Commodity mid-stage management system and method based on multi-platform data synchronization

Abstract

The present invention discloses a commodity middle-office management system and method based on multi-platform data synchronization, which relates to the technical field of data synchronization management, including: configuring basic card and coupon information and sales information in the middle-office background, creating a coupon master data model, and generating a platform-adapted valuable coupon data model based on the coupon master, extending platform-specific fields, realizing unified management and cross-platform synchronization of commodity information, and at the same time, distributing commodity data to each platform interface through the GBS layer, triggering the platform-side review process, and deciding the synchronization timing based on the interface response portrait and the user active period model, ensuring timely synchronization of commodity information and review efficiency, in addition, it also monitors the review status in real time, performs automatic rollback on conflicting operations, and responds to platform rule changes to trigger commodity removal or information update, and in the user verification link, calculates the user verification confidence in real time, and when the confidence is lower than the confidence threshold, triggers a dynamic verification strategy to effectively prevent and control risks.

Description

Technical Field

[0001] The present invention relates to the technical field of data synchronization management, and specifically to a commodity middle-office management system and method based on multi-platform data synchronization. Background Art

[0002] With the rapid development of e-commerce and mobile payments, various online platforms such as Douyin, Meituan, and Xiaohongshu have become important channels for merchants to promote and sell. On these platforms, cards and coupons, as a common marketing tool, are widely used to attract customers, increase sales, and enhance user stickiness.

[0003] In terms of synchronizing product information, traditional methods often lack an efficient synchronization mechanism. After updating product information, merchants need to log in to each platform one by one to manually synchronize. This is not only inefficient, but also prone to untimely synchronization or omissions. At the same time, the review processes and rules of each platform are also different. Merchants need to pay close attention to the review status of each platform in order to promptly deal with situations where the review fails.

[0004] In addition, in the user verification process, traditional methods often rely on users to consciously abide by the rules and lack effective risk prevention and control measures. For example, users may abuse coupons through virtual positioning or cross-store verification, causing losses to merchants. In addition, data consistency and accuracy in the verification process are also a major challenge. Merchants need to ensure the consistency of the middle-office records and the platform billing records in order to promptly discover and handle differences. Summary of the Invention

[0005] (1) Technical problems solved

[0006] In response to the shortcomings of the existing technology, the present invention provides a commodity middle-office management system and method based on multi-platform data synchronization. By guiding users to complete account creation and store claiming on the target platform, creating a voucher master data model, and generating a platform-adapted voucher data model based on the voucher master, unified management and cross-platform synchronization of commodity information are achieved. At the same time, the present invention also distributes commodity data to each platform interface through the GBS layer, triggering the platform-side review process, and decides the synchronization timing based on the interface response portrait and the user active period model, ensuring timely synchronization of commodity information and review efficiency. In addition, the present invention also monitors the review status in real time, performs automatic rollback on conflicting operations, and responds to platform rule changes to trigger commodity removal or information update. In the user verification link, the user verification confidence is calculated in real time. When the confidence is lower than the confidence threshold, the dynamic verification strategy is triggered to effectively prevent and control risks.

[0007] (2) Technical solution

[0008] To achieve the above objectives, the present invention is implemented through the following technical solutions: a commodity mid-stage management method based on multi-platform data synchronization, comprising:

[0009] Guide users to complete merchant account registration and store claiming on the target platform, configure basic card and coupon information and sales information in the middle and back-end, and create a coupon master data model that contains common data for all platforms;

[0010] Generate a platform-adapted voucher data model based on the voucher master template, expand platform-specific fields, associate limited-time sales plans, and configure price and points rules;

[0011] The product bridging service layer distributes product data to various platform interfaces, triggering the platform-side review process. Synchronization timing is determined based on the interface response profile and user activity periods. The platform review status is monitored in real time, and reverse rollback is performed for synchronization failures.

[0012] In response to changes in platform rules, product removal or information update is triggered. The user verification confidence level is calculated when the user verifies the product. When the confidence level is lower than the confidence threshold, secondary verification is triggered.

[0013] Furthermore, multiple platform accounts are associated and mapped to generate a unique merchant ID. Basic information includes but is not limited to input display information, consumption rules, and applicable store information. Sales information includes but is not limited to inventory strategies and purchase limit rules. The voucher master data model includes but is not limited to validity period type, applicable store list, and redemption rules.

