2318 results about "CAN bus" patented technology

The invention discloses a vehicle idling start and stop control system and method. The control system judges whether a vehicle has idling start and stop conditions by monitoring the water temperatureof an engine, the capacity of a storage battery, a safety belt and other information, judges whether the vehicle has conditions of stopping the engine by monitoring the rotating speed of the engine, the speed of the vehicle, the brake air pressure and other conditions, and judges whether the vehicle has conditions of starting the engine by monitoring the gear of a gearbox, the speed of the vehicle, a hand brake and other information. When the relevant conditions are met, start and stop work signals are sent to the engine though a CAN bus. In order to guarantee the reliability of frequent startand stop of the engine, the system strengthens flywheel gear rings of a starter and the engine. In order to ensure that the battery does not lose power, a storage battery capacity sensor is added tothe system on the basis of increasing the capacity of the storage battery, and the idling start and stop are allowed when the state of charge (SOC) of the storage battery satisfies a safe starting threshold. The idling start and stop control system has advantages of low cost and high reliability, and is especially suitable for commercial vehicles.

The invention relates to the technical field of liquid cooling servers, and discloses a liquid cooling server safety management system and method, and the method employs a multi-mode sensor network to synchronously collect the thermodynamic parameters of a liquid cooling system at 100 Hz, and constructs a three-dimensional thermal field digital twinborn model after the processing of an extended Kalman filtering algorithm. A distributed cooling strategy is designed based on a federated learning framework, and each node locally trains a thermal dynamic prediction model and is optimized by a central aggregator. A dynamic control instruction is generated by using a near-end strategy optimization algorithm, and cooling liquid flow distribution is optimized in combination with a quantum derivative simulated annealing algorithm. And designing a dual-threshold phase change control mechanism, establishing a block chain log to ensure traceability and tamper resistance of the instruction, and realizing closed-loop feedback control through a CAN bus. The system and the method can accurately monitor and intelligently control the liquid cooling system, improve the heat dissipation efficiency, reduce the power consumption, and guarantee the data safety and the system stability.

The invention provides a multilevel caching method and system based on CAN bus communication, and is applied to the field of data transmission. According to the method, the influence coefficient of the CAN bus is obtained by presetting load scenes (idle, low load and high load), whether the influence coefficient is limited by hardware is judged, the optimization direction is ensured to be accurate, data cache management is performed by adopting an annular buffer area, and the data transmission interval is dynamically adjusted and the data packaging strategy is optimized (small data merging, data alignment and filling byte reduction), so that the data transmission efficiency is improved. The CAN ID priority is dynamically adjusted by collecting communication data category information, it is ensured that high-priority data (such as ECU control signals) are transmitted in time, bandwidth occupation of low-priority data is reduced, in addition, the processing response speed of data frames is monitored, and if the processing response speed does not reach the standard, the data sending frequency is optimized, and the real-time performance is improved.

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 relates to the technical field of battery control, and discloses an energy storage battery output power self-adjusting system, which comprises a data acquisition module used for acquiring working parameters of an energy storage battery in real time; the historical database is used for storing previous operation data of the energy storage battery and the load equipment; the data processing and analyzing module is used for cleaning and normalizing the historical data; a self-adaptive adjustment strategy generation module; the invention discloses an energy storage battery output power self-adjusting method. The method comprises the following steps: S1, collecting battery pack data through a CAN bus; s2, establishing a load equipment priority list; s3, constructing a multi-time scale optimization model; s4, implementing dynamic impedance matching; s5, generating a power distribution instruction set; according to the invention, the output power prediction model is established by using the machine learning algorithm in combination with the previous operation data in the historical database, and the load demand trend in a period of time in the future can be accurately predicted.

The invention relates to the technical field of driving, and particularly discloses a mobile phone management and control method and a control system in a driving process. Vehicle running state data and mobile phone multi-mode sensing data are obtained, vehicle CAN bus information and mobile phone sensor data are combined, a motion coupling degree value is calculated on the basis of deviation of a steering wheel angle and mobile phone acceleration, the operation synchronization rate is analyzed through accelerator operation and gyroscope response, hand action coordination is evaluated, and the hand action coordination is evaluated. The method comprises the following steps: calculating a spatial constraint deviation value by combining a magnetometer orientation and a driving position coordinate offset degree; monitoring an abnormal posture of a driver; generating a risk assessment value by fusing three indexes; dynamically controlling a mobile phone function; according to the system, through multi-source data fusion and a hierarchical management and control strategy, emergency communication requirements are balanced on the premise of ensuring driving safety, distracted driving behaviors are accurately identified, and driving potential safety hazards caused by use of a mobile phone are solved.

