CN114079795A - Network live broadcast silent frame and silent fault detection method - Google Patents
Network live broadcast silent frame and silent fault detection method Download PDFInfo
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- H04N21/20—Servers specifically adapted for the distribution of content, e.g. VOD servers; Operations thereof
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Abstract
The invention discloses a method for detecting network live broadcast silent frame and silent fault, which comprises deploying video quality monitoring equipment at a live broadcast program source and a CDN node, injecting an IP video stream into the video quality monitoring equipment in a mode of probe active stream pulling or switch mirror image, and calculating related network layer and code stream layer alarm indexes causing silent frame and silence by performing packet capturing and protocol analysis on a live broadcast media stream; detecting a content layer alarm index by performing audio and video decoding analysis on the media stream; the accurate mute alarm of the silent frame is realized by carrying out correlation analysis on alarm indexes of a network layer, a code stream layer and a content layer; according to the broadcasting attribute of a specific live program, a live program feature library is established, and mute alarm of a silent frame caused by non-broadcasting faults is filtered. The method for detecting the live broadcast mute frame and mute fault of the network can effectively detect the live broadcast mute frame and mute fault of the network caused by the broadcast control platform, the CDN server and the network fault, and avoid false alarm.
Description
Technical Field
The invention belongs to the technical field of communication detection, relates to a fault detection method, and particularly relates to a live broadcast static frame and mute fault detection method.
Background
In recent years, with the comprehensive popularization of three-network convergence and the rapid development of internet video services in China, the IPTV and network live broadcast services and the flow are increasing at an incredible speed. For charged live video services, consumers no longer meet the quality experience of the traditional free network video best effort. In order to win the market and users in the fierce video service competition, network operators and live broadcast platform companies increasingly pay more attention to the quality guarantee of the live broadcast service so as to improve the competitiveness. In order to effectively manage and guarantee the quality of the live broadcast service, maintenance personnel not only need to rapidly process serious faults complained by users, but also can actively sense various live broadcast fault phenomena (such as silent frame, silence, screen splash, black screen, asynchronous audio and video and the like) which are not complained by the users but affect the user experience, so that the maintenance and optimization work of a live broadcast platform and a network can be carried out more pertinently, the problems of the live broadcast platform and the network can be prevented, and the loss of the users can be avoided.
According to the existing IPTV and Internet video user behavior big data analysis reports, live webcast is still the service with the most watching users and the highest use frequency. As the majority of television users are used to the audience quality level of the live broadcast service transmitted by the traditional broadcast digital television through a special channel, the quality of the network live broadcast video transmitted based on the unreliable IP network also generates similar or even higher expected value, thus providing extremely high requirements for the quality guarantee of the live broadcast video of operators. In the network live broadcast service guarantee, besides the problems of screen splash and mosaic caused by network packet loss, picture mute and mute are one of important factors influencing user experience. Live mute and mute problems are affected by a number of factors, including the following:
(1) mute the still frames due to the characteristics of the live program content itself, such as: the static character pictures which often appear in PPT and news simulcast important notices of remote education teaching, but have the sound read by teachers teaching and broadcasters; some drama programs (e.g., dumb) or silent movies have a situation where the picture is normally played but there is no sound.
(2) In the recording or encoding and transcoding process of a live program source, various problems are caused by the fault of related equipment, including: the content layer appears silent frames or silence; or the video PID loss or the audio PID loss occurs in an MPEG2-TS code stream layer; or a large number of empty frames are filled in the audio-video PID.
(3) In the link of video server output or network transmission, live broadcast video has 'cutoff' fault due to server fault or network interruption, and also has silent frame and silent sound.
