Commission tracking and accurate settlement system of private domain ecological multi-level recommendation network

Abstract

The invention discloses a commission tracking and accurate settlement system of a private domain ecological multi-level recommendation network, and relates to the technical field of application software development, and the system comprises a multi-level recommendation network construction module which supports 1-5 levels of recommendation relationships, is bound through invitation codes and the like, activates authority through identity authentication, and stores the relationships through a MySQL cluster; the recommendation behavior full-link tracking module is used for distributing a unique UUID to track full-process data and monitoring abnormal behaviors; the dynamic commission rule configuration module supports fixed or proportional commission and realizes differentiated configuration by using a rule engine; a commission accurate settlement module; the visual income display and detail query module is used for displaying data and supporting screening and exporting; and the system interaction and log management module is used for pushing notifications and recording operation logs. According to the invention, accurate commission tracking and reasonable distribution are realized, the system stability and the operation efficiency are improved, the cost and the risk are reduced, and a private domain multi-level recommendation scene is adapted.

Description

Technical Field

[0001] This invention relates to the field of application software development technology, and in particular to a commission tracking and accurate settlement system for a multi-level recommendation network in a private domain ecosystem. Background Technology

[0002] With the large-scale development of private domain traffic operations, multi-level referral networks have become a core means for enterprises to achieve user growth and business expansion. Through a hierarchical architecture of "direct referrals + indirect referrals," these networks incentivize users to actively promote the business and rapidly expand the private domain user pool. Commissions, as the core driving force of the referral network, directly impact user enthusiasm and enterprise operational efficiency through their tracking accuracy, settlement timeliness, and allocation rationality. Currently, commission settlement systems in the private domain ecosystem are mostly based on simple hierarchical ratio settings, lacking refined control over the entire referral process. This makes them ill-suited for complex multi-level referral scenarios, gradually exposing numerous technical and operational shortcomings.

[0003] Existing systems have significant flaws in commission calculation and distribution. Most solutions use a fixed tier decay rate, determining commission rates solely based on the referral tier, ignoring key influencing factors such as business conversion rates and user activity. This results in insignificant differences in earnings between high-value and ordinary referrers, leading to a loss of motivation for indirect referrers due to low commissions, while top-level referrers may monopolize profits, disrupting the balanced development of the referral network. Furthermore, commission calculation rules lack flexibility. Different services (such as SIM card sales and data package purchases) have significantly different profit margins, but most systems use uniform rules, failing to achieve differentiated configurations. This makes it difficult to ensure the attractiveness of referrals for low-profit services while potentially causing excessively high commission costs for high-profit services. In addition, rule changes require a system restart to take effect, resulting in a delayed response and hindering rapid adaptation to market strategy adjustments.

[0004] Insufficient accuracy in referral behavior tracking and settlement exacerbates operational risks. Traditional systems suffer from broken tracking chains, recording only two nodes: "referral binding" and "final conversion," lacking data collection on intermediate stages such as user clicks, applications, and payments. This makes it impossible to provide complete evidence of behavioral traceability when commission disputes arise. The ability to identify fraudulent referrals is weak, relying solely on simple IP address verification, which fails to identify fraudulent activities such as virtual device registration and bulk click fraud, leading to substantial invalid commission expenditures and increased business costs. Commission handling in refund scenarios is even more rudimentary; most systems simply recover settled commissions without distinguishing between full and partial refunds, or considering the differences between platform and user responsibilities. This easily provokes resistance from referrers and can even lead to operational disputes.

[0005] Insufficient user incentives and system stability further constrain business development. Existing incentive mechanisms are mostly directly linked to commission amounts, lacking quantitative assessment of recommendation quality and failing to provide differentiated benefits for high-contribution recommenders, thus hindering the formation of a sustainable incentive effect. Cross-business commission settlements are independent, requiring users to deal with frequent small commission payments, resulting in cumbersome operations with low user awareness, while also increasing payment processing costs for businesses. Abnormal commission monitoring is lagging, relying solely on manual checks for large settlements, failing to identify abnormal scenarios such as "sudden increases in daily commissions" or "multiple commissions associated with the same user" in real time, making them vulnerable to malicious exploitation and financial losses. Furthermore, data backups often employ a single storage model; in the event of disk failure or system malfunction, core data such as recommendation relationships and settlement records are easily lost, with low recovery efficiency, severely impacting continuous system operation. These combined problems lead to decreased recommendation network activity, increased operating costs, and reduced user trust, becoming the main bottleneck for the large-scale development of private domain multi-level recommendation models. Summary of the Invention

[0006] The commission tracking and accurate settlement system for a multi-level recommendation network in a private domain ecosystem proposed in this invention aims to solve the problems mentioned in the prior art.

[0007] To achieve the above objectives, the present invention adopts the following technical solution: a commission tracking and accurate settlement system for a multi-level recommendation network in a private domain ecosystem, comprising: Multi-level recommendation network construction module: Supports the definition of 1-5 levels of recommendation relationships, with the upper limit of levels configurable through the administrator backend; users bind their accounts via exclusive invitation codes, sharing links with unique identifiers, or QR codes; access to the identity authentication unit, recommendation permissions are activated after verification via mobile phone number verification code and facial recognition; topology relationships are stored using a MySQL cluster. Recommendation Behavior Full-Link Tracking Module: Assigns a unique UUID to each recommendation behavior, covering the entire process from clicking a link, submitting an application, making a payment conversion, to after-sales confirmation; collects data from 20 tracking nodes and transmits it to the tracking database via Kafka; has a built-in abnormal behavior monitoring unit that identifies anomalies through IP blacklist comparison and device fingerprint verification. Dynamic commission rule configuration module: Provides flexible commission calculation rule management, supports fixed amount and percentage commission, preset basic commission ratios for SIM cards and data packages, and administrators can adjust parameters through the web interface; it adopts the Drools7.0 rule engine for parsing and supports setting differentiated rules according to business type, referral level, and user level; Commission Precision Settlement Module: Performs commission calculation, deduction, and payment operations, supporting real-time, daily, and monthly settlement modes; calculated based on "actual business payment amount × corresponding level ratio," automatically deducting 20% ​​personal income tax and 5% platform service fee; settlement status is divided into four categories; Visualized earnings display and detailed query module: Presents commission data to users, adapted to both mobile and PC platforms via H5; details support filtering; provides Excel 2007 format export function.

[0008] System interaction and log management module: The user end sends notifications via official account template messages; the administrator end provides a data statistics dashboard that supports filtering and analysis; and a built-in operation log unit records various operations performed by users and administrators.

[0009] Furthermore, it also includes a multi-level commission allocation coefficient optimization module, which is linked with the dynamic commission rule configuration module; the allocation coefficient is calculated as follows: Where K_i is the commission allocation coefficient for the i-th level referrer; i is the referral level; α is the base level attenuation coefficient; and θ is the business conversion coefficient. This is the user activity coefficient; through this calculation, commission distribution can take into account both hierarchical fairness and be linked to business quality and user contribution, while also connecting to the business conversion database to update the θ value hourly.

[0010] Furthermore, it also includes a recommendation behavior validity verification module, which is connected to the recommendation behavior full-link tracking module to build a three-level verification system of "basic verification + deep verification + dynamic review". The dynamic review adopts a sliding window mechanism, which performs a second check on the valid recommendations of the previous 7 days every 24 hours. If there is no behavior within 72 hours, it is marked as "suspicious" and commission settlement is suspended. The verification results are divided into three categories: "valid", "suspicious" and "invalid". Suspicious data is stored in the pending review database. Administrators can view the behavior trajectory map and verification log, and after manual judgment, synchronize it to the commission settlement module.

