Adaptive code rate control method and system based on ultra-high definition video

Abstract

The application discloses a deep learning-based adaptive code rate control method and system for ultra-high-definition video, and relates to the technical field of video coding and transmission. The application solves the problem that traditional coding behavior is prone to causing data congestion or frame loss due to network space fluctuation. The application accurately calculates characteristic flow by comprehensively considering the current total buffer amount of network nodes and the video frame flow to be processed, further determines the network interval required by the processing period, determines the data amount that can be processed in the processing period based on the network interval, and precisely controls the interaction flow of multiple path nodes by analyzing the past data characteristics of the video stream path nodes and other nodes. The application not only can reasonably allocate network resources to prevent data congestion of network nodes due to excessive flow, but also can ensure that the video code rate reaches the best state in the adjustment and verification process, thereby improving the video transmission efficiency and quality.

Description

Technical Field

[0001] This invention relates to the field of video encoding and transmission technology, specifically to an adaptive bitrate control method and system for ultra-high-definition video. Background Technology

[0002] With the rapid development of internet technology and the widespread adoption of smart devices, ultra-high-definition video is increasingly being used in people's daily lives. For example, 4K, 8K, and even higher resolution video content is used in online video playback, video conferencing, virtual reality (VR) / augmented reality (AR) fields. Ultra-high-definition video, with its delicate image quality, rich details, and realistic visual effects, brings users an unprecedented immersive experience and is gradually becoming the mainstream development direction in the video field.

[0003] Ultra-high-definition video, due to its extremely high resolution and frame rate, generates massive amounts of data, placing stringent demands on network transmission bandwidth. In real-world network environments, network conditions are complex and constantly changing, with bandwidth fluctuating dynamically. This is influenced by various factors, including network congestion, changes in the number of users, network service provider scheduling strategies, and differences in network load at different times. For example, during peak network usage periods, a large number of users are online simultaneously, leading to strained network bandwidth resources and potential congestion. Furthermore, differences in network infrastructure across different regions can result in variations in bandwidth stability.

[0004] Traditional video bitrate control methods often exhibit significant limitations when dealing with applications like ultra-high-definition video, which involve large data volumes and high real-time requirements. Some fixed bitrate control strategies cannot dynamically adjust to real-time changes in network bandwidth. When network bandwidth is insufficient, videos frequently experience stuttering, slow loading, or even become unplayable, severely impacting the user experience. On the other hand, simple adaptive bitrate algorithms, lacking in-depth analysis and accurate prediction of network conditions, struggle to effectively adapt to ultra-high-definition video bitrates in complex and ever-changing network environments. They fail to fully utilize network bandwidth resources, potentially resulting in an unsatisfactory balance between video quality and smoothness. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides an adaptive bitrate control method and system for ultra-high-definition video, which solves the problem that traditional encoding methods are prone to data congestion or frame loss due to network space fluctuations.

[0006] To achieve the above objectives, the present invention provides the following technical solution: an adaptive bitrate control method for ultra-high-definition video, comprising the following steps:

[0007] Step 1: Identify the network node associated with this ultra-high-definition video. Based on the historical operational data of this network node, identify and record the different bandwidth characteristics associated with different buffer sizes within this network node. The specific method is as follows:

[0008] Step 11: From the historical operation data of this network node, identify the different bandwidth values ​​associated with different cache amounts in the cache area, and take the several sets of bandwidth values ​​associated with a single cache amount as the bandwidth set of this cache amount.

