Vehicle performance testing method and device based on mechanical arm, electronic equipment and medium

Abstract

The invention relates to a vehicle performance testing method and device based on a mechanical arm, electronic equipment and a medium, and the method comprises the steps: opening a vehicle door through a first mechanical arm to wake up a vehicle system; in response to the situation that the vehicle-mounted terminal system is awakened, a target application in the vehicle-mounted terminal system is operated through a second mechanical arm, and a plurality of first images in the operation process of the target application are obtained; and determining a performance test result of the in-vehicle infotainment system based on the plurality of first images. According to the technical scheme, the mechanical arm operates the real vehicle door to achieve dormancy awakening of the vehicle machine system, the application is operated through the mechanical arm, the effect close to the use scene that a user actually opens and closes the vehicle door and operates the application can be obtained, the performance test result is determined according to the multiple first images, framing operation on a video is avoided, and the performance test efficiency is improved. According to the scheme, automatic testing in the whole process is achieved, and the execution efficiency is high.

Description

Technical Field

[0001] The present disclosure relates to the field of automated testing technology, and in particular to a vehicle computer performance testing method, device, electronic equipment and medium based on a robotic arm. Background Art

[0002] Sleep wakeup is a common function of the Digital Cockpit Head Unit (DHU, also known as the car system), and the performance stability of the DHU after sleep wakeup is crucial to the user's real car experience. At present, in the relevant technology, the test method for the performance stability of the DHU after sleep wakeup is usually implemented by manual operation. The DHU is woken up manually on the actual car, and then the application in the car system is manually opened. At the same time, the video is shot and the log is captured. Whether there is a black screen or stuck abnormal situation, the recorded video is divided into frames to calculate the response time and fluency results of the application.

[0003] However, the above-mentioned manual testing method requires manual operation during the testing process, and has low execution efficiency for repeated long-term tests. In addition, there are certain errors in the response time and fluency performance results of manual analysis, resulting in poor accuracy of the test results. Summary of the invention

[0004] In order to solve the above technical problems or at least partially solve the above technical problems, at least one embodiment of the present disclosure provides a vehicle performance testing method, device, electronic device and medium based on a robotic arm.

[0005] In a first aspect, the present disclosure provides a vehicle computer performance testing method based on a robotic arm, comprising:

[0006] Open the door through the first mechanical arm to wake up the vehicle computer system;

[0007] In response to the vehicle system being awakened, operating a target application in the vehicle system through a second mechanical arm, and acquiring a plurality of first images during the operation of the target application;

[0008] A performance test result of the vehicle system is determined based on the multiple first images.

[0009] In a second aspect, the present disclosure provides a vehicle performance testing device based on a robotic arm, comprising:

[0010] A wake-up module, used for waking up the vehicle system by opening the vehicle door through the first mechanical arm;

[0011] an operating module, configured to operate a target application in the vehicle-mounted system through a second mechanical arm in response to the vehicle-mounted system being awakened, and acquire a plurality of first images during the operation of the target application;

[0012] A result determination module is used to determine the performance test result of the vehicle system based on the multiple first images.

[0013] In a third aspect, the present disclosure provides an electronic device, including: a processor and a memory;

[0014] The processor is used to execute the vehicle computer performance testing method based on the robotic arm as described in the first aspect by calling the program or instruction stored in the memory.

[0015] In a fourth aspect, the present disclosure provides a computer-readable storage medium, wherein the computer-readable storage medium stores a program or instruction, wherein the program or instruction enables a computer to execute the vehicle-machine performance testing method based on a robotic arm as described in the first aspect.

[0016] In a fifth aspect, the present disclosure provides a computer program product, which is used to execute the vehicle computer performance testing method based on a robotic arm as described in the first aspect.

[0017] Compared with the prior art, the technical solution provided by the embodiments of the present disclosure has at least the following advantages:

[0018] In the disclosed embodiment, the vehicle door is opened by the first mechanical arm to wake up the vehicle system; in response to the vehicle system being awakened, the target application in the vehicle system is operated by the second mechanical arm, and multiple first images of the target application being operated are obtained; the performance test results of the vehicle system are determined based on the multiple first images. By adopting the above technical scheme, the vehicle system is awakened from sleep mode by operating the door of the real vehicle by the mechanical arm, and the application is operated by the mechanical arm, so that the effect close to the user's actual opening and closing of the door and the use scenario of operating the application can be obtained, and the performance test results are determined based on multiple first images, avoiding the operation of framing the video, and the implementation is simple. This scheme realizes full-process automated testing with high execution efficiency, and solves the problem of low execution efficiency through manual operation during repeated tests. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and, together with the description, serve to explain the principles of the present disclosure.

