Three-network integration content distribution network system based on edge computing

Abstract

The application discloses a three-network fusion content distribution network system based on edge computing and concretely relates to the three-network fusion technical field, and comprises a user request module, an edge cooperative node cluster, a central cloud platform and a TV large-screen terminal; the user request module is used for collecting the interactive operation of the user, generating a standardized content request message based on the collection result, and intelligently routing the request message to the optimal edge cooperative node access point; the edge cooperative node cluster comprises an intelligent scheduling module and a cooperative distribution module, directly provides the content distribution service for the TV large-screen terminal, the central cloud platform is connected to the edge cooperative node cluster through a management channel, generates a preliminary content distribution strategy template according to the request message forwarded by the intelligent scheduling module, the TV large-screen terminal receives the content data slices distributed by the edge cooperative node through different network protocols, and performs time sequence alignment, decryption and stream recombination locally according to the embedded synchronization signaling.

Description

Technical Field

[0001] This invention relates to the field of triple play technology, and more specifically, to a triple play content distribution network system based on edge computing. Background Technology

[0002] With the rapid development of digital technology, network technology, and multimedia terminals, home entertainment and information consumption centered on large-screen televisions are undergoing profound changes. Currently, content providers, operators, and service providers are actively leveraging the respective advantages of broadcast television networks, telecommunications networks, and the Internet to provide users with diversified traffic monetization services, including television channel transmission, content on demand, video shopping, and merchandise sales, aiming to create a comprehensive service platform integrating content distribution, interactive services, and commercial monetization.

[0003] However, in the field of integrated "large-screen TV" services, content providers generally face challenges in transmission channels, traditionally relying on a single network for business operations. While broadcast television networks possess stable and efficient downlink broadcasting capabilities, suitable for large-scale live channel distribution, their traditional closed technical systems lack two-way interactive capabilities, making it difficult to support modern business demands such as personalized on-demand and real-time interactive shopping. Telecommunications networks, with their reliable two-way transmission and quality of service assurance capabilities, have advantages in interactive video and data services, but their relatively high bandwidth costs place enormous pressure on the core and access networks when distributing high-concurrency content such as ultra-high-definition video, limiting scalability. Internet TV, relying on the open internet, features abundant resources, flexible access, and rapid innovation; however, its "best-effort" service model cannot guarantee the transmission quality and stability of critical services, resulting in experience bottlenecks such as stuttering and latency in demanding scenarios like live broadcasts and real-time interactions. Summary of the Invention

[0004] To overcome the aforementioned deficiencies of the prior art, embodiments of the present invention provide a triple-play content delivery network system based on edge computing. By employing edge computing and cluster node technology, the system improves the efficiency and stability of content transmission, meets the needs of personalization and high concurrency, and solves the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a triple-play content delivery network system based on edge computing, comprising a user request module, an edge collaborative node cluster, a central cloud platform, and a large-screen TV terminal;

[0006] The user request module is used to collect user interaction operations, generate standardized content request messages based on the collection results, and intelligently route the request messages to the optimal edge collaborative node access point based on the performance detection of the network access point.

[0007] The edge collaboration node cluster is deployed on the network access side and directly provides content distribution services to the large-screen TV terminal, including an intelligent scheduling module and a collaborative distribution module;

[0008] The intelligent scheduling module is embedded in the access point of the edge collaborative node cluster. It is used to receive and parse the request messages sent by the user request module, forward them to the central cloud platform through the management channel, and receive the preliminary content distribution strategy template issued by the central cloud platform. The collaborative distribution module is connected to the intelligent scheduling module, obtains the local three-network status parameters, performs secondary optimization through the local decision engine, generates a collaborative distribution instruction set, and intelligently slices the content. Through the established broadcast television network, telecommunications network and Internet connection, it pushes different content slices concurrently to the target TV large screen terminal.

[0009] The central cloud platform connects to the edge collaboration node cluster through a management channel, and generates a preliminary content distribution strategy template based on the request messages forwarded by the intelligent scheduling module, combined with the associated content identifier, user profile tags, and global network resource view.

[0010] The large-screen TV terminal receives content data slices from edge collaborative nodes via different network protocols, and performs timing alignment, decryption, and stream reassembly locally based on the embedded synchronization signaling.

[0011] Preferably, as a preferred embodiment of the edge computing-based triple-play content delivery network system of the present invention, it includes the user request module, deployed on the large-screen TV terminal, specifically including the following:

[0012] The user interaction perception unit captures the user's button, voice, and remote control operation interaction behaviors on the large-screen TV terminal and uses them as the original interaction event stream.

