Digital video monitoring system and method based on Internet of Things

Abstract

The invention relates to the technical field of video monitoring, in particular to a digital video monitoring system and method based on the Internet of Things, and the system comprises a video monitoring management platform, a video information unit, a live image quality unit, a display working condition unit, a network steady state evaluation unit, a display interference unit, a video stabilization unit and a video management unit. According to the method, analysis is carried out from two points of display performance and front-end interference of the monitoring video, so that the display image quality condition of each monitoring video can be intuitively known, the display image quality and stability of the monitoring video are improved, analysis is carried out from two angles of a monitoring video network and a monitoring video display through an information mode, and the analysis accuracy is improved. Therefore, whether the monitoring video display abnormity is caused by the abnormity of the monitoring video network and the monitoring video display can be known, the influence of the monitoring video network and the monitoring video display on the monitoring video display can be reduced, and the monitoring video display picture quality and the display stability can be improved.

Description

Technical Field

[0001] The present invention relates to the field of video monitoring technology, and in particular to a digital video monitoring system and method based on the Internet of Things. Background Art

[0002] At present, with the rapid development of digital video surveillance system technology, digital video surveillance systems have been applied in more and more places, and with the development of network technology, the country's investment in security and the participation of operators, many large-scale digital video surveillance systems have been established; in digital video surveillance systems, more and more various extended functions are added, connecting multiple systems together, realizing some new functions and expanding the application scope of the original system;

[0003] However, in the prior art, it is impossible to stably monitor the actual display screen of the video surveillance, thereby reducing the display quality and stability of the surveillance video, and it is impossible to perform multi-factor interference analysis on the abnormal display screen of the surveillance video, thereby failing to reasonably and targetedly manage multiple interference factors, resulting in an increase in the risk of surveillance video abnormality, and at the same time, it is not conducive to the overall rational management of the surveillance video, thereby reducing the stability and monitoring effect of the entire video surveillance;

[0004] In view of the above technical defects, a solution is now proposed. Summary of the invention

[0005] The purpose of the present invention is to provide a digital video surveillance system and method based on the Internet of Things to solve the above-mentioned technical defects. The present invention analyzes the display performance of the surveillance video and the front-end interference to intuitively understand the display quality of each surveillance video, so as to improve the display quality and stability of the surveillance video. In addition, the present invention analyzes from the two perspectives of the surveillance video network and the surveillance video display in the form of information to understand whether the abnormal display of the surveillance video is caused by the abnormality of the surveillance video network and the surveillance video display, so as to reduce the influence of the surveillance video network and the surveillance video display on the surveillance video display, so as to improve the display quality and display stability of the surveillance video.

[0006] The object of the present invention can be achieved by the following technical solutions: A digital video monitoring system based on the Internet of Things, comprising a video monitoring management platform, a video information unit, a live picture quality unit, a display working condition unit, a network steady-state evaluation unit, a display interference unit, a video stabilization unit, and a video management unit;

[0007] The video information unit is used to collect the display picture data and working condition data of the front-end camera, and send the display picture data and working condition data to the live picture quality unit and the display working condition unit respectively;

[0008] After receiving the display picture data, the live picture quality unit immediately performs a live picture supervision analysis on the display picture data, compares and analyzes the obtained picture quality deviation assessment coefficient Hi, and obtains a normal signal and a picture quality risk signal;

[0009] After receiving the working condition data, the display working condition unit immediately performs screen display front-end interference division analysis on the working condition data, compares and analyzes the obtained interference index and aging risk value, and obtains the display impact index JY;

[0010] The network stability assessment unit is used to collect dynamic data of the monitoring video network immediately after responding to the image quality risk signal, and perform network stability interference assessment analysis on the dynamic data, and compare and analyze the obtained video stability risk index to obtain the interference signal;

[0011] The display interference unit is used to respond to the image quality risk signal, collect display impact information of the monitoring video display, and perform screen display interference evaluation analysis on the display impact information, compare and analyze the obtained display deviation coefficient Z, and obtain a display impact signal;

[0012] The video stabilization unit is used to respond to the image quality risk signal, and perform in-depth imbalance risk analysis of the monitoring video, compare and analyze the obtained joint risk assessment coefficient K, and obtain the pipe adjustment signal.

