A method and system for optimizing network video transmission

Abstract

The present invention belongs to the technical field of video transmission optimization. The present invention provides an optimization method and system for network video transmission, comprising: based on the real-time transmission data of the network video and the rate data of the network, analyzing to obtain the abnormal risk value of the network video transmission and obtaining the transmission risk signal; based on the transmission high risk signal, combining and analyzing the bandwidth data and the real-time transmission deviation value to obtain the impact association value; analyzing whether the cause of the abnormal network video transmission is bandwidth fluctuation according to the impact association value, and generating the impact signal; based on the impact signal, generating the corresponding feedback signal, wherein the feedback signal comprises a detection signal and a control signal; based on the generation of the control signal, obtaining the control coefficient; and controlling and optimizing the network video transmission according to the control coefficient. The present invention can timely discover potential video transmission problems, quickly control and optimize the abnormal network video transmission, and ensure the transmission quality and stability of the network video.

Description

Technical Field

[0001] The invention belongs to the technical field of video transmission optimization, and in particular to an optimization method and system for network video transmission. Background Art

[0002] With the rapid development of the Internet, network video transmission has become an indispensable part of people's daily life and work. However, problems such as delay, freeze, and insufficient bandwidth in the process of network video transmission have seriously affected the user's viewing experience. In order to solve these problems, the present invention proposes a new network video transmission optimization method and system to improve the efficiency, quality and stability of video transmission.

[0003] A Chinese patent application with publication number CN117135401A discloses a video transmission optimization method based on a 5G network, including: dividing a target video to obtain sub-videos, collecting information of the target video as a video information packet, wherein the video information includes video format and parameters; processing the target video to obtain video sending data, and the sending end sends the video sending data to the receiving end based on the 5G network; predicting the transmission time of the target video, and comparing the sending time of the target video with the time of receiving the video sending data to obtain an efficiency evaluation index.

[0004] In the prior art, the transmission quality, efficiency and stability of network video are improved through different qualitative methods, but there is a lack of dynamic analysis of network video transmission anomalies, because there are many reasons for network video transmission anomalies and in some cases normal transmission can be achieved without intervention. The present invention monitors network video transmission in real time, analyzes the cause of the anomaly and performs precise regulation and optimization when the network video transmission is abnormal, which can reduce unnecessary optimization resource costs on the one hand and ensure the quality, efficiency and stability of network video transmission on the other hand.

[0005] To this end, the present invention provides an optimization method and system for network video transmission. Summary of the invention

[0006] In order to make up for the deficiencies of the prior art, at least one technical problem raised in the background technology is solved.

[0007] The present invention monitors network video transmission anomalies in real time, which is conducive to timely discovering potential video transmission problems, and analyzing the causes of network video transmission anomalies, so as to timely troubleshoot network video transmission anomalies, determine the causes of the anomalies, and quickly regulate and optimize the network video transmission anomalies. It also obtains the regulation coefficient, which is conducive to timely regulating the network video transmission when the network video transmission is abnormal, so as to ensure the transmission quality and stability of the network video.

[0008] The technical solution adopted by the present invention to solve the technical problem is: an optimization method for network video transmission, comprising:

[0009] Step 1: Based on the real-time transmission data of network video and the rate data of the network, analyze and obtain the abnormal risk value of network video transmission;

[0010] Step 2: Based on the network video transmission abnormality risk value, a transmission risk signal is obtained, wherein the transmission risk signal includes a transmission high risk signal and a transmission low risk signal;

[0011] Step 3: Based on the transmission high-risk signal, the bandwidth data of the network video transmission is obtained, wherein the bandwidth data includes the real-time bandwidth share value, and the impact association value is obtained based on the combined analysis of the bandwidth data and the real-time transmission deviation value. According to the impact association value, whether the cause of the network video transmission abnormality is bandwidth fluctuation is analyzed, and an impact signal is generated, wherein the impact signal includes a strong impact association signal and a weak impact association signal;

[0012] Step 4: Based on the impact signal, generate a corresponding feedback signal, wherein the feedback signal includes a detection signal and a control signal;

[0013] Step 5: Based on the generated control signal, the control coefficient is obtained, and the network video transmission is controlled and optimized according to the control coefficient, wherein the control coefficient includes a bandwidth control value and a bandwidth control rate.

