Data processing method and device based on business qualification and storage medium

Abstract

This invention relates to the field of intelligent management technology and discloses a data processing method, apparatus, and storage medium based on business qualifications. First, business qualification documents are identified and processed to obtain qualification validity period data, wherein the business qualification documents include the qualification documents of the target object and / or the qualification documents of the corresponding product. Then, based on the qualification validity period data and the effective warning threshold range, a tiered warning is triggered, and a warning pending notification is sent. Finally, if the warning pending notification is not processed and the qualification validity period data expires, an automatic delisting module is triggered to delist the target product. Through the above implementation method, the entire process from business qualification identification, warning notification to delisting is automated and intelligent, which can intercept non-compliant products at the source, effectively improve qualification management efficiency, support compliant platform operation, reduce compliance risks, protect user rights, enhance user trust, and further strengthen user stickiness.

Description

Technical Field

[0001] This invention relates to the field of intelligent management technology, and in particular to a data processing method, apparatus and storage medium based on business qualifications. Background Technology

[0002] Against the backdrop of the rapid development of the e-commerce industry, the verification and management of supplier and product qualifications by e-commerce platforms has become a core aspect of ensuring product quality and protecting consumer rights.

[0003] In related technologies, e-commerce platforms primarily rely on manual offline verification to confirm the qualifications of suppliers and products. However, it is difficult for humans to monitor the validity period of all supplier and product qualifications in real time and with high accuracy. Therefore, a new data processing method based on business qualifications is needed. Summary of the Invention

[0004] The embodiments described in this specification aim to at least partially address one of the technical problems in the related art. To this end, the embodiments described in this specification propose a data processing method, apparatus, and storage medium based on business qualifications.

[0005] This specification provides a data processing method based on business qualifications, characterized in that the method includes: The business qualification documents are identified and processed to obtain qualification validity period data, wherein the business qualification documents include the qualification documents of the target object and / or the qualification documents of the product corresponding to the target object; Based on the qualification validity period data and the effective early warning threshold range, a tiered early warning is triggered, and an early warning pending notification is sent. If the warning notification is not processed and the qualification validity period expires, the automatic delisting module will be triggered to remove the target product from the shelves.

[0006] In one implementation, the process of identifying and processing the business qualification documents to obtain qualification validity period data includes: Information is extracted from the business qualification documents to obtain preliminary qualification validity period data; The business qualification document is extracted and semantically analyzed using a target text extraction model to obtain text qualification validity period data. The business qualification document is subjected to image feature extraction using a target image recognition model to obtain an image feature vector; Verification is performed based on the preliminary qualification validity period data and the text qualification validity period data to determine the qualification validity period data; The qualification verification result is determined by verifying the image feature vector and the formal qualification feature vector in the predefined knowledge base.

[0007] In one implementation, before extracting information from the business qualification document, the method further includes: The initial business qualification document is normalized in resolution to obtain a normalized business qualification document; The normalized business qualification document is subjected to text and background contrast enhancement processing to obtain an enhanced business qualification document; The enhanced business qualification document is tilted to obtain the corrected business qualification document; The business qualification document is verified based on the corrected business qualification document to determine the business qualification document.

[0008] In one implementation, the qualification validity period data includes target object qualification validity period data and product qualification validity period data. If the warning pending notification is not processed and the qualification validity period data expires, an automatic delisting module is triggered to delist the target product, including: If the warning notification is not processed and the validity period of the target object's qualification expires, the automatic delisting module will be triggered to delist all products corresponding to the target object. If the warning notification is not processed and the product qualification validity period expires, the automatic delisting module will be triggered to delist the product whose product qualification validity period has expired. And / or, Trigger the automatic delisting module to remove the target product from the shelves, including: Update the status information of the target product in the product database table to "out of stock"; The removal log of the target product is recorded in the removal log table, wherein the removal log includes the status information and product information of the target product.

[0009] In one embodiment, the method further includes: Publish the status information and product information of the target product to the message queue; The message queue is used to notify the associated subsystem, so that the associated subsystem updates the relevant information of the target product based on the status information and product information. And / or, Before removing a target product from the shelves, a verification process is performed based on the product database table and the removal log table to avoid duplicate removal of the target product.

[0010] In one embodiment, the method further includes: If a failure is detected during execution, the transaction compensation mechanism is invoked to roll back the successfully executed operations.

[0011] In one implementation, the effective early warning threshold range includes a primary early warning threshold range and a secondary early warning threshold range. The step of triggering tiered early warnings based on the qualification validity period data and the effective early warning threshold range, and sending an early warning pending notification, includes: The remaining days of the qualification are determined based on the qualification validity period data. If the remaining days of the qualification reach the threshold range of the Level 2 warning, a Level 2 warning will be triggered and a Level 2 warning pending notification will be sent. If the remaining days of the qualification reach the threshold range of Level 1 warning, a Level 1 warning will be triggered and a Level 1 warning pending notification will be sent.

[0012] In one embodiment, the method further includes: If the warning notification has been processed, obtain the new business qualification document and identify and process the new business qualification document to obtain the new qualification validity period data; Based on the new qualification validity period data and the effective early warning threshold range, a tiered early warning is triggered, and a new early warning pending notification is sent. If the new warning notification remains unprocessed and the new qualification validity period expires, the automatic delisting module will be triggered to remove the target product from the platform.