[0013] Preferably, the live monitoring and analysis process of the display screen of the live picture quality unit is as follows:

[0014] A monitoring cycle is set, and the monitoring cycle is set as a time threshold, and display picture data of each front-end camera in the monitoring video display within the time threshold is obtained, and the display picture data includes actual picture clarity, picture jitter frequency and picture change value, and then the difference between the actual picture clarity and the preset actual picture clarity threshold, the part of the picture jitter frequency exceeding the preset picture jitter frequency threshold and the part of the picture change value exceeding the preset picture change value threshold are obtained, and the difference between the actual picture clarity and the preset actual picture clarity threshold, the part of the picture jitter frequency exceeding the preset picture jitter frequency threshold and the part of the picture change value exceeding the preset picture change value threshold are set as clarity deviation value, jitter deviation value and change deviation value respectively, and the monitoring video is set as i, i is a natural number greater than zero, and the clarity deviation value, jitter deviation value and change deviation value are labeled as QPi, DQi and LPi respectively, and the picture change value represents the actual time difference between the display picture and the monitoring picture;

[0015] The image quality deviation evaluation coefficient Hi of each surveillance video in the surveillance video display is obtained according to the formula, and the image quality deviation evaluation coefficient Hi is compared and analyzed with the preset image quality deviation evaluation coefficient threshold recorded and stored in the live image quality unit to obtain a normal signal and an image quality risk signal.

[0016] Preferably, the screen display of the working condition unit shows the front-end interference division analysis process as follows:

[0017] The working condition data of each front-end camera in the monitoring video display within the time threshold is obtained, and the working condition data includes an interference index and an aging risk value. The interference index and the aging risk value are compared and analyzed with the preset interference index threshold and the preset aging risk value threshold stored in the display working condition unit content to obtain the low-level display influence and the high-level display influence, and the low-level display influence and the high-level display influence are set as the display influence index JY, JY=2,4.

[0018] Preferably, the interference index represents the product of the total volume of dust particles on the mirror of the front-end camera and the number corresponding to working parameters lower than a preset threshold after data normalization, and the working parameters include pixel value, horizontal resolution, and aperture number; the aging risk value represents the product of the input time of the front-end camera and the maximum value of the component over-temperature operation time after data normalization, the input time represents the time from the time of being put into use to the current time, and the maximum value of the component over-temperature operation time represents the maximum value of the time corresponding to the operating temperature of the component in the front-end camera exceeding the preset threshold.

[0019] Preferably, the network stability interference assessment and analysis process of the network stability assessment unit is as follows:

[0020] The time threshold is divided into g sub-time periods, where g is a natural number greater than zero, and dynamic data of the monitoring video network in each sub-time period is obtained, the dynamic data including a transmission floating index and a network offset index. The transmission floating index and the network offset index are compared and analyzed with the preset transmission floating index threshold and the preset network offset index threshold recorded and stored in the network steady-state assessment unit. If the transmission floating index is less than the preset transmission floating index threshold, and the network offset index is less than the preset network offset index threshold, then the corresponding sub-time period is determined to be an unbalanced period. If the transmission floating index is greater than or equal to the preset transmission floating index threshold, or the network offset index is greater than or equal to the preset network offset index threshold, then the corresponding sub-time period is determined to be a normal period. The ratio between the number of sub-time periods corresponding to the unbalanced period and the number of sub-time periods corresponding to the normal period is set as a video stability risk index. The video stability risk index is compared and analyzed with the preset video stability risk index threshold recorded and stored in the network steady-state assessment unit to obtain an interference signal.

[0021] Preferably, the transmission floating index represents the product of the maximum change value of the transmission network speed and the corresponding time length when the delay rate exceeds the preset delay rate threshold after data normalization processing; the network offset index represents the product of the part of the actual network bandwidth that is lower than the preset network bandwidth threshold and the part of the network frame rate that is lower than the preset network frame rate threshold after data normalization processing.

[0022] Preferably, the screen display interference evaluation and analysis process of the interference display unit is as follows:

[0023] The display impact information of the monitoring video display within the time threshold is obtained, and the display impact information includes actual resolution, actual contrast and actual brightness. The actual resolution, actual contrast and actual brightness are compared and analyzed with the preset actual resolution threshold, the preset actual contrast threshold and the preset actual brightness threshold. The part where the actual resolution is lower than the preset actual resolution threshold, the part where the actual contrast deviates from the preset actual contrast threshold and the part where the actual brightness deviates from the preset actual brightness threshold are set as resolution interference value, contrast offset value and brightness offset value respectively. The resolution interference value, contrast offset value and brightness offset value are labeled as FB, DB and LD respectively. The display deviation coefficient Z is obtained according to the formula. The display deviation coefficient Z is compared and analyzed with the preset display deviation coefficient threshold entered and stored in the display interference unit to obtain a display impact signal.

