A management method for synchronous output of multi-device live broadcast images

Abstract

The present invention provides a management method for synchronous output of multi-device live broadcast images, which relates to the field of image communication technology. By establishing a distributed live broadcast architecture centered on SuperNode, the live broadcast signal is synchronously forwarded to multiple display devices, an edge computing module is integrated in the relay node, and connected to the cloud. The cloud has an adaptive synchronization analysis module, which monitors the network signal of the display device through the edge computing module, and performs preliminary synchronization analysis based on the live broadcast signal and sends it to the cloud. The cloud performs synchronization optimization through the adaptive synchronization analysis module based on the synchronization analysis results received from each display device, generates a synchronization strategy and sends it to the edge computing module for live broadcast synchronization control. The technical problem that the existing technology lacks refined management of multi-device live broadcast images, resulting in asynchronous images and poor user experience is solved. The technical effect of improving the synchronization and playback quality of multi-device live broadcast images and enhancing user experience is achieved.

Description

Technical Field

[0001] The present invention relates to the field of image communication technology, and in particular to a management method for synchronous output of live broadcast images of multiple devices. Background Art

[0002] In the current field of live broadcast technology, with the explosive growth of multimedia content and the increasing requirements of the audience for live broadcast experience, multi-device synchronous live broadcast has become an important means to improve the user's viewing experience. In the existing technology, although the function of collecting live broadcast materials with multiple cameras or multiple devices at the same time has been realized, there are still significant defects in the synchronous output of the picture. Specifically, due to factors such as network delays, differences in device performance, and different encoding and decoding standards between multiple devices, the live broadcast pictures on multiple platforms often have problems such as time asynchrony, uneven picture quality, and even signal loss, which seriously affects the audience's viewing experience.

[0003] The existing technology has the technical problem of lack of refined management of live broadcast images of multiple devices, resulting in asynchronous images and poor user experience. Summary of the invention

[0004] The present application provides a management method for synchronous output of live broadcast images of multiple devices, which is used to solve the technical problem in the prior art that the live broadcast images of multiple devices lack refined management, resulting in asynchronous images and poor user experience.

[0005] In view of the above problems, the present application provides a management method for synchronous output of live broadcast images of multiple devices, the method comprising: establishing a distributed live broadcast architecture centered on SuperNode, SuperNode acting as a relay node for receiving live broadcast signals, and synchronously forwarding the live broadcast signals to multiple display devices; integrating an edge computing module in the relay node, connecting to the cloud, and the cloud having an adaptive synchronization analysis module; wherein the network signal of the display device is monitored by the edge computing module, and a preliminary synchronization analysis is performed based on the live broadcast signal, and the signal is sent to the cloud; the cloud performs synchronization optimization through the adaptive synchronization analysis module based on the synchronization analysis results received from each display device, generates a synchronization strategy, and sends it to the edge computing module for live broadcast synchronization control.

[0006] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0007] The method provided in the embodiment of the present application establishes a distributed live broadcast architecture centered on SuperNode. SuperNode is used as a relay node to receive live broadcast signals and synchronously forward the live broadcast signals to multiple display devices; an edge computing module is integrated in the relay node and connected to the cloud, and the cloud has an adaptive synchronization analysis module; wherein the network signal of the display device is monitored by the edge computing module, and a preliminary synchronization analysis is performed based on the live broadcast signal and sent to the cloud; the cloud performs synchronization optimization based on the synchronization analysis results received from each display device through the adaptive synchronization analysis module, generates a synchronization strategy and sends it to the edge computing module for live broadcast synchronization control. The technical effect of improving the synchronization and playback quality of the live broadcast images of multiple devices and enhancing the user experience is achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0009] Figure 1 A flowchart of a management method for synchronously outputting live broadcast images of multiple devices provided in this application;

[0010] Figure 2 A flowchart of live broadcast synchronization control in a management method for synchronous output of live broadcast images of multiple devices provided in this application. DETAILED DESCRIPTION

[0011] This application provides a management method for synchronous output of live broadcast images of multiple devices, which is used to solve the technical problem that the live broadcast images of multiple devices lack refined management in the prior art, resulting in asynchronous images and poor user experience. The technical effect of improving the synchronization and playback quality of live broadcast images of multiple devices and enhancing user experience is achieved.

