1515 results about "Vehicle networks" patented technology

A method for constructing a vehicle networking intrusion detection model based on federated learning is provided, which includes decoupling a local model according to a feature extractor and a classifier; selecting other vehicle clients with higher similarity to each vehicle client; collecting a local feature extractor from each vehicle client and distributing them to each vehicle client along with a global classifier obtained in a previous round, by a server; aggregating the local classifier and feature extractor from other vehicle clients to form an evaluation model; updating weights to obtain an updated local feature extractor, and combining it with the global classifier in the previous round to update the local model; aggregating feature representations extracted from a local dataset, and obtaining a current feature representation; collecting all current feature representations to train a global classifier and distributing them to vehicle clients that participate a next round of training.

The invention relates to a resource allocation system for 5G-A Internet of Vehicles network slices, which belongs to the technical field of intelligent traffic systems and comprises a network slice architecture module, a management and control plane module, a programmable module, a safety and fault-tolerant module, a performance monitoring and optimizing module, a mobility management module, a resource arbitration engine and a cross-domain collaborative interface. And the network slice architecture module is used for dividing Internet of Vehicles communication into a plurality of virtual slices based on a 5G-A network slice technology. According to the resource allocation system and method oriented to the 5G-A Internet of Vehicles network slices, accurate control over end-to-end performance indexes is achieved through the slice definition template library and a QoS dynamic mapping mechanism, the problem that key service performance is degraded due to one-step resource allocation of a traditional network is solved, distributed agent collaboration based on the MAPPO algorithm, and the resource allocation efficiency is improved. LSTM load prediction and a heuristic time delay equalization algorithm are combined, so that the system can respond to business demand changes at a millisecond level, and the resource utilization rate is improved.

The invention provides an Internet of Vehicles vulnerability management method, system, device and medium based on a block chain, and relates to the technical field of vehicle networks, the method can comprehensively identify potential safety risks of vehicles through a dynamic and static combined vulnerability detection mechanism, and generates a structured vulnerability report; hash abstract chaining evidence storage is performed on key data of reports and subsequent repair links by using a block chain, so that the whole process of vulnerability discovery, analysis and repair is ensured not to be tampered and traceable, and the authenticity and credibility of data are improved; the integrity of the report is verified at the cloud end, and an AI analysis engine is combined to associate a CVE database and threat intelligence, so that intelligent generation and decision support of a repair scheme are realized; the hash abstract on the chain of the OTA patch is verified at the vehicle end, so that the credibility of the patch source and the integrity of the content are guaranteed; and finally, through feedback of an installation result and secondary uplink archiving, complete closed-loop management from vulnerability discovery to repair verification is formed.

The invention discloses an Internet of Vehicles network selection and switching decision system supporting access of multiple operators. According to the method and the device, the timeliness and the continuity of network switching are remarkably improved through prediction and planning in advance. The module depends on a trajectory-network matching prediction sub-module, combines a vehicle navigation trajectory and a global digital twinborn model, identifies a base station coverage area in a future driving path and the network service quality of each operator in advance, and avoids the lag problem that switching is triggered only after signals are weakened in traditional passive switching. The pre-switching resource reservation sub-module further sends a resource request to a target operator base station and confirms reservation before the vehicle enters a new road section, ensures that resources such as bandwidth and time slot required during switching are in place, reduces switching failure or interruption caused by resource competition, avoids common communication interruption after signal sudden drop in traditional switching, and improves the switching efficiency. And continuous transmission of key businesses such as automatic driving control instructions and real-time road conditions is ensured.

The invention relates to a virtual power plant load prediction and dynamic response method and system integrated with vehicle network interaction. The method comprises the following steps: acquiring and processing historical charging data to generate a user data set; extracting and analyzing the user data set to generate a charging behavior prediction result; collecting traffic flow data in real time and matching to generate a traffic influence coefficient; constructing a regional charging demand prediction model, and generating regional charging demand distribution; acquiring external environment event data and processing the data to generate a sudden load correction value; a comprehensive load prediction value is generated by fusing multi-level factors, the power distribution and the charging priority of the charging pile are adjusted, and a scheduling instruction is output to complete closed-loop operation, so that the problem of load prediction deviation caused by user behavior randomness, traffic dynamic coupling and unpredictability of emergencies is solved; accurate modeling and collaborative optimization of multi-level dynamic factors are realized, and the scheduling efficiency of the virtual power plant to the vehicle network interaction resources is improved.

