CN116916113A - Data stream smoothing method based on 5G video customer service - Google Patents
Data stream smoothing method based on 5G video customer service Download PDFInfo
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N21/00—Selective content distribution, e.g. interactive television or video on demand [VOD]
- H04N21/60—Network structure or processes for video distribution between server and client or between remote clients; Control signalling between clients, server and network components; Transmission of management data between server and client, e.g. sending from server to client commands for recording incoming content stream; Communication details between server and client
- H04N21/63—Control signaling related to video distribution between client, server and network components; Network processes for video distribution between server and clients or between remote clients, e.g. transmitting basic layer and enhancement layers over different transmission paths, setting up a peer-to-peer communication via Internet between remote STB's; Communication protocols; Addressing
- H04N21/647—Control signaling between network components and server or clients; Network processes for video distribution between server and clients, e.g. controlling the quality of the video stream, by dropping packets, protecting content from unauthorised alteration within the network, monitoring of network load, bridging between two different networks, e.g. between IP and wireless
- H04N21/64784—Data processing by the network
- H04N21/64792—Controlling the complexity of the content stream, e.g. by dropping packets
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- H04N21/40—Client devices specifically adapted for the reception of or interaction with content, e.g. set-top-box [STB]; Operations thereof
- H04N21/43—Processing of content or additional data, e.g. demultiplexing additional data from a digital video stream; Elementary client operations, e.g. monitoring of home network or synchronising decoder's clock; Client middleware
- H04N21/442—Monitoring of processes or resources, e.g. detecting the failure of a recording device, monitoring the downstream bandwidth, the number of times a movie has been viewed, the storage space available from the internal hard disk
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- H04N21/00—Selective content distribution, e.g. interactive television or video on demand [VOD]
- H04N21/40—Client devices specifically adapted for the reception of or interaction with content, e.g. set-top-box [STB]; Operations thereof
- H04N21/43—Processing of content or additional data, e.g. demultiplexing additional data from a digital video stream; Elementary client operations, e.g. monitoring of home network or synchronising decoder's clock; Client middleware
- H04N21/442—Monitoring of processes or resources, e.g. detecting the failure of a recording device, monitoring the downstream bandwidth, the number of times a movie has been viewed, the storage space available from the internal hard disk
- H04N21/4424—Monitoring of the internal components or processes of the client device, e.g. CPU or memory load, processing speed, timer, counter or percentage of the hard disk space used
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- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N21/00—Selective content distribution, e.g. interactive television or video on demand [VOD]
- H04N21/60—Network structure or processes for video distribution between server and client or between remote clients; Control signalling between clients, server and network components; Transmission of management data between server and client, e.g. sending from server to client commands for recording incoming content stream; Communication details between server and client
- H04N21/63—Control signaling related to video distribution between client, server and network components; Network processes for video distribution between server and clients or between remote clients, e.g. transmitting basic layer and enhancement layers over different transmission paths, setting up a peer-to-peer communication via Internet between remote STB's; Communication protocols; Addressing
- H04N21/632—Control signaling related to video distribution between client, server and network components; Network processes for video distribution between server and clients or between remote clients, e.g. transmitting basic layer and enhancement layers over different transmission paths, setting up a peer-to-peer communication via Internet between remote STB's; Communication protocols; Addressing using a connection between clients on a wide area network, e.g. setting up a peer-to-peer communication via Internet for retrieving video segments from the hard-disk of other client devices
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- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N21/00—Selective content distribution, e.g. interactive television or video on demand [VOD]
- H04N21/60—Network structure or processes for video distribution between server and client or between remote clients; Control signalling between clients, server and network components; Transmission of management data between server and client, e.g. sending from server to client commands for recording incoming content stream; Communication details between server and client
- H04N21/63—Control signaling related to video distribution between client, server and network components; Network processes for video distribution between server and clients or between remote clients, e.g. transmitting basic layer and enhancement layers over different transmission paths, setting up a peer-to-peer communication via Internet between remote STB's; Communication protocols; Addressing
- H04N21/647—Control signaling between network components and server or clients; Network processes for video distribution between server and clients, e.g. controlling the quality of the video stream, by dropping packets, protecting content from unauthorised alteration within the network, monitoring of network load, bridging between two different networks, e.g. between IP and wireless
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Abstract
The invention relates to the technical field of video image processing, and provides a data stream smoothing processing method based on 5G video customer service, which comprises the following steps: acquiring relevant parameters of video customer service data flow at each moment, calculating the fluctuation change characteristics of the network throughput at each moment, further calculating and acquiring an overflow boundary and an underflow boundary of a buffer zone according to the fluctuation trend of the network throughput at each moment, acquiring a smooth code rate of the video customer service data flow according to the overflow boundary and the underflow boundary of the buffer zone, and carrying out smooth processing on the video customer service data flow according to the dynamic smooth code rate of the video customer service data flow. The invention realizes smooth and stable transmission processing of the video customer service data stream.
