548 results about "Simulation testing" patented technology

The invention discloses an e-government affair platform management method and system based on cloud data, and relates to the technical field of data processing, and the method comprises the steps: collecting multi-source data, carrying out the preprocessing, extracting keywords, carrying out the multi-scale time coding and multi-scale space coding of the data, and storing the data in a database; constructing a government affair knowledge graph based on the database, mining association relationships of department services and analyzing affair handling habits of users; generating a government affair examination and approval path based on the knowledge graph, optimizing the government affair examination and approval path through reinforcement learning, dynamically adjusting the examination and approval process, performing a simulation test by using a digital twinning technology, and verifying the user satisfaction after the examination and approval process is optimized; and storing the government affair data in a database and performing authority management. According to the method, the electronic government affair data processing capability can be improved, the examination and approval efficiency is improved, the verifiability of an optimization strategy is enhanced, and technical support is provided for digital and intelligent transformation of government affair services.

The invention discloses a laser radar point cloud real-time three-dimensional target detection method suitable for an unmanned vehicle. The method comprises the following steps: preparing a target detection data set; model training; updating parameters; simulation data preparation; performing model reasoning and result evaluation based on simulation data; model deployment and real vehicle data preparation; performing model reasoning and data storage based on real vehicle data; uploading and marking the data; and incremental learning and parameter updating. The three-dimensional target detection model provided by the invention specifically comprises a preprocessing module; a backbone network module; a neck network module; a feature selection module; and a detection head module. According to the three-dimensional target detection model provided by the invention, the point cloud data is converted into various aerial view images, and richer spatial information is mined; a two-dimensional distance sensing sparse convolutional layer is adopted to perform feature extraction, and multi-view feature fusion is realized through a double-branch network; a multi-scale feature pyramid network and a feature selection module are combined, so that the detection precision of a long-distance target and a small target is effectively improved; by constructing an efficient neural network architecture and combining training, simulation testing and real data incremental learning strategies, the robustness, precision and adaptive capacity of a target detection model are effectively improved, and powerful perception support is provided for an unmanned driving system.

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.

The invention provides a low-altitude typical scene-oriented aircraft cockpit simulation method, system and device and a storage medium, and the method comprises the steps: constructing a dynamic extensible low-altitude scene library through fusing mixed reality (MR) and a high-precision digital twin model; in combination with a man-machine in-the-loop multimode interaction technology (eye movement-gesture-voice cooperation), the operation naturalness is improved; and an intelligent optimization algorithm driven by reinforcement learning is introduced, adaptive adjustment of flight control parameters is realized, and finally a'perception-decision-evaluation 'closed-loop training system is formed. According to the invention, digital twinning, mixed reality (MR), multi-modal sensing and adaptive control technologies are fused, pilot low-altitude flight skill training, man-machine work efficiency evaluation and intelligent aircraft control system verification are carried out, and the method can be expanded to UAM operator training and unmanned aircraft simulation testing. The technical barrier of low-altitude flight skill training at present is solved, and the technical blank of low-altitude dynamic scene simulation is filled up.

The invention discloses a multi-parameter ultrahigh frequency sensor performance comprehensive evaluation method, which belongs to the technical field of sensor performance evaluation, and effectively breaks through the limitation of the traditional static test on a transient interference response blind area through a dynamic interference field simulation test and an original fidelity-false triggering joint evaluation model. The adaptive adjustment mechanism of the multi-source interference field can vividly simulate the space-time non-uniform characteristic of electromagnetic noise in an industrial field, and in combination with a time-frequency domain composite evaluation algorithm, not only is the signal distortion degree of the sensor in typical interference modes such as frequency sweeping and pulse train quantified, but also the error action acceleration rule caused by high-frequency-band noise coupling is disclosed. The dynamic immunity evaluation capability enables a designer to accurately position the electromagnetic shielding weakness of the sensor, optimizes a front-end filter circuit or a signal processing algorithm in a targeted manner, and remarkably improves the reliability of a product in strong interference scenes such as radar detection and wireless communication.

