2470 results about "Vehicle networks" patented technology

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 the technical field of data mining, in particular to a data security management system and method based on cloud computing. The method comprises the following steps: acquiring real-time vehicle driving data, and performing homomorphic modular exponentiation dangerous case sensing fragmentation encryption to obtain dangerous case sensing encrypted fragmentation data; acquiring an Internet of Vehicles data stream, and implementing zero-trust adaptive data stream encryption to obtain a zero-trust authentication credential; implementing federal learning privacy feature mapping on the dangerous case sensing encrypted fragmented data to obtain an anonymized encrypted feature mapping model; performing Merkle tree aggregation on the anonymized encrypted feature mapping model to obtain a block chain global dangerous case fingerprint; extracting a threat behavior quantization vector according to the block chain global dangerous case fingerprint and the anonymization encryption feature mapping model; and carrying out transmission strategy optimization according to the threat behavior quantization vector and the zero-trust authentication credential to obtain a global dynamic transmission strategy matrix. According to the invention, the safety of vehicle data uploading and the overall uploading efficiency can be improved.

Disclosed is a method for using deep reinforcement learning to optimize dependent task offloading in the Internet of Vehicles. The steps of the present invention comprise: S1, constructing a vehicle system network; S2, constructing a task model for an application; S3, constructing a task load model: on the basis of the vehicle network system and the task model, calculating time delay, power consumption, and paid incentive rewards for each of the following three offloading modes: local computing, offloading to a nearby vehicle, and offloading to a nearby roadside unit; S4, determining task priorities: first, on the basis of the allocation of predecessor nodes of sub-tasks in combination with a dynamic network environment, determining the priority of each sub-task, and then carrying out scheduling on the basis of a multi-queue algorithm; and S5, searching for an optimal offloading strategy by means of deep reinforcement learning. The method of the present invention does not require excessive prior knowledge, has good reusability in similar application scenarios, and has high practical value.

An intelligent reflecting surface assistance-based task unloading and resource allocation method for an unmanned aerial vehicle mobile edge computing network system, comprising: constructing an intelligent reflecting surface-assisted unmanned aerial vehicle mobile edge computing network system model; constructing an intelligent reflecting surface-assisted unmanned aerial vehicle mobile edge computing network communication model ; according to the communication model, obtaining a time delay model and an energy consumption model; establishing a multivariable joint optimization model so as to minimize the energy consumption of an unmanned aerial vehicle; and using a deep reinforcement learning method to solve the objective optimization model. By means of intelligent reflecting surface assistance, joint optimized unloading decision and resource allocation, the present application effectively reduces the energy consumption of the unmanned aerial vehicle and improves the performance of the unmanned aerial vehicle network system.

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 provides an intelligent network connection automobile abnormity detection system and method based on big data analysis, and the method comprises the steps: obtaining metadata in the communication between a vehicle CAN bus and a cloud, the metadata comprising a protocol type and a load feature, carrying out the analysis of the load feature according to the protocol type, and extracting a key field of a data package; constructing a load feature matrix according to the key field, and normalizing the heterogeneous data to obtain a feature data set in a uniform format; a mahalanobis distance algorithm is adopted to calculate the distance between each data point in the feature data set and a preset base line, and if the distance exceeds a preset threshold value, abnormal traffic is judged, and an abnormal traffic mark list is generated; fingerprint features of a transmission link are obtained from the 5G network slices, the fingerprint features of different slices are subjected to standardization processing, and a cross-slice consistent fingerprint feature data set is generated; therefore, security threats in a complex Internet of Vehicles environment can be effectively handled, and the accuracy and reliability of abnormal traffic detection are improved.

The invention provides a vehicle network interaction type ordered charging scheduling method. The method comprises the following steps: acquiring photovoltaic, energy storage and charging pile configuration parameters, electric vehicle charging requirements, system topology, operation data of each device and meteorological data; regression analysis is carried out on charging behaviors and energy storage operation modes of various charging pile electric vehicles, and controllable load and energy storage response capability of each response time period are evaluated; generating power and load power are predicted, and the overall response capability is evaluated; according to the issued instruction, determining a response time period, a response task type and a planned operation load; based on the response demand, the operation mode of each device, the response subsidy profit sharing scheme and the demand constraint, an optimization scheduling model is established; solving an optimal planning problem, outputting a day-ahead response scheme, and declaring and confirming a clearing response demand; and correcting the scheduling strategy in real time. According to the invention, an effective resource control scheme is provided for the vehicle network interaction system, the electric vehicle and the power grid are decoupled, and the utilization rate and the economical efficiency of resources are improved.