[0014] Perform logical verification on the generated voucher master, including but not limited to checking whether the inventory is greater than or equal to the sold quantity and detecting whether there is a conflict in the purchase limit rules. If the verification passes, proceed to the next step. If the verification fails, modify the corresponding configuration information and try again.

[0015] Furthermore, platform-specific fields are expanded to include:

[0016] Capture the audit rule update logs of each platform in real time, extract the rule fields from the logs, classify the extracted rule fields, and collect the indicator values ​​of each rule field on each platform, including counting the number of changes to the rule field on each platform in the past 30 days, counting the audit pass rate of the rule field, and recording the number of cases that require manual processing due to changes in the rule field. Calculate the information entropy of each rule field on different platforms through the indicator value. .

[0017] Furthermore, the weight of each field on different platforms is calculated using information entropy: The rule fields with weights greater than the weight threshold are high-weight rule fields, otherwise, they are low-weight rule fields. Focused injection is performed on high-weight rule fields, including but not limited to supplementing category keywords and replacing risk words in coupon names according to the latest sensitive word library. For low-weight rule fields, only required information is filled in.

[0018] Furthermore, based on the interface response profile and the user's active time period, the synchronization timing is determined, including:

[0019] Record the historical response delay data of each platform interface in real time. Based on this historical response delay data, build a probability distribution model for the response time of each platform interface. This model categorizes operations into two categories: high-conflict-sensitive operations and low-conflict field operations. High-conflict-sensitive operations are changes to core data, while low-conflict field operations are changes to non-core information.

[0020] For high-conflict sensitive operations, the average response delay of each platform interface is calculated based on the interface response time probability distribution model, and the platforms are sorted from low to high by delay. The platform interface with the lowest delay is synchronized first. For low-conflict field operations, data on the active period of users on each platform is obtained, and information updates are completed before the user active period.

[0021] Furthermore, the return status of the synchronization operation is monitored in real time. If the platform interface returns a failure, it is marked as a conflicting operation. According to the operation sequence log, all related operations before the conflicting operation are located, and the rollback is performed in reverse order of the operations. For highly conflict-sensitive operations, it is rolled back directly to the original state before the conflict occurred. For low-conflict field operations, if the field update has not taken effect, the update is canceled. If it has taken effect, it is marked as pending manual review.

[0022] Furthermore, compensation measures are triggered based on the conflict type: if there is an inventory oversold conflict, an alarm notification is pushed; if there is a rule verification failure conflict, the low-weight rule field is corrected and synchronization is resubmitted; if there is an interface timeout conflict, synchronization is automatically retried; if the retried synchronization fails, it is marked as a "manual processing work order."

[0023] Furthermore, when the user triggers the cancellation action, the user's cancellation request is received, the user's geographic location, device signal strength, and number of competing stores are obtained in real time, and the cancellation confidence C is calculated: ,in, Indicates the effective duration. Indicates the total monitoring time. represents the signal fluctuation variance, represents the maximum allowed variance, k represents the attenuation coefficient, , Indicates the number of competing stores.

[0024] Furthermore, after successful cancellation, the card or coupon will be marked as cancelled in the middle-office database, and the cancellation status will be synchronized to all related platforms through the platform interface. The cancellation record will be pulled through the billing interface provided by the platform, and the middle-office cancellation record will be compared with the platform billing record to check whether there are any differences. If any differences are found, manual verification will be triggered.

[0025] The product management system based on multi-platform data synchronization includes:

[0026] The coupon master generation module guides users to complete merchant account registration and store claiming on the target platform, configures basic card and coupon information and sales information in the middle and back-end, and creates a coupon master data model that contains common data for all platforms;

[0027] The coupon generation module generates a platform-adapted coupon data model based on the coupon master template, extends platform-specific fields, associates limited-time sales plans, and configures price and points rules.

[0028] The product bridging module distributes product data to various platform interfaces through the product bridging service layer, triggering the platform-side review process. It determines the synchronization sequence based on the interface response profile and user activity period, monitors the platform review status in real time, and performs reverse rollback for synchronization failures.

