IPTV terminal edge calculation method and system

Abstract

The invention relates to an IPTV terminal edge calculation method and system, and relates to the technical field of IPTV terminals, and the IPTV terminal edge calculation method comprises the following steps: firstly, analyzing user interaction behaviors to obtain a behavior mode; based on the mode, the system performs dynamic matching and context processing on content requests to generate a content preference matrix. And then, optimizing task scheduling of local edge nodes by utilizing the matrix. And the system carries out hardware resource mapping to obtain an equipment adaptation parameter, and carries out pipeline adaptation on a video decoder according to the equipment adaptation parameter to generate a local rendering instruction. And finally, asynchronous rendering is performed on the video frame cache based on the instructions, and a smooth video playing stream is output. The problems that when a traditional centralized IPTV system faces large-scale user access, network delay often exists, and service quality is reduced are solved.

Description

Technical Field

[0001] This invention relates to the field of IPTV terminal technology, and in particular to an IPTV terminal edge computing method and system. Background Technology

[0002] With the rapid development of internet technology, IPTV services are becoming increasingly popular, and users' demands for video content quality and smoothness are constantly rising. However, traditional centralized IPTV systems often suffer from network latency and service quality degradation when faced with large-scale user access. At the same time, the diversity of user behavior and preferences also poses challenges to content recommendation and resource scheduling. These factors seriously affect the user's viewing experience and hinder the further development of IPTV services.

[0003] To address these issues, researchers have begun focusing on the application of edge computing technology in the IPTV field. Edge computing, by offloading computing and storage resources to the network edge, can effectively reduce network latency and improve service response speed. However, how to effectively utilize the edge computing capabilities of IPTV terminals to achieve intelligent and personalized video decoding and rendering, including user behavior analysis, content preference prediction, resource optimization scheduling, and video decoding, remains a pressing problem. Furthermore, how to achieve efficient resource allocation and task scheduling on heterogeneous terminal devices, and how to ensure the smoothness and quality of video playback, are also significant challenges currently facing IPTV edge computing research. Summary of the Invention

[0004] The purpose of this invention is to at least partially solve one of the technical problems existing in the prior art.

[0005] To achieve the above objectives, the present invention provides an edge computing method for IPTV terminals, comprising the following steps: Multidimensional time series analysis was performed on user interaction data from IPTV terminals to obtain user behavior patterns. By dynamically matching and context-aware processing the content request sequence of the IPTV terminal based on the user behavior pattern, a content preference distribution matrix is ​​obtained. Based on the content preference distribution matrix, task scheduling optimization is performed on the preset resource status information of local edge nodes to obtain an edge deployment task queue; Based on the edge deployment task queue, heterogeneous hardware resource mapping analysis is performed on the IPTV terminal to obtain device adaptation configuration parameters; Based on the device adaptation configuration parameters, the video decoder module of the IPTV terminal is subjected to decoding and rendering pipeline adaptation processing to generate local rendering instructions. The video frame buffer of the IPTV terminal is asynchronously rendered based on the local rendering instructions to obtain a smooth video playback output stream.

[0006] Furthermore, the multi-dimensional time series analysis of user interaction behavior data from IPTV terminals to obtain user behavior patterns includes: The user interaction behavior data of the IPTV terminal is processed by time slicing based on the sliding window mechanism to obtain multiple time segment data. Each time segment dataset is then labeled with behavior event type to obtain labeled time segment data. Weights are assigned to different behavioral event types in the labeled time segment data to obtain weighted time segment data, and feature vectors are extracted in the time dimension based on the weighted time segment data to obtain time feature vectors. Cluster analysis is performed on the time feature vectors to obtain initial user behavior patterns, and pattern merging and optimization are performed on the initial user behavior patterns to obtain the final user behavior patterns.

[0007] Furthermore, the step of dynamically matching and context-awarely processing the content request sequence of the IPTV terminal based on the user behavior pattern to obtain a content preference distribution matrix includes: Semantic tag mapping is performed on each behavior pattern in the user behavior pattern to obtain behavioral semantic features, and metadata parsing is performed on the content request sequence of the IPTV terminal to extract program content feature vectors; Based on the semantic similarity calculation results between the behavioral semantic features and the program content feature vector, a behavior-content association matrix is ​​constructed, and the behavior-content association matrix is ​​subjected to context weight modulation to obtain a context-enhanced preference matching matrix; The context-enhanced preference matching matrix is ​​used to dynamically assign tags to the content classification system in the preset local content library, generate a content preference tag sequence, and generate a candidate set for program items in the local content library based on the content preference tag sequence, thus obtaining a content recommendation candidate set. Based on the matching degree between the historical playback popularity of each program item in the content recommendation candidate set and the user behavior pattern, the candidate program items are prioritized and a content preference distribution matrix is ​​generated.

[0008] Furthermore, the step of optimizing task scheduling based on the content preference distribution matrix and the preset resource status information of local edge nodes to obtain an edge deployment task queue includes: The resource status information of the preset local edge nodes is monitored in real time to obtain an edge node status monitoring table, and the resource availability in the edge node status monitoring table is evaluated to obtain a node resource status matrix. The matching degree of the content preference distribution matrix and the node resource status matrix is ​​calculated to obtain a task resource matching degree table, and the available edge nodes in the task resource matching degree table are filtered to obtain a list of available nodes. A multi-dimensional resource constraint analysis is performed on the available node list to obtain resource scheduling constraints. The task allocation is then optimized based on the resource scheduling constraints to generate a task allocation strategy graph. Based on the task allocation strategy graph, task deployment planning is performed on the list of available nodes to obtain an edge deployment task queue.

[0009] Furthermore, the heterogeneous hardware resource mapping analysis performed on the IPTV terminal based on the edge deployment task queue to obtain device adaptation configuration parameters includes: The task feature fingerprint is encoded on the edge deployment task queue to obtain the task feature fingerprint vector, and the instruction semantic parsing is performed on the task feature fingerprint vector to obtain the instruction-level computation density matrix. The IPTV terminal is marked with heterogeneous hardware resources based on the instruction-level computation density matrix to obtain a heterogeneous resource marking map. The interface feature protocols in the IPTV terminal are extracted based on the heterogeneous resource marking map to obtain an interface protocol feature table. Based on the instruction-level computation density matrix, the interface protocol feature table is detected to determine whether there is a resource mapping conflict. If there is, the resource mapping conflict result is obtained, and the resource conflict marker sequence is reconstructed based on the resource mapping conflict result to obtain a conflict-free resource path graph. Based on the conflict-free resource path graph, the task feature fingerprint vector is quantified using hardware affinity to obtain a device affinity score table. Then, the device affinity score table is mapped to a parameter register level configuration to obtain device adaptation configuration parameters.

