product is a low-exposure, high-conversion product, and the exposure conversion type benchmark label can be set to low-exposure, high-conversion;

[0183] If the exposure UV of the target product is greater than 100 and the CT-CVR of the target product is equal to 0, it can be determined that the correct exposure conversion type of the target product is an invalid exposure product, and the reference label of the exposure conversion type can be set to invalid exposure accordingly.

[0184] The first threshold is greater than the fourth threshold. In one example, the first threshold, the second threshold, and the third threshold may be set at the 75th percentile, and the fourth threshold may be set at the 25th percentile, which may be set specifically according to usage requirements.

[0185] 8. Check the dimension for high GMV contribution products

[0186] For another example, the classification of a product can be reflected in whether it is a high GMV contribution product. From the perspective of the accuracy of the classification of high GMV contribution products, the correctness of the data processing logic for marking the target product as a high GMV contribution product can be verified.

[0187] In this example, the data type may include: the GMV contribution ratio of the target product. Correspondingly, the benchmark label is the correct classification information of the target product under high GMV contribution products, and the to-be-verified label is the actual classification information of the target product under high GMV contribution products. Among them, high GMV contribution products are products whose GMV contribution ratio is not less than the preset ratio value.

[0188] The verification rule can be reflected as follows: the labeled classification information of the target product under high GMV contribution products matches the correct classification information.

[0189] In this example, based on the product information feeds displayed on all pages of the online store, GMV contribution statistics can be performed from different dimensions, and benchmark labels under high GMV contribution products can be added to the target products according to the following labeling rules:

[0190] Labeling rule 1 can obtain the GMV contribution ratio of the target products associated with different regions according to the statistical dimension of region. If the GMV contribution ratio of the target product is ≥ the first preset ratio value, it can be determined that the correct classification information of the target product under high GMV contribution products is a high GMV contribution product, and the benchmark label of the high GMV contribution product can be set to 1 accordingly; otherwise, it can be determined that the correct classification information of the target product under high GMV contribution products is a non-high GMV contribution product, and the benchmark label of the high GMV contribution product can be set to 0 accordingly.

[0191] Among them, the first preset proportion value can be a manually set value; or the target products associated with different regions can be sorted according to the GMV contribution proportion, and then determined according to the GMV contribution proportions of the top M target products with the highest proportions, for example, M=10, that is, the GMV contribution proportion of the 10th-ranked target product is determined as the first preset proportion value, and the corresponding top 10 target products are all high-GMV contribution products. Under this implementation method, 10 high-GMV contribution products can be determined for each region.

[0192] Labeling rule 2 can obtain the GMV contribution ratio of target products associated with different categories according to the product category as the statistical dimension. If the GMV contribution ratio of the target product is ≥ the second preset ratio value, it can be determined that the correct classification information of the target product under high GMV contribution products is a high GMV contribution product, and the benchmark label of the high GMV contribution product can be set to 1 accordingly; otherwise, it can be determined that the correct classification information of the target product under high GMV contribution products is a non-high GMV contribution product, and the benchmark label of the high GMV contribution product can be set to 0 accordingly.

[0193] In this implementation, the product category can be reflected as the front-end category of the product, such as the first-level front-end category, which is the highest-level category in the product classification displayed on the front-end by the online store to consumers. This is because the back-end category is more standardized and stable, while the front-end category is more flexible and can better meet the browsing experience of consumers. Sorting the GMV contribution ratio by the front-end category is more in line with the browsing preferences of consumers and has more statistical flexibility.

[0194] Similarly, the second preset percentage value may be a manually set value, or may be determined based on the GMV contribution percentage ranking of target products associated with different categories. For details, please refer to the above introduction, and no examples will be given here.

[0195] Regardless of the dimension used to perform GMV contribution statistics, if N5 high GMV contribution products are identified from the target products (where N5 ≥ 1), that is, the baseline label of these N5 target products under the high GMV contribution product label is 1, then the labels to be verified of these N5 target products under the high GMV contribution product label can be read from the product database of the target event venue. If the values ​​of the labels to be verified are all 1, that is, the marked classification information is consistent with the correct classification information, then it can be determined that the rule verification of the high GMV contribution products has passed, that is, the classification of high GMV contribution products is accurate; if there are products with a label to be verified of 0 among the N5 products, then it can be determined that the rule verification of the high GMV contribution products has failed.

