619 results about "Car model" patented technology

The invention relates to the technical field of automobile education virtual practical training, in particular to a virtual practical training method for automobile vocational education. According to the technical scheme, the method comprises the following steps: initializing a multi-modal interaction system, specifically comprising the following steps: starting a virtual reality module, and creating an automobile practical training scene, including whole automobile internal structure display, engine operation simulation and automobile driving environment simulation; starting an augmented reality module, and preparing to superpose virtual information on a real automobile model; and starting the mixed reality module. Through the steps of multi-modal interaction system initialization, trainee practical training operation, intelligent cooperative tutoring network analysis, ability evaluation and course adjustment, dynamic updating and continuous learning and virtual competition module setting, a vivid practical training scene, personalized tutoring and learning paths are provided for trainees, learning enthusiasm and competition awareness are stimulated, and the training efficiency is improved. The ability of trainees is accurately evaluated, the teaching content is ensured to closely follow the industry development, and the effect of automobile vocational education is significantly improved.

The invention discloses a large model driven automobile model modeling method based on a Modelica language, and belongs to the technical field of intelligent modeling and automobile simulation. The method comprises the following steps: firstly, accurately analyzing a natural language demand into a structured triple by adopting a BERT-CRF (domain knowledge enhanced) multi-task model; matching an optimal component combination through a multi-objective optimization algorithm driven by a graph neural network, and cooperatively predicting an interdisciplinary parameter feasible region in combination with symbolic mathematical derivation and machine learning; a topological connection matrix is innovatively optimized by using a graph attention network, and intelligent generation and dynamic verification of simulation codes are realized by fusing a template engine and syntax tree analysis; and finally, constructing a multi-target reward function optimization control strategy through reinforcement learning, and establishing a closed-loop knowledge iteration mechanism. Compared with a traditional modeling method, through deep combination of the large model and Modelica, the technical difficulty of automobile system modeling is remarkably reduced while the preciseness of physical modeling is kept, and the method is particularly suitable for complex scenes such as new energy vehicle model development and intelligent driving system integration.

The invention provides a visual vehicle control method and device based on a vehicle model and a storage medium. The method comprises the following steps: generating a vehicle model image of a vehicle model according to a controllable function list; cutting a controllable component in the car model image, and determining coordinate information of the controllable component; drawing the car model image and the controllable component on the interface of the client according to the interface size of the client and the coordinate information of the controllable component; binding the vehicle control state of the controllable component with the picture processing logic; in response to an interactive operation of a user on the vehicle model image, converting parameters collected by the interactive operation into a vehicle control instruction; and a vehicle control instruction is sent to the vehicle, the loading dynamic effect is displayed in the controllable component area in the vehicle model image interacted by the user, a control result returned by the vehicle is received, and the display state of the controllable component in the vehicle model image is updated according to the control result. The vehicle control operation is more visual and comprehensive, the operation is easy, and the user experience and the operation efficiency are improved.

The invention relates to an unfamiliar environment state cognitive model training method for an online driving scene, and belongs to the crossing field of new energy automobiles and artificial intelligence. The method comprises the steps that S1, a three-dimensional driving scene model is loaded, a hybrid electric vehicle model is built, and multi-angle driving pictures of a driving position at each moment are collected; s2, obtaining Camera BEV features of the pure visual feature extraction stream, and combining the Camera BEV features with an original two-dimensional variable state tensor; the method comprises the following steps of: forming an Actor-Critic-Guard network architecture by using a near-end strategy optimization algorithm based on an Actor-Critic architecture as an intelligent agent; s3, constructing a loss function containing loss values about Actor, Critic and Guard and an L2 regular term to train the network; and S4, loading a test environment similar to a real world, and evaluating the Guard environment cognitive network in a dynamic driving scene.

