430 results about "Real time networks" patented technology

The present invention relates to the technical field of data transmission. Disclosed is an application-driven three-dimensional spatial data transmission method. The method comprises: determining system key performance indicators (KPIs) by means of qualitative and quantitative analysis; constructing an AI-driven adaptive three-dimensional data transmission mechanism, and dynamically adjusting a transmission strategy on the basis of a real-time network state, a device capability, an application scenario and the KPIs; developing an adaptive compression algorithm set oriented to three-dimensional data, so as to meet differentiated compression requirements of different application scenarios; performing loop execution of a test, and adjusting and optimizing the data transmission mechanism and the compression algorithm set on the basis of a test feedback result and the real-time network state; and deploying an optimized transmission method to a production environment, and collecting field data to optimize the system performance and verify the achievement of the KPIs. By constructing a qualitative and quantitative analysis framework based on machine learning, the present invention quantifies differentiated transmission requirements of different application scenarios and formulates transmission strategies meeting the scenario requirements.

The invention discloses a multi-source heterogeneous data synchronization system, and relates to the field of computer information management application. The system fuses key technologies such as flow dynamic regulation and control, self-adaptive load balancing and intelligent semantic mapping. Through a real-time network state monitoring and self-adaptive flow regulation and control algorithm, the data transmission rate is dynamically adjusted according to the server resource utilization rate, the data priority and the network bandwidth, data backlog and system overload are avoided, and the robustness and the real-time performance of a synchronization task are remarkably improved. The intelligent field mapping engine is based on semantic analysis, word vector representation and a machine learning model, automatically recognizes field semantic consistency of heterogeneous data sources, generates an optimal mapping rule and dynamically optimizes a mapping result, the manual configuration cost is reduced, and the mapping accuracy rate reaches 98% or above. The system has high expandability and self-learning ability, is suitable for a large-scale multi-source heterogeneous data integration scene, and provides an efficient, intelligent and self-adaptive full-stack solution for a data synchronization task.

The invention relates to the technical field of artificial intelligence, can be applied to the fields of finance, medical treatment and the like, and provides a network fault processing method based on artificial intelligence, and the method comprises the following steps: carrying out the trend prediction and anomaly detection through a machine learning model based on real-time network performance indexes and historical alarm data, and obtaining a network fault detection result; generating an alarm set containing fault type inference; performing association analysis on the alarm set and network topology and change records, constructing a fault propagation map, and outputting a root cause node list sorted according to probability; generating an equipment parameterized repair script according to the root cause node list; executing the repair script and monitoring an execution state in real time to obtain a repair result; and verifying the repair result. Compared with the prior art, the method has the advantages that the end-to-end fault processing period is remarkably shortened, the manual intervention requirement is reduced, and the service continuity is guaranteed in a complex network environment.

The invention discloses an edge-cloud collaborative civil aviation equipment intelligent detection platform, which relates to the technical field of intelligent detection and comprises an edge computing layer, a cloud computing layer, a collaborative scheduling layer, a data collaborative channel and an image processing layer. According to the method, a dynamic edge-cloud task scheduling mechanism is adopted, a traditional static task allocation mode is broken through, millisecond-level elastic task migration is achieved through real-time network state monitoring and computing power perception, incremental federated learning collaborative modeling and a gradient aggregation mechanism based on differential privacy are adopted, collaborative optimization of a multi-edge-node model is supported, and the task migration efficiency is improved. Multi-modal sensor fusion and lightweight anomaly detection are fused, the composite fault detection precision is improved, adaptive sampling and an edge knowledge graph are adopted, the sampling rate is dynamically adjusted based on the equipment risk level, offline rapid diagnosis can be achieved in combination with a graph database, and the problems that data redundancy is caused by fixed sampling, and the detection accuracy is high are solved. And the traditional system loses the diagnosis capability when the network is interrupted.

The invention discloses a network space map surveying and mapping method and system based on multi-source data fusion, and the method comprises the steps: obtaining a multi-source data set in a unified format based on network flow data, equipment information data and geographic position data; obtaining a network asset entity and an incidence relation graph thereof through entity identification and correlation analysis based on the multi-source data set with the uniform format; obtaining a network space three-dimensional map model through three-dimensional space mapping and visual rendering based on the network asset entity and the association relationship map thereof; based on the network space three-dimensional map model, performing dynamic updating according to the accessed real-time data flow to obtain real-time network space situation data; and obtaining a network security risk assessment result through anomaly detection and threat identification based on the real-time network space situation data. According to the invention, visual display and security situation awareness of the network space are realized, and a brand new decision support tool is provided for network security management.

