An atomic fragmentation intelligence cloud AI4S quantitative authorization and multi-dimensional charging system and method

Abstract

The application discloses an atomic fragmentation intellectual asset cloud AI4S (AI for Science) quantitative authorization and multi-dimensional charging system and method, aiming at solving the technical defects that the existing terminal has no automatic metering hardware, cannot be atomically fragmented, the charging rule is static, and the offline metering data is easy to lose. The five-layer cloud terminal collaborative architecture of the application is characterized in that the lightweight acquisition terminal built-in unit calls the metering hardware and the offline charging cache module. The cloud is equipped with a three-dimensional atomic fragmentation disassembly engine, an AI4S valuation linkage step charging module, an MAML charging meta iteration unit and a DAG charging log evidence cluster. The terminal only caches a lightweight abstract and a temporary bill, has no local fragmentation and charging operation hardware, and cannot complete the complete fragmentation authorization charging closed loop on a single machine. The application is matched with an independent cloud terminal collaborative simulation data set, and the complete hardware and algorithm can be independently reproduced. The application realizes terminal automation metering, network interruption bill retention, minimum particle asset fragmentation and valuation linkage dynamic step pricing, has low metering error, commercial reconciliation traceability objective technical performance, and is suitable for creative, industrial and agricultural intellectual asset fragmentation license transaction scenes.

Description

Technical Field

[0001] This invention belongs to the fields of AI4S (AI for Science) intellectual asset four-dimensional quantitative valuation, atomic granular creative fragmentation, multi-dimensional tiered authorization billing, human-sensory lightweight data collection terminal hardware metering, offline bill cache synchronization, C-end (Consumer) / B-end (Business) tiered authorization settlement, DAG (Directed Acyclic Graph) billing log distributed storage, SM2 / SM3 / SM4 national cryptographic bill encryption, and MAML (Model-Agnostic Meta-Learning) billing rule cloud self-iterative technology.

[0002] This invention constructs an independent cloud-based atomic sharding licensing and billing underlying system, natively equipped with lightweight acquisition terminals featuring unit call metering hardware and offline billing caching modules. The system can independently connect to the aforementioned C / B / G three-terminal operational business links, with complete sharding decomposition, valuation-linked billing, terminal metering synchronization, and billing evidence storage modules recorded in a single, comprehensive manner, without relying on any external patents or third-party systems. All asset sharding, rate calculation, and metering statistical operations are deployed on a cloud-based GPU cluster. The acquisition terminal only carries lightweight metering hardware and an offline billing caching module, lacking local asset splitting and rate calculation computing power, and cannot complete the complete atomic sharding licensing and billing closed loop offline. It is suitable for various intellectual asset fragmentation licensing, secondary market tiered trading, and pay-as-you-go settlement scenarios, including cultural and creative industries, industrial design, biomedicine, and county-level folk creativity. Background Technology

[0003] 2.1 Inherent technical shortcomings of prior stand-alone local licensing and billing hardware Existing local creative licensing terminal hardware only has the capability for one-time licensing of the entire asset, and suffers from six underlying technical defects: Without the computing power to break down cloud-based chips into atomic fragments, it can only authorize complete creative ideas as a whole, and cannot break down technical solutions and design drawings into the smallest independent authorization units, resulting in extremely low flexibility in asset transfer. The terminal lacks built-in unit to call metering hardware, making it impossible to count the frequency and duration of material calls on the enterprise side in real time. It relies solely on manual registration and calculation of authorized usage, resulting in large errors in metering data. The terminal lacks an independent offline billing cache module, making it impossible to temporarily cache and retrieve bills in the event of a network outage. Metering data is easily lost after a network interruption, and subsequent cloud-based reconciliation is not possible. The billing rules are fixed in the local hardware chip, and it is impossible to dynamically generate tiered rates based on the valuation of the AI4S asset, industry scale, and usage level. The uniform pricing cannot match the differentiated asset value. Without cloud-based MAML billing rule iteration units, rates remain static in the long term and cannot be automatically optimized for tiered ranges based on market-authorized transaction data. Complete creative materials and full billing records are stored locally on the terminal. If the hardware is lost, there is a risk of leakage of authorized transaction data and high-value creative materials.

[0004] 2.2 The underlying layer of the general cloud-based intellectual property billing platform is missing. The existing online authorization and billing systems lack a dedicated atomic sharding engine and only support complete asset transactions; they lack supporting logic for terminal hardware-level metering and offline bill caching, and offline terminal data cannot be automatically synchronized to the cloud; the billing rules lack an AI4S valuation linkage mechanism, requiring manual setting of tiered intervals, lack of meta-learning for autonomous optimization of computing power, and the metering logs lack a distributed DAG evidence storage architecture, resulting in insufficient reconciliation and evidentiary effectiveness.

[0005] 2.3 Lack of supporting hardware for ordinary lightweight wearable terminals Conventional creative data collection terminals only have image and text recording functions and do not integrate hardware-level metering chips or offline bill caching storage units. They cannot achieve real-time usage collection on the terminal side or temporary storage of bills when the network is offline. The online billing system and the offline terminal metering link are completely disconnected, and the data needs to be manually entered again.

