Intelligent digital entertainment platform equipment management method, system and equipment

Abstract

The invention discloses an intelligent digital entertainment platform equipment management method, system and equipment, and relates to the technical field. Equipment state data and user behavior data can be collected in real time through an equipment state sensing module and a user behavior sensing module on any number of entertainment terminals of an entertainment platform, and data stream sets are respectively input into a preset equipment state model and a preset user intention model for processing. Generating an equipment state model gradient and a user intention model gradient; and the central server performs aggregation operation on the received model gradient through a federal aggregation algorithm to generate a global equipment state model, generates a global decision instruction based on the model and the user intention model gradient, and executes resource scheduling operation or peripheral equipment parameter adjustment operation, so that resource allocation can be dynamically adjusted, the management efficiency is improved, and the user experience is improved. User experience is improved, and efficient operation of the system and safety and stability of equipment are ensured.

Description

Technical Field

[0001] This application relates to the field of intelligent entertainment device management technology, and more particularly to an intelligent digital entertainment platform device management method, system and device. Background Technology

[0002] With the widespread application of intelligent digital entertainment devices, various entertainment platform devices play a vital role in scenarios such as gaming, audio-visual entertainment, and interactive experiences. However, existing technologies still have many shortcomings in device management, especially in areas such as device status monitoring, user behavior adaptation, and intelligent system management. These shortcomings make it difficult to meet the management needs of modern intelligent digital entertainment platforms for efficient, intelligent, and real-time responsiveness. Existing device management systems often focus on single-function monitoring or basic data collection, lacking a collaborative optimization mechanism between device operating status, user behavior, and platform services. This leads to problems such as low management efficiency, delayed response, and poor user experience.

[0003] The aforementioned issues indicate that existing equipment monitoring and management platforms are mostly focused on industrial automation or general equipment monitoring, lacking support for the collaborative management of equipment, users, and content in intelligent digital entertainment scenarios. Existing solutions have significant shortcomings in user behavior perception, failing to meet the platform-level intelligent and personalized entertainment management needs. Therefore, there is an urgent need for a device management method for entertainment platforms to address the deficiencies of current technologies in user experience adaptation, dynamic resource optimization, and system decision-making, providing a more intelligent, flexible, and efficient device management solution. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of the prior art and provide an intelligent digital entertainment platform device management method, system and device, which solves the above problems.

[0005] To achieve the above objectives, in a first aspect, this application provides a method for managing intelligent digital entertainment platform devices, comprising the following steps: Based on device status sensing modules and user behavior sensing modules deployed in two or more physical spaces, device status data and user behavior data are obtained. Based on the device status data, gradient calculation is performed according to the chain rule to obtain the device status model gradient, which is used to characterize the current health status of peripheral devices. Based on user behavior data and the chain rule, gradient calculation is performed to obtain the gradient of the user intent model, and a gradient of the user intent model representing the user's current needs is generated. Based on the gradient of the device state model, obtain the global device state model; Based on the global device status model and combined with user behavior data, global decision instructions are obtained; Based on global decision-making instructions, execute resource scheduling operations or peripheral device parameter adjustment operations to optimize resource allocation and user experience.

[0006] In one possible implementation, the method is applied to a digital entertainment platform comprising a central server and entertainment terminals deployed in two or more physical spaces; wherein the central server is electrically connected to each entertainment terminal, and each entertainment terminal is connected to peripheral devices; Obtain device status data and user behavior data, specifically including: By deploying a device status sensing module in each entertainment terminal, device status data of peripheral devices associated with each entertainment terminal are collected in real time, and the device status data constitutes the first data stream set; wherein, the device status sensing module is configured to periodically or based on preset event triggers monitor the device status parameters of the entertainment terminal and the hardware operation parameters of the peripheral devices. By deploying a user behavior sensing module in each entertainment terminal, user behavior data in the physical space where the entertainment terminal is located is collected in real time, and the user behavior data constitutes a second data stream set; wherein, the user behavior sensing module is configured to monitor user behavior non-intrusively using sensors.

[0007] In one possible implementation, on each entertainment terminal, the first data stream set is input into a preset device state model for processing to obtain the device state model gradient.

[0008] On each entertainment terminal, the second data stream set is input into a preset user intent model for processing to obtain the user intent model gradient.

[0009] In one possible implementation, the central server receives the device state model gradient and the user intent model gradient uploaded by the entertainment terminals respectively, and performs aggregation operation on the received two or more device state model gradients through a preset federated aggregation algorithm to obtain the global device state model.

[0010] In one possible implementation, the central server obtains global decision instructions based on a global device state model and combined with the gradient of the user intent model uploaded by the entertainment terminal.

[0011] The global decision-making instructions are sent to one or more target entertainment terminals.

[0012] The target entertainment terminal executes corresponding resource scheduling operations or peripheral device parameter adjustment operations based on the received global decision instructions, wherein: Resource scheduling operations include at least the reallocation of computing or storage tasks among two or more entertainment terminals.

[0013] Adjusting peripheral device parameters involves at least modifying the operating parameters of the software or hardware associated with the peripheral device.

[0014] In one possible implementation, a health index is obtained for each entertainment terminal based on a global device state model. The health index of the entertainment terminal is a quantitative indicator.

[0015] The health index of entertainment terminals is recorded as follows: The entertainment terminal serial number is i. This entertainment terminal has n device status parameters, which are denoted as i. The weighting coefficient for each device status parameter is as follows: ,but .

