656 results about "Network analysis" patented technology

The invention relates to the technical field of network security, in particular to a network security analysis method and system based on big data. Comprising the following steps: collecting related multi-source heterogeneous data of a network, and carrying out standardized processing such as cleaning and de-noising; network analysis is carried out based on the preprocessed data, network traffic is analyzed in real time by using machine learning and deep learning algorithms, and abnormal conditions are detected; constructing a risk prediction model according to a network analysis result and related information, and predicting a future network security risk level; if the risk level exceeds the threshold value, determining a security event source and a responsibility subject through data tracing; and finally, generating a safety response strategy according to risk prediction and data traceability results, and performing disposal. The corresponding system covers the modules of data acquisition, preprocessing, network analysis, risk prediction, data tracing, security response and disposal and the like, and all the modules work cooperatively to form a complete network security analysis and guarantee system, so that the stable operation of the network system is guaranteed.

The invention discloses an inquiry model training method and device based on a long thinking chain, and relates to the field of large models, semantic information is extracted through strategy network analysis of a model, and an initial step decision is generated in combination with context information in a historical memory library; sending the initial step decision into a reasoning path generator, and reasoning to generate a primary diagnosis disease source and an intermediate diagnosis step; sending the primary diagnosis source and the intermediate diagnosis step into a verification module, performing pathological logic verification according to a case diagnosis report and a medical knowledge base, and feeding back a verification result; the reasoning path generator updates the historical memory bank based on the feedback result, the preliminary diagnosis disease source and the intermediate diagnosis steps; the strategy network continues reasoning based on user feedback input and the updated context information in the historical memory bank, and finally an inquiry result is output. According to the scheme, technical means such as reinforcement learning, self-adaptive backtracking and memory enhancement are introduced into a long thinking chain reasoning framework, so that a large language model realizes multi-aspect comprehensive improvement in medical question and answer and auxiliary diagnosis scenes.

The invention discloses an intelligent early warning method for pipeline blockage of a slurry circulation system of a slurry shield, which relates to the field of intelligent early warning, and comprises the following steps of: performing spatial-temporal feature analysis on a standardized multi-dimensional data stream, constructing a blockage feature knowledge graph based on pipeline position and time sequence correlation analysis, and generating a blockage feature vector through a graph neural network; based on the blockage feature vector, analyzing the dynamic change trend of particle distribution through a long-short-term memory network and predicting the particle blockage risk in combination with an acoustic signal, then performing adaptive judgment by fusing geological conditions and construction stage information to obtain a risk assessment result, and inputting the risk assessment result and the blockage feature vector into digital twinborn simulation to obtain the particle blockage risk. A blockage scene is predicted based on fluid dynamics and a particle sedimentation model, early warning parameters are adjusted through Bayesian optimization, and graded early warning signals are generated; according to the invention, by generating the blockage feature vector, the recognition capability of the early local abnormal propagation trend is enhanced, and a reliable basis is provided for accurately predicting the blockage risk.

The invention discloses a market supervision data asset multi-dimensional evaluation method and system based on a hierarchy-network dynamic fusion model, and relates to the technical field of data asset evaluation. According to the method, a semantic association and index alignment relationship between data assets is comprehensively described by constructing a semantic map of the data assets; a hierarchy-network dynamic fusion model is formed by combining an analytic hierarchy process and a network analysis process, so that the determination of the index weight is more scientific and reasonable; a graph neural network is utilized to perform embedded representation learning on a data asset graph structure, comprehensive feature expression of each data asset is extracted, a multi-objective optimization method is combined, a Pareto optimal solution set is generated, scoring, sorting and grading are performed on the data assets, and reliability and interpretability of an evaluation result are ensured; the evaluation report is output in a structured data format and is provided for a market supervision system through an interface, thereby facilitating effective management and decision support for data assets by a supervision department, and having a wide application prospect.

