CN118658095A - Method, device, computer equipment and medium for determining the operating status of detection equipment - Google Patents

Abstract

The embodiment of the invention provides a judging method, a judging device, a computer device and a medium for detecting the running state of equipment, wherein the method comprises the following steps: acquiring a video of the detection equipment in operation as a detection video through a video acquisition platform, and acquiring the type of the detection video through the type of the detection equipment; if the detected video is a chessboard video, identifying the number of point bits of the chessboard in the detected video, judging the number of equipment of the detection equipment through the number of point bits, and if the detected video and the equipment are inconsistent, judging the running state of the detection equipment to be abnormal; if the detected video is the navigation icon video, identifying the navigation icon in the detected video, comparing the navigation icon with a preset correct navigation pattern in similarity, generating a comparison result, and if the comparison result is smaller than a similarity threshold, judging that the running state of the detection equipment is abnormal. The scheme constructs a video acquisition platform, and the running state of the detection equipment is automatically judged through the video acquisition platform.

Description

Method, device, computer equipment and medium for determining the operating status of detection equipment

Technical Field

The present invention relates to the technical field of equipment detection, and in particular to a method, device, computer equipment and medium for determining the operating status of a detection device.

Background Art

The automotive industry is changing rapidly. In order to verify the stability of the equipment, it is inevitable to confirm the output of the vehicle's equipment system screen. The screen monitoring is to check whether the test equipment is running normally each time. The existing technology is limited to single-item testing of each module for power-on and power-off testing, which needs to be checked through logs, and the video during operation requires a lot of manpower to confirm again.

The existing technology for power-on and power-off detection usually includes: module testing, hardware testing, vehicle testing, and power-on and power-off testing of devices with screens. The module test determines whether the module communication is normal through the agreed serial communication protocol, and the screen output requires manual inspection to see if the screen is normal. The hardware test controls the power on and off of the device through the existing programmable power supply, and manually checks whether the screen is normal. The vehicle test performs power-on and power-off tests according to the actual vehicle environment, and manually checks the screen. The screen device power-on and power-off test records the screen, and the screen is manually checked.

Therefore, whether it is a real vehicle test or a bench test, a lot of time is needed to check whether the image is normal.

Summary of the invention

In view of this, an embodiment of the present invention provides a method for determining the operating status of a detection device to solve the technical problem in the prior art that the operating status of the detection device needs to be manually checked and cannot be automatically determined. The method includes:

Construct a video acquisition platform for monitoring the operating status of the detection equipment, collect the running video of the detection equipment as the detection video through the video acquisition platform, and obtain the type of the detection video according to the type of the detection equipment;

If the detection video is a chessboard video, identify the number of points on the chessboard in the detection video, determine the number of devices of the detection device according to the number of points, compare the number of devices with the preset correct number of devices, and if the two are consistent, determine that the operating state of the detection device is normal; if the two are inconsistent, determine that the operating state of the detection device is abnormal;

If the detection video is a navigation icon video, identify the navigation icon in the detection video, compare the navigation icon with the preset correct navigation pattern for similarity, and generate a comparison result. If the comparison result is greater than or equal to the similarity threshold, the operating status of the detection device is judged to be normal. If the comparison result is less than the similarity threshold, the operating status of the detection device is judged to be abnormal.

The embodiment of the present invention also provides a device for determining the operating status of a detection device to solve the technical problem that the operating status of the detection device in the prior art needs to be manually checked and cannot be automatically determined. The device includes:

The video acquisition module is used to build a video acquisition platform for monitoring the operating status of the detection equipment, collect the running video of the detection equipment as the detection video through the video acquisition platform, and obtain the type of the detection video according to the type of the detection equipment;

The number of points detection module is used to identify the number of points on the chessboard in the detection video if the detection video is a chessboard video, determine the number of devices of the detection device according to the number of points, compare the number of devices with the preset correct number of devices, and if the two are consistent, determine that the operating state of the detection device is normal; if the two are inconsistent, determine that the operating state of the detection device is abnormal;

The navigation icon detection module is used to identify the navigation icon in the detection video if the detection video is a navigation icon video, compare the similarity between the navigation icon and the preset correct navigation pattern, and generate a comparison result. If the comparison result is greater than or equal to the similarity threshold, the operating status of the detection device is judged to be normal; if the comparison result is less than the similarity threshold, the operating status of the detection device is judged to be abnormal.

