5478 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 digital twinborn-based park pipe network monitoring and early warning method and system, and relates to the technical field of pipe network monitoring and early warning, and the method comprises the steps: constructing a park pipe network domain knowledge graph, collecting real-time monitoring data and historical operation and maintenance data, and forming a knowledge enhanced pipe network data set; extracting pipe network performance attenuation characteristics by using a bidirectional gating recurrent neural network, and deeply fusing the pipe network performance attenuation characteristics with expert empirical rules in the domain knowledge graph to construct a performance attenuation analysis model; a performance degradation curve and remaining useful life are calculated based on the model, potential fault risks are predicted, and maintenance suggestions are generated. According to the invention, precise evaluation of pipe network performance degradation, early warning of fault risks and intelligent generation of maintenance suggestions are realized, and the intelligent level and reliability of operation and maintenance of the park pipe network are improved.

The invention discloses a remote monitoring and intelligent management and control system for fire-fighting equipment, and belongs to the technical field of fire-fighting early warning, and the system specifically comprises the steps: deploying a multi-parameter sensor array, and enabling each sensor node to form a redundant monitoring network through a honeycomb topological structure; carrying out real-time fusion analysis on the sensor network data through an edge computing gateway, and triggering an abnormal region positioning module when detecting that any monitoring parameter exceeds a preset dynamic threshold value; the abnormal region positioning module constructs a three-dimensional thermodynamic diagram model based on the parameter gradient change of adjacent sensor nodes, and dynamically calculates the diffusion direction and the potential risk level of an abnormal region; the mobile inspection robot cluster receives the coordinates and risk parameters of the abnormal area, a multi-stage response path is automatically generated based on a building digital twin model updated in real time, a primary path quickly approaches along a preset main road, and a secondary path is dynamically adjusted according to field obstacle distribution; and the tail end path adopts a contact type detection module to recheck the abnormal area.

The invention discloses an intelligent operation and maintenance management and control system for an information communication network, and belongs to the technical field of communication. The system comprises the following modules: an intelligent sensing and data acquisition module which is responsible for real-time acquisition of multi-source network data, dynamic monitoring of cross-domain equipment states and conversion of heterogeneous data into a unified standard; the network intelligent analysis module integrates network performance prediction, fault risk assessment, abnormal behavior identification and security threat monitoring, and provides comprehensive network operation and maintenance insight; the intelligent resource scheduling module is used for realizing dynamic allocation of cross-domain network resources and ensuring efficient and safe utilization of the resources through multi-dimensional optimization and a real-time load balancing strategy; the intelligent decision support module is used for continuously optimizing an operation and maintenance strategy based on machine learning, analyzing a long-term operation trend and providing suggestions for management decisions; and the open integration and collaboration module provides integration of an open API interface and a third-party system, supports cross-domain collaboration management, and improves the flexibility and expansibility of the system.

The invention discloses a substation network security defense system based on artificial intelligence. The system comprises a data collection module for collecting transformer substation network data; the anomaly detection module is used for identifying an abnormal mode and an intrusion behavior; the threat intelligence analysis module is used for identifying potential threat and vulnerability information; the intelligent security monitoring and response module is used for evaluating the network security state; the malicious code detection module is used for detecting, analyzing and isolating malicious software; the intelligent security prediction module is used for predicting a network security event and carrying out early warning prompt; the self-adaptive security policy module is used for automatically adjusting or updating an optimal security policy; the system updating and maintaining module is used for updating and maintaining system modules and functions; the user interface and alarm module is used for providing safety state display and alarm for a user; the data backup and recovery module is used for backing up and recovering key data; and a compliance auditing module which makes operation and configuration accord with security standards. According to the invention, hierarchical, intelligent and active protection of the transformer substation network is realized.