[0014] As a further technical solution of the present invention, the real-time transmission data of the network video includes a real-time transmission rate, and the rate data of the network includes a real-time network rate;

[0015] The real-time transmission rate of the network video is subtracted from the real-time network rate, and the absolute value of the difference is taken to obtain the real-time transmission deviation value, and the real-time transmission deviation value is compared with the real-time transmission deviation threshold:

[0016] If the real-time transmission deviation value is greater than or equal to the real-time transmission deviation threshold, a transmission abnormality signal is generated;

[0017] If the real-time transmission deviation value is less than the real-time transmission deviation threshold, a transmission normal signal is generated;

[0018] Based on the transmission abnormality signal, the transmission abnormality data is analyzed and obtained, wherein the transmission abnormality data includes the abnormal transmission time ratio, the transmission abnormality degree ratio and the transmission abnormality trend performance value. After multiplying the abnormal transmission time ratio with the transmission abnormality degree ratio, the obtained result is multiplied with the transmission abnormality trend performance value to obtain the network video transmission abnormality risk value.

[0019] As a further technical solution of the present invention, the abnormal transmission time ratio is obtained in the following manner:

[0020] The time during which the transmission abnormal signal continues to appear is marked as the abnormal transmission time, and the abnormal transmission time is compared with the abnormal transmission time threshold to obtain the abnormal transmission time ratio;

[0021] The transmission anomaly ratio is obtained as follows:

[0022] All real-time transmission deviation values ​​within the abnormal transmission time are obtained, and the difference between them and the real-time transmission deviation threshold is processed respectively, and the absolute value of the difference is taken to obtain the real-time transmission relative deviation value, all real-time transmission relative deviation values ​​are summed up to obtain the total value of real-time transmission relative deviation, the total value of real-time transmission relative deviation is divided by the abnormal transmission time to obtain the mean value of real-time transmission relative deviation, the mean value of real-time transmission relative deviation is processed by ratio with the real-time transmission deviation threshold to obtain the transmission abnormality degree ratio.

[0023] As a further technical solution of the present invention, the method for obtaining the transmission abnormal trend performance value is:

[0024] Mark all real-time transmission deviation values ​​within the abnormal transmission time in the XY two-dimensional coordinate system, and connect all the real-time transmission deviation value mark points with straight lines to obtain the real-time transmission deviation value change line within the abnormal transmission time, and obtain the slope of each sub-line segment in the real-time transmission deviation value change line. If the slope of the sub-line segment is greater than or equal to 0, mark the sub-line segment as an increasing sub-line segment; if the slope of the sub-line segment is less than 0, mark the sub-line segment as an increasing or decreasing sub-line segment;

[0025] The number of all sub-line segments and the number of growing sub-line segments are counted, and the number of growing sub-line segments is divided by the number of all sub-line segments to obtain the transmission abnormality trend performance value.

[0026] As a further technical solution of the present invention, the network video transmission abnormality risk value is compared with the network video transmission abnormality risk threshold, and the specific comparison process is as follows:

[0027] If the network video transmission abnormality risk value is greater than or equal to the network video transmission abnormality risk threshold, a transmission high risk signal is generated;

[0028] If the network video transmission abnormality risk value is less than the network video transmission abnormality risk threshold, a transmission low risk signal is generated.

[0029] As a further technical solution of the present invention, the method for obtaining the influence association value is:

[0030] Divide the abnormal transmission time into a number of continuous and equal time sub-units, and mark the time transition nodes between all time sub-units as analysis nodes;

[0031] Based on the real-time transmission deviation change value of the analysis node and the real-time bandwidth share change value, the processing output is obtained to obtain the impact change ratio;

[0032] If the influence change ratio corresponding to the analysis node is greater than zero, the analysis node is marked as a suspected associated node;

[0033] If the impact change ratio corresponding to the analysis node is less than or equal to zero, no operation is performed;

[0034] Obtain the impact change ratios corresponding to all suspected associated nodes, sum and average them to obtain the impact change ratio mean, perform difference processing on the impact change ratio corresponding to the suspected associated node and the impact change ratio mean, take the absolute value of the difference to obtain the impact change ratio difference, and compare the impact change ratio difference with the impact change ratio difference threshold;

[0035] If the impact change ratio difference is less than or equal to the impact change ratio difference threshold, the suspected associated node is marked as an associated node;

[0036] If the impact change ratio difference is greater than the impact change ratio difference threshold, no operation is performed;

[0037] The number of associated nodes and the number of all analyzed nodes are counted and ratio processed to obtain the impact association value.