[0011] Furthermore, it includes a refund commission retrospective module, which is linked with the commission accuracy settlement module and the business after-sales system; it distinguishes between full refund and partial refund scenarios: for full refunds, it traces all settled commissions associated with the business within the past 90 days, and deducts them first from the referrer's pending commissions; for partial refunds, it calculates the amount to be recovered according to the refund ratio, and each level shares the responsibility for recovery according to the original allocation ratio; it adds a retrospective exemption mechanism, exempting referrer from commission recovery for refunds caused by platform system failures, inaccurate business descriptions, etc., and the system automatically marks "platform responsibility exemption" and synchronizes it to the detailed records; for refunds caused by user reasons, detailed vouchers are pushed when recovering; the entire retrospective process is traceable, and the recovery status is updated in real time.

[0012] Furthermore, it also includes a recommendation contribution evaluation module, which connects to the recommendation behavior tracking module and the commission settlement module; the contribution calculation method is as follows: Where C represents the recommendation contribution; β represents the conversion timeliness weight, T represents the conversion time score; γ represents the business value weight, R represents the actual payment amount score; δ represents the repurchase impact weight, S represents the repurchase score of the referred user; η represents the retention weight, U represents the retention score of the referred user; the score data is updated hourly from the business system and user behavior database, and the contribution is calculated and sorted in real time; the module has a built-in contribution decay mechanism to incentivize the recommender to remain active and identify the common characteristics of high-value recommenders.

[0013] Furthermore, it includes a differentiated incentive configuration module, which links with the recommendation contribution assessment module and the dynamic commission rule configuration module to implement a three-dimensional incentive system of "level incentives + scenario incentives + growth incentives". Level incentives are divided into three levels based on contribution: high contribution levels enjoy a 1% increase in basic commissions, T+0 real-time commission payments, dedicated customer service, and priority promotion rights for new businesses; medium contribution levels receive standardized promotion packages, monthly conversion ranking exposure, and access to live operation strategy courses; low contribution levels receive one-on-one training and recommendation skills toolkits; scenario incentives target specific periods: commissions are doubled during holidays; growth incentives include promotion acceleration packages; levels are dynamically adjusted monthly; and the incentive effect tracking module monitors changes in user conversion rates and activity levels at each level, automatically optimizing incentive weights and rights configurations.

[0014] Furthermore, it also includes a dynamic service fee adjustment module, connected to the commission accuracy settlement module and the referral contribution evaluation module; the service fee calculation method is as follows: Where F is the actual service fee ratio; F0 is the benchmark service fee ratio; C is the recommended contribution; ε is the differential adjustment coefficient; Fmin is the lower limit of the service fee; a service fee return mechanism is added; service fee adjustment records are synchronized to the blockchain and connected to the State Taxation Administration's electronic invoice system to automatically generate VAT general invoices based on the actual service fee amount; the module has a built-in service fee calculation tool, and administrators can input the expected contribution range.

[0015] Furthermore, it includes a cross-business commission consolidation and settlement module, connected to the commission precision settlement module; supports user-defined settlement cycles; the consolidation and settlement process includes: generating a pre-settlement list 24 hours before the settlement date, allowing users to submit appeals for abnormal items, with the processing results affecting the current settlement; automatically consolidating valid commissions on the settlement date, calculating the percentage by business type, and generating a consolidated settlement amount after deducting taxes and service fees; pushing an electronic settlement statement containing business composition, deduction details, and revenue trends after settlement; providing a "small amount consolidation and accumulation" option for multiple small commissions; and the module connects to the financial system to automatically generate consolidated settlement vouchers.

[0016] Furthermore, it includes an abnormal commission early warning and handling module, connected to the commission accurate settlement module and log management module; the early warning threshold is subdivided according to business type and recommendation level; the early warning level is divided into yellow, orange and red: yellow warning initiates automatic verification; orange warning triggers manual review; red warning immediately freezes account commission payments; intelligent diagnosis of abnormal causes is added, and abnormal data is analyzed through decision tree algorithm to output the top 3 possible causes and verification directions; after processing, an "Abnormal Handling Review Report" is generated, the early warning model parameters are updated, and the entire processing process is stored in the operation log to support traceability inspection by regulatory authorities.

[0017] Furthermore, it includes a data backup and recovery module, connected to all system modules; it adopts a "real-time incremental backup + daily full backup + weekly off-site backup" strategy: real-time incremental backup only synchronizes newly added and changed data to the local disk array; a full backup is performed at 3:00 AM every day, and the data is encrypted and stored in Alibaba Cloud OSS; off-site backup is performed and synchronized to Tencent Cloud COS; recovery supports "full system recovery" and "module-level recovery": full system recovery is used for major failures; module-level recovery is used for partial failures; a temporary query interface is provided during the recovery process, and an integrity check is automatically performed after the recovery is completed. The system can only be opened after the check pass rate is 100%; it adopts cold and hot data separation storage, with hot data of the past 30 days stored on high-speed SSD disks, and cold data older than 30 days migrated to low-cost object storage.

[0018] Compared with existing technologies, the beneficial effects of this invention are: This invention addresses the pain points of traditional commission settlement systems, such as unfair distribution, inaccurate tracking, insufficient incentives, and poor stability, through multi-module collaborative innovation and refined design throughout the entire process. It upgrades commission management from "extensive settlement" to "refined operation," providing strong support for the healthy operation of private domain multi-level referral networks.

[0019] The fairness and flexibility of commission allocation are significantly improved. This invention, through a multi-level commission allocation coefficient optimization module, deeply integrates referral levels with multi-dimensional indicators such as business conversion rate and user activity, dynamically adjusting commission allocation weights. This ensures reasonable income for indirect referrers while highlighting the contributions of high-value referrers through differentiated coefficients, avoiding incentive imbalances caused by a single-level ratio. The dynamic commission rule configuration module, based on a rule engine, enables flexible configuration, supporting custom rules by business type and user level. Rule changes take effect in real time without requiring a system restart, accurately matching the profit margins and operational needs of different businesses. This multi-dimensional and dynamic allocation mechanism effectively balances the income of referrers at different levels and with different values, stimulating the enthusiasm of the entire referral chain and promoting the balanced development of the referral network.

[0020] The accuracy of referral tracking and settlement has achieved a qualitative leap. The end-to-end tracking module covers all nodes from clicking an invitation to after-sales confirmation, assigning a unique identifier to each referral behavior to ensure traceability of behavioral data and providing complete evidence for commission disputes. A three-tiered validity verification system combines basic verification, deep verification, and dynamic review, using multi-dimensional verification such as device fingerprinting, behavior sequence, and geographical location to accurately identify fraudulent referrals and significantly reduce invalid commission payments. The refund commission retrospective module distinguishes between full and partial refund scenarios, clearly defining the responsibilities for collection at each level, while also setting up a platform liability exemption mechanism to protect both corporate fund security and the legitimate rights and interests of referrers. The cross-business merging function in the settlement process supports user-defined settlement cycles and small-amount cumulative settlements, reducing the cumbersome operations caused by frequent payments, improving user experience while reducing corporate costs.

[0021] A differentiated incentive system effectively enhances user stickiness and promotion motivation. The referral contribution assessment module constructs a multi-dimensional quantitative model, comprehensively considering conversion timeliness, business value, repurchase impact, and user retention to accurately identify high-value referrers. Based on contribution, a three-dimensional incentive system provides tailored incentive plans for users of different values ​​through a combination of tiered benefits, scenario rewards, and growth acceleration packages. High-contribution users enjoy core benefits such as increased commissions and priority promotion rights, while low-contribution users receive training support, forming a virtuous cycle of "incentive-growth-re-incentive." The dynamic service fee adjustment module links the service fee ratio to contribution, with high-contribution users enjoying lower rates and service fee rebates, further strengthening the incentive effect and driving referrers to continuously improve the quality of their promotions.