[0024] Preferably, the in-depth imbalance risk analysis process of the monitoring video of the video stabilization unit is as follows:

[0025] Obtain the display impact index JY, video stability risk index SW and display deviation coefficient Z within the time threshold, according to the formula A joint risk assessment coefficient is obtained, wherein v1 is a preset error correction coefficient, v1 is greater than zero, and K is a joint risk assessment coefficient. The joint risk assessment coefficient K is compared and analyzed with a preset joint risk assessment coefficient threshold recorded and stored in the video stabilization unit to obtain a pipe adjustment signal.

[0026] A video monitoring method based on the Internet of Things comprises the following steps:

[0027] Step 1: Collect the display screen data and working condition data of each front-end camera, and substitute the display screen data into step 2 for real-time monitoring analysis of the display screen and substitute the working condition data into step 3 for front-end interference division analysis of the screen display;

[0028] Step 2: Perform real-time monitoring and analysis of the displayed image through information progression. If a normal signal is obtained, the normal video is marked as "green"; if a quality risk signal is obtained, the defective video is marked as "red";

[0029] Step 3: Perform image display front-end interference division analysis in an information progressive manner to obtain a display impact index JY;

[0030] Step 4: When a quality risk signal is generated, dynamic data of the surveillance video network is collected and network stability interference assessment and analysis is performed. If an interference signal is obtained, feedback management is provided;

[0031] Step 5: Collect display impact information through information feedback and conduct screen display interference evaluation and analysis. If a display impact signal is obtained, feedback management is performed;

[0032] Step 6: Perform in-depth imbalance risk analysis of the monitoring video on the display impact index JY, video stability risk index and display deviation coefficient Z through information fusion. If a control signal is obtained, feedback management is performed.

[0033] The beneficial effects of the present invention are as follows:

[0034] (1) The present invention analyzes the display performance of the surveillance video and the front-end interference to intuitively understand the display quality of each surveillance video, so as to reasonably and specifically manage the abnormal front-end camera to improve the display quality and stability of the surveillance video, and analyzes from the two perspectives of the surveillance video network and the surveillance video display in the form of information to understand whether the abnormal display of the surveillance video is caused by the abnormality of the surveillance video network and the surveillance video display, so as to reduce the influence of the surveillance video network and the surveillance video display on the surveillance video display, so as to improve the display quality and display stability of the surveillance video;

[0035] (2) The present invention performs in-depth imbalance risk analysis of surveillance videos in a progressive and information fusion manner to understand whether the abnormality of the surveillance video image is affected by the combined effect of multiple factors, so as to perform overall control of the surveillance video based on the information feedback, so as to improve the overall display stability and display clarity of the surveillance video. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] The present invention will be further described below in conjunction with the accompanying drawings;

[0037] Figure 1 It is a flowchart of the system of the present invention;

[0038] Figure 2 It is a reference analysis diagram of the method of the present invention. DETAILED DESCRIPTION

[0039] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0040] Embodiment 1:

[0041] See also Figure 1 to Figure 2 As shown, the present invention is a digital video monitoring system based on the Internet of Things, including a video monitoring management platform, a video information unit, a live picture quality unit, a display working condition unit, a network steady-state evaluation unit, a display interference unit, a video stabilization unit and a video management unit, the video monitoring management platform is connected to the video information unit in a one-way communication, the video information unit is connected to the live picture quality unit and the display working condition unit in a one-way communication, the display working condition unit is connected to the live picture quality unit in a one-way communication, the live picture quality unit is connected to the network steady-state evaluation unit, the display interference unit, the video stabilization unit and the video management unit in a one-way communication, the network steady-state evaluation unit and the display interference unit are connected to the video stabilization unit in a one-way communication, and the video stabilization unit is connected to the video management unit in a one-way communication;

[0042] When the video surveillance management platform generates a supervision instruction, it sends the supervision instruction to the video information unit. After receiving the supervision instruction, the video information unit immediately collects the display screen data and working condition data of the front-end camera, and sends the display screen data and working condition data to the live picture quality unit and the display working condition unit respectively. After receiving the display screen data, the live picture quality unit immediately performs real-time display screen supervision and analysis on the display screen data to determine whether the current monitoring video display screen of each front-end camera is normal, so as to carry out reasonable and targeted management according to the information feedback to ensure the stability and clarity of the monitoring video display screen. The specific display screen real-time supervision and analysis process is as follows:

[0043] A monitoring cycle is set, and the monitoring cycle is set as a time threshold, and display picture data of each front-end camera in the monitoring video display within the time threshold is obtained, and the display picture data includes actual picture clarity, picture jitter frequency and picture change value, and then the difference between the actual picture clarity and the preset actual picture clarity threshold, the part of the picture jitter frequency exceeding the preset picture jitter frequency threshold and the part of the picture change value exceeding the preset picture change value threshold are obtained, and the difference between the actual picture clarity and the preset actual picture clarity threshold, the part of the picture jitter frequency exceeding the preset picture jitter frequency threshold and the part of the picture change value exceeding the preset picture change value threshold are set as clarity deviation value, jitter deviation value and change deviation value respectively, and the monitoring video is set as i, i is a natural number greater than zero, and the clarity deviation value, jitter deviation value and change deviation value are labeled as QPi, DQi and LPi respectively, and the picture change value represents the actual time difference between the display picture and the monitoring picture;

[0044] According to the formula The image quality deviation evaluation coefficient of each monitoring video in the monitoring video display is obtained, wherein a1, a2, a3 and a4 are preset proportional factor coefficients of the clarity deviation value, the jitter deviation value, the change deviation value and the display impact index, respectively; JYi is the display impact index of each front-end camera; the proportional factor coefficient is used to correct the deviation of each parameter in the formula calculation process, so as to make the calculation result more accurate; a5 is the preset correction factor coefficient; a1, a2, a3, a4 and a5 are all greater than zero; Hi is the image quality deviation evaluation coefficient of each monitoring video in the monitoring video display; the image quality deviation evaluation coefficient Hi is compared and analyzed with the preset image quality deviation evaluation coefficient threshold value recorded and stored in the live image quality unit:

[0045] If the ratio between the image quality deviation evaluation coefficient Hi and the preset image quality deviation evaluation coefficient threshold is less than 1, the corresponding surveillance video is determined to be a normal video, and a normal signal is generated;

[0046] If the ratio between the image quality deviation evaluation coefficient Hi and the preset image quality deviation evaluation coefficient threshold is greater than or equal to 1, the corresponding monitoring video is determined to be a defective video, a quality risk signal is generated, and the normal signal and the quality risk signal are sent to the video management unit. After receiving the normal signal and the quality risk signal, the video management unit immediately performs the preset warning operation corresponding to the normal signal and the quality risk signal, that is, marking the normal video as "green" and marking the defective video as "red", so as to intuitively understand the display quality of each monitoring video, so as to reasonably and specifically manage the abnormal front-end camera, so as to improve the display quality and stability of the monitoring video;

[0047] After receiving the working condition data, the display working condition unit immediately performs screen display front-end interference division analysis on the working condition data to understand the impact of each front-end camera on the monitoring video display screen at the current moment, so as to improve the accuracy of the analysis results. The specific screen display front-end interference division analysis process is as follows:

[0048] The working condition data of each front-end camera in the monitoring video display within the time threshold is obtained. The working condition data includes interference index and aging risk value. The interference index and aging risk value are compared and analyzed with the preset interference index threshold and preset aging risk value threshold stored in the display working condition unit content:

[0049] If the interference index is less than the preset interference index threshold, and the aging risk value is less than the preset aging risk value threshold, it is determined to be a low-level display impact;

[0050] If the interference index is greater than or equal to the preset interference index threshold, or the aging risk value is greater than or equal to the preset aging risk value threshold, it is determined to be a high-level display impact, wherein the surveillance video display interference risk degrees corresponding to the low-level display impact and the high-level display impact are increased in sequence, and the low-level display impact and the high-level display impact are set as the display impact index JY, JY=2, 4, that is, the display impact index JY=2, indicating a low-level display impact, and the display impact index JY=4, indicating a high-level display impact;

[0051] In the embodiment of the present invention, the interference index represents the product value obtained by multiplying the total volume of dust particles on the mirror of the front-end camera by the number of working parameters corresponding to being lower than a preset threshold after data normalization processing. The working parameters include pixel value, horizontal resolution, aperture number, etc. It should be noted that the larger the value of the interference index, the greater the impact of the front-end camera itself on the monitoring video quality;

[0052] In the embodiment of the present invention, the aging risk value represents the product of the input time of the front-end camera and the maximum value of the component over-temperature operation time after data normalization processing, the input time represents the time from the time of being put into use to the current time, and the maximum value of the component over-temperature operation time represents the maximum value of the time corresponding to the operating temperature of the component in the front-end camera exceeding the preset threshold. It should be noted that the larger the value of the aging risk value, the greater the impact of the front-end camera itself on the quality of the monitoring video.

