All-media all-signal integrated management platform

Abstract

The application discloses a full-media full-signal integrated management platform, and belongs to the technical field of information processing and communication. The application comprises: a timing perception intelligent core, a network transmission control module, and a full-media signal processing module. The timing perception intelligent core comprises a global clock synchronization module, a delay prediction compensation module, an intelligent signal routing module, and an adaptive coding and decoding engine. The network transmission control module comprises a network adaptive transmission engine, a multi-path load balancer, and a fault switching recovery module. The full-media signal processing module is constructed based on a micro-service architecture and comprises a multi-modal signal acquisition module, a signal preprocessing module, a media content management center, a timing synchronization processing tool, and a unified API gateway. The intelligent resource scheduling pool provides intelligent scheduling management of computing, storage, network, and AI computing power, and realizes resource demand prediction, multi-target optimization distribution, and automatic elastic expansion and contraction.

Description

Technical Field

[0001] This invention relates to the field of information processing and communication technology, specifically to an integrated management platform for all media and signals based on a distributed cloud-native architecture, which is suitable for unified access, timing control, encoding and decoding processing, and transmission scheduling management of multi-source and multi-type media data. Background Technology

[0002] In recent years, with the continuous enrichment of media dissemination methods and the ongoing expansion of multimedia application scenarios, media content has shown a diversified and integrated development trend. The collaborative needs of various audio and video signals, sensor data, and graphic information in actual business operations are constantly increasing. Traditional media management systems are mostly loosely constructed and generally suffer from problems such as dispersed processing capabilities, limited signal type support, and low efficiency of inter-system collaboration, making it difficult to adapt to the current technical requirements for unified access and management of multi-source heterogeneous signals.

[0003] Meanwhile, the rapid evolution of cloud computing, edge computing, and network communication technologies has driven media processing systems towards distributed and virtualized architectures. However, in complex network environments, multiple media signals are susceptible to latency fluctuations, bandwidth jitter, and node clock skew during transmission, leading to timing imbalances and severely limiting the synchronous presentation effect on the terminal side. Furthermore, existing systems often use statically configured encoding, compression, and transmission protocols, lacking the ability to dynamically optimize based on actual network conditions, which is detrimental to ensuring stable operation of media services under high load or weak network environments.

[0004] On the other hand, existing resource management methods are mainly based on static allocation and have failed to establish an elastic scheduling mechanism based on dynamic changes in business. This results in uneven distribution of computing resources, storage capacity and network bandwidth in high-concurrency or low-utilization scenarios, slow response, and affects the overall system performance and service quality.

[0005] In summary, how to achieve unified access, timing synchronization, intelligent encoding and decoding, and efficient scheduling control of multiple types of media signals under a distributed cloud-native architecture, and build an integrated management platform with elastic scalability and network adaptability, has become an urgent technical problem to be solved. Summary of the Invention

[0006] To overcome a series of shortcomings in existing technologies, the purpose of this application is to provide an integrated management platform for all media and signals, based on a distributed cloud-native architecture, including:

[0007] The timing-aware intelligent hub includes a global clock synchronization module, a delay prediction and compensation module, an intelligent signal routing module, and an adaptive encoding and decoding engine. Specifically: the global clock synchronization module establishes a unified time base and predicts and corrects clock drift at each node in real time; the delay prediction and compensation module predicts node delay trends, dynamically generates compensation strategies, and adjusts buffer parameters; the intelligent signal routing module optimizes multi-target routes based on quality of service and network conditions, dynamically selecting the optimal transmission path; and the adaptive encoding and decoding engine adjusts encoding parameters according to content complexity, network conditions, and latency requirements.

[0008] The network transmission control module includes a network adaptive transmission engine, a multi-path load balancer, and a fault switching and recovery module. Specifically: the network adaptive transmission engine dynamically selects the transmission protocol based on network conditions to achieve media-aware congestion control and FEC coding rate adjustment; the multi-path load balancer distributes the load to multiple paths after receiving transmission tasks and achieves balanced transmission through dynamic weight adjustment; and the fault switching and recovery module monitors the link status in real time and supports predictive switching to backup paths.

[0009] The multimedia signal processing module, built on a microservice architecture, includes a multimodal signal acquisition module, a signal preprocessing module, a media content management center, a timing synchronization processing tool, and a unified API gateway. Specifically: the multimodal signal acquisition module uniformly accesses and standardizes various audio and video signals; the signal preprocessing module performs signal format conversion, noise suppression, and basic quality optimization; the media content management center enables unified storage and retrieval of multimedia content; the timing synchronization processing tool focuses on precise timing alignment and frame-level synchronization of multiple signals; and the unified API gateway is responsible for standardized data exchange and protocol conversion.

[0010] The intelligent resource scheduling pool provides intelligent scheduling and management of computing, storage, network and AI computing power, enabling resource demand prediction, multi-objective optimized allocation and automatic elastic scaling.

[0011] Furthermore, the global clock synchronization module combines the IEEE 1588 precision clock protocol and the NTP network time protocol to construct a three-level synchronization architecture consisting of a master clock source, slave clock nodes, and edge clock units: the master clock source is configured with an atomic clock or GPS clock as a reference source, with an accuracy of ±10 nanoseconds; the slave clock nodes maintain synchronization with the master clock source through the PTP protocol, with clock deviation controlled within ±50 nanoseconds; the edge clock units are responsible for the clock calibration of local devices, supporting automatic frequency offset compensation and phase-locked loop adjustment; when a clock offset exceeding ±100 nanoseconds is detected, it automatically switches to a backup clock source to ensure the continuity and reliability of clock synchronization.

[0012] Furthermore, the latency prediction compensation module includes an input layer, two LSTM hidden layers, and an output layer. The input layer receives a 64-dimensional feature vector, covering network bandwidth, packet loss rate, round-trip time, buffer utilization, CPU load, and memory utilization. The two LSTM hidden layers are configured with 128 and 64 neurons respectively, with a learning rate of 0.001, a batch size of 32, and a training window length of 200 time steps, used to predict the network latency change trend within the next 1 to 10 seconds.

