4219 results about "Data access" patented technology

The invention discloses a computer equipment fault monitoring system and method based on artificial intelligence, and relates to the technical field of computer equipment fault monitoring. The system comprises a data access module, a semantic analysis module, a knowledge graph construction module, a dynamic semantic association module, a data fusion processing module, a decision output module and an adaptive optimization module. The data access module collects and standardizes hardware, software and network data; the semantic analysis module extracts and enhances semantic tags; the knowledge graph construction module forms a data semantic relation network; the dynamic semantic association module screens potential semantic relationships; the data fusion processing module generates a multi-dimensional feature vector; the decision output module triggers fault early warning; and constructing a feedback knowledge graph of the self-adaptive optimization module. According to the method, through event-driven interpolation, dynamic weight fusion, closed-loop feedback optimization and the like, the problems of multi-source data alignment, semantic fusion and dynamic adaptation are solved, the fault monitoring accuracy and the system adaptability are improved, and the method is suitable for fault monitoring and early warning of computer equipment.

The invention provides a multi-source heterogeneous data integration and governance-oriented data weaving method, which comprises the following steps of: performing data acquisition from an accessed multi-source heterogeneous data source to generate an original multi-source heterogeneous data stream; performing standardization processing on the original multi-source heterogeneous data stream to generate a standardized multi-source heterogeneous data set; performing active content scanning processing on the standardized multi-source heterogeneous data set to determine business metadata, and performing consanguinity tracking processing on the business metadata to generate enhanced business metadata; calling a domain ontology framework to carry out standardized constraint on the enhanced service metadata to obtain standardized service metadata without cross-data source semantic ambiguity, and carrying out implicit association mining processing on the standardized service metadata based on a graph neural network to generate a semantic knowledge graph containing core entities and relationships; and performing logic abstraction processing on the distributed data resources according to the semantic knowledge graph to generate a unified data access interface.

The invention relates to the technical field of financial data security, and discloses an intelligent ERP financial system data security management and authentication method. The method comprises the following steps: acquiring an original transaction data stream in an ERP system, extracting key financial fields, and dividing the key financial fields into a sensitive data set and a common data set according to a preset rule; a dynamic encryption strategy framework is constructed based on sensitive data set attributes, the framework comprises multiple levels of encryption strength parameters, and the corresponding encryption strength can be automatically matched according to the authentication level of an access request. And monitoring a system data access behavior in real time, collecting feature data, inputting the feature data into the anomaly detection model, and triggering access blocking when an output anomaly access probability exceeds a threshold value. And generating a periodic integrity verification instruction according to the sensitive data updating frequency, performing integrity verification by using a hash chain technology, recording a result and marking a tampering risk level. And based on the association relationship between the tampering risk level and the abnormal access probability, generating an updated security policy and synchronizing the updated security policy to each data access node.

The present invention discloses an artificial intelligence-based adaptive big data storage and retrieval optimization system and method designed to intelligently manage and optimize large-scale distributed data environments. The system integrates data acquisition, distributed storage, metadata processing, adaptive learning, and retrieval optimization units configured to work collaboratively for continuous self-optimization. The invention employs deep reinforcement learning and predictive neural network techniques to dynamically analyze system telemetry, workload behavior, and data access patterns in real time, enabling proactive adjustment of data placement, caching, replication, and compression parameters across distributed nodes. The metadata processing framework utilizes graph-based dependency modeling to maintain semantic and contextual relationships among datasets, facilitating intelligent and context-aware data retrieval. The retrieval optimization unit interprets user queries semantically and computes the optimal retrieval route using latency prediction models and dynamic routing techniques.

The invention discloses a cloud-edge collaborative intelligent storage node dynamic deployment method and system. The method comprises the following steps: monitoring performance indexes such as edge node data traffic and storage resource state in real time; a deep learning algorithm combining time sequence analysis and an LSTM neural network is adopted to analyze traffic information, and a data access demand is predicted; edge nodes and cloud storage resource configuration are dynamically adjusted based on a prediction result, and an intelligent scheduling algorithm, a data cold and hot separation strategy and a self-adaptive fragmentation technology are introduced to allocate resources; the storage node layout is optimized in real time, and an optimal data distribution path is selected through a reinforcement learning strategy; data security and access control are realized by adopting end-to-end encryption, multi-level access control and block chain technologies. The system comprises a monitoring acquisition module, a prediction analysis module, a resource scheduling module, a path optimization module and a security control module. According to the method, the utilization efficiency of storage resources is improved, data delay is reduced, system stability and data security are enhanced, and the method is suitable for a cloud edge collaborative storage scene.

