Method and system for enterprise deployment asset valorization and business support based on a meta-linguistic system

Abstract

The present application proposes an enterprise deployment asset value-added and business support method and system based on a meta-language system. Economic assetization, governance decision-making, reliable execution, performance adaptation, open operation, capability asset value-added, business support, etc. are achieved. The present application realizes integrated deployment, business self-certification, asset value-added, closed-loop unified anchor point, and audit book throughout all steps. The method, system, storage medium, and electronic device form a protection link. Under the same business load conditions, the system throughput is improved, the average response time is reduced, and the total computing power consumption is reduced. It can be deployed in cloud GPU clusters, enterprise private servers, edge inference devices, and other enterprise computing environments running large model agents, and can be integrated into mainstream infrastructure such as PyTorch, DeepSpeed, vLLM, Kubernetes, and Docker, with industrial applicability.

Description

Technical Field

[0001] This invention relates to the fields of artificial intelligence and enterprise deployment technology, and in particular to methods and systems for enterprise deployment asset value enhancement and business support based on the meta-language system. Background Technology

[0002] Enterprise-level AI kernels generally suffer from deficiencies in deployment and commercialization. The deployment layer lacks integrated support for economic assetization, two-layer governance, trusted execution, adaptive granularity, formal verification, standardized openness, and unified runtime. The execution process lacks verifiable commercial credential infrastructure; cost measurement, execution statistics, and audit ledgers are not bound to verifiable credentials, making compliance, cost, transaction, and cross-domain collaboration scenarios unverifiable. Capability asset memory data, skills, training data, operating costs, and human feedback are managed in isolation, lacking categorized and graded accumulation, collaborative evolution processes, inference-level economic scheduling, privacy data provision, and human-in-the-loop governance. There is no unified anchor point between these capabilities and the commercial closed loop; the registry and audit ledger are missing, preventing the formation of a complete commercial closed loop encompassing deployment, value-added services, and monetization. Summary of the Invention

[0003] In view of this, in order to solve the problems existing in the technical background, the present invention proposes a method and system for enterprise deployment of asset value enhancement and business support based on the meta-language system. Specifically, it includes the following: A method for enterprise deployment of asset enhancement and business support based on a meta-language system, including Economic assetization quantifies the frequency of use of capability units, the quality of skill combinations, and the contribution of computing power into tradable economic assets, and adopts a decaying minting quantity for repeated execution to prevent coin fraud. Governance decisions are made through either automated technical review or community voting. When both automated technical review and community voting are used, the results of automated technical review take precedence over community voting. Trusted execution enables high-security capabilities to be performed in a trusted execution environment. The trusted execution environment can be a hardware-level trusted execution environment or a software-isolated execution environment. Execution in a hardware-level trusted execution environment includes a five-stage process: instance creation, confidential loading, secure computation, sealed output, and remote authentication. Performance adaptation: Adaptively selects atomic, combinatorial, and model-level execution granularity according to task complexity; dynamically compiles virtual capabilities by combining high-frequency capabilities and performs formal verification of high-security-level capabilities to generate verification certificates. Open operation exposes capability units through standardized tool interfaces or provides unified runtime execution services; Capability asset value enhancement involves classifying and storing execution data in a hierarchical manner to form capability assets, and performing at least one of the following: co-evolution, inference-level economic scheduling, differential privacy-synthesized data supply, and human-in-the-loop processing. For business support, it collects cost measurement and execution statistics summaries, binds them to the audit ledger to generate verifiable credentials with audit chain pointers, and distributes them to compliance, cost, transaction, and cross-domain collaboration scenarios by tag; each step runs collaboratively based on the capability unit registry, and the audit ledger records execution and governance events in a chain.

[0004] Furthermore, the economic assetization and governance decision-making steps also include at least one of the following: minting tokens based on the overall cost of skill combinations, where the overall cost of remapping is the smaller of the maximum path cost of the combination and the upper limit constraint; minting verified and registered skill combinations into non-fungible tokens for trading; transferring only the right to use skill arrangement templates; obtaining equity points based on computing power contribution and periodically exchanging them; third-party incubation of proposal sponsorship tokens; sponsors receiving a share of the minting amount after the capability review is passed until the sponsorship limit is reached; adopting a three-layer smart contract governance consisting of a proposal contract, a voting contract, and an execution contract; proposals are accompanied by technical specifications and a security deposit; the voting weight is the square root of the pledged amount; proposals must simultaneously meet the voting power ratio threshold and the participation rate threshold to pass; staking is subject to a cooldown period; large-amount votes are delayed in taking effect; if a governance attack is detected, the governance window is suspended and unexecuted proposals are rolled back; high-security-level adjustment proposals must be accompanied by evaluation opinions and cannot rely solely on community voting for approval.