The embodiment of the invention relates to the technical field of robot control, and discloses a wire harness assembling method for a vehicle ceiling and electronic equipment, the wire harness assembling method for the vehicle ceiling is applied to a mechanical arm, and the method comprises the steps of obtaining a vehicle image of a current vehicle; determining a current vehicle type corresponding to the current vehicle according to the vehicle image; based on the current vehicle type, a preset parameter mapping library is determined and inquired, control parameters corresponding to the current vehicle type are determined, and the control parameters comprise an assembly track and force control parameters; and according to the assembly track and the force control parameters, a mechanical arm is controlled to conduct wire harness assembly on the car roof of the current car model. The vehicle model of the current vehicle is determined, the control parameters corresponding to the current vehicle model are determined by using the preset parameter mapping library, and the mechanical arm is controlled to perform wire harness assembly on the vehicle roof of the current vehicle model through the assembly track and the force control parameters, so that the assembly cost can be reduced.

The invention provides a charging load prediction method and device, a medium and a program product. The method comprises the steps that a single electric vehicle model and a dynamic traffic road network model are built; based on the charging cost, the battery replacement cost and the charging / battery replacement waiting duration, calculating a comprehensive charging cost; calculating the driving cost based on the driving distance, the driving energy consumption and the driving time consumption; according to the driving cost and the comprehensive charging cost, predicting a charging decision result of each electric vehicle; and according to the charging and swapping decision result, calculating the charging and swapping load. According to the charging and swapping load prediction method, the possible charging and swapping condition of each electric vehicle is predicted according to the driving cost and the comprehensive charging cost, the space-time distribution of the charging and swapping load of the electric vehicle can be effectively predicted, and finally the electrical load of each charging / swapping station is calculated according to the space-time distribution. Accurate prediction of the charging load in one day in one region is realized.

The invention relates to the technical field of vehicle safety auxiliary devices, and discloses an automobile multifunctional crawler-type anti-skid escape device which comprises tires, electric hydraulic cylinders and four sets of independently-controlled driving crawlers. Wherein the two groups of driving tracks are transversely arranged at the edge positions of the front end and the rear end of the automobile chassis to form a transverse track assembly; the vehicle body is lifted through the transverse track assembly, key parts are prevented from being eroded by accumulated water, and water flow impact is relieved; and thirdly, by means of independent control of the four sets of caterpillar bands, the vehicle is endowed with flexible maneuvering functions such as pivot steering and transverse movement, and accurate parking in a narrow space and obstacle avoidance are assisted. The whole structure is designed based on vehicle model customization, and an active and multifunctional driving safety guarantee means is provided for a driver on the premise that an original vehicle framework is not changed.

The invention provides a curve braking force distribution control method for an electric vehicle carrying an EMB braking system, and the method comprises the following steps: obtaining ideal values of a yaw velocity and a side slip angle during the steering of the vehicle through a two-degree-of-freedom vehicle model; the actual values of the yaw velocity and the side slip angle during vehicle steering are obtained through a Kalman filtering method and a vehicle sensor, and after the deviation is obtained through calculation, a sliding mode additional yaw moment controller is designed to calculate and correct the additional yaw moment needed by vehicle steering; obtaining the vertical load of each wheel according to a vehicle dynamics model, and controlling the EMB system by using a differential braking method according to the obtained vertical load of each wheel and the obtained additional yawing moment required for correcting the steering of the vehicle. The EMB system is controlled in a differential braking mode, the posture of the vehicle body is adjusted, and the curve braking stability of the vehicle is improved while the braking efficiency is guaranteed.