The invention discloses an enterprise data asset intelligent analysis method and system based on AI recognition, and the method comprises the steps: receiving an enterprise multi-source heterogeneous data stream, carrying out the joint feature extraction and semantic alignment through a pre-trained multi-modal fusion recognition model, and generating a structured data asset recognition result; constructing a dynamic enterprise data asset atlas according to the structured data asset identification result in combination with the data access trajectory and authority metadata collected in real time; performing spatio-temporal evolution analysis on the dynamic enterprise data asset map, and extracting potential data value density features and risk exposure features; inputting the data value density features and the risk exposure features into a self-organizing mapping network to generate a data asset grading topological graph; and based on the data asset grading topological graph, through strategy constraint reinforcement learning, generating an executable data governance action sequence. According to the embodiment of the invention, the identification precision and real-time analysis capability of special assets of enterprises can be improved.

The invention relates to the technical field of data processing, can be applied to business scenes of financial science and technology, medical health and the like, and discloses a data processing method, device, equipment and medium based on a context agreement, comprising: receiving a business demand event, extracting a demand text, and disassembling the demand text to generate a plurality of subtasks; obtaining a data source context according to the sub-tasks and task subject information, generating a corresponding data extraction path and an analysis model strategy, connecting a target data source through an access strategy, collecting original data, preprocessing the original data, and dividing the original data into training data and verification data; and training the model based on the analysis model strategy and generating a reasoning result, and completing verification output through a result verification model. According to the method, task disassembly, data access and model generation processes are driven through a model context protocol, and a knowledge graph reasoning and verification mechanism is combined, so that a closed-loop automatic analysis path is formed, and the processing efficiency and intelligent adaptation capability of cross-domain data tasks are effectively improved.

The invention discloses a data access permission control method and system based on a block chain, and relates to the technical field of block chains, the system is composed of a plurality of function modules, and the system comprises a hierarchical permission module which adopts a role hierarchical strategy and defines the access granularity of a role through an intelligent contract; the dynamic permission adjustment module is used for dynamically adjusting the permission range by analyzing user behaviors in real time through machine learning according to the principle of following the minimum permission in the permission distribution process; attribute encryption and zero-knowledge proof are introduced, an access key is dynamically generated according to user attributes, and sensitive information is hidden in the verification process; the multi-chain collaboration module is used for coding an intelligent contract through an authority rule, automatically executing access request verification, storing encrypted data through a private chain, managing an index and an access strategy through an alliance chain, isolating a storage domain from an access domain, and carrying out process traceability on a data request through a cross-chain verification mechanism; and the key management module is used for generating a true random number private key by adopting a hardware security unit.

The invention relates to the cross technical field of business intelligence and natural language processing, in particular to an interactive AI report generation method and system based on intelligent semantic driving, and the method comprises the steps: (1) a large language model analyzes user question semantics, calculates the matching degree with a report subject, and dynamically selects a question correction or report generation process; (2) the fuzzy query is converted into a deterministic request based on the domain knowledge base, and semantic ambiguity is eliminated; (3) analyzing field association in combination with database metadata, and generating an executable SQL (Structured Query Language); (4) dynamically rewriting SQL (Structured Query Language) according to user roles, adding permission filtering conditions, executing query and desensitizing sensitive data; (5) analyzing result features, automatically matching a visual form and generating natural language interpretation; and (6) collecting user feedback, and optimizing a semantic model and a query strategy. According to the scheme, the accuracy of natural language query and the adaptive capacity of the system can be remarkably improved, and the security of data access is guaranteed.