The invention relates to a network information security protection method and system based on artificial intelligence dynamic defense. The method comprises the following steps: constructing a multi-level security policy library containing basic defense, scene defense and emergency response based on preprocessed network data features (including protocol features, behavior sequences and abnormal indexes); then, combining real-time threat intelligence and a network environment state, and utilizing a strategy combination optimization algorithm to generate a weight matrix reflecting strategy priorities and dependency relationships; dynamically extracting and fusing various defense strategies from a strategy library according to the weight matrix to form a customized defense scheme; and eliminating resource competition and logic contradiction between strategies through a conflict detection mechanism, and finally deploying a conflict-free optimization scheme to a target node. By adopting the method, collaborative deployment of multi-dimensional security policies can be realized, and the accuracy and real-time performance of security protection in a complex network environment are remarkably improved.

The invention, which relates to the technical field of data security monitoring, discloses a network data risk assessment system for a computer, comprising a data monitoring platform. The data monitoring platform is in communication connection with a data acquisition module, a data preprocessing module, an anomaly detection module, a threat evaluation module, an automatic alarm module, a response strategy generation module and a report generation module, and all the modules are in electric signal connection. According to the network data risk assessment system for the computer, the functions of anomaly detection, threat assessment, response strategy generation and automatic alarm are integrated, rapid detection and response to network threats are achieved, abnormal mode recognition is conducted through the machine learning technology, potential security threats are found, the recognition accuracy of abnormal behaviors is improved, and in addition, the network data risk assessment system for the computer has the advantages of being high in practicability and easy to popularize. And preset protection measures are automatically triggered, so that quick response is realized when high-risk threats are detected, and the damage of security incidents to the system is reduced.

The invention relates to the technical field of Internet of Things, and discloses an OpenHarmony-based Internet of Things control method and platform, and the method comprises the steps: carrying out the distributed discovery and lightweight authentication of Internet of Things equipment, and constructing a dynamic equipment topological graph; generating a device capability description file according to the authentication device set, and creating a virtualization layer; acquiring multi-source sensor data from the virtualization layer and performing fusion processing to obtain a scene control strategy; acquiring network data packets at key nodes of the dynamic equipment topological graph and extracting features to obtain a security situation assessment result; performing deep packet detection based on a security situation assessment result and a scene control strategy, identifying an abnormal behavior, and generating a network optimization strategy; the cluster head nodes are selected according to the network optimization strategy to perform load-balanced data transmission, and the node working mode and the network topology structure are dynamically adjusted to realize global energy efficiency optimization, so that the global energy efficiency optimization is realized, and the overall operation time of the Internet of Things system is prolonged.

A system and method are provided for placing security operations at selected enforcement points in a distributed security fabric. The enforcement points at which the security operations are placed can be endpoints, nodes, and / or network devices within the network. The security operations can be updated by monitoring data flows through the network to generate network data, and then determining, based on the network data, one or more changes to the security operations, based on the generated network data. Recommended changes can be obtained by applying the network data to a machine-learning model that indicates suspicious data packets (e.g., disseminates packets suspected of being malicious from normal traffic) and crafts new policies to deny the suspicious data packets. Performance of the network can also be improved by analyzing the security operations for redundancies and / or inefficiencies and modifying the security operations to mitigate them.

The invention provides a communication network resource optimization method and system based on artificial intelligence, and relates to the technical field of communication optimization, and the method comprises the steps: collecting historical network data, carrying out the feature extraction through time series decomposition and an LSTM algorithm, carrying out the weight calculation of a performance index through combining an attention mechanism, and predicting the network flow through a multi-scale prediction mechanism. And dynamic calibration is carried out based on historical errors, so that the network flow change trend can be accurately predicted, the prediction precision is improved, reasonable distribution and optimal configuration of network resources are realized, and the network operation efficiency is effectively improved.

The invention relates to the technical field of image processing, and discloses an AR technology-based precise positioning method and system for minimally invasive surgery of hepatobiliary surgery. The method comprises the steps that blood vessel network data are extracted through a medical image sequence, a three-dimensional structure diagram is generated, operation target points are marked, and a coordinate system is established; collecting dynamic data for mapping matching, and establishing a real-time tracking model; constructing an augmented reality scene and implanting a navigation path; acquiring an instrument position calculation track, and comparing with a navigation map to generate an instruction; constructing a projection interface to display the key area; recording the whole process and evaluating the operation quality. By establishing the real-time tissue deformation tracking model, a multi-source data fusion accurate positioning mechanism and an intelligent operation quality evaluation system, the accuracy and reliability of operation navigation are effectively improved.

