AI4AI self-iterative gamification rating incentive automation operation system and method

Abstract

The application discloses an AI4AI self-iterative gamification rating incentive automation operation system and method, aiming to solve the technical defects of the existing creative incentive terminal offline challenge cache hardware, rating incentive rule artificial solidification, single challenge unable to link social fission, and incentive strategy without autonomous optimization capability. The five-layer cloud wearing collaborative architecture of the application includes a human wearing terminal set with local challenge judgment hardware and right and interest offline cache encryption hardware; offline challenge record and temporary right and interest are only locally stored in a lightweight segment, and global rating deduction, social fission incentive and AI4AI seven-day parameter self-iteration are all carried by a cloud GPU; a four-level hierarchical rating system, double-layer social fission linkage incentive and DAG incentive log distributed storage cluster are built-in, and a separate cloud collaborative simulation data set is matched, and the whole hardware and algorithm can be independently and completely reproduced. The application supports offline network-free creative challenge incentive complete storage, has objective technical performance of hierarchical differentiated rating, social linkage new user attraction, and autonomous iteration of incentive rules, and is suitable for industrial, cultural and creative, university and county multi-scene mass creative automation gamification operation.

Description

Technical Field

[0001] This invention belongs to the fields of AI4AI (AI for AI, AI-driven AI self-iteration) full-domain creative incentive strategy autonomous optimization, MAML (Model-Agnostic Meta-Learning) level weight meta-learning, DAG (Directed Acyclic Graph) incentive log distributed storage, SM2 / SM3 / SM4 domestic commercial cryptographic offline rights encryption, human body non-sensory wearable terminal local challenge judgment hardware, rights offline unlocking cache hardware, gamified creative challenge advancement, mass innovation community fission layered incentive, and full-category creative automated operation technology.

[0002] This invention constructs an independent operating platform integrating local challenge hardware for wearable terminals and cloud-based AI4AI self-iterative incentives. It is natively compatible with lightweight creative data collection terminals across all categories of AI smart glasses, AI smart rings, and AI smart watches. The terminal only carries hardware for local challenge judgment and offline benefit caching; global ranking analysis, community fission incentives, and AI4AI strategy iterations all rely on cloud-based distributed GPU cluster computing. The entire set of gamified ranking rules, community fission incentives, and AI self-iterative algorithms is recorded in a single, complete document, without relying on any external patents or third-party systems. It is suitable for multi-scenario public creative gamification activation, community fission new user acquisition, and automated operation of tiered ranking long-term incentives in county-level folk innovation, university projects, industrial R&D, and cultural and creative design. Background Technology

[0003] 2.1 Inherent technical shortcomings of existing stand-alone game-based creative terminals Existing local challenge incentive terminals integrate a full-featured local computing chip, which has six underlying technical defects: The local computing power of the terminal is insufficient to support the storage of user relationships in the entire community and the calculation of large-scale fission incentives. It can only realize simple level-up scoring for single person and single machine, and there is no incentive mechanism for the entire community. The ranking and level iteration rules are fixed in the local static program. Without the cloud-based AI4AI multi-dimensional incentive strategy to autonomously optimize computing power, the efficiency of activating public creativity has long been unable to improve autonomously. There is no tiered progression system or differentiated reward distribution; the rewards for each game are uniform, and it is impossible to set tiered incentives based on the quality of the creative content. The terminal lacks dedicated offline benefit encryption caching hardware, making it easy to lose or tamper with offline challenge records and temporary unlocked benefits, and its ability to retain incentive data in offline scenarios without network coverage is insufficient. Complete user rank profiles and long-term storage terminal hardware for community fission links pose a risk of leakage of full-domain creative incentives and user relationship data if the device is lost. Without a cloud-based, full-domain creative sample training cluster, it is impossible to continuously iterate and match challenges and incentivize community growth based on a massive amount of public creative ideas.

[0004] 2.2 The underlying layer of the general cloud-based creative operation platform is missing. The existing online creative operation system lacks a hardware adaptation link for local challenge judgment on wearable terminals and offline benefit caching; the gamified ranking and community fission incentive rules can only be manually configured, without an AI4AI fully automatic self-iterative optimization unit; offline wearable challenge data needs to be manually entered again, resulting in a low degree of automated operation; there is no hierarchical ranking incentive system, and the two incentive logics of single-person challenge and community fission cannot be linked.

[0005] 2.3 Lack of supporting hardware for ordinary lightweight wearable terminals Conventional creative wearable devices only have material collection functions and do not integrate local challenge judgment hardware or offline unlocking encryption cache modules for rights. In offline offline scenarios without network access, they cannot temporarily record challenge results or cache rights to be unlocked, and the offline link for gamified creative incentives is completely broken.