[0029] The verification module responds to changes in platform rules, triggers product removal or information update, and calculates the user's verification confidence when the user verifies. When the confidence is lower than the confidence threshold, secondary verification is triggered.

[0030] (3) Beneficial effects

[0031] The present invention provides a product mid-stage management system and method based on multi-platform data synchronization, which has the following beneficial effects:

[0032] (1) By guiding users to complete account creation and store claiming on the target platform, and uniformly configuring the basic information and sales information of the products in the middle and back-end, the tedious process of manually configuring the cards and coupons on each platform is avoided. By creating a master card data model and storing universal card and coupon information for all platforms, the synchronization of card and coupon information across multiple platforms is achieved, which not only reduces duplication of work, but also reduces the management complexity caused by differences between platforms.

[0033] (2) By extracting rule fields and constructing a set of field factors, as well as building a rule field weight model based on historical data, it is possible to identify and process the audit points of different platforms. By focusing on injecting high-weight rule fields, such as supplementing category keywords, replacing risk words, etc., it can significantly improve the review pass rate of cards and coupons on various platforms, and help reduce repeated submissions and modifications caused by failed reviews.

[0034] (3) Based on the interface response time probability distribution model and user active period data, different types of data changes are prioritized and time-series planned to ensure the timeliness and effectiveness of data updates. Through synchronized timing decision-making, conflict handling and compensation mechanisms, operational efficiency and accuracy are significantly improved, and user complaints and churn caused by inconsistent data or delayed updates are reduced.

[0035] (4) By regularly pulling product status data and monitoring the platform rule change log in real time, it can quickly respond to any changes in the platform's rules, thereby updating the product status or information in a timely manner, avoiding products being removed from the shelves or causing other unnecessary troubles due to non-compliance with platform rules. By calculating the user's verification confidence, it can identify high-risk verification behaviors, such as cross-store verification or the use of virtual positioning, thereby triggering a secondary verification mechanism to ensure the authenticity and accuracy of the verification. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 This is a schematic diagram of the steps of the commodity middle-stage management method based on multi-platform data synchronization of the present invention;

[0037] Figure 2 This is a schematic diagram of the connection between the product platform of the present invention and various platforms;

[0038] Figure 3 This is a schematic diagram of the bridging service process for the product in the present invention;

[0039] Figure 4 This is a structural diagram of the commodity middle-office management system based on multi-platform data synchronization of the present invention. DETAILED DESCRIPTION

[0040] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0041] See also Figure 1-Figure 3 The present invention provides a commodity middle-stage management method based on multi-platform data synchronization, comprising the following steps:

[0042] Step 1: Guide users to complete account creation and store claiming on the target platform, create a master coupon data model, and store universal card and coupon information across all platforms;

[0043] The step 1 includes the following contents:

[0044] Step 101: According to the rules of the target platform (such as Douyin, Meituan, Xiaohongshu, etc.), guide users to complete merchant account registration, store claiming and qualification review on the target platform. The following uses Douyin as an example to explain:

[0045] Douyin platform operation process: Register a "Douyin Visitor" account, go to "Join Douyin Stores", select "Claim Single Store" or "Chain Store", enter the store and address to search, and submit the claim application after confirming that the information is correct. If the store cannot be found, you can select "Create New Store", fill in the detailed address, contact number, business hours and other information, and upload the business license, legal person ID card, industry qualifications (such as food business license, etc.) according to the platform requirements. After submission, wait for 1-3 working days for review. After the review is passed, you can manage the store information;

[0046] Step 102: Associate and map multiple platform accounts, generate a unique merchant ID, and uniformly configure basic product information in the middle and backend platforms. This includes entering display information (such as card and voucher name and subtitle), consumption rules (such as usage threshold and validity period), and applicable store information. Configure product sales information, including setting inventory policies (total inventory, whether to display sold quantities, etc.) and purchase restriction rules (the upper limit for single-store redemption, whether to limit repeated redemption at a single store per launch, etc.);

[0047] Step 103: Generate a coupon master data model based on the above configuration. Use the snowflake algorithm to assign a platform-unique code to the coupon master. The coupon master data model serves as the basic template for coupons and includes a series of configuration items from basic information to sales information. It supports unified management and adaptation on multiple platforms. Description of some fields:

[0048] Coupon module: general marketing and valuable coupons;

