Video active inspection system based on Internet of Things driving

Abstract

The invention relates to the field of video inspection, in particular to an active video inspection system based on Internet of Things driving, which comprises a current analysis module, an image input module, an image input detection module, an image output detection module and an audio detection module. According to the method, the current of the conference room equipment is monitored, abnormal equipment is found in time, rapid fault diagnosis is achieved, the effectiveness of the video stream is effectively judged by obtaining and processing the video stream and analyzing the grayscale image, it is ensured that the input picture is always in an available state, the blank screen phenomenon and picture distortion are avoided, and the user experience is improved. The image output detection module verifies the display effect, ensures the accuracy of an output picture and determines an abnormal area when the output picture is abnormal, the audio detection module plays a test audio and collects feedback, the sound receiving effect of a microphone is effectively evaluated, the conference room equipment is automatically and actively inspected, the equipment state is continuously monitored and evaluated, and the sound receiving efficiency is improved. Potential faults can be found and prevented in time, and the sudden fault risk of equipment is remarkably reduced.

Description

Technical Field

[0001] The present invention relates to the field of video inspection, and in particular to an active video inspection system driven by the Internet of Things. Background Art

[0002] With the rapid development of information technology, conference rooms have become important communication spaces for modern enterprises and institutions, and the normal operation of their equipment is crucial to efficient communication and collaboration. Traditional conference room equipment detection methods often rely on manual inspections, which have many limitations. Manual inspections are costly and require specialized technicians to regularly inspect conference room equipment. Secondly, the periodicity and randomness of manual inspections may result in equipment failures being missed or discovered late, and accuracy and comprehensiveness are difficult to guarantee.

[0003] Modern conference rooms are usually equipped with complex video conferencing systems, including many conferencing devices. The normal operation of these devices is crucial to the smooth progress of the meeting. However, traditional detection methods are difficult to accurately and comprehensively monitor the working status of these devices, resulting in problems that cannot be discovered and handled in a timely manner, seriously affecting the progress of the meeting and the work efficiency of the enterprise.

[0004] Patent document with publication number CN117857779A discloses a digital inspection method for equipment based on the Internet of Things. The method is to input the target video to the multimedia terminal of each conference room at a set time point through the multimedia management background, analyze the output parameters of the monitored audio equipment and video equipment in each conference room, diagnose the working status of the audio equipment and video equipment in each conference room, screen out each audio-abnormal conference room and each video-abnormal conference room and sort them by maintenance priority, and display the audio maintenance priority sorting and video maintenance priority sorting on the multimedia early warning display terminal.

[0005] This shows the following problems: the inspection of conference equipment in the prior art has the problems of single detection dimension, low detection accuracy, and inability to comprehensively evaluate the equipment status. Summary of the invention

[0006] To this end, the present invention provides a video active inspection system driven by the Internet of Things to overcome the problems in the prior art of inspection of conference equipment, such as single detection dimension, low detection accuracy, and inability to comprehensively evaluate the equipment status.

[0007] To achieve the above object, the present invention provides a video active inspection system driven by the Internet of Things, comprising: A current analysis module, used to compare the current difference by turning on and off the conference room equipment, and determine whether the conference room equipment is in normal use based on the current difference, wherein the conference room equipment includes a signal source device, an output device and a microphone; An image input module, which is connected to the current analysis module, and is used to control the signal source device to start up, transmit the video signal, and convert it into a video stream when the result is determined to be normal use of the device; An image input detection module, which is connected to the image input module, is used to collect video stream images, and convert the video stream images into grayscale images, calculate the average grayscale value, determine whether the video stream image is a black screen or has an image according to the first threshold, the second threshold and the entropy value, and determine whether the image is normal according to the grayscale difference when there is an image; An image output detection module, which is connected to the image input module and the image input detection module respectively, and is used to receive the video signal when the video stream screen is normal and transmit it to the output device for screen output to obtain a test screen, and to determine the test color area according to the test screen, and compare it with the original color area to determine whether the output is normal, and to obtain the abnormal area by comparing the standard test block with the original test block when the output is abnormal, and to obtain the color change time of each standard test block when the test screen changes when the output is normal to determine whether there is a problem with the screen change; An audio detection module connected to the image output detection module, used to play a test audio when the video signal is converted to screen output, and to receive the test audio through a microphone, and to determine whether there is a problem with the microphone receiving sound according to the received audio; The storage module is respectively connected to the current analysis module, the image input detection module, the image output detection module, and the audio detection module, and is used to generate an abnormality log when the conference room is abnormal.

[0008] Furthermore, the image input detection module includes: A picture acquisition unit, used for acquiring video stream pictures of the video stream; A grayscale conversion unit, connected to the picture acquisition unit, for converting the video stream picture into a grayscale image; A picture analysis unit connected to the grayscale conversion unit, used to calculate the average grayscale value of the grayscale image, and determine whether the video stream picture is a black screen or a picture according to the grayscale threshold and the image entropy value, wherein the grayscale threshold includes a first threshold and a second threshold; The picture determination unit is connected to the picture analysis unit and is used to calculate the grayscale difference according to the maximum grayscale value and the minimum grayscale value of the grayscale image when determining whether the video stream has a picture, and compare the similarity with the standard grayscale difference to determine whether the picture is normal.