[0010] Furthermore, the step of performing decoding and rendering pipeline adaptation processing on the video decoder module of the IPTV terminal based on the device adaptation configuration parameters to generate local rendering instructions includes: Based on the device adaptation configuration parameters, the decoding capability mapping analysis of the video decoder module in the IPTV terminal is performed to obtain the decoder hardware mapping capability. Based on the decoder hardware mapping capability, the decoding pipeline is divided into segments to obtain a multi-level decoding pipeline configuration descriptor. Based on the multi-level decoding pipeline configuration descriptor, a data path is established for the rendering pipeline hardware interface in the IPTV terminal to obtain a decoding rendering data path mapping diagram. Based on the decoding rendering data path mapping diagram, a rendering buffer memory layout is planned to obtain a rendering buffer memory allocation strategy. The rendering buffer memory allocation strategy is input into the IPTV terminal GPU rendering unit to compile the shader program, obtain the GPU shader program bytecode, and perform rendering instruction conversion processing based on the GPU shader program bytecode to generate local rendering instructions.

[0011] Furthermore, the asynchronous rendering process of the video frame buffer of the IPTV terminal based on the local rendering instructions to obtain a smooth video playback output stream includes: The local rendering instructions are decomposed into frame-level rendering tasks to obtain a frame rendering task sequence; Based on the frame rendering task sequence, the video output interface of the IPTV terminal is subjected to frame rate synchronization control to obtain a video frame rate synchronization control signal. The video frame rate synchronization control signal is then used for streaming data transmission processing to obtain a smooth video playback output stream.

[0012] The present invention also provides an IPTV terminal edge computing system, comprising: The analysis module is used to perform multi-dimensional time series analysis on user interaction behavior data of IPTV terminals to obtain user behavior patterns; The matching module is used to dynamically match and context-awarely process the content request sequence of the IPTV terminal based on the user behavior pattern to obtain a content preference distribution matrix; The optimization module is used to optimize task scheduling based on the content preference distribution matrix and the preset resource status information of local edge nodes to obtain an edge deployment task queue. The mapping module is used to perform heterogeneous hardware resource mapping analysis on the IPTV terminal based on the edge deployment task queue to obtain device adaptation configuration parameters; The decoding module is used to perform decoding and rendering pipeline adaptation processing on the video decoder module of the IPTV terminal based on the device adaptation configuration parameters, and generate local rendering instructions. The rendering module is used to perform asynchronous rendering processing on the video frame buffer of the IPTV terminal based on the local rendering instructions, so as to obtain a smooth video playback output stream.

[0013] The present invention also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of any of the methods described above.

[0014] The present invention also provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of any of the methods described above.

[0015] This invention provides an edge computing method for IPTV terminals, comprising the following steps: performing multi-dimensional time series analysis on user interaction behavior data of the IPTV terminal to obtain user behavior patterns; dynamically matching and context-aware processing of the content request sequence of the IPTV terminal using the user behavior patterns to obtain a content preference distribution matrix; optimizing task scheduling based on the resource status information of preset local edge nodes based on the content preference distribution matrix to obtain an edge deployment task queue; performing heterogeneous hardware resource mapping analysis on the IPTV terminal based on the edge deployment task queue to obtain device adaptation configuration parameters; performing decoding and rendering pipeline adaptation processing on the video decoder module of the IPTV terminal based on the device adaptation configuration parameters to generate local rendering instructions; and performing asynchronous rendering processing on the video frame buffer of the IPTV terminal based on the local rendering instructions to obtain a smooth video playback output stream. This method solves the problems of network latency and service quality degradation that often occur in traditional centralized IPTV systems when facing large-scale user access, and achieves the technical effect of responding to user requests more quickly and reducing latency during video loading and switching. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This is a schematic diagram of the steps of an IPTV terminal edge computing method in one embodiment of the present invention; Figure 2 This is a schematic diagram of the steps of an IPTV terminal edge computing system in one embodiment of the present invention; Figure 3 This is a schematic block diagram of the structure of a computer device according to an embodiment of the present invention.

[0018] The objectives, features, and advantages of this invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0019] The embodiments of the present invention are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention. The step numbers in the following embodiments are set only for ease of explanation, and there is no limitation on the order between the steps. The execution order of each step in the embodiments can be adaptively adjusted according to the understanding of those skilled in the art.

[0020] The following describes in detail, with reference to the accompanying drawings, an IPTV terminal edge computing method proposed according to an embodiment of the present invention. First, the IPTV terminal edge computing method proposed according to an embodiment of the present invention will be described with reference to the accompanying drawings.

[0021] Figure 1 This invention provides an IPTV terminal edge computing method according to one embodiment, comprising the following steps: Step S1: Perform multidimensional time series analysis on the user interaction behavior data of the IPTV terminal to obtain user behavior patterns.

[0022] Step S2: Dynamically match and context-awarely process the content request sequence of the IPTV terminal through the user behavior pattern to obtain a content preference distribution matrix.

[0023] Step S3: Based on the content preference distribution matrix, optimize the task scheduling of the preset local edge node resource status information to obtain the edge deployment task queue.

[0024] Step S4: Perform heterogeneous hardware resource mapping analysis on the IPTV terminal based on the edge deployment task queue to obtain device adaptation configuration parameters.

[0025] Step S5: Based on the device adaptation configuration parameters, perform decoding and rendering pipeline adaptation processing on the video decoder module of the IPTV terminal to generate local rendering instructions.

[0026] Step S6: Asynchronous rendering processing is performed on the video frame buffer of the IPTV terminal based on the local rendering instructions to obtain a smooth video playback output stream.

[0027] Specifically, the IPTV terminal edge computing method proposed in this invention achieves efficient and personalized video services through a series of steps. First, in step S1, the system performs multi-dimensional time series analysis on the user interaction behavior data of the IPTV terminal to obtain user behavior patterns. This process may involve collecting data such as the user's viewing history, click behavior, and search records, and using time series analysis techniques, such as the Autoregressive Integrated Moving Average (ARIMA) model or Long Short-Term Memory (LSTM) network, to identify patterns such as the user's viewing habits, preferred time periods, and content interests. For example, the system may find that a user tends to watch news and documentaries between 8 pm and 10 pm on weekdays, while preferring to watch movies and variety shows on weekends.

[0028] Next, in step S2, the system uses the user behavior patterns obtained in step S1 to dynamically match and context-awarely process the content request sequences of the IPTV terminal, thereby generating a content preference distribution matrix. This step may involve using machine learning algorithms, such as collaborative filtering or content-based recommendation systems, to dynamically adjust recommendation strategies based on the user's real-time behavior and current context (such as time, location, device type, etc.). The content preference distribution matrix may be a multi-dimensional array containing the user's preference for different types of content and how these preferences change over time and context. For example, the matrix might show that the user has a higher preference for sports events when using a smart TV on Friday night, while preferring to watch short video content when using a mobile device on a weekend morning.

