Vehicle-mounted system anomaly detection method, device, equipment, medium and program product

Abstract

The invention provides a vehicle-mounted system anomaly detection method, device and equipment, a medium and a program product, and relates to the technical field of vehicles, and the vehicle-mounted system anomaly detection method comprises the steps: obtaining a target image displayed by a vehicle-mounted system in a pressure test process in response to the wake-up of the vehicle-mounted system, pressure conditions of the pressure test at least comprise voice pressure, pressure for performing random operation on the vehicle-mounted system and message playback pressure; based on the target image, whether the vehicle-mounted system is abnormal or not is detected, and the abnormity comprises color abnormity of a display interface of the vehicle-mounted system and / or jamming of the display interface of the vehicle-mounted system. According to the invention, the anomaly detection efficiency of the vehicle-mounted system can be effectively improved.

Description

Technical Field

[0001] The present invention relates to the field of vehicle technology, and in particular to a method, device, equipment, medium and program product for detecting abnormality of an in-vehicle system. Background Art

[0002] With the development of intelligent vehicles, more and more intelligent applications are integrated into the vehicle system. If the vehicle system has an abnormality during use, such as freezing after waking up, startup failure, white screen, black screen or gray screen, it will seriously affect the driver's driving experience. Therefore, it is necessary to capture the abnormality of the vehicle system in use in time to ensure the driver's driving experience.

[0003] At present, the vehicle system is usually detected for abnormalities through manual testing. Specifically, the vehicle system is manually awakened from sleep mode, and after awakening, the vehicle system interface is manually entered to view the vehicle system screen to detect whether the vehicle system has abnormal problems such as freezing, startup failure, white screen, black screen or gray screen. However, the above method of detecting abnormalities in the vehicle system has the problem of low detection efficiency. Summary of the invention

[0004] The purpose of the present invention is to provide a method, device, equipment, medium and program product for detecting abnormalities in a vehicle-mounted system, so as to solve the problem of low detection efficiency when performing abnormality detection on a vehicle-mounted system in the current manner.

[0005] In order to achieve the above object, the technical solution adopted by the present invention is as follows:

[0006] A method for detecting an abnormality of an in-vehicle system comprises: in response to the in-vehicle system being awakened, obtaining a target image displayed by the in-vehicle system during a stress test, wherein the stress conditions of the stress test include at least voice stress, stress of performing random operations on the in-vehicle system, and message playback stress; based on the target image, detecting whether the in-vehicle system is abnormal, wherein the abnormality includes abnormal color of a display interface of the in-vehicle system and / or freeze of a display interface of the in-vehicle system.

[0007] According to the above-mentioned technical means, when the vehicle-mounted system is stress tested, the stress conditions of the stress test include at least voice pressure, pressure of random operations on the vehicle-mounted system and message playback pressure, which can enhance the stress test, cause more pressure on the vehicle-mounted system, and cover more scenarios; based on the target image, detect whether the vehicle-mounted system is abnormal, and the abnormalities include abnormal colors of the display interface of the vehicle-mounted system and / or freezes in the display interface of the vehicle-mounted system, so as to realize automatic detection of whether the vehicle-mounted system is abnormal, without manual intervention, and can effectively improve the efficiency of abnormality detection of the vehicle-mounted system.

[0008] Furthermore, the abnormality includes an abnormal color of the display interface of the vehicle-mounted system. Based on the target image, detecting whether the vehicle-mounted system is abnormal includes: grayscale processing the target image to obtain an image after grayscale processing; resizing the grayscale processed image to obtain an image after size resetting; obtaining the pixel value of each pixel in the image after size resetting; obtaining the target quantities corresponding to different pixel values; sorting the target quantities in order from large to small to obtain the sorted quantities; if the number of sorted quantities is greater than or equal to the first color number, taking the pixel value corresponding to the first color number from the sorted quantity as the target pixel value, and obtaining the weighted average of the target pixel values ​​of the first color number; if the weighted average is within the preset color threshold range, determining that the color of the display interface of the vehicle-mounted system is abnormal; if the weighted average is not within the preset color threshold range, determining that the color of the display interface of the vehicle-mounted system is normal; if the number of sorted quantities is less than the second color number, determining that the color of the display interface of the vehicle-mounted system is abnormal, and the second color number is less than the first color number.

[0009] Furthermore, the preset color threshold range includes a first threshold range corresponding to white, a second threshold range corresponding to black, and a third threshold range corresponding to gray. If the weighted average value is within the preset color threshold range, it is determined that the color of the display interface of the vehicle system is abnormal, including: if the weighted average value is within the first threshold range, it is determined that the display interface of the vehicle system is a white screen; if the weighted average value is within the second threshold range, it is determined that the display interface of the vehicle system is a black screen; if the weighted average value is within the third threshold range, it is determined that the display interface of the vehicle system is a gray screen.

[0010] Further, grayscale processing is performed on the target image to obtain a grayscale processed image, including: obtaining a target area image from the target image according to configuration information, the configuration information is used to determine coordinate information of a to-be-detected area of ​​the target image, the to-be-detected area including the entire area or sub-area of ​​the target image; grayscale processing is performed on the target area image to obtain a grayscale processed image.

[0011] Furthermore, the abnormality includes a freeze in the display interface of the vehicle-mounted system. Based on the target image, detecting whether the vehicle-mounted system is abnormal includes: obtaining first pixel histogram data corresponding to the target image and second pixel histogram data corresponding to a previous image adjacent to the target image; obtaining the similarity between the target image and the previous image based on the first pixel histogram data and the second pixel histogram data; if the similarity is within a preset similarity threshold range, determining that the target image and the previous image are similar images; if the number of consecutive similar images is greater than the number threshold, determining that the display interface of the vehicle-mounted system is freezed; if the number of consecutive similar images is less than or equal to the number threshold, determining that the display interface of the vehicle-mounted system is not freezed.

[0012] Furthermore, the vehicle system abnormality detection method also includes: within a first preset time period after the vehicle system is awakened, detecting whether the display interface of the vehicle system is in a bright screen state; if the display interface of the vehicle system is in a bright screen state, determining that the vehicle system is normal; if the display interface of the vehicle system is not in a bright screen state, determining that the vehicle system is abnormal, and the abnormality includes a failure to start the vehicle system or an abnormal restart.

[0013] Furthermore, the vehicle-mounted system abnormality detection method also includes: obtaining a test log at the end of the stress test; if there is an abnormality in the vehicle-mounted system or the test log contains an abnormality log, outputting an alarm message.

