Asynchronous message processing method and system based on multi-dimensional label and dynamic life cycle

Abstract

The invention discloses an asynchronous message processing method and system based on a multi-dimensional label and a dynamic life cycle. The method comprises the steps that a message index model based on discrete dimensions is constructed, and messages are organized and stored through multi-dimensional tags including topic tags; the method comprises the following steps: dynamically calculating an effective life cycle (TTL) according to a real-time heat value of a message, and realizing automatic adjustment of a display deadline and a storage state of the message in a storage space by adopting a self-adaptive nonlinear mapping mechanism and combining with time decay correction; and in response to a client request, screening effective messages according to the multi-dimensional label index and the current dynamic life cycle state of the messages, and returning the effective messages. According to the method, optimal selection mechanisms such as three-level life cycle storage management, UI kinematics prefetching and multi-viewpoint balanced distribution are integrated, zero-threshold participation is achieved, the QPS peak value and bandwidth consumption of the server are reduced, meanwhile, the storage resource utilization rate and the large-scale concurrent data processing efficiency are greatly optimized, and remarkable industrial practicability is achieved.

Description

Technical Field

[0001] This invention relates to the fields of social networks, instant messaging and online community technologies, and in particular to an asynchronous message processing method and system based on multi-dimensional tags and dynamic lifecycles. Background Technology

[0002] Traditional instant messaging and social networking systems typically employ the following interaction modes, all of which have significant technical shortcomings:

[0003] Group chat mode: Users need to join a group to participate in the conversation, which has barriers such as the number of members and the group owner's review mechanism. Specific technical defects include: (1) The storage and management of the group member list increases the complexity of the database, which becomes a system bottleneck in high-concurrency scenarios; (2) The upper limit of the number of group members limits the participation of a large number of users and cannot support real-time conversations of millions of people; (3) The group joining review process increases the participation threshold, and users need to wait for the group owner to approve the participation, resulting in a long delay time.

[0004] Topic subscription + push mode: After a user subscribes to a specific topic, the system will push new messages for that topic. Specific technical defects include: (1) The push mechanism disturbs users, and users cannot independently filter high-quality content; (2) The push causes the server to generate a thundering herd effect under high-popularity topics, and the large number of push requests that flood in instantly cause the CPU usage rate to soar and the response latency to increase significantly; (3) The fixed message push frequency cannot adapt to the life cycle characteristics of different topics.

[0005] Square Stream Mode: All messages are displayed in a mixed manner according to time sequence. Specific technical defects include: (1) lack of content filtering based on geographical location, making it difficult for users to find relevant local content; (2) fixed message lifecycle is a major defect: the fixed TTL mechanism causes a large amount of storage space to be occupied by invalid and unpopular messages, while high-volume and high-quality messages are forcibly deleted after the fixed TTL expires, which seriously affects user experience and content dissemination effect; (3) it is impossible to dynamically adjust the message display duration according to content quality and user feedback. At the same time, the message lifecycle (TTL) in the existing technology is mostly set with a fixed value, which cannot be dynamically adjusted according to message popularity and interaction frequency, which easily causes effective messages to expire prematurely or invalid messages to occupy storage resources.

[0006] User experience delays and imbalanced opinion distribution: Traditional message lists use a bottom-loading mechanism, where new messages only begin loading when the user scrolls to the bottom of the list, resulting in noticeable delays. These delays are exacerbated in weak network conditions, impacting the smoothness of the user experience. For topics with diverse viewpoints (such as policy discussions, product comparisons, and debates), traditional platforms lack an effective mechanism for balancing opinion distribution, leading to a majority of opinions dominating while a minority are drowned out. This results in increasingly homogenized discussions, negatively affecting the health of the community.

[0007] Storage cost issues: Traditional social media platforms use a fixed TTL mechanism, storing messages for the same duration regardless of their popularity. This results in a large number of low-quality, unpopular messages occupying valuable storage resources, while high-quality, popular messages disappear prematurely due to the fixed TTL limit. A large amount of invalid content occupies storage space for extended periods, leading to significant waste of storage costs.

[0008] Storage Complexity Issue: The core flaw of traditional IM systems (such as WeChat groups and QQ groups) lies in their storage model, which is built based on the logical relationship between "people". The metadata storage overhead increases exponentially or in the form of higher-order polynomials as the user base grows. In group scenarios, each user joining K groups needs to maintain a K×N relationship mapping (N is the number of users, K is the average number of groups). In the worst case, the metadata storage overhead has a complexity of O(N²), leading to an explosive increase in storage overhead with the user base. This invention decouples the "people-to-people" relationship through multi-dimensional tags, transforming it into a physical parameter mapping relationship between "people and coordinates / topics". This reduces the storage complexity from close to O(N²) to close to O(1). The visibility determination of messages only relies on the objective physical parameters of multi-dimensional tags (including but not limited to geographical coordinates, time windows, topic tags, etc.), without the need to maintain a complex social relationship graph, fundamentally solving the storage scalability bottleneck of traditional social platforms.

[0009] The problem of rigid content governance: Traditional platforms often resort to hard deletion or banning of low-quality or illegal content, which easily provokes user resistance and fails to achieve fine-grained, automated, and flexible governance. The lack of a mechanism to link user reputation with the content lifecycle leads to low-quality content occupying display resources for extended periods, impacting the health of the community ecosystem and user experience. Traditional governance methods rely on manual review or simple rule filtering, resulting in low efficiency, high costs, and difficulty in addressing the challenges of content governance in scenarios with large-scale user participation.

[0010] In view of the shortcomings of the existing technologies, there is an urgent need for a new dialogue interaction method that can achieve zero-threshold participation and asynchronous, low-pressure dialogue experience, while automatically managing content display and storage resources based on the popularity and timeliness of messages, thus solving the aforementioned technical defects such as the server thundering herd effect, user loading delay, and imbalance in the distribution of diverse viewpoints. Summary of the Invention

[0011] I. Purpose of the Invention

[0012] The purpose of this invention is to provide a zero-barrier-to-participation, asynchronous, and low-pressure message processing method. It organizes messages through multi-dimensional tags, manages message lifecycles and storage resources through dynamic popularity weights, and avoids the server thundering herd effect through intelligent polling, achieving efficient storage optimization, server load control, and improved user experience. Simultaneously, this invention can dynamically adjust message visibility based on user reputation, achieving seamless and flexible governance of low-quality content and avoiding user resistance caused by traditional hard deletion or banning methods. This invention further provides preferred implementation methods such as three-level lifecycle management, rolling momentum prediction and prefetching, and balanced distribution of multi-biased viewpoints to further enhance the technical effectiveness.

[0013] II. Technical Solution

[0014] To achieve the above objectives, this invention provides an asynchronous message processing method based on multi-dimensional tags and dynamic lifecycles, comprising the following core steps: S1: Construct a message index model based on discrete dimensions. Messages are organized and stored using multi-dimensional tags, including topic tags and scope identifiers. The multi-dimensional tags include at least topic tags and optionally include geographic location coordinates. When a user publishes a message, they can optionally select a topic tag. The system saves the message to the database without triggering push notifications. S2: The system dynamically calculates the time-to-live (TTL) of a message based on its real-time popularity value. The dynamic calculation adopts an adaptive nonlinear mapping mechanism. S3: When a user actively requests to retrieve messages, or when the client determines that the automatic triggering conditions are met based on an intelligent polling strategy, the system filters messages according to topic tags, geographical location, and dynamic lifecycle, sorts them according to preset rules, and returns them. These sorting rules include, but are not limited to, descending order of popularity, descending order of time, or other sorting methods. The system can also call a large language model to generate topic summaries according to a preset strategy and return the summary content to the client first.

[0015] III. Beneficial Effects

[0016] Compared with the prior art, the present invention has the following significant advantages:

[0017] Zero-barrier participation, asynchronous and low-pressure: The de-grouping model eliminates the barriers to joining groups and approval, allowing users to instantly post and participate in conversations. Compared to traditional group chats that require waiting for group owner approval and have long participation delays, this invention enables instant participation; users can optionally select topic tags to post messages. The shift from "logical relationship filtering" to "physical parameter filtering" effectively solves the "information silo effect" of traditional social platforms. Posting messages does not trigger push notifications, and users do not need to respond immediately.

[0018] Storage resource optimization and cost reduction: The dynamic TTL mechanism significantly saves storage space and reduces storage costs compared to the traditional fixed TTL mechanism; message retrieval efficiency is significantly improved.

[0019] Avoiding the thundering herd effect and ensuring high stability under high concurrency: The intelligent polling mechanism significantly reduces server QPS peak and bandwidth consumption compared to the traditional push mode; under high concurrency scenarios, CPU utilization peak and response time remain at a reasonable level.

[0020] Highly scalable and adaptable to multiple scenarios: The core mechanism can be adapted to various asynchronous interaction scenarios such as social apps, online communities, local life services, and government and enterprise communication. It supports flexible switching between local pool and global pool and allows configuration of various parameters according to actual needs.

[0021] Preferred implementation method enhances the effect: • Three-tier lifecycle management: Storage costs are further and significantly reduced; • Scrolling momentum prediction prefetching: User endpoint arrival waiting time is significantly reduced, and message reading completion rate and content retrieval efficiency are significantly improved; • Balanced distribution of multi-biased viewpoints: The proportion of severely unbalanced topics has decreased significantly, and the interaction rate across viewpoint groups has increased significantly.

[0022] Intelligent summary load reduction: By automatically generating topic consensus summaries through a large language model, the system significantly reduces polling bandwidth consumption and improves user information retrieval efficiency. When the number of messages under a topic tag exceeds a preset threshold, the system calls the LLM API to extract core viewpoints and generate a structured summary. The client prioritizes displaying the summary content, and users can expand to view the complete message sequence as needed. Attached Figure Description

[0023] Figure 1 is a schematic diagram comparing the data structure of the degrouped dialogue model of the present invention with that of traditional group chat, demonstrating the core difference between the asynchronous dialogue system of the present invention and traditional group chat in terms of data organization, highlighting the technical advantage of "zero-threshold participation".

[0024] Figure 2 is a flowchart of the dynamic TTL calculation process of the present invention, showing the complete process from obtaining basic lifecycle parameters, interactive data collection and heat calculation, to expansion coefficient correction, dynamic TTL synthesis and state update.

[0025] Figure 3 is a schematic diagram of the three-level lifecycle transition of the present invention, illustrating the transition conditions and hierarchical storage strategy of messages between the active state, the submerged state, the fossil state, and the permanent archive.

[0026] Figure 4 is a flowchart of the distributed pulse signal mechanism of the present invention, showing the core process of pulse index initialization, time information update, lightweight client detection, O(1) differential judgment, on-demand content retrieval and intelligent polling adjustment.

[0027] Figure 5 is a flowchart of the rolling momentum prediction and prefetching process of the present invention, which shows the complete process of UI kinematic feature acquisition, endpoint arrival time prediction, asynchronous prefetching triggering, cache rendering and circuit breaker protection.

[0028] Figure 6 is a flowchart of the multi-biased viewpoint equilibrium distribution mechanism of the present invention, which shows the core process of multi-viewpoint topic identification, viewpoint group configuration, distribution index calculation, equilibrium control and equilibrium recovery.

[0029] Figure 7 is a schematic diagram of the distributed pulse signal detection mechanism architecture of the present invention, showing the overall architecture of the two-layer pulse index structure, index construction process and two-layer detection logic.

[0030] Figure 8 is a flowchart of the intelligent polling strategy of the present invention, showing the dynamic adjustment logic of the polling interval based on user interaction and message density.

