17444 results about "Feature data" patented technology

The invention relates to the technical field of intelligent operation and maintenance, and discloses an intelligent operation and maintenance method fusing multi-modal data and active learning, and the method comprises the steps: deploying a hierarchical Internet of Things equipment network in a target operation and maintenance region, collecting a heterogeneous operation and maintenance data set, and generating an operation and maintenance feature data set through employing a unified building operation and maintenance service framework cooperating with a plurality of MCPs; inputting the operation and maintenance feature data set and the user feedback information into a deep learning intention recognition model for intention recognition and demand analysis to obtain structured user demand data and an operation and maintenance task priority sequence; according to the operation and maintenance feature data set and the structured user demand data, performing real-time evaluation on the equipment operation state to obtain fault risk early warning data; autonomous decision analysis is carried out through an agent type artificial intelligence engine, an equipment maintenance scheme and a resource scheduling scheme are generated, and then the problems that in traditional operation and maintenance, fault prediction is single, the model adaptability is poor, and prediction results are difficult to convert into effective decisions are solved.

The invention relates to the technical field of data processing, and discloses a multi-source heterogeneous marine data intelligent fusion and marine disaster prediction method and platform, and the method comprises the steps: carrying out the hierarchical deployment and network topology construction of marine environment monitoring equipment, communication relay equipment and data processing equipment, and obtaining a sea-air-shore integrated monitoring network; collecting marine environment data with a space-time identifier; carrying out ocean element modeling and dynamic feature extraction to obtain an ocean environment digital model; performing feature fusion and correlation analysis on the multi-source heterogeneous ocean data to obtain spatio-temporal feature data; and training and multi-model integration are carried out on the typhoon path prediction model, the storm surge prediction model and the red tide prediction model by adopting a transfer learning method to obtain a marine disaster prediction model set, so that the prediction precision and timeliness are improved, the allocation and scheduling of computing resources are optimized, and the real-time requirement of mass data processing is met.

The invention relates to the technical field of industrial automation control, and discloses a multi-machine collaborative industrial robot intelligent scheduling system and an application method.The system comprises a data acquisition module for acquiring real-time motion data through a multi-modal sensor; the feature fusion module is used for generating environment dynamic feature data based on graph neural network fusion data; the strategy generation module is used for generating collaborative strategy parameters by utilizing a collaborative reinforcement learning model; the trajectory optimization module is used for constructing a multi-target trajectory optimization model to plan an optimal synchronous trajectory; the motion control module is used for establishing a layered motion control model to realize cooperative motion control; a dynamic resource allocation module can also be included to optimize resource allocation. The application method sequentially executes corresponding steps according to the modules of the system. According to the method, the environment can be accurately sensed, the cooperation strategy can be optimized, the efficient track can be planned, accurate motion control and reasonable resource allocation can be realized, and the cooperative motion control capability and the production efficiency of the industrial robot in the complex environment can be effectively improved.

The invention discloses a ring main unit monitoring method and system based on the Internet of Things, and the method comprises the steps: building a multi-source data collection network of a ring main unit, extracting the multi-dimensional time-frequency domain features of a signal through an edge computing device, generating a multi-source feature data set, and constructing a dynamic digital twinborn model, real-time mapping of the running state of the ring main unit is achieved through a physical-virtual synchronization algorithm, and twin data volumes are output; according to the node influence coefficient of the twin data volume, analyzing a topological structure of a ring network system by adopting a graph neural network, identifying a key fault propagation path and generating a priority monitoring node list; based on the priority monitoring node list and the twin data volume, dynamically generating an adaptive monitoring strategy through a reinforcement learning algorithm, executing the adaptive monitoring strategy and collecting feedback data in real time, and completing closed-loop iteration of the monitoring strategy through an online learning algorithm; through closed-loop control of multi-source perception-digital twinning-intelligent decision, high efficiency, intelligence and reliability of ring main unit monitoring are realized.