The invention discloses a second-hand car transaction information management method, and relates to the field of data: obtaining second-hand car historical transaction data, and performing hierarchical processing on the obtained data to obtain a hierarchical data set; the hierarchical data set adopts a brand-vehicle series-vehicle type three-level data structure; establishing a hierarchical graph neural network model through transfer learning according to the hierarchical data set; according to the second-hand car historical transaction data, establishing an evaluation model based on a residual value rate; mapping to-be-evaluated vehicle data to corresponding nodes in the hierarchical data set, and calculating a data sufficiency coefficient of the to-be-evaluated vehicle; according to the data sufficiency coefficient, selecting a hierarchical graph neural network model or an evaluation model to carry out second-hand car price prediction; and performing second-hand car transaction information management according to the predicted second-hand car price. As the sparse data vehicle type price prediction precision is low, the second-hand vehicle price prediction precision is improved.

The invention discloses a power grid frequency modulation optimization method, a medium and a program product in the technical field of electric vehicle and power grid interaction, and the method comprises the steps: building a single electric vehicle model in multiple network access states, and determining an operation boundary condition when a single electric vehicle interacts with power grid energy; establishing a relationship between the incentive electricity price and the user responsivity based on the Weber-Fechner law; evaluating the time sequence response of the electric vehicle under the excitation electricity price in the whole network access time period, and establishing an electric vehicle cluster model according to the operation boundary condition and the user responsivity when the single electric vehicle interacts with the power grid energy; and a collaborative frequency modulation operation optimization objective function of the electric vehicle aggregator and the frequency modulation unit is established, and collaborative operation of the electric vehicle aggregator and the frequency modulation unit is realized. The method can effectively improve the responsivity of an electric vehicle user to power grid frequency modulation, the high responsivity of the user not only expands the range of frequency modulation resources, but also enhances the flexibility in the frequency modulation process, and optimizes the power grid frequency modulation effect.

The invention relates to a production line vehicle type configuration transmission and verification method, and the method comprises the steps: obtaining vehicle type configuration data corresponding to a work-in-process when an intelligent transportation trolley enters an initial station of a production line; the vehicle model configuration data are written into the flexible conveying bracket provided with the work-in-process product; the intelligent transportation trolley is controlled to carry the flexible conveying bracket to enter a plurality of target stations matched with the vehicle type configuration data; when the intelligent transportation trolley arrives at any target station, vehicle type configuration data are read from the flexible transportation bracket; verifying the configuration state of the current station based on the vehicle type configuration data; if verification is not passed, the target station is controlled to execute configuration switching operation, and verification is executed again after configuration switching is completed; and if the verification is passed, controlling the target station to execute the corresponding production operation. By adopting the method and the device, the consistency of the vehicle type configuration data acquired by the station and the actual vehicle type configuration data of the product in process can be improved, and the processing abnormity caused by wrong station configuration is reduced.

The invention belongs to the technical field of automobile control, and provides a method and device for determining a vehicle handling stability area, a medium and equipment, and the method comprises the steps that under different vehicle speeds, vehicle body states at a plurality of continuous moments are obtained; for each vehicle body state, obtaining a side slip angle, a yaw velocity and a state matrix through a nonlinear vehicle model and transformation, calculating a Lyapunov index based on the definition of the Lyapunov index, and judging whether the vehicle is in a stable state, an unstable state or a periodic motion based on the Lyapunov index; for each vehicle speed, the side slip angle and the yaw velocity of the vehicle in periodic motion are extracted, an analytic curved surface expression of a stability envelope space is obtained through a radial basis function curved surface interpolation method, a quantitative calculation result can be provided, and the method is easy to popularize and use to a multi-degree-of-freedom vehicle model.

1. Name of the Design Product: Inflator (SUP Aston Martin Car Model). 2. Use of the Design Product: This design product is used for inflation. 3. Design Key Points of the Design Product: Lies in the shape. 4. Picture or Photograph that Best Illustrates the Design Key Points: Perspective view.