[0038] As a further technical solution of the present invention, the specific process of obtaining the real-time transmission deviation change value and the real-time bandwidth proportion change value of the analysis node is as follows:

[0039] The real-time transmission deviation value corresponding to the analysis node and the real-time transmission deviation value of the previous analysis node are obtained, and the difference is processed to obtain the real-time transmission deviation change value. The real-time bandwidth share change value is obtained in the same way as the real-time transmission deviation change value is obtained.

[0040] As a further technical solution of the present invention, if the generated influence signal is an influence-related weak signal, a detection signal is generated to detect other influencing factors in the network video transmission process, and the detection includes but is not limited to network congestion, equipment operation and transmission protocol;

[0041] If the generated influence signal is a strong influence correlation signal, a regulation signal is generated.

[0042] As a further technical solution of the present invention, the bandwidth control value is obtained by:

[0043] Obtain the impact change ratios of all associated nodes, sum them up and take the average, and obtain the target impact change ratio;

[0044] Obtaining a real-time transmission deviation value at the end of the abnormal transmission time, dividing the real-time transmission deviation value at the end of the abnormal transmission time by the target impact change ratio, to obtain a bandwidth control value;

[0045] The bandwidth control rate is obtained as follows:

[0046] Obtain the abnormal transmission time, perform difference processing on the abnormal transmission time and the abnormal transmission time threshold, and take the absolute value of the difference to obtain the control time;

[0047] The bandwidth control value is ratioed to the control time to obtain the bandwidth control rate.

[0048] An optimization system for network video transmission, comprising:

[0049] Data acquisition module: Based on the real-time transmission data of network video and the rate data of the network, the abnormal risk value of network video transmission is analyzed;

[0050] Risk analysis module: based on the abnormal risk value of network video transmission, a transmission risk signal is obtained, wherein the transmission risk signal includes a high-risk transmission signal and a low-risk transmission signal;

[0051] Impact analysis module: based on the transmission high-risk signal, obtain the bandwidth data of network video transmission, where the bandwidth data includes the real-time bandwidth share value, and obtain the impact association value based on the combined analysis of the bandwidth data and the real-time transmission deviation value. According to the impact association value, analyze whether the cause of the abnormal network video transmission is bandwidth fluctuation, and generate an impact signal, where the impact signal includes a strong impact association signal and a weak impact association signal;

[0052] Work execution module: based on the impact signal, generates a corresponding feedback signal, wherein the feedback signal includes a detection signal and a control signal;

[0053] Control optimization module: based on the generation of control signals, obtains control coefficients, and performs control optimization on network video transmission according to the control coefficients, wherein the control coefficients include bandwidth control values ​​and bandwidth control rates.

[0054] The beneficial effects of the present invention are as follows:

[0055] 1. The present invention analyzes and obtains the abnormal risk value of network video transmission based on the real-time transmission data of network video and the rate data of the network, and obtains the transmission risk signal based on the abnormal risk value of network video transmission, wherein the transmission risk signal includes a high-risk transmission signal and a low-risk transmission signal. The present invention determines the degree of abnormal network video transmission by analyzing the real-time transmission data of network video and the rate data of the network, which is conducive to timely discovery of potential video transmission problems.

[0056] 2. The present invention obtains bandwidth data of network video transmission based on the transmission high-risk signal, obtains impact association value based on the combined analysis of bandwidth data and real-time transmission deviation value, analyzes whether the cause of network video transmission abnormality is bandwidth fluctuation according to the impact association value, and generates impact signal, wherein the impact signal includes impact association strong signal and impact association weak signal. The present invention can determine whether the cause of network video transmission abnormality is bandwidth fluctuation by combining and analyzing bandwidth data and real-time transmission deviation value, so as to facilitate timely troubleshooting of network video transmission abnormality, determine the cause of the abnormality, and facilitate rapid regulation and optimization of network video transmission abnormality.

[0057] 3. The present invention generates a corresponding feedback signal based on the influencing signal, wherein the feedback signal includes a detection signal and a control signal. Based on the control signal, a control coefficient is obtained, and the network video transmission is regulated and optimized according to the control coefficient. This is beneficial for timely regulation of the network video transmission when the network video transmission is abnormal, thereby ensuring the transmission quality and stability of the network video. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] The present invention will be further described below in conjunction with the accompanying drawings.

