3257 results about "Network data" 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 dynamic routing selection method and system, electronic equipment and a storage medium, and aims to solve the problem that a routing strategy is difficult to adapt to a dynamically changing network, the method comprises the following steps: a terminal layer collects the state of a terminal and network data, and performs lightweight feature extraction; the edge node layer receives the data of the terminal layer, carries out space-time-semantic feature aggregation, and generates a region-level resource scheduling and routing decision strategy based on fragmented reinforcement learning; the central cloud service layer gathers whole network data, generates a global optimization strategy and issues the global optimization strategy; the edge node layer fuses global optimization and a region-level strategy, and executes dynamic routing selection; and security and privacy protection are provided through the trusted chain layer. According to the invention, adaptive path selection can be realized, the network resource utilization rate is improved, and the network stability is improved.

The invention discloses a network data intelligent tool system and method based on a model context protocol MCP, and relates to the technical field of computer networks. The system comprises an AI analysis main body, an MCP Pcap tool engine, context management, a Pcap data source, various MCP Pcap tools, a Pcap tool gateway interface, a Pcap tool interface and a data packet interface. The invention provides an interaction mechanism of direct embedding and bypass data forwarding in tool calling. According to the method, direct embedded transmission of small-size data in tool calling is supported, separation of data transmission and instruction calling is achieved, the technical bottleneck that large-size Pcap data cannot be efficiently exchanged through a text channel is effectively overcome, efficient processing of the AI model on network packet capture data is achieved, and the data transmission efficiency is improved. And the efficiency and the automation degree of large-scale network traffic analysis are improved.

The invention discloses a computer network data secure transmission system and method, and particularly relates to the technical field of network data secure transmission, and the system comprises a dynamic key management module which generates a key seed through a quantum random number generator, generates a dynamic key bound with a data packet in combination with a timestamp, and transmits the dynamic key to a server; after being encrypted by a receiving end public key, the data are transmitted through an independent verification channel; the dual-path transmission control module is used for establishing dual channels of a main path and a shadow path, a data packet of the main path is embedded into a random camouflage protocol header to simulate a non-sensitive protocol, and a blank data packet is filled in the shadow path to maintain traffic characteristics; according to the real-time verification engine, a receiving end constructs an encrypted hash tree to achieve fragment-level integrity verification, and meanwhile, requests key state three-state verification from the key management module. Through key dynamic generation and separation transmission, dual-path adaptive fragmentation distribution, fragmentation hash tree reconstruction and key linkage verification, and abnormal triggering fragmentation level retransmission, the problems of long-term effectiveness of a static key, predictable transmission path and verification lag are solved.

The invention relates to an internet big data extraction method and device, equipment and a storage medium, and the method comprises the following steps: carrying out distributed crawler collection on an internet data source, obtaining original network data, and converting the original network data into a structured data matrix; and performing multi-level semantic analysis on the matrix, constructing a semantic feature map, and performing topic segmentation and classification to form a topic domain knowledge tree. Association rules in the knowledge tree are further mined, and an implicit knowledge network is constructed. Performing semantic decomposition and expansion on query conditions based on the network to generate expanded query data, and performing similarity matching with the knowledge network to obtain a candidate data set; and finally, performing multi-factor sorting and extraction on the candidate data, and outputting target data, thereby solving the technical problem that in the second-hand car market, due to wide data sources and fuzzy semantics, an existing system has relatively large deviation when performing price prediction and maintenance cost analysis.

The invention discloses a fault root cause positioning method and system for a server cluster, and relates to the technical field of network fault diagnosis. According to the method, nanosecond-level synchronous acquisition of micro-service call chains, container indexes, physical nodes and network data is realized through a precise time protocol, and a consistent data set is constructed through entity association and standardized processing; a service-resource topological graph is dynamically constructed, and an inter-service calling edge weight model is innovatively designed: a real-time load factor and a historical fault index attenuation sum processed by a Sigmoid function are fused, and the weight is periodically updated to accurately quantify the inter-node influence intensity; converting the topological graph into a Bayesian network; when a fault occurs, a three-level assembly line compression alarm is adopted, frequent item sets are mined through bitmap indexes and parallel FP-Growth, and strong causal association item sets are screened in combination with topological edge weights and KL divergence; strong causal alarm is taken as evidence, probabilistic root cause sorting is output through reverse random walk sampling, and high-precision positioning of complex distributed system faults is achieved.

