4461results about "Vehicle testing" patented technology

Disclosed is a digital twin-based fault diagnosis method for logistics mobile equipment, capable of achieving online monitoring of equipment state and fault diagnosis by means of fusion of digital twin technology and sensor data, thereby allowing for proactive fault prevention and precise maintenance, improving the reliability and operation efficiency of logistics equipment and prolonging the service life thereof. The technical solution of the method comprises: step 1, on the basis of logistics mobile equipment, establishing a corresponding digital twin model; step 2, acquiring real-time data of a battery entity for adaptive model updating; step 3, inputting fault information into a bidirectional GRU model having a multi-head attention mechanism; step 4, performing outlier detection on the basis of an improved fuzzy isolation forest algorithm, comprising introducing a type-2 fuzzy theory into a fuzzy isolation forest algorithm and making improvements; and step 5, using the obtained fault data to implement corrective measures on the basis of risk levels.

The invention belongs to the field of automatic driving test, and provides an end-to-end automatic driving system closed-loop confrontation test method based on a world model, and the method comprises the steps: building an end-to-end automatic driving system test platform based on the world model, providing end-to-end automatic driving system perception input information, and carrying out the modeling of a camera sensor, the accurate alignment among the multi-modal scene information is realized; constructing a traffic flow module, and providing map and scene global state information; a traffic vehicle multi-agent collaborative confrontation strategy balancing strategy convergence and strategy diversity is designed, a dense reward function is constructed and matched with an agent heterogeneous strategy adaptive adjustment method, a global exploration target is planned as a whole, and the test efficiency is improved; all the modules are integrated, an end-to-end automatic driving system to be tested is integrated, and efficient closed-loop testing is achieved. According to the method, efficient testing of the performance of the end-to-end automatic driving system can be achieved, the robustness and safety of the end-to-end automatic driving system can be evaluated, and functional defects of the system can be rapidly excavated.

The present invention proposes an autonomous driving testing method based on multi-alliance cluster confrontation, aiming to improve the efficiency and accuracy of autonomous driving simulation test, and comprising S1—initialization of testing environment of autonomous driving; S2—decision-making for dividing background vehicle clusters; S3—decision-making for confrontation behaviors of the background vehicles; S4—trajectory planning for the background vehicles; and S5—looping through the steps S2, S3, and S4 until cluster confrontation testing tasks are completed. The method of the present invention uses reinforcement learning and alliance games to dynamically generate testing scenarios that are highly confrontational to vehicles being tested, which can find dangerous boundary scenarios for autonomous driving more quickly and improve the efficiency of simulation testing.

A system for accidentology-based measuring of accident risk-indexing score values for a motor vehicle to be tested providing a measured accident probability value for an occurrence of an accident event having a physical impact to the tested motor vehicle. The system includes a driving scenario module, a test setting module, and a scoring module. The driving scenario module is configured for determining various driving scenarios for the motor vehicle by defining a set of measurable scenario characteristics, which include at least one ADAS variable. The test setting module is configured for determining a test setting in form of a multi-dimensional test matrix, which includes testing protocols for each of the measurable scenario characteristics for providing measured values of measurable scenario characteristics of the various driving scenarios. The scoring module is configured for generating the risk-indexing score by receiving the multi-dimensional test result signal and a historical data information signal.

The invention relates to the technical field of obstacle prediction, and provides a vehicle fault real-time diagnosis method based on multi-source heterogeneous data and a dynamic stream processing system, and the method comprises the steps: obtaining multi-source heterogeneous operation data from a vehicle sensor, a vehicle-mounted bus and an external environment; based on historical fault samples, determining a parameter critical value interval of each fault for each historical fault sample through reverse reasoning, and constructing a fault critical value library; performing dynamic streaming processing on the multi-source heterogeneous operation data, extracting time sequence features, comparing the time sequence features with a fault critical value library in real time, and outputting early warning through a multi-source voting mechanism; and when the operation data is close to or exceeds the corresponding critical value interval, outputting a fault prediction result, and triggering a dynamic updating mechanism to correct the critical value library. According to the method, the accuracy, timeliness and interpretability of fault identification are improved, meanwhile, the system resource consumption and real-time requirements are considered, and the method is suitable for long-term health state monitoring of large-scale vehicles.

