Multi-channel collaborative acceleration method for set-top box data processing and transmission

Abstract

The present invention provides a multi-channel collaborative acceleration method for set-top box data processing and transmission, which relates to the field of data processing technology. It adopts edge computing and photon computing technologies, and uses distributed edge computing nodes and photon computing units to send data requests to edge nodes closer to users for preliminary processing. At the same time, photon computing technology is used to accelerate the processing of high-resolution videos, so that the set-top box can quickly respond to user requests, achieve a smooth user experience, and reduce the delay of video and audio data. Through the network slicing technology of 5G / 6G networks, independent network resources are allocated to different types of data streams. Quantum key distribution provides powerful data encryption protection to ensure data security during transmission. Network slicing technology ensures the priority and bandwidth allocation of different data streams, improves the reliability of transmission, reduces packet loss and delay, and ensures stable transmission of data in the network.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a multi-channel collaborative acceleration method for set-top box data processing and transmission. Background Art

[0002] According to a household multi-channel smart set-top box system disclosed by patent number "CN112511879A", it includes a control module, the control module is electrically connected to an information module, the information module is electrically connected to a communication module, the information module is electrically connected to a first noise reduction module, a first gain module, a multi-channel TUNER, a DEMO decoding, and a cache module in sequence, the cache module is electrically connected to a second noise reduction module, the second noise reduction module is electrically connected to a second gain module, the second gain module is electrically connected to a transmission interface, the transmission interface is electrically connected to multiple groups of smart TVs, and the control module is electrically connected to multiple TUNERs, storage modules and auxiliary modules; the present invention processes signals through multiple groups of noise reduction and gain modules, so that the signals will not be lost and are stably transmitted and played, and decodes multiple signals through multi-channel TUNER and DEMO decoding, and plays them in different channels.

[0003] According to a set-top box data processing method and a set-top box disclosed in patent number "CN105554573B", the method includes: the set-top box receives a first segment of data carrying a data identifier sent by a server through a driver layer, and generates a second segment of data carrying the data identifier based on the first segment of data; the set-top box detects whether there is a segment of data carrying the data identifier in a first circular buffer through an intermediate layer; if it is detected that there is no segment of data carrying the data identifier in the first circular buffer, the set-top box detects whether the first circular buffer is filled with unread data through the intermediate layer; if it is detected that the first circular buffer is not filled with unread data, the set-top box obtains the second segment of data through the intermediate layer. The technical solution provided by the embodiment of the present invention is conducive to reducing the packet loss rate and CPU occupancy rate when the set-top box processes received data, ensuring the logic and stability of data processing and reception, and thus ensuring the stability of the set-top box during use.

[0004] According to the patent number "CN115134665B", a data processing method and device based on a set-top box, a storage medium, and an electronic device are disclosed, wherein the data processing method based on the set-top box includes: determining a target broadband account whose number of set-top boxes in use is greater than a quantity threshold according to the movie viewing data of the set-top boxes connected under each broadband account; clustering the movie viewing data of the set-top boxes in use under the target broadband account to obtain at least two clusters; marking the installation location of the set-top boxes contained in each cluster according to the movie viewing data of the set-top boxes contained in each cluster in a specified time period and the correspondence between the pre-stored movie viewing data and the installation location; constructing a knowledge graph corresponding to the set-top box user according to the installation location of the set-top box connected under each broadband account, and ordering a movie viewing service package for the set-top box user according to the knowledge graph. The present disclosure can quickly and accurately order service packages for users who install multiple set-top boxes.

[0005] The above patent documents and prior art have the following technical problems when used:

[0006] Problem 1: Most of the data processing for set-top boxes uses the internal CPU and GPU for direct analysis and processing. The processing speed of high-resolution data sources is low, the delay is long, and the response speed is insufficient, which affects the user experience.

[0007] Problem 2: For set-top box output transmission, a single channel is generally used for transmission. For multi-channel transmission, multiple single channels are accumulated. Due to the different types of data sources after parsing and the different transcoding steps, the resources occupied during the transmission process are not compatible, the security during the transmission process is low, and data packet loss is prone to occur, which is not conducive to actual use.

[0008] Problem 3: When processing and transmitting data in set-top boxes, the internal resource distribution cannot adapt to the data processing requirements, resulting in a waste of resources and poor overall computing power.

[0009] Problem four: When displaying set-top box data, it is impossible to make predictions based on the user’s data preferences, and the lack of intelligence leads to a poor user experience. Summary of the invention

[0010] Technical issues solved

[0011] In view of the shortcomings of the prior art, the present invention provides a multi-channel collaborative acceleration method for set-top box data processing and transmission, which solves the following problems:

[0012] 1. The set-top box has the problem of single data processing, low speed and long delay;

[0013] 2. The resources occupied by the internal channel of the set-top box during transmission are not suitable, the security during transmission is low, and data packet loss is prone to occur;

[0014] 3. The internal computing of the set-top box cannot be intelligently allocated on demand, resulting in resource waste and shortage;

[0015] 4. The problem of being unable to analyze and intelligently predict based on user viewing needs leads to poor user experience.

[0016] Technical Solution

[0017] To achieve the above objectives, the present invention is implemented through the following technical solutions: a multi-channel collaborative acceleration method for set-top box data processing and transmission, the method comprising the following steps:

[0018] Sp1: Data request and initial analysis. The user sends a data request through the set-top box. The request content includes the user's required data. The set-top box sends the requested data to the nearest edge computing node. After receiving the request data, the edge computing node first performs a preliminary analysis and accelerates the data processing task through quantum computing. It uses AI models to predict the user's required data and cache the required data in advance.

[0019] The AI ​​model’s prediction formula for request data is:

[0020]

[0021] in, The user demand predicted by the user represents the possible user request at time t+1;

[0022] f is the prediction function, a function based on the AI ​​model, used to map historical data to future needs;

[0023] θ is the model function, which determines the behavior and accuracy of the prediction function;

[0024] y(t) is the actual user demand data at time t, indicating the user's behavior at time t;

[0025] Sp2: Data processing and content optimization. Edge computing nodes use local AI models to analyze and process request data and predicted demand data in real time. The data content is automatically identified through AI models and optimized according to the user's points of interest. Edge computing nodes use photonic computing technology to process high-resolution videos and complex computing tasks. Edge computing nodes allocate computing resources through dynamic resource scheduling, intelligently adjust according to real-time load and user needs, and AI models dynamically allocate resources according to predicted load conditions.