[0014] Furthermore, the vehicle system abnormality detection method also includes: at the end of the stress test, putting the vehicle system into sleep mode; when the vehicle system has been in sleep mode for a second preset time, waking up the vehicle system, and executing the step of obtaining a target image displayed by the vehicle system during the stress test in response to the vehicle system being awakened.

[0015] Furthermore, the vehicle-mounted system includes a vehicle-mounted central control system and a vehicle-mounted instrument system.

[0016] A vehicle-mounted system abnormality detection device includes: an acquisition module, which is used to respond to the vehicle-mounted system being awakened and obtain a target image displayed by the vehicle-mounted system during a stress test, wherein the stress conditions of the stress test at least include voice pressure, pressure of performing random operations on the vehicle-mounted system, and message playback pressure; a detection module, which is used to detect whether the vehicle-mounted system is abnormal based on the target image, wherein the abnormality includes abnormal color of the display interface of the vehicle-mounted system and / or freeze of the display interface of the vehicle-mounted system.

[0017] Furthermore, the abnormality includes an abnormal color of the display interface of the vehicle-mounted system, and the detection module is specifically used to: perform grayscale processing on the target image to obtain an image after grayscale processing; perform size resizing processing on the image after grayscale processing to obtain an image after size resizing processing; obtain the pixel value of each pixel point in the image after size resizing processing; obtain the target quantity corresponding to different pixel values; sort the target quantity in order from large to small to obtain the sorted quantity; if the number of the sorted quantity is greater than or equal to the first color number, then take the pixel value corresponding to the first color number from the sorted quantity as the target pixel value, and obtain the weighted average of the target pixel values ​​of the first color number; if the weighted average is within the preset color threshold range, it is determined that the color of the display interface of the vehicle-mounted system is abnormal; if the weighted average is not within the preset color threshold range, it is determined that the color of the display interface of the vehicle-mounted system is normal; if the number of the sorted quantity is less than the second color number, it is determined that the color of the display interface of the vehicle-mounted system is abnormal, and the second color number is less than the first color number.

[0018] Furthermore, the preset color threshold range includes a first threshold range corresponding to white, a second threshold range corresponding to black, and a third threshold range corresponding to gray. When the detection module is used to determine that the color of the display interface of the vehicle system is abnormal if the weighted average value is within the preset color threshold range, it is specifically used to: if the weighted average value is within the first threshold range, determine that the display interface of the vehicle system is a white screen; if the weighted average value is within the second threshold range, determine that the display interface of the vehicle system is a black screen; if the weighted average value is within the third threshold range, determine that the display interface of the vehicle system is a gray screen.

[0019] Furthermore, when the detection module is used to perform grayscale processing on the target image to obtain the grayscale processed image, it is specifically used to: obtain the target area image from the target image according to the configuration information, the configuration information is used to determine the coordinate information of the area to be detected of the target image, and the area to be detected includes the entire area or sub-area of ​​the target image; perform grayscale processing on the target area image to obtain the grayscale processed image.

[0020] Furthermore, the abnormality includes a freeze in the display interface of the vehicle-mounted system, and the detection module is specifically used to: obtain first pixel histogram data corresponding to the target image and second pixel histogram data corresponding to the previous image adjacent to the target image; based on the first pixel histogram data and the second pixel histogram data, obtain the similarity between the target image and the previous image; if the similarity is within a preset similarity threshold range, determine that the target image and the previous image are similar images; if the number of consecutive similar images is greater than the number threshold, determine that the display interface of the vehicle-mounted system is freezed; if the number of consecutive similar images is less than or equal to the number threshold, determine that the display interface of the vehicle-mounted system is not freezed.

[0021] Furthermore, the detection module is also used to: detect whether the display interface of the vehicle system is in a bright screen state within a first preset time period after the vehicle system is awakened; if the display interface of the vehicle system is in a bright screen state, determine that the vehicle system is normal; if the display interface of the vehicle system is not in a bright screen state, determine that the vehicle system is abnormal, and the abnormality includes failure to start the vehicle system or abnormal restart.

[0022] Furthermore, the detection module is also used to: obtain the test log at the end of the stress test; if there is an abnormality in the vehicle system or the test log contains an abnormal log, output an alarm message.

[0023] Furthermore, the vehicle-mounted system abnormality detection device also includes a sleep and wake-up module, which is used to: put the vehicle-mounted system into sleep mode at the end of the stress test; wake up the vehicle-mounted system when the vehicle-mounted system has been in sleep mode for a second preset time, and execute the step of acquiring a target image displayed by the vehicle-mounted system during the stress test in response to the vehicle-mounted system being awakened through the acquisition module.

[0024] Furthermore, the vehicle-mounted system includes a vehicle-mounted central control system and a vehicle-mounted instrument system.

[0025] An electronic device comprises: a processor, and a memory communicatively connected to the processor;

[0026] Memory stores computer-executable instructions;

[0027] The processor executes the computer-executable instructions stored in the memory to implement the vehicle-mounted system abnormality detection method as described above in the present invention.

[0028] A computer-readable storage medium stores computer program instructions, and when the computer program instructions are executed, the vehicle-mounted system abnormality detection method as described above in the present invention is implemented.

[0029] A computer program product includes a computer program, and when the computer program is executed, the vehicle-mounted system abnormality detection method as described above in the present invention is implemented.

[0030] Beneficial effects of the present invention: The vehicle-mounted system abnormality detection method, device, equipment, medium and program product provided by the present invention, in response to the vehicle-mounted system being awakened, obtains the target image displayed by the vehicle-mounted system during the stress test, the stress conditions of the stress test include at least voice pressure, pressure of random operations on the vehicle-mounted system and message playback pressure, which can enhance the stress test, cause more pressure on the vehicle-mounted system, and cover more scenarios; based on the target image, detect whether the vehicle-mounted system is abnormal, the abnormality includes abnormal color of the display interface of the vehicle-mounted system and / or the display interface of the vehicle-mounted system is stuck, and automatically detect whether the vehicle-mounted system is abnormal without manual intervention, which can effectively improve the efficiency of abnormality detection of the vehicle-mounted system, and discover or reproduce problems existing in the vehicle-mounted system as much as possible, thereby reducing problems that cannot be tested by manual testing and achieving the purpose of reducing costs and increasing efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0032] Figure 1 A schematic diagram of an application scenario provided by an embodiment of the present invention;

[0033] Figure 2 A flow chart of a method for detecting abnormality of an in-vehicle system provided by an embodiment of the present invention;

[0034] Figure 3A schematic diagram of a test bench provided in one embodiment of the present invention;

[0035] Figure 4 A flowchart of a vehicle system abnormality detection method provided by another embodiment of the present invention;

[0036] Figure 5 A schematic diagram of the structure of a vehicle-mounted system abnormality detection device provided by an embodiment of the present invention;

[0037] Figure 6 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0038] The following will describe the embodiments of the present invention with reference to the accompanying drawings and preferred embodiments. Those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present invention. It should be understood that the preferred embodiments are only for illustrating the present invention, not for limiting the scope of protection of the present invention.

