600 results about "Response control" patented technology

The invention discloses a geological disaster networking monitoring and early warning method, and relates to the technical field of geological disaster monitoring and early warning, and the method comprises the following steps: S1, collecting the original data of multiple types of monitoring equipment in a monitoring region, extracting a high-amplitude sudden change region and a frequency drift factor according to the time and frequency distribution, constructing a high-frequency disturbance sensing matrix, and carrying out the recognition of the high-frequency disturbance sensing matrix; and generating a disturbance characteristic index map for representing the spatial distribution of the unnatural disturbance source. According to the method, active identification and modeling of non-natural interference are realized by constructing a high-frequency disturbance perception matrix and a disturbance index map, disturbance propagation analysis and residual difference are combined to strengthen precursor signal features, the risk level is accurately judged through trend identification and causal analysis, and finally, early warning model parameters are dynamically optimized based on response regulation factors, so that the early warning accuracy is improved. A closed-loop mechanism of interference identification, signal purification, trend extraction, risk judgment and strategy adjustment is formed, and the early warning stability, accuracy and practicability of the system in a high-interference environment are remarkably improved.

The invention discloses a photovoltaic inverter insulation resistance intelligent detection system and method, and particularly relates to the technical field of photovoltaic equipment operation safety monitoring, and the method comprises the following steps: generating frozen window shielding interference data through collecting a bus voltage in a state transition stage and constructing a dynamic convergence model; identifying an abnormal trend based on resistance derivative change in a stable state, inputting a dual-path decision process by combining environment and power grid parameters, and dynamically outputting an operation state level; control strategies such as remote prompt, power drop adjustment or output disconnection are executed according to the state grade, and accurate identification and grading response control of the insulation state are achieved; according to the invention, by constructing a voltage disturbance shielding mechanism, a trend derivative identification and delay confirmation strategy and a double-path safety state decision process with dynamic switching capability, high accuracy of insulation abnormity judgment, stability of trend identification and adaptability of a decision strategy are realized, and robustness and application range of a detection system are improved.

The invention, which relates to the technical field of network awareness anomaly detection, discloses a network awareness anomaly detection system based on big data, comprising a data acquisition module, a feature fusion module, a map construction module, a model calculation module, a root cause reasoning module, a threshold decision module and a response control module. The data acquisition module receives network flow data, equipment state data and system log data and outputs a standardized feature set; the feature fusion module is connected with the data acquisition module, dynamically calculates a weight coefficient of each data source based on information entropy, performs feature aggregation of privacy protection through a federated learning framework, and outputs a fusion feature vector; the atlas construction module is connected with the feature fusion module, maintains a network equipment node set and a communication edge set in real time, and updates a space-time association atlas according to a topology change event; and the model calculation module is connected with the atlas construction module, extracts topological features through a space-time diagram convolutional network, and updates a detection model based on an incremental learning mechanism.

The invention relates to the technical field of power system automation, in particular to a substation management system and method based on the Internet of Things technology, and the system comprises a ubiquitous Internet of Things sensing matrix, an electric power intelligence brain evolution center, a space-time fusion twin module, a nonlinear optimization decision module, and a multi-level vibration risk management and control model. Wherein the ubiquitous internet-of-things sensing matrix acquires substation equipment, environment and power grid data in real time; the electric power intelligence brain evolution center carries out health assessment, life prediction and abnormity positioning on the equipment; the space-time fusion twinborn module constructs a digital twinborn body, fuses equipment space-time data and historical data, and performs operation and maintenance simulation and fault reproduction; the nonlinear optimization decision-making module is used for generating a response regulation and control strategy aiming at the nonlinear and uncertain problems in the operation of the power grid; and the multi-level vibration risk management and control model carries out dynamic assessment and strategy optimization on equipment faults, power grid safety and environmental risks. Therefore, the problems of low inspection efficiency, high false detection risk, poor transmission stability and the like in the prior art are solved.