The invention relates to the field of Internet of Vehicles security, and particularly discloses a lightweight Internet of Vehicles intrusion detection method integrating multi-teacher distillation and boundary perception enhancement, which comprises the following steps: firstly, acquiring in-vehicle CAN bus flow data and out-vehicle network flow data, and dividing the data into a training set, a test set and a verification set; and the traffic data is converted into two-dimensional image data by using a Gramian Angular Field technology. Then, constructing a lightweight student model LAD Net, and adopting a multi-path feature extraction structure, a CBAM convolution attention module and a time convolution network TCN module; meanwhile, a multi-teacher fusion model is constructed based on a ResNet32 network, and optimization training is performed on the LAD Net through a boundary perception dynamic feature enhancement mechanism and an adaptive distillation temperature adjustment mechanism. And finally, the trained LAD Net is used for performing prediction and performance evaluation on the test set, so that the problems of high model complexity, data imbalance and insufficient generalization ability in the prior art are effectively solved.

The invention provides an Internet of Vehicles broadcast frame radio frequency fingerprint identification method based on LMMSE channel estimation. The method comprises the following steps: obtaining subcarrier data in a resource grid based on an obtained signal of a physical side link broadcast channel; performing root-mean-square delay expansion and channel autocorrelation matrix construction based on the subcarrier data, and combining priori signal-to-noise ratio regularization and time domain windowing operation to obtain a channel estimation value; initial radio frequency fingerprint features are obtained through the channel equalization and the channel estimation value, and the improved neural network and the initial radio frequency fingerprint features are utilized to carry out classification identification on the Internet of Vehicles equipment. Influences of noise and channels on fingerprints are effectively considered, and the purpose of extracting fingerprints of different devices in a complex environment more accurately is achieved.

The invention provides a large-scale ultrafast charging facility vehicle network interaction-oriented adaptive regulation and control system, and belongs to the technical field of charging pile load scheduling. During working, a multi-source data acquisition and vehicle network state sensing module acquires and fuses multiple types of data, and a vehicle network interaction strategy optimization module generates a strategy according to the data; the power dynamic distribution module distributes power according to a strategy, and the energy storage coordinated control module optimizes energy storage charging and discharging. The real-time response and feedback module quickly responds to a power grid instruction and corrects a strategy, and the security and protocol adaptation module guarantees security and docking. During use, after the terminal is started, fusion data is automatically collected, a regulation and control strategy is generated and executed, instructions are responded and corrected in real time, and safety and cross-platform adaptation are guaranteed in the whole process.

In a system including a plurality of in-vehicle networks, information regarding the network configuration of function units connected to different types of transmission paths can be easily used. A relay device includes: an acquisition unit configured to acquire second function unit information of the function unit connected to the second transmission path, the second function unit information being function unit information that includes information regarding a network configuration of a layer of a lower order than an application layer; and a conversion unit configured to perform a conversion process of converting the second function unit information acquired by the acquisition unit into information that is able to be registered into a database of one or a plurality of pieces of first function unit information being function unit information of one or a plurality of the function units each connected to a corresponding first transmission path.

A method of optimizing dependent task offloading in an Internet of Vehicles using deep reinforcement learning is provided, including the following steps: S1: constructing a vehicle system network; S2: constructing a task model for an application program; S3: constructing a task load model: calculating delay, energy consumption and incentive compensation for local computing, offloading to nearby vehicles and offloading to nearby RSUs three offloading methods based on the vehicle network system and the task model, respectively; S4: determining task priorities: first determining the priorities of each of subtasks according to allocation of predecessor nodes of the subtasks combining with dynamic network environment, and then scheduling according to a multi-queue algorithm; and S5: finding an optimal offloading strategy by using the deep reinforcement learning.