[0020] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0021] Figure 1 A schematic flow chart of a vehicle computer performance testing method based on a robotic arm provided by an exemplary embodiment of the present disclosure;

[0022] Figure 2 A schematic flow chart of a vehicle machine performance testing method based on a robotic arm provided by another exemplary embodiment of the present disclosure;

[0023] Figure 3 A schematic structural diagram of a vehicle computer performance testing device based on a robotic arm provided in one embodiment of the present disclosure. DETAILED DESCRIPTION

[0024] In order to more clearly understand the above-mentioned purposes, features and advantages of the present disclosure, the present disclosure is further described in detail below in conjunction with the accompanying drawings and embodiments. It is understandable that the described embodiments are part of the embodiments of the present disclosure, rather than all of the embodiments, and the specific embodiments described herein are only used to explain the present disclosure, rather than to limit the present disclosure. In the absence of conflict, the embodiments of the present disclosure and the features in the embodiments can be combined with each other. Based on the described embodiments of the present disclosure, all other embodiments obtained by ordinary technicians in the field belong to the scope of protection of the present disclosure.

[0025] In the following description, many specific details are set forth to facilitate a full understanding of the present disclosure, but the present disclosure may also be implemented in other ways different from those described herein; it is obvious that the embodiments in the specification are only part of the embodiments of the present disclosure, rather than all of the embodiments.

[0026] Currently, there are two main testing methods for DHU performance stability after sleep and wake-up:

[0027] (1) The DHU is manually awakened from sleep mode by operating the actual vehicle, and then the APP is manually opened. A high-speed camera is used to record videos and capture logs. The black screen and freeze conditions are manually checked. The recorded video is then divided into frames to calculate the response time and fluency of the APP. However, the performance results obtained by manually awakening the DHU from sleep mode and detecting black screen and freeze conditions, manually operating the APP and recording videos, and then analyzing the videos have errors and poor accuracy, and the execution efficiency is low when the test is repeated many times.

[0028] (2) Send a sleep wake-up signal to the DHU test bench through a signal simulation device to simulate the sleep wake-up scenario of the real vehicle DHU, then use the Android Debug Bridge (ADB) command to open the specified APP, and use the ADB command to record the video and capture the relevant logs, then divide the video into frames and parse the logs, and finally detect the response time, fluency and other performance of the APP after the DHU sleep wake-up, as well as whether there are stability anomalies such as black screen and stuck. However, although this test method can achieve full automation, it simulates the sleep wake-up of the DHU through signals, which is not close enough to the actual use scenario of the real vehicle DHU. The performance results measured by opening the APP and recording the video through the ADB command, and then dividing the recorded video into frames and parsing the measured results have errors, poor accuracy, and complex code implementation. In addition, there are certain omissions in the detection of black screen and stuck anomalies through log analysis, which makes the test results inaccurate.

[0029] In view of the above problems, the present disclosure provides a vehicle computer performance test solution based on a robotic arm. In this solution, the robotic arm is used to simulate the user's actions of opening the car door and operating the application, and multiple first images are collected during the operation of the application. The multiple first images are used to analyze the performance test results of the vehicle computer system, including but not limited to the response time, fluency, and whether a black screen or freeze (stuck) occurs in the application. Compared with the above existing solutions, this solution has the following advantages:

[0030] 1) The DHU is awakened from sleep mode by operating the door of the real vehicle through a mechanical arm. Compared with simulating DHU awakening from sleep mode by sending signals through a signal simulation device, this solution is closer to the actual door opening and closing usage scenario of the user, and the test results are more accurate. This solves the problem of sleep awakening failure or inaccurate test results when simulating DHU awakening from sleep mode by sending signals through a signal simulation device. Compared with manually operating the real vehicle to awaken DHU from sleep mode, this solution can realize automated testing with high execution efficiency.