[0022] System stability and risk control capabilities have been significantly enhanced. The abnormal commission early warning and handling module constructs a tiered early warning system, subdividing early warning thresholds according to business type and level. Through collaborative processing of intelligent verification and manual review, it intercepts abnormal commission payments in real time. Combined with intelligent diagnosis of abnormal causes and model optimization, it continuously reduces the probability of similar risks recurring. The data backup and recovery module adopts a "three-site storage + multi-strategy backup" architecture to achieve hot and cold data separation storage. This ensures the security and consistency of core data while reducing storage costs. At the same time, it supports rapid recovery at the system and module levels, ensuring that the system can quickly restart in the event of a failure and avoid business interruption.

[0023] Overall, this invention, through the collaborative innovation of precise tracking, reasonable allocation, differentiated incentives, and security guarantees, constructs a full lifecycle commission management system adapted to multi-level recommendation scenarios in private domains. This effectively improves the activity and operational efficiency of the recommendation network, reduces enterprise costs and operational risks, and provides key technical support for the large-scale expansion and refined operation of private domain traffic. Attached Figure Description

[0024] Figure 1This is a schematic block diagram of the commission tracking and accurate settlement system for the multi-level recommendation network in the private domain ecosystem proposed in this invention; Figure 2 This is a comparison chart of key performance characteristics between the traditional and the commission settlement systems of this invention; Figure 3 Performance graphs for different business scenarios; Figure 4 A diagram showing the relationship between referral contribution and incentive benefits; Figure 5 This is a screenshot showing the effect of abnormal commission warning processing. Detailed Implementation

[0025] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0026] In the description of this invention, it should be understood that the terms "center," "longitudinal," "lateral," "length," "width," "thickness," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," "clockwise," and "counterclockwise," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this invention.

[0027] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, features defined with "first" and "second" may explicitly or implicitly include one or more of the stated features. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified. Furthermore, the terms "installed," "connected," and "linked" should be interpreted broadly; for example, they may refer to a fixed connection, a detachable connection, or an integral connection; they may refer to a mechanical connection or an electrical connection; they may refer to a direct connection or an indirect connection through an intermediate medium; and they may refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances. The invention will now be described in further detail with reference to the accompanying drawings.

[0028] Reference Figures 1 to 5A commission tracking and accurate settlement system for a multi-level referral network in a private domain ecosystem, comprising: A multi-level recommendation network building module is used to construct a hierarchical private domain recommendation architecture. It supports 1-5 levels of recommendation relationships, with the first-level referrer being the direct inviter and levels two through five being indirect referrers. The upper limit of levels can be adjusted through administrator configuration. Users bind their accounts via a unique invitation code, a sharing link containing a unique identifier, or a QR code. The invitation code is generated from an 8-digit combination of numbers and letters, and the sharing link embeds the user ID parameter to ensure traceability and uniqueness. An identity authentication unit is integrated, using mobile phone verification codes and facial recognition to verify user identity. Recommendation permissions are activated only after successful verification. A MySQL cluster is used to store the recommendation network topology, synchronizing newly bound data every 10 seconds to ensure real-time accuracy of hierarchical relationships and supporting network storage and querying for millions of users.

[0029] The referral behavior end-to-end tracking module enables full-node data collection from invitation to conversion. Each referral behavior is assigned a unique UUID identifier, spanning the entire process from user clicking the invitation link, submitting a business application, completing payment conversion, to after-sales confirmation. Tracking nodes include 20 data items such as click time, device model, IP address, application business type, payment amount, and conversion completion time, transmitted to the tracking database via a Kafka message queue with a data synchronization latency of ≤80ms. A built-in abnormal behavior monitoring unit identifies anomalies such as repeated clicks and fake registrations through IP blacklist comparison and device fingerprint verification. Once an abnormal behavior is marked, commission calculation is paused, awaiting manual review.

[0030] The dynamic commission rule configuration module provides flexible management of commission calculation rules. It supports two rule types: fixed amount and percentage-based commission. Preset base rates are 5% for first-level commissions, 3% for second-level commissions, and 1% for third-level commissions for SIM card services; and 3% for first-level commissions and 2% for second-level commissions for data package services. Administrators can adjust parameters through the web-based configuration interface. The module uses the Drools 7.0 rule engine to parse rules, supporting differentiated rule settings based on service type, referral level, and user level. Rules take effect immediately after setting, without requiring a system restart. Rule storage uses a distributed file system, generating a version record for each rule change, allowing for retrospective viewing of historical configurations.

[0031] The commission settlement module performs commission calculation, deduction, and payment operations. Settlement cycles support real-time, daily, and monthly modes, with real-time settlement as the default, triggered when the transaction is completed and there is no refund risk (72 hours after completion). The calculation logic is: Commission Amount = Actual Payment Amount × Corresponding Commission Rate, automatically deducting 20% ​​personal income tax and 5% platform service fee, with deduction details synchronized to the transaction records. It integrates with WeChat Pay and Alipay Enterprise Payment interfaces, with a payment response time of ≤3 seconds. A 3-retry mechanism is initiated for payment failures, with a 5-minute interval. Settlement status is categorized into four types: pending settlement, settled, refunded, and recovered. Status changes are synchronized to both the user and management ends in real time.

[0032] The visualized earnings display and detailed query module presents commission-related data to users. Adapted for both mobile and PC platforms via an H5 page, the homepage displays key indicators such as cumulative earnings, pending settlement amount, and earnings trends over the past 7 days, visually illustrating earnings changes using a line chart. Detailed queries support filtering by time range (today, past 7 days, past 30 days, custom) and service type (SIM cards, data packages, value-added services). Each detail includes information such as referral level, referred user's nickname, service name, commission amount, deductions, and arrival time. It provides an Excel 2007 format export function, with exported data including an electronic signature to ensure validity, and supports batch export of monthly details.

[0033] The system interaction and log management module enables two-way interaction and operation recording between users and administrators. On the user side, notifications such as commission payments, rule changes, and refund collection are pushed via WeChat official account template messages, with a message delivery rate of ≥98%. The administrator side provides a data statistics dashboard displaying key metrics such as daily active recommended users, conversion orders, and total commission, supporting filtering and analysis by region and level. A built-in operation log unit records all user bindings, conversions, settlements, and administrator rule configurations and exception handling operations. Logs include the operator, operation time, operation content, and device information, with a storage period of ≥2 years, and support keyword retrieval.

[0034] This invention also includes a multi-level commission allocation coefficient optimization module, which works in conjunction with the dynamic commission rule configuration module to dynamically adjust commission allocation weights based on recommendation level, business conversion rate, and user activity. The allocation coefficient is calculated as follows: Where K_i is the commission allocation coefficient for the i-th level referrer, without units; i is the referral level, ranging from 1 to 5; α is the basic level decay coefficient, ranging from 0.1 to 0.2, with a fixed 0.15 for SIM card services and a fixed 0.1 for data package services; θ is the business conversion coefficient, ranging from 0.8 to 1.2, with 1.2 for conversion rates >30%, 1.0 for 15%-30%, and 0.8 for <15%; φ is the user activity coefficient, ranging from 0.9 to 1.1, with 1.1 for monthly referrals ≥20, 1.0 for 5-20, and 0.9 for <5. This calculation ensures that commission allocation balances level fairness while linking it to business quality and user contribution, avoiding incentive imbalances caused by a single decay mechanism. Furthermore, it connects to the business conversion database to update the θ value hourly and the φ value daily based on user behavior logs, ensuring that the coefficients adapt to business dynamics in real time.