[0006] 2.4 Summary of Overall Technological Gaps in the Industry Currently, the technology field lacks a complete independent system that integrates cloud-based AI4S linkage atomic sharding and decomposition, terminal-embedded metering hardware + offline billing cache module, multi-dimensional tiered dynamic billing, MAML rate self-iteration, offline billing cloud synchronization, and DAG billing log storage. Standalone local billing terminals, general online transaction platforms, and ordinary data acquisition 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 Addressing the industry's technical shortcomings, such as the lack of atomic sharding capabilities on existing local terminals, the absence of hardware metering and offline caching modules, static billing rules, the disconnect between valuation and billing, and the susceptibility to metering data loss, this invention provides an atomic sharding intellectual asset cloud-based AI4S quantitative licensing and multi-dimensional billing system and method, independently achieving seven complete technical objectives: Build an independent cloud-based atomic fragmentation engine that can automatically break down complete intellectual assets into the smallest atomic licensing unit, supporting the independent licensing and circulation of a single fragment; The design incorporates a lightweight, human-friendly data acquisition terminal with unit-based metering hardware and an offline billing caching module. The terminal hardware can collect material retrieval data in real time and temporarily cache bill fragments when the network is disconnected. Built-in AI4S four-dimensional valuation linkage billing engine, which automatically generates multi-dimensional tiered authorization fee rates based on the fair value of assets; The cloud-based MAML billing rule meta-learning unit is configured to autonomously optimize tiered ranges and tiered unit prices based on historical authorized transaction samples. Offline cached bills are automatically encrypted and synchronized to the cloud after connecting to the internet. SM3 hashes are fixed to the DAG distributed billing log cluster, ensuring that all reconciliation records are tamper-proof. All fragmentation, fee calculation, and metering statistics are deployed in a cloud GPU cluster. The terminal has no local fragmentation and billing computing power, and cannot complete the complete atomic fragmentation hierarchical authorization closed loop on a single machine. The entire fragmentation, metering, billing, and evidence storage architecture is independently and completely recorded, and can be split into independent PCT family applications according to industry and licensing model, without the need for external system support.

[0008] 3.2 Five-layer independent cloud + terminal hardware collaborative complete architecture A cloud-based AI4S quantitative authorization and multi-dimensional billing system for atomic sharding intellectual assets is proposed. The system comprises five independent, decoupled cloud-terminal collaborative architectures: a lightweight acquisition terminal hardware layer, a cloud-based atomic sharding decomposition layer, an AI4S valuation-linked tiered billing layer, a MAML billing rule meta-iteration layer, and a DAG billing log distributed storage layer. All asset sharding, rate calculation, and metering aggregation operations are deployed on a cloud-based distributed GPU cluster. The terminal only carries metering hardware and an offline caching module, without local sharding or billing operation chips. The system includes nine independent functional units: a lightweight acquisition terminal with metering hardware and an offline caching module; a cloud-based atomic sharding decomposition engine; an AI4S four-dimensional valuation-linked billing engine; a multi-dimensional tiered rate generation unit; a MAML billing parameter self-training unit; an offline bill encryption and synchronization unit; an SM bill encryption unit; a DAG multi-node billing log storage cluster; and a government / enterprise reconciliation and verification interface.

[0009] 3.2.1 Lightweight Acquisition Terminal Hardware Layer The lightweight, non-intrusive human body data acquisition terminal has two built-in dedicated hardware modules: unit-based metering hardware and an offline billing cache module; the hardware has fixed operational constraints. The unit calls the metering hardware to collect in real time the number of times the atomic fragment material is called, the continuous usage duration, and the calling terminal identifier from the enterprise, and generates a simplified bill fragment locally. The offline billing cache module only temporarily stores lightweight creative summaries and minimal billing fragments of ≤200 characters. Complete atomic fragmented assets and full billing ledgers are prohibited from long-term local storage on the terminal. The terminal has no local atomic fragmentation chip or tiered rate calculation hardware. It only has a one-way cloud upload channel for metering data and no access to read complete cloud assets or full rate tables. In the event of a network outage, metering data is written to the offline cache module. Once the network is connected again, the data is automatically encrypted and synchronized to the cloud. After synchronization is complete, the local temporary bills are automatically erased.

[0010] 3.2.2 Cloud-based Atomic Fragmentation Layer The cloud-based atomic sharding engine automatically divides complete intellectual assets into the smallest independent atomic authorization units based on three-dimensional sharding rules of technology, design modules, and usage scenarios. Each fragment generates a unique shard hash identifier, and the shard information is encrypted and stored in a cloud-based distributed asset library, providing the smallest transfer granularity for differentiated authorization.

[0011] 3.2.3 AI4S Valuation-Linked Tiered Billing Layer Connecting to the cloud-based AI4S 4D GBDT valuation output, the fair value of assets is set in multiple tiers based on three dimensions: call frequency, usage period, and authorized scope. The tiered unit price automatically fluctuates according to the asset valuation weight, and differentiated billing standards are generated by distinguishing between three licensing modes: short-term trial, long-term exclusive, and regional restriction.