The invention relates to the technical field of twinborn model simulation, and discloses a twinborn model simulation method and system for hot working of large forgings. The method comprises the following steps: collecting and preprocessing process parameters, quality data and environment information in a multi-source manner, and obtaining hot working characteristic data; performing correlation analysis to construct a process knowledge graph; calculating the distribution of a temperature field, a stress field and an organization field by using a self-sensing multi-field coupling var value neural network; comparing and analyzing to obtain deviation data and correction parameters; adjusting a network parameter optimization prediction result; and executing multi-objective optimization calculation, and generating a whole-process technological parameter and a control instruction. According to the method, full-process multi-physics field coupling calculation from smelting, casting, forging and pressing to heat treatment can be achieved, model parameters are dynamically adjusted according to real-time production data, the optimal process scheme is generated, and therefore the manufacturing quality and efficiency of large forgings are improved.

The invention belongs to the technical field of visual special effects, and discloses an AI visual special effect dynamic generation system fusing multi-modal perception. Through cooperative work of six core modules of multi-modal input processing, importance analysis, parameter configuration, parameterized rendering, parameter optimization and rendering and output, deep fusion of special effects and contents is realized. An image sequence, audio data and scene parameters can be processed at the same time, a multi-modal feature data set is constructed, a scene key visual area is scientifically recognized, a visual importance distribution map is generated, and the special effect parameter configuration and rendering process is guided. An improved neural radiation field technology and a physical constraint model are adopted to ensure the consistency and reality of the special effect at different visual angles; and a multi-dimensional quality evaluation and parameter automatic optimization mechanism is introduced, so that the visual expressive force and the artistic value of the special effect are guaranteed. The creation threshold is remarkably reduced, the cooperative expression ability of the special effect and the content is improved, and the special effect becomes a powerful tool for enhancing the narration and enhancing the emotion.

The invention discloses a water quality prediction method based on graph neural network and spatial-temporal feature fusion, and the method comprises the steps: obtaining hydrological data and water quality data of a plurality of monitoring stations in a preset drainage basin in a preset time period, representing the monitoring stations as nodes of a graph, and representing the adjacency relation between the monitoring stations as edges of the graph, a graph topology network mapping multiple monitoring station pollutant data is constructed, a spatial-temporal feature fusion module is adopted to perform feature extraction and fusion on the graph topology network to obtain water pollution spatial-temporal feature data, and the spatial-temporal feature fusion module is obtained by training a graph convolutional network, a gating circulation unit and a self-attention mechanism. And performing fusion processing on the extracted feature data to form a time sequence data set of a graph topology structure, and finally predicting the data set through a graph time sequence prediction model to obtain water quality prediction results of the plurality of monitoring stations in a target time period. The method can effectively deal with the spatial and temporal change process of water pollution, reduce the water quality prediction cost and improve the water quality prediction precision.

The invention provides a road traffic anomaly detection method and related equipment based on holographic perception, and the method comprises the steps: carrying out the time-space registration and preprocessing of video streams, point clouds, vehicle motion states, traffic parameters and environment data collected by multi-source sensor equipment of a target road network, and obtaining a multi-source data stream; performing feature acquisition and fusion processing through a preset target detection model and a feature extraction network to obtain target feature data and scene semantic information; establishing a space-time diagram data structure according to the target feature data and the scene semantic information, and performing target trajectory analysis through a space-time attention mechanism; and carrying out association analysis processing in combination with the analysis result and the environment data to obtain target behavior characteristics and interaction relation data, and realizing road traffic anomaly detection through the target behavior characteristics and the interaction relation data. Through multi-source data fusion and a deep learning model, the accuracy of environment perception is improved, and multi-dimensional information in a complex road network is captured for road traffic anomaly detection.