[0031] 2) This solution uses a high-speed camera to shoot images during the APP operation process, and then uses a picture grayscale value comparison algorithm to analyze the pictures to calculate the corresponding response time and fluency results, and can detect black screen and stuck problems. Compared with the method of recording videos through ADB commands and then dividing the video into frames to obtain screenshots to calculate the results, this solution skips the steps of recording and dividing the frames, and is simple to implement and has high execution efficiency. It solves the problem of opening the APP and recording videos through ADB commands and then dividing the video into frames to obtain screenshots for performance analysis, which leads to many and complex code implementation steps and low execution efficiency. In addition, compared with detecting black screen and stuck by parsing logs, the detection results of this solution are more accurate, and there is no problem of missing problems, which solves the problem of missing problems when detecting black screen and stuck by parsing logs;

[0032] 3) Opening the APP through the ADB command will cause errors in determining the starting frame of the response time, resulting in inaccurate response time test results. However, this solution uses a robotic arm to click to open the APP, which is closer to the actual usage scenario, and the starting frame is accurately determined, so the response time test results are more accurate;

[0033] 4) This solution can realize full-process automated testing with high execution efficiency, solving the problem of low execution efficiency through manual operation during repeated testing.

[0034] The specific implementation methods of the vehicle performance testing method, device, electronic equipment and medium based on a robotic arm disclosed in the present invention are explained in detail below with reference to the accompanying drawings.

[0035] Figure 1 A flow chart of a vehicle computer performance testing method based on a robotic arm provided for an exemplary embodiment of the present disclosure. The method can be executed by a vehicle computer performance testing device based on a robotic arm provided for an embodiment of the present disclosure. The vehicle computer performance testing device based on a robotic arm can be implemented by software and / or hardware and can be integrated in an electronic device. The electronic device can be a host computer that executes test cases to perform performance stability tests on the vehicle computer system of a real vehicle.

[0036] like Figure 1 As shown, the vehicle computer performance testing method based on the mechanical arm may include the following steps:

[0037] Step 101, opening the vehicle door by a first robotic arm to wake up the vehicle computer system.

[0038] The first robot arm (including the robot and its own high-speed camera) is placed next to the door of the real car to operate the door of the real car and collect images of the door area. High-speed cameras are a type of industrial cameras. Compared with ordinary cameras, high-speed cameras have high image stability, high transmission capacity and high anti-interference ability.

[0039] In this embodiment, during the test, the door of the actual vehicle is in a closed state, the DHU enters a dormant state, and the electronic device opens the door of the actual vehicle through the first mechanical arm. After the door is successfully opened, the DHU of the actual vehicle is awakened.

[0040] Step 102 : in response to the vehicle computer system being awakened, operating a target application in the vehicle computer system through a second robotic arm, and acquiring a plurality of first images during the operation of the target application.

[0041] Among them, the second robotic arm (including a robotic arm and its own high-speed camera) is placed inside the actual vehicle, and is used to operate the application in the vehicle system and collect images during the operation.

[0042] In this embodiment, when the vehicle system (DHU) is awakened, the electronic device can operate the target application in the vehicle system through the second mechanical arm, wherein the target application is the application program that the currently executed test case is testing. Before conducting the test, for each performance test type (such as response time, fluency, black screen, freeze), it is necessary to write a corresponding test case for each application participating in the test, operate the corresponding application (i.e., target application) by executing the test case, and obtain multiple images of the target application during the operation (for ease of description and distinction, referred to as first images), and no image is obtained when no operation is performed. Among them, the high-speed camera of the second mechanical arm can be used to capture multiple first images, and the multiple first images captured are related to the performance test of the corresponding performance test type. By analyzing the multiple first images, the performance of the corresponding performance test type can be analyzed.

[0043] As an example, when the high-speed camera of the second robotic arm captures multiple first images, the high-speed camera can capture the first images throughout the process of the second robotic arm operating the target application until the operation is completed.

[0044] As an example, when the high-speed camera of the second robotic arm collects multiple first images, it can collect corresponding first images when the second robotic arm performs operations related to the performance test type. For example, when executing a test case for response time test, the high-speed camera of the second robotic arm starts collecting the first image when the second robotic arm clicks the icon of the target application in the vehicle system interface, collects the first image according to the frequency of the high-speed camera itself, and stops collecting images when the duration of collecting images reaches a preset duration; when executing a test case for fluency test, the high-speed camera of the second robotic arm does not collect images when the second robotic arm clicks the icon of the target application in the vehicle system interface, and only starts collecting the first image after the target application is started and the second robotic arm slides the display page of the target application.

[0045] Step 103: determining a performance test result of the vehicle system based on the multiple first images.