The invention relates to the technical field of intelligent decision making of production equipment, and discloses a decision making method and system based on a knowledge graph, and the decision making method based on the knowledge graph comprises the following steps: processing input data through a multi-granularity knowledge graph construction system, and obtaining a multi-scale knowledge graph; processing the real-time sensor data and the multi-scale knowledge graph through a time sequence knowledge dual representation learning framework to obtain a dynamic representation model; processing the dynamic representation model and the multi-scale knowledge graph through a time-varying causal propagation network decision system to obtain a decision analysis result; processing the decision analysis result and the multi-scale knowledge graph through a multi-hypothesis reasoning algorithm to obtain a root cause analysis report; a multi-scale knowledge graph is constructed by fusing multi-source heterogeneous data, and intelligent decision with high accuracy and strong interpretation is realized by combining time sequence knowledge dual representation learning and time-varying causal propagation network analysis.

The present disclosure includes systems and methods for multi-cloud network analysis and threat intelligence correlation. In various embodiments, systems are adapted to perform steps of retrieving network flow logs associated with one or more Virtual Private Cloud (VPC) networks; processing the network flow logs to identify new files and enriching critical fields from the network flow logs; transforming and aggregating the network flow logs for further processing; and identifying threats associated with the one or more VPC networks.

The invention relates to the technical field of fracture network fracture water channel identification, and discloses an identification method for analyzing a dominant flow channel in a three-dimensional fracture network based on a topological network. According to the method, probability distribution parameters such as geometric occurrence, gap width and density of a rock mass multi-scale fracture system in a target area are obtained through field surveying and mapping and three-dimensional scanning, and a spatial topological structure of the three-dimensional fracture system is reconstructed by adopting Monte Carlo method simulation and discrete fracture network modeling technology; secondly, abstracting the fracture network into a three-dimensional topological graph model based on a graph theory principle, defining fracture center points and cross points as graph nodes, and converting fracture sections into weighted edges; the method breaks through the continuous medium hypothesis limitation of traditional seepage analysis, and has important engineering application value in the fields of deep geological energy storage reservoir seepage risk assessment, shale gas fracture network optimization design, rock slope stability analysis and the like; compared with a traditional numerical simulation method, the method has the technical advantages of being high in calculation efficiency, good in prediction precision, high in multi-scale applicability and the like.

The invention discloses a chemical hidden danger management method and system based on sequential network analysis, and relates to the technical field of hidden danger management. Compared with a previous hidden danger checking method, the hidden danger checking method solves the problems that a traditional hidden danger checking method depends on manpower, an existing static knowledge graph and an existing system have many defects such as incapability of reflecting dynamic characteristics, lack of time sequence analysis and the like, and difficulty in accurately judging hidden dangers and providing dynamic query services; modeling is carried out on chemical enterprise data, a time sequence knowledge graph is constructed on the basis, the dynamic process of entities and relationships thereof changing along with time can be recorded and analyzed, and the dynamic characteristics of the data are reflected; possible hidden dangers are accurately judged according to time sequence analysis and changes of risk points along with time; a complex network analysis technology is utilized to deeply analyze the time sequence knowledge graph, and a dynamic automatic reminding mechanism is combined to help managers to better manage and prevent safety problems; and an intelligent question-answering system is further integrated to help managers to carry out intelligent centralized management.

The invention discloses a photovoltaic power grid fault identification method and system based on circuit analysis, and relates to the technical field of fault identification, and the method comprises the following steps: obtaining the operation parameters of a photovoltaic power grid, and constructing a circuit analysis model; based on the circuit analysis model, equivalent response curves in different fault scenes are extracted, and the reference operation state is compared to generate a differential residual sequence; performing time-frequency joint decomposition on the differential residual sequence, and stripping photovoltaic output fluctuation from a load disturbance component to obtain a pure circuit characteristic component; based on the pure circuit characteristic component, a multi-dimensional characteristic coordinate space is formed, and the fault type is judged by using the dynamic bending rate of the fault response track; and mapping a fault type discrimination result back to the circuit analysis model, and positioning the position of a fault branch in combination with local disturbance distribution of the node impedance matrix. According to the method, pure circuit characteristic component extraction and multi-dimensional characteristic space dynamic analysis are combined, and accurate judgment of complex fault types and fault branch positioning are achieved.