[0013] This specification provides a data processing device based on business qualifications, the device comprising: The qualification document recognition and processing module is used to recognize and process business qualification documents to obtain qualification validity period data, wherein the business qualification documents include the qualification documents of the target object and / or the qualification documents of the product corresponding to the target object; The qualification validity period graded early warning module is used to trigger graded early warnings based on the qualification validity period data and the effective early warning threshold range, and send early warning pending notifications; The target product automatic delisting module is used to automatically delist the target product when the warning pending notification is not processed and the qualification validity period data expires.

[0014] This specification provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method described in any of the above embodiments.

[0015] This specification provides a computer program product that includes instructions that, when executed by a processor of a computer device, enable the computer device to perform the steps of the method described in any of the above embodiments.

[0016] In the above-described implementation method, firstly, business qualification documents are identified and processed to obtain qualification validity period data. These documents include the qualification documents of the target object and / or the qualification documents of the corresponding product. Then, based on the qualification validity period data and the effective warning threshold range, a tiered warning is triggered, and a warning pending notification is sent. Finally, if the warning pending notification is not processed and the qualification validity period expires, an automatic delisting module is triggered to delist the target product. This implementation method achieves full automation and intelligence throughout the entire process from business qualification identification and warning notification to delisting, forming a data closed loop. It can intercept non-compliant products at the source, creating an unattended qualification management system that effectively improves qualification management efficiency, supports compliant platform operation, reduces compliance risks, protects user rights, enhances user trust, and further strengthens user stickiness. By reviewing and issuing warnings for the qualification documents of the target object and / or the qualification documents of the corresponding product, the onboarding and product listing cycle of the target object is shortened, making it suitable for intelligent management scenarios of business qualifications in various e-commerce platforms. Attached Figure Description

[0017] Figure 1a A flowchart illustrating the data processing method based on business qualifications provided for the implementation of this specification; Figure 1b A schematic diagram of pseudocode for the data processing method based on business qualifications provided in the embodiments of this specification; Figure 2a A flowchart illustrating the process of obtaining qualification validity period data provided for the implementation of this specification; Figure 2b A schematic diagram illustrating an example of JSON formatted data provided for implementation of this specification; Figure 3 A flowchart illustrating the automatic delisting module provided for implementation of this specification; Figure 4 A flowchart illustrating the process of sending early warning and pending task notifications for the implementation of this specification; Figure 5 A flowchart illustrating the process of removing a target product from shelves based on new qualification validity data, provided for the implementation of this specification. Figure 6 A flowchart illustrating the preprocessing of initial business qualification documents provided for the implementation of this specification; Figure 7 A flowchart illustrating the notification association subsystem provided in the embodiments of this specification; Figure 8 A schematic diagram of a data processing apparatus based on business qualifications provided for embodiments of this specification; Figure 9An internal structural diagram of a computer device provided for embodiments of this specification. Detailed Implementation

[0018] Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain the present invention, and should not be construed as limiting the present invention.

[0019] Against the backdrop of the rapid development of the e-commerce industry, the verification and management of supplier and product qualifications by e-commerce platforms has become a core aspect of ensuring product quality and protecting consumer rights.

[0020] In related technologies, e-commerce platforms mainly rely on manual offline verification to check the qualifications of suppliers and products. This model has the following significant shortcomings: 1. Low review efficiency: Manual review relies on a large amount of manpower and has a long processing cycle, making it difficult to meet the qualification verification needs of e-commerce platforms with a large number of suppliers and products. This problem is even more prominent during promotional activities when the number of products surges.

[0021] 2. Lack of monitoring of qualification validity period: It is difficult for humans to monitor the validity period of all supplier and product qualifications in real time and accurately. There are often cases where qualifications have expired but related products are still being sold, which not only brings consumer risks, but also exposes the platform to compliance risks.

[0022] 3. Limited ability to verify authenticity: Some suppliers may provide forged or altered qualification documents, and manual review is limited by the experience and professional level of the reviewers, making it difficult to effectively identify false materials, resulting in non-compliant suppliers and products entering the platform.

[0023] 4. Lack of automated handling mechanism: Even if expired qualifications are discovered, the existing mechanism still relies on manual operations such as removing products from shelves, which makes it difficult to stop the circulation of non-compliant products in a timely manner and cannot protect consumer rights in the first instance.

[0024] The relevant technologies have failed to establish a complete management loop from "qualification verification" to "expiration warning" and then to "automatic removal of expired products." The entire process still relies heavily on manual intervention and cannot systematically solve the core problems of the current qualification management model in terms of efficiency, authenticity identification, and automatic handling.

[0025] Based on the above analysis, this specification provides a data processing method based on business qualifications. First, business qualification documents are identified and processed to obtain qualification validity data, whereby the business qualification documents include the qualification documents of the target object and / or the qualification documents of the corresponding product. Then, based on the qualification validity data and the effective warning threshold range, a tiered warning is triggered, and a warning pending notification is sent. Finally, if the warning pending notification is not processed and the qualification validity data expires, an automatic delisting module is triggered to delist the target product. This implementation method automates and intelligently manages the entire process from business qualification identification and warning notification to delisting, forming a data closed loop. It can intercept non-compliant products at the source, creating an unattended qualification management system that effectively improves qualification management efficiency, supports compliant platform operation, reduces compliance risks, protects user rights, enhances user trust, and further strengthens user stickiness. By reviewing and issuing warnings for the qualification documents of the target object and / or the qualification documents of the corresponding product, the onboarding and product listing cycle of the target object is shortened, making it suitable for intelligent management scenarios of business qualifications in various e-commerce platforms.

