An Efficient Power Management Method for a Multi-Device Compatible Wireless Screen Mirroring VR Box

Abstract

The present invention relates to the technical field of power management, and particularly to an efficient power management method for a multi-device compatible wireless screen mirroring VR box. The method includes the following steps: obtaining device power consumption acquisition data; analyzing the VR box working mode for the device power consumption acquisition data to generate VR box working mode data, where the VR box working mode data includes a standby mode, a charge and discharge mode, and a wireless screen mirroring mode; extracting power consumption dynamic characteristics from the device power consumption acquisition data to obtain power consumption mode dynamic characteristic data; performing mode parameter mapping based on the power consumption mode dynamic characteristic data to generate a power consumption mode mapping table; and performing intelligent prediction of device power consumption on the power consumption mode mapping table to generate device power consumption prediction data. The present invention improves the efficiency and comprehensiveness of power management of the wireless screen mirroring VR box through accurate power consumption prediction, intelligent power distribution strategies, and efficient power scheduling optimization.

Description

Technical Field

[0001] The present invention relates to the technical field of power management, and particularly to an efficient power management method for a multi-device compatible wireless screen mirroring VR box. Background Art

[0002] Early wireless screen mirroring VR boxes generally adopted traditional power management methods, suffering from problems such as poor battery life, serious heat accumulation, and low energy efficiency utilization. With the progress of technology, more and more research has begun to focus on how to optimize power management strategies to support efficient wireless transmission and continuous VR usage experience. In recent years, power management technology has been continuously developing towards the direction of intelligence and dynamics. By integrating multiple power management modes, adopting high-efficiency batteries, and intelligent power scheduling and other technologies, it is possible to significantly extend the usage duration of the VR box and optimize the power distribution between devices. Especially in the multi-device compatible wireless screen mirroring scenario, through accurate power monitoring and management, it is possible to ensure the stability and high efficiency of the device under high-load operation, while reducing power consumption and improving the user experience. However, currently, the power management of traditional VR boxes often lacks accurate power consumption prediction and reasonable power distribution strategies, resulting in unstable battery life in different working modes, and at the same time, the power management efficiency is low, failing to effectively perform task parallel processing and power scheduling, resulting in the power consumption management of the VR box not being optimized during multi-device operation, and thus leading to low efficiency and comprehensiveness of the power management of the wireless screen mirroring VR box. Summary of the Invention

[0003] Based on this, it is necessary to provide an efficient power management method for a multi-device compatible wireless screen mirroring VR box to solve at least one of the above technical problems.

[0004] To achieve the above object, an efficient power management method for a multi-device compatible wireless screen mirroring VR box, the method includes the following steps:

[0005] Step S1: Obtain device power consumption acquisition data; analyze the VR box working mode of the device power consumption acquisition data to generate VR box working mode data, where the VR box working mode data includes a standby mode, a charge and discharge mode, and a wireless screen mirroring mode; extract the power consumption dynamic characteristics of the device power consumption acquisition data to obtain power consumption mode dynamic characteristic data; perform mode parameter mapping based on the power consumption mode dynamic characteristic data to generate a power consumption mode mapping table;

[0006] Step S2: Perform intelligent prediction of device power consumption on the power consumption mode mapping table to generate device power consumption prediction data; based on the device power consumption prediction data, perform transmission distance power consumption weight allocation on the VR box working mode data to generate transmission power consumption weight allocation data; based on the transmission power weight allocation data, generate a power distribution strategy for the standby mode, charging and discharging mode, and wireless screen mirroring mode to obtain the VR box power supply power distribution strategy;

[0007] Step S3: Perform distributed task parallel processing on the VR box working mode data through the VR box power supply power distribution strategy to generate device power supply power scheduling data; analyze the high-frequency power consumption power usage situation of the VR box working mode data according to the device power supply power scheduling data to generate high-frequency power power scheduling feedback record data;

[0008] Step S4: Perform power management energy efficiency evaluation on the high-frequency power power scheduling feedback record data to generate power management energy efficiency evaluation data; verify the management optimization of the VR box power supply power distribution strategy through the power management energy efficiency evaluation data to generate VR box power management optimization verification data for performing efficient power management operations of multi-device compatible wireless screen mirroring VR boxes.

[0009] The present invention helps to deeply understand the power requirements of the device in each mode by collecting the power consumption data of the device and analyzing different working modes (standby, playback, screen mirroring), laying a foundation for subsequent power consumption optimization. By extracting dynamic features and performing mode parameter mapping, the changing rules of the device power consumption can be accurately captured, providing data support for power consumption management and ensuring the accuracy of subsequent power distribution. By intelligently predicting the power consumption of the device, the power consumption of the device in different operating environments can be estimated in advance, avoiding excessive or insufficient power allocation and improving the battery usage efficiency. Based on the transmission distance, weight allocation is performed on the power consumption to ensure reasonable power consumption distribution when the device performs wireless screen mirroring and avoid unnecessary power waste caused by too long signal transmission distance. By optimizing the power distribution for each mode, the power usage in different working modes is ensured to be reasonable, improving the overall power efficiency of the system and extending the device usage time. Through distributed parallel processing, efficient generation of power supply scheduling data is achieved, enabling multiple tasks to be processed simultaneously, reducing the power consumption bottleneck of the device during multitasking operation and improving the processing efficiency. Analyzing the high-frequency power consumption usage and generating feedback records helps to monitor the power consumption changes in real time, optimize the power usage strategy and avoid unnecessary energy waste. By evaluating the energy efficiency of the power management, potential problems in the power distribution can be identified and improved, ensuring the efficient utilization of power resources and enhancing the overall energy efficiency of the VR box. Optimizing and validating the power management strategy based on the evaluation data enables continuous improvement and optimization of the power management, ensuring high performance and long battery life in the usage environment of multi-device compatible wireless screen mirroring. Therefore, the present invention improves the efficiency and comprehensiveness of the power management of the wireless screen mirroring VR box through accurate power consumption prediction, intelligent power distribution strategy and efficient power scheduling optimization.

[0010] Preferably, step S1 includes the following steps:

[0011] Step S11: Use the power consumption monitoring module in the VR box to collect the power consumption parameters of the connected device to obtain the device power consumption collection data, where the device power consumption collection module includes the internal device power consumption collection data and the external connected device power consumption collection data;

[0012] Step S12: Analyze the internal device power consumption collection data and the external connected device power consumption collection data for the working mode of the VR box to generate the VR box working mode data, where the VR box working mode data includes the standby mode, the charge and discharge mode, and the wireless screen mirroring mode;

[0013] Step S13: Perform data preprocessing on the device power consumption collection data to generate the standard device power consumption collection data, where the data preprocessing includes data cleaning, data denoising, missing value filling, and data standardization; classify the standard device power consumption collection data according to the VR box working mode data to generate the power consumption mode classification data;

[0014] Step S14: Extract the dynamic power consumption features from the power consumption mode classification data to obtain the dynamic power consumption mode feature data; perform mode parameter mapping based on the dynamic power consumption mode feature data, thereby generating a power consumption mode mapping table.

[0015] By using the power consumption monitoring module in the VR box, the present invention can obtain the power consumption parameters of the device in detail, including not only the power consumption of internal devices but also the power consumption of externally connected devices, so as to comprehensively understand the energy consumption status of the device. By analyzing the working mode of the power consumption acquisition data of the device, the working mode data of the VR box is generated, including the standby mode, the charge and discharge mode, and the wireless screen projection mode, which can effectively distinguish the power consumption performance under different working states and provide a basis for subsequent power consumption optimization. The collected data is preprocessed, including data cleaning, denoising, missing value filling, and standardization, to ensure the quality and consistency of the data, thereby improving the accuracy of power consumption analysis. At the same time, combining the working mode data for mode classification can clearly divide each power consumption mode, thereby providing data support for optimization under different modes. By extracting the dynamic features of the power consumption mode classification data to generate the dynamic feature data of the power consumption mode, the law of power consumption change over time can be captured, and further, based on these dynamic features, mode parameter mapping is performed to generate a power consumption mode mapping table. This process provides a scientific basis for formulating more accurate power consumption control strategies. By analyzing the power consumption of the VR box in different modes, it can provide a basis for subsequent energy-saving optimization strategies, such as reducing unnecessary power consumption in the standby mode and optimizing resource usage in the charge and discharge mode, thereby improving the energy usage efficiency and performance of the device. This process can help achieve precise control and optimization of the power consumption of the VR box device, improve energy usage efficiency, extend the service life of the device, and provide data support for future energy-saving designs.

[0016] Preferably, the VR box working mode analysis of the internal device power consumption acquisition data and the externally connected device power consumption acquisition data includes:

[0017] Perform power consumption acquisition discrimination on the internal device power consumption acquisition data and the externally connected device power consumption acquisition data. When only the internal device power consumption acquisition data is collected, a first mode mark is made to generate the standby mode;

[0018] When both the internal device power consumption acquisition data and the externally connected device power consumption acquisition data are collected, perform power consumption fluctuation analysis on the externally connected device power consumption acquisition data to generate power consumption fluctuation analysis data; perform power consumption fluctuation spectrum conversion on the externally connected device power consumption acquisition data based on the power consumption fluctuation analysis data to generate a power consumption fluctuation spectrum diagram;

[0019] Perform a stability analysis of the fluctuation frequency of the power consumption fluctuation spectrum diagram to generate power consumption fluctuation stability data and power consumption fluctuation instability data; based on the power consumption fluctuation stability data, perform a second mode marking on the power consumption acquisition data of internal devices and the power consumption acquisition data of externally connected devices to obtain a charging and discharging mode;

[0020] Based on the power consumption fluctuation instability data, perform a third mode marking on the power consumption acquisition data of internal devices and the power consumption acquisition data of externally connected devices to obtain a wireless screen mirroring mode.

[0021] Through the power consumption acquisition discrimination of the power consumption acquisition data of internal devices and the power consumption acquisition data of externally connected devices, the present invention can effectively distinguish different working states. When only the power consumption data of internal devices is collected, it can be accurately identified as the standby mode, reflecting the performance of the device in a low-energy and low-activity state. When the power consumption data of both internal and external devices is collected simultaneously, by analyzing the fluctuations in the power consumption of externally connected devices, the power consumption fluctuations caused by external devices can be captured, providing a basis for subsequent power consumption stability analysis. The generation function of the power consumption fluctuation spectrum diagram can effectively identify the details of frequency changes, thereby helping to understand the working characteristics of the device. By performing a stability analysis of the fluctuation frequency of the power consumption fluctuation spectrum diagram, the stability data and instability data of the power consumption fluctuation can be generated, accurately reflecting the degree of influence of external devices on the power consumption. This analysis provides a scientific basis for determining different working modes (such as the charging and discharging mode and the wireless screen mirroring mode), enabling early warning of unstable power consumption fluctuations, and thus optimizing the power consumption management strategy. Through the discrimination of the second and third mode markings, according to the stability and instability data of the power consumption fluctuation, the working mode of the VR box can be intelligently identified and marked as the charging and discharging mode and the wireless screen mirroring mode respectively. This mechanism can dynamically identify the working state and optimize and adjust according to different modes to ensure the most reasonable power consumption control and performance adjustment of the VR box during playback and screen mirroring. Through the refined classification of multiple modes such as standby, playback, and screen mirroring and the analysis of power consumption fluctuations, the system can adjust the power consumption according to the requirements of different modes, effectively reducing unnecessary energy consumption, improving the overall energy efficiency, and avoiding additional energy consumption caused by unstable states. The system can analyze and mark the power consumption changes in different modes in real time, enabling the VR box to automatically switch the working mode according to the influence of external devices, adapt to environmental changes, enhance the user experience while reducing the battery burden, and extend the service life of the device.