The invention provides a real-time network topology and parameter identification method and system for a power distribution system, and the method comprises the steps: collecting the data of a node for installing an intelligent electric meter in a historical power distribution network, and constructing a data set; calculating an admittance matrix, deducing a network topology structure, and preliminarily estimating line parameters; modeling the power grid topology based on a GCN (Graph Convolutional Network), calculating the importance of each node, and iteratively optimizing the placement strategy of the SMD through a loss function; on this basis, SMD data is adopted as the input of a graph neural network GNN, and real-time network topology and parameter identification are carried out on the system; and if the change of the network topology is identified, performing fine adjustment on the pre-trained GNN parameters through a transfer learning method to adapt to a new topological structure, and re-identifying the parameters. According to the method, under the condition of limited measurement equipment, the placement strategy of the measurement equipment can be optimized, and the identification real-time performance and precision of the topology and parameters of the power distribution network are improved.

The invention discloses a distributed computing power dynamic scheduling method and device and a medium, and the method comprises the steps: packaging heterogeneous computing power resources of a home terminal and an edge cloud node through a lightweight containerization technology, and collecting the hardware resource state data of the home terminal and the load data of the edge cloud node in real time; generating a dynamic scheduling strategy according to the task calculation type and the real-time network state of the to-be-calculated task, and splitting the to-be-calculated task into a plurality of sub-tasks according to the dynamic scheduling strategy; distributing the sub-tasks to home terminals or edge cloud nodes according with a dynamic scheduling strategy, and aggregating calculation results of the sub-tasks; and performing end-to-end encryption and fragmentation verification on the cross-domain transmitted data stream, and dynamically adjusting the task allocation permission of the home terminal based on the equipment security score.

The invention relates to the field of multimedia communication and network control, in particular to an intelligent network control method for low-delay video return and related equipment. The intelligent network control method comprises the following steps: acquiring network key indexes including bandwidth, delay, jitter and packet loss rate of a network link in real time, and providing real-time network environment data support for transmission strategy adjustment. According to the method, an intelligent control mechanism combining network state perception and video content feature recognition is constructed, so that the technical problem of low-delay video return in a complex network environment is effectively solved. Specifically, key indexes of a network link are collected in real time, and a lightweight CNN model is introduced to analyze the video content activeness, so that dual perception capabilities for a network environment and content features are formed, data support is provided for dynamic adjustment of coding parameters, and accurate balance between video quality and network adaptability is realized.

The invention provides a dynamic network security defense method and system oriented to real-time network state adaptation, and the method comprises the steps: building a standardized state vector sequence through collecting key indexes, such as network flow rate, abnormal connection ratio, topology change frequency, protocol distribution deviation degree and the like; based on the sequence, utilizing a time sequence prediction model embedded with a structured attention mechanism to predict attack risk trends of a plurality of time windows in the future in a prospective manner; and in combination with the current state and the risk trend, dynamically selecting an optimal strategy path in a predefined defense action map, mapping the optimal strategy path into a standardized control command which can be executed by equipment, and dispatching, issuing and executing the standardized control command according to the priority. According to the method, closed-loop self-adaptive defense from state perception and risk prediction to strategy generation and execution is realized, and the real-time performance, intelligence and engineering deployability of a network security system are remarkably improved.

The invention discloses a wireless data uploading method and system for an industrial internet of things, and relates to the field of wireless data processing, and the method comprises the steps: firstly, comprehensively considering the inherent service attributes of data to determine the initial service priority of the data; meanwhile, terminal side sensing data and global network state data obtained from the gateway are fused, and a more accurate and comprehensive network quality score is calculated through double-source information. Finally, the initial priority and the network quality are dynamically combined according to the two key dimensions, and a final transmission strategy is generated. According to the method, a data uploading decision is no longer static and one-dimensional, but a two-dimensional dynamic decision which can be adaptively adjusted according to a real-time network condition, so that the sending opportunity or mode of the high-priority data is intelligently adjusted when the network is congested, the blocking of key services due to blind sending is effectively avoided, and the service quality is improved. And the robustness and the high efficiency of data uploading in a complex industrial environment are ensured.

