A 5G Video Service Quality Enhancement Method, Platform, Device and Medium

Abstract

The present invention discloses a method, platform, device and medium for enhancing the quality of 5G video services, relating to the technical field of 5G services. The method includes: invoking historical service communication data to mine the adversarial contradiction relationship in communication transmission; establishing a service enhancement module; generating a video interaction task and establishing an interaction docking to separate the network control plane and the data plane; interacting with the 5G communication network status, performing transmission bitrate adaptation according to the adversarial contradiction relationship to determine a transmission strategy; performing two-way redundant compression on the pre-interaction video before and after transmission to determine a data strategy; and fusing the transmission strategy and the data strategy to perform 5G video service management. The present invention solves the technical problem that in existing 5G video services, it is difficult to balance bandwidth, computing power and latency, resulting in low quality and stability of 5G video services, and achieves the technical effect of effectively balancing bandwidth, computing power and latency in 5G video services and significantly enhancing the quality and stability of the overall 5G video services.

Description

Technical Field

[0001] The present invention relates to the technical field of 5G services, and particularly to a method, platform, device and medium for enhancing the quality of 5G video services. Background Art

[0002] With the large-scale commercial deployment of 5G technology, 5G video services have developed rapidly. However, there are still many technical challenges in its development process. In terms of video transmission, it is difficult to achieve an effective balance among bandwidth, computing power and latency. Although 5G networks have the characteristic of high bandwidth, when facing the transmission of massive video data, especially high-definition, ultra-high-definition videos and high-frame-rate videos, bandwidth bottlenecks are still likely to occur. At the same time, the encoding and decoding processing of videos consumes a large amount of computing power resources. If not properly processed, it will lead to an increase in latency, seriously affecting the real-time performance and smoothness of video services. For example, in some large-scale online video live events, due to the large number of concurrent viewers, the pressure on the transmission and processing of video data increases sharply, and phenomena such as video freezing and excessive latency often occur, greatly reducing the viewing experience of users. There are also deficiencies in the redundant processing technology of videos. In high-concurrency service scenarios, there is a lack of effective service management strategies. When a large number of users request video services simultaneously, problems such as network congestion and a sharp decline in service quality are likely to occur.

[0003] There are technical problems in the existing 5G video services that it is difficult to balance bandwidth, computing power and latency, resulting in low quality and stability of 5G video services. Summary of the Invention

[0004] The present application provides a method, platform, device and medium for enhancing the quality of 5G video services, aiming to solve the technical problems in the existing 5G video services that it is difficult to balance bandwidth, computing power and latency, resulting in low quality and stability of 5G video services.

[0005] In view of the above problems, the present application provides a method, platform, device and medium for enhancing the quality of 5G video services.

[0006] In the first aspect of the present application, a method for enhancing the quality of 5G video services is provided, and the method includes:

[0007] Call historical service communication data, mine the adversarial contradiction relationship in communication transmission, where the adversarial contradiction relationship is the relative balance relationship among bandwidth, computing power, and latency; establish a service enhancement module, where the service enhancement module includes a bidirectional redundancy branch and an AI generation block; generate a video interaction task and establish an interaction docking, separate the network control plane and the data plane, where the network control plane has the adversarial contradiction relationship built in, and the data plane has the service enhancement module built in; interact with the 5G communication network status, based on the network control plane, perform transmission bitrate adaptation on the video interaction task according to the adversarial contradiction relationship, and determine the transmission strategy; based on the data plane, combined with the service enhancement module, perform bidirectional redundant compression on the pre-interaction video of the video interaction task before and after transmission, determine the data strategy, where there is video fusion of AI generation tasks; fuse the transmission strategy and the data strategy, coordinate the network control plane and the data plane, and perform 5G video service management.

[0008] In the second aspect of the present application, a 5G video service quality enhancement platform is provided, and the platform includes:

[0009] A historical service communication data calling unit, which is used to call historical service communication data and mine the adversarial contradiction relationship in communication transmission, where the adversarial contradiction relationship is the relative balance relationship among bandwidth, computing power, and latency; a service enhancement module establishing unit, which is used to establish a service enhancement module, where the service enhancement module includes a bidirectional redundancy branch and an AI generation block; an interaction docking establishing unit, which is used to generate a video interaction task and establish an interaction docking, separate the network control plane and the data plane, where the network control plane has the adversarial contradiction relationship built in, and the data plane has the service enhancement module built in; a transmission strategy determining unit, which is used to interact with the 5G communication network status, based on the network control plane, perform transmission bitrate adaptation on the video interaction task according to the adversarial contradiction relationship, and determine the transmission strategy; a data strategy determining unit, which is based on the data plane, combined with the service enhancement module, performs bidirectional redundant compression on the pre-interaction video of the video interaction task before and after transmission, determines the data strategy, where there is video fusion of AI generation tasks; a service management unit, which is used to fuse the transmission strategy and the data strategy, coordinate the network control plane and the data plane, and perform 5G video service management.

[0010] In a third aspect of the present application, an electronic device is provided, which includes: a processor; a memory for storing executable instructions of the processor; wherein, the processor is configured to execute a 5G video service quality enhancement method provided by the present application.

[0011] In a fourth aspect of the present application, a computer-readable storage medium is provided, storing a computer program, and the computer program is configured to execute a 5G video service quality enhancement method provided by the present application.

[0012] One or more technical solutions provided in the present application have at least the following technical effects or advantages:

[0013] Call historical service communication data, mine the adversarial contradiction relationship in communication transmission; establish a service enhancement module; generate video interaction tasks and establish interaction docking, separate the network control plane and the data plane; interact with the 5G communication network status, based on the network control plane, perform transmission bitrate adaptation according to the adversarial contradiction relationship, and determine the transmission strategy; based on the data plane, combine the service enhancement module, perform bidirectional redundant compression on the pre-interaction video before and after transmission, and determine the data strategy; fuse the transmission strategy and the data strategy, coordinate the network control plane and the data plane, and perform 5G video service management. It achieves the technical effect of effectively balancing bandwidth, computing power, and latency in 5G video services, and significantly enhancing the quality and stability of the overall 5G video service. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0015] Figure 1 It is a schematic flowchart of a 5G video service quality enhancement method provided by an embodiment of the present application;

[0016] Figure 2 It is a schematic structural diagram of a 5G video service quality enhancement platform provided by an embodiment of the present application.