[0046] In this embodiment, the performance test result of the vehicle system can be determined by comparing and calculating the obtained multiple first images. The performance of the vehicle system can be reflected by the performance data of the application. For example, a short response time of the application reflects that the performance of the vehicle system is good.

[0047] As an example, by directly comparing multiple first images, it is possible to detect whether the target application has abnormal conditions such as black screen and freeze, and by comparing the differences between the first images, the response start frame and end frame in the multiple first images can be determined to determine the response time performance, and the smoothness performance can be determined by analyzing the same images in the multiple first images.

[0048] As an example, for the multiple first images obtained, the multiple first images can be gray-processed first to obtain a gray image corresponding to each first image (referred to as a first gray image for ease of description and distinction), thereby obtaining multiple corresponding first gray images, and then, based on the gray values ​​of the multiple first gray images, determine the performance test results of the vehicle system. Specifically, the response time and fluency results of the APP and whether there are black screen or stuck problems are calculated by comparing the image gray values. The specific detection methods are explained below for different performance detection types.

[0049] In an optional embodiment of the present disclosure, the performance test results include the response time of the target application. When determining the performance test results of the vehicle system based on the grayscale values ​​of multiple first grayscale images, a candidate starting image having a grayscale difference with a preset interface grayscale image greater than a first threshold value can be determined based on the grayscale values ​​of the multiple first grayscale images, and a candidate ending image having the same grayscale value as a preset application homepage grayscale image can be determined, wherein the interface grayscale image refers to a grayscale image corresponding to an image of the vehicle system display interface including an icon of the target application, that is, a grayscale image of the interface image before the second mechanical arm clicks the target application, by clicking the target application in the interface, the target application is started to respond to the click operation of the second mechanical arm, and the homepage of the target application is gradually displayed, the application homepage grayscale image is a grayscale image of the homepage page image of the complete target application, and when the homepage of the target application is displayed in the vehicle system display interface, it indicates that the target application response has ended, and the value of the first threshold can be pre-set according to actual needs. It can be understood that when the icon of the target application is clicked, the icon will usually appear to be enlarged or reduced. By determining this change, the response start frame of the target application can be determined. In this embodiment, the first grayscale image whose grayscale difference with the interface grayscale image is greater than a first threshold can be screened out as a candidate starting image based on the grayscale difference between each first grayscale image and the interface grayscale image. These images are all images that have changed compared to the interface image when the second robotic arm opens the target application, reflecting the process of the target application starting to respond. Next, based on the acquisition time of the multiple first images corresponding to the multiple first grayscale images, the earliest acquisition time among the acquisition times of the candidate start images is determined as the response start time, and the earliest acquisition time among the acquisition times of the candidate end images is determined as the response end time. That is to say, the multiple first grayscale images have the same acquisition time as the first images corresponding to them. For the determined candidate start images, according to the acquisition time of each candidate start image, the earliest acquisition time is determined as the response start time, and the candidate start image with the earliest acquisition time is the response start frame; for the acquired candidate end images, according to the acquisition time of each candidate end image, the earliest acquisition time is determined as the response end time, and the candidate end image with the earliest acquisition time is the response end frame. Finally, according to the difference between the response start time and the response end time, the response time of the target application is determined.

[0050] In addition, optionally, after determining the response time of the target application, the response time can be compared with the expected response time. If the expected response time is not reached (i.e., the calculated response time is greater than the expected response time), the first image corresponding to the response start frame and the response end frame is automatically saved and the log information is captured to facilitate developers to trace back and solve the problem later.

[0051] In an optional embodiment of the present disclosure, the performance test result includes fluency. When the performance test result of the vehicle system is determined based on the grayscale values ​​of the plurality of first grayscale images, a candidate grayscale image within a preset time length can be determined from the plurality of first grayscale images according to the operation time of performing the page sliding operation through the second mechanical arm and the acquisition time of the plurality of first images, wherein the acquisition time of the candidate grayscale image is not earlier than the operation time, and the value of the preset time length can be set according to actual needs, such as setting the preset time length to 1 second. It should be noted that the plurality of first grayscale images have the same acquisition time as the plurality of first images corresponding thereto, the acquired candidate grayscale images are a plurality of continuous images within the preset time length whose acquisition time is not earlier than the operation time and is closest to the operation time among the plurality of first grayscale images, the candidate grayscale images are adjacent plurality of first grayscale images, the first grayscale image among the candidate grayscale images is not earlier than the operation time and is closest to the operation time, and the time interval between the last grayscale image among the candidate grayscale images and the first grayscale image is not greater than the preset time length. Next, the number of consecutive images with the same grayscale value in the candidate grayscale image can be counted, and then the fluency can be determined based on the number. It can be understood that when sliding the page, if the same continuous images are collected, the task fluency is not good, and the more the number of consecutive identical images in the candidate grayscale image, the worse the fluency. Therefore, the fluency can be determined based on the number of identical consecutive images. For example, the correspondence between different numbers of consecutive identical images and fluency values ​​can be pre-defined. After counting the number of consecutive images with the same grayscale value in the candidate grayscale image, the fluency of the target application can be determined by querying the correspondence.