The invention discloses a protocol self-adaption method and device for household photovoltaic data access, and the method comprises the steps: transmitting a predefined multi-protocol detection instruction set to a newly accessed household photovoltaic terminal when the access of the household photovoltaic terminal is detected for the first time, and the predefined multi-protocol detection instruction set comprises standardized instructions of at least two heterogeneous communication protocols; monitoring response data of the household photovoltaic terminal, and identifying a communication protocol type supported by the household photovoltaic terminal based on a feature matching relationship of the response data; dynamically generating a corresponding data analysis template based on the identified communication protocol type, and establishing a protocol adaptation channel; and data acquisition and analysis are carried out on the household photovoltaic terminal through the protocol adaptive channel, so that protocol adaptive access is realized. Parallel detection of heterogeneous protocols is realized by pre-defining a multi-protocol detection instruction set, non-preset library identification of protocol types is completed in combination with response feature matching, and edge-side adaptive access is achieved by using a dynamically generated analysis template and a protocol adaptive channel.

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 relates to the field of internet attack detection, in particular to a data security event real-time monitoring method and system, and the method comprises data collection, multi-modal fusion, threat detection, causal reasoning, dynamic response and feedback optimization. Compared with the traditional security analysis which depends on isolated data dimension or simple rule association, is difficult to capture a cross-data-source complex attack mode, and is faced with the limitations of low calculation efficiency, slow link traceability, storage access bottleneck and the like in mass data association analysis, the scheme integrates multi-source heterogeneous data into a dynamic association network through graph structure modeling, so that the security analysis efficiency is improved. Hidden association and behavior patterns among users, equipment and IPs are deeply mined by utilizing a GNN framework, the suspicious degree among entities can be accurately quantified, and hidden attack chains can be identified; and meanwhile, a hybrid storage architecture and a query optimization technology are adopted, so that a security analyst can backtrack a complex attack path in a second level while breaking through the bottleneck of large-scale graph data access performance, and the threat hunting efficiency and the high-level attack traceability are remarkably improved.

The invention relates to the technical field of data processing, in particular to a multi-AI algorithm collaborative intelligent management system based on a large model, and the system comprises a data access module which collects original messages of terminal equipment of multiple manufacturers; the protocol adaptation module analyzes a device communication characteristic spectrum through a quantum entanglement separator, and outputs a standardized time-space event stream; a federation modeling module creates a parallel calculation instance, and a quantum tunneling mechanism is utilized to fuse the feature vectors to generate an equipment association topology model; the chaotic scheduling module generates an atomic task fractal network and assigns the atomic task fractal network to edge nodes; the interface optimization module calculates an interface field mapping relation through a quantum Boltzmann machine; and the closed-loop correction module triggers equipment communication characteristic spectrum re-calibration. Multi-source device communication differences are eliminated through quantum protocol analysis, federated quantum fusion breaks through algorithm collaboration barriers, chaotic fractal scheduling realizes resource dynamic optimization, a closed-loop collaboration system of device access to interface configuration is formed, and the problems of protocol incompatibility of multi-source heterogeneous devices and AI algorithm collaboration obstacles are solved.

The invention provides an RAG knowledge base construction method and system based on hierarchical semantic indexes. The method belongs to the cross technical field of artificial intelligence and information retrieval. The method comprises the following steps: performing multi-level semantic analysis on an input original document set to generate document semantic hierarchical structure data; constructing a hierarchical semantic index tree based on the document semantic hierarchical structure data; performing dynamic knowledge graph initialization according to the hierarchical semantic index tree to generate an initial dynamic cognitive graph; and collecting real-time interaction data through a user feedback interface and a new data access module, and performing incremental updating on the initial dynamic cognitive map to form a knowledge representation system supporting life cycle evolution. Through multi-level semantic analysis and construction of a hierarchical semantic index tree (HSIT), deep semantic analysis can be performed on an original document set, the context integrity of knowledge is ensured, structured storage is realized, the knowledge can be expressed and stored more accurately, and information loss or semantic ambiguity is avoided.