The invention relates to the technical field of data analysis, and provides a power network data-driven optimization method and system based on dynamic authority modeling, which are used for improving the self-adaptability and anti-risk capability of a power network in a complex operation environment. The method comprises the steps of obtaining a power network operation data set, performing dynamic permission modeling processing on the power network operation data set, generating a user permission feature set and an equipment permission feature set, and generating a power resource dynamic allocation strategy according to the user permission feature set and the equipment permission feature set, and feeding back the power resource dynamic allocation strategy to the power network control system to activate a permission configuration updating operation. Therefore, through deep coupling of the permission model and resource scheduling, a technical path giving consideration to both elasticity and reliability is provided for intelligent upgrading of a power system, so that the self-adaptability and anti-risk capability of a power network in a complex operation environment can be improved.

The invention relates to the technical field of cloud computing, in particular to a network data encryption and privacy protection system in a cloud environment, which comprises a key management unit driven by a wolf pack algorithm, an encryption algorithm optimization unit, a privacy protection strategy dynamic adjustment unit and a safety monitoring and abnormity response unit. The invention discloses a cloud environment network data encryption and privacy protection system constructed based on a wolf pack algorithm. High-security keys are dynamically generated and distributed through a key management unit, security performance and resource consumption are balanced through an encryption algorithm optimization unit, multi-target dynamic gaming and compliance guarantee are achieved through a privacy protection strategy unit, distributed attack detection and cooperative defense are completed through a security monitoring unit, and the security performance is improved through a cooperative feedback mechanism between the units. Intelligent encryption protection, dynamic strategy adjustment and efficient attack response of the full life cycle of the data in the cloud environment are realized, and the system security, the resource utilization rate and the compliance capability are remarkably improved.

The invention discloses a computer network security threat real-time monitoring method and system, and relates to the technical field of network security, and the method comprises the steps: collecting and preprocessing multi-source network data, organizing the multi-source network data into a behavior sequence according to a time sequence, and forming time sequence behavior data for analysis; constructing an attack atlas based on the preprocessed multi-source network data, and in the atlas construction process, forming dynamic representation of the attack atlas in combination with time attributes of behavior events and inter-entity contexts; time sequence behavior data are input into an RCLNet architecture for analysis, the RCLNet extracts spatial features through CNN, the LSTM captures time features, key behavior features are concerned by using an adaptive attention mechanism, and a high-dimensional behavior embedding vector is generated. High-precision and real-time detection and response to network threats are realized, and the intelligence and actual combat adaptability of the system are greatly improved.

The invention discloses an intelligent crawler generation method and system based on a large language model and an MCP protocol, belongs to the technical field of network data collection, and solves the problem that the capability of LLM in dynamic webpage analysis and anti-crawling strategy generation links cannot be fully exerted due to the fact that LLM and browser interaction protocols cannot be effectively integrated in the prior art. The method comprises the steps of analyzing an acquisition demand based on a large language model and generating a standardized demand description document, realizing interaction between the large language model and a browser based on an MCP protocol, analyzing a page complete DOM tree structure through a crawler script generation system, and performing quality verification and intelligent repair on a generated crawler script. According to the method, the complete DOM tree and the dynamic data rendered by the browser are obtained through the MCP, and the large language model can be called to automatically analyze the element positioning strategy, so that the collection script is adaptively generated, and high efficiency, intelligence and automation of webpage data collection are ensured.