[0016] Among them, the device status parameters and weight coefficients are queried and matched by the central server from the pre-set device status parameter and weight coefficient database according to the specific hardware device of each entertainment terminal.

[0017] In one possible implementation, the resource scheduling operation is triggered when the health index of an entertainment terminal is detected to be lower than a preset health threshold.

[0018] In one possible implementation, when the health index of an entertainment terminal is lower than a preset health threshold, the corresponding entertainment terminal sends a resource scheduling request to the central server, and the entertainment terminal that sends the resource scheduling request is designated as the first entertainment terminal.

[0019] The central server retrieves available entertainment terminals that are currently idle or under low load, excluding the first entertainment terminal, from the global device status model.

[0020] It is determined that the available entertainment terminal has the hardware required to execute the task being performed by the first entertainment terminal.

[0021] Based on the global device status model, the current health index of available entertainment terminals is sorted, and the entertainment terminal with the highest health index is selected as the second entertainment terminal.

[0022] In one possible implementation, when a resource scheduling operation is triggered, a global decision instruction executes a cross-space resource scheduling instruction.

[0023] Establish a point-to-point data stream transmission channel between the first entertainment terminal and the second entertainment terminal.

[0024] The first entertainment terminal has completed the first part of the specific task and is currently executing the second part of the specific task. The second part is unloaded to the second entertainment terminal for execution, and the data stream obtained after executing the specific task is transmitted back through the transmission channel. The first entertainment terminal itself only retains and processes the first part of the specific task.

[0025] Secondly, this application provides an intelligent digital entertainment platform device management system, including a central server and two or more entertainment terminals electrically connected thereto.

[0026] Each entertainment terminal includes a multimodal data sensing module and a local processing module, wherein: The multimodal data sensing module is configured to acquire device status data and user behavior data.

[0027] The local processing module is configured to obtain the gradient of the device state model based on device state data, and to obtain the gradient of the user intent model based on user behavior data.

[0028] The central server includes an aggregation decision-making unit and an instruction distribution unit, wherein: The aggregation decision unit is configured to obtain a global device state model based on the device state model gradient; and to obtain global decision instructions based on the global device state model and user behavior data.

[0029] The instruction distribution unit is configured to obtain resource scheduling operations or peripheral device parameter adjustment operations based on global decision instructions.

[0030] Thirdly, this application provides a computer device, including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of any one of the methods in the intelligent digital entertainment platform device management method.

[0031] In summary, the beneficial effects that this application can achieve are: This application provides a method, system, and device for managing intelligent digital entertainment platform equipment. It generates global decision instructions by integrating equipment status data and user behavior data, and realizes dynamic resource scheduling and parameter optimization based on a federated aggregation algorithm. This solves the problems of single monitoring dimensions and isolated data processing in traditional methods, and has the advantages of improving the monitoring dimensions of equipment status, realizing collaborative optimization of user behavior and equipment status, and enhancing dynamic decision-making capabilities. Attached Figure Description

[0032] The accompanying drawings, which are included to provide a further understanding of embodiments of the invention and form part of this application, do not constitute a limitation thereof. In the drawings: Figure 1This is a schematic diagram of the method steps in an embodiment of this application; Figure 2 This is a schematic diagram of the method flow of an embodiment of this application; Figure 3 This is a schematic diagram of resource scheduling operations according to an embodiment of this application; Figure 4 This is a schematic diagram of the system structure according to an embodiment of this application. Detailed Implementation

[0033] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. It should be understood that the accompanying drawings in this application are for illustrative and descriptive purposes only and are not intended to limit the scope of protection of this application. Furthermore, it should be understood that the schematic drawings are not drawn to scale. The flowcharts used in this application illustrate operations implemented according to some embodiments of this application. It should be understood that the operations in the flowcharts may not be implemented in sequence, and steps without logical contextual relationships may be reversed or implemented simultaneously. In addition, those skilled in the art, guided by the content of this application, may add one or more other operations to the flowcharts, or remove one or more operations from the flowcharts.

[0034] Furthermore, the described embodiments are merely some, not all, of the embodiments of this application. The components of the embodiments of this application described and illustrated herein can typically be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of the application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0035] In existing technologies, digital entertainment platform device management often employs a single-dimensional monitoring approach, focusing only on hardware operating parameters or basic service status, lacking collaborative analysis of user behavior characteristics and the health status of entertainment terminals. Traditional systems struggle to dynamically perceive changes in user needs, leading to rigid resource allocation. For example, in multi-device virtual reality scenarios, when a terminal experiences performance degradation due to excessive load, the system cannot promptly migrate computing tasks to idle devices, causing screen delays or interactive stutters, severely impacting the user experience.

[0036] To address the aforementioned issues, the inventors discovered that the key to device management and user experience optimization lies in establishing a correlation analysis mechanism between device operating status and user behavior data. By analyzing the shortcomings of existing technologies, such as lagging device status perception, lack of user intent recognition, and difficulties in cross-device resource scheduling, a two-dimensional data-driven management approach was gradually developed. First, the approach considers fusing device sensor data with user interaction data to construct a dual indicator reflecting both the health of entertainment terminals and user needs. Second, it explores the use of federated technology to achieve secure aggregation of multi-terminal data, overcoming the limitations of single-device data silos. Finally, a collaborative decision-making mechanism based on a global model and local gradient updates was designed, forming a closed-loop control system for device resource optimization and user service adaptation.

