12455 results about "Vehicle control" patented technology

The invention provides an unmanned aerial vehicle autonomous obstacle avoidance and path planning method and system based on deep learning, and relates to the field of unmanned aerial vehicle control, and the method comprises the steps: obtaining position information and environment perception data, constructing a spatial-temporal feature matrix, extracting target motion and background feature vectors, and mapping the target motion and background feature vectors into a target-environment fusion feature field; calculating an accessibility matrix and a cost matrix to construct a track search space, generating a candidate track set and determining an optimal planned track; and performing segmented optimization on the planned track to obtain a continuous attitude sequence, and generating an adaptive control strategy. According to the invention, intelligent obstacle avoidance and efficient path planning of the unmanned aerial vehicle in a complex environment are realized.

The invention provides an end-to-end automatic driving decision control method and system, and belongs to the technical field of automatic driving. The invention relates to an end-to-end automatic driving decision control method based on multi-modal perception and hierarchical trajectory optimization, and the method comprises the steps: constructing an end-to-end decision closed loop through combining the zero sample migration capability of a vision-language-action (VLA) model with a hierarchical optimization architecture: analyzing multi-modal input (vision, language and point cloud) by using a pre-trained VLA model to generate path points; the vehicle pose is dynamically adjusted through upper-layer optimization to expand a feasible solution space, a smooth track meeting dynamics and collision avoidance constraints is solved in real time through lower-layer optimization, and finally a vehicle control instruction is output. According to the method, a multi-modal sensing and hierarchical trajectory optimization mechanism is fused, and the sensing generalization ability, the path planning feasibility and the control execution robustness of the system in a complex traffic environment are effectively improved.

The present invention provides an unmanned aerial vehicle-based electric power patrol inspection control method and system. The method comprises: creating a flight path on the basis of an electric power patrol inspection area, and collecting images and position information of electric power devices by means of various types of devices carried by an unmanned aerial vehicle, the images and the position information being transmitted to a receiving terminal at a ground station in a wireless transmission mode; the ground station receiving the images and the position information that are collected in real time, processing and analyzing the images to obtain analytic status, such as defects, damage and corrosion, of the electric power devices; and, on the basis of the analytic status from the ground station, using the position information as distinguishing features to generate an electronic patrol inspection report, providing service lives, maintenance costs and maintenance measures of the electric power devices on the basis of the electronic patrol inspection report, and notifying maintenance staff of the position information of the electric power devices. The system comprises an unmanned aerial vehicle control module, an image processing and analysis module and an electronic patrol inspection report generation module. The present invention is of great significance to maintenance and management of electric power devices.

The invention relates to the technical field of unmanned aerial vehicle control, in particular to a task allocation and conflict resolution system and method for multi-unmanned aerial vehicle collaborative operation, and provides the following scheme: dividing an initial operation area and generating a response weight by constructing a crop growth state map and a three-dimensional plot model; based on path planning and resource adaptation, a flight route is dynamically generated, and the crop state and the unmanned aerial vehicle state are monitored in real time; when adjustment conditions are met, a multi-dimensional dynamic task evaluation model is constructed, and task migration and conflict decoupling are completed in combination with particle swarm optimization and an autonomous negotiation mechanism. The method is suitable for a precision agriculture scene, the unmanned aerial vehicle path dynamic scheduling in the operation area and the high-priority area precision coverage are realized, and the operation efficiency and the resource cooperation capability are improved.

Various electric energy supply vehicles and electric energy supply vehicle control system; a to-be-replaced electric vehicle and an electric vehicle battery replacement control system; a robot, a robot driving system, a carrying robot and a carrying robot control system; a plurality of programmable controllers, a plurality of wireless programmable controllers, a plurality of charging and replacing cabinets, a charging and replacing cabinet control system, a plurality of programmable controllers, a plurality of wireless programmable controllers, a plurality of to-be-replaced electric vehicles with different structures, a constructed electric vehicle finding mode, a plurality of electric vehicles to be subjected to battery replacement, a construction battery swapping station link is omitted, one electric energy supply vehicle can provide replenishment for a plurality of electric vehicles, an electric energy supply vehicle is put according to the amount of electric vehicles, invalid investment is avoided, and a vehicle owner does not need to center the battery again.

