2744 results about "Shard" patented technology

The invention relates to the technical field of data encryption, and discloses a data encryption protection system and method for intelligent controller hardware, and the method comprises the steps: generating a random confusion factor based on a real-time operation state through constructing a dynamic confusion algorithm, and carrying out the nonlinear transformation of original data; implementing a fragment encryption strategy, dividing fragments according to data sensitivity levels, and adopting a heterogeneous encryption algorithm; generating a dynamic binding key in combination with the environment parameters and the hardware unique identifier; hardware-level integrity check is executed, and data and hardware are verified through cyclic redundancy check codes and hardware fingerprints; establishing a self-adaptive key updating mechanism; performing multi-layer defense linkage; and implementing anti-side channel protection, and constructing a security audit log. According to the method, the hardware data security of the intelligent controller is comprehensively guaranteed, various security threats are effectively resisted, and the data confidentiality, integrity and availability are improved.

The invention belongs to the technical field of contract security management, and discloses a supplier contract security management system and method based on a block chain. Encryption fragmentation storage and multi-node verification are carried out on the supplier contract to ensure that data cannot be tampered; based on this, supplier multi-dimensional qualification evaluation and credit point recording are carried out; contract term coding and trigger condition setting are realized, and a self-execution contract control chain is formed; contract execution state consensus is achieved through cross-organization cooperation and distributed consensus; the privacy security is guaranteed by adopting data hierarchical encryption and authority fine control; generating a risk situation map in combination with compliance requirement mapping and real-time risk assessment; constructing a contract performance traceability chain based on historical data indexes and association analysis; performing abnormal behavior detection and security event classification processing; and finally, an optimization suggestion and a performance evaluation report are generated. According to the method, the safety, efficiency and transparency of supplier contract management are remarkably improved.

The invention relates to the technical field of GPU resource dynamic scheduling, in particular to a cloud environment GPU resource dynamic scheduling method, which comprises the following steps: monitoring operation indexes of GPU instances in a cloud environment in real time, collecting GPU video memory allocation records, GPU video memory fragmentation degree magnitude and page migration activity count, and predicting task resource demand total quantity through statistics. And generating a video memory state evaluation result. According to the method, the resource pool adjustment decision and the task matching degree list are jointly optimized, the instance models and the number are screened under the double constraints of the instance starting time parameter and the release cost parameter, and operation delay and extra cost caused by blind capacity expansion and contraction are reduced. Based on a strong binding mechanism started by a video memory pre-allocation instruction and a task process, in combination with continuous tracking of a resource consumption trajectory, a video memory peak deviation and an execution time deviation are fed back in real time, and a closed-loop scheduling optimization link is formed.

The invention relates to the technical field of data mining, in particular to a data security management system and method based on cloud computing. The method comprises the following steps: acquiring real-time vehicle driving data, and performing homomorphic modular exponentiation dangerous case sensing fragmentation encryption to obtain dangerous case sensing encrypted fragmentation data; acquiring an Internet of Vehicles data stream, and implementing zero-trust adaptive data stream encryption to obtain a zero-trust authentication credential; implementing federal learning privacy feature mapping on the dangerous case sensing encrypted fragmented data to obtain an anonymized encrypted feature mapping model; performing Merkle tree aggregation on the anonymized encrypted feature mapping model to obtain a block chain global dangerous case fingerprint; extracting a threat behavior quantization vector according to the block chain global dangerous case fingerprint and the anonymization encryption feature mapping model; and carrying out transmission strategy optimization according to the threat behavior quantization vector and the zero-trust authentication credential to obtain a global dynamic transmission strategy matrix. According to the invention, the safety of vehicle data uploading and the overall uploading efficiency can be improved.

The invention provides a fragment data cross-modal label generation system and method based on deep transfer learning. The method comprises the following steps: extracting first high-dimensional feature vectors in different modes; mapping the first high-dimensional feature vectors of different modals into the same semantic space through a cross-modal comparison loss function to realize multi-modal alignment and fusion to obtain second high-dimensional feature vectors; labeling semantic tags corresponding to the second high-dimensional feature vectors based on the fragmented data components by adopting a small sample transfer learning algorithm; a multi-channel Hash encoder is adopted, a self-adaptive encoding strategy is called according to different modal data combinations, and the second high-dimensional feature vector is encoded into a multi-channel binary Hash code; in combination with an incremental graph neural network, the binary hash codes and the corresponding semantic tags are dynamically expanded into the historical knowledge graph; matched fine-grained tags are established for semantic differentiation features of different entity combinations in the target knowledge graph, and a cross-modal tag tree is obtained by combining three-matrix hierarchical construction.