[0017] Figure 3 It is a schematic structural diagram of an electronic device provided by the present application.

[0018] Explanation of the accompanying drawings: historical service communication data calling unit 10, service enhancement module establishing unit 20, interactive docking establishing unit 30, transmission strategy determining unit 40, data strategy determining unit 50, service management unit 60, processor 21, memory 22, input device 23, output device 24. DETAILED DESCRIPTION

[0019] This application provides a 5G video service quality enhancement method, platform, device and medium to solve the technical problem that it is difficult to balance bandwidth, computing power and delay in existing 5G video services, resulting in low quality and stability of 5G video services.

[0020] The technical solutions in the embodiments of the present application will be described clearly and completely below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0021] Embodiment 1, as Figure 1 As shown, the present application provides a 5G video service quality enhancement method, the method comprising:

[0022] Step S100: calling historical service communication data to mine the antagonistic and contradictory relationship of communication transmission, wherein the antagonistic and contradictory relationship is a relative balanced relationship among bandwidth, computing power and delay.

[0023] Specifically, first, historical service communication data needs to be accurately retrieved from a vast historical database. These data have a wide range of sources, including transmission records in various 5G video service scenarios, such as communication data for different time periods, different regions, different user groups, and different video types (high-definition, ultra-high-definition, live broadcast, on-demand, etc.). The data covers computing power-related information such as the initial bandwidth allocation during each video transmission process, the maximum and minimum bandwidth values that can be dynamically allocated during transmission, the CPU usage rate of the server when processing video data, GPU acceleration, etc., as well as the latency time from when data is sent from the video source end to when it is successfully received at the receiving end, and detailed information such as the corresponding network resource cost. Advanced data mining algorithms and intelligent analysis models are used to deeply analyze these data. When exploring the relationship between bandwidth, computing power, and latency, through statistical analysis and machine learning algorithms, for example, multiple linear regression analysis is used to preliminarily explore the linear correlation trend among the three, and then a deep neural network is used to further fit the non-linear relationship to construct an accurate mathematical model to describe their internal connection. When it is found that as the bandwidth increases and the computing power enhances, the latency of data transmission shows an obvious downward trend. This is because a wider bandwidth can transmit more data per unit time, and sufficient computing power can quickly perform operations such as encoding, decoding, compressing, and decompressing video data, reducing the waiting time of data during transmission. However, this improvement comes at a cost. As the bandwidth is broadened and the computing power increases, both the construction and maintenance costs of the network infrastructure and the energy consumption of the server, equipment purchase, and upgrade costs will increase accordingly. In the general environment of full coverage of the communication network, an adaptive adjustment mechanism is established based on the discovered antagonistic and contradictory relationship. By deploying intelligent monitoring and scheduling systems at key nodes of the network, the current network bandwidth usage status, the computing power load of each server, and the latency data of ongoing video transmissions are monitored in real time. Once it is found that the latency has an increasing trend, within the allowable range, bandwidth resources are preferentially increased or more computing power is allocated to relevant tasks according to the current cost budget and resource allocation situation to quickly reduce the latency and ensure the smoothness of the video service. When the network is idle or in a low-load state, the occupancy of bandwidth and computing power will be appropriately reduced to save costs, achieving the dynamic balance and optimization of the entire communication network under different service requirements and resource conditions, laying a solid data foundation and decision-making basis for the subsequent improvement of 5G video service quality.

[0024] Step S200: Establish a service enhancement module, where the service enhancement module includes a bidirectional redundancy branch and an AI generation block.

[0025] Specifically, a service enhancement module is established. This module is crucial for improving the quality of 5G video services. The bidirectional redundancy branch is one of its core components, which utilizes various redundancy characteristics in video data. Based on the temporal redundancy between adjacent frames before and after, it fully explores the similarity of the video in the temporal dimension. By comparing the regions with little pixel change between adjacent frames, it accurately removes redundant information and effectively compresses the data volume. At the same time, relying on the spatial redundancy between adjacent pixels, it deeply analyzes the laws of image spatial distribution and optimizes the regions where adjacent pixel values are similar or present specific patterns, further reducing unnecessary data storage and transmission. Moreover, supplemented by the statistical redundancy of cells, according to the probability distribution characteristics of cell appearance, it efficiently encodes the frequently occurring cells and reduces the overall complexity of the data. Based on these redundancy elements, the principle of information theory is introduced. After repeated training with a large number of sample data, a bidirectional redundancy branch capable of accurately performing video lossy compression is constructed, significantly reducing the data volume and improving the transmission efficiency while ensuring the basic visual effect of the video. In addition, the service enhancement module also includes an AI generation block. With powerful artificial intelligence algorithms, this block can intelligently generate a rich variety of content according to the user's personalized needs, the characteristics of the video content, and the current service scenario. For example, when watching a sports event video, it dynamically generates real-time score announcements, athlete data analysis, etc., providing users with a more rich and in-depth viewing experience. The bidirectional redundancy branch and the AI generation block cooperate with each other, jointly injecting powerful impetus into 5G video services and enabling a qualitative leap in data processing and content innovation.

[0026] Step S300: Generate a video interaction task and establish an interaction connection, separating the network control plane from the data plane, where the network control plane has the built-in adversarial contradiction relationship and the data plane has the built-in service enhancement module.