A control system for controlling a second unmanned aerial vehicle that flies while holding a first cable connected to a first unmanned aerial vehicle that performs work and a second cable extending from a cable reeling machine, includes a sensor to sense a surrounding environment and output sensor data, and a controller configured or programmed to control operation of the unmanned aerial vehicle and, during flight of the second unmanned aerial vehicle, detect the first cable and the second cable based on the sensor data, and upon predicting that at least one of the first cable and the second cable will contact the ground or an obstacle on the ground, change a trajectory of the second unmanned aerial vehicle to avoid the contact.

The invention belongs to the technical field of vehicle control, and particularly relates to a vehicle-mounted image recognition and target detection system based on deep learning, and the system comprises a distributed monitoring module which collects the operation, obstacle and traffic signal information of a target vehicle through multi-modal classification and scene matching, completes the marking of a shielding region and the matching of information through the combination of shared data, and achieves the recognition of the target vehicle. Forming an enhanced monitoring set; the label planning module constructs an enhanced topological space based on the enhanced monitoring set, and adjusts moving tracks in different scenes by combining with vehicle and pedestrian track probability distribution fed back by dynamic intention recognition; the action recognition module predicts trajectory parameters and collision probabilities of non-target vehicles and pedestrians by using Bayesian and multi-modal algorithms; the decision-making module generates a real-time control instruction through particle swarm optimization and fuzzy control, and optimal control parameters are fed back through simulation; according to the invention, intelligent track planning and real-time control in a complex scene are realized, and the detection precision and control robustness of the shielded and label-free area are improved.

The invention discloses a cloud control self-driving automobile control method considering automobile cloud communication time delay. By constructing a time delay prediction model based on historical communication delay data, the back-and-forth communication time delay at the current moment is accurately predicted, and forward compensation is carried out on the vehicle state by adopting an iterative prediction formula in combination with a vehicle transverse dynamic model. According to the method, a process disturbance correction item is introduced, system modeling errors caused by environment disturbance are effectively corrected, and the state prediction precision is improved. And deducing a future vehicle state through the predicted time delay step number in combination with a historical vehicle state and control input so as to realize dynamic compensation of the time delay influence. The method effectively solves the problems of control lag and state information lag caused by communication delay in vehicle-cloud cooperative control, improves the control real-time performance and precision of a cloud control automatic driving system, enhances the robustness and safety of the system in a complex environment, and is suitable for automatic driving scenes with high dynamic and high safety requirements.

The invention discloses an unmanned aerial vehicle inspection method and system applied to foundation pit accumulated water monitoring, and the method comprises the steps: determining an optimal route node sequence, and generating an unmanned aerial vehicle control instruction set; acquiring internal and external parameters of a multi-source sensor, and performing time alignment compensation on image frames and sensor data; acquiring a ponding area image coordinate set and attribute data; a three-dimensional projection point set of the ponding area is obtained, and the real coverage area of the ponding area is calculated; generating a three-dimensional overview map of the foundation pit by using the multi-view image of the foundation pit area; determining a three-dimensional coordinate point set and an area estimation result of the ponding area; and projecting the three-dimensional coordinate point set of the ponding area to the three-dimensional overview map of the foundation pit to generate a hidden danger monitoring report. By integrating the unmanned aerial vehicle, the sensor data and the advanced image processing technology, the accuracy, stability and visualization effect of foundation pit accumulated water monitoring are greatly improved, more reliable and comprehensive technical support is provided for safety management of foundation pit engineering, and the safety risk in the construction process is remarkably reduced.

The invention relates to the technical field of artificial intelligence, in particular to a driver fatigue state monitoring system and method based on multi-modal biological feature fusion, and the system comprises a sensing module, a data processing and analysis module, an environment risk assessment module, a personalized model module, a self-adaptive decision fusion module, and a vehicle interaction and vehicle control module. Compared with the prior art in which fatigue judgment is performed by adopting a fixed threshold value, the fatigue judgment cannot adapt to a complicated and changeable driving environment and may cause excessive alarm or missing alarm under a monotonous road condition in a dangerous road section, an environment perception and dynamic threshold value adjustment algorithm is introduced, so that the system can intelligently evaluate the environmental risk and monotonicity, and the fatigue judgment accuracy is improved. The alarm sensitivity is automatically adjusted, and the early warning accuracy and the scene adaptability are both improved.