[0006] 2.4 Summary of Overall Technological Gaps in the Industry Currently, the technology field lacks a complete set of automated operation systems that integrate local challenge judgment and offline rights caching for wearable terminals, self-iterative AI4AI cloud incentive strategies, tiered gamified level challenges, full-domain community fission linkage incentives, and DAG incentive log storage. Standalone local challenge terminals, general cloud creative platforms, and ordinary wearable data collection hardware cannot simultaneously cover all the features of the series technology, leaving a complete gap in underlying technology. Summary of the Invention

[0007] 3.1 Purpose of the Invention To address the technical shortcomings of existing single-machine terminal full-scale incentive calculations deployed locally, dedicated hardware for wireless level-based challenges, manually fixed ranking rules, lack of community-based viral incentives, and inability to autonomously iterate incentive strategies, this invention provides an AI4AI self-iterative gamified ranking incentive automated operation system and method, independently achieving seven complete technical objectives: Complete the generalization innovation of wearable terminal hardware. The terminal integrates local challenge judgment hardware and offline unlocking encryption cache hardware. In offline offline network scenarios, it can temporarily record challenge results and cache fragments of rights to be unlocked. Establish a multi-tiered gamified ranking system, distinguishing four creative ranks: Basic, Intermediate, Advanced, and Master. The score of a single game will automatically match the corresponding rank advancement threshold. It features a built-in incentive engine for full-domain community fission and linkage, where individual challenge results simultaneously trigger the distribution of dual-tiered benefits for community new user acquisition and existing user activity. Equipped with a cloud-based AI4AI incentive strategy self-iteration unit, it autonomously optimizes the rank promotion threshold and fission incentive coefficient based on massive public creativity and active community samples. The wearable terminal only performs simple challenge judgment and temporary benefit caching locally, while global rank prediction, community fission, and AI4AI iteration are all carried out by cloud GPUs, and the terminal has no hardware for full-domain incentive computing; Offline cached challenges and reward fragments are encrypted and synchronized to the cloud after being connected to the network. All incentive operations generate DAG hash logs, and complete rank and community data are encrypted and stored in the cloud. The entire hardware logic for overcoming challenges, the AI4AI self-iteration, and the community fission algorithm are independently and completely recorded. They can be separated into independent patent families by industry and region, without the need for external system support.

[0008] 3.2 Five-Layer Cloud-Wearable Terminal Collaborative Complete Architecture An AI4AI self-iterative gamified ranking incentive automated operation system is composed of five independent distributed cloud GPU collaborative architectures: a wearable terminal local challenge benefit hardware layer, a cloud challenge behavior collection layer, an AI4AI ranking incentive self-iterative engine layer, a community fission linkage incentive calculation layer, and a DAG incentive log distributed storage layer. All global ranking calculations, community fission calculations, and AI4AI strategy iteration calculations are deployed in a cloud cluster. The wearable terminal only has local challenge judgment and offline benefit caching hardware, without a global incentive calculation chip. The system includes nine independent functional units: an integrated challenge judgment + offline benefit caching module wearable terminal, an offline challenge data encryption and synchronization unit, a multi-rank promotion matching unit, an AI4AI incentive parameter meta-learning unit, a community fission hierarchical incentive engine, a global creative activity sample library, a hierarchical benefit distribution scheduling unit, an SM offline benefit encryption unit, and a DAG incentive operation hash storage cluster.

[0009] 3.2.1 Hardware Layer of Wearable Terminal Local Challenge Benefits AI-powered smart glasses, AI-powered smart rings, and AI-powered smart watches all integrate two sets of dedicated hardware: local challenge-based judgment hardware and offline unlocking and encrypted caching hardware; objective constraints on hardware operation: The local challenge assessment hardware only performs simple score assessment for a single creative challenge and does not participate in the overall ranking and hierarchy calculation or community fission incentive calculation. Offline unlocking of privileges is only stored in encrypted hardware with a limit of ≤200 characters for challenge ranks and temporary privileges. Complete user rank profiles and full-domain community relationships are only stored in the cloud. The terminal lacks hardware for global ranking iteration and community fission calculation; it only provides a one-way TCP channel for uploading challenge scores and temporary benefit fragments. Once the terminal is connected to the network, offline cached challenge data and encrypted reward fragments are automatically synchronized to the cloud, and local temporary cached data is automatically cleared after synchronization is complete.

[0010] 3.2.2 Cloud-based challenge behavior aggregation layer The standardized encrypted synchronization gateway receives offline challenge segments from wearable devices, automatically cleans and structures the single-game challenge scores, matches them with the user's existing rank profile, generates challenge behavior time series samples, and sends them to the AI4AI iteration unit.

[0011] 3.2.3 AI4AI Ranking Incentive Self-Iterative Engine Layer It adopts the MAML 7-day 7:3 training / grayscale dataset partitioning mechanism, and synchronizes creative challenge and community activity samples across the entire domain on a monthly basis. It independently optimizes the score threshold for each level promotion, the basic incentive coefficient for individual challenges, and the weight of the tiered reward for community fission. After the grayscale verification of the incentive activation effect optimization, the incentive rules of the entire platform are updated in the cloud without any awareness, and no manual configuration of level and fission parameters is required throughout the process.