[0027] Specifically, first, various video interaction tasks are carefully generated based on the diverse video service requirements of users and the preset functions of the system. These tasks cover rich operations such as video playback, pause, fast forward, resolution switching, real-time interactive comments, etc., as well as the transmission and processing requirements of different types of videos (such as live broadcasts, video-on-demand, video calls, etc.). Subsequently, an accurate and efficient interactive docking mechanism is established to ensure seamless and real-time data interaction and instruction transmission among the video source end, user terminals, and network nodes. On this basis, the entire network architecture is separated into a network control plane and a data plane. The network control plane precisely incorporates the previously discovered confrontation and contradiction relationship model between bandwidth, computing power, and latency. It constantly monitors the overall operating state of the network, including the real-time occupancy of each link's bandwidth, the allocation of computing power resources among different tasks, and the latency data feedback during data transmission. Based on this information, through calculations and strategic decisions, it dynamically adjusts key control parameters such as network resource allocation, routing selection, and transmission protocols to maintain the stability and efficiency of network services. The data plane is like a high-speed transportation channel of the network, which incorporates a powerful service enhancement module. The two-way redundant branches in this module focus on deeply compressing and optimizing video data before data transmission, removing various redundant information in the video data, effectively reducing the data transmission volume, and improving the transmission efficiency. At the same time, the AI-generated block intelligently generates rich additional information or special effects according to the video content and user requirements during data transmission or at the receiving end, bringing a more personalized and innovative video service experience to users. Through such separation of the network architecture and functional layout, a solid and flexible infrastructure is laid for the all-round improvement of 5G video service quality.

[0028] Step S400: Interact with the 5G communication network state, and based on the network control plane, perform transmission bitrate adaptation on the video interaction task according to the confrontation and contradiction relationship to determine the transmission strategy.

[0029] Specifically, it starts to interact with the 5G communication network status in a comprehensive and real-time manner. As the core decision-making unit, the network control plane performs adaptive adjustment of the transmission bit rate to determine the optimal transmission strategy based on the conflicting relationship between its built-in bandwidth, computing power and delay. It first accurately detects the real-time bandwidth of the network to understand the current bandwidth capacity available for data transmission, and closely monitors the allocation and remaining status of computing power resources, because sufficient computing power can support more complex video encoding and decoding operations, which in turn affects the setting of the transmission bit rate. In addition, delay feedback information during data transmission is collected, because delay is one of the key indicators for measuring the quality of video services. When the network bandwidth is relatively abundant and the computing power is sufficient, such as during low network usage periods or areas with strong network infrastructure performance, in order to provide users with the ultimate video viewing experience, the network control plane will increase the transmission bit rate so that the video can be transmitted at a higher resolution and smoother frame rate, showing a clear, delicate and coherent picture effect. On the contrary, if it is detected that bandwidth resources are tight, such as during network peak hours or in local network congestion areas, or computing power is limited due to multiple tasks running in parallel, in order to avoid video freezes or even interruptions, the network control plane will decisively reduce the transmission bit rate. At the same time, it may adjust the video encoding method and adopt a more efficient compression algorithm to minimize delays under limited bandwidth and computing power conditions, ensure that the video can be transmitted continuously and stably, and allow users to watch the video content normally. Therefore, under different network conditions, through this dynamic and intelligent transmission bit rate adaptation mechanism, the most appropriate transmission strategy can be determined to ensure the stability and smoothness of 5G video services.

[0030] Step S500: Based on the data plane and in combination with the service enhancement module, bidirectional redundant compression is performed on the pre-interaction video of the video interaction task before and after transmission to determine a data strategy, wherein there is video fusion of the AI ​​generated task.

[0031] Specifically, relying on its built-in service enhancement module, before transmission, for pre-interactive videos, the bidirectional redundancy branch in the service enhancement module quickly starts working. It deeply explores the temporal redundancy between adjacent frames in the video, precisely captures the similarities in the picture content of adjacent frames, and efficiently compresses this repetitive information; carefully analyzes the spatial redundancy between adjacent pixels, and reduces unnecessary pixel data storage according to the pixel distribution law; at the same time, it makes full use of the statistical redundancy of cells to optimize cell coding to reduce the data volume. Through this series of complex and precise operations, pre-compression processing of pre-interactive videos is achieved, effectively reducing the data volume to be transmitted and lightening the burden on the subsequent transmission process. When the video is transmitted to the receiving end, the reverse function of the bidirectional redundancy branch, that is, the redundancy removal processing ability, is used again. According to the pre-set rules and algorithms, the redundant information in the compressed video is accurately restored, perfectly reproducing the original picture quality and content of the pre-interactive video. And, if there is an AI generation task, in this process, the rich content generated by the AI generation block will also be deeply integrated with the video. For example, if the AI generates relevant special effects, text explanations or personalized recommendation information according to the video scene, these elements will be seamlessly integrated into the video picture or audio track at the appropriate time point and in the appropriate way, thus not only optimizing the transmission and restoration of video data, but also greatly enriching the connotation and expression form of the video, and finally determining a complete data strategy to comprehensively improve the quality of 5G video services and the user experience.

[0032] Step S600: Integrate the transmission strategy and the data strategy, coordinate the network control plane and the data plane, and perform 5G video service management.

[0033] Specifically, the transmission strategy and the data strategy begin to deeply integrate, and the network control plane and the data plane cooperate to jointly promote the management of 5G video services. The transmission strategy led by the network control plane focuses on network resource allocation and transmission link optimization. Based on the dynamic balance of bandwidth, computing power, and latency, it precisely regulates key elements such as transmission bitrate and route selection to ensure the efficient flow of video data in the network. The data strategy relying on the data plane focuses on the processing and optimization of video data itself. It compresses and restores the video through a two-way redundant branch and integrates AI-generated tasks to enrich video content. The functions of the two are decoupled but interrelated. Through a dedicated coordination mechanism, the network control plane transmits network status information to the data plane in real time so that the data plane can flexibly adjust the data processing method according to the transmission conditions. The data plane reports the requirements and feedback of data processing to the network control plane to assist in optimizing the transmission strategy. In the entire 5G video service process, from the push of the video source to the reception and presentation at the user end, each link can be precisely managed and optimized to achieve a smooth and high-quality video playback experience, effectively improving the overall quality and stability of 5G video services and meeting the diverse needs of users for 5G video services in different scenarios.

[0034] In a possible implementation manner, step S600 further includes:

[0035] Step S610: Set a high-concurrency service threshold.

