222 results about "Hardware-in-the-loop simulation" patented technology

A hardware-in-the-loop test system (100) and a hardware-in-the-loop test method. The hardware-in-the-loop test system (100) comprises: a bidirectional power supply (110), a processor (120) and a battery simulator (130). The processor (120) is connected to the bidirectional power supply (110) and is used for controlling the bidirectional power supply (110) to output test parameters to a controller (200) under test. The battery simulator (130) is connected to the processor (120), so as to receive the test parameters sent by the processor (120) and regulate the state of the battery simulator (130) on the basis of the test parameters; the battery simulator (130) is further used for being connected to a first functional unit in said controller (200), and the first functional unit is used for performing state measurement for the battery simulator (130). The processor (120) is further used for being in communication connection with said controller (200), so as to receive response data of a second functional unit in said controller (200) at the test parameters and measured state information of the battery simulator (130), and, on the basis of the response data and the state information, determine a test result of said controller (200).

The invention relates to the technical field of simulation, and particularly discloses a real-time simulation method for detecting photoelectric tracking equipment, which comprises the following steps of: performing feature decoupling processing on photoelectric signals through a dual-channel adaptive neural network architecture, and constructing an equipment mathematical model by adopting a dynamic gating fusion mechanism based on a processing result; the method comprises the following steps: establishing a multi-physics field coupling simulation environment based on a heterogeneous computing architecture, introducing an equipment mathematical model, performing three-field co-evolution through light transmission modeling, electromagnetic field distribution calculation and target motion prediction, and generating a dynamic test scene containing space-time relevance; according to the method, a mode of combining mixed feature analysis and fuzzy reasoning is adopted, multi-dimensional performance indexes are extracted from simulation data, and multi-target dynamic optimization is carried out through a strategy of combining a quantum evolution algorithm and swarm intelligence optimization; by means of a digital twin platform and a hardware-in-loop interface, real-time verification and closed-loop optimization are achieved, and the response speed and tracking precision of photoelectric tracking equipment in a complex environment are greatly improved.

The invention discloses an aerospace equipment data-real fusion performance test method and device based on reinforcement learning, and the device comprises a digital twin model building module which is used for building an aerospace equipment high-fidelity mechanism model fusing multi-physical field features, a lightweight digital twinborn body adaptive to reinforcement learning training is generated through a model order reduction technology; the digital twinborn model correction module is used for carrying out consistency calibration of multi-dimensional features and key physical parameter correction on the reduced-order model based on object reference data; the performance test module based on reinforcement learning actively explores a coupling scene of an extreme working condition and multiple faults in an interaction environment by constructing an attacker agent, and autonomously explores a performance boundary and a limit envelope of equipment; and the number-real fusion verification module is used for carrying out a hardware-in-the-loop point compensation test by taking the performance limit obtained by the digital test as guidance so as to assist in decision making and optimization of a test scheme. According to the method, the coverage, the safety and the test efficiency of the limit performance test of the aerospace equipment are improved.

The invention discloses a hardware-in-the-loop test method and device and electronic equipment, relates to the technical field of automotive electronics, and adopts a simulated annealing algorithm in an optimization stage, adjusts parameters according to characteristics of an electronic stability control system, optimizes data distribution, and improves test data quality and fault detection capability. In the verification stage, the generated test data is subjected to actual operation test, the data problem is fed back by comparing actual output with an expected result, and the optimized test data which does not pass verification is sent to the target hardware-in-loop test model for optimization adjustment, so that a closed-loop optimization verification process is formed. The iterative closed-loop process comprehensively covers all possible working conditions and fault modes, key test points are prevented from being omitted, the reliability of the electronic stability control system is enhanced, the accuracy, effectiveness, comprehensiveness and sufficiency of hardware-in-the-loop test of the electronic stability control system are ensured, and the reliability of the electronic stability control system is improved. And the limitation of hardware-in-the-loop test of the electronic stability control system is reduced.

The invention discloses a real-time SLMP fuel cell hybrid electric vehicle energy management method. The method comprises the following steps: firstly, collecting vehicle operation data in real time, constructing an operation state vector containing kinetic parameters, and initializing a state transition matrix as agent action input; on the basis, the speed and the acceleration are predicted through a self-learning Markov predictor, and dynamic self-adaptive power distribution of the fuel cell and the power cell is achieved in combination with a multi-objective optimization function and power distribution constraints. The method passes hardware-in-the-loop (HIL) verification, aims at reducing hydrogen consumption and improving efficiency, and exits if two times of circulation are not passed. Compared with a traditional method based on rules or static optimization, the method innovatively embeds a self-learning mechanism, has the advantages of being high in adaptability, high in prediction precision and good in generalization performance, and is suitable for energy management optimization under complex working conditions.

