CN109936769B - Video jamming detection method, video jamming detection system, mobile terminal and storage device - Google Patents
Video jamming detection method, video jamming detection system, mobile terminal and storage device Download PDFInfo
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Abstract
The invention provides a video blockage detection method, a detection system, a mobile terminal and a storage device, comprising the following steps: step S1, capturing video message information; step S2, drawing a step accumulation graph in real time according to the length and the timestamp of the video message information; step S3, according to the streaming media video fragment transmission mechanism and the maximum video downloading amount in unit time, matching and filtering the ladder accumulation graph, and screening out abnormal fragments; and step S4, judging whether the abnormal segment is a stuck region according to the data interaction information in the abnormal segment. The invention can realize pause detection on the video stream of HTTP and HTTPs protocols without analyzing the video message, has low cost, can adapt to various video sources, and is insensitive to the fluctuation of the video transmission technology of the video sources.
Description
Technical Field
The invention belongs to the technical field of video monitoring, and particularly relates to a video blockage detection method, a video blockage detection system, a mobile terminal and a storage device.
Background
With the development of video transmission technology, the video service demand is continuously increased, and the bandwidth resource is limited, in order to improve the video service quality, a service provider improves the network video technology, including the video coding and decoding technology and the video transmission technology, and the purpose of the service provider is to improve the user satisfaction (QoE). The user satisfaction indicators include: video quality, pause time, pause frequency, initial delay, sharpness conversion frequency, etc., wherein a video pause event is a key factor influencing user satisfaction. It is therefore necessary to investigate the video stuck event.
Traditional studies of video stuck events are mainly to evaluate client buffer situation by parsing video packets. However, with the importance of privacy of users, more and more video websites use the HTTPS encryption protocol instead of the conventional HTTP protocol as an application layer protocol for streaming video transmission, which makes the conventional method that needs to analyze video packets to obtain key parameters to estimate the buffer change no longer applicable. Therefore, the prior art has yet to be developed.
Disclosure of Invention
The invention aims to solve the technical problem of providing a video jam detection method, a video jam detection system, a mobile terminal and a storage device, and aims to solve the problem that the existing video jam detection method needs to analyze video messages.
In order to solve the technical problem, the invention is realized in such a way that a video stuck detection method comprises the following steps:
step S1, capturing video message information;
step S2, drawing a step accumulation graph in real time according to the length and the timestamp of the video message information;
step S3, according to the streaming media video fragment transmission mechanism and the maximum video downloading amount in unit time, matching and filtering the ladder accumulation graph, and screening out abnormal fragments;
and step S4, judging whether the abnormal segment is a stuck region according to the data interaction information in the abnormal segment.
Further, in step S1, the traffic capturing tool used for capturing the video packet is a scapy packet or wireshark in python.
Further, in step S3, the step cumulative graph is matched and filtered by using the following matching algorithm:
wherein F represents a matching function, q (t) represents the length of the video message information downloaded at the time t, t0T, Q is a parameter to be estimated at the current moment, T represents unit time, Q represents the maximum length of the downloaded video message information in the T time period, and T1Represents a time period, and T1T or less, and filtering by a matching functionThe area reached is an abnormal area.
Further, the method for determining whether the abnormal segment is a stuck region comprises the following steps: if at least one rule of the following rules (1), (2) and (3) is met, judging that the abnormal fragment is a non-stuck region; otherwise, judging the abnormal fragment as a stuck region;
rule (1), the anomaly fragment contains an initial point;
rule (2) in the data interaction information in the abnormal fragment, keepalive and ack signal interaction exists, and the time interval of any two keepalive signals is larger than a set threshold value;
in the rule (3) and the data interaction information in the abnormal fragment, keepalive and ack signals do not exist, but fin and corresponding ack signals exist for the same source IP and the same destination IP.
A video stuck detection system comprising:
the capturing module is used for capturing video message information;
the drawing module is used for drawing a step accumulation graph in real time according to the length and the timestamp of the video message information;
the screening module is used for matching and filtering the step accumulation graph according to a streaming media video fragment transmission mechanism and the maximum video downloading amount in unit time to screen out abnormal fragments;
and the judging module is used for judging whether the abnormal fragment is a stuck region according to the data interaction information in the abnormal fragment.