The invention discloses a production process state monitoring scheduling optimization method based on real-time data acquisition, relates to the technical field of manufacturing process scheduling, and is used for solving the problem of insufficient real-time performance and stability of process scheduling. According to the method, a process monitoring mechanism based on real-time acquisition and closed-loop scheduling is constructed, a time sequence structured data frame is formed under a unified time reference, operation stability and quality offset characteristics are extracted, a data credible label and a current process state factor vector are generated, an optimized scheduling model is input, and task conflicts and resource bottlenecks are identified according to the data credible label and the current process state factor vector. According to the method, path compression and sequence adjustment are implemented in combination with scheduling priority mapping and a resource path diagram, scheduling deviation vectors are constructed through task response time delay and process blocking in operation, rules and parameters are triggered to be updated online and locally rearranged, and solidification is performed after verification in a prediction window, so that equipment idling and switching fragmentization in the production process are reduced, and the production efficiency is improved. And the real-time performance of the production process, the resource utilization rate and the system stability are improved.

The invention provides a data all-flash storage optimization method and system based on cloud computing, and the method comprises the steps: receiving a storage operation request stream of a user terminal through collecting a multi-dimensional performance data set of each node of an all-flash storage cluster in real time, wherein the multi-dimensional performance data set comprises a storage space fragmentation rate, an input / output request queue depth and a network link bandwidth utilization rate; analyzing a to-be-stored data block identifier set and an operation mode label, traversing a historical access record library based on to-be-stored data block identifiers, extracting historical access time sequence characteristics and physical storage position proximity of associated data blocks, and combining the multi-dimensional performance data and the historical access time sequence characteristics to obtain a data block identifier set; according to the method, a dynamic access popularity prediction model in a preset time period is constructed, and finally, a cross-node fragmentation storage topological graph and a cache preloading strategy matrix are generated according to the proximity of the dynamic access popularity prediction model and a physical storage position, so that the optimization of all-flash storage is realized, and the data storage performance in a cloud computing environment is improved.

Disclosed are systems and methods for a hybrid cloud-edge computing platform using decentralized networks for solving resource-intensive computation tasks such as machine learning and video processing tasks. In some respects, the cloud-edge computing platform comprises a coordinator cluster and worker nodes partitioned into shards, which include cloud-based networks and edge networks, and which cater to a wide range of computation requirements. The coordinator cluster manages workloads by selecting the appropriate shard and worker nodes to execute tasks based on computed efficiency scores. Worker nodes may be incentivized with rewards such as cryptocurrency tokens for successful completion of tasks. The disclosed architecture also supports the pipelining of multiple tasks in a workload, where the output of one task is used as input for another task. In some respects, the computing architecture includes a blockchain-supported edge network, which enables verifying compute tasks and managing reward distribution for worker nodes using smart contracts.

The invention provides a blockchain-driven distributed private data storage and access control method and system, and relates to the technical field of access control, and the method comprises the steps: carrying out the sensitivity scoring and fragmentation processing of private data, organizing a data fingerprint into a Bloom filter, and constructing an integrity proof tree, the data fragments are encrypted and then submitted to a distributed storage network, and an evidence storage record of the data fingerprints is generated in the block chain; during data access, a hierarchical access key is dynamically generated according to behavior characteristics of a visitor, and the hierarchical access key is distributed to an authorized node group after threshold segmentation; the authorization node group generates an authorization voucher by using a threshold signature scheme based on a lattice password, and writes the authorization voucher into the block chain; and the visitor obtains the data fingerprint and the encrypted data fragment by using the authorization certificate, and decrypts the data fragment of the corresponding authority level through the secure multi-party calculation recombination key component after verifying the integrity.

The invention discloses a heterogeneous AI computing power resource scheduling method and system, and the method comprises the steps: constructing a heterogeneous AI computing power resource pool, wherein the heterogeneous AI computing power resource pool integrates the computing resources of a plurality of heterogeneous AI acceleration chips; obtaining a scheduling demand of the AI task, wherein the scheduling demand comprises a task type, a resource request quantity, a priority identifier and a task group association relationship; generating a multi-dimensional scheduling strategy according to task requirements, wherein the scheduling strategy comprises a priority scheduling rule, an affinity scheduling rule and a resource preemption rule; based on a multi-dimensional scheduling strategy, the AI tasks are dynamically allocated to target computing power nodes of the heterogeneous AI computing power resource pool, and the task execution state and the resource utilization rate are monitored in real time; and dynamically adjusting computing resource allocation according to the resource utilization rate. Through the heterogeneous AI computing power resource pool, the resource utilization rate is remarkably improved, dynamic resource allocation is realized through a multi-dimensional scheduling strategy, and meanwhile, a communication path is optimized through an affinity scheduling strategy, so that the problem of task starvation caused by resource fragmentation is avoided.