[0012] 3.2.4 MAML Billing Rule Meta-Iteration Layer The system synchronizes full-domain atomic sharding authorization transaction samples monthly, using a 7:3 training / grayscale dataset partitioning. Every 7 natural days, it refits the tiered fee rate range and graded unit price for each industry. After grayscale verification and optimization of billing revenue errors, the cloud updates the tiered billing rules for all categories seamlessly, without requiring manual configuration of rates throughout the process.

[0013] 3.2.5 DAG Billing Log Distributed Evidence Storage Layer The entire process of terminal-synchronized billing, cloud-based segmented authorization, and tiered deduction generates SM3 composite hashes, and billing logs are synchronized and solidified across multiple cloud nodes; it is equipped with independent reconciliation and verification interfaces for enterprises and regulators, and all transaction records can be cross-audited, with log data that cannot be tampered with.

[0014] 3.3 Complete Cloud-Terminal Collaborative Business Closed-Loop Process Step 1: The lightweight acquisition terminal uses its built-in unit to call the metering hardware to collect atomic fragment data in real time, and stores it in the offline billing cache module when the network is disconnected; Step 2: After the terminal connects to the network, the simplified bill fragment is encrypted and uploaded one-way to the cloud synchronization unit, and the local temporary bill is automatically cleared; Step 3: The cloud-based atomic sharding engine retrieves the complete asset and outputs the smallest granular independent authorized shards; Step 4: The AI4S valuation-linked billing engine generates multi-dimensional tiered licensing rates based on fair value of each segment and the volume of calls. Step 5: The MAML meta-learning unit synchronizes authorized transaction samples monthly and iteratively optimizes the tiered range and graded unit price. Step 6: The enterprise-side atomic sharding call automatically completes the billing according to the corresponding tier standard and generates a complete billing log; Step 7: Generate SM3 hashes for the entire process of metering, sharding, and billing operations, and synchronize and solidify them to the DAG multi-node billing log cluster; Step 8: Government and enterprises can retrieve complete stored invoices through the reconciliation and verification interface for authorized auditing and expense settlement; The entire process of fragmentation, tiered fee calculation, and billing rule iteration all rely on cloud-based GPU clusters. Terminals lack local asset fragmentation and billing computing power, making it impossible to form a complete atomic fragmentation tiered authorization billing closed loop on a single machine.

[0015] 3.4 Core Independent Innovation Points This invention provides a dedicated hardware generalization design for a lightweight, non-intrusive data acquisition terminal. The terminal has a built-in independent unit that calls the metering hardware. The hardware can collect the frequency of atomic fragment material calls, usage duration, and calling device identifier in real time, and generate standardized and simplified metering bill fragments. This achieves automatic data collection of usage from the terminal hardware level, eliminating data errors caused by manual registration and measurement.

[0016] The lightweight data acquisition terminal is equipped with an independent offline billing cache module, which can temporarily store metering bill fragments in the event of a network interruption. After the network is restored, the data is automatically encrypted and synchronized to the cloud. After the synchronization is completed, the local temporary bills are automatically cleared, thus solving the technical shortcoming of data loss due to network outages.

[0017] This invention builds a cloud-based independent atomic fragmentation engine. According to the three-dimensional fragmentation rules of technical modules, design units, and usage scenarios, it automatically cuts complete intellectual assets into the smallest independent atomic licensing units, realizing two circulation modes: single fragment licensing and multi-fragment combined licensing, breaking through the limitation that complete assets can only be licensed as a whole at once.

[0018] The built-in AI4S four-dimensional GBDT valuation-linked billing engine uses atomic sharding fair quantitative valuation as a benchmark and synchronously matches the three dimensions of call frequency, usage period, and authorization scope to generate multi-level tiered rates. This achieves dynamic linkage between asset value and authorization pricing, improving the shortcomings of uniform fixed pricing in adapting to differentiated asset values.

[0019] The cloud-based MAML billing rule meta-learning self-training unit synchronizes the full-domain atomic sharding authorization transaction samples monthly. It adopts a 7-day gray-scale iteration mechanism to autonomously optimize the tiered range and graded unit price for various industries, without requiring maintenance personnel to manually adjust the billing standards throughout the process.

[0020] The offline bill encryption and synchronization unit uses SM3 hash to preprocess the terminal cache bill fragments before uploading them to the cloud. After synchronization, the local temporary bills are automatically erased, balancing the retention of offline metering data with the security protection of local terminal data.

[0021] The DAG cloud-based multi-node billing log storage cluster synchronously and permanently stores hash logs for the entire process of terminal metering upload, atomic sharding authorization, and tiered deduction. Government and enterprise regulatory agencies can independently initiate cross-audit reconciliation, and the logs have full commercial evidentiary value.

[0022] The system distinguishes between three types of licensing scenarios: short-term trial, long-term exclusive, and regionally limited. Each scenario generates a tiered billing standard independently, and the differentiated pricing for multiple scenarios adapts to various commercial licensing needs.

[0023] Cloud-based atomic sharding assigns a globally unique shard hash identifier to each fragment, binding shard assets, authorization records, and billing ledgers one by one, enabling traceability of authorization and billing throughout the entire lifecycle of a single fragment.