[0052] In addition, optionally, after determining the fluency of the target application, the fluency can be compared with the expected fluency. If the expected fluency is not achieved (i.e., the calculated fluency is greater than the expected fluency), the first image corresponding to the candidate grayscale image is automatically saved and the log information is captured to facilitate developers to trace back and solve the problem later.

[0053] In an optional embodiment of the present disclosure, the performance test result includes a black screen. When determining the performance test result of the vehicle system based on the grayscale values ​​of multiple first grayscale images, it can be determined based on the grayscale values ​​of multiple first grayscale images whether there is a first grayscale image in the multiple first grayscale images whose grayscale values ​​of the entire image are all target values; if so, it is determined that a black screen appears in the target application. The target value is the pixel value representing black in the grayscale image. For example, 0 in the grayscale image represents black, and the target value is set to 0. If the grayscale values ​​of all pixels of a first grayscale image are the target values, the first grayscale image is determined to be a completely black image, and the corresponding first image is also a completely black image, so that it can be determined that a black screen appears in the target application during operation.

[0054] In addition, optionally, when it is determined that a black screen appears on the target application, the first image corresponding to the first grayscale image whose grayscale values ​​are all target values ​​can also be saved, and relevant log information can be captured to facilitate developers to trace and solve the problem later.

[0055] In an optional embodiment of the present disclosure, the performance test results include freezes (stuck). When the performance test results of the vehicle system are determined based on the grayscale values ​​of multiple first grayscale images, it can be determined that the target application has freezes when the grayscale values ​​of multiple continuous grayscale images in the multiple first grayscale images are the same. For example, when the grayscale values ​​of the middle multiple continuous grayscale images in the multiple first grayscale images are the same, but the grayscale values ​​of these grayscale images are different from the grayscale values ​​of the subsequent partial grayscale images, it can be determined that the target application has recovered after freezing. If the grayscale value of the last first grayscale image in the multiple first grayscale images is the same as the grayscale value of the adjacent multiple continuous grayscale images, it can be determined that the target application has not recovered after freezing, that is, it has been frozen. It can be understood that when the application is stuck, the first image captured by the high-speed camera of the second robotic arm no longer changes, and thus the grayscale value of the first image no longer changes. Therefore, if the grayscale values ​​of these first grayscale images are the same from a certain first grayscale image to the last first grayscale image, it can be determined that the target application is stuck.

[0056] In addition, optionally, when it is determined that the target application is stuck, the first image corresponding to the relevant first grayscale image may be saved, and relevant log information may be captured to facilitate developers to trace back and solve the problem later.

[0057] The vehicle computer performance test method based on a robotic arm in the disclosed embodiment opens the vehicle door through the first robotic arm to wake up the vehicle computer system; in response to the vehicle computer system being awakened, operates the target application in the vehicle computer system through the second robotic arm, and obtains multiple first images during the operation of the target application; and determines the performance test result of the vehicle computer system based on the multiple first images. By adopting the above technical scheme, the vehicle computer system is awakened from sleep by operating the door of the real vehicle through the robotic arm, and the application is operated through the robotic arm, so that the effect close to the user's actual opening and closing of the door and the use scenario of operating the application can be obtained, and the performance test result is determined based on the multiple first images, avoiding the operation of framing the video, and the implementation is simple. This scheme realizes the whole process of automated testing with high execution efficiency, and solves the problem of low execution efficiency through manual operation during repeated tests.

[0058] In an optional embodiment of the present disclosure, Figure 2 As shown, based on the above embodiment, step 101 may include the following sub-steps:

[0059] Step 201: operate the car key through the first mechanical arm to unlock the car door.

