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

Abstract

The invention discloses a multi-platform data synchronization-based commodity middle platform management system and method, and relates to the technical field of data synchronization management, and the method comprises the steps: configuring card and coupon basic information and selling information at a middle platform background, creating a coupon mother set data model, generating a valuable coupon data model adaptive to a platform based on the coupon mother set, expanding the specific field of the platform, and carrying out the management of the middle platform; unified management and cross-platform synchronization of commodity information are achieved, meanwhile, commodity data are distributed to all platform interfaces through a GBS layer, a platform side auditing process is triggered, timing sequences are synchronized based on interface response portraits and user active period model decisions, timely synchronization and auditing efficiency of the commodity information are ensured, in addition, the auditing state is monitored in real time, and the auditing efficiency is improved. The method comprises the steps of performing automatic rollback on conflict operation, responding to platform rule change, triggering commodity off-shelf or information updating, calculating user cancel-after-verification confidence coefficient in real time in a user cancel-after-verification link, and triggering a dynamic cancel-after-verification strategy when the confidence coefficient is lower than a confidence coefficient threshold value, thereby effectively preventing and controlling risks.

Description

Technical Field

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

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

[0003] In terms of the synchronization of commodity information, traditional methods often lack an efficient synchronization mechanism. After merchants update commodity information, they need to log in to each platform manually one by one for synchronization, which is not only inefficient but also prone to situations of untimely synchronization or omission. At the same time, there are also differences in the review processes and rules of each platform, and merchants need to closely monitor the review status of each platform in order to handle the situation of non-pass in a timely manner.

[0004] In addition, in the user redemption link, 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 redemption, causing losses to merchants. In addition, data consistency and accuracy during the redemption process are also a major challenge, and merchants need to ensure the consistency between the middle platform records and the platform bill records in order to discover and handle differences in a timely manner. Summary of the Invention

[0005] (I) Technical Problems to be Solved In view of the deficiencies of the prior art, the present invention provides a commodity middle platform 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 coupon master data model, and generating a platform-adapted valuable coupon data model based on the coupon master, the unified management and cross-platform synchronization of commodity information are realized. At the same time, the present invention also distributes commodity data to each platform interface through the GBS layer, triggers the platform-side review process, and determines the synchronization timing based on the interface response portrait and the user active period model, ensuring the timely synchronization and review efficiency of commodity information. In addition, the present invention also monitors the review status in real time, performs automatic rollback on conflicting operations, and responds to changes in platform rules, triggering commodity delisting or information update. In the user redemption link, the user redemption confidence is calculated in real time. When the confidence is lower than the confidence threshold, a dynamic redemption strategy is triggered, effectively preventing risks.

[0006] (II) Technical Solutions To achieve the above objectives, the present invention is realized through the following technical solutions: A commodity middle platform management method based on multi-platform data synchronization, including: 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 backend, and create a coupon master data model containing common data for the entire platform; Generate a platform-adapted voucher data model based on the voucher master, expand platform-specific fields, associate limited-time sales plans, and configure price and points rules; The product data is distributed to each platform interface through the product bridging service layer, triggering the platform-side review process. The synchronization sequence is determined based on the interface response profile and the user's active period, the platform review status is monitored in real time, and reverse rollback is performed for synchronization failure operations. In response to changes in platform rules, product removal or information update is triggered. When users redeem products, the user redemption confidence level is calculated. When the confidence level is lower than the confidence level threshold, secondary verification is triggered.

[0007] Furthermore, the multi-platform accounts are associated and mapped to generate a unique merchant ID. The 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 strategy and purchase limit rules, and the voucher master data model includes but is not limited to validity period type, applicable store list and redemption rules; Perform logical verification on the generated voucher master, including but not limited to checking whether the inventory is greater than or equal to the quantity sold 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.

[0008] Furthermore, the platform-specific fields are expanded to include: 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 need to be manually processed due to the change of the rule field. Calculate the information entropy of each rule field on different platforms through the indicator value .

[0009] Furthermore, 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 but not limited to supplementing category keywords and replacing risk words in the voucher name according to the latest sensitive word library. For low-weight rule fields, only the required information is filled in.

