Efficient power management method for multi-device compatible wireless projection screen VR box

Abstract

The invention relates to the technical field of power management, in particular to an efficient power management method for a multi-device compatible wireless projection screen VR box. The method comprises the following steps: acquiring equipment power consumption acquisition data; vR box working mode analysis is carried out on the equipment power consumption collection data, VR box working mode data are generated, and the VR box working mode data comprise a standby mode, a charging and discharging mode and a wireless screen projection mode; performing power consumption dynamic feature extraction on the equipment power consumption acquisition data to obtain power consumption mode dynamic feature data; performing mode parameter mapping based on the power consumption mode dynamic characteristic data so as to generate a power consumption mode mapping table; and performing equipment power consumption intelligent prediction on the power consumption mode mapping table to generate equipment power consumption prediction data. Through accurate power consumption prediction, an intelligent power supply distribution strategy and efficient power supply scheduling optimization, the efficiency and comprehensiveness of power supply management of the wireless projection screen VR box are improved.

Description

Technical Field

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

[0002] Early wireless projection VR boxes generally adopted traditional power management methods, which had problems such as poor battery life, severe heat accumulation, and low energy efficiency. With the advancement 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 experience. In recent years, power management technology has continued to develop in the direction of intelligence and dynamics. By integrating multiple power management modes, using high-efficiency batteries, and intelligent power scheduling technologies, the use time of VR boxes can be significantly extended and the power distribution between devices can be optimized. Especially in the wireless projection scenario compatible with multiple devices, through accurate power monitoring and management, the stability and high efficiency of the device under high load can be ensured, while reducing power consumption and improving user experience. However, 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, low power management efficiency, and failure to effectively perform task parallel processing and power scheduling, resulting in the failure to optimize the power consumption management of VR boxes when multiple devices are operated, which leads to low efficiency and comprehensiveness of power management of wireless projection VR boxes. Summary of the invention

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

[0004] To achieve the above purpose, a high-efficiency power management method for a multi-device compatible wireless projection VR box is provided, the method comprising the following steps:

[0005] Step S1: acquiring device power consumption data; performing VR box working mode analysis on the device power consumption data to generate VR box working mode data, wherein the VR box working mode data includes a standby mode, a charging and discharging mode, and a wireless projection mode; performing power consumption dynamic feature extraction on the device power consumption data to obtain power consumption mode dynamic feature data; performing mode parameter mapping based on the power consumption mode dynamic feature data to generate a power consumption mode mapping table;

[0006] Step S2: Intelligently predict the device power consumption of the power consumption mode mapping table to generate device power consumption prediction data; perform transmission distance power consumption weight allocation on the VR box working mode data based on the device power consumption prediction data to generate transmission power consumption weight allocation data; generate power allocation strategies for standby mode, charging and discharging mode, and wireless projection mode based on the transmission power weight allocation data to obtain the VR box power supply power allocation strategy;

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

[0008] Step S4: Perform power management energy efficiency evaluation on the high-frequency power dispatch feedback record data to generate power management energy efficiency evaluation data; perform management optimization verification on the VR box power distribution strategy through the power management energy efficiency evaluation data to generate VR box power management optimization verification data to perform efficient power management operations for multi-device compatible wireless projection VR boxes.

[0009] The present invention collects the power consumption data of the device and analyzes different working modes (standby, playback, and screen projection), which helps to deeply understand the power requirements of the device in each mode and lay the foundation for subsequent power consumption optimization. By extracting dynamic features and mapping mode parameters, the changing law of 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 battery efficiency. The power consumption is weighted based on the transmission distance to ensure that the power consumption of the device is reasonably distributed during wireless screen projection, and unnecessary power waste caused by the long signal transmission distance is avoided. By optimizing the power distribution of each mode, the power consumption in different working modes is ensured to be reasonable, the overall power efficiency of the system is improved, and the use time of the device is extended. The efficient generation of power supply power scheduling data is achieved through distributed parallel processing, and multiple tasks can be processed simultaneously, reducing the power consumption bottleneck of the device when multi-tasking is running, and improving processing efficiency. The high-frequency power consumption power usage is analyzed and feedback records are generated, which helps to monitor power consumption changes in real time, optimize power usage strategies, and avoid unnecessary energy waste. By evaluating the energy efficiency of power management, potential problems in power distribution can be identified and improved to ensure efficient use of power resources and improve the overall energy efficiency of the VR box. The power management strategy is optimized and verified based on the evaluation data, so that power management is continuously improved and optimized to ensure high performance and long battery life in a multi-device compatible wireless projection environment. Therefore, the present invention improves the efficiency and comprehensiveness of power management of wireless projection VR boxes through accurate power consumption prediction, intelligent power distribution strategy and efficient power scheduling optimization.