An embodiment of the present invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements any of the above-mentioned methods for determining the operating status of the detection device when executing the computer program, so as to solve the technical problem in the prior art that the operating status of the detection device needs to be manually checked and cannot be automatically determined.

An embodiment of the present invention also provides a computer-readable storage medium, which stores a computer program for executing any of the above-mentioned methods for determining the operating status of the detection device, so as to solve the technical problem in the prior art that the operating status of the detection device needs to be manually checked and cannot be automatically determined.

Compared with the prior art, the beneficial effects that can be achieved by at least one of the above technical solutions adopted in the embodiments of this specification include at least:

The video acquisition platform is used to collect the detection video, and the detection video is automatically detected. The detection video is analyzed through the video acquisition platform to realize the detection of the operating status of different detection equipment.

BRIEF DESCRIPTION OF THE DRAWINGS

In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

FIG1 is a flow chart of a method for determining the operating status of a detection device provided by an embodiment of the present invention;

FIG2 is a schematic diagram of the structure of a video acquisition platform provided by an embodiment of the present invention;

FIG3 is a structural block diagram of a computer device provided by an embodiment of the present invention;

FIG. 4 is a structural block diagram of a device for determining the operating status of a detection device provided in an embodiment of the present invention.

DETAILED DESCRIPTION

The embodiments of the present application are described in detail below with reference to the accompanying drawings.

The following describes the implementation methods of the present application through specific specific examples, and those skilled in the art can easily understand other advantages and effects of the present application from the content disclosed in this specification. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. The present application can also be implemented or applied through other different specific implementation methods, 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 application. It should be noted that, in the absence of conflict, the following embodiments and the features in the embodiments can be combined with each other. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without making creative work belong to the scope of protection of the present application.

In an embodiment of the present invention, a method for determining the operating status of a detection device is provided. As shown in FIG1 , the method includes:

Step S101: construct a video acquisition platform for monitoring the operating status of the detection device, collect the video of the detection device during operation as the detection video through the video acquisition platform, and obtain the type of the detection video according to the type of the detection device;

Step S102: if the detection video is a chessboard video, identify the number of points on the chessboard in the detection video, determine the number of devices of the detection device according to the number of points, compare the number of devices with the preset correct number of devices, and if the two are consistent, determine that the operating state of the detection device is normal; if the two are inconsistent, determine that the operating state of the detection device is abnormal;

Step S103: If the detection video is a navigation icon video, identify the navigation icon in the detection video, compare the navigation icon with the preset correct navigation pattern for similarity, and generate a comparison result. If the comparison result is greater than or equal to the similarity threshold, the operating status of the detection device is judged to be normal; if the comparison result is less than the similarity threshold, the operating status of the detection device is judged to be abnormal.

Specifically, the video acquisition platform can detect two types of videos. The existing vehicle test is a manual test on the vehicle, which is time-consuming and laborious. The present invention builds a power-on and power-off environment on the test bench and uses a camera to shoot a chessboard or a device with a navigation icon.

In specific implementation, the following steps are used to identify the number of points on the chessboard in the detection video, and the number of detection devices is determined by the number of points:

Capture multiple frames within a fixed time period in the detection video to generate a detection object picture group; detect each corner point of each detection object picture in the detection object picture group respectively, and obtain the total number of corner points corresponding to each detection object picture until all the detection object pictures in the detection object picture group are detected; calculate the average value of the total number of corner points of all the detection object pictures in the detection object picture group, and use the average value as the number of points on the chessboard; set the corresponding relationship between the number of points on the chessboard and the number of detection devices, and obtain the number of devices corresponding to the number of points through the corresponding relationship.

In the specific implementation, in order to detect the corner points of each image, the following steps are performed to detect each corner point of each detection object image in the detection object image group:

The image of the detection object is converted into a grayscale image and then filtered to generate a preprocessed image; edge detection is performed on the preprocessed image to extract the line group that constitutes the chessboard, and it is determined whether the lines in the line group are straight lines. If they are straight lines, the corresponding lines are taken as valid lines to generate a valid line group; all valid lines in the valid line group are looped to determine whether there are valid lines that are perpendicular to each other; for each pair of valid lines that are perpendicular to each other, the corresponding intersection point is taken as a corner point.