[0009] Furthermore, the picture analysis unit includes: A grayscale value acquisition subunit, used to acquire the grayscale value of each pixel of the grayscale image; A gray value calculation subunit, which is connected to the gray value acquisition subunit and is used to calculate the average gray value according to the gray value of each pixel; a gray value comparison subunit, connected to the gray value calculation subunit, for comparing the average gray value with the first threshold and the second threshold to obtain a comparison result; A determination subunit is connected to the grayscale value comparison subunit, and is used to determine that there is a picture when the average grayscale value is greater than the second threshold, and to determine that it is a black screen when it is lower than the first threshold. When the average grayscale is neither less than the first threshold nor greater than the second threshold, the image entropy value is calculated and compared with the minimum entropy value, and based on the comparison result, it is determined whether the video stream screen is black or has a screen.

[0010] Furthermore, the image output detection module includes: A picture output unit, used for converting the video signal into an output signal recognizable by a display device when it is determined that the video stream has a picture; A picture generating unit connected to the picture output unit, for generating a test picture according to the output signal; An image judgment unit is connected to the picture generation unit, and is used to obtain the test image and process it to obtain a standard image, and to compare the standard image with the original image to determine whether the output is normal, and to determine the abnormal area when it is abnormal, and to determine whether there is a problem with the picture change according to the picture change speed when it is normal.

[0011] Furthermore, the image determination unit includes: An image acquisition subunit, used for photographing the test screen to obtain the test image; An image processing subunit, connected to the image acquisition subunit, for scaling the test image to obtain a standard image of the same size as the original image; A color acquisition subunit, connected to the image processing subunit, for acquiring a test color area and an original color area corresponding to the standard image and the original image; A first determination subunit, connected to the color acquisition subunit, for comparing any of the test color areas with the corresponding original color area, and determining whether the screen output is abnormal based on the comparison result; A block division subunit, connected to the first determination subunit, for dividing the standard image into a certain number of blocks to obtain standard test blocks; A region determination subunit, connected to the block division subunit, for comparing the standard test block with the corresponding original test block, and determining an abnormal region based on the comparison result; The second determination subunit is connected to the first determination subunit and the block division subunit respectively, and is used to determine the color change time of each standard test block and compare it with the standard change time. If the color change time of any standard test block is greater than the standard change time, it is determined that there is a problem with the picture change.

[0012] Furthermore, the current analysis module includes: A switch control unit, used to control the opening and closing of the conference room equipment; A current measuring unit connected to the switch control unit, for measuring a turn-on current of the conference room device when the conference room device is turned on and a turn-off current of the conference room device when the conference room device is turned off; a difference calculation unit connected to the current measurement unit and used to calculate the difference between the on current and the off current to obtain a current difference; A threshold comparison unit is connected to the difference calculation unit and is used to compare the current difference with a preset threshold. When the current difference is less than the preset threshold, it is determined that the device is in normal use; when the current difference is greater than or equal to the preset threshold, it is determined that the device is abnormal.

[0013] Furthermore, the audio detection module includes: An audio playback unit, used to play a test audio of a preset frequency and loudness when the video signal is converted to a screen output; An audio collection unit, connected to the audio playback unit, for collecting the test audio through a microphone to obtain received audio; The audio analysis unit is connected to the audio acquisition unit and is used to analyze the loudness and frequency of the received audio and determine whether there is a problem with the microphone sound collection based on the loudness and frequency.

[0014] Furthermore, the audio analysis unit comprises: A data acquisition subunit, used for analyzing the received audio to obtain loudness and frequency; A calculation subunit, connected to the data acquisition subunit, for calculating a loudness deviation value and a frequency deviation value by respectively comparing the loudness or frequency with the corresponding preset loudness and preset frequency; The determination subunit is connected to the calculation subunit and is used to compare the loudness deviation value and the frequency deviation value with the loudness deviation range and the frequency deviation range respectively, and determine whether there is a problem with the microphone sound collection based on the comparison result.

[0015] Furthermore, the image input module comprises: A video control unit, configured to send a start instruction to the signal source device when it is determined that the conference room devices are all in normal use; A signal receiving unit, connected to the signal source control unit, for receiving the video signal generated by the signal source device; The signal conversion unit is connected to the signal receiving unit and is used to convert the video signal into a video stream.

[0016] Furthermore, the data storage module comprises: An abnormal marking unit is used to mark the conference room when any abnormality occurs, such as the equipment in the conference room cannot be used normally, the video stream screen is black, the output screen is normal, or there is a problem with the microphone sound collection; The abnormality storage unit is connected to the abnormality marking unit and is used to store the conference room serial number and abnormality information after marking the conference room to obtain the abnormality log.

[0017] Compared with the prior art, the beneficial effect of the present invention lies in that, by monitoring the current of conference room equipment, abnormal equipment can be discovered in time, and rapid fault diagnosis can be achieved. The linkage between the image input module and the image input detection module enables the system to automatically turn on the signal source when the equipment is normal, obtain and process the video stream, and the analysis of the grayscale image can effectively determine the validity of the video stream, ensure that the input picture is always in a usable state, avoid black screen phenomenon and picture distortion, the image output detection module verifies the display effect, ensures the accuracy of the output picture and determines the abnormal area when an abnormality occurs, and the audio detection module can effectively evaluate the microphone's sound reception effect by playing the test audio in real time and collecting feedback. The system can periodically and automatically perform active inspections on conference room equipment. By continuously monitoring and evaluating the equipment status, potential faults can be discovered and prevented in time, significantly reducing the risk of sudden equipment failures. The storage module enables the system to generate a detailed abnormality log when an abnormality is detected, which provides important data support for subsequent troubleshooting and maintenance, and helps to optimize equipment management and maintenance strategies.