[0012] Below, the technical solutions in the present invention will be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments of the present invention. It should be understood that the present invention is not limited to the example embodiments described herein. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention. It should also be noted that, for the convenience of description, only the parts related to the present invention are shown in the accompanying drawings, rather than all of them.

[0013] like Figure 1As shown, the present application provides a management method for synchronous output of live broadcast images of multiple devices, the method comprising:

[0014] A distributed live broadcast architecture centered on SuperNode is established. SuperNode acts as a relay node to receive live broadcast signals and synchronously forward the live broadcast signals to multiple display devices.

[0015] Specifically, first deploy a high-performance SuperNode server as a relay node, which is located in a data center with excellent network conditions, to ensure that the SuperNode can stably and efficiently receive and forward large-volume live broadcast signals. SuperNode not only serves as a signal reception and forwarding center, but also acts as the hub of a distributed live broadcast architecture, connecting with multiple other relay nodes and terminal devices (such as display devices) to form a highly scalable distributed network. SuperNode uses efficient data transmission protocols, such as UDP, to ensure low latency and high reliability, and performs preliminary analysis and optimization processing on the received live broadcast signal. Then, using multicast technology, SuperNode simultaneously forwards the processed signal to multiple display devices. Each device is configured with a time synchronization mechanism to ensure consistent playback time, ensuring the synchronous output of images between multiple display devices.

[0016] An edge computing module is integrated in the relay node and connected to the cloud, and the cloud has an adaptive synchronization analysis module.

[0017] Specifically, the edge computing module is integrated into the SuperNode relay node, which can process live broadcast data nearby to reduce the delay and bandwidth consumption of data transmission to the cloud. Through the preliminary analysis of the live broadcast signal, the edge computing module can quickly identify the characteristics of the video frame rate, resolution, etc., and optimize the processing to improve the processing efficiency of the live broadcast content.

[0018] At the same time, the edge computing module maintains a close connection with the cloud, and uploads key data and analysis results to the cloud through a high-speed and secure network channel. The cloud-based adaptive synchronization analysis module receives the analysis results from the edge computing module, and optimizes and adjusts the synchronization status of each display device through big data analysis and machine learning algorithms. By integrating edge computing and cloud-based adaptive synchronization analysis, not only the real-time and stability of the live broadcast service are improved, but also the intelligent management and optimization of the live broadcast content are realized, bringing users a better and smarter live broadcast viewing experience.

[0019] The network signal of the display device is monitored by the edge computing module, and a preliminary synchronization analysis is performed based on the live broadcast signal and sent to the cloud.

[0020] Furthermore, the network signal of the display device is monitored by the edge computing module, and a preliminary synchronization analysis is performed based on the live broadcast signal, including: real-time monitoring of the network signal of the display device to identify network transmission characteristics, including bandwidth, latency, and packet loss rate; performing signal characteristic analysis based on the live broadcast signal to obtain live broadcast characteristics, including video frame rate, video bit rate, and audio bit rate; the edge computing module compares the network transmission characteristics with the live broadcast characteristics to evaluate the carrying relationship of the network transmission data volume and determine the preliminary synchronization analysis results.

[0021] Specifically, the edge computing module monitors the network signals of each display device in real time, identifies and obtains the transmission characteristics of each network, including bandwidth, latency and packet loss rate, monitors the available bandwidth of the display device in real time, and indicates the amount of data that the network can transmit per unit time. Latency refers to the time required to measure the signal from the source to the destination, and calculates the proportion of the number of data packets lost during the transmission process, which affects the integrity of the signal. The network transmission characteristics reflect the transmission capacity and quality of the network. At the same time, the edge computing module performs signal feature analysis on the received live signal and extracts live broadcast features such as video frame rate, video bit rate, audio bit rate, and resolution. Video frame rate refers to the number of frames transmitted per second, which affects the smoothness of the video. Video bit rate refers to the transmission rate of video data. Audio bit rate refers to the transmission rate of audio data, which affects the sound quality. Resolution refers to the clarity of the video. The live broadcast feature refers to the quality and playback requirements of the signal. Then, the edge computing module compares the obtained network transmission characteristics with the live broadcast characteristics to evaluate the carrying relationship of the network transmission data volume, that is, whether the network can support the current live broadcast requirements. The preliminary synchronization analysis results are obtained through the evaluation results, and the synchronization analysis results are further optimized by the cloud-based adaptive synchronization analysis module. Preliminary analysis achieved through edge computing helps to quickly identify and resolve network synchronization issues, improving the efficiency of signal transmission and the accuracy of synchronized playback.