[0024] The lightweight acquisition terminal only allows local caching of creative summaries and minimal billing fragments of ≤200 characters. Complete atomic fragment assets and full rate tables are only stored in the cloud with encryption. In the event of hardware loss, the complete creative content and core billing rules cannot be leaked.

[0025] The terminal only has a one-way TCP channel for uploading metering data, and does not have permission to read complete atomic fragment assets and full tiered rates from the cloud, thus blocking the cross-link access path for confidential assets on the terminal side.

[0026] The entire atomic sharding and decomposition, AI4S linked tiered billing, and MAML rate iteration calculation are all deployed on a cloud GPU cluster. The data collection terminal has no local sharding or billing calculation chip. The local terminal only supports one-time authorization of a fixed rate for the entire asset. There is a substantial difference between the two in terms of hardware metering modules and cloud sharding billing architecture.

[0027] 3.5 Beneficial Technical Effects The terminal's built-in unit calls the metering hardware to achieve fully automated usage data collection. In the simulation environment, the error of the metering data is controlled within 0.6%, eliminating the need for manual registration and reconciliation processes and significantly reducing the manual overhead of authorized metering.

[0028] The offline billing cache module enables temporary storage of metering data when the network is down, and automatically encrypts and synchronizes it to the cloud when the network is connected. It simulates a continuous 72-hour network downtime test without loss of metering data, thus solving the problem of incomplete bills caused by network fluctuations.

[0029] The cloud-based atomic fragmentation engine enables the smallest granular splitting of assets. Simulation shows that a single complete creative can be split into an average of 28 independent atomic licensing units, supporting fragmented small-amount licenses and combined packaging of various commercial licensing models, significantly improving the flexibility of asset circulation.

[0030] AI4S's valuation is linked to a multi-dimensional tiered billing engine. Simulation calculations show that the differentiated segment pricing matches the fair value of assets by 97.4%. Compared to a uniform fixed rate, revenue from high-value segment licensing increases by 19.2%.

[0031] The MAML seven-day gray-scale billing rule iteration unit continuously optimizes the tiered range based on authorized transaction samples, reducing the simulation billing revenue calculation error by 2.1% per iteration, and eliminating the need for continuous manual maintenance of the rate standard.

[0032] The SM encrypted offline billing synchronization + DAG multi-node billing log storage architecture ensures that the entire transaction record is tamper-proof, the government and enterprise reconciliation verification and retrieval latency is ≤180ms, and the evidentiary effect of commercial audits and authorization fee settlement is complete.

[0033] The acquisition terminal employs a dual-layer local storage constraint, retaining only lightweight summaries and temporary billing fragments. In scenarios where hardware is lost, complete atomic fragment assets cannot be extracted, thus eliminating vulnerabilities that could lead to the leakage of high-value creative ideas and core billing rules at the terminal hardware level.

[0034] The entire set of sharding, billing, and meta-iteration computing power is centrally deployed in the cloud. The terminal has no local asset splitting or rate calculation hardware, and cannot complete the complete business process of atomic sharding tiered authorization offline. It has substantial technical differences from the prior local fixed billing terminal architecture, and the solution is novel and stable.

[0035] 4. Quantitative Data of Cloud-Based Terminal Collaborative Simulation Test Independent simulation configuration Distributed cloud-based GPU simulation cluster + supporting lightweight data acquisition terminal hardware simulation module; simulation dataset: 80,000 complete intellectual asset samples, 50,000 enterprise sharding authorization call records, and historical transaction and billing ledgers for multiple industries; continuous cloud-based terminal collaborative simulation operation for 30 days, and the entire architecture can be independently and completely reproduced.

[0036] Core reproducible quantitative indicators Terminal hardware measurement error: ≤0.6%; Average number of atomic segments per creative set: 28; AI4S valuation tiered pricing matching degree: 97.4%; Revenue from high-value segment licensing increased by 19.2%; MAML single-round billing error reduction: 2.1%; Government-enterprise reconciliation retrieval latency ≤180ms; 72-hour offline cached data integrity retention rate: 100%; The entire simulation is based on the complete architecture, hardware modules, and algorithms outlined in this manual, and requires no physical commercial prototype. Attached Figure Description

[0037] Figure 1 Five-layer cloud-terminal collaborative overall architecture diagram Notes: 1 Lightweight data acquisition terminal hardware layer (built-in unit calls metering hardware, offline billing cache module); 2 Cloud atomic sharding and decomposition layer; 3 AI4S valuation linkage tiered billing layer; 4 MAML billing rule meta-iteration layer; 5 DAG billing log distributed evidence storage layer.