[0059] Figure 1 This is a flow chart of transmission anomaly analysis in an optimization method for network video transmission according to Embodiment 1 of the present invention;

[0060] Figure 2 This is a flow chart of impact analysis in an optimization method for network video transmission according to Embodiment 2 of the present invention;

[0061] Figure 3 This is a flow chart of control optimization in an optimization method for network video transmission according to Embodiment 3 of the present invention;

[0062] Figure 4 This is a flowchart of a system for optimizing network video transmission according to Embodiment 4 of the present invention. DETAILED DESCRIPTION

[0063] In order to make the technical means, creative features, objectives and effects achieved by the present invention easy to understand, the present invention is further explained below in conjunction with specific implementation methods.

[0064] Example 1

[0065] like Figure 1 As shown, an optimization method for network video transmission according to an embodiment of the present invention includes:

[0066] Step 1: Based on the real-time transmission data of network video and the rate data of the network, analyze and obtain the abnormal risk value of network video transmission;

[0067] The real-time transmission data of the network video includes the real-time transmission rate, and the rate data of the network includes the real-time network rate;

[0068] In some embodiments, the real-time transmission rate of the network video and the real-time network rate are monitored and obtained by a network speed meter;

[0069] In some embodiments, the real-time transmission rate of the network video is subtracted from the real-time network rate, and the absolute value of the difference is taken to obtain a real-time transmission deviation value, and the real-time transmission deviation value is compared with a real-time transmission deviation threshold:

[0070] If the real-time transmission deviation value is greater than or equal to the real-time transmission deviation threshold, a transmission abnormality signal is generated;

[0071] If the real-time transmission deviation value is less than the real-time transmission deviation threshold, a transmission normal signal is generated;

[0072] Based on the transmission abnormality signal, the transmission abnormality data is analyzed and obtained, wherein the transmission abnormality data includes the abnormal transmission time ratio, the transmission abnormality degree ratio and the transmission abnormality trend performance value. After the abnormal transmission time ratio is multiplied by the transmission abnormality degree ratio, the obtained result is multiplied by the transmission abnormality trend performance value to obtain the network video transmission abnormality risk value;

[0073] Exemplarily, the abnormal transmission time ratio is obtained in the following manner:

[0074] The time during which the transmission abnormal signal continues to appear is marked as the abnormal transmission time, and the abnormal transmission time is compared with the abnormal transmission time threshold to obtain the abnormal transmission time ratio;

[0075] It should be noted that the abnormal transmission time threshold is set by those skilled in the art based on experimental data;

[0076] Exemplarily, the transmission anomaly degree ratio is obtained in the following manner:

[0077] Obtain all real-time transmission deviation values ​​within the abnormal transmission time, perform difference processing on them and the real-time transmission deviation threshold value respectively, and take the absolute value of the difference to obtain the real-time transmission relative deviation value, sum all the real-time transmission relative deviation values ​​to obtain the total value of the real-time transmission relative deviation, divide the total value of the real-time transmission relative deviation by the abnormal transmission time to obtain the mean value of the real-time transmission relative deviation, perform ratio processing on the mean value of the real-time transmission relative deviation and the real-time transmission deviation threshold value to obtain the transmission abnormality degree ratio;

[0078] Exemplarily, the transmission abnormality trend performance value is obtained in the following manner:

[0079] Mark all real-time transmission deviation values ​​within the abnormal transmission time in the XY two-dimensional coordinate system, and connect all the real-time transmission deviation value mark points with straight lines to obtain the real-time transmission deviation value change line within the abnormal transmission time, and obtain the slope of each sub-line segment in the real-time transmission deviation value change line. If the slope of the sub-line segment is greater than or equal to 0, mark the sub-line segment as an increasing sub-line segment; if the slope of the sub-line segment is less than 0, mark the sub-line segment as an increasing or decreasing sub-line segment;

[0080] Count the number of all sub-broken line segments and the number of growing sub-broken line segments, divide the number of growing sub-broken line segments by the number of all sub-broken line segments, and obtain the transmission abnormality trend performance value;

[0081] Step 2: Based on the network video transmission abnormality risk value, a transmission risk signal is obtained, wherein the transmission risk signal includes a transmission high risk signal and a transmission low risk signal;

[0082] In some embodiments, the network video transmission abnormality risk value is compared with the network video transmission abnormality risk threshold, and the specific comparison process is as follows:

[0083] If the network video transmission abnormality risk value is greater than or equal to the network video transmission abnormality risk threshold, a transmission high risk signal is generated;

[0084] If the network video transmission abnormality risk value is less than the network video transmission abnormality risk threshold, a transmission low risk signal is generated;

[0085] The technical solution of the embodiment of the present invention is: based on the real-time transmission data of the network video and the rate data of the network, the network video transmission abnormality risk value is analyzed, and based on the network video transmission abnormality risk value, a transmission risk signal is obtained, wherein the transmission risk signal includes a high-risk transmission signal and a low-risk transmission signal. The present invention determines the degree of network video transmission abnormality by analyzing the real-time transmission data of the network video and the rate data of the network, which is conducive to timely discovery of potential video transmission problems.