[0035] This invention also includes a recommendation behavior validity verification module, which is connected to the recommendation behavior full-link tracking module to construct a three-level verification system of "basic verification + deep verification + dynamic review". Basic verification covers IP address uniqueness (connecting to the IP2Location database, with ≤5 registrations per IP per day), device authenticity (extracting device MAC address, IMEI code, and operating system version, comparing with the virtual device feature database), and user new registration attributes (verifying the phone number's registration records for the past 30 days). Deep verification targets high-commission businesses (single commission > 100 RMB), adding behavioral timing verification (the interval between clicking and registration must be within 1-120 minutes), geographical location verification (the referrer and the referred user must reside in the same prefecture-level city; cross-regional verification requires manual review), and facial recognition verification (randomly selecting 10% of high-commission referral scenarios, requiring the referred user to complete liveness detection). Dynamic review uses a sliding window mechanism, performing a second verification every 24 hours on valid referrals from the previous 7 days, comparing the referred user's subsequent behavior (such as whether they completed their first order or made repeat purchases). If there is no behavior within 72 hours, it is marked as "suspicious" and commission settlement is suspended. The verification results are divided into three categories: "valid", "suspicious", and "invalid". Suspicious data is stored in the pending review database. Administrators can view the behavior trajectory map and verification log, and after manual judgment, synchronize it to the commission settlement module. Invalid data will automatically cancel commission calculation and generate an "Invalid Recommendation Analysis Report".

[0036] This invention also includes a refund commission retrospective module, which works in conjunction with the commission precision settlement module and the business after-sales system to achieve refined adjustment of commissions for refunds across all scenarios. It distinguishes between full and partial refund scenarios: For full refunds, it traces all settled commissions from all levels associated with the business within the past 90 days, recovering them in the order of "recent to distant," prioritizing deductions from the referrer's pending commissions. Any shortfall generates a recovery order, supporting proactive payments via WeChat and Alipay or automatic deductions in 3 installments (each installment 7 days apart, with a 0.01% late payment fee charged daily for overdue payments); for partial refunds, the recovery amount is calculated based on the refund ratio (recovery amount = original total commission × refund ratio), with each level sharing the recovery responsibility according to the original allocation ratio. A retrospective exemption mechanism is added: for refunds caused by platform system failures, inaccurate business descriptions, or other platform-related responsibilities, the referrer's commission recovery is exempted, and the system automatically marks it as "platform responsibility exempt" and synchronizes it to the detailed records; for refunds due to user-caused reasons, detailed vouchers (including business order number, refund approval form, and original commission settlement record) are pushed during the recovery process. The entire process of tracing back is recorded, and the status of the recovery (pending deduction, in progress, completed, overdue) is updated in real time. Recovery orders that are overdue for more than 30 days will be included in the credit warning system, and the referrer's subsequent promotion rights will be restricted.

[0037] This invention also includes a recommendation contribution evaluation module, connected to the recommendation behavior tracking module and the commission settlement module, to construct a multi-dimensional quantitative evaluation model. The contribution is calculated as follows: Where C is the recommendation contribution, ranging from 0 to 100; β is the conversion timeliness weight, with a value of 0.25; T is the conversion time score (≤1 hour 100 points, 1-24 hours 80 points, 24-72 hours 60 points, >72 hours 40 points); γ is the business value weight, with a value of 0.35; R is the actual payment amount score (≤50 yuan 60 points, 50-100 yuan 80 points, >100 yuan 100 points, value-added services add 10 points); δ is the repurchase impact weight, with a value of 0.2; S is the repurchase score of the referred user (≥3 times within 30 days 100 points, 1-2 times 80 points, 0 times 50 points); η is the retention weight, with a value of 0.2; U is the retention score of the referred user (30-day retention 100 points, 15-30-day retention 80 points, <15-day retention 40 points). The scoring data is updated hourly from the business system and user behavior database. Contribution is calculated and ranked in real time, generating a Top 100 recommender list (updated every 24 hours). The list is labeled with tags such as "Contribution Star" and "Growth Star". The module has a built-in contribution decay mechanism, with historical contributions decaying by 10% each month to incentivize recommenders to remain active. It also outputs a "Contribution Analysis Report" to identify common characteristics of high-value recommenders (such as promotion periods and business preferences), providing data support for operational strategies.

[0038] This invention also includes a differentiated incentive configuration module, which works in conjunction with the recommendation contribution assessment module and the dynamic commission rule configuration module to implement a three-dimensional incentive system of "level incentive + scenario incentive + growth incentive". Level incentives are divided into three levels based on contribution: high contribution (C≥80) enjoys a 1% increase in basic commission, T+0 real-time commission payment, dedicated customer service (response time ≤10 minutes), and priority promotion rights for new businesses (obtaining promotional materials 48 hours earlier than ordinary users); medium contribution (50≤C<80) receives a standardized promotion package (including copywriting, posters, and short video scripts), monthly conversion ranking exposure, and access to live-streaming operational strategy courses; low contribution (C<50) receives 1-on-1 training (2 live streams per week, case study manual), and a recommendation skills toolkit (including user profile analysis templates and communication scripts). Scenario incentives target specific periods: commissions are doubled during holiday promotions (such as Spring Festival and Double 11), an additional reward of 2 yuan per order is given during the first week of a new business launch, and a 5 yuan reward is given for inviting new referrers to register (issued after the invited person completes their first order). The growth incentive program includes an accelerated promotion package. Lower-level users who increase their contribution by ≥20% for three consecutive days can be promoted ahead of schedule and receive a "Growth Gift Pack" (including promotional traffic support and commission discount coupons). Levels are dynamically adjusted monthly, with SMS and WeChat official account notifications explaining the reasons for promotion / demotion and the current benefits. The incentive effect tracking module monitors changes in conversion rates and activity levels for users at each level, generating an "Incentive Effect Review Report" every two weeks and automatically optimizing incentive weights and benefit configurations.

[0039] This invention also includes a dynamic service fee adjustment module, connected to the commission accuracy settlement module and the referral contribution evaluation module, to achieve dynamic matching of service fees and user contributions. The service fee is calculated as follows: Where F represents the actual service fee percentage, ranging from 3% to 5%; F0 represents the base service fee percentage, 5% for SIM card services and 4% for data packages; C represents the referral contribution, ranging from 0 to 100; ε represents the differential adjustment coefficient, 0.03% for high-contribution users, 0.02% for medium-contribution users, and 0.01% for low-contribution users; and Fmin represents the lower limit of the service fee, uniformly 3% for all services. A service fee rebate mechanism is implemented: high-contribution users with ≥100 monthly conversion orders or ≥5000 RMB in monthly commission will receive a 10% service fee rebate for that month. The rebate amount will be automatically included in the commission to be settled in the following month, with details marked "Service Fee Rebate" and calculation basis attached. Service fee adjustment records are synchronized to the blockchain (using a consortium blockchain architecture) to ensure that each adjustment is tamper-proof. It is connected to the State Taxation Administration's electronic invoice system, automatically generating VAT general invoices based on the actual service fee amount, supporting users to apply for and download them online. The module includes a built-in service fee calculation tool, allowing administrators to input an expected contribution range to simulate and calculate service fee expenditures and platform revenue, thus assisting in rule optimization.

[0040] This invention also includes a cross-business commission merging and settlement module, which connects with the commission precision settlement module to achieve integrated management of commissions from multiple businesses. It supports user-defined settlement cycles (daily, weekly, and monthly settlements), with weekly settlement as the default (settling the previous week's commissions every Monday). The settlement scope covers all private domain recommendation businesses, including SIM card processing, data package purchases, value-added service subscriptions, and equipment leasing. The merging and settlement process includes: generating a pre-settlement list 24 hours before the settlement date and pushing it to the user for verification; users can submit appeals for anomalies (such as inconsistent commission amounts or incorrect business attribution), with an appeal processing time of ≤24 hours, and the processing result affecting the current settlement; automatically merging valid commissions on the settlement day, statistically analyzing the percentage by business type (generating a pie chart for visualization), and generating a merged settlement amount after deducting taxes and service fees, with the payment remarks uniformly set as "Private Domain Merged Commission - YYYYMMDD"; after settlement, pushing an electronic settlement statement containing business composition, deduction details, and revenue trends, supporting PDF download and including an electronic signature (CA certification). For multiple small commission transactions (each less than 10 yuan), a "Small Amount Consolidation and Accumulation" option is provided. Once the accumulated amount reaches ≥50 yuan, automatic settlement is performed, reducing transaction costs and operational complexity associated with frequent payments. The module integrates with the financial system, automatically generating consolidated settlement vouchers that include business transaction records, payment vouchers, and invoice information, thus automating financial reconciliation.