[0036] Step S620: If the high-concurrency service threshold is met, establish an intermediate service node, where the intermediate service node is an edge node of the access network at the receiving end.

[0037] Step S630: Perform primary service management based on the transmitting end and the intermediate service node, and perform secondary service management based on the intermediate service node and the receiving end.

[0038] Specifically, first, a high-concurrency service threshold is set. This threshold is a key indicator determined based on a comprehensive analysis of the system's carrying capacity, network resource status, and past service data. It defines the upper limit of the number of video service requests that the system can effectively process under normal operating conditions. When the number of connections or data traffic requesting 5G video services simultaneously exceeds this set value, it is considered to enter a high-concurrency service scenario.

[0039] Once it is detected that the current service request meets the set high-concurrency service threshold, the process of establishing an intermediate service node is immediately initiated. This intermediate service node is positioned as the access network edge node at the receiving end, and its location selection and construction fully consider the network topology and user distribution. For example, intermediate service nodes are set up at the centers of various urban areas or large user aggregation areas, which can be closest to the user terminals to the greatest extent, effectively shortening the last mile of data transmission, reducing signal transmission loss and delay.

[0040] Based on this network architecture adjustment, hierarchical service management is carried out. In the first-stage service management, the transmitting end first sends the video data to the intermediate service node. At this time, the intermediate service node undertakes the functions of data aggregation, caching, and preliminary processing. It can perform operations such as splitting and format conversion on the video stream from the transmitting end, and adaptively process the video data according to the network conditions and device performance of each receiving end, avoiding network jams or crashes caused by a large amount of data directly rushing to the receiving end. For example, for some receiving ends with low bandwidth, the intermediate service node can convert the video into a low-resolution version for transmission. In the second-stage service management, the intermediate service node accurately distributes the preliminarily processed data to each receiving end. Since the intermediate service node has a deeper understanding and adaptation of the local network environment, it can adjust the transmission strategy in a timely manner according to the real-time feedback of the receiving end, such as dynamically adjusting the transmission bit rate or switching the transmission path in case of local network congestion, so as to ensure that each receiving end can obtain a relatively stable and high-quality video service, effectively improving the effect and reliability of the entire 5G video service management in a high-concurrency scenario and guaranteeing the viewing experience of users.

[0041] In a possible implementation manner, step S200 further includes:

[0042] Step S210: Use the time redundancy between adjacent frames before and after as the first redundancy factor, the spatial redundancy between adjacent pixels as the second redundancy factor, and the statistical redundancy of cells as the third redundancy factor.

[0043] Step S220: Based on the first redundancy factor, the second redundancy factor, and the third redundancy factor as the processing targets, introduce the information theory principle and use sample training as the method to construct a redundancy branch, where the redundancy branch is used to perform video lossy compression.

[0044] Specifically, the temporal redundancy between adjacent frames is established as the first redundant element. As a continuous sequence of images, adjacent frames of a video usually have a high degree of similarity. For example, in a video showing a calm lake, the ripples on the lake and the outlines of the surrounding scenery remain almost unchanged in multiple consecutive frames. This large amount of repeated image information in the temporal dimension constitutes the available temporal redundancy. Secondly, the spatial redundancy between adjacent pixels plays an important role as the second redundant element. An image is composed of many pixels. In a local area, adjacent pixels often show similarities or gradients in attributes such as color and brightness. For example, in an image with a blue sky background, the blue hue and brightness of adjacent pixels change slightly and continuously. By identifying and processing this spatial pixel correlation, the amount of data storage can be effectively reduced. Finally, the statistical redundancy of cells is regarded as the third redundant element. At the cell level involved in video data transmission, the probability of occurrence of different cells varies. Some cells appear frequently, while others appear less frequently. Based on this statistical law, high-frequency cells can be specially encoded to reduce the redundancy of the overall data. These three redundant elements together provide the core basis and data processing direction for the subsequent construction of efficient redundant branches.

[0045] Based on the above three redundant elements, the redundant branch is constructed. The principle of information theory is introduced. Information theory provides a theoretical foundation for data compression, which can measure the amount of information and redundancy in data. A large number of video samples are used as training materials. During the training process, the entropy coding and other technical means in information theory are used. For temporal redundancy, the difference between adjacent frames is calculated and encoded through the inter-frame prediction algorithm to remove duplicate information; for spatial redundancy, the discrete cosine transform method is used to convert the image pixel block to the frequency domain, highlight its energy concentration characteristics and then quantize and encode; for the statistical redundancy of the signal element, variable length coding operations such as Huffman coding are performed according to the probability of the signal element. Through such sample training and principle application, a redundant branch capable of performing lossy video compression is gradually constructed. Under the premise of sacrificing a small amount of video quality, the storage and transmission volume of video data is significantly reduced, the transmission efficiency and resource utilization of 5G video services are improved, and a solid data processing foundation is laid for the subsequent enhancement of video service quality.

[0046] In a possible implementation, step S220 further includes:

[0047] Step S221: invert the redundant branch to serve as a de-redundant branch.

[0048] Step S222: establishing a trigger rule for the receiving end of the redundant branch and a trigger rule for the transmitting end of the de-redundant branch.

[0049] Specifically, in order to restore the video data processed by the redundancy branch at the receiving end of video transmission, it is necessary to invert the redundancy branch to construct a redundancy removal branch. First, deeply analyze a series of algorithms and operation processes used by the redundancy branch when processing video. In the temporal redundancy processing of adjacent frames before and after, the inter-frame prediction algorithm is adopted, and the motion vectors and residual information between frames are recorded to achieve compression; for the spatial redundancy between adjacent pixels, techniques such as discrete cosine transform, quantization, and entropy coding are used to transform and compress the pixel data; in terms of the statistical redundancy of cells, the Huffman coding operation is implemented according to the probability distribution of cell occurrences. Then, take the inverse processes of these algorithms as the core to construct the redundancy removal branch. For temporal redundancy, based on the stored motion vectors and residual information, the received compressed video frames are gradually restored to the original video frame sequence through reverse motion compensation and residual addition operations; for spatial redundancy, first perform the inverse quantization operation, and then use the inverse discrete cosine transform to convert the frequency-domain data back to the spatial-domain pixel data; for cell statistical redundancy, perform the decoding operation according to the Huffman coding table to restore the encoded data to the original cell sequence. Through the integration and application of this series of inverse operation algorithms for different redundancy types, the redundancy removal branch is successfully constructed, enabling it to effectively restore the video data when the video is transmitted to the receiving end and ensuring that users receive high-quality and complete video content.