[0037] Therefore, this application proposes to obtain device status data and user behavior data; obtain device status model gradient based on device status data; obtain user intent model gradient based on user behavior data; obtain global device status model based on device status model gradient; obtain global decision instructions based on global device status model and combined with user behavior data; and execute resource scheduling operations or peripheral device parameter adjustment operations based on global decision instructions.

[0038] Example 1 Please refer to the following: Figure 1 This is a flowchart illustrating a method for managing intelligent digital entertainment platform devices provided in an embodiment of the present invention. Further, a method for managing intelligent digital entertainment platform devices may specifically include the content described in steps S1-S5.

[0039] Obtain device status data and user behavior data; Based on the equipment status data, obtain the gradient of the equipment status model; Based on user behavior data, obtain the gradient of the user intent model; Based on the gradient of the device state model, obtain the global device state model; Based on the global device status model and combined with user behavior data, global decision instructions are obtained; Based on global decision-making instructions, execute resource scheduling operations or peripheral device parameter adjustment operations.

[0040] This invention provides a method for managing devices on an intelligent digital entertainment platform. By installing a device status sensing module and a user behavior sensing module in each entertainment terminal, the method collects real-time device status data and user behavior data from peripheral devices associated with the entertainment terminal. This data is then input into pre-set device status models and user intent models for processing, generating device status model gradients and user intent model gradients. A central server receives the gradient data uploaded from each entertainment terminal, generates a global device status model using a pre-set federated aggregation algorithm, and generates global decision commands based on this model and the user intent model gradients. This enables the execution of resource scheduling operations or peripheral device parameter adjustments, achieving collaborative optimization of device status monitoring and user behavior.

[0041] This invention can dynamically adjust resource allocation, improve management efficiency, and enhance user experience. Specifically, it generates a health index for each entertainment terminal. ,in For entertainment terminal serial number, For device status parameters, The weighting coefficients accurately assess the operational status of each terminal. When the health index of an entertainment terminal falls below a preset threshold, resource scheduling is automatically triggered. A global decision-making command selects the optimal replacement terminal to complete the task relay, effectively improving the system's response speed. Furthermore, this invention proposes a global decision-making method based on federated aggregation, ensuring personalized services in a multi-user environment. This solves the problem of adapting to rapidly changing entertainment service demands and multi-terminal collaborative management in existing technologies.

[0042] In the implementation of this application, a sensor array deployed on the entertainment terminal periodically collects status parameters such as device temperature, memory usage, and network latency, forming a time-series data stream reflecting the health of the entertainment terminal. Simultaneously, computer vision technology is used to capture changes in user facial expressions and body movements, constructing a user emotional state recognition model. In the local processing stage, device status data is input into a pre-trained neural network model, outputting a gradient vector representing the degree of device performance degradation; user behavior data is input into the neural network model, generating an intent gradient reflecting changes in user preferences. The central server securely aggregates and fuses the gradient data uploaded from each terminal, generating a federated model that reflects the global distribution of entertainment terminal health. This model, along with real-time user behavior data, is used to calculate the optimal resource allocation scheme. When a terminal's health value is detected to exceed a threshold, a cross-device task migration instruction is generated, splitting the tasks of high-load terminals to low-load terminals for execution, while simultaneously adjusting to maintain device smoothness.

[0043] Compared to existing technologies, traditional equipment monitoring systems can only provide alarms for anomalies in single-point devices and cannot dynamically adjust resource configurations based on user behavior. While existing technologies enable remote monitoring of equipment status, they do not involve user interaction data analysis or cross-terminal collaborative decision-making mechanisms.

[0044] Through the above technical solution, this application can perceive the operating status and user behavior characteristics of multiple terminal devices in real time, and construct a global entertainment terminal health assessment model through federated learning to achieve dynamic resource scheduling across physical spaces. When the health value of a specific terminal falls below a threshold, it automatically triggers the migration of computing tasks and the adjustment of device parameters to ensure service continuity.

[0045] Example 2 Based on Example 1, please refer to Figure 1 , Figure 2 and Figure 3 , Figure 2 This is a flowchart illustrating a method for managing intelligent digital entertainment platform devices according to an embodiment of the present invention. Figure 3 The resource scheduling operation diagram provided in this embodiment of the invention further includes the following:

[0046] Step S1: Collect two types of heterogeneous data, including device status data and user behavior data.

[0047] In one possible implementation, a device status sensing module deployed in each entertainment terminal collects device status data of peripheral devices associated with each entertainment terminal in real time, and the device status data constitutes a first data stream set; wherein, the device status sensing module is configured to periodically or based on preset events to monitor the device status parameters of the entertainment terminal and the hardware operating parameters of the peripheral devices; preset events include the occurrence of resource scheduling requests, instruction execution, and user login.

[0048] This embodiment can be applied to entertainment venues such as KTVs, including a central server and multiple entertainment terminals distributed in various rooms (physical spaces). Each private room (physical space) in the KTV has one entertainment terminal (usually a computer or smart device with built-in dedicated software). The entertainment terminal connects to the room's peripheral equipment: microphones, displays, song selection touchscreens, lighting equipment, etc. The central server is deployed in the KTV's control room and connects to the entertainment terminals in all private rooms via wired or wireless networks.

[0049] In the implementation of this embodiment, the device status data collected by the device status sensing module includes: For audio devices, real-time monitoring data of frequency response curves are collected to characterize the risk of microphone feedback.

[0050] For display devices, screen refresh rate data and display latency data are collected to characterize the smoothness of the display.

