A multi-order-source-based dual-track commission data mutual exclusion processing method and system

Abstract

This invention provides a method and system for processing mutually exclusive dual-track commission rebate data based on multiple order sources. It runs referral commissions and queuing bonuses in parallel within the same queue. Through technical means such as dual-factor time lock, valid identity identification, two-level queuing bonus mechanism, automatic release of referral relationships, commission mutual exclusion verification, queue head locking and idle processing, cross-platform order identification, multi-dimensional anti-fraud, third-party automatic settlement, and product information push based on commission rebate results, it fundamentally eliminates double compensation and fraudulent arbitrage, achieves fair and efficient operation of referral commissions and queuing bonuses, and automatically recommends products when distributing commissions.

Description

Technical Field

[0001] This invention relates to the field of e-commerce data processing and automated settlement technology, and in particular to a method, system, computer-readable storage medium, and computer program product for processing mutually exclusive dual-track commission rebate data based on multiple order sources. Background Technology

[0002] Existing rebate systems mainly fall into two categories: referral rebate models (where orders are placed through a referrer's exclusive referral code, and the referrer receives a referral commission) and queue-based rebate models (where users queue according to their consumption order, and users in the queue receive a share of the profits when later users make purchases). These two models typically operate independently. A few platforms have attempted to integrate them, but this has fundamental flaws: Double commission issue: The same order may trigger two commission mechanisms at the same time, resulting in double commission for the same order, which will undermine the platform's profits and make it impossible to automatically distinguish between referral relationships and individual user relationships.

[0003] Single-level queueing bonus distribution: Existing queueing commission systems typically only have a single-level queueing bonus distribution. When referral commissions adopt a two-level commission architecture, queueing bonus distribution is only one level, resulting in inconsistent distribution logic between the two types of commissions. Some queueing users are prematurely removed from the queue before receiving the full bonus they are entitled to.

[0004] Fraudulent order arbitrage: The existing system puts users into a queue simply by placing an order, which cannot prevent users from maliciously returning goods after receiving rebates.

[0005] Recommended order gap issue: There is a lack of mechanism to handle the interruption of queue continuity when multiple recommended orders appear in the queue consecutively. Therefore, there is an urgent need for a technical solution that can run referral commissions and queued commissions in parallel within the same system, and that queued commissions and referral commissions adopt a unified two-level allocation logic, and can automatically achieve mutual exclusion of commissions and prevent the same order from being counted twice. Summary of the Invention

[0006] The purpose of this invention is to provide a method and system for processing mutually exclusive dual-track commission data based on multiple order sources, so as to solve the problems in the prior art where referral commissions and queued commissions cannot be effectively integrated in the same system and the same order is easily double-paid. To achieve the above objectives, this invention provides a method for processing mutually exclusive dual-track commission data based on multiple order sources, characterized by the following steps: Order aggregation steps: Receive order data from multiple e-commerce platforms, extract the order timestamp, order source identifier, and recommendation relationship identifier for each order, and write them into the same queue; wherein, the multiple e-commerce platforms include, but are not limited to, self-owned mini-programs, JD.com, Tmall, Douyin, Pinduoduo, etc.

[0007] Queue sorting steps: All orders in the queue are uniquely sorted according to the order timestamp to generate a queue number; orders with an empty referral relationship identifier are marked as retail orders; orders with a non-empty referral relationship identifier are marked as recommended orders.

[0008] The rebate exclusion handling steps are as follows: When a new order enters the queue, the rebate type is determined based on its referral relationship identifier. If the new order is a referral order, the referral rebate module is triggered to issue a referral commission to the corresponding referrer, and the queue contribution value of the order is set to zero, meaning it does not participate in the queue bonus counting. If the new order is a retail order, the referral rebate is not triggered, and the queue contribution value of the order is set to one, meaning it participates in the queue bonus counting. The referral commission can be either a first-level commission or a second-level commission. This step ensures that the same order generates only one type of rebate—a referral commission or a queue bonus—based on its referral attribute in the queue, thus eliminating double compensation at the source.

[0009] Queue bonus triggering steps: When the accumulated queuing contribution value in the queue reaches a preset trigger condition, the queue bonus module is triggered to distribute queuing bonuses to the user at the head of the queue. The preset trigger condition is that a queue bonus is triggered once for every N new retail orders with a queuing contribution value of one, where N is a pre-set positive integer. In the above method, all users entering the queue simultaneously enjoy both referral commission rights and queue bonus rights. Referral commission rights refer to the user's right to receive a commission after referring a new user to place an order through their exclusive referral code; queue bonus rights refer to the user's right to receive a bonus as the first user in the queue when the cumulative number of new individual orders reaches a preset trigger condition. The referral commission rights and queue bonus rights operate independently; only one type of commission can be triggered for the same order from the same user, and commissions will not be issued repeatedly for the same order. Furthermore, this invention also provides a method for pushing product information based on commission rebate results. After the commission rebate distribution module completes the commission distribution, the system automatically generates a notification message containing the commission type, product source identifier, and product details entry point, which is then displayed through the user interface. Users can click the product details entry point in the notification message to directly jump to the corresponding product purchase page. This method organically combines commission settlement with product recommendation, improving the platform's business conversion efficiency and user experience. Attached Figure Description

[0010] Figure 1System Overall Architecture and Data Flow Diagram. This diagram is the top-level view of the system's overall architecture, showing the entire data flow from order aggregation, two-factor time lock verification, order identity recognition and allocation, queuing and bonus engine execution, automatic removal of recommendation relationships, anti-fraud monitoring, notification generation and push, to blockchain audit and evidence storage, as well as the logical connections between the core modules. Detailed Implementation

[0011] To make the objectives, technical solutions, and advantages of the present invention clearer, the technical solutions of the present invention will be described in detail below with reference to the accompanying drawings. The system involved in this invention mainly consists of the following core modules, which operate sequentially according to a fixed logical order: Unified Order Gateway (Entry Module): Responsible for receiving order data from multiple different e-commerce platforms, converting the heterogeneous order fields of each platform into a standardized order data structure preset within the system, and converting time data from different time zones or formats of each platform into UTC timestamps as the sole time reference for subsequent queuing.