The invention belongs to the technical field of hardware-in-loop simulation testing, and particularly relates to a hardware-in-loop interface board card. According to the hardware-in-the-loop interface board card disclosed by the invention, mutual conversion between a simulation machine input / output low-voltage signal and a controller output / input high-voltage signal is realized through a simulation machine signal photoelectric conversion sending circuit and a controller signal photoelectric conversion sending circuit in the signal conditioning module; the low-voltage signal output by the simulation machine interface can indirectly control the high-voltage signal input into the controller interface, and the high-voltage signal output by the controller interface can be converted into the low-voltage signal matched with the simulation machine interface, so that the problems of specification difference and signal matching difference between the IO interface and the controller interface are solved; the low-voltage simulator system is electrically isolated from the high-voltage controller system, so that the influence of the high-voltage controller on the simulator is avoided; and in the aspect of structural design, the hardware-in-the-loop interface board card is divided into an upper layer board and a lower layer board, so that the occupied area is reduced.

The invention discloses a topological data analysis-based special clothing function region segmentation and division method. The method comprises the following steps of S1, preprocessing three-dimensional point cloud data and dynamic pressure distribution data; s2, calculating a preliminary functional region division scheme based on node importance of the topological network model; s3, forming a preliminary division framework of the functional region based on geometric characteristics; s4, matching the preliminary functional region division scheme with the preliminary division framework to form a functional demand optimization scheme; s5, obtaining a special clothing function region division scheme adapted to function requirements through multiple iterative optimization; and S6, performing simulation testing on the optimized special clothing function region division scheme, and adjusting the special clothing function region division scheme based on a simulation result to obtain a final special clothing function region division scheme. According to the method, the boundary of the functional region is optimized through the combination of the topological entropy and the fractal dimension, so that the division result can better reflect the dynamic pressure distribution and geometric complexity characteristics in the actual use environment.

The invention provides a failure probability estimation method and device for an automatic driving system, and relates to the technical field of automatic driving, and the method comprises the steps: carrying out the sampling of a uniform distribution space, obtaining an initial sampling result, and constructing a plurality of initial testing scenes according to the initial sampling result; respectively performing simulation testing on the target automatic driving system in the plurality of initial testing scenes to obtain initial simulation testing results, and constructing an evaluation index prediction model according to the initial simulation testing results and the initial sampling results; determining a solving target for obtaining a reference failure sample based on the evaluation index prediction model, and solving the solving target by using an optimization algorithm to obtain a reference failure sample set; and sampling in the proposal distribution space by using the reference failure sample set, and determining a final failure probability according to a final sampling result. By adopting the method and the device for estimating the failure probability of the automatic driving system, the problems of low verification efficiency and inaccurate failure probability estimation of the automatic driving system in the prior art are solved.

The invention provides a hardware-in-the-loop simulation test method and system for a network construction type wind turbine generator. The method comprises the following steps: establishing a pneumatic model, a mechanical model and a control model of the wind turbine generator in Bladed software; building a wind turbine generator electrical part, a power grid short-circuit ratio adjustable device, a load simulation device, an energy storage device and a power grid simulator model in the RT-LAB simulation platform; and secondly, carrying out simulation test on the low-short-circuit-ratio operation characteristics of the network construction type wind turbine generator through the constructed hardware-in-the-loop platform. According to the method, simulation verification can be carried out on the low-short-circuit-ratio operation characteristic control parameters of the grid-forming type wind turbine generator set, and then the optimal control logic is determined.

The invention, which relates to the technical field of equipment simulation testing, discloses a simulation evaluation method for automatic testing equipment of an in-vehicle infotainment system, and the method comprises the steps: collecting operation test data, carrying out the recognition processing through a deep convolutional neural network model, obtaining test task data, constructing a test action sequence, mapping the test action sequence into a control instruction, and carrying out the simulation evaluation of the in-vehicle infotainment system. The control instruction is optimized and adjusted according to a preset action rule base; driving the in-vehicle automatic test equipment to execute the control instruction to complete the test operation, recording the operation response time and interface state change data in the test process, calculating a movement track deviation value, calculating a test accuracy score, and when the test accuracy score is smaller than a preset threshold value, starting the in-vehicle automatic test equipment; and if not, supplementing the optimization parameters to the preset action rule base and triggering a new test. According to the invention, accurate control of the test equipment is realized by adopting the TCP / IP communication protocol, and the test strategy is continuously optimized by dynamically updating the preset action rule base, so that the automatic test efficiency of the vehicle machine is improved.

