Whole vehicle test method, system, storage medium and equipment based on digital twinning

Abstract

The application relates to a whole vehicle test method and system based on digital twinning, a storage medium and equipment, wherein a state bit of a multi-state switch is numbered in advance to obtain a corresponding number; when the conditions for vehicle emergency unlocking are met, the emergency password input mode is started by simultaneous action of the parking brake and the service brake; the state bit corresponding to the number is input into the ECU by switching the state bit of the multi-state switch, and when the input password is correct, the ECU controls the vehicle door to be unlocked, thereby realizing emergency unlocking. The input mode of the emergency unlocking password is started in the linkage mode of the parking brake and the service brake, different state bit signals are input into the ECU by utilizing the state switching characteristic of the multi-state switch, the input of the emergency unlocking password is realized, and thus the emergency unlocking is realized.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of motor vehicle control, in particular to a vehicle test method, system, storage medium and equipment based on digital twinning. BACKGROUND

[0002] The statements in this section merely provide background information related to the present application and do not necessarily constitute the prior art.

[0003] Vehicle test is a process of testing and checking a completed vehicle as a whole, aiming to verify the performance and safety of the whole vehicle, and to reproduce various working conditions encountered during vehicle operation, and to detect and locate faults. Since it is a test stage, some working conditions are difficult to reproduce by field test, and some faults may be delayed due to various unknown reasons, so that the efficiency of vehicle test is not ideal, thereby delaying the time of vehicle delivery. SUMMARY

[0004] In order to solve the technical problems existing in the background art, the present application provides a vehicle test method, system, storage medium and equipment based on digital twinning, which uses digital twinning technology to build a virtual model of the actual vehicle operation condition, discovers and records abnormalities through the running condition of the virtual model, and reversely improves the cognition, monitoring and fault warning of the test vehicle operation condition. In addition, the virtual model records data in real time, traces back to the working condition at the time of problem occurrence, so as to avoid the problem of difficult reproduction of complex working conditions.

[0005] In order to achieve the above purpose, the present application adopts the following technical solutions:

[0006] The first aspect of the present application provides a vehicle test method based on digital twinning, comprising the following steps:

[0007] Obtain attribute information of a vehicle to be tested, driving data and environmental data during the test, and preprocess them;

[0008] According to the attribute information after preprocessing, an attribute model reflecting the appearance constraint and shape constraint of the vehicle is constructed;

[0009] According to the driving data and environmental data after preprocessing, a behavior model reflecting the position transformation and motion logic relationship of the vehicle during the test is determined, and the attribute model and the behavior model are associated through the logic model;

[0010] According to the associated attribute model and behavior model, the corresponding dynamic parameter data of the vehicle driving is determined, the corresponding curve change is obtained, and the fault is warned according to the curve change and the set threshold. After the fault occurs, the fault is traced back to the working condition according to the vehicle operating parameters in the set time period before the fault time point.

[0011] Further, the attribute information of the vehicle to be tested is static data, including at least one or more of vehicle appearance, brand, color, number, evaluation information, key component service life and maintenance information.

[0012] Further, the attribute information of the test vehicle, the driving data during the test period and the environmental data are saved into a database, specifically: the database includes a real-time database and a storage database, the driving data during the test period and the environmental data are saved in the real-time database, and the attribute information is saved in the static database.

[0013] Further, the attribute model associated with the logical model and the behavior model form a virtual model, and the virtual model obtains the running parameters of the vehicle during the test period and the corresponding curve change.

[0014] Further, through the vehicle driving parameters and the corresponding curve change in the virtual model and the set threshold, the fault is prewarned.

[0015] Further, during the prewarning period, the determined fault condition is classified into the corresponding fault level and category according to the monitored variables in the curve, and the corresponding processing measures are obtained according to the expert library.

[0016] Further, after the fault occurs, the fault time point is determined through the data in the virtual model, and the vehicle running parameters in a set time period before the fault time point are obtained, the state of the vehicle when the fault occurs is displayed, and the fault working condition is traced.

[0017] The second aspect of the application provides a whole vehicle test system based on digital twinning, comprising:

[0018] The data acquisition module is configured to: acquire and preprocess the attribute information of the vehicle to be tested, the driving data during the test period and the environmental data;

[0019] The digital twinning module is configured to: construct an attribute model capable of reflecting vehicle appearance constraints and shape constraints according to the preprocessed attribute information;

[0020] The digital twinning module is further configured to: determine a behavior model capable of reflecting the position transformation and motion logical relationship of the vehicle during the test period according to the preprocessed driving data and environmental data, and associate the attribute model with the behavior model through the logical model;

[0021] The fault monitoring and working condition reproducing module is configured to: determine corresponding dynamic parameter data when the vehicle is running according to the associated attribute model and the behavior model, obtain corresponding curve changes, and give a warning according to the curve changes and a set threshold; and trace the working condition of the fault according to the vehicle operating parameters in a set time period before the fault time point after the fault occurs.