The invention discloses a driving vehicle road cloud collaborative safety management and control method and system based on trusted computing, and the method comprises the steps: generating an HMAC-SM3 data fingerprint through employing an SM3 Hash algorithm engine, and proposing an LSTM lightweight dynamic trust evaluation model based on a TinyML framework; through a step-by-step Hash verification and rollback mechanism, through Geohash coding and SM3 Hash joint calculation, based on an aggregation signature of a BLS12-381 curve and public key aggregation, the communication overhead during multi-RSU collaborative signature is reduced; distributed private key fragmentation decryption is realized by adding a homomorphic aggregation ciphertext; aggregating and delaying the compressed ciphertext; dynamically evaluating the credibility of the nodes by a Krum dynamic defense mechanism; the system comprises a vehicle side credible sensing layer, a roadside credible broadcast layer and a cloud credible decision-making layer. According to the invention, through layered credibility verification, dynamic trust chain extension and a data privacy protection mechanism, the security and credibility problems of multi-node cooperation in an automatic driving scene are solved.

The invention provides a cache information processing method and device of a port platform and a medium, and belongs to the technical field of port distributed information processing. An update request is received, and authentication and flow limiting processing are performed on the request; obtaining a cache key prefix and a data structure type, routing a cache key to a fragment node based on a consumption write-back event, executing a write-in operation in a distributed cache service, and setting survival time and multi-copy synchronization; delivering the message body to the message middleware and obtaining delivery confirmation; the messages are pulled from the queue according to the configured concurrency, deduplication verification is carried out on idempotent identifiers of the messages, and data persistence operation is executed after the verification message version is higher than the current version of the database; index collection points are implanted into all modules of the whole link, and the request delay, the cache hit rate and the message accumulation depth index are visually displayed. The reasonable distribution and optimization of system resources are realized; the consistency and integrity of the data are guaranteed, and the reliability of message transmission is improved at the same time.

The invention relates to a data security processing method and system based on distributed storage, and relates to the technical field of computer information processing. The method comprises the following steps: cutting data into encryption fragments with a configurable number by adopting a dynamic fragmentation strategy, and generating a physically isolated dynamic check block in combination with a timestamp to realize tampering prevention; a dynamic threshold value is dynamically calculated based on the data sensitivity index and the node load, and the node is optimized through the reliability score for cooperative decryption; a database table is divided into independent marshalling storage according to main foreign key association, foreign key fields are encrypted by adopting cross keys, and cross-marshalling access needs to meet a multi-key threshold condition; an intelligent threat perception engine is constructed, access logs and threat intelligence are analyzed in real time, and key rotation, fragment replacement and joint defense response mechanisms are dynamically triggered. According to the invention, full life cycle protection of data is realized, and the problems of key leakage risk and cross-table association attack are effectively solved.

The invention relates to the technical field of network monitoring, and provides a network situation monitoring system and method.The method comprises the steps that entities and relations of multi-source data are extracted through a sensing fusion module, a network situation map is constructed in combination with causal rules of an attack behavior knowledge base, and causal association of attack chains is recognized in combination with time sequence analysis and a causal discovery algorithm; the problems of attack chain identification fragmentation and risk path prediction distortion in the prior art are solved. The simulation verification module generates a dynamic mirror image based on a behavior feature library of an entity so as to construct an isolation simulation environment, and associates the causal strength of an attack chain with a defense effect to calculate an efficiency value so as to provide a precise basis for defense strategy screening; and the response feedback module takes the efficiency value as a reward function, optimizes a defense strategy through reinforcement learning, updates a network situation map, an attack behavior knowledge base and an isolation simulation environment through a feedback mechanism, and remarkably improves the coping capacity of the system to complex attacks and attack variations.