[0009] Step 12: Confirm the bandwidth characteristics of the bandwidth set of a single cache size: Sort several sets of bandwidth values ​​in the bandwidth set in ascending order and confirm the sorting sequence;

[0010] A numerical segment is randomly selected from the sorted sequence, and its characteristics are confirmed to lock the first bandwidth value D1 of the numerical segment. i and the final bandwidth value D2 i Using D2 i -D1 i =Ca i Confirm the range of values ​​for Ca. i Then, the total number of bandwidth values ​​included in this range is denoted as G. i Using Ca i ÷G i =M i , where i represents different numerical ranges;

[0011] Will satisfy: M i The numerical range ≥Y1 is defined as the calibration numerical range, where Y1 is a preset value;

[0012] Step 13: From the confirmed range of calibration values, select Ca... i The calibrated value segment associated with max is denoted as the characteristic value segment. Based on the first and last bandwidth values ​​associated with the characteristic value segment, the bandwidth characteristic associated with this cache size is determined. This bandwidth characteristic is a characteristic interval. If there are identical Ca values... i If there are multiple sets of calibration value segments for max, then a set of calibration value segments is randomly selected as the feature value segment and the feature interval is confirmed.

[0013] Step 2: Based on the recorded bandwidth characteristics corresponding to different buffer sizes, determine the current video frame traffic to be processed for ultra-high-definition video, and based on the total buffer size of this network node at the current moment, determine the network range that the processing period needs to reach. The specific method is as follows:

[0014] Step 21: Define the total buffer size associated with this network node at the current time as HH, and define the video frame traffic LH to be processed for this ultra-high-definition video at the current time as ZL=LH+HH to confirm the characteristic value ZL associated with the current time. Then, based on the current time, confirm the unit traffic DL processed by the corresponding network node in the time period between the previous time and the current time, and confirm the characteristic traffic associated with the corresponding network node as ZL-DL=TL.

[0015] Step 22: Based on the confirmed characteristic traffic and the different bandwidth characteristics corresponding to different buffer amounts, confirm the bandwidth characteristics corresponding to this characteristic traffic and mark it as the network range that needs to be reached during the processing period.

[0016] Step 3: Based on the network interval associated with the processing time period, determine the amount of data that the processing time period can handle. Then, based on the past data characteristics of video stream path nodes and other nodes, perform flow control on multiple path nodes with data interaction, and simultaneously monitor the video frame traffic of the video stream path nodes to identify whether there is frame dropping during the interaction process. The specific method is as follows:

[0017] Step 31: Based on the determined network interval, directly lock the range of data volume that can be processed within the processing time period;

[0018] Step 32: Using the current time as the time reference, identify the data characteristics associated with other path nodes that have data interactions within this network node in past time periods, and label the interaction volume associated with other path nodes at the current time as L. k , where k represents different other path nodes;

[0019] For a single group of nodes along other path nodes: extract the interaction quantity from the data features associated with the single group of nodes, and satisfy the condition: interaction quantity ≤ L k The data features are selected as the selected features;

[0020] Then, identify the interaction volume and cache volume associated with different times from the selected features. Use the formula: interaction volume ÷ (interaction volume + cache volume) = standard feature to identify the standard features associated with different interaction volumes. Select the maximum value from several standard features and use the maximum value as the reference value for the interaction volume associated with it.

[0021] For other path nodes, the same processing method is used to sequentially confirm the reference quantities associated with the corresponding path nodes;

[0022] Step 33: Based on the baseline data volume confirmed by other path nodes and the data volume range associated with the processing time period, confirm the data volume range of the video stream path nodes within the processing time period:

[0023] Several baseline quantities are summed to confirm the total baseline characteristics of other path nodes and denoted as Zz. The associated data volume interval is denoted as [L1, L2]. The data volume interval [SJ1, SJ2] of the processing period is confirmed by using L1-Zz=SJ1 and L2-Zz=SJ2.

[0024] Identify and confirm whether there are negative values ​​in this data range [SJ1, SJ2]. If they exist, generate a baseline adjustment signal and execute Step 34 to reconfirm the data range. If they do not exist, mark this data range as a defined range.

[0025] Step 34: Based on the baseline values ​​confirmed by other path nodes, synchronously reduce the baseline values. For each set of reduction processes, the baseline value is reduced by one unit value. Stop when there are no negative values ​​in the data column interval corresponding to the total baseline feature associated with several sets of baseline values. Record the current reduced baseline value and synchronously mark the corresponding data interval as a defined interval.