[0022] A third aspect of the present application provides a computer readable storage medium, which stores a computer program, and the program is executed by a processor to implement the steps of the whole vehicle test method based on digital twinning.

[0023] A fourth aspect of the present application provides a computer device, which comprises a memory, a processor and a computer program stored in the memory and executable on the processor, and the processor implements the steps of the whole vehicle test method based on digital twinning when executing the program.

[0024] Compared with the prior art, the above one or more technical solutions have the following beneficial effects:

[0025] 1. The digital twinning technology is applied to the test scene of the whole vehicle, a virtual model is built through the digital twinning technology to reflect the actual vehicle running condition, the data during the test is simulated through the virtual model, the running condition of the whole vehicle is reflected, and the curve change of a vehicle parameter output by the virtual model is used to find and record abnormalities, the virtual model is used to compensate for the working conditions that cannot be covered during the whole vehicle test, the cognition, monitoring and fault warning of the test vehicle running condition are improved in the reverse direction, and thus the efficiency of the whole vehicle test is improved.

[0026] 2. The virtual model constructed through the digital twinning technology records data in real time, traces back to the working condition at the moment when the problem or fault occurs, and thus supports subsequent fault positioning and analysis work, so as to avoid the problem that complex working conditions are difficult to reproduce. BRIEF DESCRIPTION OF DRAWINGS

[0027] The drawings accompanying the specification of the present application form a part thereof and serve to provide further understanding of the present application, the exemplary embodiments of the present application and the explanations thereof serve to explain the present application, and do not constitute improper limitations on the present application.

[0028] Figure 1 is a whole vehicle test process schematic diagram based on digital twinning provided by one or more embodiments of the present application;

[0029] Figure 2 is a schematic diagram of data preprocessing provided by one or more embodiments of the present application;

[0030] Figure 3 is a schematic diagram of constructing a twinning model provided by one or more embodiments of the present application;

[0031] Figure 4 This is a schematic diagram of fault early warning and operating condition tracing using a twin model provided in one or more embodiments of the present invention. Detailed Implementation

[0032] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0033] It should be noted that the following detailed descriptions are exemplary and intended to provide further illustration of the invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0034] Terminology Explanation:

[0035] LOF Algorithm: Local Outlier Factor (LOF)

[0036] Digital twin technology: A digital twin (DT) is a virtual mirror image of a physical product that can reflect the entire lifecycle of the corresponding physical product.

[0037] Fault condition reproduction: Fault condition reproduction mainly occurs during the troubleshooting stage after a fault occurs. It often requires repeating the fault occurrence process or key points to locate and resolve the fault.

[0038] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the scope of exemplary embodiments according to the invention. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. Furthermore, it should be understood that when the terms “comprising” and / or “including” are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0039] Most vehicle testing relies on manual inspection, with fault diagnosis performed through human intervention. This diagnostic technique requires highly skilled personnel and depends on expert experience or instrument detection. The inspection is subject to lag and inconsistent evaluation standards, resulting in a high rate of false alarms and missed detections.

[0040] The instrument panel alarm fault detection method is a diagnostic approach based on signal processing technology. This method mainly requires sensors and instrument detection technologies to collect and analyze the fault information. Although this diagnostic technology does not rely entirely on experience and knowledge as in the first stage and its accuracy has been improved accordingly, it still requires human assistance to some extent, and the fault alarm has a lag, which cannot meet the user's requirements.

[0041] Therefore, the progress of the whole vehicle off-line is often delayed because it is difficult to promote the reproduction of the working condition. Therefore, the following embodiments give a whole vehicle test method, system, storage medium and equipment based on digital twinning, aiming to realize data recording and fault backtracking by building a virtual model, and improve the fault positioning efficiency and off-line progress.

[0042] Embodiment one:

[0043] As shown in Figures 1-4 The whole vehicle test method based on digital twinning includes the following steps:

[0044] 1. Data acquisition stage: collect basic data including static data and dynamic data.