The multi-picture analyzer is a common network live channel monitoring and fault detection tool in the industry at present. The multi-picture analyzer is generally deployed at the head end of a video program source, multi-channel live broadcast signals are introduced into the analyzer in an IP video stream pulling mode, then image decoding is carried out on video streams, and the decoded multi-channel programs are displayed on a large screen in a set screen combination mode. Because the multi-picture analyzer decodes and restores the video image frame by frame, various abnormal picture phenomena (such as static frames, blue screens, black screens, color bars and the like) can be detected through the image pattern recognition and analysis technology. Because the video decoding needs to consume higher CPU resource of x86 equipment, currently, a single multi-picture analyzer can generally support video decoding display and picture fault detection on 32-100 road high definition programs (or 8-25 road high definition programs) at the same time. With the increasing number of network direct dialing channels and the development of high definition of program resolution, the performance of a multi-picture analyzer cannot meet the requirement of operation and maintenance of network live broadcast quality. In addition, because the multi-picture analyzer mainly analyzes and detects the audio and video content layer of the live program, the silent frame and the silent problem caused by the faults of the content layer, the code stream layer and the network layer cannot be further distinguished.
In view of the above, there is a need to design a new fault detection method to overcome at least some of the above-mentioned defects of the existing fault detection methods.
Disclosure of Invention
The invention provides a method for detecting live broadcast mute frames and mute faults of a network, which can effectively detect live broadcast mute frames and mute faults of the network caused by broadcast control platforms, CDN servers and network faults and avoid false alarms.
In order to solve the technical problem, according to one aspect of the present invention, the following technical solutions are adopted:
a method for detecting live broadcast silent frame and silent fault of a network, the method comprises the following steps:
step S1, video quality monitoring equipment is deployed at a live program source and CDN nodes, live media stream data is subjected to packet capturing and protocol analysis, audio and video content is subjected to decoding analysis, and KPI indexes and alarms of various network layers, code stream layers and content layers are calculated;
step S2, the video quality monitoring device analyzes various video stream alarm indicators, including: video cut-out Outage, video PID loss Vpid, audio PID loss Apid, video TS null packet VTnull, audio TS null packet ATnull, video frame similarity Sd and video volume Vs;
the video cut-off Outage tracks and analyzes the interval time of adjacent data packets of the video stream, and if the interval time of the adjacent network data packets exceeds To, the video cut-off is judged;
the method comprises the following steps that Vpid is lost by a video PID, tracking analysis is carried out on a video PID program identification number of a video stream MPEG2-TS layer, if the TS packet interval time of adjacent video PIDs is detected To exceed To, the video PID is judged To be lost, and Vpid is recorded as 1; otherwise, recording Vpid as 0;
the method comprises the following steps that Apid is lost by an audio PID, tracking analysis is carried out on an audio PID program identification number of a video stream MPEG2-TS layer, if the TS packet interval time of adjacent audio PID exceeds To, the audio PID is judged To be lost, and Apid is recorded as 1; otherwise, remember Apid ═ 0;
the method comprises the steps that video TS Null packets VTnull are counted in a video stream, and the TS Null packet rate Null% is calculated and is the ratio of the TS Null packet number to the total TS packet number; recording the video TS null packets when the video TS null packet rate exceeds N1;
audio TS Null packets ATnull, which counts the audio TS Null packets in the video stream and calculates the TS Null packet rate Null%, wherein the TS Null packet rate Null% is the ratio of the TS Null packet number to the total TS packet number; recording the audio TS null packets when the audio TS null packet rate exceeds N1;
and (3) video frame similarity Sd, extracting a frame of video picture every second by decoding the video content layer, calculating the similarity Sd of adjacent picture frames, and judging as a static frame when Sd is greater than T1 and the duration Dt of the similar picture frame is greater than T2.