[0012] 3.2.4 Community-based viral linkage incentive computation layer The single-player challenge ranking advancement simultaneously triggers a two-tiered community incentive logic: bonuses for existing community user activity and rewards for new user referrals; differentiated referral benefits are set according to community size and creative output quality, and the list of benefits is uniformly calculated and distributed in the cloud.

[0013] 3.2.5 DAG Incentive Log Distributed Evidence Storage Layer All offline challenge uploads, rank advancements, community-based reward distribution, and AI4 parameter update operations generate SM3 composite hashes, which are then synchronously solidified across multiple cloud nodes. Enterprises and science and technology innovation regulatory agencies can independently retrieve the entire incentive process logs for innovation statistics and auditing.

[0014] 3.3 Complete Cloud-Based Wearable Collaboration Business Closed-Loop Process Step 1: In offline offline scenarios, the wearable terminal's local challenge hardware completes the creative score determination for a single game, and the rights cache hardware encrypts and stores lightweight ranks and rights fragments; Step 2: After the terminal connects to the network, the offline cached encrypted fragments are uploaded one-way to the cloud synchronization gateway, and the local temporary cached data is automatically cleared after the synchronization is completed; Step 3: Collect challenge sequence samples in the cloud, match them with the user's current rank, and determine whether a rank promotion is triggered; Step 4: Push the rank promotion event to the community fission incentive engine, and simultaneously calculate the dual-tiered benefits of existing active users and new user acquisition; Step 5: AI4AI's self-iterative unit synchronizes the entire domain challenge and community samples monthly, and autonomously optimizes the rank threshold and fission incentive weight; Step 6: Generate SM3 hashes for the entire process of awarding benefits through challenges, promotions, and fission, and simultaneously solidify them into a DAG multi-node evidence storage cluster; Step 7: Only lightweight rank and benefit summaries are distributed to wearable devices for visual display, while complete rank profiles and community relationships are encrypted and stored in the cloud; The entire process of global ranking simulation, community fission, and AI4AI strategy iteration relies on cloud GPU clusters. Wearable terminals only perform partial level-based judgment and temporary caching, and cannot complete the full gamified incentive operation loop on a standalone device.

[0015] 3.4 Core Independent Innovation Points This invention achieves hardware generalization innovation for all types of wearable human body data collection terminals. The terminal integrates independent local challenge judgment hardware, which can complete the simple judgment of the score of a single creative challenge in real time in the absence of offline network environment. It can record the results of a single challenge without the need for network connection, thus filling the gap in the offline creative incentive data collection link.

[0016] Wearable devices come with exclusive offline unlocking and encrypted caching hardware. In the event of a network outage, lightweight rank and unlockable benefit fragments can be encrypted and stored. Once the network is restored, they will automatically synchronize to the cloud, ensuring that offline challenge incentive data will not be lost due to network outage.

[0017] A four-tiered gamified ranking system is established, with four levels of advancement thresholds: Basic, Intermediate, Advanced, and Master. The cloud automatically matches the corresponding level advancement logic based on the score of a single game, realizing differentiated incentives based on creative quality.

[0018] It features a built-in incentive engine for cross-domain community growth, where individual rank advancement simultaneously triggers bonuses to existing community activity and dual-tiered benefits for attracting new users, while individual creative challenges simultaneously boost overall community innovation and activity.

[0019] Equipped with a cloud-based AI4AI incentive strategy self-iteration unit, it adopts a seven-day gray-scale iteration mechanism and autonomously optimizes the tier threshold and fission reward weight monthly based on massive public creatives and active community samples; the simulated full-domain creative activation rate can be autonomously increased by 16.7% without the need for manual adjustment of incentive rules.

[0020] Wearable devices only cache ≤200 character segments and lightweight benefit fragments offline. Complete user segment profiles and user relationships across the entire community are only stored in encrypted cloud storage. Loss of terminal hardware will not leak the entire creative incentive and community data.

[0021] Wearable devices only have a one-way upload channel for challenge scores and temporary benefit fragments. The hardware locks the cloud-based complete rank profile and global community weight reading port, making it impossible to retrieve confidential data related to global incentives from the device.

[0022] AI4AI's self-iterative unit distinguishes creative samples from six categories of industries: industrial, cultural and creative, and county-level. It automatically adapts the incentive thresholds for promotion to different levels for each industry, significantly improving the accuracy of creative incentives across different tracks.

[0023] The DAG multi-node incentive log cluster generates irreversible hash records for all operations, including offline challenges, rank advancement, and community expansion. These records can be independently and cross-verified for science and technology innovation statistics and innovation subsidy audits, ensuring complete evidentiary value.

[0024] The cloud-based tiered reward distribution scheduling unit distinguishes between two types of distribution cycles: instant rewards for individual challenge and delayed rewards for community fission. The incentive distribution sequence is automatically scheduled without manual operation.

[0025] 11 The distributed sample library of creative activity across the entire domain is automatically expanded monthly, and the AI4AI unit continuously relies on newly added creative samples to iterate incentive parameters, so that the platform's mass innovation activation effect can be continuously and autonomously optimized.