[0049] Data types: basic coupons, old Douyin photos, old Xiaohongshu photos, old Gaode photos, and merchant-approved versions;

[0050] Coupon master ID: ID randomly generated by the Snowflake algorithm (unique on the entire platform);

[0051] Associated [base voucher] id: used to identify the base voucher of the old photo or merchant review version;

[0052] Master Coupon Code: A platform-wide unique code automatically generated according to certain rules;

[0053] Business management number id: business management number;

[0054] Applicable sub-account group ID: There can only be one;

[0055] Validity period type: Absolute time (specific date range) / relative time (valid within a few days after receiving);

[0056] Applicable store list: associated store ID and platform POI_ID (such as Amap POI_ID);

[0057] Write-off rules: the upper limit of write-offs per store, whether cross-store write-offs are allowed;

[0058] Step 104: Perform a logical check on the generated coupon master, including but not limited to checking whether the inventory is greater than or equal to the sold quantity, detecting whether there is a conflict with the purchase limit rules, etc. If the check passes, proceed to the next step. If the check fails, modify the corresponding configuration information according to the prompts and try again;

[0059] When using, combine the contents of steps 101 to 104:

[0060] By guiding users to complete account creation and store claiming on the target platform, and uniformly configuring basic product information and sales information in the middle and back-end, the tedious process of manually configuring cards and coupons on each platform is avoided. By creating a coupon master data model and storing universal card and coupon information for all platforms, the synchronization of card and coupon information across multiple platforms is achieved, which not only reduces repetitive work, but also reduces the management complexity caused by differences between platforms.

[0061] Step 2: Generate a platform-adapted voucher data model based on the voucher master template, associate it with a limited-time sales plan, configure the price, points, and platform-specific distribution channels, and expand platform-specific fields.

[0062] The second step includes the following contents:

[0063] Step 201: Extract relevant information from the coupon master data model based on the target platform (e.g., Douyin, Xiaohongshu, etc.). For each platform to be supported, add or adjust necessary fields based on their specific requirements. For example, add the correct category enumeration value to the coupon generated for Douyin, and specify the accurate POI_ID (geographic location identifier) ​​for the coupon generated for AutoNavi.

[0064] Step 202: Set a limited-time sales plan based on the marketing strategy, including but not limited to price, discount information, points redemption rules, etc., configure specific distribution channels for different sales plans, for example, set certain coupons to be sold only within the Douyin live broadcast room, and associate the sales plan with the corresponding coupons;

[0065] Step 203: Capture the review rule update logs of each platform in real time (such as changes to Douyin's category whitelist and updates to Xiaohongshu's sensitive word library), extract specific rule fields (such as "category restrictions" and "sensitive word blocking") from the logs, and classify them as rule field factors;

[0066] Specifically, obtain rule change information through crawlers or log analysis tools (must comply with the platform agreement), use scheduled tasks (such as cron) or message queues (such as Kafka) to pull logs in real time, and store the logs in a database (such as MySQL). The database fields include: platform name, rule field type, change time, specific content, etc.

[0067] Use natural language processing to segment log text, remove stop words, and identify entities to extract specific rule fields. Alternatively, use regular expressions or templates to match rule fields through rule pattern matching, and categorize the extracted rule fields into a set of rule field factors, such as category restrictions (adding / deleting category IDs, whitelist updates), sensitive word blocking (adding blocked words, illegal keywords), regional restrictions (adding / removing blocked regions), and validity period requirements (shortening review cycles and extending validity periods).

[0068] Step 204: Collect the indicator values ​​of each rule field on different platforms, including counting the number of changes to specific rule fields on each platform in the past 30 days (e.g., Douyin's "category restrictions" change 5 times per week), counting the approval rate of different rule fields, and recording the number of cases requiring manual processing due to rule field changes (e.g., Xiaohongshu's "sensitive word blocking" requires manual intervention 15 times per week);

[0069] Step 205: Normalize the indicators of different dimensions to the interval [0, 1] and calculate the information entropy of each rule field on different platforms: ,in, , Indicates the j The first rule field i indicator values, , n represents the number of indicators, , m Indicates the number of rule fields;