The invention provides a wind turbine generator set safety chain hardware-in-the-loop test system and method, and relates to the technical field of simulation test. The system comprises an upper computer, a simulation test module, a main control module and a safety chain, the safety chain is actual safety chain hardware equipment of a wind turbine generator, and a shutdown signal is sent to the simulation test module and the main control module under the condition that it is determined that a safety chain fault is triggered; the main control module controls the simulation test module to simulate the running state of the wind turbine generator; the simulation test module is used for generating a safety chain fault signal according to a safety chain test requirement when the wind turbine generator is in a grid-connected power generation mode, and carrying out hardware-in-loop safety test on a safety chain; the main control module is used for monitoring whether the safety chain triggers a safety chain fault based on the safety chain fault signal or not, and sending a safety chain fault signal reset instruction to the simulation test module after receiving the shutdown signal; and after receiving the shutdown signal, the simulation test module executes a shutdown process of the wind turbine generator and executes a safety chain fault signal resetting process.

The invention discloses a system and method for supporting rapid iteration and quantitative deployment of a trajectory prediction algorithm, and the system comprises a data sample processing module which is used for integrating multi-source heterogeneous data, generating a dynamic sample library in a standard format, and generating a normalized preprocessing trajectory prediction sample set; the model training and quantification module is used for carrying out multi-stage training on the trajectory prediction model in the model library by using the preprocessed trajectory prediction sample set, and the multi-stage training comprises the steps of firstly carrying out floating point model training and then carrying out quantification perception training; and the model deployment and test module is used for carrying out hardware-in-the-loop test and automatic driving real vehicle test on the trajectory prediction model after multi-stage training so as to further iteratively optimize the trajectory prediction model according to a test result. The trajectory prediction model of the system and method disclosed by the invention shortens the iteration period of the automatic driving trajectory prediction algorithm, reduces the quantitative precision loss of the model, improves the real vehicle verification efficiency, and improves the algorithm development efficiency and deployment reliability.

According to the FPGA + CPU-based permanent magnet synchronous motor hardware-in-the-loop test simulation method provided by the invention, rapid verification and optimization of a motor control system can be realized through automatic tool chain development and a high-real-time simulation technology. The method comprises the following steps: firstly, building a mathematical model of a high-precision permanent magnet synchronous motor system on the basis of an HDL Coder modeling specification, and developing a whole set of complete solution covering model design, code generation, deployment and debugging on the basis of an HDL Coder Workflow Advisor; the method has the advantages of being high in universality, high in usability and low in cost, is suitable for various power electronic models with high-frequency requirements, optimizes and adapts to an FPGA + CPU multi-core heterogeneous computing architecture through a code generation technology, and achieves efficient hardware-in-loop simulation testing.

A hardware-in-the-loop test method and system for a protection device. The method comprises: constructing a simulation scenario in a hardware-in-the-loop simulation test system; on the basis of the mounting position of a protection device (4) to be verified in the simulation scenario, determining the minimum sampling signals required for said protection device (4) to work normally, and establishing a current transformer model capable of restoring a transient saturation scenario; performing a power-on operation on said protection device (4), and enabling the hardware-in-the-loop simulation test system to output an electrical signal; and after a correct sampling reading is acquired on said protection device (4), simulating a fault scenario in the simulation scenario, and on the basis of a current signal having transient saturation characteristics calculated by a model and a logic determination of the protection device, determining whether an action result of the fault scenario satisfies a setting value and an action logic. The present hardware-in-the-loop test method for a protection device restores a true current in a non-ideal fault condition, thereby deeply testing the working characteristics of a relay protection device in a power station.