The invention relates to the technical field of network flow analysis, in particular to a non-intrusive network flow real-time analysis method and system based on an eBPF, and the method comprises the steps: deploying an eBPF program in a kernel mode, and capturing a network data packet entering a kernel protocol stack in real time; identifying an application layer protocol type and analyzing a key field; the structured data is transmitted to the user mode through the annular buffer area; and the user state carries out TCP stream recombination and session-level aggregation statistics on the structured data. According to the invention, on the premise that application codes are not modified, real-time and structured protocol analysis can be carried out on various common application layer protocols in a kernel layer; end-to-end delay is accurately disassembled through a full-link delay disassembling mechanism, and a performance bottleneck link is positioned; the analysis result is output in the form of structured data, second-level performance statistics and abnormal behavior detection are supported, and the operation and maintenance analysis efficiency and the system observability are remarkably improved.

The invention discloses a data exchange system for performing real-time secure communication with an intranet system, and relates to the technical field of network data security. The system comprises a network environment identification module, a challenge authentication module, a hardware key binding module, a data communication module, an abnormal response module, a strategy updating module and a visual monitoring and log module. According to the invention, by introducing the trust map evaluation module, map modeling is carried out on the characteristics of the equipment in the historical operation process, the rationality of the current operation environment of the system is judged based on the map coincidence evaluation result, the communication module is allowed to operate only when the evaluation result meets the condition, and a judgment mechanism based on the historical trusted behavior is formed; and the prevention and control capability of the system on gradual-change attacks and behavior counterfeit is enhanced.

The invention discloses a viscous coarse-grained soil permeability coefficient prediction method based on a physical constraint neural network, and the method comprises the following steps: carrying out an indoor viscous coarse-grained soil seepage test, and establishing a viscous coarse-grained soil permeability coefficient formula considering porosity and grain composition characteristics, further constructing a mixed model containing a physical driving item and a neural network data driving item, forming a complete data set through a numerical simulation technology and literature investigation on the basis of a seepage test, complementarily collecting porosity, grain composition characteristics and corresponding permeability coefficient data of the viscous coarse-grained soil sample, and dividing the complete data set into a training set and a test set; according to the method, optimal hyper-parameters are dynamically searched in combination with Bayesian optimization for model training, a loss function curve and permeability coefficients of the viscous coarse-grained soil under different porosity and grading characteristics are obtained, tests show that high-precision prediction of the permeability coefficients of the viscous coarse-grained soil is achieved, and the problems that a traditional method is insufficient in physical constraint and low in prediction precision are solved.

The invention discloses a crop disease diffusion prediction method and system based on multi-modal fusion, and the method comprises the following steps: S1, collecting and preprocessing an RGB image sequence and a sensor data sequence of a crop growth environment, and generating an RGB image time sequence difference result and a sensor difference result through time difference processing; s2, mapping the RGB image time sequence difference result and the sensor difference result to a shared time sequence space through a time alignment algorithm, and generating a sensor alignment result and an RGB alignment result; s3, an FD-ViT prediction model is constructed; inputting the sensor alignment result and the RGB alignment result into an FD-ViT prediction model for prediction, and generating a prediction result; and S4, generating a disease diffusion thermodynamic diagram and early warning information according to a prediction result. According to the method, RGB image data and sensor network data are fused, a Transform-based time sequence prediction model is constructed, and early recognition and diffusion trend prediction of crop diseases are realized.

The invention relates to the technical field of edge computing, and discloses a network data efficient caching method and system based on edge computing, which realize dynamic identification of hotspot data and optimal caching position decision by analyzing edge node data flow characteristics and network topology states in real time. Firstly, data packet meta-information is extracted from an edge node, a content feature vector is generated, and data popularity is evaluated; secondly, analyzing a network topology structure and a link state, and screening candidate cache nodes; then, factors such as service priority, network reliability and topology stability are fused, and an optimal cache position is determined by adopting a reinforcement learning algorithm; and finally, the cache content is updated, the cache state is synchronized between the edge nodes, and the cache distribution is optimized. The data access efficiency and the resource utilization rate of the edge network can be effectively improved, and the method adapts to dynamically changing network environments and user requirements.