Description
Technical Field
The invention relates to the technical field of image data processing, in particular to a data stream smoothing processing method based on 5G video customer service.
Background
The mature development and wide application of 5G technology makes high-definition video data high-speed and low-delay transmission realistic. However, in the actual communication process, due to the blocking of the transmission channel barrier, the communication network quality is poor, so that the problems of overlong starting time of a customer service video signal, black screen, blocking, asynchronous audio and the like in the video data signal communication transmission process occur in the actual communication process of customer service users, and the actual experience of the customer service video communication is greatly influenced.
Disclosure of Invention
The invention provides a data stream smoothing processing method based on 5G video customer service, which aims to solve the problem of abnormal blocking of video customer service caused by code rate fluctuation in the video customer service data stream transmission process, and adopts the following technical scheme:
the invention relates to a data stream smoothing processing method based on 5G video customer service, which comprises the following steps:
acquiring relevant parameters of video customer service data streams at each moment;
acquiring a video customer service data stream network throughput slice according to the video customer service data stream network throughput, and acquiring a network throughput fluctuation change characteristic of the video customer service data stream at each moment according to the video customer service data stream network throughput slice;
calculating the network throughput fluctuation trend of the video customer service data at each moment according to the network throughput fluctuation change characteristics of the video customer service data stream at each moment, calculating the buffer filling degree of the video customer service data stream at each moment according to the network throughput fluctuation trend of the video customer service data at each moment, and calculating the buffer overflow boundary and the buffer underflow boundary of the video customer service data stream at each moment according to the buffer filling degree of the video customer service data stream at each moment;
and calculating the flow smooth code rate of the video customer service data stream according to the overflow boundary and the underflow boundary of the buffer area at each moment of the video customer service data stream, and adjusting the code rate of the video customer service data stream.
Preferably, the method for acquiring the relevant parameters of the video customer service data stream at each moment comprises the following steps:
the relevant parameters of the video customer service data stream at each moment specifically comprise the transmission code rate of the video customer service data stream at each moment, the network throughput of the video customer service data stream at each moment and the size of a video customer service data stream buffer zone at each moment.
Preferably, the method for obtaining the video customer service data stream network throughput slice according to the video customer service data stream network throughput comprises the following steps:
and taking the data at each moment in the throughput of the obtained video customer service data flow network as a starting point, backwardly taking all the data with the preset length to form a data slice, and not forming the data slice for the data points with the number of the data points backwardly from the starting point not meeting the preset length.
Preferably, the method for acquiring the fluctuation and change characteristics of the network throughput at each moment of the video customer service data stream comprises the following steps:
calculating the average value of network throughput at different moments in the video customer service data flow network throughput slice at the starting point of the current moment, recording the average value as a first average value, calculating the variance of network throughput at different moments in the video customer service data flow network throughput slice at the starting point of the current moment, recording the first variance, calculating the difference between the video customer service data flow network throughput at each different moment in the video customer service data flow network throughput slice at the starting point of the current moment and the first average value, recording the peak accumulated sum of the first difference value and the first difference value as a first accumulated sum, and recording the average value of the first accumulated sum as the fluctuation change characteristic of the network throughput at each moment of the video customer service data flow.