The invention discloses an AMT function security test method based on hardware-in-the-loop, which comprises the following steps: implementing the AMT function security test method based on HIL according to an AMT HIL function security test process, including hardware platform building and software platform building; hardware comprises a load electromagnetic valve group, an HIL cabinet, a TCU and an upper computer, the TCU is connected with a corresponding pin of the HIL cabinet, a pin of a control signal of the load electromagnetic valve group needs to be led out from a Load board card of the HIL cabinet and is connected with the load electromagnetic valve group, and the upper computer is connected with the HIL cabinet to provide operation software; the AMT function security test method based on hardware-in-the-loop provided by the invention has correct function performance, accuracy and time sequence of a security mechanism; consistency and correctness of interface realization are realized; and verifying the validity and robustness level of the diagnosis or failure coverage of the security mechanism.

The invention relates to the technical field of new energy, and discloses an optimization method for an intelligent yaw system of a wind generating set, and the method comprises the steps: building the intelligent yaw system, enabling the system to be internally provided with seven modules, and enabling a multi-sensor data collection module to collect wind direction, wind speed and yaw angle data in real time, the data fusion module performs Kalman filtering fusion processing to obtain a high-confidence state estimation value, the intelligent decision output module generates an optimal yaw control instruction by adopting a multi-mode control strategy and a parameter self-adaptive mechanism based on fused data, an execution mechanism is driven to realize accurate wind alignment, and the system integrated hardware-in-the-loop test module is used for realizing accurate wind alignment. Omnibearing verification can be carried out before deployment, after actual operation, operation indexes are continuously monitored through the performance evaluation module, control parameters and fusion algorithm parameters are adjusted online through the optimization module on the basis of methods such as machine learning, closed-loop optimization is formed, finally, yaw precision, response speed and system stability are remarkably improved, and maintenance cost is reduced.

The invention discloses an input-output method and system realized based on a calculation engine in the coal chemical industry, and relates to the technical field of coal chemical industry, and the method comprises the steps: building a molecular model based on a reaction network of a coal molecular structure, and updating the parameters of the molecular model in real time through extended Kalman filtering; constructing a data fusion layer through a hybrid modeling technology, and constructing a coal chemical industry residual learning architecture by using a mechanism model and a neural network series structure; evaluating a data-driven model prediction confidence interval according to a Monte Carlo method; a digital twin verification platform is constructed based on hardware-in-the-loop test, and a data-driven adaptive iteration mechanism is constructed through an online and offline collaborative updating mechanism. The method has the beneficial effects that the medium component fluctuation is accurately captured, the output accuracy is improved, reliable evaluation dependence is provided, and the real-time response force and long-term stability of the system in a complex environment are improved.

The invention provides a desktop-level hardware-in-the-loop test system building method, device and equipment, and relates to the technical field of hardware-in-the-loop simulation, and the method comprises the steps: configuring a vehicle network simulation and vehicle dynamics model in an upper computer with a CAN development environment and vehicle dynamics simulation software, a real-time data channel between the two is established through a full duplex transmission protocol; a driver instruction is generated through the driving control panel, and a vehicle state signal is generated through vehicle dynamics software; the wheel speed in the vehicle state signal is converted into a physical excitation signal, and a wheel cylinder pressure signal returned by the controller is collected; the pressure signal is fed back to the vehicle dynamics model to form a closed loop, a test scene is executed, and the desktop-level hardware-in-loop test of the brake controller is completed. By using vehicle dynamics simulation software, the test confidence coefficient is improved; data exchange is carried out by utilizing an FDX protocol of a CAN development environment, and complexity and potential risks of self-development of a communication interface are avoided.

The invention relates to the technical field of electric power simulation, and discloses a hardware-in-the-loop simulation-oriented new energy equipment model verification method and system, and the method comprises the steps: constructing a new energy equipment basic model based on a physical equation, carrying out the compensation of a dynamic working condition error through combining with an LSTM neural network, and generating a hybrid parameterized model; fitting voltage and current data through a particle swarm optimization algorithm, and dynamically adjusting impedance parameters to complete model calibration; a time domain step response test, frequency domain characteristic analysis and a voltage sag fault experiment are executed on a hardware-in-loop platform, and the performance of the model is verified in a multi-dimensional mode; and generating a standardized verification report containing a steady-state error and a simulation step length index based on the deviation between the dynamic response curve and the measured data. According to the method, a mechanism model and a data driving technology are fused, the problems that dynamic characteristic adaptation is insufficient and parameter calibration depends on experience in traditional verification are solved, and the simulation precision and verification efficiency of the model under complex working conditions are improved.