[0026] This specification provides a data processing method based on business qualifications. Please refer to [link / reference]. Figure 1a The data processing method based on business qualifications may include the following steps: S110. Identify and process the business qualification documents to obtain qualification validity period data.

[0027] Among them, business qualification documents include the qualification documents of the target object and / or the qualification documents of the product corresponding to the target object.

[0028] Specifically, during the formal onboarding process of target entities (such as enterprises and merchants), they need to submit basic qualification documents related to their main entity, such as business licenses and industry-specific operating permits, as the basis for onboarding review and identity verification. Simultaneously, to facilitate subsequent business operations, target entities also need to prepare and upload relevant qualification documents for the products to be listed, such as product quality inspection reports and production licenses, so that the platform can conduct a preliminary review of the product's compliance and safety. After receiving the basic qualification documents related to the entity and the qualification documents related to the listed products, preprocessing is performed to obtain standardized documents, resulting in business qualification documents that include the target entity's qualification documents and / or the qualification documents for the corresponding products. After obtaining the business qualification documents, in order to obtain the required information from the documents, the business qualification documents need to be identified and processed to extract key information and obtain qualification validity period data.

[0029] For example, the initial qualification documents for the target object can be uploaded via the HTTPS REST interface ( / supplier / apply). The file format must be PDF, JPG, or PNG, and the file size must not exceed 20MB. Batch uploading is supported when uploading qualification documents related to the products to be listed.

[0030] For example, the preprocessing of the uploaded basic qualification documents related to the entity and the qualification documents related to the listed products includes resolution normalization (2048×1536), adaptive binarization (35×35 window, threshold k=0.2), and tilt correction (maximum angle ±5°). After completing the preprocessing operations on the uploaded qualification documents, the business qualification documents are obtained.

[0031] S120. Trigger tiered early warnings based on qualification validity data and valid early warning threshold range, and send early warning pending notifications.

[0032] S130. If the warning notification is not processed and the qualification validity period expires, the automatic delisting module will be triggered to delist the target product.

[0033] Specifically, to achieve precise control over the validity period of qualifications and ensure the compliance of product sales, a routine data monitoring mechanism for qualification validity periods needs to be established. This involves comparing qualification validity period data with valid early warning threshold ranges. Based on the comparison results, the valid early warning threshold range to which the qualification validity period data belongs is determined. Based on the valid early warning threshold range, tiered early warnings are triggered, and a warning notification is sent to business personnel, reminding them to pay attention to the validity status of the qualifications. If business personnel fail to handle the issue within the specified time after receiving the warning notification, and the qualification validity period expires, an automatic delisting module will be triggered to remove the target product from the shelves, effectively preventing the continued circulation of non-compliant products and ensuring the legality and compliance of operations.

[0034] For example, please refer to Figure 1b , Figure 1b The pseudocode demonstrates a data processing method based on business qualifications.

[0035] In the above implementation, firstly, business qualification documents are identified and processed to obtain qualification validity period data. These documents include the qualification documents of the target object and / or the qualification documents of the corresponding product. Then, based on the qualification validity period data and the effective warning threshold range, tiered warnings are triggered, and a warning pending notification is sent. Finally, if the warning pending notification is not processed and the qualification validity period expires, an automatic delisting module is triggered to delist the target product. This implementation achieves full automation and intelligence from business qualification identification and warning notification to delisting, forming a data closed loop. It can intercept non-compliant products at the source, creating an unattended qualification management system that effectively improves qualification management efficiency, supports compliant platform operation, reduces compliance risks, protects user rights, enhances user trust, and further strengthens user stickiness. By reviewing and issuing warnings for the qualification documents of the target object and / or the qualification documents of the corresponding product, the onboarding and product listing cycle of the target object is shortened, making it suitable for intelligent management scenarios of business qualifications in various e-commerce platforms.

[0036] In some implementations, please refer to Figure 2a The process of identifying and processing business qualification documents to obtain qualification validity period data may include the following steps: S210. Extract information from business qualification documents to obtain preliminary qualification validity period data.

[0037] Specifically, after obtaining the business qualification documents, the information extraction module or service is invoked to extract key information from the documents, thereby obtaining preliminary qualification validity period data. It should be noted that in addition to obtaining the preliminary qualification validity period data, basic information such as the issuing authority, issuance date, and certificate number can also be obtained, and this information is converted into structured data for storage. For example, a master-slave architecture using MySQL 8.0 is used for structured data storage. For instance, the table structure can be as follows: Table name: T_CERT.

[0038] Fields: certId (primary key PK), issueOrg (issuing authority), issueDate (issuance date), expiryDate (preliminary qualification validity period data), status (status information), certNo (certificate number).

