Instrument black screen detection method, system, device and storage medium

Abstract

The application discloses a kind of instrument black screen detection method, system, equipment and storage medium, camera and relay are connected with terminal equipment respectively by USB interface, instrument is connected with relay, the method comprises: obtaining the equipment address of relay, controls relay to power on instrument based on equipment address;Judge whether the start-up stage log is extracted from the serial port log of instrument;If yes, control camera to collect the current display screen image of instrument, and store current display screen image in the file system of terminal equipment by the imwrite function in OpenCV library;Current display screen image is input into AI automatic classification identification model, and instrument black screen detection result is obtained.In the present application, the efficiency of data transmission is improved through the connection between the camera, relay, terminal device and instrument. Then, the AI automatic classification identification model is used to automatically identify the black screen phenomenon without manual intervention, thereby improving the test efficiency.

Description

Technical Field

[0001] This invention relates to the field of black screen detection technology, and in particular to a method, system, device and storage medium for detecting black screen on instruments. Background Technology

[0002] In the field of modern display device black screen detection, due to electrical differences in hardware manufacturing processes and software complexity, display devices such as automotive and motorcycle instrument panels may exhibit subtle variations, leading to timing discrepancies and causing black screen phenomena with a very low probability. However, these issues require extensive power-on and power-off testing on multiple devices, a process that necessitates tens of thousands of burn-in cycles, making manual testing extremely time-consuming. Therefore, how to achieve automatic identification of instrument panel black screen phenomena without human intervention has become a pressing problem to be solved.

[0003] The above content is only used to help understand the technical solution of the present invention and does not represent an admission that the above content is prior art. Summary of the Invention

[0004] The main objective of this invention is to provide a method, system, device, and storage medium for detecting instrument blackouts, aiming to solve the technical problem of how to automatically identify instrument blackouts without human intervention.

[0005] To achieve the above objectives, the present invention provides a method for detecting a black screen on an instrument. A camera and a relay are connected to a terminal device via USB interfaces, and the instrument is connected to the relay. The method for detecting a black screen on the instrument includes:

[0006] Obtain the device address of the relay, and control the relay to power on the instrument based on the device address;

[0007] Determine whether to extract the startup phase log from the instrument's serial port log;

[0008] If so, control the camera to capture the current display image of the instrument, and use the imwrite function in the OpenCV library to store the current display image in the file system of the terminal device;

[0009] The current display screen image is input into the AI ​​automatic classification and recognition model to obtain the instrument black screen detection result.

[0010] Optionally, the step of controlling the relay to power on the instrument based on the device address includes:

[0011] The relay is powered on based on the device address;

[0012] After the relay is powered on, the instrument is powered on.

[0013] Optionally, after the step of determining whether to extract the startup phase log from the instrument's serial port log, the method further includes:

[0014] If not, the camera is controlled to capture the current display image of the instrument through a timeout judgment mechanism;

[0015] The current display image is stored in the file system of the terminal device using the imwrite function in the OpenCV library.

[0016] Optionally, the step of intelligently controlling the camera to capture the current display image of the instrument through timeout judgment includes:

[0017] Determine the power-on duration of the instrument;

[0018] Determine whether the power-on duration of the instrument is greater than a preset power-on threshold;

[0019] When the power-on duration of the instrument exceeds the preset power-on threshold, the camera is controlled to capture the current display image of the instrument.

[0020] Optionally, before the step of inputting the current display screen image into the AI ​​automatic classification and recognition model to obtain the instrument black screen detection result, the method further includes:

[0021] Acquire multiple normal instrument interface images and multiple black screen instrument interface images, and then label the multiple normal instrument interface images and multiple black screen instrument interface images according to their categories to obtain labeled multiple normal interface images and labeled multiple black screen interface images.

[0022] The labeled normal interface images and the labeled black screen interface images are input into the image classification model for training to obtain an AI automatic classification and recognition model.