[0196] As an example, the above verification rules can be implemented through negation logic. For example, the verification rules are specifically reflected as follows: for target products classified as high GMV contribution products, the number of products with a to-be-verified label of 0 under the high GMV contribution product label can be counted from the product database of the target event venue. If the number of products is 0, it can be determined that the rule verification of the high GMV contribution product has passed; if the number of products is greater than 0, it can be determined that the rule verification of the high GMV contribution product has failed.

[0197] That is to say, for the target products classified as high GMV contribution products, their labels to be verified should all be 1. If the number of products with labels to be verified of 0 is greater than 0, it means that the data processing logic for labeling the target products as high GMV contribution products is abnormal.

[0198] In actual applications, verification dimensions can also be divided from the perspective of C-end labeling. For example, the 0-exposure products, exposure conversion types, and high GMV contribution products in the above example all belong to the verification dimensions determined under C-end labeling.

[0199] 9. Verify dimensions for recommended products

[0200] For another example, the classification of a product can be reflected in whether it is a main product, that is, a product that is recommended with priority. From the perspective of the accuracy of the classification of the main products, the correctness of the data processing logic for marking the target products with the main products can be verified.

[0201] In this example, the data types may include: the exposure conversion type of the target product, whether it is a high GMV contribution product, whether it is a main promotion intervention product, and whether it is a 0-exposure product, that is, the benchmark labels of the target product under multiple other verification dimensions are integrated to achieve the labeling of the target product in the main promotion dimension. Correspondingly, the benchmark label is the correct classification information of the target product under the main promotion product, and the label to be verified is the marked classification information of the target product under the main promotion product.

[0202] The verification rule can be reflected as follows: the marked classification information of the target product under the main product matches the correct classification information.

[0203] In this example, you can add a base tag under the main product to the target product according to the following tagging rules:

[0204] If the target product meets at least one of the following conditions, the correct classification information of the target product under the main product can be determined to be the main product, and the benchmark label of the main product can be set to 1; otherwise, the correct classification information of the target product under the main product is determined to be a non-main product, and the benchmark label of the main product can be set to 0:

[0205] Condition 1: The exposure conversion type of the target product is high exposure and high conversion or low exposure and high conversion, that is, the base label of the exposure conversion type is high exposure and high conversion or low exposure and high conversion;

[0206] Condition 2: The target product is a high GMV contribution product, that is, the benchmark label of the high GMV contribution product is 1;

[0207] Condition 3: The target product is the main intervention product and a 0-exposure product, that is, the benchmark label of the main intervention product is 1, and the benchmark label of the 0-exposure product is 1.

[0208] If N6 main promoted products (where N6 ≥ 1) are identified from the target products, that is, the reference label of these N6 target products under the main promoted product label is 1, then the labels to be verified of these N6 target products under the main promoted product label can be read from the product database of the target event venue. If the labels to be verified are all 1, that is, the marked classification information is consistent with the correct classification information, then it can be determined that the rule verification of the main promoted product has passed, that is, the classification of the main promoted product is correct; if there is a product with a label to be verified of 0 among the N6 products, it can be determined that the rule verification of the main promoted product has failed.

[0209] As an example, the above verification rules can be implemented through negation logic. For example, the verification rules are specifically reflected as follows: for target products classified as main promoted products, the number of products with a to-be-verified label of 0 under the main promoted product label can be counted from the product database of the target event venue. If the number of products is 0, it can be determined that the rule verification of the main promoted product has passed; if the number of products is greater than 0, it can be determined that the rule verification of the main promoted product has failed.

[0210] That is to say, for the target products classified as the main promoted products, their labels to be verified should all be 1. If the number of products with labels to be verified of 0 is greater than 0, it means that the data processing logic for labeling the target products as the main promoted products is abnormal.

[0211] Understandably, when the verification dimensions are seasonal products, new products, zero-exposure products, high GMV contribution products, and main products, rule verification can also be performed based on the verification rule that the tag to be verified cannot be null or empty; and the number of products with the tag to be verified as 1 is counted based on the inverted logic to implement rule verification of target products classified as non-seasonal products, non-new products, non-zero-exposure products, non-high GMV contribution products, and non-main products. In this way, comprehensive data verification of all target products is achieved.

[0212] 10. Verification dimension is product stratification level

[0213] In practical applications, commodities can be stratified according to usage requirements, and the stratified levels can be used to represent the weight of the target commodity at the target event venue, thereby affecting whether the target commodity is more easily seen by consumers at the target event venue. In this way, the data processing logic for stratification and grading of target commodities can be verified for correctness from the perspective of stratification accuracy.