The invention relates to a vehicle control method and device, computer equipment and a storage medium. The method comprises the steps of obtaining target crosswind information of crosswind acting on a vehicle according to driving information of the vehicle, weather information of a scene where the vehicle is located and scene image information; determining a target yaw velocity of the vehicle according to the vehicle state information of the vehicle; determining a first angular velocity difference value between the target yaw velocity and an actual yaw velocity obtained through a sensor of the vehicle, and determining driving condition information of the vehicle according to the first angular velocity difference value; and target control information of the vehicle is obtained by using the first angular velocity difference value and / or the target crosswind information according to the driving working condition information of the vehicle. By adopting the method, the driving stability of the vehicle in the crosswind environment can be improved.

The invention relates to the field of automatic driving vehicle control, in particular to a sightseeing vehicle anti-collision control method and system based on a multi-mode radar. Comprising the following steps: acquiring a multi-modal radar original signal, and performing timestamp alignment and noise reduction processing to generate standardized fusion data; performing multi-target classification based on a convolutional neural network, and outputting a target recognition result in combination with Doppler frequency shift compensation and distance calculation; generating a hierarchical response strategy through dual-threshold comparison and environment interference dynamic weight adjustment; the strategy is converted into a PWM control signal, and a control instruction is generated through relay driving adaptation and hardware compatibility detection; a relay is driven in real time, motor response is collected, and execution effect evaluation and time sequence marking are completed; and brake release and system parameter synchronization are realized through PID closed-loop control. The problems of insufficient multi-target identification precision, poor environmental interference adaptability and response delay are solved, and the active safety performance of the sightseeing vehicle is remarkably improved.

The invention relates to the technical field of unmanned aerial vehicle control, and discloses an unmanned aerial vehicle group scheduling and simulation integrated system based on digital twinning and deep reinforcement learning, and the system comprises a digital twinning mapping module, a deep reinforcement learning task scheduling module, a heterogeneous interface adaptive module, a fault prediction and self-healing module, and a scenarized strategy optimization module. Through an unmanned aerial vehicle group intelligent scheduling and simulation integrated technology fusing digital twinning and deep reinforcement learning, all links of unmanned aerial vehicle group management and control can be comprehensively covered, common task scheduling and path planning indexes are concerned, and factors such as heterogeneous unmanned aerial vehicle compatibility, fault prediction and self-healing are also included; and the collaborative process of scheduling and simulation is deeply optimized, so that a system operation result more accurately reflects the real operation condition of the unmanned aerial vehicle group, and a more scientific basis is provided for intelligent scheduling and reliable management of the unmanned aerial vehicle group.

The invention discloses a fast training type unmanned aerial vehicle reinforcement learning strategy method suitable for a microcontroller, and belongs to the technical field of unmanned aerial vehicle control, and the method comprises the steps: constructing a standard four-rotor dynamic model containing the first-order delay characteristic of a motor, and simulating a real physical system; an asymmetric actor-commentator architecture is adopted to ensure the robustness and mobility of the strategy; a reward function coefficient is dynamically adjusted based on a course learning mechanism, and network parameters are optimized by adopting an offline strategy algorithm; performing network training and process optimization; and carrying out lightweight processing and deployment on the trained strategy network, landing the strategy in simulation to a real hardware platform with limited resources, jointly realizing rapid training, reliable migration and lightweight deployment, and realizing high-frequency autonomous flight control of the unmanned aerial vehicle on an embedded platform. According to the method, the high-performance reinforcement learning control strategy can be stably and efficiently operated on the microcontroller with limited resources, and the hardware cost and the power consumption of the intelligent unmanned aerial vehicle are greatly reduced.

The present application relates to the technical field of vehicles, and provides a vehicle control method and apparatus, an electronic device, and a medium. The method comprises: on the basis of the transverse motion state quantity and the longitudinal motion state quantity of a first vehicle, solving for a tracking control problem for tracking a planning path and a planning speed, so as to obtain a first wheel steering angle control quantity and an expected acceleration of the first vehicle, wherein the first wheel is a front wheel or a rear wheel, the planning path comprises a reference value of the transverse motion state quantity of the vehicle at a series of time points in the future, and the planning speed comprises a reference value of the longitudinal motion state quantity of the vehicle at a series of time points in the future; obtaining the acceleration control quantity of the first vehicle on the basis of the expected acceleration of the first vehicle and the actual acceleration thereof; and controlling the first vehicle on the basis of the first wheel steering angle control quantity and the acceleration control quantity of the first vehicle. In the present application, vehicle control is carried out in a transverse and longitudinal combined tracking control mode, so that the speed tracking accuracy can be improved as much as possible while the path tracking accuracy is ensured.