The invention relates to the technical field of data access security, and discloses a container mirror image security management method and system, and the method comprises the steps: constructing a container mirror image, generating a differential encryption key through a strategy center, carrying out the encryption strategy of a kernel dependence layer, a runtime environment layer, an application code layer and a sensitive configuration layer, and forming a hierarchical protection basis. If an access request is triggered, firstly collecting equipment fingerprints and geofence information and evaluating an environmental risk score, verifying access authority and an access scene matching degree through attribute-based encryption, analyzing operation track characteristics in real time, identifying an abnormal mode, and if the three-layer verification is passed, generating a temporary access token; according to the method, access request authority is verified, a temporary access token is matched, key fragments are synthesized, a master key is only temporarily generated in a memory and encrypted and stored, and through combination of a double-layer encryption channel and inner-layer and outer-layer defense, the anti-attack ability of container mirror image transmission is improved, and man-in-the-middle attack and data tampering are effectively coped with.

The invention discloses a data management system and method based on distributed cloud storage, and relates to the technical field of distributed cloud storage, and the system comprises an access log collection module, a node state monitoring module, a data thermal evaluation module, a redundancy strategy decision module and a storage execution scheduling module. A fragmentation access behavior is recorded through an access log acquisition module, a node state monitoring module acquires a node resource state in real time, and a data thermal evaluation module constructs a three-dimensional thermal model based on an access frequency, a network hop count and a storage cost to calculate a thermal value and supports dynamic weight adjustment; the redundancy strategy decision module divides the fragments into high frequency, medium frequency and low frequency according to the thermodynamic value, and single copy + local cache, erasure code storage and dynamic adjustment strategies are adopted respectively; the storage execution scheduling module executes fragmentation operation and optimizes reading node selection, and data access efficiency is improved through load awareness and a bandwidth-delay joint model; according to the invention, dynamic balance between data access efficiency and storage cost is realized.

The invention provides an AI-based multi-user data storage docking and secure transmission method, which comprises the following steps of: acquiring a propagation path of a hot topic according to a multi-modal content association graph, performing fragmentation storage on the propagation path by adopting a clustering algorithm, and generating a fragmentation storage index table; when the relevance of different modal contents is higher than a preset threshold value, determining that the sensitivity grading result is consistent with the multi-modal relevance, performing dynamic rule adjustment by adopting a deep learning model, generating a dynamic auditing rule set, and obtaining boundary judgment parameters of the rule set; and distributing the dynamic auditing rule set and the decision transparency index to each service line by adopting a cross-platform data synchronization protocol according to the auditing decision log, generating a cross-platform consistency auditing standard, and obtaining a synchronization log of standard execution.

The invention relates to the technical field of information retrieval, in particular to a software copyright management system based on a smart contract, which comprises a copyright level right confirmation module, an NFT fragmentation evidence storage module, a dynamic right confirmation verification module, a fragment recombination tracking module and a transaction authorization management module. According to the method, copyright data hash mapping is constructed based on a block chain hierarchical right confirmation mode, a software copyright evolution path is defined, evolution integrity is ensured by calculating hierarchical hash values and adopting Merkle tree structure storage and association of copyright information of different hierarchies, right confirmation data is split and stored in a fragmentation evidence storage mode, safety is improved, and storage and retrieval burdens are reduced; a dynamic right confirmation mechanism is combined with hash comparison and public key signature, real-time verification of newly added copyright information is achieved, it is ensured that the copyright inheritance relation is accurate and checkable, hash calculation based on code fragments, code change tracking and derivative copyright confirmation verification are achieved, and an intelligent contract is used for transaction authorization management, transaction matching and rule verification execution and copyright circulation compliance is ensured.

The invention discloses an intelligent private cloud resource scheduling management system based on a hyper-converged architecture, particularly relates to the technical field of virtual resource scheduling management, and is used for solving the problem of overall utilization rate reduction caused by cascade resource oscillation and fragmentation due to fault recovery in the prior art. Candidate buffer nodes are screened based on the resource occupancy state, a migration priority queue is generated in combination with the node resource isomerism matching degree and the real-time load, and an independent buffer resource pool is reserved at a target node; key resources are directionally reserved through resource locking labels, task dependence periods are associated, and task conflict risks possibly triggered after the resources are released are dynamically predicted; adjusting a label effective period to cover a task execution window based on a conflict identification result; and removing the label in the preset time window, releasing the resources to the buffer pool, synchronously triggering global resource integration and candidate node list updating, and realizing efficient recovery and dynamic redistribution of fragmented resources, thereby inhibiting multi-node resource fluctuation.