[0045] Static data mainly refers to vehicle appearance attribute data, evaluation information and other data. Vehicle appearance attribute data such as brand, vehicle number, color, evaluation information such as vehicle key component service life, maintenance information, etc.; such data is determined after the vehicle is manufactured and almost does not change in a long period of time.

[0046] Dynamic data includes vehicle running data and environmental data. Vehicle running data such as vehicle speed, gear state, temperature, speed, etc. Sensor collected data, environmental data such as road condition information, surrounding vehicle information, weather information. Such data changes dynamically with the operation of the vehicle (whether in the test site or actually on the road).

[0047] The data acquisition stage writes data into the database, which is divided into real-time database and storage database. For the storage database, the driving information can be dynamically maintained, and the information is preliminarily pretreated and cached. When the upper control terminal has the demand of extracting data, the data can be directly transmitted to the control terminal. Dynamic data generated during the vehicle running process, such as engine speed, oil temperature, etc. need to be transmitted through the real-time database, the real-time database does not pretreat the data, and directly uploads the data to the control terminal, which is directly processed and used by the control terminal, so as to realize timely and efficient early warning response.

[0048] The data acquisition stage adopts a distributed data storage method. For some static information such as vehicle attributes, the cached data in the storage database can be directly obtained, and for dynamic data during vehicle driving, it is not processed and directly uploaded to the control terminal. The distributed data storage method reduces the complexity of storing large capacity data in the local database, and also improves the response speed of the control terminal.

[0049] 2. Data transmission stage: using 5G transmission technology, the transmitted data includes static data generated by the vehicle and dynamic data generated during the test driving process, the transmission direction is from the memory to the application layer controller, CRC check (Cyclic Redundancy Check) is performed at the sending and receiving end, and the data that does not pass the check is considered to be caused by network anomalies and is excluded.

[0050] 3. Data preprocessing stage: since in the actual production process, the data generated by each system is nonlinear, highly random and uncertain, and the types of data collected by each system are not the same, and some invalid data and missing data caused by equipment wear and failure also need to be avoided, therefore, data preprocessing is performed before the virtual model is built, the main purpose of which is to unify the data standards and perform adaptive processing.

[0051] Data preprocessing includes storage database static data preprocessing and control terminal dynamic data preprocessing. Missing or abnormal static data will affect the integrity and accuracy of the digital screen display information, and has little effect on the virtual twin system, so it will not affect the accuracy of the normal twin system fault warning and fault backtracking function. Therefore, the interpolation algorithm can be used to complete the data cleaning process for static data preprocessing. The accuracy of dynamic data will seriously affect the accuracy of fault warning, so data preprocessing is required before building the digital twin system to filter out abnormal data, as shown in Figure 2 .

[0052] Dynamic data collection requires good network upload performance, and driving into areas with weak signals during vehicle testing will cause great difficulties in signal collection. From the proportion of abnormal data collected, the preprocessing of continuous data recorded by the vehicle terminal in time sequence mainly focuses on the identification of outliers. In this embodiment, the LOF algorithm based on distance density is used for data preprocessing.

[0053] First, use the LOF algorithm to find the abnormal points in the data. The calculation formula for judging the abnormal points P in the data group C1 is as follows:

[0054]

[0055] Assign a score to each point as the LOF score through the ratio of the reachable distance, the greater the score, the more obvious the abnormality of point P. Set the LOF score threshold for judging abnormal values, and carefully determine the value of the score threshold C. Modify the value of C to avoid modifying the originally abnormal data to normal values. For outliers with a score threshold exceeding the limit, the original value of the abnormal point is calculated in reverse according to the time to obtain the similar distance, and is modified to normal data to ensure the continuity and integrity of the data.

[0056]

[0057] 4. Virtual model construction stage: the construction of the virtual model is mainly divided into attribute model and behavior model. The entire model is constructed by using a three-dimensional modeling software. Static data is used to construct the attribute model. The appearance constraint and shape constraint of the vehicle reflected by the static data are used to build the attribute model. The behavior model is built mainly by the standard dynamic parameters of vehicle driving, position transformation and motion logic relationship. The 1:1 mapping of the actual scene and the virtual scene is completed from the dimensions of space, time and physics. The virtual geometric model for vehicle driving is established, as shown in FIG. 4. Figure 3

[0058] The model can receive data in real time, which is equivalent to a physical model of the entire vehicle. The model receives and stores data for a period of time in real time. The trend of data change is analyzed in the physical model to predict the possibility of fault occurrence and give a warning. For the fault that has occurred, the physical model is used for tracing and positioning.