[0023] Optionally, after the step of obtaining the device address of the relay and controlling the relay to power on the instrument based on the device address, the method further includes:

[0024] Control the camera to record the display screen of the instrument;

[0025] The displayed screen is recorded and encoded into a video file using the VideoCapture function in the OpenCV library, and the video file is stored in the file system of the terminal device.

[0026] Furthermore, to achieve the above objectives, the present invention also proposes a detection system for instrument blackout, wherein a camera and a relay are connected to a terminal device via USB interfaces, and the instrument is connected to the relay. The instrument blackout detection system includes:

[0027] The power-on module is used to obtain the device address of the relay and control the relay to power on the instrument based on the device address;

[0028] The judgment module is used to determine whether to extract the startup phase log from the instrument's serial port log;

[0029] The storage module is used to control the camera to capture the current display image of the instrument and store the current display image to the file system of the terminal device using the imwrite function in the OpenCV library.

[0030] The classification module is used to input the current display screen image into the AI ​​automatic classification and recognition model to obtain the instrument black screen detection result.

[0031] Furthermore, to achieve the above objectives, the present invention also proposes a device for detecting instrument blackouts. The device includes: a memory, a processor, and an instrument blackout detection program stored in the memory and executable on the processor. The instrument blackout detection program is configured to implement the steps of the instrument blackout detection method described above.

[0032] In addition, to achieve the above objectives, the present invention also proposes a storage medium storing a detection program for a black screen of an instrument, wherein when the detection program for a black screen of an instrument is executed by a processor, the steps of the detection method for a black screen of an instrument as described above are implemented.

[0033] This invention connects a camera and a relay to a terminal device via USB interfaces, with the instrument connected to the relay. First, the device address of the relay is obtained. Based on this address, the relay powers on the instrument. Then, it checks if the startup log is extracted from the instrument's serial port log. If so, the camera captures the current display image of the instrument and stores it in the terminal device's file system using the `imwrite` function from the OpenCV library. Finally, the current display image is input into an AI automatic classification and recognition model to obtain the instrument's black screen detection result. Compared to existing technologies that require tens of thousands of manual tests, making manual testing cumbersome, this invention improves data transmission efficiency by connecting the camera and relay to the terminal device via USB interfaces, and the instrument to the relay. The AI ​​automatic classification and recognition model then enables automatic identification of black screen phenomena without manual intervention, thereby improving testing efficiency. Attached Figure Description

[0034] Figure 1 This is a schematic diagram of the structure of the instrument black screen detection device in the hardware operating environment involved in the embodiment of the present invention;

[0035] Figure 2 This is a flowchart illustrating the first embodiment of the instrument black screen detection method of the present invention;

[0036] Figure 3 This is a control system diagram of an automatic detection display device for black screen, representing the first embodiment of the instrument black screen detection method of the present invention.

[0037] Figure 4 A flowchart of the software program for the first embodiment of the instrument black screen detection method of the present invention;

[0038] Figure 5 This is a structural block diagram of the first embodiment of the instrument blackout detection system of the present invention.

[0039] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0040] It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the scope of the invention.

[0041] Reference Figure 1 , Figure 1 This is a schematic diagram of the instrument black screen detection device in the hardware operating environment involved in the embodiment of the present invention.

[0042] like Figure 1 As shown, the detection device for the black screen instrument may include: a processor 1001, such as a central processing unit (CPU), a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. The communication bus 1002 is used to establish communication between these components. The user interface 1003 may include a display screen and an input unit such as a keyboard; optionally, the user interface 1003 may also include a standard wired interface or a wireless interface. The network interface 1004 may optionally include a standard wired interface or a wireless interface (such as a Wireless-Fidelity (Wi-Fi) interface). The memory 1005 may be high-speed random access memory (RAM) or stable non-volatile memory (NVM), such as a disk storage device. Optionally, the memory 1005 may also be a storage system independent of the aforementioned processor 1001.