[0060] Among them, the car key can be fixedly placed at a position next to the car door where the first mechanical arm can operate.

[0061] In this embodiment, at the beginning of the test, the door of the actual vehicle is in a locked state, the DHU enters a dormant state, and the electronic device can operate the car key through the first mechanical arm to unlock the door.

[0062] Step 202: Open the vehicle door by using the first robotic arm, and obtain a plurality of second images captured by the high-speed camera of the first robotic arm.

[0063] In this embodiment, after the first robotic arm operates the car key to unlock the car door, the first robotic arm can continue to open the car door. The first robotic arm can open and close the handle-type car door and the button-type car door. While the first robotic arm performs the action of opening the car door, the high-speed camera of the first robotic arm is used to capture images of the door area to obtain multiple images of the process of opening the car door (referred to as second images for ease of description and distinction).

[0064] Step 203: grayscale processing is performed on the plurality of second images to obtain a plurality of second grayscale images.

[0065] In this embodiment, after obtaining multiple second images, each second image can be grayscale processed separately to obtain a grayscale image corresponding to each second image (referred to as a second grayscale image for ease of description and distinction), thereby obtaining multiple second grayscale images.

[0066] Step 204 : when there are differences in the grayscale values ​​of the plurality of second grayscale images, determine that the vehicle door is open.

[0067] In this embodiment, after obtaining multiple second grayscale images, it is possible to determine whether the door is successfully opened by analyzing whether there is a difference in the grayscale values ​​of the multiple second grayscale images. After the door is successfully opened, the DHU of the actual vehicle is awakened.

[0068] It can be understood that when the door changes from closed to open, the position of the door corresponding to the frame will change, the captured second image will change, and the grayscale value in the second grayscale image corresponding to the second image will change. Therefore, in this embodiment, by analyzing the grayscale value changes of multiple second grayscale images during the door opening process, it can be determined whether the door is successfully opened. If the door is not opened, the multiple second grayscale images will have the same grayscale value, and if the door is opened, the grayscale values ​​of the multiple second grayscale images will be different.

[0069] In an optional embodiment of the present disclosure, for multiple second grayscale images, it is possible to analyze whether there are differences in the grayscale values ​​of the target areas of the multiple second grayscale images to determine whether the door is successfully opened. The target area is the area including the door, and it can also be the area including the junction between the door and the car frame. It can be understood that after the second mechanical arm is fixed, the camera angle of its built-in high-speed camera is also fixed. If the door does not move in the captured second image, the door is a fixed area in the second image. Therefore, if the grayscale of the fixed area changes, it can be determined that the door is successfully opened. For example, the target area can be determined as the right half of the image area of ​​the second grayscale image. If the grayscale value of this part of the area changes, it is determined that the door is successfully opened.

[0070] The vehicle computer performance testing method based on a robotic arm in the disclosed embodiment operates the car key to unlock the vehicle door through the first robotic arm, opens the vehicle door through the first robotic arm, and obtains multiple second images captured by the high-speed camera of the first robotic arm, performs grayscale processing on the multiple second images to obtain multiple second grayscale images, and determines that the vehicle door is open when there are differences in the grayscale values ​​of the multiple second grayscale images. Thus, the DHU sleep wake-up is realized by operating the real vehicle door through the robotic arm, which is closer to the user's actual door opening and closing usage scenario.

[0071] In an optional implementation of the present disclosure, after the performance test result is determined, the vehicle door can be closed by the first mechanical arm, and the vehicle key can be operated by the first mechanical arm to lock the vehicle door. After the vehicle door is locked for a preset time (e.g., 10 minutes), it is determined that the DHU enters a dormant state. After that, the vehicle door can be opened by the first mechanical arm to wake up the vehicle system, and the next test case can be executed to complete the test of the next application or the next performance test type.

[0072] In summary, the vehicle computer performance test solution based on the robotic arm provided by the present disclosure can be fully automated. The operation of waking up the DHU of the real vehicle based on the robotic arm is closer to the actual user scenario. The test results are accurate, and the acquisition of images for performance analysis skips the step of recording video frames. The code is simple to implement, and repeated long-term tests can be performed many times with high execution efficiency. At present, this solution has been implemented in the performance stability automation script, which is suitable for the performance stability test of DHU waking up of various brands and models, and has good applicability.