Provided are an active defense system and method for an unknown threat. The system includes an intelligent threat early-warning module (10), an unknown threat detection module (20) and a self-adaption defense processing module (30). The intelligent threat early-warning module (10) is configured to perform threat prediction on a power grid situation data set collected from a power information network in real time to obtain threat early-warning information and send the information to the unknown threat detection module (20). The unknown threat detection module (20) is configured to perform threat detection and analysis on collected unknown threat network data when receiving the threat early-warning information to generate a threat analysis report and send the report to the self-adaption defense processing module (30). The self-adaption defense processing module (30) is configured to trigger a defense processing operation corresponding to a preset threat defense strategy according to the threat analysis report.

The invention discloses a pollutant anomaly detection method based on multi-pollutant collaboration and spatial-temporal feature fusion. The method comprises the following steps: S1, constructing a multi-pollutant sensor network data set; s2, cleaning and preprocessing the multi-pollutant sensor network data set in the research area; s3, learning a directed graph adjacency matrix of the single-pollutant sensor network for the multi-pollutant sensor network data set through Bayesian variation inference, and modeling an asymmetric causal relationship between sensors; s4, processing the sensor nodes of the predicted target location by using a single-pollutant spatial feature extraction module, and performing spatial feature fusion among multiple pollutants based on a hierarchical attention mechanism; s5, performing multi-pollutant data prediction on the multi-pollutant sensor network data of the predicted target location by fusing spatial features and a directed graph by using a spatial-temporal feature fusion module; and S6, performing anomaly detection based on an anomaly score and a dynamic update threshold value of the air pollutant monitoring value generated by cooperation of multiple pollutants.

The invention relates to the field of electric energy metering, and discloses a real-time metering method of an intelligent modular electric energy metering box, which comprises the following steps: deploying a sensor array in the electric energy metering box; acquiring original electric energy data and environmental parameters through the sensor array to obtain an original electric energy metering data stream; performing dynamic threshold filtering on the original electric energy metering data stream to obtain clean electric energy metering data; constructing an electric energy topology model according to a graph neural network, and inputting the key electric energy parameters into the electric energy topology model to obtain initial electric energy topology network data; and abnormal nodes in the electric energy topology model are detected through an isolated forest algorithm to obtain optimized electric energy topology data, the dynamic threshold filtering and Kalman filtering algorithms are adopted, noise and abnormal values in the original electric energy data can be effectively removed, the reliability of the electric energy metering data is ensured, and especially in a complex environment, the reliability of the electric energy metering data is improved. Errors may be caused by factors such as electromagnetic interference.

The invention relates to the field of intelligent operation and maintenance, and discloses a multi-source network data operation and maintenance system based on micro-service architecture and AI collaboration, comprising the steps of collecting multi-source data of a micro-service system, and performing cleaning, format unification and time alignment on the collected data; based on a data result of the data acquisition module, constructing a micro-service call chain and a dependency graph, embedding a real-time performance index and a log feature in each node, and dynamically updating a service relation graph; carrying out real-time anomaly detection on the multi-source data, and judging the alarm effectiveness in combination with a dynamic threshold and an AI alarm confidence self-learning mechanism; the AI conducts reasoning along the call chain anomaly map, causal relationship reasoning is added, and the anomaly propagation influence range is predicted; and feeding back a root cause positioning result and the optimized alarm information to an operation and maintenance system, optimizing an alarm threshold and decision parameters in combination with historical records, and outputting an updated operation and maintenance decision scheme. The method has the advantage of improving the operation stability of the system.

The invention discloses a method for determining an electrical fire risk assessment weight index coefficient, and relates to the technical field of risk assessment, and the method comprises the steps: deploying a plurality of types of sensors to collect original environment data streams including historical fault data, environment parameter data and communication network data in real time, carrying out the preprocessing of the original environment data streams, and carrying out the calculation of the original environment data streams; forming a preprocessed feature data set; performing sparse optimization on the preprocessed feature data set by using an Elastic Net regression function, optimizing regularization parameters in the regression function through a cross validation method, and finally obtaining an optimized feature set and a preliminary weight vector; and processing the time evolution sequence of the preliminary weight vector by using a convolution mode to generate a convolution feature tensor, and carrying out nonlinear mapping on the convolution feature tensor by using a ReLU activation function to obtain a predicted weight sequence. Effective fusion of multi-scale features is realized through a dynamic segmentation strategy of an adjustable time window in combination with a high-frequency signal analysis and long-term trend extraction technology.

