Dynamic right and interest distribution method and device based on user contribution value, equipment and medium

Abstract

The invention discloses a user contribution value-based dynamic right and interest allocation method, apparatus and device, and a medium. According to the method, a user interaction behavior event is captured through a front-end system, is forwarded to an event center of a system B after being verified and enhanced by a system A, and is asynchronously delivered to a message queue for processing. And the consumption end calculates the contribution value variation according to the dynamically configured rule base table, and supports positive and negative adjustment of the civilized contribution value and the platform contribution value. The system adopts a MySQL and Redis dual-storage architecture, and data consistency under high concurrency is ensured through atomic operation. The user contribution proportion is calculated in real time based on the formula beta (i) = U (i) / (G (t)-G (0)), and three distribution strategies of proportion type right release, stepped ranking weighting and threshold type screening incentive are provided. And through an event-driven architecture and a cache consistency algorithm, millisecond-level contribution value updating and second-level proportion accounting are realized. The problems of single contribution measurement mechanism, right configuration rigidity, calculation performance bottleneck and the like of a traditional integral system are solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of computer software and Internet technology, and particularly relates to a dynamic benefit allocation method and device based on user contribution value, equipment and medium. BACKGROUND

[0002] With the rapid development of Internet technology and the in-depth promotion of digital operation mode, various platform applications have shown explosive growth, and users' ways of participating in platform activities have become increasingly diversified. In the fields of e-commerce, community services, enterprise management, etc., how to scientifically quantify user contribution and reasonably allocate platform benefits has become a key problem to improve user activity and platform value. Traditional user incentive systems mostly adopt a static point mode, i.e., fixed point rules are set in advance, and users obtain points through specific behaviors, and then exchange the points for corresponding benefits. This mode can meet the basic needs in the early stage of Internet development, but as the user scale expands and the business scenarios become more complex, its limitations become increasingly prominent.

[0003] The user contribution evaluation system in the prior art generally has three technical bottlenecks. First, the contribution degree measurement mechanism is too single, and most systems only support simple cumulative scoring, lacking the ability to flexibly regulate multi-event types, multi-contribution levels and multi-time periods. For example, it is unable to distinguish between civilized behavior contribution and platform business contribution, unable to dynamically adjust the contribution weight according to the time period, and unable to realize differentiated incentives based on user historical performance. Secondly, the benefit configuration presents a rigid feature, and the system's preset exchange rules are difficult to adapt to cross-type incentive strategies and differentiated allocation logic in complex scenarios. When the platform needs to adjust the benefit allocation scheme according to the operation strategy, it often needs to modify the underlying code, lacking flexible configuration management capability. Finally, there is a serious performance bottleneck in contribution data calculation. The traditional real-time calculation scheme based on a relational database will have obvious delay and throughput limitation when facing high-frequency user behavior triggers, and it is difficult to support real-time benefit accounting needs in large-scale concurrent scenarios.

[0004] In addition, the existing technology also has many deficiencies in system architecture design. Most systems adopt a synchronous processing mode, and user behavior triggers are closely coupled with contribution value calculation and benefit allocation, resulting in long system response time and poor user experience. In terms of data consistency guarantee, there is a lack of effective distributed transaction processing mechanism, and in high-concurrency scenarios, data inconsistency problems are prone to occur. In terms of fault tolerance, the system components have strong dependencies, and single-point failures can easily trigger a chain reaction, affecting the overall service availability. In terms of scalability, the traditional architecture is difficult to support horizontal expansion, and when the user scale reaches tens of millions, the system performance will decrease sharply. These technical defects seriously restrict the promotion and implementation of user incentive systems in large-scale commercial applications. SUMMARY

[0005] The application aims to provide a dynamic benefit allocation method based on user contribution value, device, equipment and medium, to solve the technical problems of single contribution degree measurement mechanism, rigid benefit allocation, high contribution data calculation delay and obvious throughput bottleneck in the prior art, and to realize accurate quantification of diversified user behavior, real-time dynamic adjustment of contribution value and differentiated intelligent allocation of benefits.

[0006] To achieve the above-mentioned purpose, the application provides the following technical solutions: In the first aspect, the application provides a dynamic benefit allocation method based on user contribution value, which realizes full-process automatic processing from user behavior capture to benefit allocation by constructing an event-driven multi-level processing architecture. The core of the method is to establish a complete contribution value dynamic adjustment mechanism and a real-time proportion calculation system based on cache consistency.