The invention discloses a real-time monitoring and fault response control device for an industrial and commercial liquid cooling energy storage system, and particularly relates to the technical field of liquid cooling energy storage fault management. Temperature, pressure, vibration and flow data of key components in a liquid cooling system are collected and preprocessed; a time sequence analysis and signal processing technology is utilized to extract feature vectors reflecting key component states from the multi-dimensional operation data set, the saliency of weak abnormal signals is enhanced, and a key feature set containing weak abnormal features is generated; constructing an anomaly recognition model by adopting a long-short-term memory network, inputting a key feature set, outputting an anomaly probability corresponding to the key component, and setting an early warning threshold value of the anomaly probability to trigger early warning; and predicting a fault maintenance time window of the key component based on a Weibull reliability model, and dynamically adjusting the priority of maintaining the key component and executing preventive maintenance based on a fault maintenance time window prediction result and an abnormal probability.

The invention provides an intelligent monitoring and early warning method and device for photovoltaic energy storage equipment, and the method comprises the steps: carrying out the multi-time scale sliding window feature extraction of the operation data of a photovoltaic energy storage system, calculating a power coupling degree quantitative index and a system response feature index, and generating a real-time feature data set; based on the real-time feature data set, performing deviation propagation path identification under thermal dynamic constraint to obtain a real-time deviation propagation path diagram marking deviation sensitive nodes; performing multi-level dynamic deduction in combination with the real-time feature data set and the deviation propagation path map to obtain a dynamic deviation accumulation situation map displaying a deviation accumulation risk level and a development trend; and performing adaptive threshold early warning judgment according to the dynamic deviation accumulation situation map, and executing response control according to a hierarchical early warning mechanism to obtain a hierarchical early warning response control instruction. According to the method, the cumulative effect of the power prediction deviation can be effectively identified, and early warning of coordination imbalance of the photovoltaic energy storage system is realized.

The invention provides a new energy vehicle aggregation prevention and control system based on clustering identification and regional risk modeling, and aims to solve the potential safety hazards such as thermal runaway caused by high-density vehicle aggregation. The system comprises a state acquisition module, a clustering identification module, a regional risk modeling module, a risk grade determination module, a response control scheduling module and a thermal risk prediction module. The system collects running state data such as vehicle position, battery temperature and charge state, uses a density-based spatial clustering algorithm to identify clusters, calculates risk score values of the clusters, and implements current limiting and path guiding control according to risk levels. Meanwhile, the risk change trend of a future area is predicted through the trained long-short-term memory neural network model, and early warning and response are achieved in advance. The system is suitable for safety management scenes of new energy vehicles in high-density areas such as charging stations and underground garages.

The invention discloses a multi-modal monitoring and anomaly detection method, system and device for a belt conveying system and a medium, and belongs to the technical field of anomaly detection.The method comprises the steps that a plurality of multi-modal sensors are arranged on the whole path along the path of the belt conveying system, and original signals in the running process of the belt conveying system are collected; the original signals are preprocessed; performing environment compensation calculation to generate engineering data; mapping the engineering data to the same space coordinate system of the belt conveying system, and completing feature extraction to obtain feature vectors; multi-modal fusion analysis is carried out on the feature vectors, abnormal state identification and fault level classification are realized, and an early warning signal and a risk level are output; and executing a matched response control strategy according to the early warning signal, the risk level and the corresponding space coordinate system position. According to the method, the abnormal state can be dynamically recognized under the complex working condition, differential response control is achieved, and the recognition capability and processing precision of early-stage, multi-source and dynamic anomalies are improved.