[0013] Furthermore, the adaptive codec engine supports encoding standards including H.264, H.265, AV1, and VP9, ​​and includes a bitrate control module, a resolution adaptation module, and a frame rate adjustment module. Specifically: the bitrate control module adjusts the encoding bitrate in real time based on network bandwidth, supporting dynamic adjustment within the range of 500Kbps to 50Mbps, with an adjustment step size of 100Kbps; the resolution adaptation module enables automatic resolution switching from 480p to 4K, including 720p, 1080p, and 2K resolutions; and the frame rate adjustment module supports dynamic adjustment within the range of 15fps to 60fps, with an adjustment step size of 5fps.

[0014] Furthermore, the network adaptive transmission engine includes a protocol performance analyzer, a network status monitor, and a transmission protocol switching controller. The protocol performance analyzer supports TCP, UDP, QUIC, WebRTC, and SRT protocols, and performs protocol adaptability assessments for different media types and network environment characteristics. The network status monitor collects link bandwidth, round-trip time (RTT), packet loss rate, and network jitter metrics in real time. The transmission protocol switching controller comprehensively considers media stream characteristics, QoS requirements, and current network status to dynamically select the optimal transmission protocol. For real-time interactive scenarios, it prioritizes low-latency UDP or QUIC protocols, while for file transfers with high reliability requirements, it selects TCP. Protocol switching employs a parallel connection establishment method, and data streams are smoothly migrated after a new connection is established to ensure service continuity.

[0015] Furthermore, the fault handover and recovery module includes a link layer detector, a network layer monitor, and an application layer detector. The link layer detector monitors the physical link status, detecting parameters including signal strength, bit error rate, and link utilization, with a detection period of 500 milliseconds. The network layer monitor monitors route reachability and network latency, using ICMP and Traceroute tools for path probing, with a probing interval of 5 seconds and a probing timeout of 5 seconds. The application layer detector detects service availability through a heartbeat mechanism, with a heartbeat interval of 10 seconds and a timeout threshold of 3 consecutive failures. When the comprehensive fault risk assessment exceeds the threshold, a preventative handover is triggered, with the handover execution time controlled within 2 seconds. It also supports automatic back-off after fault recovery, with a back-off delay set to 30 seconds.

[0016] Furthermore, the multimodal signal acquisition module supports SDI, HDMI, IP stream, USB 3.0, and Gigabit Ethernet access, and includes a signal format detector, a standard converter, and a quality calibrator. The signal format detector automatically identifies the resolution, frame rate, color space, and encoding format of the input signal; the standard converter converts various format signals into a unified internal processing format; and the quality calibrator performs noise reduction, sharpening, and color correction on the signal.

[0017] Furthermore, the media content management center adopts a distributed storage architecture, including a content hash calculator, a shard storage manager, and a replica consistency controller. Specifically: the content hash calculator uses the SHA-256 algorithm to generate unique content identifiers; the shard storage manager divides large files into fixed-size data blocks, with a default block size of 2MB, supporting dynamic adjustments within the range of 1MB-16MB, and generating a CRC32 checksum for each data block; the replica consistency controller manages the consistency of data replicas, with a default number of 3 replicas, configurable to 2 to 5 replicas.

[0018] Furthermore, the timing synchronization processing tool is specifically responsible for the timing alignment and synchronization of multiple signals, and is compatible with SMPTE timecode, AES / EBU timecode, and user-defined timecode formats. It includes frame-level synchronizers, line-level synchronizers, and sample-level synchronizers. Specifically: the frame-level synchronizer ensures the time consistency between video frames, with synchronization errors controlled within ±2 frames; the line-level synchronizer is responsible for the field synchronization of interlaced video, achieving line-level synchronization accuracy; and the sample-level synchronizer is used to achieve precise synchronization of audio samples, with an accuracy of 1 sampling point, supporting sampling rates of 44.1kHz, 48kHz, and 96kHz.

[0019] Furthermore, the intelligent resource scheduling pool includes a resource demand predictor, a multi-objective optimization scheduler, and a resource efficiency analyzer. Specifically: the resource demand predictor predicts changes in resource demand for computing, storage, network, and AI computing power within a 1-hour to 24-hour time window based on historical usage data and business load patterns; the multi-objective optimization scheduler comprehensively considers resource utilization, service quality assurance, cost control, and energy efficiency objectives to find an approximate optimal solution for resource allocation; and the resource efficiency analyzer continuously monitors the usage and performance of various resources, identifies resource waste and performance bottlenecks, generates resource configuration optimization suggestions and adjustment strategies, and supports continuous improvement and dynamic optimization of resource configuration.

[0020] Compared with the prior art, this application has the following beneficial effects:

[0021] This application achieves high-precision timing consistency, transmission reliability, and resource optimization collaborative management of media signals in complex network environments by using multi-level clock synchronization, intelligent signal scheduling, adaptive encoding and decoding, and transmission mechanisms, combined with multi-modal acquisition and intelligent resource scheduling. Attached Figure Description

[0022] Figure 1 This is a schematic diagram of the all-media, all-signal integrated management platform disclosed in the embodiments of this application.

[0023] Figure 2 This is a timing interaction flowchart of the global clock synchronization module in this application, which shows in detail the synchronization process between the master clock source, slave clock nodes and edge clock units.

[0024] Figure 3 This is a schematic diagram of the overall architecture of the delay prediction compensation module in this application. Detailed Implementation

[0025] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of the embodiments of this invention will be described in more detail below with reference to the accompanying drawings. In the drawings, the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The described embodiments are some embodiments of this invention, but not all embodiments.

[0026] Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0027] The embodiments and directional terms described below with reference to the accompanying drawings are exemplary and intended to explain the present invention, and should not be construed as limiting the present invention.

[0028] In a broad embodiment of the present invention, the all-media, all-signal integrated management platform, based on a distributed cloud-native architecture resource construction, includes:

[0029] The timing-aware intelligent hub includes a global clock synchronization module, a delay prediction and compensation module, an intelligent signal routing module, and an adaptive encoding and decoding engine. Specifically: the global clock synchronization module establishes a unified time base and predicts and corrects clock drift at each node in real time; the delay prediction and compensation module predicts node delay trends, dynamically generates compensation strategies, and adjusts buffer parameters; the intelligent signal routing module optimizes multi-target routes based on quality of service and network conditions, dynamically selecting the optimal transmission path; and the adaptive encoding and decoding engine adjusts encoding parameters according to content complexity, network conditions, and latency requirements.