[0041] This invention also includes an abnormal commission early warning and processing module, which is connected to the commission accuracy settlement module and the log management module to construct an "intelligent early warning - hierarchical processing - closed-loop optimization" system. Early warning thresholds are subdivided according to business type and referral level: daily commission for SIM card services > 8000 yuan, data package services > 3000 yuan, value-added services > 2000 yuan; daily commission growth for first-level referrers > 50%, and for second-level and above referrers > 70%; the same referred user is associated with 3 or more referrers and generates commissions. The alert levels are divided into yellow (single threshold trigger), orange (double threshold trigger), and red (triple threshold or above trigger): A yellow alert triggers automatic verification (comparing device fingerprints, IP addresses, and behavioral patterns), outputting verification results within 10 minutes; if no anomalies are found, the alert is lifted. An orange alert triggers manual review; administrators must complete data tracing within 2 hours (checking recommendation links, conversion records, and user profiles). The alert is lifted upon successful review; otherwise, it is marked as abnormal. A red alert immediately freezes account commission payments and initiates a special investigation (jointly with the risk control department to verify whether there is bulk registration, business fraud, or false conversion). The investigation period is ≤3 working days, and the investigation results are synchronized to users and regulators. Intelligent diagnosis of abnormal causes is incorporated, using a decision tree algorithm to analyze abnormal data and output the top 3 possible causes (such as false registration, machine operation, and channel fraud) and investigation directions. After processing, an "Abnormal Handling Review Report" is generated, updating the alert model parameters (such as adjusting thresholds and supplementing abnormal features) to reduce the frequency of similar alerts. The entire processing flow is stored in the operation log, supporting source tracing and inspection by regulatory authorities.

[0042] This invention also includes a data backup and recovery module, connected to all system modules, to build a "multi-regional, multi-layered, and traceable" data security system. It adopts a "real-time incremental backup + daily full backup + weekly off-site backup" strategy: real-time incremental backup is performed every 5 minutes, synchronizing only newly added and changed data to the local disk array (RAID5 architecture); a full backup is performed daily at 3 AM, with data encrypted (AES-256 algorithm) and stored on Alibaba Cloud OSS; an off-site backup is performed every Sunday at midnight, synchronizing to Tencent Cloud COS to achieve cross-cloud storage redundancy. Backup data consistency verification is performed hourly, using MD5 value comparison to ensure data consistency across the three locations; if inconsistency is found, a re-backup is immediately triggered and an alarm is pushed. Recovery supports "full system recovery" and "module-level recovery": full system recovery is used for major failures, capable of recovering data from any point in time within the last 90 days, with a recovery time ≤30 minutes; module-level recovery is used for partial failures (such as only commission settlement module data anomalies), recovering only the target module data without affecting the operation of other modules, with a recovery time ≤10 minutes. During the recovery process, a temporary query interface is provided, allowing users to view historical data but not perform any operations. Once recovery is complete, an integrity check is automatically performed (checking recommendation relationship chains, settlement record continuity, and user data integrity). The system can only be reopened after a 100% pass rate. Hot and cold data are stored separately. Hot data from the past 30 days is stored on high-speed SSDs (query response ≤100ms), while cold data older than 30 days is migrated to low-cost object storage. The migration process is performed in the background and does not affect queries, reducing storage costs by more than 30%.

[0043] The following two examples further illustrate the specific implementation of this system: Example 1: Operator's Private Domain SIM Card Data Package Promotion System (A Provincial Telecom Private Domain Operation Scenario) This embodiment addresses the private domain ecosystem deployment needs of a provincial telecommunications company. The system serves 2 million private domain users, focusing on two core businesses: SIM card processing (5G packages) and data package purchases. It constructs a 5-level referral network. The original system suffered from unbalanced commission distribution, frequent false referrals, and monthly invalid commission expenditures exceeding 80,000 yuan. The activity rate of referrers was only 35%. The solution of this invention achieves refined management. The specific implementation process is as follows.

[0044] 1. System module setup and parameter configuration The multi-level recommendation network building module uses a MySQL 8.0 cluster (3 master-3 slave architecture) to store recommendation topology relationships, and synchronizes and adds new bound data every 10 seconds. Invitation codes are generated according to the rule of "2 uppercase letters + 6 numbers", and the sharing link embeds a 16-digit user ID parameter. Identity authentication is integrated with Alibaba Cloud's face recognition API, and mobile phone verification codes are provided through Alibaba Cloud's SMS service. After successful verification, recommendation permissions are activated, supporting 5-level relationship configuration. The upper limit of the level can be adjusted in real time through the administrator backend developed in Vue.

[0045] The recommendation behavior end-to-end tracking module assigns a UUID to each recommendation and tracks 20 data points: click time (accurate to milliseconds), device model (e.g., iPhone 14), IP address (region resolved by the IP2Location database), service type (5G SIM card / 10GB data package), payment amount (99 yuan / 20 yuan), etc. Data is transmitted via a Kafka 2.8.1 message queue, with synchronization latency controlled within 72ms. The abnormal behavior monitoring unit accesses a fake device signature database (containing over 100,000 virtual device fingerprints), and the IP blacklist is updated weekly.

[0046] The dynamic commission rule configuration module uses the Drools 7.0 rule engine, and the web configuration interface supports drag-and-drop rule editing. Preset rules: For SIM card services, Level 1: 5%, Level 2: 3%, Level 3: 1%, Level 4: 0.5%, Level 5: 0.3%; For data package services, Level 1: 3%, Level 2: 2%, Level 3: 1%, Level 4: 0.3%, Level 5: 0.2%. Rules are stored in the MinIO distributed file system, and each change generates a version number, supporting retrospective viewing.

[0047] The commission precision settlement module integrates with WeChat Pay V3 and Alipay Enterprise Payment API, with a payment response time of 2.3 seconds and a 5-minute retry interval for failed transactions. The settlement cycle is real-time by default, triggering payment after 72 hours without refund risk, automatically deducting 20% ​​personal income tax and a 5% basic service fee. The deduction criteria are stored in the settlement details field (e.g., "Personal income tax: 1.98 yuan, service fee: 0.495 yuan"). The settlement status is synchronized in real-time to both the user and management ends via Redis caching.

[0048] The revenue visualization module uses React to develop an H5 page, compatible with iOS and Android systems. The homepage line chart is drawn using ECharts. Detailed queries support dual filtering by time and business type. Excel exports include CFCA electronic signatures, with a monthly batch export limit of 100,000 records. The system interaction module pushes template messages via the enterprise WeChat official account, achieving a delivery rate of 98.5%. Operation logs are stored in Elasticsearch and retained for 2 years, supporting keyword search.

[0049] 2. Implementation of core functions and application of formulas Multi-level commission allocation coefficient optimization: For SIM card services, α=0.15. A first-level referrer makes 25 referrals per month (φ=1.1), with a conversion rate of 35% (θ=1.2), Ki=1-(1-1)×0.15×1.2×1.1=1; a second-level referrer makes 10 referrals per month (φ=1.0), with a conversion rate of 20% (θ=1.0), Ki=1-(2-1)×0.15×1.0×1.0=0.85, and the actual commission rate is 3%×0.85=2.55%. For data package services, α=0.1. A third-level referrer makes 3 referrals per month (φ=0.9), with a conversion rate of 12% (θ=0.8), Ki=1-(3-1)×0.1×0.8×0.9=0.856, and the actual commission rate is 1%×0.856=0.856%. The θ value is updated hourly from the business conversion database, and the φ value is updated daily from the user behavior logs.

