A service data restoration method, device, equipment and medium

Abstract

The application provides a business data restoration method, device, equipment and medium, relates to the field of intelligent medical treatment, and comprises the following steps: acquiring mall business data, generating a commodity data table of a single commodity according to the mall business data; comparing the commodity data table with a preset data template to obtain a difference data item; comparing the difference feature with a node feature of a preset processing node in terms of similarity to obtain a matched processing node, which is used for processing the difference data item to generate a standard data table of corresponding commodity data; and calling the standard data table to generate current commodity data. The application can be used for automatically restoring test data of products such as medicines before the products are put on the market, and improves the efficiency of verification and the reusability of data.

Description

Technical Field

[0001] This application relates to the field of smart healthcare, and more particularly to a method, apparatus, device, and medium for restoring business data. Background Technology

[0002] As e-commerce platforms become standard equipment for internet companies, the trend towards platform-based e-commerce capabilities is becoming increasingly apparent. The types of business entities integrating with these platforms include in-app integration, external app integration, and WeChat official account / mini-program integration. Different businesses have different data requirements, with variations in data usage frequency, cycle, and type. E-commerce platforms offer a rich variety of data, including sales of regular goods, virtual goods, imported goods, and pharmaceuticals, as well as marketing data such as flash sales, limited-time offers, and group buying. This marketing data is used in various business lines and subsidiary projects, so rapid data recovery after expiration reduces the cost of integration testing and ensures continuous data availability.

[0003] However, before products or promotions can be listed, they need to be tested. Creating test data involves complex processes such as merchant product acquisition, promotion creation, promotion registration, and page setup, relying on personnel familiar with the process. If existing product or promotion data becomes invalid, it requires manual confirmation of previous requirements and configurations with the business side, followed by manual data restoration from the operations backend, which is time-consuming, tedious, and has high communication costs. Summary of the Invention

[0004] In view of the problems existing in the prior art, this application proposes a business data restoration method, apparatus, equipment and medium, which mainly solves the problem that existing e-commerce data relies on manual restoration and is complicated to operate.

[0005] To achieve the above and other objectives, the technical solution adopted in this application is as follows.

[0006] This application provides a method for restoring business data, including:

[0007] Obtain e-commerce business data, and generate a product data table for each individual product based on the e-commerce business data;

[0008] The product data table is compared with a preset data template to obtain the difference data items;

[0009] Feature extraction is performed on the difference data items to obtain difference features. The difference features are compared with the node features of preset processing nodes to obtain matching processing nodes for processing the difference data items to generate standard product data. A standard data table for corresponding product data is constructed based on the standard product data.

[0010] In response to a product business request, the standard data table is invoked to generate the current product data.

[0011] In one embodiment of this application, after obtaining the e-commerce business data, the method further includes:

[0012] Generate a data table of individual product activities to be restored based on the mall's business data;

[0013] In response to a product activity business request, the data table to be restored is invoked to generate the current product activity data.

[0014] In one embodiment of this application, generating a product data table for a single product based on the e-commerce business data includes:

[0015] Iterate through all products in the store and retrieve the corresponding product data based on the first identifier of each product.

[0016] The product data is categorized, and product data items are generated based on the categorization results;

[0017] The product data items are stored in a preset spare table to obtain the product data table for the corresponding product.

[0018] In one embodiment of this application, generating a data table to be restored for a single product activity based on the mall business data includes:

[0019] Obtain the second identifier of each product activity in the mall, and retrieve the associated product activity data based on the second identifier;

[0020] The product activity data is input into a preset feature extraction model to obtain product activity features;

[0021] Based on the product activity characteristics, product activity data items are generated to construct the corresponding product activity data table to be restored.

[0022] In one embodiment of this application, the product data table is compared with a preset data template to obtain difference data items, including:

[0023] Construct a product graph based on the entities in the product data table and the relationships between them;

[0024] The nodes in the product map are compared with the data in the data template. Nodes with matching data are marked as marked nodes, and unmarked nodes connected to the marked nodes are taken as difference data items.

[0025] In one embodiment of this application, the similarity comparison between the difference features and the node features of a preset processing node is performed to obtain a matching processing node for processing the difference data items to generate standard product data, and a standard data table corresponding to the product data is constructed based on the standard product data, including:

[0026] Based on the difference characteristics, a matching processing node is retrieved from a preset processing node library. The processing nodes include: removing activity tags, removing activity prices, restoring inventory, restoring sales areas, and checking the listing status of stores and merchants.

[0027] All matching processing nodes are connected in series into a processing chain according to the preset node interface. The difference data items are input into the processing chain to obtain standard product data.

[0028] The standard product data is used to replace the difference data items in the product data table to obtain a standard data table.

[0029] In one embodiment of this application, calling the data table to be restored to generate current product activity data includes:

[0030] Determine the first key feature of the current product activity based on the product activity business request;

[0031] Based on the first key feature, all historical product activities are retrieved to obtain the matching product activities;

[0032] Retrieve the data table to be restored corresponding to the matched product activity;

[0033] Determine the current product activity time based on the product activity business request;

[0034] The time in the data table to be restored is adjusted according to the current product activity time so that the data in the corresponding data table to be restored can be made effective again.

[0035] In one embodiment of this application, generating current product data by calling the standard data table includes:

[0036] Determine the second key feature of the current product based on the product business request;

[0037] The second key feature is compared with the standard data table of each product to obtain the matching standard data table;

[0038] The current product parameters in the product business request are associated with the corresponding data items in the standard data table to generate the current product data, wherein the current product parameters include: merchant type, channel price, product images and text, and sales channel.