The invention provides a network interference positioning method and device based on multi-source data fusion, and relates to the technical field of communication, and the method comprises the steps: extracting a frequency domain feature vector of radio frequency signal data, a spatial feature vector of environment perception data, and a topological feature vector of network topological data, carrying out cross-modal alignment on the frequency domain feature vector and the spatial feature vector; dynamically adjusting a fusion weight based on a real-time network load, and fusing each feature vector based on the fusion weight to obtain a fusion feature tensor; and performing non-line-of-sight propagation correction on the fused feature tensor to obtain a positioning parameter set, and determining the position of the interference source based on the positioning parameter set. According to the method and the device provided by the invention, multi-source data is adopted for positioning, cross-modal alignment is carried out on features of different modals, the fusion weight is dynamically adjusted based on the real-time network load, and through targeted non-line-of-sight propagation correction, the interference source is accurately identified and quickly positioned when the network is in a heavy load state.

The invention provides a video coding method and device, electronic equipment and a storage medium, relates to the technical field of image processing, in particular to the field of video coding and the like, and can be applied to application scenes such as video live broadcast and the like. The specific implementation scheme is as follows: acquiring a reference frame which is adjacent to a current frame and has established initial ROI hierarchical division; multiplexing a motion vector generated by the reference frame in a motion compensation time domain filtering process, and determining an ROI position predicted value of the current frame; extracting feature points of the five sense organs of the current frame, and correcting an ROI position predicted value based on a feature point spatial topological relation in the initial ROI; respectively configuring differential quantization parameters for the corrected main face region, the corrected secondary face region and the corrected background region; dynamically allocating a three-layer area code rate according to a real-time network bandwidth; and outputting the coded frame of the current frame and the associated ROI level metadata. According to the scheme, the coding efficiency and quality can be improved.

The invention discloses a multiplexing method and system for improving 5G mobile communication spectrum efficiency, and belongs to the technical field of dynamic spectrum multiplexing communication. The method comprises the steps of collecting real-time information, predicting spectrum availability and inter-band interference by utilizing a neural network model, generating a dynamic spectrum multiplexing strategy, issuing the strategy, adjusting parameters and continuously monitoring and optimizing. According to the method, an intelligent prediction model fusing MLP and LSTM networks is adopted, spectrum availability and inter-band interference prediction are provided, and prediction confidence information is generated; a self-adaptive multi-target intelligent optimization algorithm is applied, and the algorithm can dynamically adjust and optimize the weight according to a real-time network; local optimality judgment is carried out by combining local real-time information through the intelligent reconstruction capability of the 5G base station; and continuous monitoring and closed-loop optimization are realized by using an online learning mechanism. According to the invention, dynamic intelligent management of 5G network spectrum resources can be realized, and the 5G mobile communication spectrum efficiency is significantly improved.

The invention discloses a data migration method and device, computer equipment and a storage medium, and realizes a data migration processing mode oriented to an unstable network environment by combining neural network prediction, decision tree anomaly recognition and a distributed cache mechanism. Therefore, the data transmission stability and the migration task completion rate in the cross-network environment are obviously improved. Firstly, a pre-trained neural network is utilized to carry out deep prediction on a real-time network state, and a potential performance reduction trend is identified in advance, so that a migration process can actively adjust a path or a strategy before a risk occurs. And secondly, by performing decision tree identification on a data packet sending sequence, the system can position a potential interruption point based on fine-grained time sequence characteristics, and quantify the risk in an interpretable manner, so that migration control can be accurate to a specific data packet level. Finally, under the condition that the risk is unacceptable, the system automatically activates a distributed cache mechanism, and the durability and the restorability of the data are guaranteed in a redundant copy storage mode.

The invention provides a cross-platform user behavior analysis method and system based on transfer learning, and relates to the technical field of network security, first, historical network behavior record data of a source domain security platform and real-time network behavior flow data of a target domain security platform are obtained, the historical network behavior record data comprise security behavior sequences of source domain users in different access scenes, and the real-time network behavior flow data are stored in the target domain security platform; the method comprises the following steps of: performing cross-domain security feature extraction on two types of data, constructing a cross-domain security behavior association graph, migrating source domain historical malicious behavior mode knowledge to a target domain through a migration learning model based on the graph, generating cross-platform migration security features, and performing cross-domain security feature extraction on the target domain historical malicious behavior mode knowledge. And calling a security behavior analysis model to carry out joint modeling and time sequence security association analysis, identifying an abnormal security behavior mode of a target domain user, and finally matching a network security disposal rule base according to the abnormal mode, generating and issuing a protection strategy, and realizing real-time risk interception.