[0214] In this example, the data types may include: the exposure conversion type of the target product, UV value, whether to promote intervention products, and whether to promote the product. Correspondingly, the benchmark label is the correct stratification level of the target product, and the label to be verified is the marked stratification level of the target product.

[0215] The verification rule can be reflected as follows: the labeled classification level of the target product matches the correct classification level.

[0216] Taking the division of four product layers as an example, the corresponding level points of each layer can be set respectively, and the level points can be used for marking. For example, the level points of the layers are: level point of L1 = 1, level point of L2 = 2, level point of L3 = 3, and level point of L4 = 4. The number of product layers and the corresponding level points of different layers can be determined according to usage requirements.

[0217] Specifically, you can add the benchmark label at the hierarchical level to the target product according to the following labeling rules:

[0218] If the target product is the main intervention product (i.e., the benchmark label of the main intervention product is 1), and the exposure conversion type of the target product is high explosion and high conversion or low exposure and high conversion, then the correct stratification level of the target product can be determined to be L1, and the benchmark label of the stratification level label can be set to 1 accordingly;

[0219] If the target product is the main product and the UV value of the target product is ≥ the fifth threshold, it can be determined that the correct stratification level of the target product is L2, and the benchmark label of the stratification level can be set to 2 accordingly;

[0220] If the target product is the main product, and the sixth threshold ≤ UV value of the target product ≤ the fifth threshold, then the correct stratification level of the target product can be determined to be L3, and the benchmark label of the stratification level can be set to 3 accordingly;

[0221] If the benchmark labels of the target product under the main intervention product, exposure conversion type, main product, and UV value meet other conditions besides the above conditions, it can be determined that the correct stratification level of the target product is L4, and the benchmark label of the stratification level label can be set to 4 accordingly.

[0222] Among them, the value of the fifth threshold can be the 95th percentile, and the value of the sixth threshold can be the 75th percentile, which can be set specifically according to usage requirements.

[0223] 11. The verification dimension is the comprehensive rating of the product

[0224] In practical applications, the products can be comprehensively scored according to usage requirements. Correspondingly, the data processing logic for calculating and marking the comprehensive scores of the target products can be verified for correctness from the perspective of the accuracy of the calculation logic of the comprehensive scores.

[0225] In this example, the data types may include: the hierarchical grade score of the target product, and the UV value ranking score of the product under the hierarchy. Correspondingly, the benchmark label is the correct comprehensive score of the target product, and the label to be verified is the marked comprehensive score of the target product.

[0226] The verification rule can be reflected as follows: the labeled comprehensive score of the target product matches the correct comprehensive score.

[0227] As an example, a benchmark label under the comprehensive score can be added to the target product according to the following labeling rules: the comprehensive score S of the target product = the hierarchical level score of the target product * K + the UV value ranking score of the target product at the hierarchical level. The value of the coefficient K can be determined according to application requirements. The embodiment of the present application does not limit the specific calculation formula of the comprehensive score.

[0228] Specifically, for the same target product, its benchmark label under the comprehensive score can be obtained according to the above comprehensive score calculation formula, and the target product's label to be verified can be read from the product database of the target event venue to compare the label information. If the two are the same, the marked comprehensive score is consistent with the correct comprehensive score. In this way, when the marked comprehensive scores of all target products are consistent with the correct comprehensive scores, it can be determined that the comprehensive score rule verification has passed; otherwise, it is determined that the comprehensive score rule verification has failed, indicating that the data processing logic for marking the target product with a comprehensive score is abnormal.

[0229] In summary, the embodiments of the present application can set corresponding verification dimensions for different data processing logics of smart investment applications, and configure the data types and verification rules required for verification analysis of each verification dimension. In this way, labels can be added to the target products associated with the target event venue based on the target data associated with the target product under the data type, and the labeling rules that have passed accuracy verification, and the label can be used as a benchmark label.

[0230] In addition, a product database established by the smart betting application for the target event venue can also be obtained. The database stores the labels generated by the smart betting application for the target product under each verification dimension through different data processing logics, and uses the labels as the labels to be verified. In this way, according to the verification rules associated with the verification dimension, the labels to be verified and the reference labels associated with the target product under the verification dimension can be compared, and the correctness of the target product associated with the target event venue can be verified based on the information comparison results.

[0231] The implementation process of such data verification is more convenient and is not affected by human factors, which helps to improve the accuracy and reliability of the verification results.

[0232] In actual applications, if the target product fails the correctness check, the data processing logic corresponding to the target verification dimension is fault-located based on the target verification dimension associated with the mismatched tag to be inspected and the reference tag.