The invention relates to an algorithm fusion and fault early warning system in a battery safety management platform, and aims to realize multi-dimensional risk identification and active response control in a battery operation process. The system comprises a multi-source data acquisition module, a fusion calculation engine, a fault early warning response module and an execution interface module. By collecting voltage, current, temperature, humidity, sound wave signals, electrochemical impedance spectroscopy, gas concentration and other multi-mode operation data, a fusion calculation engine completes time sequence correlation modeling, causal feature recognition and risk trend deduction on the basis of a constructed hierarchical algorithm fusion structure, and a risk response signal reflecting a safety state is generated; the fault early warning response module generates hierarchical control instructions corresponding to risk levels according to the signals, the hierarchical control instructions comprise virtual fusing, cooling scheduling, BMS refreshing and the like, the execution interface module issues the control instructions to a physical controller of the battery system, response behaviors are driven, and therefore intelligent, closed-loop and dynamic battery safety management is achieved.

The invention discloses a flight control method and system for an electric power inspection unmanned aerial vehicle, and relates to the technical field of unmanned aerial vehicle inspection flight control. According to the flight control method of the electric power inspection unmanned aerial vehicle, the current flight position of the unmanned aerial vehicle is obtained in real time, deviation analysis is performed on the current flight position and a target inspection position, and when the deviation exceeds a preset threshold value, disturbance sensing data in a set area is further obtained and a disturbance sensing intensity index is calculated; according to a comparison result of the index and a disturbance attribution judgment threshold value, an offset cause is judged, and a corresponding disturbance response control strategy or a flight error correction strategy is executed respectively, so that the flight stability and the control precision of the electric power inspection unmanned aerial vehicle are dynamically guaranteed; according to the invention, through dual discrimination of the disturbance perception intensity index and the flight error adjustment index, external interference and flight control error causes are accurately distinguished, path offset cause identification and strategy matching are realized, and the flight stability and control precision of the electric power inspection unmanned aerial vehicle in a complex environment are improved.

The invention discloses an archive digitization full-life-cycle encryption integrity verification method and system, relates to the field of digital archive safety management, and is used for solving the problem of integrity and safety guarantee in archive digitization management. Logic units are divided through a semantic analysis engine, non-uniform blocks are generated based on sensitivity dynamic partitioning, local hash values are calculated, and a multi-level verification tree is constructed to generate root hash and a hash path mapping table; acquiring visitor identities, terminal environments and timestamps in each stage of an archive life cycle, generating state snapshots in combination with a hash path, and constructing a life cycle state snapshot chain; reconstructing a target logic unit verification path according to the access request, analyzing the structural difference between a current path and a historical snapshot chain through a path reconstruction type difference verification algorithm, and identifying structural offset, node tampering or path loss; and response control is triggered for abnormal verification, including session key updating and permission freezing, so that the security integrity guarantee of the digital archive in the full life cycle is improved.

The invention discloses an intelligent water supply management system and method based on wind-light-water multi-source data fusion, and relates to the technical field of electric digital data processing. The system comprises a time alignment deviation quantification module, a time alignment regulation and control module, a data fusion timeliness response quantification module and a timeliness response regulation and control module. According to the method, the time alignment deviation quantization result is obtained by quantizing the multi-source data time parameters, whether time alignment regulation is carried out or not is judged, if yes, the timeliness response parameters are obtained after regulation, and if not, the timeliness response parameters are directly obtained and quantized to obtain the data fusion timeliness response quantization result; and whether PLC scanning interval regulation and monitoring verification are carried out or not is judged based on the data fusion timeliness response quantification result, so that the timeliness of water supply management demand response is improved, and the problem that in the prior art, the water supply management execution response flexibility is poor due to the fact that the time difference of multi-source data processing is not fully considered is solved.

The invention provides a remote intelligent operation monitoring method and system for an intelligent substation, relates to the technical field of intelligent operation and maintenance of substations, and relates to multi-source heterogeneous sensing, depth feature modeling, fault prediction evaluation and model self-optimization. According to the method, electrical, environmental and meteorological data are collected through heterogeneous sensors, a structured original data set is constructed, time sequence prediction is carried out in combination with a convolution-LSTM model, a Transform fusion network is utilized to output a fault probability and a confidence interval, online early warning and response control are realized, and the method has a federated learning driven adaptive updating capability.