The invention belongs to the technical field of reusable aircraft control, and relates to an intelligent fault diagnosis and control method for a reusable aircraft. According to the method, a control-oriented aircraft dynamics model is established, a neural network adaptive fault observer and a particle swarm optimization algorithm are fused, and high-precision tracking control over the attitude and trajectory of the aircraft is achieved. When faults such as performance loss of an actuator and unmeasurable deviation occur, fault information can be estimated quickly and accurately, the reconstructed fault information is integrated into control law design, and it is guaranteed that the aircraft still keeps stable flight in a serious fault state. Meanwhile, the method can effectively deal with composite disturbances such as pneumatic uncertainty, elastic modal coupling and external environment interference, and improves the real-time performance and accuracy of fault diagnosis by means of high-precision joint online estimation. In addition, the particle swarm optimization algorithm further enhances the overall performance and adaptive ability of the control system, so that the aircraft operates more efficiently and stably in the flight process.

The invention provides a new energy automobile high-voltage system dynamic risk assessment method and device based on multi-source data fusion, and is applied to the technical field of data processing. According to the method, data such as high-voltage component operation parameters, battery states, environment perception, fault history and whole vehicle control instructions are obtained. Pre-processing the first working condition label, the second working condition label and the third working condition label, analyzing a CONTROLSIGNAL field to obtain a working mode of the high-voltage system, and generating a dynamic working condition label; combining a Pearson's correlation coefficient and XGBoost feature importance, screening risk sensitive features from the two, and forming a risk related feature subset; the method comprises the following steps of: establishing an improved Bayesian network model, training a subset by using an improved Bayesian network model, establishing an exclusive risk assessment model for different working conditions, reasoning real-time data by using the model to obtain a probability value of each risk dimension, and generating a dynamic risk assessment result and an early warning signal in combination with grade standard judgment.

The invention discloses an RFID multi-label information fusion and unmanned vehicle path real-time correction method, system and device based on improved whale optimization, and a storage medium, and belongs to the technical field of the crossing field of the Internet of Things and an intelligent traffic system, and the method comprises the steps: collecting received signal strength indication information and label position information, constructing a label signal propagation model and a positioning probability distribution model; performing fusion processing of multi-label positioning information, adopting a dynamic weighting strategy to adjust signal contribution, obtaining an unmanned vehicle position estimation value, taking the unmanned vehicle position estimation value as state input of path optimization, constructing a path cost function, introducing a whale optimization search mechanism based on dynamic update parameters, performing iterative optimization on a path candidate solution, and obtaining a path optimization result; outputting a path correction result; and a real-time path instruction is issued to the vehicle control module, and the unmanned vehicle is guided to complete path correction and navigation control in a dynamic environment. According to the method, the problems of poor path adaptability, low fusion precision and local optimum in the existing method are solved.

The invention relates to a multi-unmanned aerial vehicle disaster site collaborative search method fused with a big language model consultation mechanism. Compared with the prior art, the multi-unmanned aerial vehicle disaster site collaborative search method solves the defect that multi-unmanned aerial vehicle control cannot adaptively combine historical task experience and multiple big language model decision suggestions. The method comprises the following steps: starting a collaborative search task and initial path configuration; starting real-time monitoring on the disaster search task cooperatively executed by the plurality of unmanned aerial vehicles; a plurality of unmanned aerial vehicles cooperatively execute monitoring of a disaster search task; generating an unmanned aerial vehicle task abstract and a response specification; performing big language model consultation processing; and updating the credibility of each model. According to the method, the knowledge of the large language model and historical task data are utilized to supplement the defects of a traditional planning algorithm, continuous adaptive optimization of the search task is realized, and the safety and efficiency of an unmanned aerial vehicle system in a disaster scene are improved.

The invention belongs to the field of unmanned aerial vehicle control, and particularly relates to a complex task-oriented unmanned aerial vehicle modular integrated control platform, which comprises an acquisition module for acquiring and processing information such as a real-time flight state of an unmanned aerial vehicle group, a complex task text, meteorological data and the like; the task analysis module decomposes a complex task into a single subtask set and an incidence matrix, and evaluates the task complexity for resource allocation; the atmospheric analysis module is used for inverting a real-time flight association adjustment graph based on the corrected meteorological data, flight states and the like in combination with a target optimization algorithm and an optimization function taking resource quantity minimization and task completion probability maximization as targets; the record analysis module generates a performance transfer matrix by using a hidden Markov algorithm; and the flight control module responds to the adjustment graph to correct the flight state, and ensures that the task completion probability meets a preset safety threshold. According to the platform, efficient cooperative control and resource optimization allocation of the unmanned aerial vehicle group under complex tasks are realized through modular integration.