The invention relates to the technical field of data storage, can be applied to business scenes such as medical health and financial science and technology, and discloses a multi-Pool storage resource adaptive writing method which comprises the following steps: monitoring available storage space, read-write input / output rate, average response time and data access frequency of each Pool; dynamically adjusting the current write-in weight of the Pool according to the monitoring result; converting the current write-in weights of all Pool into probability values, and constructing a discrete probability distribution table; selecting a target Pool through a pseudo-random number generator according to the probability distribution table; and writing the to-be-stored data into the target Pool. According to the method, the running state of each Pool is continuously monitored, and the writing weight is adjusted in real time, so that the problem of resource allocation rigidity under a fixed weight strategy is avoided; in combination with a discrete probability distribution table constructed based on normalized weight and a pseudo-random selection mechanism, selection of the target Pool has certain load balancing elasticity, and data can be more effectively guided to be written into the Pool with light load and excellent performance.

The invention belongs to the technical field of computers, and particularly relates to an information integration management method and system supporting real-time updating, and the method comprises the steps: accessing a heterogeneous data source through a distributed gateway, carrying out the adaptive analysis of a protocol, and standardizing a structure; adding a millisecond timestamp and a unique identifier, and generating a semantic data cluster through streaming engine sliding window association analysis; after multi-dimensional consistency verification is executed, writing and storing are carried out by adopting a double-buffering and snapshot isolation mechanism; and dynamically assembling the customized view according to the user role and the context and safely returning the customized view. The system correspondingly comprises a protocol analysis module, a time sequence marking module, a streaming association module, a semantic verification module, a double-buffer control module, an index updating module, a view assembling module and a secure transmission module. According to the invention, millisecond-level real-time convergence, conflict-free fusion and on-demand view output are realized, and the data access flexibility, the quality guarantee capability and the service adaptation flexibility are comprehensively improved.

The invention discloses a data storage control method and device, a storage medium and electronic equipment, and relates to the technical field of computers, and the data storage control method comprises the steps that the access frequency of multiple pieces of data accessed in a future time period is predicted according to historical access characteristics of the multiple pieces of data in a storage system; detecting whether the data access types of the data are changed or not according to the frequency range in which the access frequency falls; under the condition that it is detected that the data access type of the target data is changed, a storage strategy of the target data is adjusted according to the target data access type obtained after the target data is changed, and a target storage strategy is obtained; and executing the target change operation on the target data, the target change operation being used for changing the storage strategy of the target data to the target storage strategy, thereby solving the technical problem that it is difficult for a storage system to consider both storage overhead and storage performance during data storage. The technical effect that the storage cost and the storage performance are both considered when the storage system stores the data is achieved.

The invention relates to the technical field of data processing, can be applied to business scenes of financial science and technology, medical treatment and health and the like, and discloses a data hierarchical storage method, device, equipment and medium based on access characterization. Writing the data into a matched first target storage device according to a preset device performance corresponding relation; after the data are stored, continuously monitoring subsequent access characteristics of the data, and dynamically judging whether the heat state is changed or not; and if the popularity state is changed and a specific migration condition is met, redistributing the second target storage device and completing data migration operation so as to realize automatic hierarchical storage management of cold and hot data. By acquiring the data access representation and dynamically identifying the popularity state, hierarchical migration of data with different popularity among storage devices with different performances is realized, the resource use efficiency and access performance of a storage system are improved, and the storage cost is reduced.

The invention discloses a Direct3D memory model compatible method based on adaptive placeholder resources, which comprises the following steps: establishing resource metadata, accessing a scene library, checking an instruction template and double sandboxes when a DXVK is started, describing related resources by the DXVK through the metadata after a D3D application is started, completing resource mapping and metadata dynamic updating, and allocating the resources to the corresponding sandboxes; the DXVK compiles an application shader code, identifies a resource access instruction, matches an access scene and a check template by combining a pipeline stage and binding slot query metadata, instantiates the check instruction and adds the check instruction to the front of a target instruction; according to the unbound resources, adaptive generation of corresponding occupied resources is carried out, the authority and the state are configured, the unbound resources are bound to a Vulkan descriptor set to replace VKNULLHANDLE, and adaptation of the shader interface and the pipeline state parameters is completed to create PSO (Particle Swarm Optimization); and intercepting an application rendering instruction analysis parameter to construct a command buffer area, submitting the command buffer area to a Vulkan queue to trigger a GPU (Graphic Processing Unit) to execute rendering, and finally completing complete conversion and adaptation from the D3D rendering logic to the Vulkan.