[0059] The appearance constraint and shape constraint of the vehicle reflected by the static data are used to build the attribute model. ATBModel = {AppearConstraint, ShapConstraint}. The static data required for the appearance constraint model includes vehicle model, color, vehicle weight and other information. After the vehicle is produced and delivered, the parameter information is input to the 3D drawing software to generate the appearance constraint model. The static data required for the shape constraint model includes vehicle engine model, transmission system model, brake system model and other information. The shape constraint model construction process is similar to that of the appearance constraint model.

[0060] The dynamic data reflects the real-time running condition of the entire vehicle and the relative displacement condition of the road space. The behavior model is built by using this part of data. BEHModel = {vehic, posit}. The actual condition of the entire vehicle running is mainly driven by the sensor collected data, and finally determines the parameters such as the speed and pressure of the model. The relative displacement condition of the road space is mainly calculated by fusing the road information and the vehicle speed, and finally determines the parameters such as the space map and road information of the physical model. After the above data parameters are processed by the LOF algorithm, they are input to the Unity virtual reality software for dynamic simulation of the entire vehicle.

[0061] ​5. Data visualization processing: The digital visualization large screen system displays the corresponding data according to the data obtained from the data interface, according to the designed visualization effect. Data transmission is mainly responsible for the data interaction between the data acquisition system and the digital visualization large screen system, to ensure that the large screen system can quickly and completely receive the real-time data of the field device and the key data collected from various related information systems. Data is interacted through the data interface between the data acquisition system and the visualization large screen system, and through the subscription and publication mode, various core data is directly distributed to each related unit of the large screen system through the classification standard interface.

[0062] 6. Fault early warning processing: The virtual model is built to monitor the real-time dynamic parameter data of the whole vehicle, and the threshold of each parameter is set. Through the detection of the change curve of the parameter, the early warning of the fault is carried out. In this process, the fault classification is carried out through the monitoring of the variable name, the threshold judgment is added to divide the fault level into three levels of high, medium and low. Finally, the fault classification and fault level are determined by the binary indicator light flashing mode, and the determination result is submitted to the expert library to identify the corresponding processing measures.

[0063] 7. Traceable working condition processing: The virtual model records the whole vehicle data fragments. Once a fault occurs during the operation of the whole vehicle, the running parameters before the fault occurs can be traced through the twin virtual model, and the running parameters are input into the virtual model to intuitively display the state of the whole vehicle at the moment of the fault, and then the fault reason is located. In the process of fault early warning and working condition tracing of the twin model, as shown in Figure 4

[0064] In the above process, the digital twin technology is used to build a virtual model of the actual vehicle operation, to find and record abnormalities through the running of the virtual model, to improve the cognition, monitoring and fault early warning of the test vehicle operation; in addition, the virtual model records data in real time, traces the working condition at the moment of the problem, to avoid the problem of difficult reproduction of complex working conditions.

[0065] Embodiment two:

[0066] The whole vehicle test system based on digital twin includes:

[0067] The data acquisition module is configured to obtain attribute information of a vehicle to be tested, driving data and environment data during a test period, and save and preprocess the attribute information;

[0068] The digital twin module is configured to construct an attribute model capable of reflecting vehicle appearance constraints and shape constraints according to the preprocessed attribute information.

[0069] ​The digital twin module is further configured to determine a behavior model capable of reflecting position transformation and motion logic relationship of the vehicle during the test according to the preprocessed driving data and the environmental data, and associate the attribute model with the behavior model through the logic model;

[0070] The fault monitoring and working condition reproduction module is configured to determine corresponding dynamic parameter data of the vehicle during driving according to the associated attribute model and behavior model, obtain corresponding curve changes, and perform early warning on the fault according to the curve changes and a set threshold; and trace the fault working condition according to vehicle operating parameters in a set time period before a fault time point after the fault occurs.

[0071] Embodiment three:

[0072] The embodiment provides a computer readable storage medium, which stores a computer program, and the program is executed by a processor to implement steps in the whole vehicle test method based on the digital twin.

[0073] Embodiment four:

[0074] The embodiment provides a computer device, which comprises a memory, a processor, and a computer program stored in the memory and capable of running on the processor, and the processor implements steps in the whole vehicle test method based on the digital twin when executing the program.

[0075] The steps involved in the above embodiments two to four correspond to the embodiment one, and the specific implementation can be referred to the related description part of the embodiment one. The term "computer readable storage medium" should be understood as including a single medium or multiple media of one or more instruction sets; and should also be understood as including any medium capable of storing, encoding, or carrying instruction sets for execution by a processor and causing the processor to perform any method in the present application.