[0051] For on-demand terminals, touch response latency evaluation data is collected to characterize the human-computer interaction response speed.

[0052] In one possible implementation, user behavior perception modules deployed in each entertainment terminal collect user behavior data in real time within the physical space where the entertainment terminal is located, and the user behavior data constitutes a second data stream set; wherein, the user behavior perception module is configured to monitor user behavior non-intrusively using a sensor array.

[0053] In the implementation of this application embodiment, the user behavior data captured by the user behavior perception module includes: User voiceprint data collected by the voiceprint recognition unit is used to distinguish the identities of different singers and record their corresponding historical vocal range characteristics.

[0054] The motion recognition unit captures user gesture data and body posture data from the motion capture camera. The gesture data is used to trigger non-contact adjustment of peripheral device parameters, while the body posture data is used to analyze the user's continuous activity state.

[0055] Real-time emotional state data is generated by processing the singing audio stream through the emotional computing subunit. The emotional state data is obtained by analyzing the pitch, loudness, or rhythm deviations during the user's singing process.

[0056] Entertainment terminals deployed in two or more physical spaces continuously monitor the operating parameters of peripheral devices, such as processor load, storage device status, and network communication quality, through local device status awareness modules. When a resource scheduling request or user login event is detected, high-frequency data acquisition is automatically triggered, forming a first data stream set. Simultaneously, a user behavior awareness module uses a non-intrusive sensor array to capture user location changes, movement trajectories, and voice interaction information in real time, generating a second data stream set. Both types of data sets are preprocessed by local processors and then uploaded to a central server. The central server enables cross-space device collaborative management based on multi-node data. The classified collection mechanism of device status data and user behavior data avoids the resource contention problem caused by mixed data processing in traditional solutions. The distributed architecture supports independent operation and centralized management of devices in different physical spaces.

[0057] This system enables comprehensive monitoring of device operating status and seamless collection of user behavior data across multiple physical space deployments, enhancing the intelligence level of entertainment platform device management. By classifying and processing device status data and user behavior data, data processing efficiency is optimized. An event-triggered mechanism ensures the integrity of data collection during critical operations. A non-invasive sensor array acquires accurate behavioral data while guaranteeing user experience, providing reliable data support for subsequent resource scheduling and device collaboration.

[0058] Step S2: Process the data locally. Based on the device status data, obtain the gradient of the device status model; based on the user behavior data, obtain the gradient of the user intent model.

[0059] In one possible implementation, on each entertainment terminal, the first data stream set is input into a preset device state model for processing to generate a device state model gradient that characterizes the current health status or performance of the peripheral device.

[0060] On each entertainment terminal, the second data stream set is input into a preset user intent model for processing to generate a user intent model gradient that represents the user's current needs or emotional state.

[0061] In the implementation of this application embodiment, the device state model and user intent model are learned through a neural network model based on historical records. Then, the partial derivatives of the loss function with respect to the model parameters are calculated using the obtained criminal device state data and user behavior data to form a gradient vector. The gradient calculation adopts the chain rule, and the output and loss are calculated through forward propagation in the neural network, and the gradient is calculated through back propagation.

[0062] Device status models are typically lightweight neural networks used to predict the health of entertainment devices.

[0063] Let the model device state parameters be The loss function is We introduce a loss function to measure the deviation between the predicted value and the actual device state, then the gradient... This is the partial derivative of the loss function with respect to the parameters.

[0064] For example, based on the most recently collected data, the device state model calculates that the current operating efficiency of the audio equipment has decreased by 10% compared to the initial state by substituting partial derivatives, thus obtaining a gradient of -0.1 for the device state model.

[0065] The user intent model is based on neural network deep learning. The input is quantified user behavior data, including voiceprints, gestures, and sentiment scores. The output is the probability of user needs, such as increasing volume or changing songs. The gradient calculation logic is consistent with the device state model, using backpropagation to solve for the partial derivatives of the loss function with respect to the model parameters, reflecting the weight of user behavior characteristics on demand prediction.

[0066] For example, the user intent model analyzes the user's gestures and voice commands to determine if the user might need to increase the volume. Therefore, the gradient of the user intent model is +0.3, which represents the strength of the user's need to increase the volume.

[0067] By deploying localized models at edge nodes to achieve real-time data processing, hardware operating parameters collected by the device status awareness module are directly input into the device status model for feature extraction, generating gradient information reflecting the device's operating status and avoiding delays caused by raw data transmission. User behavior data is semantically encoded through a local intent model to generate abstract feature vectors representing user needs, reducing the risk of sensitive behavioral data being leaked. Locally generated gradient data is uploaded to the central server in a standardized format, providing a unified input interface for global model aggregation under the federated learning framework, enabling collaborative optimization of distributed data and centralized decision-making.

[0068] Traditional methods require uploading raw device status data and user behavior data to a central server for centralized processing, leading to increased response latency and privacy risks. This solution, by performing data feature extraction and gradient generation locally on the terminal, shortens the data processing time, protects user privacy through data abstraction, and provides a technical foundation for cross-terminal model collaborative training through standardized gradient formats.

[0069] This application enables real-time monitoring of the health status of entertainment terminals and instant parsing of user intent, effectively solving the problem of global decision-making lag. The localized gradient generation mechanism avoids the transmission of raw data over the network, reducing system communication load and security risks. Standardized feature representation supports cross-terminal federated learning, providing a unified decision-making basis for subsequent resource scheduling and parameter adjustment, and enhancing the personalized adaptation capabilities of entertainment services.