The invention relates to the technical field of information synchronization, in particular to a cross-platform electronic communication synchronization system, which comprises a network construction and path optimization module for collecting basic network parameters of communication equipment of a plurality of platform networks, obtaining cross-platform network data according to the basic network parameters, selecting an optimal transmission path according to the cross-platform network data, and transmitting the optimal transmission path to a server; and generating real-time optimized path information. In the invention, the basic network parameters of the communication equipment of the plurality of platform networks are collected, the cross-platform network data are acquired, the edge weight is dynamically updated according to the delay and the bandwidth, and the optimal transmission path is selected based on the network state, so that the effect of efficiently utilizing network resources is achieved, and the real-time performance and the accuracy of synchronous data transmission are remarkably improved. The to-be-synchronized data is classified and subjected to priority setting, and the data processing sequence is adjusted in combination with the real-time network data, so that accurate scheduling of the synchronization task is realized, the delay and conflict problems in data transmission are reduced, and the efficiency and reliability of data synchronization are improved.

The invention relates to the technical field of data processing, and discloses a comprehensive dispatching system for a digital intelligent water service pipe network. Comprising a processing module, a layering module, an identification module, a scheduling module, an analysis module and an evaluation module which are connected in sequence. The processing module preprocesses the pipe network data and constructs a dynamic boundary area; the layering module generates operation parameters according to function classification; the identification module analyzes equipment fault features to obtain state data; the scheduling module formulates a preliminary scheme according to a time-sharing rule; the analysis module performs multi-target processing based on the dynamic weight to generate a scheduling instruction; and the evaluation module evaluates pressure, flow and water quality indexes to form a target strategy. The operation efficiency of the water supply network is improved.

The invention relates to the technical field of communication networks, and discloses a service channel time slot allocation method, which comprises the following steps of: 1, acquiring network data in real time from network equipment, the network data comprising bandwidth utilization rate, delay, packet loss rate and service flow type; 2, predicting future network flow fluctuation through a long-short-term memory network model based on historical flow data and real-time network data; and step 3, dynamically adjusting the allocation of time slot resources according to the network flow fluctuation prediction result, and preferentially allocating the time slot resources for high-priority services. According to the method, the dynamic adjustment of the time slot resources is realized by combining the flow prediction of the LSTM model and the Q-learning reinforcement learning algorithm, so that the system can flexibly allocate the resources according to the actual network load condition, the resource waste is reduced, the utilization efficiency of the bandwidth is improved, and the utilization rate of the overall network resources is remarkably improved especially under the condition of high network load.

Apparatuses, methods, and systems are disclosed for determining application data and / or analytics. One method includes associating, at an application enablement unit, an application data analytics event with a combination of data collection events and network data analytics events based on an application request for end-to-end application related performance analytics. The method includes obtaining a first set of data from data producing entities and / or data analytics producing entities based on the application data analytics event. The method includes configuring a second set of data based on the first set of data. The second set of data includes at least a portion of and / or an abstraction of the first set of data based on the application data analytics event. The method includes deriving application data analytics corresponding to the application data analytics event based on the second set of data.

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.

An active defense system and method for an unknown threat. The system comprises: an intelligent threat early warning module (10), an unknown threat detection module (20), and an adaptive defense processing module (30). The intelligent threat early warning module (10) is configured to perform threat prediction on a power grid situation data set acquired in real time from a power information network, so as to obtain threat early warning information, and send the threat early warning information to the unknown threat detection module (20); the unknown threat detection module (20) is configured to perform threat detection and analysis on acquired unknown threat network data when receiving the threat early warning information, generate a threat analysis report, and send the threat analysis report to the adaptive defense processing module (30); and the adaptive defense processing module (30) is configured to trigger, on the basis of the threat analysis report, a defense processing operation corresponding to a preset threat defense strategy.