Specifically, start the host computer, and compare the chessboard obtained from the detection video with the model in the algorithm through the algorithm for detecting the number of points on the chessboard integrated in the host computer. Start timing from the frame where the number of points on the chessboard is identified. After running for 10 seconds (to ensure that the device is operating normally), start counting the number of points on the chessboard in each frame within 1 second, and calculate the average number of points in all frames within one second. In one embodiment, a corresponding relationship between the average value and the number of devices is set. For example, an average value between 5 and 9 is determined as one device, an average value between 10 and 16 is determined as 2 devices, and so on. Finally, compare the number of devices with the number of devices set in the host computer. If the two are consistent, it is recorded as a normal operating state, and if they are inconsistent, it is recorded as an abnormal operating state. Among them, the fixed time period is one second after running for 10 seconds.

The correct number of devices for the test device can be set in the host computer. When the number of detected chessboard points is inconsistent with the set correct number of devices, the image of the frame with the problem will be saved to the set path and recorded in the error file of the host computer.

In specific implementation, the following steps are performed to recognize and detect the navigation icon in the video, and the navigation icon is compared with the preset correct navigation pattern for similarity to generate a comparison result:

Capture multiple frames within a fixed time period in the detection video to generate a matching source icon group; calculate the feature value of each matching source icon in the matching source icon group until all the matching source icons in the matching source icon group are calculated; calculate the ratio of the feature value of the matching source icon to the feature value of the correct navigation pattern, and use the ratio as the matching degree; calculate the average value of the matching degrees corresponding to all the matching source icons in the matching source icon group, and use the average value as the comparison result.

In specific implementation, the characteristic value of each matching source icon in the matching source icon group is calculated through the following steps:

The matching source icon is scaled to the same size as the correct navigation pattern to generate a scaled matching source icon; the scaled matching source icon is gray-scaled to generate a matching source gray-scale icon; the average value of the pixel points in each row of the matching source gray-scale icon is calculated in turn; the variance of all the average values is calculated, and the variance is used as the characteristic value of the matching source icon.

Specifically, start the host computer, integrate the preset correct navigation pattern in the host computer, and compare the navigation icon obtained from the detection video with the correct navigation pattern. First, start timing from the first frame in which the navigation icon is recognized. After running for 10 seconds (to ensure that the device is operating normally), calculate the matching degree between the navigation icon and the correct navigation pattern of each frame within 1 second, and then calculate the average matching degree. When the average matching degree reaches 80% (preset similarity threshold) or above, it is judged that the operating status of the detection device is normal, otherwise it is judged that the operating status of the detection device is abnormal. Among them, the fixed time period is 1 second after running for 10 seconds.

Specifically, comparing the similarity of navigation icons is to compare the variance of the navigation icon obtained from the detection video with the variance of the correct navigation pattern. The smaller the difference in variance, the more similar the icons are. The variance of the correct navigation pattern can be calculated in advance and saved in the host computer.

In specific implementation, as shown in FIG2 , the video acquisition platform is constructed through the following steps:

The acquisition camera, host computer and programmable power supply are provided. The acquisition camera is connected to the host computer, and the programmable power supply is connected to the detection equipment. The acquisition camera is set toward the detection equipment, and the acquisition camera transmits the recorded detection video to the host computer. The detection video is displayed in real time on the host computer, and the detection video is analyzed, counted and the detection results are output. The host computer controls the power on and off of the detection equipment through the programmable power supply.

Specifically, the host computer is used to display and record the images captured by the acquisition camera in real time, analyze and count the images, and output the results. The host computer will output the images captured by the acquisition camera in real time, perform detection in real time, display the detection details, and record the detected images.

The host computer can automatically determine whether the picture is normal at different time intervals for different detection devices. The pictures of the adapted detection devices (including central control devices with lens output devices and devices without lens devices) are automatically detected. The host computer automatically counts the detection results of the running status of the detection equipment and forms an Excel table (error file), which contains the results of each detection; the host computer also marks the abnormal detection video and records it in the Excel table (error file).

The acquisition camera is used to monitor the video of the testing equipment during operation. The programmable power supply is used to control the power on and off of the testing equipment.