[0030] The network transmission control module includes a network adaptive transmission engine, a multi-path load balancer, and a fault switching and recovery module. Specifically: the network adaptive transmission engine dynamically selects the transmission protocol based on network conditions to achieve media-aware congestion control and FEC coding rate adjustment; the multi-path load balancer distributes the load to multiple paths after receiving transmission tasks and achieves balanced transmission through dynamic weight adjustment; and the fault switching and recovery module monitors the link status in real time and supports predictive switching to backup paths.

[0031] The multimedia signal processing module, built on a microservice architecture, includes a multimodal signal acquisition module, a signal preprocessing module, a media content management center, a timing synchronization processing tool, and a unified API gateway. Specifically: the multimodal signal acquisition module uniformly accesses and standardizes various audio and video signals; the signal preprocessing module performs signal format conversion, noise suppression, and basic quality optimization; the media content management center enables unified storage and retrieval of multimedia content; the timing synchronization processing tool focuses on precise timing alignment and frame-level synchronization of multiple signals; and the unified API gateway is responsible for standardized data exchange and protocol conversion.

[0032] The intelligent resource scheduling pool provides intelligent scheduling and management of computing, storage, network and AI computing power, enabling resource demand prediction, multi-objective optimized allocation and automatic elastic scaling.

[0033] Furthermore, the global clock synchronization module combines the IEEE 1588 precision clock protocol and the NTP network time protocol to construct a three-level synchronization architecture consisting of a master clock source, slave clock nodes, and edge clock units: the master clock source is configured with an atomic clock or GPS clock as a reference source, with an accuracy of ±10 nanoseconds; the slave clock nodes maintain synchronization with the master clock source through the PTP protocol, with clock deviation controlled within ±50 nanoseconds; the edge clock units are responsible for the clock calibration of local devices, supporting automatic frequency offset compensation and phase-locked loop adjustment; when a clock offset exceeding ±100 nanoseconds is detected, it automatically switches to a backup clock source to ensure the continuity and reliability of clock synchronization.

[0034] Furthermore, the latency prediction compensation module includes an input layer, two LSTM hidden layers, and an output layer. The input layer receives a 64-dimensional feature vector, covering network bandwidth, packet loss rate, round-trip time, buffer utilization, CPU load, and memory utilization. The two LSTM hidden layers are configured with 128 and 64 neurons respectively, with a learning rate of 0.001, a batch size of 32, and a training window length of 200 time steps, used to predict the network latency change trend within the next 1 to 10 seconds.

[0035] The size of the transmission buffer is dynamically adjusted based on the prediction results. The buffer capacity ranges from 512KB to 8MB. The adjustment step size adopts an adaptive algorithm, which is 64KB for small capacity and 512KB for large capacity. Combined with historical statistical data, it realizes active compensation for latency jitter, thereby improving the stability and quality of network transmission.

[0036] Furthermore, the adaptive codec engine supports encoding standards including H.264, H.265, AV1, and VP9, ​​and includes a bitrate control module, a resolution adaptation module, and a frame rate adjustment module. Specifically: the bitrate control module adjusts the encoding bitrate in real time based on network bandwidth, supporting dynamic adjustment within the range of 500Kbps to 50Mbps, with an adjustment step size of 100Kbps; the resolution adaptation module enables automatic resolution switching from 480p to 4K, including 720p, 1080p, and 2K resolutions; and the frame rate adjustment module supports dynamic adjustment within the range of 15fps to 60fps, with an adjustment step size of 5fps.

[0037] Furthermore, the network adaptive transmission engine includes a protocol performance analyzer, a network status monitor, and a transmission protocol switching controller. The protocol performance analyzer supports TCP, UDP, QUIC, WebRTC, and SRT protocols, and performs protocol adaptability assessments for different media types and network environment characteristics. The network status monitor collects link bandwidth, round-trip time (RTT), packet loss rate, and network jitter metrics in real time. The transmission protocol switching controller comprehensively considers media stream characteristics, QoS requirements, and current network status to dynamically select the optimal transmission protocol. For real-time interactive scenarios, it prioritizes low-latency UDP or QUIC protocols, while for file transfers with high reliability requirements, it selects TCP. Protocol switching employs a parallel connection establishment method, and data streams are smoothly migrated after a new connection is established to ensure service continuity.

[0038] Furthermore, the multi-path load balancer employs a hybrid strategy of weighted round-robin scheduling and least-connection scheduling. Weight calculation considers path bandwidth capacity, current utilization, historical performance metrics, and link stability. The load allocation process includes four stages: task decomposition, path evaluation, load assignment, and performance monitoring. It supports granular load splitting and can perform differentiated allocation based on packet size, service priority, and latency sensitivity. The balancer also features load backflow and dynamic rebalancing capabilities. When a path experiences congestion or performance degradation, it can migrate some load to other paths while recording historical path performance data for weight adjustment and path selection optimization, ensuring maximum overall transmission efficiency and balanced load distribution.

[0039] Furthermore, the fault handover and recovery module includes a link layer detector, a network layer monitor, and an application layer detector. The link layer detector monitors the physical link status, detecting parameters including signal strength, bit error rate, and link utilization, with a detection period of 500 milliseconds. The network layer monitor monitors route reachability and network latency, using ICMP and Traceroute tools for path probing, with a probing interval of 5 seconds and a probing timeout of 5 seconds. The application layer detector detects service availability through a heartbeat mechanism, with a heartbeat interval of 10 seconds and a timeout threshold of 3 consecutive failures. When the comprehensive fault risk assessment exceeds the threshold, a preventative handover is triggered, with the handover execution time controlled within 2 seconds. It also supports automatic back-off after fault recovery, with a back-off delay set to 30 seconds.

[0040] Furthermore, the multimodal signal acquisition module supports SDI, HDMI, IP stream, USB 3.0, and Gigabit Ethernet access, and includes a signal format detector, a standard converter, and a quality calibrator. The signal format detector automatically identifies the resolution, frame rate, color space, and encoding format of the input signal; the standard converter converts various format signals into a unified internal processing format; and the quality calibrator performs noise reduction, sharpening, and color correction on the signal.

[0041] Furthermore, the signal preprocessing module includes a format normalizer, a noise suppressor, and a quality enhancer, wherein: the format normalizer unifies the signal format and parameters; the noise suppressor uses an adaptive filtering algorithm to reduce signal noise; and the quality enhancer optimizes signal quality through techniques such as sharpening and contrast adjustment to prepare for subsequent encoding and transmission.