[0013] The original interactive event stream is mapped to standard operation intents through a local lightweight parser. Based on the intents and the local state of the TV large-screen terminal, a structured request message containing content identifier, service type, QoS requirements, session ID and timestamp fields is generated.

[0014] Parallel lightweight probing is performed on M edge collaborative node access points to construct a real-time performance state vector for each access point. ,in, , , , These represent the network latency, available bandwidth, packet loss rate, and current load metrics of the j-th access point, respectively.

[0015] Based on the real-time performance state vector and the service type and QoS requirements in the structured request message. ,in, This indicates the user's latency requirements. This indicates the user's bandwidth requirements. This indicates the user's requirements regarding packet loss rate;

[0016] Using a routing optimization algorithm, the routing score of each available edge collaborative node access point is calculated. The route scoring By delay scoring Bandwidth rating Packet loss rate score and load rating We obtain the following by performing a weighted summation: ,in, The weights are dynamically adjusted based on the service type and QoS requirements, corresponding to the weighting factors of latency score, bandwidth score, packet loss rate score, and load score, respectively. Before calculating the score, access points that do not meet the basic QoS requirements are excluded. Finally, the edge collaborative node access point with the best routing score is selected as the target, and the structured request message is encrypted and transmitted to the selected optimal edge collaborative node access point to ensure the integrity and security of the transmission process.

[0017] Preferably, as a preferred embodiment of the edge computing-based triple-play content delivery network system of the present invention, it includes the intelligent scheduling module, which is embedded in the access point of the edge collaborative node cluster. This module receives and parses request messages sent by the user request module, forwards them to the central cloud platform through a management channel, and receives a preliminary content distribution strategy template from the central cloud platform, specifically including the following:

[0018] Receive encrypted structured request messages from the user request module, decrypt and verify their integrity, and parse out the content identifier. Business Type QoS requirements Session ID Terminal device information and timestamp Key information is used to form a structured request data object. ;

[0019] The parsed request data object Encapsulated in a standard format, the request is forwarded to the central cloud platform via a secure management channel, and a preliminary session context for this request is created and stored within the edge collaboration node cluster. The Includes session ID, request timestamp, and request data object. and the current processing status;

[0020] Receive the preliminary content distribution strategy template issued by the central cloud platform, perform preliminary format verification and security checks on the template, and store it in the local policy cache.

[0021] Preferably, as a preferred embodiment of the edge computing-based triple-play content delivery network system of the present invention, it includes the collaborative distribution module, connected to the intelligent scheduling module, which acquires local triple-play status parameters, performs secondary optimization through a local decision engine, generates a collaborative distribution instruction set, and intelligently slices the content. Through established broadcast television network, telecommunications network, and internet connections, it concurrently pushes different content slices to the target large-screen TV terminal. Specifically, it includes:

[0022] The system monitors the operational status of the broadcast television network, telecommunications network, and Internet it is connected to, as well as the computing, storage, and caching status of the edge collaborative node itself. It also collects the local three-network status vector of the node, which includes real-time bandwidth, latency, packet loss rate, network status parameters, CPU utilization, memory usage, storage space, and cache hit rate of the edge collaborative node resource status.

[0023] The initial content distribution strategy template, local three-network status parameters, and edge collaborative node resource status are taken as inputs. The local decision engine performs secondary optimization and outputs a complete execution plan that includes content slicing strategy, network path mapping, transmission parameter configuration, and timing control information. This plan is used as the collaborative distribution instruction set.

[0024] Based on the generated collaborative distribution instruction set, the original content is retrieved from the local cache and intelligently sliced. According to the content characteristics, network status and terminal requirements, the content is divided into media slices of different sizes and encoding formats. Each slice is marked with the corresponding transmission priority and network adaptation parameters. Through the established broadcast television network, telecommunications network and Internet connection, the media slices and the adapted content slices are pushed to the target TV large screen terminal.