[0039] This application also provides a business data restoration apparatus, including:

[0040] The business data processing module is used to acquire e-commerce business data and generate a product data table for a single product based on the e-commerce business data.

[0041] The difference acquisition module is used to compare the product data table with a preset data template to obtain the difference data items;

[0042] The standard data acquisition module is used to compare the similarity of the difference features with the node features of the preset processing nodes, obtain the matching processing nodes for processing the difference data items to generate standard product data, and construct a standard data table of corresponding product data based on the standard product data.

[0043] The business response module is used to respond to business requests by calling the data table to be restored to generate current product activity data, or calling the standard data table to generate current product data.

[0044] This application also provides a computer device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the business data restoration method.

[0045] This application also provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the business data restoration method described above.

[0046] As described above, the business data restoration method, apparatus, device, and medium of this application have the following beneficial effects.

[0047] This application acquires e-commerce business data and generates a product data table for each individual product based on that data. The product data table is compared with a preset data template to obtain discrepancy data items. Feature extraction is performed on these discrepancy data items to obtain discrepancy features. These features are then compared with the node features of preset processing nodes to determine matching processing nodes. These matching nodes are used to process the discrepancy data items to generate standard product data. A standard data table for the corresponding product data is then constructed based on this standard product data. In response to a product business request, the standard data table is invoked to generate the current product data. This application can restore historical product data from the e-commerce platform, allowing for automatic regeneration of the required product data based on the restored product data table. This avoids repeated communication with suppliers, improves the reuse rate of e-commerce business data, and ensures rapid response to test data before the corresponding products are listed. Attached Figure Description

[0048] Figure 1 This is a schematic diagram illustrating the application environment of the mall business data restoration method in one embodiment of this application.

[0049] Figure 2 This is a flowchart illustrating the method for restoring e-commerce business data in one embodiment of this application.

[0050] Figure 3 This is a schematic diagram of the process for obtaining a product data table in one embodiment of this application.

[0051] Figure 4 This is a block diagram of a data restoration device for e-commerce business in one embodiment of this application.

[0052] Figure 5 This is a schematic diagram of the device in one embodiment of this application. Detailed Implementation

[0053] The following specific examples illustrate the implementation of this application. Those skilled in the art can easily understand other advantages and effects of this application from the content disclosed in this specification. This application can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of this application. It should be noted that, unless otherwise specified, the following embodiments and features in the embodiments can be combined with each other.

[0054] It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of this application. Therefore, the drawings only show the components related to this application and are not drawn according to the number, shape and size of the components in actual implementation. In actual implementation, the form, quantity and proportion of each component can be arbitrarily changed, and the layout of the components may also be more complex.

[0055] Technical terms:

[0056] The concept of a pipeline can be abstracted as follows: a repetitive task is divided into different stages (for example, preparing food for a customer can be divided into four stages: plate, fries, peas, and beverage), and each stage is handled by an independent unit (four producers are responsible for different parts). All objects to be executed enter the job queue in sequence (here, all customers are lined up and served in sequence; except for a short period at the beginning and end, at any given time, all four customers are served simultaneously).

[0057] The embodiments of this application can be applied to various scenarios, including but not limited to cloud technology, artificial intelligence, and digital healthcare. The following describes exemplary applications of the devices provided in the embodiments of this application. These devices can be implemented as various types of user terminals such as smartphones, smartwatches, laptops, tablets, desktop computers, set-top boxes, mobile devices (e.g., mobile phones, portable music players, personal digital assistants, dedicated messaging devices, portable gaming devices), intelligent voice interaction devices, smart home appliances, and in-vehicle terminals. They can also be implemented as servers. The following describes exemplary applications when the device is implemented as a server.

[0058] See Figure 1 , Figure 1 This is a schematic diagram of an optional application environment for the business data restoration method provided in this application embodiment. Terminal 400 (terminal 400-1 and terminal 400-2 are shown as examples) connects to server 200 through network 300, which can be a wide area network, a local area network, or a combination of both.

[0059] Terminal 400-1 belongs to the company's business or management personnel. Terminal 400-1 runs a client 410-1 for managing e-commerce business data. Taking the management of e-commerce medicines as an example, it is used to enter images, descriptions, prices, and the creation and listing of new inventory of medicines. The entered information is uploaded to server 200 via network 300.

[0060] Server 200 is used for drug shelf management based on entered information. This includes assembling drug information into a pre-defined pipeline on Server 200, completing a streamlined process of adding product tags, setting promotional prices, and defining sales areas. Terminal 400-2 retrieves the listed drugs from Server 200 via Network 300, reviews the listed drugs, and associates them with specific promotional activities, generating product codes or activity codes. All associated data for the drugs is then fed back to Server 200. While retrieving drug data, Server 200 backs up the drug data and its associated data to a temporary table. Server 200 can also pre-store standard data templates for each product, allowing clients on Terminal 400 to compare the standard data templates in Server 200 with the data in the temporary table. After retrieving activity data for each product, Server 200 can generate a data table to be restored, which may include information such as activity session time, activity plan time, and product promotional price cycle. Associate the data table to be restored with the product activity code so that the data table can be called and edited based on the code.