[0053] Embodiment 2:

[0054] When the image quality risk signal is generated, the network stability assessment unit is used to respond to the image quality risk signal, immediately collect dynamic data of the surveillance video network, and perform network stability interference assessment and analysis on the dynamic data to understand whether the abnormal quality of the surveillance video picture is caused by the abnormality of the surveillance video network, so as to reduce the impact of the surveillance video network on the quality of the surveillance video picture, thereby helping to improve the picture display quality of the surveillance video. The specific network stability interference assessment and analysis process is as follows:

[0055] The time threshold is divided into g sub-time periods, where g is a natural number greater than zero, and dynamic data of the monitoring video network in each sub-time period is obtained, the dynamic data including the transmission floating index and the network offset index, and the transmission floating index and the network offset index are compared and analyzed with the preset transmission floating index threshold and the preset network offset index threshold recorded and stored in the network stability assessment unit. If the transmission floating index is less than the preset transmission floating index threshold, and the network offset index is less than the preset network offset index threshold, then the corresponding sub-time period is determined to be an unbalanced time period, and if the transmission floating index is greater than or equal to the preset transmission floating index threshold, or the network offset index is greater than or equal to the preset network offset index threshold, then the corresponding sub-time period is determined to be a normal time period, and the ratio between the number of sub-time periods corresponding to the unbalanced time period and the number of sub-time periods corresponding to the normal time period is set as the video stability risk index, and the video stability risk index is compared and analyzed with the preset video stability risk index threshold recorded and stored in the network stability assessment unit:

[0056] If the ratio between the video stability risk index and the preset video stability risk index threshold is less than 1, no signal is generated;

[0057] If the ratio between the video stability risk index and the preset video stability risk index threshold is greater than or equal to 1, an interference signal is generated, and the interference signal is sent to the video management unit via the video stabilization unit. After receiving the interference signal, the video management unit immediately performs a preset warning operation corresponding to the interference signal, so as to manage the stability of the surveillance video network, so as to reduce the impact of the surveillance video network on the quality of the surveillance video picture, thereby helping to improve the picture display quality of the surveillance video;

[0058] In the embodiment of the present invention, the transmission floating index represents the product value obtained by multiplying the maximum change value of the transmission network speed and the corresponding time length of the delay rate exceeding the preset delay rate threshold after data normalization. It should be noted that the larger the value of the transmission floating index is, the greater the impact of the surveillance video network on the surveillance video display is;

[0059] In the embodiment of the present invention, the network deviation index represents the product value obtained by multiplying the portion of the actual network bandwidth lower than the preset network bandwidth threshold and the portion of the network frame rate lower than the preset network frame rate threshold after data normalization processing. It should be noted that the larger the value of the network deviation index, the greater the risk of imbalance between the monitoring video quality and the transmission efficiency;

[0060] After the image quality risk signal is generated, the display interference unit is used to respond to the image quality risk signal, collect the display impact information of the monitoring video display, and perform screen display interference evaluation and analysis on the display impact information to understand whether the video display has an impact on the monitoring video display screen, so as to reduce the interference degree of the video display on the monitoring video display screen, so as to improve the monitoring video display screen quality and display stability. The specific screen display interference evaluation and analysis process is as follows:

[0061] The display influence information of the monitoring video display within the time threshold is obtained, and the display influence information includes actual resolution, actual contrast and actual brightness. The actual resolution, actual contrast and actual brightness are compared and analyzed with the preset actual resolution threshold, the preset actual contrast threshold and the preset actual brightness threshold. The part where the actual resolution is lower than the preset actual resolution threshold, the part where the actual contrast deviates from the preset actual contrast threshold and the part where the actual brightness deviates from the preset actual brightness threshold are respectively set as resolution interference value, contrast offset value and brightness offset value. The resolution interference value, contrast offset value and brightness offset value are respectively labeled as FB, DB and LD. According to the formula Z=(FB×f1+DB×f2+LD×f3) 2 ÷f4 to obtain the display deviation coefficient, where f1, f2 and f3 are respectively the preset weight factor coefficients of the resolution interference value, the contrast offset value and the brightness offset value, f4 is the preset fault tolerance factor coefficient, f1, f2, f3 and f4 are all greater than zero, and Z is the display deviation coefficient. The display deviation coefficient Z is compared and analyzed with the preset display deviation coefficient threshold value stored in the display interference unit:

[0062] If the ratio between the display deviation coefficient Z and the preset display deviation coefficient threshold is less than 1, no signal is generated;

[0063] If the ratio between the display deviation coefficient Z and the preset display deviation coefficient threshold is greater than or equal to 1, a display influence signal is generated, and the display influence signal is sent to the video management unit via the video stabilization unit. After receiving the display influence signal, the video management unit immediately performs a preset warning operation corresponding to the display influence signal to reduce the interference degree of the video display on the monitoring video display screen, so as to improve the monitoring video display screen quality;

[0064] When the image quality risk signal is generated, the video stabilization unit is used to respond to the image quality risk signal and conduct an in-depth imbalance risk analysis of the surveillance video to understand whether the abnormality of the surveillance video image is affected by multiple factors, so as to reasonably control and improve the overall quality of the surveillance video image. The specific process of the in-depth imbalance risk analysis of the surveillance video is as follows:

[0065] Obtain the display impact index JY, video stability risk index SW and display deviation coefficient Z within the time threshold, according to the formula The joint risk assessment coefficient is obtained, where v1 is a preset error correction coefficient, v1 is greater than zero, and K is a joint risk assessment coefficient. The joint risk assessment coefficient K is compared with the preset joint risk assessment coefficient threshold recorded and stored in the video stabilization unit for analysis:

[0066] If the ratio between the joint risk assessment coefficient K and the preset joint risk assessment coefficient threshold is less than 1, no signal is generated;

[0067] If the ratio between the joint risk assessment coefficient K and the preset joint risk assessment coefficient threshold is greater than or equal to 1, a control signal is generated and sent to the video management unit. After receiving the control signal, the video management unit immediately performs the preset early warning operation corresponding to the control signal, so as to perform overall control over the monitoring video according to the information feedback, so as to improve the overall display stability and display clarity of the monitoring video.

[0068] Embodiment three:

[0069] A video monitoring method based on the Internet of Things comprises the following steps:

[0070] Step 1: Collect the display screen data and working condition data of each front-end camera, and substitute the display screen data into step 2 for real-time monitoring analysis of the display screen and substitute the working condition data into step 3 for front-end interference division analysis of the screen display;

[0071] Step 2: Perform real-time monitoring and analysis of the displayed image through information progression. If a normal signal is obtained, the normal video is marked as "green"; if a quality risk signal is obtained, the defective video is marked as "red";

[0072] Step 3: Perform image display front-end interference division analysis in an information progressive manner to obtain a display impact index JY;

[0073] Step 4: When a quality risk signal is generated, dynamic data of the surveillance video network is collected and network stability interference assessment and analysis is performed. If an interference signal is obtained, feedback management is provided;

[0074] Step 5: Collect display impact information through information feedback and conduct screen display interference evaluation and analysis. If a display impact signal is obtained, feedback management is performed;

[0075] Step 6: Perform in-depth imbalance risk analysis of the monitoring video on the display impact index JY, video stability risk index, and display deviation coefficient Z by means of information fusion. If a control signal is obtained, feedback management is performed;

[0076] In summary, the present invention analyzes the display performance and front-end interference of the monitoring video so as to intuitively understand the display quality of each monitoring video, so as to reasonably and specifically manage the abnormal front-end cameras to improve the display quality and stability of the monitoring video, and analyzes from the two perspectives of the monitoring video network and the monitoring video display in the form of information to understand whether the abnormal display of the monitoring video is caused by the abnormal monitoring video network and the monitoring video display, so as to reduce the impact of the monitoring video network and the monitoring video display on the monitoring video display, so as to improve the display quality and display stability of the monitoring video, and conduct in-depth imbalance risk analysis of the monitoring video in a progressive and information fusion manner to understand whether the abnormal monitoring video picture is affected by the combined effect of multiple factors, so as to perform overall management and control of the monitoring video according to the information feedback, so as to improve the overall display stability and display clarity of the monitoring video.

[0077] The threshold is set to facilitate comparison. The size of the threshold depends on the amount of sample data and the number of bases set by technicians in this field for each group of sample data; as long as it does not affect the proportional relationship between the parameter and the quantized value.