The invention relates to the technical field of vehicle network control, in particular to an efficient vehicle network control system and method based on CAN-FD, and the system comprises a data transmission optimization module, a bandwidth management module, a signal scheduling control module, an encryption security control module and an anomaly detection module. In the invention, the bit rate of a data frame, the length of a data packet and the proportion of filling bytes are optimized, the transmission efficiency is improved, the bandwidth occupied by invalid data is reduced, a priority screening mechanism is called, the stable transmission of high-priority signals is ensured, the bandwidth allocation is adjusted based on the flow state and node frequency monitoring, the burst flow congestion is relieved, and the resource allocation is balanced; in combination with arbitration priority and bus conflict state, non-emergency signal priority is optimized, key data blocking risk is reduced, encryption and integrity verification are adopted, data security is ensured, tampering and leakage are prevented, data abnormity score and detection are introduced, abnormal data are screened, identification capability is improved, and network potential safety hazard is reduced.

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 a harmonic detection method and system for a vehicle network electrical system, and relates to the technical field of harmonic detection, and the harmonic detection method for the vehicle network electrical system comprises the steps: obtaining a current signal of a vehicle network bus, and carrying out the band limit processing of the current signal; constructing a variable window width S transformation kernel function with self-adaptive frequency; based on bispectrum analysis of a third-order cumulant, harmonic phase coupling characteristics are extracted, and an enhanced spectrum is generated through principal component projection; generating a dynamic detection threshold by using a non-harmonic interval background noise model; and carrying out harmonic parameter fitting by adopting an exponential decay model with frequency domain priori constraint. According to the method, through a cascade architecture of adaptive time-frequency analysis, high-order statistical feature extraction, dynamic threshold detection and prior constraint fitting, full-process closed-loop optimization from signal preprocessing to parameter optimization is realized, and the method is particularly suitable for accurate detection of heavy-frequency, nonlinear and time-varying harmonics of a vehicle network system; and a diagnosis basis with higher reliability can be provided for electric energy quality treatment.

The invention discloses a vehicle network interaction regulation and control method and system based on charging pile data. The method comprises the steps that electric quantity data of each adjustable vehicle at the current collection moment, power supply load data of each node in a power distribution network, position data of each charging pile and charging power data, charging load data and environment data at each collection moment are collected, and the charging load of each charging pile is predicted; obtaining load prediction data of each charging pile, calculating to obtain a vehicle-network interaction suitability index of each charging pile, and determining the power supply load prediction data of each node based on the vehicle-network interaction suitability indexes of all the charging piles, the electric quantity data of all the adjustable and controllable vehicles and the determined matching degree among the power supply load prediction data of each node. And executing a vehicle network interaction regulation and control strategy corresponding to the matching result. According to the charging pile data-based vehicle network interaction regulation and control method and system provided by the embodiment of the invention, the efficiency of vehicle network interaction regulation and control is improved.

The invention discloses an unmanned aerial vehicle communication secrecy energy efficiency optimization method and system based on deep reinforcement learning. The method comprises the following steps: constructing an intelligent reflector-assisted NOMA unmanned aerial vehicle communication network security secrecy energy efficiency optimization model; performing iterative optimization solution on the intelligent reflector-assisted NOMA unmanned aerial vehicle communication network security secrecy energy efficiency optimization model to obtain unmanned aerial vehicle communication network security secrecy parameters; setting a state vector, an action space and a reward function of a deep reinforcement learning algorithm, and constructing a deep reinforcement learning optimization algorithm; and performing iterative optimization on the security secrecy parameters of the unmanned aerial vehicle communication network based on a deep reinforcement learning optimization algorithm to obtain the optimal unmanned aerial vehicle NOMA secrecy communication energy efficiency. According to the embodiment of the invention, deep reinforcement learning can be combined and the flight path energy loss of the unmanned aerial vehicle is considered, so that the confidential energy efficiency in an unmanned aerial vehicle communication system is improved. The method can be widely applied to the technical field of unmanned aerial vehicle network communication energy efficiency.