[0039] For example, an Optical Character Recognition (OCR) service is invoked to extract information from business qualification documents, generating structured data containing information such as the issuing authority (issueOrg), issuance date (issueDate), preliminary qualification validity period (expiryDate), and certificate number (certNo). The returned data follows a specific JSON format. For example, please refer to... Figure 2b , Figure 2b An example of JSON formatted data is shown.

[0040] S220. Use the target text extraction model to extract text and perform semantic analysis on the business qualification documents to obtain text qualification validity period data.

[0041] S230. Use the target image recognition model to extract image features from the business qualification documents and obtain image feature vectors.

[0042] Specifically, a target text extraction model is used to extract text and perform semantic analysis on the business qualification documents. Through in-depth analysis of the document content, key data can be accurately extracted, and the validity period data of the qualification documents can be obtained. A target image recognition model is used to extract image features from the business qualification documents, capturing the image features in the documents and obtaining image feature vectors. It should be noted that during text extraction and image feature extraction, the target text extraction model and the target image recognition model can utilize GPU acceleration to improve processing speed and efficiency.

[0043] For example, the LayoutLMv3-base model is used to perform text extraction and semantic analysis on the business qualification document to obtain the text qualification validity period data. The ResNet50 model is used to extract image features from the business qualification document, thereby extracting a 2048-dimensional feature vector from the document to obtain the image feature vector.

[0044] S240. Verify the validity period data of the qualification based on the preliminary qualification validity period data and the text qualification validity period data to determine the qualification validity period data.

[0045] Specifically, after obtaining the preliminary qualification validity period data and the text-based qualification validity period data, in order to improve the accuracy of the data, it is necessary to compare the preliminary qualification validity period data and the text-based qualification validity period data to verify the data. If the preliminary qualification validity period data and the text-based qualification validity period data are consistent, it indicates that the preliminary qualification validity period data is correct and can be directly used as the qualification validity period data. If the preliminary qualification validity period data and the text-based qualification validity period data are inconsistent, the text-based qualification validity period data shall prevail, overriding the preliminary qualification validity period data, and the updated text-based qualification validity period data shall be used as the qualification validity period data.

[0046] S250. Verify the qualification based on the image feature vector and the formal qualification feature vector in the predefined knowledge base to determine the qualification verification result.

[0047] Specifically, due to the risk of forgery of key image elements such as seals and logos in business qualification documents, the authenticity of these documents can be verified by comparing the image feature vector with legitimate qualification feature vectors in a predefined knowledge base. The image feature vector and the legitimate qualification feature vectors in the predefined knowledge base are compared for similarity. If the similarity between the image feature vector and the legitimate qualification feature vectors in the predefined knowledge base is not lower than a predefined similarity threshold, the qualification is deemed legitimate, and a qualification verification result is marked as genuine. If the similarity between the image feature vector and the legitimate qualification feature vectors in the predefined knowledge base is lower than the predefined similarity threshold, a forgery risk is identified, and a qualification verification result is marked as having a forgery risk (FORGERY_RISK). For example, the predefined similarity threshold can be 0.95. For example, the cosine similarity between the image feature vector and the legitimate qualification feature vectors in the predefined knowledge base is calculated, and the qualification verification result is determined based on this cosine similarity.

[0048] In some implementations, information is extracted from business qualification documents, and the obtained data may include the issuing authority. The official query interface publicly available to the authority (such as a government qualification verification platform or an authoritative certificate verification system) can be further invoked as a predefined knowledge base. The formal qualification feature vector returned by the interface is compared with the image feature vector to enhance the reliability of the verification results.

[0049] In the above implementation, information is extracted from the business qualification documents to obtain preliminary qualification validity period data. A target text extraction model is used to extract text and perform semantic analysis on the business qualification documents to obtain text qualification validity period data. A target image recognition model is used to extract image features from the business qualification documents to obtain image feature vectors. Verification is performed based on the preliminary qualification validity period data and the text qualification validity period data to determine the qualification validity period data. Verification is performed based on the image feature vectors and the formal qualification feature vectors in a predefined knowledge base to determine the qualification verification result, thereby reducing compliance risks and ensuring business security.

[0050] In some implementations, the qualification validity period data includes the qualification validity period data of the target object and the qualification validity period data of the product. If the warning pending notification is not processed and the qualification validity period data expires, the automatic delisting module is triggered to delist the target product. This may include: if the warning pending notification is not processed and the qualification validity period data of the target object expires, the automatic delisting module is triggered to delist all products corresponding to the target object.

[0051] Specifically, an unprocessed alert notification indicates that the business qualification documents have not been updated. Therefore, if the alert notification is unprocessed and the target entity's qualification validity period expires, the target entity's qualification will be considered invalid. In this case, due to the invalid qualification, the target entity's operating qualifications on the platform no longer comply with relevant regulations, and all its products lose the necessary compliance qualifications for listing and cannot continue to be sold on the platform. To ensure the validity and compliance of the platform's product qualifications, an automatic delisting module will be triggered to remove all products corresponding to the target entity from the platform. At this point, the target products refer to all products corresponding to the target entity.

[0052] In the above implementation, if the warning notification is not processed and the validity period of the target object's qualification expires, the automatic delisting module is triggered to delist all products corresponding to the target object. This effectively improves the efficiency of qualification management, supports the platform's compliant operation, reduces compliance risks, protects user rights, enhances user trust, and further strengthens user stickiness.