This disclosure belongs to the field of energy management technology, specifically to a hybrid electric vehicle energy management method and system based on multi-objective optimization. The method comprises the following steps: obtaining hybrid electric vehicle operating data and constructing a hybrid electric vehicle model; establishing a multi-objective optimized hybrid electric vehicle energy management model based on the constructed hybrid electric vehicle model, and solving the weight coefficients between the multiple objectives using a sum-of-squares optimal method; and solving the hybrid electric vehicle energy management model using a proximal strategy optimization algorithm to implement hybrid electric vehicle energy management. This disclosure fully considers the temperature of the power battery and introduces an energy management framework based on a proximal strategy optimization algorithm to improve fuel economy, maintain a stable power battery state of charge (SOC), and reduce battery temperature rise.

The invention provides a multi-sensor calibration system, a multi-sensor external parameter calibration method, electronic equipment and a readable storage medium, and relates to the technical field of artificial intelligence such as automatic driving and intelligent transportation. The multi-sensor calibration system comprises an open calibration room, a corner reflector assembly, an automobile rotating table and an automobile model assembly, wherein the automobile model assembly comprises a camera to be calibrated, a laser radar to be calibrated, a millimeter wave radar to be calibrated and an inertial measurement unit. The multi-sensor external parameter calibration method comprises the following steps: acquiring a motion state of an automobile rotating table in the multi-sensor calibration system, wherein the motion state is one of a static state and a rotating state; and performing external parameter calibration on a target sensor on a car model assembly in the multi-sensor calibration system according to the motion state. According to the invention, based on the motion state of the automobile rotating table in the multi-sensor calibration system, external parameter calibration is carried out on different sensors on the automobile model assembly in the multi-sensor calibration system, so that the calibration efficiency and the calibration accuracy can be improved.

The invention discloses an air conditioner control method and system for optimizing the thermal comfort of a subway carriage, and relates to the technical field of fluid simulation, and the method comprises the following steps: utilizing a subway carriage model established by AnsysFluent to carry out numerical simulation on an unoptimized subway carriage, and then carrying out error analysis with actual data so as to establish a more accurate carriage model. The method comprises the following steps: performing numerical simulation on subway carriage thermal environments under different stand mat densities, optimizing air supply parameters based on evaluation indexes such as related standard specifications and PMV, and classifying the stand mat densities according to an optimization result. The interval where the density of the standing seats in the carriages is located at the starting moment of each train is calculated in real time through a detection algorithm of the density of the standing seats in the carriages based on a space attention module of a self-adaptive receptor, and fuzzy PID control is adopted to replace a traditional switch to control a carriage air conditioning system. And adjusting the temperature in the carriage by linking the calculated standing position density category interval with a fuzzy PID controller.

The invention provides an automatic driving vehicle pose estimation method. The method comprises the following steps: firstly, acquiring a road color picture by using an automobile camera, and inputting the road color picture into a Mask-RCNN-based network in combination with a 3D automobile model to obtain an automobile instance image and a 3D point cloud; then, features are extracted through a PSPNet network and a PointNet + + network, feature fusion is carried out through a Transform fusion module, an FSAM module and an FCAM module, and global features are obtained; meanwhile, global and category-level features of the 3D automobile model are extracted by using a ResNet-1 network GSENet and a PointNet + + network. And inputting the features into an adaptive module to obtain new global features, and fusing the new global features with instance global features to generate an attention matrix. And finally, deforming the priori point cloud by using an attention module and an attention matrix based on Transform so as to estimate the pose of the vehicle. According to the method, the local and global features of the automobile can be effectively extracted, and the accuracy of pose estimation is improved through feature fusion and the attention matrix.

The utility model relates to the technical field of automobile models, and discloses a quick connecting structure for an automobile chassis and an automobile shell, which comprises the automobile chassis, the automobile shell and a locking mechanism, a clamping part is arranged on the chassis, a clamping groove is formed in the clamping part, and a through hole is formed in the side wall of the clamping groove; a clamping piece is arranged on the inner side of the vehicle shell and comprises a clamping column extending towards one side of the vehicle chassis, and the clamping column is inserted into the clamping groove and provided with a clamping hole opposite to the through hole. The locking mechanism is connected to the clamping part and comprises a locking rod and a driving piece, the locking rod is arranged in the clamping hole in a sliding and penetrating mode, and the driving piece is movably arranged, connected with the locking rod and used for driving the locking rod to penetrate into or be separated from the clamping hole. According to the rapid structure of the chassis and the automobile shell, rapid disassembly and assembly of the automobile shell and the chassis can be achieved, and therefore the maintenance and replacement efficiency of functional parts in an automobile model can be improved.