Preferably, the specific calculation method for the network throughput fluctuation trend at each moment of acquiring customer service data comprises the following steps:
in the above-mentioned formula(s),stability constant for network fluctuations, +.>Respectively indicate->Maximum value and +.about.H of video customer service data stream network throughput in video customer service data stream network throughput slice with each time position as starting point>First->Maximum value of video customer service data stream network throughput in video customer service data stream network throughput slice with each moment as starting point, +.>Respectively indicate->Minimum value and +.f. of video customer service data stream network throughput in video customer service data stream network throughput slice with each time position as starting point>First->Minimum value of video customer service data stream network throughput in video customer service data stream network throughput slice with each moment as starting point, < >> />Respectively indicate->Fluctuation change feature vector and +.f. of network throughput data slice in video customer service data flow network throughput slice with each moment position as starting point>First->Fluctuation variation feature vector of network throughput data slice in video customer service data flow network throughput slice with each moment as starting point>Shows the similarity between the two vectors, < >>Represents +.>Network throughput fluctuation trend at each moment.
Preferably, the method for obtaining the feature vector according to the fluctuation of the network throughput of the video customer service data stream comprises the following steps:
and arranging the network throughput fluctuation change characteristics calculated at each moment in the video customer service data stream network throughput slice with the current moment as a starting point according to the time sequence to obtain the video customer service data stream network throughput fluctuation change characteristic vector at the current moment.
Preferably, the method for calculating the filling degree of the video customer service data stream buffer zone comprises the following steps:
and calculating the difference between the size of the buffer area at the current moment and the average value of the size of the buffer area at all different moments before the current moment as a second difference value, taking the network throughput fluctuation trend value at all different moment positions before the current moment as input, obtaining the network throughput fluctuation trend value at the next moment by using a time sequence prediction algorithm, recording the network throughput fluctuation trend value as a first predicted value, calculating the normalization result of the ratio of the network throughput fluctuation trend value at the current moment to the first predicted value, and recording the normalization result as the filling degree of the video customer service data stream buffer area.
Preferably, the specific calculation method for the overflow boundary and the underflow boundary of the video customer service data stream buffer zone is as follows:
in the above-mentioned formula(s),representing a normalization function, ++>Time point +.>Total number of positions at all different times before, < ->Indicating +.>The largest buffer fluctuation difference coefficient at all previous different time positions,indicating +.>The smallest buffer fluctuation difference coefficient at all previous time positions,/>Represents +.>Buffer size at each instant +.>Represents +.>Overflow boundary of video customer service data stream buffer at each moment +.>Represents +.>Video customer service data stream buffer underflow boundaries at each instant.
Preferably, the method for calculating the fluctuation difference coefficient of the buffer area comprises the following steps:
and calculating the actual video customer service data stream buffer fullness and the predicted video customer service data stream buffer fullness by using the actual network throughput fluctuation trend value and the first predicted value at the next moment of each different moment before the current moment, and recording the difference value between the actual video customer service data stream buffer fullness and the predicted actual video customer service data stream buffer fullness as a buffer fluctuation difference coefficient.
Preferably, the method for calculating the smooth code rate of the video customer service data stream flow according to the overflow boundary and the underflow boundary of the buffer area at each moment of the video customer service data stream and adjusting the code rate of the video customer service data stream comprises the following steps:
if the current time buffer area is larger than or equal to the overflow boundary of the video customer service data stream buffer area, enabling the dynamic smooth code rate of the video customer service data stream to be a first preset code rate, if the current time buffer area is larger than the underflow boundary of the video customer service data stream buffer area and smaller than the overflow boundary of the video customer service data stream buffer area, enabling the dynamic smooth code rate of the video customer service data stream to be a second preset code rate, and if the current time buffer area is smaller than the underflow boundary of the video customer service data stream buffer area, enabling the dynamic smooth code rate of the video customer service data stream to be a third preset code rate, and adjusting the video customer service data stream based on the dynamic smooth code rate of the video customer service data stream.
The beneficial effects of the invention are as follows: the invention provides a data flow smoothing processing method based on 5G video customer service, which well characterizes network throughput variation conditions at different time positions through network throughput calculation at different time positions, and simultaneously well characterizes network throughput fluctuation trend at different time positions through network throughput fluctuation characteristic calculation, and further acquires buffer filling degree through network throughput variation conditions, performs calculation characterization on the buffer size in the video customer service data flow transmission process, and calculates smoothing of video customer service data flow through buffer filling degree and smoothing boundary of video customer service data flow, thereby effectively avoiding influence of incorrect data smooth transmission caused by inaccurate estimation of the buffer size in the video data smoothing process in the traditional algorithm based on buffer adjustment code rate.