[0053] In some implementations, the qualification validity period data includes the qualification validity period data of the target object and the qualification validity period data of the product. If the warning pending notification is not processed and the qualification validity period data expires, the automatic delisting module is triggered to delist the target product. This may include: if the warning pending notification is not processed and the product qualification validity period data expires, the automatic delisting module is triggered to delist the product whose product qualification validity period data has expired.

[0054] Specifically, an unprocessed alert notification indicates that the business qualification documents have not been updated. Therefore, if the alert notification is unprocessed and the product qualification validity period expires, the product's qualification will be considered invalid. This means that the product no longer qualifies for listing and cannot continue to be sold on the platform. To ensure the validity and compliance of product qualifications on the platform, an automatic delisting module will be triggered to remove products whose product qualification validity period has expired. In this case, the target product is the one whose product qualification validity period has expired.

[0055] In the above implementation, if the warning notification is not processed and the product qualification validity period data expires, the automatic delisting module is triggered to delist the product whose product qualification validity period data has expired. This effectively improves the efficiency of qualification management, supports the platform's compliant operation, reduces compliance risks, protects user rights, enhances user trust, and further strengthens user stickiness.

[0056] In some implementations, please refer to Figure 3 The automatic delisting module is triggered to remove the target product from the shelves, which may include the following steps: S310. Update the status information of the target product in the product database table to "out of stock".

[0057] S320. Record the removal log of the target product in the removal log table.

[0058] Specifically, after identifying the target product to be removed from the shelves, the system first updates the corresponding status information of the target product in the product database table to indicate that the product has been removed from sale. To comprehensively record all information related to product removal, the system maintains a removal log table. This log table includes, but is not limited to, the following: verification records, warning events, and removal operations. The removal log table uses a structured log format and is designed with a query index to support queries and exports by time, module, and keyword. Each time a product is removed from the shelves, the system generates a new removal log entry and writes it to the removal log table. The log entry includes the target product's status information and product information, which may include logId (primary key), productId (product ID), delistTime (removal time), and reason (removal reason: expired qualification). After completing the database operation, the system publishes a message of type "ProductDelisted" to a message queue (such as the Apache Kafka 3.5 message queue). The message body may contain basic information about the removed product (such as productId, delistTime, reason, etc.). Subsystems within the platform that rely on product status (such as search and recommendation) can subscribe to this message to achieve real-time awareness and update their local status, ensuring that the data of each module remains consistent with the main database.

[0059] It should be noted that the two operations of updating the product database table and recording the delisting log table are executed collaboratively through a distributed transaction mode (such as the Saga mode).

[0060] In some implementations, the product database table and the delisting log table can use a master-slave architecture in MySQL 8.0 for data storage to ensure high availability and read / write separation. For example, the product database table structure can be as follows: Table name: T_PRODUCT_CERT.

[0061] Fields: productId (product ID), certId (business qualification document ID), isPrimary (whether it is legitimate), INDEX (productId) (index).

[0062] The structure of the delisting log table can be as follows: Table name: T_DELIST_LOG.

[0063] Fields: logId (primary key PK), productId (product ID), delistTime (delisting time), reason (delisting reason).

[0064] In the above implementation, the status information of the target product in the product database table is updated to the delisted status, and the delisting log of the target product is recorded in the delisting log table. This facilitates regulatory backtracking, improves security and traceability, and can also be used for subsequent business analysis.

[0065] In some implementations, the method may further include: invoking a transaction compensation mechanism to roll back successfully executed operations if an execution step failure is detected.

[0066] Specifically, the transaction compensation mechanism uses a compensation logic to undo or correct already executed operations, ensuring system consistency and preventing the entire process from failing due to an error in a single step. When a failure is detected in an execution step, the transaction compensation mechanism is triggered, rolling back previously successfully executed operations to prevent the entire business process from being interrupted due to a partial failure. For example, in the Saga transaction pattern, if any subtask in a distributed transaction fails, the corresponding compensation operation is invoked, rolling back the completed steps in reverse order to restore the system state.

[0067] In the above implementation, if an execution step fails, the transaction compensation mechanism is invoked to roll back the successfully executed operation, thereby improving system stability and ensuring fault tolerance and robustness in a high-concurrency environment.

[0068] In some implementations, the method may further include: performing a delisting verification based on a product database table and a delisting log table before delisting the target product to avoid duplicate delisting of the target product.

[0069] Specifically, because a transaction compensation mechanism is invoked to roll back successfully executed operations when a failure is detected, inconsistencies may arise between the product database table and the delisting log table. For example, if the product database table has already updated the target product's status information to "delisted," a rollback might result in the delisting log table not recording the corresponding delisting status. Alternatively, if the business qualification documents have already been updated, and the target product's status information in the product database table has changed from "delisted" to "normal," a rollback might result in the delisting log table still recording the target product's status as "delisted." Therefore, before delisting a target product, a consistency check must be performed, synchronously checking the relevant records of the target product's status information in the product database table and the delisting log table to confirm whether the target product has been marked as delisted and whether a corresponding delisting status log exists. Delisting is only permitted when both the product database table and the delisting log table show the target product's status as "delisted," effectively avoiding duplicate delistings, data conflicts, or transaction anomalies, and ensuring the idempotency and data consistency of the delisting process. For example, this mechanism can be implemented through an idempotent interface design, such as generating a unique identifier based on productId+delistTime to ensure that the same request is not processed repeatedly.