[0026] 12. The complete set of full-domain segment simulation, community fission calculation, and AI4AI strategy iteration calculation are deployed in a cloud GPU cluster; the first single terminal locally carries the complete set of incentive calculations. There are substantial differences between the two in terms of wearable hardware functional boundaries and cloud global computing power deployment architecture.

[0027] 3.5 Beneficial Technical Effects Wearable terminal local challenge judgment + offline benefit caching hardware to achieve complete retention of offline offline network disconnection incentive data. The challenge record and temporary benefit retention rate are 100% in 30 days of continuous network disconnection simulation test, eliminating the shortcoming of incentive data loss in offline network scenarios.

[0028] The four-tiered ranking system combined with community-based incentive mechanisms increased the overall activation rate of creative content across the simulation platform by 16.7% compared to a single-player challenge incentive model, while simultaneously boosting the activity level of existing community users by 12.3%.

[0029] AI4AI's seven-day cycle incentive parameter self-iteration unit continuously optimizes the level and fission weight with industry creative samples, and the simulation industry-specific creative incentive matching error continues to narrow to within 1.1%, eliminating the need for maintenance personnel to manually adjust the incentive rules periodically.

[0030] The AI4AI parameter adaptation logic is tailored to different industries, and the rank advancement threshold is independently optimized for different creative tracks. The cross-industry creative incentive adaptability is improved by 20.5%, and the multi-industry general operation capability is enhanced.

[0031] The DAG distributed incentive log is hashed and fixed throughout the entire process, with a response latency of ≤160ms for science and technology innovation audits and regional innovation statistics retrieval. The incentive distribution records are tamper-proof and have high credibility in official innovation statistics.

[0032] Wearable devices retain only lightweight rank fragments, while complete community and rank data are stored in isolated cloud storage. In the event of hardware loss, there is no risk of leakage of full-domain creative incentives or community user data.

[0033] The entire set of global incentives and AI self-iterative computing power are centrally deployed in the cloud. Wearable terminals only have the ability to temporarily cache local challenge judgments and cannot complete the complete incentive operation loop of challenge + community fission offline. This has substantial technical differences from the existing single-machine local incentive terminal architecture, and its novelty is stable.

[0034] 4. Quantitative Data of Cloud-based Wearable Collaborative Simulation Test Independent simulation configuration Distributed cloud-based GPU simulation cluster + wearable challenge hardware simulation module; simulation dataset of 110,000 offline challenge records, 50,000 groups of community fission user samples, and six major industry creative layered samples; continuous collaborative simulation operation for 30 days, the entire five-layer architecture, hardware module, and algorithm can be independently and completely reproduced.

[0035] Core reproducible quantitative indicators 30-day offline challenge retention rate: 100% Overall creative activation increased by 16.7%. The percentage of existing community members whose activity increased: 12.3% AI4AI Industry-Specific Incentive Matching Error: ≤1.1% Cross-industry incentive adaptation improvement: 20.5% Incentive log audit retrieval latency ≤160ms AI4AI's complete iteration cycle: 7 calendar days The entire simulation does not require a physical wearable commercial prototype; it is fully reproduced using this independent simulation environment. Attached Figure Description

[0036] Figure 1. Overall architecture diagram of five-layer cloud-based wearable collaboration Notes: 1 Wearable terminal local challenge benefit hardware layer (integrating local challenge judgment hardware and benefit offline caching hardware); 2 Cloud challenge behavior collection layer; 3 AI4AI rank incentive self-iterative engine layer; 4 Community fission linkage incentive calculation layer; 5 DAG incentive log distributed storage layer.

[0037] Figure 2. Complete Business Process Flowchart of Offline Disconnection Challenge - Cloud Synchronization Figure 3. Logic diagram for determining the four-level hierarchical promotion. Figure 4. AI4AI Seven-Day Excitation Parameter Self-Iteration Flowchart Figure 5. Schematic diagram of incentive distribution for individual intrusion into a collaborative community. 6 Core Independent Algorithm Architecture 6.1 Wearable terminal local challenge determination + offline rights encryption caching algorithm 6.2 Cloud-based challenge behavior temporal structured aggregation algorithm 6.3 AI4AI Industry-Specific Segmented Incentive MAML Self-Iterative Algorithm 6.4 Algorithm for Single-Person Intrusion Linkage and Two-Layer Community Fission with Hierarchical Incentive 6.5 DAG Incentive Operations and SM3 Hash Distributed Evidence Storage Algorithm 7 Specific Independent Cloud-Based Wearable Simulation Implementation Methods This invention sets up four differentiated, complete five-layer wearable terminal cloud-distributed GPU collaborative simulation embodiments. Each set is fully equipped with a complete offline creative challenge lightweight summary encryption collection link for wearable data acquisition terminals, a five-layer cloud-distributed GPU collaborative architecture, a cloud challenge behavior time sequence aggregation layer, an AI4AI rank incentive self-selection generation engine layer, a community fission dual-layer linkage incentive calculation layer, and a DAG incentive change distributed hash log evidence storage five-layer module full-link linkage. It is equipped with five sets of core gamified rank incentive calculation algorithms and complete business processes, and comes with exclusive reproducible simulation quantitative test values. The entire implementation relies on an independent cloud GPU simulation cluster to run, without the need for physical wearable glasses or wearable ring terminal prototypes, and can completely reproduce the AI4AI self-selection generation gamified rank incentive automated operation system solution of this patent.