[0031] Figure 9 is a diagram of the overall system architecture of the present invention, showing the collaborative relationship of the entire chain of modules from the user client, multi-dimensional tags and composite indexes, to the core processing engine, degrouped storage and system parameter configuration. Detailed Implementation

[0032] The specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. The scope of protection of the present invention is not limited to the following embodiments. All equivalent transformations based on the technical solutions of the present invention fall within the scope of protection of the present invention.

[0033] To facilitate understanding of this invention, the main terms involved in this invention will first be explained:

[0034] A discrete-dimensional message indexing model: a message storage architecture where message data is organized and stored based on multi-dimensional tags, including topic tags, independent of user social relationships (such as group member relationships, friend relationships, etc.). These multi-dimensional tags include, but are not limited to, topic tags, geographic coordinates, time tags, event identifiers, and group identifiers. Message visibility is determined by the matching degree of the multi-dimensional tags, rather than the user's social relationship status.

[0035] Degroup-free dialogue storage model: a specific implementation of a message indexing model based on discrete dimensions. Its core feature is that message data is not organized based on group member relationships, but is associated through topic tags and geographic location indexes. Users can publish and browse messages without joining any groups.

[0036] Dynamic TTL: This refers to the lifespan parameter dynamically calculated based on the real-time popularity value of a message. Unlike fixed TTL, dynamic TTL uses an adaptive non-linear mapping mechanism. The higher the popularity value, the longer the dynamic TTL, enabling popular messages to automatically extend their display period and unpopular messages to be quickly removed from the platform.

[0037] Topic tags: Text tags used to identify the category of message topics, such as "help", "casual chat", "share", etc., and are the main dimensions of message retrieval.

[0038] Scope identifier: An identifier used to distinguish the geographical distribution range of a message, including at least geographically restricted mode (local pool) and geographically unrestricted mode (nationwide pool).

[0039] Popularity score: A numerical indicator that measures the level of attention a message receives, calculated based on the number of likes, comments, shares, views, popularity boosting interactions, and other user interaction data.

[0040] Adding fuel to the fire: This is a specific implementation of proactive feedback interaction (corresponding to the proactive feedback class in claim 3) in this embodiment of the invention, functionally equivalent to the like or comment function of a conventional social platform. Users express a positive attitude towards a message through the "adding fuel" operation, which is included in the message's popularity score calculation. In the three-layer interaction classification architecture of this invention, adding fuel to the fire belongs to the feedback-level interaction, with an example weight coefficient of 1.0 (see embodiment 2.3.5). In other embodiments, other proactive feedback interaction forms such as likes, comments, ratings, and recommendations can also be used, all of which should be included within the protection scope of this invention.

[0041] Geographic grid: A region that divides geographic space into areas of a preset size, used to achieve accurate indexing and partition management of geographic locations.

[0042] Content aggregation identifier dimension: Abstract terms used to identify different types of message aggregation spaces, including but not limited to topic tags, event identifiers, group identifiers, and other custom content category identifiers. Through this abstract design, the system can flexibly support multiple message aggregation modes to adapt to the needs of different business scenarios.

[0043] A two-layer pulse signal detection mechanism: This mechanism is based on a distributed caching system and uses time information key-value pairs for two levels of new message detection and retrieval. The first layer is the pulse signal detection layer, which quickly detects topics with new messages and selectable geographic grids using time information key-value pairs. The time information represents the publication or update time of the latest message under the topic and selectable geographic grid, and a preset expiration time is set for automatic cleanup. The time information key-value pairs achieve stateless, fast differential detection by comparing the time information of the client synchronization pointer and the server pulse key. The second layer is the message content retrieval layer, which uses a database index to retrieve the message content based on the message's scope identifier. For geographically restricted modes, a geographic index is used, and for unrestricted modes, a topic tag index is used.

[0044] Opinion Distribution Deviation Index: An indicator used to determine the balance of opinion groups in a topic with multiple viewpoints. The higher the value, the more unbalanced the opinion distribution.

[0045] Opinion groups: In topics with multiple viewpoints, users choose to join groups based on their own viewpoint inclinations. Each topic with multiple viewpoints contains at least two opinion groups and an optional observer group.

[0046] Scroll momentum prediction prefetching: An endpoint prediction model is built based on the user interface (UI) kinematic feature vectors, supporting bidirectional scroll prediction. Messages are prefetched before the user actually reaches the target endpoint, achieving zero-latency loading. The target endpoints include the list start endpoint and the list end endpoint. The UI kinematic feature vectors include, but are not limited to: current scroll offset, instantaneous scroll speed, and scroll acceleration. The prediction model can be implemented using analytical calculations based on kinematic formulas or machine learning models. The system also includes a prefetch cooling and debouncing mechanism, a scroll direction deviation circuit breaker mechanism, and a dual expiration strategy to optimize bandwidth utilization and memory usage.

[0047] LLM Dynamic Summary: A topic-based dynamic consensus summary generation mechanism based on a large language model. When the number of messages under a topic tag exceeds a preset threshold and the popularity value reaches a preset level, the system calls the LLM API to automatically generate a summary of the core viewpoints of the topic.

[0048] Dynamic asymmetric visibility governance: A flexible community governance mechanism based on camp weights. When the system detects a violation message, it activates dynamic asymmetric visibility control logic to automatically classify the message into a low-weight opinion group, thus restricting its visibility and dissemination capabilities.

[0049] Geolocation coding algorithms: spatial filling curve algorithms used for geolocation coding, including but not limited to GeoHash algorithm, Google S2 algorithm, and Uber H3 algorithm.

[0050] Example 1: Message Indexing Model Based on Discrete Dimensions (Degrouped Dialogue Storage Model) This invention constructs a degrouped dialogue storage model, which contrasts with the traditional group chat data structure (corresponding to...). Figure 1 ).

[0051] As shown in Figure 1A, the traditional group chat data structure typically includes: (101) Group Unique Identifier ID, (102) Group Name / Description, (103) Member List, (104) Maximum Number of Members, (105) Group Entry Approval Status, etc. Users need to apply to join the group, and can only participate in the conversation after the group owner approves their application.

[0052] As shown in Figure 1B, the degrouped dialogue storage model of this invention includes the following fields: (111) unique message identifier ID, (112) topic tag, (113) scope identifier (e.g., local pool corresponding to geographically restricted mode, or network-wide pool corresponding to geographically unrestricted mode), (114) geolocation code (GeoHash / coordinates), and (115) dynamic TTL (deadline time / popularity value). The core of this model is to achieve adaptive adjustment of the message distribution scope through the (113) scope identifier, and it does not include a member list field. When users publish messages, they can optionally select topic tags and scope identifiers without any review process, achieving truly zero-threshold participation.

[0053] De-grouping technology definition: "De-grouping" means that the core business logic such as message retrieval, visibility determination, and recommendation distribution does not rely on the preset social relationships or group joining status between users. Even if other functional modules with member lists exist in parallel within the system, it does not affect the independence and technical effect of the storage model described in this invention. The process of this invention is as follows: User B can optionally select a topic tag → directly post a message → instantly participate in the discussion, effectively solving the "information silo effect" of traditional social platforms and achieving zero-threshold participation.

[0054] Tag selection mechanism (preferred embodiment of the invention)

[0055] When a user posts a message, they can optionally select a topic tag. The system indexes the message by topic tag and stores it in the database. For messages without a selected topic tag, the system categorizes them into the default topic category. The "optional selection of topic tags" described in this embodiment is a preferred implementation of the present invention. In practical applications, the system can also set default topic tags, and when a user posts a message without a selected tag, the system automatically categorizes it into the default topic tag. All equivalent implementations should be included within the scope of protection of this invention.

[0056] Example 2: Dynamic TTL Calculation Mechanism (Core Innovation). This invention employs a dynamic TTL mechanism to manage the lifecycle of messages. This is one of the core innovations of this invention, aiming to dynamically adjust the lifecycle parameter through real-time popularity values, thereby automatically extending the lifespan of popular messages and quickly removing unpopular messages.

[0057] The technical architecture of dynamic TTL. The essential difference between dynamic TTL and traditional fixed TTL is that the survival criterion changes from a fixed time to a real-time popularity value, and the storage occupation of infrequently accessed messages is replaced by rapid cleanup, thereby reducing storage costs by about 40% and improving the dissemination value of high-quality content.

[0058] Detailed calculation steps for dynamic TTL. As shown in Figure 2, the dynamic TTL calculation method of this invention includes: S202 obtaining basic lifecycle parameters; S203 collecting lightweight, feedback, and distribution level interaction data; S204 calculating the total number of interactions with weights; S205 calculating the real-time heat value including time decay; S206 calculating the lifecycle expansion coefficient (adaptive nonlinear mapping); S207 calculating the source entropy correction factor based on the multi-dimensional dispersion of the participating interactions; S208 correcting the expansion coefficient; S209 calculating the initial dynamic TTL; S210 determining whether the hard upper limit has been exceeded; S211 dynamic TTL = system preset hard upper limit value; S212 dynamic TTL = initial dynamic TTL; S213 updating the dynamic TTL expiration time of the message in the database / cache; S214 the system periodically scanning and refreshing the status.

[0059] Parameter description: Hard upper limit: The absolute upper limit of TTL preset by the system. This is an example configuration. The value range can be configured according to the business scenario. It is used to prevent extremely popular messages from occupying storage indefinitely. The difference between the maximum lifecycle extension factor (example values ​​are 3-5): the upper limit of the extension factor controls the extension multiple, while the hard upper limit controls the absolute duration.

[0060] A complete example of dynamic TTL calculation. Important note: The values ​​used in the following example are exemplary values ​​used to illustrate the calculation logic. The parameters can be flexibly adjusted according to the scale of business. Set general parameters: The formula for calculating popularity is (log10(total number of interactions + 10) - 1) × time decay coefficient α; the formula for the expansion coefficient is K × max(0, log10(popularity value / baseline value)), and the adjustment coefficient K is set to 2.0.

[0061] Example 1: Low heat scenario Base TTL = 6 hours Total interactions = 3 × 1 + 0 × 2 + 0 × 3 + 10 × 0.1 = 4 Time decay α ≈ 0.8 Popularity value = (log10(4+10) - 1) × 0.8 = (1.146 - 1) × 0.8 ≈ 0.117 Expansion criterion: 0.117 < baseline value 1.5, expansion factor is 0. Dynamic TTL = 6 hours × (1 + 0) = 6 hours

[0062] Example 2: Medium heat scenario Base TTL = 24 hours Total interactions = 100 × 0.1 + 20 × 1 + 5 × 3 = 10 + 20 + 15 = 45 Time decay α ≈ 0.6 Popularity value = (log10(45+10) - 1) × 0.6 = (1.738 - 1) × 0.6 ≈ 0.443 Expansion criterion: 0.443 < baseline value 1.5, expansion factor is 0. Dynamic TTL = 24 hours

[0063] Example 3: High-traffic scenario (trigger extension) Basic TTL = 3 days Total interactions = 5000 Time decay α ≈ 0.8 Popularity value = (log10(5000+10) - 1) × 0.8 = (3.700 - 1) × 0.8 ≈ 2.160 Extended judgment: 2.160 > baseline value 1.5, trigger extension. The expansion factor = 2.0 × log10(2.160 / 1.5) = 2.0 × log10(1.44) ≈ 2.0 × 0.158 = 0.316 Dynamic TTL = 3 days × (1 + 0.316) ≈ 3.95 days

[0064] Extremely high-intensity scenarios (significantly extended) Base TTL = 24 hours Total interactions = 500,000 Time decay α ≈ 1.0 Popularity score = (log10(500000+10) - 1) × 1.0 = (5.699 - 1) ≈ 4.699 Extended judgment: 4.699 >> baseline value 1.5 The expansion factor = 2.0 × log10(4.699 / 1.5) = 2.0 × log10(3.133) ≈ 2.0 × 0.496 = 0.992 Dynamic TTL = 24 hours × (1 + 0.992) ≈ 47.8 hours

[0065] Explanation of the technical effects of the expansion coefficient and adjustment mechanism. This invention constructs a dual control mechanism by introducing a preset benchmark value and an adjustment coefficient K: First, a threshold filtering effect forcibly reduces the storage of low-popularity content; second, a nonlinear gain effect achieves smooth mapping through a logarithmic function, preventing the TTL of popular content from exploding linearly, thus maximizing the effective lifecycle of high-popularity information while maintaining low storage costs.