[0026] AI model optimization formula for data content:

[0027]

[0028] Wherein, P is the optimized structure of video coding parameters;

[0029] p is the value of the encoding parameter;

[0030] N is the total number of samples, indicating the number of data processed.

[0031] f(x i , p) is the optimized model output based on the input data and encoding parameters;

[0032] x i is the i-th input data sample;

[0033] y i is the i-th input data sample x i actual feedback or expected output;

[0034] Sp3: Multi-channel data transmission. The optimized data processed by Sp2 is divided into multiple channels and classified according to the content type. Each type of data is transmitted through different network slices and encrypted through quantum key distribution technology. The data is transmitted through multiple slices of the 5G / 6G network, and each slice provides an optimized transmission path for a specific type of data.

[0035] The bandwidth allocation formula for network slicing is:

[0036]

[0037] Among them, B i is the bandwidth of the i-th network slice;

[0038] B total is the total bandwidth, which indicates the total amount of broadband available in the 5G / 6G network;

[0039] M is the number of slices, which indicates the number of independent channels allocated in the network;

[0040] The optimization goal is:

[0041]

[0042] Among them, α i is the priority weight of the i-th slice;

[0043] β i is the bandwidth demand of the i-th slice;

[0044] Sp4: Final presentation of data and user feedback. After the set-top box receives data transmitted from multiple channels of Sp3, it uses the photon computing unit to quickly decode it and transmits the processed data to the user. The AI ​​model learns the user's behavior and feedback content in real time inside the set-top box, continuously optimizes the prediction model and presentation content, and uses the data on the edge computing node.

[0045] Preferably, in the data request and initial analysis, the user request is further responded to through delay optimization, setting the network delay to L and the data processing delay time to T p , then the total delay T total for:

[0046] T total =L cloud +T p,cloud

[0047] After the introduction of edge computing:

[0048] T total =L edge +T p,edge

[0049] Among them, L edge is the network delay of the edge node, which refers to the delay time from the set-top box to the edge computing node;

[0050] T p,edge is the data processing time of the edge node, which refers to the time it takes for the edge node to complete data processing, and L edge <L cloud and T p,edge <T p,cloud Represents the latency reduction achieved through edge computing.

[0051] Preferably, in the data processing and content optimization, the processing time formula of photon calculation is:

[0052]

[0053] Among them, N is the amount of data to be processed, that is, the total number of samples, indicating the total size of the data to be processed;

[0054] v is the frequency of photons, which indicates the processing speed of photon calculations;

[0055] T q Calculate processing time for photons;

[0056] In the data processing and content optimization, the optimization formula for dynamic resource scheduling and allocation is:

[0057]

[0058] Among them, Li is the current load of the i-th node, indicating the amount of data currently processed by the node;

[0059] C i is the processing capacity of the i-th node, indicating the maximum processing capacity of the node;

[0060] Q is the number of edge nodes.

[0061] Preferably, in the multi-channel data transmission, the quantum key distribution formula in the quantum key distribution technology is used:

[0062]

[0063] Among them, K is the generated quantum key, which is used to encrypt data transmission;

[0064] H is a hash function;

[0065] S A and S B It is the measurement result of the sender and the receiver, which is generated by the superposition of quantum states; the transmission delay formula of 5G / 6G network in multi-channel data transmission is:

[0066]

[0067] Where D is the transmission delay, which indicates the time required for data to be transmitted in the network;

[0068] L is the length of the data packet, indicating the amount of data that needs to be transmitted;

[0069] B is the network bandwidth, which indicates the transmission capacity provided by the 5G / 6G network.

[0070] Preferably, in the data request and initial analysis, the edge computing node is a computing resource distributed at the edge of the network, close to the set-top box, and is used to process large amounts of real-time data. By distributing computing tasks to multiple edge nodes, the load and transmission delay of the cloud data center are reduced. The edge computing node completes complex data analysis, video encoding, and AI reasoning tasks through parallel processing, and dynamically adjusts resources to intelligently allocate computing power.

[0071] Preferably, in the data request and initial analysis, the AI ​​prediction model is used to analyze the user's historical behavior and preferences and predict future needs. By training the deep learning model, the user's possible requests can be predicted and the required data can be cached in advance. The model is trained based on time series data and uses a neural network structure to capture the time dependency of user behavior. The prediction results will be used to optimize the data caching strategy and reduce latency and bandwidth usage.

[0072] Preferably, the 5G / 6G network in the multi-channel data transmission supports high-bandwidth, low-latency communication, allows multi-channel data to be transmitted in parallel, and allocates independent channels to different types of data streams through network slicing technology. The network slicing technology virtualizes network resources to allocate dedicated bandwidth and priority to each channel, and dynamically adjusts bandwidth allocation based on the AI-based scheduling algorithm.

[0073] Preferably, quantum computing in the multi-channel data transmission can process large-scale data in parallel, and quantum encryption technology can ensure the security of data transmission through quantum key distribution.

[0074] Preferably, the photonic computing in the final presentation of the data and user feedback utilizes photonic chips for high-speed computing, and the photonic computing unit is embedded in a set-top box or edge node to process high-resolution video and its user computing-intensive tasks. The photonic computing unit data is transmitted at high speed through optical fiber communication.

[0075] Beneficial Effects

[0076] The present invention provides a multi-channel collaborative acceleration method for set-top box data processing and transmission.

[0077] Beneficial effects:

[0078] 1. The present invention adopts edge computing and photon computing technology, and uses distributed edge computing nodes and photon computing units to send data requests to edge nodes closer to users for preliminary processing. At the same time, photon computing technology is used to accelerate the processing of high-resolution videos. This processing transfers computing tasks from the cloud to edge nodes, and uses the high-speed processing capabilities of photon computing to reduce data delays. The combination of edge computing and photon computing significantly reduces data processing and transmission delays, allowing the set-top box to quickly respond to user requests and achieve a smooth user experience. The delay of video and audio data is reduced, which improves the user's experience quality when watching videos and playing games.

[0079] 2. The present invention adopts quantum key distribution technology and 5G / 6G network slicing technology, and utilizes quantum encryption technology and high-bandwidth and low-latency 5G / 6G networks. Quantum key distribution technology is applied for encryption during data transmission to ensure the security of data transmission. At the same time, through the network slicing technology of 5G / 6G networks, independent network resources are allocated to different types of data streams. Quantum key distribution provides powerful data encryption protection to ensure data security during transmission. Network slicing technology ensures the priority and bandwidth allocation of different data streams, improves transmission reliability, reduces packet loss and delay, and ensures stable transmission of data in the network.