The invention discloses a method and a system for testing and evaluating energy consumption of a hybrid power system in a plateau road environment, is used for testing and evaluating energy consumption of a hybrid power system vehicle, and relates to the technical field of energy consumption testing of hybrid power vehicles. Dividing the plateau road into a plurality of working condition segments according to driving working conditions, and recording road features of each working condition segment; obtaining driving behavior characteristics of a driver in each working condition segment, and combining the driving behavior characteristics with the corresponding working condition segments to form a plateau working condition segment pool; and extracting vehicle data corresponding to each driving behavior working condition fragment, and calculating vehicle energy consumption of each driving behavior working condition fragment according to the vehicle data, thereby completing vehicle energy consumption test evaluation.

The invention relates to the technical field of insertion and extraction force testing, and discloses a detection device and method for automobile exterior trimming part assembling, and the device comprises a bottom frame, a tension testing assembly, a vibration mechanism, and an extrusion mechanism, the vibration mechanism comprises a gear-missing column, a moving plate, a first electric push rod, a placing plate, and a vibration spring, and the bottom frame is provided with a sliding groove. The moving plate is slidably connected into the sliding groove, the first electric push rod is fixedly installed in the sliding groove, the output end of the first electric push rod is fixedly connected with the side wall of one end of the moving plate, one end of the vibration spring is fixedly connected to the bottom frame, the placing plate is fixedly connected to the other end of the vibration spring, and a mounting block is arranged on the bottom frame. According to the device, the vibration mechanism and the extrusion mechanism are matched, so that the bumper support on the placement plate continuously shakes, various road surface vibrations borne by an automobile in the driving process are simulated, and the reliability of the buckle under long-term vibration is accurately evaluated.

The invention provides a new energy vehicle fault diagnosis and prediction method based on deep learning, and the method comprises the steps: collecting the brake intensity sudden change data in the operation of a new energy vehicle in real time, and determining the trend and amplitude range of the current brake intensity change according to the brake demand signals under various working conditions in combination with the historical fault mode record analysis, thereby achieving the fault diagnosis and prediction of the new energy vehicle. Obtaining a preliminary braking demand characteristic value; according to the braking demand characteristic value, calling a pre-established deep learning prediction model, analyzing the relevance between a historical fault mode and current braking strength sudden change data, calculating the probability distribution of feedback energy anomaly detection, and determining the potential fault risk level of the new energy vehicle; and for the potential fault risk level, acquiring real-time data of surface temperature scanning of the brake disc, identifying a thermal load area, analyzing temperature distribution uniformity, judging whether a key area exceeds a preset threshold value or not, and obtaining a thermal load state evaluation result.

The invention relates to the technical field of edge computing, electric automobile testing and the like, and provides an electric automobile instrument testing method based on end-cloud collaboration, which comprises the following steps of: constructing a lightweight virtualized resource isolation environment at an edge computing node, loading signal generation, communication agency and state monitoring micro-service; a test platform with elastic capacity expansion and contraction and fault self-recovery capability is formed; historical test data are collected at a cloud end, and an equipment health degree prediction model is trained and deployed to an edge node to support a local intelligent test; constructing a dynamic test service chain, generating and pre-distorting and compensating an enhanced test signal, and then acting the enhanced test signal on the instrument; through multi-modal signal acquisition and unified preprocessing, in combination with wavelet packet decomposition and a deep neural network, high-dimensional feature extraction is realized, a test report containing a health trend is finally output, and a test process is actively intervened based on a trend prediction result. According to the method, the authenticity of test signals, the health assessment precision and the safety and efficiency of the test process are effectively improved.

The invention provides a method and a device for controlling and testing formation driving of autonomous vehicles. The method comprises the following steps: acquiring current scene features based on a sensing device configured on a road side unit and / or an autonomous vehicle; the current scene feature is matched with a preset scene library, target formation control information is obtained, the preset scene library stores a mapping relation between multiple groups of scene features and formation control information, and the target formation control information comprises pilot vehicle control information and control information corresponding to at least one following vehicle; and based on the target formation control information, controlling the leading vehicle and the following vehicle to perform formation driving. According to the method and the device, the formation scene of the automatic driving vehicles in multiple scenes such as expressways is introduced, the problems of scattered test organization and limited scene coverage in the traditional road test are solved, multi-source heterogeneous sensing fusion dynamic correction can be constructed, and the formation cooperative control of the automatic driving vehicles in the high-speed environment is realized; the problem of perception degradation caused by failure of a single sensor is solved.