[0025] Preferably, as a preferred embodiment of the edge computing-based triple-play content delivery network system of the present invention, it includes the central cloud platform, which is connected to the edge collaborative node cluster through a management channel. Based on the request messages forwarded by the intelligent scheduling module, and combined with associated content identifiers, user profile tags, and a global network resource view, a preliminary content distribution strategy template is generated. The template includes distribution network priority, caching suggestions, and service quality targets, and is then distributed to the intelligent scheduling module, specifically including the following:

[0026] After unified access, encoding, transcoding, packaging, storage, tagging management and copyright review of various multimedia content, it is stored in the content metadata database and the content source is provided to the edge collaborative node cluster;

[0027] Listen to the management channel between the edge collaboration node cluster and capture all inbound data objects encapsulated with user requests. The system retrieves standard messages and decapsulates them to extract structured user request data objects. ;

[0028] The parsed and extracted user request data object Using session IDs as clues, historical interests and payment levels are obtained, and user profile tags are generated by analyzing user behavior and preferences; based on... Content identifier in Query and obtain detailed attributes of the content, including content type, resolution, encoding format, copyright information, popularity, and size; and obtain the channel utilization rate of the target area's broadcast television network, the health status of the telecommunications network slice, the quality of the Internet link, and the load of major CDN nodes by accessing the real-time updated global network resource view.

[0029] Based on the above Business types in QoS requirements The overall priority of this content distribution is calculated based on user payment level in user profile tags, and content type and popularity in content attributes. ;in, It is a comprehensive priority. These are weighting factors for service type, QoS requirements, payment level, content type, and popularity, respectively. The priority scores are based on service type, QoS requirements, payment level, content type, and popularity.

[0030] Based on the popularity and size of the content attributes, as well as the caching pressure of the edge collaboration nodes in the global network resource assessment, caching recommendations are generated for the edge collaboration node cluster. , It is a content identifier. This indicates the caching strategy and validity period for this content;

[0031] According to the above The QoS requirements in the data, the user level in the user profile tags, and the available resources in the global network resource assessment are mapped to generate the specific service quality target for this distribution. ;

[0032] The calculated , and Encapsulated as a preliminary content distribution strategy template ,in, This indicates the generation timestamp of the initial content distribution strategy template; and it is then distributed to the intelligent scheduling module via a secure management channel.

[0033] Preferably, as a preferred embodiment of the edge computing-based triple-play content delivery network system of the present invention, the large-screen TV terminal receives content data slices from edge collaborative nodes via different network protocols, and performs timing alignment, decryption, and stream reassembly locally according to embedded synchronization signaling, specifically including the following:

[0034] It receives encrypted content data slices from broadcast television networks, telecommunications networks and the Internet, caches them in the high-speed buffer inside the terminal, and performs integrity verification and legality checks on each received content data slice;

[0035] Synchronization signaling, including timestamp, sequence number, fragment index and content ID, is extracted from the verified content data fragments. This enables precise time-series alignment of content fragments from different network channels that arrive out of order, and the removal of duplicate fragments.

[0036] Using the terminal's built-in preset key, the verified and aligned content data slices are decrypted in real time to restore their original media data format. The decrypted and time-aligned media data slices are then seamlessly reassembled to form a complete media stream. Based on the terminal's own decoding capabilities, display parameters, and user settings, the reassembled media stream is adapted for playback and then output to the TV screen and audio system through a multimedia rendering engine.

[0037] On the other hand, the present invention provides a computer device including a memory and a processor, wherein the memory stores a computer program, wherein when the computer program is executed by the processor, it implements the functional modules of the edge computing-based triple-play content delivery network system as described above.

[0038] On the other hand, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, it implements a triple-play content delivery network system based on edge computing as described above.

[0039] The technical effects and advantages provided by the present invention in the above technical solution are as follows:

[0040] By deeply integrating edge computing, triple-play integration, and content delivery networks, this solution aims to address the problems of high latency, high bandwidth pressure, unstable service quality, and low resource utilization faced by traditional content delivery. Through the user request module, the performance of network access points is detected, and content requests are intelligently routed to the optimal edge collaborative node access point. This avoids all requests converging on the central cloud platform. The edge collaborative node cluster is deployed on the network access side, directly providing services to large-screen TV terminals, which significantly shortens the content transmission path, reduces the round-trip time of content transmission, and lowers the latency of content loading and playback. Attached Figure Description

[0041] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0042] Figure 1 This is a flowchart of the method for the edge computing-based triple-play content delivery network system of the present invention.

[0043] Table 1 is a data record table of the simulation experiment of the triple-play content delivery network system based on edge computing of the present invention. Detailed Implementation

[0044] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0045] In the description of this application, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the stated features. In the description of this application, "multiple" means two or more, unless otherwise explicitly specified.

[0046] In the description of this application, the term "for example" is used to mean "used as an example, illustration, or description." Any embodiment described as "for example" in this application is not necessarily to be construed as being more preferred or advantageous than other embodiments. The following description is provided to enable any person skilled in the art to make and use the invention. Details are set forth in the following description for purposes of explanation. It should be understood that those skilled in the art will recognize that the invention can be made without using these specific details. In other instances, well-known structures and processes will not be described in detail to avoid obscuring the description of the invention with unnecessary detail. Therefore, the invention is not intended to be limited to the embodiments shown, but is consistent with the broadest scope of the principles and features disclosed in this application.