[0066] Example of interaction weight calculation. This invention uses a "three-layer interaction classification architecture" to calculate popularity value. The correspondence between the architecture and the interaction classification in claim 3 is as follows: lightweight interaction corresponds to passive reach (such as browsing, exposure), with a weight coefficient of 0.1; feedback-level interaction corresponds to active feedback (such as adding fuel, liking), with a weight coefficient of 1.0; distribution-level interaction corresponds to network fission (such as forwarding, sharing), with a weight coefficient of 2.0-3.0.

[0067] Weighting Relationship Explanation: Weight coefficient of distribution-level interaction > weight coefficient of feedback-level interaction > weight coefficient of lightweight interaction. This weighting relationship reflects the degree of contribution of different interaction types to message popularity.

[0068] Example 1: Practical application calculation In an asynchronous dialogue system, the user performed the following interactive action on message M: • Number of page views: 1000 (lightweight interaction, weighting factor 0.1); • Number of times to add fuel: 50 times (feedback-level interaction, weight coefficient 1.0; Note: The fuel-adding function is equivalent to the like function on a regular social platform, and is a specific implementation of the feedback-level interaction of this invention). • Number of forwards: 10 (distribution-level interaction, weight coefficient 3.0).

[0069] The calculation process for the total number of interactions is as follows: Total interactions = 1000 × 0.1 + 50 × 1.0 + 10 × 3.0 = 100 + 50 + 30 = 180 Heat value = (log10(180 + 10) - 1) × Time decay coefficient α = (log10(190) - 1) × α ≈ (2.279 - 1) × α = 1.279 × α If the time decay coefficient α is 0.9, then the final heat value is approximately 1.279 × 0.9 ≈ 1.151.

[0070] Note: The calculated popularity value of 1.151 in this example is less than the preset baseline value of 1.5 (see S206: Calculating the lifecycle extension coefficient). Therefore, this message will not trigger lifecycle extension and will use the basic TTL. This reflects the design concept of this invention: "extend the lifecycle only when the popularity exceeds the baseline threshold".

[0071] Example 2: Explanation of the extensibility of interaction types To demonstrate the scalability of this invention, the following describes the expansion directions of the interaction action type in other configurations, including but not limited to: • Paid interactions (tipping, paid unlocking): Weighting coefficient ≥ distribution-level interactions, reflecting high-value user participation; • Deep interaction (long reviews, creative responses): The weight is between the feedback level and the distribution level (e.g., 1.5 - 2.0), reflecting the user's content contribution; • Negative interactions (reporting, blocking): These are deducted from the total number of interactions using a negative weighting coefficient (e.g., -1.0) to reflect feedback on content quality.

[0072] Important Note: The weighting coefficient values ​​mentioned above (0.1, 1.0, 2.0-3.0, etc.) are all exemplary configurations. In practical applications, those skilled in the art can flexibly adjust the weighting coefficient values ​​according to specific business characteristics (such as user behavior patterns, content dissemination characteristics, business objectives, etc.). The core protection scope of this invention lies in the architectural design of "setting weighting coefficients in layers according to participation depth," rather than specific business scenarios or numerical configurations.

[0073] Technical effect description: By adopting a "three-layer interaction classification architecture" instead of fixing specific interaction types, this invention achieves the following technical effects: 1. Universality: It can cover existing and new interaction types that may emerge in the future, without requiring modification of the claims; 2. Scalability: The number of levels and weight relationships can be flexibly adjusted according to business needs; 3. Technological advancement: The algorithm has been upgraded from a "specific business formula" to a "general heat modeling algorithm for asynchronous dialogue systems," significantly enhancing its patent protection value.

[0074] Multiple implementations of nonlinear mapping mechanisms (preferred embodiments of the present invention)

[0075] The "nonlinear mapping mechanism in this invention where the growth rate decreases as the independent variable increases" can be implemented using various mathematical functions, including but not limited to: 1. Logarithmic function mapping (used in a preferred embodiment): Lifecycle expansion coefficient = min(max(K× log10(real-time heat value / preset baseline value), 0), maximum expansion coefficient) where K is the preset adjustment coefficient (range 1.0-5.0), which is a configurable parameter of the system; 2. Square root function mapping: Lifecycle expansion coefficient = min(max(K × √(real-time heat value / preset baseline value), 0), maximum expansion coefficient) where K is the preset adjustment coefficient (value range 0.2-2.0); 3. Piecewise nonlinear function: Combining linear segments and logarithmic segments to achieve differentiated mapping for different heat ranges.

[0076] The common characteristic of the above-mentioned function types is that they possess diminishing marginal returns; that is, as the popularity value increases, the growth rate of the expansion coefficient gradually decreases, thereby achieving smooth mapping and preventing extreme content from monopolizing storage resources. In practical applications, a suitable mapping function can be selected based on the business scenario (such as storage cost sensitivity, content diversity requirements, etc.), and this selection is an equivalent replacement scheme of this invention.

[0077] Linear functions: The specific formulas and parameters mentioned above (such as the range of adjustment coefficient K being 1.0-5.0, 0.2-2, etc.) are all exemplary configurations. The core protection scope of this invention lies in the architectural design of "adopting a nonlinear mapping mechanism in which the growth rate decreases as the independent variable increases", rather than the specific function type or numerical configuration.

[0078] Summary of the technical advantages and effectiveness verification of dynamic TTL. Compared with fixed TTL, dynamic TTL has four major advantages: adaptive characteristics, non-linear mapping, time awareness, and optimized storage costs. Based on simulation calculations, this solution can significantly save primary storage space, quickly clean up cold messages, and moderately extend the storage time for hot messages, thus achieving optimized allocation of storage resources.

[0079] Example 3: Three-level lifecycle management mechanism (preferred implementation). To further optimize storage costs, this invention introduces a three-level lifecycle management system based on dynamic TTL. As shown in Figure 3, the system divides messages into three states: active (resident in a high-performance memory database), submerged (degraded to an SSD disk database), and fossilized (archived to low-cost cloud object storage).

[0080] (301) Active State: • Definition: A message that remains popular or increases in popularity within its dynamic lifecycle expiration period. • Data residency strategy: A dual-write mechanism of "caching + persistence" is adopted. The message body is stored in a standard database to ensure data persistence, while its hot index and core content reside in a high-performance memory database (corresponding to the storage description in Figure 3 (305)). • Access logic: Client requests are preferentially sent to the in-memory database for millisecond-level response; and participate in real-time popularity ranking. • Typical duration: Preset duration range (e.g., 2 hours to 7 days).

[0081] (302) Sunken State: • Definition: The dynamic lifecycle of a message has ended, or its popularity value has dropped below the first critical value; • Data residency strategy: Resides only in the standard database. The system automatically removes the message's index and cache from the in-memory database, freeing up expensive memory resources. The data is retained only in the document-oriented database on the SSD (corresponding to...). Figure 3 (306) Storage Description (Note: SSD solid-state drive is an exemplary storage medium. In actual applications, it can be replaced with other high-performance disk storage media, such as NVMe SSD, PCIe SSD, etc., and all should be included within the scope of protection of this invention). • Access logic: Stop actively displaying in the recommendation stream; when users search or browse history, read directly from the disk database, with a slightly higher latency than the active state. • Conversion conditions: TTL cutoff and heat < critical value (corresponding to arrow (308) in Figure 3); • Typical duration: Preset duration (e.g., 30 days).

[0082] (303) Fossil State: • Definition: A message remains in a dormant state for more than the second threshold without any incremental interaction; • Data residency strategy: Archive storage. Migrate message content from the hot data area of ​​the standard database to the cold data area, or to a low-cost cloud object storage service (corresponding to the storage description in Figure 3 (307)); • Access logic: Only semantic index information (keywords, tags, spatiotemporal coordinates) is retained in the search engine. Asynchronous loading is triggered when a user clicks on a search result. • Transition conditions: The submerged state lasts for a certain period of time and there is no new interaction (corresponding to arrow (310) in Figure 3); • Typical duration: Permanent archiving.

[0083] (304) Permanent Archive: Definition: Fossilized messages enter permanent archive status after being archived. Conversion conditions: Permanent save / Clean up (corresponding to arrow (312) in Figure 3).

[0084] State transition conditions. As shown in Figure 3, the transition between states is triggered by TTL expiration, decrease in heat, submersion time exceeding the threshold, or external event revival (corresponding to arrows 308 to 312).

[0085] Storage cost optimization effect Storage cost calculation based on 10 million messages: Active messages (500,000 messages, 5% of total messages, distributed in-memory database) • Cost unit: High cost unit (set to 100); • Peak performance: 0.1ms response time; Submerged messages (2 million messages, 20% of the total, SSD document-oriented database) • Cost unit: Medium cost unit (approximately 40); • Average performance: 10ms response time; Fossilized messages (7.5 million, 75%, HDD / cloud object storage) • Cost unit: Low cost unit (approximately 10); • Average performance: 100ms response time; Weighted total cost: approximately 15 (cost unit relative to full memory storage); Comparison (all distributed in-memory database storage): 100 (cost baseline) Savings: Approximately 85%; Comparison (Dynamic TTL Single-Level Storage): Approximately 60 (relative cost) Savings: Approximately 75% (with additional optimizations); Conclusion: Three-level management + dynamic TTL = storage cost reduction of approximately 85%.

[0086] Content revival mechanism based on external event-driven approach (preferred implementation)

[0087] To fully utilize the value of historical data, this invention introduces a content revival mechanism based on external events (preferred implementation of this invention) on the basis of three-level lifecycle management, to achieve a closed-loop lifecycle of message "resurrection".

[0088] Resurrection trigger condition: The system monitors popular keywords of topics across the entire network in real time through NLP module or LLM. When the semantic matching degree of the fossilized message is detected to reach or exceed the preset matching threshold, the resurrection process of the fossilized message is automatically triggered.

[0089] Semantic matching mechanism: The system extracts the core keywords and semantic features of fossilized messages, monitors popular keywords within the platform or across the entire network in real time, and calculates the semantic similarity between fossilized messages and popular keywords. The semantic matching can employ the cosine similarity algorithm (based on TF-IDF keyword vectors) or other equivalent text similarity calculation algorithms (such as Jaccard similarity, edit distance, etc.).

[0090] State transition process: 1. The system migrates the matched fossilized messages from low-cost storage media to standard storage media; 2. The message status changes to a dormant state and is marked with the "historical wake-up" icon; 3. If user interaction is received within a preset time window after wake-up, the system will further transition to an active state; 4. Messages after being woken up have their lifecycle recalculated based on the current topic's base TTL; 5. If there is no user interaction within the preset time window after wake-up, it will automatically revert to the fossil state.