[0022] The cloud performs synchronization optimization through the adaptive synchronization analysis module based on the synchronization analysis results received from each display device, generates a synchronization strategy and sends it to the edge computing module for live broadcast synchronization control.

[0023] Specifically, the cloud receives the synchronization analysis results from each display device, and analyzes each received synchronization analysis result according to the adaptive synchronization analysis module to identify the synchronization status of the current live broadcast and potential optimization points. Using time series analysis and prediction models, the changing trend of future network status is evaluated, and a synchronization strategy is generated, including suggestions such as adjusting network bandwidth usage, optimizing playback rate, and dynamic buffering strategy. The synchronization strategy is then sent back to the edge computing module to perform real-time live broadcast synchronization control and achieve fine synchronization control of the live stream. Through intelligent analysis and strategy adjustment, the synchronization consistency between display devices is ensured, and the transmission quality of the live broadcast signal and user experience are improved.

[0024] In one embodiment, Figure 2 As shown, the generated synchronization strategy is sent to the edge computing module for live broadcast synchronization control, including: configuring a time synchronization mechanism on the relay node and the multiple display devices to unify the time synchronization of all devices; determining the playback time according to the synchronization strategy, and generating a timestamp or synchronization signal based on the playback time and embedding it into the live broadcast signal; the edge computing module identifies the playback time, and synchronously forwards the live broadcast signal to the multiple display devices, and the multiple display devices perform synchronous live broadcast display based on the time synchronization mechanism.

[0025] Specifically, first, a time synchronization mechanism, such as NTP (Network Time Protocol) or PTP (Precision Time Protocol), is configured on the relay node and multiple display devices to ensure that the time bases on all display devices are consistent, providing an accurate time reference for subsequent synchronization control. Then, according to the generated synchronization strategy, the playback time of the live content is determined, and a timestamp or synchronization signal is generated according to the playback time, and the timestamp or synchronization signal is embedded in the live signal to guide the playback behavior of each device and ensure that the live signal can maintain a high degree of time consistency during the transmission process. The edge computing module identifies the playback time information in the signal and accurately and synchronously forwards the live signal to multiple display devices accordingly. After receiving the live signal, each display device will use the previously configured time synchronization mechanism to fine-tune according to the timestamp or synchronization signal in the signal to ensure that the playback time of the live content on all display devices is strictly aligned, ensuring the synchronization consistency between the display devices and improving the transmission quality and user experience of the live signal.

[0026] Further, the evaluation of the bearing relationship of the network transmission data volume and determining the preliminary synchronization analysis result include: using the bearing relationship evaluation formula: , determine the matching coefficient S, and use the matching coefficient S as the preliminary synchronization analysis result, wherein, is the network bandwidth, L is the latency, P is the packet loss rate, is the video bit rate, is the video frame rate, is the audio bit rate, Ideal video frame rate, is the weight coefficient.

[0027] Specifically, when evaluating the bearer relationship of the network transmission data volume, the edge computing module uses a comprehensive bearer relationship evaluation formula to determine the matching coefficient S, which reflects the degree of matching between the network transmission capacity and the live signal demand. The bearer relationship evaluation formula is: ,in, is the network bandwidth, L is the latency, P is the packet loss rate, is the video bit rate, is the video frame rate, is the audio bit rate, For the ideal video frame rate, is the weight coefficient.

[0028] By calculating the matching coefficient S, we can quantify the matching degree between the network transmission characteristics and the live broadcast requirements. The choice can be adjusted according to the actual scenario to balance the impact of various indicators on synchronization evaluation. For example, in a scenario where bandwidth is critical, increasing The weight of can better reflect the priority of bandwidth. In this process, the level of matching coefficient S directly determines the adaptability of the network. A high S value means that the network is in good condition and can effectively support the current live broadcast needs, while a low S value indicates that the live broadcast parameters may need to be adjusted or the network may need to be optimized. Finally, the matching coefficient S is used as a preliminary synchronization analysis result to provide a basis for subsequent synchronization strategy optimization. Through this step, the network status can be dynamically evaluated and adapted to ensure the smooth transmission of the live broadcast signal and the synchronous playback of each device.

