4649 results about "Driving mode" patented technology

The invention relates to the technical field of intelligent transportation control, in particular to a tomato transportation speed self-adaptive adjustment method based on path road condition feedback, which comprises the following steps: acquiring road images, vibration waveforms and altitude data through a multi-source sensing unit, and constructing real-time road condition information; identifying a driving mode and extracting a corresponding bumping parameter; detecting the maturity grade of the tomato by combining multispectrum and thermal imaging, and querying a maturity-compressive strength corresponding table to calculate a cargo damage threshold value; constructing a dynamic mapping model under multiple working conditions, predicting vibration response and converting the vibration response into equivalent pressure; comparing the equivalent pressure with a damage threshold value to obtain a safety margin, constructing a speed adjustment decision tree and generating a maximum allowable speed value of each road section; and dynamically generating a segmented variable-speed control instruction based on the speed decision matrix, and controlling a throttle valve and a braking system to cooperatively change speed. The method has the advantages of accurate working condition identification, dynamic fruit adaptation, closed-loop speed regulation and control and the like, and is suitable for fine speed control of a high-sensitivity fruit and vegetable transportation scene.

The invention relates to the technical field of orchard unmanned vehicles, in particular to an orchard unmanned vehicle intelligent navigation and control method based on multi-sensor fusion, and the method comprises the following steps: S1, global path planning: generating discrete plane path points, and then pre-fitting a prior navigation global path; s2, three-dimensional environment perception: extracting static obstacle and dynamic obstacle information in a working path in real time, detecting and identifying a target obstacle at the same time, and performing sensor information fusion; s3, path tracking control: controlling the orchard unmanned vehicle to run according to a priori navigation global path; and S4, dynamic path optimization: realizing dynamic planning of an obstacle avoidance trajectory, synchronously establishing a trajectory overlap ratio evaluation mechanism, and adjusting a driving mode to avoid the obstacle. According to the invention, the reliability of environment perception and path dynamic adjustment is improved, and the three-dimensional motion control model is constructed, so that the unmanned vehicle can safely and accurately complete autonomous navigation work.

The invention discloses a multi-mode energy recovery optimization control system of a hybrid electric vehicle, and relates to the technical field of energy recovery control. The state sensing module is integrated with a sensor to collect vehicle speed, acceleration, battery state and road condition signals, and data accuracy is ensured through Kalman filtering fusion; the driving mode recognition module is used for recognizing modes such as urban congestion and high-speed cruise through an algorithm based on driving behaviors and road conditions; the energy recovery strategy decision module is used for dynamically adjusting recovery priorities and parameters according to different modes, and balancing efficiency and smoothness; the power distribution execution module converts and stores energy through cooperative control of double clutches and a gearbox, and is linked with an ABS (Anti-lock Brake System) to guarantee safety; and the feedback module is optimized in real time, battery and motor states are monitored, strategies are dynamically corrected, and efficiency and component protection are both considered. The energy recovery efficiency is improved, the driving smoothness and the braking safety are improved, the system adapts to multiple scenes, the service life of the battery is prolonged, and the performance of the hybrid electric vehicle is comprehensively improved.

A safety strategy determining method, applied to a hybrid transmission control unit (HTCU), and comprising: monitoring an environment signal in a hybrid transmission; when an error occurs to the environment signal, determining an abnormal event corresponding to the environment signal; analyzing the abnormal event, and determining a safety state corresponding to the abnormal event; on the basis of the safety state and the current gear of a vehicle, determining a safety strategy corresponding to the abnormal event, wherein the current gear comprises one gear in different driving modes, and the safety strategy comprises at least one of the following: maintaining the current gear, reverting the gear in the hybrid transmission to the current gear, and switching from the current gear to a corresponding downshifted gear; sending a gear switching request carrying the safety strategy to a vehicle electronic control unit (VECU), wherein the gear switching request is used for requesting the VECU to switch to a target gear on the basis of the safety strategy; and switching the gear in the hybrid transmission to the target gear. The problem of over-protection or under-protection in the hybrid transmission is avoided, and therefore the driving safety is improved. Also provided is a security strategy determining apparatus.