[0070] Step 206: Calculate the weight of each field on different platforms using information entropy: , pre-set the weight threshold. Rule fields with weights greater than the weight threshold are high-weight rule fields, otherwise, they are low-weight rule fields. Focus injection is performed on high-weight rule fields, including but not limited to supplementing category keywords and replacing risk words in coupon names based on the latest sensitive word library. For low-weight rule fields, only the required information is filled in, and no additional operations are required. Based on historical data and business experience, a reasonable weight threshold (such as 0.35) is set. The weight threshold can be adjusted according to actual conditions to ensure that high-weight rule fields receive sufficient attention and processing;

[0071] It should be noted that category keywords are keywords associated with products or coupons based on the target platform's product classification system (i.e., categories). These keywords help products be correctly categorized on the platform and can improve product search visibility. For example, if you want to publish a restaurant coupon, you may need to explicitly include restaurant-related category keywords such as "restaurant" and "food" in the description;

[0072] Risky words refer to sensitive words that may trigger the platform's review mechanism or directly lead to the rejection of products or coupons. These words generally include but are not limited to sensitive content, vulgar content, and false advertising terms. For example, exaggerated expressions such as "cheapest" and "absolutely effective" or words referring to sensitive historical events may be considered risky words.

[0073] Required information refers to the information that must be filled in when creating a product or coupon according to the requirements of each platform. This information is crucial to ensuring the integrity and legitimacy of the product or coupon. For example, the product title, subtitle, instructions, expiration date, and scope of application are all common required information. For coupons, this may also include the selling price, discount information, and inventory quantity.

[0074] When using, combine the contents of steps 201 to 204:

[0075] By extracting rule fields and constructing a set of field factors, as well as building a rule field weight model based on historical data, we can identify and process the audit points of different platforms. By focusing on injecting high-weight rule fields, such as supplementing category keywords and replacing risk words, we can significantly improve the approval rate of cards and coupons on various platforms, helping to reduce repeated submissions and modifications caused by failed audits.

[0076] Step 3: Distribute product data to each platform interface through the GBS layer, triggering the platform-side review process. Based on the interface response profile and the user active period model, the synchronization timing is determined. The review status is monitored in real time, the product status of the middle platform is updated, and conflicting operations are automatically rolled back.

[0077] The step three includes the following contents:

[0078] Step 301: The product data generated by the middle platform (such as coupon information and product information related to the coupon, sales plan, etc.) is distributed to the target platform (such as Douyin, Xiaohongshu, and Amap) through the GBS layer, triggering the review process of the target platform and recording the historical response delay data of each platform interface in real time (such as the average delay of 200ms for Meituan and 500ms for Xiaohongshu), including interface call time, response time, success rate, error type, etc.

[0079] Step 302: Based on historical response delay data, a probability distribution model (e.g., normal distribution, exponential distribution) is constructed for the response time of each platform interface. The average response time, fluctuation range (standard deviation), and timeout threshold (e.g., maximum delay within a 95% confidence interval) of each interface are calculated.

[0080] Step 303: Based on the degree of impact of the operation on data consistency, the operations are divided into two categories: high-conflict sensitive operations, which involve changes to core data such as inventory, price, and write-off rules (such as inventory adjustments and price modifications); low-conflict field operations, which involve updates to non-core data (such as titles, cover images, and descriptions);

[0081] For high-conflict sensitive operations, we calculate the average response latency of each platform's interfaces based on the interface response time probability distribution model. We then sort the platforms by latency from low to high, prioritizing synchronization with the platform interfaces with the lowest latency. For low-conflict field operations, we obtain data on user activity periods on each platform (e.g., Douyin is most active from 8 PM to 12 AM, and Xiaohongshu is most active from 10 AM to 2 PM). We prioritize completing information updates before these user activity periods to ensure users see the latest data (e.g., Douyin cover image updates are completed before 7 PM, and Xiaohongshu title modifications are completed before 9 AM).