[0233] As mentioned above, different verification dimensions are used to verify different data processing logics. If an unmatched target verification dimension is identified during the data verification process, the data processing logic of the smart betting application can be fault-located accordingly. For example, the identification information of the target verification dimension can be provided to relevant personnel, who can then locate the data processing logic related to the target verification dimension from the implementation code of the smart betting application and perform targeted troubleshooting on the problems therein.

[0234] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with the relevant laws, regulations and standards of the relevant countries and regions, and provide corresponding operation entrances for users to choose to authorize or refuse.

[0235] Corresponding to the aforementioned method embodiment, the present application embodiment also provides a data verification device for verifying the correctness of commodities in the commodity database automatically established by the intelligent betting application for the target event venue. Figure 2 , the device may include:

[0236] The data acquisition unit 201 is used to obtain the verification configuration information of the target activity venue and the commodity database, wherein the verification configuration information includes multiple verification dimensions, and data types and verification rules required for verification analysis on different verification dimensions; different verification dimensions correspond to different data processing logics used by the smart betting application when establishing the commodity database, and the data processing logic is used to select a target commodity from a data source and generate a label of the target commodity under the verification dimension, and add it to the commodity database; wherein the label added to the target commodity by the data processing logic is a label to be verified;

[0237] A reference label adding unit 202 is used to obtain target data associated with the target product under the data type, add a label to the target product according to the target data and a labeling rule that has been verified for accuracy, and use the label as a reference label;

[0238] The information comparison unit 203 is used to perform information comparison on the to-be-verified label and the reference label associated with the target product under the verification dimension according to the verification rules required for verification analysis of the verification dimension, and to judge the correctness of the target product associated with the target activity venue according to the corresponding matching of the label information associated with the target product under the multiple verification dimensions.

[0239] Wherein, the verification configuration information is stored in a rule database.

[0240] The data acquisition unit is specifically used to: after determining the multiple verification dimensions that need to be verified and analyzed, dynamically splice the coding information of the multiple verification dimensions in the request link for accessing the rule database; and obtain the data types and verification rules required for verifying and analyzing the multiple verification dimensions from the rule database through an access request carrying the coding information of the multiple verification dimensions.

[0241] The verification dimension is defined according to the natural attributes of the product, including at least one of the following items:

[0242] Taking the number of commodities at the venue as the verification dimension, the correctness of the data processing logic for selecting the target commodity from the data source is verified;

[0243] Taking the source of goods as the verification dimension, the correctness of the data processing logic for marking the source of the target goods according to the data source is verified;

[0244] Taking the main intervention products as the verification dimension, the correctness of the data processing logic for labeling the main intervention products for the target products is verified;

[0245] Taking seasonal products as the verification dimension, the correctness of the data processing logic for seasonal product tagging for the target products is verified;

[0246] Taking new products as the verification dimension, the data processing logic for new product labeling of the target product is verified for correctness.

[0247] The data types required for verification analysis at different verification dimensions include:

[0248] The verification dimension is the number of commodities in the venue, and the data type includes: identification information of the target commodity;

[0249] The verification dimension is the source of the commodity, and the data type includes: identification information of the data source;

[0250] The verification dimension is the main intervention product, and the data type includes: main intervention product determination logic and / or main intervention product preset identification;

[0251] The verification dimension is the seasonal product, and the data type includes: the seasonal start and end months of the target product;

[0252] The verification dimension is the new product, and the data type includes: the creation time of the target product.

[0253] The verification dimension is defined according to the product exposure conversion situation, including at least one of the following items:

[0254] Taking 0-exposure products as the verification dimension, the correctness of the data processing logic for marking 0-exposure products for the target products is verified;

[0255] Taking the product exposure conversion type as the verification dimension, the correctness of the data processing logic for marking the exposure conversion type of the target product is verified;

[0256] Taking high GMV contribution products as the verification dimension, the correctness of the data processing logic for marking high GMV contribution products for the target products is verified; the GMV contribution ratio of the high GMV contribution products is not less than the preset ratio value;

[0257] Taking the main promoted product as the verification dimension, the correctness of the data processing logic for marking the main promoted product for the target product is verified;

[0258] Taking the commodity stratification level as the verification dimension, the correctness of the data processing logic for stratification level labeling of the target commodity is verified;

[0259] Taking the comprehensive rating of the product as the verification dimension, the correctness of the data processing logic for calculating and marking the comprehensive rating of the target product is verified.