[0061] Terminal 400-2 is used to call data stored on server 200 or to access pending drug information issued by server 200 to review drugs or to restore existing product data to standardized product data based on the called table data. This allows for the generation of required product data based on the standardized product data. The generated product data includes comparing product data with a standard data template to obtain differential data, correcting the differential data, and replacing the differential data with standardized data. This includes removing promotional tags, removing promotional prices, restoring inventory, restoring sales areas, and checking the store and merchant's attitude towards product listing. A standardized check is performed to restore the product data.

[0062] Terminal 400-2 is also used to send business requirement information to server 200 via a request. Server 200 responds to the business request by specifying the restoration of specific product data or performing customized restoration of product data. For example, based on the latest requirements of the current business, it fills in merchant type, channel price, product images and text, sales channels, etc. on the basis of standardized product data, automatically generating corresponding product data, which is stored in database 500 and / or forwarded to terminal 400-1 for display in client 410 of terminal 400-1.

[0063] In some embodiments, server 200 may be a standalone physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms. Terminal 400 may be a smartphone, tablet, laptop, desktop computer, smart speaker, smartwatch, smart voice interaction device, smart home appliance, or in-vehicle terminal, but is not limited to these. Terminals and servers can be directly or indirectly connected via wired or wireless communication, which is not limited in this embodiment.

[0064] In some embodiments, terminal 400-1 can also be directly connected to terminal 400-2. Terminal 400-1 performs pre-listing assembly of products, including creating and listing product images, descriptions, prices, and inventory via a pipeline to obtain product data. The product data is then output to terminal 400-2, where it is converted into a data table and stored in a temporary table for comparison with a preset standard data template. Based on the differentiated data, a standardized product data table for the corresponding product is obtained. Terminal 400-2 also restores product activity data to generate a data table to be restored. This allows for direct matching of the corresponding data table when expired product activities need to be retrieved. Note that all times of the current activity are obtained, its period is calculated, and after being extended by an equal amount compared to the current time, the data is re-stored, tagged, and refreshed to make the activity data effective again.

[0065] Please see Figure 2 This application provides a method for restoring business data, which includes the following steps:

[0066] Step S200: Obtain e-commerce business data and generate a product data table for a single product based on the e-commerce business data.

[0067] In one embodiment, by traversing all product types in the online store, new products can be assembled according to product type, enabling the creation and listing of product data such as images, descriptions, prices, and inventory. Product listing can be implemented through a pipeline. That is, through assembly line operations, the product listing process is broken down into multiple stages, with multiple products being assembled synchronously based on the pipeline queue. After listing, a unique identifier is generated for each product. The data to be restored for the product can be obtained by input parameters based on the unique identifier of the product, resulting in the product's table data and related table data. The table data stores data such as product name, category, price, and image, while the related table data records data such as product inventory, listing area, activity tags, activity price, and sales channel. The specific content stored in the table data and related table data can be selected and adjusted according to actual application needs, and there are no restrictions here. Based on the product's table data and related table data, a corresponding product data table can be generated. After each new product is listed, a corresponding product data table can be generated and stored in a temporary table. When the product data expires, the corresponding product data can be quickly restored by calling the product data table in the temporary table.

[0068] Please see Figure 3 , Figure 3 This is a schematic diagram illustrating the process of obtaining a product data table in one embodiment of this application. In this embodiment, generating a product data table for a single product based on the e-commerce business data includes the following steps:

[0069] Step S300: Traverse all products in the mall and retrieve the corresponding product data based on the first identifier of each product.

[0070] In one embodiment, an identifier table can be generated by traversing all products in the online store and extracting the first identifier of each product. The first identifier can be a unique code for the product. Based on the records in the identifier table, the first identifier of each product is extracted one by one, and the corresponding product data is retrieved based on the first identifier.

[0071] Step S301: Classify the product data and generate product data items based on the classification results.

[0072] In one embodiment, a classification model can be constructed using a neural network. Product data is input into the pre-trained classification model to obtain different classifications of the product data. These product data categories may include product images, product descriptions, product prices, product inventory, etc. Specific categories can be set and adjusted according to actual application needs, and are not limited here. Before training the classification model, various types of product data can be collected, and each type of product data can be labeled with a category. The labeled data is then used as training samples to train the classification model. A loss function is constructed based on the similarity between the training samples and the labeled categories, and the model parameters are updated using gradient descent until the loss value reaches a set threshold. The network parameters are then fixed, resulting in the corresponding classification model. Each product data category can be considered as a product data item, and multiple product data items can be obtained after classification.

[0073] Step S302: Store the product data items into a preset spare table to obtain the product data table for the corresponding product.

[0074] In one embodiment, a spare table (i.e., a temporary table) can be set up for each category of goods. The goods data items for each goods are input into this spare table to obtain the goods data table for that goods. The goods data table can be associated with the unique product code of the corresponding goods. Managers can use the unique product code to call the corresponding goods data table for review and verification, and to edit and modify statistical deviation information, etc., to ensure the accuracy of subsequent goods data restoration.

[0075] In one embodiment, after obtaining the e-commerce business data, the following steps are also included:

[0076] Generate a data table of individual product activities to be restored based on the mall's business data;

[0077] In response to a product activity business request, the data table to be restored is invoked to generate the current product activity data.

[0078] Specifically, since the e-commerce business data may contain both product data and product activity-related data, in order to facilitate the reuse of historical product activity data, a data table to be restored for the corresponding historical product activities can be generated based on the e-commerce business data. Based on the data table to be restored, historical product activity data can be generated, or new product activity data that meets the current needs can be generated.