The invention belongs to the technical field of automatic driving test, and provides an automatic driving confrontation test scene generation method and system based on strategy switching, and the method comprises the steps: obtaining an automatic driving simulation scene; setting positions and initial speeds of a target vehicle and an intelligent body vehicle in the acquired simulation scene; in combination with the set position and initial speed of the target vehicle, the running state of the target vehicle is optimized by adopting a multi-agent near-end strategy, and the target vehicle is controlled; determining a current traffic state according to the control driving state of the target vehicle; in combination with the dynamic quantitative index of the artificial risk potential field, sensing the traffic flow risk of the intelligent body vehicle in the determined current traffic state in real time; and according to the obtained traffic flow risk level, automatically switching a defense strategy and an attack strategy of the agent vehicle, and driving the multi-agent vehicle to cooperatively generate an automatic driving confrontation test scene.

The application provides a test method for intelligent auxiliary driving test, constructs a test problem description grabbing model, directly collects signals uploaded by an intelligent vehicle in real time through a vehicle-mounted Ethernet and a CAN FD direct connection interface without using other data interfaces, triggers a test problem description reporting process once abnormal signals are monitored, and ensures that the vehicle meets safety requirements of real vehicle road test.

The invention provides a keyless entry system calibration method and apparatus, an electronic device and a medium. The method comprises the steps of determining a first region and a second region according to a to-be-calibrated function of a target antenna, a first field intensity threshold and a second field intensity threshold; the first field intensity threshold value is greater than the second field intensity threshold value; the second area comprises the first area; based on the position of the key in the expansion area, determining a first field intensity measurement value and a second field intensity measurement value corresponding to the position; the first field intensity measurement value is the field intensity measurement value of the key relative to the target antenna, and the second field intensity measurement value is the field intensity measurement value of the key relative to at least one other antenna on the vehicle; the expansion area is an area which is in the second area and is not in the first area; determining a judgment rule set according to the to-be-calibrated function; and determining a target field intensity threshold value corresponding to the to-be-calibrated function according to the judgment rule set, the first field intensity measurement value and the second field intensity measurement value.

The utility model relates to the technical field of controller detection, and more specifically relates to a vibration detection device used for a controller, the vibration detection device comprises a vibration table and a clamp assembly, the clamp assembly is connected to the vibration table, the clamp assembly comprises a clamp base, a pressing mechanism, a linear driving mechanism and a test connecting plug, one end of the pressing mechanism is fixedly connected with the clamp base, the other end of the pressing mechanism is used for abutting against the controller so as to press the controller on the clamp base, and the fixed end of the linear driving mechanism is fixedly connected with the clamp base. And a moving end of the linear driving mechanism is fixedly connected with the test connecting plug and is used for driving the test connecting plug to be plugged with or separated from the controller. The linear driving mechanism is arranged to drive the test connecting plug to move, so that the test efficiency can be remarkably improved.

The invention relates to the technical field of new energy automobile battery testing, and discloses a new energy automobile battery aluminum alloy bottom shell impact testing device and method. The new energy automobile battery aluminum alloy bottom shell impact test device comprises a base, a driving frame is slidably arranged on the base, an acceleration mechanism for driving the driving frame to move at a high speed is installed on the base, a rotary driving seat is fixedly connected to the upper end of the driving frame, and a servo motor is installed in the rotary driving seat. And the driving end of the servo motor is fixedly connected with a multi-module mounting seat. According to the new energy automobile battery aluminum alloy bottom shell impact test method, an acceleration mechanism on a base drives a driving frame to move at a high speed, a rotary driving seat is driven to impact a battery pack mounted in an automobile battery detection positioning mechanism, and a servo motor drives a multi-module mounting seat to rotate and locks a required model seat for impact test. Therefore, various impact working conditions possibly encountered by the battery bottom case in actual use are comprehensively simulated, and the diversity and accuracy of testing are ensured.

The invention belongs to the field of vehicles, and discloses a method and system for evaluating the front view of a vehicle, and the method comprises the steps: obtaining a vehicle three-dimensional model, and determining the eye point coordinates of a driver model in the vehicle three-dimensional model; determining a view checking area according to the eye point coordinates; taking an eye point corresponding to the eye point coordinate as a projection point, and projecting a part in the vehicle three-dimensional model in a visual field checking area; and evaluating the visual field in front of the vehicle based on the projection of the part and the weight of the sub-region. According to the method, the view checking area and the sub-area weight thereof are determined based on the eye point coordinates of the driver, the view condition in the real driving process can be reflected, and the driving safety can be improved. Meanwhile, the front view of the vehicle is evaluated through the projection of the part in the view checking area and the sub-area weight, the subjective view feeling of a driver is converted into a design index capable of being objectively and quantitatively measured, the evaluation result is more scientific, and the driving comfort and safety of the vehicle are improved.