The invention discloses a transmission network fault positioning method, system and device and a storage medium, and relates to the field of communication, and the method comprises the steps: firstly, collecting network data to construct a multi-granularity network topology model, then obtaining original alarm data, analyzing the topology association relationship between alarms, and obtaining a multi-granularity network topology model; and mining horizontal peer association and vertical upstream and downstream association among the alarm objects, and identifying multi-alarm data of each alarm object. Thirdly, constructing an associated alarm directed graph based on the multi-alarm data, and performing associated alarm compression on the associated alarm directed graph to obtain a compressed alarm set; and finally, constructing an alarm propagation graph based on the compressed alarm set and the multi-granularity network topology model, querying the alarm propagation graph through a reverse hierarchical sorting algorithm, and determining a root alarm. According to the method, through innovative methods such as topological modeling, association mining, graph compression and reverse sequencing, alarm data and a topological structure are closely combined, an alarm propagation rule is analyzed, and fault root causes are diagnosed.

The invention provides a 5G network intelligent switching management method based on artificial intelligence, and the method comprises the following steps: collecting and processing network related data, and obtaining a network data set; constructing a switching demand prediction model used for predicting network switching time and a target and a switching effect prediction model used for predicting network performance after switching; optimizing the initial switching strategy in combination with reinforcement learning to obtain an optimal switching strategy; deploying a network switching engine model on the edge computing node, and adjusting an optimal switching strategy in real time according to the network environment change; a virtual simulation digital environment is constructed for network simulation, an optimal switching strategy is evaluated according to the simulated virtual network environment, and then a network switching engine model is optimized in real time according to an evaluation result. According to the invention, the intelligent switching decision is realized, the precision and efficiency of the switching decision can be improved, the network adaptability and diversified requirements are enhanced, and the user experience is improved.

A method of simulating a network for machine learning and training is provided. The method can include generating a simulated model (digital twin) of a network, wherein the simulated model is based on receiving network data from the network. The method can also include training a machine learning model based on data received from the simulated model and operating the trained machine learning model within the network. In addition, the network may be based on an open radio access network (O-RAN). Further, the method may include operating the simulated model within a non-real-time (Non-RT) radio access network intelligent controller (RIC) framework. Also, the method may include operating the simulated model within a near-real-time (Near-RT) radio access network intelligent controller (RIC) framework. In addition, the method may include operating the simulated model in parallel with the O-RAN.

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 network traceability data processing method, system and device and a medium, and the method comprises the steps: collecting network data, carrying out the preprocessing of the network data, and generating the preprocessed network data; extracting behavior characteristics from the preprocessed network data to obtain network behavior characteristics; constructing an attack graph according to the obtained network behavior characteristics, and forming a dynamic graph representing an attack path; and carrying out traceability decision on the dynamic atlas, and positioning an attack source and an attack path through the traceability decision. Through the technical means of six-dimensional feature vector construction, quantum derivation genetic algorithm optimization detection and attack atlas construction through the tense graph convolutional network, the problems of data dimension missing, correlation analysis lagging and insufficient traceability precision are effectively solved.

A system and method are provided for routing traffic through a network to ensure load balancing and avoid untrustworthy nodes. Based on network data (e.g., telemetry data), a machine learning model generates trust scores, which are used for routing decisions by determining preferred routes from a source to a destination. The trust scores for nodes along a potential route can be combined into a cumulative trust score. The potential route with the lowest cumulative trust score (i.e., most trustworthy) is preferred, when all other factors are equal. Traffic is routed along the preferred routes, until their capacity is exceeded. Then to achieve load balancing, traffic flows are extended to the next most preferred routes (e.g., the next lowest cumulative trust score), and so forth. When traffic flows include a mix of sensitive and non-sensitive data, the sensitive data is preferentially directed along the most preferred routes.

