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CN-122027339-A - Quantized deployment and multi-scene adaptation system of distributed trusted solidification nodes

CN122027339ACN 122027339 ACN122027339 ACN 122027339ACN-122027339-A

Abstract

The invention discloses a quantitative deployment and multi-scene adaptation system of a distributed trusted curing node, and belongs to the technical field of artificial intelligence safety and distributed deployment. The system comprises six modules of quantitative configuration, dynamic scheduling, trusted isolation, scene adaptation, global collaboration and full-link tracing, standardized node scheduling is realized through quantifiable indexes, dual isolation is adopted to ensure safety, multi-field multiplexing is realized through scene adaptation, overall consistency is maintained through global collaboration, and the whole process is traced through chain storage. The invention solves the problems of wide deployment, centralized safety risk, poor multiplexing performance across scenes, difficult compliance tracing and other industry pain points of the existing distributed AI, can be widely applied to large-scale trusted AI landing scenes such as government affairs, finance, medical treatment, industry, intelligent home furnishings and the like, and has extremely high practicability and commercial value.

Inventors

  • YAN XINXIN

Assignees

  • 闫鑫鑫

Dates

Publication Date
20260512
Application Date
20260325

Claims (7)

  1. 1. The quantitative deployment and multi-scene adaptation system for the distributed trusted curing nodes is characterized by comprising a distributed node quantitative configuration module, a node dynamic scheduling module, a trusted isolation and authority management and control module, a multi-scene self-adaptive matching module, a global collaborative optimization module and a full-link tracing and evidence-storing module; the distributed node quantitative configuration module is used for setting calculation power weight, storage capacity grade, credible strength index, response priority and deployment density parameter of the credible solidification node, and constructing a quantifiable and schedulable standardized node resource pool, wherein the quantification index comprises a node number interval, single node calculation power ratio, storage safety grade, delay threshold range and credible verification frequency; The node dynamic scheduling module realizes hybrid scheduling based on the quantization index, and automatically distributes tasks according to the service load and the equipment state to realize load balancing; The trusted isolation and authority management and control module adopts a hardware sandbox and software layer dual isolation mechanism to independently and safely divide different nodes and scenes so as to realize strict authority management and control; The multi-scene self-adaptive matching module is internally provided with a scene feature library and a strategy library, and can automatically match deployment modes according to government affairs, finance, medical treatment, industry and household scenes; the global collaborative optimization module is used for converging the node states of the whole network in real time, uniformly calibrating logic deviation and optimizing resource allocation; The full-link tracing and evidence storing module adopts hash encryption and chain evidence storing, records the whole process data and realizes non-tamperable tracing.
  2. 2. The system of claim 1, wherein the node dynamic scheduling module employs a hybrid scheduling architecture combining decentralization and centralization, which avoids single point bottlenecks and improves large-scale deployment stability.
  3. 3. The system of claim 1, wherein the trusted quarantine and rights management module supports multi-level rights partitioning to prevent cross-node data pollution and privacy disclosure.
  4. 4. The system of claim 1, wherein the multi-scenario adaptive matching module supports one-time configuration, multi-scenario multiplexing, and significantly reduces the cost of repeated development and deployment.
  5. 5. The system of claim 1, wherein the global co-optimization module is configured to implement global node behavior consistency management and avoid local anomaly diffusion.
  6. 6. The system of claim 1, wherein the full link trace back and certification module supports full certification of deployment records, dispatch logs, rights changes, and behavioral data, meeting industry compliance and audit requirements.
  7. 7. The quantitative deployment and multi-scene adaptation method of the distributed trusted solidification node is characterized by comprising the following steps of: s1, setting quantitative indexes such as node computing power, storage, credibility level, delay threshold, deployment density and the like through a distributed node quantitative configuration module, and constructing a standardized credible node resource pool; S2, the node dynamic scheduling module performs task allocation and load balancing scheduling according to the real-time service load; s3, the trusted isolation and authority management and control module starts a dual isolation environment and performs authority management and control; s4, automatically loading a corresponding strategy by the multi-scene self-adaptive matching module according to the service scene to complete scene deployment; S5, the global collaborative optimization module monitors and uniformly optimizes the global node state in real time; s6, the whole-link tracing and evidence-storing module encrypts and stores the whole-process data to realize auditable and traceable data; and S7, circularly executing S2-S6 to realize continuous and stable distributed trusted AI deployment operation.