[0026] Step 35: During the processing period, limit the interaction traffic of other path nodes based on the baseline data recorded by other path nodes. Interact with video frame traffic through video stream path nodes, and monitor the interaction traffic during the interaction process to identify any frame drops. Based on the identification results, quickly fill in the lost bytes of data. Specifically:

[0027] Step 351: Based on the defined interval, monitor the interactive traffic associated with the video stream path nodes within the processing time period in real time, identify interactive traffic that does not belong to this defined interval and record it as abnormal traffic, record the specific time associated with the abnormal traffic as the abnormal time, mark the byte data associated with the abnormal time as pending data, and confirm the sequence number of the corresponding pending data in turn.

[0028] Step 352: Based on the chronological order, sort the sequence numbers of the data to be determined, confirm the sequence number sequence, and determine whether adjacent sequence numbers satisfy the following condition: the next sequence number = the previous sequence number + 1. If not, generate a missing sequence number signal, and based on the difference between adjacent sequence numbers, lock the missing sequence number. The method for determining the missing sequence number is as follows:

[0029] The next set of adjacent sequence numbers is labeled as XL1, and the previous set of sequence numbers is labeled as XL2. The missing feature Tz is confirmed by XL1-XL2=Tz. Then, the missing sequence number is defined by XL2+(Tz-j), where j is a positive integer and 1≤j<Tz.

[0030] Based on the confirmed missing sequence number, the byte data associated with the missing sequence number is filled in in real time.

[0031] Preferably, in Step 352, if the adjacent sequence numbers satisfy the condition that the next sequence number = the previous sequence number + 1, then the next group of undetermined data is marked as correct. For the undetermined data that is ranked first, the sequence number of the byte data of the previous moment of the first abnormal moment is selected as the benchmark.

[0032] Preferably, the ultra-high-definition video adaptive bitrate control system includes:

[0033] The bandwidth characteristic confirmation end identifies the network node associated with this ultra-high-definition video. Based on the historical operating data of this network node, it identifies and records the different bandwidth characteristics associated with different buffer volumes within this network node.

[0034] The network interval confirmation end, based on the recorded bandwidth characteristics corresponding to different buffer amounts, confirms the video frame traffic to be processed for ultra-high-definition video at the current moment, and based on the total buffer amount of this network node at the current moment, confirms the network interval that the processing period needs to reach.

[0035] On the flow control end, based on the network interval associated with the processing period, the amount of data that can be processed in the processing period is determined, and then based on the past data characteristics of the video stream path nodes and other nodes, flow control is performed on multiple path nodes that have data interaction.

[0036] The video node monitoring terminal interacts with video frame traffic through video stream path nodes, and monitors the interaction traffic during the interaction process to identify whether there are frame drops during the interaction, and quickly fills in the lost byte data based on the identification results.

[0037] This invention provides a method and system for adaptive bitrate control based on ultra-high-definition video. Compared with existing technologies, it has the following advantages:

[0038] This invention determines the bandwidth fluctuation characteristic range corresponding to different cache amounts by meticulously analyzing the correlation between cache amount and bandwidth characteristics in the historical operation data of network nodes. This measure enables more targeted adjustment of bitrate based on real-time network conditions during video bitrate adjustment, significantly improving the accuracy and effectiveness of bitrate adjustment, thereby ensuring smooth playback of ultra-high-definition video in complex network environments.

[0039] By comprehensively considering the current total buffer size of network nodes and the traffic of video frames to be processed, the characteristic traffic is accurately calculated, and the network interval that needs to be reached during the processing period is further determined. Based on the network interval, the amount of data that can be processed during the processing period is determined. By analyzing the past data characteristics of video stream path nodes and other nodes, the interaction traffic of multiple path nodes is precisely controlled. This not only enables reasonable allocation of network resources and prevents data congestion at network nodes due to excessive traffic, but also ensures that the video bitrate reaches the optimal state during the adjustment and verification process, thereby improving video transmission efficiency and quality.