[0010] Furthermore, the synchronization sequence is determined based on the interface response profile and the user's active time period, including: Real-time record the historical response latency data of each platform interface. Based on the historical response latency data, construct a probability distribution model of the response time of each platform interface. The operations are divided into two categories: high-conflict sensitive operations and low-conflict field operations. High-conflict sensitive operations are the changes of core data, and low-conflict field operations are the changes of non-core information; For high-conflict sensitive operations, according to the probability distribution model of the interface response time, calculate the average response latency of each platform interface, sort the platforms in ascending order of latency, and preferentially synchronize the platform interface with the lowest latency. For low-conflict field operations, obtain the data of the active periods of users on each platform, and preferentially complete the information update before the active periods of users.

[0011] Furthermore, real-time monitor the return status of the synchronization operation. If the platform interface returns a failure, mark it as a conflict operation. According to the operation timing log, locate all associated operations before the conflict operation occurs, and perform a rollback in reverse order of the operations. For high-conflict sensitive operations, directly roll back to the original state before the conflict occurs. For low-conflict field operations, if the field update is not effective, cancel the update. If it is effective, mark it as pending manual review.

[0012] Furthermore, trigger compensation measures according to the conflict type: if there is an over-sold inventory conflict, push an alarm notification; if there is a conflict in rule verification failure, correct the low-weight rule fields and resubmit the synchronization; if there is an interface timeout conflict, automatically retry the synchronization. If the retry synchronization fails, mark it as a "manual processing work order".

[0013] Furthermore, when the user triggers the verification action, receive the user's verification request, real-time obtain the user's geographical location, device signal strength, and the number of competitor stores, and calculate the verification confidence C: , where represents the effective duration, represents the total monitoring duration, represents the signal fluctuation variance, represents the maximum allowable variance, k represents the attenuation coefficient, , represents the number of competitor stores.

[0014] Furthermore, after the verification is successful, mark the coupon as the verified status in the middleware database, synchronize the verification status to all associated platforms through the platform interface, pull the verification record through the billing interface provided by the platform, compare the middleware verification record with the platform billing record, check if there are any differences, and if differences are found, trigger a manual verification.

[0015] A commodity middleware management system based on multi-platform data synchronization includes: The coupon master template generation module guides users to complete merchant account registration and store claiming on the target platform, configures the basic information and selling information of coupons in the background of the middle platform, and creates a coupon master template data model containing data common to all platforms. The valuable coupon generation module generates a valuable coupon data model adapted to the platform based on the coupon master template, expands the platform-specific fields, associates with the limited-time selling plan, and configures the selling price and integral rules. The commodity bridging module distributes commodity data to each platform interface through the commodity bridging service layer, triggers the platform-side review process, determines the synchronization timing based on the interface response portrait and the user active period, monitors the platform review status in real time, and performs reverse rollback on the failed synchronization operations. The verification module responds to changes in platform rules, triggers commodity delisting or information update, calculates the user verification confidence when the user verifies, and triggers secondary verification when the confidence is lower than the confidence threshold.

[0016] (III) Beneficial effects The present invention provides a commodity middle platform management system and method based on multi-platform data synchronization, having the following beneficial effects: (1) By guiding users to complete account creation and store claiming on the target platform and uniformly configuring the basic information and selling information of commodities in the background of the middle platform, the cumbersome process of manually configuring coupons for each platform one by one is avoided. By creating a coupon master template data model and storing the coupon information common to all platforms, the synchronization of coupon information among multiple platforms is realized, which not only reduces repetitive labor but also reduces the management complexity caused by differences among platforms.

[0017] (2) By extracting rule fields and constructing a field factor set, and constructing a rule field weight model based on historical data, the review key points of different platforms can be identified and processed. By focusing on injecting high-weight rule fields, such as supplementing category keywords, replacing risk words, etc., the review passing rate of coupons on each platform can be significantly improved, which helps to reduce the repetitive submission and modification work caused by failed reviews.