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

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

[0012] Step S12: performing VR box working mode analysis on the internal device power consumption collection data and the external connection device power consumption collection data to generate VR box working mode data, wherein the VR box working mode data includes a standby mode, a charging and discharging mode, and a wireless screen projection mode;

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

[0014] Step S14: extracting power consumption dynamic features from the power consumption mode classification data to obtain power consumption mode dynamic feature data; and performing mode parameter mapping based on the power consumption mode dynamic feature data to generate a power consumption mode mapping table.

[0015] The present invention can obtain the power consumption parameters of the device in detail by utilizing the power consumption monitoring module in the VR box, including not only the power consumption of the internal device, but also the power consumption of the externally connected device, so as to fully understand the energy consumption status of the device. By performing working mode analysis on the power consumption acquisition data of the device and generating VR box working mode data, including standby mode, charging and discharging mode and wireless projection mode, the power consumption performance under different working states can be effectively distinguished, providing 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, by combining the working mode data for mode classification, each power consumption mode can be clearly divided, thereby providing data support for optimization under different modes. By performing dynamic feature extraction on the power consumption mode classification data and generating dynamic feature data of the power consumption mode, the law of power consumption changing over time can be captured, and mode parameter mapping is further performed based on these dynamic features to generate a power consumption mode mapping table. This process provides a scientific basis for formulating a more accurate power consumption control strategy. By analyzing the power consumption of the VR box in different modes, we can provide a basis for subsequent energy-saving optimization strategies, such as reducing unnecessary power consumption in standby mode, optimizing resource usage in charging and discharging modes, etc., thereby improving the energy 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 efficiency, extend the service life of the device, and provide data support for future energy-saving designs.

[0016] Preferably, performing VR box operation mode analysis on the internal device power consumption collection data and the external connection device power consumption collection data includes:

[0017] Perform power consumption collection and discrimination on the power consumption collection data of the internal device and the power consumption collection data of the externally connected device, and when only the power consumption collection data of the internal device is collected, perform a first mode mark to generate a standby mode;

[0018] When the power consumption data collected by the internal device and the power consumption data collected by the externally connected device are collected at the same time, the power consumption fluctuation analysis is performed on the power consumption data collected by the externally connected device to generate the power consumption fluctuation analysis data; based on the power consumption fluctuation analysis data, the power consumption fluctuation spectrum is converted to the power consumption fluctuation spectrum diagram of the power consumption data collected by the externally connected device;

[0019] Perform fluctuation frequency stability analysis on the power consumption fluctuation spectrum diagram to generate power consumption fluctuation stability data and power consumption fluctuation instability data; perform second mode marking on the internal device power consumption collection data and the external connection device power consumption collection data based on the power consumption fluctuation stability data to obtain the charging and discharging mode;

[0020] Based on the power consumption fluctuation instability data, the internal device power consumption collection data and the external connection device power consumption collection data are marked with a third mode to obtain the wireless screen projection mode.