[0040] By setting a defined interval to monitor the interactive traffic of video stream path nodes in real time, abnormal traffic and frame drops can be quickly identified. By using sequence number sorting and calculation to accurately locate missing sequence numbers and promptly fill in missing bytes, the burden of data interaction monitoring is greatly reduced, while ensuring the smoothness of ultra-high-definition video playback, avoiding stuttering, and effectively improving the user viewing experience. Attached Figure Description

[0041] Figure 1 This is a schematic diagram of the method flow of the present invention;

[0042] Figure 2 This is a schematic diagram of the principle framework of the present invention. Detailed Implementation

[0043] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0044] First Embodiment

[0045] Please see Figure 1 This application provides an adaptive bitrate control method based on ultra-high-definition video, including the following steps:

[0046] Step 1: Identify the network node associated with this ultra-high-definition video. Based on the historical operating data of this network node, identify and record the different bandwidth characteristics associated with different buffer sizes within this network node. Specifically, the reason why there are specific network fluctuations in the corresponding network node is generally due to excessive buffer size, causing data congestion and thus causing corresponding bandwidth fluctuations. In order to effectively adjust the bitrate of this ultra-high-definition video, it is necessary to determine the corresponding bandwidth fluctuation characteristics in advance for effective adjustment.

[0047] The specific method for confirming the different bandwidth characteristics associated with different cache characteristics of network nodes is as follows:

[0048] Step 11: From the historical operation data of this network node, identify the different bandwidth values ​​associated with different cache amounts in the cache area, and take the several sets of bandwidth values ​​associated with a single cache amount as the bandwidth set of this cache amount.

[0049] Step 12: Confirm the bandwidth characteristics of the bandwidth set of a single cache size: Sort several sets of bandwidth values ​​in the bandwidth set in ascending order and confirm the sorting sequence;

[0050] A numerical segment is randomly selected from the sorted sequence, and its characteristics are confirmed to lock the first bandwidth value D1 of the numerical segment. i and the final bandwidth value D2 i Using D2 i -D1 i =Ca i Confirm the range of values ​​for Ca. i Then, the total number of bandwidth values ​​included in this range is denoted as G. i Using Ca i ÷G i =M i , where i represents different numerical ranges;

[0051] Will satisfy: M i The numerical range ≥Y1 is defined as the calibration numerical range, where Y1 is a preset value, and its specific value is determined by the operator based on experience.

[0052] Step 13: From the confirmed range of calibration values, select Ca... i The calibrated value segment associated with max is denoted as the characteristic value segment. Based on the first and last bandwidth values ​​associated with the characteristic value segment, the bandwidth characteristic associated with this cache size is determined. This bandwidth characteristic is a characteristic interval. If there are identical Ca values... i If there are multiple sets of calibration value segments for max, then a set of calibration value segments is randomly selected as the feature value segment and the feature interval is confirmed.

[0053] Specifically, its characteristic range corresponds to the bandwidth fluctuation characteristics of the corresponding cache size. In the subsequent processing, the video stream bitrate can be adjusted based on the corresponding bandwidth fluctuation characteristics. Moreover, the bandwidth fluctuation characteristics confirmed by the corresponding cache size are obvious and highly characteristic, which facilitates subsequent feature processing and confirmation.