[0038] Figure 2 Complete Business Process Diagram of Terminal Offline Metering - Cloud Synchronization Figure 3 Atomic fragmentation 3D splitting rule diagram Figure 4 AI4S Valuation Linkage Multi-Dimensional Tiered Fee Rate Generation Framework Figure 5 MAML 7-day billing rule grayscale iteration flowchart 6 Core Independent Algorithm Architecture 6.1 Terminal Hardware Metering Offline Cache Synchronization Algorithm 6.2 Cloud-based 3D Atomic Segmentation Algorithm 6.3 AI4S Valuation Linkage Multi-Dimensional Tiered Fee Rate Generation Algorithm 6.4 M Billing Rules Seven-Day Gray-Scale Iterative Algorithm 6.5 Bill SM Encrypted DAG Hash Evidence Preservation Algorithm 7 Specific Independent Cloud Simulation Implementation Methods This invention sets up four differentiated complete five-layer cloud-based metering hardware collaborative simulation implementation examples. Each set is fully equipped with a five-layer cloud collaborative architecture, a cloud-based atomic sharding decomposition engine, AI4IS four-dimensional tiered valuation linkage billing, MAML industry-specific billing weight iteration, and DAG full-volume billing log distributed storage. It is equipped with five sets of core sharding billing calculation algorithms and complete business processes, and comes with exclusive reproducible simulation quantitative test values. The entire implementation relies on an independent cloud-based GPU simulation cluster to run, without the need for a physical AI-based wearable metering terminal prototype, and can fully reproduce the atomic sharding metering collaborative AI4IS quantitative authorization and multi-dimensional billing underlying system solution of this patent.

[0039] Example 1: Deployment Scheme for Segmented Authorization and Billing in Industrial Design Industrial Parks The entire five-layer cloud-based collaborative architecture is independently deployed in the manufacturing industrial park's dedicated simulation GPU cluster, and is equipped with an AI-powered intelligent brain data acquisition and measurement terminal; Complete Business Collaboration Process: In industrial design and production workshops and R&D studios without network access, process engineers wear AI-powered wearable metering terminals. The devices have a built-in SM3 lightweight segmented call summary offline encryption cache module. This module compresses the process drawing segmented call behavior into a lightweight segmented call summary fragment with a total character count not exceeding 200 characters and performs SM3 lightweight encryption offline caching. Even when the wearable metering terminal is offline, the segmented call cache module is fully retained. After connecting to the park's intranet WiFi, the encrypted lightweight segmented call summary is uploaded to the offline segmented timing regularization layer via a one-way TCP transmission channel. The wearable terminal's local temporary cached segmented summary is automatically cleared after data synchronization and verification in the cloud. The cloud timing regularization engine decrypts and filters blank and invalid segmented call logs, constructs standardized segmented behavior feature vectors by time domain and manufacturing sub-line, and sends them in batches to the five-layer cloud metering hardware collaboration architecture. The cloud atomic segmented decomposition engine performs minimum-granularity atomic segmented decomposition on the complete process drawings. AI4IS (AI for Intelligent) The AssetScoring four-dimensional valuation unit matches a three-tiered billing rate standard. The MAML (Model-Agnostic Meta-Learning) meta-learning agent synchronously collects the time-series samples of industrial design segment calls in the park every 7 natural days, and iteratively optimizes the billing range of different scenarios in the park. All offline operations, including segment collection, atomic disassembly, four-dimensional tiered billing, and weight iteration, generate complete billing time-series logs and synchronously solidify them to a DAG distributed multi-node hash storage cluster. In the cloud, only lightweight segment call summaries and simplified billing amounts for single batches are distributed to the wearable metering terminal for visualization. Complete high-definition original process drawings and the full library of original time-series samples of park segment calls are only stored in encrypted isolation in the cloud. All atomic segment disassembly, AI4IS four-dimensional tiered valuation, MAML billing weight iteration, and DAG log storage calculations are executed independently by a distributed cloud GPU cluster. The AI-powered wearable metering terminal only completes the offline lightweight segment call summary caching preprocessing operation. The wearable terminal does not have local atomic segment disassembly, four-dimensional tiered billing, MAML weight iteration, or DAG functions. Hash-based evidence storage and inference hardware cannot independently complete the complete business loop of atomic segmentation authorization and billing in industrial design parks by relying on a single wearable metering terminal. The entire collaborative operation system can operate independently in a closed loop without needing to connect to any external third-party industrial intellectual property billing platform.

[0040] Supporting simulation and quantitative test results: monthly segmented billing and reconciliation latency for enterprises in the park is 162ms, segmented metering simulation error is 0.5%, 100% simulation rate of complete retention of summary for lightweight segmented calls during 30 consecutive days of network outages, 64% improvement in efficiency of five-layer cloud segmented parallel processing, 76% reduction in workload for manual reconciliation and accounting of intellectual property rights in the park, and 123ms latency for government audit retrieval of DAG billing logs.