The invention provides a three-dimensional point cloud data segmentation method and system based on multi-scale point features, and relates to the technical field of point cloud processing, and the method comprises the steps: obtaining original point cloud data, constructing a three-dimensional Hash index table, carrying out the self-adaptive hierarchical division through a local maximum value of a density distribution curve, and obtaining a local maximum value of a density distribution curve; noise point elimination is carried out based on a region density count value and a geometric distribution coefficient, an enhanced feature vector containing a main feature direction, a main curvature value and the like is constructed for non-eliminated points, a local geometric descriptor is constructed by adopting four spherical search regions with increasing radiuses, a three-branch space attention network is input to extract spatial features, and the spatial features are extracted; and fused feature data are obtained through a multi-level geometric feature extraction network and a bidirectional feature propagation network. And obtaining a final segmentation result through segmentation energy function optimization and a region merging strategy.

The invention provides a network attack risk mapping assessment method and system, and relates to the technical field of network attack risks, and the method comprises the steps: collecting a network flow data packet to extract a multi-dimensional attack feature vector, employing a deep neural network to carry out feature learning to obtain an attack type recognition result and an attack intensity assessment value, and constructing an attack feature standard model; based on the attack type identification result and the attack strength evaluation value, combining an asset weight coefficient, a vulnerability hazard level and a vulnerability utilization difficulty coefficient to construct an asset vulnerability evaluation model; and performing association analysis on the real-time attack feature data and the asset vulnerability assessment score, constructing a risk mapping matrix, calculating a comprehensive risk score, and generating risk early warning information. According to the invention, accurate identification, dynamic assessment and timely early warning of the network attack risk are realized, and the accuracy and real-time performance of risk assessment are improved.

The invention provides an ecological meteorology and satellite remote sensing combined dynamic environment monitoring method and system. Wherein spatio-temporal dynamic concentration data and spectral reflection characteristic data are obtained at a pollutant emission node; generating an associated data block by the spatio-temporal dynamic concentration data and the spectral reflection characteristic data according to a pollution concentration abrupt change event trigger time sequence, and performing chain storage on Hash fingerprints and pollution source geographic coordinate information through a distributed node consensus mechanism; integrating the ecological meteorological observation data to generate a pollutant migration path map; and establishing a pollution influence boundary judgment model according to the pollutant migration path map and the vegetation stress response characteristic data, and monitoring the diffusion range and influence boundary of pollutants in real time through the model to generate an environment monitoring report. According to the technical scheme provided by the invention, the industrial environment pollution dynamic monitoring precision and the decision response efficiency are remarkably improved.

The invention relates to the technical field of data processing, and discloses a bridle machine fault monitoring method and system based on the Internet of Things. The method comprises the following steps: collecting multi-source operation data through a sensor; performing filtering segmentation processing on the data to obtain feature data; analyzing the feature data to obtain an abnormal identifier and a fault type; establishing a component association map to position a fault source and predict a development trend; calculating a maintenance priority to generate a maintenance scheme; and recording the actual maintenance result and the prediction deviation to update the fault library and the knowledge parameters. Through multi-source data fusion, intelligent fault diagnosis, predictive maintenance and knowledge self-adaptive updating mechanisms, early recognition and accurate positioning of faults of the bridle machine are realized, the non-planned downtime is effectively shortened, and the equipment reliability and the production efficiency are improved.

The invention discloses an intelligent file classification and retrieval method and system, and the method comprises the steps: carrying out the unified format conversion and feature extraction of text, image and audio data according to the original data format of a file through a multi-modal data fusion algorithm, and obtaining a standardized file feature data set; based on a deep learning adaptive classification model, performing classification processing on the standardized archive feature data set to generate multi-level archive classification labels; analyzing the semantic relationship between the archive classification labels through an enhanced semantic network construction technology, generating a semantic association map, and optimizing the semantic association map to obtain an optimized semantic association map; and according to a retrieval request input by a user, performing semantic matching and correlation sorting by utilizing the optimized semantic correlation graph, and outputting archive information most related to the retrieval request. By utilizing the embodiment of the invention, the relevance and accuracy of retrieval can be improved, and modernization and intellectualization of archive management are promoted.