[0076] The above only describes the preferred embodiments of the present application and is not used to limit the present application. For those skilled in the art, the present application can have various modifications and changes. Any modification, equivalent replacement, improvement, etc. within the spirit and principles of the present application should be included in the protection scope of the present application.

Claims

1. A vehicle test method based on digital twinning, characterized in that, The method comprises the following steps: Obtaining attribute information of a vehicle to be tested, driving data and environmental data during the test, and preprocessing; The attribute information of the vehicle to be tested is static data, and the driving data and environmental data during the test are dynamic data; The data preprocessing comprises static data preprocessing of a storage database and dynamic data preprocessing of a control terminal, the preprocessing of the static data can complete a data cleaning process by using an interpolation algorithm, and the preprocessing of the dynamic data is performed by using a LOF algorithm based on distance density; According to the attribute information after preprocessing, an attribute model capable of reflecting vehicle appearance constraints and shape constraints is constructed; According to the driving data and environmental data after preprocessing, a behavior model capable of reflecting position transformation and motion logical relationship of the vehicle during the test is determined, and the attribute model and the behavior model are associated through a logical model; According to the associated attribute model and behavior model, corresponding dynamic parameter data of the vehicle during driving is determined, corresponding curve changes are obtained, and a fault is prewarned according to the curve changes and a set threshold value. After the fault occurs, the fault is traced back to the working condition according to the vehicle operating parameters in a set time period before the fault time point. 2.The digital-twin-based whole vehicle test method of claim 1, wherein, The attribute information of the vehicle to be tested comprises at least one or more of vehicle appearance, brand, color, number, evaluation information, key component service life and maintenance information. 3.The digital-twin-based whole vehicle test method of claim 1, wherein, The attribute information of the vehicle to be tested, the driving data and the environmental data during the test are saved into a database, specifically, the database comprises a real-time database and a storage database, the driving data and the environmental data during the test are saved in the real-time database, and the attribute information is saved in the static database. 4.The digital-twin-based whole vehicle test method of claim 1, wherein, The attribute model and the behavior model associated through the logical model form a virtual model, the virtual model obtains the operating parameters of the vehicle during the test and corresponding curve changes. 5.The digital-twin-based whole vehicle test method of claim 1, wherein, The fault is prewarned through the vehicle driving parameters and corresponding curve changes in the virtual model and a set threshold value. 6.The vehicle test method based on digital twinning according to claim 1, wherein, During the prewarning, the determined fault condition is classified into a corresponding fault level and category according to the monitored variables in the curve, and corresponding processing measures are obtained according to an expert library. 7.The digital-twin-based whole vehicle test method of claim 1, wherein, After the fault occurs, the fault time point is determined through the data in the virtual model, the vehicle operating parameters in a set time period before the fault time point are obtained, the state of the vehicle when the fault occurs is displayed, and the fault working condition is traced back.

8. A whole vehicle test system based on digital twinning, characterized by It comprises: A data acquisition module configured to obtain attribute information of a vehicle to be tested, driving data and environmental data during the test, and save and preprocess the attribute information, the driving data and the environmental data; The attribute information of the vehicle to be tested is static data, and the driving data and environmental data during the test are dynamic data; The data preprocessing comprises static data preprocessing of a storage database and dynamic data preprocessing of a control terminal, the preprocessing of the static data can complete a data cleaning process by using an interpolation algorithm, and the preprocessing of the dynamic data is performed by using a LOF algorithm based on distance density; A digital twin module configured to construct an attribute model capable of reflecting vehicle appearance constraints and shape constraints according to the attribute information after preprocessing; The digital twin module is further configured to determine a behavior model capable of reflecting position transformation and motion logic relationship of the vehicle during the test according to the preprocessed driving data and the environmental data, and associate the attribute model with the behavior model through the logic model; The fault monitoring and working condition reproduction module is configured to determine corresponding dynamic parameter data of the vehicle during driving according to the associated attribute model and behavior model, obtain corresponding curve changes, and give a warning according to the curve changes and a set threshold value. After the fault occurs, the working condition of the fault is traced according to the vehicle operating parameters in a set time period before the fault time point.

9. A computer-readable storage medium, characterized in that, A computer program is stored thereon, which is executed by a processor to implement the steps in the whole vehicle test method based on digital twin according to any one of claims 1-7.

10. A computer device, comprising: A computer program is stored thereon, which is executed by a processor to implement the steps in the whole vehicle test method based on digital twin according to any one of claims 1-7.

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