[0086] Example 2

[0087] like Figure 2 As shown, based on Example 1, an optimization method for network video transmission according to an embodiment of the present invention includes:

[0088] Step 3: Based on the transmission high-risk signal, the bandwidth data of the network video transmission is obtained, wherein the bandwidth data includes the real-time bandwidth share value, and the impact association value is obtained based on the combined analysis of the bandwidth data and the real-time transmission deviation value. According to the impact association value, whether the cause of the network video transmission abnormality is bandwidth fluctuation is analyzed, and an impact signal is generated, wherein the impact signal includes a strong impact association signal and a weak impact association signal;

[0089] It should be noted that the real-time bandwidth ratio represents the ratio of the bandwidth occupied by network video transmission to the total network bandwidth;

[0090] Exemplarily, the method of obtaining the impact association value is:

[0091] Divide the abnormal transmission time into a number of continuous and equal time sub-units, and mark the time transition nodes between all time sub-units as analysis nodes;

[0092] It should be noted that the time transition node is defined as: the time connection point between two adjacent time sub-units, which is actually the end time point of the previous time sub-unit or the start time point of the next time sub-unit;

[0093] The analysis nodes are numbered in chronological order, and are numbered as the first analysis node, the second analysis node, the third analysis node, ... the i-th analysis node, where i represents the analysis node number;

[0094] Obtain the real-time transmission deviation value and the corresponding real-time bandwidth share value corresponding to each analysis node;

[0095] Based on the real-time transmission deviation value and the corresponding real-time bandwidth share value corresponding to each analysis node, the real-time transmission deviation change value and the real-time bandwidth share change value of each analysis node from the second analysis node to the i-th analysis node are obtained;

[0096] Specifically, the specific process of obtaining the real-time transmission deviation change value and the real-time bandwidth proportion change value of the analysis node is as follows:

[0097] Get the real-time transmission deviation value corresponding to the analysis node and the previous analysis node of the analysis node

[0098] The real-time transmission deviation value is processed by difference to obtain the real-time transmission deviation change value. The real-time bandwidth proportion change value is obtained in the same way as the real-time transmission deviation change value is obtained.

[0099] Here, an example is given for an analysis node. For example, the process of obtaining the real-time transmission deviation change value of the second analysis node is: obtaining the real-time transmission deviation value of the second analysis node and the real-time transmission deviation value of the first analysis node, subtracting the real-time transmission deviation value of the first analysis node from the real-time transmission deviation value of the second analysis node, and obtaining the real-time transmission deviation change value of the second analysis node;

[0100] The real-time transmission deviation change value of the analysis node is processed by ratio with the real-time bandwidth share change value to obtain the impact change ratio;

[0101] If the influence change ratio corresponding to the analysis node is greater than zero, the analysis node is marked as a suspected associated node;

[0102] If the impact change ratio corresponding to the analysis node is less than or equal to zero, no operation is performed;

[0103] It should be noted that if either the real-time transmission deviation change value or the bandwidth real-time share change value is equal to zero, the impact change ratio defaults to zero;

[0104] Obtain the impact change ratios corresponding to all suspected associated nodes, sum and average them to obtain the impact change ratio mean, perform difference processing on the impact change ratio corresponding to the suspected associated node and the impact change ratio mean, take the absolute value of the difference to obtain the impact change ratio difference, and compare the impact change ratio difference with the impact change ratio difference threshold;

[0105] If the impact change ratio difference is less than or equal to the impact change ratio difference threshold, the suspected associated node is marked as an associated node;

[0106] If the impact change ratio difference is greater than the impact change ratio difference threshold, no operation is performed;

[0107] Count the number of associated nodes and the number of all analysis nodes, and perform ratio processing to obtain the impact association value;

[0108] The impact correlation value is compared with the impact correlation threshold. The specific comparison process is as follows:

[0109] If the impact correlation value is greater than or equal to the impact correlation threshold, it means that the cause of the network video transmission abnormality is the bandwidth fluctuation during the network video transmission, and a strong impact correlation signal is generated;

[0110] If the impact correlation value is less than the impact correlation threshold, it means that the cause of the network video transmission abnormality is not the bandwidth fluctuation during network video transmission, and an impact correlation weak signal is generated;

[0111] It should be noted that the strong signal affecting the correlation indicates that during the abnormal transmission time, the real-time transmission deviation value will change accordingly with the change of the real-time bandwidth proportion value. Conversely, the weak signal affecting the correlation indicates that the real-time transmission deviation value will not change accordingly with the change of the real-time bandwidth proportion value.

[0112] The technical solution of the embodiment of the present invention is: based on the transmission high-risk signal, the bandwidth data of the network video transmission is obtained, based on the combined analysis of the bandwidth data and the real-time transmission deviation value, the impact association value is obtained, and whether the cause of the abnormal network video transmission is bandwidth fluctuation is analyzed according to the impact association value, and an impact signal is generated, wherein the impact signal includes an impact association strong signal and an impact association weak signal. By combining the analysis of the bandwidth data and the real-time transmission deviation value, the present invention can determine whether the cause of the abnormal network video transmission is bandwidth fluctuation, which is convenient for timely troubleshooting of the abnormal network video transmission fault problem, determining the abnormal cause, and facilitating rapid regulation and optimization of the abnormal network video transmission.

[0113] Example 3

[0114] like Figure 3 As shown, based on Example 1 and Example 2, an optimization method for network video transmission described in an embodiment of the present invention includes:

[0115] Step 4: Based on the impact signal, generate a corresponding feedback signal, wherein the feedback signal includes a detection signal and a control signal;

[0116] Specifically, if the generated impact signal is an impact-related weak signal, a detection signal is generated to detect other impact factors in the network video transmission process, including but not limited to network congestion, equipment operation, and transmission protocol;

[0117] If the generated influence signal is a strong influence correlation signal, a control signal is generated to control and optimize the network video transmission;

[0118] Step 5: Based on the generated control signal, a control coefficient is obtained, and the network video transmission is controlled and optimized according to the control coefficient, wherein the control coefficient includes a bandwidth control value and a bandwidth control rate;

[0119] Specifically, the bandwidth control value is obtained as follows:

[0120] Obtain the impact change ratios of all associated nodes, sum them up and take the average, and obtain the target impact change ratio;

[0121] Obtaining a real-time transmission deviation value at the end of the abnormal transmission time, dividing the real-time transmission deviation value at the end of the abnormal transmission time by the target impact change ratio, to obtain a bandwidth control value;

[0122] The bandwidth control rate is obtained as follows:

[0123] Obtain the abnormal transmission time, perform difference processing on the abnormal transmission time and the abnormal transmission time threshold, and take the absolute value of the difference to obtain the control time;

[0124] The bandwidth control value is ratioed with the control time to obtain the bandwidth control rate;

[0125] The technical solution of the embodiment of the present invention is: based on the influencing signal, a corresponding feedback signal is generated, wherein the feedback signal includes a detection signal and a control signal, based on the control signal, a control coefficient is obtained, and the network video transmission is regulated and optimized according to the control coefficient, which is beneficial to timely regulate the network video transmission when the network video transmission is abnormal, thereby ensuring the transmission quality and stability of the network video.

[0126] Example 4

[0127] like Figure 4 As shown, an optimization system for network video transmission according to an embodiment of the present invention includes:

[0128] Data acquisition module: Based on the real-time transmission data of network video and the rate data of the network, the abnormal risk value of network video transmission is analyzed;

[0129] Risk analysis module: based on the abnormal risk value of network video transmission, a transmission risk signal is obtained, wherein the transmission risk signal includes a high-risk transmission signal and a low-risk transmission signal;

[0130] Impact analysis module: based on the transmission high-risk signal, obtain the bandwidth data of network video transmission, where the bandwidth data includes the real-time bandwidth share value, and obtain the impact association value based on the combined analysis of the bandwidth data and the real-time transmission deviation value. According to the impact association value, analyze whether the cause of the abnormal network video transmission is bandwidth fluctuation, and generate an impact signal, where the impact signal includes a strong impact association signal and a weak impact association signal;

[0131] Work execution module: based on the impact signal, generates a corresponding feedback signal, wherein the feedback signal includes a detection signal and a control signal;

[0132] Control optimization module: based on the generation of control signals, obtains control coefficients, and performs control optimization on network video transmission according to the control coefficients, wherein the control coefficients include bandwidth control values ​​and bandwidth control rates.