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In order to more clearly illustrate the embodiments of the invention or the technical solutions of the prior art, the drawings which are used in the description of the embodiments or the prior art will be briefly described, it being obvious that the drawings in the description below are only some embodiments of the invention, and that other drawings can be obtained according to these drawings without inventive faculty for a person skilled in the art.
Fig. 1 is a flow chart of a data flow smoothing method based on 5G video customer service according to an embodiment of the present invention;
fig. 2 is a schematic diagram of dynamic smooth code rate of a video customer service data stream.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1, a flowchart of a data flow smoothing method based on 5G video customer service according to an embodiment of the present invention is shown, and the method includes the following steps:
and S001, acquiring relevant parameters of the video customer service data stream at each moment.
Assuming that the customer service video transmission process is a simple end-to-end transmission process, namely, the customer service video is transmitted from the user endTo the user side->And transmitting. Obtaining the transmission code rate of customer service video stream in the transmission process, and recording the transmission code rate as +.>Meanwhile, in order to analyze the network quality in the actual video data transmission process, the network throughput and the buffer area size at different moments are acquired, and the network throughput is recorded as +.>Obtaining the buffer area size at different moments。
Step S002, obtaining a network throughput slice according to the network throughput of the video customer service data stream, and obtaining the fluctuation and change characteristics of the network throughput at each moment according to the network throughput slice.
The network condition of the video customer service data stream is continuously changed along with the environment in the transmission process, at the moment, the throughput of the network is also fluctuated up and down along with time, and when the throughput of the network is suddenly reduced, the missed transmission and the wrong transmission of the customer service video data are easy to occur in the transmission process, so that the poor experience of blocking and blacking of the customer service video data is caused; otherwise, when the throughput of the network suddenly increases, if the code rate of the video customer service data stream transmission cannot be adjusted in time at this time, the throughput of the network cannot be fully utilized at this time, and the efficiency of the video customer service data stream transmission is affected. Therefore, it is first necessary to calculate the network throughput variation in the video customer service data stream transmission process.
In general, the network throughput will have corresponding fluctuation variation with time during transmission, and corresponding fluctuation variation trend will occur within a certain period of time, so that in order to facilitate further calculation of the network throughput variation, a length of each time point can be obtainedNetwork throughput data slice of +.>The empirical value is taken to be 16, two bytes long.
In the above-mentioned formula(s),representing +.>The mean size of all data in the network throughput data slice as starting point, +.>Expressed as time +.>Variance value size of all data in network throughput data slice as starting point, +.>Representing the +.sup.th in the data slice>The network throughput at each instant can be calculated by the above formula to obtain the instant +.>Fluctuation variation characteristic of network throughput data slice as starting point +.>Is a numerical value of (a).
When calculating the fluctuation variation characteristics of the network throughput data sliceThe larger the value is, the unstable network throughput change is shown at different moments in the corresponding network throughput data slice, and the larger difference exists in the network throughput values at different moments; on the contrary, if the fluctuation variation characteristic of the network throughput data slice calculated at the moment is +.>The smaller the value, the less obvious the difference of throughput values between different moments in the network throughput data slice at the moment, and the more stable the network state at the moment.
Step S003, calculating the buffer fullness according to the network throughput fluctuation trend calculated according to the network throughput fluctuation change characteristics, and calculating the buffer overflow boundary and the buffer underflow boundary according to the buffer fullness.
The fluctuation change characteristics of the network throughput data slices can be calculated and obtained for the network throughput data at different moments, the network throughput fluctuation sequences are rearranged according to the original moments, and in order to further analyze the trend of the network throughput change characteristics, calculation and analysis are carried out on the data change conditions of two adjacent data slices.
In the above-mentioned formula(s),stability constant for network fluctuations, +.>Respectively are provided withRepresents +.>Maximum value and +.about.H of video customer service data stream network throughput in video customer service data stream network throughput slice with each time position as starting point>First->Maximum value of video customer service data stream network throughput in video customer service data stream network throughput slice with each moment as starting point, +.>Respectively indicate->Minimum value and +.f. of video customer service data stream network throughput in video customer service data stream network throughput slice with each time position as starting point>First->Minimum value of video customer service data stream network throughput in video customer service data stream network throughput slice with each moment as starting point, < >> />Respectively indicate->Fluctuation change feature vector and +.f. of network throughput data slice in video customer service data flow network throughput slice with each moment position as starting point>First->Fluctuation variation feature vector of network throughput data slice in video customer service data flow network throughput slice with each moment as starting point>Shows the similarity between the two vectors, < >>Represents +.>Network throughput fluctuation trend at each moment.