The invention relates to an Internet of Vehicles multi-agent cooperative sensing method and system. The method comprises the following steps: constructing a collaborative sensing network, wherein the collaborative sensing network comprises a self-agent and a plurality of collaborative agents; acquiring and processing sensing data through the collaborative sensing network; performing feature extraction to obtain intermediate features; self-adaptive sparsification is carried out to obtain sparse features, and the sparse features are compressed and transmitted to a self-agent; performing time sequence feature enhancement on the features of all the agents at the self-agent end; fusing the features to obtain fused features; and constructing an Internet of Vehicles perception model, and realizing perception by the detection model according to the fused features. According to the method, the calculation complexity of traditional global attention is reduced from the square level to the linear level through an adaptive sparsification mechanism, the calculation overhead is remarkably reduced while the multi-agent feature interaction precision is kept, and the method is more suitable for real-time operation on the vehicle-mounted edge equipment with limited resources.

The invention provides a vehicle-mounted network security threat sensing system and method based on edge and cloud collaboration, and belongs to the technical field of vehicle network security. Comprising the following steps: S1, collecting vehicle multi-modal data through an edge layer; s2, preprocessing the multi-modal data of the vehicle; s3, performing feature extraction on the preprocessed vehicle multi-modal data to obtain a high-dimensional feature vector; s4, calculating a threat confidence score based on the high-dimensional feature vector; s5, performing hierarchical response decision based on the threat confidence score and a predefined response strategy library, and generating hierarchical response data; s6, extracting threat metadata based on the hierarchical response data; s7, performing global association analysis based on the threat metadata, and upgrading a defense system; and S8, based on the global threat intelligence and the upgraded defense system, carrying out edge layer updating, and then skipping to S1. The method is beneficial to eliminating response delay, relieving network bandwidth pressure and optimizing cloud computing resource load.

The invention relates to the technical field of intelligent power grid and electric vehicle cooperative control, and discloses a plug-and-play and flexible power regulation and control integrated vehicle network interaction cooperative control system, which comprises a protocol adaptive conversion module, a multi-modal data alignment module, a flexible power regulation and control module, a vehicle pile identity authentication module and a power grid state sensing module, and realizing inter-generation vehicle millisecond-level identification and data unified modeling through the protocol middleware. According to the method, the problems of protocol compatibility and data alignment in a plug-and-play scene are solved, and deep coordination between the charging process and the power grid state is realized through flexible power regulation and control and a multi-stage safety mechanism. The system guarantees the non-inductive charging experience of a user, reduces the impact on a power grid to the maximum extent, prolongs the service life of a battery, and improves the energy utilization efficiency.

A system including a vehicle, a controller, and an intrusion response component. The vehicle has a plurality of network zones, each network zone including a plurality of end points. The controller includes: a network monitoring component configured to interpret network communications associated with at least one of the network zones; and a network intrusion detection component configured to detect an intrusion event in response to the network communications. The intrusion response component is configured to perform an intrusion response operation in response to the detected intrusion event.

The invention discloses a security resource allocation method for a low-altitude economic unmanned aerial vehicle semantic communication network, which relates to the technical field of wireless communication network resource allocation and comprises the following steps: acquiring communication data and network state sensing data of each node in an unmanned aerial vehicle network; performing semantic analysis on the communication data, determining semantic importance and setting a transmission priority; monitoring a security state based on the network state sensing data, identifying a potential security threat and determining a threat level; establishing a security constraint condition set according to the threat level and the semantic importance; establishing a multi-objective optimization model, and performing optimization allocation on network resources under the security constraint condition set; performing hierarchical processing and resource scheduling on the communication task based on the transmission priority and the optimal distribution result; and monitoring a resource allocation effect, and triggering resource reallocation when the performance index deviates from a threshold value. According to the method, adaptive resource allocation considering communication efficiency and safety protection is realized, and the overall performance of the unmanned aerial vehicle semantic communication network is improved.