The invention provides a gear switching method, a vehicle and a storage medium, the method is applied to the technical field of hybrid power control, and the method comprises the steps that in the process that the vehicle runs based on a direct drive mode, if it is determined that the vehicle needs to switch gears, the whole vehicle driving power corresponding to the direct drive mode and the discharging power of a power battery are determined; based on the whole vehicle driving power, the discharging power and the gear change state of the vehicle, a target torque reduction gradient is determined; based on the target torque reduction gradient, the actual torque of the engine is controlled to be reduced to the target torque; and under the condition that the actual torque of the engine is equal to the target torque, the clutch is controlled to be in an open state, so that the vehicle is shifted under the condition that the clutch is in the open state. The method can ensure that the power is not interrupted in the vehicle gear shifting process, the vehicle stability is improved, and the user driving experience is improved.

The invention provides a two-wheeled electric vehicle control system and method, and the system comprises a user side control platform which is used for detecting the operation of a user on a reverse turning handle, and generating a corresponding control instruction according to the pre-configured function setting; the communication module is used for bidirectionally transmitting instruction signals and vehicle state information in real time between the user end control platform and the vehicle end control system; and the vehicle end control system is used for receiving and analyzing the control instruction, judging the running or static state of the vehicle, executing kinetic energy recovery braking or driving mode switching in the running state, executing a reversing function or a self-defining function in the static state, and feeding back a vehicle execution result or vehicle state information through the communication module. The technical problems that in the prior art, the control function of a control terminal is single, the operation mode is not convenient enough, and the integration degree of the control terminal and vehicle functions is low are at least solved.

The invention provides a wind power plant unit state monitoring and fault early warning system and method based on deep learning, and belongs to the field of wind power generation and artificial intelligence. According to the system, a cloud edge collaborative architecture is adopted, an edge computing terminal operates a data-driven space-time prediction model and a physical digital twinborn model in parallel, and abnormity is preliminarily screened by calculating a double-track residual error and comparing the double-track residual error with a dynamic early warning threshold value. And when an exception occurs, the cloud platform receives multi-modal data including a sensor, a model state and an operation and maintenance text, performs deep root cause analysis by using a diagnosis model fused with a wind power fault knowledge graph, and generates an interpretable diagnosis report. According to the method, deep fusion of data and a physical model is realized, and the accuracy of fault monitoring, the interpretability of diagnosis and the intelligent level of operation and maintenance decision are remarkably improved through a data-physical double-track driving mode.

The invention discloses a vehicle operation control system, method and equipment, a medium and a product. The system comprises an operation state determination subsystem used for determining predicted operation situation information associated with a vehicle; the operation constraint subsystem is used for determining vehicle operation constraint parameters based on the predicted operation situation information and determining a to-be-driven mode of the vehicle; the motion control subsystem is used for determining an actuator reference instruction based on the predicted operation situation information, the to-be-driven mode, the vehicle operation constraint parameters and the to-be-driven track; the control distribution subsystem is used for determining an actuator distribution instruction of a plurality of actuators in the vehicle based on the actuator reference instruction and the predicted operation situation information; and the coordination control subsystem is used for determining target actuator instructions of the plurality of actuators based on the to-be-driven mode and the actuator distribution instruction, and controlling the actuators to operate based on the target actuator instructions. The control consistency, the response real-time performance, the operation safety and the comfort of the whole vehicle in a complex dynamic scene are improved.

The invention discloses a fully-mechanized coal mining equipment group sensing-control integrated twinning device and method, and relates to the technical field of coal mine intelligent mining digital twinning. The method comprises a sensing information driving mode, a physical instruction driving mode and a dual-driving mode. When the sensing information driving mode lacks physical instruction information, real-time sensing data of the hydraulic support are collected, sensing-control integration is achieved, and the shape is consistent; the physical instruction driving mode is used for speculating a current posture through a speed parameter training model and an optimal force training model when sensing information is lost, so that virtual-real synchronization is realized; and the dual-drive mode is used for realizing real-time consistent action and regulation of the virtual monitoring model and the virtual control model according to an adaptive control strategy when the physical instruction transmission path and the sensing information transmission path are both intact. According to the method, high-fidelity actions of the virtual control model and the virtual monitoring model can be driven through the physical entity, and virtual reconstruction of the working space is completed in the virtual space.