[0007] Specifically, the method first captures the multi-dimensional interaction behavior events of the user in real time through the front-end system, including but not limited to likes, check-ins, reports, content uploads, and single payment operations. Each event type is marked by a unique event identification code, such as "QD" for check-in and "SDZF" for single payment. When the user triggers a specific behavior, the front-end system constructs a request body containing user identification, event identification code, trigger timestamp and other core information, and sends it to the A system access layer.

[0008] The A system acts as an access gateway, responsible for request verification, parameter verification and message enhancement. The system not only verifies the legality of the user's identity, but also adds user portrait information, device fingerprint, geographic location and other context data, encapsulating the original request into a standardized message body containing complete business semantics. This message enhancement mechanism ensures that the subsequent processing links can obtain sufficient decision basis.

[0009] The standardized message body is then forwarded to the B system event hub. The B system first performs idempotency check to prevent abnormal accumulation of contribution value caused by repeated events through message unique identification. After the check, the B system serializes the message and asynchronously delivers it to the high-reliability message queue, realizing the decoupling of request reception and business processing, and significantly improving the concurrent processing capability of the system.

[0010] The consumer group mode is used for concurrent consumption of the consumer end of the message queue, and the batch pulling strategy is used to improve the processing efficiency. After the consumer end parses the message body, it queries the pre-configured rule base table, which maintains the mapping relationship between events and contribution value adjustment rules. The rule configuration supports high flexibility, including contribution value type (civil contribution value or platform contribution value), adjustment value (supporting positive increase and negative deduction), effective condition, cooling period, trigger threshold, and upper limit of points.

[0011] Based on the matched rules, the system calculates the final contribution value change amount through the rule engine. The calculation process takes into account complex business logic, such as the first check-in within 24 hours being valid, additional rewards for consecutive check-ins, and deductions for violations. After the calculation is complete, the system updates the user contribution value detail table and the summary table through a database transaction, ensuring strong consistency of the data.

[0012] To support real-time query requirements in high-concurrency scenarios, the system uses a dual-storage architecture of MySQL and Redis. While the data is being written to the database, Lua scripts are used to ensure the atomicity of Redis cache updates, and the update operations for user personal contribution values and global contribution values are performed in the same transaction. This design avoids data inconsistency that may occur in extremely high concurrency (100,000 requests per second).

[0013] The calculation of contribution proportion is the core basis for interest allocation. The system freezes a global contribution value snapshot as the initial base value G(0) at the beginning of the activity, and updates the current global contribution value G(t) in real time as user behavior continues to trigger. The contribution proportion of each user is calculated by the formula β(i)=U(i) / (G(t)-G(0)), where U(i) represents the cumulative contribution value of user i during the activity. This incremental-based calculation method ensures accuracy and avoids interference from historical data on the current activity.

[0014] In the interest allocation link, the system provides three differentiated allocation strategies to adapt to different business scenarios. The proportion-based interest release strategy is suitable for profit distribution scenarios, and the system calculates the user's share of the total distributable income R(i)=R×β(i) based on the contribution proportion β(i) of each user and the total distributable income R. The ladder ranking empowerment strategy is aimed at the allocation of scarce interests, and uses Redis ordered sets to achieve millisecond-level ranking calculation, giving special interests to users with high rankings. The threshold screening incentive strategy sets a fixed contribution value threshold, and automatically triggers interest distribution when the user's contribution value reaches a certain threshold.

[0015] To ensure the high availability of the system, the method also includes a comprehensive fault tolerance mechanism. When the Redis cache service is unavailable, the system automatically degrades to MySQL for direct read and write; when the rule engine encounters an exception, the default rules are enabled to ensure the normal operation of the basic functions. For cases where message processing fails, the system uses an exponential backoff strategy for retries, with a maximum of 5 retries before entering the dead letter queue for manual processing.

[0016] In a second aspect, the present application provides a dynamic interest allocation device based on user contribution values, which includes a behavior event capture module, a message processing module, a message queue module, a rule engine module, a data storage module, and an interest allocation module. Each module works together to dynamically manage contribution values and intelligently allocate interests.

[0017] In a third aspect, the present application provides an electronic device comprising a processor and a memory, wherein the memory stores a computer program, and the processor executes the program to implement the method described above.

[0018] In a fourth aspect, the present application provides a computer readable storage medium storing a computer program, and the program is executed to implement the method described above.