[0260] The data types required for verification analysis at different verification dimensions include:

[0261] The verification dimension is the zero-exposure product, and the data type includes: the number of exposed unique visitors UV of the target product;

[0262] The verification dimension is the product exposure conversion type, and the data type includes: the exposure UV of the target product, the exposure to transaction conversion rate CT-CVR, and the independent visitor UV value;

[0263] The verification dimension is the high GMV contribution product, and the data type includes: the GMV contribution ratio of the target product;

[0264] The verification dimension is the main recommended product, and the data types include: the exposure conversion type of the target product, whether it is a high GMV contribution product, whether it is a main recommended intervention product, and whether it is a 0-exposure product;

[0265] The verification dimension is the product stratification level, and the data type includes: the exposure conversion type, UV value, whether the intervention product is promoted, and whether the product is promoted;

[0266] The verification dimension is the comprehensive score of the product, and the data type includes: the hierarchical level score of the target product, and the UV value ranking score of the product under the stratification.

[0267] Wherein, the verification dimension is the product exposure conversion type, and the product exposure conversion type includes high exposure and high conversion, high exposure and low conversion, low exposure and high conversion, and invalid exposure.

[0268] The reference label adding unit is specifically used to add a reference label under the commodity exposure conversion type to the target commodity according to the following labeling rules:

[0269] If the exposure UV of the target product is ≥ the first threshold and the CT-CVR of the target product is ≥ the second threshold; or, the exposure UV of the target product is ≥ the first threshold and the UV value of the target product is ≥ the third threshold, then it is determined that the benchmark tag associated with the target product under the product exposure conversion type is high exposure and high conversion;

[0270] If the exposure UV of the target product is greater than or equal to the first threshold and the CT-CVR of the target product is less than the second threshold; or, the exposure UV of the target product is greater than or equal to the first threshold and the UV value of the target product is less than the third threshold, then it is determined that the benchmark tag associated with the target product under the product exposure conversion type is high exposure and low conversion;

[0271] If the exposure UV of the target product is less than the fourth threshold and the CT-CVR of the target product is greater than or equal to the second threshold; or, the exposure UV of the target product is less than the fourth threshold and the UV value of the target product is greater than or equal to the third threshold, then it is determined that the benchmark tag associated with the target product under the product exposure conversion type is low exposure and high conversion;

[0272] If the exposure UV of the target product is greater than 100 and the CT-CVR of the target product is equal to 0, it is determined that the reference tag associated with the target product under the product exposure conversion type is invalid exposure;

[0273] The first threshold is greater than the fourth threshold.

[0274] Among them, the verification dimension is the main recommended product, then

[0275] The reference label adding unit is specifically configured to: if the labeling rule is that the target product satisfies at least one of the following conditions, determine that the reference label associated with the target product under the main product is a main product; otherwise, determine that the reference label associated with the target product under the main product is a non-main product:

[0276] The product exposure conversion type of the target product is the high exposure and high conversion or the low exposure and high conversion;

[0277] The target product is a high GMV contribution product;

[0278] The target product is the main intervention product and has 0 exposure.

[0279] The intelligent investment application is run in an online environment of a commodity information service system, and the device further comprises:

[0280] The page generation unit is used to generate a target page corresponding to the target activity venue according to the tag saved by the commodity database for the target commodity when the target commodity passes the correctness check, so as to put the target page into the commodity information service system and provide shopping services for the target commodity to the consumer user.

[0281] The intelligent betting application is run in the test environment of the commodity information service system, and the venue generation capability of the intelligent betting application in the application iteration process is verified through the commodity correctness verification. The device also includes:

[0282] The application publishing unit is used to determine that the venue generation capability verification of the smart betting application has passed when the target product passes the correctness check, and publish the smart betting application to the online environment of the product information service system to realize automatic venue construction and delivery.

[0283] Wherein, the device further comprises:

[0284] The fault location unit is used to locate the fault of the data processing logic corresponding to the target verification dimension according to the target verification dimension associated with the mismatched tag to be inspected and the reference tag when the target commodity fails to pass the correctness verification.

[0285] In addition, an embodiment of the present application further provides a computer-readable storage medium on which a computer program is stored, and when the program is executed by a processor, the steps of any one of the methods in the aforementioned method embodiments are implemented.

[0286] And an electronic device, comprising:

[0287] one or more processors; and

[0288] A memory associated with the one or more processors, the memory being used to store program instructions, wherein the program instructions, when read and executed by the one or more processors, execute the steps of the method described in any one of the aforementioned method embodiments.