[0079] In one embodiment, generating a data table to be restored for a single product activity based on the e-commerce business data includes the following steps:

[0080] Step S400: Obtain the second identifier of each product activity in the mall, and call the associated product activity data according to the second identifier.

[0081] In one embodiment, product promotions in the online store may include flash sales, limited-time offers, group buying, and bundled deals. When creating a product promotion, a second identifier can be assigned to each promotion; this second identifier can serve as a unique code for the promotion. By iterating through all product promotions in the online store, the second identifiers of all promotions are recorded in an activity table. The corresponding product promotion data can be retrieved one by one based on the unique identifier recorded in the activity table.

[0082] Step S401: Input the product activity data into a preset feature extraction model to obtain product activity features.

[0083] In one embodiment, a feature extraction model can be trained using a deep neural network. The product activity data obtained in the aforementioned steps is then input into the feature extraction model to extract product activity features. These features may include activity session duration, activity schedule, and product activity price cycle. The deep neural network can be a long short-term memory neural network, and the specific neural network structure can be selected and adjusted according to actual application requirements; no restrictions are imposed here.

[0084] Step S402: Generate product activity data items based on the product activity characteristics to construct a data table to be restored for the corresponding product activity.

[0085] In one embodiment, each extracted product activity feature is treated as a product activity data item, or the corresponding original data is extracted from the product activity data based on the product activity features as the content of the product activity data item. Then, a data table to be recovered is generated based on all product activity data items. The data table to be recovered records the activity features of the corresponding product activities.

[0086] When launching new product promotions, effective pre-launch testing can be conducted by quickly obtaining test data from the corresponding product promotion's pending recovery data table. Specifically, testers can generate a product promotion business request based on the product promotion to be tested and send it to the server. After receiving the corresponding product promotion business request, the server parses the request and then outputs targeted business response data.

[0087] In one embodiment, generating current product activity data by calling the data table to be restored includes the following steps:

[0088] Step S231: Determine the first key feature of the current product activity based on the product activity business request.

[0089] In one embodiment, the description information of the product activity in the product activity business request is obtained, and the description information is used to extract features to obtain the first key feature of the product activity.

[0090] Step S232: Retrieve all historical product activities based on the first key feature to obtain matching product activities.

[0091] In one embodiment, the similarity comparison can be performed between the first key feature corresponding to the product activity business request and the activity data of all historically listed product activities to determine whether the currently requested product activity has similar historical product activities.

[0092] Step S233: Call the data table to be restored corresponding to the matched product activity.

[0093] In one embodiment, if it is determined that there are similar historical product activities based on the first key feature, the corresponding product activity's data table to be restored can be called so that the product activity data can be restored based on the data table to be restored, and the product activity data can be made effective again.

[0094] Step S234: Determine the current product activity time based on the product activity business request;

[0095] In one embodiment, the time information of the current product activity in the product activity business request can be extracted, including the start time and end time of the product activity.

[0096] Step S235: Adjust the time in the data table to be restored according to the current product activity time, so that the data in the corresponding data table to be restored can be made effective again.

[0097] In one embodiment, the activity period can be calculated based on the time information of the current product activity and compared with the time information in the data table to be restored. Using the start time of the current product activity as a benchmark, the time information in the data table to be restored is extended by an equal amount, or historical product activity data can be directly restored, making the activity data valid again. For example, a business unit once created a five-day 618 flash sale activity, with four sessions per day and five flash sale items per session, each with different prices and inventory. If the product activity expires, restoring the flash sale activity data can help the business quickly test and validate its strategies. In another embodiment, the time information can also be extended or adjusted based on the data to be restored, and then re-stored, tagged, and refreshed in the cache to make the new activity data effective.

[0098] Step S210: Compare the product data table with the preset data template to obtain the difference data items.

[0099] In one embodiment, when restoring product data, it is necessary to convert the product data into standardized data. Standardized data only records basic product information and does not include e-commerce business-related information such as product activity tags. Standardized product data can be used to restore historical product data that has been listed in the e-commerce platform, and can also be used to generate new product data to be listed, facilitating pre-listing testing and verification. Therefore, a data template can be set, specifying which data constitutes basic product information, to facilitate standardized product data detection and restoration based on the template. The data template can be a general template for various types of product data or a template specific to a particular product category. If the data template is specific to a particular product category, matching can be performed based on the product category, comparing the product data table with the data template to identify the differing data items in the product data table.

[0100] In one embodiment, comparing the product data table with a preset data template to obtain the difference data items includes the following steps:

[0101] Step S211: Construct a product graph based on the entities in the product data table and the relationships between them.

[0102] In one embodiment, entity information in a product data table can be extracted using a preset entity recognition model. Based on the preset hierarchical relationship between the corresponding product data items in the product data table, the temporal connection relationship between the entities is determined. For example, the product name can be set as the first level, the product activity tag as the second level, and the activity price as the third level. The specific level settings can be adjusted according to actual application needs and are not limited here. A product graph is constructed based on the entities and the connections between them, with the entities corresponding to the products serving as nodes in the product graph.

[0103] Step S212: Compare the similarity between the nodes in the product map and the data in the data template. Mark the nodes with matching data as marked nodes, and take the unmarked nodes connected to the marked nodes as difference data items.

[0104] In one embodiment, matching data can be retrieved from the data template based on each node in the product map. Specifically, the similarity between the entity in each node and the data in the data template can be calculated. If there is data with a similarity that reaches a set threshold, the node is marked. After all nodes are retrieved, the marked nodes are counted, and the unmarked nodes are regarded as difference data items that differ from the data template.