[0082] Step 304: Obtain the current audit status of the product through the query interface provided by the platform (such as the / audit_status interface of Douyin). If the distribution fails or the audit status is not updated, trigger the alarm mechanism and notify relevant personnel (such as through email or message reminders); set a scheduled task (such as polling every 5 minutes), obtain the latest audit status through the platform interface, and update the middle platform product status to one of the following categories based on the audit status returned by the platform: Under review: the product has been submitted but the review has not yet been completed, Synchronized: the product has passed the review and is successfully listed, Audit failed: the product has not passed the review and needs to be modified and resubmitted;

[0083] Step 305: Monitor the return status of the synchronization operation in real time. If the platform interface returns "failure" (e.g., inventory oversold, field not meeting the rules, interface timeout), mark it as a conflicting operation, and record the conflict type (e.g., insufficient inventory, rule verification failure), occurrence time, operation details, and other related platform operations.

[0084] Step 306: Based on the operation sequence log, locate all related operations before the conflicting operation (for example, if the inventory adjustment of the same ticket master has been synchronized to other platforms). Roll back the operations in reverse order. For highly conflict-sensitive operations, roll back directly to the original state before the conflict (for example, restore inventory quantity, undo price adjustment). For low-conflict field operations, if the field update has not taken effect, cancel the update; if it has taken effect, mark it for manual review.

[0085] Step 307: Triggering compensation measures based on the conflict type:

[0086] If there is an inventory oversold conflict, a free coupon (such as a "compensation coupon") will be issued to affected users, with the amount being 10% of the original coupon's face value. An alert notification will be sent to the merchant, prompting them to manually check the inventory logic.

[0087] If rule verification fails or conflicts occur, the low-weight rule fields will be automatically corrected (e.g., replacing sensitive words) and resubmitted for synchronization. If high-weight rule field conflicts occur (e.g., category restrictions are not met), manual intervention is required for adjustment.

[0088] If the interface times out and conflicts, the synchronization will be automatically retried (up to 3 times, with increasing intervals). If failures persist, it will be marked as a "manual processing work order" and the operation and maintenance team will be notified.

[0089] When using, combine the contents of steps 301 to 307:

[0090] Based on the interface response time probability distribution model and user active period data, different types of data changes are prioritized and time-series planned to ensure the timeliness and effectiveness of data updates. Through synchronized timing decision-making, conflict resolution and compensation mechanisms, operational efficiency and accuracy are significantly improved, and user complaints and churn caused by inconsistent data or delayed updates are reduced.

[0091] Step 4: Respond to changes in platform rules, trigger product removal or information update, calculate user verification confidence in real time, and trigger a dynamic verification strategy when the confidence is lower than the confidence threshold.

[0092] The fourth step includes the following contents:

[0093] Step 401: Regularly pull product status data through the platform API, map the status returned by the platform to a unified status classification of the middle platform (such as "on the shelf", "in the warehouse", "off the platform"), and monitor the platform rule change log in real time (such as store claim failure, category blocking, etc.). If store claim failure is detected, the product status is updated to "off the platform". If category blocking is detected, trigger a product information update (such as modifying the category ID);

[0094] Step 402: When the user triggers the redemption action, the redemption request is received through the middle platform interface and legitimacy verification is performed, including checking whether the card or coupon is valid (e.g., not expired, not redeemed), checking whether the user has the authority to redeem the card or coupon, etc. The user's geographic location, device signal strength, and surrounding POI density (e.g., the number of competing stores within 500 meters) are obtained in real time, and the redemption confidence level C is calculated: ,in, Indicates the effective duration (the percentage of time the user is within the store's geofence). Indicates the total monitoring time, which is the entire monitoring time from the time the user triggers the cancellation action to the completion of the operation. Indicates the signal fluctuation variance, which is the variance value of the Wi-Fi or cellular signal strength of the user device during the monitoring period. Indicates the maximum allowable variance, which is the upper limit of the signal fluctuation variance obtained through historical data statistics. k Represents the attenuation coefficient, a constant adjusted according to the business scenario, which controls the impact of the number of competing products on confidence. , Indicates the number of competitor stores within a specific radius (e.g., 500 meters) around the user's current location;

[0095] Step 403: When the verification confidence level is lower than the confidence threshold, it is determined to be a high-risk verification (e.g., the user may verify across stores or use virtual location), triggering secondary verification (e.g., facial recognition + store-specific QR code).

[0096] When the write-off confidence level is lower than the confidence threshold and the number of competing stores is greater than three, a limited-time bonus offer (e.g., "add 10 yuan to redeem a popular item after write-off") is automatically pushed to suppress conversions from competing products. In other cases, no additional action is taken. Based on historical data and business experience, a reasonable confidence threshold is set to accurately distinguish high-risk write-offs from low-risk write-offs.