[0289] In addition, an embodiment of the present application also provides a computer program product, including a computer program / computer executable instructions, which, when executed by a processor in an electronic device, implements the steps of any of the methods described in the aforementioned method embodiments.

[0290] in, Figure 3 The architecture of an electronic device is exemplarily shown. For example, device 300 can be a mobile phone, a computer, a digital broadcast terminal, a messaging device, a game console, a tablet device, a medical device, a fitness device, a personal digital assistant, an aircraft, etc.

[0291] Reference Figure 3 , device 300 may include one or more of the following components: a processing component 302 , a memory 304 , a power component 306 , a multimedia component 308 , an audio component 310 , an input / output (I / O) interface 312 , a sensor component 314 , and a communication component 316 .

[0292] The processing component 302 generally controls the overall operation of the device 300, such as operations associated with display, phone calls, data communications, camera operations, and recording operations. The processing component 302 may include one or more processors 320 to execute instructions to complete all or part of the steps of the method provided by the technical solution of the present disclosure. In addition, the processing component 302 may include one or more modules to facilitate the interaction between the processing component 302 and other components. For example, the processing component 302 may include a multimedia module to facilitate the interaction between the multimedia component 308 and the processing component 302.

[0293] The memory 304 is configured to store various types of data to support operations on the device 300. Examples of such data include instructions for any application or method operating on the device 300, contact data, phone book data, messages, pictures, videos, etc. The memory 304 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk.

[0294] The power supply component 306 provides power to the various components of the device 300. The power supply component 306 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power for the device 300.

[0295] The multimedia component 308 includes a screen that provides an output interface between the device 300 and the user. In some embodiments, the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen may be implemented as a touch screen to receive input signals from the user. The touch panel includes one or more touch sensors to sense touch, slide, and gestures on the touch panel. The touch sensor may not only sense the boundaries of the touch or slide action, but also detect the duration and pressure associated with the touch or slide operation. In some embodiments, the multimedia component 308 includes a front camera and / or a rear camera. When the device 300 is in an operating mode, such as a shooting mode or a video mode, the front camera and / or the rear camera may receive external multimedia data. Each front camera and rear camera may be a fixed optical lens system or have a focal length and optical zoom capability.

[0296] The audio component 310 is configured to output and / or input audio signals. For example, the audio component 310 includes a microphone (MIC), and when the device 300 is in an operating mode, such as a call mode, a recording mode, and a speech recognition mode, the microphone is configured to receive an external audio signal. The received audio signal can be further stored in the memory 304 or sent via the communication component 316. In some embodiments, the audio component 310 also includes a speaker for outputting audio signals.

[0297] The input / output (I / O) interface 312 provides an interface between the processing component 302 and peripheral interface modules, such as keyboards, click wheels, buttons, etc. These buttons may include but are not limited to: a home button, a volume button, a start button, and a lock button.

[0298] The sensor assembly 314 includes one or more sensors for providing various aspects of status assessment for the device 300. For example, the sensor assembly 314 can detect the open / closed state of the device 300, the relative positioning of components, such as the display and keypad of the device 300, and the sensor assembly 314 can also detect the position change of the device 300 or a component of the device 300, the presence or absence of user contact with the device 300, the orientation or acceleration / deceleration of the device 300, and the temperature change of the device 300. The sensor assembly 314 may include a proximity sensor configured to detect the presence of nearby objects without any physical contact. The sensor assembly 314 may also include an optical sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, the sensor assembly 314 may also include an accelerometer, a gyroscope sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.

[0299] The communication component 316 is configured to facilitate wired or wireless communication between the device 300 and other devices. The device 300 can access a wireless network based on a communication standard, such as WiFi, or a mobile communication network such as 2G, 3G, 4G / LTE, 5G, etc. In an exemplary embodiment, the communication component 316 receives a broadcast signal or broadcast-related information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communication component 316 also includes a near field communication (NFC) module to facilitate short-range communication. For example, the NFC module can be implemented based on radio frequency identification (RFID) technology, infrared data association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology and other technologies.

[0300] In an exemplary embodiment, the device 300 may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the above methods.

[0301] In an exemplary embodiment, a non-transitory computer-readable storage medium including instructions is also provided, such as a memory 304 including instructions, and the above instructions can be executed by a processor 320 of the device 300 to complete the method provided by the technical solution of the present disclosure. For example, the non-transitory computer-readable storage medium can be a ROM, a random access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, etc.