The embodiment of the invention provides a new energy automobile charging pile fault monitoring method and system, and relates to the technical field of charging pile fault detection. The new energy automobile charging pile fault monitoring method comprises the following steps: S1, collecting multi-source data in the operation process of a charging pile; s3, preprocessing the multi-source data to generate a multi-dimensional feature data set; s3, constructing a dynamic fault threshold model; s4, on the basis of the multi-dimensional feature data set and the dynamic fault threshold model, multi-level fault detection is executed; s5, establishing a fault prediction mechanism; and S6, generating a fault monitoring report according to the fault type and the fault severity evaluation index. Through multi-source data acquisition, dynamic threshold construction and a multi-level fault detection and prediction mechanism, real-time accurate monitoring and fault early warning of the operation state of the charging pile can be realized, and compared with a traditional method, the accuracy and the advancement of fault identification are remarkably improved.

The invention provides a power prediction precision improvement method and system for a wind and light station, and relates to the technical field of new energy power generation, and the method comprises the steps: obtaining the historical power, weather and equipment operation data of the wind and light station through a data preprocessing module, and carrying out the time sequence decomposition, spatial interpolation, abnormal value detection and correction, and obtaining the fusion feature data. And constructing a deep learning prediction model based on the fused feature data, extracting spatial features by using a convolutional neural network, processing the spatial features by using a long-short-term memory network to obtain an initial prediction result, introducing an attention mechanism to perform weight allocation to obtain weighted features, and combining to obtain power prediction data. And judging the weather type according to the real-time meteorological condition, and correcting by adopting the matched correction model when the extreme weather is identified. And finally, an error compensation model is constructed based on historical prediction errors for compensation, a final power prediction result is obtained, error correction is performed according to actual power data, and compensation model parameters are updated.

The invention provides a flow analysis and threat detection method and device based on machine learning, and the method comprises the steps: collecting a real-time flow data package of a target network environment, carrying out the protocol analysis and session recombination, and generating a real-time flow feature data set containing multi-dimensional flow features; loading a pre-trained multi-level threat classification model, inputting the real-time traffic feature data set into a feature extraction layer of the model, carrying out normalized coding on traffic features of corresponding dimensions through feature coding channels, generating a real-time feature vector sequence, inputting the real-time feature vector sequence into a primary classifier of the model, and classifying the real-time traffic features according to the real-time feature vector sequence; and performing abnormal probability calculation and cluster division on the real-time feature vector sequence through a mixed detection unit, outputting a primary threat tag and an abnormal confidence coefficient corresponding to each real-time feature vector, inputting the primary threat tag and the abnormal confidence coefficient into an aggregation classifier, performing dynamic weighted aggregation, and generating a comprehensive threat score so as to judge whether a threat response strategy is triggered or not. According to the invention, the accuracy and timeliness of threat detection in a complex network environment can be improved.

The invention relates to the technical field of firmware protection, in particular to a firmware protection method and system based on a security coprocessor. The method comprises the following steps: acquiring characteristic data of the coprocessor; extracting hardware entropy source data according to the coprocessor feature data; generating a hardware trust root key based on the hardware entropy source data; performing partition processing on the firmware based on the hardware trust root key to obtain firmware partition data; constructing hierarchical verification structure data according to the firmware partition data; implementing firmware starting security verification based on the hierarchical verification structure data to obtain integrity verification data; establishing a secure communication channel between the processors according to the integrity verification data; executing a bidirectional authentication process based on the secure communication channel, and generating session key data; a firmware runtime environment is monitored based on the session key data. According to the method, the hardware entropy source is extracted by measuring the physical characteristics of the coprocessor, and an unforgeable hardware trust root is established, so that the security is improved fundamentally.