The video volume Vs is obtained by decoding the audio content layer, and is judged to be silent when Vs < T3 and the duration Dt > T4;
step S3, if the video cutoff Outage index is detected, the system prompts a silent frame and a silent alarm;
if detecting that the audio PID loses Apid or the video PID loses Vpid, entering a PID loss judgment sub-process; if the audio PID is lost and the video PID is normal, the system prompts a mute alarm; if the audio PID is normal and the video PID is lost, the system prompts a static frame alarm; if audio PID loss and video PID loss occur simultaneously, the system prompts a mute frame and a mute alarm;
if detecting an audio TS empty packet ATNull or a video TS empty packet VTNull, entering a TS empty packet judgment sub-process; if the audio TS is empty and the video TS is normal, the system prompts a mute alarm; if the audio TS packet is normal and the video TS packet is empty, the system prompts a static frame alarm; if the audio TS empty packet and the video TS empty packet occur at the same time, the system prompts a mute frame and a mute alarm;
if the similarity Sd > T1 of the adjacent video picture frames is detected, the adjacent video picture frames are judged to be the same, if the duration Dt > T2 of the same picture frame, the video picture is judged to be abnormal and static, a static frame fault possibly occurs, and the value of SFtmp is 1, otherwise the value of SFtmp is 0;
if the video volume Vs < T3 and the duration Dt > T4, determining that the video is abnormal and has no sound, possibly having a mute fault, and recording SLtmp as 1, otherwise, recording SLtmp as 0;
step S4, entering a live program special scene alarm suppression flow, and establishing a program feature library to record corresponding channel names and program broadcasting time periods aiming at special picture live programs and special sound live programs;
step S5, when SFtmp is 1 and SLtmp is 1, that is, when the video image is still and there is no sound, the system prompts a still frame mute failure; when SFtmp is 1 and SLtmp is 0, namely the video picture is still but there is sound, if the warning program is in the special picture live broadcast channel list Ch1 and the warning Time is in the specific Time period Time1 of the feature library, no fault is prompted, otherwise the system prompts a still frame warning; when SFtmp is 0 and SLtmp is 1, namely the video picture is normal but has no sound, if the alarm program is in the special sound live program channel list Ch2 and the alarm Time is in the specific Time period Time2 of the feature library, no fault is prompted, otherwise the system prompts a mute alarm; and when the SFtmp is 0 and the SLtmp is 0, namely the video picture is normal and the sound is normal, judging that the video broadcasting is normal, and ending the special scene alarm suppression flow of the live program.
As an embodiment of the present invention, the default value of To is 1000 ms.
As an embodiment of the present invention, the default value of N1 is 99%.
In one embodiment of the invention, the audio/video TS empty packet PID is a TS packet of 0x1 FFF.
As an embodiment of the present invention, the default value of T1 is 99.99%, and the default value of T2 is 5 seconds.
As an embodiment of the present invention, the default value of T3 is-50 dB and the default value of T4 is 20 seconds.
The invention has the beneficial effects that: the method for detecting the live broadcast mute frame and mute fault of the network can effectively detect the live broadcast mute frame and mute fault of the network caused by the broadcast control platform, the CDN server and the network fault, and avoid false alarm.
Drawings
Fig. 1 is a flowchart of a live webcast silent frame and silent fault detection method according to an embodiment of the present invention.
FIG. 2 is a flow chart of the PID loss sub-process in an embodiment of the invention.
Fig. 3 is a flow chart of a TS null sub-process in an embodiment of the present invention.
Fig. 4 is a flowchart of a specific program scene determination sub-process in an embodiment of the present invention.
Detailed Description
Preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings.
For a further understanding of the invention, reference will now be made to the preferred embodiments of the invention by way of example, and it is to be understood that the description is intended to further illustrate features and advantages of the invention, and not to limit the scope of the claims.
The description in this section is for several exemplary embodiments only, and the present invention is not limited only to the scope of the embodiments described. It is within the scope of the present disclosure and protection that the same or similar prior art means and some features of the embodiments may be interchanged.