The invention relates to a plug-in hybrid electric vehicle model predictive control energy management method based on reinforcement learning, and aims to solve the energy management optimization problem of a plug-in hybrid electric vehicle. The method comprises the following steps: firstly, predicting a vehicle speed in a future time domain by using a least square support vector machine-neural network (LSSVM-BP) model; then, calculating power required by a vehicle in a prediction time domain based on a vehicle dynamics model, and solving an energy management problem through a model prediction control algorithm; and finally, performing rolling optimization based on a reinforcement learning method to obtain a control variable sequence of each stage in a prediction time domain, and taking a first element of the control variable sequence as output. According to the method, reinforcement learning and a model prediction control algorithm are combined, optimal distribution of the energy source of the plug-in hybrid electric vehicle is achieved, and the fuel economy of the vehicle is improved.

The utility model discloses an ink jet device for automobile model processing, which comprises a base, a driving assembly, a transmission assembly, a nozzle, a clamping plate and a nozzle, when ink is jetted, a telescopic part drives the clamping plate to clamp and fix a model, and the telescopic part drives a mounting frame to move downwards to move the model to one side of the nozzle; a suction pump arranged on a connecting pipe conveys raw materials in a containing cavity into a mounting pipe, then the raw materials are sprayed to a model through a spray head, a driving assembly drives a rotating shaft to rotate and a transmission assembly to operate, the rotating shaft rotates to drive the clamped automobile model to rotate at a constant speed, and the spray head conducts orderly ink spraying on the automobile model at multiple angles; the transmission assembly drives the stirring rod connected with the transmission assembly to rotate, the stirring rod rotates to stir the ink jet raw materials in the containing cavity, on one hand, the raw materials are evenly mixed, and on the other hand, the fluidity of the raw materials is improved.

1. The name of this design product: Car model toy (HT). 2. Purpose of the product of this design: toys. 3. The design key point of this appearance design product lies in the shape. 4. The picture or photo that best illustrates the design points: Stereoscopic diagram 1.

The invention provides a driving system, a turning system and a wind generating set, and the driving system comprises a converter cabinet which comprises a first converter and a second converter, and each of the first converter and the second converter comprises a machine-side converter and a grid-side converter which are connected through a DC bus; the grid-side circuit is connected between a grid-side converter of the first converter and a grid-connected point of the wind generating set; the barring circuit is used for connecting one ends, far away from each other, of the grid-side converter and the machine-side converter of the first converter; the first bus circuit and the second bus circuit are connected between the positive direct-current bus of the first converter and the positive direct-current bus of the second converter and between the negative direct-current bus of the first converter and the negative direct-current bus of the second converter respectively, the driving system has a turning mode and a grid-connected mode, in the turning mode, the grid-side circuit is disconnected, and in the grid-connected mode, the grid-side circuit is disconnected; and the turning circuit, the first bus circuit and the second bus circuit are conducted and are opposite in a grid-connected mode. According to the invention, the additional tool cost is obviously reduced, and the cost of the whole machine can be controlled.

The invention provides a control method and device and a vehicle. The method comprises the steps that a computing platform controls a first display device in a vehicle cabin to display a first scene, wherein the first scene comprises a first environment and a first vehicle model; in response to a first input for the first scene, the computing platform controls the first display device to switch from displaying the first scene to displaying a first dynamic effect, the first dynamic effect including a first environmental change dynamic effect and the first vehicle model. The embodiment of the invention can be applied to an intelligent vehicle or an electric vehicle, so that a user can directly control the dynamic effect of the vehicle model desktop scene on the vehicle model desktop, thereby increasing the interactivity and interestingness of the vehicle model desktop scene, and improving the user experience.