[0080] 3. The present invention adopts dynamic resource scheduling and photon computing technology, and utilizes AI prediction models and photon computing units. The AI ​​model predicts the load situation in real time, and optimizes the resource allocation of edge computing nodes through dynamic resource scheduling. At the same time, photon computing technology is used to efficiently process large amounts of data and complex computing tasks. The combination of dynamic resource scheduling and photon computing optimizes resource utilization, reduces resource waste and processing bottlenecks. The system can efficiently process large-scale data, improve overall computing efficiency and processing capabilities, and thus support higher quality content processing and transmission.

[0081] 4. Adopt AI model optimization technology, utilize deep learning and user behavior analysis, AI model analyzes user's historical behavior and preferences, optimizes video encoding and content presentation, predicts user needs, caches data in advance, and reduces waiting time. The application of AI model can intelligently adjust the content presentation method and provide personalized services. Users can obtain higher quality content and faster loading speed, thereby improving satisfaction and experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0082] Figure 1 is a flow chart of the method of the present invention;

[0083] Figure 2 A comparison chart of user demand prediction and actual demand of the present invention;

[0084] Figure 3 This is a delay comparison diagram of edge computing and cloud computing of the present invention;

[0085] Figure 4 A time diagram for photon calculation processing of the present invention;

[0086] Figure 5 is a dynamic resource scheduling diagram of the present invention;

[0087] Figure 6 The network slice bandwidth allocation diagram of the present invention;

[0088] Figure 7 It is a quantum key distribution diagram of the present invention;

[0089] Figure 8 is a real-time processing efficiency diagram of the present invention;

[0090] Fig. 9 This is a control signal delay diagram of the present invention. DETAILED DESCRIPTION

[0091] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other user embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention. Specific embodiment one:

[0093] like Figure 1-Figure 9 As shown, a multi-channel collaborative acceleration method for set-top box data processing and transmission, the set-top method includes the following steps:

[0094] Sp1: Data request and initial analysis. The user sends a data request through the set-top box. The request content includes video, music, games and other user content needs. The set-top box sends the request data to the nearest edge computing node. After receiving the request data, the edge computing node first performs a preliminary analysis. The edge computing node is executed after receiving the data request from the set-top box. The edge computing node is close to the user-end device and can process data under low latency conditions. User request analysis: When the set-top box sends a data request, the edge computing node will first receive the request and perform a preliminary identification of the user's needs. This recognition task is completed through the AI ​​prediction model. The model judges the user's needs at the current time point based on historical user behavior data, and accelerates the data processing tasks of the edge computing nodes through quantum computing. Quantum computing acceleration is mainly reflected in the parallel processing and acceleration of large-scale data by edge computing nodes. The specific acceleration process is as follows: Parallel computing: Quantum computing can process multiple data states at the same time through principles such as superposition and entanglement, thereby achieving efficient parallel computing. Compared with traditional computing, quantum computing does not process data one by one, but calculates multiple inputs simultaneously through the superposition state of quantum bits. Processing flow: During the quantum computing acceleration stage, the edge computing node will transfer the complex computing tasks of the data (multi-channel data classification, coding optimization, etc.) to the quantum computing unit. The quantum computing unit completes the calculation and output results of multiple possible states in an instant, greatly shortening the data processing time. Acceleration effect: Quantum computing reduces the time required for data transmission and calculation through efficient parallel processing, especially when processing high-resolution videos. It shows obvious advantages, uses AI models to predict users' possible needs, and caches the required data in advance;

[0095] The AI ​​model’s prediction formula for request data is:

[0096]

[0097] in, The user demand predicted by the user represents the possible user request at time t+1;

[0098] f is the prediction function, a function based on the AI ​​model, used to map historical data to future needs;

[0099] θ is the model function, which determines the behavior and accuracy of the prediction function;

[0100] y(t) is the actual user demand data at time t, which represents the user's behavior at time t.

[0101] Users make requests for videos, music, games, and other content through set-top boxes;

[0102] The set-top box sends the request to the nearest edge computing node;

[0103] Edge computing nodes use AI prediction models to analyze requests and predict future user needs. Edge computing nodes complete real-time data processing and optimization by combining AI prediction, quantum computing, photon computing and other technologies. The specific process is as follows:

[0104] Request analysis: AI models predict demand based on historical user behavior;

[0105] Quantum computing acceleration: Quantum computing accelerates data processing and completes parallel tasks;

[0106] Photon computing processing: Photon computing accelerates video processing to ensure real-time decoding and playback of high-resolution videos;

[0107] Resource scheduling: Edge computing nodes dynamically adjust computing resources based on real-time load and user needs to ensure the overall efficiency of the system;

[0108] The prediction function f uses historical data y(t), y(t-1)… and model parameters θ to predict the user’s possible needs at time t+1;

[0109] The edge node stores the predicted demand data (t+1) in the cache, ready to respond to future requests;

[0110] Further respond to user requests through delay optimization, set the network delay to L, and the data processing delay time to T p , then the total delay T total for:

[0111] T total =L cloud +T p,cloud

[0112] After the introduction of edge computing:

[0113] T total =L edge +T p,edge

[0114] in:

[0115] L edge is the network delay of the edge node, which refers to the delay time from the set-top box to the edge computing node;

[0116] T p,edge The data processing time of the edge node refers to the time it takes for the edge node to complete data processing;

[0117] L edge <L cloud and T p,edge <T p,cloud Represents the latency reduction achieved through edge computing.

[0118] Network delay: L cloud and L edge represent the delay from the set-top box to the cloud and edge computing nodes respectively; since the edge computing node is closer to the user, L edge <L cloud ;

[0119] Data processing time: T p,cloud and T p,edge Represent the data processing time in the cloud and edge computing nodes respectively. Edge computing nodes usually have a shorter processing time, so T p,edge <T p,cloud ;

[0120] The introduction of edge computing reduces overall latency and improves response speed.

[0121] Sp2: Data processing and content optimization. Edge computing nodes use local AI models to analyze and process request data and predicted demand data in real time. They automatically identify data content through AI models and optimize it according to user interests. Edge computing nodes use photon computing technology to process high-resolution videos and complex computing tasks. Photon computing units are embedded in edge computing nodes and process data through photon chips and fiber optic communication technology. Photons have extremely high computing speeds. Since the speed of light is several orders of magnitude faster than the speed of electrons, photon computing can quickly process big data and complex computing tasks, such as compression, decompression, and encoding of high-resolution videos. Processing flow: The processing time of photon computing is T. p The processing time formula is related to the amount of data N and the photon frequency v. The larger the amount of data N to be processed, the higher the photon frequency v and the longer the processing time T. q High-resolution video processing: In high-resolution video processing, the edge computing node divides the video frame and uses the photon computing unit to process each frame in parallel. This processing method can greatly improve the real-time playback performance of high-resolution video, ensuring that users experience smooth and delay-free viewing.