The invention relates to the technical field of fault diagnosis, and discloses an intelligent fault diagnosis method and system for a multi-channel gas sensor, and the method comprises the steps: carrying out the random geometric analysis and response limitation region construction of a multi-channel gas sensor array, and obtaining an effective sensor set; performing correlation analysis and sensitivity precision dilution measurement function screening to obtain an optimal sensor combination; performing periodic sampling and feature extraction to obtain a feature data matrix; performing deep Q network analysis to generate a fault diagnosis trigger signal; performing sensor channel block mapping and position coding on the feature data matrix according to the fault diagnosis trigger signal to obtain a space-time separation feature vector; and inputting the space-time separation feature vector into a bidirectional long-short-term memory network to perform fault type determination, and outputting a fault type determination result, thereby realizing accurate identification and classification of different types of faults of the multi-channel gas sensor.

The invention relates to the technical field of automobile education virtual practical training, in particular to a virtual practical training method for automobile vocational education. According to the technical scheme, the method comprises the following steps: initializing a multi-modal interaction system, specifically comprising the following steps: starting a virtual reality module, and creating an automobile practical training scene, including whole automobile internal structure display, engine operation simulation and automobile driving environment simulation; starting an augmented reality module, and preparing to superpose virtual information on a real automobile model; and starting the mixed reality module. Through the steps of multi-modal interaction system initialization, trainee practical training operation, intelligent cooperative tutoring network analysis, ability evaluation and course adjustment, dynamic updating and continuous learning and virtual competition module setting, a vivid practical training scene, personalized tutoring and learning paths are provided for trainees, learning enthusiasm and competition awareness are stimulated, and the training efficiency is improved. The ability of trainees is accurately evaluated, the teaching content is ensured to closely follow the industry development, and the effect of automobile vocational education is significantly improved.

The invention provides an intelligent urban municipal planning method and system based on geographic information, and relates to the technical field of data processing, and the method comprises the steps: obtaining urban spatial data of different data sources, including terrain, land utilization, building distribution, traffic network, population distribution and environment index data; and analyzing the urban spatial data, including spatial overlay analysis, buffer analysis and network analysis, and identifying key problems and potential risks in urban municipal planning to obtain a data analysis result. The urban space data can be comprehensively and accurately analyzed, the optimized municipal planning scheme is automatically generated and updated and adjusted in real time, and the urban traffic fluency, the environment quality and the public facility utilization efficiency are improved.

Embodiments relate to generating specialized large language models by performing transfer learning on a base large language model. The base large language model is trained using network traffic capture files as training data to predict information in a network traffic capture file during inference. The base large language model is modified into specialized large language models for including in different applications for performing communication network analysis. In this way, the specialized large language models may be developed in an expedient and efficient manner by leveraging the training performed on the base large language model.

The invention provides an energy digitization platform resource scheduling method based on cloud edge cooperative computing, which comprises the following steps: acquiring real-time supply and demand data, an energy price signal and network topology information from a distributed energy management system, and preprocessing to obtain a structured dynamic supply and demand scene data set meeting a unified format requirement; aiming at a dynamic supply and demand scene data set, respectively detecting the fluctuation frequency and amplitude of an energy price on different time scales by adopting a time sequence analysis method, detecting the change condition of a network topology structure in real time by adopting a network analysis technology, and extracting key parameters reflecting scene dynamic characteristics from the change condition; and extracting a scheduling demand of cross-regional energy flow from the adjusted edge node permission configuration, and optimizing a cross-regional energy flow path in combination with real-time inter-regional supply and demand difference data and network state evaluation to obtain a globally optimized cross-regional energy scheduling scheme.