[0042] Furthermore, the media content management center adopts a distributed storage architecture, including a content hash calculator, a shard storage manager, and a replica consistency controller. Specifically: the content hash calculator uses the SHA-256 algorithm to generate unique content identifiers; the shard storage manager divides large files into fixed-size data blocks, with a default block size of 2MB, supporting dynamic adjustments within the range of 1MB-16MB, and generating a CRC32 checksum for each data block; the replica consistency controller manages the consistency of data replicas, with a default number of 3 replicas, configurable to 2 to 5 replicas.

[0043] Furthermore, the timing synchronization processing tool is specifically responsible for the timing alignment and synchronization of multiple signals, and is compatible with SMPTE timecode, AES / EBU timecode, and user-defined timecode formats. It includes frame-level synchronizers, line-level synchronizers, and sample-level synchronizers. Specifically: the frame-level synchronizer ensures the time consistency between video frames, with synchronization errors controlled within ±2 frames; the line-level synchronizer is responsible for the field synchronization of interlaced video, achieving line-level synchronization accuracy; and the sample-level synchronizer is used to achieve precise synchronization of audio samples, with an accuracy of 1 sampling point, supporting sampling rates of 44.1kHz, 48kHz, and 96kHz.

[0044] Furthermore, the unified API gateway includes a service registration and discovery center, an API route manager, and a protocol conversion adapter. The service registration and discovery center is compatible with multiple registration services, including Consul, Eureka, and Etcd, with a registration response time controlled within 500 milliseconds, a health check interval of 30 seconds, a service offline detection time of 90 seconds, and supports the registration management of up to 1000 service instances. The API route manager supports routing rules based on paths, header information, and parameters, with a maximum of 5000 routing rules and a route lookup time controlled within 1 millisecond. The protocol conversion adapter supports multiple protocol conversions, including HTTP, HTTPS, WebSocket, and gRPC, with conversion latency controlled within 100 milliseconds.

[0045] Furthermore, the elastic computing implements containerized dynamic resource management, including a container orchestration scheduler, a resource monitoring and analysis system, and an auto-scaling controller. Specifically: the container orchestration scheduler, based on the Kubernetes architecture, supports automated deployment, service discovery, and load balancing of microservices, and intelligently schedules and allocates resources based on service priorities; the resource monitoring and analysis system collects real-time CPU utilization, memory usage, network I / O throughput, and disk I / O metrics, and assesses changes in resource demand through time series analysis and trend prediction algorithms; the auto-scaling controller automatically adjusts the number of computing instances based on preset elastic scaling strategies and real-time load monitoring data, supports scaling decisions based on multi-dimensional metrics, controls scaling response time to within 2-3 minutes, and employs a graceful shutdown mechanism to ensure uninterrupted service during scaling down. Simultaneously, it achieves an optimal balance between resource utilization and operating costs through resource reservation and cost optimization algorithms.

[0046] Furthermore, the intelligent resource scheduling pool includes a resource demand predictor, a multi-objective optimization scheduler, and a resource efficiency analyzer. Specifically: the resource demand predictor predicts changes in resource demand for computing, storage, network, and AI computing power within a 1-hour to 24-hour time window based on historical usage data and business load patterns; the multi-objective optimization scheduler comprehensively considers resource utilization, service quality assurance, cost control, and energy efficiency objectives to find an approximate optimal solution for resource allocation; and the resource efficiency analyzer continuously monitors the usage and performance of various resources, identifies resource waste and performance bottlenecks, generates resource configuration optimization suggestions and adjustment strategies, and supports continuous improvement and dynamic optimization of resource configuration.

[0047] The preferred embodiments of the present invention are described below with reference to the accompanying drawings, and the present invention will be further described in detail.

[0048] Example 1: Overall Architecture Implementation of the All-Media, All-Signal Integrated Management Platform

[0049] like Figure 1 As shown, the all-media all-signal integrated management platform provided by the present invention includes a timing-aware intelligent hub, a network transmission control module, an all-media signal processing module, and an intelligent resource scheduling pool.

[0050] 1. Specific Implementation of the Time-Aware Intelligent Hub

[0051] The timing-aware intelligent hub includes a global clock synchronization module, a delay prediction and compensation module, an intelligent signal routing module, and an adaptive encoding and decoding engine.

[0052] 1.1 Global Clock Synchronization Module

[0053] like Figure 2 As shown, the global clock synchronization module adopts a three-level synchronization architecture:

[0054] Master clock source: Equipped with a rubidium atomic clock as the reference clock source, achieving a clock accuracy of ±5 nanoseconds. The master clock source is calibrated via GPS satellite signals, performing automatic calibration every hour. When GPS signals are unavailable, it automatically switches to the built-in rubidium atomic clock to maintain the time reference.

[0055] Slave clock nodes: Deployed on various data center nodes, they maintain synchronization with the master clock source via the IEEE 1588 PTP protocol. Each slave clock node incorporates a temperature-compensated crystal oscillator (TCXO) and automatically selects the optimal clock path using the Optimal Master Clock Algorithm (BMCA) of the PTP protocol. A clock skew monitoring mechanism checks every 100 milliseconds, triggering a fast synchronization process when the skew exceeds ±30 nanoseconds.

[0056] Edge clock unit: Responsible for clock calibration of local devices, using phase-locked loop (PLL) technology to automatically compensate for frequency offset. The edge clock unit has a built-in clock quality assessment algorithm that comprehensively considers indicators such as clock offset, frequency stability, and phase noise to automatically evaluate the clock quality level.

[0057] The specific synchronization process is as follows: The master clock source broadcasts an Announce message every second, containing clock quality information and priority; after receiving the Announce message, the slave clock node runs the BMCA algorithm to select the best master clock; the slave clock node sends a Delay_Req message to the selected master clock to measure the path delay; the master clock source replies with a Delay_Resp message, and the slave clock node calculates the clock offset and adjusts its local clock; the edge clock unit synchronizes with the slave clock node periodically to maintain clock consistency within the local area network.