[0022] Preferably, step S2 includes the following steps:

[0023] Step S21: Perform a timing sorting on the power consumption mode mapping table to generate a power consumption feature timing sorting data set; perform a time series segmentation on the power consumption feature timing sorting data set to generate power consumption timing segmentation data; perform a sliding window setting on the power consumption timing segmentation data to generate a timing power consumption segment data set;

[0024] Step S22: Perform intelligent prediction of device power consumption on the time-series power consumption segment dataset to generate device power consumption prediction data;

[0025] Step S23: Based on the device power consumption prediction data, analyze the device power requirements for the standby mode, charging and discharging mode, and wireless screen mirroring mode to generate device power requirement data; allocate transmission distance power consumption weights through the device power requirement data to generate transmission power consumption weight allocation data;

[0026] Step S24: Based on the transmission power weight allocation data, generate a power distribution strategy for the standby mode, charging and discharging mode, and wireless screen mirroring mode to obtain the power distribution strategy for the VR box power supply.

[0027] By performing a timing sorting on the power consumption mode mapping table, the present invention can clearly display the timing changes of power consumption characteristics. Based on the data generated by segmenting according to the time series, the power consumption can be effectively segmented and analyzed to capture the power consumption trends of the device at different time points, providing accurate basic data for subsequent power consumption prediction. By setting a sliding window to fragment the power consumption timing data, a timing power consumption fragment data set is generated, which can more precisely capture the dynamic changes of the device's power consumption, ensuring the accuracy and adaptability of power consumption prediction in the short term. This process optimizes the timing structure of the data, making subsequent power consumption prediction more accurate. By performing intelligent prediction on the timing power consumption fragment data set, the power consumption of the device can be estimated in advance, thereby predicting the energy consumption of the device in different modes. This prediction can provide real-time data support for the power demand analysis and power distribution strategy generation of the device, ensuring more efficient power consumption management in different application modes. Based on the device power consumption prediction data, power demand analysis can be performed separately on the standby mode, charging / discharging mode, and wireless screen mirroring mode, and then accurate device power demand data can be generated. This analysis can help determine the power consumption of the device in each mode, providing a basis for the precise control of power distribution. By assigning power consumption weight according to the transmission distance for the device power demand data, a reasonable power weight can be allocated according to the distance and power consumption requirements between different devices. This optimization helps to improve the energy efficiency transmission between devices, avoid excessive energy consumption, and at the same time improve the overall power utilization efficiency of the system. Based on the data of transmission power weight assignment, accurate power distribution strategies for different modes (standby mode, charging / discharging mode, wireless screen mirroring mode) can be generated to ensure the best balance of power consumption management in each mode. The optimization of the power distribution strategy will enable the VR box to maintain good power performance in different usage scenarios, avoid excessive consumption, and extend the device usage time. The power distribution strategy generated in this process can intelligently adjust the power supply of the device in different working modes, reasonably allocate power, avoid unnecessary power consumption, and improve the overall energy usage efficiency. Especially in long-term usage scenarios, it can extend the usage time of the VR box and enhance the user experience. Through the dynamic processing and predictive analysis of timing data, the system can adapt to changes in device power consumption and power demand in real time.

[0028] Preferably, step S22 includes the following steps:

[0029] Step S221: Extract the key power consumption timing characteristics from the device power consumption fragment data set to obtain key power consumption timing characteristic data, where the extraction of key power consumption timing characteristics includes the extraction of the power consumption moving average and the extraction of the power consumption change rate;

[0030] Step S222: Divide the key power consumption timing feature data into a data set to generate a model training set and a model test set; use the recursive neural network algorithm to train the model training set to generate a pre-power consumption prediction model; optimize and iterate the pre-power consumption prediction model through the model test set to generate a power consumption prediction model.

[0031] Step S223: Import the device power consumption segment data set into the power consumption prediction model to perform intelligent prediction of device power consumption and generate device power consumption prediction data.

[0032] Through the extraction of key power consumption timing features (such as power consumption moving average and power consumption change rate) from the device power consumption segment data, the present invention can effectively capture the timing change law of power consumption. These features are crucial for identifying power consumption patterns and trends, providing high-quality input data for subsequent intelligent prediction and ensuring the accuracy of prediction. Reasonably dividing the key power consumption timing feature data into a data set to generate a model training set and a test set ensures the effectiveness and stability of model training, avoids data overfitting, and provides reliable test data for model optimization. Using the recursive neural network (RNN) algorithm to train the training set can capture the dynamic change characteristics of power consumption timing and generate a preliminary model for power consumption prediction. Optimizing and iterating the pre-power consumption prediction model through the model test set can further improve the accuracy and generalization ability of the prediction model. The process of continuous iteration and optimization enables the model to adapt to power consumption changes under different devices and usage scenarios, improving the applicability and prediction accuracy of the model. By importing the device power consumption segment data set into the finally optimized power consumption prediction model, efficient and intelligent device power consumption prediction can be performed. This prediction can not only grasp the energy consumption trend of the device in advance but also provide data support for subsequent power distribution and management to ensure the energy efficiency optimization of the device under different working modes. By extracting power consumption timing features and combining with recursive neural network for training and optimization, the model can more accurately identify the law of power consumption changes. Especially for complex timing data, RNN can better capture long-term dependence relationships, improving the accuracy of power consumption prediction. This intelligent power consumption prediction system provides basic data support for subsequent power consumption optimization. By accurately predicting the power consumption demand of the device, a reasonable basis can be provided for power distribution, device scheduling, etc., avoiding excessive power consumption and improving energy efficiency. Through the intelligent prediction of device power consumption, the system can better adapt to the power consumption changes during the use of the device, perform real-time adjustment and optimization, which not only improves the operation efficiency of the device but also extends the battery life, reduces energy consumption and environmental burden.

[0033] Preferably, step S24 includes the following steps:

[0034] Step S241: Obtain the total power supply power data of the VR box device.

[0035] Step S242: Based on the standby mode, mark the total power supply power data of the VR box device with a low-power state, and based on the low-power state, perform minimum power resource allocation on the device power consumption segment data set to generate standby mode power allocation data;

[0036] Step S243: Based on the charge and discharge mode, screen the operation data of the charge and discharge mode working modules for the total power supply power data of the VR box device to generate charge and discharge mode working module operation screening data, where the charge and discharge mode working module screening data includes audio and video decoding module operation data, data transmission module operation data, and screen control module operation data;

[0037] Step S244: Perform medium-power state marking according to the audio and video decoding module operation data, data transmission module operation data, and screen control module operation data, and based on the medium-power state, perform hierarchical power control on the device power consumption segment data set to generate charge and discharge mode power allocation data;

[0038] Step S245: Based on the wireless screen mirroring mode, screen the operation data of the wireless screen mirroring mode working modules for the total power supply power data of the VR box device to generate wireless screen mirroring mode working module operation screening data, where the wireless screen mirroring mode working module operation screening data includes data transmission module operation data, screen refresh module operation data, and graphics processing module operation data;

[0039] Step S246: Perform high-power state marking according to the data transmission module operation data, screen refresh module operation data, and graphics processing module operation data, and based on the high-power state, use the power high-load regulation formula to perform power adaptive allocation on the device power consumption segment data set, thereby generating wireless screen mirroring mode power allocation data;

[0040] Step S247: Integrate the power strategies through the standby mode power allocation data, charge and discharge mode power allocation data, and wireless screen mirroring mode power allocation data to generate the VR box power supply power allocation strategy.

[0041] Through detailed power distribution for different working modes (standby mode, charge-discharge mode, wireless mirroring mode), the system can accurately allocate power resources in different modes according to the actual operation requirements of the device. In standby mode, the system ensures minimum power consumption through low-power state marking; in charge-discharge mode, the system provides reasonable power for the device through hierarchical power control; in wireless mirroring mode, through high-power state marking and using a high-power load regulation formula, it ensures that the device can obtain appropriate power supply when the power demand is high. This distribution strategy can effectively optimize the overall energy efficiency of the VR box device. The system marks the power states of the device in different working modes (low-power, medium-power, high-power states) and adjusts power distribution based on these states. This accurate power state identification helps ensure that the device obtains reasonable power support in different behavioral modes, avoiding excessive consumption or unnecessary power waste. By finely screening and distributing power for the specific working modules of the VR box (such as audio and video decoding module, data transmission module, screen control module, etc.), reasonable power control can be carried out according to the power consumption requirements of different modules. For example, in charge-discharge mode, the power consumption requirements of different modules are different, and the system will perform power distribution in the medium-power state according to actual needs. In wireless mirroring mode, the system further adaptively distributes power according to high-power requirements to ensure the efficient operation of the device. By optimizing the power distribution of different modes, the system can effectively reduce power waste and extend the usage time of the device. Especially the low-power and medium-power control strategies in standby mode and charge-discharge mode can significantly reduce unnecessary power consumption. Using the high-power load regulation formula to provide adaptive power distribution for wireless mirroring mode ensures that even when the device is heavily loaded (such as when tasks like graphics processing and screen refreshing are heavy), power can be reasonably allocated to prevent power shortage or device overheating problems. By integrating the power distribution data of standby mode, charge-discharge mode, and wireless mirroring mode, the system can generate a comprehensive power distribution strategy. This strategy can comprehensively consider the different working requirements and power consumption situations of the device to ensure the best power management and efficient operation of the VR box device in all working modes. This power distribution strategy is not only applicable to device management in different working modes but also can flexibly adjust power distribution in various actual application scenarios. For example, in the case of long-term standby required, the system will give priority to entering the low-power state; when playing high-definition videos or performing mirroring, the system can automatically adjust power supply according to real-time power consumption requirements to ensure smooth operation.

[0042] Preferably, the high-power load regulation formula in step S245 is specifically as follows:

[0043]

[0044] In the formula, P aDenoted as the adaptive power distribution in the wireless screen mirroring mode, T represents the running time of the entire wireless screen mirroring mode, α represents the priority coefficient of the transmission module power consumption in the total power, P TX (t) represents the data transmission power varying with time t, β represents the regulation coefficient of the graphics processing module, P GPU (t) represents the instantaneous power consumption change rate of the graphics processing module power consumption at time t, P CPU (t) represents the sine wave modulation term of the processor power consumption varying with time t, γ represents the CPU power consumption weight coefficient, ω represents the frequency parameter, Denoted as the total RAM power consumption at time τ from the start time to the current time t.