An attack path modeling and discovery system and process of the present combines comprehensive network data with machine learning to determine the greatest risk to a network environment by exposing the path of least resistance an attacker would likely take. This system and process considers both the likelihood of a threat agent to exploit a vulnerability, and the potential for loss when that threat occurs. The system utilizes two key models to accomplish this goal. First, Knowledge Graphs (KG) are leveraged to comprehensively model relationships across an environment, and second, Graph Neural Networks (GNNs) are used to predict the path of least resistance to the network's user-defined most valuable assets.

The invention relates to the technical field of pipe network data processing, in particular to an automatic monitoring data processing method for pipe network pressure. The method comprises the following steps: acquiring pipe network structure data; constructing a pipeline topology network based on the pipe network structure data to generate the pipeline topology network; screening main pipelines and branch pipes of the pipeline topology network to obtain pipe network circulation data; carrying out pipe wall microwave resonator deployment on the main pipeline and the branch pipelines based on the pipe network circulation data, and capturing pipeline circumferential deformation harmonic waves in real time through the deployed microwave resonators; longitudinal wave propagation time delay of pipe network circulation data is analyzed, and a pipe body structure dynamic resonance atlas is constructed in combination with pipeline circumferential deformation harmonic waves; and carrying out pipe network internal pressure and pipe wall equivalent stress calculation on the pipeline topology network through a pipe body structure dynamic resonance map. By combining pipeline topology analysis, microwave resonator monitoring, dynamic resonance spectrum and bidirectional fluid-solid coupling analysis, the accuracy of pipe network pressure monitoring and identification is improved.

The invention provides a network flow control method and system, an engine, a node and related equipment, and relates to the technical field of communication. The method comprises the steps that communication information of a source computing power node is received, and the communication information comprises identification numbers of the source computing power node and a target computing power node and an identification number of a source port selected by the source computing power node; determining an optimal source port and a sending rate of the source computing power node based on the communication information; determining a forwarding path of the source computing power node based on the optimal sending port; configuration information is sent to the source computing power node, and the configuration information comprises the sending rate and the forwarding path. By means of the technical means, the problems that in network data transmission in the related technology, the link bandwidth is not fully utilized, and burst transmission flow cannot be dealt with are solved.

The invention belongs to the technical field of network communication, and particularly relates to an operation and maintenance network fault solving system and method.The operation and maintenance network fault solving method comprises the steps that a data collection module collects multi-source original network data of target network equipment and preprocesses the multi-source original network data to obtain multi-source standard network data; a semantic reconstruction module identifies non-business purpose fields in the multi-source standard network data and performs semantic reconstruction processing on the non-business purpose fields to obtain a target state set; the fault diagnosis module carries out fault analysis on the target state set and determines a fault type and a root position; and the execution module generates and executes a predictive isolation strategy according to the fault type and the root position, records a fault processing process and updates a dynamic coding rule and a diagnosis weight distribution strategy. According to the invention, the problems of low efficiency and insufficient accuracy caused by dependence on artificial experience and rule analysis in a network fault diagnosis method can be solved.

The invention discloses a cooperative scheduling method for a zero-carbon park complementary energy storage system, and the method comprises the steps: enabling an electric energy quality index to be explicitly incorporated into an optimization target through multi-source resource dynamic modeling and scene prediction, and building a strong coupling relation between a physical constraint and a scheduling decision; the hierarchical execution mechanism gives consideration to global optimization and local quick response, realizes undisturbed switching under abnormal working conditions, forms a prediction-optimization-execution-feedback closed-loop control system, and can accurately describe physical connection and electrical characteristics of a park power grid by establishing a power distribution network equivalent model and acquiring topological parameters, thereby realizing the optimal control of the park power grid. And basic network data is provided for subsequent optimization. By determining the controllable resource set and completing topological mapping, the position and the regulation and control range of each device in the power grid can be determined, and mistaken sending or conflict of instructions can be avoided. An apparent power upper limit constraint and SOC dynamic model is established, overload operation of equipment can be avoided, the energy storage charging and discharging capacity can be accurately represented, and the performability of a scheduling scheme is ensured.