The embodiment of the invention provides a memory access method and device, electronic equipment and a readable storage medium. The method comprises the following steps: receiving a cache line access request; extracting label information from a target memory address corresponding to the cache line access request, wherein the label information comprises a shared label, a private label, a group index, inter-block offset and intra-block offset; querying whether the cache line access request hits the L1 cache or not based on the label information; the group index is used for determining a target group, the shared tag and the private tag are used for determining a target cache line in the target group, and the inter-block offset is used for determining a target cache block in the target cache line; and if the cache line access request hits the L1 cache, determining a target data position in a hit target cache block based on the in-block offset, and executing a corresponding memory access request based on the target data position. According to the embodiment of the invention, on the basis of ensuring the data access efficiency, the storage space of the cache line is fully utilized, and the waste of the cache space is reduced.

The invention relates to the technical field of computers, and provides a request management method and device, electronic equipment and a storage medium, and the method comprises the steps: when it is detected that a data access request is subjected to a cache miss, based on a first address carried by the data access request, executing the cache miss on the data access request; checking whether an uncompleted request entry corresponding to the first address exists in a preset request management structure or not, wherein the preset request management structure is a pre-constructed multi-path group associative cache structure used for performing grouping management on a cache miss request; and adding the data access request into a waiting queue of the uncompleted request entry under the condition that the uncompleted request entry exists in the preset request management structure. According to the method and the device, the cache miss requests are subjected to grouping management by adopting a multi-path group associative cache structure, so that the parallel search of the cache miss requests can be realized, and the processing efficiency of the cache miss requests is greatly improved.

The invention relates to the technical field of microprocessors, and discloses a safety debugging method of an embedded microprocessor and the embedded microprocessor, the method comprises the following steps: implanting a random module in the microprocessor, constructing a resistance network array through random distribution of silicon substrate lattice defects to generate a disordered sequence, and if microprocessor data access is carried out, starting the random module; if physical intrusion is detected, a debugging session is established, three types of physical characteristics are synchronously generated to construct a composite key, a triple dynamic confusion layer is constructed in the microprocessor, the dynamic confusion layer and the composite key construct physical logic linkage protection, a probe access instruction logic address is called, if physical intrusion is detected, a probe triggers an invisible check point, and the probe accesses the invisible check point. The integrity of an execution environment is verified, if abnormal debugging is detected, a hardware defense mechanism is triggered, an access attack model is established, confusion parameters are trained and optimized, and a module interconnection mode is changed, so that the learning cycle of an attacker is broken, and the risk of attacking a processor in a machine learning mode is reduced.

The invention discloses an information security management method and system based on sensitive data, relates to the technical field of data management, and comprises the following steps: realizing quantum encryption transmission of remote high-sensitive data by combining a quantum key distribution link and a dynamic chaotic key stream; the partitioning and synchronization priority is dynamically adjusted according to the node load, the data integrity is verified through CRC, the distributed storage disaster recovery system with extremely high safety and usability is achieved, it is ensured that data can still be recovered even under extreme disasters, a zero-trust data channel based on an IPSec tunnel is constructed in the data transmission process, and the reliability of data transmission is improved. The tamper-proof, anti-counterfeiting and anti-leakage capabilities of a data access link are greatly improved, the access frequency abnormity is detected through the isolated forest model, the behavior trend is detected through the LSTM time sequence prediction model, the baseline deviation analysis is performed through user behavior portrait comparison, and the real-time, multi-modal and chained security monitoring of the user data operation behavior is realized.

The invention relates to the technical field of blockchain privacy protection, and discloses a homomorphic encryption and blockchain privacy protection method and system, and aims to encrypt a transaction amount through multi-key fully homomorphic encryption, store transaction metadata in combination with a Hash commitment mechanism, ensure that data on a chain is always in an encrypted state, and prevent unauthorized access. And secondly, in order to solve the problem that the smart contract cannot directly process the ciphertext, homomorphic calculation is combined with zero-knowledge proof, so that the smart contract can verify the transaction legality under the condition of not decrypting data, and the calculation burden is reduced by combining multi-party calculation. And finally, in order to optimize the storage and query efficiency, in combination with under-chain encryption storage, the encryption hash value is only stored on the chain, and meanwhile, proxy homomorphic signature is adopted to enhance data access authority management, so that the expandability and calculation efficiency of the block chain are improved.