[0045] The present invention analyzes and integrates a power high-load regulation formula. The purpose of the formula is to adaptively regulate the power of the system in the wireless screen mirroring mode. Each term of the formula corresponds to the power consumption contribution of different hardware modules. Finally, a total adaptive power distribution (Pa) is obtained. This power distribution takes into account the power consumption and its change rate of different hardware modules (such as the transmission module, GPU, CPU, RAM), and dynamically adjusts the power distribution according to their priorities to keep the system running efficiently. Among them, α is the priority coefficient of the transmission module power consumption in the total power. It determines the proportion of the transmission module in the power distribution. A larger α value means that the transmission module will preferentially obtain more power to ensure that data transmission is not restricted. P TX (t) is the data transmission power consumption varying with time. The power consumption of the transmission module changes with time and is usually related to factors such as data traffic and transmission frequency. The transmission module requires a certain amount of power to maintain the stability of data transmission. β is the regulation coefficient of the graphics processing module. It controls the influence weight of the GPU power consumption change rate on the power distribution. The magnitude of the β value determines the instantaneous change rate of the GPU power consumption The importance in power regulation. A larger β will make the influence of GPU power consumption change on power distribution greater. Is the instantaneous change rate of the GPU power consumption at time t, reflecting the change of the graphics processing load. If the load of the GPU changes greatly, then this item will generate a large power regulation demand to ensure that the GPU is always within a reasonable power consumption range. P CPU (t) is the processor power consumption modulation term varying with time t. The power consumption of the CPU is dynamically changing during the calculation process and is usually related to the system load and running tasks. This power consumption is modulated by a sine wave to simulate the periodic change of the CPU load, such as the alternation of peak load periods and low load periods. γ is the CPU power consumption weight coefficient, which determines the priority of the CPU power consumption in the power distribution. A larger γ value indicates that the system will give priority to considering the change of the CPU power consumption to maintain the CPU load balance. is the total RAM power consumption from the initial time to the current time t. The change in RAM power consumption varies with the system memory usage. Especially when processing large data or complex tasks, the power consumption of the memory fluctuates significantly. This item reflects the cumulative power consumption of the RAM through integration. As a storage module, the RAM requires a relatively high power supply during long-term operation to ensure the stability of data reading and writing. By monitoring its power consumption, power can be allocated to the RAM in a timely manner to avoid a decline in system performance due to insufficient memory resources. When using the conventional power high-load regulation formula in this field, the adaptive power distribution power in the wireless screen mirroring mode can be obtained. By applying the power high-load regulation formula provided by the present invention, the adaptive power distribution power in the wireless screen mirroring mode can be calculated more accurately. By adjusting the power consumption weights of different modules (transmission module, GPU, CPU, RAM), the system can dynamically adjust the power distribution according to the real-time load conditions, avoiding unnecessary power consumption waste or performance bottlenecks caused by too much or too little power for a single module. Using sine wave modulation to control the CPU power consumption and calculating the cumulative RAM power consumption through integration can better predict and regulate the change of power consumption, reduce the power volatility, and thus improve the system stability. This formula dynamically regulates the power consumption and its change rate of each hardware module to optimize the overall power efficiency of the system. Through the priority coefficient, instantaneous power consumption change rate, modulation term, and integration calculation, the power distribution can be adjusted according to the actual needs of the system to ensure the smooth and efficient operation of each module and avoid excessive power consumption waste and performance bottlenecks.

[0046] Preferably, step S3 includes the following steps:

[0047] Step S31: Split the power management task for the VR box working mode data through the VR box power distribution strategy to generate power management task data; perform distributed task scheduling on the power management task data to generate a device power scheduling micro-task data set;

[0048] Step S32: Perform micro-task parallel processing on the device power scheduling micro-task set to generate device power supply scheduling data;

[0049] Step S33: Analyze the power consumption usage of the VR box working mode data according to the device power supply scheduling data to generate power consumption usage data for power scheduling;

[0050] Step S34: Perform high-frequency power consumption power adjustment feedback on the power consumption usage data for power scheduling to generate high-frequency power scheduling feedback record data.

[0051] By splitting the power management tasks for the working mode data of the VR box, complex power management tasks can be refined into multiple small tasks (power management task data). This process makes power management more precise and controllable, providing a reliable data basis for subsequent scheduling and processing. After generating the power management task data, the system distributes the tasks to different computing units through a distributed task scheduling method. This scheduling method can effectively utilize the computing resources of the system, enabling parallel processing of power management tasks, thereby improving the efficiency of power scheduling for the entire VR box device, reducing response time, and enhancing the processing capacity of the system. Micro-task parallel processing allows multiple power scheduling tasks to be carried out simultaneously, which not only significantly improves the processing efficiency of power scheduling but also enables rapid response to changes in power demand in different modes during device operation, ensuring that the device always maintains optimal power support under different workloads. By analyzing the power scheduling data of the device, the power consumption during the power scheduling process can be grasped in real time, providing accurate data support for subsequent optimization of power management strategies, ensuring that the device can operate with the lowest energy consumption in different working modes, and timely adjusting power distribution to avoid power shortage or waste. Through high-frequency power consumption adjustment feedback for power scheduling (step S34), the system can dynamically adjust the power scheduling plan and optimize the power distribution strategy in real time. The high-frequency power adjustment feedback record data can provide refined power control for the system, ensuring that power distribution remains in the best state during device load changes or emergencies, thus avoiding affecting device performance due to insufficient power supply or unreasonable distribution. Through the above power scheduling process, the VR box device can maintain stable power supply under different working modes and load conditions, avoid overload or power shortage, and enhance the stability and long-term reliability of the device, which is particularly crucial for application scenarios that require continuous high-power operation (such as video playback, screen mirroring, etc.). By using the high-frequency power scheduling feedback mechanism, the system can be adjusted according to the real-time power consumption of power scheduling, further optimizing power distribution. This intelligent scheduling system can not only improve the operation efficiency of the device but also adapt to changes in power demand in different usage scenarios, providing a more flexible and intelligent power management solution. By optimizing power scheduling and feedback, avoiding power waste and excessive consumption, it helps to improve the battery life and extend the service life of the device. Especially in mobile devices or scenarios that require long-term operation, reasonable power distribution can significantly improve the user experience and reduce the charging frequency.

[0052] Preferably, the distributed task scheduling for the power management task data includes:

[0053] Dividing the device power tasks for the power management task data to generate power management micro-task data; performing micro-task load analysis on the power management micro-task data to generate power management micro-task load data;

[0054] Define the power management optimization objectives for the power management micro-task load data to generate power management optimization objective data; formulate a task scheduling strategy for the power management micro-task load data based on the power management optimization objective data, thereby generating a power management task scheduling strategy;

[0055] Perform task mapping on the power management micro-task data to generate power management task mapping data; allocate resources to the power management micro-task data through the power management task scheduling strategy to generate power management resource allocation data; perform distributed dynamic task migration on the power management task mapping data according to the power management resource allocation data to generate a power management scheduling micro-task data set.

[0056] Through the division of power management task data, the present invention generates power management micro-task data, breaking down complex power management tasks into small and independent micro-tasks. This process makes task management more refined, facilitating subsequent optimization and scheduling, and providing a clear data structure for distributed task processing. Conducting a load analysis on the power management micro-task data can evaluate the computing requirements, resource consumption, and running priorities of each micro-task, thereby generating power management micro-task load data. This analysis provides a quantitative basis for subsequent task scheduling, helps allocate resources reasonably, avoids task resource competition or overload phenomena, and improves the overall system efficiency. Through the analysis of the power management micro-task load data, the system can set clear power management optimization target data. This optimization target data helps formulate a task scheduling strategy to ensure that each micro-task can be efficiently scheduled and executed according to the optimization target. For example, reasonably allocate task resources, reduce power consumption peaks, and optimize device working efficiency, ultimately generating a power management task scheduling strategy. After task mapping of the power management micro-task data, the system makes appropriate resource allocation for the tasks according to the resource allocation strategy. This process can ensure that each micro-task can be allocated the most suitable resources according to its own needs, avoid resource waste, and ensure the efficient execution of tasks. Through dynamic task migration of the power management task mapping data, intelligent migration and load balancing of tasks between multiple computing units are achieved. This distributed dynamic task migration can dynamically adjust the execution location of tasks according to the current system state and load conditions, thereby improving the flexibility and adaptability of task scheduling and ensuring the optimal allocation of power resources. The entire process can monitor the execution of power tasks in real time and optimize the task scheduling strategy based on the load analysis results to ensure that the device can adaptively adjust according to changes in power demand at any time during operation. This real-time nature and high efficiency significantly improve the operation stability of the device and the response speed of power management. Through the intelligent power management task scheduling strategy, excessive power consumption can be avoided and the system burden can be reduced. The execution of each micro-task can be completed at the most appropriate moment and on the most suitable device, thus effectively saving power, improving energy usage efficiency, and reducing unnecessary power waste. Through dynamic task migration and distributed resource allocation, the system can adapt to different working environments and load conditions, thereby improving the overall performance of the system. When the device faces changes or high loads, it can maintain stable operation through fine-grained power resource management, avoiding performance bottlenecks or insufficient power supply. Reasonable resource allocation and task scheduling not only help improve immediate performance but also effectively reduce the power consumption and excessive consumption of the device, thereby reducing device wear and extending service life. Especially for devices that need to operate stably for a long time, the sustainability of the device can be significantly improved by optimizing power management.

[0057] Preferably, step S4 includes the following steps:

[0058] Step S41: Conduct a power management energy efficiency assessment on the high-frequency power grid dispatching feedback record data to generate power management energy efficiency assessment data;

[0059] Step S42: Optimize the power distribution strategy of the VR box power supply through the power management energy efficiency assessment data to generate an optimized power distribution strategy for the VR box power supply;

[0060] Step S43: Use the optimized power distribution strategy of the VR box power supply to conduct management optimization verification on the high-frequency power grid dispatching feedback record data to generate VR box power management optimization verification data for performing efficient power management operations on the multi-device compatible wireless screen mirroring VR box.

[0061] Through the energy efficiency assessment of the high-frequency power grid dispatching feedback record data, the present invention can comprehensively understand the power usage efficiency of the VR box under different working conditions. The energy efficiency assessment data provides a reliable basis for further optimizing power grid dispatching, helping to discover potential problems in power management, such as power waste or unreasonable resource allocation. By optimizing the power distribution strategy of the VR box power supply based on the power management energy efficiency assessment data, more efficient power grid dispatching can be achieved. The optimized power distribution strategy can adjust the power distribution plan according to the device requirements and energy efficiency assessment results, thereby reducing power consumption waste, extending the device operation time, and reducing energy consumption while meeting the performance requirements. Using the optimized power distribution strategy of the VR box power supply to conduct management optimization verification on the high-frequency power grid dispatching feedback record data can ensure the effectiveness of the optimized strategy in practical applications. This verification process can not only further verify the effectiveness and accuracy of the optimized strategy but also simulate the power grid dispatching behavior under different working conditions to ensure that the VR box can achieve efficient power management in different environments. Through the power management optimization verification, it can be ensured that the VR box can still maintain efficient power management operations in the case of multi-device compatible wireless screen mirroring. This optimization scheme can address power conflicts or power consumption problems that occur when multiple devices are running simultaneously, ensuring that each device can operate efficiently within a reasonable power consumption range, thereby improving the system stability and user experience. After optimizing the power distribution strategy and management verification, power consumption waste can be minimized to the greatest extent. In the case of multiple devices running simultaneously, the optimized power management can ensure the high efficiency of battery usage and reduce the situations of overcharging or over-discharging, thereby extending the service life of the device. The entire process can achieve efficient power grid dispatching operations through the comprehensive assessment and strategy optimization of power management energy efficiency, thereby optimizing the working mode of the VR box. Whether in the standby, playback, or wireless screen mirroring mode, it can maintain the maximum energy efficiency. At the same time, under the high-efficiency compatibility of multiple devices, the optimized power management strategy ensures the stable operation of the system under high load conditions. Description of the Drawings

[0062] Figure 1Schematic diagram of the step process of an efficient power management method for a multi-device compatible wireless screen mirroring VR box;

[0063] Figure 2 For Figure 1 Detailed implementation step process schematic diagram of step S2 in

[0064] Figure 3 For Figure 1 Detailed implementation step process schematic diagram of step S3 in

[0065] The realization, functional characteristics and advantages of the object of the present invention will be further described with reference to the embodiments and the accompanying drawings. Specific implementation manner

[0066] The technical method of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts fall within the scope of protection of the present invention.