The time point can be calculated by the formulaNetwork throughput fluctuation trend at ∈>The numerical value of (2) when +.>When the throughput variation in two adjacent data slices in each moment is obvious, the difference between the maximum and minimum peak values of the throughput calculated at the moment is large, and the similarity between the characteristic vectors of the fluctuation variation of the network throughput is small, the moment point calculated at the moment is +.>Network throughput fluctuation trend at ∈>The value is larger, which indicates the current moment +.>The more obvious the fluctuation trend of the network throughput is at the position, otherwise, when the change of the network throughput is stable, the fluctuation trend of the network throughput is calculated at the momentThe smaller the value.
The network throughput fluctuation trend values at different moments are obtained through the analysis and calculation, in the actual transmission communication process, in order to ensure the normal transmission of the video customer service data stream, a buffer area is generally arranged, and if the network throughput is suddenly increased in the video customer service data stream transmission process, a large amount of data cannot be received in the buffer area at one time, the buffer area overflows, and at the moment, the video customer service data stream cannot timely process the information loss of the video customer service data stream at certain moments, and abnormal blocking occurs; conversely, when the network throughput suddenly decreases, the buffer area is overflowed, and the effective video stream data in the buffer area is insufficient to support the normal running of customer service video at the next moment. Therefore, the video customer service data stream needs to be dynamically and smoothly regulated according to the network throughput change condition, so that the transmission code rate of the video customer service data stream is coordinated with the network throughput change condition in the current state, and buffer overflow or underflow in the video customer service data stream transmission process is avoided.
Assume the current timeThe network throughput fluctuation trend value is +.>The +.sup.th can be obtained by Holt prediction algorithm>Obtaining the network throughput fluctuation trend value at each moment +.>The value of the filling degree of the buffer area can be calculated according to the fluctuation trend of the network throughput, wherein the specific calculation method of the Holt prediction algorithm is a known technology and is not repeated here.
In the above-mentioned formula(s),representing a normalization function, ++>And->Respectively indicate->Time and thNetwork throughput fluctuation trend value size at time position, +.>Represents +.>Buffer size at the respective time instant position, < >>The minimum buffer size at all different times is shown. The +.sup.th can be obtained by the above formula>Buffer fullness at the respective time position>When the network throughput is reduced, the fluctuation difference of the network throughput at the front moment and the rear moment is obvious.
When the filling degree value of the buffer area calculated by the formula is larger, the buffer area at the current moment is larger than the minimum buffer area difference of all different moments, and meanwhile, if the fluctuation trend of the network throughput is smaller at the moment, data accumulation exists in the buffer area at the moment, so that the normal transmission of the video customer service data stream is influenced; otherwise, if the buffer area at the current moment is smaller than the minimum buffer area difference at all different moments, and the fluctuation trend of the network throughput is larger at the moment, the filling degree of the calculated buffer area is smaller at the moment, and the transmission of the video customer service data stream is not greatly influenced.
The filling degree of the buffer area at different time points can be obtained through the steps, and the change condition of the filling degree of the buffer area can dynamically adjust the change of the transmission code rate of the video customer service data stream.
In each moment of the video customer service data stream, the filling degree value of the buffer area at the next moment can be obtained through the predictive calculation of the formula, and the first moment is recordedThe difference coefficient of the predicted calculation value at each time position and the buffer fluctuation obtained by the actual network throughput is +.>。
In the above-mentioned formula(s),representing a normalization function, ++>Time point +.>Total number of positions at all different times before, < ->Indicating +.>The largest buffer fluctuation difference coefficient at all previous different time positions,indicating at the moment of time/>The smallest buffer fluctuation difference coefficient at all previous non-temporal positions, +.>Represents +.>Buffer size at each instant.
The first video customer service data stream can be calculated by the formulaBuffer overflow boundary at time instant +.>And buffer underflow boundary->Is a numerical value of (a). In order to avoid the phenomenon of uncoordinated network throughput, buffer area and video code rate of customer service video data in the transmission process, the buffer area overflow boundary and the buffer area underflow boundary are calculated according to the average change condition of the maximum and minimum values of the fluctuation difference coefficient of the buffer area at different moments.