The invention relates to the technical field of data processing, and provides a multi-source heterogeneous data fusion sky-ground intelligent monitoring system which obtains ground sensor network data and satellite remote sensing data, and carries out space-time calibration on the obtained ground sensor network data and satellite remote sensing data; a multi-dimensional space-time correlation model is constructed, the data calibrated by the space-time dynamic calibration module is further analyzed, and the fusion weight of a space and time correlation matrix is optimized; collaborative correction of multi-source data is implemented, the space-time joint incidence matrix is optimized, and space and time deviations are eliminated; and finally fusing and calibrating the processed multi-source heterogeneous data, and providing data output. According to the method, the multi-source data can be fused under the same precision, the dynamic response capability of the system to the credibility of different data sources is improved, errors caused by data deviation and inconsistency are effectively reduced, and the precision and reliability of a monitoring result are improved.

The invention relates to the technical field of data analysis, and discloses a precise targeted early warning method and device for meteorological disasters of kiwifruit canker and a medium. The method comprises the following steps: performing standardization processing on historical occurrence time, morbidity degree and distribution range data of kiwifruit canker as well as meteorological observation data and cultivation management data of corresponding time periods to obtain a historical disease database; performing multivariate statistical analysis processing on the three-dimensional meteorological monitoring network data and the historical disease database to obtain a disease risk assessment index; performing feature extraction processing on the disease risk assessment indexes through a deep learning algorithm to obtain disease early warning parameters; performing gridding partition processing on the disease early warning parameters to obtain partition early warning data; and performing dynamic optimization processing on the accuracy, the advance and the coverage rate of the partition early warning data to obtain accurate targeted early warning information of the meteorological disaster of the kiwifruit canker. According to the application, the accuracy and timeliness of the existing kiwi fruit canker meteorological disaster early warning method are improved.

The invention discloses a private network data security detection method based on big data, and belongs to the field of security detection, and the method comprises the steps: obtaining a real-time data packet from private network traffic, encoding the data packet through a forward error correction code technology, generating redundancy check information, and obtaining an encoded data packet; if a bit error is detected in the transmission process of the coded data packet, requesting a sending end to retransmit a damaged data packet through an automatic retransmission request mechanism to obtain a retransmitted data packet; adjusting the redundancy of a forward error correction code and a trigger threshold of an automatic retransmission request according to the adaptive error correction parameter, generating an optimized error correction scheme, and obtaining a repaired data packet; and aiming at the alarm processing instruction, automatically adjusting a network flow control strategy, limiting the transmission bandwidth of an abnormal destination, updating the sensitive data fingerprint database, and obtaining optimized protection configuration.

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 provides a bus network data transmission control method based on priority scheduling, which relates to the technical field of network data transmission, and comprises the following steps: dividing a bus network into an edge layer, a core layer and a terminal layer; the terminal layer comprises a plurality of actuator nodes; performing priority classification on the terminal data set in the edge layer; distributing a transmission time slot of priority rating data on the core layer based on a preemptive scheduling algorithm; a priority data transmission strategy is dynamically adjusted in combination with the dynamic time slot allocation table and the local load state; and when a plurality of actuator nodes initiate transmission requests at the same time, outputting a final transmission sequence according to a priority data transmission strategy. Through a static rule base and a dynamic correction mechanism, key data features are accurately identified, the real-time performance and reliability of high-priority data are ensured, and the key business delay risk is reduced. Intelligent allocation of bandwidth resources is achieved through a preemptive scheduling algorithm, real-time service requirements are guaranteed to the maximum extent, and the network throughput efficiency is remarkably improved.

The invention relates to the technical field of medical information processing, in particular to a main diagnosis rationality judgment method and system based on artificial intelligence. The method comprises the following steps: acquiring diagnosis request data, and performing candidate main diagnosis generation on the diagnosis request data by using preset medical knowledge graph data and preset intelligent detection rule data to obtain candidate main diagnosis data; performing diagnosis execution parameter generation according to the candidate main diagnosis data to obtain candidate main diagnosis parameter data; acquiring historical diagnosis request data and historical candidate main diagnosis data, and performing diagnosis linkage processing according to the historical diagnosis request data and the historical candidate main diagnosis data to obtain diagnosis linkage network data; and performing node interaction processing on the candidate main diagnosis data according to the diagnosis linkage network data to obtain candidate main diagnosis weight data. According to the method, the intelligent level of diagnosis rationality judgment, the credibility of a scoring result and the adaptability of a clinical system are improved.

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.