[0012] Two-factor time lock validator: Receives standardized order data processed by the unified order gateway and performs dual-condition verification on each order: the first factor is whether the user has confirmed receipt, and the second factor is whether the preset protection period has expired and no return has occurred. Only when both conditions are met simultaneously is the order marked as a "valid order," and a unique activation timestamp is issued by the system.

[0013] Order Identification and Allocation Unit: This unit determines the type of orders that have obtained valid identities. If an order carries a referral relationship identifier, it is identified as a referral order, triggering the referral commission module to distribute commissions to the corresponding referrer. After commission distribution, the order is marked as "receiving dividends only," and its queue contribution value is set to zero. If an order does not have a referral relationship identifier, it is identified as a retail order, no referral commission is triggered, and its queue contribution value is set to one.

[0014] The queuing and profit-sharing engine (core scheduling module) sorts orders that have obtained valid identities and completed identity verification according to their activation timestamps, generating unique queuing numbers. When a valid retail order enters the queue, the engine automatically skips all referral orders in the queue that are not participating in payment, and locks the payment target onto the two valid orders with queuing permissions at the front of the queue, paying them first-level and second-level queuing profit-sharing in sequence according to the rules. If the order at the front of the queue only receives the first-level profit-sharing, it is locked at the front of the queue by the system and waits for the second-level profit-sharing to arrive before being removed from the queue. If there are multiple consecutive referral orders in the queue, the engine always keeps the order at the front of the queue unchanged, and continues to execute payment after the next valid retail order appears.

[0015] Referral relationship termination module: After the referral commission is paid out, the hierarchical referral relationship corresponding to the order will be automatically terminated, and the referral relationship status of the referred person will be reset to "unbound".

[0016] Notification generation and push module: After the commission distribution module completes the commission distribution, it generates structured notification data containing commission type, product source brand identifier, commission amount, and product details entry link, and displays it to the user through the user interface.

[0017] Anti-fraud monitoring system: continuously scans order data in circulation in real time from multiple dimensions, including abnormal consumption frequency, network of associated accounts, abnormal geographical distribution of delivery addresses, and malicious return arbitrage.

[0018] Blockchain audit and evidence storage module: All key operational data across the entire chain generates hash values ​​and is written into the blockchain ledger as tamper-proof electronic evidence. The invention will now be described in detail with reference to specific embodiments. Assume user A purchases an Ananda leak protection device through the Ananda mini-program for 1560 yuan. This order does not carry any referral relationship identifier; the system marks it as a retail order and activates it as a valid order after the user confirms receipt and the 7-day protection period expires, obtaining an activation timestamp and entering the queue. User B purchases the same leak protection device through user A's referral link, also for 1560 yuan. This order carries user A's referral relationship identifier; the system marks it as a referral order and issues a first-level referral commission of approximately 400 yuan to user A, while simultaneously setting user B's order queue contribution value to zero. Subsequent users C, D, E, etc., continue to place orders through different channels, and the system automatically calculates commissions and queues rewards according to the above logic until user A at the head of the queue receives the full second-level reward and leaves the queue, with subsequent users taking their place. Through the above technical solution, the present invention realizes the core functions of parallel operation of referral rebates and queued rebates within the same system, mutual exclusion of commissions, automatic termination of referral relationships, and dual anti-fraud measures, thereby fundamentally solving the problems of double compensation and fraudulent arbitrage in existing rebate systems.

Claims

1. A method for processing multiple order source based dual track commissioning data exclusivity, the method comprising: receiving a plurality of order source based dual track commissioning data; and processing the plurality of order source based dual track commissioning data exclusivity. Includes the following steps: Order aggregation steps: Receive order data from multiple e-commerce platforms, extract the order timestamp, order source identifier, and recommendation relationship identifier for each order, and write them into the same queue. Queue sorting steps: All orders in the queue are uniquely sorted according to the order timestamp to generate a queue number; among them, orders with an empty referral relationship identifier are marked as retail orders, and orders with a non-empty referral relationship identifier are marked as recommended orders; Rebate mutual exclusion processing steps: When a new order enters the queue, the rebate type is determined based on its referral relationship identifier; if the new order is a referral order, the referral rebate module is triggered to issue a referral commission to the corresponding referrer, and the queue contribution value of the order is set to zero, i.e., it does not participate in the queue bonus counting; if the new order is a retail order, the referral rebate is not triggered, and the queue contribution value of the order is set to one, i.e., it participates in the queue bonus counting; wherein, the referral commission is a first-level commission or a second-level commission; Queue bonus triggering steps: When the accumulated queuing contribution value in the queue reaches the preset triggering condition, the queue bonus module is triggered to distribute the queue bonus to the user at the head of the queue.

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