The invention discloses a method for obtaining high-temporal-spatial-resolution port traffic observation data from AIS data, and the method comprises the following steps: S1, carrying out the preprocessing of the historical trajectory of a ship based on AIS data; s2, based on a stay index formula and a K-means algorithm, dividing the preprocessed historical ship trajectory into a stay section and a moving section; using an isolated forest algorithm to extract center point coordinates of the stay section to obtain a ship travel chain; marking a staying type and a port to which the staying type belongs for each staying section based on a space rule and a process logic; s3, key indexes reflecting the port traffic state are constructed through time-space statistics of mooring and anchoring behaviors; a directed shipping network with ports as nodes is constructed, and traffic relation and characteristics between the ports are mined in combination with a network analysis method. According to the method, the timeliness and accuracy of port traffic situation monitoring can be improved, and the problems of slow updating, coarse granularity and high heterogeneity of traditional statistical data are solved.

The invention belongs to the technical field of low-altitude flight safety early warning, and provides a multi-modal data fusion method for low-altitude flight risk early warning. Comprising the steps of flight state data and meteorological data alignment processing, missing data filling, spatial feature extraction, spatial feature and flight state data fusion and space-time convolutional neural network and space-time diagram convolutional network analysis. According to the invention, the flight state and the meteorological data are fused, so that the prediction accuracy and reliability are improved; through the space-time convolutional neural network and the space-time diagram convolutional network, the space and time dependency relationship in the flight data and the meteorological data can be captured at the same time; through spatio-temporal data fusion, missing data can be accurately aligned and complemented, and the data quality and the real-time performance of the system are improved.

The invention discloses a rice nitrogen response regulation and control network analysis and breeding target identification system and method based on multi-omics data. According to the system, organic combination of regulation and control network construction based on single or multiple varieties of materials, key transcription factor recognition and accurate positioning of regulation and control areas where transcription factors play roles is achieved through an expression-chromatin accessibility correlation research method, and cis-trans effect distinguishing of the regulation and control areas is achieved through a deep learning model. The method comprises the following steps: carrying out nitrogen starvation pretreatment on rice, then carrying out nitrogen resupply, collecting a root sample, and carrying out ATAC-seq and RNA-seq sequencing; an eCAAS method is adopted to construct a regulation and control network, and key transcription factors are identified and accurately positioned; the chromatin accessibility difference of different varieties is predicted through a deep learning model, the cis-action effect and the trans-action effect are distinguished, an upstream transcription factor target is provided for genes dominated by the trans-effect, and haplotype and editable regulatory region targets available for direct breeding are provided for genes dominated by the cis-effect.

The invention relates to the technical field of network analysis, and discloses an industrial network topology structure safety robustness simulation analysis method and system, and the method comprises the steps: carrying out the modeling of each equipment node in an industrial network as a node, carrying out the modeling of a communication link between equipment as an edge, and constructing an industrial network topology model; setting a disturbance scene model; performing disturbance simulation on the industrial network topology model in a simulation environment, and recording a topological structure change process and connectivity evolution information thereof after disturbance; performing feature learning and embedding on the network topological graph before and after disturbance by adopting a graph neural network model, and outputting a robustness score R-score in a corresponding disturbance scene; calculating a robustness evaluation index based on the simulation data; and outputting a weakness identification result and an optimization suggestion of the topological structure. According to the invention, the structure robustness, safety controllability and adaptive adjustment capability of an industrial network in the face of a complex operation environment and potential threats are improved.

The invention relates to the technical field of automobile exhaust pollution, in particular to a sensor-based automobile exhaust networking analysis system, which comprises a data acquisition module, a data processing and fusion module, an analysis and prediction module, an intelligent decision module, a data storage module and a user interaction module, the data processing and fusion module is used for processing the received pollutant component data, the data storage module is used for storing the pollutant component data, the analysis and prediction module is used for analyzing the pollutant component data in real time and predicting the tail gas emission trend, and the intelligent decision module is used for generating optimized vehicle operation parameters and providing maintenance suggestions. And the user interaction module provides a visual interaction interface and displays the tail gas emission data, the analysis result and the decision suggestion of the vehicle in real time. According to the automobile exhaust monitoring system, the technical problems existing in an existing automobile exhaust monitoring system are effectively solved, and the accuracy and reliability of data are remarkably improved.