[0005] Furthermore, the trusted execution steps and performance adaptation steps also include at least one of the following: a five-stage process and a four-stage protocol phase mapping in the isolated execution cabin; remote authentication failure resulting in destruction and reconstruction of trusted instances downgrading to software isolated execution; introducing randomized execution duration disturbances to resist timing side-channel inference; using zero-knowledge proof output to verify results without viewing the internal state; relying on a five-dimensional complexity vector to complete granularity selection; high-frequency combination compilation of virtual capabilities and mounting of governance rules, including inheritance of the highest security level, registration and filing, automatic invalidation and recompilation upon changes, and auditing traces; formal verification using model testing and relational analysis to generate verification certificates; the highest security level capability registration must hold a certificate; if a counterexample occurs, the path is output; multiple verification failures result in downgrading or refusal of registration; combining capabilities to construct a deep learning computation graph, completing operator mapping, graph optimization, reverse compilation, and batch scheduling; the compiled product is registered and bound to the source version in the registry; upgrade failures result in rollback to the default backend or interpreted execution; and registering approximate gradients for non-microoperable operations to support backpropagation.

[0006] Furthermore, the open operation steps also include at least one of the following: converting the complete capability definition into a standardized tool interface schema description; mapping security levels to corresponding authentication requirements; registering tool description sets to list endpoints, including grouping and dependency matrices; delegating pipelines to complete call validity verification, parameter mapping, registry calls, and result encapsulation; being compatible with synchronous and asynchronous modes; synchronizing registry changes via callbacks; discovering cross-server aggregation and filtering by security level, version, and latency; using multiple priority queues for cost-aware scheduling; triggering deceleration when budget exceeds limits; configuring resource limits, system call whitelists, prohibiting outbound networks, and terminating upon timeout in isolation chambers; storing execution results in two-level caches; batch invalidating caches for registry changes; collecting and monitoring indicators in multiple dimensions; performing elastic scaling with a cooldown period based on load delays; and only reducing queue weights when computing power budget is exhausted, without rejecting business requests.

[0007] Furthermore, the business support steps also include at least one of the following: aggregating formal verification certificates, trusted hardware execution credentials, and governance decision-making traceability to generate compliance evidence packages; outputting reports according to regulatory templates; supporting lightweight verification of single credentials; and automatically remapping security levels and execution strategies when regulatory rules are updated. Verifiable credentials include credential identifiers, capability identifiers, execution statistics summaries, cost measurements, audit chain pointers, security levels, business scenario tags, and issuer signatures. Multi-source evidence hash aggregation is used to bind credentials, and the distribution scope of credentials is limited according to security levels. Credentials are converted into compliance reports, cost invoices, settlement credentials, and cross-domain call credentials, and the validity period and verifiable scope of credentials are set.

[0008] Furthermore, the cost scenario, transaction scenario, and cross-domain collaboration scenario also include at least one of the following: multi-dimensional cost allocation based on execution cost; budget overrun triggering scheduling deceleration and outputting feedback reports; fault location based on capability-level call chains; joint optimization of cache capacity and scheduling strategies; batch invalidation of associated caches due to registry changes; scaling up or down execution levels based on cost delay thresholds; adjusting scheduling weights based on gateway statistical feedback; generating valuation ranges based on execution statistical summaries; generating settlement vouchers with audit chain pointers for cross-entity call metering; automatically collecting evidence for arbitration in transaction disputes; completing asset settlement using a points plus settlement voucher model, with points not convertible to fiat currency; an integrated cross-organization call execution link, directly rejecting calls due to any abnormality in any link; resolving directory version and permission conflicts and leaving audit trails; storing cross-domain call evidence in a shared audit chain; and supporting lightweight verification.

[0009] Furthermore, the capability asset value-added steps also include at least one of the following: classifying execution data into five categories, scoring it in four dimensions, filtering it in three layers, and storing it in layers as capability assets; refining the model and allowing it to evolve with feedback; each asset having an attached lineage chain; the main agent and sub-agents completing co-evolution according to a five-step process, executing a ten-level closed-loop cycle, with sub-agent capabilities registered in the registry; inference request execution model downgrading routing, semantic caching, batch inference, hint compression, and quantitative inference at least one of these to complete cost-effectiveness attribution; generating desensitized synthetic data using a differential privacy framework, registering lineage traceability information to supplement training data; manually processing and returning low-confidence request routing, implementing incentive control according to labeled economics; identifying underutilized assets and performing archiving to release computing power, market authorization, transformation and upgrading, and depreciation revaluation for recovery and monetization.

[0010] A system for enterprise-deployed asset value enhancement and business support based on a meta-language system includes an economic incentive unit, a governance unit, a trusted execution unit, a granularity selection unit, a formal verification unit, a framework adaptation unit, a protocol integration unit, a runtime management unit, a measurement unit, a voucher unit, an audit unit, a distribution unit, a compliance unit, a cost unit, a transaction unit, a cross-domain unit, a data storage unit, a collaboration unit, a supply unit, an economic scheduling unit, and a human-in-the-loop unit. Each unit implements the method steps corresponding to any one of claims 1 to 7, and all units operate collaboratively with the capability unit registry and audit ledger as the data hub.

[0011] A computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the above method.

[0012] An electronic device includes a processor and a memory, the memory storing instructions executable by the processor, which, when executed, implement the steps of the method described above.