[0018] Furthermore, by setting up a picture acquisition unit, a grayscale conversion unit and a picture analysis unit, intelligent judgment of the video stream picture is realized, wherein the picture acquisition unit can obtain the video stream picture in real time, the grayscale conversion unit converts the color picture into a grayscale image to simplify the calculation complexity, and the picture analysis unit determines the picture state by calculating the average grayscale value and comparing it with the preset standard value, and further determines whether the picture is normal based on the grayscale difference. This detection method based on grayscale value analysis can not only accurately identify black screen faults, but also reduce the system computing load and improve detection efficiency. At the same time, the preset standard grayscale value is used as the judgment basis, which makes the detection result more objective and accurate, effectively avoids the errors caused by subjective judgment in traditional manual inspections, and improves the reliability and stability of the system. Furthermore, the grayscale information of each pixel of the image is obtained by the grayscale value acquisition subunit, and the average grayscale value is calculated by the grayscale value calculation subunit to provide basic data support for the picture status judgment. Secondly, a dual-threshold judgment mechanism is set up, and the average grayscale value is compared with the first threshold and the second threshold by the grayscale value comparison subunit, so that the obvious normal picture and black screen state can be quickly identified. Finally, for the situation where the grayscale value falls in the fuzzy interval, the entropy analysis method is innovatively introduced. By calculating the image entropy value and comparing it with the minimum entropy value, the boundary situation that is difficult to accurately identify by the traditional single judgment method is effectively solved, which greatly improves the accuracy of the picture status judgment and effectively avoids the occurrence of misjudgment.

[0019] Furthermore, the image acquisition subunit accurately captures the test screen to ensure the real-time and effectiveness of image acquisition. The image processing subunit adjusts the test image to be completely consistent with the original image through proportional transformation technology, eliminating errors that may be caused by size and resolution. The block division subunit divides the image into the same number of blocks according to a unified standard to achieve accurate image comparison. Finally, the second judgment subunit can accurately locate the abnormal position and degree of image output through detailed comparison of each block. This multi-dimensional and multi-level image detection method not only greatly improves the sensitivity and accuracy of detection, but also can quickly identify minor display anomalies, such as local color distortion, slight blur or subtle deformation, which are difficult to detect with traditional detection methods. At the same time, it has strong versatility and scalability, and can be widely used in intelligent inspection of conference room video equipment, effectively improving the efficiency and accuracy of equipment maintenance.

[0020] Furthermore, through the cooperation of the image acquisition and processing sub-units, the consistency in size between the test image and the original image is ensured, laying the foundation for subsequent accurate comparative analysis. The color acquisition sub-unit and the first judgment sub-unit can quickly identify abnormalities at the overall picture level, improving the efficiency of abnormality detection. When an abnormality is found, the block division sub-unit divides the inconsistent area into equal parts, accurately locates the specific abnormal area, effectively improves the accuracy of fault diagnosis, and further determines whether there is an abnormality when the picture changes when the output is normal. The entire detection process not only ensures the comprehensiveness of the detection, but also improves the accuracy of positioning. It can timely discover and accurately point out the abnormal parts of the display device, which is conducive to subsequent maintenance and processing.

[0021] Furthermore, the on and off of the conference room equipment is accurately controlled by the switch control unit, and the current measurement unit is used to capture the current change when the equipment is turned on and off. The difference between the on current and the off current is calculated by the difference calculation unit, and the threshold comparison unit performs a strict comparison. This can quickly and accurately judge the operating status of the equipment, realize intelligent and precise diagnosis of the equipment status, and greatly reduce the workload of manual inspections and the risk of missed detection.

[0022] Furthermore, by automatically playing test audio of preset frequency and loudness while outputting the video signal, and collecting and analyzing the audio signal in real time through the microphone, and quantitatively detecting the sound reception quality of the microphone, the intelligence and automation level of the inspection of conference room audio equipment has been greatly improved, and the objectivity and accuracy of audio equipment fault diagnosis has been improved.

[0023] Furthermore, by performing multi-dimensional and refined quantitative analysis on the audio signals received by the microphone and calculating the deviation values ​​of loudness and frequency, a quantitative evaluation of the performance of the audio equipment is achieved, and possible anomalies of the microphone are fully captured, which significantly improves the scientificity and objectivity of audio detection and effectively detects possible equipment failures.

[0024] Furthermore, intelligent control of the signal source equipment is achieved through the video control unit, and the start-up command is only sent after confirming that the conference room equipment is in normal use, effectively avoiding unnecessary equipment startup, significantly improving the system's energy utilization efficiency and the accuracy of equipment use, and ensuring fast and real-time processing of video signals by optimizing the technical path of signal reception and conversion, providing timely and efficient data support for subsequent image detection.