The invention discloses an automatic test system for detecting an IO port on an MCU controller, and particularly relates to the technical field of automatic test and hardware-in-the-loop verification, the system takes a TSMaster as an upper computer, a programmable power supply, a digital multimeter and a controllable relay matrix are combined, and input injection, output measurement and controlled power supply disturbance are realized through a CAN / Ethernet / SCPI. The system comprises an input injection and channel switching module, an output drive and high-resistance isolation module, a power supply disturbance and alarm verification module, a unified acquisition and self-calibration compensation module, a criterion evaluation / report module, a regression module and the like, completes alignment and online small signal calibration and crosstalk compensation under a unified time base, constructs a minimum evidence unit, and implements interval criterion and time sequence robustness evaluation. And outputting an auditing report and triggering regression scheduling to realize high coverage of IO port functions and boundary working conditions and repeatable verification.

The invention discloses a high-speed magnetic levitation power load hardware-in-the-loop simulation test platform and method, and relates to the technical field of hardware-in-the-loop, and the high-speed magnetic levitation power load hardware-in-the-loop simulation test platform comprises a tested controller and a power load simulation module. The power load simulation module is connected with the tested controller through a power cable and a digital signal line, and the power load simulation module can provide a power test environment required by operation for the tested controller. According to the high-speed magnetic levitation power load hardware-in-loop simulation test platform, high-power signal transmission and power characteristic real-time online simulation are realized, and the defect that signal-level hardware-in-loop simulation cannot meet the high-power test requirement of a controller driving part in the prior art is overcome; and the integrity and the accuracy of the test of the high-speed magnetic levitation system controller are obviously improved.

The invention provides a hardware-in-the-loop test device. The hardware-in-the-loop test device comprises a simulation model verification system used for obtaining real-time data of input and output of an IO terminal of a DCS system in various operation states; the real-time data is transmitted to the fault state detection system; the fault state detection system is used for converting real-time data into a control signal and transmitting the control signal to the simulation data signal IO system; the simulation data signal IO system is used for converting the control signal into an electric signal; the electric signal is transmitted to a fault signal injection system; and the fault signal injection system is used for superposing the fault signal on the electric signal to obtain various types of faults, and transmitting the electric signal superposed with the fault signal to the DCS. According to the invention, the hardware-in-the-loop test device simulates each external signal fault of the DCS system, and injects the fault signal into the DCS system, thereby verifying and improving the robustness of the DCS system and a simulation model, and meeting the reliability test of the DCS system under an extreme working condition.

The invention discloses a reinforcement learning-power hardware-in-the-loop training system and method for a power converter, and belongs to the technical field of power electronic control and artificial intelligence crossing. The system comprises a real-time digital simulator, a power amplifier, a controlled power converter and an AI accelerator. The method comprises the steps of S1, system initialization and environment configuration; s2, performing safe online training; and S3, strategy convergence and solidification. Through the power hardware-in-the-loop architecture, the risk of reinforcement learning exploration is completely avoided, and hardware damage caused by overvoltage and overcurrent is fundamentally eradicated in combination with a real-time hardware protection strategy. According to the method, the real object power converter controller is incorporated into a training closed loop, so that an intelligent agent strategy directly learns and adapts to the nonlinear dynamic state of real hardware, the gap from simulation to reality is thoroughly filled up, and it is ensured that a training result can be directly applied to an actual system. According to the invention, a full-real-time training system is constructed, and synchronous concurrent processing of data acquisition, strategy execution and model updating is realized.

The invention belongs to the technical field of power systems, and particularly discloses a controller hardware-in-the-loop wind turbine generator transmission chain test platform simulation method, which comprises the following steps: designing parameters of a wind turbine generator, and constructing a wind turbine generator simulation model; establishing a wind field model, and embedding the wind field model into the wind turbine generator simulation model; constructing a transmission chain model and performing data connection with the wind turbine generator simulation model; constructing a control system comprising a main controller model and a subsystem control system, and establishing a control instruction channel and a feedback signal channel for the subsystem control system and the transmission chain model; deploying a control system in a rapid control prototype, and connecting the control system with the simulation platform through a physical interface; transmission chain operation data are collected in the simulation process, and the control strategy of the experimental platform and the transmission chain performance are evaluated. The hardware-in-the-loop simulation technology is utilized, closed-loop operation between the controller entity and the virtual model is achieved, and the authenticity of transmission chain experiment platform control strategy verification and the flexibility of experiment platform debugging are effectively improved.