[0067] In addition, the accompanying drawings are only schematic diagrams of the present invention and are not necessarily drawn to scale. The same reference numerals in the drawings represent the same or similar parts, and thus repeated descriptions thereof will be omitted. Some of the block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software, or in one or more hardware modules or integrated circuits, or in different networks and / or processor methods and / or microcontroller methods.

[0068] It should be understood that although the terms "first", "second", etc. may be used here to describe each unit, these units should not be limited by these terms. These terms are only used to distinguish one unit from another. For example, without departing from the scope of the exemplary embodiments, the first unit may be called the second unit, and similarly the second unit may be called the first unit. The term "and / or" used here includes any and all combinations of one or more of the listed related items.

[0069] To achieve the above object, please refer to Figures 1 to 3 , an efficient power management method for a multi-device compatible wireless screen mirroring VR box, the method includes the following steps:

[0070] Step S1: Obtain the device power consumption collection data; analyze the working mode of the VR box for the device power consumption collection data to generate VR box working mode data, where the VR box working mode data includes the standby mode, the charge and discharge mode, and the wireless screen mirroring mode; extract the dynamic characteristics of the power consumption from the device power consumption collection data to obtain the dynamic characteristic data of the power consumption mode; perform mode parameter mapping based on the dynamic characteristic data of the power consumption mode, thereby generating a power consumption mode mapping table.

[0071] Step S2: Perform intelligent prediction of the device power consumption on the power consumption mode mapping table to generate device power consumption prediction data; allocate the power consumption weight of the transmission distance for the VR box working mode data based on the device power consumption prediction data to generate transmission power consumption weight allocation data; generate a power distribution strategy for the standby mode, the charge and discharge mode, and the wireless screen mirroring mode based on the transmission power weight allocation data to obtain the power supply power distribution strategy of the VR box.

[0072] Step S3: Perform distributed task parallel processing on the VR box working mode data through the power supply power distribution strategy of the VR box to generate device power supply power scheduling data; analyze the high-frequency power consumption power usage situation of the VR box working mode data according to the device power supply power scheduling data to generate high-frequency power power scheduling feedback record data.

[0073] Step S4: Evaluate the energy efficiency of the power supply management for the high-frequency power power scheduling feedback record data to generate power supply management energy efficiency evaluation data; verify the management optimization of the power supply power distribution strategy of the VR box through the power supply management energy efficiency evaluation data to generate VR box power supply management optimization verification data, so as to execute the efficient power supply management operation of the multi-device compatible wireless screen mirroring VR box.

[0074] The present invention collects the power consumption data of the device and analyzes different working modes (standby, playback, screen mirroring), which helps to deeply understand the power requirements of the device in each mode and lays a foundation for subsequent power consumption optimization. By extracting dynamic features and performing mode parameter mapping, the changing rules of the device power consumption can be accurately captured, providing data support for power consumption management and ensuring the accuracy of subsequent power distribution. By intelligently predicting the power consumption of the device, the power consumption of the device in different operating environments can be estimated in advance, avoiding excessive or insufficient power distribution and improving the battery usage efficiency. Based on the transmission distance, weight distribution is performed on the power consumption to ensure reasonable power consumption distribution when the device is wirelessly screen mirroring, and to avoid unnecessary power waste caused by too long signal transmission distance. By optimizing the power distribution for each mode, the power consumption in different working modes is ensured to be reasonable, improving the overall power efficiency of the system and extending the device usage time. Through distributed parallel processing, efficient generation of power supply power scheduling data can be achieved, enabling multiple tasks to be processed simultaneously, reducing the power consumption bottleneck of the device during multitasking operation, and improving the processing efficiency. Analyzing the high-frequency power consumption usage and generating feedback records helps to monitor the power consumption changes in real time, optimize the power usage strategy, and avoid unnecessary energy waste. By evaluating the energy efficiency of the power management, potential problems in the power distribution can be identified and improved, ensuring the efficient use of power resources and enhancing the overall energy efficiency of the VR box. Optimizing and validating the power management strategy based on the evaluation data enables the continuous improvement and optimization of the power management, ensuring high efficiency and long battery life in the usage environment of multi-device compatible wireless screen mirroring. Therefore, the present invention improves the efficiency and comprehensiveness of the power management of the wireless screen mirroring VR box through accurate power consumption prediction, intelligent power distribution strategy, and efficient power scheduling optimization.

[0075] In an embodiment of the present invention, referring to Figure 1 as described, it is a schematic diagram of the step flow of an efficient power management method for a multi-device compatible wireless screen mirroring VR box of the present invention. In this example, the efficient power management method for a multi-device compatible wireless screen mirroring VR box includes the following steps:

[0076] Step S1: Obtain the device power consumption collection data; analyze the working mode of the VR box for the device power consumption collection data to generate VR box working mode data, where the VR box working mode data includes standby mode, charge and discharge mode, and wireless screen mirroring mode; extract the dynamic features of the power consumption for the device power consumption collection data to obtain the dynamic feature data of the power consumption mode; perform mode parameter mapping based on the dynamic feature data of the power consumption mode to generate a power consumption mode mapping table.

[0077] In the embodiments of the present invention, power consumption data of the VR box during actual operation is obtained through sensors and monitoring modules in the device. Data collection should cover the power consumption of the device in standby, playback, and wireless screen mirroring modes. The collected data includes key parameters such as the change of power consumption over time, current, and voltage to ensure that the power consumption characteristics of the device can be comprehensively reflected. The collected data is cleaned and preprocessed to remove abnormal data, fill in missing data, and sorted according to the time axis to form complete power consumption time series data. According to the power consumption characteristics and the operating state of the device, the characteristics of standby, playback, and wireless screen mirroring modes are defined. Each mode has specific power consumption mode characteristics. For example, the power consumption in the standby mode is relatively low, the power consumption in the charging and discharging mode is medium, and the power consumption in the wireless screen mirroring mode is relatively high. By analyzing the time series characteristics of the power consumption data, such as the peak value, duration, and fluctuation amplitude of the power consumption, it is determined which working mode the device is in. By classifying and labeling the power consumption data in different modes, VR box working mode data is generated. This data records the power consumption characteristics and time periods of each mode for subsequent analysis and optimization. Through statistical methods and signal processing techniques, such as moving average, Fourier transform, etc., dynamic feature extraction is performed on the power consumption data. The main extracted features include the average value, peak value, fluctuation frequency, growth rate, etc. of the power consumption, reflecting the power consumption changes of the device in different modes. The extracted features are summarized into power consumption mode dynamic feature data. This data provides a basis for subsequent power consumption mode mapping and power scheduling optimization. The various feature parameters in the power consumption mode dynamic feature data are parameterized. For example, for the standby mode, the low power consumption range parameters are mapped; for the charging and discharging mode and the wireless screen mirroring mode, the feature parameters of medium and high power consumption are mapped. Key parameters such as the upper and lower limits of power consumption, the power consumption fluctuation range, and the power load of each mode are determined, and a mode parameter mapping is established. The mapped parameters are sorted into a table to form a power consumption mode mapping table. This table describes the power consumption characteristics of each working mode and provides the power consumption parameter range of each mode under different loads. The power consumption mode mapping table provides data support for the power distribution strategy in the subsequent steps.

[0078] Step S2: Perform intelligent prediction of the device power consumption on the power consumption mode mapping table to generate device power consumption prediction data; based on the device power consumption prediction data, perform power consumption weight allocation for the transmission distance on the VR box working mode data to generate transmission power consumption weight allocation data; based on the transmission power weight allocation data, generate a power distribution strategy for the standby mode, the charging and discharging mode, and the wireless screen mirroring mode to obtain the VR box power supply power distribution strategy;

[0079] In the embodiments of the present invention, by using the dynamic feature data in the power consumption mode mapping table as input, which includes key parameters such as the average power consumption, power consumption fluctuation, load upper and lower limits, etc. under different working modes. A Recurrent Neural Network (RNN) or Long Short-Term Memory Network (LSTM) model is used to better capture the time series features of power consumption. The power consumption mode mapping table data is divided into a training set and a test set for model training and verification. The real-time data under different working modes is input into the trained model to generate future power consumption prediction data for each working mode. According to the output results of the model, power consumption prediction data is generated, which can provide a basis for the subsequent power distribution to ensure the reasonable distribution of power and the efficient utilization of resources. Considering the influence of the transmission distance, the device power consumption prediction data and the transmission distance are weighted and allocated. For a longer transmission distance, a higher power consumption weight is allocated to ensure transmission stability; for a shorter transmission distance, a lower power consumption weight is allocated to optimize power resources. Using the power consumption prediction data and the weight allocation algorithm, power consumption weight allocation data is generated, recording the power consumption requirements for the transmission distance under different modes. The transmission power consumption requirements of different working modes are integrated to obtain the transmission power consumption weight allocation data, providing input support for the power resource scheduling of each mode. According to the transmission power consumption weight allocation data, power is distributed to each working mode. The specific steps are as follows: Based on the low power consumption demand, the minimum power resources are allocated to keep the device in the lowest operating state. According to the power consumption weight in the charge and discharge mode and the transmission requirements of the device, medium power is allocated to ensure smooth playback of audio and video. In the case of a longer transmission distance and a higher load, higher power resources are allocated to ensure the smoothness and stability of the screen mirroring. For each mode, the allocation strategy is dynamically adjusted, and according to the real-time power consumption prediction data and mode switching situations, the usage efficiency of power resources is optimized. The power distribution schemes for all modes are integrated into a complete power supply power distribution strategy, so that this strategy can be automatically called for power management during the operation of the device.

[0080] Step S3: Perform distributed task parallel processing on the VR box working mode data through the VR box power supply power distribution strategy to generate device power supply power scheduling data; Analyze the high-frequency power consumption power usage situation of the VR box working mode data according to the device power supply power scheduling data to generate high-frequency power power scheduling feedback record data;

[0081] In the embodiments of the present invention, according to the power distribution strategy of the power supply, the working modes of the VR box (standby mode, charge and discharge mode, wireless screen mirroring mode) are divided into multiple power management task units. For example, the charge and discharge mode can be further subdivided into tasks such as audio and video decoding, data transmission, and screen refreshing. Corresponding power resource requirements are allocated to each task unit, the tasks are split into micro-tasks that can be processed in parallel, and a device power scheduling micro-task data set is generated. A distributed computing framework (such as edge computing or task parallel processing mechanism) is used to process each micro-task in parallel to ensure that each task unit operates efficiently within the allocated power budget. During parallel processing, the power consumption of task execution is monitored in real time to ensure that it does not exceed the power limit of the power distribution strategy. The power consumption of each micro-task is collected, integrated, and device power scheduling data is generated, which serves as the basic data for subsequent high-frequency power consumption analysis. High-frequency power consumption data in each working mode is extracted from the device power scheduling data, with particular attention paid to tasks that frequently run in the charge and discharge mode and the wireless screen mirroring mode (such as screen refreshing, data transmission, and graphics processing). The sliding window technique is used to perform time series analysis on the high-frequency power consumption data to identify the power consumption characteristics during peak periods. Power fluctuation analysis is carried out to find the main influencing factors of power consumption (such as transmission bandwidth, power load changes), and the high-frequency power consumption electricity usage in each mode is generated. The analysis results are recorded as high-frequency power scheduling feedback record data to provide real-time feedback information for power management. This feedback data can be used as the basis for the next step of strategy optimization to ensure the reasonable allocation of power resources in high-frequency tasks and improve the overall power consumption efficiency of the device.