[0058] 1.2 Delay Prediction Compensation Module

[0059] like Figure 3 As shown, the delay prediction compensation module adopts a deep learning network architecture:

[0060] Input layer: Receives a 64-dimensional feature vector, including but not limited to: network bandwidth utilization (current value, 1-minute average, 5-minute average); packet loss rate (instantaneous value, sliding window average); round-trip time (Minimum, Average, Maximum, Standard Deviation); transmit buffer utilization and receive buffer utilization; CPU load (1-minute, 5-minute, 15-minute average load); memory usage and available memory; network interface queue length and interrupt frequency;

[0061] LSTM Hidden Layers: The first LSTM layer contains 128 neurons, using the tanh activation function, with a dropout rate of 0.2 to prevent overfitting. The second LSTM layer contains 64 neurons, also using the tanh activation function. The network uses the Adam optimizer with an exponentially decaying learning rate strategy, starting at 0.001 and decreasing by 0.96 every 1000 steps.

[0062] Output layer: Outputs the predicted delay sequence for the next 1 to 10 seconds using a linear activation function. The prediction results include the mean delay, variance, and confidence interval.

[0063] Buffer dynamic adjustment algorithm:

[0064] IF Predicted Delay Growth Rate > Threshold 1 THEN

[0065] Buffer size = MIN(current size × 1.5, 8MB)

[0066] ELSE IF Predicted Delay Decline Rate > Threshold 2THEN

[0067] Buffer size = MAX(current size × 0.8, 512KB)

[0068] END IF

[0069] The training process uses a sliding window approach with a window length of 200 time steps and a step size of 1. The model is updated every 1000 new samples collected, and an online learning method is used to adapt to changes in the network environment.

[0070] 1.3 Intelligent Signal Routing Module

[0071] The intelligent signal routing module employs a multi-objective optimization algorithm, comprehensively considering four objective functions: transmission delay, bandwidth utilization, link reliability, and load balancing.

[0072] Objective function definition:

[0073] Delay target: minΣ(path delay × traffic weight);

[0074] Bandwidth target: maxΣ (available bandwidth utilization);

[0075] Reliability target: maxΣ(link stability score);

[0076] Load balancing objective: minmax(load variance of each path).

[0077] The routing decision uses an improved genetic algorithm with a population size of 50, a crossover probability of 0.8, a mutation probability of 0.1, and 200 generations. The routing table is recalculated every 10 seconds, supporting dynamic route updates.

[0078] 1.4 Adaptive Encoding / Decoding Engine

[0079] The adaptive codec engine supports four encoding standards: H.264, H.265, AV1, and VP9. The specific implementation is as follows:

[0080] Bitrate control module: Employs a dynamic bitrate allocation algorithm based on scene complexity. First, it calculates the spatial complexity SI and time complexity TI of the video frame: Spatial complexity SI = standard deviation (Sobel edge detection results); Time complexity TI = standard deviation (difference between adjacent frames).

[0081] The target bitrate is dynamically adjusted based on the complexity: Target bitrate = base bitrate × (1 + α × SI + β × TI) × available network bandwidth coefficient, where α = 0.3 and β = 0.2 are empirical coefficients.

[0082] Resolution adaptation module: Resolution switching decisions are based on network bandwidth and device performance.

[0083] If available bandwidth < 1 Mbps, then resolution = 480p.

[0084] ELSE IF Available bandwidth < 3Mbps THEN Resolution = 720p

[0085] ELSE IF Available bandwidth < 8Mbps THEN Resolution = 1080p

[0086] ELSE IF Available bandwidth < 20Mbps THEN Resolution = 2K

[0087] ELSE resolution = 4K

[0088] Frame rate adjustment module: The frame rate adjustment uses a content-aware algorithm to maintain a high frame rate in fast-moving scenes and reduce the frame rate in static scenes to save bandwidth: Motion intensity = Σ|current frame - previous frame| / total number of pixels

[0089] If motion intensity > threshold, then frame rate = 60fps

[0090] ELSE frame rate = 30fps;

[0091] 2. Specific implementation of the network transmission control module

[0092] 2.1 Network Adaptive Transmission Engine

[0093] Protocol Performance Analyzer: Establishes performance evaluation models for different protocols.

[0094] TCP protocol score = reliability weight × 0.9 + latency weight × 0.3 + throughput weight × 0.7;

[0095] UDP protocol score = latency weight × 0.9 + throughput weight × 0.8 + reliability weight × 0.2;

[0096] QUIC protocol score = latency weight × 0.8 + reliability weight × 0.8 + throughput weight × 0.9.

[0097] Protocol switching strategy:

[0098] 1) Real-time interactive scenarios (RTT < 50ms requirement): Prioritize QUIC > UDP > WebRTC

[0099] 2) File transfer scenario: TCP > SRT > QUIC preferred

[0100] 3) Streaming media scenarios: Prioritize SRT > QUIC > UDP

[0101] Protocol switching employs a dual-connection parallel establishment method. After the new connection is successfully established, data flow migration is performed, and the old connection is closed after confirming that data transmission is complete.

[0102] 2.2 Multi-path load balancer

[0103] The load balancing algorithm employs a hybrid strategy of weighted round-robin and least connections.

[0104] Path weight calculation formula: W i = (Bandwidth capacity i / Σ bandwidth capacity) × (1 - current utilization i) × stability coefficient i;

[0105] The specific process of load balancing is as follows: After receiving a transmission task, determine whether fragmentation is needed based on the data size; data larger than 1MB is fragmented, and the fragment size is dynamically adjusted according to the path MTU; calculate the real-time weight and load score of each path; use a weighted round-robin algorithm to allocate data fragments to different paths; monitor the transmission progress of each path and dynamically adjust the subsequent allocation strategy.

[0106] 2.3 Fault Switching and Recovery Module

[0107] Three-layer detection mechanism:

[0108] Link layer detector: Monitors physical link parameters;

[0109] Signal strength detection: RSSI < -70dBm triggers an alarm;

[0110] Bit error rate detection: BER>10 -6 Trigger an alarm;

[0111] Link utilization: A congestion alarm is triggered when the utilization rate is >85%.

[0112] Network layer monitor:

[0113] Traceroute uses an improved ICMP protocol for path probing, supporting both IPv4 and IPv6 dual-stack. It probes latency and packet loss at each hop, building network topology maps and performance heatmaps.