[0070] In the above implementation, before removing the target product from the shelves, a removal check is performed based on the product database table and the removal log table to avoid duplicate removal of the target product and improve system stability.

[0071] In some implementations, please refer to Figure 4 The effective warning threshold range includes the first-level warning threshold range and the second-level warning threshold range. Triggering a tiered warning based on qualification validity data and the effective warning threshold range, and sending a warning pending notification, may include the following steps: S410. Determine the remaining days of the qualification based on the qualification validity period data.

[0072] S420. If the remaining days of qualification reach the threshold range of Level 2 warning, trigger Level 2 warning and send Level 2 warning pending notification.

[0073] S430. If the remaining days of qualification reach the threshold range of Level 1 warning, trigger Level 1 warning and send Level 1 warning pending notification.

[0074] The effective warning threshold range can be defined based on the number of days remaining until the qualification validity period expires. For example, the level 2 warning threshold range can be set between 71 and 90 days remaining until the qualification validity period expires. The level 1 warning threshold range can be set between 8 and 30 days remaining until the qualification validity period expires.

[0075] Specifically, the system obtains the current date, for example, using the server's standard time. After obtaining the current date, it calculates the difference between the current date and the qualification validity period data to determine the remaining days until the qualification expires. Then, it compares this remaining day with several preset effective warning threshold ranges to determine the specific warning interval within which the remaining days fall. These preset effective warning thresholds can be configured in the threshold configuration center. When the remaining days reach the secondary warning threshold range, a secondary warning is automatically triggered. At this time, the system generates a secondary warning pending notification and sends it to business personnel through a multi-channel notification interface to remind them that their business qualification documents are nearing expiration and require timely renewal or update operations to ensure business compliance and continuity. When the remaining days reach the primary warning threshold range, a primary warning is automatically triggered. At this time, the system generates a primary warning pending notification and sends it through a multi-channel notification interface to ensure that business personnel are aware that their qualifications are nearing expiration and can take appropriate measures as soon as possible to prevent qualification expiration. The alert notification can include key information such as qualification type, qualification number, validity period, and remaining days. It's important to note that the system will only send one alert notification within each valid alert threshold range to avoid information redundancy caused by duplicate notifications. That is, once a threshold range is triggered, a new alert will not be re-evaluated and sent until the valid alert threshold range is updated or the alert has been processed.

[0076] In some implementations, the qualification validity period data can be the qualification validity period data of the target object and / or the qualification validity period data of the product.

[0077] For example, the effective warning threshold range may include the number of days remaining until the qualification validity period expires: 71 to 90 days, 8 to 30 days, 1 to 7 days, and 0 days (expiring today). When the remaining days are between 71 and 90 days, a regular warning is triggered, and a warning notification is sent via in-app message. When the remaining days are between 8 and 30 days, an important warning is triggered, and a warning notification is sent simultaneously via email and office software. When the remaining days are between 1 and 7 days, an emergency warning is triggered, and a warning notification is sent simultaneously via SMS and office software. When the remaining days of the qualification expires and there are 0 days left (expiring on the same day), an emergency warning will be triggered, and a warning notification will be sent simultaneously through three channels: SMS, office software, and telephone.

[0078] In the above implementation, the remaining days of the qualification are determined based on the qualification validity period data. When the remaining days of the qualification reach the secondary warning threshold range, a secondary warning is triggered and a secondary warning pending notification is sent. When the remaining days of the qualification reach the primary warning threshold range, a primary warning is triggered and a primary warning pending notification is sent. This improves the automation, accuracy and risk control capabilities of qualification management.

[0079] In some implementations, please refer to Figure 5 The method may also include the following steps: S510. If the warning pending notice has been processed, obtain the new business qualification document and identify and process the new business qualification document to obtain the new qualification validity period data.

[0080] S520: Trigger tiered warnings based on new qualification validity period data and effective warning threshold range, and send new warning pending notifications.

[0081] S530: If a new warning notification is not processed and the new qualification validity period expires, the automatic delisting module will be triggered to delist the target product.

[0082] Specifically, after receiving a warning notification, business personnel update their business qualification documents to process the notification. If the warning notification has been processed, the newly uploaded file is retrieved and preprocessed to obtain a new business qualification document. After obtaining the new document, it needs to be identified and processed to extract key information and obtain new qualification validity data. This new validity data is then compared to the valid warning threshold range. Based on the comparison results, the valid warning threshold range to which the new validity data belongs is determined. A tiered warning is triggered based on this range, and a new warning notification is sent to the business personnel to remind them of the qualification's validity status. If the business personnel fail to process the new warning notification within the specified time, and the new qualification validity data expires, an automatic delisting module will be triggered to remove the target product from the platform, effectively preventing the continued circulation of non-compliant products and ensuring the legality and compliance of the company's operations.