[0078] The above formulas are obtained by collecting a large amount of data for software simulation and selecting a formula that is close to the actual value. The coefficients in the formula are set by technical personnel in this field according to actual conditions. The above is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited to this. Any technical personnel familiar with the technical field within the technical scope disclosed by the present invention, according to the technical solution and the inventive concept of the present invention, make equivalent replacement or change, which should be covered within the protection scope of the present invention.

Claims

1. A digital video surveillance system based on the Internet of Things, characterized in that: It includes a video surveillance management platform, a video information unit, a live picture quality unit, a display condition unit, a network steady-state evaluation unit, a display interference unit, a video stabilization unit, and a video management unit; The video information unit is used to collect the display picture data and working condition data of the front-end camera, and send the display picture data and working condition data to the live picture quality unit and the display working condition unit respectively; After receiving the display picture data, the live picture quality unit immediately performs a live picture supervision analysis on the display picture data, compares and analyzes the obtained picture quality deviation assessment coefficient Hi, and obtains a normal signal and a picture quality risk signal; After receiving the working condition data, the display working condition unit immediately performs screen display front-end interference division analysis on the working condition data, compares and analyzes the obtained interference index and aging risk value, and obtains the display impact index JY; The network stability assessment unit is used to collect dynamic data of the monitoring video network immediately after responding to the image quality risk signal, and perform network stability interference assessment analysis on the dynamic data, and compare and analyze the obtained video stability risk index to obtain the interference signal; The display interference unit is used to respond to the image quality risk signal, collect display impact information of the monitoring video display, and perform screen display interference evaluation analysis on the display impact information, compare and analyze the obtained display deviation coefficient Z, and obtain a display impact signal; The video stabilization unit is used to respond to the image quality risk signal, and perform in-depth imbalance risk analysis of the monitoring video, compare and analyze the obtained joint risk assessment coefficient K, and obtain the pipe adjustment signal.

2. According to the digital video monitoring system based on the Internet of Things according to claim 1, it is characterized in that: The live monitoring and analysis process of the display image of the live picture quality unit is as follows: A monitoring cycle is set, and the monitoring cycle is set as a time threshold, and display picture data of each front-end camera in the monitoring video display within the time threshold is obtained, and the display picture data includes actual picture clarity, picture jitter frequency and picture change value, and then the difference between the actual picture clarity and the preset actual picture clarity threshold, the part of the picture jitter frequency exceeding the preset picture jitter frequency threshold and the part of the picture change value exceeding the preset picture change value threshold are obtained, and the difference between the actual picture clarity and the preset actual picture clarity threshold, the part of the picture jitter frequency exceeding the preset picture jitter frequency threshold and the part of the picture change value exceeding the preset picture change value threshold are set as clarity deviation value, jitter deviation value and change deviation value respectively, and the monitoring video is set as i, i is a natural number greater than zero, and the clarity deviation value, jitter deviation value and change deviation value are labeled as QPi, DQi and LPi respectively, and the picture change value represents the actual time difference between the display picture and the monitoring picture; The image quality deviation assessment coefficient Hi of each surveillance video in the surveillance video display is obtained according to the formula, and the image quality deviation assessment coefficient Hi is compared and analyzed with the preset image quality deviation assessment coefficient threshold recorded and stored in the live image quality unit to obtain a normal signal and an image quality risk signal.

3. The digital video surveillance system based on the Internet of Things according to claim 2 is characterized in that: The screen display of the working condition unit shows the front-end interference division analysis process as follows: The working condition data of each front-end camera in the monitoring video display within the time threshold is obtained, and the working condition data includes an interference index and an aging risk value. The interference index and the aging risk value are compared and analyzed with the preset interference index threshold and the preset aging risk value threshold stored in the display working condition unit content to obtain the low-level display influence and the high-level display influence, and the low-level display influence and the high-level display influence are set as the display influence index JY, JY=2,4.

4. The digital video surveillance system based on the Internet of Things according to claim 3 is characterized in that: The interference index represents the product of the total volume of dust particles on the mirror of the front-end camera and the number corresponding to working parameters lower than the preset threshold after data normalization, and the working parameters include pixel value, horizontal resolution, and aperture number; the aging risk value represents the product of the input time of the front-end camera and the maximum value of the over-temperature operation time of the component after data normalization, the input time represents the time from the time of being put into use to the current time, and the maximum value of the over-temperature operation time of the component represents the maximum value of the time corresponding to the operating temperature of the component in the front-end camera exceeding the preset threshold.