[0047] Example 1

[0048] This embodiment provides, for example Figure 1 The triple-play content delivery network system based on edge computing shown includes a user request module, an edge collaborative node cluster, a central cloud platform, and a large-screen TV terminal.

[0049] The user request module is used to collect user interaction operations, generate standardized content request messages based on the collection results, and intelligently route the request messages to the optimal edge collaborative node access point based on the performance detection of the network access point.

[0050] The edge collaboration node cluster is deployed on the network access side and directly provides content distribution services to the large-screen TV terminal, including an intelligent scheduling module and a collaborative distribution module;

[0051] The intelligent scheduling module is embedded in the access point of the edge collaborative node cluster. It is used to receive and parse the request messages sent by the user request module, forward them to the central cloud platform through the management channel, and receive the preliminary content distribution strategy template issued by the central cloud platform. The collaborative distribution module is connected to the intelligent scheduling module, obtains the local three-network status parameters, performs secondary optimization through the local decision engine, generates a collaborative distribution instruction set, and intelligently slices the content. Through the established broadcast television network, telecommunications network and Internet connection, it pushes different content slices concurrently to the target TV large screen terminal.

[0052] The central cloud platform connects to the edge collaboration node cluster through a management channel, and generates a preliminary content distribution strategy template based on the request messages forwarded by the intelligent scheduling module, combined with the associated content identifier, user profile tags, and global network resource view.

[0053] The large-screen TV terminal receives content data slices from edge collaborative nodes via different network protocols, and performs timing alignment, decryption, and stream reassembly locally based on the embedded synchronization signaling.

[0054] In this embodiment, the user request module needs to be specifically described. This user request module is deployed on the large-screen TV terminal and specifically includes the following:

[0055] The user interaction perception unit captures the user's button, voice, and remote control operation interaction behaviors on the large-screen TV terminal and uses them as the original interaction event stream.

[0056] The original interactive event stream is mapped to standard operation intents through a local lightweight parser. Based on the intents and the local state of the TV large-screen terminal, a structured request message containing content identifier, service type, QoS requirements, session ID and timestamp fields is generated.

[0057] Parallel lightweight probing is performed on M edge collaborative node access points to construct a real-time performance state vector for each access point. ,in, , , , These represent the network latency, available bandwidth, packet loss rate, and current load metrics of the j-th access point, respectively.

[0058] Based on the real-time performance state vector and the service type and QoS requirements in the structured request message. ,in, This indicates the user's latency requirements. This indicates the user's bandwidth requirements. This indicates the user's requirements regarding packet loss rate;

[0059] Using a routing optimization algorithm, the routing score of each available edge collaborative node access point is calculated. The route scoring By delay scoring Bandwidth rating Packet loss rate score and load rating We obtain the following by performing a weighted summation: ,in, The weights are dynamically adjusted based on the service type and QoS requirements, corresponding to the weighting factors of latency score, bandwidth score, packet loss rate score, and load score, respectively. Before calculating the score, access points that do not meet the basic QoS requirements are excluded. Finally, the edge collaborative node access point with the best routing score is selected as the target, and the structured request message is encrypted and transmitted to the selected optimal edge collaborative node access point to ensure the integrity and security of the transmission process.

[0060] In this embodiment, the intelligent scheduling module needs to be specifically described. This module is embedded in the access point of the edge collaborative node cluster and is used to receive and parse request messages sent by the user request module, forwarding them to the central cloud platform through the management channel; and to receive a preliminary content distribution strategy template issued by the central cloud platform, specifically including the following:

[0061] Receive encrypted structured request messages from the user request module, decrypt and verify their integrity, and parse out the content identifier. Business Type QoS requirements Session ID Terminal device information and timestamp Key information is used to form a structured request data object. ;

[0062] The parsed request data object Encapsulated in a standard format, the request is forwarded to the central cloud platform via a secure management channel, and a preliminary session context for this request is created and stored within the edge collaboration node cluster. The Includes session ID, request timestamp, and request data object. and the current processing status;

[0063] Receive the preliminary content distribution strategy template issued by the central cloud platform, perform preliminary format verification and security checks on the template, and store it in the local policy cache.