[0083] The above implementation methods achieve full automation and intelligence across the entire process, from business qualification identification and early warning notification to delisting, forming a data closed loop. This allows for the interception of non-compliant products at the source, creating an unattended qualification management system. This effectively improves qualification management efficiency, supports compliant platform operations, reduces compliance risks, protects user rights, enhances user trust, and further strengthens user stickiness. By reviewing and issuing early warnings on the qualification documents of target entities and / or the qualification documents of their corresponding products, the onboarding and product listing cycles of target entities are shortened. This system is suitable for intelligent management scenarios of business qualifications in various e-commerce platforms.

[0084] In some implementations, please refer to Figure 6 Before extracting information from business qualification documents, the following steps may also be included: S610. Perform resolution normalization on the initial business qualification document to obtain a normalized business qualification document.

[0085] S620. Perform text and background contrast enhancement processing on the normalized business qualification document to obtain the enhanced business qualification document.

[0086] S630. Adjust the skewness of the enhanced business qualification document to obtain the corrected business qualification document.

[0087] S640. Verify the business qualification documents based on the calibration documents to determine the business qualification documents.

[0088] In some cases, due to variations in equipment performance, shooting environment, or print quality during the initial scanning, photographing, or printing of initial business qualification documents, issues such as inconsistent resolution, low text contrast, or page tilt often arise. These factors directly interfere with the accuracy and reliability of subsequent automated information extraction. Therefore, preprocessing of initial business qualification documents is necessary.

[0089] Specifically, firstly, the business qualification documents need to be resolution normalized according to preset standards. By uniformly adjusting the pixel density of the images, the differences in clarity caused by inconsistent resolution are eliminated, converting them into normalized business qualification documents that conform to the preset resolution. Secondly, to reduce the risk of text being interfered with by backgrounds and losing readability, the normalized documents need to undergo text-background contrast enhancement processing. Algorithms such as adaptive grayscale adjustment or local binarization can be used to strengthen the visual difference between text areas and the background, effectively suppressing image noise, shadows, or background color interference, thereby obtaining enhanced business qualification documents with significantly improved clarity. Further, to correct any layout shifts that may occur during document acquisition, the enhanced business qualification documents need to be tilted. If the tilt angle of the enhanced business qualification documents is detected to be outside the preset angle range, rotation correction is performed to obtain corrected business qualification documents. Finally, the corrected business qualification documents are verified, for example, by verifying whether the file format is the preset format and whether the file size is within the preset threshold range, ensuring the compliance of the documents, thus obtaining the final business qualification documents.

[0090] In the above implementation, the initial business qualification document is normalized to obtain a normalized business qualification document. The normalized business qualification document is then enhanced to improve the text and background contrast to obtain an enhanced business qualification document. The enhanced business qualification document is then skewed to obtain a corrected business qualification document. The corrected business qualification document is then used for verification to determine the business qualification document, thereby improving the reliability of the business qualification document.

[0091] In some implementations, please refer to Figure 7 The method may also include the following steps: S710: Publish the status information and product information of the target product to the message queue.

[0092] S720: Notify the associated subsystems through a message queue so that the associated subsystems can update the relevant information of the target product based on the status information and product information.

[0093] Specifically, the status information of the target product (e.g., whether it's available or unavailable) and product information (e.g., product ID, unavailability time, and reason for unavailability) are encapsulated into a message in a unified format and published to a message queue in real time. Through the asynchronous communication mechanism of the message queue, all related subsystems (e.g., search subsystem, recommendation subsystem, order system, etc.) are reliably notified. Upon receiving the message, each related subsystem can independently and promptly trigger its internal business logic based on the status and product information, completing the synchronous update of the target product's relevant information in local storage or the business context, thereby ensuring data consistency and business status coordination between systems.

[0094] For example, the system will publish a message of type "ProductDelisted" to a message queue (such as an Apache Kafka 3.5 message queue). The message body can contain basic information about the delisted product (such as productId, delistTime, reason, etc.). Subsystems within the platform that rely on product status (such as search, recommendation, etc.) can subscribe to this message to realize and update their local status in real time, thereby removing the display and recommendation index of the target product in real time and ensuring that the data of each module is consistent with the main database.

[0095] In the above implementation, the status information and product information of the target product are published to a message queue, and the associated subsystems are notified through the message queue so that the associated subsystems can update the relevant information of the target product based on the status information and product information, thereby improving the reliability of the subsystems.

[0096] This specification provides a data processing device 800 based on business qualifications. Please refer to [link / reference]. Figure 8 The data processing device 800 based on business qualifications includes: a qualification document recognition and processing module 810, a qualification validity period graded early warning module 820, and a target product automatic removal module 830.

[0097] The qualification document recognition and processing module 810 is used to recognize and process business qualification documents to obtain qualification validity period data, wherein the business qualification documents include the qualification documents of the target object and / or the qualification documents of the product corresponding to the target object; The qualification validity period graded early warning module 820 is used to trigger graded early warnings based on the qualification validity period data and the effective early warning threshold range, and send early warning pending notifications. The target product automatic delisting module 830 is used to trigger the automatic delisting module to delist the target product when the warning pending notification is not processed and the qualification validity period data expires.

[0098] For a detailed description of the data processing device based on business qualifications, please refer to the description of the data processing method based on business qualifications above, which will not be repeated here.