A mobile terminal, comprising: a processor, and a memory communicatively connected to the processor, the memory storing a computer program for, when executed, implementing the method as described above; the processor is adapted to invoke a computer program in the memory to implement the method as described above.
A storage device storing a computer program executable to implement a method as described above.
Compared with the prior art, the invention has the beneficial effects that: the method estimates the state of a buffer area of a video client according to a streaming media video fragment transmission mechanism and the maximum video downloading amount in unit time, screens out abnormal fragments, and then analyzes data interaction information in the abnormal fragments to judge whether the video is blocked or not. The technical scheme does not need to analyze the video message, can realize pause detection on the video stream of the HTTP and the HTTPs protocol, has low cost, can adapt to various video sources, and is insensitive to the fluctuation of the video transmission technology of the video sources.
Drawings
Fig. 1 is a flowchart of an embodiment of a video stuck detection method according to the present invention.
Fig. 2 is a diagram of a video message buffer change.
Fig. 3 is a graph of video packet accumulation (packet length) versus time in accordance with an embodiment of the present invention.
FIG. 4 is a graph of the cumulative variation of buffer size, video download speed, and video packets in one embodiment of the present invention.
Fig. 5 is a diagram of TCP packet accumulation in accordance with an embodiment of the present invention.
Fig. 6 is a diagram of TCP packet accumulation after matching corresponding to fig. 4.
Fig. 7 is an abnormal area matching diagram corresponding to fig. 4.
Fig. 8 is a diagram of network fluctuation analysis.
Fig. 9 is a block diagram of a video stuck detection system according to the present invention.
Fig. 10 is a diagram of a mobile terminal for video stuck detection according to the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
The preferred embodiment of the video stuck detection method provided by the present invention, as shown in fig. 1, includes the steps of:
and step S1, capturing video message information.
And step S2, drawing a step accumulation graph in real time according to the length and the time stamp of the video message information.
And step S3, according to the streaming media video fragment transmission mechanism and the maximum video downloading amount in unit time, performing matching filtering on the step cumulative graph, and screening out abnormal fragments.
And step S4, judging whether the abnormal segment is a stuck region according to the data interaction information in the abnormal segment.
The video chudun is generally embodied in the bottom layer client side that the client side buffer video segment is exhausted, and the client side buffer zone segment is smaller than the maximum playing threshold value, so that the video cannot be normally played. As shown in FIG. 2, the horizontal axis represents time stamp (time), the vertical axis represents client video buffer size (buffer size), and the video is buffered from click-to-play to Δ t1When the playing threshold is reached, the video starts to play, and the video is in the playing stagnation stage before the playing threshold is reached; the video play buffer is exhausted until at2When the video reaches the playing threshold, the video starts to play again. In these two phases, the dashed part of the curve indicates that the video is in a stuck (steady) state.
Most of the existing video jam detection methods are directed at video streams of an HTTP (hyper text transport protocol), and some key parameters involved in the methods often need to analyze messages. And 75-90% of traffic on the internet is encrypted, which makes the traditional method that needs to analyze video messages to obtain key parameters to estimate the buffer change no longer applicable. The method estimates the state of a buffer area of a video client according to a streaming media video fragment transmission mechanism and the maximum video downloading amount in unit time, screens out abnormal fragments, and then analyzes data interaction information in the abnormal fragments to judge whether the video is blocked or not. The technical scheme does not need to analyze the video message, can realize pause detection on the video stream of the HTTP and the HTTPs protocol, has low cost, can adapt to various video sources, and is insensitive to the fluctuation of the video transmission technology of the video sources.
Specifically, in step S1, the flow capturing means may be: the scapy packet in wireshark and python captures the video message, and realizes the positioning of the video flow by counting the length (download amount) of message download and the domain name of the main stream video website. And then drawing a step accumulation graph according to the length and the timestamp of the video message information and a streaming media fragment transmission mechanism, wherein the graph is a graph of the length of the video message information and the time, and is shown in fig. 3. The step accumulation chart embodies the loop process of video playing-downloading: playing, the video message in the buffer area of the client end is consumed to the downloading threshold value, starting to download and buffer a new video fragment, completing the current fragment downloading and the like until the message is consumed to the buffering threshold value. This continuous loop represents the periodicity of the video message download.