The invention provides an underwater multi-modal identification method and system for hidden micro defects of a high dam, and the method comprises the following steps: obtaining displacement monitoring data, stress monitoring data and temperature monitoring data, generating a multi-source monitoring data matrix through time synchronization processing, extracting abnormal features through employing a self-adaptive weight method, and carrying out the recognition of the abnormal features. Obtaining a suspicious area list with priority; planning a sonar scanning path according to the suspicious area list, obtaining edge feature data through multi-stage signal enhancement processing, and determining an accurate defect area; acoustic data and optical data are collected, and a fusion feature matrix is obtained through space-time registration and feature fusion; and carrying out feature discrimination on the fusion feature matrix, determining defect types and levels, obtaining a positioning result and a reliability evaluation result through multi-level analysis, and finally generating a defect identification report. According to the invention, the accuracy and reliability of underwater hidden micro defect identification are improved.

The invention discloses a personalized learning path recommendation system based on artificial intelligence, and relates to the technical field of path recommendation, firstly, the system collects multi-dimensional feature data of a learner, and constructs a personalized feature vector; secondly, in combination with knowledge graph modeling and graph neural network technologies, deeply mining explicit and implicit knowledge point association; then, predicting an optimal learning path by using a sequence recommendation model, and ensuring reasonable sorting of knowledge points; in the learning process, the system combines real-time interaction data, dynamically adjusts a learning path, and continuously optimizes a recommendation strategy through an adaptive optimization algorithm; and finally, based on the learning result and the behavior data, evaluating the effectiveness of the learning path, and updating the knowledge point weight and recommendation strategy through a feedback mechanism, thereby realizing intelligent and self-adaptive personalized learning recommendation, the accuracy and adaptability of learning path recommendation can be effectively improved, learners are helped to master knowledge more efficiently, and the learning recommendation efficiency is improved. And the learning experience and effect are improved.

The invention relates to the technical field of red tide anomaly detection, in particular to an ocean red tide anomaly detection method and system fusing multi-source remote sensing and a graph neural network. The method comprises the following steps: acquiring remote sensing image data, unmanned aerial vehicle image data and monitoring data of a monitoring point; performing data preprocessing on the acquired remote sensing image data and unmanned aerial vehicle image data; performing feature extraction and feature fusion on the remote sensing image and the unmanned aerial vehicle image to obtain remote sensing feature data; constructing a space-time diagram structure based on the monitoring data of the monitoring points to obtain diagram structure data; based on a cross-modal comparison self-supervised learning mechanism, carrying out consistency representation learning on a remote sensing feature mode and a graph structure feature mode; by introducing multi-source heterogeneous data and fusing a graph neural network modeling means, the limitation of a single data driving method in the aspects of coarse red tide recognition granularity, low space-time precision and the like is effectively broken through, and the meticulous property and global perception ability of red tide feature modeling are remarkably improved.

The invention discloses a concrete surface intelligent construction method and system based on multi-modal sensing. The method comprises the following steps: firstly, receiving multi-source sensing data, and generating standardized feature data and defect feature vectors through standardization processing and feature extraction; performing time sequence alignment processing on the sensing data, and generating time sequence alignment data in combination with multi-scale analysis; constructing a state evaluation model based on the dynamic Bayesian network, and generating an environment state vector and a construction difficulty graph; carrying out region segmentation and optimizing task allocation by adopting a spectral clustering algorithm, and generating a task allocation scheme and a collaboration strategy; then, construction parameters are determined through multi-objective optimization, tool configuration is planned, and an optimization parameter set and a tool configuration scheme are generated; and finally, trajectory planning is carried out based on an improved A * algorithm, and a multi-robot cooperative construction trajectory is generated through comprehensive optimization. Full-process control of intelligent construction of the concrete surface is achieved, and the construction quality and efficiency are improved.