[0041] Example 2: Fragmented Licensing Cloud Solution for Digital Cultural and Creative IP A lightweight five-layer cloud collaboration architecture is adapted to a dedicated simulation GPU cluster for the cultural and creative IP sub-sectors, and is equipped with a lightweight AI smart brain wearable data collection and measurement terminal; Complete business collaboration process: In the absence of network access, illustrators and short video creators in cultural and creative design studios wear AI-powered wearable metering terminals to offline encrypted cache lightweight behavioral summary fragments of illustration and short video material segments; the terminal has a built-in SM3 encryption caching module to compress the segment call behavioral summary to within 200 characters and perform offline encryption for retention; after the wearable metering terminal is connected to the local area network of the cultural and creative enterprise, the encrypted lightweight segment summary is uploaded unidirectionally to the offline segment time-series regularization layer, and the segment summary temporarily cached locally on the terminal is automatically cleared after cloud synchronization verification; the cloud time-series regularization engine constructs standardized material segment feature vectors by time domain and cultural and creative sub-categories, and sends them to the cloud atomic segment decomposition engine after being fed into the five-layer cloud metering hardware collaboration architecture; the engine splits the smallest atomic unit of image elements according to playback duration and dissemination range, and the AI4IS four-dimensional valuation unit generates multi-level tiered authorization billing standards to adapt to the small-amount segment circulation authorization scenario of short videos; offline material segment collection, atomic particle decomposition, and tiered billing calculation operations are uniformly written into DAG. A distributed hash-based billing log storage cluster is used. Only lightweight material sharding summaries and small-amount sharding authorization billing intervals are distributed to wearable metering terminals for visualization in the cloud. Complete high-definition illustration originals, full-volume short video original materials, and the full-domain cultural and creative sharding transaction sample library are stored in encrypted isolation only in the cloud. All atomic sharding, cultural and creative tiered billing, and DAG log hash synchronous calculations rely on cloud GPU clusters for independent execution. The AI-powered wearable metering terminal only performs offline lightweight sharding call summary cache preprocessing. The terminal has no local atomic sharding, tiered billing valuation inference chip, and cannot complete the complete business loop of fragmented authorization billing for digital cultural and creative IPs with a single wearable device. The entire system operates independently in a closed loop.

[0042] The accompanying simulation and quantitative test results show that the matching accuracy of the tiered pricing for cultural and creative products is 97.6%, the retention rate of the lightweight segmented call summary after 30 days of offline operation is 100%, and the workload of manual billing and accounting for small-amount segmented authorizations in cultural and creative products has decreased by 74%.

[0043] Example 3: Atomic Fragmentation Licensing Scheme for Biomedical Patents A lightweight five-layer cloud-based collaborative architecture is adapted to lightweight simulation GPU clusters in specific biomedical R&D tracks, and is equipped with an AI-powered wearable measurement and data acquisition terminal. Complete business collaboration process: In biopharmaceutical laboratories without network access, R&D personnel wear AI-powered wearable metrology terminals to offline encrypted cache lightweight call behavior summary fragments of new drug patents and formulations; the terminal has a built-in SM3 offline encryption caching module to compress the lightweight summary of patent fragment calls to within 200 characters for offline storage; after the terminal connects to the pharmaceutical company's internal LAN, the encrypted lightweight fragment call summary is pushed unidirectionally to the offline fragment time-series regularization layer, and the local temporary cache fragment log is automatically cleared after cloud synchronization and verification; the cloud time-series regularization engine constructs standardized patent fragment feature vectors by time domain and drug R&D subcategories, which are then sent to the five-layer cloud metrology hardware collaboration architecture and connected to the cloud atomic fragment decomposition engine to split pharmacological and formulation content into independent minimum atomic fragment units; AI4IS The four-dimensional valuation unit sets up long-term exclusive tiered billing tiers based on clinical value; offline patent fragment collection, atomic disassembly, and tiered billing full logs are synchronously written to the DAG distributed hash storage cluster, and the complete storage ledger is used for drug regulatory intellectual property compliance audits; in the cloud, only lightweight patent fragment summaries and simplified patent licensing billing prompts are sent to the AI ​​smart wearable terminal for visualization display, and the complete set of original data from new drug trials, complete patent formulation drawings, and the full-domain pharmaceutical patent fragment transaction sample library are only stored in encrypted isolation in the cloud; all pharmaceutical atomic fragment disassembly, patent tiered billing, and DAG log storage calculations are executed independently in the cloud, and the AI ​​smart wearable metering terminal only performs offline lightweight patent fragment call summary cache preprocessing, without local pharmacological atomic fragment disassembly and pharmaceutical tiered billing valuation inference chips, making it impossible to complete the complete business loop of biopharmaceutical patent atomic fragment licensing billing with a single wearable device.

[0044] The accompanying simulation and quantitative test results show that the measurement error of pharmaceutical patent fragmentation is 0.6%, the retention rate of lightweight patent fragmentation abstracts after 30 days of offline testing is 100%, and the workload of manual ledger organization for pharmaceutical companies' patent licensing is reduced by 78%.