[0025] Furthermore, by monitoring and recording conference room equipment failures, the comprehensiveness and accuracy of equipment management have been significantly improved. Conference rooms with problems can be marked immediately and accurately, and the conference room numbers and specific abnormal information can be stored in a structured manner to generate standardized abnormality logs, providing clear positioning information for subsequent maintenance and management, greatly improving the efficiency of fault handling. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 A system framework diagram of a video active inspection system driven by the Internet of Things provided in an embodiment of the present invention; Figure 2 This is a schematic diagram of the structure of an image input detection module according to an embodiment of the present invention; Figure 3 Schematic diagram of the structure of an image output detection module according to an embodiment of the present invention; Figure 4 Schematic diagram of the structure of the current analysis module according to an embodiment of the present invention. DETAILED DESCRIPTION

[0027] In order to make the objects and advantages of the present invention more clearly understood, the present invention is further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0028] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only used to explain the technical principles of the present invention and are not intended to limit the protection scope of the present invention.

[0029] It should be noted that, in the description of the present invention, terms such as "up", "down", "left", "right", "inside" and "outside" indicating directions or positional relationships are based on the directions or positional relationships shown in the drawings. This is merely for the convenience of description and does not indicate or imply that the device or element must have a specific orientation, be constructed and operated in a specific orientation. Therefore, it cannot be understood as a limitation on the present invention.

[0030] In addition, it should be noted that in the description of the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For those skilled in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0031] See also Figure 1 ,like Figure 1 As shown, it is a schematic diagram of the structure of a video active inspection system based on Internet of Things drive provided by an embodiment of the present invention; Specifically, the video active inspection system based on the Internet of Things drive provided by the embodiment of the present invention includes: A current analysis module, used to compare the current difference by turning on and off the conference room equipment, and determine whether the conference room equipment is in normal use based on the current difference, wherein the conference room equipment includes a signal source device, an output device and a microphone; An image input module, which is connected to the current analysis module, and is used to control the signal source device to start up, transmit the video signal, and convert it into a video stream when the result is determined to be normal use of the device; An image input detection module, which is connected to the image input module, is used to collect video stream images, and convert the video stream images into grayscale images, calculate the average grayscale value, determine whether the video stream image is a black screen or has an image according to the first threshold, the second threshold and the entropy value, and determine whether the image is normal according to the grayscale difference when there is an image; An image output detection module, which is connected to the image input module and the image input detection module respectively, and is used to receive the video signal when the video stream screen is normal and transmit it to the output device for screen output to obtain a test screen, and to determine the test color area according to the test screen, and compare it with the original color area to determine whether the output is normal, and to obtain the abnormal area by comparing the standard test block with the original test block when the output is abnormal, and to obtain the color change time of each standard test block when the test screen changes when the output is normal to determine whether there is a problem with the screen change; An audio detection module connected to the image output detection module, used to play a test audio when the video signal is converted to screen output, and to receive the test audio through a microphone, and to determine whether there is a problem with the microphone receiving sound according to the received audio; The storage module is respectively connected to the current analysis module, the image input detection module, the image output detection module, and the audio detection module, and is used to generate an abnormality log when the conference room is abnormal.

[0032] Specifically, in this embodiment, when inspecting the conference room, the equipment in the conference room is switched on and off to determine whether there is an abnormality based on the current difference. When the equipment is switched on and off normally, the video input and output are detected to ensure the normal use of the video screen in the conference room during actual use, and the audio is released. The sound is collected and detected through the microphone to determine whether the entire sound reception link of the microphone is normal. The signal source device can be a device that generates video signals, such as a conference scene camera, a remote conference host, a player, etc., and its specific structure is not limited here.

[0033] Specifically, by monitoring the current of conference room equipment, abnormal equipment is discovered in time and rapid fault diagnosis is achieved. The linkage between the image input module and the image input detection module enables the system to automatically turn on the signal source when the equipment is normal, obtain and process the video stream, and analyze the grayscale image to effectively determine the validity of the video stream, ensuring that the input picture is always in a usable state, avoiding black screen phenomena and picture distortion. The image output detection module verifies the display effect, ensures the accuracy of the output picture and determines the abnormal area when an abnormality occurs. The audio detection module can effectively evaluate the microphone's sound reception effect by playing test audio in real time and collecting feedback. The system can periodically and automatically perform active inspections on conference room equipment. By continuously monitoring and evaluating the equipment status, potential faults can be discovered and prevented in time, significantly reducing the risk of sudden equipment failures. The storage module enables the system to generate detailed abnormality logs when an abnormality is detected, providing important data support for subsequent troubleshooting and maintenance, and helping to optimize equipment management and maintenance strategies.

[0034] Please continue reading Figure 2 ,like Figure 2As shown, it is a structural diagram of an image input detection module according to an embodiment of the present invention; Specifically, the image input detection module includes: A picture acquisition unit, used for acquiring video stream pictures of the video stream; A grayscale conversion unit, connected to the picture acquisition unit, for converting the video stream picture into a grayscale image; A picture analysis unit connected to the grayscale conversion unit, used to calculate the average grayscale value of the grayscale image, and determine whether the video stream picture is a black screen or a picture according to the grayscale threshold and the image entropy value, wherein the grayscale threshold includes a first threshold and a second threshold; The picture determination unit is connected to the picture analysis unit and is used to calculate the grayscale difference according to the maximum grayscale value and the minimum grayscale value of the grayscale image when determining whether the video stream has a picture, and compare the similarity with the standard grayscale difference to determine whether the picture is normal.