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 invention provides an IBS micro-ecological transplantation intelligent prediction method and system based on multi-omics driving, and relates to the technical field of biomedicine. The method comprises the following steps: establishing a multi-omics data fusion subsystem to collect metagenome, metabolome, host genome and clinical phenotype group data of a target patient; inputting the data into a flora-metabolite combined network analysis model to construct an interaction network and extracting features; generating an incidence matrix based on the features and the host genome data and calculating indexes; generating indexes through a dynamic response algorithm in combination with the clinical phenotypic data and the indexes; and outputting a curative effect prediction result by using a transfer learning framework combined with modeling. The system comprises a data acquisition module, a network analysis module, a correlation calculation module, a dynamic response module and a joint modeling module. According to the method, multiple omics data are integrated, the flora and host relation is accurately mined, intelligent prediction of the micro-ecological transplantation curative effect is achieved, powerful support is provided for IBS personalized treatment, and meanwhile data processing and safety guarantee measures are taken.

The invention discloses an intelligent adjusting system and method for soil layer penetrating power of a shield tunneling machine. The intelligent adjusting system comprises a sensor array, wherein the sensor array is annularly and uniformly distributed around a cutter head of the shield tunneling machine; the risk level module is used for calculating a similarity matrix between section rings by adopting a Gaussian kernel function, dividing performance communities through a Louvain algorithm and outputting geological risk levels and corresponding coordinates; and the intelligent decision module integrates a multilayer convolutional neural network, fuses sensing data and a network analysis result in real time, and generates dynamic instructions of the cutterhead rotating speed, the propelling speed and the grouting pressure. The shield tunneling machine soil layer penetrating power intelligent adjusting system achieves real-time monitoring and intelligent analysis of multi-dimensional information such as soil layer hardness, pore water pressure, ground stress and temperature by integrating a high-precision multi-dimensional sensing module, a risk level module and an intelligent decision module. The system can automatically adjust the cutterhead rotating speed, the propelling speed and the grouting pressure of the shield tunneling machine according to the geological risk grade.

The invention discloses a functional connection network analysis method based on a group sparse attention converter. The method mainly comprises the following steps: preprocessing brain functional magnetic resonance imaging data output by a functional magnetic resonance instrument; dividing the preprocessed brain function image into a plurality of brain regions according to a standard; based on Pearson correlation and group sparse characteristics, calculating a dependency relationship between brain regions, and respectively constructing an initialized brain network and a guidance network; taking the initialized brain network as the input of a group sparse attention converter, and carrying out learning under the assistance of a guidance network so as to learn a brain network with a group similar topological structure; and subsequent classification is carried out. The method integrates priori knowledge of a population level, allows more comprehensive modeling across multiple brain networks, is designed for end-to-end adaptive learning, dynamically updates the brain networks through back propagation, and significantly improves the construction and analysis performance of the brain networks.

The invention relates to the technical field of chip testing, in particular to chip internal signal transmission impedance matching testing equipment which comprises a vector network analysis module and a testing socket module, the testing socket module is internally provided with an inert gas cavity and a cylindrical cavity groove, the inert gas cavity and the cylindrical cavity groove are communicated with each other, and the inert gas cavity and the cylindrical cavity groove are communicated with each other. Gas in the inert gas cavity is inert gas; according to the impedance matching test equipment for signal transmission in the chip, the probe part is actively controlled to retract during test, and the probe part is controlled to eject out to establish test connection after the chip is placed in place, so that hard friction or collision between a solder ball contact of the chip and the probe part can be effectively avoided.

The invention discloses a temperature and humidity sensor data association analysis method based on deep learning, and relates to the technical field of data association analysis. Comprising the following steps: calculating a temperature and humidity gradient difference value of any two adjacent nodes based on a continuous temperature and humidity data sequence acquired by a sensor, and performing partition division in combination with a geographic distance; constructing a correlation coefficient matrix based on a historical time window for all sensor nodes in the same partition, and dividing the nodes into a plurality of correlation groups with high correlation; dividing a master node and a slave node for each association group; and generating a redundancy substitution matrix based on the correlation coefficient matrix, and when the main node is in an abnormal state, screening a standby node as a new main node in combination with the real-time data difference degree, and maintaining the stability of an inference link. The method effectively reduces the node energy consumption and communication redundancy while guaranteeing the prediction accuracy and reliability, is suitable for a large-scale deployment scene, and is especially suitable for a temperature and humidity sensing network analysis task in an edge environment.