[0013] The above technical solution has the following beneficial effects: This invention achieves integrated deployment, commercial self-verification, asset appreciation, and a closed-loop system with a unified anchor point. The registry and audit ledger connect all steps, and the method, system, storage media, and electronic devices form a protective link. Under the same business load, system throughput is improved, average response latency is reduced, and total computing power consumption is lowered. It can be deployed in various enterprise computing environments running large-scale intelligent models, such as cloud GPU clusters, enterprise private servers, and edge inference devices. It can be integrated with mainstream infrastructures such as PyTorch, DeepSpeed, vLLM, Kubernetes, and Docker, demonstrating industrial applicability. Attached Figure Description

[0014] Figure 1 This is a flowchart illustrating the overall process of the seven-group fusion framework of the present invention; Figure 2 This is a detailed diagram of the deployment layer of the present invention; Figure 3 This is a flowchart of the commercial credential infrastructure of the present invention; Figure 4 A flowchart of five paths to enhance capability assets; Figure 5 This is a diagram of the system module structure. Detailed Implementation

[0015] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0016] Example 1: A method for enterprise deployment of asset enhancement and business support based on a meta-language system, including... Economic assetization quantifies the frequency of use of capability units, the quality of skill combinations, and the contribution of computing power into tradable economic assets, and adopts a decaying minting quantity for repeated execution to prevent coin fraud. Governance decisions are made through either automated technical review or community voting. When both automated technical review and community voting are used, the results of automated technical review take precedence over community voting. Trusted execution enables high-security capabilities to be performed in a trusted execution environment. The trusted execution environment can be a hardware-level trusted execution environment or a software-isolated execution environment. Execution in a hardware-level trusted execution environment includes a five-stage process: instance creation, confidential loading, secure computation, sealed output, and remote authentication. Performance adaptation: Adaptively selects atomic, combinatorial, and model-level execution granularity according to task complexity; dynamically compiles virtual capabilities by combining high-frequency capabilities and performs formal verification of high-security-level capabilities to generate verification certificates. Open operation exposes capability units through standardized tool interfaces or provides unified runtime execution services; Capability asset value enhancement involves classifying and storing execution data in a hierarchical manner to form capability assets, and performing at least one of the following: co-evolution, inference-level economic scheduling, differential privacy-synthesized data supply, and human-in-the-loop processing. For business support, it collects cost measurement and execution statistics summaries, binds them to the audit ledger to generate verifiable credentials with audit chain pointers, and distributes them to compliance, cost, transaction, and cross-domain collaboration scenarios by tag; each step runs collaboratively based on the capability unit registry, and the audit ledger records execution and governance events in a chain.

[0017] The economic assetization and governance decision-making steps also include at least one of the following: minting tokens based on the overall cost of skill combinations, where the overall cost of remapping is the smaller of the maximum path cost of the combination and the upper limit constraint; minting verified and registered skill combinations into non-fungible tokens for trading; transferring only the right to use skill orchestration templates; obtaining equity points based on computing power contribution and exchanging them periodically; third-party incubation of proposal sponsorship tokens; sponsors receiving a share of the minting amount after the capability review is passed until the sponsorship limit is reached; adopting a three-layer smart contract governance consisting of a proposal contract, a voting contract, and an execution contract; proposals are accompanied by technical specifications and a security deposit; the voting weight is the square root of the pledged amount; proposals must simultaneously meet the voting power ratio threshold and the participation rate threshold to pass; staking is subject to a cooldown period; large-amount votes are delayed in taking effect; if a governance attack is detected, the governance window is suspended and unexecuted proposals are rolled back; high-security-level adjustment proposals must be accompanied by evaluation opinions and cannot rely solely on community voting for approval.

[0018] The trusted execution steps and performance adaptation steps also include at least one of the following: a five-stage process and a four-stage protocol phase mapping in the isolated execution cabin; remote authentication failure resulting in destruction and reconstruction of trusted instances downgrading to software isolated execution; introducing randomized execution duration perturbations to resist timing side-channel inference; using zero-knowledge proof output to verify results without viewing the internal state; relying on a five-dimensional complexity vector to complete granularity selection; high-frequency combination compilation of virtual capabilities and attachment of governance rules, including inheritance of the highest security level, registration and filing, automatic invalidation and recompilation upon changes, and audit trails; formal verification using model testing and relational analysis to generate verification certificates; registration of the highest security level capability must be accompanied by a certificate; outputting the path if a counterexample occurs; multiple verification failures result in downgrading or refusal of registration; constructing a deep learning computation graph by combining capabilities, completing operator mapping, graph optimization, reverse compilation, and batch scheduling; registering the compiled product in the registry and binding the source version; reverting to the default backend or interpreting execution upon upgrade failure; registering approximate gradients for non-microoperable operations to support backpropagation.

[0019] Furthermore, the open operation steps also include at least one of the following: converting the complete capability definition into a standardized tool interface schema description; mapping security levels to corresponding authentication requirements; registering tool description sets to list endpoints, including grouping and dependency matrices; delegating pipelines to complete call validity verification, parameter mapping, registry calls, and result encapsulation; being compatible with synchronous and asynchronous modes; synchronizing registry changes via callbacks; discovering cross-server aggregation and filtering by security level, version, and latency; using multiple priority queues for cost-aware scheduling; triggering deceleration when budget exceeds limits; configuring resource limits, system call whitelists, prohibiting outbound networks, and terminating upon timeout in isolation chambers; storing execution results in two-level caches; batch invalidating caches for registry changes; collecting and monitoring indicators in multiple dimensions; performing elastic scaling with a cooldown period based on load delays; and only reducing queue weights when computing power budget is exhausted, without rejecting business requests.