[0039] It should be noted that the illustrations provided in the following embodiments are only schematic illustrations of the basic concept of the present invention, and thus the drawings only show components related to the present invention rather than being drawn according to the number, shape and size of components in actual implementation. In actual implementation, the type, quantity and proportion of each component may be changed arbitrarily, and the component layout may also be more complicated.

[0040] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in the present invention are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant laws, regulations and standards, and corresponding operation entrances shall be provided for users to choose to authorize or refuse.

[0041] With the development of intelligent vehicles, more and more intelligent applications are integrated into the vehicle system. If the vehicle system has abnormalities during use, such as freezing, startup failure, black screen or gray screen after waking up, it will seriously affect the driver's driving experience. Therefore, it is necessary to strengthen the stability test of the vehicle system's sleep and wake-up, and timely capture the abnormalities that occur during the use of the vehicle system to ensure the driver's driving experience.

[0042] At present, the vehicle system is usually detected for abnormalities through manual testing. Specifically, the vehicle system is manually awakened from sleep mode, and after awakening, the vehicle system interface is manually entered to view the vehicle system screen to detect whether the vehicle system has abnormal problems such as freezing, startup failure, black screen or gray screen. However, the above method for detecting abnormalities in the vehicle system has the problem of low detection efficiency, and some problems of the vehicle system will only be exposed after the vehicle system has been used for a long time, which may not be covered by manual testing. Specifically, (1) the inspection efficiency of manual testing is too low, and it is necessary to pay attention to the vehicle system for a long time and perform repeated operations, which makes the tester easily fatigued and make mistakes; (2) some occasional problems appear too quickly and then disappear, and there is no time to capture logs and screenshots, and the logs are flushed; (3) some low-probability problems will only appear after the vehicle system has been used for a long time, which is not easy to find through manual testing, and some problems are easily missed online; (4) The current sleep and wake-up stress test of the vehicle system is only a single automated test, which does not take into account the complex effects on the actual vehicle, affecting the actual test results.

[0043] Based on the above problems, the present invention provides a method for detecting abnormalities in a vehicle-mounted system. By simulating the stress test of the sleep and wake-up of the vehicle-mounted system of a real vehicle, the stress conditions of the stress test include at least voice pressure, pressure of random operations on the vehicle-mounted system and message playback pressure, which can enhance the stress test and cover more scenarios; based on the target image displayed by the vehicle-mounted system during the stress test, it is detected whether the vehicle-mounted system is abnormal, and the abnormalities include abnormal colors of the display interface of the vehicle-mounted system and / or freezes in the display interface of the vehicle-mounted system, so as to realize automatic detection of whether the vehicle-mounted system is abnormal, without manual intervention, and can effectively improve the efficiency of abnormality detection of the vehicle-mounted system.

[0044] In the following, the application scenario of the solution provided by the present invention is firstly described by way of example.

[0045] Figure 1 Schematic diagram of an application scenario provided by an embodiment of the present invention. Figure 1 As shown, in this application scenario, the vehicle-mounted system 102 is awakened from a dormant state by the electronic device 101, and the vehicle-mounted system 102 is subjected to a stress test, and the stress conditions of the stress test include at least voice pressure, pressure of performing random operations on the vehicle-mounted system, and message playback pressure. The electronic device 101 obtains the target image displayed by the vehicle-mounted system during the stress test through the camera 103, and detects whether the vehicle-mounted system is abnormal based on the target image, and the abnormality includes abnormal color of the display interface of the vehicle-mounted system and / or freeze of the display interface of the vehicle-mounted system.

[0046] It should be noted that Figure 1 This is only a schematic diagram of an application scenario provided by an embodiment of the present invention. Figure 1does not limit the equipment included in Figure 1 The positional relationship between the devices is limited.

[0047] The technical solution of the present invention is described in detail below through specific embodiments. It should be noted that the following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described in detail in some embodiments.

[0048] Figure 2 The flowchart of the vehicle system abnormality detection method provided by one embodiment of the present invention. The vehicle system abnormality detection method can be executed by software and / or hardware devices. For example, the hardware device can be a vehicle system abnormality detection device, and the vehicle system abnormality detection device can be an electronic device or a processing chip in the electronic device. Figure 2 As shown, the method of the embodiment of the present invention includes:

[0049] S201. In response to the vehicle-mounted system being awakened, a target image displayed by the vehicle-mounted system during a stress test is obtained, where stress conditions of the stress test include at least voice stress, stress of performing random operations on the vehicle-mounted system, and message playback stress.

[0050] In an embodiment of the present invention, the vehicle-mounted system is awakened from a dormant state and a stress test is performed on the vehicle-mounted system. Among them, the stress conditions of the stress test include at least voice pressure, pressure of random operations on the vehicle-mounted system, and message playback pressure, and other stress conditions can be added as needed; for how to wake up the vehicle-mounted system from a dormant state, reference can be made to the subsequent embodiments. The pressure of random operations on the vehicle-mounted system is, for example, the pressure generated by a random operation test on the vehicle-mounted system based on the Android Debug Bridge (ADB), specifically, for example, the pressure generated by a monkey test (also known as a funny test, that is, using a strange test method to test the system under test to test the stability of the system). For this step, exemplarily, in response to the vehicle-mounted system being awakened, when the vehicle-mounted system is stress-tested, for example, a monkey test is performed and a voice command is sent every half a minute, and at the same time, the running message signal collected from the real vehicle is played back to simulate the real vehicle environment, and the target image displayed by the vehicle-mounted system during the stress test is obtained.

[0051] Optionally, the vehicle-mounted system includes a vehicle-mounted central control system and a vehicle-mounted instrument system.