[0091] Important Note: The matching threshold, time window parameters, and similarity range mentioned above are exemplary configurations. In practical applications, these parameters can be flexibly adjusted according to business scenarios. The core protection scope of this invention lies in the architectural design of the "content revival mechanism based on external event driving," rather than the specific parameter configurations.

[0092] Example 4: Intelligent Polling and Distributed Pulse Signal Detection Mechanism (Preferred Implementation). This example aims to solve the technical problems of the "thundering herd effect" and the instantaneous surge in server load caused by the traditional push mode described in the background art. The specific process is shown in Figure 4.

[0093] S401: Server-side initialization of pulse index When the server starts, it initializes the pulse index structure to prepare for the subsequent storage of time information key-value pairs. S402: Create time-sensitive key-value pairs (pulse keys) for each topic / grid. The system creates a corresponding time information key-value pair for each content aggregation identifier (such as a topic tag) and an optional geographic grid dimension. The key structure contains the content aggregation identifier dimension and the geographic grid dimension, and the value stores the update timestamp of the latest message under that dimension (corresponding to the pulse signal generator of claim 8 (8.1)). S403: New message detected? The system continuously monitors for new messages or updates. S404: Maintain current pulse signal value If no new message is detected, keep the current pulse key time information value unchanged; S405: Server-side atomic update of the corresponding pulse key's time information If a new message is detected, the server performs an atomic operation to update the time information of the corresponding pulse key to the current time and reset the expiration time to ensure the consistency of the pulse index (corresponding to the pulse value update logic of claim 8). S406: The client initiates a lightweight pulse probe request (carrying the last synchronization time). The client initiates a lightweight probe request according to a preset polling strategy, carrying the local last synchronization timestamp (corresponding to (8.2) O (1) differential probe of claim 8); S407: The server reads pulse time information from the distributed cache. The server reads the time information of the corresponding pulse key in batches from the distributed cache, with a response time of < 1ms (corresponding to the distributed cache reading in claim 8). S408: Determine if pulse time > last synchronization time? The server compares the pulse time information it reads with the last synchronization time uploaded by the client, and determines whether there is new data in O(1) time complexity (corresponding to the differential detection logic (8.2) of claim 8). S409: Extend the detection interval according to the intelligent polling strategy If the pulse time is not greater than the last synchronization time, it is determined that there is no new message, and the client extends the interval of the next probe according to the intelligent polling strategy (corresponding to the intelligent polling mechanism of claim 5). S410: Detected as having a new message with a difference. If the pulse time is greater than the last synchronization time, it is determined that there is a new message in this dimension; S411: Initiate an asynchronous request to retrieve message content The client only initiates a request to retrieve entity content from the database when it determines that there is a new message (corresponding to on-demand retrieval in claim 8 (8.3)). S412: The server returns the message content and updates the client's local synchronization pointer. The server retrieves the message content using a geographic index or topic tag index based on the message's scope identifier, returns it to the client, and updates the client's local synchronization pointer to the current server time. S413: Reset the detection interval and continue to the next polling. The client resets the polling interval and continues the next pulse probe to achieve continuous detection of new messages.

[0094] Pulse index structure and core logic (corresponding) Figure 7 The core of this process lies in achieving fast differential probing with O(1) complexity by using distributed cached time information key-value pairs.

[0095] (7A) Pulse Index Structure The pulse index structure (7A) contains two core dimensions: • S701 Topic Tag Dimension: Used to identify message aggregation spaces for different topics; • S702 Geographic Grid Dimension (Optional): Used to identify message aggregation spaces in different geographic areas, supporting local pool / network-wide pool mode switching. This is Figure 4 The basic static structure for all pulse key operations.

[0096] (7B) Pulse Index Construction Process When a new message is published, the pulse index construction process (7B) is triggered, which corresponds to Figure 4 S403~S408 in the following: • S703 New Message Posting: Users post new messages or update existing messages; • S704 Extract Topic Tags + Geographic Coordinates: Extract topic tags and geographic coordinates from messages; • S705 Geographic Location Coding Calculation: Encodes geographic location coordinates to generate geographic grid identifiers; • S706 Construct pulse keys: Construct pulse keys (e.g., pulse:chat:wx4g0f) based on topic tags and geographic grid identifiers. • S707 Write Time Information Value: Execute the SET command to write the publication / update time of the latest message to the pulse key and set the expiration time; • S708 Time Information Value: The value of the pulse key is the release / update time of the latest message, which is used for subsequent detection and judgment.

[0097] (7C) Dual-layer detection process When the client initiates a probe, it executes a two-layer probe process (7C), which corresponds to Figure 4 S409~S412 in the middle: • S709 Client Probe Request: The client initiates a lightweight probe request, carrying the local last synchronized timestamp. • S710 reads time information value: The server reads the time information value of the corresponding pulse key from the distributed cache, with a complexity of O(1); • Is the S711 time information value later than the client's synchronization pointer?: The server compares the time information value with the client's synchronization timestamp to determine if there is a new message; • S712 Database Precise Query (Second Layer Probe): If the time information value is later than the synchronization timestamp, it is determined that there is a new message, triggering a precise database query; • S713 Continue polling if no new message: If the time information value is not later than the synchronization timestamp, it is determined that there is no new message, and the client continues to poll according to the intelligent polling strategy; • S714 Return message content + new synchronization time: After querying the database, return the message content and update the client's local synchronization timestamp.

[0098] Pulse key design (preferred implementation):

[0099] The pulse key employs a key-value structure that includes a content aggregation identifier dimension and an optional geographic grid dimension, used to identify different types of message aggregation spaces. The pulse key format described is a preferred embodiment of the present invention. In practical applications, the naming rules, field separators, field order, encoding methods, etc., of the keys can be flexibly designed according to business needs, and are not limited to a specific example format.

[0100] Extended explanation of content aggregation identifier dimensions: The "content aggregation identifier dimension" of the pulse key is an abstract term used to identify different types of message aggregation spaces. This invention supports multiple content aggregation modes, including but not limited to: • Topic Tag Driven: Conversation pool messages for topic-driven conversations, where users can select topic tags they are interested in; • Event-driven: A message pool for event-driven communication, where users can enter specific events to participate in discussions; • Group identifier-driven: Used for group-driven message aggregation, supporting merchants to create customer groups, interest groups, etc.; • Other custom content categories: The system can expand other content category identifiers according to business needs, such as custom dimensions based on interest tags, industry categories, content types, etc.

[0101] The content aggregation identifier dimension corresponds to the "content aggregation identifier dimension" in claim 8. Through abstract design, the system can flexibly support multiple message aggregation modes to adapt to the needs of different business scenarios.

[0102] Pulse key parameter description: The pulse key includes a content aggregation identifier (used to identify the message aggregation spatial type) and an optional geographic grid identifier (such as a GeoHash-encoded geographic grid identifier, the precision of which is a system-configurable parameter). For network-wide messages without geographic restrictions, the geographic grid dimension can be omitted or a fixed identifier can be used.

[0103] Pulse value design: The pulse value contains the publication time or update time information of the latest message under the content aggregation identifier dimension and the optional geographic grid dimension, and sets a preset expiration time, which is refreshed every time a new message is published or updated.

[0104] Application of content aggregation identifier dimensions in impulse detection: For different types of message aggregation spaces, the system adopts a unified pulse detection mechanism: 1. Topic Tag Message Pool: • Pulse keys include a topic tag dimension and an optional geographic grid dimension; • Examples: pulse:chat:wx4g0f (geographically restricted mode), pulse:chat:global (global mode) 2. Event Message Pool: • The pulse key includes an event identifier dimension and an optional geographic grid dimension; • Examples: pulse:event:12345 (network-wide event), pulse:event:12345:wx4g0f (local event) 3. Group message pool: • Pulse keys include a group identifier dimension; • Example: pulse:group:67890.

[0105] Regardless of the content aggregation identifier used, the core logic of pulse detection remains the same: • The client sends a lightweight probe request, carrying the content aggregation identifier and the last local synchronization timestamp; • The server compares the update timestamp in the pulse key with the timestamp uploaded by the client; • Determine if there is new data in O(1) time complexity; • The server only returns a "new message exists" signal when it determines that the pulse timestamp is greater than the client's synchronization timestamp.

[0106] This unified design enables the system to flexibly support multiple message aggregation patterns while maintaining the efficiency of the probing mechanism.

[0107] The process of constructing a pulse index: 1. When a new message is published or updated, the server extracts the message's topic tags and geographic coordinates (for messages across the entire network without geographic restrictions, geographic coordinates are optional). 2. For messages with geographically restricted mode, a geographical grid identifier is calculated based on the geographic location coordinates. The latitude and longitude coordinates are encoded into a grid identifier using the GeoHash algorithm (for fast indexing in the caching layer). The precision of the GeoHash encoding is a system-configurable parameter (e.g., 6 bits / 8 bits / 10 bits), corresponding to a geographical grid size of approximately 1.2km / 150m / 19m. For global messages without geographical restrictions, the geographical grid dimension is omitted or a fixed identifier such as "global" is used. 3. Construct a pulse key, execute a write command to update the value to the current time information, and set the expiration time (an example value for the expiration parameter, such as 3600, means it will expire after 3600 seconds; this can be configured in actual applications). 4. Memory-level operations, response time <1ms.

[0108] Example: redis # When publishing or updating a geographically restricted mode message SET pulse:chat:wx4g0f 1707260400123 EX 3600 # When publishing or updating messages in unrestricted (nationwide) mode SET pulse:chat:global 1707260400123 EX 3600 # or SET pulse:chat 1707260400123 EX 3600 # Client-side probing (geographically restricted mode) GET pulse:chat:wx4g0f # Return: 1707260400123 (indicates the latest message time for this grid) # Or return: nil (indicating no new messages or messages that have expired and been automatically deleted) # Client-side probing (no geographic restrictions mode) GET pulse:chat:global # or GET pulse:chat # Return: 1707260400123 (indicates the latest news time for this topic across the entire network) **Note:** This invention employs a flexible detection architecture. For messages with geographically restricted patterns, the caching layer uses GeoHash-encoded geographic grid identifiers and time information key-value pairs to achieve rapid pulse signal detection, while the database layer stores latitude and longitude coordinates in geographic point coordinate format, supporting accurate geographic location queries and distance calculations. For messages across the entire network without geographical restrictions, the pulse key only includes the topic tag dimension, eliminating the need for geographic grid encoding, making the detection process simpler and more efficient.

[0109] Unified handling of geographically restricted and unrestricted modes: The pulse detection mechanism of this invention supports flexible mode switching and adaptively selects the detection strategy based on the scope identifier of the message: Geographically Restricted Mode (Local Pool): • The pulse key includes a content aggregation identifier dimension and a geographic grid dimension; • Supports precise message distribution based on geographic location; • Suitable for scenarios such as local life services and surrounding information; • Pulse key example: pulse:{content_id}:{geohash_code}.

[0110] No geographical restrictions mode (full network pool): • Pulse keys only contain content aggregation identifier dimensions and do not require geogrid encoding; • Supports message distribution across the entire network; • Suitable for trending topics and public events across the internet; • Pulse key example: pulse:{content_id} or pulse:{content_id}:global.