[0018] (3) Based on the interface response time probability distribution model and user active period data, the priority sorting and timing planning of different types of data changes are carried out to ensure the timeliness and effectiveness of data updates. Through synchronization timing decision-making, conflict handling and compensation mechanisms, the operation efficiency and accuracy are significantly improved, and user complaints and losses caused by data inconsistency or delayed updates are reduced.

[0019] (4) By regularly pulling product status data and real-time monitoring of platform rule change logs, any rule changes on the platform can be quickly responded to, so as to update the product status or information in a timely manner, avoiding the product being taken off the shelf or causing other unnecessary troubles due to non-compliance with platform rules. By calculating the user verification confidence, high-risk verification behaviors such as cross-store verification or using virtual positioning can be identified, thus triggering a secondary verification mechanism to ensure the authenticity and accuracy of the verification. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 It is a schematic diagram of the steps of the product middle platform management method based on multi-platform data synchronization of the present invention; Figure 2 It is a schematic diagram of the product middle platform docking with each platform of the present invention; Figure 3 It is a schematic diagram of the product middle platform bridging service process of the present invention; Figure 4 It is a schematic diagram of the structure of the product middle platform management system based on multi-platform data synchronization of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0021] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0022] Please refer to Figures 1-3 , the present invention provides a product middle platform management method based on multi-platform data synchronization, including the following steps: Step 1: Guide the user to complete account creation and storefront claim on the target platform, create a coupon master data model, and store the general coupon information for the entire platform; The content of the first step includes the following: Step 101: According to the rules of the target platform (such as Douyin, Meituan, Xiaohongshu, etc.), guide the user to complete merchant account registration, storefront claim and qualification review on the target platform. The following takes Douyin as an example for illustration: Douyin platform operation process: Register a "Douyin Merchant" account, enter "Entering the Douyin Store", select "Claiming a single store" or "Chain store", enter the store and address for search, after confirming that the information is correct, submit a claim application. If the store is not found, you can choose to "Create a new store", fill in detailed address, contact phone number, business hours and other information, upload the business license, legal person ID card, industry qualification (such as food business license, etc.) according to the platform requirements, and wait for 1-3 working days for review after submission. After the review is passed, you can manage the store information; Step 102: Associate and map multi-platform accounts to generate a unique merchant identifier, and uniformly configure the basic information of the product in the back-end of the middle platform, including input display information (such as coupon name, subtitle), consumption rules (such as usage threshold, validity period type), and information on applicable stores. Configure the product sales information, including setting inventory strategies (total inventory, whether to display the sold quantity, etc.) and purchase limit rules (single-store verification limit, whether to limit the repeat verification of a single store for each delivery, etc.); Step 103: Generate a coupon master data model based on the above configuration, and use the Snowflake algorithm to assign a globally unique code to the coupon master. The coupon master data model serves as the basic template for coupons and contains a series of configuration items from basic information to sales information, supporting unified management and adaptation across multiple platforms. Some field descriptions are as follows: Coupon module: Marketing general, valuable coupon; Data type: Basic coupon, old Douyin photo, old Xiaohongshu photo, old Amap photo, merchant review version; Coupon master id: An ID randomly generated by the Snowflake algorithm (globally unique); Associated [Basic coupon] id: Used to identify the basic coupon for old photos or merchant review versions; Coupon master code: A globally unique code automatically generated according to certain rules; Business management number id: The affiliated business management number; Applicable profit sharing group id: There can only be one; Validity period type: Absolute time (specific date range) / Relative time (valid within a certain number of days after receipt); List of applicable stores: Associated store ID and platform POI_ID (such as Amap POI_ID); Verification rule: Single-store verification limit, whether cross-store verification is allowed; Step 104: Conduct logical verification 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 are conflicts in the purchase limit rules, etc. If the verification passes, proceed to the next step; if the verification fails, modify the corresponding configuration information according to the prompt and try again; When using, combine the content of Step 101 to Step 104: 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 product in the back-end of the middle platform, the cumbersome process of manually configuring coupons for each platform one by one is avoided. By creating a coupon master data model and storing the globally applicable coupon information, the synchronization of coupon information across multiple platforms is achieved, which not only reduces repetitive labor but also reduces the management complexity caused by platform differences.