Description

Quantized deployment and multi-scene adaptation system of distributed trusted solidification nodes Technical Field The invention relates to the technical fields of artificial intelligent security management, distributed system deployment, trusted execution environment and multi-scenario intelligent adaptation, in particular to a quantitative deployment strategy, node isolation scheduling, scenario self-adaptive matching and full-link collaborative traceability system based on distributed trusted solidification nodes, which is suitable for unified deployment and security management and control of a large-scale AI cluster, government service, financial wind control, medical diagnosis, industrial control, intelligent home and other multi-field trusted AI. Background Along with the large-scale landing of artificial intelligence technology, a single AI node cannot meet the complex service requirements, and multi-node, multi-device and multi-scene distributed AI deployment becomes a mainstream trend of the industry. The prior art has the common problems of extensive node deployment, unreasonable resource allocation, incapability of quantitative evaluation of credibility, poor cross-scene reusability, centralized security risk, incomplete behavior tracing and the like. The traditional deployment mode adopts a centralized management and control architecture, so that heterogeneous equipment and a differentiated scene are difficult to adapt, a standardized trusted isolation mechanism is lacking among nodes, data leakage, logic pollution and core deviation are easy to occur, and meanwhile, a quantitative evaluation system and a dynamic scheduling strategy are lacking, so that the operation and maintenance cost is high, the expansibility is weak, and the safety compliance is difficult to ensure uniformly. Aiming at the industry pain point, the invention provides a quantized deployment and multi-scene adaptation system of a distributed trusted curing node, which realizes the quantization, deployment schedulability, scene adaptation and global traceability of the node and fills the technical blank of large-scale landing of the current distributed trusted AI. Disclosure of Invention System overall architecture The system core control framework comprises six modules, namely a distributed node quantitative configuration module, a node dynamic scheduling module, a trusted isolation and authority management and control module, a multi-scene self-adaptive matching module, a global collaborative optimization module and a full-link tracing and evidence storage module. All modules are mutually linked to form a complete technical closed loop of 'quantitative deployment dynamic scheduling scene adaptation collaborative optimization traceability evidence storage'. (II) Module functionality Distributed node quantization configuration module The method is used for carrying out standardized quantitative definition on the trusted solid node, configuring node calculation force weight, storage capacity grade, trusted strength index, response priority and deployment density parameter, and forming a quantifiable, comparable and schedulable node resource pool. The quantization index may include, but is not limited to, a node calculation interval, a single node calculation ratio, a storage security level, a delay threshold range, a trusted verification frequency, converting the deployment process from experience to datamation. Node dynamic scheduling module And the mixed scheduling combining the decentralization and the centralization is realized based on the quantitative indexes, the node tasks are automatically distributed according to the service load, the scene pressure and the equipment state, the single-point overload and the resource waste are avoided, and the overall stability and the response efficiency of the system under large-scale deployment are ensured. Trusted isolation and authority management and control module By adopting a hardware sandbox and software layer dual isolation mechanism, independent trusted environments are divided for nodes in different scenes and different levels, the access authority of core data is strictly controlled, cross-node pollution, data leakage and illegal tampering are prevented, and the overall safety in a distributed environment is ensured. Multi-scene self-adaptive matching module The scene feature library and the adaptation strategy library are built in, and the corresponding node deployment mode, the credibility strength and the operation strategy are automatically matched according to compliance requirements and business logic of different scenes such as government affairs, finance, medical treatment, industry and home furnishing, so that one-time configuration and multi-scene multiplexing are realized. Global collaborative optimization module And carrying out real-time convergence analysis on the running state of the nodes of the whole network, uniformly calibrating deviation and optimizing