The invention relates to the technical field of network communication, in particular to a DNS (Domain Name Server) hierarchical cache resolution acceleration method and system based on a prefetching mechanism, which comprises the following steps: by constructing a hierarchical cache model comprising a client layer, a local DNS server layer and an upstream DNS server layer, caching a resolved domain name and a corresponding resolution result into each layer of cache; a DNS request of a terminal user is received, whether the request hits the cache or not is judged, and corresponding operation is adopted; historical query logs are collected, a next DNS request is predicted in combination with a prefetching mechanism of real-time network topology perception, and an analysis result of the domain name is cached to a local DNS server layer in advance; a cross-layer collaborative cache updating mechanism is adopted to ensure synchronous updating of cache data of different levels; and a symmetric encryption algorithm and a TLS protocol are introduced, so that the security of the data in the storage and transmission process is ensured. According to the method, the response of DNS analysis is accelerated through a prefetching mechanism and layered caching.

The invention provides an AI model intelligent training and reasoning integration method and system, and the method comprises the following steps: receiving a model training instruction, and carrying out the preprocessing of original data, and obtaining a training data set; executing model training based on the training data set, monitoring task priorities and resource requirements through a dynamic resource scheduling algorithm, and dynamically adjusting training resource allocation according to a real-time monitoring result; when the model reaches a preset performance index, performing model pruning and quantification to generate an optimization model, and performing parameter fine tuning on the optimization model to obtain a final deployment model; generating reasoning service configuration according to calculation characteristics of the final deployment model, migrating the model and the configuration to a reasoning environment, and starting reasoning service; and dynamically adjusting the number of reasoning nodes according to the real-time network flow of the reasoning service. By implementing the technical scheme provided by the invention, through bidirectional dynamic resource scheduling and model deep optimization, the computing resource utilization rate and the model deployment efficiency are improved, and the high availability of the reasoning service is guaranteed.

A system for enhancing an energy efficiency of a mobile network includes a digital twin connected to the mobile network. The digital twin is programmed to: receive, from the mobile network, real-time network traffic data and base station data of base stations operating in the mobile network; generate a first prediction of network traffic at a first time-scale based on the real-time network traffic data; generate a second prediction of network traffic at a second time-scale based on the real-time network traffic data and the base station data; and generate a first power consumption prediction for the mobile network based on the first prediction, the second prediction, and a topology of the mobile network, and the digital twin emulates at least a portion of the mobile network.

The invention discloses a network processing system based on artificial intelligence, and the system comprises an intelligent sensing layer which is responsible for collecting the flow, delay and topology original data of network equipment in real time, carrying out the initial processing at an edge node, generating structured network state information, and outputting the structured network state information to a cognitive analysis layer; the cognitive analysis layer is used for performing four-dimensional modeling and intelligent analysis on the network state after receiving the structured data of the intelligent perception layer; then generating an optimization decision and outputting the optimization decision to an adaptive execution layer; the self-adaptive execution layer is responsible for converting an optimization decision into specific network configuration and operation according to the decision of the cognitive analysis layer, feeding back an execution result to the man-machine collaboration layer, and receiving an administrator confirmation or adjustment instruction from the man-machine collaboration layer at the same time; the man-machine collaboration layer is responsible for interaction between the system and an administrator and displaying a real-time network state and an AI decision causal chain; and an administrator monitors the network state through the man-machine collaboration layer, confirms or rejects AI suggestions, and feeds back an operation log to the cognitive analysis layer.

A method of scheduling data transmission in a radio access network is provided. The radio access network comprises a scheduler configured to orchestrate communication of data between one or more base stations and a plurality of User Equipments, UEs, according to a set of instructions. The method comprises receiving real-time network usage data. The method further comprises dynamically modifying the set of instructions based on the network usage data. The method further comprises implementing the modified set of instructions so that the scheduler is configured to orchestrate communication of data between the one or more base stations and the plurality of UEs according to the modified set of instructions.