[0023] Step 2: Generate a valuable coupon data model adapted to the platform based on the coupon master template, associate it with the limited-time sales plan, configure the selling price, points, and platform-specific delivery channels, and expand the platform-specific fields; The above Step 2 includes the following: Step 201: According to the target platform (such as Douyin, Xiaohongshu, etc.), extract relevant information from the coupon master template data model. For each platform that needs to be supported, add or adjust necessary fields according to its specific requirements. For example, add the correct category enumeration value to the valuable coupon generated for Douyin, and specify the accurate POI_ID (geographical location identifier) for the valuable coupon generated for Amap; Step 202: Set the limited-time sales plan according to the marketing strategy, including but not limited to the selling price, discount information, points redemption rules, etc., and configure specific delivery channels for different sales plans. For example, set that some coupons are only for sale within the Douyin live broadcast room, and associate the sales plan with the corresponding valuable coupon; Step 203: Real-time capture the audit rule update logs of each platform (such as the change of the category white list of Douyin, the update of the sensitive word library of Xiaohongshu), extract specific rule fields (such as "category restriction", "sensitive word shielding") from the logs, and classify them as rule field factors; Specifically, obtain the rule change information through a crawler or log analysis tool (which needs to comply with the platform agreement), use a scheduled task (such as cron) or message queue (such as Kafka) to pull the 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.; Use natural language processing to perform word segmentation, stop word removal, and entity recognition on the log text to extract specific rule fields, or through rule pattern matching, use regular expressions or template matching rules to extract rule fields, and classify the extracted rule fields into a set of rule field factors. For example: category restriction (new / delete category ID, white list update), sensitive word shielding (new shielding words, violation keywords), geographical restriction (new / remove geographical shielding area), validity period requirements, etc. (shorter audit cycle, extended validity period); Step 204: Collect the metric values of each rule field on different platforms, including counting the number of changes of each platform's specific rule field in the past 30 days (such as the "category restriction" of Douyin changes 5 times a week), counting the audit passing rates of different rule fields, and recording the number of cases that need to be manually processed due to rule field changes (such as the "sensitive word shielding" of Xiaohongshu has 15 manual interventions per week); Step 205: Normalize the metrics of different dimensions to the [0,1] interval, and calculate the information entropy of each rule field on different platforms: , where , represents the jThe i index value of a rule field, , n indicating the number of metrics, , m indicating the number of rule fields; Step 206: Calculate the weight of each field on different platforms through information entropy: , a weight threshold is set in advance. 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 the coupon name according to the latest sensitive word library. For low-weight rule fields, only fill in the required information without additional operations. According to historical data and business experience, a reasonable weight threshold (such as 0.35) is set, and the weight threshold can be adjusted according to the actual situation to ensure that high-weight rule fields receive sufficient attention and processing; It should be noted that category keywords are keywords associated with products or coupons according to the product classification system (i.e., categories) of the target platform. These keywords help products be correctly classified on the platform and can improve the search visibility of products. For example, if you want to publish a dining coupon, you may need to clearly include category keywords related to dining such as "restaurant" and "delicious food" in the description; Risk words refer to sensitive words that may trigger the platform's review mechanism or directly result in the rejection of products or coupons. These words usually include but are not limited to sensitive content, vulgar content, false propaganda terms, etc. For example, exaggerated expressions such as "the cheapest" and "absolutely effective", or words related to sensitive historical events may be regarded as risk words; Required information refers to the information items that must be filled in according to the requirements of each platform when creating products or coupons. These information are crucial for ensuring the integrity and legality of products or coupons. For example, product titles, subtitles, usage instructions, expiration dates, applicable scopes, etc. are common required information. For vouchers, it may also involve selling prices, discount information, inventory quantities, etc.; When using, combine the content of steps 201 to 204: By extracting rule fields and constructing a field factor set, and constructing a rule field weight model based on historical data, the review key points of different platforms can be identified and processed. By performing focused injection on high-weight rule fields, such as supplementing category keywords and replacing risk words, the review pass rate of coupons on each platform can be significantly improved, which helps reduce the repetitive submission and modification work caused by failed reviews.