And S004, calculating the flow smooth code rate of the video customer service data flow according to the overflow boundary and the underflow boundary of the buffer area at each moment of the video customer service data flow, and adjusting the code rate of the video customer service data flow.
On the premise of not affecting the transmission quality of the video customer service data stream, a proper code rate is required to be selected to transmit the video customer service data stream, so as shown in fig. 2, the invention dynamically smoothes the transmission code rate of the video customer service data stream according to the buffer zone.
In the above-mentioned formula(s),for a first code rate, its value is +.>The smooth communication of customer service videos can be ensured; />For the second code rate, its value is +.>Clear communication of customer service videos can be ensured; />A third code rate with a value of +.>Can ensure high-definition communication of customer service video, < >>And dynamically smoothing the code rate for the video customer service data stream.
The video customer service data stream transmission rate in the original state cannot meet the current network transmission state, so that video customer service data stream transmission is blocked, at this time, the video customer service data stream transmission process is dynamically and smoothly regulated through the video customer service data stream dynamic smooth rate, and the video customer service data stream transmission rate can quickly meet the current network transmission state through smoothing the current transmission rate in the transmission process with unstable network state, and the video customer service data stream is transmitted by using a 5G modulation method according to the dynamic rate, wherein the specific modulation process is a known technology and is not repeated here.
The foregoing description of the preferred embodiments of the invention is not intended to be limiting, but rather is intended to cover all modifications, equivalents, alternatives, and improvements that fall within the spirit and scope of the invention.
Claims (10)
1. A data stream smoothing processing method based on 5G video customer service is characterized by comprising the following steps:
acquiring relevant parameters of video customer service data streams at each moment;
acquiring a video customer service data stream network throughput slice according to the video customer service data stream network throughput, and acquiring a network throughput fluctuation change characteristic of the video customer service data stream at each moment according to the video customer service data stream network throughput slice;
calculating the network throughput fluctuation trend of the video customer service data at each moment according to the network throughput fluctuation change characteristics of the video customer service data stream at each moment, calculating the buffer filling degree of the video customer service data stream at each moment according to the network throughput fluctuation trend of the video customer service data at each moment, and calculating the buffer overflow boundary and the buffer underflow boundary of the video customer service data stream at each moment according to the buffer filling degree of the video customer service data stream at each moment;
and calculating the flow smooth code rate of the video customer service data stream according to the overflow boundary and the underflow boundary of the buffer area at each moment of the video customer service data stream, and adjusting the code rate of the video customer service data stream.
2. The method for smoothing data flow based on 5G video customer service according to claim 1, wherein the method for obtaining relevant parameters of video customer service data flow at each moment is as follows:
the relevant parameters of the video customer service data stream at each moment specifically comprise the transmission code rate of the video customer service data stream at each moment, the network throughput of the video customer service data stream at each moment and the size of a video customer service data stream buffer zone at each moment.
3. The method for smoothing data flow based on 5G video customer service according to claim 1, wherein the method for obtaining video customer service data flow network throughput slices according to video customer service data flow network throughput is as follows:
and taking the data at each moment in the throughput of the obtained video customer service data flow network as a starting point, backwardly taking all the data with the preset length to form a data slice, and not forming the data slice for the data points with the number of the data points backwardly from the starting point not meeting the preset length.
4. The method for smoothing data flow based on 5G video customer service according to claim 2, wherein the method for obtaining the network throughput fluctuation variation characteristics of each moment of the video customer service data flow is as follows:
calculating the average value of network throughput at different moments in the video customer service data flow network throughput slice at the starting point of the current moment, recording the average value as a first average value, calculating the variance of network throughput at different moments in the video customer service data flow network throughput slice at the starting point of the current moment, recording the first variance, calculating the difference between the video customer service data flow network throughput at each different moment in the video customer service data flow network throughput slice at the starting point of the current moment and the first average value, recording the peak accumulated sum of the first difference value and the first difference value as a first accumulated sum, and recording the average value of the first accumulated sum as the fluctuation change characteristic of the network throughput at each moment of the video customer service data flow.