[0070] Step S3: Federated aggregation calculation: Obtain the global device state model based on the device state model gradient.

[0071] In one possible implementation, the central server receives the device state model gradient and user intent model gradient uploaded by the entertainment terminals respectively, and the entertainment terminals in each physical space form a federation node to share the device state model gradient.

[0072] The global device state model is generated by aggregating gradients from two or more device state models using a pre-defined federated aggregation algorithm.

[0073] The global device status model represents the overall operational status of peripheral devices.

[0074] In the implementation of this application, the federated aggregation algorithm refers to a parameter fusion method based on a distributed machine learning framework. Specifically, it can be implemented using weighted averaging or gradient compression algorithms to integrate multi-terminal model update information while protecting data privacy. This algorithm fuses the model update amounts generated by local training on each terminal through mathematical operations, avoiding the uploading of raw device status data.

[0075] The device state model gradient refers to the parameter update generated through local device state model training. Specifically, it can be generated using a neural network backpropagation algorithm and is used to characterize the trend of device operating state changes. This gradient data only reflects the direction of model parameter adjustments and does not include specific device operating data.

[0076] The global device status model refers to a global data model generated by fusing the gradients of the device status models of any entertainment terminal using a federated aggregation algorithm. Specifically, it can be implemented using weighted averaging or gradient compression algorithms. This model reflects the comprehensive operating status of all devices in the entertainment platform and enables collaborative device management across physical spaces. It can capture common characteristics among different peripheral devices to obtain the operating status of the entertainment terminal; this embodiment focuses on the health of the entertainment terminal.

[0077] Suppose there are K entertainment terminals in the system, the gradient of the device state model of the k-th terminal is sk, and the proportion of terminal data volume is... Where nk is the amount of data at the k-th terminal and N is the total amount of data, the global gradient S is the weighted average of the gradients of all terminals, expressed as: .

[0078] For example, the central server collects the device state model gradients from 10 entertainment terminals, calculates the global device state model using the average weighted method, and reflects the average health status of the peripheral devices of the overall terminals.

[0079] In one possible implementation, a health index for each entertainment terminal is generated based on a global device state model. The health index of an entertainment terminal is a quantitative indicator.

[0080] The health index of entertainment terminals is recorded as follows: The entertainment terminal serial number is i. This entertainment terminal has n device status parameters, which are denoted as i. The weighting coefficient for each device status parameter is as follows: ,but .

[0081] In this method, the device status parameters and weight coefficients are queried and matched by the central server from a pre-set database of device status parameters and weight coefficients based on the specific hardware of each entertainment terminal. This enables intelligent adjustment of the weight coefficients, making the method more universal and accurate for devices with different configurations.

[0082] In the implementation of this application's embodiments, for example, the device status parameters of each entertainment terminal include at least the central processing unit load, storage device fragmentation rate, and network communication jitter, which are then weighted and summed, i.e. The weighting coefficient , , The value is adjusted according to the specific hardware of the entertainment terminal.

[0083] Among them, the health index of entertainment terminals refers to the integration of multi-dimensional device status parameters into a single comparable numerical indicator through mathematical calculation, which is used to compare the overall operating status of different terminals.

[0084] Among them, device status parameters refer to monitoring data that reflect the operating status of terminal hardware. Specifically, they can be implemented using parameters such as terminal load, storage device fragmentation rate, and network communication jitter, which are used to quantify its performance in computing, storage, and communication dimensions.

[0085] The weighting coefficient refers to the contribution ratio of each device status parameter in the health index calculation. Specifically, it can be implemented using preset matching rules, adjusting the parameter weights according to the specific hardware device of the terminal to adapt to the performance evaluation focus of different terminals.

[0086] Specifically, the central server extracts device status parameters from each terminal using a global device status model and matches corresponding weight coefficients from a pre-set library. For example, for high-performance terminals undertaking real-time rendering tasks, the CPU load is assigned a higher weight; for storage-intensive terminals responsible for data caching, the weight of storage fragmentation rate is correspondingly increased. A health index is generated through a weighted summation formula, enabling terminals with different hardware to quantify their status based on a unified standard. When a terminal's health index falls below a preset threshold, the system automatically triggers resource scheduling operations, migrating the computational task to a terminal with a higher health index.

[0087] Step S4: Global decision-making. Based on the global device status model and combined with user behavior data, a global decision-making instruction is obtained.

[0088] In one possible implementation, please refer to Figure 3The central server generates global decision instructions based on the global device state model and combined with the gradient of the user intent model uploaded by the entertainment terminal. The global decision instructions aim to optimize the resource allocation and user experience of the entire system.

[0089] Step S5: Execute instructions based on global decision instructions, such as... Figure 3 As shown, perform resource scheduling operations or peripheral device parameter adjustment operations. Issue global decision commands to one or more target entertainment terminals.

[0090] The target entertainment terminal executes corresponding resource scheduling operations or peripheral device parameter adjustment operations based on the received global decision instructions, wherein: Resource scheduling operations include at least the reallocation of computing or storage tasks among two or more entertainment terminals.

[0091] Adjusting peripheral device parameters involves at least modifying the operating parameters of the software or hardware associated with the peripheral device.

[0092] In one possible implementation, the resource scheduling operation is triggered when the health index of an entertainment terminal is detected to be lower than a preset health threshold. In this embodiment, the health threshold is set to 60. When the health index of entertainment terminal Y drops to 58, the central server immediately initiates the resource scheduling operation.