The embodiment of the invention relates to a vehicle remote driving control method and device based on a visual language model. The method is applied to a vehicle, and corresponding vehicle sensors are installed at multiple positions of the vehicle; comprising the following steps: after a vehicle enters an automatic driving mode, acquiring a multi-view image sequence, a user instruction and a navigation instruction, and processing the user instruction, the navigation instruction and the multi-view image sequence to obtain a first prediction track point set and a second prediction track point set; and performing detection based on the first prediction trajectory point set and the second prediction trajectory point set to obtain a detection result, and when the detection result is that the predicted driving fails, data including the key frame image is sent to a cloud server so as to process the key frame image to obtain a scene semantic description text, and a control strategy is generated and sent to a remote driving platform so as to perform driving control on the vehicle. Therefore, the sensing and decision-making capabilities of the automatic driving system in a complex environment are improved, so that the remote driving control of the vehicle is more efficient and safer.

The invention provides a series-parallel hybrid power tractor driving system. The series-parallel hybrid power tractor driving system comprises an engine, a P1 motor used for generating electricity or starting the engine, a PS motor used for independent or coupled driving and a series-parallel hybrid power tractor transmission system. The invention further provides a control method of the driving system. Confluence and diversion of engine power and speed change of the tractor are controlled through the P1 motor, the PS motor, the rotating speed coupling mechanism, the torque coupling mechanism, the clutch and the brake. According to the series-parallel connection type hybrid power tractor driving system, a multi-mode driving mode can be achieved to adapt to operation in a complex environment, the dynamic property and economical efficiency of a tractor are improved, and the series-parallel connection type hybrid power tractor driving system has the advantages of interval stepless speed change, precise control and the like.

The invention provides a vehicle control method and device, a vehicle and a storage medium, the method is applied to the technical field of hybrid power control, and the method comprises the steps that based on the current electric quantity of a power battery of the vehicle and the whole vehicle demand power of the vehicle, an initial driving mode of the vehicle is firstly determined; calculating the total power consumption corresponding to each of multiple candidate power distribution proportions in the initial driving mode, determining a target power distribution proportion in the multiple candidate power distribution proportions according to the total power consumption corresponding to each of the multiple candidate power distribution proportions, and finally determining the target power distribution proportion in the multiple candidate power distribution proportions based on the target power distribution proportion. And correcting the initial driving mode to obtain a target driving mode, and controlling the vehicle to run in the target driving mode. According to the method, the energy utilization efficiency of the whole vehicle can be maximized, it is ensured that the engine and the motor operate in respective efficient intervals as much as possible, and more stable power output can be provided for the vehicle under different road conditions.

The invention discloses a device for monitoring the moisture content of soil at different depths and application of the device. The device comprises a main rod, a driving structure and a sensor, a cavity is formed in the main rod, a plurality of detection windows are formed in one side of the main rod, and the detection windows are sealed by elastic bodies; the driving structure comprises a driving belt, and the sensor is driven to be close to or far away from the detection window, so that the probe penetrates through the elastic body or retracts. According to the device for monitoring the moisture content of the soil at the different depths, the driving belt is used for driving the sensors to move in the radial direction of the main rod in series, so that the sensors penetrate through the elastic body arranged at the detection window to achieve moisture content detection, a gap between two times of detection can retract into the main rod, long-term erosion to the sensors is avoided, and the detection accuracy is improved. The service life of the sensor is obviously prolonged; the detection precision of the sensor is maintained; in addition, the driving mode of the driving belt avoids failure caused by entrainment of a small amount of soil particles, and the long-term stable monitoring capability of the device is further improved.

The invention discloses a vehicle stability control method, system and device, a medium and a program product, and relates to the technical field of vehicle dynamic control, and the method comprises the steps: calculating a vehicle speed, a pavement friction coefficient, a slip rate, a load transfer rate and a preview path curvature; the vehicle speed, the road friction coefficient, the load transfer rate and the preview path curvature serve as input, the yaw velocity deviation and the slip rate serve as reward signals, and a yaw velocity dynamic threshold value is generated; according to the actual yaw velocity, the expected yaw velocity and the yaw velocity dynamic threshold, obtaining a judgment result that the current vehicle is under-steering or over-steering, and generating a control strategy in combination with a vehicle driving form; and the optimal braking torque adjustment amount is calculated with the minimum yaw velocity deviation and the slip rate as the optimization target, and the front and rear shaft driving torque is obtained according to the deviation between the actual side slip angle and the expected side slip angle. The self-adaptive dynamic environment and the differentiated adaptive driving form are realized, and the safety and the controllability under the complex working condition are improved.