An embodiment intrusion detection system for detecting intrusions in an in-vehicle network includes a message queue module configured to store network messages collected from the in-vehicle network in a message queue, a memory configured to securely store a ruleset comprising a set of detection rules used in a plurality of detection techniques, a rule engine configured to apply the plurality of detection techniques to the collected network messages to detect security events and configured to determine a severity score and a reliability score for each detected security event, and an interface manager configured to transmit detection reports to a remote backend server in response to detection of the security events.

The invention relates to the technical field of automobile driving, and particularly discloses an intelligent headlamp self-adaptive control method and system based on multi-modal data fusion. The method comprises the following steps: acquiring an ambient light value outside a vehicle and a vehicle pose vector in real time; requesting a road side unit to obtain a tunnel or garage feature point set based on the ambient light value through an Internet of vehicles V2X communication protocol; based on the vehicle pose vector and the tunnel or garage feature point set, calculating a relative distance D by using an improved Euclidean distance, introducing a speed-adaptive distance compensation coefficient, and generating a corrected distance D '; the corrected distance D'is compared with a first preset early warning distance threshold K1 and a second preset early warning distance threshold K2, and if D 'is smaller than or equal to K1, a progressive lighting scheme is started; and if D 'is greater than or equal to K2, starting a light brightness attenuation scheme. The invention aims to provide a high-precision and safe mode, so that the self-adaptive control of the automobile headlamp is closer to the actual driving requirement of the automobile.

The invention provides a double-chain-based air-space-ground fusion vehicle-mounted network security authentication system and method, and belongs to the technical field of intelligent transportation, in an air-space-ground fusion vehicle-mounted network, a vehicle-mounted unit, a road side unit, an unmanned aerial vehicle node and a satellite node interact through a trusted mechanism and a double-chain architecture, after interaction in a single domain is completed, cross-domain interaction is performed, and the security authentication of the vehicle-mounted unit, the road side unit, the unmanned aerial vehicle node and the satellite node is completed. The authentication token is stored on the block chain after being obtained in the first interaction, and the quick interaction can be completed by directly calling the authentication token subsequently. According to the invention, through the double-chain block chain architecture, the authentication time delay is optimized, the cross-domain authentication efficiency is improved, and the privacy protection problem of the node in a high dynamic environment is effectively solved.

The invention relates to a virtual local area network configuration method and device based on an Ethernet gateway controller, equipment and a medium. The method comprises the following steps: acquiring vehicle multi-dimensional state data and calculating a network reconstruction demand degree through an Ethernet gateway controller, and generating a reconstruction trigger signal when the demand degree exceeds a threshold value; responding to the signal, dynamically selecting an optimal VLAN configuration template from a preset library by adopting a reinforcement learning strategy, and generating a configuration scheme with space-time constraint in combination with network topology; the risk level is analyzed and evaluated based on the scheme and the entropy value of the network topology, a configuration instruction sequence for risk optimization is generated, and the configuration instruction sequence is reliably issued to target equipment through a security protocol; a sensing-decision-execution-verification closed-loop control system is formed by verifying the configuration effect through the nodes and triggering a rollback mechanism when the configuration is abnormal, accurate triggering, intelligent decision, safe execution and reliable recovery of configuration of the vehicle-mounted network in a complex dynamic environment are achieved, and the network resource utilization rate, the safe response speed and the system reliability are remarkably improved.

The invention relates to the technical field of predictive driving, in particular to a decision-making method for intelligent predictive driving of a networked automobile based on machine learning. Comprising the following steps: acquiring and preprocessing multi-source data of a networked automobile, real-time data of the Internet of Vehicles and historical driving data, and performing feature extraction on the preprocessed multi-source data of the networked automobile and the preprocessed real-time data of the Internet of Vehicles to obtain preliminary feature data; introducing a multi-layer recursive feature fusion algorithm to perform fusion processing on the preliminary feature data to obtain final fused data; and constructing and training a predictive driving model by using a networked automobile intelligent predictive driving prediction algorithm based on deep cross adaptability, and performing prediction processing on the finally fused data by using the predictive driving model to obtain a driving prediction result and generate a driving decision. The technical problems that processing of multi-source heterogeneous data is not accurate enough in intelligent predictive driving of the networked automobile, the predictive adaptability of driving decisions is poor, and the accuracy rate is low are solved.