[0054] Step 2: Based on the recorded bandwidth characteristics corresponding to different cache sizes, determine the current video frame traffic to be processed for ultra-high-definition video. Based on the total cache size of this network node at the current moment, determine the network range that the processing period needs to reach. Specifically, at the current moment, the corresponding network node has a caching process, and its caching process still contains relevant data from previous stages that were not cached. Therefore, based on the past data and the video data to be processed now, the specific network bandwidth is determined. The specific method for determining the network range is as follows:

[0055] Step 21: Define the total buffer size associated with this network node at the current moment as HH, and determine the characteristic value ZL associated with this ultra-high-definition video at the current moment as LH using the formula: ZL = LH + HH. Then, based on the current moment, determine the unit traffic DL processed by the corresponding network node in the time period between the previous moment and the current moment, and determine the characteristic traffic associated with the corresponding network node using the formula: ZL - DL = TL (that is, the buffer size that the corresponding network node may generate in the next moment, that is, the specific buffer size between the current moment and the next moment, which will affect the corresponding bandwidth characteristics).

[0056] Step 22: Based on the confirmed characteristic traffic and the different bandwidth characteristics corresponding to different buffer amounts, confirm the bandwidth characteristics corresponding to this characteristic traffic and mark it as the network range that needs to be reached during the processing period. Specifically, different traffic values ​​have different bandwidth characteristics, which will cause different bandwidth fluctuations. In order to confirm the bitrate characteristics associated with different bandwidth conditions, specific adjustments are made.

[0057] Step 3: Based on the network interval associated with the processing time period, determine the amount of data that the processing time period can process. Then, based on the past data characteristics of video stream path nodes and other nodes, perform flow control on multiple path nodes with data interaction to ensure that the video bitrate reaches the optimal adjustment and verification state during the actual adjustment and verification process. The specific processing method for this adjustment is as follows:

[0058] Step 31: Based on the determined network interval, directly lock the range of data volume that can be processed within the processing time period (the bandwidth of the network interval is directly related to the corresponding data transmission capacity, so the data volume can be directly confirmed).

[0059] Step 32: Using the current time as the time reference, identify the data characteristics associated with other path nodes that have data interactions within this network node in past time periods, and label the interaction volume associated with other path nodes at the current time as L. k , where k represents different other path nodes;

[0060] For a single group of nodes along other path nodes: extract the interaction quantity from the data features associated with the single group of nodes, and satisfy the condition: interaction quantity ≤ L k The data features are selected as the selected features;

[0061] Then, identify the interaction volume and cache volume associated with different times from the selected features. Use the formula: interaction volume ÷ (interaction volume + cache volume) = standard feature to identify the standard features associated with different interaction volumes. Select the maximum value from several standard features and use the maximum value as the reference value for the interaction volume associated with it.

[0062] For other path nodes, the same processing method is used to sequentially confirm the reference quantities associated with the corresponding path nodes;

[0063] Step 33: Based on the baseline data volume confirmed by other path nodes and the data volume range associated with the processing time period, confirm the data volume range of the video stream path nodes within the processing time period:

[0064] Several baseline quantities are summed to confirm the total baseline characteristics of other path nodes and denoted as Zz. The associated data volume interval is denoted as [L1, L2]. The data volume interval [SJ1, SJ2] of the processing period is confirmed by using L1-Zz=SJ1 and L2-Zz=SJ2.

[0065] Identify and confirm whether there are negative values ​​in this data range [SJ1, SJ2]. If they exist, generate a baseline adjustment signal and execute Step 34 to reconfirm the data range. If they do not exist, mark this data range as a defined range.

[0066] Step 34: Based on the baseline values ​​confirmed by other path nodes, synchronously reduce the baseline values. For each set of reduction processes, the baseline value is reduced by one unit value (the unit value is a preset value, determined by the operator based on experience, generally 10 bytes). Stop when there are no negative values ​​in the data column interval corresponding to the total baseline feature associated with several sets of baseline values ​​after reduction. Record the current reduced baseline value and synchronously mark the corresponding data interval as the defined interval.