Because the video playing has a buffer threshold, when the video message consumption of the buffer area of the client reaches the threshold, a new fragment starts to be downloaded, and at the moment, the client still has the residual fragment and can support continuous watching. The size of the flat area varies with the fluctuation of the network, so that a unit time T is required for filtering, and all areas (flat areas) which may be stuck are filtered out. And T needs to be the maximum time span, the screening rule is: and in the unit time T time period, if the maximum download quantity Q is lower than a set value, judging the area to be an abnormal area. The gentle region is maximally matched through Q and T, so that the occurrence is avoided: matching two adjacent flattish areas into one, or dividing one flattish area (A1C1) into two (A1B1 and B1C1), the ideal matching result is as in A2C2 flattish area in fig. 3; meanwhile, in order to realize the prejudgment of the impending stuck region, the T value cannot be too large; in order to match each flat region individually, the Q value cannot be too large, so a matching algorithm needs to be adopted to achieve the optimal matching.
Based on the above analysis, the present invention provides a better matching algorithm as follows:
wherein F represents a matching function, q (t) represents the length of the video message information downloaded at the time t, t0T, Q is a parameter to be estimated at the current moment, T represents unit time, Q represents the maximum length of the downloaded video message information in the T time period, and T1Represents a time period, and T1And (5) less than or equal to T, and obtaining an area filtered by the matching function as an abnormal area.
To further explain by taking fig. 4 as an example, the graph shows a TCP packet accumulation (TCP _ Segment _ Sum), a Buffer Size (Buffer _ Size), and a video Download Speed (Download _ Speed) in a video playing process in a case. The speed of the network is limited manually, so that the video buffer zone returns to zero, the video is blocked, and the expressed mapping relation is as follows: the video is at delta t in the buffer2In the state (see fig. 2), the buffer gradient becomes gentle, which means that the amount of video clip downloaded per unit time becomes small. The captured TCP message step cumulative graph is shown in fig. 5, and then the matching algorithm is used to perform matching, so as to obtain the matched TCP message step cumulative graph shown in fig. 6. The parameters Q and T are estimated from the matching graph of fig. 6, and an abnormal region matching graph as shown in fig. 7 is obtained, where the regions in the circles are abnormal regions.
Compared with the prior art, the abnormal region matching method is more simplified, strong in real-time performance and low in cost, can adapt to various video sources, and is insensitive to the fluctuation of the video transmission technology of the video sources.
After the abnormal regions are determined, it is necessary to further determine which abnormal regions are real video stuck events, fig. 8 is a network fluctuation analysis diagram, and a vertical line segment in a curve represents that there is message interaction. The marking area in the area 1 is in a network delay state, wherein the first marking point is that the network is in a low-speed state and is blocked; the second mark area is in a medium-low speed state, and normal playing can be just maintained at the time; the marked area in area 2 is a state when the network is interrupted. According to the information displayed in the graph, the message interaction state of the abnormal area can be analyzed.
A stuck event:
(1) network latency (congestion): the client and the server frequently interact with messages. Due to network delay, a congestion control mechanism based on TCP determines that the network environment of a client is poor when a server does not respond to a message sent by the client, so that a congestion window is adjusted to a lower value for transmission, and the download quantity of the message is lower than a certain value within a certain time. The residual fragments of the client buffer area are less, and the consumption speed of the user on the video message is unchanged, so that the buffer area frequently reaches a playing threshold value, and data are requested to the server at short intervals. Thus presenting a less sloped region where the client communicates with the server more frequently.
(2) Network interruption: the client and the server exchange information and lack response. Since the ISP is at the middle end of the network, the network disruption to the ISP is divided into two categories: the client area network is interrupted and the server area network is interrupted. At this time, data sent by any party through the network has no return through the ISP end, and both parties are in a state of waiting for response, and at this time, an extremely flat area is presented on the flow accumulation diagram, and as can be seen from the diagram, the flat area message interaction information is less.