The invention discloses a live webcast mute frame and mute fault detection method, and FIG. 1 is a flow chart of the live webcast mute frame and mute fault detection method in an embodiment of the invention; referring to fig. 1, the method includes:
step S1, video quality monitoring equipment is deployed at a live program source and CDN nodes, live media stream data is subjected to packet capturing and protocol analysis, audio and video content is subjected to decoding analysis, and KPI indexes and alarms of various network layers, code stream layers and content layers are calculated;
step S2, the video quality monitoring device analyzes various video stream alarm indicators, including: video cut-out Outage, video PID loss Vpid, audio PID loss Apid, video TS null packet VTnull, audio TS null packet ATnull, video frame similarity Sd and video volume Vs;
video cut-off Outage, tracking and analyzing the interval time of adjacent data packets of a video stream, and judging that the video is cut off if the interval time of the adjacent network data packets exceeds To (the default value is 1000 ms);
a video PID loses Vpid, a video PID program identification number of a video stream MPEG2-TS layer is tracked and analyzed, if the TS packet interval time of adjacent video PIDs is detected To exceed To (the default value is 1000ms), the video PID is judged To be lost, and Vpid is recorded as 1; otherwise, recording Vpid as 0;
the method comprises the following steps that Apid is lost by an audio PID, tracking analysis is carried out on an audio PID program identification number of a video stream MPEG2-TS layer, if the TS packet interval time of adjacent audio PID exceeds To (the default value is 1000ms), the audio PID is judged To be lost, and Apid is recorded as 1; otherwise, remember Apid ═ 0;
a video TS Null packet VTnull, which counts video TS Null packets (namely TS packets with PID being 0x1 FFF) in a video stream, calculates a TS Null rate Null% (namely the ratio of the TS Null packet number to the total TS packet number), and marks the video TS Null packets when the video TS Null rate exceeds N1 (the default value is 99%);
audio TS Null packets ATnull, which are statistics of audio TS Null packets (i.e., TS packets with PID of 0x1 FFF) in the video stream, and calculate a TS Null rate Null%, which is a ratio of the number of TS Null packets to the total number of TS packets; recording the audio TS null packet when the audio TS null packet rate exceeds N1 (the default value is 99%);
and (3) video frame similarity Sd, extracting one frame of video picture per second by decoding the video content layer, calculating the similarity Sd of adjacent picture frames, and judging as a static frame when Sd is greater than T1 (the default value is 99.99%) and the duration Dt of the similar picture frame is greater than T2 (the default value is 5 seconds).
A video volume Vs, which is obtained by decoding the audio content layer, and is judged to be silent when Vs < T3 (default value is-50 dB) and the duration Dt > T4 (default value is 20 seconds);
step S3, if the video cutoff Outage index is detected, the system prompts a silent frame and a silent alarm;
FIG. 2 is a flow chart of a PID miss sub-process in accordance with an embodiment of the invention; referring to fig. 2, in an embodiment of the present invention, if it is detected that the audio PID loses Apid or the video PID loses Vpid, a PID loss determining sub-process is entered; if the audio PID is lost and the video PID is normal (i.e., Apid is 1 and Vpid is 0), the system prompts a mute alarm; if the audio PID is normal and the video PID is lost (i.e. Apid is 0 and Vpid is 1), the system prompts a static frame alarm; if audio PID loss and video PID loss (Apid is 1 and Vpid is 1) occur at the same time, the system prompts a mute frame and a mute alarm;
FIG. 3 is a flow chart of a TS null sub-flow in an embodiment of the present invention; referring to fig. 3, in an embodiment of the present invention, if an audio TS null packet ATNull or a video TS null packet VTNull is detected, a TS null packet determination sub-process is performed; if the audio TS is empty and the video TS is normal (ATNull is 1 and VTNull is 0), the system prompts a mute alarm; if the audio TS packet is normal and the video TS packet is empty (ATNull is 0 and VTNull is 1), the system prompts a static frame alarm; if an audio TS empty packet and a video TS empty packet (ATNull is 1 and VTNull is 1) simultaneously appear, the system prompts a mute frame and a mute alarm;
if the similarity Sd > T1 (the default value is 99.99%) of the adjacent video picture frames is detected, the adjacent video picture frames are determined to be the same, if the duration Dt > T2 (the default value is 5 seconds) of the same picture frame, the video picture is determined to be abnormal static, a static frame fault possibly occurs, and the SFtmp is recorded as 1, otherwise, the SFtmp is recorded as 0;
if the video volume Vs < T3 (default is-50 dB) and the duration Dt > T4 (default is 20 seconds), determining that the video is abnormal and has no sound, possibly having a mute fault, and recording SLtmp as 1, otherwise recording SLtmp as 0;
step S4, entering a live program special scene alarm suppression flow, and establishing a program feature library to record corresponding channel names and program broadcasting time periods aiming at special picture live programs (such as news simulcast, weather forecast, remote education and the like) and special sound live programs (such as drama, silent drama and the like);
step S5 and fig. 4 are flowcharts of a special program scene determination sub-process in an embodiment of the present invention; referring to fig. 4, when SFtmp is 1 and SLtmp is 1 (i.e., the video frame is still and there is no sound), the system prompts a "silent frame mute" fault; when the SFtmp is 1 and the SLtmp is 0 (i.e. the video picture is still but there is sound), if the alert program is in the special picture live channel list Ch1 and the alert Time is within the feature library specific Time period Time1, no fault is prompted, otherwise, the system prompts a "still frame" alert; when the SFtmp is 0 and the SLtmp is 1 (i.e. the video picture is normal but there is no sound), if the alert program is in the special sound live program channel list Ch2 and the alert Time is within the feature library specific Time period Time2, then no fault is prompted, otherwise, the system prompts a "mute" alert; and when the SFtmp is 0 and the SLtmp is 0 (namely the video picture is normal and the sound is normal), judging that the video broadcasting is normal, and ending the special scene alarm suppression flow of the live program.