The invention discloses an intelligent thermal management method and system for a power battery, and the method comprises the steps: obtaining current vehicle driving state data, and recognizing whether a vehicle enters a driving mode or not and whether a driving thermal management process is started or not; obtaining current driving working condition state data, and calculating vehicle demand power and battery allowable power based on the vehicle historical power and the current power data; obtaining current power battery temperature state data, judging a current driving thermal management requirement, and adaptively switching a management process corresponding to a thermal management strategy; and obtaining the current temperature of the cooling liquid at the inlet of the power battery, and judging whether to start thermal management or perform thermal cycle. According to the method, by judging the relation between the whole vehicle demand power and the battery allowable discharge power, under the condition that the whole vehicle power use and the battery safety are not affected, the heat management starting frequency is reduced to optimize the heat management energy consumption; heat management energy is fully utilized, the purpose of meeting the required power of the whole vehicle is achieved, unnecessary heat management can be effectively reduced, and heat management energy consumption is reduced.

The present application provides a fault identification method and apparatus for an autonomous driving system. The method comprises: acquiring travelling-related data of an ego vehicle (at least including ego vehicle travelling data and target obstacle motion data); respectively performing feature extraction on the travelling-related data of the ego vehicle on the basis of a plurality of feature extraction networks to obtain feature vectors of the ego vehicle from a plurality of viewing angles; fusing the feature vectors from the plurality of viewing angles to obtain a global driving scenario feature vector of the ego vehicle; and on the basis of the global driving scenario feature vector, determining a target driving scenario where the ego vehicle is located in a current driving mode and whether a fault occurs to an autonomous driving system in the target driving scenario. In the present application, dynamic change information of an ego vehicle and a surrounding environment of the ego vehicle is captured from a plurality of viewing angles, the information from the plurality of viewing angles is adaptively fused to obtain comprehensive representations of driving scenarios, and a target driving scenario where the ego vehicle is currently located and whether a fault occurs to an autonomous driving system in the target driving scenario are identified on the basis of the comprehensive representations, thereby improving the accuracy of fault identification results of the autonomous driving system.

Apparatus, systems, and methods are contemplated for electric powered vertical takeoff and landing (eVTOL) aircraft. Such are craft are engineered to carry safely carry at least 500 pounds (approx. 227 kg) using a few (e.g., 2-4) rotors, generally variable speed rigid (non-articulated) rotors. It is contemplated that one or more rotors generate a significant amount of lift (e.g., 70%) during rotorborne flight (e.g., vertical takeoff, hover, etc), and tilt to provide forward propulsion during wingborne flight. The rotors preferably employ individual blade control, and are battery powered. The vehicle preferably flies in an autopilot or pilotless mode and has a relatively small (e.g., less than 45′ diameter) footprint.

The invention provides an off-line test method and an off-line test device for a vehicle steering system, and the method comprises the steps: controlling a vehicle to run according to a preset test track through activating an automatic driving mode of the vehicle; in the running process of the vehicle, running state data of the vehicle are collected in real time; and comparing the operation state data with a preset quality standard, and generating an offline test result of the vehicle steering system based on a comparison result. In the mode, the vehicle is controlled to run along the preset test track by adopting automatic driving, so that the test control precision of the vehicle steering system can be improved, and the accuracy and consistency of off-line test are further improved.

A lane-changing trajectory planning method, an autonomous driving method, and a related apparatus. The autonomous driving method comprises: on the basis of first motion state information of an intelligent driving device and second motion state information of an opponent, controlling the intelligent driving device to change to a target lane within a first lane-changing time period and to be located in front of the opponent, wherein the opponent is located on the target lane for the intelligent driving device, and is located between a first position and a second position, wherein the first position is located behind the intelligent driving device and longitudinally distant from the intelligent driving device by a first distance, and the second position is located in front of the intelligent driving device and longitudinally distant from the intelligent driving device by a second distance. The technical solution of the present application can be applied to intelligent vehicles such as a new energy vehicle and an electric vehicle, and facilitates improvement of lane-changing success rates and traffic efficiency when the vehicles are in an autonomous driving mode in complex lane-changing scenarios such as merging into congested roads and merging into ramps.