[0024] Step 3: Distribute product data to each platform interface through the GBS layer, trigger the platform-side review process, determine the synchronization timing based on the interface response portrait and user active period model, monitor the review status in real time, update the product status in the middle platform, and perform automatic rollback on conflicting operations; The third step includes the following: Step 301: Distribute the product data generated by the middle platform (such as coupon information, product information related to coupons, sales plans, etc.) to the target platforms (such as Douyin, Xiaohongshu, Amap, etc.) through the GBS layer, trigger the review process of the target platforms, and record the historical response delay data of each platform interface in real time (such as the average delay of Meituan is 200ms, the average delay of Xiaohongshu is 500ms), including interface call time, response time, success rate, error type, etc.; Step 302: Based on the historical response delay data, construct a probability distribution model of the response time of each platform interface (such as normal distribution, exponential distribution), and calculate the average response time, fluctuation range (standard deviation), and timeout threshold (such as the maximum delay within the 95% confidence interval) of each interface; Step 303: According to the impact degree of the operation on data consistency, the operations are divided into two categories: highly conflict-sensitive operations: operations involving changes to core data such as inventory, price, and redemption rules (such as inventory adjustment, price modification), and low-conflict field operations: operations involving non-core data updates (such as title, cover image, description text); For highly conflict-sensitive operations, according to the probability distribution model of the interface response time, calculate the average response delay of each platform interface, sort the platforms in ascending order of delay, and preferentially synchronize the platform interface with the lowest delay. For low-conflict field operations, obtain the user active period data of each platform (such as high activity on Douyin from 8 pm to 12 am, high activity on Xiaohongshu from 10 am to 2 pm), and preferentially complete the information update before the user active period to ensure that users can see the latest data (for example: the cover image update on Douyin is completed before 7 pm, and the title modification on Xiaohongshu is completed before 9 am); Step 304: Obtain the current review 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 review status is not updated, trigger the alarm mechanism and notify relevant personnel (such as through email or message reminder); set a scheduled task (such as polling every 5 minutes), obtain the latest review status through the platform interface, and update the product status in the middle platform to one of the following categories according to the review status returned by the platform: Under review: The product has been submitted but the review has not been completed yet, Synced: The product has passed the review and been successfully listed, Review failed: The product has not passed the review and needs to be modified and resubmitted; Step 305: Monitor the return status of the synchronization operation in real time. If the platform interface returns "failure" (such as oversold inventory, field does not meet the rules, interface timeout), mark it as a conflict operation, and record the conflict type (such as insufficient inventory, rule verification failure), occurrence time, operation details and other related platform operations; Step 306: According to the operation sequence log, locate all related operations before the conflicting operation occurs (for example, the inventory adjustment of the same ticket master has been synchronized to other platforms), and perform rollback in reverse order of the operations. For highly conflict-sensitive operations, roll back directly to the original state before the conflict occurs (such as restoring inventory quantity and canceling price adjustment). For low-conflict field operations, if the field update is not effective, cancel the update; if it is effective, mark it as pending manual review; Step 307: Triggering compensation measures according to the conflict type: If there is an inventory oversold conflict, a free-threshold coupon (such as a "compensation coupon") will be issued to the affected users, with the amount being 10% of the original coupon face value. An alarm notification will be pushed to the merchant, prompting the need to manually check the inventory logic; If the rule verification fails or conflicts, the low-weight rule fields will be automatically corrected (such as replacing sensitive words) and resubmitted for synchronization. If the high-weight rule fields conflict (such as category restrictions not being met), manual intervention is required for adjustment. If there is an interface timeout conflict, the synchronization will be automatically retried (up to 3 times, with increasing intervals). If failures continue, it will be marked as a "manual work order" and the operation and maintenance team will be notified; When used, combine the contents of step 301 to step 307: Based on the interface response time probability distribution model and user active period data, different types of data changes are prioritized and time-planned to ensure the timeliness and effectiveness of data updates. Through synchronized timing decisions, conflict resolution and compensation mechanisms, operational efficiency and accuracy are significantly improved, reducing user complaints and churn caused by inconsistent data or delayed updates.