[0133] The above shows and describes the basic principles, main features and advantages of the present invention. It should be understood by those skilled in the art that the present invention is not limited to the above embodiments. The above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. An optimization method for network video transmission, characterized in that: include: Step 1: Based on the real-time transmission data of network video and the rate data of the network, analyze and obtain the abnormal risk value of network video transmission; Step 2: Based on the network video transmission abnormality risk value, a transmission risk signal is obtained, wherein the transmission risk signal includes a transmission high risk signal and a transmission low risk signal; Step 3: Based on the transmission high-risk signal, the bandwidth data of the network video transmission is obtained, wherein the bandwidth data includes the real-time bandwidth share value, and the impact association value is obtained based on the combined analysis of the bandwidth data and the real-time transmission deviation value. According to the impact association value, whether the cause of the network video transmission abnormality is bandwidth fluctuation is analyzed, and an impact signal is generated, wherein the impact signal includes a strong impact association signal and a weak impact association signal; Step 4: Based on the impact signal, generate a corresponding feedback signal, wherein the feedback signal includes a detection signal and a control signal; If the generated impact signal is an impact-related weak signal, a detection signal is generated to detect other impact factors in the network video transmission process, including network congestion, equipment operation, and transmission protocol; If the generated influence signal is a strong influence correlation signal, a regulation signal is generated; Step 5: Based on the generated control signal, the control coefficient is obtained, and the network video transmission is controlled and optimized according to the control coefficient, wherein the control coefficient includes a bandwidth control value and a bandwidth control rate.

2. The optimization method for network video transmission according to claim 1, characterized in that: The real-time transmission data of the network video includes a real-time transmission rate, and the rate data of the network includes a real-time network rate; The real-time transmission rate of the network video is subtracted from the real-time network rate, and the absolute value of the difference is taken to obtain the real-time transmission deviation value, and the real-time transmission deviation value is compared with the real-time transmission deviation threshold: If the real-time transmission deviation value is greater than or equal to the real-time transmission deviation threshold, a transmission abnormality signal is generated; If the real-time transmission deviation value is less than the real-time transmission deviation threshold, a transmission normal signal is generated; Based on the transmission abnormality signal, the transmission abnormality data is analyzed and obtained, wherein the transmission abnormality data includes the abnormal transmission time ratio, the transmission abnormality degree ratio and the transmission abnormality trend performance value. After multiplying the abnormal transmission time ratio with the transmission abnormality degree ratio, the obtained result is multiplied with the transmission abnormality trend performance value to obtain the network video transmission abnormality risk value.

3. The optimization method for network video transmission according to claim 2, characterized in that: The abnormal transmission time ratio is obtained as follows: The time during which the transmission abnormal signal continues to appear is marked as the abnormal transmission time, and the abnormal transmission time is compared with the abnormal transmission time threshold to obtain the abnormal transmission time ratio; The transmission anomaly ratio is obtained as follows: All real-time transmission deviation values ​​within the abnormal transmission time are obtained, and the difference between them and the real-time transmission deviation threshold is processed respectively, and the absolute value of the difference is taken to obtain the real-time transmission relative deviation value, all real-time transmission relative deviation values ​​are summed up to obtain the total value of real-time transmission relative deviation, the total value of real-time transmission relative deviation is divided by the abnormal transmission time to obtain the mean value of real-time transmission relative deviation, the mean value of real-time transmission relative deviation is processed by ratio with the real-time transmission deviation threshold to obtain the transmission abnormality degree ratio.

4. The optimization method for network video transmission according to claim 2, characterized in that: The method for obtaining the transmission abnormal trend performance value is as follows: Mark all real-time transmission deviation values ​​within the abnormal transmission time in the XY two-dimensional coordinate system, and connect all the real-time transmission deviation value mark points with straight lines to obtain the real-time transmission deviation value change line within the abnormal transmission time, and obtain the slope of each sub-line segment in the real-time transmission deviation value change line. If the slope of the sub-line segment is greater than or equal to 0, mark the sub-line segment as an increasing sub-line segment; if the slope of the sub-line segment is less than 0, mark the sub-line segment as an increasing or decreasing sub-line segment; The number of all sub-line segments and the number of growing sub-line segments are counted, and the number of growing sub-line segments is divided by the number of all sub-line segments to obtain the transmission abnormality trend performance value.