[0035] Specifically, if the similarity between the grayscale difference and the standard grayscale difference is less than the similarity threshold, the picture is determined to be abnormal, and if the similarity between the grayscale difference and the standard grayscale difference is greater than the similarity threshold, the picture is determined to be normal.

[0036] In the specific implementation process, when determining whether the video stream has a picture, the maximum grayscale value is 250, the minimum grayscale value is 10, the calculated grayscale difference is 240, and the preset standard grayscale difference of the picture is 200. The calculated similarity is 83%, and the preset similarity threshold is 95%, then the picture is determined to be abnormal.

[0037] Specifically, by setting up a picture acquisition unit, a grayscale conversion unit and a picture analysis unit, intelligent judgment of the video stream picture is realized. Among them, the picture acquisition unit can obtain the video stream picture in real time, the grayscale conversion unit converts the color picture into a grayscale image to simplify the calculation complexity, and the picture analysis unit determines the picture state by calculating the average grayscale value and comparing it with the preset standard value, and further determines whether the picture is normal based on the grayscale difference. This detection method based on grayscale value analysis can not only accurately identify black screen faults, but also reduce the system computing load and improve detection efficiency. At the same time, the preset standard grayscale value is used as the judgment basis, which makes the detection result more objective and accurate, effectively avoiding the errors caused by subjective judgment in traditional manual inspections, and improving the reliability and stability of the system.

[0038] Specifically, the picture analysis unit includes: A grayscale value acquisition subunit, used to acquire the grayscale value of each pixel of the grayscale image; A gray value calculation subunit, which is connected to the gray value acquisition subunit and is used to calculate the average gray value according to the gray value of each pixel; a gray value comparison subunit, connected to the gray value calculation subunit, for comparing the average gray value with the first threshold and the second threshold to obtain a comparison result; A determination subunit is connected to the grayscale value comparison subunit, and is used to determine that there is a picture when the average grayscale value is greater than the second threshold, and to determine that it is a black screen when it is lower than the first threshold. When the average grayscale is neither less than the first threshold nor greater than the second threshold, the image entropy value is calculated and compared with the minimum entropy value, and based on the comparison result, it is determined whether the video stream screen is black or has a screen.

[0039] In the specific implementation process, the first grayscale value is preset to 20, the second grayscale value is 100, and the minimum entropy value is 4.0. The video stream screen is obtained and converted into a grayscale image. When the average grayscale value of each pixel of the grayscale image is calculated to be 120, which is greater than the standard grayscale value, it is determined that the video stream has a screen. When the average grayscale value of the grayscale image is calculated to be 80, which is greater than the first threshold and less than the second threshold, the image entropy value representing the complexity of the image is calculated. The calculated entropy value is 2.5, which is less than the minimum entropy value of 4.0, and the video stream is determined to be a black screen. The calculation of the image entropy value is a prior art and will not be repeated here.

[0040] Specifically, the grayscale information of each pixel of the image is obtained by the grayscale value acquisition subunit, and the average grayscale value is calculated by the grayscale value calculation subunit to provide basic data support for the picture status judgment. Secondly, a dual-threshold judgment mechanism is set up, and the average grayscale value is compared with the first threshold and the second threshold by the grayscale value comparison subunit. The normal picture and the black screen state can be quickly identified. Finally, for the situation where the grayscale value falls in the fuzzy interval, the entropy analysis method is innovatively introduced. By calculating the image entropy value and comparing it with the minimum entropy value, the boundary situation that is difficult to accurately identify by the traditional single judgment method is effectively solved, which greatly improves the accuracy of the picture status judgment and effectively avoids the occurrence of misjudgment.

[0041] Please continue reading Figure 3 ,like Figure 3 As shown, it is a structural diagram of an image output detection module according to an embodiment of the present invention; Specifically, the image output detection module includes: A picture output unit, used for converting the video signal into an output signal recognizable by a display device when it is determined that the video stream has a picture; A picture generating unit connected to the picture output unit, for generating a test picture according to the output signal; An image judgment unit is connected to the picture generation unit, and is used to obtain the test image and process it to obtain a standard image, and to compare the standard image with the original image to determine whether the output is normal, and to determine the abnormal area when it is abnormal, and to determine whether there is a problem with the picture change according to the picture change speed when it is normal.

[0042] Specifically, the image acquisition subunit accurately captures the test screen to ensure the real-time and effectiveness of image acquisition. The image processing subunit uses proportional transformation technology to adjust the test image to be completely consistent with the original image, eliminating errors that may be caused by size and resolution. The block division subunit divides the image into the same number of blocks according to a unified standard to achieve accurate image comparison. Finally, the second judgment subunit can accurately locate the abnormal position and degree of image output through detailed comparison of each block. This multi-dimensional and multi-level image detection method not only greatly improves the sensitivity and accuracy of detection, but also can quickly identify minor display anomalies, such as local color distortion, slight blur or subtle deformation, which are difficult to detect with traditional detection methods. At the same time, it has strong versatility and scalability, and can be widely used in intelligent inspection of conference room video equipment, effectively improving the efficiency and accuracy of equipment maintenance.