The invention belongs to the field of data analysis and processing, and discloses a virtuality and reality combined automobile automatic driving test method. Comprising the following steps: connecting a sensor suite and an automatic driving system of an automobile with a virtual twin center by using a CAN bus or an Ethernet interface, operating the automatic driving system, and generating a performance report; the method comprises the following steps: initializing raindrop attributes, setting the transparency of raindrops based on real environment data, introducing a complex wind field model and a surface ponding dynamic evolution mechanism, carrying out collision detection on all raindrop pairs by using a space segmentation algorithm, and simulating dynamic behaviors of the raindrops in the air based on a discrete element method and a smoothed particle fluid dynamics method. Visual rendering is carried out on the water spray effect; under the influence of raindrop physical characteristic modeling, output of laser radar point cloud scattering modeling, camera image fuzzy modeling, millimeter wave radar signal attenuation and scattering modeling and ultrasonic sensor interference modeling is iteratively updated, and automatic driving testing of an automobile under virtual-real combination is achieved.

The invention discloses a ship mobile energy storage system CAN communication protocol automatic test system and method, and the system comprises a protocol modeling and test configuration module, a simulation node behavior simulation module, a CAN bus interaction and power closed loop module, and a test analysis and result evaluation module. A CAN communication protocol specification of the ship mobile energy storage system and parameters of tested equipment can be obtained, a protocol analysis model and a test configuration file are constructed for format verification and logic consistency verification, meanwhile, a corresponding test case sequence is automatically generated, and the message sending time is controlled to generate a time sequence simulation message flow; and sending the time sequence simulation message flow to the tested equipment, simulating energy interaction behavior acquisition to generate a power feedback data set, testing to obtain a signal consistency deviation and a power execution error to construct a protocol conformity evaluation matrix, and generating a visual test report. According to the invention, the high coverage rate and automatic closed-loop test of the CAN communication protocol can be realized.

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 relates to the technical field of automobile active safety, in particular to an intelligent brake pad abrasion measuring method and system based on multi-sensor fusion, and the method comprises the steps that S1, multi-source sensing signals are synchronously collected, and wheel speed pulse signals and brake pressure signals from a vehicle CAN bus are received; s2, multi-modal signal feature extraction: carrying out time domain filtering processing on the original thickness signal to form a thickness change feature vector, carrying out space partition calculation on the temperature distribution signal to form a temperature gradient feature matrix, and carrying out wavelet packet decomposition on the braking vibration signal to form a frequency domain energy feature set; s3, dynamically fusing decisions, outputting a joint feature vector, and generating a wear loss estimated value and a confidence index; and S4, performing Kalman filtering correction on the wear loss estimated value according to the confidence index to form final wear thickness data. According to the brake pad wear intelligent measurement method and system based on multi-sensor fusion, the problem that brake pad wear cannot be accurately monitored and intelligently diagnosed online in real time can be solved.

The invention discloses an electric heating deicing system and method based on machine learning prediction and adaptive adjustment, and belongs to the technical field of wind power generation. The system comprises a multi-physical sensor array and a dynamic heating execution module which are arranged on a blade. The multi-physical sensor array and the dynamic heating execution module are both connected with a CAN bus; the CAN bus is connected with an intelligent control module, a cloud collaboration platform and an edge computing node. The method comprises the following steps: acquiring historical operation data of an original unit, and preprocessing the historical operation data to obtain preprocessed data; the original unit historical operation data comprises environmental parameters and blade state monitoring data; analyzing the preprocessed data based on a machine learning algorithm, and establishing a blade icing prediction model; real-time environment parameters and blade state monitoring data are obtained, and the blade surface icing condition is predicted based on the blade icing prediction model; and determining the heating requirement of the blade based on the surface icing condition of the blade, and carrying out deicing.