[0043] Those skilled in the art will understand that Figure 1The structure shown does not constitute a limitation on the detection device for instrument blackout, and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0044] like Figure 1 As shown, the memory 1005, which serves as a storage medium, may include an operating system, a network communication module, a user interface module, and a detection program for instrument blackout.

[0045] exist Figure 1 In the instrument blackout detection device shown, the network interface 1004 is mainly used for data communication with the network server; the user interface 1003 is mainly used for data interaction with the user; the processor 1001 and memory 1005 in the instrument blackout detection device of the present invention can be set in the instrument blackout detection device, and the instrument blackout detection device calls the instrument blackout detection program stored in the memory 1005 through the processor 1001 and executes the instrument blackout detection method provided in the embodiment of the present invention.

[0046] This invention provides a method for detecting a black screen on an instrument, referring to... Figure 2 , Figure 2 This is a flowchart illustrating the first embodiment of the instrument black screen detection method of the present invention.

[0047] In this embodiment, the camera and relay are connected to the terminal device via USB interfaces, and the instrument is connected to the relay. (Refer to...) Figure 3 , Figure 3 This is a control system diagram of an automatic detection display device for black screen, which is the first embodiment of the instrument black screen detection method of the present invention.

[0048] It should also be understood that the terminal device can be a computer, etc. The camera and relay are connected to the computer's USB port using a USB cable, and each instrument is connected to the relay. The computer runs the main control program.

[0049] It should also be noted that the relay can control about 10 display devices at the same time. Assuming a power-on / off cycle of 15 seconds, each instrument can be tested 60 / 15*60*12 = 2880 times in 12 hours, and a total of 28,800 tests can be performed on 10 instruments, which greatly improves testing efficiency and saves development time.

[0050] The method for detecting a black screen on the instrument includes the following steps:

[0051] Step S10: Obtain the device address of the relay, and control the relay to power on the instrument based on the device address.

[0052] It is easy to understand that the execution subject of this embodiment can be a detection system for instrument black screen with functions such as data processing, network communication and program operation, or other computer devices with similar functions. This embodiment does not limit it.

[0053] Furthermore, the method of powering on the instrument by controlling the relay based on the device address is to power on the relay based on the device address; after the relay is powered on, the instrument is powered on.

[0054] In the specific implementation, refer to Figure 4 , Figure 4 The flowchart of the software program for the first embodiment of the instrument black screen detection method of the present invention shows that the device address is connected to the relay via USB, and the relay switch is controlled to power on, thereby controlling the power supply of the instrument.

[0055] Obtain the device address of the relay, control the relay to power on the instrument based on the device address, then control the camera to record the display screen of the instrument; use the VideoCapture function in the OpenCV library to encode the display screen recording into a video file, and store the video file in the file system of the terminal device.

[0056] In this embodiment, the system communicates with the camera via USB to acquire the camera's current real-time image, and uses the VideoCapture function in the OpenCV library to encode it into a video file, which is then stored in the computer's file system.

[0057] Step S20: Determine whether to extract the startup phase log from the instrument's serial port log.

[0058] In the specific implementation, if not, the camera is controlled to capture the current display image of the instrument through a timeout judgment mechanism; the current display image is stored in the file system of the terminal device through the imwrite function in the OpenCV library.

[0059] Furthermore, the processing method for intelligently controlling the camera to acquire the current display image of the instrument through timeout judgment is as follows: determine the power-on duration of the instrument; determine whether the power-on duration of the instrument is greater than a preset power-on threshold; when the power-on duration of the instrument is greater than the preset power-on threshold, control the camera to acquire the current display image of the instrument.

[0060] It should also be noted that the preset power-on threshold can be customized by the user, such as 12s, 15s, etc.

[0061] In this embodiment, if no startup log is received from the instrument serial port log, a timeout judgment mechanism is entered. If more than 12 seconds have passed since power-on, the imwrite function in the OpenCV library is used to save the current screen as an image and store it in the computer's file system.