[0093] In one possible implementation, when the health index of an entertainment terminal is lower than a preset health threshold, the corresponding entertainment terminal sends a resource scheduling request to the central server, and the entertainment terminal that sends the resource scheduling request is designated as the first entertainment terminal.

[0094] The central server retrieves available entertainment terminals that are currently idle or under low load, excluding the first entertainment terminal, from the global device status model.

[0095] Determine if available entertainment terminals possess the hardware required to execute the task being performed by the first entertainment terminal, and then screen for hardware matching terminals.

[0096] Based on a global device state model, the current health index of available entertainment terminals is sorted, and the entertainment terminal with the highest health index is selected as the second entertainment terminal. In this embodiment, if Y is processing 4K video and suddenly falls below a threshold, entertainment terminals capable of image processing are retrieved and sorted. The health threshold for entertainment terminal Z is set to 100 (out of 100), and entertainment terminal Z is selected as the second entertainment terminal.

[0097] In one possible implementation, when a resource scheduling operation is triggered, a global decision instruction executes a cross-space resource scheduling instruction.

[0098] Establish a point-to-point data stream transmission channel between the first entertainment terminal and the second entertainment terminal.

[0099] The first entertainment terminal has completed the first part of the specific task and is currently executing the second part of the specific task. The second part is unloaded to the second entertainment terminal for execution, and the data stream obtained after executing the specific task is transmitted back through the transmission channel. The first entertainment terminal itself only retains and processes the first part of the specific task.

[0100] When this application embodiment is implemented, when the resource scheduling operation is triggered, the global decision instruction executes the cross-room resource scheduling instruction.

[0101] The execution process of cross-package resource scheduling instructions includes: The central server retrieves the healthiest second entertainment terminal, excluding the first entertainment terminal, from the global device status model.

[0102] It is determined that the second entertainment terminal has the hardware devices required to perform specific tasks, such as a graphics processing unit.

[0103] Generate instructions to establish a point-to-point video data stream transmission channel between the first entertainment terminal and the second entertainment terminal.

[0104] Task segmentation is performed. The first entertainment terminal (terminal Y) offloads the video decoding part of a specific task to the second entertainment terminal (terminal Z) for execution, and sends the decoded video stream back through the direct transmission channel. The first entertainment terminal (terminal Y) itself only retains and processes the audio decoding part associated with the specific task. Combined with the video decoding sent back by the second entertainment terminal (terminal Z), a complete media stream is output.

[0105] This specific task refers to the task being executed on the first entertainment terminal (terminal Y). The specific meaning is that this task can be divided into several parts. In this embodiment, these parts may include audio decoding and video decoding. The first entertainment terminal (terminal Y) can first complete the execution of these parts of the specific task, and the second entertainment terminal (terminal Z) can complete the execution of the unfinished parts of the current specific task.

[0106] When implementing the embodiments of this application, the peripheral device parameter adjustment operation includes at least modifying the software or hardware operating parameters associated with the peripheral device.

[0107] The peripheral device parameter adjustment operation includes: If the user's gesture data indicates an increased waving amplitude, a control command is generated to automatically and linearly increase the microphone input gain associated with the entertainment terminal.

[0108] If the body posture data analysis results indicate that the user has been in a sedentary state for a preset duration threshold, a control command is generated to automatically adjust the brightness of the ambient lighting equipment in the physical space where the entertainment terminal is located.

[0109] When implemented in this application embodiment, the method further includes a personalized configuration synchronization mechanism, which includes: Once a user completes identity authentication on any entertainment terminal, the system automatically retrieves and loads the historical personalized device parameter configuration file bound to that user's identity from the user database on the central server.

[0110] The target entertainment terminal automatically adjusts the parameters of its peripheral devices according to the configuration file. The parameters include at least the equalizer curve of the microphone, the screen color temperature preference of the display device, and the genre weight coefficient used for the song recommendation algorithm.

[0111] When implemented in this application embodiment, the method further includes a fault pre-handling mechanism, which includes the following steps: The device status awareness module continuously monitors the hard drive read latency parameters of the entertainment terminal.

[0112] When the system detects that the duration of hard drive read latency exceeds a preset latency threshold (for example, we often set it to 200 milliseconds in practical applications), it determines that there is a potential risk that the storage health value is below the threshold.

[0113] In response, a pre-processing operation is triggered, which includes: pre-caching media files of songs identified as popular or frequently played from the hard drive to the memory of the entertainment terminal; and pushing a predictive maintenance alarm containing the entertainment terminal identifier and fault prediction information to the back-end operation and maintenance management system.

[0114] When it is determined that there is a potential risk that the storage health value is below the threshold, the audio decoding channel of a backup second entertainment terminal is started simultaneously and runs on the audio decoding channel of the backup second entertainment terminal to ensure that the continuity of audio playback is not affected when the first entertainment terminal is congested.

[0115] In Example 3: This is the third embodiment of the present invention. Based on embodiments 1 and 2, please refer to the following references. Figure 4 This is a schematic diagram of the system structure of an intelligent digital entertainment platform equipment management system. This embodiment provides an intelligent digital entertainment platform equipment management system, including a central server and two or more entertainment terminals electrically connected to it.

[0116] In the implementation of this application embodiment, each entertainment terminal includes a multimodal data sensing module and a local processing module, wherein: The multimodal data sensing module is configured to acquire device status data and user behavior data.