[0099] In some embodiments, a computer device is provided, which may be a terminal, and its internal structure diagram may be as follows: Figure 9 As shown. The computer device includes a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. When the computer program is executed by the processor, it implements a data processing method based on business qualifications. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad mounted on the computer device casing, or an external keyboard, touchpad, or mouse.

[0100] Those skilled in the art will understand that Figure 9 The structures shown are merely block diagrams of some structures related to the solutions disclosed in this specification, and do not constitute a limitation on the computer device to which the solutions disclosed in this specification are applied. Specifically, the computer device may include more or fewer components than shown in the figures, or combine certain components, or have different component arrangements.

[0101] In some embodiments, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the method steps described above.

[0102] This specification provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method in any of the above embodiments.

[0103] One embodiment of this specification provides a computer program product including instructions that, when executed by a processor of a computer device, enable the computer device to perform the steps of the method described in any of the above embodiments.

[0104] It should be noted that the logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be specifically implemented in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which programs can be printed, because programs can be obtained electronically, for example, by optically scanning the paper or other media, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

Claims

1. A data processing method based on business qualifications, characterized in that, The method includes: The business qualification documents are identified and processed to obtain qualification validity period data, wherein the business qualification documents include the qualification documents of the target object and / or the qualification documents of the product corresponding to the target object; Based on the qualification validity period data and the effective early warning threshold range, a tiered early warning is triggered, and an early warning pending notification is sent. If the warning notification is not processed and the qualification validity period expires, the automatic delisting module will be triggered to remove the target product from the shelves.

2. The method according to claim 1, characterized in that, The process of identifying and processing business qualification documents to obtain qualification validity period data includes: Information is extracted from the business qualification documents to obtain preliminary qualification validity period data; The business qualification document is extracted and semantically analyzed using a target text extraction model to obtain text qualification validity period data. The business qualification document is subjected to image feature extraction using a target image recognition model to obtain an image feature vector; Verification is performed based on the preliminary qualification validity period data and the text qualification validity period data to determine the qualification validity period data; The qualification verification result is determined by verifying the image feature vector and the formal qualification feature vector in the predefined knowledge base.

3. The method according to claim 2, characterized in that, Before extracting information from the business qualification documents, the method further includes: The initial business qualification document is normalized in resolution to obtain a normalized business qualification document; The normalized business qualification document is subjected to text and background contrast enhancement processing to obtain an enhanced business qualification document; The enhanced business qualification document is tilted to obtain the corrected business qualification document; The business qualification document is verified based on the corrected business qualification document to determine the business qualification document.

4. The method according to claim 2, characterized in that, The qualification validity period data includes the qualification validity period data of the target object and the qualification validity period data of the product. If the warning pending notification is not processed and the qualification validity period data expires, the automatic delisting module is triggered to delist the target product, including: If the warning notification is not processed and the validity period of the target object's qualification expires, the automatic delisting module will be triggered to delist all products corresponding to the target object. If the warning notification is not processed and the product qualification validity period expires, the automatic delisting module will be triggered to delist the product whose product qualification validity period has expired. And / or, Trigger the automatic delisting module to remove the target product from the shelves, including: Update the status information of the target product in the product database table to "out of stock"; The removal log of the target product is recorded in the removal log table, wherein the removal log includes the status information and product information of the target product.

5. The method according to claim 4, characterized in that, The method further includes: Publish the status information and product information of the target product to the message queue; The message queue is used to notify the associated subsystem, so that the associated subsystem updates the relevant information of the target product based on the status information and product information. And / or, Before removing a target product from the shelves, a verification process is performed based on the product database table and the removal log table to avoid duplicate removal of the target product.

6. The method according to claim 1, characterized in that, The method further includes: If a failure is detected in the execution step, the transaction compensation mechanism is invoked to roll back the successfully executed operations.

7. The method according to claim 1, characterized in that, The effective early warning threshold range includes a first-level early warning threshold range and a second-level early warning threshold range. The triggering of tiered early warnings based on the qualification validity period data and the effective early warning threshold range, and the sending of early warning pending notifications, includes: The remaining days of the qualification are determined based on the qualification validity period data. If the remaining days of the qualification reach the threshold range of the Level 2 warning, a Level 2 warning will be triggered and a Level 2 warning pending notification will be sent. If the remaining days of the qualification reach the threshold range of the first-level warning, a first-level warning will be triggered and a first-level warning pending notification will be sent.

8. The method according to claim 1, characterized in that, The method further includes: If the warning notification has been processed, obtain the new business qualification document and identify and process the new business qualification document to obtain the new qualification validity period data; Based on the new qualification validity period data and the effective early warning threshold range, a tiered early warning is triggered, and a new early warning pending notification is sent. If the new warning notification remains unprocessed and the new qualification validity period expires, the automatic delisting module will be triggered to remove the target product from the platform.

9. A data processing device based on business qualifications, the device comprising: The qualification document recognition and processing module is used to recognize and process business qualification documents to obtain qualification validity period data, wherein the business qualification documents include the qualification documents of the target object and / or the qualification documents of the product corresponding to the target object; The qualification validity period graded early warning module is used to trigger graded early warnings based on the qualification validity period data and the effective early warning threshold range, and send early warning pending notifications; The target product automatic delisting module is used to automatically delist the target product when the warning pending notification is not processed and the qualification validity period data expires.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 8.

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