[0019] The present application has the following beneficial effects: Through the event-driven architecture and flexible rule configuration, the precise quantification of multi-dimensional user behavior is supported, the point rule can be dynamically adjusted according to the business needs, and the complex incentive system combining positive incentive and negative constraint is supported; by using the asynchronous message mechanism and batch processing strategy, combined with the multi-level cache architecture, millisecond-level point update and second-level proportion accounting are realized, which can support 100,000 query requests per second and 10,000 update requests per second, and meet the high concurrency demand of tens of millions of users.

[0020] Through the Redis atomic operation and database transaction mechanism, the accuracy of the contribution value data in the extremely high concurrency scenario is ensured. The regular audit and reconciliation mechanism further improves the data reliability. The three kinds of equity allocation strategies can be used alone or in combination, which can realize fair allocation based on contribution proportion and meet the differentiated configuration demand of scarce resources, and adapt to diversified business scenarios.

[0021] The perfect fault tolerance degradation mechanism and message retry strategy ensure that the system can still provide basic services when some components fail. The full-link tracking and monitoring alarm system facilitates problem positioning and system optimization; the modular system architecture and standardized message format facilitate the access of new functions and the upgrade of old functions. The rule-based configuration management reduces the system maintenance cost and improves the business adaptability. BRIEF DESCRIPTION OF DRAWINGS

[0022] In order to more clearly illustrate the technical solutions in the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, the drawings in the following description only illustrate the present application, and for those skilled in the art, other drawings can also be obtained without creative labor on the basis of these drawings.

[0023] Fig. 1 The contribution value dynamic adjustment process diagram of the present application; Fig. 2 The contribution proportion calculation diagram of the present application; Fig. 3 The system architecture diagram of the present application; Fig. 4 The three kinds of equity allocation strategies diagram of the present application. DETAILED DESCRIPTION

[0024] The present application will be described in detail below with reference to the drawings and specific embodiments. It should be noted that, in order to make the embodiments more detailed, the following embodiments are the best, preferred embodiments, and other alternative ways can also be implemented by those skilled in the art for some known technologies; and the drawings are only used to more specifically describe the embodiments, and are not intended to specifically limit the present application.

[0025] It should be noted that in the specification, "one embodiment", "embodiment", "exemplary embodiment", "some embodiments" and the like indicate that the described embodiment can include a specific feature, structure or property, but not necessarily every embodiment includes this specific feature, structure or property. In addition, when a specific feature, structure or property is described in combination with an embodiment, it should be within the knowledge of those skilled in the related art to implement this feature, structure or property in combination with other embodiments (whether or not explicitly described).

[0026] Generally, the terms can be understood at least in part from the context of their usage. For example, depending at least in part upon the context, the term "one or more" as used herein can be used to describe any feature, structure, or characteristic in the singular or can be used to describe combinations of features, structures, or characteristics in the plural. Further, the term "based on" can be understood as not necessarily of a set of exclusive factors, but, depending at least in part on the context, can instead allow for a fact not necessarily explicitly described to be present.

[0027] Reference Figs. 1 to 4 Illustrated The present application provides a dynamic benefit allocation method, device, equipment and medium based on user contribution value, and the technical solutions of the present application will be described in detail below with reference to the drawings and specific embodiments.

[0028] Embodiment 1: Specific implementation of dynamic adjustment of contribution value This embodiment details the complete implementation process of user behavior event triggering to contribution value updating. The system is pre-configured with a set of dynamic rule base tables, including the identification code of various events (EventCode), contribution value type, adjustment weight, and other parameters. For example, the check-in event corresponds to the identification code "QD", which can be configured to increase 5 platform contribution values; the report of illegal content corresponds to the identification code "JB", which can be configured to increase 10 civilization contribution values; and the publication of illegal content may trigger a negative incentive of deducting 20 platform contribution values. When the user performs a check-in operation, the front-end system identifies the action and constructs a request body, including user ID (such as user_123456), event identification code "QD", trigger timestamp, and other basic information, and initiates an interface call to the A system access layer. After receiving the request, the A system performs identity verification and parameter checking, while attaching user portrait information, device fingerprint, geographic location, and other context data, and assembles a standardized message body: {userId: "user_123456", eventCode: "QD", timestamp: 1719900000, deviceId: "device_abc", location: "Beijing Chaoyang District", extra: {...}}. Subsequently, the A system forwards the message body to the B system event hub through the HTTP / RPC protocol. The B system, as the message processing hub, first performs idempotency checking to prevent abnormal accumulation of contribution values caused by repeated events, then serializes the message and delivers it to a high-reliability message queue, such as a specific topic of RocketMQ.