[0020] The business support steps also include at least one of the following: aggregating formal verification certificates, trusted hardware execution credentials, and governance decision-making traceability to generate compliance evidence packages; outputting reports according to regulatory templates; supporting lightweight verification of single credentials; and automatically remapping security levels and execution strategies when regulatory rules are updated. Verifiable credentials include credential identifiers, capability identifiers, execution statistics summaries, cost measurements, audit chain pointers, security levels, business scenario tags, and issuer signatures. Multi-source evidence hash aggregation is used to bind credentials, and the distribution scope of credentials is limited according to security levels. Credentials are converted into compliance reports, cost invoices, settlement credentials, and cross-domain call credentials, and the validity period and verifiable scope of credentials are set.

[0021] The cost scenario, transaction scenario, and cross-domain collaboration scenario also include at least one of the following: multi-dimensional cost allocation based on execution cost; budget overrun triggering scheduling deceleration and outputting feedback reports; fault location based on capability-level call chains; joint optimization of cache capacity and scheduling strategies; batch invalidation of associated caches due to registry changes; scaling up or down execution levels based on cost delay thresholds; adjusting scheduling weights based on gateway statistical feedback; generating valuation ranges based on execution statistical summaries; generating settlement vouchers with audit chain pointers for cross-entity call metering; automatically collecting evidence for arbitration in transaction disputes; completing asset settlement using a points plus settlement voucher model, with points not convertible to fiat currency; an integrated cross-organization call execution link, directly rejecting calls due to any abnormality in any link; resolving directory version and permission conflicts and leaving audit trails; storing cross-domain call evidence in a shared audit chain; and supporting lightweight verification.

[0022] The capability asset enhancement steps also include at least one of the following: classifying execution data into five categories and scoring it in four dimensions; filtering it in three layers and storing it in layers as capability assets; refining the model and allowing it to evolve with feedback; each asset having an attached lineage chain; the main agent and sub-agents co-evolving according to a five-step process, executing a ten-level closed-loop cycle, with sub-agent capabilities registered in the registry; inference request execution model downgrading routing, semantic caching, batch inference, hint compression, and quantitative inference at least one of these to complete cost-effectiveness attribution; generating desensitized synthetic data using a differential privacy framework, registering lineage traceability information to supplement training data; manually processing and returning low-confidence requests, implementing incentive control according to labeled economics; identifying underutilized assets and performing archiving to release computing power, market authorization, transformation and upgrading, and depreciation revaluation for recovery and monetization.

[0023] In this embodiment, the system quantifies the frequency of use of capability units, the quality of skill combinations, and computing power contributions into tradable economic assets. Tokens are minted based on the overall cost after skill combination remapping, where the overall remapping cost equals the smaller of the maximum path cost of the combination and the upper limit constraint. An exponential decaying minting amount is used to prevent token fraud for repeated executions. Verified and registered skill combinations are minted into non-fungible tokens and listed for trading; transfers only transfer the template usage right. Rights points are awarded based on computing power contributions and are periodically redeemed. Third-party incubation proposals sponsor tokens; after a capability passes review, the sponsor receives a share of the minting amount until the upper limit is reached. Market disputes are adjudicated through a smart contract arbitration channel using execution statistical summaries as evidence. Governance employs a two-tiered structure of technical automatic review and community voting, with technical judgment taking priority. The technical automatic review is executed by the registry review engine, performing atomicity checks on proposals, verifying whether candidate capabilities can be expressed by combinations of existing capabilities, orthogonality checks, checking for substantial overlap between functional domains and existing capabilities, verifying the sufficiency of evidence, and confirming whether closed-loop evidence meets a predetermined threshold. The review process outputs a technical assessment conclusion. Proposals rejected by the technical assessment are terminated immediately and do not proceed to community voting. Proposals that pass the technical assessment proceed to community voting. In case of conflict between the voting results and the technical assessment, the technical assessment prevails. The review report, with structured supporting evidence, is included in the audit ledger. A three-tiered contract system—proposal, voting, and execution—is implemented. Voting weight is calculated based on the square root of the pledged amount. Approval requires both a minimum approval vote percentage and a minimum participation rate threshold. A cooling-off period is applied to staking; large-scale voting is delayed; and in the event of a governance attack, the process is immediately paused and rolled back. Adjustments to high security levels must be accompanied by an assessment opinion, and pure community voting cannot pass alone.