[0067] Step 35: During the processing period, limit the interaction traffic of other path nodes based on the baseline data recorded by other path nodes (i.e., the maximum interaction traffic cannot exceed the recorded baseline data). Interact with video frame traffic through video stream path nodes, and monitor the interaction traffic during the interaction process to identify whether frame drops occur (real-time monitoring is performed based on a defined interval, which is a baseline; when video stream traffic data has frame drops or other issues, it will exceed or fall below this defined interval). The specific method for identification is as follows:

[0068] Step 351: Based on the defined interval, monitor the interactive traffic associated with the video stream path nodes within the processing time period in real time, identify interactive traffic that does not belong to this defined interval and record it as abnormal traffic, record the specific time associated with the abnormal traffic as the abnormal time, mark the byte data associated with the abnormal time as pending data, and sequentially confirm the sequence number of the corresponding pending data (the byte data inside the data or each frame is marked with a sequence number in advance before sending to facilitate subsequent frame verification).

[0069] Step 352: Based on the chronological order, sort the sequence numbers of the pending data, confirm the sequence number sequence, and determine whether adjacent sequence numbers satisfy the following condition: the next sequence number = the previous sequence number + 1. If satisfied, the next group of pending data is marked as correct. For the pending data at the top, select the sequence number of the byte data before the first abnormal time as the benchmark (that is, among the pending data, the sequence number of the previous group of data is used to evaluate whether the sequence number of the next group of data meets the standard). If not satisfied, generate a missing sequence number signal, and based on the difference between adjacent sequence numbers, lock the missing sequence number. The method for determining the missing sequence number is as follows:

[0070] The next set of adjacent sequence numbers is labeled as XL1, and the previous set of sequence numbers is labeled as XL2. The missing feature Tz is confirmed by XL1-XL2=Tz. Then, the missing sequence number is defined by XL2+(Tz-j), where j is a positive integer and 1≤j<Tz.

[0071] Based on the confirmed missing sequence number, the byte data associated with the missing sequence number is filled in in real time to ensure the smoothness of the corresponding ultra-high-definition video during playback and prevent stuttering.

[0072] Based on the confirmed range, the monitoring burden during data interaction can be effectively reduced. Only the corresponding interaction traffic needs to be monitored, without the need for real-time monitoring of the sequence number. Based on the existence time of the corresponding abnormal traffic, the missing byte data can be quickly located and quickly supplemented, thereby achieving a faster and more effective data filling process. This not only ensures the normal playback of ultra-high-definition video, but also achieves a more effective data loss monitoring effect.

[0073] Second Embodiment

[0074] Combination Figure 2 Based on an ultra-high-definition video adaptive bitrate control system, including:

[0075] The bandwidth characteristic confirmation end identifies the network node associated with this ultra-high-definition video. Based on the historical operating data of this network node, it identifies and records the different bandwidth characteristics associated with different buffer volumes within this network node.

[0076] The network interval confirmation end, based on the recorded bandwidth characteristics corresponding to different buffer amounts, confirms the video frame traffic to be processed for ultra-high-definition video at the current moment, and based on the total buffer amount of this network node at the current moment, confirms the network interval that the processing period needs to reach.

[0077] On the flow control end, based on the network interval associated with the processing period, the amount of data that can be processed in the processing period is determined, and then based on the past data characteristics of the video stream path nodes and other nodes, flow control is performed on multiple path nodes that have data interaction.

[0078] The video node monitoring terminal interacts with video frame traffic through video stream path nodes, and monitors the interaction traffic during the interaction process to identify whether there are frame drops during the interaction, and quickly fills in the lost byte data based on the identification results.

[0079] Some of the data in the above formulas are numerical calculations with dimensions removed, and the contents not described in detail in this specification are all prior art known to those skilled in the art.

[0080] The above embodiments are only used to illustrate the technical methods of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical methods of the present invention without departing from the spirit and scope of the technical methods of the present invention.