The invention provides a water surface target grabbing system and a control method thereof, and relates to the technical field of marine operation equipment. The system comprises an unmanned ship and a cooperative mechanical arm, the unmanned ship and the cooperative mechanical arm share a processor; the unmanned ship supplies power to the cooperative mechanical arm; the unmanned ship is positioned through an inertial navigation system on the unmanned ship and a scene camera and a depth camera on the cooperative mechanical arm; a scene camera, a depth camera and an integrated sensor on the cooperative mechanical arm transmit data to a processor on the unmanned ship through a local area network, the processor calculates and analyzes the data transmitted by the scene camera and the depth camera, and an action instruction is sent to the cooperative mechanical arm through a CAN bus. According to the water surface target grabbing system, automatic grabbing is achieved, and the grabbing precision, stability and adaptability of the system in the dynamic marine environment are improved.

The invention relates to an automatic switching method and system adaptive to multiple communication protocols, and the method comprises the steps: executing the wake-up operation of a controller after an instrument is powered on, and monitoring a data frame on a CAN bus; extracting a CANID (Controller Area Network Identifier) in the data frame, matching the CANID with a preset manufacturer handshake command list, and judging whether a handshake command exists or not; if the matched handshake instruction exists, determining a corresponding manufacturer communication protocol type; according to the manufacturer communication protocol type, determining a corresponding CAN message analysis method and a CAN message sending method; receiving a first CAN message, and analyzing the first CAN message by using a CAN message analysis method to generate display data; and obtaining operation data of the user, converting the operation data into a second CAN message according to the CAN message sending method, and sending the second CAN message to the controller. The application has the effect of improving the communication efficiency.

The invention discloses a multi-dimensional intelligent analysis and fault traceability system for ACU final test data, and relates to the technical field of data analysis. The multi-source heterogeneous data acquisition module is used for acquiring operation parameters, function test data and assembly data through a CAN bus, test equipment and a production system, and is provided with a high-precision timestamp; the data cleaning and preprocessing module is used for standardizing data, filtering and denoising, synchronizing time and complementing missing values; the multi-dimensional feature extraction module is used for extracting features from a time domain, a frequency domain, a time sequence and a space; the intelligent fault diagnosis module is used for classifying faults by using a random forest and a D-S evidence theory and evaluating severity; the fault traceability reasoning module is used for constructing a fault propagation graph and positioning root causes through a Bayesian network; and the visual display and early warning module is used for generating a three-dimensional fault tree and a thermodynamic diagram and triggering graded early warning. According to the invention, the fault detection rate and traceability efficiency are improved, the false alarm rate is reduced, early warning is realized, the ACU test process is optimized, and the product quality and safety are guaranteed.

The invention discloses an Internet of Vehicles intrusion detection method based on hierarchical feature enhancement and a dynamic sparse attention mechanism, and the method comprises the steps: firstly obtaining an original CAN bus message data stream, and carrying out the multi-modal data preprocessing and hierarchical feature construction; secondly, for hierarchical features, performing message-level depth feature compression and space-time statistical feature enhancement by adopting a dual-channel feature compression mechanism to form an enhanced mixed feature vector, and then constructing a neural network model of a dynamic sparse attention mechanism based on content awareness to perform feature extraction; and inputting a sparse attention calculation result into a hierarchical classifier, and predicting and outputting a detection result. And training the network model, and deploying the trained network model in a vehicle-mounted embedded device to realize real-time message monitoring and intrusion detection. According to the method, the calculation complexity and parameter quantity of the model are greatly reduced, the real-time performance in a vehicle-mounted environment is remarkably improved, and the detection capability on complex hidden attacks is effectively improved.

The invention discloses a new energy vehicle endurance display method and system based on a power battery SOH, electronic equipment and a vehicle. The method comprises the steps that a vehicle, a vehicle control unit, a battery management system and a display module are triggered to be started through a KL15 signal wake-up signal, and power-on initialization and self-inspection are executed; after the self-inspection is completed, reading the stored current state-of-health initial value SOH0 of the power battery and the stored current state-of-charge initial value SOC0 of the power battery from the EEPROM; performing dynamic correction on the read SOC value, performing fixed value locking on the SOH value, and sending the corrected SOC value and the locked SOH value to a CAN bus of the whole vehicle; based on the SOH0 and the SOC1 sent by the battery management system, the remaining endurance mileage S is calculated in combination with the total delivery mileage L of the new vehicle; and the instrument and large-screen display module is used for displaying the current remaining endurance mileage in real time. According to the method, the problem of display deviation caused by neglecting battery aging in a traditional method is solved, and the user driving experience and the battery management efficiency are improved.