5. The digital video surveillance system based on the Internet of Things according to claim 4 is characterized in that: The network stability interference evaluation and analysis process of the network stability evaluation unit is as follows: The time threshold is divided into g sub-time periods, where g is a natural number greater than zero, and dynamic data of the monitoring video network in each sub-time period is obtained, the dynamic data including a transmission floating index and a network offset index. The transmission floating index and the network offset index are compared and analyzed with the preset transmission floating index threshold and the preset network offset index threshold recorded and stored in the network steady-state assessment unit. If the transmission floating index is less than the preset transmission floating index threshold, and the network offset index is less than the preset network offset index threshold, then the corresponding sub-time period is determined to be an unbalanced period. If the transmission floating index is greater than or equal to the preset transmission floating index threshold, or the network offset index is greater than or equal to the preset network offset index threshold, then the corresponding sub-time period is determined to be a normal period. The ratio between the number of sub-time periods corresponding to the unbalanced period and the number of sub-time periods corresponding to the normal period is set as a video stability risk index. The video stability risk index is compared and analyzed with the preset video stability risk index threshold recorded and stored in the network steady-state assessment unit to obtain an interference signal.

6. The digital video surveillance system based on the Internet of Things according to claim 5, characterized in that: The transmission floating index represents the product of the maximum change in transmission speed and the duration corresponding to the delay rate exceeding the preset delay rate threshold after data normalization; the network deviation index represents the product of the portion of the actual network bandwidth lower than the preset network bandwidth threshold and the portion of the network frame rate lower than the preset network frame rate threshold after data normalization.

7. The digital video surveillance system based on the Internet of Things according to claim 6, characterized in that: The screen display interference unit shows the interference evaluation and analysis process as follows: The display impact information of the monitoring video display within the time threshold is obtained, and the display impact information includes actual resolution, actual contrast and actual brightness. The actual resolution, actual contrast and actual brightness are compared and analyzed with the preset actual resolution threshold, the preset actual contrast threshold and the preset actual brightness threshold. The part where the actual resolution is lower than the preset actual resolution threshold, the part where the actual contrast deviates from the preset actual contrast threshold and the part where the actual brightness deviates from the preset actual brightness threshold are set as resolution interference value, contrast offset value and brightness offset value respectively. The resolution interference value, contrast offset value and brightness offset value are labeled as FB, DB and LD respectively. The display deviation coefficient Z is obtained according to the formula. The display deviation coefficient Z is compared and analyzed with the preset display deviation coefficient threshold entered and stored in the display interference unit to obtain a display impact signal.

8. The digital video monitoring system based on the Internet of Things according to claim 7, characterized in that: The in-depth imbalance risk analysis process of the surveillance video of the video stabilization unit is as follows: Obtain the display impact index JY, video stability risk index SW and display deviation coefficient Z within the time threshold, according to the formula A joint risk assessment coefficient is obtained, wherein v1 is a preset error correction coefficient, v1 is greater than zero, and K is a joint risk assessment coefficient. The joint risk assessment coefficient K is compared and analyzed with a preset joint risk assessment coefficient threshold recorded and stored in the video stabilization unit to obtain a pipe adjustment signal.

9. A video monitoring method based on the Internet of Things, applied to a digital video monitoring system based on the Internet of Things as claimed in any one of claims 1 to 8, characterized in that: The following steps are involved: Step 1: Collect the display screen data and working condition data of each front-end camera, and substitute the display screen data into step 2 for real-time monitoring analysis of the display screen and substitute the working condition data into step 3 for front-end interference division analysis of the screen display; Step 2: Perform real-time monitoring and analysis of the displayed image through information progression. If a normal signal is obtained, the normal video is marked as "green"; if a quality risk signal is obtained, the defective video is marked as "red"; Step 3: Perform image display front-end interference division analysis in an information progressive manner to obtain a display impact index JY; Step 4: When a quality risk signal is generated, dynamic data of the surveillance video network is collected and network stability interference assessment and analysis is performed. If an interference signal is obtained, feedback management is provided; Step 5: Collect display impact information through information feedback and conduct screen display interference evaluation and analysis. If a display impact signal is obtained, feedback management is performed; Step 6: Perform in-depth imbalance risk analysis of the monitoring video on the display impact index JY, video stability risk index and display deviation coefficient Z through information fusion. If a control signal is obtained, feedback management is performed.

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