In specific implementation, in order to automatically generate an error file, the following steps are performed to output the error file:

When the running state of the detection device is abnormal, the frame with error in the detection video is captured as the error frame; the timestamp corresponding to the error frame is extracted, and the timestamp, the file name of the detection video and the file path corresponding to the detection video are output as the error file.

In this embodiment, a computer device is provided, as shown in FIG3 , including a memory 301, a processor 302, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, any of the above-mentioned methods for determining the operating status of the detection device is implemented.

Specifically, the computer device may be a computer terminal, a server or a similar computing device.

In this embodiment, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program for executing any of the above-mentioned methods for determining the operating status of a detection device.

Specifically, computer-readable storage media include permanent and non-permanent, removable and non-removable media, and information storage can be achieved by any method or technology. Information can be computer-readable instructions, data structures, modules of programs or other data. Examples of computer-readable storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, tape disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable storage media does not include temporary computer-readable media (transitory media), such as modulated data signals and carrier waves.

Based on the same inventive concept, a device for determining the operating status of a detection device is also provided in an embodiment of the present invention, as described in the following embodiments. Since the principle of solving the problem by the device for determining the operating status of the detection device is similar to the method for determining the operating status of the detection device, the implementation of the device for determining the operating status of the detection device can refer to the implementation of the method for determining the operating status of the detection device, and the repeated parts will not be repeated. As used below, the term "unit" or "module" can be a combination of software and/or hardware that implements a predetermined function. Although the device described in the following embodiments is preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceived.

FIG4 is a structural block diagram of a device for determining the operating status of a detection device according to an embodiment of the present invention. As shown in FIG4 , the device comprises: a video acquisition module 401 , a point number detection module 402 and a navigation icon detection module 403 . The structure is described below.

The video acquisition module 401 is used to construct a video acquisition platform for monitoring the operating status of the detection device, collect the running video of the detection device as the detection video through the video acquisition platform, and obtain the type of the detection video according to the type of the detection device;

The point number detection module 402 is used to identify the number of points on the chessboard in the detection video if the detection video is a chessboard video, determine the number of devices of the detection device according to the number of points, compare the number of devices with the preset correct number of devices, and if the two are consistent, determine that the operating state of the detection device is normal; if the two are inconsistent, determine that the operating state of the detection device is abnormal;

The navigation icon detection module 403 is used to identify the navigation icon in the detection video if the detection video is a navigation icon video, compare the similarity between the navigation icon and the preset correct navigation pattern, and generate a comparison result. If the comparison result is greater than or equal to the similarity threshold, the operating status of the detection device is judged to be normal; if the comparison result is less than the similarity threshold, the operating status of the detection device is judged to be abnormal.

In one embodiment, the video acquisition module includes:

The platform construction unit is used for a collection camera, a host computer and a program-controlled power supply, wherein the collection camera is connected to the host computer, and the program-controlled power supply is connected to the detection equipment;

A video acquisition unit is used to acquire the camera orientation detection device settings, and the acquisition camera transmits the recorded detection video to the host computer;

The video detection unit is used to display the detection video in real time on the host computer, analyze and count the detection video, and output the detection results;

The power on and off control unit is used for the host computer to control the power on and off of the detection equipment through a programmable power supply.

In one embodiment, the number of points detection module includes:

A detection object picture group generating unit, used for capturing multiple frames within a fixed time period in the detection video to generate a detection object picture group;

A corner point total number obtaining unit is used to respectively detect each corner point of each detection object picture in the detection object picture group, and obtain the total number of corner points corresponding to each detection object picture until all detection object pictures in the detection object picture group are detected;

A point number calculation unit is used to calculate the average value of the total number of corner points of all the detection object pictures in the detection object picture group, and use the average value as the number of points of the chessboard;

The device quantity determination unit is used to set the corresponding relationship between the number of points on the chessboard and the number of devices of the detection device, and obtain the number of devices corresponding to the number of points through the corresponding relationship.

In one embodiment, a corner point total number acquisition unit is used to convert the detection object image into a grayscale image and then perform a filtering operation to generate a preprocessed image; perform edge detection on the preprocessed image, extract the line group that constitutes the checkerboard, and determine whether the lines in the line group are straight lines. If they are straight lines, the corresponding lines are used as valid lines to generate a valid line group; loop all valid lines in the valid line group to determine whether there are valid lines that are perpendicular to each other; for each pair of valid lines that are perpendicular to each other, the corresponding intersection point is used as a corner point.