The invention discloses a vehicle test boundary scene generation method and system based on a pre-boundary scene. The method comprises the following steps: acquiring a first feature data set; obtaining and discriminating a real boundary scene and a real pre-boundary scene based on the first feature data set and a preset risk discrimination criterion; constructing a second feature data set based on the real pre-boundary scene data and the discriminated real boundary scene data; based on the real pre-boundary scene data, utilizing the generation model to obtain generated pre-boundary scene data; training a prediction model based on the real pre-boundary scene data and the discriminated real boundary scene data; and obtaining test prediction boundary scene data based on the generated pre-boundary scene data and the prediction model. According to the method provided by the invention, the test prediction boundary scene data is acquired based on the real pre-boundary scene data, and data expansion is performed on the real boundary scene data by using the generation model and the prediction model, so that the technical problem of sparse generated test scene data caused by limited scene generation data space in the prior art is solved.

The invention discloses an order noise evaluation method and device, and relates to the technical field of noise analysis. The method comprises the following steps: acquiring a noise time-domain signal and a rotating speed time-domain signal, and obtaining a three-dimensional noise spectrum signal; determining all to-be-selected frequency points corresponding to the target rotating speed based on the three-dimensional noise spectrum signal; determining the frequency points of which the intermediate critical frequency bands of the to-be-selected frequency points meet preset requirements, and obtaining a plurality of target frequency points; based on the three-dimensional noise spectrum signal, calculating protrusion ratios corresponding to the plurality of target frequency points, and determining the maximum protrusion ratio as a target protrusion ratio; and evaluating the to-be-evaluated order noise based on the target outburst ratio. According to the method and the device, the frequency points of the intermediate critical frequency band including the order noise frequency band in the to-be-selected frequency points are determined, so that the evaluation of the whole frequency band range covering the order noise is ensured, the full-band evaluation of the influence range of the order noise is realized, the comprehensiveness of order noise rating is improved, and the accuracy of the evaluation result of the order noise is improved.

The invention relates to an automatic driving test scene simulation generalization generation method and system based on knowledge distillation. The method comprises the following steps: acquiring real trajectory data, constructing a scene-level multi-agent trajectory generation framework based on a denoising diffusion probability model, and generating diversified and vivid agent trajectories through an iterative denoising process so as to realize modeling of multi-agent interaction behaviors in a complex traffic environment; a mixed knowledge self-distillation framework is further proposed, multi-level knowledge of a teacher model is migrated in a student model, and task loss and distillation loss are dynamically balanced in combination with a simulated annealing strategy, so that the cross-scene adaptability and generation stability of the model are improved; simulation verification shows that the generated intelligent agent track can effectively reduce the collision event rate and the retrograde event rate, and interaction coordination and traffic rule constraints are ensured. According to the system, the authenticity, diversity and cross-scene adaptive capacity of an automatic driving simulation scene can be improved.

The invention relates to the technical field of vehicle intelligent management, and discloses a vehicle state monitoring method and system based on deep learning, and the method comprises the steps: analyzing the physical dependence relation among vibration abnormality detection, loosening positioning and fatigue evaluation monitoring tasks based on structural mechanics constraints, and carrying out the monitoring of the vibration abnormality detection, the loosening positioning and the fatigue evaluation; generating a task dependence directed graph; inputting the multi-modal monitoring data matrix into a shared encoder of a multi-task learning network to generate a unified representation vector; respectively inputting the unified representation vector into a vibration anomaly detection decoder, a loose positioning decoder and a fatigue evaluation decoder, and outputting an initial prediction result of each task; and establishing a conditional probability model between task outputs based on the constraint relationship defined by the task dependent directed graph, and generating a consistency diagnosis result meeting physical constraints through maximum posteriori estimation. The technical problems of one-sided diagnosis results, mutual contradiction and low calculation efficiency are solved.