Non-stuck events:
(1) there is a normal handshake (syn) or interrupt (fin) signal. For example, in the middle of a video, a normal fin interrupt signal occurs, and the advertisement is generally udp transmission and cannot be considered as a video message, so that a long flat area occurs, and the length is: the advertisement duration + the playing duration of the previous video segment may exceed the unit time T.
(2) There is a normal keepalive interaction. For example, a video is normally played, because the playable time is too long, the length of a flat area exceeds the time T and is identified by a matching method, because a keepalive mechanism exists in a tcp link, the tcp is a link with a state, when the video is originated from a client and has no information transmission, a server sends keepalive signals to the client at regular intervals, usually 45 seconds, to determine the state of the client, at this time, if the network is normal, the client feeds back an ack determination signal to the server, generally, any two keepalive time intervals are more than or equal to 35 seconds, and it can be determined that the video belongs to a normal flat area, for example, a user pauses behavior, and the fragment playing time is greater than a matching threshold, the playing time of a previous video fragment is greater than the matching threshold, and the abnormal areas are actually non-stuck areas. If the keepalive sent by the server is not fed back for three times continuously, the system is directly disconnected, which is understood as network interruption. The client state can be determined by analyzing keepalive message interactions.
Based on the analysis of the message interaction state of the abnormal area, the invention provides a judgment model for judging whether the abnormal area is a video pause event. The method specifically comprises the following three rules, and if at least one of the three rules is met, the abnormal fragment is judged to be a non-stuck region; otherwise, judging the abnormal fragment as a stuck region. The three rules are as follows:
rule (1), the anomaly fragment contains an initial point.
In the rule (2) and the data interaction information in the abnormal fragment, keepalive and ack signal interaction exists, and the time interval of any two keepalive signals is greater than a set threshold value.
In the rule (3) and the data interaction information in the abnormal fragment, keepalive and ack signals do not exist, but fin and corresponding ack signals exist for the same source IP and the same destination IP.
The existing machine learning algorithm flow often includes: selecting the characteristics of the data set, training the model and outputting the result. Previous methods based on machine learning all belong to supervised learning, and a good model can be trained only by taking a large number of labeled video sample characteristics as input. Firstly, the video playing time is from several minutes to several hours, so the marking work cost is huge, and more cost is needed to support the process for ISP; secondly, if the model is complex, the time sequence span of the selection of the input features is long, and the real-time detection is often difficult to support; finally, once the training of the machine learning model is completed, the parameters are solidified, and once the video transmission technology is updated and changed, the video is often required to be labeled and trained again. The abnormal region is screened by the matching algorithm, and the stuck detection is finally realized, and compared with the previous method, the method has the advantages that: (1) the video message does not need to be analyzed, and the video stream of the HTTP and HTTPs protocols can be subjected to pause detection; (2) compared with the prior method, the model is more simplified and has stronger real-time property; (3) the method has low cost, can adapt to various video sources, and is insensitive to the fluctuation of the video transmission technology of the video sources.
Based on the video stuck detection method, the present invention further provides a video stuck detection system, as shown in fig. 9, including:
the capturing module 1 is used for capturing video message information;
the drawing module 2 is used for drawing a step accumulation graph in real time according to the length and the timestamp of the video message information;
the screening module 3 is used for matching and filtering the ladder accumulation graph according to a streaming media video fragment transmission mechanism and the maximum video downloading amount in unit time, and screening out abnormal fragments;
and the judging module 4 is used for judging whether the abnormal segment is a stuck region according to the data interaction information in the abnormal segment.
Based on the video stuck detection method, the present invention further provides a mobile terminal, as shown in fig. 10, the mobile terminal includes: a processor (processor)10, a memory (memory)20, a communication Interface (Communications Interface)30, and a bus 40; wherein the content of the first and second substances,
the processor 10, the memory 20 and the communication interface 30 complete mutual communication through the bus 40;
the communication interface 30 is used for information transmission between communication devices of the mobile terminal;
the processor 10 is configured to call the computer program in the memory 20 to execute the method provided by the above method embodiments, for example, including: when the system is started, the mobile terminal captures video message information; estimating the state of a video client buffer area according to a streaming media video fragment transmission mechanism and the maximum video downloading amount in unit time, and screening out abnormal fragments; and judging whether the abnormal fragment is a stuck region or not according to the data interaction information in the abnormal fragment.