[0029] Furthermore, the determination of the preliminary synchronization analysis results also includes: the edge computing module performs preliminary synchronization strategy analysis according to the preliminary synchronization analysis results, calculates the buffer length based on the latency and packet loss rate, and calculates the playback rate based on the current network bandwidth, video bit rate, and audio bit rate; uses the buffer length and the playback rate as the preliminary synchronization strategy for the corresponding display device, and adds the buffer length and the playback rate to the preliminary synchronization analysis results.

[0030] Specifically, the edge computing module performs preliminary synchronization strategy analysis on the preliminary synchronization analysis results. The synchronization strategy includes calculating the buffer length and playback rate of each device. Among them, the buffer length B is calculated based on the latency and packet loss rate. The calculation formula of the buffer length B is: ,in, is the buffer length of the i-th device, is the network delay of the ith device, is the packet loss rate of the ith device, is a weight coefficient used to adjust the impact of latency and packet loss rate on buffer length. By calculating the buffer length, the appropriate buffer size can be dynamically configured for each device to adapt to changes in real-time network conditions and reduce playback interruptions caused by network instability.

[0031] In addition to calculating the buffer length, the playback rate R is calculated based on the current network bandwidth, video bit rate, and audio bit rate. The calculation formula for the playback rate R is: ,in, is the playback rate of the i-th device, is the current network bandwidth, is the total bit rate, including the sum of the video bit rate and the audio bit rate. To adjust the weight coefficient of the playback rate, set It can effectively control the device playback rate in a complex and changing network environment. By calculating the playback rate, it can ensure that the device plays video content smoothly without exceeding the network bandwidth limit, thereby optimizing the user experience.

[0032] Finally, the calculated buffer length and playback rate are used as the preliminary synchronization strategy for each display device, and these strategy information are integrated into the preliminary synchronization analysis results. Through this dynamic adjustment, it is possible to respond to different network conditions and device requirements in real time, achieve more accurate and efficient synchronization of live broadcasts, and thus improve the overall live broadcast service quality.

[0033] Furthermore, the cloud performs synchronization optimization through the adaptive synchronization analysis module based on the synchronization analysis results received from each display device, and generates a synchronization strategy that is sent to the edge computing module for live synchronization control, including: obtaining preliminary synchronization strategies of all relay nodes, and building a buffer length strategy set and a playback rate strategy set; taking picture synchronization output as the goal, screening consistent strategy parameters in the buffer length strategy set and the playback rate strategy set, the consistent strategy parameters being a synchronization control strategy with a maximum buffer length and a minimum playback rate; determining whether the consistent strategy parameters meet the live picture output requirements; and when the live picture output requirements are met, sending the consistent strategy parameters as a synchronization strategy to the edge computing module for live synchronization control.

[0034] Specifically, in the cloud-based adaptive synchronization analysis module, the preliminary synchronization strategies of all relay nodes are obtained, and the buffer length strategy set and the playback rate strategy set are constructed based on the buffer length and playback rate of the preliminary synchronization strategy. In order to achieve the goal of screen synchronization, the synchronization control strategy parameters with the maximum buffer length and the minimum playback rate are selected from the strategy set and regarded as consistent strategy parameters. The consistent strategy parameters are the node devices with the worst network conditions. If all devices can synchronize with this node, it can ensure that the screens of all devices can be displayed synchronously.

[0035] After determining the consistent policy parameters, determine whether the consistent policy parameters meet the live screen output requirements, that is, determine whether they meet the output quality. Optionally, pre-set playback quality assessment thresholds, such as minimum frame rate, maximum delay, minimum buffer, etc., and compare the consistent policy parameters with the pre-set playback quality assessment parameter thresholds. If the consistent policy parameters are equal to or greater than the playback quality assessment parameter thresholds, it means that the consistent policy parameters meet the live screen output requirements, indicating that the network of all devices meets the live screen output requirements, and can be directly executed according to this policy and sent to the edge computing module for live synchronization control. Based on this, both picture synchronization and playback quality are guaranteed, and effective management of live quality and synchronization is achieved in a dynamic network environment, providing a high-quality live experience.