The invention discloses an enterprise financial management system based on financial information processing, which relates to the technical field of financial management, is used for solving the problem of risk assessment misjudgment caused by financial data volume reduction and financial risk and controllable force change, and comprises the steps of collecting historical data, determining financial data types and enterprise risk assessment indexes according to the historical data, and determining the financial data types and the enterprise risk assessment indexes. Setting a statistical deadline, collecting enterprise data and financial change data within the statistical deadline, integrating the enterprise data and the financial change data, analyzing the controllability of different types of financial data by using a hierarchical anomaly detection method, and collecting environmental data when the controllability degree of the financial data is low. Constructing a support vector machine model by integrating the controllability degree of the financial data and the environmental data, evaluating financial risk changes according to an output result of the support vector machine model, adjusting risk evaluation indexes according to the financial risk changes, performing simulation testing, and collecting feedback data; the statistical deadline is dynamically planned according to the feedback data, so that the risk assessment index is adjusted more timely, and the decision-making strain is improved.

The invention relates to a physical-data hybrid drive vehicle driving state estimation method, and belongs to the technical field of intelligent vehicle chassis dynamics state estimation. Constructing a mapping relation between the state estimation vector and sensor data by adopting a ridge regression method so as to explicitly express a vehicle movement mechanism; secondly, constructing a data driving model to implicitly extract vehicle sensor data features, and constructing an enhanced LSTM neural network with a double-stage attention mechanism as the data driving model; then, performing dynamic weighted fusion on an output result of the physical model and a sensor sample sequence output by the data driving model to realize deep coupling of physical mechanism knowledge and data driving characteristics; according to the method, the superiority of the method compared with other methods is verified through simulation testing and real vehicle testing.

The invention discloses an automatic driving test scene generation method based on a large-scale language model and a probabilistic scene generation language. The automatic driving test scene generation method comprises the following steps: S1, analyzing a natural language instruction input by a user by using the large-scale language model; s2, performing structured decomposition on the scene description; s3, generating a code snippet of a probabilistic scene generation language; and S4, assembling and compiling a script of the probabilistic scene generation language. The invention further discloses an automatic driving test scene generation device based on the large language model and the probabilistic scene generation language. According to the method, direct, efficient and reliable conversion from the natural language intention to the complex dynamic simulation scene is realized, and the method can be widely applied to the technical field of simulation testing and artificial intelligence.

The invention discloses an unmanned ship state information online modeling and forecasting method based on a simulation technology, and relates to the technical field of unmanned ship simulation testing, an unmanned ship database system is constructed, and simulation data obtained through simulation of an unmanned ship model in a start-ccm + real flow field and a Nometic marine environment and data collected in a real marine test are included; and designing a nonlinear reserve pool calculation algorithm based on a sliding window algorithm to carry out online modeling forecasting on the state information. According to the method, dynamic characteristics of computational fluid mechanics, a physical simulation environment and a real marine environment are combined, online modeling forecasting of state information of the unmanned ship in the real marine environment can be realized by using small sample data, effective prior information is provided for collision avoidance, planning and control of the unmanned ship, and a navigation task is safely and effectively completed.

The invention provides an assembly type plate type foundation splitting design method based on the optimal economic size of a prefabricated part, and relates to the technical field of tower plate type foundations of power transmission lines, and the method comprises the following specific steps: under set constraint conditions, randomly generating various splicing combinations of a predetermined plate type foundation, a finite element analysis model is used for carrying out performance simulation testing and calculating cost, comprehensive performance indexes and comprehensive cost are generated through data processing, a cost-performance coupling coefficient is calculated, a double-optimal parameter combination is screened, a prediction model is constructed and trained, and an optimal combination of spliced combinations is obtained through iterative optimization in combination with a genetic algorithm. And the size is used as the optimal economic size of the splicing combination. According to the method, multi-dimensional collaborative optimization is achieved, traditional design limitation is broken through, performance and cost are accurately balanced through intelligent optimization, engineering practice is fully considered, design landing performance is enhanced, construction efficiency is effectively improved, cost is reduced, and foundation safety is guaranteed.