Claims

1. A method for adaptive bitrate control based on ultra-high-definition video, characterized in that, Includes the following steps: Step 1: Identify the network node associated with this ultra-high-definition video. Based on the historical operating data of this network node, identify and record the different bandwidth characteristics associated with different buffer volumes within this network node. Step 2: Based on the recorded bandwidth characteristics corresponding to different buffer sizes, determine the current video frame traffic to be processed for ultra-high-definition video, and based on the total buffer size of this network node at the current moment, determine the network range that the processing period needs to reach. The specific method is as follows: Step 21: Define the total buffer size associated with this network node at the current time as HH, and define the video frame traffic LH to be processed for this ultra-high-definition video at the current time as ZL=LH+HH to confirm the characteristic value ZL associated with the current time. Then, based on the current time, confirm the unit traffic DL processed by the corresponding network node in the time period between the previous time and the current time, and confirm the characteristic traffic associated with the corresponding network node as ZL-DL=TL. Step 22: Based on the confirmed characteristic traffic and the different bandwidth characteristics corresponding to different buffer amounts, confirm the bandwidth characteristics corresponding to this characteristic traffic and mark it as the network range that needs to be reached during the processing period. Step 3: Based on the network interval associated with the processing time period, determine the amount of data that the processing time period can handle. Then, based on the past data characteristics of video stream path nodes and other nodes, perform flow control on multiple path nodes with data interaction, and simultaneously monitor the video frame traffic of the video stream path nodes to identify whether there is frame dropping during the interaction process. The specific method is as follows: Step 31: Based on the determined network interval, directly lock the range of data volume that can be processed within the processing time period; Step 32: Using the current time as the time reference, identify the data characteristics associated with other path nodes that have data interactions within this network node in past time periods, and label the interaction volume associated with other path nodes at the current time as L. k , where k represents different other path nodes; For a single group of nodes along other path nodes: extract the interaction quantity from the data features associated with the single group of nodes, and satisfy the condition: interaction quantity ≤ L k The data features are selected as the selected features; Then, identify the interaction volume and cache volume associated with different times from the selected features. Use the formula: interaction volume ÷ (interaction volume + cache volume) = standard feature to identify the standard features associated with different interaction volumes. Select the maximum value from several standard features and use the maximum value as the reference value for the interaction volume associated with it. For other path nodes, the same processing method is used to sequentially confirm the reference quantities associated with the corresponding path nodes; Step 33: Based on the baseline data volume confirmed by other path nodes and the data volume range associated with the processing time period, confirm the data volume range of the video stream path nodes within the processing time period: Several baseline quantities are summed to confirm the total baseline characteristics of other path nodes and denoted as Zz. The associated data volume interval is denoted as [L1, L2]. The data volume interval [SJ1, SJ2] of the processing period is confirmed by using L1-Zz=SJ1 and L2-Zz=SJ2. Identify and confirm whether there are negative values ​​in this data range [SJ1, SJ2]. If they exist, generate a baseline adjustment signal and execute Step 34 to reconfirm the data range. If they do not exist, mark this data range as a defined range. Step 34: Based on the baseline values ​​confirmed by other path nodes, synchronously reduce the baseline values. For each set of reduction processes, the baseline value is reduced by one unit value. Stop when there are no negative values ​​in the data column interval corresponding to the total baseline feature associated with several sets of baseline values. Record the current reduced baseline value and synchronously mark the corresponding data interval as a defined interval. Step 35: During the processing period, limit the interaction traffic of other path nodes based on the baseline data recorded by other path nodes, and interact with video frame traffic through video stream path nodes. During the interaction process, monitor the interaction traffic to identify whether there are any frame drops during the interaction, and quickly fill in the lost byte data based on the identification results.