The utility model discloses an automobile model part which comprises an automobile model body and a coating, and the coating is arranged on the surface of the automobile model body. Wherein the coating comprises a putty layer and a paint layer, and the putty layer is located between the paint layer and the surface of the automobile model body. According to the automobile model part disclosed by the utility model, the putty layer is arranged in the coating of the automobile model part, so that pinholes in an automobile model can be more fully filled by the coating, the shrinkage degree of the putty layer is smaller during drying, grains are not easy to generate on the surface, the hardness of the putty layer is higher, and the putty layer is not easy to collapse at a hole structure; the surface of the coating can be prevented from being uneven, and the surface of the coating of the automobile model part is smoother.

The utility model discloses a positioning support for paint spraying of an automobile model part, which comprises a processing table, the rear end face of the processing table is connected with a connecting seat in a sliding manner, the processing table is provided with a fixing assembly corresponding to the connecting seat, and one end of the connecting seat is fixedly connected with a fixing frame; rotating shafts are rotatably connected to the two sides of the fixing frame, a positioning assembly is arranged between the two rotating shafts, a gear is fixedly connected to the outer wall of one rotating shaft, a limiting frame is fixedly connected to the side wall of the fixing frame, and a hydraulic cylinder is fixedly connected to the side wall of the fixing frame. According to the device, the connecting base can be rapidly detached, then the fixing frame is detached, the using portability is improved, the fixing frame and the positioning assembly which are painted are conveniently cleaned and maintained in the later period, the using effect is improved, meanwhile, mounting and detaching operation is convenient, detaching operation can be completed with one hand, and the workload is reduced.

The invention discloses a method for assisting in generating a special fire emergency car model through artificial intelligence, and belongs to the crossing field of artificial intelligence and car design. The method aims to overcome the defects of traditional forest fire fighting vehicle design, and the forest fire rescue efficiency and the firefighter safety are improved. Data such as fire fighting truck 2D drawings, forest fire cases, fire fighting demand feedback and the like are widely collected through multiple channels and are cleaned, labeled and structured, and effective data are divided into a training set, a verification set and a test set according to a specific proportion. A deep learning model (such as GAN or VAE) is selected and optimized according to the design characteristics of the forest fire-fighting vehicle, and the model is stabilized through a large amount of iterative training. A design keyword is input to generate a 2D sketch, big data analysis is utilized to screen, evaluate and optimize for multiple times, a 3D model framework is generated by means of a 3D generation AI model, fine optimization is performed after multi-dimensional evaluation, and finally a 3D model which is vivid in color and meets actual combat requirements is generated. By utilizing the artificial intelligence technology, the design efficiency is greatly improved, the adaptability of the automobile to the forest environment is enhanced, the safety of firefighters is guaranteed, the industrial technology progress is promoted, and remarkable economic and social benefits are achieved.

1. The name of this design product: Sterilizer and Deodorizer (Pinggao Jingxiang Series Car Model). 2. Purpose of this product design: This product is used for deodorizing, sterilizing and disinfecting indoor air or internal spaces of products. 3. The key point of the design of this product lies in its shape. 4. The picture or photo that best illustrates the design points: three-dimensional picture.

1. Name of the designed product: car model toy (JIL). 2. Use of the designed product: toy. 3. Design points of the designed product: in shape. 4. Picture or photo best indicating the design points: perspective view.

1. Name of the product of this design: Car Model Speaker (Gu‑152). 2. Purpose of this design product: Equipment for playing audio. 3. The key point of the design of this product lies in its shape. 4. The picture or photo that best illustrates the design points: Stereoscopic drawing 1.