[0036] Furthermore, it is determined whether the consistent strategy parameters meet the live screen output requirements, and then it also includes: when the live screen output requirements are not met, the adaptive synchronization analysis module performs time series prediction based on the synchronization analysis results of each device input and the network signal, wherein the adaptive synchronization analysis module has a pre-trained time series prediction model to obtain timing synchronization features, and the timing synchronization features characterize future network feature change information and the synchronization relationship of each device; based on the timing synchronization features, with picture synchronization as the goal, the node pre-buffer compensation strategy is optimized in combination with the preliminary synchronization strategy of all relay nodes, and the live screen output requirements are used as constraints to obtain the synchronization strategy and send it to the edge computing module for live synchronization control.

[0037] Specifically, when the consistent strategy parameter is less than the playback quality evaluation parameter threshold, it means that the consistent strategy parameter does not meet the live screen output requirements, there are quality or synchronization problems, and the strategy needs to be adjusted. The pre-trained time series prediction model built into the adaptive synchronization analysis module is used to perform time series prediction on the synchronization analysis results and network signals of each device input, and output the timing synchronization features. The timing synchronization features include predictions of network conditions (such as bandwidth changes, delay trends, packet loss rates) and device performance changes in the future, characterizing future network feature change information and the synchronization relationship between devices. The time series prediction model is ARIMA, LSTM or GRU, etc., and historical network data, including bandwidth, delay, packet loss rate and other information, is collected through network monitoring tools or logs. The performance data of each device, such as processing power, playback delay, buffer status, etc., is collected through device logs, user feedback or special performance monitoring software. Record the synchronization status and playback quality indicators of each device, such as the number of playback interruptions, audio and video asynchrony, etc. Remove noise data and outliers, fill in missing data, ensure data quality, standardize or normalize data to improve the efficiency and effectiveness of model training, extract useful features, generate input data for training, for example, calculate the past bandwidth change rate, delay change rate, etc. as input features, and divide the data into training set, validation set, and test set. Use the training set to train the model, adjust the hyperparameters to optimize model performance, use the validation set to evaluate the model's prediction accuracy, adjust the model structure and parameters, and perform a final evaluation of the model through the test set to ensure that the model performs well on unseen data. According to the test results, make necessary adjustments and optimizations to the model to obtain the final trained time series prediction model, and integrate the trained model into the adaptive synchronization analysis module.

[0038] According to the timing synchronization characteristics, with the synchronization of multi-device live screens as the goal, combined with the preliminary synchronization strategies of all relay nodes, the node pre-buffer compensation strategy is optimized. By fine-tuning the pre-buffer compensation strategy of each node, the impact of network delay and device performance differences is balanced to ensure the smoothness and synchronization of live content. In the optimization process, the live screen output requirements are always taken as strict constraints to ensure that all adjustment strategies can meet the user's high standards for live quality. Finally, the optimized synchronization strategy is obtained and sent to the edge computing module, which executes the synchronization strategy in real time during the live broadcast process to perform live broadcast synchronization control and achieve precise synchronization control of the live stream. By utilizing the future network trends provided by the prediction model and combining the real-time device and network analysis results, the synchronization strategy can not only adapt to the current network conditions, but also respond to possible changes in advance, ensuring that the live screen can maintain a high degree of synchronization and stability under any circumstances, and improving the user's viewing experience.

[0039] Further, the synchronization strategy is obtained, including: based on the preliminary synchronization strategy of each relay node, the deviation synchronization node is screened with the live screen output requirement as the baseline, and the deviation synchronization node is a display device that does not meet the live screen output requirement; the deviation amount of the deviation synchronization node is used for pre-buffer compensation analysis to determine the first pre-buffer compensation strategy; the first pre-buffer compensation strategy and the preliminary synchronization strategy are sent as the first synchronization strategy to the edge computing module for live synchronization control; the adaptive synchronization analysis module optimizes the node buffer compensation strategy for the deviation synchronization node device according to the preliminary synchronization strategy of the relay node based on the timing synchronization characteristics obtained in real time, with the goal of minimizing the playback time difference between each device, to obtain a second pre-buffer compensation strategy, and integrates the second pre-buffer compensation strategy with the preliminary synchronization strategy of the corresponding timing relationship to generate a second synchronization strategy, and sends the second synchronization strategy to the edge computing module for live synchronization control; based on the adaptive synchronization analysis module, according to the timing synchronization characteristics and the preliminary synchronization strategy, iteratively optimizes with the goal of minimizing the playback time difference between each device and with the live screen output requirement as a constraint condition, to obtain an adaptive synchronization strategy and send it to the edge computing module for live synchronization control until all live screens are output.