[0062] Step S30: If yes, control the camera to capture the current display image of the instrument, and store the current display image in the file system of the terminal device using the imwrite function in the OpenCV library.

[0063] It should also be understood that when the startup log is received from the instrument's serial port log, the `imwrite` function from the OpenCV library is used to save the current screen as an image to the computer's file system. Afterwards, the instrument is powered off via a USB-controlled relay switch, and then the process of powering on the instrument by controlling the relay based on the device address is repeated.

[0064] Step S40: Input the current display screen image into the AI ​​automatic classification and recognition model to obtain the instrument black screen detection result.

[0065] Further, before inputting the current display screen image into the AI ​​automatic classification and recognition model to obtain the instrument black screen detection result, multiple normal interface images and multiple black screen interface images of the instrument are acquired, and the multiple normal interface images and multiple black screen interface images of the instrument are labeled with categories respectively to obtain labeled multiple normal interface images and labeled multiple black screen interface images; the labeled multiple normal interface images and labeled multiple black screen interface images are input into the image classification model for training to obtain the AI ​​automatic classification and recognition model.

[0066] It should also be understood that the category label content is either normal or black screen.

[0067] In the specific implementation, images of normal and black screen interfaces are collected as training sets and labeled with image categories; a convolutional neural network is constructed to train the image classification model; images saved during the testing process are input into the model, and the classification results are output.

[0068] It should also be understood that the classification result (i.e. the instrument black screen detection result) is the real-time classification and identification of the black screen image.

[0069] In this embodiment, the camera and relay are connected to the terminal device via USB interfaces, and the instrument is connected to the relay. First, the device address of the relay is obtained. Based on the device address, the relay is controlled to power on the instrument. Then, it is determined whether to extract the startup phase log from the instrument's serial port log. If so, the camera is controlled to capture the current display image of the instrument, and the current display image is stored in the terminal device's file system using the `imwrite` function in the OpenCV library. The current display image is then input into the AI ​​automatic classification and recognition model to obtain the instrument's black screen detection result. Compared to existing technologies that require tens of thousands of manual tests, making manual testing cumbersome, this embodiment improves data transmission efficiency by connecting the camera and relay to the terminal device via USB interfaces, and the instrument to the relay. The AI ​​automatic classification and recognition model then achieves automatic identification of black screen phenomena without manual intervention, thereby improving testing efficiency.

[0070] Reference Figure 5 , Figure 5 This is a structural block diagram of the first embodiment of the instrument blackout detection system of the present invention.

[0071] like Figure 5 As shown, the instrument blackout detection system proposed in this embodiment of the invention includes:

[0072] In this embodiment, the camera and relay are connected to the terminal device via USB interfaces, and the instrument is connected to the relay. (Refer to...) Figure 3 , Figure 3 This is a control system diagram of an automatic detection display device for black screen, which is the first embodiment of the instrument black screen detection method of the present invention.

[0073] It should also be understood that the terminal device can be a computer, etc. The camera and relay are connected to the computer's USB port using a USB cable, and each instrument is connected to the relay. The computer runs the main control program.

[0074] It should also be noted that the relay can control about 10 display devices at the same time. Assuming a power-on / off cycle of 15 seconds, each instrument can be tested 60 / 15*60*12 = 2880 times in 12 hours, and a total of 28,800 tests can be performed on 10 instruments, which greatly improves testing efficiency and saves development time.

[0075] The power-on module 5001 is used to obtain the device address of the relay and control the relay to power on the instrument based on the device address.

[0076] Furthermore, the method of powering on the instrument by controlling the relay based on the device address is to power on the relay based on the device address; after the relay is powered on, the instrument is powered on.

[0077] In the specific implementation, refer to Figure 4 , Figure 4 The flowchart of the software program for the first embodiment of the instrument black screen detection method of the present invention shows that the device address is connected to the relay via USB, and the relay switch is controlled to power on, thereby controlling the power supply of the instrument.