[0029] The consumer side of the message queue uses the consumer group mode for concurrent consumption. Each consumer instance pulls messages from the queue in batches, with a batch size of 100-500, and after parsing the message body, it queries the corresponding rule configuration. The rule engine matches the rule table according to the event identification code to obtain the contribution value adjustment parameters of the event, including the contribution value type, civilization contribution value or platform contribution value, adjustment value, effective condition, cooling period, etc. For example, the rules of the check-in event may include: only the first check-in within 24 hours is valid, and an additional 20 contribution values are awarded for consecutive check-ins for 7 days. The consumer calculates the final contribution value change based on the rules, and updates the user contribution value detail table and summary table through database transactions to ensure data consistency. If message processing fails, the system will re-deliver the message to the delay queue, retry according to the exponential backoff strategy, such as 1 second, 5 seconds, 30 seconds, and after a maximum of 5 retries, enter the dead letter queue for manual processing.

[0030] Embodiment 2: Real-time calculation of contribution proportion in high-concurrency scenarios This embodiment focuses on how to achieve real-time and accurate calculation of contribution value proportion in a high-concurrency environment. The system uses MySQL to store the persistent data of contribution value, and uses Redis cache layer to realize high-frequency read-write operation. At the beginning of the activity, the system performs an initialization process: first, it calculates the total contribution value of all users at the current time from the MySQL database as the initial baseline, for example, the total contribution value of the platform on June 1, 2025, at 0 o'clock is 1,000,000, which is stored in the Redis key, such as contribution:baseline:202506. At the same time, two key cache data structures are initialized: global contribution value counter (contribution:total:202506) and user personal contribution value hash table (contribution:user:202506).

[0031] When a user triggers a contribution value change event, the system uses Lua script to ensure the atomicity of Redis operation. For example, user A completes a payment of 100 yuan, which should increase 50 platform contribution values according to the rules, the system will execute HINCRBYcontribution:user:202506userA50 (increase user A's contribution value) and INCRBYcontribution:total:20250650 (increase global contribution value) in the same Redis transaction. This atomic operation ensures the strong consistency of data in the case of extremely high concurrency (100,000 requests per second). The system synchronizes the incremental data in Redis to MySQL every minute through a timing task, and uses batch insertion and update strategy to reduce database pressure.

[0032] The calculation of contribution proportion uses the following formula: User contribution proportion β(i)=U(i) / (G(t)-G(0)); Where U(i) represents the cumulative contribution value of user i during the activity, G(t) represents the total global contribution value at the current time, and G(0) represents the initial baseline at the beginning of the activity. For example, when the activity has been going on for 15 days, the global contribution value reaches 5,000,000, and user A's contribution value is 20,000, then its proportion is: 20,000 / (5,000,000-1,000,000)=0.5%.

[0033] To support real-time ranking function, the system uses Redis ordered set data structure, with contribution value as score and user ID as member, to realize O(logN) complexity ranking query through ZADD and ZREVRANK commands. At the same time, set a reasonable cache expiration time (such as 5 minutes) and preheating mechanism to avoid cache avalanche problem.

[0034] Embodiment 3: Implementation of multi-dimensional equity distribution strategy: This embodiment details the specific implementation of three equity distribution strategies. First is the proportion-based equity release strategy, suitable for platform income distribution and other scenarios. Suppose the platform generated 1 million yuan of distributable income in June 2025, the system allocates according to the contribution proportion of each user. The specific calculation process is as follows: first, obtain the total incremental contribution value during the activity (e.g., 4,000,000), then calculate the income of each participating user. User A's contribution value is 20,000, with a proportion of 0.5%, so the income should be 100 million x 0.5% = 5,000 yuan. The system calculates the allocation list and pushes it to the financial system for payment after the activity ends.

[0035] Second is the ladder ranking empowerment strategy, mainly used for the distribution of scarce rights. For example, the "green channel" rights in cooperation with government departments, only open to the top 1000 users in terms of contribution value. The system performs a ranking snapshot at 2 a.m. every day, stores the ranking results persistently, and notifies users who obtain rights through message push. The rights take effect using a list synchronization mechanism, pushing the user ID list that meets the conditions to the partner system to ensure that the rights take effect in time. For users at the critical point of the ranking, such as ranks 990-1010, the system will send a reminder message to encourage them to improve their contribution value.