[0050] Referral Contribution Assessment: A referrer recommends a 129 RMB SIM card, which converts within 2 hours (T=80 points). The referred user makes two repeat purchases within 30 days (S=80 points) and remains active for 25 days (U=80 points). C=0.25×80+0.35×100+0.2×80+0.2×80=20+35+16+16=87, thus the contribution is considered high. Another referrer recommends a 19 RMB data package, which converts within 72 hours (T=40 points). The referred user makes no repeat purchases (S=50 points) and remains active for 7 days (U=40 points). C=0.25×40+0.35×60+0.2×50+0.2×40=10+21+10+8=49, thus the contribution is considered low.

[0051] Dynamic service fee adjustment: For SIM card services, F0=5%, for high-contribution users C=87, ε=0.03%, F=5%×(1-87×0.03%)=5%×0.739=3.695%≥3%, the actual rate charged is 3.695%. If this user converts 120 orders per month, a 10% service fee will be refunded (e.g., if the total monthly service fee is 500 yuan, 50 yuan will be refunded), which will be included in the commission to be settled in the following month. The service fee adjustment record will be uploaded to the consortium blockchain (AntChain), automatically generating a general VAT invoice, which users can apply for on the H5 page.

[0052] Refund Commission Retrospective: A user received a full refund after purchasing a 99 RMB SIM card. This involved a three-tier commission structure (Level 1: 4.95 RMB, Level 2: 2.97 RMB, Level 3: 0.99 RMB). The system first deducted 4.95 RMB from the Level 1 referrer's outstanding commission (15 RMB), leaving 10.05 RMB. The Level 2 referrer's outstanding commission was 8 RMB, after which 2.97 RMB was deducted, leaving 5.03 RMB. The Level 3 referrer's outstanding commission was 0.5 RMB; after deducting 0.5 RMB, a supplementary payment of 0.49 RMB was required, generating a follow-up order that supports 3 installments. Refunds due to discrepancies in the platform's business description are exempt from all levels of follow-up payments and are marked "Platform Liability Exemption".

[0053] Abnormal Commission Warning: A referrer's daily commission for a SIM card reached 9200 yuan (exceeding the 8000 yuan threshold), triggering a red alert. The administrator received a notification via both the app and email. Upon investigation, it was discovered that the referrer used 10 virtual devices to register referred users. Commissions were immediately frozen, and after a two-day special investigation, the account was marked as invalid. Operation logs were recorded, and the warning model threshold was updated (the daily commission cap for this service was adjusted to 7000 yuan).

[0054] Data backup and recovery: Real-time incremental backups are performed every 5 minutes and stored on a local RAID5 disk array; a full backup is performed daily at 3 AM to Alibaba Cloud OSS (AES-256 encryption); and an off-site backup is performed every Sunday at 3 AM to Tencent Cloud COS. A recovery test is conducted on the last day of each month, with data recovery from June 1st taking 22 minutes and a 100% integrity verification pass rate. Hot data (last 30 days) is stored on SSDs, with a query response time of 80ms; cold data is migrated to low-cost storage, reducing storage costs by 32%.

[0055] 3. Performance data representation Table 1: Performance Comparison between Traditional System and Invention Explanation: Table 1 data is based on two months of operational statistics. Traditional systems, due to single-level allocation and weak verification, resulted in an average of 82,000 RMB in invalid commissions per month, with a referrer activity rate of only 35%. Broken commission tracking led to an average of 12 disputes per month, requiring system restarts for rule changes, resulting in a 2-hour response delay and 2-hour data recovery time. This invention identifies fraudulent recommendations through three-level verification, reducing invalid expenditures to 11,000 RMB; multi-dimensional incentives boost activity to 68%; end-to-end tracking and reasonable backtracking reduce disputes to 1 per month; the rule engine enables instant changes, and the backup system shortens recovery time to 25 minutes. It perfectly suits the large-scale private domain referral scenarios of telecom operators, balancing incentive and cost control needs.

[0056] Example 2: E-commerce Private Domain Multi-Category Product Promotion System (Private Domain Scenario of a Beauty Brand) This embodiment is a private domain ecosystem deployment project for a beauty brand. The system serves 800,000 private domain users, covering three business categories: beauty and skincare products (high profit), beauty tools (medium profit), and personal care samples (low profit). It constructs a three-level referral network. The original system suffered from chaotic cross-business settlements, crude refund processing, a user complaint rate of 18%, and low commission settlement efficiency. The solution of this invention is adopted to optimize operations. The specific implementation process is as follows.

[0057] 1. Configuration and debugging of highly adaptable systems The multi-level recommendation network building module uses a MySQL 8.0 cluster (2 masters and 2 slaves), supports 3-level recommendation relationships, and the invitation code is "brand abbreviation + 2 numbers + 4 letters" (e.g., MZ12ABCE). The sharing link embeds the user's OpenID parameter. Identity authentication is integrated with Tencent Cloud face recognition; successful verification activates permissions, and administrators can configure tiered privileges in the backend.

[0058] The recommendation behavior end-to-end tracking module has added "product browsing duration" and "add-to-cart time" to its tracking nodes, transmitted via Kafka 2.8.1 with a synchronization latency of 78ms. Anomaly monitoring integrates with a third-party device fingerprinting platform (YiDun), achieving 99% accuracy in identifying virtual devices.

[0059] The dynamic commission rule configuration module has the following presets: beauty and skincare products: Level 1 8%, Level 2 5%; beauty tools: Level 1 5%, Level 2 3%; personal care samples: Level 1 3%, Level 2 2%. Rule editing supports individual configuration by product SKU, stored in MinIO, and the version history includes operator information.

[0060] The commission settlement module integrates with WeChat Pay and Alipay, with a payment response time of 2.7 seconds and 3 retries for failed payments. The settlement cycle can be customized by the user (weekly settlement by default), and automatically deducts 20% personal income tax and basic service fees of 4% (cosmetics), 3.5% (tools), and 3% (samples).

[0061] The cross-business consolidated settlement module is developed in Java, supports daily, weekly, and monthly settlement options, and pushes the pre-settlement list 24 hours in advance. Appeals are processed within 20 hours. Consolidated settlement details generate ECharts pie charts, and electronic settlement statements include CA signatures and support PDF download.

[0062] 2. Implementation of core functions and application of formulas Multi-level commission allocation coefficient optimization: For beauty and skincare products, α=0.2. A first-level referrer makes 30 referrals per month (φ=1.1), with a conversion rate of 38% (θ=1.2), Ki=1-(1-1)×0.2×1.2×1.1=1, resulting in an actual commission of 8%. A second-level referrer makes 15 referrals per month (φ=1.0), with a conversion rate of 25% (θ=1.0), Ki=1-(2-1)×0.2×1.0×1.0=0.8, resulting in an actual commission of 5%×0.8=4%. For shampoo and body sample products, α=0.1. A second-level referrer makes 4 referrals per month (φ=0.9), with a conversion rate of 18% (θ=0.8), Ki=1-(2-1)×0.1×0.8×0.9=0.928, resulting in an actual commission of 2%×0.928=1.856%.

[0063] Recommendation Contribution Assessment: One recommender promotes a 299 yuan face cream (R=100 points), with a conversion rate of 1 hour (T=100 points), and the referred customer makes 3 repeat purchases in 30 days (S=100 points) and retains the product for 30 days (U=100 points). C=0.25×100+0.35×100+0.2×100+0.2×100=100, indicating a high contribution. Another recommender promotes a 19 yuan sample (R=60 points), with a conversion rate of 48 hours (T=60 points), no repeat purchases (S=50 points), and a retention rate of 10 days (U=40 points). C=0.25×60+0.35×60+0.2×50+0.2×40=15+21+10+8=54, indicating a medium contribution.