[0064] In this embodiment, the collaborative distribution module is specifically described. This module is connected to the intelligent scheduling module, obtains local three-network status parameters, performs secondary optimization through a local decision engine, generates a collaborative distribution instruction set, and intelligently slices the content. Through established broadcast television network, telecommunications network, and internet connections, it concurrently pushes different content slices to the target large-screen TV terminal. Specifically, this includes:

[0065] The system monitors the operational status of the broadcast television network, telecommunications network, and Internet it is connected to, as well as the computing, storage, and caching status of the edge collaborative node itself. It also collects the local three-network status vector of the node, which includes real-time bandwidth, latency, packet loss rate, network status parameters, CPU utilization, memory usage, storage space, and cache hit rate of the edge collaborative node resource status.

[0066] The initial content distribution strategy template, local three-network status parameters, and edge collaborative node resource status are taken as inputs. The local decision engine performs secondary optimization and outputs a complete execution plan that includes content slicing strategy, network path mapping, transmission parameter configuration, and timing control information. This plan is used as the collaborative distribution instruction set.

[0067] Based on the generated collaborative distribution instruction set, the original content is retrieved from the local cache and intelligently sliced. According to the content characteristics, network status and terminal requirements, the content is divided into media slices of different sizes and encoding formats. Each slice is marked with the corresponding transmission priority and network adaptation parameters. Through the established broadcast television network, telecommunications network and Internet connection, the media slices and the adapted content slices are pushed to the target TV large screen terminal.

[0068] In this embodiment, the central cloud platform is specifically described. This central cloud platform connects to the edge collaboration node cluster via a management channel and, based on the request messages forwarded by the intelligent scheduling module, combines associated content identifiers, user profile tags, and a global network resource view to generate a preliminary content distribution strategy template. This template includes distribution network priority, caching suggestions, and service quality targets, and is then sent to the intelligent scheduling module. Specifically, it includes the following:

[0069] After unified access, encoding, transcoding, packaging, storage, tagging management and copyright review of various multimedia content, it is stored in the content metadata database and the content source is provided to the edge collaborative node cluster;

[0070] Listen to the management channel between the edge collaboration node cluster and capture all inbound data objects encapsulated with user requests. The system retrieves standard messages and decapsulates them to extract structured user request data objects. ;

[0071] The parsed and extracted user request data object Using session IDs as clues, historical interests and payment levels are obtained, and user profile tags are generated by analyzing user behavior and preferences; based on... Content identifier in Query and obtain detailed attributes of the content, including content type, resolution, encoding format, copyright information, popularity, and size; and obtain the channel utilization rate of the target area's broadcast television network, the health status of the telecommunications network slice, the quality of the Internet link, and the load of major CDN nodes by accessing the real-time updated global network resource view.

[0072] Based on the above Business types in QoS requirements The overall priority of this content distribution is calculated based on user payment level in user profile tags, and content type and popularity in content attributes. ;in, It is a comprehensive priority. These are weighting factors for service type, QoS requirements, payment level, content type, and popularity, respectively. The priority scores are based on service type, QoS requirements, payment level, content type, and popularity.

[0073] Based on the popularity and size of the content attributes, as well as the caching pressure of the edge collaboration nodes in the global network resource assessment, caching recommendations are generated for the edge collaboration node cluster. , It is a content identifier. This indicates the caching strategy and validity period for this content;

[0074] According to the above The QoS requirements in the data, the user level in the user profile tags, and the available resources in the global network resource assessment are mapped to generate the specific service quality target for this distribution. ;

[0075] The calculated , and Encapsulated as a preliminary content distribution strategy template ,in, This indicates the generation timestamp of the initial content distribution strategy template; and it is then distributed to the intelligent scheduling module via a secure management channel.

[0076] In this embodiment, the specific focus is on the large-screen TV terminal. The large-screen TV terminal receives content data slices from edge collaboration nodes via different network protocols. Based on the embedded synchronization signaling, it performs timing alignment, decryption, and stream reassembly locally, specifically including the following:

[0077] It receives encrypted content data slices from broadcast television networks, telecommunications networks and the Internet, and caches them in the high-speed buffer inside the terminal. It performs integrity verification and legality checks on each received content data slice to ensure that the data has not been tampered with during transmission and removes invalid slices.

[0078] Synchronization signaling, including timestamps, sequence numbers, fragment indexes, and content IDs, is extracted from the verified content data fragments. This allows for precise time-series alignment of content fragments from different network channels that arrive out of order, while removing duplicate fragments to ensure the uniqueness of content segments and the correctness of the playback order.