[0114] Application layer detectors:

[0115] The heartbeat mechanism uses a lightweight probe packet containing a timestamp, sequence number, and checksum. Customizable heartbeat frequency and timeout thresholds are supported.

[0116] Failover decision algorithm:

[0117] Comprehensive fault risk assessment = Σ(detection layer weight i × anomaly score i); when the assessment value exceeds 0.7, a preventive handover is triggered, and when it exceeds 0.9, an emergency handover is triggered.

[0118] 3. Specific Implementation of the Multimedia Signal Processing Module

[0119] 3.1 Multimodal signal acquisition module

[0120] Signal format detector: Employs an automatic detection mechanism based on EDID (Extended Display Identification Data), supporting hot-plug detection. For IP stream signals, format parameters are obtained by parsing RTP / RTSP header information.

[0121] Detection process: The hardware interface detects the signal input; reads EDID information or parses the streaming media header; identifies resolution, frame rate, color space, and encoding format; generates a standardized signal descriptor; and triggers the format conversion process.

[0122] Standard converter:

[0123] The internal processing format uses a 4:2:2 YUV color space and supports 8-bit and 10-bit color depths. The conversion process includes color space conversion, resolution scaling, and frame rate conversion.

[0124] Color space conversion matrix (RGB to YUV):

[0125] Y = 0.299R + 0.587G + 0.114B

[0126] Cb=-0.169R-0.331G+0.500B+128

[0127] Cr=0.500R-0.419G-0.081B+128;

[0128] 3.2 Media Content Management Center

[0129] Distributed storage architecture: It adopts a 3-replica storage strategy, and the replica distribution algorithm ensures that replicas are distributed on different nodes in different racks.

[0130] Content Hash Calculator:

[0131] def calculate_hash(file_path):

[0132] sha256_hash=hashlib.sha256()

[0133] with open(file_path,"rb")as f:

[0134] for byte_block in iter(lambda:f.read(4096),b""):

[0135] sha256_hash.update(byte_block)

[0136] return sha256_hash.hexdigest().

[0137] Sharded Storage Manager: The sharding algorithm supports Reed-Solomon error correction coding and uses an (n,k) configuration, where n is the total number of shards and k is the number of data shards. The default configuration is (6,4), allowing a maximum of two shards to be lost without affecting data recovery.

[0138] The fragmented storage process is as follows: the file is divided into fixed-size data blocks; a CRC32 checksum is calculated for each data block; Reed-Solomon encoding is applied to generate redundant fragments; the fragments are distributed and stored on different storage nodes; and the metadata index and fragment location information are updated.

[0139] 3.3 Timing Synchronization Processing Tools

[0140] Multi-level synchronization architecture:

[0141] Frame-level synchronizer:

[0142] It employs a timecode locking mechanism and supports standard frame rates such as 25fps, 29.97fps, 30fps, 50fps, 59.94fps, and 60fps. The synchronization algorithm achieves frame synchronization by dynamically adjusting the buffer depth by comparing the timecode differences between adjacent signals.

[0143] Line-level synchronizer: Enables field synchronization for interlaced video by detecting the vertical sync signal VSync to achieve precise field boundary alignment. Supports interlaced scanning formats such as 576i and 480i.

[0144] Sample-level synchronizer:

[0145] Audio synchronization employs timestamp resampling technology, supporting sampling rates of 44.1kHz, 48kHz, and 96kHz. Synchronization accuracy reaches 1 sampling point, and latency jitter is controlled within ±10 sampling points.

[0146] Synchronization process: Extract time code information from each signal; calculate time deviation between signals; determine the reference signal (usually the most stable signal); perform delay compensation or acceleration processing on non-reference signals; monitor synchronization status in real time and adjust dynamically.

[0147] 4. Specific Implementation of the Intelligent Resource Scheduling Pool

[0148] 4.1 Flexible Computing Management

[0149] Container orchestration scheduler:

[0150] Built on Kubernetes v1.25, it supports custom scheduling policies and resource constraints.

[0151] Scheduling strategies include:

[0152] Affinity scheduling: Schedules related microservices to the same node to reduce network latency;

[0153] Anti-affinity scheduling: Reschedule backup services to different nodes to improve availability;

[0154] Resource reservation and scheduling: Reserve dedicated resources for critical services;

[0155] Load-aware scheduling: making scheduling decisions based on the real-time load of nodes.

[0156] Pod scheduling algorithm:

[0157] 1) Node filtering phase: Filtering nodes that do not meet resource requirements;

[0158] 2) Node scoring stage: Score candidate nodes based on multiple dimensions;

[0159] 3) Optimal node selection: Select the node with the highest score for Pod scheduling.

[0160] Automatic scaling controller: Implemented based on HPA (HorizontalPodAutoscaler) and VPA (VerticalPodAutoscaler):

[0161] HPA scaling strategy:

[0162]

[0163]

[0164] Expansion decision: Expansion is triggered when CPU or memory utilization exceeds a threshold for 3 consecutive minutes;

[0165] Shrinkage decision: Shrinkage is triggered when CPU and memory utilization remain below the threshold for 10 minutes.

[0166] 4.2 Intelligent Resource Scheduling

[0167] Resource demand forecaster: Employs a forecasting method combining time series analysis and machine learning.

[0168] ARIMA models are used to capture trends and seasonality in historical data:

[0169] In the ARIMA(p,d,q) model, p is the number of autoregressive terms, d is the difference order, and q is the number of moving average terms.

[0170] Random forest models are used to handle multidimensional features:

[0171] Features include: historical resource usage, business load metrics, time characteristics (hours, weeks, months), and external event markers;

[0172] Model parameters: number of trees 100, maximum depth 15, minimum number of split samples 20.

[0173] Multi-objective optimization scheduler: Optimization objective function: minF = w1 × resource utilization variance + w2 × service response time + w3 × operating cost + w4 × energy consumption.

[0174] Constraints:

[0175] Resource capacity constraint: Σ resource demand ≤ resource capacity;

[0176] QoS constraint: Response time ≤ SLA requirement;

[0177] Affinity constraint: Satisfy service deployment rules.

[0178] The NSGA-II multi-objective genetic algorithm was used to solve the problem. The population size was 100, the number of generations was 500, the crossover probability was 0.9, and the mutation probability was 0.1.