The invention relates to the field of laboratory environment control, and discloses a laboratory environment abnormity self-adaptive linkage control system based on multi-source heterogeneous perception, which comprises the following steps: when any environment parameter exceeds a set safety threshold value, judging whether comprehensive environment abnormity exists in combination with the numerical value change trend of other types of sensors; based on historical monitoring data and real-time sensing data, constructing a multi-dimensional environment characteristic spectrum, and identifying internal association and change paths between abnormal signals; whether linkage control measures are needed or not is evaluated by analyzing the current anomaly type and the influence area; dynamically generating a linkage control path and a priority sequence according to a system topological structure and an equipment operation state, and determining equipment objects needing to be linked; and according to the generated control path and parameters, the operation state of related equipment is automatically adjusted, active response control for abnormity is completed, and an environment feedback result after control is monitored. The method has the advantage of improving the intelligent control level.

The invention relates to a low-delay edge reasoning deployment method for power terminal equipment. The method comprises the following steps: acquiring data under a unified time reference to form an operation data sequence; inputting the operation data sequence into a pre-trained reasoning model to generate a reasoning parameter set adaptive to the local operation capability; performing reasoning calculation based on the reasoning parameter set to obtain a load identification result and an electric quantity prediction result; determining a communication scheduling strategy according to the reasoning result, wherein the communication scheduling strategy comprises communication priority information, sending time sequence information and target receiving node information; a communication instruction is generated according to the communication scheduling strategy, and data content is sent to a target receiving node according to the communication instruction within a limited time, so that rapid data synchronization and dynamic response control based on load state change are realized; according to the invention, intelligent data processing and rapid communication scheduling can be locally realized at the power terminal equipment, the system response time delay is reduced, and the processing efficiency of a load change event is improved.

The invention discloses a high dynamic response control method and system for a linear actuator of a humanoid robot, and the method comprises the steps: collecting the pressure and moment data of a contact point, carrying out the data preprocessing and signal calibration processing, and obtaining the calibration pressure and moment data; based on the calibrated pressure and torque data, performing high-frequency signal extraction to obtain dynamic change characteristics, and performing distribution diagram construction in combination with the pressure and torque data to obtain a pressure distribution diagram and a torque distribution diagram; performing data fusion processing according to the pressure distribution diagram and the torque distribution diagram, generating real-time contact area distribution, and performing dynamic prediction model construction based on the pressure and torque data to obtain a dynamic prediction model; according to the dynamic prediction model, actuator output force and joint angle adjustment amount are calculated, output torque and angle compensation amount are generated, torque distribution optimization and force feedback compensation are carried out, and an optimized final control signal is obtained. According to the method, high-dynamic response control of the controller can be realized.

The invention relates to an intelligent inspection control system and method. The system comprises an environment sensing module, a situation recognition module, a task management and scheduling module, a path planning module, an execution control module and a communication linkage module. The system can collect various environmental parameters of an inspection area in real time, dynamically adjust the priority of an inspection task after identifying an abnormal state, and generate an optimal path to drive a robot to execute response operation; meanwhile, abnormal information, images, videos, sensor snapshots and other contents are uploaded to a background management platform through a communication linkage module, and sound-light alarm equipment, broadcast equipment and other equipment are linked to carry out field response. The control method has task preemption and path reconstruction capabilities, and supports distributed sensing deployment and whole-process execution state monitoring. Compared with the prior art, integrated fusion of environment situation awareness, task dynamic scheduling and response control is achieved, and the adaptive capacity, the response speed and the safety guarantee level of the system to complex scenes are improved.