[0079] Using the terminal's built-in preset key, the verified and aligned content data slices are decrypted in real time to restore their original media data format; the decrypted and time-aligned media data slices are seamlessly reassembled to form a complete media stream; based on the terminal's own decoding capabilities, display parameters, and user settings, the reassembled media stream is adapted for playback and output to the TV screen and audio system through the multimedia rendering engine, providing users with a continuous, high-quality audio-visual experience.

[0080] The computer device can be a terminal, comprising a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0081] This embodiment also provides a storage medium storing a computer program. When executed by a processor, the program implements the functional modules of the edge computing-based triple-play content delivery network system proposed in the above embodiments. The storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Red-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0082] Example 2

[0083] The following is another embodiment of the present invention, which provides a triple-play content delivery network system based on edge computing. In order to verify the beneficial effects of the present invention, a simulation experiment is conducted for scientific demonstration.

[0084] This experiment aims to verify the effectiveness of a triple-play content delivery network system based on edge computing. By simulating user requests, intelligent routing, multi-network collaborative distribution, and terminal reception and reassembly technologies, it improves the efficiency, reliability, and user experience of content distribution in high-concurrency, multi-service scenarios. The experiment uses simulated user interaction data, content distribution requests, and dynamically changing network status data of broadcast television networks, telecommunications networks, and the Internet. By analyzing the system's service quality, resource utilization, and user satisfaction under different network conditions and service requirements, the experiment verifies the system's performance advantages in intelligent scheduling, collaborative distribution, and terminal adaptation.

[0085] The simulation experiment steps are implemented based on the content of the edge computing-based triple-play content delivery network system provided in Example 1, and the specific steps include:

[0086] A test environment was simulated, consisting of one central cloud platform, five edge collaborative node clusters, and 1,000 large-screen TV terminals. Three network channels were configured, including broadcast television network, telecommunications network, and Internet. The edge cache capacity was set to 100GB per node, and an LRU cache replacement strategy was adopted.

[0087] Simulate 1000 large-screen TV terminals initiating content requests, including HD live streaming, 4K video-on-demand, and interactive shopping, and randomly set different QoS requirements;

[0088] Each request message contains a content ID, service type, QoS requirements, session ID, and terminal device information. The simulated user request module intelligently routes the request to the optimal edge collaborative node access point based on the real-time performance detection of the network access point.

[0089] The intelligent scheduling module receives and parses simulated user request messages, forwards them to the central cloud platform through the management channel, and generates a preliminary content distribution strategy template based on the request message, user profile, and global network resource view, and then distributes it to the intelligent scheduling module. The collaborative distribution module obtains local three-network status parameters, combines them with the preliminary strategy template, performs secondary optimization through the local decision engine, generates a collaborative distribution instruction set, and performs intelligent slicing of the content.

[0090] According to the collaborative distribution instructions, different content slices are pushed concurrently to the simulated large-screen TV terminal through the simulated broadcast television network, telecommunications network and Internet connection.

[0091] The analog TV large-screen terminal receives content data slices from the collaborative distribution module through different network protocols. Based on the embedded synchronization signaling, the terminal performs timing alignment, decryption, and stream reassembly locally, and records the arrival order, integrity, reassembly time, and final playback quality of the content slices received by the terminal.

[0092] The specific data from the above simulation experiment are as follows:

[0093] 0-10 U001 LiveTV01 HD live streaming Area A Telecommunications: Excellent; Internet: Good; Broadcasting: Excellent ECN-A01 Broadcasting + Telecommunications 98% excellent 90% 75% 10-20 U002 VOD4K02 4K on-demand Area B Telecommunications: Good; Internet: Average; Broadcasting: Poor ECN-B03 Telecommunications + Internet 95% excellent 85% 80% 20-30 U003 ShopItem Interactive shopping Area C Telecommunications: Medium; Internet: Excellent; Broadcasting: Excellent ECN-C02 internet 99% excellent 92% 70% 30-40 U004 LiveTV01 HD live streaming Area A Telecommunications: Poor; Internet: Good; Broadcasting: Excellent ECN-A01 Broadcasting 90% good 90% 65% 40-50 U005 VODHD03 HD on-demand Region D Telecommunications: Excellent; Internet: Excellent; Broadcasting: Good ECN-D01 telecommunications 97% excellent 88% 78% 50-60 U006 LiveTV02 HD live streaming Area B Telecommunications: Excellent; Internet: Excellent; Broadcasting: Excellent ECN-B02 Broadcasting + Telecommunications 99% excellent 91% 82%

[0094] Table 1

[0095] Experimental results show that under varying network conditions, user requests can be intelligently routed to the optimal edge collaboration node, effectively reducing initial access latency. Through dynamic scheduling of different network resources and intelligent content slicing, the system can maximize the use of the downlink advantages of broadcast television networks, the reliability of telecommunications networks, and the flexibility of the Internet while ensuring QoS. Especially when a single network experiences performance bottlenecks, it can achieve rapid cross-network switching and compensation, ensuring the continuity of content transmission.