[0052] For example, the prerequisites can be prepared in advance, that is, the vehicle-mounted central control system, the vehicle-mounted instrument system and the camera are set up, and the camera monitors the vehicle-mounted central control system and the vehicle-mounted instrument system, for example, taking pictures every 1 second, so as to obtain the target images displayed by the vehicle-mounted central control system and the vehicle-mounted instrument system during the stress test.

[0053] S202: Based on the target image, detect whether the vehicle-mounted system is abnormal, where the abnormality includes abnormal color of the display interface of the vehicle-mounted system and / or freezing of the display interface of the vehicle-mounted system.

[0054] In this step, after obtaining the target image displayed by the vehicle-mounted system during the stress test, it is possible to detect whether the vehicle-mounted system is abnormal based on the target image. Exemplarily, based on the target image, a screen abnormality detection algorithm can be used to detect whether the display interface of the vehicle-mounted system has color abnormalities, such as whether it is a white screen, a black screen, a gray screen, or a screen with other abnormal colors; based on the target image, a freeze abnormality detection algorithm can be used to detect whether the display interface of the vehicle-mounted system is frozen. For specific information on how to detect whether the vehicle-mounted system is abnormal based on the target image, please refer to the subsequent embodiments, which will not be repeated here.

[0055] Optionally, the vehicle system anomaly detection method provided by the embodiment of the present invention may further include: putting the vehicle system into sleep mode at the end of the stress test; waking up the vehicle system when the vehicle system has been in sleep mode for a second preset time, and executing step S201.

[0056] Exemplarily, the second preset duration is, for example, 3 minutes, and the second preset duration is not limited in the embodiment of the present invention. It can be understood that by waking up the vehicle system from the sleep state, performing a stress test, and detecting whether the vehicle system is abnormal, at the end of the stress test, the vehicle system is put into sleep; when the vehicle system is in sleep for the second preset duration, the vehicle system is woken up, and a stress test is performed on the vehicle system to detect whether the vehicle system is abnormal, that is, each round of sleep-wake-up stress testing process is automatically performed, and during the sleep-wake-up process, the vehicle system is automatically detected through a stress test to detect whether the vehicle system is abnormal, so as to detect abnormalities in a timely manner.

[0057] The vehicle-mounted system abnormality detection method provided by an embodiment of the present invention obtains a target image displayed by the vehicle-mounted system during a stress test in response to the vehicle-mounted system being awakened. The stress conditions of the stress test include at least voice pressure, pressure of performing random operations on the vehicle-mounted system, and message playback pressure. The stress test can be enhanced, causing more pressure on the vehicle-mounted system and covering more scenarios. Based on the target image, it is detected whether the vehicle-mounted system is abnormal. The abnormalities include abnormal colors of the display interface of the vehicle-mounted system and / or freezes in the display interface of the vehicle-mounted system. Automatic detection of whether the vehicle-mounted system is abnormal is achieved without manual intervention. The efficiency of abnormality detection on the vehicle-mounted system can be effectively improved, and problems existing in the vehicle-mounted system can be discovered or reproduced as much as possible, thereby reducing problems that cannot be tested by manual testing and achieving the purpose of reducing costs and increasing efficiency.

[0058] Based on the above embodiment, optionally, an embodiment of the present invention provides a test bench for detecting abnormalities in a vehicle-mounted system, and the test bench is constructed to simulate the operation scenario of a real vehicle and execute the method for detecting abnormalities in a vehicle-mounted system. Figure 3 A schematic diagram of a test bench provided in one embodiment of the present invention is shown in FIG. Figure 3 As shown, the test bench includes an electronic device 301, an on-board system 302, a camera 303, a relay 304, a controller area network (CAN) box 305, a microcontroller unit (MCU) serial port 306, and a programmable power supply 307. The electronic device 301 is connected to the on-board system 302 through the relay 304, the CAN box 305, the MCU serial port 306, and the programmable power supply 307, respectively. The electronic device 301 is also connected to the camera 303. In the above connection relationship, the connection can be made by wire or by wireless, which is not limited in the embodiment of the present invention; the electronic device 301, for example, calls the relay 304, the CAN box 305, the MCU serial port 306, the programmable power supply 307, and the camera 303 based on a python (a programming language) script to run a stability test script. During the stability test, the on-board system abnormality detection method is executed. The electronic device 301 is a computer, for example; the vehicle system 302 includes the vehicle central control system 3021 and the vehicle instrument system 3022; the camera 303 can be used to capture the image displayed by the vehicle system 302 and transmit the acquired image to the electronic device 301, and the camera 303 can be fixed to monitor the display interface of the vehicle system 302; the relay 304 is used to control the accessory (Accessory, ACC) line of the vehicle system 302 under test; the CAN box 305 is used to send the message collected by the real vehicle (simulating the corresponding real vehicle environment); the MCU serial port 306 is used to print the MCU log; the programmable power supply 307 is used to power the vehicle system 302. It can be understood that Figure 3 The test bench shown is a stress test bench that simulates the sleep and wake-up of a real vehicle. It can be as close as possible to the actual user's usage scenario. Therefore, based on the test bench, a series of abnormal monitoring mechanisms based on the stability test after the vehicle system is woken up can be provided to detect whether the vehicle system is abnormal.

[0059] Figure 4 This is a flow chart of a vehicle system abnormality detection method provided by another embodiment of the present invention. Based on the above embodiment, the embodiment of the present invention further describes the vehicle system abnormality detection method. Figure 4 As shown, the method of the embodiment of the present invention may include:

[0060] S401, waking up the vehicle system from a dormant state.

[0061] For example, reference Figure 3 The test bench shown in the figure, the electronic device executing the embodiment of the present method calls the CAN box to replay the wake-up message collected on the actual vehicle, wakes up the vehicle system, simulates the actual vehicle environment, and at the same time, monitors the power-on signal of the wake-up message playback. If the power-on signal is received, the ACC is powered on, and the screen light detection of the vehicle system is immediately started, that is, step S402 is executed.

[0062] Optionally, after waking up the vehicle system, you can first perform a basic smoke test case test to detect basic functional problems and fuzzy problems of the vehicle computer, and then perform a stability test on the vehicle system to detect whether the vehicle system is abnormal during the stability test.

[0063] S402: within a first preset time period after the vehicle-mounted system is awakened, detecting whether a display interface of the vehicle-mounted system is in a bright screen state.