[0117] The multimodal data perception module includes a device status perception module and a user behavior perception module.

[0118] The device status sensing module is configured to collect device status data of peripheral devices associated with the entertainment terminal in real time. The device status data constitutes a first data stream set and reflects the hardware operating parameters of the peripheral devices.

[0119] The device status sensing module is further subdivided in terms of function into: An audio device monitoring unit that integrates an algorithm module for real-time monitoring of microphone frequency response curves.

[0120] A display device monitoring unit that integrates sensors or software probes for detecting screen refresh rate and end-to-end display latency.

[0121] Additionally, a video-on-demand terminal monitoring unit, which integrates latency evaluation logic for assessing the time between touch panel input and system response.

[0122] The user behavior perception module is configured to capture user behavior data in real time within the physical space where the entertainment terminal is located. The user behavior data constitutes a second data stream set and reflects the user's physiological or behavioral characteristics.

[0123] The user behavior awareness module includes: A voiceprint recognition unit includes one or more microphone arrays coupled to a voiceprint recognition subunit configured to extract user voiceprint features from audio input for identity differentiation.

[0124] A motion recognition unit includes one or more motion capture cameras deployed in compliance with privacy regulations, coupled with a gesture recognition subunit and a posture analysis subunit. The gesture recognition subunit is used to map specific user gestures to device control commands, and the posture analysis subunit is used to recognize the user's long-term continuous posture.

[0125] In addition, an emotion computing unit, which runs as a software module on the local processor, is configured to perform real-time analysis of the user's singing audio stream to infer their emotional state.

[0126] The local processing module is configured with a processor to perform local model processing tasks, including obtaining the device state model gradient based on device state data and obtaining the user intent model gradient based on user behavior data.

[0127] The local processing module's memory receives global decision instructions, and its internal logic is configured as follows when performing peripheral device parameter adjustment operations: When a signal indicating an increase in the amplitude of a wave is received from the gesture recognition subunit, the input gain parameter value associated with the microphone device driver is automatically queried and increased.

[0128] Furthermore, when a signal indicating that a user's sitting time exceeds a preset threshold is received from the posture analysis subunit, a dimming command is automatically sent to the controller connected to the ambient lighting equipment via the control bus.

[0129] It also includes a communication interface configured to upload the generated device state model gradient and user intent model gradient to the central server, without including the original data of the first data stream set and the second data stream set during the upload process.

[0130] The central server includes an aggregation decision-making unit and an instruction distribution unit, wherein: The aggregation decision unit, electrically connected to the processor of the local processing module, is configured to obtain a global device state model based on the device state model gradient; and to obtain global decision instructions based on the global device state model and user behavior data.

[0131] The aggregation decision unit is used to receive the model gradient uploaded by any entertainment terminal, and aggregate the device state model gradient through a preset federated aggregation algorithm to update a global device state model. Then, it generates a global decision instruction based on the global device state model and the user intent model gradient.

[0132] The aggregation decision unit is further configured as follows: After each update of the global device state model, a dynamic health index for each entertainment terminal in the system is calculated and maintained.

[0133] The calculation logic of the health index of the entertainment terminal is encoded as a weighted sum of multiple dimensions of the device status that affect the terminal, such as normalized CPU load, normalized storage fragmentation rate, and normalized network jitter. The device status parameters used for the summation and their corresponding weight coefficients are stored in a configurable parameter table, which allows for adaptive lookup and application based on the specific hardware of the terminal.

[0134] The instruction dispatch unit, electrically connected to the memory of the local processing module, is configured to obtain resource scheduling operations or peripheral device parameter adjustment operations based on global decision instructions.

[0135] The instruction distribution unit is used to distribute global decision instructions to one or more target entertainment terminals specified by the aggregation decision unit; wherein, after receiving the global decision instructions, the local processing module's memory of the target entertainment terminal is further configured to perform corresponding resource scheduling operations or peripheral device parameter adjustment operations. When the resource scheduling decision process is activated and it is determined that cross-terminal resource sharing is required, the aggregation decision unit, the instruction distribution unit, the first entertainment terminal, and the selected second entertainment terminal work together to: The aggregation decision unit is responsible for querying the global device status model to locate the second entertainment terminal with the required hardware (such as image processing capabilities).

[0136] The instruction distribution unit is responsible for generating and distributing a series of instructions, which first instruct the first and second entertainment terminals to establish a P2P direct data channel using their communication interfaces.

[0137] Subsequently, the instruction directs the first entertainment terminal to serialize a specific processing step (such as video decoding) of a high-load task within it and send it to the second entertainment terminal via a channel.

[0138] At the same time, the instruction directs the second entertainment terminal to receive the task portion, utilize its idle computing power to perform processing, and send the processing result back to the first entertainment terminal through the channel.

[0139] Example 4 The fourth embodiment of the present invention differs from the previous embodiments in that: Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0140] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, two or more units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the mutual coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces, devices, or units, or it may be an electrical, mechanical, or other form of connection.

[0141] The units described as separate components may or may not be physically separate. As will be apparent to those skilled in the art, the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0142] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0143] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or grid device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0144] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for managing intelligent digital entertainment platform equipment, characterized in that, The method includes the following steps: Based on device status sensing modules and user behavior sensing modules deployed in two or more physical spaces, device status data and user behavior data are obtained. Based on the device status data, gradient calculation is performed according to the chain rule to obtain the device status model gradient, which is used to characterize the current health status of the peripheral device. Based on the user behavior data, gradient calculation is performed according to the chain rule to obtain the user intent model gradient and generate a user intent model gradient that represents the user's current needs. Based on the gradient of the device state model, a global device state model is obtained; Based on the global device state model and combined with user behavior data, global decision instructions are obtained; Based on the global decision instructions, resource scheduling operations or peripheral device parameter adjustment operations are executed to optimize resource allocation and user experience.