[0302] It can be known from the description of the above implementation methods that those skilled in the art can clearly understand that the present application can be implemented by means of software plus a necessary general hardware platform. Based on such an understanding, the technical solution of the present application can be essentially or partly contributed to the prior art in the form of a software product, which can be stored in a storage medium such as ROM / RAM, a magnetic disk, an optical disk, etc., and includes several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in the various embodiments of the present application or certain parts of the embodiments.

[0303] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can refer to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system or system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can refer to the partial description of the method embodiment. The system and system embodiments described above are merely schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without creative work.

[0304] The above is a detailed introduction to the scheme provided by the present application. The principles and implementation methods of the present application are described in detail using specific examples. The description of the above embodiments is only used to help understand the method and core idea of ​​the present application. At the same time, for those skilled in the art, according to the idea of ​​the present application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting the present application.

Claims

1. A data verification method, characterized in that: The method is used to verify the correctness of commodities in a commodity database automatically established by the intelligent investment application for a target event venue, and includes: The verification configuration information of the target activity venue and the commodity database are obtained, wherein the verification configuration information includes multiple verification dimensions, and data types and verification rules required for verification analysis on different verification dimensions; different verification dimensions correspond to different data processing logics used by the smart betting application when establishing the commodity database, and the data processing logic is used to select a target commodity from a data source and generate a label for the target commodity under the verification dimension, and add the label to the commodity database; wherein the label added to the target commodity by the data processing logic is a label to be verified; Obtain target data associated with the target product under the data type, add a label to the target product according to the target data and a labeling rule that has been verified for accuracy, and use the label as a reference label; According to the verification rules required for verification analysis of the verification dimension, information comparison is performed between the to-be-verified label and the reference label associated with the target product under the verification dimension, and based on the corresponding matching of the label information associated with the target product under the multiple verification dimensions, the correctness of the target product associated with the target activity venue is judged.

2. The method according to claim 1, characterized in that The verification configuration information is stored in a rule database. The obtaining of the verification configuration information of the target activity venue includes: After determining the multiple verification dimensions that need to be verified and analyzed, dynamically splicing the encoding information of the multiple verification dimensions in the request link for accessing the rule database; The data types and verification rules required for verification analysis of the multiple verification dimensions are obtained from the rule database through an access request carrying the coding information of the multiple verification dimensions.

3. The method according to claim 1, characterized in that The verification dimension is defined according to the natural attributes of the product, including at least one of the following items: Taking the number of commodities at the venue as the verification dimension, the correctness of the data processing logic for selecting the target commodity from the data source is verified; Taking the source of goods as the verification dimension, the correctness of the data processing logic for marking the source of the target goods according to the data source is verified; Taking the main intervention products as the verification dimension, the correctness of the data processing logic for labeling the main intervention products for the target products is verified; Taking seasonal products as the verification dimension, the correctness of the data processing logic for seasonal product labeling for the target products is verified; Taking new products as the verification dimension, the data processing logic for new product labeling of the target product is verified for correctness.

4. The method according to claim 3, characterized in that The data types required for verification analysis at different verification dimensions include: The verification dimension is the number of commodities in the venue, and the data type includes: identification information of the target commodity; The verification dimension is the source of the commodity, and the data type includes: identification information of the data source; The verification dimension is the main intervention product, and the data type includes: main intervention product determination logic and / or main intervention product preset identification; the main intervention product is a commodity that is preferentially recommended through pre-configuration; The verification dimension is the seasonal product, and the data type includes: the seasonal start and end months of the target product; The verification dimension is the new product, and the data type includes: the creation time of the target product.

5. The method according to claim 1, characterized in that The verification dimension is defined according to the product exposure conversion situation, including at least one of the following items: Taking 0-exposure products as the verification dimension, the correctness of the data processing logic for marking 0-exposure products for the target products is verified; Taking the product exposure conversion type as the verification dimension, the correctness of the data processing logic for marking the exposure conversion type of the target product is verified; Taking high GMV contribution products as the verification dimension, the correctness of the data processing logic for marking high GMV contribution products for the target products is verified; the GMV contribution ratio of the high GMV contribution products is not less than the preset ratio value; Taking the main recommended product as the verification dimension, the correctness of the data processing logic for marking the main recommended product for the target product is verified; the main recommended product is a preferentially recommended product; Taking the commodity stratification level as the verification dimension, the correctness of the data processing logic for stratification level marking of the target commodity is verified; the commodity stratification level is used to indicate the weight of the target commodity in the target activity venue; Taking the comprehensive rating of the product as the verification dimension, the correctness of the data processing logic for calculating and marking the comprehensive rating of the target product is verified.