[0036] The third is the threshold screening incentive strategy, which sets a fixed contribution value threshold to distribute rights. For example, users with a contribution value of 5000 points can enjoy platform VIP privileges, and users with a contribution value of 10000 points can obtain priority enrollment rights for offline activities. The system implements real-time rights issuance through a trigger mechanism: when a user's contribution value crosses the threshold, the rights issuance process is triggered immediately, including updating the user's rights status, sending notification messages, and synchronizing to related business systems. To prevent abuse of rights, the system also implements rights expiration management, regularly checking and recycling expired rights.

[0037] Embodiment 4: System fault tolerance and performance optimization scheme: This embodiment explains the fault tolerance mechanism and performance optimization scheme of the system in actual operation. In terms of fault tolerance design, the system adopts a multi-level degradation strategy: when Redis cache is unavailable, it automatically degrades to MySQL direct read-write, although the performance decreases, but ensures service availability; when the message queue is blocked, enable local buffer queue to store messages, and batch them after recovery; when the rule engine is abnormal, enable the default rule to ensure the normality of basic functions. The system also implements a perfect monitoring and alarm mechanism, including contribution value abnormal fluctuation alarm, such as single user single day contribution value exceeding threshold, system performance alarm, such as Redis hit rate below 80%, business index alarm, such as abnormal decrease in daily active users, etc.

[0038] For performance optimization, the system adopts multiple strategies to improve processing capacity. At the database level, the system supports ten million user data by dividing the database and table, hashing shards by user ID, and establishing composite indexes to optimize query performance. At the cache level, the system uses a multi-level cache architecture, with local cache storing hot data and distributed cache storing full data. At the message processing level, the system achieves horizontal expansion by increasing consumer instances and improves throughput by using batch consumption and asynchronous processing. After optimization, the system can support 10,000 contribution value query requests per second and 10,000 contribution value update requests per second, meeting the business needs of large platforms.

[0039] To verify the accuracy of the system, a contribution value audit mechanism is also implemented. The system performs reconciliation tasks regularly, such as every morning, comparing contribution value data in Redis cache and MySQL database, and automatically correcting and recording audit logs when inconsistencies are found. The system also provides a full-link tracking function for contribution value changes, generating a unique tracking ID for each change to facilitate problem troubleshooting and user complaint handling.

[0040] Example 5: Extended application scenarios This example demonstrates the application of the invention in different business scenarios. In the e-commerce platform scenario, the system maps user behaviors such as purchasing, evaluating, and sharing to different contribution values, and converts purchase amounts to platform contribution values in proportion. For example, consuming 1 yuan earns 1 contribution value, and high-quality evaluations earn additional civilization contribution value rewards. The platform allocates part of the advertising revenue as a reward pool to users based on their monthly contribution proportion, forming a positive cycle of user growth and platform revenue.

[0041] In the community governance scenario, the system supports a more complex contribution value system. Residents earn civilization contribution values for behaviors such as garbage classification, volunteer service, and neighborhood mutual assistance, and earn platform contribution values for participating in community voting and feedback. Contribution values are not only used for material reward distribution but also linked to community service priorities, such as high-contribution-value residents having priority in using community activity rooms and signing up for community activities. The system also supports seasonal adjustments to contribution values, such as increasing the contribution value weight of snow shoveling in winter and increasing the weight of water-saving behaviors in summer.

[0042] In the enterprise management scenario, the system is transformed into an employee contribution evaluation system. Employee behaviors such as overtime, project participation, knowledge sharing, and team collaboration are converted into contribution values for year-end bonus distribution, promotion evaluation, and training opportunity allocation. The system supports both departmental independent accounting and company-wide unified accounting to meet different management needs. The system displays departmental and individual contribution value rankings in real-time through visual screens, creating a positive work atmosphere.

[0043] As can be seen from the above embodiments, the dynamic right distribution method based on user contribution value provided by the present application has the following technical advantages: first, the fine and real-time management of contribution value is realized, and a complex rule system combining positive incentive and negative constraint is supported; second, through the event-driven architecture and asynchronous message mechanism, high concurrency and low delay contribution value processing capability is realized, which can support millions of users; third, the cache consistency algorithm and incremental calculation strategy are adopted to ensure the accuracy and real-time of contribution ratio calculation; fourth, flexible and diverse right distribution strategies are provided, which can adapt to the incentive needs of different business scenarios; fifth, the perfect fault-tolerant mechanism and performance optimization scheme ensure the high availability and scalability of the system.