The invention relates to the technical field of spacecrafts, and discloses a satellite solid storage and data transmission integrated system which comprises a data transmission solid storage master control FPGA, an FPGA loading and refreshing system and an ARM system, the FPGA loading and refreshing system and the ARM system are connected with the data transmission solid storage master control FPGA, the ARM system is connected with the FPGA loading and refreshing system, and meanwhile the ARM system is connected with satellite service through a CAN bus; the FPGA loads and refreshes a system, and periodically refreshes rewrite configuration or system reset rewrite configuration to ensure normal operation of the configuration; and the ARM system forwards the satellite remote control instruction received from the CAN bus to the data transmission solid memory master control FPGA and the loading FPGA through the SPI bus. According to the technical scheme, storage and transmission are efficiently integrated, the data storage rate, the data transmission rate and the data transmission capacity are improved, the system architecture is simplified, flexible selection of a data transmission modulation mode and the data transmission rate is achieved, the system reliability is improved, data access and transmission can be more accurately regulated and controlled through control and management of the FPGA, and the system reliability is improved. And the system instability caused by hardware faults or interface problems is effectively reduced.

The invention discloses an anti-rollover control method and system for a balanced heavy forklift truck, and relates to the field of forklift truck intelligent control, and the method comprises the steps: collecting forklift truck working condition data through a sensor group, transmitting the data to a central controller through a CAN bus, and carrying out the preprocessing; inputting a rollover risk assessment algorithm, calculating a boundary condition of a static stability triangle and dynamic lateral moment balance, generating a rollover risk coefficient in combination with working condition data of the forklift, and outputting a decision instruction set; dynamic load adjustment is carried out through a hydraulic servo controller, and load pose parameters are obtained; a motor controller is driven to implement torque vector distribution, a braking energy recovery unit is involved, controllable slow-release braking is applied to wheels on the inner side, and adjusted wheel motion state parameters are obtained; and comparing the theoretical prediction model to carry out residual analysis, triggering a residual correction strategy, and carrying out repetitive control. The working state of the forklift is collected in real time and dynamically analyzed, so that the safety of the forklift in the operation process is improved.

The invention discloses an LLM-based controller CAN communication test method and system, and the method comprises the steps: converting a DBC file into a configuration file, reading a VSS and CAN signal corresponding relation file, and inputting the VSS and CAN signal corresponding relation file to an LLM; calling an LLM model, and generating a test case according to the cue word; constructing an automatic framework to implement an execution function based on a behavior-driven development BDD mode; and associating the generated test case with an execution function in the automation framework, constructing a complete test suite, and operating the test suite to execute a CAN bus communication test. VSS path and CAN Signal verification is carried out on the basis of the controller which already realizes the VSS technology, the test case conforming to the BDD natural language is generated by using the LLM, the test case is directly used as the test script, the step of manually compiling the test case is omitted, the test efficiency is improved, and the test cost is reduced.

The invention relates to the technical field of automobile charging, and particularly discloses a vehicle circuit system with a reverse charging function, comprising a main battery pack; the bidirectional inverter is electrically connected to the main battery pack and is configured to convert external alternating current into direct current to charge the main battery pack in a charging mode and convert the direct current of the main battery pack into alternating current to be output in a discharging mode; the charging control module is connected to the output end of the bidirectional inverter and used for processing input / output electric energy of different specifications; the multifunctional output interface comprises at least one alternating current interface and a direct current interface; and the system control unit, the integrated current detection module, the temperature monitoring module and the fault diagnosis module are respectively in communication connection with the bidirectional inverter, the charging control module and the output interface through a CAN bus. The vehicle power battery can reversely supply power to external equipment or other vehicles, meanwhile, the conventional charging function and the energy recycling function are compatible, and the method is suitable for new energy vehicles, hybrid electric vehicles and other scenes.