[0050] The receiving - end trigger rule for constructing redundant branches and the transmitting - end trigger rule for removing redundancy branches are key links to ensure the efficient and accurate processing of video data. For the receiving - end trigger rule of redundant branches, when the video source is ready and sends a transmission request to the network control plane, the network control plane comprehensively evaluates various factors such as the current network bandwidth, computing power resources, and the overall transmission task queue. If it is determined that the network has the ability to handle the new video transmission task and there is no conflict with higher - priority tasks, it will send an instruction to start the redundant branch to the receiving end, and at the same time transmit relevant video source information and network parameters. After receiving the instruction and information, the receiving end immediately starts the redundant branch and begins to perform compression processing on the incoming video data based on temporal, spatial, and cell - statistical redundancy to optimize the data volume for subsequent transmission. For the transmitting - end trigger rule of the redundancy - removal branch, when the compressed video data reaches the network buffer at the receiving end and passes the preliminary integrity and legality checks, the receiving end will feedback the receiving status information to the network control plane. The network control plane judges whether it is suitable to start the redundancy - removal branch for decompression operations based on this information and the current local network conditions. If the conditions are met, it will send an instruction to start the redundancy - removal branch to the receiving end, and at the same time attach key parameters required for decompression, such as coding table information, motion vector data, etc. After receiving the instruction, the receiving end immediately activates the redundancy - removal branch and performs decompression processing on the compressed video data according to the established reverse algorithm to gradually restore the original video data, so as to ensure that the video can be effectively redundantly processed and restored according to reasonable rules before and after transmission, and guarantee the smoothness and stability of the entire video service process.

[0051] In a possible implementation manner, step S500 further includes:

[0052] Step S510: Based on the redundant branch, perform pre - processing on the pre - interactive video to determine the compressed video.

[0053] Step S520: With the transmission strategy, perform communication transmission of the compressed video. At the receiving end, perform redundancy processing on the compressed video based on the redundancy - removal branch to restore the pre - interactive video.

[0054] Specifically, make full use of the previously constructed redundant branches to perform pre - processing on the pre - interactive video. Based on its precise processing capabilities for temporal redundancy between adjacent frames, spatial redundancy between adjacent pixels, and cell statistical redundancy, the redundant branches conduct in - depth analysis and optimization of the pre - interactive video. Through the inter - frame prediction and motion compensation algorithms for temporal redundancy, a large amount of similar image information between adjacent frames is identified and removed, and only the key inter - frame change data is retained; for spatial redundancy, the discrete cosine transform technology is used to convert the image pixel data into the frequency domain for quantization and encoding processing, effectively reducing the redundant data volume between adjacent pixels; in terms of cell statistical redundancy, operations such as Huffman coding are performed according to the probability distribution of cell occurrences to further compress the data. After this series of complex and efficient processing procedures, the original pre - interactive video is converted into a compressed video with a significantly reduced data volume, creating favorable conditions for subsequent communication transmission.

[0055] Strictly carry out the communication transmission work of the compressed video in accordance with the determined transmission strategy. The transmission strategy is formulated comprehensively based on various factors such as network bandwidth, computing power, and latency monitored by the network control plane, and it clarifies key elements such as the most suitable transmission bitrate, route selection, and transmission protocol in the current network environment. During the transmission process, the compressed video data is stably pushed from the source end to the receiving end according to these strategies. When the compressed video reaches the receiving end, redundant processing is performed based on the pre - set de - redundancy branch. The de - redundancy branch, according to the reverse algorithm corresponding to the redundant branch, for temporal redundancy, uses the stored motion vectors and residual information for reverse motion compensation and residual addition operations to restore the original inter - frame relationship; for spatial redundancy, inverse quantization is first performed and then the frequency - domain data is converted back to spatial - domain pixel data through the inverse discrete cosine transform; for cell statistical redundancy, decoding operations are performed according to the Huffman coding table. Through this series of de - redundancy processing steps, the compressed video is gradually restored to the original pre - interactive video, ensuring that the receiving - end users can view video content that is basically the same as that at the source end, thereby effectively improving the transmission efficiency and resource utilization rate of video data in the 5G network environment while ensuring the video service quality.

[0056] In a possible implementation manner, step S500 further includes:

[0057] Step S530: Identify the pre - interactive video and determine the multimedia elements.

[0058] Step S540: Traverse the multimedia elements, extract the first multimedia element for data processing, and determine the first processing result.

[0059] Step S550: Map the first processing result to the pre - interactive video and integrate to determine the processing result of the multimedia content in the pre - interactive video.

[0060] Specifically, a comprehensive and detailed identification of the pre-interactive video is carried out to accurately determine various multimedia elements contained therein. Through video analysis tools and algorithms, the multi-dimensional information contained in the video is deeply explored. First, focus on the image part of the video, identify the main elements in the picture, such as people, scenery, objects, etc., and at the same time analyze the basic attributes of the image, such as resolution, color mode, encoding method, etc. Further analysis will also be carried out on the changes of the image in the time dimension, such as the movement trajectory of objects in the picture, the switching rhythm of scenes, etc., because these elements have a key mapping relationship with the time dimension in subsequent processing, which can help us better locate and restore the processing effect. In addition to images, detailed discrimination is also carried out on voice elements to determine key information such as the number of voice channels, sampling frequency, voice content, and the start and end times of the voice. Similarly, attention is paid to the distribution of the voice on the time axis to clarify its coordination relationship with the video picture. In addition, other multimedia elements such as subtitles, video effects, hyperlinks, etc. are also identified one by one, and their respective presentation forms, appearance times, and associations with the overall video in time and space are clarified. The entire identification process aims to construct a complete and clear multimedia element framework. For example, when processing one of them, such as an image, according to the previously determined mapping relationships such as the time dimension, the processed image result is accurately located, mapped, and extracted in the original video. In this way, not only can the accuracy of the processing be guaranteed, but also the processing efficiency can be greatly improved, avoiding ineffective work, and laying a solid foundation for further optimizing the overall quality of the pre-interactive video.