[0105] Step S220: Extract features from the difference data items to obtain difference features, compare the difference features with the node features of preset processing nodes to obtain matching processing nodes for processing the difference data items to generate standard product data, and construct a standard data table for corresponding product data based on the standard product data.

[0106] In one embodiment, to restore product data to standardized data, it is necessary to correct discrepancies by replacing them with standard data. Therefore, a standardized discrepancy detection and processing workflow is required to restore product data and obtain reusable standardized product data.

[0107] In one embodiment, the difference features are compared with the node features of preset processing nodes to obtain matching processing nodes for processing the difference data items to generate standard product data, and a standard data table corresponding to the product data is constructed based on the standard product data, including the following steps:

[0108] Step S221: Based on the difference characteristics, retrieve matching processing nodes from the preset processing node library. The processing nodes include: removing activity tags, removing activity prices, restoring inventory, restoring sales areas, and checking the listing status of stores and merchants.

[0109] In one embodiment, since standardized product data is required, the difference data items obtained through the aforementioned steps may include activity tags, activity prices, historical inventory, historical sales areas, and historical store or merchant status records. Features of the difference data items can be extracted using a neural network to obtain the difference features of each difference data item. The corresponding standardized processing node for the difference data item is retrieved based on these difference features. Specifically, processing nodes for various types of difference data can be pre-constructed, and after associating the processing nodes with the features of keywords or key phrases in the difference data, they are stored in a database to form a processing node library. After obtaining the difference data items through the aforementioned steps, matching processing nodes can be retrieved from the processing node library based on the difference features of the difference data items.

[0110] Step S222: Connect all matching processing nodes into a processing chain according to the preset node interface, and input the difference data items into the processing chain to obtain standard product data.

[0111] In one embodiment, after obtaining the matching processing nodes for the differential data items, the processing nodes for different differential data items can be chained together to form a processing chain. The node interfaces between each processing node can be set as universal interfaces to allow for arbitrary combination and connection between processing nodes. For example, the obtained processing nodes may include: removing activity tags nodes, removing activity prices nodes, restoring inventory nodes, restoring sales areas nodes, checking store and merchant status nodes, etc., and these processing nodes are chained together to form a processing chain. Each differential data item is input into this processing chain, and the corresponding standardization processing is completed through different processing nodes in the processing chain, such as removing activity tags, removing activity prices, clearing inventory, setting sales areas to empty, and clearing store and merchant status, etc. After completing this series of standardization processes, standard data for the differential data items can be obtained.

[0112] Step S223: Use the standard product data to replace the difference data items in the product data table to obtain a standard data table.

[0113] In one embodiment, combining the standard data with the data items corresponding to the marked nodes in the product map yields the standard product data for the corresponding product. Recording the standard product data in a preset standard table for the corresponding product results in the product's standard data table.

[0114] Step S230: In response to the product business request, the standard data table is called to generate the current product data.

[0115] In one embodiment, when a new product needs to be listed, test data can be quickly obtained by calling the product's standard data table to conduct effective pre-listing testing. Specifically, relevant testers can generate a product business request based on the product to be tested and send it to the server. After receiving the corresponding product business request, the server parses the request and then outputs targeted business response data.

[0116] In one embodiment, generating current product data by calling the standard data table includes the following steps:

[0117] Step 236: Determine the second key feature of the current product based on the product business request.

[0118] In one embodiment, if the business request is a product business request, the description text of the current product in the business request is obtained, and key features in the description text are extracted as the second key features of the current product. The second key features may include product category, product name, etc. The description text in the business request can be set according to the needs of administrators or testers, and is not limited here.

[0119] Step 237: Compare the second key feature with the standard data table of each product to obtain the matching standard data table;

[0120] In one embodiment, a standard data table for each product in the online store is obtained through the standardized detection process described in the preceding steps. After receiving a business request, it is necessary to determine whether there are any products in the online store that are similar to the products tested in the business request. If so, the corresponding approximate product data is retrieved to perform pre-listing verification for the current product. Specifically, a matching standard data table can be obtained from the standard data tables corresponding to all products in the online store through the second key feature.

[0121] Step 238: Associate the current product parameters in the product business request with the corresponding data items in the standard data table to generate current product data, wherein the current product parameters include: merchant type, channel price, product images and text, and sales channel.

[0122] In one embodiment, the product business request may also include product-specific parameters, such as activity tags, planned listing time, and inventory. Two methods of product data retrieval can be performed based on the business request. The first is specified product restoration, which involves calling the standard data table for a specific product through the business request to restore the corresponding expired product data, making the product data valid again. The other is customized product data restoration, which involves calling the standard data table for the product, entering the specific parameters of the currently requested product, and recombining it with the product data in the standardized data table to obtain the required product data. Specifically, based on the latest business requirements, corresponding data can be automatically generated by filling in merchant type, channel price, product images and text, and sales channels in the input boxes.

[0123] Based on the above technical solutions, the business data restoration method of this application can quickly restore product data and activity data. By matching standardized product data with the original target data of the product, such as product tags and promotional prices, restored data consistent with the configuration of expired products can be obtained. Alternatively, by combining input requirement data with standardized product data, the original product tags and inventory are replaced with requirement data during data restoration, quickly generating the required product data to complete the construction of test data, greatly increasing the reusability and maintainability of test data. By generating standardized data, the impact of dirty data on test verification is significantly reduced; the extension and reuse of activity data are directly achieved through the unrecovered data table of activity data, reducing communication costs between systems and improving the efficiency of test data construction.