The invention discloses a network data integration analysis system and method based on a model context protocol MCP, and relates to the technical field of computer networks. The system comprises an MCP-LSP adaptation layer, a network analysis language server, an IDE plug-in, a remote cooperation management module, an MCP analysis engine, an LSP client and an IDE UI component. According to the invention, deep fusion of the network protocol analysis capability and the integrated development environment is realized; by constructing an MCP-LSP adaptation layer, a network analysis language server can convert a semantic analysis result of an MCP analysis engine into an LSP standard message format, and standardized services of functions such as protocol analysis, session state and anomaly detection are realized; the working efficiency of a developer is improved by the IDE plug-in; the remote cooperation management module realizes synchronization and sharing of session states, improves integration, expandability and cooperation efficiency of network analysis, and is suitable for diversified requirements of modern software development teams.

The present invention extends to methods, systems, and computer program products for optimizing resource allocation in view of predicted network traffic patterns and predicted power consumption. Network packets defining a network traffic flow can be received at a platform over time. Metrics can be derived from one or more applications executing on resources of the platform and processing data contained in the network data packets. Model training data can be formulated from the metrics. A resource adjustment model can be trained using the model training data. Executing the model can be automated to adjust resource allocation at the platform. Additional network packets defining an additional network traffic flow can be received at a platform over time. Data contained in the additional network packets can be processed using the adjusted resource allocation.

The invention discloses an urban rail transit station site selection multi-objective optimization method and a related device, and belongs to the crossing field of urban planning and traffic engineering, and the method comprises the following steps: according to preprocessed urban road network data, screening crossing points of urban three-level roads and roads above the three-level roads as candidate stations; the coordinates of the candidate sites are coded; based on the preprocessed crowd travel origin and destination data, POI data, building data and land utilization data, quantifying the service people flow, facility accessibility and land development intensity of each candidate site, and forming a feature matrix of each candidate site; and constructing a multi-target traffic station site selection model, screening candidate stations meeting distance constraints through a greedy search algorithm according to the feature matrix, inputting the candidate stations to NSGA-II, and solving a multi-target optimization function of the multi-target traffic station site selection model to obtain an optimal planning result. The method can solve the problems that in the prior art, the target is single during site selection, and the inter-site distance constraint is not considered.

Embodiments described herein provide techniques to facilitate hierarchical scaling when quantizing neural network data to a reduced-bit representation. The techniques includes operations to load a hierarchical scaling map for a tensor associated with a neural network, partition the tensor into a plurality of regions that respectively include one or more subregions based on the hierarchical scaling map, hierarchically scale numerical values of the tensor based on a first scale factor and second scale factor via the matrix accelerator circuitry, the first scale factor based on a statistical measure of a subregion of numerical values of within a region of the plurality of regions and the second scale factor based on a statistical measure of the region that includes the subregion, and generate a quantized representation of the tensor via quantization of hierarchically scaled numerical values.

The invention discloses an unmanned aerial vehicle power distribution network inspection tour image real-time identification method based on artificial intelligence, and relates to the field of image identification, and the method comprises the steps: carrying out the space-time calibration of a multi-modal data packet, extracting the features of each modal, fusing the features through a cross-modal attention mechanism, obtaining a multi-modal feature vector, constructing a heterogeneous graph through the topological data of a power distribution network, and carrying out the recognition of the power distribution network inspection tour image. Setting nodes and edges, assigning the multi-modal feature vectors to the nodes, aggregating neighbor equipment features by using a graph convolutional neural network, updating node representation, outputting an anomaly classification result and an anomaly propagation path prediction result of power distribution network equipment, and receiving the anomaly classification result and the anomaly propagation path prediction result by a bandwidth network center. Historical network data and environmental factors are continuously monitored and utilized to train a bandwidth prediction model, and the bandwidth change trend is predicted; according to the invention, the capturing of the complex dependency relationship between equipment and the accurate prediction of the abnormal propagation path are realized, and the comprehensiveness and the fault prevention capability of the inspection tour are obviously improved.