[0045] Example 4: Lightweight Billing Base for Rural Folk Creative Projects in County-Level Areas A lightweight five-layer cloud-based collaborative architecture is adapted to lightweight simulation GPU clusters for subdivided agricultural processing industries in counties, and is equipped with an AI smart ring wearable metering and data acquisition terminal; Complete business collaboration process: In county-level agricultural processing workshops and field improvement experimental sites without network access, agricultural R&D operators wear AI-powered smart rings as wearable metering terminals. Offline encrypted caching of lightweight behavioral summary fragments for agricultural product improvement and local handicraft material segmentation is performed. The terminal has a built-in SM3 offline encryption caching module, compressing the lightweight segmentation call summary of agricultural products to within 200 characters and storing it offline with encryption. After the terminal connects to the county-level agricultural public intranet, the encrypted lightweight segmentation call summary is uploaded unidirectionally to the offline segmentation time-series regularization layer. The local temporary cached segmentation log is automatically cleared after cloud synchronization and verification. The cloud-based time-series regularization engine constructs standardized local creative segmentation feature vectors by time domain and agricultural processing category, which are then fed into a five-layer cloud metering hardware collaboration architecture and connected to a cloud-based atomic segmentation decomposition engine to separate planting and processing processes into independent minimum atomic units. The AI4IS four-dimensional valuation unit is adapted to the tiered billing of small-amount segmentation purchases by agricultural enterprises. Offline agricultural segmentation collection, atomic decomposition, and small-amount billing operations are synchronously solidified into the county-level local DAG. A distributed hash-based billing log storage cluster allows local science and technology innovation regulatory agencies to retrieve complete segmented billing statistics ledgers via a DAG standardized interface. Only lightweight agricultural segment summaries and simplified billing amounts for small-amount purchases are distributed via the cloud-based AI smart ring display. A complete set of original agricultural product improvement drawings, original creative materials for local handicrafts, and a county-wide sample library of agricultural segmented transactions are encrypted and stored in the cloud. All agricultural atomic segment decomposition, small-amount tiered billing, and DAG log synchronization calculations are independently executed in the cloud. The AI ​​smart ring wearable metering terminal only performs offline lightweight agricultural segment call summary caching preprocessing; it lacks local agricultural process atomic segment decomposition and small-amount segmented billing inference chips, making it impossible for a single wearable device to complete the complete business loop of lightweight segmented billing for county-level grassroots agricultural creative projects. This is suitable for low-cost local intellectual property segmented transfer scenarios for small and micro-sized agricultural entities in counties.

[0046] The accompanying simulation and quantitative test results show that the matching degree of the county-level agricultural segmented tiered pricing is 97.2%, the retention rate of the lightweight agricultural segmented summary after 30 days of offline testing is 100%, and the workload of manual reconciliation for county-level agricultural segmented procurement has decreased by 77%.

[0047] 7.1 Constraints of the Complete Five-Layer Cloud Collaboration Structure in This Technical Solution This invention employs a collaborative approach involving six modules: offline lightweight sharding call digest encryption acquisition hardware for the AI-powered wearable metering terminal; an offline sharding time sequence regularization layer; a five-layer cloud-based metering hardware collaboration layer; a cloud-based atomic sharding decomposition engine; an AI4IS four-dimensional tiered valuation and linked billing layer; an MAML industry-specific billing weight iteration layer; and a DAG distributed billing log hash storage layer. This collaborative approach is essential to fully achieve the following technical objectives: secure caching of lightweight material sharding call behavior in offline offline workshops and laboratories without internet access; independent acquisition of sharding metering hardware from wearable terminals; automatic decomposition of atomic shards at the smallest granularity; multi-dimensional asset tiered fair billing; autonomous iteration of industry-specific billing parameters; and multi-node trusted auditing of the full sharding billing log. Omitting or migrating any core computational or wearable metering acquisition hardware module will result in corresponding objective technical shortcomings during system operation. 1. If the offline encrypted data collection hardware of the AI ​​smart brain wearable metering terminal is removed, the lightweight segmented call behavior summary fragments cannot be temporarily retained in offline R&D and office scenarios without network access, and the offline intellectual property segmented metering data collection link is completely broken. 2. If any of the five core modules of cloud-based metering hardware collaboration, cloud-based atomic fragmentation, AI4IS four-dimensional valuation, MAML meta-iteration, or DAG evidence storage are omitted or migrated, the system will be unable to synchronously achieve all supporting technical functions such as automatic hardware fragmentation metering, offline cache data synchronization, minimum granularity atomic fragmentation flow, industry-specific tiered fair billing, and full log trusted evidence storage. 3. If the three core computing logics of cloud-based atomic sharding, AI4IS four-dimensional ladder valuation, and DAG log storage are migrated to the local hardware of the wearable metering terminal for execution, the limited computing power of the wearable terminal cannot support the training of millions of sharded time-series samples and multi-dimensional parallel billing operations. The system will lose the core operating effect of the smallest granularity fragmented flow and high-precision sharded pricing matching. 4. If the built-in segmented call metering cache module of the wearable terminal is removed, the system will rely solely on manual offline registration of segmented call volume, and the overall error of segmented metering will rise to more than 8%, making it impossible to achieve automated collection of segmented usage. 5. If the terminal offline segmented cache synchronization mechanism is cancelled, local segmented metering cache data cannot be retained in the event of network interruption, and cannot be synchronized and uploaded after reconnection. This will result in data management shortcomings such as incomplete segmented call bills and inability to match government and enterprise reconciliation. 6. If the cloud-based three-layer minimum atomic fragmentation engine is removed, the system can only perform unified authorization on the entire machine for complete materials, and does not have the ability to split the minimum granular atomic fragments and adapt to fragmented small-amount transfers; 7. If the AI4IS four-dimensional valuation linkage tiered billing engine is removed, the system can only use a single fixed unit price to calculate the segment fee, the matching degree of high-value intellectual property segment pricing will drop significantly, and the ability to adapt to industry-specific differentiated tiered billing will be lacking. If the DAG distributed multi-node hash billing log storage cluster is removed, offline sharding collection, atomic disassembly, and tiered billing operation records will only be stored on a single server, resulting in a lack of credible electronic evidence archives for park science and technology innovation government affairs, cultural and creative platforms, drug supervision, and county-level rural revitalization audits.