[0064] For example, the first preset time is 5 seconds, and the screen-on detection algorithm can be run within 5 seconds after the vehicle system is awakened to detect whether the display interface of the vehicle system is in a screen-on state. For example, if it is detected that the vehicle central control system and the vehicle instrument system are in a non-black screen state, it is determined that the vehicle central control system and the vehicle instrument system are in a screen-on state.

[0065] S403: If the display interface of the vehicle-mounted system is in a bright screen state, the vehicle-mounted system is determined to be normal; if the display interface of the vehicle-mounted system is not in a bright screen state, the vehicle-mounted system is determined to be abnormal, wherein the abnormality includes a vehicle-mounted system startup failure or abnormal restart.

[0066] In this step, whether the vehicle-mounted system fails to start or abnormally restarts is determined by detecting whether the display interface of the vehicle-mounted system is in a light-screen state.

[0067] S404: Perform a stress test on the vehicle-mounted system, where the stress conditions of the stress test include at least voice stress, stress of performing random operations on the vehicle-mounted system, and message playback stress.

[0068] S405: Acquire a target image displayed by the vehicle-mounted system during the stress test.

[0069] For detailed description of steps S404 and S405, please refer to Figure 2 The relevant description of S201 in the illustrated embodiment will not be repeated here.

[0070] In the embodiment of the present invention, Figure 2 The step S202 may further include the following five steps S406 to S410:

[0071] S406, grayscale processing is performed on the target image to obtain a grayscale processed image; resize processing is performed on the grayscale processed image to obtain a resize processed image; the pixel value of each pixel in the resize processed image is obtained; the number of targets corresponding to different pixel values ​​is obtained; the number of targets is sorted in descending order to obtain a sorted number.

[0072] For example, reference Figure 3 The test bench shown monitors the vehicle system through a camera, for example, taking pictures every 1 second, so that the target image displayed by the vehicle system during the stress test can be obtained. The target image is gray-scaled to avoid interference under conditions such as lighting, and an image after gray-scale processing is obtained. The gray-scale processed image is resized (such as reduced in size) to reduce the pressure of the operation of the electronic device executing the embodiment of the method, and an image after size resetting is obtained. The pixel value of each pixel in the image after size resetting is obtained, and the number of targets corresponding to different pixel values ​​is obtained. For example, assuming that the number of targets corresponding to pixel value 1 is 100, the number of targets corresponding to pixel value 2 is 90, and the number of targets corresponding to pixel value 3 is 95, the three target numbers are sorted in order from large to small to obtain the sorted numbers, namely 100, 95 and 90.

[0073] Optionally, performing grayscale processing on the target image to obtain a grayscale processed image may include: acquiring a target area image from the target image according to configuration information, the configuration information being used to determine coordinate information of a region to be detected of the target image, the region to be detected including the entire region or sub-region of the target image; performing grayscale processing on the target area image to obtain a grayscale processed image.

[0074] This embodiment can be understood as supporting the detection of the entire area or sub-area of ​​the target image, wherein, for sub-area detection, for example, it is possible to detect whether the map interface in the vehicle-mounted system is loaded normally, detect whether the display interface of the vehicle-mounted instrument system is a white screen, a black screen or a gray screen, and detect whether the application is a white screen, a black screen, or a gray screen after loading. The corresponding coordinates of the vehicle-mounted system under the camera field of view can be pre-configured to determine the coordinate information of the area to be detected of the target image. The default is to detect the entire area of ​​the target image (i.e., the entire image). If the coordinates of the sub-area of ​​the target image are configured, the sub-area of ​​the target image is detected. Specifically, according to the coordinate information of the sub-area of ​​the target image, the target area image is obtained from the target image, and the target area image is gray-processed to obtain the gray-processed image.

[0075] S407. If the number of sorted quantities is greater than or equal to the first color number, the pixel value corresponding to the first color number among the sorted quantities is taken as the target pixel value, and the weighted average of the target pixel values ​​of the first color number is obtained; if the weighted average is within the preset color threshold range, it is determined that the color of the display interface of the vehicle-mounted system is abnormal; if the weighted average is not within the preset color threshold range, it is determined that the color of the display interface of the vehicle-mounted system is normal.

[0076] For example, if the number of the first colors is 10 and the number of the sorted colors is 15, the pixel values ​​corresponding to the first 10 numbers in the sorted colors can be taken as the target pixel values, that is, the 10 most important colors are obtained, and the weighted average value of the 10 most important colors is obtained. It is determined whether the weighted average value is within the preset color threshold range, so as to determine whether the display interface of the vehicle system has color abnormality.

[0077] Optionally, the preset color threshold range includes a first threshold range corresponding to white, a second threshold range corresponding to black, and a third threshold range corresponding to gray. If the weighted average is within the preset color threshold range, it is determined that the color of the display interface of the vehicle system is abnormal, which may include: if the weighted average is within the first threshold range, it is determined that the display interface of the vehicle system is a white screen; if the weighted average is within the second threshold range, it is determined that the display interface of the vehicle system is a black screen; if the weighted average is within the third threshold range, it is determined that the display interface of the vehicle system is a gray screen.

[0078] Exemplarily, a first threshold range corresponding to white, a second threshold range corresponding to black, and a third threshold range corresponding to gray can be pre-set to determine whether the weighted average is within these three threshold ranges, thereby determining whether the display interface of the vehicle system is a white screen, a black screen, or a gray screen.

[0079] S408. If the number of sorted quantities is less than the number of the second colors, it is determined that the color of the display interface of the vehicle-mounted system is abnormal and the number of the second colors is less than the number of the first colors.

[0080] Exemplarily, the number of the second colors is 5. If the sorted number is less than 5, that is, the number of the most important colors is less than 5, it can be determined that the color of the display interface of the vehicle system is abnormal, such as a red screen or a green screen.

[0081] It can be understood that steps S407 and S408 provide a screen anomaly detection algorithm that supports various anomaly image detections. The specific applicable image detection types include anomaly detection types with similar colors such as white screen, black screen, gray screen, red screen, or green screen, etc. It also supports global anomaly detection or local area anomaly detection of the images displayed by the vehicle-mounted system. If local area anomaly detection is to be performed, the coordinates corresponding to the local area need to be pre-configured. If the coordinates corresponding to the local area are not configured, the entire image is defaultly matched for full-image anomaly detection.