The invention discloses a high-sensitivity detection method for the safety of a battery cell, and the method comprises the following steps: S1, collecting the data of the battery cell in real time through a tunnel magnetoresistive sensor, and outputting a preliminary detection data set; s2, generating a micro-short circuit risk identification signal based on the preliminary detection data set; s3, generating a temperature anomaly distribution map by using the preliminary detection data set; s4, generating a thermal runaway early warning signal based on the temperature anomaly distribution map; s5, performing magnetic field scanning on the pole piece to generate a magnetic distortion data atlas; s6, constructing battery cell multi-source detection data, inputting the data into the physical information neural network, and training to obtain a final battery cell aging evolution model; s7, outputting a charging and discharging strategy optimization instruction based on the final cell aging evolution model; and S8, uniformly fusing the micro short circuit risk identification signal, the thermal runaway early warning signal and the charging and discharging strategy optimization instruction to generate a comprehensive response control instruction. According to the method, magnetic resistance sensing and physical modeling are fused, and intelligent prediction and response control optimization of the risk of the battery cell are realized.

The invention provides an enterprise production safety monitoring and checking system based on multi-source data fusion, and the system is characterized in that the system comprises a data collection module which collects production environment data, processes the data, and generates a time-space alignment data set; the feature screening module is used for screening features highly related to security from the space-time alignment data set to generate a feature matrix; the model construction module is used for constructing a risk assessment model, calculating a dynamic risk value according to the multi-dimensional features, and performing risk grading on the dynamic risk value; the response control module is used for starting a corresponding response strategy according to the risk classification and outputting an early warning instruction set and an equipment control signal; the monitoring calculation module is used for monitoring control, comparing risk value changes before and after treatment and calculating response efficiency; and the adjusting and optimizing module is used for carrying out dynamic adjustment according to the response efficiency and optimizing the response strategy. And the limitation of traditional single-dimensional monitoring is broken through, accurate correlation analysis of equipment, environment and personnel risks is realized, and the composite hidden danger recognition capability is remarkably improved.

The invention relates to the technical field of man-machine interaction, in particular to a dialogue Agent interaction method based on multi-modal intention understanding, which comprises the following steps: S1, collecting multi-modal data in a user interaction process in real time, and calculating a time synchronization deviation value of each modal data source; s2, constructing a space-time fusion feature vector; s3, analyzing a dominant action instruction and a recessive behavior clue in the space-time fusion feature vector; s4, generating a multi-level intention analysis tree; s5, when the corrected confidence coefficient of any node in the intention analysis tree is lower than a set threshold value, activating a targeted sensor to complementarily collect data; and S6, analyzing a tree drive response decision according to the finally confirmed intention. According to the method, high-precision identification and response control of the dialogue Agent on the user intention in a complex scene are realized by constructing a multi-modal interaction method with space-time consistency fusion capability, an explicit and implicit intention analysis mechanism and an adaptive modal clarification strategy.

The invention belongs to the technical field related to tunnels, and provides a subsea tunnel cross-fault water inrush catastrophe early warning decision-making method and system in order to solve the problems of inaccurate water inrush recognition, lack of interpretability and the like when an existing subsea tunnel passes through a fault zone high-risk scene. Full-process response control from weighting, prediction to explanation of the input features is realized; driving force analysis is carried out by adopting an interpretable model, so that a transparent and credible risk assessment result is provided; and by combining key driving force in the multi-source heterogeneous data and through numerical simulation of the water inrush catastrophe condition, real-time identification and early warning of the tunnel water inrush risk are comprehensively realized, the identification accuracy of the water inrush risk in the tunnel construction process and the completeness of an early warning mechanism are effectively improved, and the method has important engineering application value.

The invention relates to the technical field of game development and artificial intelligence, and discloses a Qt interactive game role action response control method fused with AI emotion recognition, which comprises the following steps: S1, acquiring multi-modal data such as facial expression, voice intonation and limb action of a player by using sensors such as a camera and a microphone on game equipment; s2, the collected multi-modal data are transmitted to an AI emotion recognition module, a deep learning algorithm is adopted to analyze and process the data, and the current emotion state of the player is recognized; and S3, obtaining current game scene information and other operation instructions input by the player through the Qt framework. According to the Qt interactive game role action response control method and system fused with AI emotion recognition, through the AI emotion recognition technology, a game role can sense the emotion state of a player and make the emotion interaction action matched with the game role, the emotion resonance between the game role and the player is enhanced, and the interaction experience and immersion of a game are greatly improved.