The invention provides a vehicle control method and device for an intelligent driving scene, electronic equipment and a medium. The method comprises the steps of obtaining multi-mode information collected by multiple sensors installed on a vehicle, vehicle system fault diagnosis information and a current driving environment; on the basis of the multi-modal information, the confidence degree of each piece of modal information in the current driving environment is calculated; according to the confidence degree of each piece of modal information, performing feature fusion on the multi-modal information to obtain an environment state vector; and generating a vehicle control strategy based on the environment state vector, the system fault diagnosis information and the current driving environment. According to the method, by introducing a confidence coefficient evaluation mechanism, unreliable modes affected by environment interference or faults can be automatically inhibited, and the perception accuracy and stability under complex scenes (such as night rainstorm and strong backlight) are remarkably improved.

The invention discloses a face recognition and encryption verification multi-grid article distribution control method and a distribution vehicle, and relates to the technical field of recognition and verification safety, and the method comprises the steps: fusing three-dimensional environment information through employing a vehicle-mounted edge calculation terminal and a navigation positioning terminal, and constructing a dynamic safety response strategy; the warehouse is analyzed through the vehicle-mounted warehouse control unified management terminal and the dynamic safety response strategy, and an edge intelligent control mechanism is obtained; processing based on a vehicle control and monitoring terminal and an edge intelligent control mechanism to obtain a real-time state; a vehicle interaction terminal and an intelligent operation terminal are utilized to obtain a face image and a visual dynamic feature, and a multi-modal detection strategy is utilized to analyze the face image and the visual dynamic feature to obtain a final authorized face image and a final authorized visual dynamic feature; and a dual-mode intelligent redundant unlocking technology is constructed, and face recognition and encryption verification are performed on the distribution vehicle based on the dual-mode intelligent redundant unlocking technology. According to the invention, compliance, high-efficiency and safe distribution of high-value materials is realized in an unmanned environment.

The invention relates to an unmanned aerial vehicle control system and method for formation flight in a wind environment. The method comprises the following steps: generating a wind disturbance environment with a specified map scale and containing random obstacles; acquiring external image data and inertial measurement data of the unmanned aerial vehicle, and generating a grid map through point cloud extraction and conversion operation; the method comprises the following steps: modeling a spatial topological structure of an unmanned aerial vehicle cluster by adopting an undirected graph mode, and solving and generating a collision-free initial trajectory by adopting an unconstrained discrete optimization mode; optimizing the initial trajectory by adopting a task redistribution strategy, and re-planning a global target and a local target to obtain a re-planned optimized trajectory; and based on the optimized trajectory, solving by adopting a model prediction control mode to obtain an optimal control quantity for unmanned aerial vehicle bottom layer control so as to correspondingly control the motion state of each unmanned aerial vehicle and realize formation flight. Compared with the prior art, the unmanned aerial vehicle cluster can efficiently and accurately realize formation flight in an obstacle environment with wind influence.

The invention relates to the technical field of path planning, in particular to an intelligent automobile path planning method and system. The method comprises the following steps of: extracting obstacle orientation and vehicle braking state information in an emergency avoidance process through an intelligent vehicle control center, and performing attitude transverse phase plane instability analysis to obtain an attitude phase plane instability state of a vehicle. Based on the state and the direction of the obstacle, analyzing and generating a collision attitude track; thirdly, matching emergency avoidance safety braking parameters through a collision posture prediction track, and ensuring safety braking; and finally, collision avoidance path planning learning is carried out by utilizing the avoidance braking safety matching parameters and the collision attitude prediction trajectory, and a safe and effective collision avoidance path is generated. According to the invention, the path planning technology is optimized, so that the path planning technology is more perfect.