[0082] Step S4: Perform power management energy efficiency evaluation on the high-frequency power scheduling feedback record data to generate power management energy efficiency evaluation data; verify the management optimization of the VR box power distribution strategy through the power management energy efficiency evaluation data to generate VR box power management optimization verification data, so as to execute the efficient power management operation of the multi-device compatible wireless screen mirroring VR box.

[0083] In the embodiments of the present invention, detailed information on power consumption of each working mode (standby, playback, screen mirroring) in high-frequency tasks is extracted from the high-frequency power grid dispatching feedback record data. The power usage efficiency, power consumption fluctuation, and total energy consumption data under each working mode are sorted out. The power utilization rate and energy efficiency ratio of each mode are calculated to evaluate the resource usage of the power distribution strategy. Combining power fluctuation analysis, it is determined whether the high-frequency power consumption is within the expected range, and potential power waste or over-allocation points are identified. The analysis results are integrated into power management energy efficiency evaluation data, providing key inputs for the next-step strategy optimization. The energy efficiency evaluation data includes parameters such as the average energy efficiency ratio, power consumption peak value, and fluctuation frequency under each mode. Based on the energy efficiency evaluation data, the power distribution weights and power state thresholds in the power distribution strategy of the VR box power supply are optimized. The power distribution for high-frequency tasks under each mode is recalibrated to ensure that each mode obtains appropriate power resources only under its workload requirements. The optimized power management parameters, scheduling priorities, and power distribution ratios are integrated into a new power distribution strategy. The power distribution optimization strategy will execute the tasks of each working mode with a higher energy efficiency ratio, thereby reducing the total energy consumption of the VR box in the case of wireless screen mirroring and multi-device compatibility. The optimization strategy is applied to each working mode of the VR box to simulate the power management in the scenario of multi-device connection and wireless screen mirroring. The actual power consumption data under each mode is recorded and compared with the power usage before optimization to evaluate the effectiveness of the optimization strategy, generating VR box power management optimization verification data, including detailed comparison results of power consumption data, energy efficiency ratio, and high-frequency power feedback. The verification data is used to confirm the reliability of the optimization strategy and provide a feedback basis for further optimization of subsequent power management.

[0084] Preferably, step S1 includes the following steps:

[0085] Step S11: Use the power consumption monitoring module in the VR box to collect power consumption parameter data of the connected devices to obtain device power consumption collection data, where the device power consumption collection module includes internal device power consumption collection data and external connected device power consumption collection data;

[0086] Step S12: Perform VR box working mode analysis on the internal device power consumption collection data and the external connected device power consumption collection data to generate VR box working mode data, where the VR box working mode data includes standby mode, charge and discharge mode, and wireless screen mirroring mode;

[0087] Step S13: Perform data preprocessing on the device power consumption collection data to generate standard device power consumption collection data, where the data preprocessing includes data cleaning, data denoising, missing value filling, and data standardization; classify the standard device power consumption collection data according to the VR box working mode data to generate power consumption mode classification data;

[0088] Step S14: Extract the dynamic power consumption characteristics from the power consumption mode classification data to obtain the dynamic power consumption mode characteristic data; perform mode parameter mapping based on the dynamic power consumption mode characteristic data, thereby generating a power consumption mode mapping table.

[0089] In the embodiment of the present invention, by configuring a power consumption monitoring module, it is connected to various devices inside the VR box, including internal devices (such as VR helmets, batteries, displays) and externally connected devices (such as wireless receivers, external controllers). Start the device power consumption acquisition system, and obtain the power consumption parameters of the device in different working states by collecting data such as current and voltage. Record and store two types of power consumption data: internal device power consumption acquisition data and externally connected device power consumption acquisition data for subsequent analysis. Monitor the working state of the VR box, and common working modes include standby mode, charge and discharge mode, wireless screen projection mode, etc. Use the power consumption data to match the system log, and judge the current working mode by calculating the power consumption change trend. Generate the VR box working mode data according to the power consumption change mode for subsequent power consumption analysis and classification. Perform data preprocessing on the collected device power consumption data: remove duplicate and incorrect power consumption data to ensure data accuracy. Use a filtering algorithm (such as low-pass filtering) to remove noise caused by environmental factors or acquisition devices. Interpolate and fill in the missing power consumption data to avoid the influence of missing values on the analysis. Standardize the power consumption data of different devices to a unified scale for comparison. Classify the standardized device power consumption data according to the VR box working mode data to generate power consumption mode classification data for identifying the power consumption characteristics in different modes. Use time series analysis methods (such as Fourier transform or wavelet transform) to extract dynamic change characteristics from the power consumption mode classification data, including power consumption fluctuations, peaks, frequencies, etc. Analyze the power consumption fluctuation rules of the device in different modes, such as lower power consumption in standby mode and higher power consumption in charge and discharge mode. According to the extracted dynamic characteristics, use a mode mapping algorithm (such as neural network or regression analysis) to associate the power consumption characteristics with different working modes to generate a power consumption mode mapping table, which contains the power consumption change rules and corresponding parameters in different modes to help further optimize the power consumption management of the device.

[0090] Preferably, the VR box working mode analysis of the internal device power consumption acquisition data and the externally connected device power consumption acquisition data includes:

[0091] Perform power consumption acquisition discrimination on the internal device power consumption acquisition data and the externally connected device power consumption acquisition data. When only the internal device power consumption acquisition data is collected, perform the first mode marking to generate the standby mode;

[0092] When the power consumption acquisition data of the internal device and the power consumption acquisition data of the externally connected device are collected simultaneously, power consumption fluctuation analysis is performed on the power consumption acquisition data of the externally connected device to generate power consumption fluctuation analysis data; based on the power consumption fluctuation analysis data, power consumption fluctuation spectrum conversion is performed on the power consumption acquisition data of the externally connected device to generate a power consumption fluctuation spectrum diagram;

[0093] Fluctuation frequency stability analysis is performed on the power consumption fluctuation spectrum diagram to generate power consumption fluctuation stability data and power consumption fluctuation instability data; based on the power consumption fluctuation stability data, second-mode marking is performed on the power consumption acquisition data of the internal device and the power consumption acquisition data of the externally connected device to obtain a charge and discharge mode;

[0094] Based on the power consumption fluctuation instability data, third-mode marking is performed on the power consumption acquisition data of the internal device and the power consumption acquisition data of the externally connected device to obtain a wireless screen mirroring mode.

[0095] In the embodiments of the present invention, the current working mode of the device is determined based on the collected power consumption data. Only the power consumption data of internal devices is collected, that is, only the data of devices inside the VR box (such as the headset, battery, display screen) is collected, and there is no power consumption data of external devices. If only the power consumption data of internal devices is collected, it is speculated that the device is in the standby mode. At this time, the device has low power consumption and is basically in an idle state. Mark this mode as the standby mode and record the power consumption value for subsequent analysis. When both the power consumption data of internal devices and the power consumption data of externally connected devices are collected, it indicates that the device interacts with external devices (such as controllers, wireless receivers, sensors, etc.) and the power consumption changes significantly. Conduct power consumption fluctuation analysis on the collected power consumption data of external devices. By calculating the time-series change of power consumption, extract fluctuation characteristics, such as the periodic fluctuation of power consumption. Use algorithms (such as Fourier transform or autocorrelation analysis) to extract dynamic characteristics such as the power consumption fluctuation frequency, amplitude, and period, and generate power consumption fluctuation analysis data. Based on the power consumption fluctuation analysis data, perform spectral conversion on the power consumption fluctuation of externally connected devices to generate a power consumption fluctuation spectrogram. This diagram shows the change frequency of power consumption over time and its intensity. Conduct stability analysis on the generated power consumption fluctuation spectrogram to evaluate whether the power consumption fluctuation frequency is stable. By calculating the standard deviation, peak value, etc. of the frequency, judge the stability of the fluctuation. If the frequency is stable, generate power consumption fluctuation stability data; if the frequency fluctuates greatly, generate power consumption fluctuation instability data. Based on the power consumption fluctuation stability data, if the power consumption fluctuation frequency is stable, it is considered that the device is in the charging / discharging mode, and a second mode mark is made, that is, the charging / discharging mode. If the power consumption fluctuation is unstable, further analysis is carried out to enter the next stage of wireless screen mirroring mode determination. If the power consumption fluctuation spectrogram shows significant instability and a large fluctuation amplitude, this usually indicates that the device is in a state of frequent interaction with external devices, such as the wireless screen mirroring mode. Analyze the power consumption fluctuation instability data, especially the part with unstable frequency and large power consumption fluctuation amplitude, which is usually consistent with the power consumption characteristics of the device when performing a large amount of data transmission or processing tasks. Make a third mode mark according to the power consumption fluctuation instability data, that is, it is considered that the device is in the wireless screen mirroring mode. During wireless screen mirroring, the device will establish a connection with external devices through wireless protocols such as Wi-Fi or Bluetooth. At this time, the power consumption of internal devices and external devices should show specific mode changes, such as an increase in the power consumption of internal devices (due to data processing and display requirements during wireless transmission) and power consumption fluctuations of external devices (the display device enters the receiving state, etc.). After detecting that the device enters the wireless screen mirroring mode, use a specific identifier (such as a label or a specific power consumption threshold) to mark this period as the "wireless screen mirroring mode". According to the instability of power consumption fluctuation, define a power consumption threshold, and when exceeding this threshold, it is considered that the device enters the wireless screen mirroring mode. At this time, the VR box usually emits large power consumption fluctuations, especially when performing content transmission or receiving data, generate a mark for the wireless screen mirroring mode, and record the relevant power consumption data.

[0096] As an example of the present invention, referring to Figure 2 as shown, in this example, step S2 includes:

[0097] Step S21: Perform a timing sort on the power consumption mode mapping table to generate a power consumption feature timing sorted data set; perform a time series segmentation on the power consumption feature timing sorted data set to generate a power consumption time series segmented data; perform a sliding window setting on the power consumption time series segmented data to generate a time series power consumption segment data set;

[0098] Step S22: Perform an intelligent prediction of the device power consumption on the time series power consumption segment data set to generate device power consumption prediction data;

[0099] Step S23: Based on the device power consumption prediction data, perform an analysis of the device power requirements for the standby mode, charging and discharging mode, and wireless screen mirroring mode to generate device power requirement data; perform a transmission distance power consumption weight allocation through the device power requirement data to generate a transmission power consumption weight allocation data;

[0100] Step S24: Based on the transmission power weight allocation data, generate a power distribution strategy for the standby mode, charging and discharging mode, and wireless screen mirroring mode to obtain the power distribution strategy for the VR box power supply.

[0101] In the embodiments of the present invention, by sorting the data in the power consumption mode mapping table in chronological order, it is ensured that the power consumption data of each mode is arranged according to the actual working order of the device, and a power consumption characteristic time series sorted data set is generated. This data set contains the power consumption characteristics of each mode and their corresponding timestamps. Time segmentation is performed on the time series data set, and the data is segmented into multiple time periods according to different cycles of the device working mode, such as standby, playback, and wireless screen mirroring modes, etc., to generate power consumption time series segmented data. The power consumption data within each time period is analyzed as an independent time series. Set the size of the sliding window (such as 10 seconds, 1 minute, etc.) and the sliding step. The power consumption data within the window will be used as a complete segment for prediction, generating a time series power consumption segment data set, where each segment contains a continuous segment of power consumption data, facilitating the capture of power consumption fluctuation patterns. Time series data prediction models such as long short-term memory network (LSTM), convolutional neural network (CNN), etc. can be selected. Use the time series power consumption segment data set for model training to learn the law of power consumption data changing over time. After training is completed, use the trained model to predict the future device power consumption, generating device power consumption prediction data, that is, the predicted value of the power consumption within a future period of time. Based on the predicted power consumption data, analyze the power requirements in standby mode, charging and discharging mode, and wireless screen mirroring mode. Calculate the power consumption, duration, etc. of the device in each mode to obtain the total power requirements in different working modes, generating device power requirement data, which reflects the total amount of power required by the device in each mode. According to factors such as transmission distance, signal strength, and device power requirements, weight distribution is performed on the transmission power consumption of different working modes. If the device is far from the transmission source (such as Wi-Fi or Bluetooth), more power is required to maintain signal transmission. Generate transmission power consumption weight distribution data through power requirement data and transmission distance. Based on the power consumption data and transmission weight distribution, design a power distribution strategy. Ensure that the power consumption in different working modes can be effectively allocated to avoid excessive consumption of battery power. In standby mode, give priority to ensuring the low power consumption state of the device; in playback and wireless screen mirroring modes, reasonably allocate more power to support data transmission and high-intensity operations. Based on the above analysis, generate a power distribution strategy for the VR box power supply, which will automatically adjust the power distribution in different working modes to optimize the power usage efficiency of the device.