[0024] Example 2, based on Example 1, executes high-security capabilities in a hardware-level trusted execution environment, undergoing five stages: instance creation, confidential loading, secure computation, sealed output, and remote authentication. These stages correspond to the four-stage protocol of the isolated execution chamber. Remote authentication failures result in rejection, destruction, reconstruction, and downgrading to software isolated execution. Randomized perturbations are introduced to the execution duration to resist timing side-channels. Zero-knowledge proof outputs allow the verifier to verify results without viewing the input's internal state. Performance adaptation uses a five-dimensional complexity vector to adaptively select granularity at the atomic, combinatorial, and model levels. High-frequency combinations are dynamically compiled into virtual capabilities and loaded with governance rules, inheriting the highest security level, registering and filing, automatically recompiling upon source changes, not participating in fingerprint completeness determination, and leaving audit trails. High-security-level combinations perform formal verification, model testing, and relational analysis to generate verification certificates. These certificates participate in registration review and security dimension scoring; the highest security level requires a certificate. Counterexamples are output as paths, and consecutive failures result in downgrading or refusal of registration. Capability combinations are constructed as a deep learning framework computation graph, involving operator mapping, graph optimization, reverse compilation, and batch scheduling. The compiled artifacts are registered as independent backends and bound to the source version. Major upgrades automatically invalidate and recompile. Failures fall back to the default backend. Graph compilation failures fall back to interpretation and execution and record operators to be mapped. Approximate gradients are registered for non-differentiable operations to support backpropagation.

[0025] After high-frequency combinations are dynamically compiled into virtual capabilities, the call path is interpreted and dispatched step by step. Registry lookup, parameter mapping, sequential execution of multiple operators, and result encapsulation are performed sequentially, compressed into compiled artifacts for direct invocation, reducing single-call processing latency, and the benefits accumulate linearly with the number of calls. When the two-level cache hits, the cached result is returned directly, avoiding duplicate execution, duplicate metering, and duplicate billing. The cache key contains capability identifiers and input summaries, and changes are batch invalidated via registry callbacks to ensure consistency. The number of data accesses for hit requests is reduced from a complete execution chain to a single key lookup. Model degradation routing in economic scheduling replaces high-cost, large-model calls with low-cost capability units. Semantic caching reduces the number of duplicate requests, batch inference merges the number of requests, hint compression shortens the input length, and quantization inference reduces the computational load per request, resulting in an overall reduction in the number of high-cost, large-model calls. Computation graph compilation and execution operator mapping, graph optimization, and batch scheduling eliminate duplicate subgraph computations, reuse intermediate tensors across requests, and reduce runtime memory usage. In summary, under the same business load, system throughput is improved, average response latency is reduced, and total computing power consumption is reduced.

[0026] Example 3, based on Example 1, converts the complete capability definition into a standardized tool interface schema, and maps security levels to authentication requirements. Tool description sets are registered to list endpoints, including grouping and dependency matrices. The delegated pipeline performs legality checks, parameter mapping, registry calls, and result encapsulation, supporting both synchronous and asynchronous modes. Registry changes are automatically synchronized via callbacks. Cross-server aggregation discovery is sorted and deduplicated by security level, version, and latency. Runtime scheduling is cost-aware across multiple priority queues, using weighted fair queues and over-budget deceleration. Execution is performed in an isolated chamber, with resource limits, system call whitelists, outbound network restrictions, and timeout termination. A two-level cache is used, with keys containing capability identifiers and input summaries, and batch invalidation upon changes. Multi-dimensional monitoring collects data on latency, throughput, error rate, resources, and cache efficiency. Load latency triggers elastic scaling with a cooldown period. Execution-level scheduling receives engine-level computing power budget as queue weight constraints; budget exhaustion results in weight reduction without rejection.

[0027] Example 4, based on Example 1, collects cost measurement data and execution statistical summaries during the execution of capability units, binds them to immutable records in the audit ledger, and generates verifiable credentials carrying audit chain pointers. These credentials include a credential identifier, capability identifier, execution statistical summary, cost measurement, audit chain pointer, security level, business scenario tag, and issuer signature. Multi-source evidence is hash-aggregated and bound to the same credential. Distribution is restricted according to security level. Credentials are converted into compliance reports, cost invoices, settlement vouchers, or cross-domain call credentials based on business scenario tags, and their validity period and verifiable scope are set.

[0028] Example 5, based on Example 1, focuses on compliance. Formal verification certificates, trusted hardware execution credentials, and governance decision logs are aggregated into a compliance evidence package, and reports are generated according to regulatory templates. Lightweight verification of single credentials without requiring a complete audit ledger is provided to the verification party. Regulatory requirements are automatically mapped to security levels and execution strategies, with automatic remapping and review of existing strategies upon updates. Costs are allocated to capabilities, skill sets, and business lines based on execution costs and unit prices; budget overruns trigger scheduling slowdowns and generate feedback reports. Cross-isolation and gateway propagation contexts are used to locate root causes of failures based on capability-level call chains. Cache and scheduling are jointly optimized based on cache hit rate and computing cost mapping. Registry changes result in batch cache invalidation based on dependency impact sets. Cost and latency are scaled up and down based on joint threshold levels. Gateway call volume statistics are fed back to the cost budget and scheduling weights are adjusted. For transactions, an estimated price range is generated based on execution statistics summaries, and the transaction price is anchored; deviation is incorporated into the valuation model training. Cross-entity revenue sharing is calculated based on call metrics, generating non-repudiable settlement credentials, summarizing metrics, providing revenue sharing details, and audit chain pointers. Disputes are automatically collected as execution summary evidence for arbitration. Skill assetization and settlement are implemented using a points-based plus settlement voucher model. Points cannot be exchanged for fiat currency or pledged. A cross-domain, cross-organizational integrated chain of directory discovery, two-way authentication, metering and billing, and settlement vouchers is executed; any missing link will result in rejection. Multi-organizational directory versions and permission conflicts are merged and resolved for consistency, with audit trails maintained. Evidence of cross-organizational calls is stored in a shared audit chain and lightweight verification is provided.