The invention provides a quad-rotor unmanned aerial vehicle trajectory tracking control method and device, equipment and a storage medium, and belongs to the technical field of unmanned aerial vehicle control, and the method comprises the steps: determining a translation system state variable and an attitude system state variable of an unmanned aerial vehicle based on the real-time trajectory data of the quad-rotor unmanned aerial vehicle; calculating a translation disturbance estimated value based on the translation system state variable, the disturbance observer constant and the expected speed; thrust data is determined based on the translation system state variable, the translation disturbance estimated value, the expected speed, the expected position and outer ring controller parameters of the unmanned aerial vehicle; determining a desired attitude angle based on the thrust data and the desired yaw angle; calculating a control moment based on the expected attitude angle, the attitude system state variable and an inner ring controller parameter of the unmanned aerial vehicle; and based on the thrust data, the control torque and the physical parameters of the unmanned aerial vehicle, calculating to obtain a rotor expected rotating speed, and controlling the unmanned aerial vehicle based on the rotor expected rotating speed. The accuracy of flight control of the unmanned aerial vehicle can be improved.

The invention relates to the technical field of driving control, in particular to a new energy automobile safe driving control method and system. The method comprises the following steps that a historical urgent danger avoiding data set and a corresponding user misoperation braking state are obtained through a new energy automobile control center, sample undersampling processing is conducted firstly, and an urgent danger avoiding balance sample is obtained; thirdly, analyzing user misoperation braking abnormity by using a balance sample, and performing disordered vector intensity calculation under a time dimension based on braking abnormity data to generate disordered vector segmentation clustering data; then, braking time sequence intervention correction sensing is carried out according to the clustering data, optimized braking intervention correction data are generated, finally, the optimized data are transmitted to a new energy automobile control terminal, and safe driving control is executed. According to the invention, the driving control technology is optimized, so that the driving control technology is more perfect.

The invention relates to an amphibious vehicle body stability control method, system, equipment and medium, and belongs to the technical field of vehicle control, the amphibious vehicle body stability control method comprises the following steps: after a vehicle enters water, heeling angle data is acquired in real time; and when the heeling angle data exceeds a set threshold value and the maintaining time exceeds a preset duration, the volumes of air bags arranged on the two sides of the vehicle are adjusted in real time so that buoyancy distribution can be adjusted, and the purpose of stably controlling the vehicle body is achieved. According to the overwater vehicle body stability control method and system based on multi-sensor fusion and dynamic adjustment disclosed by the invention, the problems of turning, heeling and wave interference when the vehicle sails on the water surface are solved. The stability of amphibious vehicle sailing on water is greatly optimized, and the amphibious vehicle is suitable for development of intelligent amphibious vehicles in the future.

The invention relates to a vehicle control method and device, electronic equipment, a vehicle and a storage medium, and the method comprises the steps: obtaining the state information of a steering system and a virtual lane in front of the vehicle in response to the detection of a fault of the steering system of the vehicle; determining a risk value function corresponding to the first area based on the state information and the virtual lane; the first area is a predicted collision avoidance area; determining a target acceleration of the vehicle according to the risk value function and the operation parameters of the vehicle; and controlling the vehicle based on the target acceleration. According to the embodiment, the first area is analyzed by combining the state information of the steering system and the virtual lane in front of the vehicle, the risk value function corresponding to the first area is determined, the target acceleration is dynamically adjusted by introducing the risk value function, the safety redundancy requirements under different working conditions can be met, and the safety of the vehicle is improved. And the flexibility and safety of vehicle control are improved.

An assistance system includes: an augmented reality module configured to i) determine a path of a host vehicle, ii) detect one or more objects with one or more trajectories predicted to interfere with the path of the host vehicle, and iii) based on the one or more trajectories, generate a first digital gateway indicative that the host vehicle should yield for the one or more objects; a display control module configured to control an augmented reality head-up display to display the first digital gateway in a closed state over an environment forward of the host vehicle and along the path of the host vehicle; and a vehicle control module configured to yield to the one or more objects as the host vehicle approaches the first digital gateway.

The invention provides a fatigue driving early warning processing system and method applied to a commercial vehicle, and relates to the technical field of vehicle fatigue driving early warning. The system comprises a data acquisition device, a data processing device, a fatigue detection device and an early warning processing device, the devices are in data connection and are connected with a vehicle control unit of the commercial vehicle; the data acquisition device is used for acquiring vehicle driving behavior data and user data; the data processing device is used for preprocessing the behavior data and self data to obtain to-be-matched data; the fatigue detection device is used for performing fatigue detection processing according to the to-be-matched data, pre-stored reference data, a preset matching condition and a pre-training deep learning model, and determining a driving state; the early warning processing device is used for executing early warning processing and generating an early warning signal when the driving state is a fatigue state. The vehicle driving behavior data and the user data are fused to judge the driving state, the defect of a single data source is overcome, and the detection accuracy can be improved.