In one embodiment, the navigation icon detection module includes:

A matching source icon acquisition unit, used to capture multiple frames within a fixed time period in the detection video and generate a matching source icon group;

A feature value acquisition unit, used to calculate the feature value of each matching source icon in the matching source icon group until all matching source icons in the matching source icon group are calculated;

A matching degree calculation unit, used to calculate the ratio of the feature value of the matching source icon to the feature value of the correct navigation pattern, and use the ratio as the matching degree;

The comparison result generating unit is used to calculate the average value of the matching degrees corresponding to all the matching source icons in the matching source icon group, and use the average value as the comparison result.

In one embodiment, a feature value acquisition unit is used to scale the matching source icon to the same size as the correct navigation pattern to generate a scaled matching source icon; grayscale the scaled matching source icon to generate a matching source grayscale icon; calculate the average value of the pixels in each row of the matching source grayscale icon in turn; calculate the variance of all the average values, and use the variance as the feature value of the matching source icon.

In one embodiment, the above device further includes: an error data output module.

In one embodiment, the error data output module includes:

An error frame acquisition unit, used for capturing an error frame in the detection video as an error frame when the running state of the detection device is abnormal;

The error file output unit is used to extract the timestamp corresponding to the error frame, and output the timestamp, the file name of the detection video and the file path corresponding to the detection video as the error file.

The embodiments of the present invention achieve the following technical effects:

The embodiment of the present invention adopts the host computer display of the video acquisition platform to acquire and display the detection video recorded by the camera, and automatically detects the detection video, analyzes the detection video through the host computer, realizes the detection of the detection video under different power-on and power-off time interval requirements of different detection equipment, and can automatically complete the recording and analysis of the detection video of unlimited length, ensuring that the operation status of the detection equipment can be accurately and quickly output without any residue; the output of the detection video can be 100% confirmed by the host computer, and whether the detection equipment is running normally can be quickly determined; the video acquisition platform is simple to build and has strong operability. The host computer automatically outputs the test results and forms an error file output. After the video acquisition platform is built, no human confirmation is required. The monitoring results of the operation status, the total number of times, and the normal or abnormal state can be directly displayed on the host computer. When an abnormality occurs, the number of abnormalities and detailed data are displayed, and the detection video can be viewed at any time; the embodiment of the present invention can monitor a variety of detection equipment, can match any set power-on and power-off time interval, and meet the test processing requirements of different users for different equipment.

Obviously, those skilled in the art should understand that the modules or steps of the above-mentioned embodiments of the present invention can be implemented by a general-purpose computing device, they can be concentrated on a single computing device, or distributed on a network composed of multiple computing devices, and optionally, they can be implemented by a program code executable by a computing device, so that they can be stored in a storage device and executed by the computing device, and in some cases, the steps shown or described can be executed in a different order from that here, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. In this way, the embodiments of the present invention are not limited to any specific combination of hardware and software.

The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the embodiments of the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims (10)