The invention discloses a roadway surrounding rock grouting reinforcement method suitable for complex geological conditions, and relates to the technical field of surrounding rock grouting reinforcement. Potential dynamic changes in the grouting process are monitored in real time through sensor arrangement, and basic data are provided for follow-up grouting control. By introducing the high-response pressure sensor, the quick-response control valve and the self-adaptive module, the grouting system achieves real-time monitoring, dynamic pressure adjustment and self-adaptive material optimization, the grouting filling effect is improved, the surrounding rock fracture and grout leakage risks are reduced, the construction safety and the surrounding rock stability are ensured, and the grouting efficiency is improved. And meanwhile, manual intervention is effectively reduced, the construction period is shortened, the engineering cost is reduced, and roadway reinforcement under the complex geological condition is more efficient, economical and reliable.

The invention discloses a response control method for a charging station participating in a low-voltage treatment scene, and the method comprises the following steps: S1, collecting the voltage data of a charging pile, calculating a deviation, and judging a low-voltage grade; s2, when the deviation is in a light interval, outputting reactive power support voltage in proportion; s3, when the deviation exceeds a heavy threshold value, reducing active power and starting V2G feedback; s4, constructing a graph structure model, and extracting inter-node power coupling and user behavior factors; s5, inputting the model into an improved prediction controller, predicting a future voltage state, and optimizing and outputting a current period power control value; s6, power adjustment is executed, and feedback power is slowly output in the V2G stage; and S7, comparing the control deviation with the behavior factor change, updating the node control priority, and feeding back for rolling optimization of the next period. According to the invention, while the charging regulation and control precision is improved, the user experience and the voltage stability are effectively considered, and the method is suitable for various low-voltage treatment scenes.

A system and method for detecting anomalies in encrypted network traffic without decrypting encrypted payloads is disclosed. The invention comprises a hardware-accelerated pipeline including a packet acquisition unit configured to extract observable header attributes and timestamp values, a signal analysis unit implemented using digital signal processing circuitry to generate temporal and spectral characteristics of encrypted packet flows, and a morphometric extraction unit that produces encrypted-flow descriptors based on statistical dispersion, burst patterns, and ciphertext variability. A behavioral graph construction unit generates graph-structured representations of encrypted flows using incremental state-transition encoding. An artificial-intelligence inference unit employing a tensor-processing processor performs multi-stage anomaly analysis through temporal reconstruction, attention-based weighting of descriptors, and graph-structural deviation assessment to compute anomaly scores without decrypting any payload. A secure response control unit evaluates the scores against policy thresholds and performs mitigation actions while preserving confidentiality.

The invention relates to the technical field of defect detection, in particular to an inner package defect detection system which comprises an illumination control module, a boundary recognition module, a defect gathering module, a grade mapping module and a response linkage module. According to the method, through the convergence degree and Euclidean vector distribution of the boundary contour form, boundary offset distribution information is established, tiny offset or abnormal positions are accurately captured, aggregation extraction and spatial clustering of defect boundaries are achieved, abnormal clusters are screened through distance and number distribution parameters, and the defect detection accuracy is improved. And a defect level is mapped by combining a structure level and a functional risk factor, and differential station response operation is triggered according to level evaluation, so that on the basis of improving image quality, the component identification precision is guaranteed, the space representation capability of defect clustering is enhanced, and the grading judgment granularity of defect detection in multi-component assembly is enhanced. Dynamic linkage response control in the inner packaging process is achieved, and the accuracy of overall defect recognition and the timeliness of control decision making are improved.