5. The method for smoothing data flow based on 5G video customer service according to claim 3, wherein the specific calculation method for the network throughput fluctuation trend at each moment of acquiring the customer service data is as follows:
in the above-mentioned formula(s),stability constant for network fluctuations, +.>Respectively indicate->Maximum value and +.about.H of video customer service data stream network throughput in video customer service data stream network throughput slice with each time position as starting point>Last timeMaximum value of video customer service data stream network throughput in video customer service data stream network throughput slice with each moment as starting point, +.>Respectively indicate->Minimum value and +.f. of video customer service data stream network throughput in video customer service data stream network throughput slice with each time position as starting point>First->Minimum value of video customer service data stream network throughput in video customer service data stream network throughput slice with each moment as starting point, < >> Respectively indicate->Fluctuation change feature vector and +.f. of network throughput data slice in video customer service data flow network throughput slice with each moment position as starting point>First->Fluctuation variation feature vector of network throughput data slice in video customer service data flow network throughput slice with each moment as starting point>Shows the similarity between the two vectors, < >>Represents +.>Network throughput fluctuation trend at each moment.
6. The method for smoothing data flow based on 5G video customer service according to claim 4, wherein the method for obtaining the feature vector according to the fluctuation of network throughput of the video customer service data flow is as follows:
and arranging the network throughput fluctuation change characteristics calculated at each moment in the video customer service data stream network throughput slice with the current moment as a starting point according to the time sequence to obtain the video customer service data stream network throughput fluctuation change characteristic vector at the current moment.
7. The method for smoothing data flow based on 5G video customer service according to claim 4, wherein the method for calculating the filling degree of the video customer service data flow buffer area is as follows:
calculating the difference between the buffer size at the current moment and the average value of the buffer sizes at all different moments before the current moment as a second difference value,
and taking the network throughput fluctuation trend values at all the positions at different moments before the current moment as input, obtaining the network throughput fluctuation trend value at the next moment by using a time sequence prediction algorithm, marking the network throughput fluctuation trend value as a first predicted value, calculating the normalization result of the ratio of the network throughput fluctuation trend value at the current moment to the first predicted value, and marking the normalization result as the filling degree of the video customer service data stream buffer area.
8. The method for smoothing data flow based on 5G video customer service according to claim 6, wherein the specific calculation method for the overflow boundary and the underflow boundary of the video customer service data flow buffer zone is as follows:
in the above-mentioned formula(s),representing a normalization function, ++>Time point +.>Total number of positions at all different times before, < ->Indicating +.>The largest buffer fluctuation difference coefficient at all previous different time positions,indicating +.>The smallest buffer fluctuation difference coefficient at all previous time positions,/>Represents +.>Buffer size at each instant +.>Represents +.>Overflow boundary of video customer service data stream buffer at each moment +.>Represents +.>Video customer service data stream buffer underflow boundaries at each instant.
9. The method for smoothing data flow based on 5G video customer service according to claim 7, wherein the method for calculating the buffer fluctuation difference coefficient is as follows:
and calculating the actual video customer service data stream buffer fullness and the predicted video customer service data stream buffer fullness by using the actual network throughput fluctuation trend value and the first predicted value at the next moment of each different moment before the current moment, and recording the difference value between the actual video customer service data stream buffer fullness and the predicted actual video customer service data stream buffer fullness as a buffer fluctuation difference coefficient.
10. The method for smoothing data flow based on 5G video customer service according to claim 1, wherein the method for calculating a dynamic smoothing code rate of the video customer service data flow and adjusting the code rate of the video customer service data flow according to the buffer overflow boundary and the buffer underflow boundary at each moment of the video customer service data flow comprises the following steps:
if the current time buffer area is larger than or equal to the overflow boundary of the video customer service data stream buffer area, enabling the dynamic smooth code rate of the video customer service data stream to be a first preset code rate, if the current time buffer area is larger than the underflow boundary of the video customer service data stream buffer area and smaller than the overflow boundary of the video customer service data stream buffer area, enabling the dynamic smooth code rate of the video customer service data stream to be a second preset code rate, and if the current time buffer area is smaller than the underflow boundary of the video customer service data stream buffer area, enabling the dynamic smooth code rate of the video customer service data stream to be a third preset code rate, and adjusting the video customer service data stream based on the dynamic smooth code rate of the video customer service data stream.
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