[0096] The foregoing has only described certain exemplary embodiments of the present invention by way of illustration. Undoubtedly, those skilled in the art can modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the foregoing drawings and descriptions are illustrative in nature and should not be construed as limiting the scope of protection of the claims of the present invention.

Claims

1. A triple-play content delivery network system based on edge computing, characterized in that: It includes a user request module, an edge collaboration node cluster, a central cloud platform, and a large-screen TV terminal; The user request module is used to collect user interaction operations, generate standardized content request messages based on the collection results, and intelligently route the request messages to the optimal edge collaborative node access point based on the performance detection of the network access point. The edge collaboration node cluster is deployed on the network access side and directly provides content distribution services to the large-screen TV terminal, including an intelligent scheduling module and a collaborative distribution module; The intelligent scheduling module is embedded in the access point of the edge collaborative node cluster. It is used to receive and parse the request messages sent by the user request module, forward them to the central cloud platform through the management channel, and receive the preliminary content distribution strategy template issued by the central cloud platform. The collaborative distribution module is connected to the intelligent scheduling module, obtains the local three-network status parameters, performs secondary optimization through the local decision engine, generates a collaborative distribution instruction set, and intelligently slices the content. Through the established broadcast television network, telecommunications network and Internet connection, it pushes different content slices concurrently to the target TV large screen terminal. The central cloud platform connects to the edge collaboration node cluster through a management channel, and generates a preliminary content distribution strategy template based on the request messages forwarded by the intelligent scheduling module, combined with the associated content identifier, user profile tags, and global network resource view. The large-screen TV terminal receives content data slices from edge collaborative nodes via different network protocols, and performs timing alignment, decryption, and stream reassembly locally based on the embedded synchronization signaling.

2. The edge computing-based triple-play content delivery network system according to claim 1, characterized in that: The user request module, deployed on the large-screen TV terminal, specifically includes the following: The user interaction perception unit captures the user's button, voice, and remote control operation interaction behaviors on the large-screen TV terminal and uses them as the original interaction event stream. The original interactive event stream is mapped to standard operation intents through a local lightweight parser. Based on the intents and the local state of the TV large-screen terminal, a structured request message containing content identifier, service type, QoS requirements, session ID and timestamp fields is generated. Parallel lightweight probing is performed on M edge collaborative node access points to construct a real-time performance state vector for each access point. ,in, , , , These represent the network latency, available bandwidth, packet loss rate, and current load metrics of the j-th access point, respectively. Based on the real-time performance state vector and the service type and QoS requirements in the structured request message. ,in, This indicates the user's latency requirements. This indicates the user's bandwidth requirements. This indicates the user's requirements regarding packet loss rate; Using a routing optimization algorithm, the routing score of each available edge collaborative node access point is calculated. The route scoring By delay scoring Bandwidth rating Packet loss rate score and load rating We obtain the following by performing a weighted summation: ,in, The weights are dynamically adjusted based on the service type and QoS requirements, corresponding to the weighting factors of latency score, bandwidth score, packet loss rate score, and load score, respectively. Finally, the edge collaborative node access point with the best routing score is selected as the target, and the structured request message is encrypted and transmitted to the selected optimal edge collaborative node access point to ensure the integrity and security of the transmission process.

3. The edge computing-based triple-play content delivery network system according to claim 1, characterized in that: The intelligent scheduling module, embedded in the access point of the edge collaboration node cluster, is used to receive and parse request messages sent by the user request module, forward them to the central cloud platform through the management channel, and receive a preliminary content distribution strategy template issued by the central cloud platform, which specifically includes the following: Receive encrypted structured request messages from the user request module, decrypt and verify their integrity, and parse out the content identifier. Business Type QoS requirements Session ID Terminal device information and timestamp Key information is used to form a structured request data object. ; The parsed request data object Encapsulated in a standard format, the request is forwarded to the central cloud platform via a secure management channel, and a preliminary session context for this request is created and stored within the edge collaboration node cluster. The Includes session ID, request timestamp, and request data object. and the current processing status; Receive the preliminary content distribution strategy template issued by the central cloud platform, perform preliminary format verification and security checks on the template, and store it in the local policy cache.