[0102] Preferably, step S22 includes the following steps:

[0103] Step S221: Extract key power consumption time series features from the device power consumption segment data set to obtain key power consumption time series feature data, where the extraction of key power consumption time series features includes the extraction of power consumption moving average and the extraction of power consumption change rate;

[0104] Step S222: Divide the key power consumption timing feature data into a data set to generate a model training set and a model test set; use the recurrent neural network algorithm to train the model training set to generate a power consumption prediction pre-model; optimize and iterate the power consumption prediction pre-model through the model test set to generate a power consumption prediction model.

[0105] Step S223: Import the device power consumption segment data set into the power consumption prediction model to perform intelligent prediction of device power consumption and generate device power consumption prediction data.

[0106] In the embodiment of the present invention, the moving average of the device power consumption data is calculated by a sliding window. The moving average can smooth the power consumption data, remove short-term fluctuations, and highlight the long-term power consumption trend. Select a suitable window size (such as 5 seconds, 10 seconds, 1 minute, etc.). Perform sliding average processing on the device power consumption data to generate a power consumption moving average, which can help capture the power consumption trend over a long period of time. Calculate the change rate of the device power consumption, which is used to analyze the change rate of the device power consumption over time. Calculate the percentage change in the device power consumption between adjacent time points to obtain the power consumption change rate, which reflects the fluctuation of the power consumption in different time periods. For example: where P I and P I+1respectively represent the power consumption values at time points I and I+1. The moving average and the change rate are extracted as key timing features, and key power consumption timing feature data are generated. These feature data can help capture the pattern of power consumption fluctuations and provide inputs for subsequent model training. The key power consumption timing feature data set is divided into a training set and a test set. Usually, 80% of the data is used as the training set and 20% of the data is used as the test set, or the cross-validation method is used. The data is randomly shuffled and then divided to ensure that the data in the training set and the test set can cover the power consumption characteristics under different working modes. An RNN model is constructed, and a suitable network structure (such as LSTM or GRU) is selected, and the model training set is used as input data for training. A loss function (such as mean squared error) is defined and backpropagation is performed. The model parameters are adjusted through optimization algorithms such as gradient descent to minimize the prediction error. During the training process, an optimizer (such as Adam, SGD, etc.) is used for parameter optimization to obtain a preliminary model that can accurately predict the device power consumption. The test set is input into the trained power consumption prediction model, and the prediction error of the model on the test set is calculated. According to the results of the test set, the model is adjusted and optimized. For example, by adjusting the hyperparameters of the model (such as the learning rate, the number of network layers, etc.), the prediction ability of the model is improved. Through multiple rounds of optimization iterations, a high-precision power consumption prediction model is finally obtained. This model can accurately predict the device power consumption under different modes. The processed device power consumption segment data set is input into the trained power consumption prediction model. The model makes inferences based on the input feature data and generates power consumption prediction results. The model outputs the predicted device power consumption data, which reflect the power consumption of the device under different working modes in the future time period. The prediction results can be used to dynamically adjust the power consumption management strategy of the device, thereby extending the service life of the device and optimizing the working efficiency of the battery management system.

[0107] Preferably, step S24 includes the following steps:

[0108] Step S241: Obtain the total power data of the VR box device;

[0109] Step S242: Based on the standby mode, mark the low-power state of the total power data of the VR box device, and based on the low-power state, perform the minimum power resource allocation on the device power consumption segment data set to generate standby mode power allocation data;

[0110] Step S243: Based on the charging and discharging mode, screen the operation data of the charging and discharging mode working modules from the total power data of the VR box device to generate the screened operation data of the charging and discharging mode working modules, where the screened data of the charging and discharging mode working modules includes the operation data of the audio and video decoding module, the operation data of the data transmission module, and the operation data of the screen control module;

[0111] Step S244: Perform medium-power state marking based on the operating data of the audio-video decoding module, the operating data of the data transmission module, and the operating data of the screen control module, and perform hierarchical power control on the device power consumption segment data set based on the medium-power state to generate charge-discharge mode power distribution data;

[0112] Step S245: Filter the total power supply power data of the VR box device based on the wireless screen mirroring mode to generate the operating data screening of the wireless screen mirroring mode working module, where the operating data screening of the wireless screen mirroring mode working module includes the operating data of the data transmission module, the operating data of the screen refresh module, and the operating data of the graphics processing module;

[0113] Step S246: Perform high-power state marking based on the operating data of the data transmission module, the operating data of the screen refresh module, and the operating data of the graphics processing module, and perform power adaptive allocation on the device power consumption segment data set based on the high-power state using the power high-load regulation formula, so as to generate wireless screen mirroring mode power distribution data;

[0114] Step S247: Integrate the power strategies through the standby mode power distribution data, the charge-discharge mode power distribution data, and the wireless screen mirroring mode power distribution data to generate the VR box power supply power distribution strategy.

[0115] In the embodiments of the present invention, by obtaining the total power data of the device from the power management system of the VR box device, this data includes the real-time power consumption, power fluctuation of the device, and the power consumption under each working mode. Real-time monitoring is carried out using a power meter, a sensor or a data acquisition system, and the obtained data should include the power changes in the standby mode, charge and discharge mode, and wireless screen projection mode. In the standby mode, the power consumption of the VR box device is at the lowest state. The power management module is used to detect the power consumption in the standby mode and mark it as a low-power state. Based on the low-power state in the standby mode, the device power consumption segment data set is analyzed, and the minimum power resources are allocated to it. For example, by reducing the display brightness, turning off unnecessary modules or reducing the sensor frequency, etc., to ensure that the power consumption of the device in the standby mode is minimized. Record the power distribution strategy of each module to ensure the minimum power consumption. Identify and screen out the working modules that are active in the charge and discharge mode, and these modules include audio and video decoding modules, data transmission modules, screen control modules, etc. Monitor the actual operation data of these modules through power sensors and mark it as "operation data of working modules in the charge and discharge mode". Screen and classify the power consumption data of each module to ensure that only the module data with significant power consumption in the charge and discharge mode is retained. In the charge and discharge mode, modules such as audio and video decoding, data transmission, and screen control consume medium power. Use the power consumption data and module performance indicators to mark the medium-power state of the charge and discharge mode. Based on the medium-power state, hierarchical power control is performed on the device power consumption segment data set. Allocate appropriate power resources to different modules (such as audio and video decoding, data transmission, screen control, etc.). Power consumption control can be carried out by adjusting the decoding accuracy, reducing the data transmission rate, adjusting the screen refresh rate, etc., to ensure that the power consumption of each module in the charge and discharge mode meets the optimization requirements. Record the power distribution strategy of each module to ensure the power consumption control of the device in the charge and discharge mode. In the wireless screen projection mode, the data transmission module, screen refresh module, and graphics processing module usually consume relatively high power. Screen out the power consumption data of these modules through a power monitoring device. Mark the data of these modules as "operation data of working modules in the wireless screen projection mode". Screen and record the power consumption data of each module to ensure that the power requirements of each module during the screen projection process can be identified. In the wireless screen projection mode, the power consumption of the data transmission, screen refresh, and graphics processing modules is usually relatively high. According to the power consumption data, mark these modules as high-power states. According to the power consumption requirements of each module, use the high-power load regulation formula for adaptive power distribution, and this formula will be adjusted according to the actual power consumption of each module, system requirements, and battery status. According to the high-power requirements of the device in the wireless screen projection mode, dynamically adjust the power consumption distribution to ensure the smooth progress of the screen projection operation while avoiding excessive power consumption. Record the power distribution strategy of each module in the wireless screen projection mode to ensure power optimization.Summarize the power distribution data in the standby mode, the charge and discharge mode, and the wireless screen mirroring mode, integrate the power demands in each mode, and form a comprehensive power distribution strategy. Using the weighted average method or an optimization algorithm, according to the power consumption priorities and resource allocation requirements in different modes, integrate the power distribution strategies of each mode. Finally, generate a complete power distribution strategy to ensure that the power consumption of the VR box device in different working modes is reasonably controlled, extend the device's usage time, and optimize the battery performance.

[0116] Preferably, the power high-load regulation formula in step S245 is specifically as follows:

[0117]

[0118] In the formula, P a represents the adaptive power distribution power in the wireless screen mirroring mode, T represents the running time of the entire wireless screen mirroring mode, α represents the priority coefficient of the transmission module power consumption in the total power, P TX (t) represents the data transmission power varying with time t, β represents the regulation coefficient of the graphics processing module, P GPU (t) represents the instantaneous power consumption change rate of the graphics processing module power consumption at time t, P CPU (t) represents the sine wave modulation term of the processor power consumption varying with time t, γ represents the CPU power consumption weight coefficient, ω represents the frequency parameter, represents the total RAM power consumption at time τ from the start time to the current time t.

[0119] The present invention analyzes and integrates a power high-load regulation formula. The purpose of the formula is to adaptively regulate the power in the wireless screen mirroring mode of the system. Each term of the formula corresponds to the power consumption contribution of different hardware modules. Finally, a total adaptive power distribution (Pa) is obtained. This power distribution takes into account the power consumption and its change rate of different hardware modules (such as the transmission module, GPU, CPU, RAM), and dynamically adjusts the power distribution according to their priorities to keep the system running efficiently. Among them, α is the priority coefficient of the transmission module power consumption in the total power. It determines the proportion of the transmission module in the power distribution. A larger α value means that the transmission module will preferentially obtain more power to ensure that data transmission is not restricted. P TX (t) is the data transmission power consumption varying with time. The power consumption of the transmission module changes with time and is usually related to factors such as data traffic and transmission frequency. The transmission module requires a certain amount of power to maintain the stability of data transmission. β is the regulation coefficient of the graphics processing module. It controls the influence weight of the GPU power consumption change rate on the power distribution. The magnitude of the β value determines the instantaneous change rate of the GPU power consumption Importance in power regulation. A larger β makes the impact of GPU power consumption changes on power distribution greater. is the instantaneous change rate of GPU power consumption at time t, reflecting the change in graphics processing load. If the load change of the GPU is large, then a large power regulation requirement will be generated for this item to ensure that the GPU is always within a reasonable power consumption range. P CPU (t) is the processor power consumption modulation term that changes with time t. The power consumption of the CPU changes dynamically during the calculation process and is usually related to the system load and running tasks. The power consumption of this item is modulated by a sine wave to simulate the periodic change of the CPU load, such as the alternating change between the peak load period and the low load period. γ is the CPU power consumption weight coefficient, which determines the priority of the CPU power consumption in power distribution. A larger γ value indicates that the system will give priority to the change of the CPU power consumption to maintain the CPU load balance. is the total RAM power consumption from the initial time to the current time t. The change in RAM power consumption will vary with the system memory usage. Especially when processing large data or complex tasks, the power consumption of the memory will fluctuate significantly. This item reflects the cumulative power consumption of the RAM through integration. As a storage module, the RAM requires a relatively high power supply during long-term operation to ensure the stability of data reading and writing. By monitoring its power consumption, power can be allocated to the RAM in a timely manner to avoid a decline in system performance due to insufficient memory resources. When using the conventional power high-load regulation formula in this field, the adaptive power distribution power in the wireless screen mirroring mode can be obtained. By applying the power high-load regulation formula provided by the present invention, the adaptive power distribution power in the wireless screen mirroring mode can be calculated more accurately. By adjusting the power consumption weights of different modules (transmission module, GPU, CPU, RAM), the system can dynamically adjust the power distribution according to the real-time load situation, avoiding unnecessary power consumption waste or performance bottlenecks caused by too much or too little power for a single module. Using sine wave modulation to control the CPU power consumption and calculating the cumulative RAM power consumption through integration can better predict and regulate the change of power consumption, reduce the power volatility, and thus improve the stability of the system. This formula dynamically regulates the power consumption and its change rate of each hardware module to optimize the overall power efficiency of the system. Through the priority coefficient, instantaneous power consumption change rate, modulation term, and integration calculation, the power distribution can be adjusted according to the actual needs of the system to ensure the smooth and efficient operation of each module and avoid excessive power consumption waste and performance bottlenecks.