[0179] Example 2: Implementation of specific application scenarios

[0180] Scenario 1: Real-time transmission of 4K live streaming signals

[0181] A certain sporting event needs to transmit live 4K video signals to broadcasting platforms across the country in real time.

[0182] System Configuration:

[0183] Signal source: 4-channel 4K@60fps SDI signal input;

[0184] Encoding settings: H.265 encoding, target bitrate 25Mbps;

[0185] Transmission paths: 3 independent network paths (China Telecom, China Unicom, and China Mobile);

[0186] Target latency: End-to-end latency < 500ms.

[0187] Implementation process:

[0188] The multimodal signal acquisition module receives four SDI signals and automatically detects them as 4K@60fps format;

[0189] The signal preprocessing module performs noise reduction and color correction;

[0190] The adaptive codec engine selects H.265 encoding based on network conditions and dynamically adjusts the bitrate;

[0191] The intelligent signal routing module calculates the optimal transmission path and selects the path combination with the lowest latency;

[0192] The multipath load balancer distributes the encoded data to three paths for parallel transmission;

[0193] The receiving end timing synchronization processing tool performs multipath data merging and synchronization;

[0194] The global clock synchronization module ensures that the clock error of each node is less than 50 nanoseconds.

[0195] Scenario 2: Multipoint Video Conferencing System

[0196] The enterprise supports multi-point video conferencing, including the headquarters and five branch offices, with four camera signals in each meeting room.

[0197] System Configuration:

[0198] Signal source: 4 1080p@30fps cameras per conference room;

[0199] Number of participants: Maximum 50 people online at the same time;

[0200] Network environment: Public network transmission, unstable bandwidth;

[0201] Latency requirement: Interaction latency <200ms.

[0202] Key technology implementation:

[0203] The latency prediction and compensation module predicts network latency changes in each branch office in real time.

[0204] The adaptive encoding / decoding engine dynamically adjusts the resolution and bitrate based on network conditions;

[0205] The network adaptive transmission engine prioritizes the QUIC protocol to ensure low-latency transmission;

[0206] The fault switching and recovery module monitors the link status and automatically switches to a backup path when the network is abnormal; the intelligent resource scheduling pool dynamically adjusts the allocation of computing resources based on the number of online users.

[0207] Scene 3: Broadcast-grade production environment

[0208] Television station production and broadcasting systems need to process audio and video signals in various formats.

[0209] System requirements:

[0210] Signal formats: SDI, HDMI, IP stream and other input formats;

[0211] Processing precision: Supports 10-bit color depth and 4:2:2 color sampling;

[0212] Synchronization accuracy: Synchronization error of multiple signals < 1 frame;

[0213] Reliability: 99.99% system availability.

[0214] Technical Implementation:

[0215] The global clock synchronization module uses GPS and atomic clock as dual references to ensure nanosecond-level clock accuracy; the timing synchronization processing tool achieves precise synchronization at the SMPTE timecode level.

[0216] The media content management center uses highly reliable storage with 3 copies and error-correcting coding;

[0217] The fault switching and recovery module achieves dual redundancy at both the device and link levels;

[0218] The intelligent resource scheduling pool reserves dedicated resources for critical business operations to ensure service quality.

[0219] Example 3: Performance Optimization Implementation

[0220] 1. System performance indicators

[0221] Through large-scale testing, the system performance indicators of this invention are as follows:

[0222] Clock synchronization accuracy: ±25 nanoseconds (when GPS is available), ±100 nanoseconds (when GPS is unavailable);

[0223] End-to-end latency: <300ms (4K live streaming scenario), <150ms (1080p interactive scenario);

[0224] System throughput: Supports processing 1000 concurrent 1080p video streams;

[0225] Fault switching speed: <2 seconds to complete path switching;

[0226] Resource utilization: CPU average utilization 75%, memory average utilization 70%.

[0227] 2. Key Algorithm Optimization

[0228] Delay prediction algorithm optimization: Improving the LSTM network with an attention mechanism, resulting in a 15% improvement in prediction accuracy.

[0229]

[0230] Load balancing algorithm optimization: Introducing Markov decision process to optimize path selection strategy improves load balancing performance by 20%.

[0231] 3. System Integration and Deployment

[0232] The system supports multiple deployment modes:

[0233] Private cloud deployment: suitable for scenarios with high security requirements;

[0234] Hybrid cloud deployment: core functions in a private cloud, extended functions in a public cloud;

[0235] Edge computing deployment: Local processing reduces transmission latency.

[0236] The deployment architecture adopts a microservice design, supporting independent upgrades and expansion. Critical services are deployed in a master-slave mode to ensure high availability.

[0237] Through the above specific implementation methods, the present invention can effectively solve key technical problems such as timing synchronization, network transmission, and resource scheduling in multimedia signal processing, and achieve integrated management of high-quality, low-latency, and highly reliable multimedia signals.

[0238] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A full-media full-signal integrated management platform, based on a distributed cloud native architecture resource construction, characterized in that, Comprise: Timing-aware intelligent hub, including global clock synchronization module, delay prediction compensation module, intelligent signal routing module and adaptive coding and decoding engine, wherein: the global clock synchronization module establishes a unified time reference, real-time predicts and corrects the clock drift of each node; the delay prediction compensation module predicts the delay trend of the node, dynamically generates compensation strategy and adjusts the buffer parameters; the intelligent signal routing module realizes multi-objective routing optimization according to the quality of service and network state, and dynamically selects the optimal transmission path; the adaptive coding and decoding engine adjusts the coding parameters according to the content complexity, network condition and delay requirement; Network transmission control module, including network adaptive transmission engine, multi-path load balancer and fault switching recovery module, wherein: the network adaptive transmission engine dynamically selects the transmission protocol according to the network condition, realizes the congestion control and FEC coding rate adjustment of media awareness; the multi-path load balancer distributes the load to multiple paths after receiving the transmission task, and realizes balanced transmission through dynamic weight adjustment; the fault switching recovery module monitors the link state in real time, and supports predictive switching to the standby path; Full-media signal processing module, which is built based on micro-service architecture, including multi-modal signal acquisition module, signal preprocessing module, media content management center, timing synchronization processing tool and unified API gateway, wherein: the multi-modal signal acquisition module uniformly accesses and standardizes various audio and video signals; the signal preprocessing module realizes signal format conversion, noise suppression and basic quality optimization; the media content management center realizes unified storage and retrieval of multimedia content; the timing synchronization processing tool focuses on accurate timing alignment and frame-level synchronization of multiple signals; the unified API gateway is responsible for standardized data exchange and protocol conversion; Intelligent resource scheduling pool, which provides intelligent scheduling management of computing, storage, network and AI computing power, realizes resource demand prediction, multi-objective optimization distribution and automatic elastic expansion and contraction; The global clock synchronization module combines IEEE1588 precision clock protocol and NTP network time protocol to build a three-level synchronization architecture composed of master clock source, slave clock node and edge clock unit: the master clock source configures atomic clock or GPS clock as reference source, with an accuracy of ±10 nanoseconds; the slave clock node keeps synchronization with the master clock source through PTP protocol, with a clock deviation controlled within ±50 nanoseconds; the edge clock unit is responsible for clock calibration of local devices, supporting automatic compensation of frequency offset and phase-locked loop adjustment; when the clock offset exceeds ±100 nanoseconds, it automatically switches to the backup clock source, ensuring the continuity and reliability of clock synchronization; The delay prediction compensation module includes input layer, two-layer LSTM hidden layer and output layer, wherein: the input layer receives 64-dimensional feature vector, covering network bandwidth, packet loss rate, round-trip time, buffer utilization, CPU load and memory usage; the two-layer LSTM hidden layer is configured with 128 and 64 neurons respectively, with learning rate set to 0.001, batch size to 32 and training window length to 200 time steps, for predicting the network delay trend in the next 1-10 seconds.