[0029] Step S3, based on the content preference distribution matrix generated in the previous step, optimizes task scheduling for the pre-defined resource status information of local edge nodes, generating an edge deployment task queue. This process may involve considering the resource status of edge nodes, such as computing power, storage capacity, and network bandwidth, combined with user content preferences, and using optimization algorithms (such as genetic algorithms or particle swarm optimization) to determine which content should be cached on local edge nodes and how to allocate computing resources to handle user requests. For example, the system may decide to pre-cache the news channels and popular movies that users watch most frequently on local edge nodes, while reserving sufficient computing resources for upcoming peak viewing periods.

[0030] In step S4, based on the edge deployment task queue generated in step S3, the system performs heterogeneous hardware resource mapping analysis on the IPTV terminal to obtain device adaptation configuration parameters. This step takes into account that different users may use different types of terminal devices (such as smart TVs, set-top boxes, mobile devices, etc.), each with its specific hardware configuration and performance characteristics. The system needs to analyze the characteristics of these heterogeneous hardware, such as CPU performance, GPU capabilities, and memory size, and map each task in the task queue to the most suitable hardware resources. For example, for devices equipped with high-performance GPUs, the system may allocate more video decoding and rendering tasks; while for devices with lower performance, it may choose to rely more on edge nodes for preprocessing.

[0031] Step S5 utilizes the device adaptation configuration parameters obtained in step S4 to perform decoding and rendering pipeline adaptation processing on the video decoder module of the IPTV terminal, generating local rendering instructions. This process may involve dynamically adjusting video encoding and decoding parameters (such as bitrate, resolution, frame rate, etc.) based on the device's hardware capabilities and network conditions, selecting the most suitable decoding algorithm and rendering method. For example, for high-performance devices, the system may choose to use a hardware-accelerated H.265 decoder and OpenGL ES rendering pipeline; while for low-performance devices, a more lightweight decoder and software rendering method may be selected. The local rendering instructions may contain specific decoding parameters, rendering pipeline configuration, buffer settings, and other information.

[0032] Finally, in step S6, the system performs asynchronous rendering processing on the video frame buffer of the IPTV terminal based on the local rendering instructions generated in step S5, ultimately obtaining a smooth video playback output stream. Asynchronous rendering processing allows the system to begin processing the next frame while one frame is still being displayed, thereby improving rendering efficiency and reducing stuttering. This step may involve using multi-threading technology to distribute tasks such as decoding, rendering, and display to different threads for parallel processing. Simultaneously, the system may implement an adaptive buffering strategy, dynamically adjusting the buffer size based on network conditions and device performance to balance low latency and smooth playback. For example, when network conditions are good, the system may reduce the buffer size to lower latency; while when the network is unstable, it may increase the buffer size to ensure smooth playback.

[0033] Through this series of steps, the IPTV terminal edge computing method of the present invention achieves end-to-end optimization from user behavior analysis to personalized content recommendation, resource optimization scheduling, and efficient video rendering, effectively improving the quality of IPTV services and user experience.

[0034] In a specific embodiment, the step of performing multidimensional time series analysis on user interaction behavior data of IPTV terminals to obtain user behavior patterns includes: The user interaction behavior data of the IPTV terminal is processed by time slicing based on the sliding window mechanism to obtain multiple time segment data. Each time segment dataset is then labeled with behavior event type to obtain labeled time segment data. Weights are assigned to different behavioral event types in the labeled time segment data to obtain weighted time segment data, and feature vectors are extracted in the time dimension based on the weighted time segment data to obtain time feature vectors. Cluster analysis is performed on the time feature vectors to obtain initial user behavior patterns, and pattern merging and optimization are performed on the initial user behavior patterns to obtain the final user behavior patterns.

[0035] Specifically, in the edge computing method for IPTV terminals, a key step is to perform multi-dimensional time series analysis on user interaction behavior data to obtain user behavior patterns. This process first employs a sliding window mechanism to slice the IPTV terminal's user interaction behavior data into time segments. The sliding window mechanism allows the system to create overlapping time segments within a continuous time stream, thus capturing the continuity and trends of user behavior. For example, a 24-hour window can be set, sliding once every hour to obtain multiple overlapping time segment data. For each time segment, the system labels the behavior event type, which may include different types of user behavior such as watching, pausing, fast-forwarding, and channel switching. Next, the system assigns weights to different behavior event types in the labeled time segment data. The purpose of this step is to reflect the importance of different behavior events and their indicative role in user preferences. For example, watching a program in its entirety might be assigned a higher weight, while frequently switching channels might be assigned a lower weight. Through this weighting process, the system generates weighted time segment data, more accurately reflecting the user's true interests and behavior patterns. Based on this weighted time segment data, the system extracts feature vectors along the time dimension. This process may involve using statistical methods or machine learning techniques, such as Principal Component Analysis (PCA) or autoencoders, to extract key features from the time series, forming temporal feature vectors. These feature vectors may contain information such as the periodicity, duration, and frequency of user behavior. To further analyze these temporal feature vectors, the system performs cluster analysis. The purpose of cluster analysis is to categorize similar behavioral patterns to form initial user behavior patterns. Algorithms such as K-means clustering, hierarchical clustering, or density clustering may be used here. For example, the system may discover that some users have fixed viewing habits on weekday evenings, while exhibiting different patterns on weekends. These initial user behavior patterns provide a basic outline of user behavior, but there may still be some subtle differences or repetitive patterns. Therefore, the final step is to perform pattern merging and optimization on these initial user behavior patterns. This process may involve operations such as pattern similarity analysis, redundant pattern removal, and low-frequency pattern filtering. Through these processes, the system can obtain more refined and representative user behavior patterns. For example, the originally scattered patterns of "watching news on weekday evenings" and "watching documentaries on weekday evenings" may be merged into a pattern of "watching news programs on weekday evenings." This entire process can be illustrated with a concrete example: Suppose there is an IPTV user, and the system collects their viewing data for a week. Using a 4-hour sliding window, sliding once every hour, the system generates multiple time-segment data. Within each time segment, the system marks the user's behavior, such as "watching news," "pausing," and "switching to a movie channel." Then, the system weights these behaviors, for example, giving a higher weight to "watching a program in its entirety" and a lower weight to "quickly switching channels."Next, the system extracts time feature vectors from this weighted data, which may include peak viewing times, average viewing duration, and channel switching frequency each day. Through cluster analysis, the system may discover several initial patterns, such as "watching news on weekday evenings," "watching movie marathons on weekend afternoons," and "watching variety shows every evening." Finally, in the pattern merging and optimization phase, the system may merge "watching news on weekday evenings" and "watching documentaries on weekday evenings" into "watching news programs on weekday evenings," as they exhibit similar time characteristics and content preferences. Meanwhile, some infrequently occurring patterns, such as "watching cooking shows at 3 AM," may be filtered out, as they may be occasional behaviors rather than stable patterns. Through this series of complex data processing and analysis steps, the system ultimately obtains a set of refined behavioral patterns that accurately reflect user habits and preferences. These patterns will provide crucial decision-making support for subsequent content recommendation, resource allocation, and personalized services, thereby significantly improving the user experience and efficiency of IPTV services.