In an embodiment of the present invention, the method of the present invention includes: deploying video quality monitoring equipment at a live program source and CDN nodes, injecting an IP video stream into the video quality monitoring equipment in a mode of actively pulling a stream by a probe or mirroring an exchanger, and calculating related network layer and code stream layer alarm indexes (including cutoff, audio and video PID loss and audio and video TS packet empty packet) which cause silence frames and silence by capturing packets and analyzing a protocol of the live media stream; detecting a content layer alarm index by performing audio and video decoding analysis on the media stream; the accurate mute alarm of the silent frame is realized by carrying out correlation analysis on alarm indexes of a network layer, a code stream layer and a content layer; meanwhile, according to the broadcasting attribute of a specific live program, a live program feature library is established, and mute alarm of a silent frame caused by a non-broadcasting fault is filtered; by establishing the comprehensive processing flow by the method, live broadcast mute and mute faults of the network caused by the broadcast control platform, the CDN server and network faults can be effectively detected, and false alarm is avoided.
In summary, the live broadcast mute and mute fault detection method provided by the invention can effectively detect live broadcast mute and mute fault caused by broadcast control platform, CDN server and network fault, and avoid false alarm.
The technical features of the embodiments described above may be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the embodiments described above are not described, but should be considered as being within the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The description and applications of the invention herein are illustrative and are not intended to limit the scope of the invention to the embodiments described above. Effects or advantages referred to in the embodiments may not be reflected in the embodiments due to interference of various factors, and the description of the effects or advantages is not intended to limit the embodiments. Variations and modifications of the embodiments disclosed herein are possible, and alternative and equivalent various components of the embodiments will be apparent to those skilled in the art. It will be clear to those skilled in the art that the present invention may be embodied in other forms, structures, arrangements, proportions, and with other components, materials, and parts, without departing from the spirit or essential characteristics thereof. Other variations and modifications of the embodiments disclosed herein may be made without departing from the scope and spirit of the invention.
Claims (6)
1. A method for detecting live broadcast silent frame and silent fault is characterized in that the method comprises the following steps:
step S1, video quality monitoring equipment is deployed at a live program source and CDN nodes, live media stream data is subjected to packet capturing and protocol analysis, audio and video content is subjected to decoding analysis, and KPI indexes and alarms of various network layers, code stream layers and content layers are calculated;
step S2, the video quality monitoring device analyzes various video stream alarm indicators, including: video cut-out Outage, video PID loss Vpid, audio PID loss Apid, video TS null packet VTnull, audio TS null packet ATnull, video frame similarity Sd and video volume Vs;
the video cut-off Outage tracks and analyzes the interval time of adjacent data packets of the video stream, and if the interval time of the adjacent network data packets exceeds To, the video cut-off is judged;
the method comprises the following steps that Vpid is lost by a video PID, tracking analysis is carried out on a video PID program identification number of a video stream MPEG2-TS layer, if the TS packet interval time of adjacent video PIDs is detected To exceed To, the video PID is judged To be lost, and Vpid is recorded as 1; otherwise, recording Vpid as 0;
the method comprises the following steps that Apid is lost by an audio PID, tracking analysis is carried out on an audio PID program identification number of a video stream MPEG2-TS layer, if the TS packet interval time of adjacent audio PID exceeds To, the audio PID is judged To be lost, and Apid is recorded as 1; otherwise, remember Apid ═ 0;
the method comprises the steps that video TS Null packets VTnull are counted in a video stream, and the TS Null packet rate Null% is calculated and is the ratio of the TS Null packet number to the total TS packet number; recording the video TS null packets when the video TS null packet rate exceeds N1;
audio TS Null packets ATnull, which counts the audio TS Null packets in the video stream and calculates the TS Null packet rate Null%, wherein the TS Null packet rate Null% is the ratio of the TS Null packet number to the total TS packet number; recording the audio TS null packets when the audio TS null packet rate exceeds N1;
and (3) video frame similarity Sd, extracting a frame of video picture every second by decoding the video content layer, calculating the similarity Sd of adjacent picture frames, and judging as a static frame when Sd is greater than T1 and the duration Dt of the similar picture frame is greater than T2.