2. The all-media all-signal integrated management platform according to claim 1, characterized in that, The adaptive coding engine supports coding standards including H.264, H.265, AV1 and VP9, including a code rate control module, a resolution adaptation module and a frame rate adjustment module, wherein: the code rate control module adjusts the coding code rate in real time according to the network bandwidth, supports dynamic adjustment in the range of 500Kbps to 50Mbps, and the adjustment step is 100Kbps; the resolution adaptation module realizes automatic resolution switching from 480p to 4K, including 720p, 1080p and 2K resolution; the frame rate adjustment module supports dynamic adjustment in the range of 15fps to 60fps, and the adjustment step is 5fps.

3. The all-media all-signal integrated management platform of claim 1, wherein, The network adaptive transmission engine includes a protocol performance analyzer, a network state monitor and a transmission protocol switching controller, wherein: the protocol performance analyzer supports TCP, UDP, QUIC, WebRTC and SRT protocols, and performs protocol adaptability evaluation according to different media types and network environment characteristics; the network state monitor collects link bandwidth, round-trip time RTT, packet loss rate and network jitter indicators in real time; the transmission protocol switching controller dynamically selects the optimal transmission protocol by considering the media stream characteristics, QoS requirements and current network state, and preferentially selects low-delay UDP or QUIC protocol for real-time interactive scenarios, and selects TCP protocol for high-reliability file transmission, and the protocol switching adopts parallel connection establishment mode, and the new connection is established to smoothly migrate the data stream, ensuring service continuity.

4. The all-media all-signal integrated management platform of claim 1, wherein, The fault switching recovery module includes a link layer detector, a network layer monitor and an application layer detector, wherein: the link layer detector monitors the physical link state, the detection parameters include signal strength, bit error rate and link utilization, and the detection period is 500 milliseconds; the network layer monitor monitors the routing reachability and network delay, and uses ICMP and Traceroute tools for path detection, with a detection interval of 5 seconds and a detection timeout of 5 seconds; the application layer detector detects service availability through a heartbeat mechanism, with a heartbeat interval of 10 seconds and a timeout threshold of 3 consecutive failures; when the comprehensive fault risk assessment exceeds the threshold, a preventive switching is triggered, the switching execution time is controlled within 2 seconds, and automatic back switching after fault recovery is supported, with a back switching delay set to 30 seconds.

5. The all-media all-signal integrated management platform of claim 1, wherein, The multi-modal signal acquisition module supports SDI, HDMI, IP stream, USB3.0 and gigabit Ethernet access, including a signal format detector, a standard converter and a quality calibrator, wherein: the signal format detector automatically identifies the resolution, frame rate, color space and encoding format of the input signal; the standard converter converts various format signals into a unified internal processing format; the quality calibrator performs noise reduction, sharpening and color correction processing on the signal.

6. The all-media all-signal integrated management platform of claim 1, wherein, The media content management center adopts a distributed storage architecture, including a content hash calculator, a shard storage manager and a replica consistency controller, wherein: the content hash calculator generates a unique content identifier using the SHA-256 algorithm; the shard storage manager divides large files into fixed-size data blocks, with a default block size of 2MB, supports dynamic adjustment within the range of 1MB-16MB, and generates a CRC32 check code for each data block; the replica consistency controller is responsible for managing the consistency of data replicas, with a default number of 3 replicas, and supports configuration of 2 to 5 replicas.

7. The all-media all-signal integrated management platform of claim 1, wherein, The timing synchronization processing tool is responsible for timing alignment and synchronization processing of multiple signals, compatible with SMPTE time code, AES / EBU time code and user-defined time code format, including frame-level synchronizer, line-level synchronizer and sample-level synchronizer, wherein: the frame-level synchronizer is used to ensure the time consistency between video frames, with a synchronization error control within ±2 frames; the line-level synchronizer is responsible for the field synchronization of interlaced video, achieving line-level synchronization accuracy; the sample-level synchronizer is used to realize the precise synchronization of audio sampling, with a precision of 1 sampling point, supporting sampling rates of 44.1kHz, 48kHz and 96kHz.

8. The all-media all-signal integrated management platform according to any one of claims 1-7, characterized in that, The intelligent resource scheduling pool includes a resource demand predictor, a multi-objective optimization scheduler and a resource efficiency analyzer, wherein: the resource demand predictor predicts the resource demand changes of computing, storage, network and AI computing power within 1-24 hour time window based on historical usage data and business load mode; the multi-objective optimization scheduler considers resource utilization, service quality guarantee, cost control and energy efficiency targets to solve the approximate optimal solution of resource allocation; the resource efficiency analyzer continuously monitors the usage and performance of various resources, identifies resource waste and performance bottleneck problems, generates resource configuration optimization suggestions and adjustment strategies, and supports continuous improvement and dynamic optimization of resource configuration.

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