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 relates to the technical field of electric vehicle and power grid interaction control, in particular to a vehicle network interaction cooperative regulation and control method, which realizes cloud platform strategy generation and issuing, edge terminal deployment and data acquisition, local control mode switching and power redistribution by building a hierarchical control framework. Constructing a space-time fusion prediction model to perform dynamic load prediction and strategy generation; designing a multi-modal data interaction mechanism, and standardizing data transmission; an abnormal state co-processing mechanism is established to quickly deal with faults; and user side interaction optimization is implemented, and the user is guided to charge reasonably. Experiments show that according to the scheme, the load rate of the transformer in the area can be reduced by 18%, the charging cost of a user is reduced by 23%, the fault response time is shortened to 1 / 5 of that of a traditional scheme, the power grid safety and the charging efficiency are effectively balanced, the charging experience of the user is improved, and coordinated development of the electric vehicle and the power grid is promoted.

The invention belongs to the field of Internet of Vehicles network communication, and discloses an Internet of Vehicles federated learning dynamic sparse training method based on cluster expansion. Constructing a hierarchical federated learning training architecture under the scene of the Internet of Vehicles, wherein the hierarchical federated learning training architecture under the scene of the Internet of Vehicles is of a client-server two-layer structure and comprises an edge server and a road system; performing federated dynamic sparse training based on a hierarchical federated learning training architecture in an Internet of Vehicles scene: training a sparse network structure based on local data by a training node under coordination and management of an edge server, and periodically updating the sparse network structure according to a predefined reconstruction updating standard; and finally, aggregating and reconstructing a global network structure meeting the target sparseness at the edge server.

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 discloses a vehicle-network interaction load control system based on a peak regulation demand, and the system employs the following method to schedule a charging load: S1, obtaining the load data of a power grid and the combination data of a starting unit, and determining the peak regulation demand of the power grid; s2, acquiring historical charging load data of the charging station, and generating a load proportion curve of ordered charging; s3, constructing a vehicle network interaction master-slave game model according to the peak regulation demand of the power grid and the user behavior; and S4, scheduling the charging load of the charging station based on an output result of the vehicle network interaction master-slave game model. According to the control system, by coordinating the charging load of the electric vehicle, peak clipping and valley filling are achieved, the peak-valley difference of the power grid is reduced, and the peak shaving pressure of the power generation side is relieved.

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 cognitive unmanned aerial vehicle network spectrum sharing method based on multi-agent reinforcement learning. The method comprises the implementation steps that a cognitive unmanned aerial vehicle network model and a cognitive unmanned aerial vehicle network and multi-agent reinforcement learning interaction network model are constructed; obtaining a training data set and defining a reward function; and carrying out iterative training on the interactive network model. In the process of training the interactive network model, the multiple agents interact with the cognitive unmanned aerial vehicle network in real time through reinforcement learning, spectrum resource allocation can be adaptively adjusted according to the communication requirement of each cognitive unmanned aerial vehicle and the dynamic change of the environment, and the spectrum resource allocation efficiency is improved. And the loss of the local Q network in each time step is calculated according to the target Q value corresponding to each experience calculated by calculating the reward of interaction between each Q agent and each environment state through the throughput of each cluster member on each channel, so that the resource waste is reduced, the spectrum conflict is avoided, and the maximization of the network throughput is realized. Therefore, the spectrum utilization rate is effectively improved.