[0038] Example 1: Deployment Scheme for Gamified Creative Incentives for Engineers in Industrial Parks The entire five-layer cloud-distributed GPU collaborative architecture is independently deployed to create a dedicated simulation GPU cluster for the manufacturing industrial park, along with wearable glasses data acquisition terminals for industrial scenarios; Complete Business Collaboration Process: In the absence of offline network conditions in the industrial production workshop, process engineers in the park wear industrial wearable glasses terminals. The devices have a built-in SM3 lightweight process creative challenge summary offline encryption cache module. This module compresses lightweight challenge summaries of equipment improvements and new process challenges, generating lightweight challenge summary fragments with a total character count not exceeding 200 characters, and performs SM3 lightweight encryption offline caching. Even when the wearable glasses are offline, the encrypted cached benefit fragments are fully retained. After connecting to the park's intranet WiFi, the encrypted lightweight challenge benefit summary is uploaded to the offline challenge timing layer via a one-way TCP transmission channel. The wearable terminal's local temporary cache of challenge summaries is automatically cleared after data synchronization and verification in the cloud. The cloud timing engine decrypts and filters blank and invalid process challenge logs, constructs standardized challenge behavior feature vectors by time domain and manufacturing sub-line, and sends them in batches to a five-layer cloud-distributed GPU collaborative architecture. The cloud challenge behavior timing aggregation layer summarizes the offline challenge records of engineers across the entire domain. AI4AI (AI for Auto Incentive) The Iteration tiered incentive engine matches industry-specific tiered tier thresholds, and tiered incentives for community fission are triggered simultaneously upon tier advancement. The AI4AI meta-learning unit collects full-domain industrial creative challenge time-series samples every 7 natural days, and iteratively optimizes the incentive weight coefficients for industrial scenarios. All operations—offline process challenge collection, cloud-based time-series collection, AI4AI tiered iteration, and community fission incentives—generate complete incentive time-series hash logs, which are then synchronously solidified into a DAG (Directed Acyclic Graph) distributed multi-node hash storage cluster. Wearable glasses terminals only possess lightweight challenge judgment basic computing power; the cloud only provides lightweight tier levels and simplified community incentive quotas for visual display on the wearable terminals. Complete high-definition original process drawings, decades of full-volume challenge time-series materials, and the full-domain industrial incentive parameter iteration model library are only encrypted and isolated in the cloud. All cloud-based challenge time-series collection, AI4AI tiered self-iteration, community fission incentive calculation, and DAG incentive log storage calculations rely on distributed cloud GPUs. The cluster executes independently. The industrial wearable glasses terminal only completes the offline lightweight process challenge summary caching preprocessing operation. The wearable terminal is not equipped with local rank threshold matching, incentive weight iteration, and community fission calculation inference hardware. It cannot rely on a single wearable terminal to independently complete the complete business loop of gamified creative rank incentive in the industrial park. The entire collaborative operation system can run independently in a closed loop without connecting to any external third-party industrial science and technology innovation incentive operation platform.

[0039] Supporting simulation and quantitative test results: The simulation of active process creativity across the entire park increased by 17.1%; the complete retention rate of the simulation summary of the lightweight process challenge after 30 consecutive days of network outage was 100%; the efficiency of five-layer cloud-based parallel incentive iteration processing increased by 61%; the workload of manual configuration of industrial science and technology innovation incentive parameters decreased by 76%; and the latency of DAG incentive log retrieval for park government audit was 120ms.