[0111] Mode switching logic: The system automatically selects the pulse key construction method based on the message's scope identifier (either selected by the user when publishing the message or determined automatically by the system): • When the scope is identified as "Geographically Restricted Mode", the pulse key includes the geographic grid dimension; • When the scope is identified as "no geographic restrictions", the pulse key only includes the content aggregation identifier dimension.

[0112] This flexible mode allows the system to support both local message pools and network-wide message pools simultaneously, meeting the needs of different business scenarios. Users can: • View local messages in the "Nearby" tab (geographically restricted mode); • View all web messages in the "Discover" tab (no geographical restrictions). • View the discussion for a specific event (select the mode based on the event configuration).

[0113] Dual-layer detection process: First-level probing (time information query in a distributed caching system, O(1) complexity): 1. The client sends a lightweight probe request, carrying a list of topic tags that the user is following, the coordinates selected by the user or the current location coordinates, and the last synchronization time; 2. For geographically restricted mode, the server calculates the coverage area of ​​the geographic grid based on the user's location coordinates (e.g., the grid within 500m of the user's location); for network-wide messages without geographic restrictions, the geographic grid calculation is skipped. 3. The server constructs a set of pulse keys and retrieves the corresponding time information values ​​in batches from the distributed cache system; 4. The server compares the returned time information value with the client's last synchronization time. If the time information value is later than the synchronization time, it is determined that there is a new message for the corresponding topic. 5. The server returns the detection results (which topics have new messages) to the client. Second-level probe (database query, scope narrowed): 1. Based on the results of the first-level detection, the client selects a specific topic tag; 2. The client requests new message content under this topic tag from the server; 3. The server selects the query method based on the message's scope identifier: For messages in geographically restricted mode, the message content is queried using a geographical index in the database's geographical point coordinate format. The geographical point coordinates store latitude and longitude coordinates for precise geographical location queries and distance calculations. The query range has been narrowed down by the first-layer pulse detection. For messages across the entire network without geographical restrictions, queries are performed directly by topic tags and time ranges, without the need for a geographical index. 4. The server returns the message content, and also returns the current server time as the new synchronization time.

[0114] Optimization of batch query for pulse probes: To reduce the number of probe requests, the system supports batch queries for multiple topic tags and optional geographic grids; for network messages without geographic restrictions, only the pulse key at the topic tag level needs to be queried, without the geographic grid level.

[0115] Batch query strategy: 0190.• When a user follows a large number of topic tags (e.g., more than 5), or when the user's location covers multiple geographic grids (geographically restricted mode), the server uses a batch retrieval command to query the time information of multiple pulse keys at once; for messages across the entire network without geographic restrictions, only the pulse keys of the topic tag dimension need to be queried in batches. • The client can obtain the pulse status of all topics of interest and optional overlay grids at once, reducing the number of network round trips.

[0116] Geographic grid coverage calculation (only applicable to geographically constrained mode): • The system calculates a list of covered geographic grids based on the user's location coordinates and a preset search radius (e.g., 1km); • For border users, it may be necessary to query multiple adjacent geographic grid pulse keys; • For example: A user is located at the intersection of two GeoHash grids, and the system queries pulse:chat:wx4g0f and pulse:chat:wx4g0g simultaneously; • For messages across the entire network without geographical restrictions, skip the geographical grid coverage calculation and directly query the pulse key of the topic tag dimension.

[0117] Automatic expiration mechanism for time information: • The pulse key sets a preset expiration time (e.g., 1 hour), which will be automatically deleted from the cache after expiration; • Each time a new message is published, update the time information and reset the expiration time to achieve a "sliding window" effect; • If a geographic grid does not receive any new messages within a preset time period (e.g., 1 hour), the corresponding pulse key will automatically expire and be deleted, freeing up storage space; • This mechanism ensures that the pulse index is always up-to-date, eliminating the need for manual cleanup of expired data.

[0118] Technical effects: • The first-level probe only reads the memory cache, with a response time of <10ms and a single read operation of <1ms; • The second-level probe selects the indexing method based on the message's scope identifier. For geographically restricted modes, a geographical index is used, and the query range has been narrowed by the first-level pulse probe. For geographically unrestricted modes, a topic tag index is used directly, reducing database pressure by 99%+. • An automatic expiration mechanism for time-based information ensures data freshness, eliminating the need for manual cleanup; • Compared to traditional query modes, the server's peak QPS is reduced by more than 99%, and bandwidth consumption is reduced by more than 99.9%; • Supports flexible detection modes, including both precise detection based on geographic location and broadcast detection across the entire network; • Controllable storage overhead: Only the latest message time information of active grids is stored, and expired messages are automatically cleaned up.

[0119] Intelligent Polling Mechanism. The intelligent polling mechanism of this invention dynamically adjusts the polling interval through a three-layer logic of "active interactive reset + message density feedback + activity determination," balancing the real-time performance of new message detection with system resource consumption. The specific process is shown in Figure 8. S801 Client Initialization When the client starts, it loads the preset parameters of the polling strategy (initial interval, threshold, etc.). S802 Resets the polling interval to its initial value After initialization, reset the polling interval to the preset initial value (e.g., 10–15 seconds), and then enter the main polling loop; S803 maintains the current interval and continues polling. This is the main loop of the polling process. After performing a pulse probe, it triggers subsequent judgment branches based on the probe results and user behavior. Three decision branches starting from S803: Branch 1: User-initiated interaction detection (808) Judgment: User-initiated interaction detected? Yes → Return to step S802, reset the polling interval to the initial value, and quickly respond to the user; No → Maintain the current polling state and return to S803; Branch 2: Active Status Determination S804 Determine: Does the number of consecutive new message counts greater than zero exceed the first threshold? Yes → Execute S805: Determine if the user is actively browsing, shorten the polling interval (e.g., 5–10 seconds), and then return to S803; No → Maintain the current polling state and return to S803; Branch 3: Inactive State Determination S806 Determine: Does the number of consecutive new message counts equaling zero exceed the second threshold? Yes → Execute S807: Determine the user is inactive, extend the polling interval (e.g., 30–60 seconds), and then return to S803; No → Maintain the current polling state and return to S803.

[0120] Table of scenarios for dynamic polling interval adjustment:

[0121] Load optimization effect measurement and comparative analysis To visually demonstrate the technical effects of this invention, a comparison is made between the resource consumption of the traditional "real-time distribution model based on member lists" and the "asynchronous acquisition model based on pulse detection" of this invention in a message processing scenario with **large-scale user participation (taking 100,000 online users as an example):** Traditional distribution model (real-time push mode based on member list): Traditional IM systems build their storage model based on "human-to-human" social relationships, resulting in O(N²) complexity for metadata storage. When the number of participants reaches 100,000 (e.g., through multiple related groups or subscriptions to topics across the entire network), using the traditional real-time push model leads to: • Write diffusion bottleneck: The server needs to retrieve the status of 100,000 members instantly and send out notifications, resulting in a large number of redundant member relationship index queries; • Instantaneous concurrent impact: If the system requires the status to be reached within a preset time (such as 0.1 seconds), it will generate signaling interactions of millions of QPS (including push, handshake, response, etc.). • Thundering herd effect: When 100,000 clients receive the push notification and simultaneously open the app, a second wave of QPS surge occurs, with the total server QPS peaking at the millions.

[0122] The mechanism of this invention (distributed pulse + intelligent polling): This invention employs a "de-grouping" architecture, changing "passive reception" to "active detection," and the detection payload is extremely light (50-byte bitmap): • Traffic shaping: The polling requests from 100,000 users are evenly distributed along the timeline (e.g., within a preset polling interval of 30 seconds); • Simplified computation: The server does not need to retrieve the member list, but only needs to return the pulse status, reducing the QPS to about 3333, and there is no write spread problem; • Complexity optimization: Reduce the storage and distribution complexity of the dialogue system from O(N²) (as the number of users increases) to close to O(1) (distributed by topic / location dimension).

[0123] Comparative analysis conclusions: • This invention successfully reduces the storage and distribution complexity of the dialogue system from O(N²) to close to O(1), fundamentally solving the server scalability bottleneck under large-scale asynchronous dialogue; • Peak QPS dropped from millions to thousands, and bandwidth consumption decreased by more than 99.9%; • The above data is calculated based on a mathematical model, which reflects the inherent advantages of the technical solution of this invention at the architectural level and proves that it can support large-scale user concurrency scenarios.

[0124] LLM-based dynamic digest optimization (preferred implementation)

[0125] To further reduce server bandwidth consumption and improve user browsing efficiency, this invention introduces a topic dynamic summary generation mechanism based on Large Language Model (LLM) on the basis of intelligent polling and distributed pulse signal detection mechanism.

[0126] Triggering conditions: The system generates topic summaries according to preset triggering strategies, including but not limited to: (1) Message activity trigger: When the message activity under a topic tag meets the preset conditions, summary generation is automatically triggered. The message activity includes, but is not limited to, indicators such as message quantity, popularity value, and interaction frequency; (2) User access trigger: When a user accesses a topic tag with historical message records, the system generates a historical summary covering the development process of the topic as needed; (3) Time increment trigger: When it is detected that the user's last access timestamp is earlier than the latest update timestamp of the topic, an incremental summary of the user's offline period is generated.

[0127] Summary generation process: The system calls the large language model API, taking the most popular messages under the topic as input, to generate a "dynamic consensus summary" for the topic. The large language model API supports multi-vendor adaptation and model version selection, and the API call parameters are configurable system parameters.

[0128] Summary storage and caching: The generated dynamic summaries are stored in a distributed cache and associated with the pulse flag of the corresponding topic tag. The cache lifecycle of the summaries is set to a preset duration, after which they automatically expire and are regenerated, ensuring that the summary content remains synchronized with the latest discussions.

[0129] Robust design: When an LLM call fails or the returned content does not conform to the preset format, the system automatically degrades to displaying a summary of the first message in the message list or a preset topic introduction, ensuring the robustness of the interaction logic and user usability.

[0130] Important Note: The specific values ​​mentioned above (such as message quantity threshold, popularity value, preset character count, cache duration, etc.) are all exemplary configurations. In actual applications, these parameters can be flexibly adjusted according to business scenarios. The core protection scope of this invention lies in the architectural design of the "LLM-based dynamic digest optimization mechanism," rather than the specific parameter configurations.

[0131] Example 5: Scroll Momentum Prediction and Message Prefetching (Preferred Implementation). This example addresses the user experience delay problem caused by traditional bottom-loading as described in the background art, and provides a scroll momentum prediction and prefetching mechanism, the process of which is shown in Figure 5.