[0040] Specifically, by taking the live screen output requirements as the baseline, analyzing the preliminary synchronization strategies of each relay node, identifying the display devices that do not meet the live screen output requirements, and marking them as deviation synchronization nodes. Then calculate the deviation amount of each deviation synchronization node, such as the degree to which the delay exceeds the standard, the proportion of packet loss rate exceeding the standard, etc. According to the deviation amount, perform pre-buffer compensation analysis, calculate the appropriate buffer adjustment and rate compensation, and determine the first pre-buffer compensation strategy for each deviation synchronization node. Combine the first pre-buffer compensation strategy with the preliminary synchronization strategy to form an overall first synchronization strategy, and send the first synchronization strategy to the edge computing module, which performs live synchronization control.

[0041] During the execution of the first synchronization strategy, the adaptive synchronization analysis module analyzes the timing synchronization characteristics, such as network delay fluctuations, differences in device processing capabilities, and future network load predictions, with the goal of minimizing the playback time difference between devices. It uses genetic algorithms, particle swarm optimization algorithms, gradient descent and other optimization algorithms to perform refined node buffer compensation strategy optimization on the deviation synchronization node devices, and generates a more refined second pre-cache compensation strategy to dynamically adapt and accurately adjust the buffer and rate of the deviation synchronization node. Then, the second pre-cache compensation strategy is combined with the preliminary synchronization strategy of each device to generate the second synchronization strategy, which is sent to the edge computing module for live broadcast synchronization control.

[0042] During the whole process, with the goal of minimizing the playback time difference between devices, the adaptive synchronization analysis module continuously adjusts the synchronization strategy through an iterative optimization mechanism based on the timing synchronization characteristics and the preliminary synchronization strategy. In each iteration, the buffer length and playback rate are adjusted to reduce errors. For deviation synchronization nodes, the buffer length is increased and the playback rate is adjusted first. Gradient descent or other optimization algorithms are used to adjust the strategy according to the direction of error change until the maximum number of iterations is reached to generate the final adaptive synchronization strategy. The adaptive synchronization strategy can dynamically adapt to various changes in the live broadcast process, ensuring that while meeting the live screen output requirements, it has a high degree of synchronization on all display devices until all live screen outputs achieve the expected effect. Through dynamic adjustment and optimization, it is possible to maintain a high-quality multi-device synchronous live broadcast experience in a complex network environment, improving the user's viewing experience.

[0043] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

[0044] This specification and the drawings are merely exemplary illustrations of the present application and are deemed to cover any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, a person skilled in the art may make various modifications and variations 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 equivalents, the present application intends to include these modifications and variations.

Claims

1. A management method for synchronous output of live broadcast images of multiple devices, characterized in that: The management method for synchronously outputting live broadcast images of multiple devices includes: Establish a distributed live broadcast architecture centered on SuperNode. SuperNode acts as a relay node to receive live broadcast signals and synchronously forward the live broadcast signals to multiple display devices. An edge computing module is integrated in the relay node and connected to the cloud, and the cloud has an adaptive synchronization analysis module; The edge computing module monitors the network signal of the display device, performs preliminary synchronization analysis based on the live broadcast signal, and sends it to the cloud; The cloud performs synchronization optimization through the adaptive synchronization analysis module according to the received synchronization analysis results, generates a synchronization strategy and sends it to the edge computing module for live broadcast synchronization control; The network signal of the display device is monitored by the edge computing module, and a preliminary synchronization analysis is performed based on the live broadcast signal, including: Monitor the network signals of display devices in real time and identify network transmission characteristics, including bandwidth, latency, and packet loss rate; Performing signal feature analysis on the live broadcast signal to obtain live broadcast features, including video frame rate, video bit rate, and audio bit rate; The edge computing module compares the network transmission characteristics with the live broadcast characteristics, evaluates the carrying relationship of the network transmission data volume, and determines the preliminary synchronization analysis results; The evaluation of the carrying relationship of the network transmission data volume and determination of the preliminary synchronization analysis results include: Evaluation formula through load relationship: , determine the matching coefficient S, and use the matching coefficient S as the preliminary synchronization analysis result, wherein, is the network bandwidth, L is the latency, P is the packet loss rate, is the video bit rate, is the video frame rate, is the audio bit rate, For the ideal video frame rate, is the weight coefficient.