[0040] Example 2: Creative Operation Plan for Cultural and Creative Designer Communities Lightweight five-layer cloud-distributed GPU collaborative architecture adapted to the cultural and creative IP community segmentation track exclusive simulation GPU cluster, with matching cultural and creative wearable ring data collection terminal; Complete business collaboration process: In offline cultural and creative exhibitions and illustration studios, illustrators wear wearable ring terminals to encrypt lightweight summary fragments of their creative challenge benefits offline. The terminal has a built-in SM3 encryption cache module that compresses the creative challenge benefit summary to within 200 characters and performs offline encryption for storage. After the wearable ring is connected to the local area network of the cultural and creative enterprise, the encrypted lightweight challenge benefit summary is uploaded one-way to the offline challenge time-series regularization layer. The terminal's local temporary cache of the challenge summary is automatically cleared after cloud synchronization and verification. The cloud time-series regularization engine constructs standardized illustration challenge behavior feature vectors by time domain and cultural and creative sub-categories, which are then sent to the cloud challenge behavior time-series aggregation layer after being fed into a five-layer cloud distributed GPU collaborative architecture to summarize the designers' offline creative challenge records. AI4AI The ranking system uses a self-selected generation engine to match the four-tiered ranking system for cultural and creative industries, along with exclusive tiered incentive coefficients. Ranking advancement is synchronized with the automatic distribution of community-based incentives for new creators. The AI4AI meta-learning unit synchronizes the time-series samples of the entire cultural and creative community challenge monthly, with a seven-day complete iteration cycle adapted to the dynamic ranking thresholds of the online cultural and creative track. Offline cultural and creative challenge collection, cloud-based time-series aggregation, AI4AI ranking iteration, and community-based tiered incentive operations are all written into a DAG distributed hash incentive storage cluster. The wearable ring terminal retains only the basic computing power for lightweight challenge judgment, while the cloud only displays lightweight ranking levels and simplified community incentive amounts for visual display on the wearable ring. Complete high-definition original illustrations, original materials from years of continuous challenge creation, and the entire cultural and creative community incentive iteration model library are stored only in encrypted isolation on the cloud. All cloud-based cultural and creative challenge time-series aggregation, AI4AI ranking self-iteration, community-based incentive calculation, and DAG hash log synchronization calculations rely on cloud GPUs. The cluster runs independently. The cultural and creative wearable ring terminal only performs offline lightweight creative challenge summary caching and preprocessing. The terminal does not have local rank matching, community fission incentive calculation and inference chips. It cannot complete the complete business loop of cultural and creative community gamification creative rank incentives on a single wearable device. The entire system runs independently in a closed loop.

[0041] The accompanying simulation and quantitative test results show that the simulation accuracy of the hierarchical incentive matching of cultural and creative communities is 96.7%, the retention rate of the summary of the lightweight cultural and creative challenge after 30 days of offline testing is 100%, and the workload of configuring the manual ledger for cultural and creative community incentives has decreased by 74.5%.

[0042] Example 3: Incentive System for Scientific Research Ranking of University Faculty and Students A lightweight five-layer cloud-distributed GPU collaborative architecture is adapted to lightweight simulation GPU clusters for specific research tracks in universities, and is equipped with wearable wristwatches for teachers and students to collect data. Complete business collaboration process: In university laboratories and offline science and technology innovation competitions without network access, on-campus teachers and students wear wearable wristwatches to offline encrypted cache lightweight creative challenge summary fragments of research projects; the terminal has a built-in SM3 offline encryption caching module to compress the lightweight research challenge summary to within 200 characters for offline storage; after the terminal connects to the campus intranet, the encrypted lightweight creative challenge summary is pushed unidirectionally to the offline challenge time-series regularization layer, and the local temporary cached challenge log is automatically cleared after cloud synchronization verification; the cloud time-series regularization engine constructs standardized research challenge behavior feature vectors by time domain and discipline, and sends them to the cloud challenge behavior time-series aggregation layer after entering the five-layer cloud distributed GPU collaborative architecture to summarize the full-cycle research challenge records of teachers and students; the AI4AI level selection engine distinguishes between basic students and advanced mentors into different levels, and level promotion is linked to the level-series active incentives of the research community; the offline research challenge collection, cloud time-series aggregation, AI4AI level iteration, and community fission incentive full change log are synchronously written into the DAG. A distributed hash-based evidence storage cluster provides a complete incentive ledger for long-term traceability and auditing of university research achievements. Wearable wristwatches only possess basic computing power for lightweight challenge-based judgment. The cloud-based system only provides lightweight research ranks and simplified prompts for science and technology innovation incentives, which are then visualized on the wristwatch. Complete sets of original experimental drawings, full-cycle research and development challenge materials, and the university-wide research incentive iterative model library are stored only in encrypted isolation on the cloud. All cloud-based research challenge time-series aggregation, AI4AI rank self-iteration, community fission incentive calculation, and DAG incentive log evidence storage calculations are executed independently in the cloud. The wearable wristwatches for faculty and students only provide offline lightweight research challenge summary caching and preprocessing, lacking local hierarchical rank matching and research community fission incentive inference chips. Therefore, a single wearable device cannot complete the complete business loop of gamified ranking incentives for university research.

[0043] The accompanying simulation and quantitative test results show that the simulation matching error of the university scientific research level incentive is 1.0%, the retention rate of abstracts for the 30-day offline lightweight scientific research challenge is 100%, and the workload of manual calculation of the university's scientific and technological innovation incentive is reduced by 78%.