The invention discloses a duplex-screen intelligent cabin interaction method and system, and relates to the technical field of intelligent cabin interaction, and the method comprises the steps: collecting user operation behavior data, vehicle state data and environment perception data in real time through a vehicle-mounted sensor group, a man-machine interaction interface and a vehicle bus; establishing a multi-dimensional user data model containing an interaction feature vector, a driving mode matrix and a preference configuration tree; the method comprises the following steps: analyzing user historical interaction data based on an adaptive weighting algorithm, and calculating use frequency weight coefficients of different functional modules; according to the duplex-screen intelligent cockpit interaction method and system provided by the invention, the precision of a user portrait is remarkably improved by constructing a multi-dimensional user data model, personalized interaction experience is realized, and the interface layout and interaction logic can be dynamically adjusted according to a real-time driving scene by adopting a self-adaptive weighting algorithm and a random forest classifier, so that the user experience is improved. The real-time performance and the adaptability of the system are enhanced, and the interaction experience is optimized.

The invention provides a battery management method and a vehicle, and relates to the technical field of electric energy management, and the method comprises the steps: obtaining an original signal of the driving data of the vehicle; extracting a feature representation of the driving data from the original signal based on a first sampling rate; the first sampling rate is smaller than the sampling rate of the original signal; inputting the feature representation into a binary neural network BNN to obtain probability values of multiple driving modes; a current driving mode of the vehicle is determined based on the probability value, and a rated output current of a battery of the vehicle is adjusted. According to the method, the features are extracted from the original signals in a dimensionality reduction mode, the BNN is adopted for reasoning to obtain the probability value of the driving mode, the driving mode can be rapidly recognized, the rated output current of the battery is adjusted, the real-time performance of battery management is guaranteed, and the service life of the battery is prolonged.

The invention provides a vehicle driving state estimation method and system in a centralized driving mode, and relates to the technical field of electric automobiles, and the method comprises the following steps: determining wheel dynamic parameters based on sensor detection information and a seven-degree-of-freedom dynamic model related to wheels, and combining a preset Dugoff tire model to obtain initial tire force parameters; a vehicle-mounted infrared camera is used for obtaining road surface image information, the road surface image information is analyzed through a deep convolutional neural network based on semantic segmentation and a vehicle dynamics inversion method, an adaptive fusion mechanism is introduced for adjustment, and a target road surface adhesion coefficient is determined; correcting the initial tire force parameter by using the target road adhesion coefficient to obtain a target tire force parameter; inputting the current sensor detection information and the target tire force parameter into a self-adaptive unscented Kalman filter to obtain vehicle driving state information; the vehicle driving state information comprises the longitudinal speed, the transverse speed, the yaw velocity and the side slip angle of each tire.

Apparatus, systems, and methods are contemplated for electric powered vertical takeoff and landing (eVTOL) aircraft. Such are craft are engineered to carry safely carry at least 500 pounds (approx. 227 kg) using a few (e.g., 2-4) rotors, generally variable speed rigid (non-articulated) rotors. It is contemplated that one or more rotors generate a significant amount of lift (e.g., 70%) during rotorborne flight (e.g., vertical takeoff, hover, etc), and tilt to provide forward propulsion during wingborne flight. The rotors preferably employ individual blade control, and are battery powered. The vehicle preferably flies in an autopilot or pilotless mode and has a relatively small (e.g., less than 45′ diameter) footprint.

The invention provides an air guide assembly and air treatment equipment. The air guide assembly is installed at an air outlet of the air treatment equipment and comprises three adjusting assemblies, and the three adjusting assemblies comprise the first adjusting assembly, the second adjusting assembly and the third adjusting assembly which are movably connected in sequence in the length direction of the air outlet. The adjusting assembly comprises a bearing plate and a plurality of air guide blades, the bearing plate is movably arranged at the air outlet, and the air guide blades are connected to the bearing plate and sequentially arranged along the plate face of the bearing plate. And the first driving assemblies are connected with the adjusting assemblies and drive the bearing plates corresponding to the adjusting assemblies to move, so that the bearing plates of all the adjusting assemblies are linked. According to the air guide assembly, the air supply angle of the air treatment equipment can be flexibly adjusted, and the air supply coverage area of the air treatment equipment is enlarged. In addition, the driving mode of the air guide assembly is simpler, and space saving and energy consumption reduction are facilitated.