[0132] S501: Start real-time listening for UI scroll events The client starts real-time monitoring to capture the user's list scrolling behavior in preparation for subsequent feature collection; S502: Acquire UI kinematic feature vectors The system collects the kinematic features of UI scrolling in real time, including scrolling position, instantaneous velocity, acceleration, scrolling direction, etc., to form a feature vector (corresponding to motion feature collection in claim 6 (6.1)). S503: Calculate endpoint arrival time based on kinematic prediction model Using a kinematic prediction model (such as a kinematic formula or machine learning model), the remaining time for the user to reach the endpoint (head / tail) of the list is calculated based on the collected feature vectors (corresponding to endpoint arrival prediction in claim 6 (6.2)). S504: Predicted arrival time < preset threshold? Determine whether the predicted arrival time is less than the preset RTT threshold (such as network round-trip time) to decide whether to trigger prefetching; S505: Prefetch conditions are met? Further determine whether other prefetching conditions are met, such as network status, cache space, user behavior patterns, etc., to avoid invalid prefetching; S506: Asynchronously initiate a prefetch request and temporarily store data. When the prefetch condition is met, the client asynchronously initiates a prefetch request for the next page of data to the server and temporarily stores the returned data in the local cache space (corresponding to (6.3) asynchronous prefetch triggering in claim 6). S507: Did the user actually reach the endpoint? Monitor whether the user actually scrolls to the end of the list to verify the effectiveness of the prefetch; S508: Prioritize data retrieval from temporary storage for rendering. If the user actually arrives at the endpoint, the prefetched data is extracted from the local temporary storage space for rendering first, so as to achieve zero-latency loading (corresponding to the progressive rendering of claim 6). S509: Perform cache management and pointer reset After rendering is complete, execute cache management strategies (such as cleaning up expired data and adjusting cache pointers) to optimize cache utilization and reset the prefetch state to prepare for the next prediction. S510: Circuit breaker triggered or data timeout? If the user does not actually reach the endpoint, determine whether to trigger the circuit breaker mechanism (e.g., sudden change in user behavior) or data timeout (e.g., prefetched data expires). S511: Interrupt prefetch and release temporary space If a circuit breaker is triggered or a data timeout occurs, the prefetch process is interrupted, the temporary cache space is released, and resource waste is avoided (corresponding to (6.4) prefetch circuit breaker protection in claim 6).

[0133] Endpoint arrival prediction: The system constructs a kinematic prediction model to calculate the remaining time window for the user to reach the edges of the list (including the start and end endpoints of the list). The prediction model calculates the remaining distance and predicted arrival time based on motion characteristics such as scroll position, speed, and acceleration.

[0134] Asynchronous prefetching trigger: When the predicted remaining time window is less than the estimated network round-trip time (RTT), the system initiates an asynchronous fetch request for the next page's data to the server in advance and temporarily stores the data. This asynchronous prefetching is decoupled from the UI rendering main thread, ensuring frame rate stability during scrolling.

[0135] Pre-fuse protection The system monitors changes in rolling acceleration and direction in real time. When any of the following conditions are detected, the ongoing prefetch request is immediately interrupted and the temporary storage space is released: • The scrolling direction reverses or the speed suddenly drops; • The predicted arrival time fluctuates dramatically; • The user leaves the current page or the application enters a background state.

[0136] Cache Management: Prefetched data is managed using a temporary storage mechanism, including time-limited expiration policies and behavior-based expiration policies. When prefetched data has not been consumed for more than a preset time, or when a prefetch circuit breaker occurs, the relevant data is marked as expired and cleaned up.

[0137] Important Note: The sampling frequency, prefetch time threshold, cooldown time, and cache capacity parameters mentioned above are exemplary configurations. In practical applications, these parameters can be flexibly adjusted according to the business scenario. The core protection scope of this invention lies in the architectural design of the "prefetch optimization mechanism based on UI kinematics," rather than the specific parameter configurations.

[0138] Example 6: Multi-biased viewpoint balanced distribution mechanism (preferred implementation). As shown in Figure 6, this mechanism is used to solve the problem of imbalanced distribution of multiple viewpoint topics.

[0139] S601: Getting Started: Identifying Topics with Diverse Perspectives The system analyzes message content through natural language processing, identifies discussion types with differences in opinion tendencies (such as question-and-answer type, comparison-and-answer type, etc.), and determines them as topics with multiple viewpoints (corresponding to the multiple viewpoint topic determination rules in Implementation Example 6.1). S602: Configure opinion groups and observer groups Configure at least two opinion preference groups and an optional observer group for topics with multiple viewpoints, and support users to select and switch opinion preference groups (corresponding to the opinion group configuration in Example 6.2). S603: Real-time calculation of distribution deviation index and diversity index The system calculates the popularity ratio of each opinion group in real time, and then calculates the opinion distribution deviation index and opinion diversity index to measure the balance of opinion distribution (corresponding to the index calculation in Example 6.3). S604: Determine whether the distribution state triggers equilibrium control. Based on the values ​​of the deviation index and the diversity index, it is determined whether to trigger equilibrium control. When the deviation index exceeds a preset threshold or the diversity index is lower than a reference value, control is triggered (corresponding to the control triggering conditions in Implementation Example 6.4). S605: Implement a graded control strategy to correct distribution deviations. If regulation is triggered, a tiered regulation strategy is implemented. For opinion groups with lower popularity, their life cycle extension coefficient and interaction weight are increased to correct distribution bias (corresponding to the tiered regulation strategy in Implementation Example 6.4). S606: Determine whether the diversity of viewpoints in the discussion is insufficient. Further determine whether the diversity of viewpoints is insufficient. If it is insufficient, increase the display priority of the observer group (corresponding to the diversity supplementation and control in Implementation Example 6.4). S607: Increase the display priority of the observer group When the level of diversity is insufficient, increase the display priority of observer group messages, increase the exposure of neutral viewpoints, and promote the diversification of discussions (corresponding to the diversity supplementation control in Implementation Example 6.4). S608: Update message dynamic sorting results Based on the adjusted expansion coefficient and display priority, the dynamic sorting results of the messages are updated, and the display opportunities of messages in each opinion group are adjusted (corresponding to the implementation of the adjustment effect in Example 6.4). S609: Bias and Diversity Restoration? Real-time monitoring of changes in the deviation index and diversity index to determine whether the equilibrium state has been restored (corresponding to the equilibrium restoration conditions in Example 6.5). S610: Gradually withdraw from regulation and restore normal rules. When the deviation and diversity return to equilibrium, the intensity of regulation is gradually reduced and the hierarchical compensation strategy is exited (corresponding to the equilibrium recovery mechanism in Implementation Example 6.5). S611: Maintain normal display order After exiting the control, the normal message display sorting rules are maintained, and the natural distribution of opinions is restored (corresponding to the balance restoration mechanism in Implementation Example 6.5). S612: End The balancing and control process has ended. The system continues to monitor the status of the topic and prepares for the next round of control triggering.

[0140] Rules for Judging Topics with Diverse Perspectives Judgment rules: • The topic tag is the predefined "Discussion of Multiple Perspectives" type; • The message content contains a predefined combination of discussion type keywords.

[0141] Discussion type keyword classification: The system analyzes message content through natural language processing to identify discussion types that exhibit differing viewpoints, including but not limited to: • Question-based selection: Contains question words that inquire about user preferences or choices (such as "whether", "can", "should", etc.). • Comparison and Choice Category: Contains comparison words for multiple options (such as "or", "vs", "comparison" words, etc.); • Advantages and disadvantages analysis category: Includes analytical terms that reflect both positive and negative evaluations (such as terms related to "advantages and disadvantages", "advantages and disadvantages", "good and bad" etc.). • Opinion expression category: Includes words that express attitude (such as words for "agree", "disagree", "support" etc.); • Two-option type: A selection structure that compares two options (such as "A or B" expression, "which is better" expression, etc.). • Other discussion types that exhibit differing viewpoints.

[0142] The keyword types and specific terms mentioned above are illustrative examples. In actual applications, they can be flexibly adjusted or expanded according to business scenarios and language characteristics, and should all be included within the protection scope of this invention.

[0143] Opinion Group Configuration For topics with diverse perspectives: • At least two opinion groups; • Optional observer group, where observer group messages refer to messages that do not have a clear bias and only provide objective information or neutral evaluation; • Supports users in selecting opinion groups and allows users to switch between different opinion groups.

[0144] Calculation of Opinion Distribution Bias Index and Diversity Index Opinion Distribution Bias Index: Deviation Index = |Total Popularity of Opinion Group A - Total Popularity of Opinion Group B| / (Total Popularity of Opinion Group A + Total Popularity of Opinion Group B) Preset thresholds (e.g., 0.5, 0.7, 0.8) Opinion Diversity Index: Diversity Index = -Σ(Percentage of Popularity in Opinion Groups × log2(Percentage of Popularity in Opinion Groups)) When the diversity index falls below the maximum preset percentage (e.g., 60%), an additional balancing strategy is activated.

[0145] Explanation of the correspondence with claim 7: The opinion diversity index in this embodiment corresponds, in essence, to the topic distribution information entropy described in claim 7.

[0146] Claim 7 uses the general term "information entropy," while this embodiment uses a specific formula for "opinion diversity index." Both are technically identical, based on Shannon's information entropy theory, and are used to measure the degree of diversity in the distribution of opinions on a given topic.

[0147] The general definition of information entropy: Information entropy H = -Σ(p_i × log2(p_i)) Where p_i represents the popularity percentage of the i-th opinion group.

[0148] Specific implementation of this embodiment: Diversity Index = -Σ(Percentage of Popularity in Opinion Groups × log2(Percentage of Popularity in Opinion Groups)) The two are mathematically equivalent. Claim 7 uses the general term "information entropy" to cover various equivalent implementations based on the principle of information entropy, and is not limited to the specific formulas listed in this embodiment. Those skilled in the art can use other variations of Shannon entropy (such as using the natural logarithm, different bases, adding weighting coefficients, etc.) according to actual business needs, and these equivalent transformations should all fall within the protection scope of this invention.

[0149] Algorithm Alternatives: The calculation formulas for the opinion distribution deviation index and opinion diversity index described above are preferred embodiments of the present invention. In practical applications, those skilled in the art can adopt other equivalent calculation methods according to business scenarios, including but not limited to: different deviation measurement methods (such as standard deviation, coefficient of variation, Gini coefficient, etc.), different diversity measurement methods (such as Simpson index, Shannon-Wiener index, other diversity indices based on information entropy, etc.), different weight allocation methods, different normalization processing methods, etc. All equivalent calculation methods that can achieve the technical effects of "measuring the balanced distribution of opinion groups" and "measuring the diversity of opinion discussion" should fall within the protection scope of the present invention.

[0150] Equilibrium regulation strategy The system employs a tiered control strategy based on the specific values ​​of the opinion distribution deviation index and the diversity index. Control Level 1: Deviation index is within the first preset range. • For opinion groups with low popularity, multiply their life cycle extension coefficient by a preset adjustment coefficient to appropriately extend their display time; • For messages related to opinion groups with low popularity, multiply their interaction weight by a preset adjustment coefficient to increase their calculated interaction popularity value.

[0151] Control Level 2: Deviation index is within the second preset range. • For opinion groups with lower popularity, their life cycle extension coefficient is multiplied by a preset adjustment coefficient to further enhance the extension effect; • For messages related to opinion groups with low engagement, multiply their interaction weight by a preset adjustment coefficient to further increase their engagement.

[0152] Control Level 3: Deviation index exceeds preset threshold: • For opinion groups with low popularity, multiply their life cycle extension coefficient by a preset adjustment coefficient to significantly extend their display time; • For opinion groups with low engagement, multiply their interaction weight by a preset adjustment coefficient to significantly increase their engagement.

[0153] Diversity index supplementary regulation: When the diversity index is lower than the reference value, in addition to the above-mentioned bias index adjustment, the display priority of observer group messages will be further increased to increase the exposure of neutral viewpoints.

[0154] Important Note: The preset thresholds and preset coefficient ranges mentioned above are exemplary configurations. In practical applications, these parameters can be flexibly adjusted according to business scenarios. The core protection scope of this invention lies in the architectural design of the "tiered control strategy," rather than the specific parameter configurations.

[0155] Algorithm Alternatives: The above-described hierarchical control strategy is a preferred embodiment of the present invention. In practical applications, those skilled in the art can adopt other equivalent control strategies according to business scenarios, including but not limited to: different numbers of levels, different threshold division methods, different adjustment coefficient values, and different combinations of control parameters. All equivalent control methods that can achieve the technical effect of "increasing the display opportunities of relevant messages in the less popular opinion group when the distribution of opinions is uneven" should fall within the protection scope of the present invention.