The invention relates to an intelligent control method, system and device for an automobile seat and a medium, and belongs to the field of automobile seat adjustment control. The intelligent control method comprises the steps that a control instruction data packet from a mobile terminal is received and converted into a vehicle CAN bus protocol data frame; analyzing an operation mode identifier in a vehicle CAN bus protocol data frame, and matching a corresponding target attitude parameter set; based on the real-time temperature data in the vehicle, dynamic compensation calculation is executed on the target attitude parameter set, and an after-compensation execution instruction set is generated; a motor control signal is sent to a seat executing mechanism; calculating a deviation value between the feedback signal data and the compensated execution instruction set, and if any deviation value exceeds a preset deviation threshold value, generating a mechanism calibration instruction; and acquiring final execution state data of the seat execution mechanism, and feeding back the final execution state data to the mobile terminal. Intelligent seat adjustment based on real-time feedback can be achieved, and it is ensured that a driver obtains the best comfortable experience in different scenes.

The invention discloses an Internet of Vehicles CAN bus intrusion detection method based on noise perception active learning, and belongs to the technical field of Internet of Vehicles safety and machine learning. The invention aims to solve the technical problems of false label noise interference, high manual labeling cost, high attack missing report rate caused by class imbalance and the like. The core of the method is to execute a noise sensing mixed query strategy in an iterative loop: firstly, generating a pseudo tag through clustering and correcting by using an integrated noise detector; secondly, calculating uncertainty scores and noise probabilities of the samples, fusing the uncertainty scores and the noise probabilities to obtain a comprehensive score, and preferentially selecting the samples with high uncertainty and low noise probabilities; and then adaptively selecting a sampling strategy according to the model performance and applying category balance constraint. The query batch is used to iteratively update the model while dynamically adjusting the classification threshold to reduce the missing report rate. According to the method, the influence of pseudo label noise can be effectively suppressed, and the attack detection precision and generalization capability are remarkably improved with extremely low labeling cost.

The invention discloses an image recognition and analysis method and system for CAN bus data in the automobile field, and relates to the technical field of image recognition, and the method comprises the steps: synchronously collecting a CAN message data flow of a vehicle and a video data flow in the vehicle operation process in a collection period; according to the signal parameters, the CAN message data flow in the acquisition period is split into a plurality of signal data, a comparison signal set is determined based on all the signal data in the acquisition period, and the comparison signal set is stored in a comparison signal data source memory; according to the video parameters, dynamically identifying a video data stream in the acquisition period, determining a reference signal based on the dynamically identified data in the acquisition period, and storing the reference signal in an operation database; and analyzing the comparison signal set and the reference signal, and storing the comparison signal in the comparison signal set meeting a set condition into a signal database. According to the invention, automatic acquisition and analysis of CAN bus data in the automobile field are realized.

The invention provides a CAN bus message period adjusting method and system based on dynamic load balancing, and the system measures and calculates a network load rate and an error state through a network load real-time detection module, carries out the priority grade division and parameter configuration for different messages, carries out the load partition definition according to the load rate, and carries out the adjustment of the CAN bus message period. And dynamically adjusting a message sending period according to a measurement result, adding a feedback mechanism to realize closed-loop control, and finally realizing load balancing of the system.

The invention relates to an electrostatic vibrating diaphragm automobile audio which is mainly used for an electric vehicle and can compensate vehicle-mounted electromagnetic interference audio and solve the problem of electromagnetic interference of a motor PWM switch and rotating speed harmonics on an audio system. The electrostatic diaphragm automobile sound system comprises an electrostatic diaphragm loudspeaker and an interference feature extraction module, the rotating speed of a motor and the PWM frequency are obtained through a CAN bus, and a dynamic interference feature set is analyzed and extracted through adaptive window length FFT; the EMI quantification module is used for calculating an electromagnetic pollution index at a frequency band of 8kHz-20kHz, and triggering compensation when the electromagnetic pollution index exceeds a threshold value of-40dB; the dynamic notch compensation module is used for generating an inversion signal; according to the double-loop feedback control module, the amplitude is adjusted through PID of an inner loop, and the phase is optimized through gradient descent of an outer loop so as to minimize THD. The method comprises five steps of signal acquisition, feature extraction, interference quantification, dynamic compensation and signal injection. Interference frequency drift can be accurately tracked, a tone quality MOS value is improved after compensation, and an over-compensation risk is avoided.