[0044] The present application not only solves the technical bottleneck of the traditional integral system, but also provides a complete technical solution for building a new generation of user incentive platform through innovative architecture design and algorithm optimization. With the in-depth development of digital operation, this technical solution will play an important role in more fields, and promote the two-way improvement of user participation and platform value.

[0045] The present application covers any substitution, modification, equivalent method and scheme made on the essence and scope of the present application. In order to make the public have a thorough understanding of the present application, specific details are described in the following preferred embodiments of the present application, and the present application can also be completely understood without the description of these details for those skilled in the art. In addition, in order to avoid unnecessary confusion to the essence of the present application, well-known methods, processes, procedures, elements and circuits are not described in detail.

[0046] The above is only the preferred embodiment of the present application, and it should be pointed out that for ordinary skilled in the art, without departing from the principle of the present application, a number of improvements and refinements can be made, and these improvements and refinements should be regarded as the protection scope of the present application.

Claims

1. A dynamic equity distribution method based on user contribution value, characterized in that: The following steps are involved: S1: The front-end system identifies the interactive behavior event triggered by the user, constructs a request body containing the user ID, event ID code and trigger timestamp, and sends it to the access layer of system A; S2: System A authenticates and verifies parameters of the request, appends user profile information, device fingerprint, and geographic location data, and assembles the result into a standardized message body. S3: System A forwards the standardized message body to the event center of System B. After System B performs an idempotence check, it serializes the message and delivers it to the message queue. S4: The consumer pulls messages in batches from the message queue, parses the message body, queries the rule base table, and adjusts the parameters based on the corresponding contribution value according to the event identification code; S5: Calculate the contribution value change based on the rule engine, update the user contribution value detail table and summary table through database transactions, and use Redis atomic operations to update the user contribution value and global contribution value in the cache; S6: The system calculates the user contribution ratio according to the formula β(i)=U(i) / (G(t)-G(0)), where U(i) is the cumulative contribution value of user i, G(t) is the current global contribution value, and G(0) is the initial base value of the activity; S7: Distribute corresponding benefits to eligible users based on the preset benefits distribution strategy, including percentage-based benefits release, step-by-step ranking empowerment, and threshold-based screening incentives.

2. The method according to claim 1, characterized in that The rule base table includes event identification code, contribution value type, adjustment value, effectiveness condition, cooling period and integral upper limit parameters, among which the contribution value type includes civilization contribution value and platform contribution value, and the adjustment value supports positive value increase and negative value deduction.

3. The method according to claim 1, characterized in that The Redis atomic operation in step S5 is implemented through a Lua script, and the HINCRBY command is executed in the same transaction to update the user's personal contribution value hash table and the INCRBY command is executed to update the global contribution value counter.

4. The method according to claim 1, wherein When the message processing in step S4 fails, the system re-delivers the message to the delay queue and retries according to the exponential backoff strategy of 1 second, 5 seconds, and 30 seconds. After a maximum of 5 retries, the message enters the dead letter queue.

5. The method according to claim 1, wherein The implementation of the proportional equity release strategy includes: Get the total incremental contribution value ΔG during the activity period; Calculate the contribution ratio β(i) of each user; According to the total distributable income R, calculate the income that user i should get R(i)=R×β(i); Generate a distribution list and push it to the financial system for execution.

6. The method according to claim 1, characterized in that The described ladder-type ranking weighting strategy is implemented through the Redis ordered set data structure, with contribution value as score and user ID as member. The ranking is calculated using ZADD and ZREVRANK commands, and a ranking snapshot is executed every morning and stored persistently.

7. The method according to claim 1, characterized in that It also includes a fault-tolerant degradation mechanism: When Redis cache is unavailable, downgrade to MySQL database for direct reading and writing; When the rule engine is abnormal, the default rules are enabled to ensure the operation of basic functions.

8. A dynamic equity distribution device based on user contribution value, characterized in that: include: Behavior event capture module, used to identify user interaction behaviors and construct standard request bodies; The message processing module, including the A system access layer and the B system event center, is used for message verification, encapsulation, and routing; Message queue module, used for asynchronous message storage and distribution; The rule engine module stores the rule base table and executes the contribution value calculation logic; Data storage module, including MySQL database and Redis cache, for persistent storage and caching of contribution value data; The equity distribution module performs differentiated equity distribution based on contribution ratio, ranking and threshold value.

9. An electronic device, characterized in that: include: one or more processors; a memory for storing one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors are caused to implement the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that A computer program is stored, and when the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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