The invention belongs to the technical field of welding quality detection, and particularly relates to a visual image-based welding seam defect detection method, which comprises the steps of image acquisition, image preprocessing, data analysis, data output, defect classification and decision making, system closed-loop optimization and the like. According to the method, by synchronously collecting two-dimensional images, three-dimensional shapes and heat distribution data of metal welding seams and adopting a polarization filter and annular LED light source combination scheme, multi-dimensional conjoint analysis of physical defects and thermodynamic characteristics is achieved, basic characteristic data are extracted through primary processing, quantifiable defect coefficient indexes are generated through secondary processing, and the detection accuracy is improved. And finally, generating a comprehensive defect index through a multi-modal fusion algorithm, constructing a well-arranged intelligent analysis decision chain, and establishing a self-evolution mechanism of data acquisition-analysis decision-model iteration through real-time interaction of a detection result and an algorithm model. The system can continuously optimize the detection threshold value and the characteristic weight parameter according to the actual working condition of the production line, and the continuous improvement of the detection sensitivity is kept.

The invention provides a data transmission method and transmission device applied to a vehicle-road cloud collaborative road network system, and the method comprises the steps: collecting vehicle operation state data through a vehicle-mounted terminal, collecting real-time road network topology data through road side equipment, and transmitting the data to a cloud server for data preprocessing; performing feature extraction on the preprocessed data based on a preset time-space correlation model, generating dynamic road network feature data, calling a cooperative transmission model to perform transmission path optimization on the dynamic road network feature data, generating a path optimization parameter set, and performing dynamic adjustment on a target transmission path according to the path optimization parameter set. Generating a self-adaptive transmission path adaptive to the current road network state; and distributing the target vehicle data packet to the roadside node and the cloud node through the self-adaptive transmission path, and carrying out retransmission scheduling on the data packet which is not successfully transmitted based on a feedback confirmation mechanism. According to the invention, the communication efficiency and the system robustness in a vehicle-road cloud collaborative scene can be improved.

The invention provides a rehabilitation training action evaluation method and device based on multi-view vision. According to the method, a multi-view camera is adopted to synchronously collect a rehabilitation training image sequence, two-dimensional coordinates of key points of a human body are extracted by improving an HRNet deep learning model, three-dimensional reconstruction is performed in combination with a Gaussian process, motion feature data are extracted, and rehabilitation training quality is evaluated. Accurate motion capture without wearing mark points by the patient is realized, the training constraint feeling of the patient is effectively reduced, the rehabilitation evaluation accuracy is improved, and a personalized rehabilitation scheme can be generated.

The invention provides a micro-grid fault diagnosis and dynamic recovery method based on deep reinforcement learning, and the method focuses on the multi-modal features of key nodes through a graph attention mechanism, extracts fault features through a multi-layer graph attention layer, and captures the spatial dependence relation of micro-grid nodes to recognize a fault propagation path. Meanwhile, spatial features and historical multi-modal data are fused with the help of a gating circulation unit, space-time joint feature representation is constructed, pre-fault symptom time sequence evolution is captured, intermittency and early fault detection capacity are enhanced, the multi-modal feature data fusion problem is solved, and high-precision fault diagnosis is achieved. A knowledge distillation technology is adopted to deploy a lightweight student model at edge equipment, millisecond-level emergency response is realized, fault diffusion is prevented, and meanwhile, the accuracy of diagnosis and repair strategies is guaranteed. The optimal repair strategy is generated at the cloud through the teacher model by using the global data, the system can adapt to the topological change of the micro-grid and novel faults, and the fault processing capability is continuously improved.

The invention relates to the technical field of industrial Internet of Things, in particular to a predictive maintenance method for intelligent factory Internet of Things equipment, which comprises the following steps of: acquiring equipment operation parameters, environment monitoring data and historical maintenance records, constructing a multi-dimensional feature data set, extracting equipment degradation features by adopting a topological graph attention mechanism and a Bayesian network, and establishing a multi-dimensional feature data set; the method realizes equipment health state modeling and fault probability prediction, combines a dynamic adjacency matrix and a multi-objective optimization algorithm, comprehensively optimizes maintenance cost, equipment fault risk and associated equipment influence, dynamically generates an optimal maintenance plan, carries out constraint optimization based on a mixed integer programming method, automatically generates a maintenance instruction sequence, and achieves the optimal maintenance of the equipment. Tasks are issued through the computerized maintenance management system, the PLC control system and the industrial Internet of Things gateway, and the execution state is monitored and maintained in real time. The intelligent level of equipment maintenance is effectively improved, non-planned shutdown is reduced, and the equipment reliability and the production efficiency are improved.