[0025] Step 4: Respond to changes in platform rules, trigger product removal or information update, calculate user cancellation confidence in real time, and trigger a dynamic cancellation strategy when the confidence is lower than the confidence threshold.

[0026] The step 4 includes the following contents: 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 the product information update (such as modifying the category ID); Step 402: When the user triggers the verification action, receive the user's verification request through the middleware interface and perform legality verification, including checking whether the coupon is valid (such as not expired, not verified), checking whether the user has the right to verify the coupon, etc., and obtain the user's geographical location, device signal strength, and surrounding POI density (such as the number of competitor stores within 500 meters) in real time, and calculate the verification confidence C: , where represents the effective duration (the proportion of the time when the user is within the store's geographical fence), represents the total monitoring duration, the entire monitoring time from when the user triggers the verification action to the completion of the operation, represents the signal fluctuation variance, the variance value of the user's device's Wi-Fi or cellular signal strength during the monitoring period, represents the maximum allowable variance, 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, controlling the influence intensity of the number of competitors on the confidence level, , represents the number of competitor stores, the number of competitor stores within a specific radius (such as 500 meters) around the user's current location; Step 403: When the verification confidence is lower than the confidence threshold, it is determined as a high-risk verification (such as the user may verify across stores or use virtual positioning), and secondary verification is triggered (such as face recognition + store-exclusive QR code); When the verification confidence is lower than the confidence threshold and the number of competitor stores is greater than 3, automatically push the limited-time upsell offer (such as "add 10 yuan to exchange for popular goods after verification") to inhibit competitor conversion; in other cases, no additional operation is performed. According to historical data and business experience, set a reasonable confidence threshold, and the confidence threshold should be able to accurately distinguish high-risk verification and low-risk verification; Step 404: After successful verification, mark the coupon as verified in the middleware database, synchronize the verification status to all associated platforms through the platform interface, pull the verification record through the billing interface provided by the platform, compare the middleware verification record with the platform billing record, check for differences. If differences are found (such as the middleware marks as verified but the platform does not record), trigger manual verification; By regularly pulling the product status data and real-time listening to the platform rule change log, it is possible to quickly respond to any rule changes on the platform, thereby timely updating the product status or information, and avoiding the product being taken off the shelf or causing other unnecessary troubles due to non-compliance with platform rules. By calculating the user's verification confidence, high-risk verification behaviors such as cross-store verification or using virtual positioning can be identified, thereby triggering the secondary verification mechanism to ensure the authenticity and accuracy of the verification.

[0027] Please refer to Figure 4, the present invention also provides a commodity middle - platform management system based on multi - platform data synchronization, including: a coupon master generation module, a valuable coupon generation module, a commodity bridging module, and a verification module; wherein, The coupon master generation module guides users to complete merchant account registration and store claim on the target platform, configures basic coupon information and selling information in the middle - platform background, and creates a coupon master data model containing platform - wide common data; The valuable coupon generation module generates a platform - adapted valuable coupon data model based on the coupon master, expands platform - specific fields, associates with a limited - time selling plan, and configures price and point rules; The commodity bridging module distributes commodity data to each platform interface through the commodity bridging service layer, triggers the platform - side review process, determines the synchronization timing based on the interface response portrait and the user's active period, monitors the platform review status in real - time, and performs reverse rollback on failed synchronization operations; The verification module responds to platform rule changes, triggers commodity delisting or information update, calculates the user verification confidence when the user verifies, and triggers secondary verification when the confidence is lower than the confidence threshold.

[0028] In the application, several formulas involved are calculated by taking their numerical values after dimensionless processing. The formula is obtained by collecting a large amount of data for software simulation to get a formula closest to the real situation, and the coefficients in the formula are set by those skilled in the art according to the actual situation.