The invention discloses a fuel cell hybrid electric vehicle power distribution method based on heuristic action planning deep reinforcement learning. The method comprises the following steps: establishing a power distribution system model of a fuel cell hybrid electric vehicle equipped with a lithium battery and a super capacitor; a power layered structure is provided, a deep reinforcement learning model of the fuel cell hybrid electric vehicle is designed, a probability transfer matrix of required power is constructed, and model training is performed in a data driving mode; designing a reward function by adopting an equivalent consumption minimum strategy, and guiding optimization of an energy management strategy by fuel economy; the motion space of deep reinforcement learning is improved by adopting heuristic planning, and smooth output of the fuel cell is kept on the premise of reducing equivalent hydrogen consumption. According to the method, power distribution of the three-energy-source fuel cell hybrid electric vehicle can be achieved in a complex and random driving environment, hydrogen consumption is reduced, dynamic adaptability to different driving conditions is improved, and meanwhile the method has the potential of real-time application.

A processor of a device for providing a route according to an embodiment of the present invention may, on the basis of entering a first lane-maintaining driving mode, cause a vehicle to drive so as not to deviate from a first lane, and on the basis of a preset condition being satisfied, may enter a second lane-maintaining driving mode in which the vehicle is caused to drive in the first lane and a second lane adjacent to the first lane.

A steer by wire includes a steering wheel holding system. An emulator controller receives vehicle data from vehicle systems and determines whether a vehicle is in one of a driving mode and a non-driving mode. When the vehicle is in the driving mode, the emulator controller transmits a command to an emulator switching system to electrically couple the phases of an emulator motor to a power source to enable the emulator motor to implement steer by wire operations placing the steering wheel in an un-hold mode. When the vehicle is in the non-driving mode, the emulator controller transmits a command to the emulator switching system to electrically couple the phases of the emulator motor to ground to short the phases of the emulator motor to ground to generate a resistive torque to resist rotational movement of a steering wheel of the vehicle placing the steering wheel in a hold mode.

This document describes, among other things, a vehicle power system that includes a traction battery, a six-phase inverter divided into two groups of six switches, a six-phase motor, and a pair of switches. In drive mode, the switches configure the inverter to connect the traction battery and the motor. In plug-in mode, the system reconfigures so that one group of switches connects an AC source to the motor, while the other group connects the motor to the traction battery.

The invention relates to the technical field of active safety of forklifts, and discloses a driving assistance method for stability adjustment of a forklift, which comprises the following steps: acquiring input parameters of the forklift for driving assistance; acquiring a fuzzy set corresponding to the input parameter; establishing a fuzzy rule base for logical reasoning based on the fuzzy set of the input parameters; constructing a decision function through a weighted average method to calculate a driving mode control value, and optimizing a weight through minimizing a decision error based on supervised learning; outputting a corresponding driving mode according to the driving mode control value; the invention further discloses a driving assistance system for forklift stability adjustment. The driving assistance system comprises a road surface recognition module, a forklift state acquisition module, a parameter fuzzification module, a fuzzy rule base storage module, a driving mode decision module and a driving mode execution module. By monitoring a plurality of input parameters, the overall performance and the adaptive capacity of the forklift system are improved, and efficient operation and safe operation of the forklift system in a dynamic complex environment are ensured.

The invention discloses a vehicle engine noise optimization method and device, equipment and a storage medium, and relates to the technical field of engine control, and the method comprises the steps: obtaining a current driving mode when a user drives a vehicle, and determining environment data and vehicle driving information during vehicle driving according to the current driving mode; a preset noise-electric quantity mapping table is inquired through the environment data and the vehicle driving information based on the current driving mode, the target engine rotating speed is obtained, a vehicle engine is controlled to operate at the target engine rotating speed, and perceptible engine noise is calculated based on the environment data and the vehicle driving data; a vehicle engine is controlled based on the perceptible engine noise to complete engine noise optimization. The engine noise is optimized by inquiring the proper engine rotating speed and taking the perceptible noise as the optimization target, and the uncomfortable experience of the engine noise under the working conditions of low speed red light and the like can be greatly reduced.