[0078] Obtain the device address of the relay, control the relay to power on the instrument based on the device address, then control the camera to record the display screen of the instrument; use the VideoCapture function in the OpenCV library to encode the display screen recording into a video file, and store the video file in the file system of the terminal device.

[0079] In this embodiment, the system communicates with the camera via USB to acquire the camera's current real-time image, and uses the VideoCapture function in the OpenCV library to encode it into a video file, which is then stored in the computer's file system.

[0080] The judgment module 5002 is used to determine whether to extract the startup phase log from the serial port log of the instrument.

[0081] In the specific implementation, if not, the camera is controlled to capture the current display image of the instrument through a timeout judgment mechanism; the current display image is stored in the file system of the terminal device through the imwrite function in the OpenCV library.

[0082] Furthermore, the processing method for intelligently controlling the camera to acquire the current display image of the instrument through timeout judgment is as follows: determine the power-on duration of the instrument; determine whether the power-on duration of the instrument is greater than a preset power-on threshold; when the power-on duration of the instrument is greater than the preset power-on threshold, control the camera to acquire the current display image of the instrument.

[0083] It should also be noted that the preset power-on threshold can be customized by the user, such as 12s, 15s, etc.

[0084] In this embodiment, if no startup log is received from the instrument serial port log, a timeout judgment mechanism is entered. If more than 12 seconds have passed since power-on, the imwrite function in the OpenCV library is used to save the current screen as an image and store it in the computer's file system.

[0085] The storage module 5003 is used to control the camera to capture the current display image of the instrument and store the current display image to the file system of the terminal device through the imwrite function in the OpenCV library.

[0086] It should also be understood that when the startup log is received from the instrument's serial port log, the `imwrite` function from the OpenCV library is used to save the current screen as an image to the computer's file system. Afterwards, the instrument is powered off via a USB-controlled relay switch, and then the process of powering on the instrument by controlling the relay based on the device address is repeated.

[0087] The classification module 5004 is used to input the current display screen image into the AI ​​automatic classification and recognition model to obtain the instrument black screen detection result.

[0088] Further, before inputting the current display screen image into the AI ​​automatic classification and recognition model to obtain the instrument black screen detection result, multiple normal interface images and multiple black screen interface images of the instrument are acquired, and the multiple normal interface images and multiple black screen interface images of the instrument are labeled with categories respectively to obtain labeled multiple normal interface images and labeled multiple black screen interface images; the labeled multiple normal interface images and labeled multiple black screen interface images are input into the image classification model for training to obtain the AI ​​automatic classification and recognition model.

[0089] It should also be understood that the category label content is either normal or black screen.

[0090] In the specific implementation, images of normal and black screen interfaces are collected as training sets and labeled with image categories; a convolutional neural network is constructed to train the image classification model; images saved during the testing process are input into the model, and the classification results are output.

[0091] It should also be understood that the classification result (i.e. the instrument black screen detection result) is the real-time classification and identification of the black screen image.

[0092] In this embodiment, the camera and relay are connected to the terminal device via USB interfaces, and the instrument is connected to the relay. First, the device address of the relay is obtained. Based on the device address, the relay is controlled to power on the instrument. Then, it is determined whether to extract the startup phase log from the instrument's serial port log. If so, the camera is controlled to capture the current display image of the instrument, and the current display image is stored in the terminal device's file system using the `imwrite` function in the OpenCV library. The current display image is then input into the AI ​​automatic classification and recognition model to obtain the instrument's black screen detection result. Compared to existing technologies that require tens of thousands of manual tests, making manual testing cumbersome, this embodiment improves data transmission efficiency by connecting the camera and relay to the terminal device via USB interfaces, and the instrument to the relay. The AI ​​automatic classification and recognition model then achieves automatic identification of black screen phenomena without manual intervention, thereby improving testing efficiency.