[0029] The compliance evidence package includes formal verification certificate hashes, trusted hardware execution credentials, governance decision traces, and a hash aggregation chain. The settlement credentials include metering summaries, revenue sharing details, audit chain pointers, and signatures. The evidence package aggregates formal verification certificates, trusted hardware execution credentials, and governance decision traces, generating reports according to regulatory templates. The settlement credentials carry metering summaries, revenue sharing details, and audit chain pointers, and are signed to ensure non-repudiation, supporting two-way authentication across cross-domain call chains and integrated metering, billing, and settlement.

[0030] Example 6, based on Example 1, involves asset accumulation. Data during the execution of capability units is categorized into five types: random errors, redundancy, dirty data, invalid data, and premium data. A four-dimensional scoring system is used, with a scarcity weight of 0.25, a gain weight of 0.30, a uniqueness weight of 0.20, and a reusability weight of 0.25. After three layers of filtering, data is stored in three layers: short-term (7 days), long-term (30 days), and premium data permanently, accumulating as reusable capability assets. The purification model, based on manually labeled feedback and downstream task gain signals, triggers retraining at predetermined intervals or error rate thresholds. The retraining results are evaluated and then gradually replaced, achieving self-evolution with feedback. Each asset carries a lineage chain, including source execution records, purification paths, and audit chain pointers. Co-evolution involves a master agent and multiple sub-agents coexisting within a long context. A five-step process—exploration, comparison, distillation, transformation, and solidification—is used to collaboratively enhance capability assets. Skills are shared across the entire domain and synchronized with versions. A ten-level collaborative closed loop is implemented, including task distribution, sub-agent execution, result comparison, difference distillation, skill solidification, domain synchronization, effect verification, feedback collection, redistribution, and closed-loop auditing. Sub-agent capabilities are registered in a registry. Economic scheduling, based on the historical benefits and costs of capability units, performs at least one of the following on inference requests: model degradation routing, semantic caching, batch inference, hint compression, and quantified inference. Cost-effectiveness analysis is performed, and attribution is trained based on inference cost. Data supply generates synthetic data under differential privacy constraints, with an adjustable privacy budget ε. K-anonymity and field desensitization are implemented. After registering the synthetic data lineage and traceability information, capability asset training data is supplemented. Human-in-the-loop routing of low-confidence requests to human processing and backflow is implemented based on confidence levels. Annotator incentives are applied according to annotation economics, with incentive coefficients graded by annotation quality. Quality grading, cost constraints, and human feedback are used to feed back and solidify the closed loop. The process of recovering and monetizing underutilized assets is linked to the self-awareness of the continuous evolutionary side. The evolutionary engine marks underutilized capability units, and when there are zero calls for 14 consecutive days, and the intention to recover or transform is output, the business side executes the corresponding disposal. Downgrading and archiving releases computing power, and resources are redistributed to high-utilization assets, with 70% redistributed and 30% retained as a safety net. Monetization is achieved through skill market integration: transfers only transfer the right to use templates, rentals are billed based on calls, and packaged authorizations are charged based on scenarios. Transformation involves distilling data to supplement training data for capability assets and dismantling them into reusable components for re-registration. The asset ledger is revalued based on depreciation value, and verifiable vouchers are generated throughout the disposal process and recorded in the audit ledger.

[0031] The decay coefficient of decayed casting quantity, the short-term and long-term premium tiered storage period of capability assets, the weight of four-dimensional scoring, the privacy budget of differential privacy, the degradation ratio and cache capacity threshold of inference-level economic scheduling, and the incentive coefficient of annotation economics are all selected from a predetermined range, with at least one parameter automatically adjusted based on operational feedback within the predetermined range. Capability units can be replaced with any of the following: meta-languages, primitives, atomic functions, or micro-operations. Verifiable credentials can be replaced with any of the following: non-fungible tokens, compliance reports, settlement credentials, or cross-domain call credentials. Audit ledgers can be replaced with any of the following: blockchain, hash chain, or trusted timestamp service. Human-in-the-loop can be replaced with expert systems or automatic adjudication by rule engines. Synthetic data can be replaced with any of the following: data augmentation, distilled samples, or simulation data. Co-evolution of master and sub-agents can be replaced with peer-to-peer or federated collaboration. Formal verification can be replaced with any of the following: static analysis, model testing, or theorem proving. Hardware-level trusted execution environments can be replaced with any of the following: software isolation, container sandbox, or virtual machine isolation, all without departing from the scope of this invention.

[0032] Example 7: Based on Example 1, the program code is stored on a computer-readable storage medium, such as a USB flash drive, external hard drive, ROM, RAM, magnetic disk, or optical disk, and is executed by a processor to implement all the aforementioned method steps. The electronic device includes a processor and a memory; the instruction execution implements all the aforementioned method steps. Device forms include servers, edge computing devices, personal computers, and large-scale model inference devices.