[0036] In a specific embodiment, the step of dynamically matching and context-awarely processing the content request sequence of the IPTV terminal based on the user behavior pattern to obtain a content preference distribution matrix includes: Semantic tag mapping is performed on each behavior pattern in the user behavior pattern to obtain behavioral semantic features, and metadata parsing is performed on the content request sequence of the IPTV terminal to extract program content feature vectors; Based on the semantic similarity calculation results between the behavioral semantic features and the program content feature vector, a behavior-content association matrix is ​​constructed, and the behavior-content association matrix is ​​subjected to context weight modulation to obtain a context-enhanced preference matching matrix; The context-enhanced preference matching matrix is ​​used to dynamically assign tags to the content classification system in the preset local content library, generate a content preference tag sequence, and generate a candidate set for program items in the local content library based on the content preference tag sequence, thus obtaining a content recommendation candidate set. Based on the matching degree between the historical playback popularity of each program item in the content recommendation candidate set and the user behavior pattern, the candidate program items are prioritized and a content preference distribution matrix is ​​generated.

[0037] Specifically, in IPTV terminal edge computing methods, dynamically matching and context-aware processing of content request sequences based on user behavior patterns to obtain a content preference distribution matrix is ​​a complex and sophisticated process. First, the system performs semantic label mapping on each behavior pattern to obtain behavioral semantic features. This step may involve using natural language processing techniques to transform user behavior into understandable semantic labels. For example, "watching for extended periods on weekend evenings" might be mapped to semantic labels such as "leisure and entertainment" or "immersive experience." Simultaneously, the system performs metadata parsing on the IPTV terminal's content request sequences to extract program content feature vectors. These feature vectors may include information across multiple dimensions, such as program type, theme, actors, director, and duration. Next, the system calculates the semantic similarity between the behavioral semantic features and the program content feature vectors. This step may use word embedding models (such as Word2Vec or BERT) to calculate semantic similarity, thereby constructing a behavior-content association matrix. For example, the behavioral semantic feature of "leisure and entertainment" might have a high similarity to content features such as movies and variety shows. Then, the system modulates the contextual weights of this behavior-content correlation matrix, considering contextual factors such as time, location, and device type, to obtain a context-enhanced preference matching matrix. This modulation can make recommendations more accurate; for example, news content might receive a higher weight when using a mobile device on a weekday morning. Based on the obtained context-enhanced preference matching matrix, the system dynamically assigns tags to the content classification system in the preset local content library, generating a content preference tag sequence. This process can be understood as mapping user behavior preferences to the content library's classification system, thereby assigning a weight related to user preferences to each content category. For example, if a user's behavior pattern shows a strong interest in technology programs, then technology content might receive a higher preference tag weight. Based on this content preference tag sequence, the system further generates a candidate set of program items in the local content library, obtaining a content recommendation candidate set. This candidate set contains a list of programs most likely to match user preferences. Finally, the system needs to further optimize and sort this content recommendation candidate set. This step is based on the matching degree between the historical playback popularity of each program item in the content recommendation candidate set and the user's behavior pattern. Historical viewing popularity may consider factors such as the total number of views and recent popularity trends, while the match with user behavior patterns may involve whether the viewing time and duration of the program align with the user's habits. By combining these factors, the system prioritizes candidate program entries and ultimately generates a content preference distribution matrix. This process can be illustrated with a concrete example: Suppose there is an IPTV user whose behavior pattern shows that they frequently watch news and documentaries on weekday evenings, while preferring movies and sports events on weekends.The system first maps these behavioral patterns to semantic tags, such as "information acquisition," "knowledge learning," "leisure and entertainment," and "sports hobbies." Simultaneously, the system analyzes users' content request sequences, extracting content feature vectors such as "news," "documentaries," "action movies," and "football matches." By calculating the similarity between these semantic tags and content features, the system constructs a preliminary behavior-content association matrix. Then, considering that users typically use their smart TVs on weekday evenings and may use mobile devices more on weekends, the system modulates the context weights of this matrix to obtain a context-enhanced preference matching matrix. Based on this matrix, the system dynamically assigns tags to programs in the local content library. For example, evening news and international documentaries may receive higher "weekday evening" tag weights, while action movies and live sports broadcasts may receive higher "weekend" tag weights. The system then generates a content preference tag sequence and filters programs matching these tags from the local content library, forming a content recommendation candidate set. Finally, the system considers the historical playback popularity of these candidate programs. For example, even if a user enjoys watching documentaries, if a popular news program experiences a recent surge in viewership, the system might increase its ranking in the recommendation list. Simultaneously, the system also considers whether the program's broadcast time matches the user's viewing habits. Through this comprehensive consideration, the system ultimately generates a content preference distribution matrix, which includes the recommendation priority of various programs at different times and on different devices. This meticulous process ensures that the IPTV system can provide users with highly personalized and context-sensitive content recommendations, significantly improving user experience and content consumption efficiency.

[0038] In a specific embodiment, the step of optimizing task scheduling based on the resource status information of preset local edge nodes using the content preference distribution matrix to obtain an edge deployment task queue includes: The resource status information of the preset local edge nodes is monitored in real time to obtain an edge node status monitoring table, and the resource availability in the edge node status monitoring table is evaluated to obtain a node resource status matrix. The matching degree of the content preference distribution matrix and the node resource status matrix is ​​calculated to obtain a task resource matching degree table, and the available edge nodes in the task resource matching degree table are filtered to obtain a list of available nodes. A multi-dimensional resource constraint analysis is performed on the available node list to obtain resource scheduling constraints. The task allocation is then optimized based on the resource scheduling constraints to generate a task allocation strategy graph. Based on the task allocation strategy graph, task deployment planning is performed on the list of available nodes to obtain an edge deployment task queue.