To solve the problems of fractured identity across multiple blockchains, these methods and systems allow for a single unified identity to be managed and resolved across multiple chains within an interop network. By leveraging message passing, identity records can exist securely across different chains. The system incorporates counterfactual blockchains, enabling trust-minimized name registration that reduces costs while maintaining decentralization and security. ENS name resolution is supported across both onchain and offchain environments, with verifiable proofs ensuring efficient resolution. By structuring message passing and utilizing decentralized indexers, these methods and systems provide a scalable and reliable framework for cross-chain identity.

The invention provides a financial document automatic analysis and question-answering system based on a large model, and relates to the field of data intelligent analysis, and the method comprises the steps: firstly carrying out the multi-granularity segmentation and semantic embedding coding of a financial document, so as to map the document data to a vector space; then dynamically matching user questions and candidate document fragments in a feature space by adopting a multi-layer retrieval mechanism so as to accurately position related contexts, and then carrying out cross-paragraph semantic association modeling on the candidate fragments by introducing dynamic propagation of feature space constraint so as to break through the information fragmentation limitation of traditional paragraph matching; and finally, a model is generated in combination with thinking chain reasoning, a traceable reasoning path is synchronously constructed when an answer is output, and the illusion risk of a large model is remarkably reduced. In this way, the analysis depth of the complex financial problem can be improved, and intelligent analysis services with both accuracy and credibility are provided for users.

The invention discloses a multi-platform Internet of Things equipment unified access management method, equipment and a storage medium, and relates to the technical field of Internet of Things integration. The method comprises the step of solving the fragmentation problem of cross-platform equipment access through dynamic configuration adaptation. The method comprises the following steps: starting from receiving a front-end equipment operation request, indexing a pre-configured mapping relation database through an equipment unique identifier, and obtaining a target platform type, an API address and an encryption certificate; dynamically calling a protocol adapter based on a platform type, and injecting an account certificate into a request header or a message body according to a platform specification; converting the original parameters into a format supported by a target platform through a protocol conversion engine, and combining the original parameters with the authentication information to generate a complete request; and realizing high-reliability communication by using an asynchronous message queue, and finally returning a platform response to a front end. According to the invention, through mapping management of the configuration driver and dynamic adapter scheduling, equipment access unification across the Internet of Things platform is realized.

The invention discloses a hyper-converged server resource pooling method and system, and belongs to the technical field of resource scheduling, and the method comprises the steps: collecting spatial information, generating a multi-dimensional resource topological graph, abstracting edge node and core node resources into virtual units with position labels, constructing a virtual resource pool, and generating a three-dimensional metadata table; constructing an independent double-queue architecture, configuring a token bucket, triggering a queue clearing mechanism based on a control period, and performing micro-batch merging on edge node short transactions; binding adjacent virtual units by taking the physical position of the equipment as a fragmentation primary key, monitoring cross-virtual unit access traffic, migrating a high-frequency data copy and redirecting a write request, and adjusting fragmentation granularity according to data relevance; the virtual unit closest to the equipment group is selected as a main node in the data fragments, write operations are combined, low-delay paths are selected for synchronization, an asynchronous confirmation mechanism is adopted to respond to edge nodes, the local resource hit rate and the task processing efficiency are improved, and the real-time performance and reliability of industrial control are guaranteed.

The invention provides a reasoning service method, a processing device, equipment, a storage medium and a program product, and relates to the technical field of artificial intelligence. The large model reasoning service method comprises the following steps: monitoring first computing power resources of a plurality of decoding nodes and monitoring performance indexes of migration links between pre-filling nodes and the decoding nodes; in response to the large model reasoning task, distributing the reasoning task to a plurality of pre-filling nodes, and generating corresponding key value cache KV Cache data; performing fragmentation processing on the KV Cache data based on the first computing power resource and / or the performance index to obtain KV Cache fragments; and migrating the KV Cache fragments to the plurality of decoding nodes, so that the plurality of decoding nodes decode the KV Cache fragments based on a multi-head parallel attention balancing mechanism and / or an assembly line parallel balancing mechanism to obtain a reasoning result. According to the technical scheme, the migration efficiency and migration stability of KV Cache data can be improved, and the reasoning efficiency of a large model reasoning task can be improved in combination with a balance mechanism.

The invention discloses a big language model reasoning service optimization method, system and device and a medium. The method comprises the steps of obtaining a current reasoning request queue; performing dynamic sorting on each reasoning request in the current reasoning request queue; and performing dynamic batch processing on each reasoning request in the reasoning request queue by using an SLO-BS algorithm to obtain a current batch of reasoning requests, and inputting the current batch of reasoning requests into a large language model, the method, the system, the equipment and the medium can solve the problem of video memory fragmentation occurring during multi-task concurrent reasoning, and reduce the SLO violation rate at the same time.