[0029] The above - mentioned embodiments can be implemented in whole or in part by software, hardware, firmware, or any other combination. When implemented using software, the above - mentioned embodiments can be implemented in whole or in part in the form of a computer program product. Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by a combination of electronic hardware, computer software, and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution.

[0030] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units. They can be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0031] The above - mentioned is only the specific implementation manner of this application, but the protection scope of this application is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed in this application, and all should be covered within the protection scope of this application.

Claims

1. A commodity middle platform management method based on multi-platform data synchronization, characterized in that: 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 backend, and create a coupon master data model containing common data for the entire platform; Generate a platform-adapted voucher data model based on the voucher master, expand platform-specific fields, associate limited-time sales plans, and configure price and points rules; The product data is distributed to each platform interface through the product bridging service layer, triggering the platform-side review process. The synchronization sequence is determined based on the interface response profile and the user's active period, the platform review status is monitored in real time, and reverse rollback is performed for synchronization failure operations. In response to changes in platform rules, product removal or information update is triggered. When users redeem products, the user redemption confidence level is calculated. When the confidence level is lower than the confidence level threshold, secondary verification is triggered.

2. The commodity middle-end management method based on multi-platform data synchronization according to claim 1 is characterized in that: Map and associate multiple platform accounts 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. Perform logical verification on the generated voucher master, including but not limited to checking whether the inventory is greater than or equal to the quantity sold 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, wherein: Extended platform-specific fields, including: Real-time capture of the audit rule update logs of each platform, extraction of rule fields from the logs, classification of the extracted rule fields, collection of the metric 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 passing 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 metric values 。 4. The commodity middle-end management method based on multi-platform data synchronization according to claim 3 is characterized in that: Calculate the weight of each field on different platforms through information entropy: , and 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 the coupon name according to the latest sensitive word library. For low-weight rule fields, only fill in the required information.

5. The commodity middle platform management method based on multi-platform data synchronization according to claim 1, characterized in that: Based on the interface response profile and the user's active period, the decision synchronization sequence includes: Record the historical response delay data of each platform interface in real time. Based on the historical response delay data, build a probability distribution model for the response time of each platform interface. Classify operations into two categories: high-conflict sensitive operations and low-conflict field operations. High-conflict sensitive operations are changes to core data, and 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 in terms of delay. The platform interface with the lowest delay is synchronized first. For low-conflict field operations, data on the active time periods of users on each platform is obtained, and information updates are completed before the user active time periods.

6. The commodity middle-end management method based on multi-platform data synchronization according to claim 5 is characterized in that: Monitor the return status of synchronization operations in real time. If the platform interface returns a failure, it is marked as a conflicting operation. According to the operation sequence log, locate all related operations before the conflicting operation occurs, and perform 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-end management method based on multi-platform data synchronization according to claim 6 is characterized in that: Trigger compensation measures based on the conflict type: If there is an inventory oversold conflict, push an alarm notification; If the rule verification fails and conflicts, correct the low-weight rule field and resubmit the 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-end management method based on multi-platform data synchronization according to claim 1 is characterized in that: When the user triggers the verification action, receive the user's verification request, and obtain the user's geographical location, device signal strength, and the number of competitor stores in real time, and calculate the verification confidence level C: , where represents the effective duration, represents the total monitoring duration, represents the signal fluctuation variance, represents the maximum allowable variance, k represents the attenuation coefficient, , represents the number of competitor stores.

9. The commodity middle-end management method based on multi-platform data synchronization according to claim 8 is characterized in that: After successful cancellation, the card or voucher 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. A commodity middle - platform management system based on multi - platform data synchronization, characterized in that: 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 backend, and creates a coupon master data model containing common data for the entire platform; The voucher generation module generates a voucher data model adapted to the platform based on the voucher master, extends the platform-specific fields, associates the limited-time sales plan, and configures the price and points rules; The product bridging module distributes product data to each platform interface through the product bridging service layer, triggers the platform-side review process, decides on the synchronization sequence based on the interface response portrait and the user's active period, monitors the platform review status in real time, and performs reverse rollback for synchronization failure operations; 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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