The invention belongs to the technical field of blockchain and Internet of Vehicles security management, and relates to an Internet of Vehicles security and credible message sharing method based on blockchain and trust management, comprising: a message providing vehicle records and broadcasts a message, and a message receiving vehicle receives the message, scores the authenticity of the message, and broadcasts the message to a consensus vehicle; the consensus vehicle performs usefulness scoring on each authenticity score s1, i, when the number of high usefulness scores is greater than a threshold value Q, the data (m, s1, i, s2, i, c, t) is uploaded to the accounting node, and s2, i, c are high usefulness scores of the consensus vehicle c on s1, i; the accounting node receives and verifies the data (m, s1, i, s2, i, c, t), packages the verified data into a block and stores the block into a block chain; the RSU calculates the trust value of the vehicle in the area and shares the trust value to an RSU cluster; the RSU cluster makes a trust decision on the vehicle according to the trust value of the vehicle; according to the method, authenticity scoring and usefulness scoring are carried out on the message, the credibility of the shared message in the Internet of Vehicles is improved, and the authenticity and integrity of the message are ensured in combination with the block chain technology.

The invention relates to the technical field of intelligent power grids and interaction between electric vehicles and power grids, and provides an electric vehicle intelligent charging and discharging control method and system based on vehicle-network interaction. The method comprises the following steps: acquiring charging pile related information, load data and charging resources of a to-be-regulated area; calculating a baseline load according to the load data, and calculating the adjustable capability of the to-be-regulated region according to the baseline load; constructing a user charging behavior analysis model according to the battery initial charging electric quantity, the battery end charging electric quantity, the time of arriving at the charging place and the vehicle taking time; according to the power grid regulation and control demand and the adjustable capability, the response capability of the vehicle network resources is judged, and a vehicle network resource first backup list is obtained through screening; matching the charging resource with a power grid regulation and control target, and performing secondary recommendation matching on the first backup list of the vehicle network resource based on a matching result to obtain a second backup list of the vehicle network resource; and formulating an ordered charging regulation and control scheme based on the vehicle network resource second backup list.

A system and method in a building or vehicle for an actuator operation in response to a sensor according to a control logic, the system comprising a router or a gateway communicating with a device associated with the sensor and a device associated with the actuator over in-building or in-vehicle networks, and an external Internet-connected control server associated with the control logic implementing a PID closed linear control loop and communicating with the router over external network for controlling the in-building or in-vehicle phenomenon. The sensor may be a microphone or a camera, and the system may include voice or image processing as part of the control logic. A redundancy is used by using multiple sensors or actuators, or by using multiple data paths over the building or vehicle internal or external communication. The networks may be wired or wireless, and may be BAN, PAN, LAN, WAN, or home networks.

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.

The invention relates to a power distribution network dispatching method considering vehicle network interaction mode division, which comprises the following steps of: establishing a road network model, enabling road network nodes and power distribution network nodes to be in joint correspondence, and constructing a road network-power distribution network coupling model; dividing EV user types and constructing corresponding travel feature models; establishing respective response models based on response modes of participating in vehicle network interaction under subjective willingness of different EV users; and based on the established model and distributed photovoltaic and energy storage in the distribution network, with the minimum deviation between the network loss cost, the EV scheduling cost and the bus voltage as the target, constructing a day-ahead-real-time two-stage power distribution network operation optimization scheduling problem considering EV automobile scheduling mode diversification, and solving the problem to obtain a real-time scheduling scheme. Compared with the prior art, the method has the advantages that the EV connected into the power distribution network is called to participate in power distribution network dispatching, the power flow distribution of the power distribution network is effectively adjusted, the network loss caused by power flow delivery due to power supply of the main network is reduced, and the operation cost of the power distribution network is further reduced.

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.

The invention provides a vehicle control method and device, a vehicle and a server, the method is applied to the vehicle, the method is based on a pre-configured first online strategy, a vehicle-mounted communication terminal in the vehicle obtains a remote control instruction triggered by a user terminal and sent by a vehicle remote service server, a whole vehicle network is awakened according to the remote control instruction, and the vehicle is controlled according to the awakened whole vehicle network. And enabling the vehicle to execute an operation corresponding to the remote control instruction. In the technical scheme, the vehicle-mounted communication terminal sends the heartbeat packet to the vehicle remote service server at the preset frequency, so that the vehicle-mounted communication terminal and the vehicle remote service server are kept in the connection state, when a user side initiates a corresponding instruction of vehicle control, the instruction can be quickly transmitted to a vehicle side through the vehicle remote service server, and the vehicle control efficiency is improved. Therefore, high-efficiency and low-time-consumption control of the vehicle is realized.

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.