[0082] S409. Obtain the first pixel point histogram data corresponding to the target image and the second pixel point histogram data corresponding to the previous image adjacent to the target image; based on the first pixel point histogram data and the second pixel point histogram data, obtain the similarity between the target image and the previous image; if the similarity is within the preset similarity threshold range, determine that the target image and the previous image are similar images.

[0083] Exemplarily, for example, obtain the target image displayed by the vehicle-mounted system during the stress test once per second, and convert the target image into the first pixel point histogram data; based on the first pixel point histogram data corresponding to the target image and the second pixel point histogram data corresponding to the previous image adjacent to the target image, use the square root and variance to obtain the similarity between the target image and the previous image; if the similarity is within the preset similarity threshold range, determine that the target image and the previous image are similar images. Among them, the similarity is represented by diff, for example, and the preset similarity threshold range is 0.95 < diff < 100, indicating that the target image and the previous image are similar images.

[0084] It should be noted that the embodiments of the present invention do not limit the execution order of S406 and S409.

[0085] S410. If the number of consecutive similar images is greater than the number threshold, determine that the display interface of the vehicle-mounted system is stuck; if the number of consecutive similar images is less than or equal to the number threshold, determine that the display interface of the vehicle-mounted system is not stuck.

[0086] In this step, the number threshold is, for example, the number of similar images within 90 seconds. It can be understood that each obtained target image is compared with the previous image adjacent to the target image. If the number of consecutive similar images is greater than the number threshold, it can be determined that the display interface of the vehicle-mounted system is stuck, and at the end of the stress test, the corresponding warning information is output; if the number of consecutive similar images is less than or equal to the number threshold, it can be determined that the display interface of the vehicle-mounted system is not stuck.

[0087] It can be understood that through steps S409 and S410, a jamming anomaly detection algorithm is provided, which combines the monkey test and the image detection algorithm. The monkey test is started first, and then the algorithm is run at the same time to verify the jamming of the vehicle system under certain extreme stress conditions. The algorithm is non-intrusive to the vehicle system and will not affect the actual performance of the vehicle system. It only needs to monitor the camera and use the jamming anomaly detection algorithm to compare the changes in the image displayed by the vehicle system during the stress test, supporting jamming detection in the whole process of the vehicle system sleep wake-up stability test.

[0088] With reference to steps S406 to S410, it can be seen that, when performing abnormality detection on the vehicle-mounted system, the embodiment of the present invention does not need to prepare reference images in advance, does not need to adapt to different projects, and only needs to fix the positions of the vehicle-mounted system and the camera, and set the screen monitoring area of ​​the vehicle-mounted system. This can greatly reduce the consumption of multi-image traversal matching for preparing multiple reference images in advance on the test performance, and reduce the adaptation cost of multiple projects.

[0089] S411. At the end of the stress test, obtain the test log; if there is an abnormality in the vehicle system or the test log contains an abnormal log, output an alarm message.

[0090] Exemplarily, each round of sleep-wake-up test lasts for 40 minutes. At the end of the stress test, the test log is pulled from the vehicle system. If there is an abnormality in the vehicle system or the test log contains an abnormal log (for example, the vehicle system is detected to be unresponsive or crashed, etc.), an alarm message can be output according to the test log corresponding to the abnormality. Specifically, for example, a notification containing an alarm message is pushed to a communication group for problem solving, and the test results are automatically uploaded to the cloud. After the problem is manually confirmed, the problem (bug) process is entered.

[0091] S412, putting the vehicle-mounted system into sleep mode; when the vehicle-mounted system is in sleep mode for a second preset time, executing step S401.

[0092] It can be understood that by waking up the vehicle system from a sleep state, a stress test is performed to detect whether the vehicle system is abnormal, and at the end of the stress test, the vehicle system is put into sleep; when the vehicle system is in sleep for a second preset time, the vehicle system is woken up and a stress test is performed on the vehicle system to detect whether the vehicle system is abnormal, that is, each round of sleep-wake-up stress testing process is automatically performed, and during the sleep-wake-up process, the vehicle system is automatically detected through a stress test to facilitate timely detection of abnormalities.

[0093] The vehicle system abnormality detection method provided by the embodiment of the present invention wakes up the vehicle system from a dormant state, and detects whether the display interface of the vehicle system is in a bright screen state within a first preset time after the vehicle system is awakened; performs a stress test on the vehicle system, and the stress conditions of the stress test at least include voice stress, stress of performing random operations on the vehicle system, and message playback stress, which can enhance the stress test, cause more stress to the vehicle system, and cover more scenarios; based on the target image displayed by the vehicle system during the stress test, determine whether the display interface of the vehicle system has color abnormalities and determine whether the display interface of the vehicle system is stuck. At the end of the stress test, obtain the test log, if the vehicle system has an abnormality or the test log contains an abnormal log, then output an alarm message, and realize the automatic execution of the full-link abnormality detection from waking up the vehicle system to dormancy, which is fully automated without manual intervention, and automatically outputs the test log, which can effectively improve the efficiency of abnormality detection of the vehicle system, solve the problems of missing low-probability problems, abnormal display interface problems of the vehicle system, and insufficient simulation of real vehicle usage scenarios, and can discover or reproduce the problems of the vehicle system as much as possible, thereby reducing the problems that cannot be tested by manual testing.

[0094] Based on the above embodiments, the vehicle-mounted system anomaly detection method provided by the embodiments of the present invention has a short development cycle and low maintenance cost, can be reused in multiple projects, and the reuse of a new project takes about one person per day, and can be effectively used to perform anomaly detection on the vehicle-mounted systems of various projects.

[0095] The following are embodiments of the apparatus of the present invention, which can be used to implement the embodiments of the method of the present invention. For details not disclosed in the embodiments of the apparatus of the present invention, please refer to the embodiments of the method of the present invention.

[0096] Figure 5 A schematic diagram of the structure of a vehicle-mounted system abnormality detection device provided by an embodiment of the present invention is shown in FIG. Figure 5 As shown, the vehicle-mounted system abnormality detection device 500 of the embodiment of the present invention includes: an acquisition module 501 and a detection module 502. Among them:

[0097] The acquisition module 501 is used to acquire the target image displayed by the vehicle-mounted system during the stress test in response to the vehicle-mounted system being awakened. The stress conditions of the stress test include at least voice pressure, pressure of performing random operations on the vehicle-mounted system, and message playback pressure.