[0073] In order to implement the above embodiments, the present disclosure also provides a vehicle computer performance testing device based on a robotic arm. The vehicle computer performance testing device based on a robotic arm can be implemented using software and / or hardware and can be integrated into an electronic device.

[0074] Figure 3A schematic diagram of a vehicle performance testing device based on a mechanical arm according to an embodiment of the present disclosure is shown in FIG. Figure 3 As shown, the vehicle performance testing device 30 based on a robotic arm may include: a wake-up module 310 , an operation module 320 and a result determination module 330 .

[0075] The wake-up module 310 is used to wake up the vehicle system by opening the vehicle door through the first mechanical arm;

[0076] An operating module 320, configured to operate a target application in the vehicle-mounted system through a second mechanical arm in response to the vehicle-mounted system being awakened, and acquire a plurality of first images during the operation of the target application;

[0077] The result determination module 330 is used to determine the performance test result of the vehicle system based on the multiple first images.

[0078] Optionally, the wake-up module 310 is further configured to:

[0079] The first mechanical arm operates the vehicle key to unlock the vehicle door;

[0080] Performing an opening action on the vehicle door by using the first mechanical arm, and acquiring a plurality of second images captured by the high-speed camera of the first mechanical arm;

[0081] Performing grayscale processing on the plurality of second images to obtain a plurality of second grayscale images;

[0082] When there are differences in the grayscale values ​​of the plurality of second grayscale images, it is determined that the vehicle door is opened.

[0083] Optionally, the result determination module 330 includes:

[0084] A grayscale processing unit, used for performing grayscale processing on the plurality of first images to obtain a plurality of first grayscale images;

[0085] The result determination unit is used to determine the performance test result of the vehicle system based on the grayscale values ​​of the plurality of first grayscale images.

[0086] Further optionally, the performance test result includes a response time of the target application; and the result determination unit is further configured to:

[0087] Based on the grayscale values ​​of the plurality of first grayscale images, determining a candidate start image having a grayscale difference with a preset interface grayscale image greater than a first threshold, and determining a candidate end image having the same grayscale value as a preset application homepage grayscale image;

[0088] Based on the acquisition time of the multiple first images corresponding to the multiple first grayscale images, determining the earliest acquisition time among the acquisition times of the candidate start images as the response start time, and determining the earliest acquisition time among the acquisition times of the candidate end images as the response end time;

[0089] The response time of the target application is determined based on the difference between the response start time and the response end time.

[0090] Optionally, the performance test result includes fluency; the result determination unit is further configured to:

[0091] Determining a candidate grayscale image within a preset time period from the plurality of first grayscale images according to an operation time of performing a page sliding operation by the second mechanical arm and an acquisition time of the plurality of first images, wherein the acquisition time of the candidate grayscale image is not earlier than the operation time;

[0092] Count the number of consecutive images with the same grayscale value in the candidate grayscale images;

[0093] Determine fluency based on quantity.

[0094] Optionally, the performance test result includes a black screen; and the result determination unit is further configured to:

[0095] Based on the grayscale values ​​of the plurality of first grayscale images, determining whether there is a first grayscale image in the plurality of first grayscale images whose entire grayscale value is the target value;

[0096] If it exists, it is determined that the target application has a black screen.

[0097] Optionally, the performance test result includes jamming; and the result determination unit is further configured to:

[0098] When the grayscale values ​​of a plurality of continuous grayscale images in the plurality of first grayscale images are the same, it is determined that a freeze occurs in the target application.

[0099] The vehicle computer performance test device based on a mechanical arm provided in the embodiment of the present disclosure can execute the vehicle computer performance test method based on a mechanical arm provided in the embodiment of the present disclosure, and has the corresponding functional modules and beneficial effects of the execution method. For the contents not fully described in the embodiment of the device of the present disclosure, reference can be made to the description in any method embodiment of the present disclosure.

[0100] The disclosed embodiment also provides an electronic device including a processor and a memory; the processor calls the program or instructions stored in the memory to execute the steps of the various embodiments of the vehicle performance testing method based on the robotic arm as described above, which will not be repeated here to avoid repeated description.

[0101] The embodiments of the present disclosure also provide a computer-readable storage medium, which is non-transitory and stores programs or instructions. The programs or instructions enable a computer to execute the steps of the embodiments of the vehicle performance testing method based on a robotic arm as described above. To avoid repeated description, they will not be repeated here.