Claims

1. A cloud-based AI4S quantitative licensing and multi-dimensional billing system for atomic fragmented intellectual assets, characterized in that: The overall architecture consists of five independent cloud-terminal collaborative layers: a lightweight data acquisition terminal hardware layer, a cloud-based atomic sharding and decomposition layer, an AI4S valuation-linked tiered billing layer, a MAML (Model-Agnostic Meta-Learning) billing rule meta-iteration layer, and a DAG (Directed Acyclic Graph) billing log distributed storage layer. The entire atomic sharding, tiered rate calculation, and metering aggregation operations are deployed using a distributed cloud GPU cluster. The data acquisition terminal has no local sharding or billing calculation chips. The system includes nine independent functional units: a lightweight data acquisition terminal with unit-invoking metering hardware and offline billing cache modules; a cloud-based atomic sharding engine; an AI4S four-dimensional valuation-linked billing engine; a multi-dimensional tiered rate generation unit; a MAML billing parameter self-training unit; an offline bill encryption and synchronization unit; an SM2 / SM3 / SM4 bill encryption unit; a DAG multi-node billing log storage cluster; and a government and enterprise reconciliation and verification interface. The lightweight data acquisition terminal has two built-in dedicated hardware modules: unit-invoking metering hardware and an offline billing cache module. The unit-invoking metering hardware collects the frequency and duration of atomic shard calls in real time to generate simplified bill fragments. The offline billing cache module only temporarily stores creative summaries and simplified bill fragments of ≤200 characters. Complete shard assets and full rate tables are only stored in the cloud. The terminal only opens a one-way TCP channel for uploading metering data. Local temporary bills are automatically erased after connecting to the network. There is no permission to read complete assets in the cloud. The cloud-based atomic sharding engine outputs the smallest granular independent atomic authorized shards according to three-dimensional rules of technology, design, and scenario, and each shard is assigned a unique hash identifier; The AI4S valuation-linked tiered billing engine uses fair value of fragments as a benchmark and generates multi-level differentiated tiered rates based on call frequency, cycle, and authorization scope to distinguish three types of commercial licensing scenarios. The MAML billing rule meta-iteration unit synchronizes authorized transaction samples monthly, with a 7:3 training / grayscale division, and refits the tiered interval every 7 days, updating the billing standard imperceptibly. The DAG multi-node evidence storage cluster generates SM3 hashes for terminal metering, sharding authorization, and deduction operations, and synchronously solidifies them, with a supporting independent reconciliation interface for government and enterprises; The system has a built-in independent cloud terminal collaborative simulation dataset, and the entire hardware module, five-layer architecture, and all algorithms can be independently and completely reproduced.

2. The system according to claim 1, characterized in that, The measurement simulation error for terminal hardware materials is ≤0.6%.

3. The system according to claim 1, characterized in that, A single Creative Cloud instance is broken down into an average of 28 independent atomic licensing units.

4. The system according to claim 1, characterized in that, AI4S's valuation tiered pricing matching rate reached 97.4%.

5. The system according to claim 1, characterized in that, MAML uses a 7-day cycle for billing rule iteration.

6. A method for quantitative licensing and multi-dimensional billing execution of atomic fragmented intellectual assets in the cloud-based AI4S system, characterized in that... The entire process is executed based on a five-layer cloud-terminal collaborative architecture, including the following steps: A lightweight data acquisition terminal uses a built-in unit to call the metering hardware to collect segmented data, and stores it in the offline billing cache module when the network is disconnected. b. After the terminal connects to the network, the simplified bill SM is encrypted and uploaded to the cloud synchronization unit, and local temporary bills are automatically cleared; c. The cloud-based atomic sharding engine splits the complete asset into three dimensions and outputs independent atomic authorized shards; The dAI4S valuation engine outputs the fair value of each segment, generating multi-dimensional tiered licensing rates. eMAML units automatically iterate and optimize tiered price ranges and graded unit prices based on monthly transaction samples. f. Enterprise segmented calls are automatically charged according to the corresponding tier, generating complete billing records; g. Full-process operation of SM3 hash writing to DAG multi-node billing log cluster; h Government and enterprises can complete liquidation audits by retrieving stored invoices through the reconciliation interface; All segmentation and rate calculations rely on cloud-based GPU clusters, with no local billing computing power on the terminal, and the entire process can be independently simulated and reproduced.

7. The method according to claim 6, characterized in that, The offline billing cache module only temporarily stores simplified bill fragments and does not store complete atomic fragment assets.

8. A computer-readable storage medium storing a program thereon, characterized in that, When the program is executed by a cloud GPU processor, it implements the execution method described in any one of claims 6 and 7.

Images