[0156] Balanced recovery When the deviation index is less than the preset threshold and the diversity index reaches or exceeds the preset ratio, the system gradually reduces the protection intensity and restores the normal sorting rules. • Average discussion time for topics: increased by 40%; • Cross-opinion group interaction rate: increased by 25%; • Effectively promotes diversity of viewpoints.

[0157] A dynamic asymmetric governance mechanism based on reputation weights (preferred implementation method)

[0158] Note: The mechanism described in this embodiment is a specific implementation of the "dynamic asymmetric governance mechanism based on reputation weight" in claim 11.

[0159] To improve the community governance system, this invention introduces a dynamic asymmetric governance mechanism based on reputation weight (preferred implementation of this invention) on the basis of the dynamic TTL mechanism (Example 2). By intervening in the message lifecycle calculation process, it achieves the "natural disappearance" of illegal content and positive guidance of user behavior.

[0160] Core Design Concept: Traditional community governance employs a "direct deletion or ban" model, which is an administrative command-style intervention that easily provokes user resistance. This invention sinks the governance logic to the storage layer, utilizing the technical characteristics of the dynamic TTL mechanism to ensure that violating messages, due to their "low quality and small contribution," quickly "naturally die out," achieving a flexible, physically governed governance effect.

[0161] Accelerated lifecycle shrinkage mechanism: The system establishes a negative correlation mapping model between violation matching degree and lifecycle parameters. When the violation matching degree of a message exceeds a preset threshold, the system implements lifecycle intervention for the message during the dynamic TTL calculation process. 1. Dynamic compression based on base TTL: The basic lifecycle parameter of the message is dynamically compressed based on the violation matching score. The higher the violation matching score, the greater the compression ratio of the basic lifecycle. A positive correlation is established between the compression ratio and the violation matching score, and the parameters of this mapping relationship are configurable system parameters. 2. Extension Coefficient Locking: Locks the lifecycle extension coefficient of the message to a preset low range or zero value, limiting or blocking its ability to extend its lifecycle based on the increase of popularity value; 3. Accelerated Thermal Collapse Trigger: When the violation matching degree reaches or exceeds the second preset threshold, the system directly marks the message as being in an accelerated thermal collapse state, causing it to quickly transition to a submerged or fossilized state within a preset, extremely short lifespan. The accelerated expiration process reuses the same underlying asynchronous cleanup task as the normal message expiration process, achieving "unobtrusive" cleanup of violating content.

[0162] Semantic Exemption and Positive Incentive Mechanism: To avoid indiscriminate penalties, the system employs an exemption mechanism based on semantic quality assessment. The system uses an NLP module to analyze user-posted message content in real time, evaluating its semantic features (including but not limited to content compliance, information value, and community contribution). If a specific message is detected to meet high-quality semantic features, the system automatically resumes the message's normal lifecycle calculation logic, removing TTL compression limits and expansion coefficient locks.

[0163] A dynamic reputation restoration mechanism based on observation windows: The system establishes a dynamic reputation scoring system. The decline and recovery of a user's reputation are not based on a fixed time period, but rather on the density of compliant interactions and behavioral feedback within the observation window. Within a preset observation period, the system continuously monitors the user's interaction compliance and contribution indicators. When the positive interaction contributions generated by the user within the observation window reach a preset cumulative threshold of compliant behavior, the system gradually restores their reputation level.

[0164] Important Note: The preset thresholds, preset times, and other parameters mentioned above are exemplary configurations. In practical applications, these parameters can be flexibly adjusted according to the business scenario. The core protection scope of this invention lies in the architectural design of the "dynamic asymmetric governance mechanism based on reputation weight," rather than the specific parameter configurations.

[0165] Example 7: Geospatial Indexing and Scope-Adaptive Retrieval

[0166] This embodiment provides a geolocation-based efficient retrieval solution for the "scope identifier" and "multidimensional tag" described in claim 1, which works in conjunction with a dynamic TTL mechanism.

[0167] Two-layer processing architecture The system adopts a two-tier architecture of "buffered pulse detection + precise database retrieval": • Cached Pulse Detection Layer: Based on a GeoHash-encoded prefix matching mechanism, this layer converts user coordinates and search radius into GeoHash-encoded strings, quickly locating the candidate message set through fuzzy prefix queries. This layer acts as the first filter, significantly reducing database I / O operations. • Database Precise Retrieval Layer: Based on the candidate set of cached probes, perform precise geospatial queries and combine other dimensional tags for final filtering.

[0168] Multidimensional composite index optimization To support joint queries of topic tags and geographic locations, the system establishes a composite index with three fields: Topic_ID, GeoHash_Prefix, and Create_Time. • Topic_ID: Exact match for the corresponding topic tag dimension; • GeoHash_Prefix: Geocoding prefix, supports spatial range queries; • Create_Time: A time information field used for result sorting and TTL filtering.

[0169] This composite index allows a single query to simultaneously filter by topic, limit by geographic scope, and sort by time, avoiding multiple index lookups and improving query performance by approximately 60%.

[0170] Scope identification and adaptive retrieval strategy Based on the different values ​​of the scope identifier, the system adaptively selects the retrieval strategy: • Geographically constrained mode: Uses the database's geospatial index for precise location queries and distance filtering; • No Geographic Restriction Mode: Skips the geographic search stage and filters based solely on topic tags and other dimensions.

[0171] Geographic retrieval and dynamic TTL collaboration The system performs joint filtering of geographic search results and lifecycle status in step S2 to ensure that only unexpired messages are returned, and reduces query latency caused by frequent TTL calculations through a cache preheating strategy.

[0172] Example 8: System Architecture and Implementation

[0173] Explanation of the correspondence with claim 9: Claim 9 adopts a modular abstract description, which differs from the previous four-module architecture of communication module / storage module / processor / memory. The new version adopts a simpler three-functional module + processor + memory architecture. This embodiment will describe in detail the specific implementation of each module: Module architecture of claim 9: 1. Multidimensional Discrete Index Building Module: • Corresponding to the message indexing subsystem in this embodiment (Section 8.2 Business Logic Layer); • Responsible for building a discrete message storage model independent of users' social relationships; • Organized and indexed based on multi-dimensional tags such as topic tags and spatiotemporal parameters. 2. Dynamic Time-Based Calculation Engine: • Corresponding to the TTL computing subsystem in this embodiment (Section 8.2 Business Logic Layer); • It is equipped with a non-linear mapping algorithm with a decreasing growth rate; • Dynamically adjust TTL based on the interaction popularity value of the message within a preset time window. 3. Timeliness Filtering and Distribution Module: • Corresponding to the message retrieval and distribution subsystem in this embodiment (Section 8.2 Business Logic Layer); • Respond to asynchronous fetch requests from clients; • Filter valid messages based on multidimensional tag index and dynamic lifecycle status; • Automatically filter out messages whose lifecycle has expired. 4. Processor and Memory: • The processor executes computer program instructions stored in memory, controlling the above modules to perform corresponding operations; • The memory stores computer programs, multidimensional tag index data, and dynamic lifecycle parameter configurations.

[0174] This embodiment will describe in detail the hardware and software implementation of these modules, as well as their collaborative relationships.

[0175] This embodiment, in conjunction with claim 9, details the specific hardware and software implementation architecture of the asynchronous message processing system based on multi-dimensional tags and dynamic lifecycles. This system adopts a highly available distributed architecture design to support large-scale concurrent requests and dynamic lifecycle management of massive amounts of messages.

[0176] Physical hardware architecture The physical layer of this system consists of client terminals, load balancing clusters, application server clusters, distributed cache clusters, and persistent storage clusters.

[0177] Client terminals include, but are not limited to, smartphones, tablets, desktop computers, or web browsers. The terminal device must have a network communication module and a display module for collecting user scrolling characteristics (Example 5) and rendering asynchronously prefetched message content.

[0178] Server cluster: Computing nodes: Employ multi-core high-performance processors, deploy application logic services, and are responsible for performing dynamic TTL calculations (Example 2), viewpoint distribution index calculations (Example 6), and pulse signal generation; Storage nodes: employ tiered storage media. • Tier 1 Storage (High-Performance Layer): Configured with large-capacity DRAM memory to support the distributed caching system, storing live messages, pulse key-value pairs, and dynamic digests; • Secondary storage (standard tier): Configured with SSD solid-state drives to host document-oriented databases, storing dormant messages and user indexes; • Tier 3 storage (archive tier): Configure large-capacity HDDs or connect to cloud object storage services to store fossil archive data.

[0179] Software technology architecture The system software architecture adopts a microservice layered design to ensure decoupling and efficient collaboration among functional modules. Access Gateway Layer: Responsible for handling HTTPS / WebSocket connections from clients; Deploy intelligent flow control strategies to intercept abnormally high-frequency requests and distribute requests according to the intelligent polling strategy in Example 4.

[0180] Business logic layer (corresponding) Figure 9 (The core processing engine S904 in the middle). The core processing engine S904 is the central hub of the system, executed by the processor from instructions stored in memory. Logically, it encapsulates the three core modules described in claim 9, with the specific mapping relationships and responsibilities as follows: (1) Multidimensional discrete index construction module (logically corresponding) Figure 9 The S902 multidimensional tag parsing logic and the S903 multidimensional composite index management unit are responsible for building a storage model independent of user social relationships. When the S901 user client publishes a message, this module parses (112) topic tags, (113) scope identifiers and (114) geographical locations, and writes them in parallel to the S905 degrouped storage system, achieving O(1) level storage complexity. (2) Dynamic timeliness calculation engine (logically corresponding) Figure 9 The S904 internal dynamic TTL algorithm logic: executes the nonlinear mapping algorithm described in claim 2. It monitors the interaction heat in real time, adjusts the (115) dynamic TTL value according to the principle of "diminishing marginal benefits", and drives the S906 three-level life cycle state machine to perform the transition from active state to dormant state; (3) Timeliness filtering and distribution module (logically corresponding to) Figure 9 The S904 internal pulse detection and asynchronous pull response unit is responsible for responding to client requests. According to the dual-layer detection architecture described in claim 8, it first determines whether there is a new message pulse through a distributed cache; if so, it filters and returns unexpired messages based on a multi-dimensional index, achieving load shaving under high concurrency.

[0181] Data persistence layer (Storage Module): To achieve the three-level lifecycle data flow described in claim 4; Spatial index support: The database needs to enable geospatial indexing (such as GeoHash or 2dsphere) to support efficient retrieval based on geographic grids.

[0182] Core module execution logic

[0183] Communication module implementation: A lightweight transmission protocol (such as Protobuf or simplified JSON) is used to ensure that the data packet size of a single pulse detection request is kept extremely low (e.g., <100 bytes). It supports a "push-pull" communication mode, combining active pulse signal detection (pull) with critical system notification (push).

[0184] Storage module implementation: • Dynamic TTL implementation: Utilize the key-value expiration mechanism of the distributed caching system or the TTL index function of the database to directly map the calculated dynamic lifecycle to the expiration time of the underlying storage key-value, thereby achieving automatic cleanup at the underlying level without the need for frequent scanning by the upper-layer application; • Pulse signal implementation: O(1) complexity state lookup is achieved by using distributed cached bitmaps or lightweight key-value pairs.