[0043] Specifically, the image judgment unit includes: An image acquisition subunit, used for photographing the test screen to obtain the test image; An image processing subunit, connected to the image acquisition subunit, for scaling the test image to obtain a standard image of the same size as the original image; A color acquisition subunit, connected to the image processing subunit, for acquiring a test color area and an original color area corresponding to the standard image and the original image; A first determination subunit, connected to the color acquisition subunit, for comparing any of the test color areas with the corresponding original color area, and determining whether the screen output is abnormal based on the comparison result; A block division subunit, connected to the first determination subunit, for dividing the standard image into a certain number of blocks to obtain standard test blocks; A region determination subunit, connected to the block division subunit, for comparing the standard test block with the corresponding original test block, and determining an abnormal region based on the comparison result; The second determination subunit is connected to the first determination subunit and the block division subunit respectively, and is used to determine the color change time of each standard test block and compare it with the standard change time. If the color change time of any standard test block is greater than the standard change time, it is determined that there is a problem with the picture change.

[0044] Specifically, when the colors of the quasi-test block and the corresponding original test block are the same, the output is determined to be normal; otherwise, the output is determined to be abnormal.

[0045] In the specific implementation process, the test screen is a screen composed of various color blocks, and the color of the color blocks will change after a certain period of time. The standard image is 100cm×50cm, and the original image is 200cm×100cm. The test image is enlarged twice to obtain the standard image, and the standard image is divided into 40 standard test blocks. When the test color area of ​​the standard image is 8000cm² for red, 6000cm² for green, 2000cm² for blue, and 4000cm² for purple, the original color area is 8000cm² for red, 5000cm² for green, and 2000cm² for blue. 000cm², purple 5000cm², then the screen output is abnormal, each standard test block is compared with the corresponding original test block, and the area where the standard test block with different colors is located is determined as the abnormal area based on the comparison result. When the area of ​​each test color of the standard image and the original image is the same, it is determined that the screen output is normal. When the test screen changes, the color change time of each block is obtained. The preset standard color change time is 0.05s. The color change time of a standard test block is 0.01s, which is greater than the standard color change time, then it is determined that there is a problem with the color change.

[0046] Specifically, through the cooperation of the image acquisition and processing sub-units, the test image and the original image are ensured to maintain consistency in size, laying the foundation for subsequent precise comparative analysis. The color acquisition sub-unit and the first judgment sub-unit can quickly identify abnormalities at the overall picture level, improving the efficiency of abnormality detection. When an abnormality is found, the block division sub-unit divides the inconsistent area into equal parts, accurately locates the specific abnormal area, effectively improves the accuracy of fault diagnosis, and further determines whether there is an abnormality when the picture changes when the output is normal. The entire detection process not only ensures the comprehensiveness of the detection, but also improves the accuracy of positioning. It can timely discover and accurately point out the abnormal parts of the display device, which is conducive to subsequent maintenance and processing.

[0047] Please continue reading Figure 4 ,like Figure 4 As shown, it is a structural schematic diagram of a current analysis module according to an embodiment of the present invention; Specifically, the current analysis module includes: A switch control unit, used to control the opening and closing of the conference room equipment; A current measuring unit connected to the switch control unit, for measuring a turn-on current of the conference room device when the conference room device is turned on and a turn-off current of the conference room device when the conference room device is turned off; a difference calculation unit connected to the current measurement unit and used to calculate the difference between the on current and the off current to obtain a current difference; A threshold comparison unit is connected to the difference calculation unit and is used to compare the current difference with a preset threshold. When the current difference is less than the preset threshold, it is determined that the device is in normal use; when the current difference is greater than or equal to the preset threshold, it is determined that the device is abnormal.

[0048] In the specific implementation process, the current is 3.8A when the signal source device is turned on, and the current is 0.5A when it is turned off. The calculated current difference is 3.3A, and the preset threshold is 3A. The current difference is greater than the preset threshold, and the device is determined to be abnormal.

[0049] Specifically, the on and off of the conference room equipment is accurately controlled by the switch control unit, and the current measurement unit is used to capture the current changes when the equipment is turned on and off. The difference between the on current and the off current is calculated by the difference calculation unit, and the threshold comparison unit performs strict comparison. This can quickly and accurately judge the operating status of the equipment, realize intelligent and precise diagnosis of the equipment status, and greatly reduce the workload of manual inspections and the risk of missed detection.

[0050] Specifically, the audio detection module includes: An audio playback unit, used to play a test audio of a preset frequency and loudness when the video signal is converted to a screen output; An audio collection unit, connected to the audio playback unit, for collecting the test audio through a microphone to obtain received audio; The audio analysis unit is connected to the audio acquisition unit and is used to analyze the loudness and frequency of the received audio and determine whether there is a problem with the microphone sound collection based on the loudness and frequency.

[0051] Specifically, by automatically playing test audio of preset frequency and loudness while outputting the video signal, and collecting and analyzing the audio signal in real time through the microphone, the sound reception quality of the microphone can be quantitatively detected, which greatly improves the intelligence and automation level of the conference room audio equipment inspection and improves the objectivity and accuracy of audio equipment fault diagnosis.

[0052] Specifically, the audio analysis unit includes: A data acquisition subunit, used for analyzing the received audio to obtain loudness and frequency; A calculation subunit, connected to the data acquisition subunit, for calculating a loudness deviation value and a frequency deviation value by respectively comparing the loudness or frequency with the corresponding preset loudness and preset frequency; The determination subunit is connected to the calculation subunit and is used to compare the loudness deviation value and the frequency deviation value with the loudness deviation range and the frequency deviation range respectively, and determine whether there is a problem with the microphone sound collection based on the comparison result.