[0122] Edge computing nodes allocate computing resources through dynamic resource scheduling, intelligently adjust according to real-time load and user needs, and AI models dynamically allocate resources based on predicted load conditions;

[0123] AI model optimization formula for data content:

[0124]

[0125] Wherein, P is the optimized structure of video coding parameters;

[0126] p is the value of the encoding parameter;

[0127] N is the total sample size, indicating the number of data processed;

[0128] f(x i , p) is the optimized model output based on the input data and encoding parameters;

[0129] x i is the i-th input data sample;

[0130] y i is the i-th input data sample x i actual feedback or expected output;

[0131] The edge computing node analyzes the requested data in real time and uses the AI ​​model to automatically identify and optimize the data. The goal is to find the video encoding parameter P so that the encoded data is consistent with the actual expected output y i By minimizing the above formula, the edge node optimizes the video encoding parameters to improve the user experience.

[0132] The processing time formula for photon calculations is:

[0133]

[0134] Among them, N is the amount of data to be processed, that is, the total number of samples, indicating the total size of the data to be processed;

[0135] v is the frequency of photons, which indicates the processing speed of photon calculations;

[0136] T q Calculate processing time for photons;

[0137] Edge computing nodes use photonic computing technology to process complex tasks such as high-resolution video. The processing time is T q Depending on the amount of data N being processed and the frequency v of the photon calculations, a higher photon frequency v will reduce the processing time.

[0138] Optimization formula for dynamic resource scheduling and allocation:

[0139]

[0140] Among them, L i is the current load of the i-th node, indicating the amount of data currently processed by the node;

[0141] C i is the processing capacity of the i-th node, indicating the maximum processing capacity of the node;

[0142] Q is the number of edge nodes;

[0143] The edge node is based on the real-time load L i and processing power C i Dynamically adjust computing resources, and the optimization goal is to minimize the ratio of each node's load to its processing capacity, thereby achieving efficient resource allocation.

[0144] Sp3: Multi-channel data transmission. The processed data is divided into multiple channels and classified according to the content type. Each type of data is transmitted through a different network slice and encrypted through quantum key distribution technology. The data is transmitted through multiple slices of the 5G / 6G network. Each slice provides an optimized transmission path for a specific type of data.

[0145] The bandwidth allocation formula for network slicing is:

[0146]

[0147] Among them, B i is the bandwidth of the i-th network slice;

[0148] B total is the total bandwidth, which indicates the total amount of broadband available in the 5G / 6G network;

[0149] M is the number of slices, which indicates the number of independent channels allocated in the network; the optimization goal is:

[0150]

[0151] Among them, α i is the priority weight of the i-th slice;

[0152] β i is the bandwidth demand of the i-th slice;

[0153] The processed data is divided into multiple channels and classified according to the content type. Each type of data is transmitted through a different network slice to optimize bandwidth allocation. The optimization goal is to minimize the priority weight α of each slice. i and bandwidth requirement β iproduct to ensure effective use of the total bandwidth B total .

[0154] Through the quantum key distribution formula in quantum key distribution technology:

[0155]

[0156] Among them, K is the generated quantum key, which is used to encrypt data transmission;

[0157] H is a hash function;

[0158] S A and S B The measurement results of the sender and the receiver are generated through the superposition of quantum states;

[0159] Data is transmitted encrypted using quantum key distribution technology, and the generated quantum key K is used to encrypt data to ensure the security of data transmission.

[0160] 5G / 6G network transmission delay formula:

[0161]

[0162] Where D is the transmission delay, which indicates the time required for data to be transmitted in the network;

[0163] L is the length of the data packet, indicating the amount of data that needs to be transmitted;

[0164] B is the network bandwidth, which indicates the transmission capacity provided by the 5G / 6G network;

[0165] Calculate the delay D of data transmission in 5G / 6G network. The length of data packet L and network bandwidth B directly affect the transmission delay.

[0166] Sp4: Final presentation of data and user feedback. After receiving the transmitted data, the set-top box uses the photon computing unit to quickly decode it and transmit the processed data to the user. The AI ​​model learns the user's behavior and feedback content inside the set-top box, continuously optimizes the prediction model and presentation content, and sends the data to the edge computing node.

[0167] After the set-top box receives the data, it uses the photon computing unit to quickly decode it. The processed data is transmitted to the user through the set-top box. The AI ​​model learns the user's behavior and feedback content inside the set-top box to optimize the prediction model and data presentation.

[0168] The specific AI model training process is as follows:

[0169] Sp1: Data collection and preprocessing: First, collect a large amount of high-resolution video streams and user interaction data from the actual operating environment. This data includes video resolution, encoding method, network bandwidth, and user behavior (switching channels, pausing playback, and interactive operations). Clean and annotate this data to ensure data accuracy and consistency;

[0170] Sp2: Feature extraction: Extract key features from the raw data, such as video frame rate, resolution change, network latency, and user behavior patterns. Use data enhancement methods, such as random cropping, rotation, and brightness adjustment, to improve the robustness of the model.

[0171] Sp3: Model selection and design: Select the appropriate deep learning model architecture, Convolutional Neural Network (CNN) for image feature extraction, Recurrent Neural Network (RNN) for time series prediction. When designing the model, pay special attention to the number of network layers, filter size, and activation function parameter settings to ensure the efficiency and accuracy of the model.

[0172] The training process is as follows:

[0173] Sp1: Use the cross-validation method to divide the data set into training set, validation set and test set. During the training process, use the adaptive learning rate and momentum optimizer advanced optimization techniques to accelerate model convergence and prevent overfitting. At the same time, by adjusting the loss function weight, balance the impact of each feature on the model decision;

[0174] Sp2: Model tuning: After training, the model is evaluated and tuned through the validation set and test set. Focus on the model's prediction accuracy, real-time performance, and generalization capabilities for different scenarios. If necessary, use transfer learning technology to migrate models trained in similar scenarios to new applications to reduce training time and resource consumption;

[0175] Sp3: Through the above training, the AI ​​model can generate the following output results based on the input real-time data (including video stream, network status, and user behavior):

[0176] Optimal video encoding parameters: predict encoding parameters suitable for current network and device conditions, such as bit rate, resolution, and frame rate;

[0177] Optimized transmission path: Select the optimal data transmission path based on network conditions to reduce delays and data packet loss;

[0178] Dynamic resource allocation strategy: Intelligently allocate the computing resources of the set-top box based on user behavior and system load to ensure the overall efficiency of the system.