[0097] Step 404: After successful cancellation, the card or coupon is marked as cancelled in the middle office database. The cancellation status is synchronized to all related platforms through the platform interface. The cancellation record is pulled through the billing interface provided by the platform. The middle office cancellation record is compared with the platform billing record to check for any discrepancies. If a discrepancy is found (for example, the middle office marked it as cancelled but the platform did not record it), a manual verification is triggered.

[0098] By regularly pulling product status data and monitoring the platform rule change log in real time, we can quickly respond to any changes in the platform's rules, thereby updating product status or information in a timely manner, avoiding products being removed from the shelves or causing other unnecessary troubles due to non-compliance with platform rules. By calculating the user's verification confidence, we can identify high-risk verification behaviors, such as cross-store verification or the use of virtual positioning, thereby triggering a secondary verification mechanism to ensure the authenticity and accuracy of the verification.

[0099] See also Figure 4 The present invention also provides a commodity middle-end management system based on multi-platform data synchronization, including: a voucher master generation module, a voucher generation module, a commodity bridging module and a verification module; wherein,

[0100] The coupon master generation module guides users to complete merchant account registration and store claiming on the target platform, configures basic card and coupon information and sales information in the middle and back-end, and creates a coupon master data model that contains common data for all platforms;

[0101] The coupon generation module generates a platform-adapted coupon data model based on the coupon master template, extends platform-specific fields, associates limited-time sales plans, and configures price and points rules.

[0102] The product bridging module distributes product data to various platform interfaces through the product bridging service layer, triggering the platform-side review process. It determines the synchronization sequence based on the interface response profile and user activity period, monitors the platform review status in real time, and performs reverse rollback for synchronization failures.

[0103] The verification module responds to changes in platform rules, triggers product removal or information update, and calculates the user's verification confidence when the user verifies. When the confidence is lower than the confidence threshold, secondary verification is triggered.

[0104] In the application, the several formulas involved are all calculated by taking their numerical values ​​after removing the dimensions, and the formula is a formula obtained by collecting a large amount of data and performing software simulation to obtain the latest real situation. The coefficients in the formula are set by technical personnel in this field according to actual conditions.

[0105] The above embodiments can be implemented in whole or in part through software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. Those skilled in the art will appreciate that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented using a combination of electronic hardware, computer software, and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution.

[0106] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, and may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment as needed.

[0107] The above is only a specific implementation method of the present application, but the scope of protection of the present application is not limited thereto. Any technician familiar with this technical field can easily think of changes or replacements within the technical scope disclosed in this application, which should be covered by the scope of protection of the present application.

Claims

1. A product mid-stage management method based on multi-platform data synchronization, characterized by: include: Guide users to complete merchant account registration and store claiming on the target platform, configure basic card and coupon information and sales information in the middle and back-end, and create a coupon master data model that contains common data for all platforms; Generate a platform-adapted voucher data model based on the voucher master template, expand platform-specific fields, associate limited-time sales plans, and configure price and points rules; The product bridging service layer distributes product data to various platform interfaces, triggering the platform-side review process. Synchronization timing is determined based on the interface response profile and user activity periods. The platform review status is monitored in real time, and reverse rollback is performed for synchronization failures. In response to changes in platform rules, product removal or information update is triggered. The user verification confidence level is calculated when the user verifies the product. When the confidence level is lower than the confidence threshold, secondary verification is triggered.

2. The commodity middle-platform management method based on multi-platform data synchronization according to claim 1 is characterized by: Map multiple platform accounts to generate a unique merchant ID. Basic information includes input display information, consumption rules, and applicable store information. Sales information includes inventory strategies and purchase limit rules. The voucher master data model includes validity period type, applicable store list, and redemption rules. Perform logical verification on the generated voucher master, including checking whether the inventory is greater than or equal to the sold quantity and whether there is a conflict in the purchase limit rules. If the verification passes, proceed to the next step. If the verification fails, modify the corresponding configuration information and try again.