2. The method for adaptive bitrate control based on ultra-high-definition video according to claim 1, characterized in that, In Step 1, the specific method for confirming the different bandwidth characteristics associated with different cache sizes is as follows: Step 11: From the historical operation data of this network node, identify the different bandwidth values ​​associated with different cache amounts in the cache area, and take the several sets of bandwidth values ​​associated with a single cache amount as the bandwidth set of this cache amount. Step 12: Confirm the bandwidth characteristics of the bandwidth set of a single cache size: Sort several sets of bandwidth values ​​in the bandwidth set in ascending order and confirm the sorting sequence; A numerical segment is randomly selected from the sorted sequence, and its characteristics are confirmed to lock the first bandwidth value D1 of the numerical segment. i and the final bandwidth value D2 i Using D2 i -D1 i =Ca i Confirm the range of values ​​for Ca. i Then, the total number of bandwidth values ​​included in this range is denoted as G. i Using Ca i ÷G i =M i , where i represents different numerical ranges; Will satisfy: M i The numerical range ≥Y1 is defined as the calibration numerical range, where Y1 is a preset value; Step 13: From the confirmed range of calibration values, select Ca... i The calibration value segment associated with max is called the feature value segment. Based on the first and last bandwidth values ​​associated with the feature value segment, the bandwidth feature associated with this cache size is confirmed, and its bandwidth feature is a feature interval.

3. The method for adaptive bitrate control based on ultra-high-definition video according to claim 2, characterized in that, In Step 13, if there are identical Ca i If there are multiple sets of calibration value segments for max, then one set of calibration value segments is randomly selected as the feature value segment and the feature interval is confirmed.

4. The method for adaptive bitrate control based on ultra-high-definition video according to claim 1, characterized in that, In Step 35, the specific method for identifying whether frame loss occurs during the interaction process is as follows: Step 351: Based on the defined interval, monitor the interactive traffic associated with the video stream path nodes within the processing time period in real time, identify interactive traffic that does not belong to this defined interval and record it as abnormal traffic, record the specific time associated with the abnormal traffic as the abnormal time, mark the byte data associated with the abnormal time as pending data, and confirm the sequence number of the corresponding pending data in turn. Step 352: Based on the chronological order, sort the sequence numbers of the data to be determined, confirm the sequence number sequence, and determine whether adjacent sequence numbers satisfy the following condition: the next sequence number = the previous sequence number + 1. If not, generate a missing sequence number signal, and based on the difference between adjacent sequence numbers, lock the missing sequence number. The method for determining the missing sequence number is as follows: The next set of adjacent sequence numbers is labeled as XL1, and the previous set of sequence numbers is labeled as XL2. The missing feature Tz is confirmed by XL1-XL2=Tz. Then, the missing sequence number is defined by XL2+(Tz-j), where j is a positive integer and 1≤j<Tz. Based on the confirmed missing sequence number, the byte data associated with the missing sequence number is filled in in real time.

5. The method for adaptive bitrate control based on ultra-high-definition video according to claim 4, characterized in that, In Step 352, if the adjacent sequence numbers satisfy the condition that the next sequence number = the previous sequence number + 1, then the next group of undetermined data is marked as correct. For the undetermined data that is ranked first, the sequence number of the byte data of the previous moment of the first abnormal moment is selected as the benchmark.

6. A super-high-definition video adaptive bitrate control system, wherein the control system operates according to any one of claims 1-5, characterized in that, include: The bandwidth characteristic confirmation end identifies the network node associated with this ultra-high-definition video. Based on the historical operating data of this network node, it identifies and records the different bandwidth characteristics associated with different buffer volumes within this network node. The network interval confirmation end, based on the recorded bandwidth characteristics corresponding to different buffer amounts, confirms the video frame traffic to be processed for ultra-high-definition video at the current moment, and based on the total buffer amount of this network node at the current moment, confirms the network interval that the processing period needs to reach. On the flow control end, based on the network interval associated with the processing period, the amount of data that can be processed in the processing period is determined, and then based on the past data characteristics of the video stream path nodes and other nodes, flow control is performed on multiple path nodes that have data interaction. The video node monitoring terminal interacts with video frame traffic through video stream path nodes, and monitors the interaction traffic during the interaction process to identify whether there are frame drops during the interaction, and quickly fills in the lost byte data based on the identification results.

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