The invention provides a vehicle simulation test device and a vehicle simulation test method. The device comprises a first base, a second base and four dynamometers, the dynamometer comprises a lifting assembly, and a steering assembly, a roll assembly and a roller assembly which are arranged on the lifting assembly. The lifting assembly is used for lifting the steering assembly, the steering assembly is used for rotating the roll assembly, and the roll assembly is used for the roll roller assembly. Through the lifting assemblies which are arranged on the left side and the right side of the tested vehicle and have different heights and the side inclination angles of the side inclination assemblies which are adaptively adjusted according to the angles of the lifting assemblies on the left side and the right side of the tested vehicle, the driving conditions of the vehicle at the transverse gradient and wheel side inclination are simulated. Through the lifting assemblies which are arranged at the front end and the rear end of the tested vehicle and have different heights, the driving condition of the vehicle at the longitudinal slope is simulated; the roller assembly is driven through the steering assembly, and the driving condition of vehicle steering is simulated.

The invention relates to the technical field of frequency converter detection, in particular to a quality test board for frequency converter production and processing and a test method, an interface management module automatically identifies and manages interface connection, the problem of inaccurate data caused by misplug or misconnection is avoided, and the test safety is enhanced at the same time. The test execution module covers multiple aspects of performance test, protection function test, communication test, load simulation test and the like. The data acquisition module acquires key parameters in real time and provides detailed data support for subsequent analysis. And the data analysis module quickly identifies abnormal conditions and generates a test report through preprocessing, anomaly detection and performance evaluation. The fault diagnosis module can quickly identify fault types, accurately position fault positions, provide repair suggestions and shorten maintenance time. Therefore, the problem of inaccurate detection data of the frequency converter caused by misplug or misconnection when a tester repeatedly executes interface plugging operation is solved.

The invention provides a multi-engine collaborative robot simulation method and system according to the claim 1, and the system comprises the steps: developing a simulation management system based on a UE5 three-dimensional simulation engine, and achieving the effective management and display of various information and data in a simulation process; a high-precision map is developed, a laser radar and other sensors are simulated to realize scene data acquisition and interaction, and the authenticity and credibility of a simulation environment are optimized for practical application simulation; based on Vortex dynamics modeling software, through a VHLInterface data interaction mode, robot and scene interaction dynamics modeling is carried out, and a robot simulation result is obtained; based on FastDDS communication middleware, data communication between the UE5 and the ROS2 is established, an ROS2 message format is compatible, and a hardware-in-the-loop simulation test of the controller is carried out. The technical problems that a multivariate control algorithm and a fine dynamic model are difficult to consider, data interaction of a simulation system is difficult, complexity and cost of the simulation system are high, and reliability is low are solved.

The embodiment of the invention provides a vehicle positioning method and device, electronic equipment and a storage medium, and the method is applied to a simulation system, and comprises the steps: collecting the current coordinates and vehicle pose data of a simulation vehicle on a target map in a process of controlling the simulation vehicle to operate in an intelligent driving simulation scene; converting the current coordinate based on the projection mode of the target map to obtain a target coordinate, the target coordinate being a coordinate corresponding to the coordinate type required by the domain controller; the target coordinates and the vehicle pose data are coupled to obtain coupling data, the coupling data are sent to a domain controller, and the coupling data are applied to vehicle positioning of the auxiliary domain controller. According to the invention, the technical problem that the precision is low when a simulation test is used for vehicle positioning in the prior art is solved.