[0053] Specifically, when the loudness deviation value and the frequency deviation value are both within the corresponding loudness deviation range and frequency deviation range, it is determined that there is no problem with the microphone sound pickup; when either the loudness deviation value or the frequency deviation value is not within the corresponding loudness deviation range and frequency deviation range, it is determined that there is a problem with the microphone sound pickup.

[0054] During the specific implementation process, the frequency of the test audio is 500Hz, the loudness is 60dB, the frequency received by the microphone is 480Hz, the loudness is 28dB, the frequency deviation range is 50Hz, and the loudness deviation range is 10dB. The loudness deviation value is 32dB, which is not within the loudness deviation range, and the frequency deviation value is 20Hz, which is within the frequency deviation range, indicating that there is a problem with the microphone receiving sound.

[0055] Specifically, by performing multi-dimensional and refined quantitative analysis on the audio signals received by the microphone and calculating the deviation values ​​of loudness and frequency, we can achieve a quantitative evaluation of the performance of the audio equipment, comprehensively capture possible anomalies in the microphone, significantly improve the scientificity and objectivity of audio detection, and effectively detect possible equipment failures.

[0056] Specifically, the image input module includes: A video control unit, configured to send a start instruction to the signal source device when it is determined that the conference room devices are all in normal use; A signal receiving unit, connected to the signal source control unit, for receiving the video signal generated by the signal source device; The signal conversion unit is connected to the signal receiving unit and is used to convert the video signal into a video stream.

[0057] Specifically, the video control unit realizes intelligent control of the signal source equipment, and sends the start command only after confirming that the conference room equipment is in normal use, effectively avoiding unnecessary equipment startup, significantly improving the system's energy utilization efficiency and the accuracy of equipment use, and ensuring the fast and real-time processing of video signals by optimizing the technical path of signal reception and conversion, providing timely and efficient data support for subsequent image detection.

[0058] Specifically, the data storage module includes: An abnormality marking unit is used to mark the conference room when the equipment in the conference room cannot be used normally, or when any abnormality occurs in the video stream screen, output screen, and microphone sound collection; The abnormality storage unit is connected to the abnormality marking unit and is used to store the conference room serial number and abnormality information after marking the conference room to obtain the abnormality log.

[0059] Specifically, when inspecting the conference rooms, any conference room where an abnormality is detected will be marked as abnormal and the abnormal situation will be recorded. During the specific implementation process, if the video output screen of Conference Room 301 is detected to be abnormal, Conference Room 301 will be marked as abnormal and the abnormal area of ​​its video output screen will be recorded in the abnormal log.

[0060] Specifically, by monitoring and recording conference room equipment failures, the comprehensiveness and accuracy of equipment management have been significantly improved. Conference rooms with problems can be marked immediately and accurately, and the conference room numbers and specific abnormal information can be stored in a structured manner to generate standardized abnormality logs. This provides clear positioning information for subsequent maintenance and management, greatly improving the efficiency of fault handling.

[0061] So far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it is easy for those skilled in the art to understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present invention.

[0062] 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 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

1. A video active inspection system based on the Internet of Things, characterized in that: include: A current analysis module, used to compare the current difference by turning on and off the conference room equipment, and determine whether the conference room equipment is in normal use based on the current difference, wherein the conference room equipment includes a signal source device, an output device and a microphone; An image input module, which is connected to the current analysis module, and is used to control the signal source device to start up, transmit the video signal, and convert it into a video stream when the result is determined to be normal use of the device; An image input detection module, which is connected to the image input module, is used to collect video stream images, and convert the video stream images into grayscale images, calculate the average grayscale value, determine whether the video stream image is a black screen or has an image according to the first threshold, the second threshold and the entropy value, and determine whether the image is normal according to the grayscale difference when there is an image; An image output detection module, which is connected to the image input module and the image input detection module respectively, and is used to receive the video signal when the video stream screen is normal and transmit it to the output device for screen output to obtain a test screen, and to determine the test color area according to the test screen, and compare it with the original color area to determine whether the output is normal, and to obtain the abnormal area by comparing the standard test block with the original test block when the output is abnormal, and to obtain the color change time of each standard test block when the test screen changes when the output is normal to determine whether there is a problem with the screen change; An audio detection module connected to the image output detection module, used to play a test audio when the video signal is converted to screen output, and to receive the test audio through a microphone, and to determine whether there is a problem with the microphone receiving sound according to the received audio; The storage module is respectively connected to the current analysis module, the image input detection module, the image output detection module, and the audio detection module, and is used to generate an abnormality log when the conference room is abnormal.

2. The video active inspection system based on Internet of Things drive according to claim 1 is characterized in that: The image input detection module comprises: A picture acquisition unit, used for acquiring video stream pictures of the video stream; A grayscale conversion unit, connected to the picture acquisition unit, for converting the video stream picture into a grayscale image; A picture analysis unit connected to the grayscale conversion unit, used to calculate the average grayscale value of the grayscale image, and determine whether the video stream picture is a black screen or a picture according to the grayscale threshold and the image entropy value, wherein the grayscale threshold includes a first threshold and a second threshold; The picture determination unit is connected to the picture analysis unit and is used to calculate the grayscale difference according to the maximum grayscale value and the minimum grayscale value of the grayscale image when determining whether the video stream has a picture, and compare the similarity with the standard grayscale difference to determine whether the picture is normal.