The invention relates to the technical field of food supply chain traceability, in particular to a food supply chain quality safety traceability method and system. The method comprises the following steps: realizing feature fusion of food data through multi-source data acquisition and time sequence alignment, then identifying a food logistics mode and carrying out load analysis, carrying out adaptive fragmentation storage on fusion features based on logistics load data, constructing a storage network topology structure, and utilizing a block chain technology to carry out self-adaptive fragmentation storage on the fusion features. And performing chain structure connection on the fragmented data, deploying an intelligent contract, finally constructing a traceability coding system according to the contract and a chain structure, and realizing whole-course quality safety accurate monitoring and tracking of the food supply chain through a traceability code set. The invention realizes a more efficient and more reliable food supply chain quality safety traceability method.

The invention relates to the technical field of cloud computing financial security, and provides a financial data encryption transmission and storage method. The method is characterized by comprising the following steps of: executing sensitivity-driven data grading fragmentation on a user terminal; dynamic elliptic curve encryption is carried out on transmission data through a two-channel encryption engine, and fully homomorphic encryption is carried out on storage data; dynamically distributing the fragments to heterogeneous cloud nodes by the multi-cloud routing based on reinforcement learning; a distributed key management matrix is constructed, key fragments are dispersed and stored in a block chain and a hardware security module, and reconstruction is activated through biological characteristics. The method has the advantages that full-link ciphertext operation is realized, and the plaintext exposure risk is eliminated; ciphertext state financial calculation is supported; single point failure is resisted; the APT attack is defended dynamically; and the quantum security evolution capability is realized. The method is suitable for mobile banks, cross-border payment and other scenes.

The invention discloses an intelligent question answering system and method based on question decomposition and community semantic search. According to the method, firstly, a complex question is decomposed into a plurality of single-hop sub-questions through an original loop question decomposition technology, then information needed for answering each sub-question is obtained based on communities in a vector retrieval knowledge graph, and the sub-questions are answered by using a large language model. And finally, generating a final question and answer for the original complex question by combining the LLM with the answers of all the sub-questions. Compared with other methods in the industry, the method has the advantages that the information integration capability is remarkably improved, information fragmentation is avoided through community-level search and abstract generation, and high-quality and logically consistent answers are provided. When a complex query is processed, the effect of the method is superior to that of a general method in the industry and is improved by 29% at most; meanwhile, on the basis of ensuring a better question and answer effect, the efficiency is remarkably improved, and on the premise that the effect exceeds that of an existing optimal method GraphRAG, the token consumption is only 3.2% of that of the existing optimal method GraphRAG.

The invention relates to the technical field of large model knowledge questions and answers, and discloses an enterprise knowledge question and answer system based on a large language model, which comprises a heterogeneous semantic embedding module, a permission mask calculation module, an enterprise term alignment module, a candidate knowledge query module and a question and answer generation output module, in the prior art, an enterprise question and answer mode is realized only depending on keyword matching or a fixed FAQ rule, and particularly under the conditions of enterprise internal knowledge distribution fragmentation, authority differentiation and term ambiguity, accurate, compliant and context-consistent semantic question and answer cannot be realized. Due to the fact that multi-mode unified embedding, the semantic level authority control mechanism, the term graph alignment model and the RAG enhanced question and answer mechanism are introduced, cross-format and multi-department enterprise intelligent question and answer is achieved, the problems of semantic drift and unauthorized leakage are avoided, and the accuracy and safety of the question and answer system are improved.

The invention relates to the technical field of information security, in particular to a computing method and system for information security. The method comprises the following steps of: partitioning and fragmenting input data, and distributing the data to a plurality of storage nodes through randomization, distributed storage and Hash mapping to ensure the safety of data storage and the distribution flexibility; establishing an index table for all the fragments; regularly triggering a data confusion mechanism, and adjusting the storage position of data fragments through a pseudo-random function; the integrity of the data is detected by generating a check code and an erasure code, it is ensured that the data is not tampered or damaged in the transmission or storage process, and the data is recovered through a redundant copy when the data is lost; the identity verification and the data integrity when the user accesses the data are ensured through authority authentication; and monitoring storage nodes and data access behaviors in real time, detecting potential threats and taking dynamic adjustment measures. According to the invention, through a multi-level security policy, the security and fault-tolerant capability of data processing are effectively improved.