[0064] Dynamic service fee adjustment: Beauty tools F0=3.5%, medium contribution C=54, ε=0.02%, F=3.5%×(1-54×0.02%)=3.5%×0.892=3.122%≥3%. High contribution users have a monthly commission of 6000 yuan, and a 10% service fee (600 yuan) will be returned, which will be included in the settlement of the following month.

[0065] Refund Commission Retrospective: A user purchased a face cream for 299 yuan and received a partial refund (149.5 yuan, refund ratio 50%). The original commission was 23.92 yuan for level 1 and 14.95 yuan for level 2. The amount to be recovered is (23.92 + 14.95) × 50% = 19.435 yuan. The recovery amount for level 1 is 11.96 yuan, and the recovery amount for level 2 is 7.475 yuan, which will be deducted first from the commission pending settlement.

[0066] Cross-business consolidated settlement: The user chose weekly settlement. Last week, 3 transactions were promoted (face cream 299 yuan, eyebrow pencil 49 yuan, sample 19 yuan). The consolidated commission = 23.92 + 2.45 + 0.57 = 26.94 yuan. After deducting personal income tax of 5.39 yuan and service fee of 1.02 yuan, the actual payment was 20.53 yuan. The payment remarks were "Private domain consolidated commission - 20240512". The pie chart on the settlement statement shows that the face cream commission accounted for 88.8%.

[0067] Abnormal commission alert: A referrer's daily commission reached 4,500 yuan (beauty tools exceeded the 3,000 yuan threshold), triggering an orange alert. The administrator checked within 2 hours and found it to be a genuine high conversion rate, so the alert was lifted. Another referrer had 3 commissions associated with the same referee, triggering a yellow alert. Automatic verification showed it was a duplicate binding, so it was marked as invalid.

[0068] 3. Performance data representation Table 2: System performance under different business scenarios Explanation: The data in Table 2 comes from a 3-month test. Traditional systems suffer from chaotic cross-business settlements, with complaint rates exceeding 15% across all scenarios, a settlement efficiency of only 30 orders / second, a referrer retention rate of 50%, and an invalid commission rate of 12%. This invention reduces the complaint rate to below 1.2% by adapting differentiated rules to different profit margins; simplifies operations through consolidated settlements, resulting in a cross-business complaint rate of only 0.5%; optimizes the rule engine and payment interface, increasing settlement efficiency to 90-180 orders / second; multi-dimensional incentives and precise settlement achieve a retention rate exceeding 62%, reaching 75% for high-profit businesses; and three-level verification reduces the invalid commission rate to below 3%, perfectly adapting to e-commerce multi-category private domain promotion scenarios, improving operational efficiency and user satisfaction.

[0069] Reference Figure 2 This diagram visually illustrates the groundbreaking optimization of the traditional system by this invention. Traditional systems, due to single-level commission allocation and weak verification of false recommendations, result in monthly invalid commission expenditures as high as 82,000 yuan, with referrer activity at only 35% due to incentive imbalances. The lack of end-to-end tracking leads to an average of 12 commission disputes per month, rule changes require system restarts with a 2-hour response delay, and data failure recovery takes 2 hours. This invention reduces invalid commissions to 11,000 yuan through three-level validity verification, increases activity to 68% through multi-dimensional incentives, reduces disputes to 1 per dispute through end-to-end tracking and reasonable backtracking, enables instant rule changes, and shortens recovery time to 25 minutes through a backup system, perfectly solving the pain points of traditional systems: high cost, low efficiency, and poor user experience.

[0070] Reference Figure 3 This diagram clearly demonstrates the multi-business adaptability of this invention. Traditional systems use uniform rules to adapt to different profit businesses. In high-profit scenarios, the complaint rate exceeds 15% due to unreasonable commission distribution, while in low-profit scenarios, the retention rate is only 50%, and the cross-business settlement efficiency is only 30 orders / second. This invention adapts to business differences through dynamic rule configuration, with complaint rates in all scenarios below 1.2%, and cross-business merged settlement with a complaint rate as low as 0.5% due to simplified operations; settlement efficiency is improved to 90-180 orders / second, and the retention rate in high-profit scenarios reaches 75% due to sufficient incentives. Even with the promotion of new businesses, stable performance can still be maintained, demonstrating the system's precise support for multi-category private domain operations.

[0071] Reference Figure 4 This diagram highlights the core value of a differentiated incentive system. Traditional systems only link incentives to commission amounts, without differentiation based on contribution level. High-value referrers cannot obtain additional benefits, making it difficult to maintain their enthusiasm. This invention allocates benefits in tiers based on contribution level. High-contribution users enjoy a 1% commission increase, real-time payment, and 10-minute express service; medium-contribution users are guaranteed basic benefits; and low-contribution users are guided to grow through training. This tiered benefit design not only highlights the value of high-contribution users but also provides a growth path for low-contribution users, forming a virtuous cycle of "contribution-incentive-growth" and effectively improving the activity of the referral network.

[0072] Reference Figure 5 This diagram illustrates the precision and efficiency of the system's risk control. Traditional systems lack tiered early warning systems, relying on manual post-event verification for anomalies, resulting in processing times exceeding 24 hours, an accuracy rate of only 80%, and a fund recovery rate of less than 70%. This invention constructs a tiered early warning system: yellow alerts are automatically verified within 10 minutes with 98% accuracy; orange alerts are manually reviewed and resolved within 2 hours; and red alerts are subject to special investigation and resolved within 3 days, achieving 100% accuracy and recovery rate. This "automatic + manual" tiered processing model ensures both efficient handling of low-level anomalies and high-quality handling of high-level risks, significantly reducing corporate financial losses.

[0073] The above are merely preferred embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A commission tracking and precise settlement system for a multi-level referral network within a private domain ecosystem, characterized in that, include: Multi-level recommendation network building module: Supports the definition of 1-5 levels of recommendation relationships, and the upper limit of the level can be configured through the administrator backend; Users bind their accounts via a unique invitation code, a sharing link with a unique identifier, or a QR code; after passing the identity authentication unit verification via mobile phone verification code and facial recognition, their recommendation permissions are activated; the topology relationship is stored using a MySQL cluster, and the stored recommendation network topology relationship data provides basic hierarchical association data support for the full-link tracking module of recommendation behavior; The full-link tracking module for referral behavior assigns a unique UUID to each referral behavior, covering the entire process from clicking a link, submitting an application, making a payment, and confirming after-sales service. It collects data from 20 tracking nodes and transmits it to the tracking database via Kafka. It also has a built-in abnormal behavior monitoring unit that identifies anomalies through IP blacklist comparison and device fingerprint verification. The collected valid referral behavior data is synchronized to the commission accuracy settlement module for commission calculation and to the visualization earnings display and detailed query module for data display. Dynamic commission rule configuration module: Provides flexible commission calculation rule management, supports fixed amount and percentage commission, preset basic commission ratios for SIM cards and data packages, and administrators can adjust parameters through the web interface; It adopts the Drools7.0 rule engine for parsing, supports setting differentiated rules according to business type, referral level, and user level, and the configured and effective commission calculation rules serve as the core rule basis for the commission accuracy settlement module to perform commission calculation; Commission Precision Settlement Module: Performs commission calculation, deduction and payment operations, supporting real-time, daily settlement and monthly settlement modes; calculated based on "actual business payment amount × corresponding level ratio", automatically deducting 20% ​​personal income tax and 5% platform service fee; The settlement status is divided into four categories. The generated settlement results are synchronized to the visual revenue display and detailed query module for users to view. The operation records during the settlement process are pushed to the system interaction and log management module for storage. The Visualized Earnings Display and Detailed Query Module presents commission data to users and is adapted to both mobile and PC platforms via H5. The details support filtering and provide an Excel format export function. The commission data and detailed information displayed are derived from the settlement results of the Accurate Commission Settlement Module and the tracking node data of the Recommendation Behavior Full-Link Tracking Module, respectively. System interaction and log management module: User terminals receive notifications via official account template messages; The administrator interface provides a data statistics dashboard that supports filtering and analysis; The built-in operation log unit records various operations performed by users and administrators, and synchronously receives and stores operation log data from the multi-level recommendation network construction module, the recommendation behavior full-link tracking module, the dynamic commission rule configuration module, and the commission accurate settlement module.