4. The edge computing-based triple-play content delivery network system according to claim 1, characterized in that: The collaborative distribution module, connected to the intelligent scheduling module, acquires local three-network status parameters, performs secondary optimization through a local decision engine, generates a collaborative distribution instruction set, and intelligently slices the content. Through established broadcast television network, telecommunications network, and internet connections, it concurrently pushes different content slices to the target large-screen TV terminal. Specifically, this includes: The system monitors the operational status of the broadcast television network, telecommunications network, and Internet it is connected to, as well as the computing, storage, and caching status of the edge collaborative node itself. It also collects the local three-network status vector of the node, which includes real-time bandwidth, latency, packet loss rate, network status parameters, CPU utilization, memory usage, storage space, and cache hit rate of the edge collaborative node resource status. The initial content distribution strategy template, local three-network status parameters, and edge collaborative node resource status are taken as inputs. The local decision engine performs secondary optimization and outputs a complete execution plan that includes content slicing strategy, network path mapping, transmission parameter configuration, and timing control information. This plan is used as the collaborative distribution instruction set. Based on the generated collaborative distribution instruction set, the original content is retrieved from the local cache and intelligently sliced. According to the content characteristics, network status and terminal requirements, the content is divided into media slices of different sizes and encoding formats. Each slice is marked with the corresponding transmission priority and network adaptation parameters. Through the established broadcast television network, telecommunications network and Internet connection, the media slices and the adapted content slices are pushed to the target TV large screen terminal.

5. The edge computing-based triple-play content delivery network system according to claim 1, characterized in that: The central cloud platform specifically includes the following: After unified access, encoding, transcoding, packaging, storage, tagging management and copyright review of various multimedia content, it is stored in the content metadata database and the content source is provided to the edge collaborative node cluster; Listen to the management channel between the edge collaboration node cluster and capture all inbound data objects encapsulated with user requests. The system retrieves standard messages and decapsulates them to extract structured user request data objects. ; The parsed and extracted user request data object Using session IDs as clues, historical interests and payment levels are obtained, and user profile tags are generated by analyzing user behavior and preferences; based on... Content identifier in Query and obtain detailed attributes of the content, including content type, resolution, encoding format, copyright information, popularity, and size; and obtain the channel utilization rate of the target area's broadcast television network, the health status of the telecommunications network slice, the quality of the Internet link, and the load of major CDN nodes by accessing the real-time updated global network resource view. Based on the above Business types in QoS requirements The overall priority of this content distribution is calculated based on user payment level in user profile tags, and content type and popularity in content attributes. ;in, It is a comprehensive priority. These are weighting factors for service type, QoS requirements, payment level, content type, and popularity, respectively. The priority scores are based on service type, QoS requirements, payment level, content type, and popularity. Based on the popularity and size of the content attributes, as well as the caching pressure of the edge collaboration nodes in the global network resource assessment, caching recommendations are generated for the edge collaboration node cluster. , It is a content identifier. This indicates the caching strategy and validity period for this content; According to the above The QoS requirements in the data, the user level in the user profile tags, and the available resources in the global network resource assessment are mapped to generate the specific service quality target for this distribution. ; The calculated , and Encapsulated as a preliminary content distribution strategy template ,in, This indicates the generation timestamp of the initial content distribution strategy template; and it is then distributed to the intelligent scheduling module via a secure management channel.

6. The triple-play content delivery network system based on edge computing according to claim 1, characterized in that: The large-screen TV terminal receives content data slices from edge collaboration nodes via different network protocols, and performs timing alignment, decryption, and stream reassembly locally based on embedded synchronization signaling. Specifically, this includes the following: It receives encrypted content data slices from broadcast television networks, telecommunications networks and the Internet, caches them in the high-speed buffer inside the terminal, and performs integrity verification and legality checks on each received content data slice; Synchronization signaling, including timestamp, sequence number, fragment index and content ID, is extracted from the verified content data fragments. This enables precise time-series alignment of content fragments from different network channels that arrive out of order, and the removal of duplicate fragments. Using the terminal's built-in preset key, the verified and aligned content data slices are decrypted in real time to restore their original media data format. The decrypted and time-aligned media data slices are then seamlessly reassembled to form a complete media stream. Based on the terminal's own decoding capabilities, display parameters, and user settings, the reassembled media stream is adapted for playback and then output to the TV screen and audio system through a multimedia rendering engine.

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