5. The optimization method for network video transmission according to claim 2, characterized in that: The network video transmission abnormality risk value is compared with the network video transmission abnormality risk threshold. The specific comparison process is as follows: If the network video transmission abnormality risk value is greater than or equal to the network video transmission abnormality risk threshold, a transmission high risk signal is generated; If the network video transmission abnormality risk value is less than the network video transmission abnormality risk threshold, a transmission low risk signal is generated.

6. The optimization method for network video transmission according to claim 1, characterized in that: The way to obtain the associated value is as follows: Divide the abnormal transmission time into a number of continuous and equal time sub-units, and mark the time transition nodes between all time sub-units as analysis nodes; Based on the real-time transmission deviation change value of the analysis node and the real-time bandwidth share change value, the processing output is obtained to obtain the impact change ratio; If the influence change ratio corresponding to the analysis node is greater than zero, the analysis node is marked as a suspected associated node; If the impact change ratio corresponding to the analysis node is less than or equal to zero, no operation is performed; Obtain the impact change ratios corresponding to all suspected associated nodes, sum and average them to obtain the impact change ratio mean, perform difference processing on the impact change ratio corresponding to the suspected associated node and the impact change ratio mean, take the absolute value of the difference to obtain the impact change ratio difference, and compare the impact change ratio difference with the impact change ratio difference threshold; If the impact change ratio difference is less than or equal to the impact change ratio difference threshold, the suspected associated node is marked as an associated node; If the impact change ratio difference is greater than the impact change ratio difference threshold, no operation is performed; The number of associated nodes and the number of all analyzed nodes are counted and ratio processed to obtain the impact association value.

7. The optimization method for network video transmission according to claim 6, characterized in that: The specific process of obtaining the real-time transmission deviation change value and the real-time bandwidth proportion change value of the analysis node is as follows: Get the real-time transmission deviation value corresponding to the analysis node and the previous analysis node of the analysis node The real-time transmission deviation value is processed and the difference is performed to obtain the real-time transmission deviation change value. The real-time bandwidth proportion change value is obtained in the same way as the real-time transmission deviation change value is obtained.

8. The optimization method for network video transmission according to claim 1, characterized in that: The bandwidth control value is obtained as follows: Obtain the impact change ratios of all associated nodes, sum them up and take the average, and obtain the target impact change ratio; Obtaining a real-time transmission deviation value at the end of the abnormal transmission time, dividing the real-time transmission deviation value at the end of the abnormal transmission time by the target impact change ratio, to obtain a bandwidth control value; The bandwidth control rate is obtained as follows: Obtain the abnormal transmission time, perform difference processing on the abnormal transmission time and the abnormal transmission time threshold, and take the absolute value of the difference to obtain the control time; The bandwidth control value is ratioed to the control time to obtain the bandwidth control rate.

9. An optimization system for network video transmission, characterized in that: The system is used to implement any one of the optimization methods of claims 1 to 8, including: Data acquisition module: Based on the real-time transmission data of network video and the rate data of the network, the abnormal risk value of network video transmission is analyzed; Risk analysis module: based on the abnormal risk value of network video transmission, a transmission risk signal is obtained, wherein the transmission risk signal includes a high-risk transmission signal and a low-risk transmission signal; Impact analysis module: based on the transmission high-risk signal, obtain the bandwidth data of network video transmission, where the bandwidth data includes the real-time bandwidth share value, and obtain the impact association value based on the combined analysis of the bandwidth data and the real-time transmission deviation value. According to the impact association value, analyze whether the cause of the abnormal network video transmission is bandwidth fluctuation, and generate an impact signal, where the impact signal includes a strong impact association signal and a weak impact association signal; Work execution module: based on the impact signal, generates a corresponding feedback signal, wherein the feedback signal includes a detection signal and a control signal; If the generated impact signal is an impact-related weak signal, a detection signal is generated to detect other impact factors in the network video transmission process, including network congestion, equipment operation, and transmission protocol; If the generated influence signal is a strong influence correlation signal, a regulation signal is generated; Control optimization module: based on the generation of control signals, obtains control coefficients, and performs control optimization on network video transmission according to the control coefficients, wherein the control coefficients include bandwidth control values ​​and bandwidth control rates.

Images