The invention relates to the technical field of enterprise evaluation, in particular to an enterprise digital capability operation detection and evaluation method, which comprises the following steps of: collecting data from digital systems of a plurality of departments in an enterprise, and generating data flow information of each department; based on the data flow information of all the departments, a cross-department digital cooperation graph is constructed by adopting a graph model cooperation graph, nodes in the cooperation graph represent all the departments of the enterprise, and edges in the cooperation graph represent cooperation relations and data flow among all the departments; analyzing the collaboration map by using data flow network analysis, and identifying a data island of cross-department data flow; and constructing a cross-department cooperation efficiency scoring model, and calculating cooperation efficiency scores of all departments and the whole enterprise. According to the invention, an enterprise can accurately know the cooperation efficiency and the overall digitization capability level of each department, so that a reliable data basis is provided for a management layer, low-efficiency departments and cooperation bottlenecks can be found, and an accurate promotion plan can be made.

The invention relates to the technical field of power transmission line monitoring, and discloses a power transmission line real-time monitoring method and platform. The method comprises the following steps: establishing a multi-omics data fusion subsystem to collect metagenome, metabolome, host genome and clinical phenotype group data of a target patient; inputting the data into a flora-metabolite combined network analysis model to construct an interaction network and extracting features; generating an incidence matrix based on the features and the host genome data and calculating indexes; generating indexes through a dynamic response algorithm in combination with the clinical phenotypic data and the indexes; and outputting a curative effect prediction result by using a transfer learning framework combined with modeling. The system comprises a data acquisition module, a network analysis module, a correlation calculation module, a dynamic response module and a joint modeling module. According to the method, multiple omics data are integrated, the flora and host relation is accurately mined, intelligent prediction of the micro-ecological transplantation curative effect is achieved, powerful support is provided for IBS personalized treatment, and meanwhile data processing and safety guarantee measures are taken.

The invention provides a cell specific transcription factor regulatory network analysis method and a visualization platform, and relates to the technical field of bioinformatics, the method comprises the following steps: constructing a gene regulatory network through a GRNBoost2-cisTarget-AUCell-Cell GRN workflow based on a transcription factor in combination with a motif database; screening a direct regulation relationship in combination with the database, and calculating an activity score of a regulator in each cell; based on activity scores and cell type annotation results, grouping the single cell data by using a unified manifold approximation and projection (UMAP) dimensionality reduction method and a Leiden clustering algorithm, and displaying the following results through an interactive visualization tool: a cell clustering UMAP graph, performing color marking according to cell types; a UMAP graph and a heat map of transcription factor regulator activity; according to the visual map of the gene regulation and control network, transcription factors and target genes are distinguished through node shapes, and regulation and control relations are marked through line weights and colors. According to the invention, an accurate regulation and control network can be provided.

The invention discloses a city management AI dispatch algorithm and system based on historical mining and responsibility matching, and the method comprises the steps: building and dynamically updating a city management element evolution graph through obtaining the multi-mode description information of a city management case and the real-time state data of disposal resources; calculating potential disposal effects of different candidate dispatching schemes by using a causal inference engine, and generating a comprehensive efficiency estimation vector; on the basis, a multi-target reinforcement learning strategy is adopted to generate an optimal dispatch instruction, and system parameters are continuously optimized through online element learning during execution; cooperative processing network analysis is activated for sudden complex events, and responsibility atlas reconstruction is triggered when the matching efficiency is low. According to the method, the accuracy and efficiency of case disposal are remarkably improved, disposal timeliness optimization, resource load balancing and improvement of the first solution rate are realized, and meanwhile, the adaptive capacity and continuous optimization efficiency of the system to complex scenes are enhanced.