[0039] Specifically, in IPTV terminal edge computing methods, dynamically matching and context-aware processing of content request sequences based on user behavior patterns to obtain a content preference distribution matrix is ​​a complex and sophisticated process. First, the system performs semantic label mapping on each behavior pattern to obtain behavioral semantic features. This step may involve using natural language processing techniques to transform user behavior into understandable semantic labels. For example, "watching for extended periods on weekend evenings" might be mapped to semantic labels such as "leisure and entertainment" or "immersive experience." Simultaneously, the system performs metadata parsing on the IPTV terminal's content request sequences to extract program content feature vectors. These feature vectors may include information across multiple dimensions, such as program type, theme, actors, director, and duration. Next, the system calculates the semantic similarity between the behavioral semantic features and the program content feature vectors. This step may use word embedding models (such as Word2Vec or BERT) to calculate semantic similarity, thereby constructing a behavior-content association matrix. For example, the behavioral semantic feature of "leisure and entertainment" might have a high similarity to content features such as movies and variety shows. Then, the system modulates the contextual weights of this behavior-content correlation matrix, considering contextual factors such as time, location, and device type, to obtain a context-enhanced preference matching matrix. This modulation can make recommendations more accurate; for example, news content might receive a higher weight when using a mobile device on a weekday morning. Based on the obtained context-enhanced preference matching matrix, the system dynamically assigns tags to the content classification system in the preset local content library, generating a content preference tag sequence. This process can be understood as mapping user behavior preferences to the content library's classification system, thereby assigning a weight related to user preferences to each content category. For example, if a user's behavior pattern shows a strong interest in technology programs, then technology content might receive a higher preference tag weight. Based on this content preference tag sequence, the system further generates a candidate set of program items in the local content library, obtaining a content recommendation candidate set. This candidate set contains a list of programs most likely to match user preferences. Finally, the system needs to further optimize and sort this content recommendation candidate set. This step is based on the matching degree between the historical playback popularity of each program item in the content recommendation candidate set and the user's behavior pattern. Historical viewing popularity may consider factors such as the total number of views and recent popularity trends, while the match with user behavior patterns may involve whether the viewing time and duration of the program align with the user's habits. By combining these factors, the system prioritizes candidate program entries and ultimately generates a content preference distribution matrix. This process can be illustrated with a concrete example: Suppose there is an IPTV user whose behavior pattern shows that they frequently watch news and documentaries on weekday evenings, while preferring movies and sports events on weekends.The system first maps these behavioral patterns to semantic tags, such as "information acquisition," "knowledge learning," "leisure and entertainment," and "sports hobbies." Simultaneously, the system analyzes users' content request sequences, extracting content feature vectors such as "news," "documentaries," "action movies," and "football matches." By calculating the similarity between these semantic tags and content features, the system constructs a preliminary behavior-content association matrix. Then, considering that users typically use their smart TVs on weekday evenings and may use mobile devices more on weekends, the system modulates the context weights of this matrix to obtain a context-enhanced preference matching matrix. Based on this matrix, the system dynamically assigns tags to programs in the local content library. For example, evening news and international documentaries may receive higher "weekday evening" tag weights, while action movies and live sports broadcasts may receive higher "weekend" tag weights. The system then generates a content preference tag sequence and filters programs matching these tags from the local content library, forming a content recommendation candidate set. Finally, the system considers the historical playback popularity of these candidate programs. For example, even if a user enjoys watching documentaries, if a popular news program experiences a recent surge in viewership, the system might increase its ranking in the recommendation list. Simultaneously, the system also considers whether the program's broadcast time matches the user's viewing habits. Through this comprehensive consideration, the system ultimately generates a content preference distribution matrix, which includes the recommendation priority of various programs at different times and on different devices. This meticulous process ensures that the IPTV system can provide users with highly personalized and context-sensitive content recommendations, significantly improving user experience and content consumption efficiency.

[0040] In a specific embodiment, the step of performing heterogeneous hardware resource mapping analysis on the IPTV terminal based on the edge deployment task queue to obtain device adaptation configuration parameters includes: The task feature fingerprint is encoded on the edge deployment task queue to obtain the task feature fingerprint vector, and the instruction semantic parsing is performed on the task feature fingerprint vector to obtain the instruction-level computation density matrix. The IPTV terminal is marked with heterogeneous hardware resources based on the instruction-level computation density matrix to obtain a heterogeneous resource marking map. The interface feature protocols in the IPTV terminal are extracted based on the heterogeneous resource marking map to obtain an interface protocol feature table. Based on the instruction-level computation density matrix, the interface protocol feature table is detected to determine whether there is a resource mapping conflict. If there is, the resource mapping conflict result is obtained, and the resource conflict marker sequence is reconstructed based on the resource mapping conflict result to obtain a conflict-free resource path graph. Based on the conflict-free resource path graph, the task feature fingerprint vector is quantified using hardware affinity to obtain a device affinity score table. Then, the device affinity score table is mapped to a parameter register level configuration to obtain device adaptation configuration parameters.

[0041] Specifically, in the edge computing method for IPTV terminals, performing heterogeneous hardware resource mapping analysis on the IPTV terminal based on the edge deployment task queue to obtain device adaptation configuration parameters is a complex and meticulous process. First, the system encodes task feature fingerprints for the edge deployment task queue, obtaining task feature fingerprint vectors. This step may involve encoding the characteristics of each task (such as computational complexity, memory requirements, I / O intensity, etc.) into a unique digital fingerprint. For example, a video decoding task might be encoded as a vector containing information such as CPU utilization, GPU requirements, and memory usage. Next, the system performs instruction semantic parsing on these task feature fingerprint vectors to obtain an instruction-level computation density matrix. This process involves analyzing the computational requirements of each task at the instruction level, which may include the density distribution of different types of instructions such as floating-point operations, integer operations, and memory access. Based on the obtained instruction-level computation density matrix, the system marks the heterogeneous hardware resources of the IPTV terminal, obtaining a heterogeneous resource marking map. This step involves identifying and marking various hardware resources in the IPTV terminal, such as CPU cores, GPU units, dedicated video decoders, and memory modules. For example, a high-end IPTV set-top box might be labeled as having a multi-core CPU, a high-performance GPU, and a dedicated video processing unit. Based on this heterogeneous resource labeling map, the system further extracts interface feature protocols from the IPTV terminal to obtain an interface protocol feature table. This table may contain information such as communication protocols, bandwidth limitations, and latency characteristics between various hardware modules. Next, the system detects whether there are resource mapping conflicts in the interface protocol feature table based on the instruction-level computational density matrix. This step aims to discover potential hardware resource contention issues. For example, the system may find that multiple computationally intensive tasks are simultaneously competing for limited GPU resources, or that high-bandwidth tasks may exceed the transmission capacity of certain interfaces. If a conflict is detected, the system obtains the resource mapping conflict result and reconstructs the topology path based on this result to obtain a conflict-free resource path map. This process may involve task rescheduling, resource reallocation, or the use of time-division multiplexing strategies to resolve conflicts. Finally, based on the conflict-free resource path map, the system performs hardware affinity quantification on the task feature fingerprint vectors to obtain a device affinity scoring table. This step evaluates the matching degree between each task and available hardware resources, and may consider factors such as computational efficiency, energy consumption, and latency. For example, video decoding tasks may have a high affinity with dedicated video processing units, while complex AI inference tasks may be better suited to be executed on GPUs. The system then performs parameter register-level configuration mapping on this device affinity score table to obtain device adaptation configuration parameters. These parameters may include specific hardware allocation schemes, clock frequency settings, power management strategies, etc.This process can be illustrated with a concrete example: Suppose there is a high-end IPTV set-top box whose edge deployment task queue includes tasks such as real-time 4K video decoding, AI-based content recommendation, and user interface rendering. The system first encodes these tasks with feature fingerprints, obtaining task feature fingerprint vectors. For example, the 4K video decoding task might be encoded as a vector representing high video processing requirements, medium CPU requirements, and low memory requirements. Through instruction semantic parsing, the system generates an instruction-level computational density matrix, showing that the video decoding task mainly involves parallel matrix operations, while the AI ​​recommendation task involves a large number of floating-point operations. Based on this matrix, the system labels the IPTV set-top box's hardware resources, identifying that it has a quad-core CPU, a medium-performance GPU, and a dedicated 4K video decoder. The system also extracts the interface protocol features between these hardware modules, such as the data transfer bandwidth between the CPU and GPU, and the input / output capabilities of the video decoder. When detecting resource mapping conflicts, the system might find that the AI ​​recommendation task and the user interface rendering task both require GPU resources, while the video decoding task requires exclusive access to the video decoder. To resolve this conflict, the system restructured its resource allocation strategy, deciding to schedule AI recommendation tasks during CPU idle periods, while primarily using the GPU for user interface rendering, and ensuring the video decoder could focus on 4K video processing. Based on this conflict-free resource path graph, the system quantified hardware affinity. For example, video decoding tasks had the highest affinity with dedicated decoders, AI recommendation tasks had a relatively high affinity with the CPU, and user interface rendering had the highest affinity with the GPU. Finally, the system translated these affinity scores into specific hardware configuration parameters, such as setting the video decoder to operate in its highest performance mode, allocating two CPU cores to AI recommendation tasks, and setting an appropriate clock frequency for the GPU to balance performance and power consumption. Through this refined process, the IPTV system can fully utilize the heterogeneous hardware resources of the terminal, finding the optimal execution environment for different types of tasks, thereby significantly improving system performance and user experience. This dynamic resource mapping and optimization strategy enables IPTV terminals to handle complex multimedia tasks more efficiently, providing users with smooth, high-quality services.