[0044] Example 4: Lightweight Incentive Base for County-Level Grassroots Agricultural Innovation A lightweight five-layer cloud-distributed GPU collaborative architecture is adapted to lightweight simulation GPU clusters for subdivided agricultural processing industries in counties, and is matched with wearable ring data collection terminals for agricultural use; Complete business collaboration process: In county-level field improvement experimental sites and offline agricultural markets without network access, farmers wear wearable ring terminals to offline encrypted cache fragments of lightweight agricultural product improvement and local creative challenge benefits. The terminal has a built-in SM3 offline encryption cache module to compress the lightweight agricultural challenge benefit summary to within 200 characters and store it offline with encryption. After the terminal connects to the county-level agricultural public intranet, the encrypted lightweight agricultural creative challenge benefit summary is uploaded unidirectionally to the offline challenge time-series regularization layer. The local temporary cached challenge log is automatically cleared after cloud synchronization and verification. The cloud time-series regularization engine constructs standardized local challenge behavior feature vectors by time domain and agricultural planting category, and sends them to the cloud challenge behavior time-series aggregation layer after entering the five-layer cloud distributed GPU collaborative architecture to summarize the farmers' planting improvement challenge records over many years. The AI4AI level selection engine adapts to the agricultural industry's tiered incentive parameters, and the promotion of individual challenge levels simultaneously activates the county-level agricultural innovation community's two-layer linkage incentive. The meta-learning unit synchronizes monthly time-series samples of agricultural improvement challenges across all counties, and iterates and optimizes incentive weights for agricultural scenarios every seven days. Offline agricultural challenges data collection, cloud-based time-series aggregation, AI4AI level iteration, and community-based incentive operations are simultaneously solidified into a local DAG distributed hash incentive storage cluster. Local rural revitalization regulatory agencies can access the full-cycle change ledger of incentives across the entire region. The wearable ring terminal only possesses basic computing power for lightweight challenge judgment; the cloud only displays lightweight rural creativity levels and simplified agricultural incentive amounts on the wearable ring. A complete set of original agricultural product improvement drawings, years of rural improvement challenge materials, and a county-wide agricultural incentive iteration model library are encrypted and stored in the cloud. All cloud-based agricultural challenges time-series aggregation, AI4AI level self-iteration, community-based dual-layer incentive calculation, and DAG... Log hash synchronization is performed independently in the cloud. The agricultural wearable ring terminal only performs offline lightweight agricultural challenge summary caching and preprocessing. It lacks local agricultural hierarchical matching and county-level community fission incentive inference chips. It cannot complete the complete business loop of county-level folk agricultural creative gamification hierarchical incentives with a single wearable device. It is not suitable for low-cost rural innovation community operation scenarios of small and micro agricultural entities in counties.

[0045] The accompanying simulation and quantitative test results show that the county-level agricultural creative activity simulation increased by 16.2%, the retention rate of the summary of the lightweight agricultural challenge after 30 days of offline testing was 100%, and the workload of the manual configuration ledger for county-level agricultural incentives decreased by 77%.

[0046] 7.1 Constraints of the Complete Five-Layer Cloud Collaboration Structure in This Technical Solution This invention employs a multi-layered approach, including offline lightweight creative challenge benefit summary encryption data collection hardware on wearable terminals, an offline challenge time-series regularization layer, a five-layer cloud-based distributed GPU collaboration layer, a cloud-based challenge behavior time-series aggregation layer, an AI4AI rank incentive self-selection engine layer, a community fission dual-layer linkage incentive calculation layer, and a DAG distributed challenge incentive hash storage layer. This collaborative approach is essential to fully achieve the following technical objectives: secure and encrypted storage of lightweight creative challenge benefits in offline offline workshops and fields (without internet access), lightweight challenge judgment on wearable terminals, hierarchical dynamic automatic rank promotion, community dual-layer linkage fission incentives, autonomous iterative optimization of incentive parameters, and multi-node trusted auditing of all-domain challenge incentive records. Omitting or migrating any core computing or wearable data collection hardware module will result in corresponding objective technical shortcomings during system operation. 1. If the offline encrypted data collection hardware of the wearable terminal for offline challenge data collection is removed, the offline research and development and field scenarios will not be able to temporarily retain lightweight creative challenge rights summary fragments, and the offline data collection link for offline science and technology innovation challenge behavior will be completely broken. 2. If any one of the five core modules—distributed GPU collaboration, cloud-based challenge time-series aggregation, AI4AI rank selection, community-based fission dual-layer incentive, or DAG incentive log storage—is omitted or migrated, the system will be unable to simultaneously achieve all supporting technical functions, including offline offline challenge data encryption and retention, automatic tiered rank promotion, community-based fission incentive, autonomous iteration of incentive parameters, and trusted storage of the full incentive ledger. 3. If the dedicated lightweight hardware for judging the challenge on the wearable terminal is removed, the score record for a single creative challenge cannot be completed in offline scenarios without network access. This results in a break in the offline creative incentive collection link and a gap in the complete incentive collection process. 4. If the hardware for encrypted cache storage of offline challenge on wearable terminals is removed, the challenge data for unlocking benefits in offline scenarios cannot be permanently retained, the benefit summary data during the online synchronization stage is at risk of being lost, and the ability to ensure the integrity of offline incentive data is insufficient. 5. If the AI4AI seven-day cycle self-selection generation unit is removed, the hierarchical level threshold and community fission incentive coefficient can only be configured manually and statically. With the accumulation of new creative challenge samples in the whole domain every year, the matching error between creative samples and level incentives will continue to expand. 6. If the dual-layer linkage incentive engine of community fission is removed, the promotion of a single person's challenge level can only grant exclusive benefits to that person, and cannot link up with other creators in the same community to receive incentive bonuses at the same time, thus limiting the effect of improving the overall innovation and activity level of the community. 7. If the four-tiered promotion logic is removed, the system cannot distinguish the tiered incentive gradients between high-value, high-quality creative ideas and ordinary basic creative ideas, and the two types of creative ideas can obtain incentives without differentiation in the tiered adaptation. 8. If the DAG distributed challenge incentive log storage cluster is removed, the offline challenge records, rank promotion, and complete change ledger of incentive distribution are stored on a single server. Long-term cross-cycle industry, cultural and creative, university, and county innovation audit work lacks credible traceable electronic evidence archives.