The invention discloses a dual-channel adaptive network stability testing method and system, and belongs to the technical field of computer network testing. The system adopts a dual-channel asynchronous communication architecture in which a control channel and a data channel are physically separated, instructions and state information are transmitted through an independent TCP control channel, and high-speed test data streams are transmitted through a UDP or TCP data channel. The method comprises the following steps: dynamically and adaptively adjusting a timeout parameter based on real-time network delay detection; nanosecond constant interval packet sending control is realized by using a high-precision monotonic clock source and busy waiting compensation mechanism; a packet loss detection, positioning and ACK confirmation mechanism based on the serial number is realized in an application layer; and real-time statistical display and idle detection automatic termination are realized through multi-thread parallel processing. According to the invention, the precision, the stability and the adaptive capability of network testing are remarkably improved, and the method is suitable for long-time stability testing in various scenes such as a high-speed Ethernet, a satellite link, an industrial Internet of Things gateway and the like.

A 3D development engine-oriented adaptive flow loading method relates to the technical field of 3D development engines, and comprises the following steps: collecting environment state data through an environment sensing module; continuously acquiring a network state index through a network monitoring module; establishing a dynamic priority calculation model, taking the environment state data and the network state indexes as input parameters, and generating a quantitative priority score for each resource project; performing dynamic reconstruction and sorting processing on the resource loading task queue based on a priority scoring result, adjusting the execution number of concurrent loading tasks according to a real-time network state index, and selecting a corresponding resource compression strategy; and establishing a resource life cycle management system, and executing allocation optimization and recovery cleaning operation of the memory resources. According to the invention, adaptive flow loading of 3D resources can be realized, and the fluency of a large-scale scene, the network adaptability and the system resource utilization efficiency are improved.

The invention provides a real-time data processing method and system based on edge computing, and relates to the technical field of data processing.The data validity period is calculated through a composite decision model of terminal equipment physical sampling frequency constraint and preset service rules, the signal restoration accuracy requirement and the service scene emergency degree are effectively balanced, and the real-time data processing efficiency is improved. Overtime failure of key data or occupation of non-key data resources is avoided; the data transmission integrity in a bandwidth fluctuation environment is improved through a dynamic window cutting mechanism driven by a real-time network state; optimizing edge computing power load distribution in combination with a routing decision mapping strategy and a processing priority resource ratio; the terminal instruction reliability is improved based on hierarchical control of historical data abnormal value purification and geometric distance measurement; a complete technical chain from data acquisition, self-adaptive transmission cutting, aging precise arbitration and routing resource collaboration to closed-loop control feedback is formed, and the transmission efficiency, the resource utilization precision and the control stability of high-concurrency real-time data processing are enhanced.

The invention discloses a spaceflight electromagnetic relay failure early warning and diagnosis method based on multi-physics field coupling, relates to the technical field of spacecraft electrical system reliability monitoring, and is used for solving the problems of lack of comprehensive consideration of multi-physics field coupling characteristics under spaceflight special working conditions and low interference resistance to complex environments. The method comprises the following steps: monitoring a network state, obtaining real-time data, calculating an optimal transmission strategy by using an intelligent regulation and control model, dynamically adjusting a coding mode, adaptively adjusting a transmission rate, further ensuring data integrity through an error code detection and error correction mechanism, and adjusting transmission parameters according to a real-time network condition in combination with packet optimization and flow priority management. Effective recognition and early warning of typical failure modes such as contact adhesion and coil burnout are achieved, and the failure early warning accuracy and diagnosis efficiency of the spaceflight electromagnetic relay in complex environments such as high radiation, extreme temperature and strong vibration are improved.

The embodiment of the invention provides a mobile communication PCI (Peripheral Component Interconnect) decision-making method and device based on an improved ant colony algorithm, and the method comprises the steps: obtaining MR (Measurement Report) data of a base station and a real-time network state; the base station MR measurement report data comprises a conflict matrix, a confusion matrix and a modulo 3 interference matrix; extracting a network topology feature vector according to the MR measurement report data of the base station; according to the network topology feature vector and the real-time network state, obtaining a heuristic weight and a pheromone volatilization rate parameter; based on an ant colony algorithm, obtaining an improved ant colony algorithm according to the heuristic weight and the pheromone occurrence rate parameter; based on the quantum revolving door, determining a three-level objective function according to the conflict matrix, the confusion matrix and the modulo 3 interference matrix; and based on an improved ant colony algorithm, generating an optimal PCI allocation scheme according to the conflict matrix, the confusion matrix, the modulo 3 interference matrix, the three-level objective function and a preset constraint condition. The problem of optimization target imbalance is solved, and the reliability of the PCI decision method can be ensured.