[0120] As an example of the present invention, refer to Figure 3 shown, in this example, the step S3 includes:

[0121] Step S31: Split the power management tasks for the VR box working mode data through the VR box power distribution strategy to generate power management task data; perform distributed task scheduling on the power management task data to generate a device power scheduling micro-task data set;

[0122] Step S32: Perform micro-task parallel processing on the device power scheduling micro-task set to generate device power scheduling data;

[0123] Step S33: Analyze the power consumption usage of the VR box working mode data according to the device power scheduling data to generate power consumption usage data for power scheduling;

[0124] Step S34: Provide high-frequency power consumption power adjustment feedback on the power consumption usage data for power scheduling to generate high-frequency power scheduling feedback record data.

[0125] In the embodiments of the present invention, the working mode data of the VR box is divided into different subtasks according to tasks, for example, standby mode, charging and discharging mode, wireless screen projection mode, etc. In each working mode, specific power management tasks are split out, such as enabling the low-power module when the device is in standby, enabling the medium-power module in the charging and discharging mode, etc. Each subtask should include the corresponding power requirements, execution duration, as well as the modules and functions involved. For example, in the charging and discharging mode, the audio and video decoding, data transmission, and screen control modules are the main tasks, while the standby mode mainly involves turning off unnecessary modules. According to the split task data, the detailed information of each power management task, such as execution time, power demand, involved modules, task priority, etc., is recorded to form a power management task dataset. Based on the hardware and software capabilities of the device, a distributed task scheduling algorithm is used to allocate the power management tasks to different processing units or computing nodes. The power demand, priority, and dependency relationship between tasks are considered during the scheduling process. Each task is split into more refined micro-tasks to form a device power scheduling micro-task dataset. Each micro-task will be assigned to a different computing node to ensure that each task can be completed within the specified time while optimizing power consumption. The micro-task dataset is allocated to multiple processing units to process each micro-task in parallel. A distributed computing framework (such as Hadoop, Spark, or other edge computing platforms) is used to coordinate the execution of micro-tasks to ensure that multiple tasks are synchronized on different computing nodes. During the parallel processing process, the execution status of each micro-task is tracked in real time, and power scheduling data is generated according to the task completion status. The finally generated device power scheduling data should include the completion status of each subtask, power consumption, and scheduling results. According to the device power scheduling data, the power consumption usage in each working mode is analyzed. For example, the power consumption in the standby mode is relatively low, the power consumption in the charging and discharging mode is medium, and the power consumption in the wireless screen projection mode is relatively high. Combining the power consumption of each micro-task and the working mode, power scheduling power consumption usage data is generated. By analyzing the power consumption fluctuations, abnormal states with too high or too low power consumption are identified. According to the results of the power consumption usage analysis, power scheduling power consumption usage data is generated, including the power consumption in each working mode, the micro-task consumption, and the overall power consumption trend. Using a real-time feedback mechanism, the device power is frequently adjusted according to the power consumption anomalies or peaks in the power scheduling power consumption usage data. For example, during the operation of the device, if the power consumption is too high within a short period of time, the system will adjust the power distribution strategy to reduce the power consumption and ensure the stable operation of the device. The detailed data of each power adjustment is recorded, including the adjustment time, adjustment amplitude, change in power consumption, feedback response time, etc., to form high-frequency power scheduling feedback record data, and these records will provide a basis for further power scheduling optimization.

[0126] Preferably, the distributed task scheduling of the power management task data includes:

[0127] Divide the device power tasks for the power management task data to generate power management micro-task data; perform micro-task load analysis on the power management micro-task data to generate power management micro-task load data;

[0128] Define the power management optimization objectives for the power management micro-task load data to generate power management optimization objective data; formulate a task scheduling strategy for the power management micro-task load data based on the power management optimization objective data, thereby generating a power management task scheduling strategy;

[0129] Perform task mapping on the power management micro-task data to generate power management task mapping data; allocate resources to the power management micro-task data through the power management task scheduling strategy to generate power management resource allocation data; perform distributed dynamic task migration on the power management task mapping data according to the power management resource allocation data to generate a power management scheduling micro-task data set.

[0130] In the embodiments of the present invention, according to the working mode of the device (such as standby mode, charge and discharge mode, wireless screen projection mode, etc.) and the power management strategy, the power management task data is divided into multiple power management micro-tasks. Each micro-task corresponds to the power management operation of a specific module of the device. For example, in the standby mode, irrelevant modules are turned off, and in the charge and discharge mode, the power of audio and video decoding is adjusted. Each micro-task is defined in detail, including the power demand, execution duration, module function, etc. of the micro-task, to generate a power management micro-task data set. Load analysis is performed on the power management micro-task data to evaluate the occupancy of device resources by each micro-task during execution. For example, audio and video decoding in the charge and discharge mode occupies relatively high computing resources, while tasks in the standby mode consume almost no resources. Based on the hardware characteristics of the device (such as CPU, GPU, memory, etc.), the resource requirements of each micro-task are analyzed to generate power management micro-task load data. According to the power resource constraints of the device and the requirements in different working modes, power management optimization goals are defined, such as minimizing power consumption, improving device performance, extending battery life, etc. Multiple optimization goals are set and their priorities are determined, so as to comprehensively consider these goals during task scheduling, generate power management optimization goal data, and clarify the priority and resource allocation direction of each micro-task in the scheduling. Based on the power management optimization goal data, a task scheduling strategy is formulated for the power management micro-task load data. For example, in the standby mode, modules with higher power consumption are preferentially reduced, while in the charge and discharge mode, the smooth operation of the decoding module is preferentially ensured. The task scheduling strategy should consider the dependencies, priorities, and resource conflicts between tasks to formulate the optimal resource allocation and task execution order, and generate a power management task scheduling strategy. According to the hardware architecture and resource allocation of the device, task mapping is performed on the power management micro-task data. For example, tasks that require higher computing resources are mapped to high-performance processing units (such as GPUs or dedicated hardware modules), while tasks with lower resource consumption can be mapped to lower-power modules, to generate power management task mapping data, which details the mapping relationship between each task and device resources. Based on the power management task scheduling strategy, resources are reasonably allocated to each micro-task. Resource allocation should be carried out according to the priority, resource requirements, and available hardware resources of the task. For tasks with heavier loads, more computing resources and power can be allocated, and vice versa, to generate power management resource allocation data, which records the resource allocation details of each micro-task. During the execution of tasks, the execution status of tasks and the real-time power consumption of the device are monitored. If the execution of a certain task is restricted, or the power consumption requirements of the device change, the system will dynamically migrate the task to other computing units with sufficient resources. The migrated task will be remapped to suitable computing resources to ensure the smooth execution of the task while reducing power consumption.Generate a power management scheduling micro-task dataset through power management resource allocation and dynamic task migration to ensure that tasks can be efficiently executed on different resource units, avoid resource conflicts, and ensure reasonable power distribution.

[0131] Preferably, step S4 includes the following steps:

[0132] Step S41: Conduct a power management energy efficiency assessment on the high-frequency power scheduling feedback record data to generate power management energy efficiency assessment data;

[0133] Step S42: Optimize the power distribution strategy of the VR box power supply through the power management energy efficiency assessment data to generate an optimized power distribution strategy for the VR box power supply;

[0134] Step S43: Use the optimized power distribution strategy of the VR box power supply to conduct management optimization verification on the high-frequency power scheduling feedback record data to generate VR box power management optimization verification data for performing efficient power management operations for multi-device compatible wireless screen mirroring VR boxes.

[0135] In the embodiments of the present invention, power management-related information is extracted from the high-frequency power scheduling feedback record data, including power consumption data, execution status of scheduling strategies, task load conditions, etc. Taking time as the unit, the feedback data is sorted and classified to ensure that the historical records of each high-frequency power adjustment can be traced in detail. Energy efficiency evaluation indicators are designed, such as Energy Efficiency (EE), power consumption optimization range, ratio of task execution time to power consumption, etc. The high-frequency power scheduling feedback data is analyzed to calculate the energy efficiency score corresponding to each feedback record. For example, according to the change rate of power consumption and the load and power consumption changes of the adjusted system, it is evaluated whether the system has achieved the energy-saving effect as expected. According to the energy efficiency evaluation indicators, power management energy efficiency evaluation data is generated. This data will reflect the efficiency of the power management system in actual operation and provide the identification of performance bottlenecks and optimization spaces. Based on the energy efficiency evaluation data generated in step S41, optimization goals are set, including maximizing power usage efficiency, maintaining smooth VR experience while reducing power consumption, ensuring power load balance during peak hours, etc. The energy efficiency optimization goals for different modes (such as standby, playback, screen mirroring, etc.) are determined, and appropriate weights are set for each mode. According to the problems and bottlenecks found in the evaluation data analysis, targeted optimization strategies are designed. For example, for the mode with excessive power consumption, the working states of some modules can be adjusted, or the power resources can be reallocated. Optimization algorithms (such as dynamic programming, heuristic algorithms, etc.) are used to optimize the power distribution strategy to ensure that the optimized strategy can reduce power consumption and improve the overall energy efficiency of the device. Based on the optimization goals and algorithm results, an optimized power distribution strategy for the VR box power supply is generated. This strategy will combine the requirements of multiple working modes to ensure the reasonable allocation of power resources under different loads and maximize the energy efficiency performance of the system. The generated power management optimization strategy is applied to the high-frequency power scheduling feedback record data to verify the effectiveness of the optimization strategy in actual scenarios. By comparing the feedback data before and after optimization, it is checked whether the strategy effectively reduces power consumption and optimizes power distribution. Verification is carried out under multiple devices and usage scenarios to ensure that the optimization strategy can adapt to different load conditions and power consumption requirements. The power management requirements of different devices (such as VR boxes, wireless screen mirroring devices, etc.) are verified synchronously to ensure that the strategy has good compatibility and adaptability. Based on the data during the verification process, optimized verification data for the VR box power management is generated. This data records the effects of the optimization strategy, including the power consumption changes and load balance conditions of each device after optimization, and is compared with the original state. Through the analysis of the optimized verification data, the power management strategy is further adjusted and optimized to ensure that it can achieve the optimal power distribution and energy efficiency performance in a wireless screen mirroring environment compatible with multiple devices. This optimization strategy ultimately provides a theoretical basis and practical basis for the power management of a wireless screen mirroring VR box compatible with multiple devices, ensuring system stability, energy conservation, and extended device usage time.