The invention relates to the technical field of image processing, and discloses a detection method, system and equipment for intelligent welding of pipe pile splicing and a medium. The method comprises the steps that noise reduction is conducted on a target weld seam image through a filtering algorithm, registration and splicing are conducted on the image through feature matching, and a weld seam unfolding image is obtained; segmenting the weld joint expanded image by using a deep learning network, and extracting a weld joint center line and surface features to obtain weld joint feature data; defect detection and recognition are conducted on the weld joint feature data, hierarchical calculation is conducted through a rule set, and weld joint quality data are obtained; parameter mapping is conducted according to the weld joint quality data, a welding parameter instruction is generated, welding process variables are adjusted, and target welding parameters are obtained; and the welding seam quality data and the target welding parameters are stored, and a quality report is generated through data analysis. The detection accuracy of intelligent welding for pipe pile splicing is improved.

The invention relates to the technical field of railway data security, and provides a railway data asset security monitoring and risk early warning method and system, and the method comprises the steps: collecting multi-dimensional feature data of railway data assets, including equipment operation state data, network communication data, data access logs and environment perception data; performing heterogeneous data fusion preprocessing on the multi-dimensional feature data to generate a standardized feature vector; calculating security evaluation indexes of the standardized feature vector based on a dynamic weight distribution algorithm, wherein the security evaluation indexes comprise an integrity evaluation value, an availability evaluation value and a confidentiality evaluation value; generating a multi-level risk early warning signal according to the deviation degree of the safety evaluation index and the dynamic threshold value; and triggering dynamic control of a data asset protection strategy based on the risk early warning signal, wherein the dynamic control comprises access permission adjustment, data encryption strength grading and flow limitation. According to the invention, the safety and reliability of railway data assets can be improved, and data leakage and system fault risks are reduced.

The invention provides a real-time map updating method and system based on multi-source geographic information data fusion. Wherein a three-dimensional model is constructed by integrating traffic flow data, remote sensing images and road network topology, the road traffic pressure is quantified, and a feature correlation index is established. And obtaining a vehicle displacement vector and a speed gradient in combination with laser point cloud and video monitoring data, generating a dynamic road feature data set, and establishing a mapping relationship with the index table. On the basis, a road state prediction model is constructed, and through correlation analysis of traffic pressure data and a real-time feature data set, deep correlation between vehicle motion features and road states is established. And dynamically adjusting a road network connection structure, synchronously updating mapping parameters and predicting model precision, and realizing real-time adaptive adjustment of the road network topology. According to the technical scheme provided by the invention, the real-time performance of map updating and the road condition prediction accuracy are remarkably improved, and the urban traffic congestion index can be reduced.

The invention relates to the technical field of network operation and maintenance supervision, in particular to a network operation and maintenance environment risk assessment method and system based on a mimicry security technology, and the method comprises the steps: carrying out the collection and preprocessing of network operation and maintenance environment data, and generating standardized security data; abnormal feature data and disturbance response data are extracted through real mimicry analysis and active challenge mimicry analysis, and associated threat feature data are generated through fusion; based on the associated threat feature data, constructing a heterogeneous twin model, performing state modeling, threat modeling and resource modeling, and generating risk assessment data; and generating an optimal response strategy through a multi-objective optimization algorithm, including flow control, node isolation and resource scheduling, and dynamically updating a risk state through a feedback regulation mechanism. Through dynamic threat identification and real-time optimization response, the risk assessment accuracy and the security defense level of the network operation and maintenance environment are improved, and the method has the advantages of being efficient, intelligent and dynamic.