[0179] In data requests and initial analysis, edge computing nodes are computing resources distributed at the edge of the network, close to the set-top box, which are used to process large amounts of real-time data and reduce the load and transmission delay of the cloud data center by distributing computing tasks to multiple edge nodes. Edge computing nodes complete complex data analysis, video encoding, and AI reasoning tasks through parallel processing, dynamically adjust resources, and intelligently allocate computing power. Photonic computing technology is used to accelerate the processing of high-resolution video. This processing transfers computing tasks from the cloud to edge nodes and uses the high-speed processing capabilities of photonic computing to reduce data delays. The combination of edge computing and photonic computing significantly reduces data processing and transmission delays, allowing the set-top box to respond quickly to user requests and achieve a smooth user experience. The delay of video and audio data is reduced, which improves the user's experience quality when watching videos and playing games.

[0180] In data requests and initial analysis, AI prediction models are used to analyze users' historical behaviors and preferences and predict future needs. By training deep learning models, users' possible requests can be predicted and the required data can be cached in advance. The model is trained based on time series data and uses a neural network structure to capture the time dependency of user behavior. The prediction results will be used to optimize data caching strategies, reduce latency and bandwidth usage, and use AI model optimization technology. By leveraging deep learning and user behavior analysis, AI models analyze users' historical behaviors and preferences, optimize video encoding and content presentation, and predict user needs, cache data in advance, and reduce waiting time. The application of AI models can intelligently adjust the way content is presented and provide personalized services. Users can obtain higher quality content and faster loading speeds, thereby improving satisfaction and experience.

[0181] In multi-channel data transmission, 5G / 6G networks support high-bandwidth, low-latency communications, allowing multi-channel data to be transmitted in parallel. Through network slicing technology, independent channels are allocated for different types of data streams. Network slicing technology virtualizes network resources to allocate dedicated bandwidth and priority to each channel. The AI-based scheduling algorithm dynamically adjusts bandwidth allocation. In multi-channel data transmission, quantum computing can process large-scale data in parallel. Quantum encryption technology ensures the security of data transmission through quantum key distribution. The use of quantum key distribution technology and 5G / 6G network slicing technology, using quantum encryption technology and high-bandwidth, low-latency 5G / 6G networks, applies quantum key distribution technology for encryption during data transmission to ensure the security of data transmission. At the same time, through the network slicing technology of 5G / 6G networks, independent network resources are allocated for different types of data streams. Quantum key distribution provides strong data encryption protection to ensure data security during transmission. Network slicing technology ensures the priority and bandwidth allocation of different data streams, improves the reliability of transmission, reduces packet loss and delay, and ensures the stable transmission of data in the network.

[0182] In the final presentation of data and user feedback, photonic computing uses photonic chips for high-speed computing. Photonic computing units can be embedded in set-top boxes or edge nodes to process high-resolution videos and other user computing-intensive tasks. Through optical fiber communication, the photonic computing unit data is transmitted at high speed. By introducing photonic computing technology, the data processing time of the set-top box can be significantly reduced, and combined with optical fiber communication, ultra-high-speed data transmission can be achieved to reduce latency. Photonic computing can be combined with edge computing and 5G / 6G network technology to achieve ultra-high-speed data processing and transmission. In addition, the high efficiency of photonic computing can also complement quantum computing to improve the performance of the overall system.

[0183] According to the above content, edge computing nodes achieve fast data processing and caching through dynamic resource scheduling and AI optimization. Combined with 5G / 6G and photonic computing, the speed of data transmission and processing is greatly improved. Quantum computing provides additional computing power to support complex tasks. Quantum encryption ensures the security of data transmission. AI technology continuously optimizes the user experience throughout the entire process and achieves highly personalized and intelligent content distribution. The interconnection between various technologies ensures the efficient operation of the entire system. Edge computing nodes and photonic computing provide powerful data processing capabilities. AI technology and dynamic resource scheduling ensure the intelligence and flexibility of the system. 5G / 6G networks and quantum encryption ensure the speed and security of data transmission. Finally, the system continuously optimizes itself through a real-time feedback mechanism to provide the best user experience. Specific embodiment 2:

[0185] like Figure 1-Figure 9 As shown, according to the contents in the above specific embodiments, the following contents are further disclosed:

[0186] Visualization of technical solutions for AI prediction models, photon computing processing time, dynamic resource scheduling, quantum key distribution, and 5G / 6G network transmission delay:

[0187] For data requests and initial analysis, the output of the AI ​​prediction model can predict the user's content needs. The model is trained based on historical user behavior data to predict the user's future needs, which is used to verify the accuracy of the model and evaluate the effectiveness of the caching strategy. By simulating data requests and processing under different delay conditions, the total delay of edge computing and cloud computing is compared to verify the benefits of edge computing. A comparison chart of user demand prediction results and actual demand is drawn to demonstrate the prediction accuracy of the model. Figure 2 As shown in the figure, the line chart is a comparison between user demand prediction and actual demand: the dotted line represents the predicted demand, and the solid line represents the actual demand, which shows the prediction effect of the AI ​​model. Figure 3The bar chart in the figure compares the latency between edge computing and cloud computing, which verifies the superiority of edge computing.

[0188] For data processing and content optimization, Figure 4 As shown in the figure, the processing time of photon computing is calculated and displayed through a bar chart. The processing time of photon computing under different data amounts and photon frequencies is simulated. By adjusting the data amount and photon frequency, the processing time is calculated to verify the acceleration effect of photon computing. Figure 5 As shown, the resource allocation of each edge node is displayed, the load and processing capacity of multiple edge nodes are simulated, and the resource allocation and resource scheduling strategies under different load conditions are visualized. The effect of dynamically adjusting computing resources is tested, and the impact of different scheduling strategies on processing efficiency is compared. A bar chart is used to display the resource allocation of different nodes under different load conditions.

[0189] In set-top boxes, photon computing technology is used for decoding and transcoding of video data. Specifically, the photon processor replaces the traditional electronic processor and performs parallel computing of video data through optical chips, which greatly improves the processing speed. When processing 8K video streams, the photon computing module can decode in real time and perform efficient transcoding, adjusting the video resolution to the resolution suitable for the user's device. At the same time, due to the parallel processing capability of photon computing, it can process multiple video streams to ensure seamless playback.