[0061] Starting from the first multimedia element, each element is checked and screened in turn. When the first multimedia element is located, the corresponding data processing operation is carried out according to its type. If the first multimedia element is a specific area in the video image, such as a facial image of a person, an image recognition and processing algorithm is used to first detect the edge of the area and accurately outline the facial contour. Then, the skin color correction technology is used to adjust the facial skin color to make it more natural. At the same time, the image sharpening algorithm is used to highlight the details of the facial features, such as the clarity of the eyes and the texture of the lips. After this series of image processing, the optimized facial image of the person is obtained as the first processing result. If the first multimedia element is a voice narration in the audio track, the voice processing technology is used to first perform voice noise reduction processing to remove background noise interference, and then perform voice enhancement to improve the volume and clarity of the voice. Special effects processing such as voice speed change and pitch change can also be performed according to needs, and finally a processed audio clip is formed as the first processing result. If the first multimedia element is subtitle text, a text processing algorithm is used to check and correct grammatical errors in the subtitles. If multi-language requirements are involved, machine translation technology can be used to translate the subtitles into a specified language and adjust the display format of the subtitles, such as font color, background transparency, etc., so as to determine the processed subtitle text as the first processing result, thereby making full preparations for the subsequent mapping and integration in the pre-interactive video.

[0062] After the first processing result is obtained, precise mapping and integration operations are performed within the original framework of the pre-interactive video. If the first processing result is optimized data about the image, such as the image has been color enhanced, details of a specific area have been enlarged or blurred, etc., the processed image data is accurately replaced with the original image data according to the time code and spatial coordinate information of the image in the pre-interactive video, ensuring that when the video is played, the frame sequence where the image is located can naturally and smoothly present the processed visual effect, and maintain coherence with the transition between the previous and next frames. If the first processing result is an adjustment in audio, such as volume standardization, audio special effects addition, etc., the processed audio clip will be seamlessly integrated into the original audio track according to the starting and ending points of the audio on the timeline, so that the audio and video screen are synchronized and coordinated to avoid the phenomenon of sound and screen misalignment. For the processing results of other multimedia elements such as subtitles, the modified subtitle text, style or special effects are also completely mapped back to the corresponding position in the original video according to their time position and display layout in the pre-interactive video. By such detailed mapping and integration operations on all the first processing results, the overall processing results of the multimedia content in the pre-interaction video are finally determined, so that the video is optimized and improved in all multimedia dimensions, bringing users a better and richer video viewing experience.

[0063] In a possible implementation, step S520 further includes:

[0064] Step S521: If there is an AI generation task, perform semantic recognition and parsing on the AI generation task to determine generation elements.

[0065] Step S522: Traverse the generation elements. For standardized generation elements, call and combine them based on the generation component library. For personalized elements, perform intelligent generation processing to determine the generation result. The generation component library contains a set of standardized components.

[0066] Step S523: Integrate the pre-interaction video and the generation result as the display content for the receiving end.

[0067] Specifically, when an AI generation task is detected, start the semantic recognition and parsing process based on the deep learning framework. First, input the text related to this task into the neural network model, which adopts an architecture that combines a convolutional neural network (CNN) and a recurrent neural network (RNN). The CNN is responsible for extracting local features in the text, such as keywords, phrase structures, etc. Through the operations of the convolutional layer and the pooling layer, the text is transformed into a feature vector. The RNN then performs sequence modeling on the feature vector, using long short-term memory network (LSTM) units to capture the semantic dependencies in the text, analyze the front-back associations between words, and the overall semantic logic of the sentence. During the model training stage, a large amount of labeled task text data is used for supervised learning, continuously adjusting the weight parameters of the neural network to improve the accuracy of semantic recognition. When inputting the AI generation task text to be parsed, the model outputs a semantic feature representation, and then, based on the predefined semantic template and rule library, determines the generation elements. For example, if the task text involves a specific scene description, the model can identify elements such as key objects, environmental attributes, and emotional tendencies in the scene and transform them into a structured data form, such as an array of objects stored in JSON format, where each object contains information such as the element name, attribute value, and relevant constraint conditions, providing accurate and computer-processable data basis for subsequent generation element processing, ensuring that the AI generation task can accurately connect with the video service requirements and effectively drive the subsequent intelligent content generation process.

[0068] Initiate the traversal operation on the determined generation elements. For the parts determined to be standardized generation elements, the task is completed with the help of a generation component library that is pre-constructed and stores a rich set of standardized components. The standardized component sets in this component library cover various types, such as general video format conversion plugins and standardized video filter modules in video processing; common audio format adaptation components and basic audio effect units in the audio field; standard font style templates and general text layout rule sets in the text category, etc. According to the specific requirement description of the generation elements, by precisely matching the index information in the component library, the corresponding standardized components are quickly located and called, and then these called components are organically spliced and configured and integrated according to the preset combination logic and rules to ensure their coordinated operation in terms of function and data interaction. For those parts identified as personalized elements, an intelligent generation processing flow is initiated. Using an intelligent generation model constructed based on deep learning algorithms and adopting a generative adversarial network (GAN), which has been fully trained on a large-scale multi-modal data, it can generate personalized content with uniqueness, innovation, and high conformity to the task context through complex neural network calculations and reasoning processes according to the specific semantic information, style requirements, and associated features with the pre-interactive video carried by the generation elements. For example, create a unique background music melody for a specific video scene, or generate customized animation effects that closely match the video plot and are artistically appealing. Finally, the results of the standardized component combination and the personalized generated content are summarized and integrated to determine the complete generation result, making full preparations for the subsequent integration with the pre-interactive video to achieve the intelligent and personalized enrichment expansion of video content and enhance the overall video service experience.