The invention relates to the technical field of curved screen testing, and discloses a hardware-in-the-loop self-adaptive testing method and system for a curved screen and a storage medium. The method comprises the steps that the model of the curved screen is obtained, bending information and a two-dimensional coordinate system are extracted from a hardware model library, and a multi-axis testing arm three-axis basic assembly is initialized; starting a corresponding bending shaft assembly according to the bending dimension; the method comprises the following steps: controlling a touch pen to click a curved screen to obtain a two-dimensional click coordinate, converting the two-dimensional click coordinate into a three-dimensional point of a space coordinate system, calculating a reference tangent vector and a normal vector of a tangent plane of the point, rotating the tangent vector to a set angle to obtain an axial vector, establishing a local coordinate system and associating the local coordinate system with the space coordinate system; and controlling the test arm to move the touch pen to a target position and execute touch operation, and judging the touch state of the curved screen according to the touch test data. Accurate inclined touch control of any point of the curved screen is realized, driving scene man-machine interaction is truly restored, and test coverage and accuracy are improved.

The present invention relates to a current output module (1) which is used in 5 SLSC (Switch Load and Signal Conditioning) systems and in electronic hardware testing, particularly in HIL (Hardware-in-the-loop) testing, and which is connected to test equipment and / or products being tested and enables comprehensive testing and measurements.

The invention relates to a closed-loop virtual test flight pre-evaluation method and a medium, and relates to the field of flight tests. The method comprises the steps of constructing a digital twin module which comprises a pneumatic sub-model, a flight control sub-model, a power sub-model and a structure sub-model, defining a tilt transition specific high-risk subject and generating a test case, executing parallel Monte Carlo simulation and hardware-in-the-loop test, performing flight safety risk identification and envelope boundary quantification, and determining a tilt transition specific high-risk subject according to the tilt transition specific high-risk subject. And generating a risk assessment report and optimizing a real aircraft test flight outline. Wherein the aerodynamic sub-model adopts a dynamic vortex grid correction method based on CFD offline calibration and online query; the flight control sub-model comprises a configurable fault injection module based on a script engine, and the configurable fault injection module is used for defining a coupling fault sequence of sequential logic and a condition triggering relation. Compared with the prior art, the accuracy and the applicability of the pre-evaluation of the virtual test flight are improved.

The invention discloses a method for actually measuring and checking network-related characteristic parameters of a new energy and energy storage power station, and the method comprises the steps: S1, building a semi-physical simulation test system, and carrying out the hardware-in-the-loop test of a to-be-tested control system, so as to obtain dynamic characteristic data; s2, performing parameter identification based on the dynamic characteristic data to establish a single-machine model; s3, performing a simulation test on the single-machine model, and if a transient error between simulation data and laboratory test data is within an allowable range, determining that the single-machine model passes verification; s4, identifying and verifying model parameters of the whole station level controller by referring to the steps S1 to S3, aggregating single machine models into a station level model, and acquiring whole station simulation data; s5, downloading the station-level model parameters and the control software to field equipment to obtain field actual measurement data; s6, if the deviation between the field actual measurement data and the simulation data meets the preset standard, it is judged that checking of the network-related characteristic parameters is completed. The method has the advantages of improving the parameter checking efficiency and the like.

The embodiment of the invention provides a test environment deployment method and device, a medium and a program product, and relates to the technical field of hardware-in-loop testing. The method comprises the following steps: acquiring a test requirement and a template library, wherein the template library comprises an environment configuration template and a parameter rule; generating environment configuration based on the test requirement and the template library, wherein the environment configuration comprises an operating system version, a network parameter and a resource allocation parameter; and deploying a test environment according to the environment configuration, wherein the test environment is used for hardware-in-the-loop test. Based on the method, the test environment deployment process can realize high automation, the probability of errors caused by manual operation is reduced, and the deployment reliability is improved. And the test requirements can be dynamically adapted, the environment consistency and the resource utilization rate are improved, rapid generation and parameter optimization of the test environment are realized, the deployment time consumption is reduced, and the test environment deployment efficiency and the overall test efficiency are improved.