[0033] This invention's system comprises twenty-one functional units. The economic incentive unit executes decaying minting amounts, equity points, and three-layer smart contract arbitration. The governance unit executes governance decisions and dispute arbitration, writing the rulings to the audit ledger. The trusted execution unit supports hardware-level trusted execution and zero-knowledge proof verification. The granularity selection unit selects inference and execution granularity based on performance adaptation requirements. The formal verification unit performs model verification, relational analysis, and computational graph verification. The framework adaptation unit adapts to mainstream machine learning frameworks. The protocol integration unit exposes capability interfaces using standard protocols. The runtime management unit maintains the registry and runtime scheduling. The metering unit measures costs and call volumes by call chain. The voucher unit issues hash aggregation vouchers and settlement vouchers. The auditing unit maintains an immutable shared audit chain. The distribution unit executes directory discovery and distribution scope control. The compliance unit aggregates compliance evidence packages and maps them to regulatory requirements. The cost unit executes cost allocation and budget control. The transaction unit executes valuation, profit sharing, and settlement arbitration. The cross-domain unit executes authentication, billing, and consistency merging across organizational call chains. The data processing unit comprises three modules: a sedimentation unit, a coordinating unit, and a data supply unit. The sedimentation unit performs five-category classification, four-dimensional scoring, three-layer filtering, and three-layer storage. The coordination unit performs exploration, comparison, distillation, transformation, and solidification collaboration between the main agent and sub-agents. The supply unit generates synthetic data under differential privacy and k-anonymity constraints. The economic scheduling unit performs model degradation routing, semantic caching, and elastic scaling. The human-in-the-loop unit routes manually processed data according to confidence level and feeds it back in. All units operate collaboratively with a registry and audit ledger as the data hub, and can be deployed on servers, edge computing devices, or large model inference devices.

[0034] The basic principles and main features of the present invention have been described above. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are only illustrative of the principles of the present invention. Various changes and modifications can be made to the present invention without departing from the spirit and scope of the present invention. All such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the invention is defined by the appended claims and their equivalents.

Claims

1. A method for enterprise deployment of asset enhancement and business support based on a meta-language system, characterized in that, include Economic assetization quantifies the frequency of use of capability units, the quality of skill combinations, and the contribution of computing power into tradable economic assets, and adopts a decaying minting quantity for repeated execution to prevent coin fraud. Governance decisions are made through either automated technical review or community voting. When both automated technical review and community voting are used, the results of automated technical review take precedence over community voting. Trusted execution enables high-security capabilities to be performed in a trusted execution environment. The trusted execution environment can be a hardware-level trusted execution environment or a software-isolated execution environment. Execution in a hardware-level trusted execution environment includes a five-stage process: instance creation, confidential loading, secure computation, sealed output, and remote authentication. Performance adaptation: Adaptively selects atomic, combinatorial, and model-level execution granularity according to task complexity; dynamically compiles virtual capabilities by combining high-frequency capabilities and performs formal verification of high-security-level capabilities to generate verification certificates. Open operation exposes capability units through standardized tool interfaces or provides unified runtime execution services; Capability asset value enhancement involves classifying and storing execution data in a hierarchical manner to form capability assets, and performing at least one of the following: co-evolution, inference-level economic scheduling, differential privacy-synthesized data supply, and human-in-the-loop processing. For business support, it collects cost measurement and execution statistics summaries, binds them to the audit ledger to generate verifiable credentials with audit chain pointers, and distributes them to compliance, cost, transaction, and cross-domain collaboration scenarios by tag; each step runs collaboratively based on the capability unit registry, and the audit ledger records execution and governance events in a chain.

2. The method for enterprise deployment of asset enhancement and business support based on a meta-language system according to claim 1, characterized in that, The economic assetization and governance decision-making steps also include at least one of the following: minting tokens based on the overall cost of skill combinations, where the overall cost of remapping is the smaller of the maximum path cost of the combination and the upper limit constraint; minting verified and registered skill combinations into non-fungible tokens for trading; transferring only the right to use skill orchestration templates; obtaining equity points based on computing power contribution and exchanging them periodically; third-party incubation of proposal sponsorship tokens; sponsors receiving a share of the minting amount after the capability review is passed until the sponsorship limit is reached; adopting a three-layer smart contract governance consisting of a proposal contract, a voting contract, and an execution contract; proposals are accompanied by technical specifications and a security deposit; the voting weight is the square root of the pledged amount; proposals must simultaneously meet the voting power ratio threshold and the participation rate threshold to pass; staking is subject to a cooldown period; large-amount votes are delayed in taking effect; if a governance attack is detected, the governance window is suspended and unexecuted proposals are rolled back; high-security-level adjustment proposals must be accompanied by evaluation opinions and cannot rely solely on community voting for approval.

3. The method for enterprise deployment of asset enhancement and business support based on a meta-language system according to claim 1, characterized in that, The trusted execution steps and performance adaptation steps also include at least one of the following: a five-stage process and a four-stage protocol phase mapping in the isolated execution cabin; remote authentication failure resulting in destruction and reconstruction of trusted instances downgrading to software isolated execution; introducing randomized execution duration disturbances to resist timing side-channel inference; using zero-knowledge proof output to verify results without viewing the internal state; relying on a five-dimensional complexity vector to complete granularity selection; high-frequency combination compilation of virtual capabilities and attachment of governance rules, including inheritance of the highest security level, registration and filing, automatic invalidation and recompilation of changes, and audit trails; formal verification using model testing and relational analysis to generate verification certificates; registration of the highest security level capability must be accompanied by a certificate; if a counterexample occurs, the path is output; multiple verification failures result in downgrading or refusal of registration. The capabilities are combined to construct a deep learning computation graph, completing operator mapping, graph optimization, reverse compilation and batch scheduling. The compiled products are registered in the registry and bound to the source version. If the upgrade fails, it will fall back to the default backend or interpreted execution. Approximate gradients are registered for non-differentiable operations to support backpropagation.