The invention provides an electronic mechanical braking system, a vehicle controller and an electric vehicle, and relates to the technical field of electric vehicles. The electronic mechanical braking system comprises a central controller and four wheel end braking devices, and the central controller is used for controlling the four wheel end braking devices to output clamping force to four wheels of the electric vehicle. Wherein the central controller is in communication with the whole vehicle CAN through two public CAN buses and is in communication with each of the four wheel end braking devices through two private CAN buses. In this way, redundant backup of the communication link is achieved, and the communication reliability of the electronic mechanical braking system is improved.

The invention discloses a vehicle remote diagnosis control method and device based on a vehicle fault code, and the method comprises the steps: collecting the parameter information of a specified part of a vehicle and the operation parameters of the whole vehicle through a CAN network by a VCU, and generating important parameter information; the VCU sets a trigger condition according to the control logic, and generates a uniquely recognizable vehicle fault code in combination with the monitoring platform; the important parameter information and the vehicle fault code are sent to a vehicle-mounted terminal T-BOX through a CAN bus; the T-BOX performs analysis and recombination according to a CAN communication protocol with the VCU to generate a message; the T-BOX uploads the message to a monitoring platform; the monitoring platform analyzes the message to obtain important parameter information and a vehicle fault code, and performs matching analysis with a whole vehicle fault detail list according to the important parameter information and the vehicle fault code to generate a vehicle fault record; and informing platform supervisors, vehicle after-sale maintenance personnel and drivers of the fault details and the fault analysis conclusions. According to the method, the problems of decisive support, hysteresis quality, rapid flow increase and the like of data in vehicle fault remote diagnosis can be solved.

The invention discloses a synchronous circuit and method of an energy storage inverter parallel operation system, and belongs to the field of photovoltaic technology. The circuit comprises a CAN bus; the plurality of synchronous communication components are respectively arranged in each energy storage inverter of the energy storage inverter parallel operation system; the synchronous communication component comprises an ePWM module, and the ePWM module comprises corresponding time base counter registers; one of the energy storage inverters is a host, and the rest of the energy storage inverters are slaves; a main synchronous communication component in the host is used for sending a kth-phase carrier synchronization signal to the CAN bus when a count value of a time base counter register corresponding to the kth phase in the main synchronous communication component is 0; the slave synchronization communication component in each slave is used for setting the count value of the time base counter register corresponding to the kth phase in the slave to be 0 when receiving the kth phase carrier synchronization signal, and k is a positive integer. According to the embodiment of the invention, synchronous operation among the energy storage inverters can be realized, and the operation reliability and safety of the parallel operation system are ensured.

The invention relates to the field of vehicle-mounted bus testing, and discloses a vehicle-mounted CAN bus terminal resistor configuration method and system for node testing, and the method comprises the steps: obtaining a node identifier and a terminal resistor identifier corresponding to each test node in a vehicle-mounted CAN bus test, the terminal resistor identifier being used for identifying the terminal resistor access state of the test node; obtaining a first node position of a first test node corresponding to the first node identifier, configuring a terminal resistor for the first test node when the first node position is an end node of the vehicle-mounted CAN bus, and canceling the configured terminal resistor for the test node of a non-end node; and updating the terminal resistor identifier of each test node according to the terminal resistor access state corresponding to each test node. According to the invention, the terminal resistor can be automatically configured in the CAN bus test.

The present invention relates to a CAN bus anomaly detection method and system based on a dual attention mechanism, comprising the following steps: S1, collecting on-board data from an OBD interface of a vehicle in real time, wherein the collected on-board data comprises operation parameters of an engine, a vehicle speed, the state of a braking system, and the readings of other important sensors; and S2, performing inference analysis on the on-board data at a vehicle end by using an anomaly detection model, calculating a threshold by means of normal operation data so as to identify a potential anomaly or attack mode, once an anomaly is detected, immediately generating identification information, giving a direct alarm to a driver, and slicing, encrypting and storing abnormal data. The present invention relates to the technical field of CAN bus anomaly detection. In the present invention, an anomaly is detected by using the strong relevance of normal data characterization under different perspectives and the difference between the normal data characterization and abnormal data characterization, and by means of the characterization learning of a large amount of normal data, the objective of anomaly detection is achieved, the dependence on abnormal data is reduced, and the accuracy of CAN bus anomaly detection is improved.