3. The video active inspection system based on Internet of Things drive according to claim 2 is characterized in that: The picture analysis unit comprises: A grayscale value acquisition subunit, used to acquire the grayscale value of each pixel of the grayscale image; A gray value calculation subunit, which is connected to the gray value acquisition subunit and is used to calculate the average gray value according to the gray value of each pixel; a gray value comparison subunit, connected to the gray value calculation subunit, for comparing the average gray value with the first threshold and the second threshold to obtain a comparison result; A determination subunit is connected to the grayscale value comparison subunit, and is used to determine that there is a picture when the average grayscale value is greater than the second threshold, and to determine that it is a black screen when it is lower than the first threshold. When the average grayscale is neither less than the first threshold nor greater than the second threshold, the image entropy value is calculated and compared with the minimum entropy value, and based on the comparison result, it is determined whether the video stream screen is black or has a screen.

4. The video active inspection system based on Internet of Things drive according to claim 1 is characterized in that: The image output detection module comprises: A picture output unit, used for converting the video signal into an output signal recognizable by a display device when it is determined that the video stream has a picture; A picture generating unit connected to the picture output unit, for generating a test picture according to the output signal; An image judgment unit is connected to the picture generation unit, and is used to obtain the test image and process it to obtain a standard image, and to compare the standard image with the original image to determine whether the output is normal, and to determine the abnormal area when it is abnormal, and to determine whether there is a problem with the picture change according to the picture change speed when it is normal.

5. The video active inspection system based on Internet of Things drive according to claim 4 is characterized in that: The image determination unit comprises: An image acquisition subunit, used for photographing the test screen to obtain the test image; An image processing subunit, connected to the image acquisition subunit, for scaling the test image to obtain a standard image of the same size as the original image; A color acquisition subunit, connected to the image processing subunit, for acquiring a test color area and an original color area corresponding to the standard image and the original image; A first determination subunit, connected to the color acquisition subunit, for comparing any of the test color areas with the corresponding original color area, and determining whether the screen output is abnormal based on the comparison result; A block division subunit, connected to the first determination subunit, for dividing the standard image into a certain number of blocks to obtain standard test blocks; A region determination subunit, connected to the block division subunit, for comparing the standard test block with the corresponding original test block, and determining an abnormal region based on the comparison result; The second determination subunit is connected to the first determination subunit and the block division subunit respectively, and is used to determine the color change time of each standard test block and compare it with the standard change time. If the color change time of any standard test block is greater than the standard change time, it is determined that there is a problem with the picture change.

6. The video active inspection system based on Internet of Things drive according to claim 1 is characterized in that: The current analysis module comprises: A switch control unit, used to control the opening and closing of the conference room equipment; A current measuring unit connected to the switch control unit, for measuring a turn-on current of the conference room device when the conference room device is turned on and a turn-off current of the conference room device when the conference room device is turned off; a difference calculation unit connected to the current measurement unit and used to calculate the difference between the on current and the off current to obtain a current difference; A threshold comparison unit is connected to the difference calculation unit and is used to compare the current difference with a preset threshold. When the current difference is less than the preset threshold, it is determined that the device is in normal use; when the current difference is greater than or equal to the preset threshold, it is determined that the device is abnormal.

7. The video active inspection system based on Internet of Things drive according to claim 1 is characterized in that: The audio detection module comprises: An audio playback unit, used to play a test audio of a preset frequency and loudness when the video signal is converted to a screen output; An audio collection unit, connected to the audio playback unit, for collecting the test audio through a microphone to obtain received audio; The audio analysis unit is connected to the audio acquisition unit and is used to analyze the loudness and frequency of the received audio and determine whether there is a problem with the microphone sound collection based on the loudness and frequency.

8. The video active inspection system based on Internet of Things drive according to claim 7 is characterized in that: The audio analysis unit comprises: A data acquisition subunit, used for analyzing the received audio to obtain loudness and frequency; A calculation subunit, connected to the data acquisition subunit, for calculating a loudness deviation value and a frequency deviation value by respectively comparing the loudness or frequency with the corresponding preset loudness and preset frequency; The determination subunit is connected to the calculation subunit and is used to compare the loudness deviation value and the frequency deviation value with the loudness deviation range and the frequency deviation range respectively, and determine whether there is a problem with the microphone sound collection based on the comparison result.

9. The video active inspection system based on Internet of Things drive according to claim 1 is characterized in that: The image input module comprises: A video control unit, configured to send a start instruction to the signal source device when it is determined that the conference room devices are all in normal use; A signal receiving unit, connected to the signal source control unit, for receiving the video signal generated by the signal source device; The signal conversion unit is connected to the signal receiving unit and is used to convert the video signal into a video stream.

10. The video active inspection system based on Internet of Things drive according to claim 1 is characterized in that: The data storage module comprises: An abnormal marking unit is used to mark the conference room when any abnormality occurs, such as the equipment in the conference room cannot be used normally, the video stream screen is black, the output screen is normal, or there is a problem with the microphone sound collection; The abnormality storage unit is connected to the abnormality marking unit and is used to store the conference room serial number and abnormality information after marking the conference room to obtain the abnormality log.

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