[0190] Under the operation process:

[0191] Video stream access: The photon computing module receives high-resolution video streams;

[0192] Parallel processing: Use optical chips to decode and transcode video frames in parallel;

[0193] Output video stream: The processed video stream is output to the user terminal in the optimal format;

[0194] In edge computing nodes, quantum computing technology is used to optimize the transmission path and scheduling strategy of multi-channel data streams. Quantum computers quickly calculate the optimal transmission path by superimposing the quantum states of multiple possible paths, thereby reducing network delays and transmission losses.

[0195] Under the operation process:

[0196] Data stream access: Edge nodes receive data streams from multiple sources;

[0197] Path optimization: The quantum computing module performs parallel calculations on all possible transmission paths and selects the optimal path;

[0198] Data transmission: Transmit data along the optimal path to reduce latency and packet loss.

[0199] Simulation and visualization of multi-channel data transmission, Figure 6 As shown in the figure, the bandwidth allocation of different slices in the 5G / 6G network is simulated, and the bandwidth allocation of different network slices is plotted, including a comparison chart of bandwidth demand and actual allocation. The stacked bar chart shows the distribution of bandwidth allocation, showing the bandwidth allocation of different slices, and testing the impact of bandwidth demand and priority settings of different slices on data transmission. Figure 7 As shown, the distribution simulation of the generated quantum key is displayed, and the encrypted data flow diagram in the quantum key distribution process is displayed, including a data security comparison diagram before and after encryption, which is used to demonstrate the effect of encryption technology, the encryption effect of quantum key distribution technology under different network conditions, and evaluate the security of quantum key generation, distribution and data encryption.

[0200] Simulation and visualization of data processing and transmission of real-time interactive games, real-time processing of simulated game data, Figure 8 As shown, including the delay of control signals and the efficiency of data processing, the smoothness of the gaming experience is evaluated by simulating different network delays and processing speeds. Fig. 9 As shown, a line graph is used to display the control signal delay under different conditions, intuitively showing the improvement effect of the optimization strategy on signal delay, testing the control signal transmission delay in real-time interactive games, and comparing the effects of different network slicing and data processing technologies on the control signal response speed. Specific embodiment three:

[0202] like Figure 1-Figure 9 As shown, according to the contents in the above specific embodiments, the following contents are further disclosed:

[0203] According to the above content, three sets of experiments are carried out for real-time examples. The experimental contents and steps are as follows:

[0204] Experiment 1: Streaming Content Distribution on Smart TV

[0205] When using set-top boxes, users hope to get smooth high-definition video playback, personalized recommendations and real-time interactive experience. Set-top boxes need to provide high-quality services by optimizing data processing and transmission processes. The following are technical solutions and steps to achieve these goals based on the above content:

[0206] Sp1: Data request and initial analysis,The user sends a video playback request through the set-top box, which sends the request to the nearest edge computing node, the local data center, and the edge computing node is responsible for processing user requests and predicting content needs. AI prediction model: Use machine learning models to analyze users' historical viewing behavior, user viewing records, and ratings to predict video content that users may be interested in. Use the formula:

[0207]

[0208] in, is the predicted user demand, f is the prediction function, and θ is the model function;

[0209] When a user requests video A, the AI ​​prediction model analyzes the user's viewing history and predicts that the user may be interested in video B. Therefore, the edge node caches video B in advance to provide services quickly.

[0210] Sp2: Data processing and content optimization. After receiving the request, the edge computing node performs real-time data processing and content optimization to provide a high-quality video playback experience. Photonic computing technology is used to process high-resolution video data to improve computing speed and efficiency. The processing time formula of photonic computing is:

[0211]

[0212] Where N is the amount of data processed, and v is the frequency of photons;

[0213] The edge node dynamically adjusts computing resources based on the current load and user needs, using the optimization formula:

[0214]

[0215] Among them, L i is the load of the ith node, C i is the processing capacity of the ith node;

[0216] When processing user requests, edge nodes analyze video content in real time and adjust video encoding parameters to ensure the best video quality under bandwidth constraints;

[0217] Sp3: Multi-channel data transmission. The processed data is transmitted through multiple channels to ensure that different types of data, video streams, and control signals can be efficiently and securely transmitted to user devices. The network slicing technology of 5G / 6G networks is used to allocate dedicated bandwidth for different data streams. High-definition video streams use one slice, and low-latency control signals use another slice. The broadband allocation formula is:

[0218]

[0219] Among them, B i is the bandwidth of the ith network slice, B total is the total bandwidth;

[0220] Use quantum key distribution technology to encrypt transmitted data to ensure data security. The quantum key generation formula is:

[0221]

[0222] Among them, K is the generated quantum key, H is the hash function, S A and S B Measurement results for sender and receiver;

[0223] High-definition video is transmitted via dedicated network slices, while quantum encryption technology is used to protect data from hacker attacks;

[0224] Sp4: Final presentation of data and user feedback. After receiving the transmitted data, the set-top box quickly decodes it and presents it to the user. At the same time, the set-top box continues to collect user feedback to optimize future data processing and content recommendations. When the user watches the video, the set-top box uses the photon computing unit to quickly decode the video stream and adjust the recommended content in real time according to the user's viewing habits.

[0225] Based on the above content, it is demonstrated that advanced technical solutions are applied to actual set-top box data processing and transmission. By integrating AI prediction, photon computing, dynamic resource scheduling, quantum key distribution and 5G / 6G network technology, efficient, secure and personalized content transmission and processing are achieved. Through the combination of these technologies, users can get a smoother viewing experience and smarter content recommendations while ensuring data security.

[0226] Experiment 2: Personalized content recommendation and optimization

[0227] Users use set-top boxes to watch different types of content, such as movies, music, and games. Set-top boxes need to provide personalized content recommendations and optimization services. The following are technical solutions and steps to achieve these goals based on the above content:

[0228] Sp1: Data request and initial analysis. The user requests to watch a specific type of content. The set-top box analyzes the user's historical behavior through the edge computing node. The AI ​​prediction model uses a deep learning model to predict the user's interest in the content. The prediction formula is:

[0229]

[0230] Analyze user behavior through training models, cache and optimize content in advance;

[0231] Sp2: Data processing and content optimization. The edge computing node automatically identifies and optimizes the content and adjusts the video encoding parameters according to user interests. The optimization formula is:

[0232]

[0233] Optimize video quality by adjusting encoding parameters p to meet user personalized needs;

[0234] Sp3: Multi-channel data transmission, personalized content is transmitted through different network slices to ensure high-quality experience of various types of content, and bandwidth is allocated according to content type, using the formula:

[0235]

[0236] Among them, α is the priority weight of the i-th slice;

[0237] β i is the bandwidth demand of the i-th slice;

[0238] The predictive capabilities of the AI ​​model enable the set-top box to prepare and optimize content in advance based on user interests, and provide a high-quality video experience by dynamically adjusting encoding parameters. Network slicing technology optimizes the transmission paths for different types of content and improves the personalization level of the overall user experience.