The invention discloses an unmanned aerial vehicle cluster authentication method based on a certificateless homomorphic network coding signature, and belongs to the field of cryptography and network security. In an unmanned aerial vehicle network system comprising a KGC, a source node, an intermediate node and an authentication server CS, the following steps are executed: the KGC generates system parameters and a master key; the source node registers to the KGC and requests part of private keys; the source node carries out vector splitting and expansion on the authentication message, randomly selects a message identifier, executes a signature algorithm to respectively calculate the signature of each vector, and sends the vectors and the signatures to the intermediate node; and the intermediate node executes a verification algorithm to judge the validity of the currently received data packet, discards the invalid data packet, calculates to obtain a signature of the combined vector, and forwards the signature to the adjacent intermediate node. When the CS obtains a sufficient number of valid data packets, the original authentication message can be recovered and an authentication success message is sent to the source node unmanned aerial vehicle. According to the invention, the transmission performance and reliability of the unmanned aerial vehicle network are improved.

The invention discloses a heterogeneous Internet of Vehicles network selection method based on fuzzy logic and a related device. The method comprises the following steps: acquiring network attributes of candidate networks; inputting the network attribute of each candidate network into a membership function to obtain input membership of low, medium and high levels of the network attribute; performing fuzzy reasoning on the input membership degrees of the low, middle and high levels of the network attribute to obtain an aggregation membership degree of the output level of the network attribute; performing defuzzification on the aggregation membership degree of the output level of the network attribute to obtain a network evaluation value of each candidate network; according to the method and the related device, the network selection problem of a V2I communication scene can be solved, the optimal network can be selected, and efficient information transmission with road infrastructures is realized.

The invention provides a new energy automobile in-vehicle network real-time intrusion detection system based on edge computing, which relates to the field of new energy automobile network security and intrusion detection and comprises an in-vehicle edge computing node, a vehicle-mounted communication interface and a regional collaboration module. The in-vehicle edge computing node, the vehicle-mounted communication interface and the regional collaboration module form a distributed collaboration architecture to realize in-vehicle network real-time intrusion detection and dynamic defense, and the in-vehicle edge computing node is deployed in a vehicle electronic control unit and a gateway, collects and analyzes network traffic and sensor data, executes a local defense decision, and sends the local defense decision to the regional collaboration module. The system monitors the controller local area network flow, the electronic control unit state and the sensor signal of the in-vehicle network in real time through edge calculation, network abnormity and physical attack signs can be found in time, the safety risk caused by delay detection is effectively avoided, and the efficiency and timeliness of in-vehicle network monitoring are improved. The system adopts intelligent analysis methods such as a topological immune reconstruction algorithm and a photon trajectory aggregation algorithm.

The invention discloses an Internet of Vehicles safety protection method based on self-learning multi-mode perception, which is characterized in that a unified safety perception model is generated by fusing various data sources from a vehicle-mounted sensor, a visual sensor, a radar, a LiDAR and the like, the comprehensive perception capability of a vehicle environment is enhanced, and the safety of the vehicle environment is improved through machine learning and a self-learning mechanism. The threat detection strategy is continuously optimized, and the recognition precision and the response speed of unknown threats are improved. By making real-time safety protection decisions according to threat types and severity and triggering corresponding emergency response measures, the system can effectively prevent different levels of safety threats, the safety and stability of the Internet of Vehicles system are ensured, and the system has high adaptability and real-time performance.