The invention relates to a camera function test method, in particular to an intelligent automobile camera function simulation test method for a strong light environment, which comprises the following steps of: firstly, determining a corresponding relation between a position of a strong light source in a world coordinate system and a pixel position in an image coordinate system according to camera parameters; secondly, determining the influence radius of a light source according to the geometrical characteristics of strong light, finally outputting a pixel position influenced by the strong light as a strong light influence area, establishing a camera imaging physical model according to an optical-electrical-digital signal propagation process, and determining the specific numerical value of the influence of the strong light environment on the pixel intensity according to the physical characteristics of the strong light source; on the basis of the geometric model, a more real strong light influence area is simulated in combination with a strong light pixel threshold value; based on the strong light influence area and the corresponding pixel value, superposing the strong light influence area and the corresponding pixel value to a strong light-free image acquired by a real vehicle to synthesize a strong light simulation image, and testing the camera function; the method has the advantages of low test cost, high test efficiency and accurate and controllable analogue simulation parameters.

The invention relates to the technical field of air tightness detection, and particularly discloses an automobile axle housing welding seam air tightness detection method, which comprises the following steps of: firstly, performing high-efficiency preliminary air tightness screening on an axle housing by using an automatic pressure drop method so as to efficiently judge whether a workpiece leaks or not; based on the judgment result, a leakage signal is used as a triggering mechanism, a thermal imaging system is dynamically activated, and transient local low-temperature characteristics formed by the throttling effect of high-pressure gas at a leakage point are captured in a non-contact mode. Furthermore, the collected thermal imaging sequence is deeply analyzed, and the collected thermal imaging sequence and a digital three-dimensional model of a workpiece are subjected to spatial correlation mapping, so that a complete automatic process from macroscopic judgment of the leakage phenomenon to accurate positioning of three-dimensional space coordinates of a leakage source is finally formed. In this way, efficient automatic screening and accurate defect positioning capacity can be organically combined, and therefore the intelligent level and the closed-loop efficiency of the whole production quality control link are remarkably improved.

The invention provides a heavy-duty truck vehicle-mounted safety monitoring method and device, and the method comprises the steps: obtaining the original monitoring data of a wheel-rail force through a wheel-rail force monitoring module which is installed on an axle and has a low power consumption characteristic, and carrying out the edge calculation of the original monitoring data, and obtaining a wheel load shedding rate, a derailment coefficient and a wheel axle transverse force; the framework acceleration and the bearing temperature are obtained through the low-power-consumption vibration acceleration sensing module and the bearing temperature sensing module; performing multi-modal fusion on each index to obtain a multi-modal operation state representation vector; obtaining a dynamic threshold value of each index based on an enhanced feature vector and a multi-modal operation state representation vector obtained by an offline self-learning model; and giving current situation analysis and trend analysis of safety monitoring based on a comparison result of each index and a dynamic threshold value. According to the invention, the limitation of independent analysis of single sensor data in the prior art is overcome, the dynamic adjustment of the safety threshold is realized, and the accuracy and reliability of safety monitoring are improved.

The invention relates to the technical field of vehicle dynamic performance testing, and particularly discloses a method and a system for testing vehicle tracking recovery capability based on transverse impact. The method comprises the following steps: acquiring position information of a tested vehicle in real time in a tracking driving process of the tested vehicle on a test road; if the tested vehicle arrives at the impact triggering area, controlling a road simulation plate laid on the test road to move transversely so as to simulate an impact working condition; when a tested vehicle arrives at an impact triggering area, rear wheels of the tested vehicle are located on the road simulation board. In the transverse movement process of the road simulation board, the instantaneous yaw angle of the tested vehicle is obtained; and responding to the situation that the instantaneous yaw angle is larger than a first set threshold value for the first time, and testing the tracking recovery capability of the vehicle according to the driving performance of the tested vehicle under the control of the electronic stability system. According to the method, the technical bottlenecks of uncontrollable interference and non-quantitative data of a traditional control test are overcome, and repeatable test, controllable parameters and quantitative evaluation are realized.

The invention belongs to the technical field of traffic control, and provides a micro-excitation-triggered multi-mode road adhesion coefficient real-time detection method and system, and the method comprises the steps: 1, carrying out the excitation triggering condition judgment; 2, generating a multi-mode excitation signal; 3, collecting dynamic response data; 4, feature fusion and attachment coefficient calculation; and step 5, integrating output and control. By applying high-frequency and low-amplitude micro-torque pulses, dynamic response of the tire is actively excited, and dependence on natural slippage is avoided; tire deformation, driving shaft torque attenuation and suspension vibration frequency spectrums are synchronously collected, and multi-dimensional characteristics of mechanics, acoustics and kinematics are covered; a grouping convolutional neural network and an attention mechanism are adopted to realize efficient feature extraction and fusion, and the bottleneck of traditional road adhesion coefficient detection is broken through.