The invention provides a bandwidth distortion self-adaptive multi-layer scalable feature compressor and a method and system thereof, and relates to the technical field of machine learning. According to the invention, through a code rate distortion joint controller, bit width distribution and fragment size of each layer of a layering number are dynamically decided according to a real-time network state training stage and a precision demand; the input features are mapped to a subspace with more concentrated energy through a layered learnable orthogonal transformation module, and feature channels are clustered and grouped; different bit widths are distributed to the coefficients based on the importance degree through a non-uniform quantization module; dynamically adjusting the fragmentation size and the fragmentation sending rhythm through an RTT adaptive scheduling module according to the round-trip delay and the jitter degree; recording an accumulated reconstruction error through an error tracking compensation module and carrying out residual encoding; and periodically transmitting the anchoring layer through the key frame synchronization module to eliminate accumulated errors. According to the method, the robustness, convergence stability and communication efficiency of distributed training in a complex network environment are remarkably improved.

Embodiments here in disclose a method and system for network configuration generation using a network digital twin (NDT) comprising a spatio-temporal graph neural network (STGNN) for performance-based simulation in a communication network system is disclosed. A simulation request is received from a management service (MnS) consumer, including network parameters related to live network nodes. Performance data is retrieved from a management server, and performance is assessed against a performance threshold. Multi-domain telecom data is processed using a spatio-temporal graph neural network (STGNN) by a feature fusion attention mechanism, trained via a hybrid feedback loop with real and synthetic data. Upon meeting performance thresholds, a configuration recommendation is generated by a configuration recommendation engine, including an explanation and confidence value. The configuration recommendation is transmitted to the MnS consumer, enabling network automation without altering the live network directly.

The invention relates to the technical field of data transmission, and discloses a gas meter information secure transmission method based on response function compensation, which comprises the following steps: collecting multi-source data and network signal quality information of a gas meter; according to a real-time network environment and physical conditions, data transmission parameters are dynamically adjusted through a response function compensation algorithm, when data transmission in a static time slot fails, a dynamic retransmission time slot is automatically allocated for retransmission, a retransmission frequency threshold value is set, and packet loss caused by channel errors or collision is compensated through a retransmission response function; a multi-layer encryption mechanism is adopted to encrypt transmission data, an optimal transmission channel is intelligently selected according to network conditions, adaptive hybrid communication is performed through star flash short-distance communication or Wi-Fi wide-area transmission, and a response compensation mechanism of multi-dimensional environmental parameters is established based on a signal transmission model; transmitting the encrypted data to a monitoring management platform through a secure channel; therefore, safety early warning decision is realized, and dynamic accurate transmission and active safety protection are realized.

The invention discloses an audio and video transmission method and device based on multi-mode AI enhancement and dynamic end-to-end encryption, equipment and a storage medium, and relates to the technical field of digital audio and video processing and secure transmission, and the method comprises the steps: obtaining an original audio and video stream, and separating the original audio and video stream into audio data, video data and subtitle text data; performing multi-mode AI enhancement processing on the audio data, the video data and the subtitle text data to obtain enhanced audio and video data; generating an encryption decision instruction based on the real-time network state, and performing encryption processing on the enhanced audio and video data according to the encryption decision instruction to generate encrypted data with a tamper-proof watermark; and when the encrypted data passes integrity verification and copyright legality judgment, the encrypted data is transmitted through a secure transmission channel, and collaborative optimization of security and efficiency is realized through a core technology chain of multi-modal separation enhancement, dynamic encryption decision, hardware-level watermark binding and credible verification transmission.

The invention belongs to the technical field of network security management and control, and particularly relates to a data security storage method adaptive to network security prevention and control, which comprises the steps of data acquisition, multi-dimensional feature extraction, optimal prevention and control strategy generation, hierarchical encryption storage, abnormal behavior traceability and dynamic isolation response. According to the system, illegal data injection risks are blocked from an entrance through a source data authentication acquisition module, multi-dimensional effective features of data are extracted through a multi-dimensional feature purification module, the multi-dimensional effective features and real-time network threat situations are fused through a prevention and control strategy adaptation module, and an optimal prevention and control strategy is screened by quantifying the adaptation degree; the hierarchical encryption storage module reasonably distributes storage hierarchies and executes differential encryption and hierarchical backup, the abnormal behavior traceability module accurately identifies abnormal behaviors, and the dynamic isolation response module executes hierarchical isolation and triggers emergency response and strategy optimization, so that full-life-cycle safety management and control of data from collection, storage to access is realized, and the safety of data storage is improved. And the prevention and control accuracy and the system operation efficiency are both considered.