The present application discloses a contact network device, a smart car, a smart network system and a transportation system. The contact network device is installed on the car. The smart network system includes a network information system and a network power supply system. The network power supply system includes a contact network connected to the contact network device. The contact network provides electricity and basic sensor information for the car. The network information system interconnects the contact network device, the smart car and the contact network information to form a vehicle network. The smart car equipped with the contact network device and the smart network system constitute a new transportation system. When the car is traveling on a road with smart network system facilities, the smart network system provides electricity and sensor information, and the car is driven by a person or automatically drives forward. When the car is traveling on a road without smart network system facilities, it is powered by a battery or an internal combustion engine, and can be driven by a person or a simple remote control driving system provides information to enable the car to move forward, solving the problems of electric vehicle charging, endurance and automatic driving application.

The invention discloses a distribution network area emergency insurance supply method and system based on vehicle network interaction, and relates to the technical field of distribution network area emergency insurance supply, and the method comprises the following steps: obtaining a distribution network area fault position and electric vehicle distribution data, and constructing a vehicle scheduling instruction set; driving the electric vehicle to move to a target area according to the scheduling instruction set, and transmitting magnetic field disturbance signals of different frequency bands through a vehicle-mounted coil array to obtain a magnetic field disturbance signal set; constructing a dynamic sensing matrix for each magnetic field disturbance signal, and performing compression reconstruction by adopting an orthogonal matching pursuit algorithm to obtain a line impedance feature vector group; constructing a security constraint set based on the line impedance feature vector group, and respectively applying the security constraint set to a multi-objective optimization algorithm to solve the optimal capacity calling proportion of the electric vehicle battery pack; and controlling the electric vehicle to output reverse power to the distribution network area based on the optimal capacity calling proportion, thereby solving the problem that power supply is difficult to optimize due to the fact that difference between multi-vehicle cooperation and a power line is not considered in a traditional method.

An embodiment method for managing at least one vehicle network including at least one electronic control unit (ECU) among a plurality of ECUs in a vehicle network management apparatus is provided, which may include, based on information of at least one default partial network cluster including at least one ECU among the plurality of ECUs according to at least one default partial network function provided by default in a mapping database, configuring a new partial network cluster by adding an ECU according to user customization to the at least one default partial network cluster, and storing information of the new partial network cluster in the mapping database.

The in-vehicle device for receiving time synchronization information transmitted from each of a plurality of reference devices and transmitting the received time synchronization information to another device, includes: a communication unit configured to communicate with the other device; and an information processing unit configured to, if the communication unit receives a frame in which the time synchronization information is stored, store the time synchronization information in a specific queue with priority over other information, wherein the communication unit transmits the time synchronization information stored in the specific queue to the other device.

The invention discloses a method for pre-storing network data in a vehicle before entering a signal blind area and related equipment, and relates to the technical field of vehicle-mounted communication, the method for pre-storing the network data in the vehicle before entering the signal blind area comprises the steps of obtaining a vehicle navigation route, and determining a corresponding base station signal distribution map and navigation route historical signal data; according to the base station signal distribution map and the navigation route historical signal data, each predicted signal blind area in the vehicle navigation route is predicted through a preset blind area prediction model; and if it is monitored that the vehicle is about to enter the predicted signal blind area, obtaining current driving information of the vehicle and predicting duration of the signal blind area, and pre-storing network data in the vehicle through a preset pre-storage rule according to the duration of the signal blind area. Before the vehicle approaches the signal blind area, the signal blind area distribution is predicted according to the vehicle navigation route, and the network data is pre-stored according to the signal blind area distribution, so that the reliability of the network data in the blind area environment is improved.

Maneuver Coordination Services (MCS) may be used in various implementations of vehicular networks. Emergency Group Maneuver Coordination (EGMC) may be used for detected unexpected safety-critical situations (USCS) on the road. EGMC provides cooperative maneuver coordination among a group of vehicles to ensure safer collective actions in the case of USCS. An MCS and EGMC may utilize a Maneuver Coordination Message (MCM) based on procedures for the Generation and Transmission of MCM with reduced communication overhead.