2. The intelligent digital entertainment platform equipment management method according to claim 1, characterized in that, The method is applied to a digital entertainment platform that includes a central server and entertainment terminals deployed in two or more physical spaces; wherein the central server is electrically connected to each of the entertainment terminals, and each of the entertainment terminals is connected to peripheral devices; The acquisition of device status data and user behavior data specifically includes: By deploying a device status sensing module in each entertainment terminal, device status data of the peripheral devices associated with each entertainment terminal are collected in real time, and the device status data constitutes a first data stream set; wherein, the device status sensing module is configured to periodically or based on preset event triggers monitor the device status parameters of the entertainment terminal and the hardware operating parameters of the peripheral devices. By deploying a user behavior sensing module in each entertainment terminal, user behavior data within the physical space where the entertainment terminal is located is collected in real time, and the user behavior data constitutes a second data stream set; wherein, the user behavior sensing module is configured to monitor user behavior non-intrusively using sensors.

3. The intelligent digital entertainment platform equipment management method according to claim 2, characterized in that, On each of the entertainment terminals, the first data stream set is input into a preset device state model to obtain the device state model gradient; On each of the entertainment terminals, the second data stream set is input into a preset user intent model to obtain the user intent model gradient.

4. The intelligent digital entertainment platform equipment management method according to claim 2, characterized in that, The central server receives the device state model gradient and the user intent model gradient uploaded by the entertainment terminal respectively, and performs aggregation operation on the two or more received device state model gradients through a preset federated aggregation algorithm to obtain a global device state model.

5. A method for managing intelligent digital entertainment platform equipment according to claim 2 or 4, characterized in that, The central server obtains global decision instructions based on the global device state model and in combination with the user intent model gradient uploaded by the entertainment terminal. The global decision command is sent to one or more target entertainment terminals; The target entertainment terminal executes corresponding resource scheduling operations or peripheral device parameter adjustment operations based on the received global decision instruction; wherein: The resource scheduling operation includes at least the reallocation of computing or storage tasks among two or more of the entertainment terminals; The peripheral device parameter adjustment operation includes at least modifying the software or hardware operating parameters associated with the peripheral device; Based on the global device status model, a health index is obtained for each entertainment terminal, and the health index of the entertainment terminal is a quantitative indicator. The health index of entertainment terminals is recorded as follows: The entertainment terminal serial number is i. This entertainment terminal has n device status parameters, which are denoted as i. The weighting coefficient for each device status parameter is as follows: ,but ; The device status parameters and weight coefficients are queried and matched by the central server from a pre-set database of device status parameters and weight coefficients based on the specific hardware of each entertainment terminal.

6. The intelligent digital entertainment platform equipment management method according to claim 5, characterized in that, The resource scheduling operation is triggered when the health index of an entertainment terminal is detected to be lower than a preset health threshold.

7. The intelligent digital entertainment platform equipment management method according to claim 5, characterized in that, When the health index of the entertainment terminal is lower than the preset health threshold, the corresponding entertainment terminal sends a resource scheduling request to the central server, and the entertainment terminal that sends the resource scheduling request is designated as the first entertainment terminal. The central server retrieves available entertainment terminals, excluding the first entertainment terminal, from the global device status model that are currently idle or in a low-load state. It is determined that the available entertainment terminal has the hardware devices required to execute the task being performed by the first entertainment terminal; Based on the global device state model, the current health index of available entertainment terminals is sorted, and the entertainment terminal with the highest health index is selected as the second entertainment terminal.

8. A method for managing intelligent digital entertainment platform equipment according to claim 6 or 7, characterized in that, When the resource scheduling operation is triggered, the global decision instruction executes the cross-space resource scheduling instruction; Establish a point-to-point data stream transmission channel between the first entertainment terminal and the second entertainment terminal; The first entertainment terminal has completed the first part of a specific task and is currently executing the second part of the specific task. The second part is unloaded to the second entertainment terminal for execution, and the data stream obtained after executing the specific task is transmitted back through the transmission channel. The first entertainment terminal itself only retains and processes the first part of the specific task.

9. A smart digital entertainment platform equipment management system, characterized in that, The system is used to perform the steps of the method according to any one of claims 1 to 8, the system comprising a central server and two or more entertainment terminals electrically connected thereto; Each of the aforementioned entertainment terminals includes a multimodal data sensing module and a local processing module; wherein: The multimodal data sensing module is configured to obtain device status data and user behavior data; The local processing module is configured to obtain a device state model gradient based on the device state data, and to obtain a user intent model gradient based on the user behavior data. The central server includes an aggregation decision unit and an instruction distribution unit; the aggregation decision unit is configured to obtain a global device state model based on the device state model gradient; and to obtain a global decision instruction based on the global device state model and user behavior data. The instruction distribution unit is configured to obtain resource scheduling operations or peripheral device parameter adjustment operations based on the global decision instruction.

10. A computer system device, comprising a memory, a processor, and a computer program stored in the memory, characterized in that, The processor executes the computer program to implement the steps of the method according to any one of claims 1 to 8.

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