[0042] In a specific embodiment, the step of performing decoding and rendering pipeline adaptation processing on the video decoder module of the IPTV terminal based on the device adaptation configuration parameters to generate local rendering instructions includes: Based on the device adaptation configuration parameters, the decoding capability mapping analysis of the video decoder module in the IPTV terminal is performed to obtain the decoder hardware mapping capability. Based on the decoder hardware mapping capability, the decoding pipeline is divided into segments to obtain a multi-level decoding pipeline configuration descriptor. Based on the multi-level decoding pipeline configuration descriptor, a data path is established for the rendering pipeline hardware interface in the IPTV terminal to obtain a decoding rendering data path mapping diagram. Based on the decoding rendering data path mapping diagram, a rendering buffer memory layout is planned to obtain a rendering buffer memory allocation strategy. The rendering buffer memory allocation strategy is input into the IPTV terminal GPU rendering unit to compile the shader program, obtain the GPU shader program bytecode, and perform rendering instruction conversion processing based on the GPU shader program bytecode to generate local rendering instructions.

[0043] Specifically, in the edge computing method for IPTV terminals, adapting the video decoder module of the IPTV terminal to the video decoder module based on device adaptation configuration parameters to generate local rendering instructions is a complex and meticulous process. First, the system performs decoding capability mapping analysis on the video decoder module in the IPTV terminal based on the device adaptation configuration parameters to obtain the decoder's hardware mapping capabilities. This step involves evaluating the specific hardware characteristics of the decoder, such as supported video encoding formats (e.g., H.264, H.265, VP9), maximum resolution, frame rate capability, and parallel decoding capability. For example, a high-end IPTV set-top box might have a decoder that supports 4K resolution H.265 video, a maximum frame rate of 60fps, and hardware-level HDR processing capabilities. Next, based on this decoder hardware mapping capability, the system divides the decoding pipeline into segments to obtain multi-level decoding pipeline configuration descriptors. This process involves decomposing the entire decoding process into multiple stages, such as video bitstream parsing, macroblock decoding, motion compensation, and deblocking filtering, and determining the parallelism and processing unit allocation for each stage based on hardware capabilities. For example, the system might decide to allocate the macroblock decoding stage to multiple parallel processing units to improve decoding efficiency. This multi-level decoding pipeline configuration descriptor details the hardware resource allocation, data flow, and processing timing for each decoding stage. Based on the multi-level decoding pipeline configuration descriptor, the system further establishes data paths for the rendering pipeline hardware interface in the IPTV terminal, obtaining a decoding-rendering data path mapping. This step involves determining how to efficiently transfer decoded video data to the rendering pipeline, potentially including the configuration of direct memory access (DMA) channels and the setting of shared memory regions. For example, the system might establish a direct data path from the video decoder output buffer to the GPU input buffer to minimize data transfer latency. Based on this decoding-rendering data path mapping, the system performs rendering buffer memory layout planning, obtaining a rendering buffer memory allocation strategy. This strategy determines how to efficiently allocate and manage various buffers, such as front buffers, back buffers, and depth buffers, within limited memory resources to support smooth video rendering and display. Finally, the system inputs the rendering buffer memory allocation strategy into the IPTV terminal's GPU rendering unit for shader program compilation, obtaining the GPU shader program bytecode. This process involves compiling high-level shader programs (such as those written in GLSL or HLSL) into low-level instructions that the GPU can directly execute. Shader programs may include video post-processing effects such as color correction, sharpening, and HDR mapping. For example, for IPTV terminals that support HDR, the system might compile a dedicated HDR color mapping shader program. Based on this GPU shader program bytecode, the system performs rendering instruction translation processing, ultimately generating native rendering instructions.These native rendering instructions are low-level commands that the GPU can directly execute, including operations such as texture loading, vertex processing, and pixel shading. This process can be illustrated with a concrete example: suppose a user is watching a 4K HDR live stream. The system first analyzes the IPTV terminal's decoder capabilities, confirming that it supports 4K H.265 decoding and hardware HDR processing. Based on this capability, the system designs a five-stage decoding pipeline, including bitstream parsing, macroblock decoding, motion compensation, deblocking filtering, and HDR processing. The system allocates four parallel processing units for the macroblock decoding stage to meet the high data throughput requirements of 4K video. Next, the system establishes a zero-copy data path from decoder output to GPU input, allowing the GPU to directly access decoded video frames without additional memory copy operations. In the rendering buffer memory layout, the system allocates sufficiently large front and back buffers for 4K resolution and reserves additional intermediate buffers for HDR processing. Finally, the system compiles a dedicated HDR color mapping shader program to correctly map HDR content to the display's color space. This shader program is compiled into GPU bytecode and then converted into a series of native rendering instructions. These instructions precisely control the color processing of each pixel, ensuring that HDR content is presented at its best on the user's display device. Through this meticulous process, the IPTV system can fully utilize the decoding and rendering capabilities of the terminal hardware to provide users with a high-quality, low-latency video experience. This dynamic decoding and rendering pipeline adaptation allows IPTV terminals to flexibly handle video content of different formats and qualities, while maximizing hardware resource utilization, thereby significantly improving system performance and the user viewing experience.