The invention belongs to the technical field of automobile testing, and particularly discloses a digital twinning-based automobile instrument testing method, which comprises the following steps of: automatically generating a structured test case through a large language model, constructing a double-digital twinning architecture by relying on a high-fidelity rendering engine and a real-time simulation engine, and realizing synchronization of a virtual model and a physical instrument; multi-modal data are collected through a multi-sensor array, and defect intelligent analysis and grade judgment are completed in combination with a large language model; and test strategy self-optimization is realized through reinforcement learning based on historical test data. According to the invention, a double-engine architecture in which a high-fidelity rendering engine and a real-time simulation engine cooperatively work is adopted, and the requirements of large-scale scene and refined rendering are met at the same time. And seamless switching and data fusion between the double engines are realized through the scene transformation matrix, so that the consistency of a virtual environment and a physical entity is ensured. Meanwhile, a large language model is introduced into a test case generation and result analysis link, and natural language understanding level test script generation and anomaly analysis are achieved.

The invention relates to the technical field of passenger car evaluation, in particular to an electric passenger car motion sickness comfort evaluation method and system. The method comprises the following steps: the same fixed driver continuously drives the tested electric passenger car to a target number of turns according to a preset cycle condition; respectively fixing the motion sickness simulation robots on a co-driver seat and a rear row right seat of the tested vehicle; for the same driver, the same tested vehicle and the same cycle condition, the test steps are repeated for multiple times, and the arithmetic mean value of data collected for multiple times is taken as a multi-dimensional physical stimulation vector; and inputting the multi-dimensional physical stimulation vector into a motion sickness prediction model which is pre-calibrated and subjected to cross validation, outputting predicted motion sickness scores of a co-driver position and a rear row right side position, and if the predicted motion sickness scores are higher than a preset comfort threshold score, judging that the motion sickness comfort of the detected electric passenger vehicle at the position is unqualified, otherwise, judging that the motion sickness comfort of the detected electric passenger vehicle is not qualified. And reversely positioning the key physical stimulation component causing the over-threshold. According to the technical scheme, the motion sickness comfort of the electric passenger car can be accurately evaluated and traced.

A method for virtual evaluation of on-road energy consumption of a new energy vehicle, comprising: constructing an evaluation route on the basis of cities where a vehicle is used and typical routes of said cities; generating an on-road working condition library on the basis of the evaluation route, a month, a weather condition, a road condition and a vehicle usage period; acquiring test data of the vehicle under different working conditions; determining evaluated energy consumption on the basis of the test data; determining a nominal energy consumption discrete coefficient on the basis of the evaluated energy consumption and nominal energy consumption; determining average comprehensive energy consumption on the basis of the evaluated energy consumption; and determining an energy consumption level of the vehicle on the basis of the nominal energy consumption discrete coefficient and the average comprehensive energy consumption. Also disclosed is an apparatus for virtual evaluation of on-road energy consumption of a new energy vehicle. Even without large-scale road testing, on-road energy consumption can still be efficiently and conveniently evaluated in numerous cities and diverse vehicle usage scenarios.

The invention belongs to the technical field of vehicle-mounted controller heat dissipation evaluation, and particularly discloses a vehicle-mounted controller salt fog environment heat dissipation evaluation method and system, and the method comprises the steps: initial heat distribution data collection, salt fog environment dynamic simulation, thermal performance evaluation parameter calculation and heat dissipation capability comprehensive evaluation. Through multi-source heat distribution data acquisition and gridding processing, the limitation of a single temperature measurement mode is overcome; establishing a multi-dimensional mapping model of the salt mist sedimentation rate, the airflow velocity and the thermal resistance increment value, and dynamically adjusting test conditions; the influence of salt spray corrosion on the heat dissipation performance is analyzed and quantified in combination with corrosion morphology, and key index weight configuration is optimized. According to the invention, the accuracy and dynamic adaptability of the evaluation result can be improved, and reliable support is provided for the heat dissipation performance test of the vehicle-mounted controller.

A haptic simulation system for a vehicle door includes a haptic simulator having a simulator frame, a simulator door rotatably connected to the simulator frame through a shaft, a motor configured to rotate the shaft, and a brake configured to brake the shaft. The motor provides an active torque to the shaft and the brake provides a passive torque to the shaft.