The invention discloses a broadband oscillation suppression device and method for a new energy field, and aims to overcome the defects of inaccurate monitoring and inflexible suppression measures in the prior art. The device comprises a three-phase impedor, a transformer and an equivalent power grid, the three-phase impedor is connected in series between the transformer and the equivalent power grid, the transformer is connected with a new energy field, and a voltage transformer and a current transformer are further connected between the equivalent power grid and the new energy field and used for monitoring voltage and current signals in real time and transmitting the voltage and current signals to a broadband oscillation monitor. The broadband oscillation monitor is connected with the three-phase impedor through the impedance switching loop, and the three-phase impedor is composed of an impedance unit composed of a plurality of sub-impedors and circuit breakers. Through precise monitoring and intelligent analysis, in combination with flexible impedance adjustment and a quick response control strategy, the defects of inaccurate monitoring and inflexible suppression measures in the prior art are effectively solved, and the suppression effect of broadband oscillation of a new energy field is remarkably improved.

The invention discloses a new energy automobile charging pile multi-dimensional data fusion monitoring and early warning method and system, and relates to the technical field of new energy automobile charging safety monitoring and control. The new energy vehicle charging pile multi-dimensional data fusion monitoring and early warning method comprises the following steps: S1, constructing a charging state original data set, and preprocessing the charging state original data set; s2, performing early warning grade division in combination with a disturbance trend analysis result and discriminator output; s3, a response control condition is matched according to the early warning level state; and S4, recording the operation structured data of the charging pile, constructing an event chain, and comparing the current and historical features to identify the coincidence degree. The problem that due to the complex operation working conditions of high-frequency plugging, large day and night temperature difference and heavy environmental dust of a new energy automobile charging pile in an urban fast charging station, an existing monitoring method cannot achieve multi-dimensional sensing and dynamic adjustment, and then abnormal early warning lags is caused is solved.

The invention discloses a method for preventing a PCIe switch internal link state from being recognized to be abnormal by an OS, and relates to the field of link state recognition anomalies, a high-risk link is recognized by obtaining a historical data set, configuration processing and algorithm optimization are carried out, an injection verification mirror image is obtained, and accurate link training control analysis is carried out based on the injection verification mirror image, so that the reliability of the PCIe switch internal link state is improved. Carrying out intelligent topology mapping management on the final link stability score to obtain an optimized topology; carrying out anomaly detection and hierarchical response control on the optimized topology to obtain a response action set; and carrying out repair or degradation processing on the link based on the response action set. And finally, the link state and the topological structure are updated according to the processing result to obtain a repair result and the updated topological structure, so that the high-risk link can be identified, the OS link state can be accurately judged, the system availability is improved, the misjudgment risk is reduced, and a dynamic degradation and active repair mechanism based on the actual risk levels (high, medium and low stability) of the link is provided.

The invention provides a lane changing intention recognition method based on a driving style. The method comprises the following steps: firstly, acquiring driving behavior data in a car-following scene through a driving simulation experiment, and constructing a multi-dimensional data set comprising a self-car state, a front-car state and time-space car-following characteristics; then, on the basis of an expected safety margin model, identifying reaction characteristics, steady-state risk characteristics and operation characteristics of a driver, and extracting behavior parameters forming a driving style, including indexes such as reaction time, an expected safety margin interval and operation sensitivity; and further dividing the drivers into different style types by using a clustering algorithm, and endowing style labels with the drivers. On the basis, lane changing intention time information subjectively marked by a driver is collected through a non-control observation experiment and is aligned with the trajectory data, and a trajectory data set with a real intention label is constructed. And finally, constructing a dynamic Bayesian network model based on the data set, taking a historical driving state sequence in a fixed time window as input, fusing a driving style, interaction characteristics of a vehicle and surrounding vehicles and traffic flow characteristics, and realizing real-time identification of whether a lane changing intention exists at the current moment. The method has good interpretability, individual difference adaptability and engineering deployment, and is suitable for dynamic identification and response control of driver intention in an intelligent driving assistance system.