The present invention also provides a storage device, wherein the storage device stores a computer program, and the computer program can be executed to the video jam detection method.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents and improvements made within the spirit and principle of the present invention are intended to be included within the scope of the present invention.
Claims (7)
1. A video stuck detection method is characterized by comprising the following steps:
step S1, capturing video message information;
step S2, drawing a step accumulation graph in real time according to the length and the timestamp of the video message information;
step S3, according to a streaming media video fragment transmission mechanism and the maximum video downloading quantity in unit time, matching and filtering the ladder accumulation graph, and screening abnormal fragments by judging that the maximum downloading quantity Q in unit time T time quantum is lower than a set value;
and step S4, judging whether the abnormal segment is a stuck region according to the data interaction information in the abnormal segment.
2. The video morton detection method according to claim 1, wherein in the step S1, a traffic capturing tool used for capturing the video packet is a scapy packet or wireshack in python.
3. The video stuck detection method according to claim 1, wherein in step S3, it is ensured that T value and Q value are not too large, so that the step-sum graph is matched and filtered by using the following matching algorithm:
wherein F represents a matching function, q (t) represents the length of the video message information downloaded at the time t, t0T, Q is a parameter to be estimated at the current moment, T represents unit time, Q represents the maximum length of the downloaded video message information in the T time period, and T1Represents a time period, and T1And (5) less than or equal to T, and obtaining an area filtered by the matching function as an abnormal area.
4. The video stuck detection method of claim 1, wherein the method for determining whether the abnormal segment is a stuck region comprises: if at least one rule of the following rules (1), (2) and (3) is met, judging that the abnormal fragment is a non-stuck region; otherwise, judging the abnormal fragment as a stuck region;
rule (1), the anomaly fragment contains an initial point;
rule (2) in the data interaction information in the abnormal fragment, keepalive and ack signal interaction exists, and the time interval of any two keepalive signals is larger than a set threshold value;
in the rule (3) and the data interaction information in the abnormal fragment, keepalive and ack signals do not exist, but fin and corresponding ack signals exist for the same source IP and the same destination IP.
5. A video stuck detection system, comprising:
the capturing module is used for capturing video message information;
the drawing module is used for drawing a step accumulation graph in real time according to the length and the timestamp of the video message information;
the screening module is used for matching and filtering the step cumulative graph according to a streaming media video fragment transmission mechanism and the maximum video downloading quantity in unit time, and screening abnormal fragments by judging that the maximum downloading quantity Q in the time period T in unit time is lower than a set value;
and the judging module is used for judging whether the abnormal fragment is a stuck region according to the data interaction information in the abnormal fragment.
6. A mobile terminal, comprising: a processor, and a memory communicatively connected to the processor, the memory storing a computer program for, when executed, implementing the method of any of claims 1-4; the processor is configured to invoke a computer program in the memory to implement the method of any of claims 1-4.
7. A storage device, characterized in that the storage device stores a computer program executable to implement the method according to any one of claims 1-4.
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| CN110958494A (en) * | 2019-10-22 | 2020-04-03 | 南京宏之图信息技术有限公司 | Method for identifying video blockage based on XDR (X data Rate) record |
| CN112995702B (en) * | 2019-12-16 | 2023-09-15 | 天翼数字生活科技有限公司 | Method and system for judging video clamping based on quality monitoring probe |
| CN113453076B (en) * | 2020-03-24 | 2023-07-14 | 中国移动通信集团河北有限公司 | User video service quality evaluation method, device, computing equipment and storage medium |
| CN113836966A (en) * | 2020-06-08 | 2021-12-24 | 中国移动通信有限公司研究院 | Video detection method, device, equipment and storage medium |
| CN111657920B (en) * | 2020-06-30 | 2022-10-21 | 毕胜普生物科技有限公司 | Electrocardiogram monitoring data visualization method and device |
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