[0069] Start the fusion process of the pre-interactive video and the generated results, aiming to construct the final display content of the receiving end. For the text information in the generated results, such as commentary, subtitles, etc., according to the time code and video screen layout rules, the text is accurately superimposed on the corresponding frame of the pre-interactive video to ensure that the text display position is reasonable, clear and readable, and complements the main content of the screen. The font, color, size and other style attributes of the text are adjusted synchronously to adapt to the video style and visual effects. If the generated results contain audio elements, such as background music, special effects sound, etc., use audio mixing technology to finely set the volume ratio, channel balance, fade-in and fade-out effects and other parameters according to the role positioning of the audio and the characteristics of the original audio track of the pre-interactive video, so that the newly added audio and the original video audio are naturally integrated to create a harmonious, unified and layered auditory environment. As for the visual effects or animation clips in the generated results, with the help of video synthesis algorithms, according to the scope of the effects or animations, triggering time and matching requirements with the video scene, they are seamlessly embedded into the specific frame sequence or scene of the pre-interactive video, ensuring that during the video playback, the visual effects are presented smoothly and naturally, with smooth transitions, and closely matching the overall rhythm and picture changes of the video. Through the all-round and refined integration of the generated results of text, audio, vision and other aspects, the receiving end display content is finally generated, bringing users a rich, diverse and immersive video viewing experience, effectively improving the quality of video services.

[0070] Embodiment 2, based on the same inventive concept as a method for enhancing 5G video service quality in the aforementioned embodiment, Figure 2 As shown, the present application provides a 5G video service quality enhancement platform, and the platform and method embodiments in the embodiments of the present application are based on the same inventive concept. Among them, the platform includes:

[0071] The historical service communication data calling unit 10 is used to call historical service communication data and mine the antagonistic and contradictory relationship of communication transmission, wherein the antagonistic and contradictory relationship is a relative balance relationship among bandwidth, computing power and delay.

[0072] The service enhancement module establishment unit 20 is used to establish a service enhancement module, wherein the service enhancement module includes a bidirectional redundant branch and an AI generation block.

[0073] The interactive docking establishment unit 30 is used to generate a video interactive task and establish an interactive docking, separate the network control plane and the data plane, wherein the network control plane has the antagonistic and contradictory relationship built in, and the data plane has the service enhancement module built in.

[0074] A transmission policy determination unit 40, which is configured to interact with the 5G communication network status, and based on the network control plane, adaptively adjust the transmission bit rate of the video interaction task according to the adversarial contradiction relationship to determine the transmission policy.

[0075] A data policy determination unit 50, which is configured to perform two-way redundant compression on the pre-interaction video of the video interaction task before and after transmission based on the data plane in combination with the service enhancement module to determine the data policy, where there is video fusion of AI-generated tasks.

[0076] A service management unit 60, which is configured to integrate the transmission policy and the data policy, coordinate the network control plane and the data plane, and perform 5G video service management.

[0077] Furthermore, the service management unit 60 further includes:

[0078] A high-concurrency service threshold setting unit, which is configured to set a high-concurrency service threshold.

[0079] An intermediate service node establishment unit, which is configured to establish an intermediate service node if the high-concurrency service threshold is met, where the intermediate service node is an edge node of the access network at the receiving end.

[0080] A primary service management unit, which performs primary service management based on the transmitting end and the intermediate service node, and performs secondary service management based on the intermediate service node and the receiving end.

[0081] Furthermore, the service enhancement module establishment unit 20 further includes:

[0082] A redundant element determination unit, which is configured to use the temporal redundancy between adjacent frames before and after as the first redundant element, the spatial redundancy between adjacent pixels as the second redundant element, and the statistical redundancy of cells as the third redundant element.

[0083] A redundancy branch construction unit, which is configured to use the first redundant element, the second redundant element, and the third redundant element as processing targets, introduce the information theory principle and use sample training as a method to construct a redundancy branch, where the redundancy branch is used to perform lossy video compression.

[0084] Furthermore, the redundancy branch construction unit further includes:

[0085] A redundant branch acquisition unit, which is configured to invert the redundancy branch as a redundancy removal branch.

[0086] A receiving - end trigger rule establishment unit, which is used to establish the receiving - end trigger rule of the redundant branch and the transmitting - end trigger rule of the redundancy - removing branch.

[0087] Furthermore, the data policy determination unit 50 further includes:

[0088] A compressed video determination unit, which pre - processes the pre - interaction video based on the redundant branch to determine a compressed video.

[0089] A pre - interaction video restoration unit, which uses the transmission strategy to perform the communication transmission of the compressed video. At the receiving end, it performs redundancy processing on the compressed video based on the redundancy - removing branch to restore the pre - interaction video.

[0090] Furthermore, the data policy determination unit 50 further includes:

[0091] A multimedia element determination unit, which is used to identify the pre - interaction video and determine multimedia elements.

[0092] A first processing result determination unit, which traverses the multimedia elements, extracts the first multimedia elements for data processing, and determines the first processing result.

[0093] A pre - interaction video mapping unit, which maps the pre - interaction video for the first processing result to integrally determine the processing result of the multimedia content in the pre - interaction video.

[0094] Furthermore, the pre - interaction video restoration unit further includes:

[0095] A generated element determination unit, which is used to perform semantic recognition and parsing on the AI generation task if there is an AI generation task to determine generated elements.

[0096] A generated result determination unit, which traverses the generated elements. For the standardized generated elements, it calls and combines them based on the generation component library. For the personalized elements, it performs intelligent generation processing to determine the generated result. The generation component library contains a set of standardized components.

[0097] A display content acquisition unit, which is used to fuse the pre - interaction video and the generated result as the display content at the receiving end.

[0098] Embodiment III Figure 3This is a schematic structural diagram of the electronic device provided in Embodiment 3 of the present invention, showing a block diagram of an exemplary electronic device suitable for implementing the embodiments of the present invention. Figure 3 The displayed electronic device is merely an example and should not impose any limitations on the functions and usage scope of the embodiments of the present invention. As Figure 3 shown, the electronic device includes a processor 21, a memory 22, an input device 23, and an output device 24; the number of processors 21 in the electronic device can be one or more. Figure 3 Taking one processor 21 as an example, the processor 21, the memory 22, the input device 23, and the output device 24 in the electronic device can be connected through a bus or other means. Figure 3 Taking the connection through a bus as an example.