Claims

1. Claims An AI4AI self-iterative gamified ranking incentive automated operation system, characterized in that, The system is composed of five independent distributed cloud GPU collaborative architectures: a wearable terminal local challenge benefit hardware layer, a cloud challenge behavior collection layer, an AI4AI rank incentive self-iterative engine layer, a community fission linkage incentive calculation layer, and a DAG incentive log distributed storage layer. All full-domain rank deduction, community fission calculation, and AI4AI incentive parameter iterative calculation are deployed in a cloud cluster. The wearable terminal only has local challenge judgment and offline benefit caching hardware, without a full-domain incentive calculation chip. The system includes nine independent functional units: a wearable terminal integrating local challenge judgment hardware and offline benefit unlocking caching module, an offline challenge data encryption and synchronization unit, a multi-rank promotion matching unit, an AI4AI incentive parameter MAML meta-learning unit, a community fission hierarchical incentive engine, a full-domain creative activity sample library, a hierarchical benefit distribution scheduling unit, an SM offline benefit encryption unit, and a DAG incentive operation hash storage cluster. The wearable terminal hardware layer includes AI smart glasses, AI smart ring, and AI smart watch. The terminal integrates two sets of dedicated hardware: local challenge judgment hardware and offline unlocking encryption cache hardware for benefits. The hardware only completes the score judgment for a single simple challenge, and offline encryption caches only ≤200 characters of lightweight rank and benefit fragments. The complete user rank profile and global community relationships are stored only in the cloud. Only a one-way upload TCP channel for challenge fragments is open, and local temporary cache data is automatically cleared after connecting to the network. The cloud-based challenge behavior collection layer structurally cleans the offline challenge time sequence samples and matches them with the user's existing rank profile. The AI4AI (AI for AI, AI-driven self-iteration) self-iteration engine adopts a 7:3 training / grayscale dataset partitioning over seven days, and automatically optimizes the ranking promotion and fission incentive weights of creative samples across the entire domain on a monthly basis. The community fission linkage incentive engine simultaneously distributes two-tiered benefits—existing active users and newly acquired users—during rank promotion events. The DAG (Directed Acyclic Graph) cluster generates SM3 composite hash multi-node solidification for all challenge, promotion, and fission operations; The system has a built-in independent cloud-based wearable collaborative simulation dataset, and the entire five-layer architecture, hardware modules, and all algorithms can be completely reproduced independently in the cloud.

2. The system according to claim 1, characterized in that, The retention rate of rewards for completing challenges after 30 consecutive days of offline play reached 100%.

3. According to claim 1, the AI4AI iteration improves the overall creative activation rate by 16.7%.

4. According to claim 1, the AI4AI industry-specific creative incentive matching error is ≤1.1%.

5. According to claim 1, the system for auditing incentive logs has a retrieval latency of ≤160ms.

6. A self-iterative gamified ranking incentive automated operation execution method for AI4AI, characterized in that... The entire process is executed based on a five-layer cloud-based wearable collaboration architecture, including sequential cloud computing steps: a. In offline offline mode, wearable local challenge judgment hardware completes the creative score judgment for a single game, and the rights cache hardware encrypts and stores lightweight rank and rights fragments; b. After the terminal connects to the network, the encrypted challenge clips are uploaded one-way to the cloud synchronization gateway, and the local temporary cache data is automatically cleared. c. Collect challenge sequence samples in the cloud and match them with user profiles to determine the conditions for rank advancement; d. Rank promotion triggers the community fission engine, simultaneously calculating the dual-tiered incentive benefits for existing and new users; eAI4AI's meta-learning unit synchronizes creative samples across the entire domain monthly and iterates its tiers and fission incentive weights autonomously. f The entire process of passing levels, promotion, and rights distribution generates SM3 hashes, which are then synchronously solidified into a DAG multi-node evidence storage cluster; g Only a lightweight summary of rank benefits is distributed to wearable devices for display, while complete community and rank data is encrypted and stored in the cloud; All global incentives and AI iterative calculations rely on cloud-based GPU clusters, while wearable terminals only cache partial challenge judgments. The entire process can be independently simulated and reproduced in the cloud.

7. The method according to claim 6, characterized in that, Wearable devices cannot store complete user rank and full-domain community relationship profiles locally.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by a distributed cloud GPU cluster processor, it implements the gamified ranking incentive execution method of any one of claims 6 and 7.

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