[0239] Experiment 3: Real-time interactive games and control signal processing

[0240] When users play real-time interactive games through set-top boxes, low-latency control signals and efficient data transmission are required to ensure a good gaming experience:

[0241] Sp1: Data request and initial analysis, users request game data and real-time control signals, the set-top box processes the request through the edge computing node, the AI ​​prediction model predicts the user's real-time control needs, and optimizes the control signal processing prediction formula:

[0242]

[0243] Improve the response speed of control signals;

[0244] Sp2: Data processing and content optimization, edge computing nodes process real-time game data, use photon computing technology to accelerate computing tasks, and ensure low latency. The processing time formula of photon computing is:

[0245]

[0246] Provide fast data processing capabilities to reduce game delays;

[0247] Sp3: Multi-channel data transmission, real-time game data and control signals are transmitted through 5G / 6G network slicing technology to ensure low latency and high bandwidth. The network slicing uses the formula:

[0248]

[0249] in,

[0250] D is the transmission delay, L is the packet length, and B is the network bandwidth, which optimizes network transmission;

[0251] Through edge computing nodes and photonic computing technology, game data processing achieves low latency to meet real-time interaction requirements. Network slicing technology and low-latency optimization ensure the fast transmission of game control signals and provide a smooth interactive experience.

[0252] Through the above real-time experimental examples, it is demonstrated that the multi-channel collaborative acceleration method of set-top box data processing and transmission can be applied to actual scenarios, achieving efficient data processing, personalized recommendations, secure transmission and low-latency interactive experience. The effective application of technical solutions in each experiment enables the set-top box to provide an optimized user experience to meet different needs. Specific embodiment four:

[0254] like Figure 1-Figure 9 As shown, according to the contents in the above specific embodiments, the following contents are further disclosed:

[0255] Photonic computing technology uses photons for data processing and transmission. It is a computing technology based on optical components and systems. Compared with traditional electronic computing technology, photonic computing has the following advantages:

[0256] Ultra-high-speed processing: Photons travel extremely fast, close to the speed of light, which enables photon computing to significantly increase the speed of data processing;

[0257] High bandwidth: The wide frequency range of photons enables photonic computing to process large-scale data and high-resolution content, and is particularly suitable for high-frequency data transmission and large-scale parallel computing;

[0258] Low energy consumption: Photonic computing consumes less power than traditional electronic computing, especially when processing large-scale data.

[0259] In the process of data processing and transmission in set-top boxes, photonic computing technology is used to accelerate data processing in edge computing nodes. Especially when processing high-resolution videos and complex computing tasks, photonic computing significantly improves data processing efficiency through its high speed and low latency. In the data processing and content optimization stage, photonic computing uses fast computing power to perform real-time analysis and optimization of requested data. This includes the processing of high-resolution videos, so that content can be transmitted and displayed in a more efficient manner according to user needs. After the set-top box receives the transmitted data, it uses the photonic computing unit for rapid decoding. Through the high-speed processing power of photonic computing, users can obtain processed data more quickly, thereby improving the user experience.

[0260] Quantum computing technology is mainly used in the field of encryption. It is a computing method that encodes data into two bytes or two words. It is usually used in quantum key distribution and data encryption. It ensures data security through the superposition of quantum states. The core of quantum computing technology is to use quantum superposition and entanglement quantum properties to generate and distribute encryption keys, thereby improving the security of data transmission.

[0261] In the process of multi-channel data transmission, photon computing technology is combined with quantum computing technology to ensure the security of data transmission through quantum key distribution technology (QKD). In this way, the encrypted data can effectively resist eavesdropping and attacks during transmission. Quantum computing technology ensures the security of multi-channel data transmission in 5G / 6G networks through quantum key distribution. The data in each network slice is encrypted before transmission, thereby improving the security level of the entire data transmission system.

[0262] Photonic computing technology and quantum computing technology play a key role in the multi-channel collaborative acceleration method of set-top box data processing and transmission. Photonic computing technology significantly improves the efficiency of data processing and optimization through its ultra-high speed and low latency characteristics, while quantum computing technology achieves security in the data transmission process through quantum key distribution. The combination of these two technologies ensures that the entire system can maintain a high degree of security during transmission while efficiently processing data. Specific embodiment five:

[0264] Here is a specific application case:

[0265] The user chooses to watch a popular live sports event. The smart TV sends a request through the set-top box and sends the request data to the nearest edge computing node. This edge node not only obtains the live broadcast data, but also analyzes the user's historical viewing history through an AI prediction model to predict that the user may be interested in relevant comments, game highlights and advertisements of the event. Based on the prediction results, the edge node caches this additional data in advance to reduce the user's waiting time. At the edge node, the live sports video data is quickly decoded by photon computing units. These photon computing units can process high-resolution video data at extremely fast speeds to ensure smooth playback. At the same time, the edge node optimizes the video content, including adjusting the encoding parameters of the video to match the user's network bandwidth, thereby providing the best video quality and lowest latency. The live broadcast content is transmitted to the user's set-top box via the 5G / 6G network. Since the live broadcast includes video, audio, real-time data and interactive content, these contents are divided into multiple channels and transmitted through network slices respectively. Video data is transmitted through high-bandwidth slices to ensure high resolution and low latency; audio data is transmitted through low-latency slices; interactive data (including real-time voting and comments) is transmitted through dedicated low-latency channels. Quantum key distribution technology is used to encrypt all transmitted data to ensure information security. After receiving data from multiple channels, the set-top box uses a photon computing unit to decode the video data and combines audio data with real-time interactive data to provide a high-quality viewing experience. Users can participate in interactive voting for the game in real time and see relevant comments and data statistics on the screen. The AI ​​model recommends additional content related to the current game to users based on their viewing habits, including team history videos and player statistics. During the viewing process, the user's behavioral data (including the user's clicks on certain ads and viewing time) is fed back to the edge computing node in real time. The edge node uses this data to adjust the content recommendation strategy to further improve the user's personalized experience.

[0266] Comprehensive benefits:

[0267] Efficient data processing: The combination of photonic computing technology and edge computing nodes ensures efficient and real-time data processing, reduces latency, and improves user experience.