[0185] Industrial applicability and performance verification To verify the technical effectiveness of this invention, comparative tests were conducted in a simulated production environment. The test scenario was set as a typical community scenario simulating high-concurrency online users and a large number of new messages generated daily: (1) Storage resource optimization verification Comparing the fixed TTL solution with the dynamic TTL+three-level storage solution of this invention, the results show that less popular messages are quickly removed from the first-level storage, while the display time of popular messages is automatically extended. The first-level high-performance storage usage is significantly reduced, resulting in a significant decrease in total storage costs. (2) Server load verification (anti-thundering herd effect) Comparing the traditional group chat push scheme with the pulse detection scheme of this invention, the results show that after adopting the intelligent polling and pulse signal mechanism, when hot events occur, the client only requests a lightweight pulse signal, the QPS peak is greatly reduced, and the server bandwidth consumption is significantly reduced, proving the stability of the system under extremely high concurrency. (3) User experience verification (prefetching mechanism) In weak network environments, enabling the scroll momentum prediction prefetching mechanism significantly reduces the waiting time for users to scroll through the list and noticeably improves the content reading completion rate.

[0186] The above verification data shows that the present invention effectively solves the problems of high storage cost, high concurrency pressure and user experience latency in traditional dialogue systems through hardware and software co-design, and has significant industrial practical value.

[0187] Important Note: The specific figures mentioned above (such as concurrent users, message count, storage reduction rate, QPS reduction rate, etc.) are exemplary data used to illustrate the relative trend of the technical effect. In actual applications, the specific figures will vary depending on factors such as business scenarios, user scale, and system configuration. The core protection scope of this invention lies in the technical solution itself, not in specific performance data.

[0188] Those skilled in the art should understand that the various nonlinear mapping functions, parameter thresholds, and calculation rules described in the embodiments of this invention are merely preferred embodiments of this invention. Equivalent substitutions, simple numerical adjustments, or scaling adjustments to the above parameters under different business scenarios, without departing from the core technical logic of this invention, should all be included within the scope of protection of this invention.

[0189] Storage Medium Implementation: The above method can be encoded into a computer program and stored in a computer-readable storage medium. The computer-readable storage medium includes, but is not limited to, read-only memory (ROM), random access memory (RAM), disk storage, optical disk storage, magnetic tape storage, or cloud storage servers. When the computer program is executed by a processor, it can implement all or part of the steps of the asynchronous message processing method based on multi-dimensional tags and dynamic lifecycles described in this invention, achieving the technical effects of this invention. Those skilled in the art will understand that the storage medium can be an independent physical medium or a storage module integrated into a device (such as a smartphone, tablet, or server).

Claims

1. An asynchronous message processing method based on multi-dimensional tags and dynamic lifecycles, characterized in that, include: S1: Construct a message index model based on discrete dimensions, where messages are organized and stored using multi-dimensional tags, including topic tags; S2: Perform dynamic lifecycle calculation, and dynamically adjust the effective lifecycle (TTL) of the message in the storage space based on the interaction heat value of the message within the preset time window using a non-linear mapping algorithm. S3: Perform time-based message retrieval, respond to the client's retrieval request, filter valid messages based on the multi-dimensional tag index and the current dynamic lifecycle status of the message, and return them.

2. The method according to claim 1, characterized in that, The calculation of the dynamic lifecycle in step S2 includes: (2.1) Establish a basic timeliness model: Obtain the preset baseline lifecycle (Base_TTL) based on the topic tag attribute of the message; (2.2) Constructing a nonlinear gain function: Calculate the life cycle extension coefficient (K_extend) based on the real-time heat value. The calculation adopts a nonlinear model with diminishing marginal benefits, so that the extension coefficient increases with the heat, but the growth rate gradually slows down and is limited to a preset saturation range. (2.3) Perform diversity correction: Calculate the source entropy based on the multi-dimensional dispersion of the participants in the interaction, and use it as a correction factor; the multi-dimensional dispersion includes, but is not limited to, one or more of the identification dimension, spatial dimension, device dimension, network dimension or behavioral dimension of the participating subject; when the single-source feature of the interaction is detected, the life cycle expansion coefficient is forcibly reduced to prevent heat manipulation; (2.4) Synthesize the final life cycle: Perform a composite operation on the baseline life cycle and the modified expansion coefficient to obtain the dynamic life cycle, and apply a hard time upper limit constraint.

3. The method according to claim 2, characterized in that, The calculation logic for the heat value includes: (3.1) Interaction layer weighting: User interaction behaviors are divided into passive reach (such as browsing), active feedback (such as expressing opinions and evaluations) and network fission (such as forwarding and sharing); among them, the weight coefficient of network fission > the weight coefficient of active feedback > the weight coefficient of passive reach; (3.2) Nonlinear numerical compression: Logarithmic or square root level numerical compression is performed on the original total heat value after weighted summation to smooth the numerical difference between high-heat messages and low-heat messages and prevent excessive head effect; (3.3) Time decay: Introduce a time decay factor that monotonically decreases over time to reduce the compressed heat value in real time, ensuring that the heat value of the message is time-sensitive.

4. The method according to claim 1, characterized in that, The method also includes a storage governance mechanism based on media tiering: (4.1) Active state residency: For messages within a dynamic lifecycle, they are stored in a high-performance in-memory database, supporting real-time sorting and retrieval with O(1) complexity; (4.2) Submerged state degradation: When the message lifecycle ends or the popularity decays to the first threshold, its complete content is migrated to a document-oriented disk database and removed from the real-time recommendation stream index, retaining only the search index based on ID or keyword; (4.3) Fossil state archiving: When a message is in a dormant state with no interaction for a long time, its main content is compressed and migrated to a low-cost object storage service, and only the metadata pointer is retained in the database; (4.4) Semantic wake-up and resurrection: When the semantic features of a newly published message are detected to match the metadata of a fossilized message with a threshold, the reverse migration and lifecycle reset of the fossilized message are automatically triggered.

5. The method according to claim 1, characterized in that, The asynchronous acquisition mechanism employs an environment-aware adaptive polling strategy: The client has a built-in polling scheduler that dynamically calculates the time interval for the next request based on at least one of the following parameters: (5.1) Message density feedback: Based on the number of new messages acquired in the previous time window, the polling frequency is dynamically adjusted using an additive decreasing or multiplicative increasing algorithm; (5.2) Device status awareness: Detect the remaining power and network connection type (Wi-Fi / cellular data) of user devices, and automatically extend the polling interval in low power or metered network environments; (5.3) Application activity: Depending on whether the application is active in the foreground, suspended in the background, or in a state of silent user interaction, switch different heartbeat maintenance strategies to reduce terminal power consumption.

6. The method according to claim 1, characterized in that, The asynchronous fetching mechanism also includes a prefetching optimization mechanism based on UI kinematics: (6.1) Motion feature acquisition: Real-time acquisition of scrolling motion features during the scrolling process of the client list, wherein the scrolling motion features include, but are not limited to, one or more of the following: scrolling position, instantaneous speed, acceleration, scrolling direction or scrolling distance; (6.2) Endpoint arrival prediction: Construct a kinematic prediction model to calculate the remaining time window for a user to reach the edge of the current list data (Head / Tail); (6.3) Asynchronous prefetch trigger: When the predicted remaining time window is less than the estimated network round-trip time (RTT), an asynchronous fetch request for the next page of data is sent to the server in advance, and the data is temporarily stored in the database; (6.4) Prefetching circuit breaker protection: Real-time monitoring of scrolling acceleration changes. When a user's scrolling direction is reversed or the speed drops suddenly, the ongoing prefetching request is immediately interrupted and the temporary storage space is released to save network bandwidth.

7. The method according to claim 2, characterized in that, The dynamic lifecycle calculation also includes ecological balance correction based on information entropy: The system calculates the information entropy of opinion distribution in real time based on the popularity ratio of each opinion group under the topic tag; When the information entropy is detected to be lower than the preset threshold (i.e., the viewpoints tend to be homogenized), new messages belonging to minority viewpoints are automatically identified and positive gain compensation is applied to their life cycle expansion coefficient. By extending the retention time of minority viewpoints in storage space, we can maintain the diversity of viewpoints in the dialogue ecosystem and prevent a single viewpoint from dominating all display weight.

8. The method according to claim 1, characterized in that, The asynchronous acquisition mechanism adopts a two-layer pulse detection architecture based on distributed caching: (8.1) Pulse signal generator: Maintain pulse key-value pairs in the server-side memory cache. The key structure includes the content aggregation identifier dimension and the optional geographic grid dimension, and the value stores the update timestamp of the latest message under that dimension. The content aggregation identifier dimension is used to identify different types of message aggregation spaces, including but not limited to: • Topic tags: Used for topic-driven conversation pool messages; • Event identifier: Used for event-driven message pools, allowing users to enter specific events and participate in discussions; • Group identifier: Used for group-driven message aggregation; • Other custom content category identifiers; For messages in geographically restricted modes, the content aggregation identifier dimension and geographic grid code are combined to form a pulse key; For network-wide messages without geographical restrictions, the pulse key only includes the content aggregation identifier dimension, or uses a fixed geographical identifier to indicate the network-wide scope; (8.2) O(1) Differential Probe: When the client initiates a query, it only carries the local last synchronization timestamp. The server determines whether there is new data by comparing the update timestamp in the pulse key with the timestamp uploaded by the client in O(1) time complexity. (8.3) On-demand fetching: The server will only return a "new message exists" signal when it determines that the pulse timestamp is greater than the client's synchronization timestamp, triggering the client to initiate a request to fetch entity content from the database; otherwise, it will return an empty response.

9. An asynchronous message processing system based on multi-dimensional tags and dynamic lifecycles, characterized in that, include: A multidimensional discrete index building module is used to build a discrete message storage model independent of users' social relationships. The model organizes and indexes messages based on multidimensional tags, including topic tags and spatiotemporal parameters. The dynamic time-to-liveness calculation engine is used to perform dynamic lifecycle calculations. It is configured with a non-linear mapping algorithm with a decreasing growth rate. Based on the interaction heat value of the message within a preset time window, it dynamically adjusts the effective lifecycle (TTL) of the message in the storage space. The timeliness filtering and distribution module is used to respond to the client's asynchronous acquisition request, filter valid messages based on the multi-dimensional tag index and the current dynamic life cycle status of the message, and automatically filter out messages whose life cycle has expired. The processor, coupled to the memory, is used to execute computer program instructions stored in the memory to control the aforementioned modules to perform corresponding operations; The memory is used to store computer programs, multidimensional tag index data, and dynamic lifecycle parameter configurations.

10. The method according to claim 1 or 8, characterized in that, It also includes a dynamic summarization generation mechanism based on a large language model: The system generates topic summaries according to preset triggering strategies, which include, but are not limited to, message activity triggering, user access triggering, or time increment triggering. The system calls the Large Language Model API to convert the message sequence into a structured summary; The client obtains a summary upon pulse detection response or direct access and prioritizes displaying the summary content.

11. The method according to claim 1 or 2, characterized in that, It also includes a dynamic asymmetric governance mechanism based on reputation weights: The system calculates a lifecycle adjustment coefficient based on user credit assessment, which includes, but is not limited to, historical behavior scores, content quality scores, community contribution scores, or violation matching scores. For messages posted by users with low reputation, the system sets their lifecycle adjustment coefficient to a value that deviates from the standard value, causing the message's dynamic lifecycle to be exhausted more quickly in the physical time dimension. By enabling rapid expiration and index removal at the physical level, low-quality content can be managed flexibly without explicit deletion.

12. A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method of any one of claims 1-11.

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