[0136] Therefore, from any perspective, the embodiments should be regarded as exemplary and non-limiting. The scope of the present invention is defined by the appended claims rather than the above description. Accordingly, all changes falling within the meaning and scope of the equivalent elements of the application documents are intended to be encompassed within the present invention.

[0137] The above are only specific embodiments of the present invention, enabling those skilled in the art to understand or implement the present invention. Various modifications to these embodiments will be obvious to those skilled in the art. The general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to these embodiments shown herein, but rather to the broadest scope consistent with the principles and novel features invented herein.

Claims

1. An efficient power management method for a multi-device compatible wireless screen mirroring VR box, characterized in that, It includes the following steps: Step S1: Obtain the device power consumption acquisition data; Analyze the VR box working mode for the device power consumption acquisition data to generate VR box working mode data, where the VR box working mode data includes the standby mode, the charge and discharge mode, and the wireless screen mirroring mode; Extract the dynamic power consumption characteristics from the device power consumption acquisition data to obtain the dynamic power consumption mode characteristic data; perform mode parameter mapping based on the dynamic power consumption mode characteristic data, thereby generating a power consumption mode mapping table; Step S2: Perform intelligent prediction of the device power consumption on the power consumption mode mapping table to generate device power consumption prediction data; Based on the device power consumption prediction data, allocate the transmission distance power consumption weights to the VR box working mode data to generate transmission power consumption weight allocation data; Based on the transmission power weight allocation data, generate a power distribution strategy for the standby mode, the charge and discharge mode, and the wireless screen mirroring mode to obtain the VR box power supply power distribution strategy; Step S3: Perform distributed task parallel processing on the VR box working mode data through the VR box power supply power distribution strategy to generate device power supply power scheduling data; Analyze the high-frequency power consumption electricity usage situation for the VR box working mode data according to the device power supply power scheduling data to generate high-frequency power electricity scheduling feedback record data; Step S4: Evaluate the power management energy efficiency for the high-frequency power electricity scheduling feedback record data to generate power management energy efficiency evaluation data; Verify the management optimization of the VR box power supply power distribution strategy through the power management energy efficiency evaluation data to generate VR box power management optimization verification data, so as to execute the efficient power management operation of the multi-device compatible wireless screen mirroring VR box.

2. The efficient power management method for the multi-device compatible wireless screen mirroring VR box according to claim 1, characterized in that Step S1 includes the following steps: Step S11: Use the power consumption monitoring module in the VR box to collect the power consumption parameters of the connected devices to obtain the device power consumption acquisition data, where the device power consumption acquisition module includes the internal device power consumption acquisition data and the external connected device power consumption acquisition data; Step S12: Analyze the VR box working mode for the internal device power consumption acquisition data and the external connected device power consumption acquisition data to generate VR box working mode data, where the VR box working mode data includes the standby mode, the charge and discharge mode, and the wireless screen mirroring mode; Step S13: Perform data preprocessing on the device power consumption acquisition data to generate standard device power consumption acquisition data, where the data preprocessing includes data cleaning, data denoising, missing value filling, and data standardization; classify the standard device power consumption acquisition data according to the VR box working mode data to generate power consumption mode classification data; Step S14: Extract the dynamic power consumption characteristics from the power consumption mode classification data to obtain the dynamic power consumption mode characteristic data; perform mode parameter mapping based on the dynamic power consumption mode characteristic data, thereby generating a power consumption mode mapping table.

3. The efficient power management method of the multi-device compatible wireless screen mirroring VR box according to claim 2, characterized in that, Analyzing the VR box working mode for the internal device power consumption acquisition data and the external connected device power consumption acquisition data includes: Perform power consumption acquisition discrimination on the internal device power consumption acquisition data and the external connected device power consumption acquisition data. When only the internal device power consumption acquisition data is collected, perform the first mode marking to generate the standby mode; When the power consumption acquisition data of the internal device and the power consumption acquisition data of the externally connected device are collected simultaneously, power consumption fluctuation analysis is performed on the power consumption acquisition data of the externally connected device to generate power consumption fluctuation analysis data; based on the power consumption fluctuation analysis data, power consumption fluctuation spectrum conversion is performed on the power consumption acquisition data of the externally connected device to generate a power consumption fluctuation spectrum diagram; Fluctuation frequency stability analysis is performed on the power consumption fluctuation spectrum diagram to generate power consumption fluctuation stability data and power consumption fluctuation instability data; based on the power consumption fluctuation stability data, second-mode marking is performed on the power consumption acquisition data of the internal device and the power consumption acquisition data of the externally connected device to obtain a charge-discharge mode; Based on the power consumption fluctuation instability data, third-mode marking is performed on the power consumption acquisition data of the internal device and the power consumption acquisition data of the externally connected device to obtain a wireless screen mirroring mode.

4. The efficient power management method for the multi-device compatible wireless screen mirroring VR box according to claim 1, characterized in that Step S2 includes the following steps: Step S21: Perform time series sorting on the power consumption mode mapping table to generate a power consumption feature time series sorted data set; perform time series segmentation on the power consumption feature time series sorted data set to generate power consumption time series segmented data; perform sliding window setting on the power consumption time series segmented data to generate a time series power consumption segment data set; Step S22: Perform intelligent prediction of device power consumption on the time series power consumption segment data set to generate device power consumption prediction data; Step S23: Based on the device power consumption prediction data, perform device power demand analysis on the standby mode, charge-discharge mode, and wireless screen mirroring mode to generate device power demand data; perform transmission distance power consumption weight allocation through the device power demand data to generate transmission power consumption weight allocation data; Step S24: Based on the transmission power weight allocation data, generate a power distribution strategy for the standby mode, charge-discharge mode, and wireless screen mirroring mode to obtain a power distribution strategy for the VR box power supply.

5. The efficient power management method for the multi-device compatible wireless screen mirroring VR box according to claim 4, characterized in that, Step S22 includes the following steps: Step S221: Extract key power consumption time series features from the device power consumption segment data set to obtain key power consumption time series feature data, where the extraction of key power consumption time series features includes the extraction of the power consumption moving average and the extraction of the power consumption change rate; Step S222: Divide the key power consumption time series feature data set to generate a model training set and a model test set; use the recursive neural network algorithm to train the model training set to generate a power consumption prediction pre-model; perform model optimization iteration on the power consumption prediction pre-model through the model test set to generate a power consumption prediction model; Step S223: Import the device power consumption segment data set into the power consumption prediction model to perform intelligent prediction of device power consumption and generate device power consumption prediction data.

6. The efficient power management method of the multi-device compatible wireless screen mirroring VR box according to claim 4, wherein, Step S24 includes the following steps: Step S241: Obtain the total power supply data of the VR box device; Step S242: Based on the standby mode, perform low-power state marking on the total power supply data of the VR box device, and based on the low-power state, perform minimum power resource allocation on the device power consumption segment data set to generate standby mode power distribution data; Step S243: Based on the charge and discharge mode, filter the total power data of the VR box device for the operation data of the charge and discharge mode working module to generate the filtered operation data of the charge and discharge mode working module. The filtered data of the charge and discharge mode working module includes the operation data of the audio and video decoding module, the operation data of the data transmission module, and the operation data of the screen control module; Step S244: Mark the medium power state according to the operation data of the audio and video decoding module, the operation data of the data transmission module, and the operation data of the screen control module, and perform hierarchical power control on the device power consumption segment data set based on the medium power state to generate the power distribution data of the charge and discharge mode; Step S245: Based on the wireless screen mirroring mode, filter the total power data of the VR box device for the operation data of the wireless screen mirroring mode working module to generate the filtered operation data of the wireless screen mirroring mode working module. The filtered operation data of the wireless screen mirroring mode working module includes the operation data of the data transmission module, the operation data of the screen refresh module, and the operation data of the graphics processing module; Step S246: Mark the high power state according to the operation data of the data transmission module, the operation data of the screen refresh module, and the operation data of the graphics processing module, and perform power adaptive distribution on the device power consumption segment data set based on the high power state using the power high-load regulation formula to generate the power distribution data of the wireless screen mirroring mode; Step S247: Integrate the power strategies through the standby mode power distribution data, the charge and discharge mode power distribution data, and the wireless screen mirroring mode power distribution data to generate the power distribution strategy of the VR box power supply.

7. The efficient power management method for a multi-device compatible wireless screen mirroring VR box according to claim 6, characterized in that, The power high-load regulation formula in Step S245 is as follows: Wherein, P a represents the adaptive power distribution power in the wireless screen mirroring mode, T represents the running time of the entire wireless screen mirroring mode, α represents the priority coefficient of the transmission module power consumption in the total power, P TX (t) represents the data transmission power varying with time t, β represents the regulation coefficient of the graphics processing module, P GPU (t) represents the instantaneous power consumption change rate of the graphics processing module power consumption at time t, P CPU (t) represents the sine wave modulation term of the processor power consumption varying with time t, γ represents the CPU power consumption weight coefficient, ω represents the frequency parameter, represents the total RAM power consumption at the τ moment from the start time to the current time t.

8. The efficient power management method for a multi-device compatible wireless screen mirroring VR box according to claim 1, characterized in that, Step S3 includes the following steps: Step S31: Split the power management tasks for the VR box working mode data through the power distribution strategy of the VR box power supply to generate the power management task data; perform distributed task scheduling on the power management task data to generate the device power scheduling micro-task data set; Step S32: Perform micro-task parallel processing on the device power scheduling micro-task set to generate the device power supply scheduling data; Step S33: Analyze the power consumption usage situation of the VR box working mode data according to the device power supply scheduling data to generate the power consumption usage data of the power scheduling; Step S34: Perform high-frequency power consumption power adjustment feedback on the power consumption usage data of the power scheduling to generate the high-frequency power power scheduling feedback record data.

9. The efficient power management method of the multi-device compatible wireless screen mirroring VR box according to claim 8, characterized in that, Performing distributed task scheduling on the power management task data includes: Dividing the device power tasks for the power management task data to generate the power management micro-task data; performing micro-task load analysis on the power management micro-task data to generate the power management micro-task load data; Defining the power management optimization target for the power management micro-task load data to generate the power management optimization target data; formulating the task scheduling strategy for the power management micro-task load data based on the power management optimization target data to generate the power management task scheduling strategy; Perform task mapping on the power management micro-task data to generate power management task mapping data; perform resource allocation on the power management micro-task data through the power management task scheduling strategy to generate power management resource allocation data; perform distributed dynamic task migration on the power management task mapping data according to the power management resource allocation data to generate a power management scheduling micro-task data set.

10. The efficient power management method for the multi-device compatible wireless screen mirroring VR box according to claim 1, characterized in that Step S4 includes the following steps: Step S41: Perform power management energy efficiency evaluation on the high-frequency power electricity scheduling feedback record data to generate power management energy efficiency evaluation data; Step S42: Optimize the power distribution strategy of the VR box power supply through the power management energy efficiency evaluation data to generate an optimized power distribution strategy for the VR box power supply; Step S43: Use the optimized power distribution strategy of the VR box power supply to perform management optimization verification on the high-frequency power electricity scheduling feedback record data to generate VR box power management optimization verification data for performing efficient power management operations for multi-device compatible wireless screen mirroring VR boxes.

Images