4. The method for enterprise deployment of asset enhancement and business support based on a meta-language system according to claim 1, characterized in that, The open operation steps also include at least one of the following: converting the complete definition of capabilities into a schema description of standardized tool interfaces, mapping security levels to corresponding authentication requirements, registering tool description sets to list endpoints, including grouping and dependency matrices, delegating pipelines to complete call legality verification, parameter mapping, registry calls, and result encapsulation, being compatible with synchronous and asynchronous modes, synchronizing registry changes via callbacks, and cross-server aggregation discovery sorting and filtering by security level, version, and latency. Cost-aware scheduling is achieved using multiple priority queues, with deceleration triggered when budget limits are exceeded. Resource restrictions, system call whitelists, outbound network blocking, and timeout termination are configured in isolation chambers. Execution results are stored in two-level caches, and caches are invalidated in batches when the registry is changed. Monitoring indicators are collected from multiple dimensions, and elastic scaling with a cooldown period is performed based on load delays. When the computing power budget is exhausted, only the queue weight is reduced, and business requests are not rejected.

5. The method for enterprise deployment of asset enhancement and business support based on a meta-language system according to claim 1, characterized in that, The business support steps also include at least one of the following: aggregating formal verification certificates, trusted hardware execution credentials, and governance decision-making traceability to generate compliance evidence packages; outputting reports according to regulatory templates; supporting lightweight verification of single credentials; and automatically remapping security levels and enforcement strategies when regulatory rules are updated. Verifiable credentials include credential identifier, capability identifier, execution statistics summary, cost measurement, audit chain pointer, security level, business scenario tag, and issuer signature. Multi-source evidence hash aggregation binds the credential, and the distribution scope of the credential is limited according to the security level. The credential is converted into compliance report, cost bill, settlement credential, and cross-domain call credential, and the validity period and verifiability scope of the credential are set.

6. The method for enterprise deployment of asset enhancement and business support based on a meta-language system according to claim 1, characterized in that, The cost scenario, transaction scenario, and cross-domain collaboration scenario also include at least one of the following: multi-dimensional cost allocation based on execution cost; budget overrun triggering scheduling deceleration and outputting feedback reports; fault location based on capability-level call chains; joint optimization of cache capacity and scheduling strategies; batch invalidation of associated caches due to registry changes; performance scaling based on cost delay thresholds; adjustment of scheduling weights based on gateway statistical feedback; generation of valuation ranges based on execution statistical summaries; generation of settlement vouchers with audit chain pointers through cross-entity call metering; automatic collection of evidence arbitration for transaction disputes; completion of asset settlement using a points plus settlement voucher model; points cannot be exchanged for fiat currency. The system integrates cross-organizational call execution, directly rejecting calls if any link fails, resolving directory version and permission conflicts and leaving audit trails, storing cross-domain call evidence in a shared audit chain, and supporting lightweight verification.

7. The method for enterprise deployment of asset enhancement and business support based on a meta-language system according to claim 1, characterized in that, The capability asset value-added steps also include at least one of the following: classifying execution data into five categories and scoring it in four dimensions; filtering it in three layers and storing it in layers as capability assets; refining the model and self-evolving with feedback; each asset having an attached lineage chain; the main agent and sub-agents completing the collaborative evolution according to the five-step process and executing a ten-level closed loop; and the sub-agent capabilities being registered in the registry. The inference request executes at least one of the following: model degradation routing, semantic caching, batch inference, hint compression, and quantization inference, to complete cost-effectiveness attribution; the differential privacy framework generates de-identified synthetic data and registers pedigree information to supplement the training data; Low-confidence requests are routed back to manual processing and incentive control is implemented according to the standard economics; underutilized assets are identified and disposed of through archiving to release computing power, market authorization, transformation and upgrading, and depreciation revaluation.

8. A system for enterprise-deployed asset enhancement and business support based on a meta-language system, characterized in that, It includes an economic incentive unit, a governance unit, a trusted execution unit, a granularity selection unit, a formal verification unit, a framework adaptation unit, a protocol integration unit, a runtime management unit, a measurement unit, a voucher unit, an audit unit, a distribution unit, a compliance unit, a cost unit, a transaction unit, a cross-domain unit, a data storage unit, a collaboration unit, a supply unit, an economic scheduling unit, and a human-in-the-loop unit; each unit implements the method steps corresponding to any one of claims 1 to 7, and all units operate collaboratively with the capability unit registry and audit ledger as the data hub.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When a computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 7.

10. An electronic device, characterized in that, It includes a processor and a memory, the memory storing instructions that can be executed by the processor, which, when executed, implement the steps of the method according to any one of claims 1 to 7.

Images