The invention discloses a local friction simulation testing machine for a sealing pair of a control valve, which comprises a box body, a driving transmission system, a mounting shaft, a lower test piece rotary friction quick-change assembly or a lower test piece linear reciprocating motion quick-change assembly, a support frame, a loading assembly, an in-situ observation system and a data acquisition and control system, and is characterized in that the driving transmission system is mounted in the box body; the mounting shaft is positioned above the box body and is connected with the driving transmission system through the coupler; the lower test piece rotary friction quick-change assembly or the lower test piece linear reciprocating motion quick-change assembly is mounted on the mounting shaft; the support frame is mounted at the top of the box; the loading assembly is mounted on the support frame in a penetrating manner and corresponds to the position of the lower test piece rotary friction quick-change assembly or the lower test piece linear reciprocating motion quick-change assembly below the loading assembly; the in-situ observation system is mounted on the side surface of the support frame and aligned to the bottom of the loading assembly; the in-situ observation system and the loading assembly are electrically connected with the data acquisition and control system. The real motion trail of the control valve sealing pair is simulated, and the accuracy is improved.

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.

The invention provides a method for determining a configuration scheme of a hardware resource pool and related equipment. The method is applied to a cloud management platform. The method comprises the following steps: receiving a cloud resource use condition configured by a tenant; generating a first configuration scheme of a resource request set and a first hardware resource pool according to the cloud resource use condition; according to the resource request set and a first configuration scheme of the first hardware resource pool, performing simulation to obtain a first simulation result; when the first configuration scheme does not meet the preset condition, optimizing the first configuration scheme to obtain a target configuration scheme; and when the first configuration scheme meets a preset condition, taking the first configuration scheme as a target configuration scheme. According to the method, the configuration scheme of the resource request set and the hardware resource pool can be generated based on the cloud resource use condition of the tenant, and the simulation test is performed, so that the cost and the resource allocation capability of the configuration scheme can be evaluated more accurately, and the matching degree between the configuration scheme and the demand of the tenant can be improved.

The invention provides an automobile hub bearing environment simulation testing machine, which comprises a frame body, a temperature adjusting assembly, a muddy water assembly, a dust raising assembly and a driving assembly, and is characterized in that the frame body comprises a test box which is arranged in a sealed manner, and the driving assembly comprises a plurality of driving shafts; the front end of the driving shaft extends into the test box and can be used for mounting a bearing to be tested, the temperature adjusting assembly is arranged adjacent to the test box and is provided with a temperature adjusting pipeline communicated with the inner side of the test box, the muddy water assembly comprises a muddy water pipeline communicated with the test box, and the muddy water pipeline can be used for enabling muddy water to enter the test box. The dust raising assembly comprises a dust raising box and an air pipe, the air pipe is connected to the dust raising box, and the dust raising box can be communicated with the test box. Aiming at the defects in the prior art, the invention provides the environment simulation testing machine for the automobile hub bearing, which can better simulate an external complex environment.

The present invention provides a bearing load simulation detection device, which belongs to the field of bearing load detection technology and is used to solve the problem that existing equipment cannot quickly and effectively perform axial and radial load tests on bearings. It includes a support mechanism and a rotation mechanism, the support mechanism includes a base plate, a first support column and a second support column are fixed on the base plate, a second mounting groove is provided on the second support column, a fixing plate is fixed on the side of the second support column, a radial force measuring mechanism is arranged on the base plate, an axial force measuring mechanism is arranged on the first support column, a locking mechanism is arranged at the end of the axial force measuring mechanism, and the rotation mechanism includes a sliding plate, the sliding plate is slidably arranged inside the second mounting groove, a connecting block and a rotating motor are fixed on the sliding plate, and a mounting shaft is connected to the output shaft of the rotating motor; the bearing load simulation detection device can quickly and effectively perform axial and radial load tests on bearings.

The invention belongs to the technical field of software testing, and discloses a test case generation method and platform for aircraft performance testing. The method comprises the following steps: determining a first input parameter set, and generating a first test case by utilizing global random search; using the first test case to drive the flight management module to perform simulation test, and receiving first flight state data output after the flight management module performs the simulation test; determining a second input parameter set based on the first flight state information, and generating a second test case by using local random search; driving the flight management module to perform a simulation test by using the second test case, and receiving second flight state data output by the flight management module after the simulation test; an out-of-control input parameter is determined based on the second flight state data. Generation and execution of the test cases are optimized through an intelligent algorithm, the generation efficiency of the test cases is improved, the test cost is reduced, meanwhile, the more comprehensive test coverage rate is ensured, and the test reliability is improved.