3. The commodity middle-platform management method based on multi-platform data synchronization according to claim 1 is characterized by: Extended platform-specific fields, including: Capture the audit rule update logs of each platform in real time, extract the rule fields from the logs, classify the extracted rule fields, and collect the indicator values ​​of each rule field on each platform, including counting the number of changes to the rule field on each platform in the past 30 days, counting the audit pass rate of the rule field, and recording the number of cases that require manual processing due to changes in the rule field. Calculate the information entropy of each rule field on different platforms through the indicator value. .

4. The commodity middle-stage management method based on multi-platform data synchronization according to claim 3 is characterized by: The weight of each field on different platforms is calculated by information entropy: The rule fields with weights greater than the weight threshold are high-weight rule fields, otherwise, they are low-weight rule fields. Focused injection is performed on high-weight rule fields, including supplementing category keywords and replacing risk words in the voucher name according to the latest sensitive word library. For low-weight rule fields, only required information is filled in.

5. The commodity middle-platform management method based on multi-platform data synchronization according to claim 1 is characterized by: Synchronize timing based on interface response profiles and user active time periods, including: Record the historical response delay data of each platform interface in real time. Based on this historical response delay data, build a probability distribution model for the response time of each platform interface. This model categorizes operations into two categories: high-conflict-sensitive operations and low-conflict field operations. High-conflict-sensitive operations are changes to core data, while low-conflict field operations are changes to non-core information. For high-conflict sensitive operations, the average response delay of each platform interface is calculated based on the interface response time probability distribution model, and the platforms are sorted from low to high by delay. The platform interface with the lowest delay is synchronized first. For low-conflict field operations, data on the active period of users on each platform is obtained, and information updates are completed before the user active period.

6. The commodity middle-platform management method based on multi-platform data synchronization according to claim 5 is characterized by: Monitor the return status of synchronization operations in real time. If the platform interface returns a failure, it is marked as a conflicting operation. Based on the operation sequence log, locate all related operations before the conflicting operation occurs, and perform a rollback in reverse order. For highly conflict-sensitive operations, roll back directly to the original state before the conflict occurs. For low-conflict field operations, if the field update has not taken effect, cancel the update. If it has taken effect, mark it for manual review.

7. The commodity middle-platform management method based on multi-platform data synchronization according to claim 6 is characterized by: Trigger compensation measures based on the conflict type: If there is an inventory oversold conflict, push an alarm notification; If rule verification fails or conflicts, correct the low-weight rule field and resubmit synchronization; If there is an interface timeout conflict, the synchronization will be automatically retried. If the synchronization fails, it will be marked as a manual processing work order.

8. The commodity middle-platform management method based on multi-platform data synchronization according to claim 1 is characterized by: When a user triggers a cancellation action, the system receives the user's cancellation request, obtains the user's geographic location, device signal strength, and the number of competing stores in real time, and calculates the cancellation confidence C: ,in, Indicates the effective duration. Indicates the total monitoring time. represents the signal fluctuation variance, represents the maximum allowed variance, k represents the attenuation coefficient, , Indicates the number of competing stores.

9. The commodity middle-platform management method based on multi-platform data synchronization according to claim 8 is characterized by: After successful cancellation, the card or coupon will be marked as cancelled in the middle-office database, and the cancellation status will be synchronized to all related platforms through the platform interface. The cancellation record will be pulled through the billing interface provided by the platform, and the middle-office cancellation record will be compared with the platform billing record to check whether there are any differences. If any differences are found, manual verification will be triggered.

10. The product management system based on multi-platform data synchronization is characterized by: include: The coupon master generation module guides users to complete merchant account registration and store claiming on the target platform, configures basic card and coupon information and sales information in the middle and back-end, and creates a coupon master data model that contains common data for all platforms; The coupon generation module generates a platform-adapted coupon data model based on the coupon master template, extends platform-specific fields, associates limited-time sales plans, and configures price and points rules. The product bridging module distributes product data to various platform interfaces through the product bridging service layer, triggering the platform-side review process. It determines the synchronization sequence based on the interface response profile and user activity period, monitors the platform review status in real time, and performs reverse rollback for synchronization failures. The verification module responds to changes in platform rules, triggers product removal or information update, and calculates the user's verification confidence when the user verifies. When the confidence is lower than the confidence threshold, secondary verification is triggered.

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