[0268] Optimized transmission links: Through 5G / 6G networks and multi-channel data transmission technology, fast and secure transmission of content is ensured, while avoiding the impact of network congestion on video quality.

[0269] Personalized recommendations: The AI ​​prediction model makes intelligent recommendations based on the user’s viewing history, improving content relevance and user satisfaction.

[0270] Security guarantee: Quantum encryption technology ensures data security during transmission and protects user privacy and content copyright.

[0271] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the statement "comprising a reference structure" do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0272] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A multi-channel collaborative acceleration method for set-top box data processing and transmission, characterized in that: The method comprises the following steps: Sp1: Data request and initial analysis. The user sends a data request through the set-top box. The request content includes the user's required data. The set-top box sends the requested data to the nearest edge computing node. After receiving the request data, the edge computing node first performs a preliminary analysis and accelerates the data processing task through quantum computing. It uses AI models to predict the user's required data and cache the required data in advance. The AI ​​model’s prediction formula for request data is: in, The user needs predicted for the user, expressed in time Possible user requests; For prediction functions, AI model-based functions are used to map historical data to future needs; is the model function, which determines the behavior and accuracy of the prediction function; For time The actual user demand data indicates that users behavior; Sp2: Data processing and content optimization. Edge computing nodes use local AI models to analyze and process request data and predicted demand data in real time. The data content is automatically identified through AI models and optimized according to the user's points of interest. Edge computing nodes use photonic computing technology to process high-resolution videos and complex computing tasks. Edge computing nodes allocate computing resources through dynamic resource scheduling, intelligently adjust according to real-time load and user needs, and AI models dynamically allocate resources according to predicted load conditions. AI model optimization formula for data content: in, An optimized structure for video encoding parameters; is the value of the encoding parameter; is the total sample size, indicating the number of data processed; Output of the optimized model based on the input data and encoding parameters; For the input data samples; For the Input data samples actual feedback or expected output; In the data processing and content optimization, the processing time formula of photon calculation is: in, is the amount of data to be processed, i.e. the total number of samples, indicating the total size of the data that needs to be processed; is the frequency of photons, indicating the processing speed of photon calculations; Calculate processing time for photons; In the data processing and content optimization, the optimization formula for dynamic resource scheduling and allocation is: in, For the The current load of a node, which indicates the amount of data currently processed by the node; For the The processing capacity of a node indicates the maximum processing capacity of the node; is the number of edge nodes; Sp3: Multi-channel data transmission. The optimized data processed by Sp2 is divided into multiple channels and classified according to the content type. Each type of data is transmitted through different network slices and encrypted through quantum key distribution technology. The data is transmitted through multiple slices of the 5G / 6G network, and each slice provides an optimized transmission path for a specific type of data. The bandwidth allocation formula for network slicing is: in, For the The bandwidth of each network slice; is the total bandwidth, which indicates the total amount of broadband available in the 5G / 6G network; is the number of slices, indicating the number of independent channels allocated in the network; The optimization goal is: in, For the The priority weight of each slice; For the The bandwidth demand of each slice; Sp4: Final presentation of data and user feedback. After the set-top box receives data transmitted from multiple channels of Sp3, it uses the photon computing unit to quickly decode it and transmits the processed data to the user. The AI ​​model learns the user's behavior and feedback content in real time inside the set-top box, continuously optimizes the prediction model and presentation content, and uses the data on the edge computing node.

2. The multi-channel collaborative acceleration method for set-top box data processing and transmission according to claim 1, characterized in that: In the data request and initial analysis, the user request is further responded to through delay optimization, and the network delay is set to , the data processing delay time is , then the total delay for: After the introduction of edge computing: in, is the network delay of the edge node, which refers to the delay time from the set-top box to the edge computing node; is the data processing time of the edge node, which refers to the time it takes for the edge node to complete data processing, and and Represents the latency reduction achieved through edge computing.

3. The multi-channel collaborative acceleration method for set-top box data processing and transmission according to claim 1, characterized in that: In the multi-channel data transmission, the quantum key distribution formula in the quantum key distribution technology is: in, To generate quantum keys for encrypting data transmission; is a hash function; and The measurement results of the sender and the receiver are generated through the superposition of quantum states; The formula for 5G / 6G network transmission delay in multi-channel data transmission is: in, is the transmission delay, which indicates the time required for data to be transmitted in the network; is the length of the data packet, indicating the amount of data that needs to be transmitted; It is the network bandwidth, which indicates the transmission capacity provided by the 5G / 6G network.

4. The multi-channel collaborative acceleration method for set-top box data processing and transmission according to claim 1, characterized in that: In the data request and initial analysis, the edge computing node is a computing resource distributed at the edge of the network, close to the set-top box, and is used to process large amounts of real-time data. By distributing computing tasks to multiple edge nodes, the load and transmission delay of the cloud data center are reduced. The edge computing node completes complex data analysis, video encoding, and AI reasoning tasks through parallel processing, and dynamically adjusts resources and intelligently allocates computing power.

5. The multi-channel collaborative acceleration method for set-top box data processing and transmission according to claim 1, characterized in that: In the data request and initial analysis, the AI ​​prediction model is used to analyze the user's historical behavior and preferences and predict future needs. By training the deep learning model, the user's possible requests can be predicted and the required data can be cached in advance. The model is trained based on time series data and uses a neural network structure to capture the time dependency of user behavior. The prediction results will be used to optimize the data caching strategy and reduce latency and bandwidth usage.

6. The multi-channel collaborative acceleration method for set-top box data processing and transmission according to claim 1, characterized in that: In the multi-channel data transmission, the 5G / 6G network supports high-bandwidth, low-latency communication, allows multi-channel data to be transmitted in parallel, and allocates independent channels for different types of data streams through network slicing technology. The network slicing technology virtualizes network resources, allocates dedicated bandwidth and priority to each channel, and dynamically adjusts bandwidth allocation based on the AI-based scheduling algorithm.

7. The multi-channel collaborative acceleration method for set-top box data processing and transmission according to claim 1, characterized in that: In the multi-channel data transmission, quantum computing can process large-scale data in parallel, and quantum encryption technology can ensure the security of data transmission through quantum key distribution.

8. The multi-channel collaborative acceleration method for set-top box data processing and transmission according to claim 1, characterized in that: In the final presentation of the data and user feedback, photonic computing utilizes photonic chips for high-speed computing. The photonic computing unit is embedded in a set-top box or edge node to process high-resolution video and its user computing-intensive tasks. The photonic computing unit data is transmitted at high speed through optical fiber communication.

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