[0098] The detection module 502 is used to detect whether the vehicle-mounted system is abnormal based on the target image, and the abnormality includes abnormal color of the display interface of the vehicle-mounted system and / or freezing of the display interface of the vehicle-mounted system.

[0099] Further, the abnormality includes an abnormal color of the display interface of the vehicle-mounted system. The detection module 502 can be specifically used to: grayscale process the target image to obtain an image after grayscale processing; resize the image after grayscale processing to obtain an image after resize processing; obtain the pixel value of each pixel in the image after resize processing; obtain the target quantity corresponding to different pixel values; sort the target quantity in order from large to small to obtain the sorted quantity; if the number of sorted quantities is greater than or equal to the first color number, then take the pixel value corresponding to the first color number from the sorted quantity as the target pixel value, and obtain the weighted average of the target pixel values ​​of the first color number; if the weighted average is within the preset color threshold range, it is determined that the color of the display interface of the vehicle-mounted system is abnormal; if the weighted average is not within the preset color threshold range, it is determined that the color of the display interface of the vehicle-mounted system is normal; if the number of sorted quantities is less than the second color number, it is determined that the color of the display interface of the vehicle-mounted system is abnormal, and the second color number is less than the first color number.

[0100] Furthermore, the preset color threshold range includes a first threshold range corresponding to white, a second threshold range corresponding to black, and a third threshold range corresponding to gray. When the detection module 502 is used to determine that the color of the display interface of the vehicle system is abnormal if the weighted average value is within the preset color threshold range, it can be specifically used to: if the weighted average value is within the first threshold range, determine that the display interface of the vehicle system is a white screen; if the weighted average value is within the second threshold range, determine that the display interface of the vehicle system is a black screen; if the weighted average value is within the third threshold range, determine that the display interface of the vehicle system is a gray screen.

[0101] Furthermore, when the detection module 502 is used to perform grayscale processing on the target image and obtain a grayscale processed image, it can be specifically used to: obtain a target area image from the target image according to configuration information, the configuration information is used to determine the coordinate information of the area to be detected of the target image, and the area to be detected includes the entire area or sub-area of ​​the target image; perform grayscale processing on the target area image to obtain a grayscale processed image.

[0102] Furthermore, the abnormality includes a freeze in the display interface of the vehicle-mounted system. The detection module 502 can be specifically used to: obtain first pixel histogram data corresponding to the target image and second pixel histogram data corresponding to the previous image adjacent to the target image; based on the first pixel histogram data and the second pixel histogram data, obtain the similarity between the target image and the previous image; if the similarity is within a preset similarity threshold range, determine that the target image and the previous image are similar images; if the number of consecutive similar images is greater than the number threshold, determine that the display interface of the vehicle-mounted system is freezed; if the number of consecutive similar images is less than or equal to the number threshold, determine that the display interface of the vehicle-mounted system is not freezed.

[0103] Furthermore, the detection module 502 can also be used to: detect whether the display interface of the vehicle system is in a bright screen state within the first preset time period after the vehicle system is awakened; if the display interface of the vehicle system is in a bright screen state, it is determined that the vehicle system is normal; if the display interface of the vehicle system is not in a bright screen state, it is determined that the vehicle system is abnormal, and the abnormality includes failure to start the vehicle system or abnormal restart.

[0104] Furthermore, the detection module 502 can also be used to: obtain the test log at the end of the stress test; if there is an abnormality in the vehicle system or the test log contains an abnormal log, output an alarm message.

[0105] Furthermore, the vehicle-mounted system abnormality detection device 500 may also include a sleep and wake-up module (not shown in the figure), which is used to: put the vehicle-mounted system into sleep mode at the end of the stress test; wake up the vehicle-mounted system when the vehicle-mounted system has been in sleep mode for a second preset time, and execute the step of acquiring the target image displayed by the vehicle-mounted system during the stress test in response to the vehicle-mounted system being awakened through the acquisition module 501.

[0106] Furthermore, the vehicle-mounted system includes a vehicle-mounted central control system and a vehicle-mounted instrument system.

[0107] The device of the embodiment of the present invention can be used to execute the technical solution of any of the above-mentioned method embodiments. Its implementation principle and technical effects are similar and will not be repeated here.

[0108] Figure 6 This is a schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. Figure 6 As shown, the electronic device 600 may include: at least one processor 601 and a memory 602 .

[0109] The memory 602 is used to store programs. Specifically, the programs may include program codes, and the program codes include computer-executable instructions.

[0110] The memory 602 may include a high-speed random access memory (RAM), and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.

[0111] The processor 601 is used to execute the computer-executable instructions stored in the memory 602 to implement the vehicle-mounted system anomaly detection method described in the aforementioned method embodiment. Among them, the processor 601 may be a central processing unit (CPU), or an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiment of the present invention. Specifically, when implementing the vehicle-mounted system anomaly detection method described in the aforementioned method embodiment, the electronic device may be, for example, an electronic device with processing functions such as a server.

[0112] Optionally, the electronic device 600 may further include a communication interface 603. In a specific implementation, if the communication interface 603, the memory 602 and the processor 601 are implemented independently, the communication interface 603, the memory 602 and the processor 601 may be interconnected through a bus and communicate with each other. The bus may be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc., but it does not mean that there is only one bus or one type of bus.

[0113] Optionally, in a specific implementation, if the communication interface 603, the memory 602 and the processor 601 are integrated on a chip, the communication interface 603, the memory 602 and the processor 601 can communicate through an internal interface.

[0114] The present invention also provides a computer-readable storage medium, in which computer program instructions are stored. When a processor executes the computer program instructions, the above-mentioned vehicle-mounted system abnormality detection method is implemented.

[0115] The present invention also provides a computer program product, including a computer program, which implements the above-mentioned vehicle-mounted system abnormality detection method when executed by a processor.

[0116] The computer-readable storage medium mentioned above can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read only memory (EEPROM), erasable programmable read only memory (EPROM), programmable read only memory (PROM), read only memory (ROM), magnetic storage, flash memory, magnetic disk or optical disk. The readable storage medium can be any available medium that can be accessed by a general or special-purpose computer.

[0117] An exemplary readable storage medium is coupled to the processor so that the processor can read information from the readable storage medium and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can be located in a dedicated integrated circuit. Of course, the processor and the readable storage medium can also exist as discrete components in the vehicle-mounted system abnormality detection device.