[0099] The memory 22, as a computer-readable storage medium, can be used to store software programs, computer-executable programs, and modules, such as the program instructions / modules corresponding to a 5G video service quality enhancement method in the embodiments of the present application. The processor 21 executes various functional applications and data processing of the computer device by running the software programs, instructions, and modules stored in the memory 22, that is, implements the above-mentioned 5G video service quality enhancement method.

[0100] It should be noted that the above sequence of the embodiments of the present application is only for description and does not represent the superiority or inferiority of the embodiments. And the above describes specific embodiments of this specification. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0101] The above are only the preferred embodiments of the present application and are not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application shall be included within the protection scope of the present application.

[0102] This specification and the drawings are only exemplary descriptions of the present application and are considered to have covered any and all modifications, variations, combinations, or equivalents within the scope of the present application. Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalent technologies, the present application is intended to include these changes and modifications.

Claims

1. A 5G video service quality enhancement method, characterized in that: The method comprises: Call historical service communication data to mine the antagonistic and contradictory relationship of communication transmission, where the antagonistic and contradictory relationship is the relative balance relationship between bandwidth, computing power and latency; Establishing a service enhancement module, wherein the service enhancement module comprises a bidirectional redundancy branch and an AI generation block; the bidirectional redundancy branch comprises: a redundancy branch and a de-redundancy branch, wherein the redundancy branch is inverted as a de-redundancy branch, and the redundancy branch is used to perform video lossy compression; Generate a video interaction task and establish an interactive connection, separate a network control plane and a data plane, wherein the network control plane has the antagonistic and contradictory relationship built in, and the data plane has the service enhancement module built in; Interact with the 5G communication network status, based on the network control plane, and adaptively transmit the bit rate of the video interaction task according to the confrontation and contradiction relationship to determine the transmission strategy; Based on the data plane, in combination with the service enhancement module, bidirectional redundancy compression is performed on the pre-interaction video of the video interaction task before and after transmission, and a data strategy is determined, wherein the data strategy also includes video fusion of the AI ​​generation task; Bidirectional redundancy compression of pre-interactive video before and after transmission, including: Based on the redundancy branch, the pre-interaction video is pre-processed to determine the compressed video; the communication transmission of the compressed video is performed according to the transmission strategy, and at the receiving end, the redundancy processing of the compressed video is performed based on the de-redundancy branch to restore the pre-interaction video; The transmission strategy and the data strategy are integrated, and the network control plane and the data plane are coordinated to perform 5G video service management.

2. A 5G video service quality enhancement method as claimed in claim 1, characterized in that: The 5G video service management includes: Set a high concurrent service threshold; If the high concurrent service threshold is met, establishing an intermediate service node, wherein the intermediate service node is an access network edge node of the receiving end; A primary service management is performed based on the transmission end and the intermediate service node, and a secondary service management is performed based on the intermediate service node and the receiving end.

3. A 5G video service quality enhancement method as claimed in claim 1, characterized in that: The service enhancement module includes a bidirectional redundant branch, including: The temporal redundancy between the previous and next adjacent frames is the first redundancy factor, the spatial redundancy between adjacent pixels is the second redundancy factor, and the statistical redundancy of the information element is the third redundancy factor; Based on the first redundant element, the second redundant element and the third redundant element as processing targets, the principle of information theory is introduced and sample training is used as a method to construct redundant branches.

4. A 5G video service quality enhancement method as claimed in claim 3, characterized in that: The method further comprises: A triggering rule for the receiving end of the redundant branch and a triggering rule for the transmitting end of the de-redundant branch are established.

5. A 5G video service quality enhancement method as claimed in claim 1, characterized in that: The data strategy also includes data fusion for AI-generated tasks, including: Perform semantic recognition and analysis on AI-generated tasks to determine the generation elements; Traversing the generation elements, calling and combining the standardized generation elements based on the generation component library, performing intelligent generation processing on the personalized elements, and determining the generation results, wherein the generation component library includes a standardized component set; The pre-interaction video and the generated result are integrated as display content of the receiving end.

6. A 5G video service quality enhancement platform, characterized in that: The platform is used to execute a 5G video service quality enhancement method according to any one of claims 1 to 5, and the platform includes: A historical service communication data calling unit, the historical service communication data calling unit is used to call historical service communication data to mine the antagonistic and contradictory relationship of communication transmission, wherein the antagonistic and contradictory relationship is a relative balance relationship between bandwidth, computing power and delay; A service enhancement module establishment unit, the service enhancement module establishment unit is used to establish a service enhancement module, wherein the service enhancement module includes a bidirectional redundant branch and an AI generation block; An interactive docking establishment unit, the interactive docking establishment unit is used to generate a video interactive task and establish an interactive docking, separate a network control plane and a data plane, wherein the network control plane has the antagonistic and contradictory relationship built in, and the data plane has the service enhancement module built in; A transmission strategy determination unit, the transmission strategy determination unit is used to interact with the 5G communication network status, based on the network control plane, and perform transmission bit rate adaptation on the video interaction task according to the antagonistic and contradictory relationship to determine the transmission strategy; A data strategy determination unit, wherein the data strategy determination unit performs bidirectional redundancy compression on the pre-interaction video of the video interaction task before and after transmission based on the data plane and in combination with the service enhancement module to determine a data strategy, wherein the data strategy also includes video fusion of an AI generated task; A service management unit, wherein the service management unit is used to integrate the transmission strategy and the data strategy, coordinate the network control plane and the data plane, and perform 5G video service management.

7. An electronic device, characterized in that: The electronic device comprises: processor; a memory for storing instructions executable by the processor; The processor is used to execute a 5G video service quality enhancement method as described in any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, so that the computer program is used to execute a 5G video service quality enhancement method as described in any one of claims 1 to 5.

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