[0102] The embodiments of the present disclosure also provide a computer program product, which is used to execute the steps of the various embodiments of the aforementioned vehicle performance testing method based on a robotic arm.

[0103] It should be noted that, in this article, relational terms such as "first" and "second" are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0104] The above description is only a specific embodiment of the present disclosure, so that those skilled in the art can understand or implement the present disclosure. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present disclosure. Therefore, the present disclosure will not be limited to the embodiments described herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A vehicle computer performance testing method based on a robotic arm, characterized in that: The method comprises: Open the door through the first mechanical arm to wake up the vehicle computer system; In response to the vehicle system being awakened, operating a target application in the vehicle system through a second mechanical arm, and acquiring a plurality of first images during the operation of the target application; A performance test result of the vehicle system is determined based on the multiple first images.

2. The method according to claim 1, characterized in that The method of opening the vehicle door by the first mechanical arm to wake up the vehicle system includes: Operating the vehicle key through the first mechanical arm to unlock the vehicle door; Performing an opening action on the vehicle door by using the first mechanical arm, and acquiring a plurality of second images captured by the high-speed camera of the first mechanical arm; Performing grayscale processing on the plurality of second images to obtain a plurality of second grayscale images; When there are differences in the grayscale values ​​of the plurality of second grayscale images, it is determined that the vehicle door is opened.

3. The method according to claim 1, characterized in that: The determining the performance test result of the vehicle system based on the plurality of first images includes: Performing grayscale processing on the plurality of first images to obtain a plurality of first grayscale images; Based on the grayscale values ​​of the plurality of first grayscale images, a performance test result of the vehicle system is determined.

4. The method according to claim 3, characterized in that The performance test results include the response time of the target application; The step of determining the performance test result of the vehicle system based on the grayscale values ​​of the plurality of first grayscale images includes: Based on the grayscale values ​​of the plurality of first grayscale images, determining a candidate start image having a grayscale difference with a preset interface grayscale image greater than a first threshold, and determining a candidate end image having the same grayscale value as a preset application homepage grayscale image; Based on the acquisition times of the multiple first images corresponding to the multiple first grayscale images, determining the earliest acquisition time among the acquisition times of the candidate start images as the response start time, and determining the earliest acquisition time among the acquisition times of the candidate end images as the response end time; The response time of the target application is determined according to the difference between the response start time and the response end time.

5. The method according to claim 3, characterized in that: The performance test results include fluency; The step of determining the performance test result of the vehicle system based on the grayscale values ​​of the plurality of first grayscale images includes: Determining, from the plurality of first grayscale images, candidate grayscale images within a preset time period according to an operation time of performing a page sliding operation by the second mechanical arm and an acquisition time of the plurality of first images, wherein the acquisition time of the candidate grayscale images is not earlier than the operation time; Counting the number of consecutive images with the same grayscale value in the candidate grayscale images; The fluency is determined according to the quantity.

6. The method according to claim 3, characterized in that The performance test results include a black screen; The step of determining the performance test result of the vehicle system based on the grayscale values ​​of the plurality of first grayscale images includes: Based on the grayscale values ​​of the plurality of first grayscale images, determining whether there is a first grayscale image in the plurality of first grayscale images whose entire grayscale value is a target value; If so, it is determined that a black screen appears on the target application.

7. The method according to claim 3, characterized in that The performance test results include stuttering; The step of determining the performance test result of the vehicle system based on the grayscale values ​​of the plurality of first grayscale images includes: When the grayscale values ​​of a plurality of continuous grayscale images in the plurality of first grayscale images are the same, it is determined that a freeze occurs in the target application.

8. A vehicle performance testing device based on a robotic arm, characterized in that: include: A wake-up module, used for waking up the vehicle system by opening the vehicle door through the first mechanical arm; An operating module, configured to operate a target application in the vehicle-mounted system through a second mechanical arm in response to the vehicle-mounted system being awakened, and acquire a plurality of first images during the operation of the target application; A result determination module is used to determine the performance test result of the vehicle system based on the multiple first images.

9. An electronic device, characterized in that: include: Processor and memory; The processor is used to execute the vehicle computer performance testing method based on a robotic arm as described in any one of claims 1 to 7 by calling the program or instruction stored in the memory.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a program or instruction, and the program or instruction enables a computer to execute the vehicle-machine performance testing method based on a robotic arm as described in any one of claims 1 to 7.

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