2. The management method for synchronous output of multi-device live screens according to claim 1, characterized in that: The generated synchronization strategy is sent to the edge computing module for live broadcast synchronization control, including: Configuring a time synchronization mechanism on the relay node and the multiple display devices to unify the time synchronization of all devices; Determine the playback time according to the synchronization strategy, and generate a timestamp or a synchronization signal based on the playback time and embed it into the live signal; The edge computing module identifies the playback time and synchronously forwards the live broadcast signal to multiple display devices, and the multiple display devices perform synchronous live broadcast display based on a time synchronization mechanism.

3. The management method for synchronous output of multi-device live broadcast images according to claim 1, characterized in that: The step of determining the preliminary synchronization analysis result further includes: The edge computing module performs preliminary synchronization strategy analysis according to the preliminary synchronization analysis result, calculates the buffer length based on the latency and packet loss rate, and calculates the playback rate based on the current network bandwidth, video bit rate, and audio bit rate; The buffer length and the playback rate are used as a preliminary synchronization strategy for a corresponding display device, and the buffer length and the playback rate are added to the preliminary synchronization analysis result.

4. The management method for synchronous output of multi-device live broadcast images as claimed in claim 3, characterized in that: The cloud performs synchronization optimization through the adaptive synchronization analysis module according to the received synchronization analysis results, generates a synchronization strategy and sends it to the edge computing module for live broadcast synchronization control, including: Obtain the preliminary synchronization strategies of all relay nodes, and construct the buffer length strategy set and the playback rate strategy set; Taking synchronous output of pictures as the goal, screening consistent policy parameters in the buffer length policy set and the play rate policy set, wherein the consistent policy parameters are a synchronous control policy with a maximum buffer length and a minimum play rate; Determine whether the consistent policy parameters meet the live screen output requirements; When the live screen output requirements are met, the consistent strategy parameters are sent as a synchronization strategy to the edge computing module for live synchronization control.

5. The management method for synchronous output of multi-device live screens according to claim 4, characterized in that: Determine whether the consistent policy parameters meet the live screen output requirements, and then further include: When the live screen output requirement is not met, the adaptive synchronization analysis module performs time series prediction according to the synchronization analysis result and the network signal, wherein the adaptive synchronization analysis module has a pre-trained time series prediction model to obtain time series synchronization features, and the time series synchronization features represent future network feature change information and the synchronization relationship of each device; According to the timing synchronization characteristics, the screen synchronization is taken as the goal, and the node pre-buffer compensation strategy is optimized in combination with the preliminary synchronization strategy of all relay nodes. The live screen output requirement is used as a constraint condition to obtain the synchronization strategy and send it to the edge computing module for live synchronization control.

6. The management method for synchronous output of multi-device live screens according to claim 5, characterized in that: Obtaining the synchronization strategy includes: Based on the preliminary synchronization strategy of each relay node, the deviation synchronization node is screened with the live screen output requirement as the baseline, and the deviation synchronization node is a display device that does not meet the live screen output requirement; Performing pre-buffer compensation analysis based on the deviation amount of the deviation synchronization node to determine a first pre-buffer compensation strategy; Sending the first pre-buffer compensation strategy and the preliminary synchronization strategy as the first synchronization strategy to the edge computing module for live broadcast synchronization control; The adaptive synchronization analysis module optimizes the node buffer compensation strategy for the deviation synchronization node device according to the initial synchronization strategy of the relay node based on the timing synchronization characteristics obtained in real time, with the goal of minimizing the playback time difference between the devices, obtains the second pre-caching compensation strategy, and integrates the second pre-caching compensation strategy with the initial synchronization strategy of the corresponding timing relationship to generate a second synchronization strategy, and sends the second synchronization strategy to the edge computing module for live broadcast synchronization control; The adaptive synchronization analysis module performs iterative optimization based on the timing synchronization characteristics and preliminary synchronization strategy, with the goal of minimizing the playback time difference between devices and taking the live screen output requirements as constraints, and obtains an adaptive synchronization strategy that is sent to the edge computing module for live synchronization control until all live screen outputs are completed.

Images