[0124] In one embodiment, such as Figure 4 As shown, a business data restoration device is provided, comprising: a business data processing module 10, used to acquire e-commerce business data and generate a product data table for a single product based on the e-commerce business data; a difference acquisition module 11, used to compare the product data table with a preset data template to obtain difference data items; a standard data acquisition module 12, used to extract features from the difference data items to obtain difference features, compare the difference features with the node features of preset processing nodes to obtain matching processing nodes for processing the difference data items to generate standard product data, and construct a standard data table for corresponding product data based on the standard product data; and a business response module 13, used to respond to product business requests and call the standard data table to generate current product data.

[0125] In one embodiment, the business data processing module 10 includes: a product activity data processing unit, which, after acquiring mall business data, further includes: generating a data table to be restored for a single product activity based on the mall business data; and, in response to a product activity business request, calling the data table to be restored to generate current product activity data.

[0126] In one embodiment, the business data processing module 10 further includes: a product data table acquisition unit, used to traverse all products in the mall, call the corresponding product data according to the first identifier of each product; classify the product data, and generate product data items according to the classification results; store the product data items in a preset spare table to obtain the product data table of the corresponding product.

[0127] In one embodiment, the business data processing module 10 is further configured to generate a data table to be restored for a single product activity based on the mall business data, including: obtaining a second identifier for each product activity in the mall; calling the associated product activity data based on the second identifier; inputting the product activity data into a preset feature extraction model to obtain product activity features; and generating product activity data items based on the product activity features to construct a data table to be restored for the corresponding product activity.

[0128] In one embodiment, the difference acquisition module 11 is further configured to compare the product data table with a preset data template to obtain difference data items, including: constructing a product graph based on the entities in the product data table and the relationships between the entities; comparing the similarity between the nodes in the product graph and the data in the data template, marking the nodes with matching data as marked nodes, and taking the unmarked nodes connected to the marked nodes as difference data items.

[0129] In one embodiment, the standard data acquisition module 12 is further configured to match preset processing nodes according to the difference features to generate standard product data, and to obtain a standard data table of corresponding product data, including: retrieving matching processing nodes from a preset processing node library according to the difference features, wherein the processing nodes include: removing activity tags, removing activity prices, restoring inventory, restoring sales areas, and checking the shelf status of stores and merchants; connecting all matching processing nodes into a processing chain according to preset node interfaces, inputting the difference data items into the processing chain to obtain standard product data; and using the standard product data to replace the difference data items in the product data table to obtain a standard data table.

[0130] In one embodiment, the business response module 13 is further configured to call the data table to be restored to generate current product activity data, including: determining a first key feature of the current product activity based on the product activity business request; retrieving all historical product activities based on the first key feature to obtain a matching product activity; calling the data table to be restored corresponding to the matching product activity; determining the current product activity time based on the product activity business request; and adjusting the time in the data table to be restored based on the current product activity time to make the data in the corresponding data table to be restored effective.

[0131] In one embodiment, the business response module 13 is further configured to call the standard data table to generate current product data, including: determining the second key feature of the current product according to the product business request; comparing the second key feature with the standard data table of each product to obtain a matching standard data table; associating the current product parameters in the product business request with the corresponding data items in the standard data table to generate current product data, wherein the current product parameters include: merchant type, channel price, product images and text, and sales channel.

[0132] The above-mentioned business data restoration method can be implemented in the form of a computer program, which can be implemented in, for example... Figure 5 The computer device shown runs on the computer. The computer device includes: memory, processor, and computer programs stored in the memory and executable on the processor.

[0133] Each module in the aforementioned business data restoration device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the terminal's memory in hardware form, or stored in the terminal's memory in software form, so that the processor can call and execute the operations corresponding to each module. The processor can be a central processing unit (CPU), a microprocessor, a microcontroller, etc.

[0134] like Figure 5 The diagram shown is a schematic representation of the internal structure of a computer device in one embodiment. A computer device is provided, including: a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it performs the following steps: acquiring e-commerce business data; generating a product data table for a single product based on the e-commerce business data; comparing the product data table with a preset data template to obtain difference data items; extracting features from the difference data items to obtain difference features; comparing the difference features with the node features of a preset processing node to obtain a matching processing node for processing the difference data items to generate standard product data; and constructing a standard data table for corresponding product data based on the standard product data; and generating current product data in response to a product business request or by calling the standard data table.

[0135] In one embodiment, after the processor acquires the e-commerce business data, it further includes: generating a data table to be restored for a single product activity based on the e-commerce business data; and in response to a product activity business request, calling the data table to be restored to generate the current product activity data.

[0136] In one embodiment, when the processor is executed, the process of generating a product data table for a single product based on the mall business data includes: traversing all products in the mall and calling the corresponding product data according to the first identifier of each product; classifying the product data and generating product data items according to the classification results; and storing the product data items in a preset spare table to obtain the product data table for the corresponding product.

[0137] In one embodiment, when the processor executes the above-mentioned process, the process of generating a data table to be restored for a single product activity based on the mall business data includes: obtaining a second identifier for each product activity in the mall; calling the associated product activity data based on the second identifier; inputting the product activity data into a preset feature extraction model to obtain product activity features; and generating product activity data items based on the product activity features to construct a data table to be restored for the corresponding product activity.