The invention discloses a closed-loop test system for a hardware-in-loop control air suspension. The closed-loop test system comprises an air suspension ECU to be tested; the simulation module is used for correspondingly simulating various vehicle parameters based on various vehicle parameter simulation models; the hardware simulation module is used for providing a hardware simulation signal for the air suspension ECU; the control module is used for controlling the simulation module and the hardware simulation module based on a test case when a test instruction is received, and outputting a target vehicle parameter and a target hardware simulation signal; the air suspension ECU performs closed-loop testing based on the target vehicle parameters and the target hardware simulation signals, so that real-time interaction and closed-loop logic among various modules in the dynamic state of the whole vehicle can be truly simulated, the simulation precision of the working condition of the air suspension is improved, an accurate test result is provided, and the test efficiency is improved. And the problems of high cost, long period, low repeatability and safety risk under limiting working conditions caused by real vehicle testing are avoided.

The invention provides an MCP2515CAN controller virtual simulation and integration method oriented to a hardware-in-the-loop test. The method comprises the following steps: constructing an MCP virtual model; a virtual CAN bus is constructed; constructing an external interface; for the MCP virtual model, creating an independent event, and realizing asynchronous data transceiving and interrupt processing by using an event triggering mechanism; the MCP virtual model, the virtual CAN bus and the external interface are integrated in a unified simulation environment. The technical problems that a traditional controller communication test depends on real hardware, the cost is high, debugging is complex, and compatibility is poor are solved.

The invention discloses a whole vehicle electric quantity balance testing method, device and equipment and a storage medium, and relates to the technical field of whole vehicle simulation testing. According to the electric quantity balance test standard, a test environment based on a hardware-in-the-loop rack is constructed, a test scene model is obtained, and the test environment comprises a programmable power supply, a controller, an actuator, a load and a test board card; obtaining a power supply scene model and vehicle logic parameters according to the test scene model; and the whole vehicle electric quantity balance test is carried out based on the test environment, the power supply scene model and the vehicle logic parameters, so that the problem of how to carry out comprehensive test analysis on the whole vehicle electric quantity balance under different working conditions is solved, the test efficiency and accuracy of the whole vehicle electric quantity balance are improved, and the test cost is reduced.

The invention discloses an SVG voltage ride-through control parameter identification method based on a kernel extreme learning machine in a double-solution space, and the method comprises the following steps: S1, dividing SVG fault stages, building an SVG steady-state control model and a fault control model, and according to the difference of the influence of control parameters on steady-state characteristics and fault characteristics, carrying out the recognition of SVG voltage ride-through control parameters; dividing a solution space of SVG control parameters into a double-solution space composed of a steady-state parameter solution space and a dynamic parameter solution space; s2, extracting a corresponding observed quantity characteristic value, constructing an input-output sample, and establishing a kernel extreme learning machine model in a double-solution space; s3, training a kernel extreme learning machine by using the input-output samples, and inputting RT-LAB hardware in-loop test data into the trained kernel extreme learning machine to identify SVG control parameters; according to the method, the adaptability and the accuracy of identification are improved, so that accurate modeling of the SVG is realized.

The invention relates to the technical field of computers, and discloses a bus signal simulation method for a universal emission control system. The method comprises the following steps: acquiring equipment node topology, a protocol type and historical load data; constructing a bus network abstract model; extracting a message flow time sequence template; generating a concurrent message scheduling sequence conforming to the arbitration logic; fusing to form a high-fidelity signal substrate; superposing physical layer interference signals based on measured data; and the data is injected into a tested system through a hardware-in-the-loop interface. The system comprises a bus topology analysis module, a network modeling module, a time sequence template extraction module, a scheduling generation module, a signal fusion module, an interference injection module and a hardware output module. According to the method, through logic and physical layer combined modeling, the test authenticity, the coverage rate and the platform universality are remarkably improved.

The application discloses a kind of hardware-in-the-loop test of automatic driving domain controller, comprising: real-time processor and graphics workstation;Graphics workstation is used to generate simulation traffic scene information, simulation sensor information is generated based on simulation traffic scene information, first simulation sensor information in simulation traffic scene information and simulation sensor information, simulation traffic scene information and simulation sensor information are transmitted to automatic driving domain controller, second simulation sensor information in simulation sensor information is transmitted to real-time processor;Real-time processor is used to transmit second simulation sensor information to automatic driving domain controller, receives the vehicle control information that automatic driving domain controller feedback.This application provides the hardware-in-the-loop test system, device, storage medium of automatic driving domain controller, can avoid the problem that scene information and sensor perception information do not match when hardware-in-the-loop test, improves the accuracy of test.