[0044] In a specific embodiment, the asynchronous rendering process of the video frame buffer of the IPTV terminal based on the local rendering instruction to obtain a smooth video playback output stream includes: The local rendering instructions are decomposed into frame-level rendering tasks to obtain a frame rendering task sequence; Based on the frame rendering task sequence, the video output interface of the IPTV terminal is subjected to frame rate synchronization control to obtain a video frame rate synchronization control signal. The video frame rate synchronization control signal is then used for streaming data transmission processing to obtain a smooth video playback output stream.

[0045] Specifically, in the edge computing method for IPTV terminals, a key step is to asynchronously render the video frame buffer based on local rendering instructions to obtain a smooth video playback output stream. This process involves complex frame-level processing and synchronization control. First, the system decomposes the local rendering instructions into frame-level rendering tasks, resulting in a sequence of frame rendering tasks. This step involves breaking down the overall rendering instructions into specific operations for each frame, potentially including a series of fine-grained rendering tasks such as texture loading, geometric transformation, and pixel shading. For example, for a 4K HDR video stream, the rendering task for each frame might include loading raw video data, applying HDR color mapping, performing image post-processing (such as denoising or sharpening), and finally outputting to the display buffer. This sequence of frame rendering tasks precisely describes the complete processing flow of each frame from raw data to final display. Next, based on this sequence of frame rendering tasks, the system performs frame rate synchronization control on the IPTV terminal's video output interface, obtaining a video frame rate synchronization control signal. This step is crucial because it ensures that the rendering process remains synchronized with the refresh rate of the display device, thereby avoiding screen tearing or stuttering. Frame rate synchronization control involves several aspects, including vertical synchronization (V-Sync), adaptive synchronization technologies (such as FreeSync or G-Sync), and possible frame rate doubling or interpolation techniques. For example, if the video source is 24fps movie content and the monitor has a 60Hz refresh rate, the system might use a 3:2 pulldown technique to match these two different frame rates, ensuring smooth playback without noticeable jitter. Based on the generated video frame rate synchronization control signal, the system performs streaming data transmission processing, ultimately resulting in a smooth video playback output stream. This process involves precisely transmitting the rendered frame data to the display device according to the rhythm of the synchronization signal. Streaming data transmission processing may involve multiple buffering techniques (such as triple buffering) to smooth the time difference between rendering and display, and may also include frame interpolation or motion compensation techniques to further improve video smoothness. This process can be illustrated with a concrete example: Suppose a user is watching a 4K HDR movie on an IPTV terminal. The movie's original frame rate is 24fps, while the user's monitor supports a 60Hz refresh rate. The system first breaks down the local rendering instructions into specific tasks for each frame. For each frame, the task sequence may include: loading raw 4K HDR data from the decoding buffer, applying HDR-to-SDR color mapping (if the display does not support HDR), performing image enhancement processing (such as noise reduction and sharpening), and finally writing the processed frame data to the display buffer. Regarding frame rate synchronization control, the system detected a mismatch between the 24fps video source and the 60Hz display. To address this, the system implemented a 3:2 pull-down technique.This means that every 5 display cycles, the system displays 3 frames twice each and another 2 frames once each (2-3-2-3 mode), smoothly mapping 24fps content to a 60Hz display. The system-generated frame rate synchronization control signal precisely controls this process, ensuring each frame is displayed at the correct time. During the streaming data transmission processing phase, the system uses triple buffering technology to manage the rendering and display process. While the GPU is rendering a new frame, the display controller is reading another completed frame for display, and the third buffer acts as an intermediate buffer to store the next frame to be displayed. This method effectively reduces rendering latency and ensures the continuity of the display process. Furthermore, to further improve image quality, the system may apply motion compensation technology during the 24fps to 60Hz transition. This involves analyzing motion information between adjacent frames and inserting calculated intermediate frames between the original frames, making the motion appear smoother and more natural. This advanced processing can significantly improve the viewing experience of fast-moving scenes, reducing motion blur and jitter. Through this series of meticulous processing steps, the IPTV system can convert raw 24fps movie content into a high-quality video stream that plays smoothly on a 60Hz display. Users can enjoy a delicate, smooth 4K picture with rich colors, natural motion, and no noticeable stuttering or screen tearing. This highly optimized rendering and display process fully utilizes the hardware capabilities of the IPTV terminal, providing users with a near-cinema-level viewing experience, while also demonstrating the powerful potential of edge computing in improving multimedia processing and display quality.

[0046] The above describes an IPTV terminal edge computing method according to an embodiment of the present invention. The following describes an IPTV terminal edge computing system according to an embodiment of the present invention. Please refer to [link / reference]. Figure 2 One embodiment of an IPTV terminal edge computing system according to the present invention includes: Analysis module 21 is used to perform multi-dimensional time series analysis on user interaction behavior data of IPTV terminals to obtain user behavior patterns; Matching module 22 is used to dynamically match and context-awarely process the content request sequence of the IPTV terminal based on the user behavior pattern to obtain a content preference distribution matrix; Optimization module 23 is used to optimize task scheduling based on the content preference distribution matrix and the preset resource status information of local edge nodes to obtain an edge deployment task queue. Mapping module 24 is used to perform heterogeneous hardware resource mapping analysis on the IPTV terminal based on the edge deployment task queue to obtain device adaptation configuration parameters; Decoding module 25 is used to perform decoding and rendering pipeline adaptation processing on the video decoder module of the IPTV terminal based on the device adaptation configuration parameters, and generate local rendering instructions; The rendering module 26 is used to perform asynchronous rendering processing on the video frame buffer of the IPTV terminal based on the local rendering instructions to obtain a smooth video playback output stream.

[0047] In this embodiment, the specific implementation of each module in the above system embodiment is described in the above method embodiment, and will not be repeated here.

[0048] Reference Figure 3 This invention also provides a computer device whose internal structure can be as follows: Figure 3 As shown, the computer device includes a processor, memory, display screen, input device, network interface, and database connected via a system bus. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores the operating system, computer programs, and database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The database stores the data corresponding to this embodiment. The network interface is used to communicate with external terminals via a network connection. When the computer program is executed by the processor, it implements the above-described method.

[0049] Those skilled in the art will understand that Figure 3 The structures shown are merely block diagrams of some structures related to the present invention and do not constitute a limitation on the computer devices on which the present invention is applied.

[0050] An embodiment of the present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-described method. It is understood that the computer-readable storage medium in this embodiment can be a volatile readable storage medium or a non-volatile readable storage medium.

[0051] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the present invention and embodiments can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual-rate SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM, etc.

[0052] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, apparatus, article, or method that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, apparatus, article, or method. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, apparatus, article, or method that includes that element.