[0138] In one embodiment, when the processor executes the above-mentioned process, the comparison of the product data table with a preset data template to obtain difference data items includes: constructing a product graph based on the entities in the product data table and the relationships between the entities; comparing the similarity between the nodes in the product graph and the data in the data template, marking the nodes with matching data as marked nodes, and taking the unmarked nodes connected to the marked nodes as difference data items.

[0139] In one embodiment, when the processor executes, the process of extracting features from the difference data items to obtain difference features, comparing the difference features with the node features of preset processing nodes to obtain matching processing nodes for processing the difference data items to generate standard product data, and constructing a standard data table for corresponding product data based on the standard product data includes: retrieving matching processing nodes from a preset processing node library based on the difference features, wherein the processing nodes include: removing activity tags, removing activity prices, restoring inventory, restoring sales areas, and checking the shelf status of stores and merchants; connecting all matching processing nodes into a processing chain according to preset node interfaces, inputting the difference data items into the processing chain to obtain standard product data; and using the standard product data to replace the difference data items in the product data table to obtain a standard data table.

[0140] In one embodiment, when the processor executes the above-mentioned process, the process of calling the data table to be restored to generate current product activity data includes: determining a first key feature of the current product activity based on the product activity business request; retrieving all historical product activities based on the first key feature to obtain a matching product activity; calling the data table to be restored corresponding to the matching product activity; determining the current product activity time based on the product activity business request; and adjusting the time in the data table to be restored based on the current product activity time to make the data in the corresponding data table to be restored effective.

[0141] In one embodiment, when the processor executes, the process of calling the standard data table to generate current product data includes: determining the second key feature of the current product based on the product business request; comparing the second key feature with the standard data tables of each product to obtain a matching standard data table; associating the current product parameters in the product business request with the corresponding data items in the standard data table to generate current product data, wherein the current product parameters include: merchant type, channel price, product images and text, and sales channel.

[0142] In one embodiment, the aforementioned computer device can be used as a server, including but not limited to a standalone physical server or a server cluster consisting of multiple physical servers. The computer device can also be used as a terminal, including but not limited to mobile phones, tablets, personal digital assistants, or smart devices. Figure 5 As shown, the computer device includes a processor, non-volatile storage medium, internal memory, display screen, and network interface connected via a system bus.

[0143] The processor of this computer device provides computing and control capabilities to support the operation of the entire device. The non-volatile storage medium of the computer device stores the operating system and computer programs. These programs can be executed by the processor to implement the business data restoration method provided in the above embodiments. The internal memory of the computer device provides a cached operating environment for the operating system and computer programs stored in the non-volatile storage medium. The display interface can display data via a screen. The screen can be a touchscreen, such as a capacitive or electronic screen, and can generate corresponding instructions by receiving click operations on the controls displayed on the touchscreen.

[0144] Those skilled in the art will understand that Figure 5 The structure of the computer device shown in the figure is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. A specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0145] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon. When executed by a processor, the computer program performs the following steps: acquiring e-commerce business data; generating a product data table for a single product based on the e-commerce business data; comparing the product data table with a preset data template to obtain difference data items; extracting features from the difference data items to obtain difference features; comparing the difference features with the node features of a preset processing node to obtain a matching processing node for processing the difference data items to generate standard product data; and constructing a standard data table for corresponding product data based on the standard product data; and, in response to a product business request, calling the standard data table to generate the current product data.

[0146] In one embodiment, when the computer program is executed by the processor, after acquiring the e-commerce business data, it further includes: generating a data table to be restored for a single product activity based on the e-commerce business data; and in response to a product activity business request, calling the data table to be restored to generate current product activity data.

[0147] In one embodiment, when the computer program is executed by the processor, the process of generating a product data table for a single product based on the mall's business data includes: traversing all products in the mall and calling the corresponding product data according to the first identifier of each product; classifying the product data and generating product data items based on the classification results; and storing the product data items in a preset spare table to obtain the product data table for the corresponding product.

[0148] In one embodiment, when the computer program is executed by the processor, the process of generating a data table to be restored for a single product activity based on the mall's business data includes: obtaining a second identifier for each product activity in the mall; calling associated product activity data based on the second identifier; inputting the product activity data into a preset feature extraction model to obtain product activity features; and generating product activity data items based on the product activity features to construct a data table to be restored for the corresponding product activity.

[0149] In one embodiment, when the computer program is executed by the processor, the process of comparing the product data table with a preset data template to obtain difference data items includes: constructing a product graph based on the entities in the product data table and the relationships between the entities; comparing the similarity between the nodes in the product graph and the data in the data template, marking nodes with matching data as marked nodes, and taking unmarked nodes connected to the marked nodes as difference data items.

[0150] In one embodiment, when the computer program is executed by the processor, the following steps are implemented: extracting features from the difference data items to obtain difference features; comparing the difference features with the node features of preset processing nodes to obtain matching processing nodes for processing the difference data items to generate standard product data; and constructing a standard data table for corresponding product data based on the standard product data. These steps include: retrieving matching processing nodes from a preset processing node library based on the difference features; the processing nodes including: removing promotional tags, removing promotional prices, restoring inventory, restoring sales areas, and checking the shelf status of stores and merchants; connecting all matching processing nodes into a processing chain according to preset node interfaces; inputting the difference data items into the processing chain to obtain standard product data; and using the standard product data to replace the difference data items in the product data table to obtain the standard data table.