1. A method for determining the operating status of a detection device, characterized by comprising: Constructing a video acquisition platform for monitoring the operating status of the detection device, collecting the video of the detection device during operation as the detection video through the video acquisition platform, and obtaining the type of the detection video according to the type of the detection device; If the detection video is a chessboard video, identify the number of points on the chessboard in the detection video, determine the number of devices of the detection device according to the number of points, compare the number of devices with a preset correct number of devices, and if the two are consistent, determine that the operating state of the detection device is normal; if the two are inconsistent, determine that the operating state of the detection device is abnormal; If the detection video is a navigation icon video, identify the navigation icon in the detection video, perform a similarity comparison between the navigation icon and a preset correct navigation pattern, and generate a comparison result. If the comparison result is greater than or equal to a similarity threshold, the operating status of the detection device is judged to be normal; if the comparison result is less than the similarity threshold, the operating status of the detection device is judged to be abnormal. 2. The method for determining the operating status of a detection device according to claim 1, wherein the step of identifying the number of points on the checkerboard in the detection video and determining the number of devices of the detection device according to the number of points comprises: Intercepting multiple frames within a fixed time period in the detection video to generate a detection object picture group; Respectively detecting each corner point of each detection object picture in the detection object picture group, and obtaining a total number of corner points corresponding to each detection object picture, until all the detection object pictures in the detection object picture group are detected; Calculating an average value of the total number of corner points of all the detection object pictures in the detection object picture group, and using the average value as the number of points on the chessboard; A corresponding relationship between the number of points on the chessboard and the number of devices of the detection device is set, and the number of devices corresponding to the number of points is obtained through the corresponding relationship. 3. The method for determining the operating status of a detection device according to claim 2, wherein detecting each corner point of each detection object picture in the detection object picture group comprises: Convert the detected object image into a grayscale image and then perform a filtering operation to generate a preprocessed image; Perform edge detection on the preprocessed image, extract the line group constituting the chessboard, determine whether the lines in the line group are straight lines, and if they are straight lines, take the corresponding lines as valid lines to generate a valid line group; Loop through all the valid lines in the valid line group to determine whether there are any valid lines that are perpendicular to each other; For each pair of mutually perpendicular valid lines, the corresponding intersection point is taken as a corner point. 4. The method for determining the operating status of a detection device according to claim 1, wherein the step of identifying a navigation icon in the detection video, comparing the navigation icon with a preset correct navigation pattern for similarity, and generating a comparison result comprises: Intercepting multiple frames within a fixed time period in the detection video to generate a matching source icon group; Calculating the feature value of each matching source icon in the matching source icon group until all the matching source icons in the matching source icon group are calculated; Calculating a ratio of the feature value of the matching source icon to the feature value of the correct navigation pattern, and using the ratio as a matching degree; An average value of the matching degrees corresponding to all the matching source icons in the matching source icon group is calculated, and the average value is used as the comparison result. 5. The method for determining the operating status of a detection device according to claim 4, wherein calculating the characteristic value of each matching source icon in the matching source icon group comprises: Scaling the matching source icon to the same size as the correct navigation pattern to generate a scaled matching source icon; Performing grayscale processing on the scaled matching source icon to generate a matching source grayscale icon; Calculate the average value of the pixels in each row of the matching source grayscale icon in sequence; The variance of all the average values is calculated, and the variance is used as the feature value of the matching source icon. 6. The method for determining the operating status of a detection device according to any one of claims 1 to 5, wherein the video acquisition platform comprises: A collection camera, a host computer and a programmable power supply, wherein the collection camera is connected to the host computer, and the programmable power supply is connected to the detection device; The acquisition camera is arranged toward the detection device, and the acquisition camera transmits the recorded detection video to the host computer; Displaying the detection video in real time on the host computer, analyzing and counting the detection video and outputting the detection result; The host computer controls the power on and off of the detection device through the programmable power supply. 7. The method for determining the operating status of a detection device according to any one of claims 1 to 5, further comprising: When the operating state of the detection device is abnormal, capturing an erroneous frame in the detection video as an erroneous frame; A timestamp corresponding to the error frame is extracted, and the timestamp, the file name of the detection video, and the file path corresponding to the detection video are output as an error file. 8. A device for determining the operating status of a detection device, characterized by comprising: A video acquisition module, used to construct a video acquisition platform for monitoring the operating status of the detection device, to collect the video of the detection device during operation as the detection video through the video acquisition platform, and to obtain the type of the detection video according to the type of the detection device; a point number detection module, for, if the detection video is a chessboard video, identifying the number of points on the chessboard in the detection video, judging the number of devices of the detection device according to the number of points, comparing the number of devices with a preset correct number of devices, and judging that the operating state of the detection device is normal if the two are consistent, and judging that the operating state of the detection device is abnormal if the two are inconsistent; The navigation icon detection module is used to identify the navigation icon in the detection video if the detection video is a navigation icon video, perform similarity comparison between the navigation icon and a preset correct navigation pattern, and generate a comparison result. If the comparison result is greater than or equal to a similarity threshold, it is judged that the operating status of the detection device is normal; if the comparison result is less than the similarity threshold, it is judged that the operating status of the detection device is abnormal. 9. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method for determining the operating status of a detection device according to any one of claims 1 to 7 when executing the computer program. 10 . A computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program for executing the method for determining the operating status of a detection device according to any one of claims 1 to 7.