6. The method according to claim 5, characterized in that The data types required for verification analysis at different verification dimensions include: The verification dimension is the zero-exposure product, and the data type includes: the number of exposed unique visitors UV of the target product; The verification dimension is the product exposure conversion type, and the data type includes: the exposure UV of the target product, the exposure to transaction conversion rate CT-CVR, and the independent visitor UV value; The verification dimension is the high GMV contribution product, and the data type includes: the GMV contribution ratio of the target product; The verification dimension is the main recommended product, and the data types include: the exposure conversion type of the target product, whether it is a high GMV contribution product, whether it is a main recommended intervention product, and whether it is a 0-exposure product; The verification dimension is the product stratification level, and the data type includes: the exposure conversion type, UV value, whether the intervention product is promoted, and whether the product is promoted; The verification dimension is the comprehensive score of the product, and the data type includes: the hierarchical level score of the target product, and the UV value ranking score of the product under the stratification.

7. The method according to claim 6, characterized in that The verification dimension is the product exposure conversion type, and the product exposure conversion type includes high exposure and high conversion, high exposure and low conversion, low exposure and high conversion, and invalid exposure. The step of adding a label to the target commodity according to the target data and the labeling rules that have been verified for accuracy, and using the label as a reference label, includes: If the exposure UV of the target product is ≥ the first threshold and the CT-CVR of the target product is ≥ the second threshold; or, the exposure UV of the target product is ≥ the first threshold and the UV value of the target product is ≥ the third threshold, then it is determined that the benchmark tag associated with the target product under the product exposure conversion type is high exposure and high conversion; If the exposure UV of the target product is greater than or equal to the first threshold and the CT-CVR of the target product is less than the second threshold; or, the exposure UV of the target product is greater than or equal to the first threshold and the UV value of the target product is less than the third threshold, then it is determined that the benchmark tag associated with the target product under the product exposure conversion type is high exposure and low conversion; If the exposure UV of the target product is less than the fourth threshold and the CT-CVR of the target product is greater than or equal to the second threshold; or, the exposure UV of the target product is less than the fourth threshold and the UV value of the target product is greater than or equal to the third threshold, then it is determined that the benchmark tag associated with the target product under the product exposure conversion type is low exposure and high conversion; If the exposure UV of the target product is greater than 100 and the CT-CVR of the target product is equal to 0, it is determined that the reference tag associated with the target product under the product exposure conversion type is invalid exposure; The first threshold is greater than the fourth threshold.

8. The method according to claim 7, characterized in that If the verification dimension is the main recommended product, The step of adding a label to the target commodity according to the target data and the labeling rules that have been verified for accuracy, and using the label as a reference label, includes: The labeling rule is that if the target product satisfies at least one of the following conditions, the reference label associated with the target product under the main product is determined to be a main product; otherwise, the reference label associated with the target product under the main product is determined to be a non-main product: The product exposure conversion type of the target product is the high exposure and high conversion or the low exposure and high conversion; The target product is a high GMV contribution product; The target product is the main intervention product and has 0 exposure.

9. The method according to any one of claims 1 to 8, characterized in that: The intelligent investment application is run in an online environment of the commodity information service system, and the method further includes: If the target product passes the correctness check, a target page corresponding to the target event venue is generated based on the tag saved in the product database for the target product, so that the target page is placed in the product information service system to provide shopping services for the target product to consumers.

10. The method according to any one of claims 1 to 8, characterized in that: The intelligent betting application is run in the test environment of the commodity information service system, and the venue generation capability of the intelligent betting application in the application iteration process is verified through the commodity correctness verification. The method also includes: If the target product passes the correctness check, it is determined that the venue generation capability of the smart betting application has passed the verification, and the smart betting application is published to the online environment of the product information service system to realize automatic venue construction and delivery.

11. The method according to any one of claims 1 to 8, characterized in that: The method further comprises: If the target commodity fails the correctness check, the data processing logic corresponding to the target check dimension is fault located according to the target check dimension associated with the mismatched tag to be checked and the reference tag.

12. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the method described in any one of claims 1 to 11 are implemented.

13. An electronic device, characterized in that: include: one or more processors; as well as A memory associated with the one or more processors, the memory being used to store program instructions, wherein when the program instructions are read and executed by the one or more processors, the steps of the method according to any one of claims 1 to 11 are executed.

14. A computer program product comprising a computer program / computer executable instructions, characterized in that: When the computer program / computer executable instructions are executed by a processor in an electronic device, the steps of the method according to any one of claims 1 to 11 are implemented.

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