[0093] Other embodiments or specific implementations of the instrument black screen detection system of the present invention can be referred to the above-described method embodiments, and will not be repeated here.

[0094] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or system. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or system that includes that element.

[0095] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0096] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as read-only memory / random access memory, magnetic disk, optical disk) and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods described in the various embodiments of the present invention.

[0097] The above are merely preferred embodiments of the present invention and do not limit the scope of the patent. Any equivalent structural or procedural transformations made based on the description and drawings of the present invention, or direct or indirect applications in other related technical fields, are similarly included within the scope of patent protection of the present invention.

Claims

1. A method for detecting a blackout in an instrument screen, characterized in that, The camera and relay are connected to the terminal device via USB interfaces, and the instrument is connected to the relay. The method for detecting a black screen on the instrument includes the following steps: Obtain the device address of the relay, and control the relay to power on the instrument based on the device address; the step of controlling the relay to power on the instrument based on the device address includes: The relay is powered on based on the device address; After the relay is powered on, the instrument is powered on. Determine whether to extract the startup phase log from the instrument's serial port log; after the step of determining whether to extract the startup phase log from the instrument's serial port log, the method further includes: If not, the camera is controlled to capture the current display image of the instrument through a timeout judgment mechanism; The step of storing the current display image to the file system of the terminal device using the imwrite function in the OpenCV library; and controlling the camera to capture the current display image of the instrument through a timeout judgment mechanism, includes: Determine the power-on duration of the instrument; Determine whether the power-on duration of the instrument is greater than a preset power-on threshold; When the power-on duration of the instrument exceeds the preset power-on threshold, the camera is controlled to capture the current display image of the instrument. If so, control the camera to capture the current display image of the instrument, and use the imwrite function in the OpenCV library to store the current display image in the file system of the terminal device; The current display screen image is input into the AI ​​automatic classification and recognition model to obtain the instrument black screen detection result.

2. The method as described in claim 1, characterized in that, Before the step of inputting the current display screen image into the AI ​​automatic classification and recognition model to obtain the instrument black screen detection result, the method further includes: Acquire multiple normal instrument interface images and multiple black screen instrument interface images, and then label the multiple normal instrument interface images and multiple black screen instrument interface images according to their categories to obtain labeled multiple normal interface images and labeled multiple black screen interface images. The labeled normal interface images and the labeled black screen interface images are input into the image classification model for training to obtain an AI automatic classification and recognition model.

3. The method as described in claim 1, characterized in that, After the step of obtaining the device address of the relay and controlling the relay to power on the instrument based on the device address, the method further includes: Control the camera to record the display screen of the instrument; The displayed screen is recorded and encoded into a video file using the VideoCapture function in the OpenCV library, and the video file is stored in the file system of the terminal device.

4. A system for detecting instrument blackout using the instrument blackout detection method as described in any one of claims 1-3, characterized in that, The camera and relay are connected to the terminal device via USB interfaces, and the instrument is connected to the relay. The instrument blackout detection system includes: The power-on module is used to obtain the device address of the relay and control the relay to power on the instrument based on the device address; The judgment module is used to determine whether to extract the startup phase log from the instrument's serial port log; The storage module is used to control the camera to capture the current display image of the instrument and store the current display image to the file system of the terminal device using the imwrite function in the OpenCV library. The classification module is used to input the current display screen image into the AI ​​automatic classification and recognition model to obtain the instrument black screen detection result.

5. A detection device for instrument blackout, characterized in that, The device includes: a memory, a processor, and a black screen detection program for an instrument, stored in the memory and executable on the processor, the black screen detection program being configured to implement the steps of the black screen detection method for an instrument as described in any one of claims 1-3.

6. A storage medium, characterized in that, The storage medium stores a detection program for instrument black screen, and when the instrument black screen detection program is executed by the processor, it implements the steps of the instrument black screen detection method as described in any one of claims 1-3.

Images