[0151] In one embodiment, when the computer program is executed by the processor, the process of calling the data table to be restored to generate current product activity data includes: determining a first key feature of the current product activity based on the product activity business request; retrieving all historical product activities based on the first key feature to obtain a matching product activity; calling the data table to be restored corresponding to the matching product activity; determining the current product activity time based on the product activity business request; and adjusting the time in the data table to be restored based on the current product activity time to make the data in the corresponding data table to be restored effective.

[0152] In one embodiment, when the instruction is executed by the processor, the process of calling the standard data table to generate current product data includes: determining a second key feature of the current product based on the product business request; comparing the second key feature with the standard data tables of each product to obtain a matching standard data table; associating the current product parameters in the product business request with the corresponding data items in the standard data table to generate current product data, wherein the current product parameters include: merchant type, channel price, product images and text, and sales channel.

[0153] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. The storage medium can be a magnetic disk, optical disk, read-only memory (ROM), etc.

[0154] The above embodiments are merely illustrative of the principles and effects of this application and are not intended to limit this application. Any person skilled in the art can modify or alter the above embodiments without departing from the spirit and scope of this application. Therefore, all equivalent modifications or alterations made by those skilled in the art without departing from the spirit and technical concept disclosed in this application should still be covered by the claims of this application.

Claims

1. A method for restoring business data, characterized in that, include: Obtain e-commerce business data, and generate a product data table for each individual product based on the e-commerce business data; The product data table is compared with a preset data template to obtain difference data items, including: constructing a product graph based on the entities in the product data table and the relationships between entities; comparing the similarity between the nodes in the product graph and the data in the data template, marking the nodes with matching data as marked nodes, and taking the unmarked nodes connected to the marked nodes as difference data items; Feature extraction is performed on the differential data items to obtain differential features. The differential features are then compared with the node features of preset processing nodes to obtain matching processing nodes for processing the differential data items to generate standard product data. A standard data table corresponding to the standard product data is constructed based on the standard product data. This includes: retrieving matching processing nodes from a preset processing node library based on the differential features; the processing nodes include: removing promotional tags, removing promotional prices, restoring inventory, restoring sales areas, and checking the shelf status of stores and merchants; connecting all matching processing nodes into a processing chain according to preset node interfaces; inputting the differential data items into the processing chain to obtain standard product data containing only basic product information; and using the standard product data to replace the differential data items in the product data table to obtain the standard data table. In response to a product business request, the standard data table is invoked to generate the current product data.

2. The business data restoration method according to claim 1, characterized in that, After obtaining the e-commerce business data, the following is also included: Generate a data table of individual product activities to be restored based on the mall's business data; In response to a product activity business request, the data table to be restored is invoked to generate the current product activity data.

3. The business data restoration method according to claim 1, characterized in that, Generate a product data table for each individual product based on the aforementioned e-commerce business data, including: Iterate through all products in the store and retrieve the corresponding product data based on the first identifier of each product. The product data is categorized, and product data items are generated based on the categorization results; The product data items are stored in a preset spare table to obtain the product data table for the corresponding product.

4. The business data restoration method according to claim 2, characterized in that, Based on the aforementioned e-commerce business data, a data table for a single product activity to be restored is generated, including: Obtain the second identifier of each product activity in the mall, and retrieve the associated product activity data based on the second identifier; The product activity data is input into a preset feature extraction model to obtain product activity features; Based on the product activity characteristics, product activity data items are generated to construct the corresponding product activity data table to be restored.

5. The business data restoration method according to claim 2, characterized in that, The process of calling the data table to be restored to generate current product activity data includes: Determine the first key feature of the current product activity based on the product activity business request; Based on the first key feature, all historical product activities are retrieved to obtain the matching product activities; Retrieve the data table to be restored corresponding to the matched product activity; Determine the current product activity time based on the business request; The time in the data table to be restored is adjusted according to the current product activity time so that the data in the corresponding data table to be restored can be made effective again.

6. The business data restoration method according to claim 1, characterized in that, The process of generating current product data by calling the standard data table includes: Determine the second key feature of the current product based on the product business request; The second key feature is compared with the standard data table of each product to obtain the matching standard data table; The current product parameters in the business request are associated with the corresponding data items in the standard data table to generate the current product data, wherein the current product parameters include: merchant type, channel price, product images and text, and sales channel.

7. A business data restoration device, characterized in that, include: The business data processing module is used to acquire e-commerce business data and generate a product data table for a single product based on the e-commerce business data. The difference acquisition module is used to compare the product data table with a preset data template to obtain difference data items; including: constructing a product graph based on the entities in the product data table and the relationships between entities; comparing the similarity between the nodes in the product graph and the data in the data template, marking the nodes with matching data as marked nodes, and taking the unmarked nodes connected to the marked nodes as difference data items; A standard data acquisition module is used to extract features from the differential data items to obtain differential features, compare the differential features with the node features of preset processing nodes to obtain matching processing nodes for processing the differential data items to generate standard product data, and construct a standard data table for corresponding product data based on the standard product data. This includes: retrieving matching processing nodes from a preset processing node library based on the differential features; the processing nodes including: removing activity tags, removing activity prices, restoring inventory, restoring sales areas, and checking store and merchant shelf status; connecting all matching processing nodes into a processing chain according to preset node interfaces; inputting the differential data items into the processing chain to obtain standard product data containing only basic product information; and using the standard product data to replace the differential data items in the product data table to obtain the standard data table. The business response module is used to respond to product business requests by calling the standard data table to generate the current product data.

8. A computer device, comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, when the processor executes the computer program, it implements the steps of the business data restoration method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the business data restoration method according to any one of claims 1 to 6.

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