[0053] The above description is only a preferred embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent structural or procedural transformations made based on the content of the present invention specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. An edge computing method for an IPTV terminal, characterized in that, Includes the following steps: Multidimensional time series analysis was performed on user interaction data from IPTV terminals to obtain user behavior patterns. By dynamically matching and context-aware processing the content request sequence of the IPTV terminal based on the user behavior pattern, a content preference distribution matrix is ​​obtained. Based on the content preference distribution matrix, task scheduling optimization is performed on the preset resource status information of local edge nodes to obtain an edge deployment task queue; Based on the edge deployment task queue, heterogeneous hardware resource mapping analysis is performed on the IPTV terminal to obtain device adaptation configuration parameters; Based on the device adaptation configuration parameters, the video decoder module of the IPTV terminal is subjected to decoding and rendering pipeline adaptation processing to generate local rendering instructions. The video frame buffer of the IPTV terminal is asynchronously rendered based on the local rendering instructions to obtain a smooth video playback output stream.

2. The IPTV terminal edge computing method according to claim 1, characterized in that, The multidimensional time series analysis of user interaction behavior data from IPTV terminals to obtain user behavior patterns includes: The user interaction behavior data of the IPTV terminal is processed by time slicing based on the sliding window mechanism to obtain multiple time segment data. Each time segment dataset is then labeled with behavior event type to obtain labeled time segment data. Weights are assigned to different behavioral event types in the labeled time segment data to obtain weighted time segment data, and feature vectors are extracted in the time dimension based on the weighted time segment data to obtain time feature vectors. Cluster analysis is performed on the time feature vectors to obtain initial user behavior patterns, and pattern merging and optimization are performed on the initial user behavior patterns to obtain the final user behavior patterns.

3. The IPTV terminal edge computing method according to claim 1, characterized in that, The step of dynamically matching and context-awarely processing the content request sequence of the IPTV terminal based on the user behavior pattern to obtain a content preference distribution matrix includes: Semantic tag mapping is performed on each behavior pattern in the user behavior pattern to obtain behavioral semantic features, and metadata parsing is performed on the content request sequence of the IPTV terminal to extract program content feature vectors; Based on the semantic similarity calculation results between the behavioral semantic features and the program content feature vector, a behavior-content association matrix is ​​constructed, and the behavior-content association matrix is ​​subjected to context weight modulation to obtain a context-enhanced preference matching matrix; The context-enhanced preference matching matrix is ​​used to dynamically assign tags to the content classification system in the preset local content library, generate a content preference tag sequence, and generate a candidate set for program items in the local content library based on the content preference tag sequence, thus obtaining a content recommendation candidate set. Based on the matching degree between the historical playback popularity of each program item in the content recommendation candidate set and the user behavior pattern, the candidate program items are prioritized and a content preference distribution matrix is ​​generated.

4. The IPTV terminal edge computing method according to claim 1, characterized in that, The step of optimizing task scheduling based on the content preference distribution matrix and the preset resource status information of local edge nodes to obtain an edge deployment task queue includes: The resource status information of the preset local edge nodes is monitored in real time to obtain an edge node status monitoring table, and the resource availability in the edge node status monitoring table is evaluated to obtain a node resource status matrix. The matching degree of the content preference distribution matrix and the node resource status matrix is ​​calculated to obtain a task resource matching degree table, and the available edge nodes in the task resource matching degree table are filtered to obtain a list of available nodes. A multi-dimensional resource constraint analysis is performed on the available node list to obtain resource scheduling constraints. The task allocation is then optimized based on the resource scheduling constraints to generate a task allocation strategy graph. Based on the task allocation strategy graph, task deployment planning is performed on the list of available nodes to obtain an edge deployment task queue.

5. The IPTV terminal edge computing method according to claim 1, characterized in that, The heterogeneous hardware resource mapping analysis of the IPTV terminal based on the edge deployment task queue is used to obtain device adaptation configuration parameters, including: The task feature fingerprint is encoded on the edge deployment task queue to obtain the task feature fingerprint vector, and the instruction semantic parsing is performed on the task feature fingerprint vector to obtain the instruction-level computation density matrix. The IPTV terminal is marked with heterogeneous hardware resources based on the instruction-level computation density matrix to obtain a heterogeneous resource marking map. The interface feature protocols in the IPTV terminal are extracted based on the heterogeneous resource marking map to obtain an interface protocol feature table. Based on the instruction-level computation density matrix, the interface protocol feature table is detected to determine whether there is a resource mapping conflict. If there is, the resource mapping conflict result is obtained, and the resource conflict marker sequence is reconstructed based on the resource mapping conflict result to obtain a conflict-free resource path graph. Based on the conflict-free resource path graph, the task feature fingerprint vector is quantified using hardware affinity to obtain a device affinity score table. Then, the device affinity score table is mapped to a parameter register level configuration to obtain device adaptation configuration parameters.

6. The IPTV terminal edge computing method according to claim 1, characterized in that, The process of adapting the video decoder module of the IPTV terminal to the device adaptation configuration parameters and generating local rendering instructions includes: Based on the device adaptation configuration parameters, the decoding capability mapping analysis of the video decoder module in the IPTV terminal is performed to obtain the decoder hardware mapping capability. Based on the decoder hardware mapping capability, the decoding pipeline is divided into segments to obtain a multi-level decoding pipeline configuration descriptor. Based on the multi-level decoding pipeline configuration descriptor, a data path is established for the rendering pipeline hardware interface in the IPTV terminal to obtain a decoding rendering data path mapping diagram. Based on the decoding rendering data path mapping diagram, a rendering buffer memory layout is planned to obtain a rendering buffer memory allocation strategy. The rendering buffer memory allocation strategy is input into the IPTV terminal GPU rendering unit to compile the shader program, obtain the GPU shader program bytecode, and perform rendering instruction conversion processing based on the GPU shader program bytecode to generate local rendering instructions.

7. The IPTV terminal edge computing method according to claim 1, characterized in that, The asynchronous rendering process of the video frame buffer of the IPTV terminal based on the local rendering instructions to obtain a smooth video playback output stream includes: The local rendering instructions are decomposed into frame-level rendering tasks to obtain a frame rendering task sequence; Based on the frame rendering task sequence, the video output interface of the IPTV terminal is subjected to frame rate synchronization control to obtain a video frame rate synchronization control signal. The video frame rate synchronization control signal is then used for streaming data transmission processing to obtain a smooth video playback output stream.

8. An IPTV terminal edge computing system, characterized in that, include: The analysis module is used to perform multi-dimensional time series analysis on user interaction behavior data of IPTV terminals to obtain user behavior patterns; The matching module is used to dynamically match and context-awarely process the content request sequence of the IPTV terminal based on the user behavior pattern to obtain a content preference distribution matrix; The optimization module is used to optimize task scheduling based on the content preference distribution matrix and the preset resource status information of local edge nodes to obtain an edge deployment task queue. The mapping module is used to perform heterogeneous hardware resource mapping analysis on the IPTV terminal based on the edge deployment task queue to obtain device adaptation configuration parameters; The decoding module is used to perform decoding and rendering pipeline adaptation processing on the video decoder module of the IPTV terminal based on the device adaptation configuration parameters, and generate local rendering instructions. The rendering module is used to perform asynchronous rendering processing on the video frame buffer of the IPTV terminal based on the local rendering instructions, so as to obtain a smooth video playback output stream.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 7.

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