2. The commission tracking and precise settlement system for a multi-level recommendation network in a private domain ecosystem according to claim 1, characterized in that, It also includes a multi-level commission allocation coefficient optimization module, which works in conjunction with the dynamic commission rule configuration module; the allocation coefficient is calculated as follows: ;in The commission allocation coefficient for the i-th level referrer; i represents the recommended level; The attenuation coefficient at the basic level; This is the business conversion coefficient; This is the user activity coefficient; this calculation ensures that commission distribution balances tiered fairness while also linking it to business quality and user contribution, and it is updated hourly by connecting to the business conversion database. value.

3. The commission tracking and precise settlement system for a multi-level recommendation network in a private domain ecosystem according to claim 1, characterized in that, It also includes a recommendation behavior validity verification module, which is connected to the recommendation behavior full-link tracking module to build a three-level verification system of "basic verification + deep verification + dynamic review". The dynamic review adopts a sliding window mechanism, which performs a second check on the valid recommendations of the previous 7 days every 24 hours. If there is no behavior within 72 hours, it is marked as "suspicious" and commission settlement is suspended. The verification results are divided into three categories: "valid", "suspicious" and "invalid". Suspicious data is stored in the pending review database. Administrators can view the behavior trajectory map and verification log, and after manual judgment, synchronize it to the commission settlement module.

4. The commission tracking and precise settlement system for a multi-level recommendation network in a private domain ecosystem according to claim 1, characterized in that, It also includes a refund commission retrospective module, which is linked with the commission accuracy settlement module and the business after-sales system; it distinguishes between full refund and partial refund scenarios: for full refunds, it traces all settled commissions of all levels associated with the business within the past 90 days, and deducts them first from the referrer's pending commissions; for partial refunds, it calculates the amount to be recovered according to the refund ratio, and each level shares the responsibility for recovery according to the original allocation ratio; it adds a retrospective exemption mechanism, and for refunds caused by platform system failures or business descriptions that are not platform responsibility, the referrer's commission recovery is exempted, and the system automatically marks "platform responsibility exemption" and synchronizes it to the detailed records; for refunds caused by the user's own reasons, detailed vouchers are pushed when recovering; the retrospective process is fully traceable, and the recovery status is updated in real time.

5. The commission tracking and precise settlement system for a multi-level recommendation network in a private domain ecosystem according to claim 1, characterized in that, It also includes a referral contribution evaluation module, which connects to the referral behavior tracking module and the commission settlement module; the contribution calculation method is as follows: Where C represents the recommendation contribution; β represents the conversion timeliness weight, T represents the conversion time score; γ represents the business value weight, R represents the actual payment amount score; δ represents the repurchase impact weight, S represents the repurchase score of the referred user; η represents the retention weight, U represents the retention score of the referred user; the score data is updated hourly from the business system and user behavior database, and the contribution is calculated and sorted in real time; the module has a built-in contribution decay mechanism to incentivize the recommender to remain active and identify the common characteristics of high-value recommenders.

6. The commission tracking and precise settlement system for a multi-level recommendation network in a private domain ecosystem according to claim 1, characterized in that, It also includes a differentiated incentive configuration module, which works in conjunction with the recommendation contribution assessment module and the dynamic commission rule configuration module to implement a three-dimensional incentive system of "level incentives + scenario incentives + growth incentives". Level incentives are divided into three levels based on contribution: high contribution levels can enjoy a 1% increase in basic commissions, T+0 real-time commission payments, dedicated customer service, and priority promotion rights for new businesses; medium contribution levels can receive standardized promotion packages, monthly conversion ranking exposure, and access to live operation strategy courses; low contribution levels receive one-on-one training and recommendation skills toolkits; scenario incentives target specific periods: commissions are doubled during holidays; growth incentives include promotion acceleration packages; levels are dynamically adjusted monthly; and the incentive effect tracking module monitors changes in user conversion rates and activity levels at each level, automatically optimizing incentive weights and rights configurations.

7. The commission tracking and precise settlement system for a multi-level recommendation network in a private domain ecosystem according to claim 1, characterized in that, It also includes a dynamic service fee adjustment module, which connects with the commission accuracy settlement module and the referral contribution evaluation module; the service fee calculation method is as follows: Where F is the actual service fee ratio; F0 is the benchmark service fee ratio; C is the recommended contribution; ε is the differential adjustment coefficient; Fmin is the lower limit of the service fee; a service fee return mechanism is added; service fee adjustment records are synchronized to the blockchain and connected to the State Taxation Administration's electronic invoice system to automatically generate VAT general invoices based on the actual service fee amount; the module has a built-in service fee calculation tool, and administrators can input the expected contribution range.

8. The commission tracking and precise settlement system for a multi-level recommendation network in a private domain ecosystem according to claim 1, characterized in that, It also includes a cross-business commission consolidation and settlement module, which is connected to the commission precision settlement module; Supports user-defined settlement cycles; the consolidated settlement process includes: generating a pre-settlement list 24 hours before the settlement date, and users can submit appeals for abnormal items, the results of which affect the current settlement. On the settlement date, valid commissions are automatically merged, and the percentage is calculated according to business type. After deducting taxes and service fees, a merged settlement amount is generated. After settlement, an electronic settlement statement containing business composition, deduction details, and revenue trend is pushed. For multiple small commissions, a "small amount merged and accumulated" option is provided. The module is integrated with the financial system to automatically generate merged settlement vouchers.

9. The commission tracking and precise settlement system for a multi-level recommendation network in a private domain ecosystem according to claim 1, characterized in that, It also includes an abnormal commission early warning and handling module, which connects with the commission accurate settlement module and log management module; the early warning threshold is subdivided according to business type and recommendation level; the early warning level is divided into yellow, orange and red: yellow warning initiates automatic verification; orange warning triggers manual review; red warning immediately freezes account commission payments; intelligent diagnosis of abnormal causes is added, which analyzes abnormal data through decision tree algorithm and outputs the top 3 possible causes and verification directions; after the processing is completed, an "Abnormal Handling Review Report" is generated, the early warning model parameters are updated, and the entire processing process is stored in the operation log to support traceability inspection by regulatory authorities.

10. The commission tracking and precise settlement system for a multi-level recommendation network in a private domain ecosystem according to claim 1, characterized in that, It also includes a data backup and recovery module, which connects to all modules of the system; it adopts a strategy of "real-time incremental backup + daily full backup + weekly off-site backup": real-time incremental backup only synchronizes newly added and changed data to the local disk array; A full backup is performed every day at 3:00 AM, and the data is encrypted and stored in Alibaba Cloud OSS. Perform off-site backup and synchronize to Tencent Cloud COS; recovery supports "full system recovery" and "module-level recovery": full system recovery is used for major failures; module-level recovery is used for partial failures; a temporary query interface is provided during the recovery process, and integrity verification is automatically performed after the recovery is completed. The system can only be opened after the verification pass rate is 100%; cold and hot data are separated for storage. Hot data of the past 30 days is stored on high-speed SSD disks, and cold data older than 30 days is migrated to low-cost object storage.

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