The video volume Vs is obtained by decoding the audio content layer, and is judged to be silent when Vs < T3 and the duration Dt > T4;
step S3, if the video cutoff Outage index is detected, the system prompts a silent frame and a silent alarm;
if detecting that the audio PID loses Apid or the video PID loses Vpid, entering a PID loss judgment sub-process; if the audio PID is lost and the video PID is normal, the system prompts a mute alarm; if the audio PID is normal and the video PID is lost, the system prompts a static frame alarm; if audio PID loss and video PID loss occur simultaneously, the system prompts a mute frame and a mute alarm;
if detecting an audio TS empty packet ATNull or a video TS empty packet VTNull, entering a TS empty packet judgment sub-process; if the audio TS is empty and the video TS is normal, the system prompts a mute alarm; if the audio TS packet is normal and the video TS packet is empty, the system prompts a static frame alarm; if the audio TS empty packet and the video TS empty packet occur at the same time, the system prompts a mute frame and a mute alarm;
if the similarity Sd > T1 of the adjacent video picture frames is detected, the adjacent video picture frames are judged to be the same, if the duration Dt > T2 of the same picture frame, the video picture is judged to be abnormal and static, a static frame fault possibly occurs, and the value of SFtmp is 1, otherwise the value of SFtmp is 0;
if the video volume Vs < T3 and the duration Dt > T4, determining that the video is abnormal and has no sound, possibly having a mute fault, and recording SLtmp as 1, otherwise, recording SLtmp as 0;
step S4, entering a live program special scene alarm suppression flow, and establishing a program feature library to record corresponding channel names and program broadcasting time periods aiming at special picture live programs and special sound live programs;
step S5, when SFtmp is 1 and SLtmp is 1, that is, when the video image is still and there is no sound, the system prompts a still frame mute failure; when SFtmp is 1 and SLtmp is 0, namely the video picture is still but there is sound, if the warning program is in the special picture live broadcast channel list Ch1 and the warning Time is in the specific Time period Time1 of the feature library, no fault is prompted, otherwise the system prompts a still frame warning; when SFtmp is 0 and SLtmp is 1, namely the video picture is normal but has no sound, if the alarm program is in the special sound live program channel list Ch2 and the alarm Time is in the specific Time period Time2 of the feature library, no fault is prompted, otherwise the system prompts a mute alarm; and when the SFtmp is 0 and the SLtmp is 0, namely the video picture is normal and the sound is normal, judging that the video broadcasting is normal, and ending the special scene alarm suppression flow of the live program.
2. The method of claim 1, wherein the method comprises:
the default value for To is 1000 ms.
3. The method of claim 1, wherein the method comprises:
the default value of N1 is 99%.
4. The method of claim 1, wherein the method comprises:
and the audio and video TS empty packet PID is a TS packet of 0x1 FFF.
5. The method of claim 1, wherein the method comprises:
the default value for T1 is 99.99%, and the default value for T2 is 5 seconds.
6. The method of claim 1, wherein the method comprises:
the default value for T3 is-50 dB and the default value for T4 is 20 seconds.
Priority Applications (1)
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