[0118] Those skilled in the art can understand that all or part of the steps of implementing the above-mentioned method embodiments can be completed by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, the steps of the above-mentioned method embodiments are executed; and the aforementioned storage medium includes: ROM, RAM, disk or optical disk and other media that can store program codes.

[0119] Finally, it should be noted that the above embodiments are only preferred embodiments for fully illustrating the present invention, and the protection scope of the present invention is not limited thereto. Any equivalent substitution or change made by a person skilled in the art based on the present invention is within the protection scope of the present invention.

Claims

1. A vehicle system abnormality detection method, characterized in that: include: In response to the vehicle-mounted system being awakened, obtaining a target image displayed by the vehicle-mounted system during a stress test, wherein the stress conditions of the stress test include at least voice stress, stress of performing random operations on the vehicle-mounted system, and message playback stress; Based on the target image, it is detected whether the vehicle-mounted system is abnormal, and the abnormality includes abnormal color of the display interface of the vehicle-mounted system and / or freeze of the display interface of the vehicle-mounted system.

2. The vehicle-mounted system abnormality detection method according to claim 1, characterized in that: The abnormality includes abnormal color of the display interface of the vehicle-mounted system, and the detecting whether the vehicle-mounted system is abnormal based on the target image includes: Performing grayscale processing on the target image to obtain a grayscale processed image; Performing a resizing process on the grayscale processed image to obtain a resizing processed image; Obtaining the pixel value of each pixel in the image after the resizing process; Get the number of targets corresponding to different pixel values; Sorting the target quantities in descending order to obtain a sorted quantity; If the number of the sorted quantities is greater than or equal to the first color number, the pixel value corresponding to the first color number is taken from the sorted quantities as the target pixel value, and the weighted average of the target pixel values ​​of the first color number is obtained; if the weighted average is within the preset color threshold range, it is determined that the color of the display interface of the vehicle-mounted system is abnormal; if the weighted average is not within the preset color threshold range, it is determined that the color of the display interface of the vehicle-mounted system is normal; If the number of the sorted quantities is smaller than the number of the second colors, it is determined that the color of the display interface of the vehicle-mounted system is abnormal, and the number of the second colors is smaller than the number of the first colors.

3. The vehicle-mounted system abnormality detection method according to claim 2, characterized in that: The preset color threshold range includes a first threshold range corresponding to white, a second threshold range corresponding to black, and a third threshold range corresponding to gray, and if the weighted average value is within the preset color threshold range, determining that the color of the display interface of the vehicle-mounted system is abnormal includes: If the weighted average value is within the first threshold value range, determining that the display interface of the vehicle-mounted system is a white screen; If the weighted average value is within the second threshold value range, determining that the display interface of the vehicle-mounted system is a black screen; If the weighted average value is within the third threshold range, it is determined that the display interface of the vehicle-mounted system is a gray screen.

4. The vehicle-mounted system abnormality detection method according to claim 2, characterized in that: The grayscale processing of the target image to obtain the grayscale processed image includes: Acquire a target area image from the target image according to the configuration information, wherein the configuration information is used to determine coordinate information of a to-be-detected area of ​​the target image, wherein the to-be-detected area includes the entire area or a sub-area of ​​the target image; Grayscale processing is performed on the target area image to obtain a grayscale processed image.

5. The vehicle-mounted system abnormality detection method according to claim 1, characterized in that: The abnormality includes a freeze in the display interface of the vehicle-mounted system, and the detecting whether the vehicle-mounted system is abnormal based on the target image includes: Acquire first pixel histogram data corresponding to the target image and second pixel histogram data corresponding to a previous image adjacent to the target image; Based on the first pixel histogram data and the second pixel histogram data, obtaining a similarity between the target image and the previous image; If the similarity is within a preset similarity threshold range, determining that the target image and the previous image are similar images; If the number of consecutive similar images is greater than a number threshold, determining that the display interface of the vehicle-mounted system is stuck; If the number of consecutive similar images is less than or equal to the number threshold, it is determined that the display interface of the vehicle-mounted system is not stuck.

6. The vehicle-mounted system abnormality detection method according to any one of claims 1 to 5, characterized in that: Also includes: Within a first preset time period after the vehicle-mounted system is awakened, detecting whether a display interface of the vehicle-mounted system is in a bright screen state; If the display interface of the vehicle-mounted system is in a bright screen state, it is determined that the vehicle-mounted system is normal; If the display interface of the vehicle-mounted system is not in a bright screen state, it is determined that the vehicle-mounted system is abnormal, and the abnormality includes failure to start the vehicle-mounted system or abnormal restart.

7. The vehicle-mounted system abnormality detection method according to any one of claims 1 to 5, characterized in that: Also includes: At the end of the stress test, obtaining a test log; If there is an abnormality in the vehicle system or the test log contains an abnormality log, an alarm message is output.

8. The vehicle-mounted system abnormality detection method according to claim 7, characterized in that: Also includes: When the stress test is finished, putting the vehicle system into hibernation; When the vehicle-mounted system is dormant for a second preset time, the vehicle-mounted system is awakened, and the step of acquiring a target image displayed by the vehicle-mounted system during the stress test in response to the vehicle-mounted system being awakened is performed.

9. The vehicle-mounted system abnormality detection method according to any one of claims 1 to 5, characterized in that: The vehicle-mounted system includes a vehicle-mounted central control system and a vehicle-mounted instrument system.

10. A vehicle-mounted system abnormality detection device, characterized in that: include: an acquisition module, configured to acquire, in response to the vehicle-mounted system being awakened, a target image displayed by the vehicle-mounted system during a stress test, wherein the stress conditions of the stress test include at least voice stress, stress of performing random operations on the vehicle-mounted system, and message playback stress; A detection module is used to detect whether the vehicle-mounted system is abnormal based on the target image, and the abnormality includes abnormal color of the display interface of the vehicle-mounted system and / or freeze of the display interface of the vehicle-mounted system.

11. An electronic device, characterized in that: include: A processor, and a memory communicatively connected to the processor; The memory stores computer-executable instructions; The processor executes the computer-executable instructions stored in the memory to implement the vehicle-mounted system abnormality detection method according to any one of claims 1 to 9.

12. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer program instructions, and when the computer program instructions are executed, the vehicle-mounted system abnormality detection method according to any one of claims 1 to 9 is implemented.

13. A computer program product, comprising a computer program, characterized in that When the computer program is executed, the vehicle-mounted system abnormality detection method according to any one of claims 1 to 9 is implemented.

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