The invention relates to the related technical field of data governance analysis, in particular to an intelligent data governance system based on behavior path analysis, and the system comprises a path generation module which generates a data flow path through a graph neural network according to a data behavior log, and draws up a behavior path graph; the first risk propagation path set determination module and the second risk propagation path set determination module respectively use graph diffusion and characteristic parameter marks to determine risk paths so as to operate a blocking mechanism to carry out data access management. The technical problems that data access depends on single rule matching and single-point anomaly detection, global correlation analysis on data operation behaviors is lacked, and complex attacks cannot be effectively detected are solved, the behavior path map is generated through the graph neural network, the influence range of abnormal operation is quantified by using the graph diffusion model, and the data access efficiency is improved. The technical effects of predicting the potential path of sensitive data leakage in advance, dynamically adjusting the confidence, improving the risk node identification accuracy, carrying out real-time blocking treatment and guaranteeing the data access security are achieved.

The invention provides an unstructured data storage optimization method and system based on information entropy and a block chain, and relates to the technical field of data storage, and the method comprises the steps: building a block chain storage network to obtain data, recognizing redundant data through dimension reduction, mapping and hyperplane construction, and removing the redundant data based on fuzzy similarity. The to-be-stored data is partitioned, the access frequency is calculated, the consistent Hash algorithm is executed to determine the storage position, the access index and the verification sequence are established, and the data integrity verification is executed, so that the data access efficiency can be improved, and the storage security can be enhanced.

The invention discloses an Internet of Things operation management scheduling system based on cloud edge collaboration, relates to the technical field of task scheduling optimization, and aims to solve the problem of adaptability of edge computing and cloud edge collaboration architecture, the Internet of Things perception access technology is adapted and fused with heterogeneous equipment through an architecture adaptation module, various IOT data are stably acquired, and the cloud edge collaboration is realized. The task execution position is dynamically adjusted in combination with the task distribution unit; real-time stream calculation and an ETL processing mechanism are combined to fit a comprehensive scheduling index Zdzh, the utilization rate of calculation resources is increased, and task scheduling is optimized; internet of Things equipment data, internet data and government affair data are analyzed based on the data lake and warehouse architecture, a data access strategy is optimized, and the data fusion efficiency is improved; and calculating and evaluating a sudden anomaly prediction coefficient Tycs, triggering an anomaly early warning mechanism or a sudden anomaly emergency mechanism, optimizing task migration, calculating node load balancing and data traceability analysis, so as to improve the stability and intelligent level of an Internet of Things operation management scheduling system.

The invention provides a credible sharing method and system for multi-party industrial data, and relates to the technical field of data processing, and the method comprises the steps that multiple participants encrypt local industrial data through a credible execution space, and generate encrypted data and corresponding data fingerprints; defining a data access rule based on the smart contract; verifying and auditing the data access request, and obtaining an encrypted data authorization certificate after verification and auditing; and under the federated learning framework, the trusted execution space of each data provider executes the privacy calculation task based on the authorization certificate, and outputs a task calculation result and sends the task calculation result to the task requester. According to the method and the device, the technical problem of privacy risk of data leakage or tampering caused by contradiction between multi-party industrial data sharing and privacy protection is solved, the privacy can still be ensured while data sharing is ensured through federated learning and trusted execution space, and the security and the credibility of data sharing are improved.

The present invention relates to the technical field of network security protection. Disclosed are a security protection method and apparatus for a customer service management system. The method comprises: acquiring an employee data set, and performing permission assignment, so as to generate an employee operation permission set; performing identity verification on an access user, and determining a data management operation permission range of the access user; acquiring a sensitive data set for encryption processing, and storing same in a customer service management system; accessing data of the customer service management system, implementing an access control policy, and performing risk assessment, so as to generate a system risk level; extracting detected anomalous data, synchronizing same to a security protection network, and outputting a protection control policy; and executing the protection control policy for security assessment, regularly updating the protection control policy, and performing intelligent security protection on the customer service management system. The present invention solves the technical problem in the prior art that customer service management systems have low security protection capacity, which leads to difficulty in preventing network attacks, thus achieving the technical effect of improving the security protection capacity of customer service management systems.