The invention discloses a testing device for the cyclic working condition of a loading machine. The system belongs to the technical field of simulation testing and comprises an engine and control system, a transmission and control system, a hydraulic control system, a dynamometer, a dsPACE system, a computer and the like. The numerical cycle working condition of the loading machine is stored in the computer, executable codes are generated according to the numerical cycle working condition through a dsPACE system, the fuel injection quantity of an engine is controlled through VCU Link, and the rotating speed and the torque of the engine are controlled to run according to the cycle working condition. Controlling the pressure of the proportional overflow valve, and simulating the power consumed by the hydraulic system under the cyclic working condition of the loader; the gear of the gearbox and the load applied by the dynamometer are controlled through the TCU, and the power consumed by the driving system and the shovel loading operation in the cyclic working condition of the loader is simulated. According to the invention, the operation of each system in the working process of the loader under the cyclic working condition can be simulated, the matching rationality, the dynamic property and the fuel economy of the power transmission system of the loader are tested, and a test basis is provided for the design and development of the loader.

The invention discloses a cloud environment change management method and device based on a large model, equipment and a medium, and relates to the field of cloud computing, and the method comprises the steps: carrying out the form information extraction of a received cloud environment change request form, so as to obtain target form information; inputting the target form information to a risk assessment engine, so that the risk assessment engine performs risk analysis on the cloud environment change request form based on the target form information; determining a to-be-tested change implementation path corresponding to the cloud environment change request form based on the obtained risk assessment result, and performing a simulation test on the to-be-tested change implementation path in a preset virtual environment to obtain a corresponding test result; and performing path adjustment on the to-be-tested change implementation path based on the test result to obtain a target change implementation path, and performing cloud environment change based on the target change implementation path. Therefore, the change request can be processed automatically and intelligently, manual intervention is reduced, and the speed and accuracy of change management are improved.

The invention provides a chip simulation waveform automatic analysis and analysis method and system, and belongs to the technical field of chip simulation testing. The method comprises the following steps: generating a waveform file for chip testing through a simulation tool, and preprocessing the obtained waveform file; building a deep learning model, training the deep learning model by using the waveform data, the deep learning model being a hybrid deep learning model, combining a deep convolutional neural network and a graph neural network, and introducing an attention mechanism; analyzing the waveform file by using the trained deep learning model, and extracting a digital logic state and a time sequence feature; and comparing the extracted features with a preset test expectation, and generating a comparison result by judging whether the waveform accords with the expectation. By introducing the deep learning model, automatic analysis and analysis of the waveform file are realized, and the test efficiency is remarkably improved.

This invention provides a method for modeling the starting performance of a marine gas turbine. First, a gas turbine aero-thermodynamic performance calculation model is established using C++, and a heat transfer model, temperature sensor model, and control law model for the gas turbine's high-temperature components are established using the M language. Then, based on test data from the gas turbine startup process, the method analyzes how the pressure ratio and efficiency of typical gas turbine components change with the physical speed of the gas generator during startup. The corresponding low-speed characteristics of the gas turbine's rotating components are generated and corrected, enabling high-precision, real-time simulation of the gas turbine's starting performance. This method considers the heat exchange between the high-temperature fuel gas and the ambient atmosphere and the gas turbine itself, improving the accuracy of the gas turbine startup performance simulation. The method can provide a simulation testing tool for the design and optimization of gas turbine startup control laws.

The invention provides a vehicle-mounted system simulation platform, and relates to the technical field of simulation testing, and the simulation platform comprises an interception layer, a simulation layer, a communication layer and a vehicle-mounted intermediate layer service. The interception layer is used for intercepting interface calling of a vehicle-mounted intermediate layer service and forwarding calling parameters to the simulation layer; the simulation layer is used for abstracting a hardware opponent component corresponding to the calling parameter into a virtual control and generating an analog signal based on the virtual control; the communication layer is used for performing data synchronization between the simulation layer and the interception layer; and the vehicle-mounted intermediate layer service is used for carrying out simulation debugging according to the analog signal, and is used for realizing simulation debugging without depending on entity hardware equipment.