[0021] The present invention can effectively distinguish different working states by collecting and distinguishing the power consumption data collected by the internal device and the power consumption data collected by the external connection device. When only the power consumption data of the internal device is collected, it can be accurately identified as the standby mode, reflecting the performance of the device in a low-energy consumption and low-activity state. When the power consumption data of the internal and external devices are collected at the same time, by analyzing the fluctuation of the power consumption of the external connection device, the power consumption fluctuation caused by the external device can be captured, providing a basis for the subsequent power consumption stability analysis. The function of generating the power consumption fluctuation spectrum diagram can effectively identify the details of the frequency change, thereby helping to understand the working characteristics of the device. By performing a fluctuation frequency stability analysis on 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 the external device on the power consumption. This analysis provides a scientific basis for determining different working modes (such as charging and discharging mode and wireless projection mode), and can provide early warning for unstable power consumption fluctuations, thereby optimizing the power consumption management strategy. Through the discrimination of the second and third mode marks, the working mode of the VR box can be intelligently identified according to the stability and instability data of the power consumption fluctuation, and marked as charging and discharging mode and wireless projection mode respectively. This mechanism can dynamically identify the working status 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 projection. Through the refined division of multiple modes such as standby, playback and projection and the analysis of power consumption fluctuations, the system can adjust power consumption according to the needs of different modes, effectively reduce unnecessary energy consumption, improve overall energy efficiency, and avoid additional energy consumption caused by unstable conditions. The system can analyze and mark power consumption changes in different modes in real time, so that the VR box can automatically switch working modes according to the influence of external devices, adapt to environmental changes, improve user experience, reduce battery burden, and extend the use time of the device.

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

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

[0024] Step S22: performing intelligent prediction of device power consumption on the time series power consumption fragment data set to generate device power consumption prediction data;

[0025] Step S23: Analyze the power demand of the device in the standby mode, the charging and discharging mode, and the wireless projection mode based on the device power consumption prediction data to generate device power demand data; perform transmission distance power consumption weight allocation based on the device power demand data to generate transmission power consumption weight allocation data;

[0026] Step S24: Generate power distribution strategies for the standby mode, the charging and discharging mode, and the wireless projection mode based on the transmission power weight allocation data to obtain a VR box power distribution strategy.

[0027] The present invention can clearly display the time series changes of power consumption characteristics by performing time series sorting on the power consumption mode mapping table, and effectively segment and analyze the power consumption according to the data generated by time series segmentation, so as to capture the power consumption trend of the device at different time points and provide accurate basic data for subsequent power consumption prediction. By setting the sliding window to fragment the power consumption time series data and generate a time series power consumption fragment data set, the dynamic changes of device power consumption can be captured more finely, ensuring the accuracy and adaptability of power consumption prediction in the short term. This process optimizes the time series structure of the data and makes subsequent power consumption prediction more accurate. Intelligent prediction of the time series power consumption fragment data set can calculate the power consumption of the device in advance, so as to predict the energy consumption of the device in different modes. The prediction can provide real-time data support for the power demand analysis and power distribution strategy generation of the device, ensuring that the power consumption management in different application modes is more efficient. Based on the device power consumption prediction data, the power demand analysis can be performed for the standby mode, the charging and discharging mode and the wireless projection mode respectively, so as to generate accurate device power demand data. This analysis can help determine the power consumption of the device in each mode and provide a basis for the precise control of power distribution. By allocating power consumption weights according to the transmission distance of the device power demand data, it is possible to allocate reasonable power weights according to the distance and power consumption requirements between different devices. This optimization helps to improve the energy-efficient transmission between devices, avoid excessive energy consumption, and improve the power utilization efficiency of the overall system. Based on the transmission power weight allocation data, it is possible to generate accurate power allocation strategies for different modes (standby mode, charging and discharging mode, wireless projection mode) to ensure that the power consumption management in each mode achieves the best balance. The optimization of the power allocation strategy will enable the VR box to maintain good power performance in different usage scenarios, avoid excessive consumption, and extend the use time of the device. The power allocation 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 efficiency. Especially in long-term use scenarios, it can extend the use time of the VR box and improve the user experience. Through dynamic processing and predictive analysis of time series 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 key power consumption timing characteristics from the device power consumption fragment data set to obtain key power consumption timing characteristic data, wherein the key power consumption timing characteristic extraction includes power consumption moving average value extraction and power consumption change rate extraction;

[0030] Step S222: Divide the key power consumption timing characteristic data into data sets to generate a model training set and a model test set; perform model training on the model training set using a recursive neural network algorithm to generate a power consumption prediction pre-model; perform model optimization iteration on the power consumption prediction pre-model using the model test set to generate a power consumption prediction model;

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

[0032] The present invention can effectively capture the time series variation law of power consumption by extracting key power consumption time series features (such as power consumption moving average and power consumption change rate) from the power consumption fragment data of the device. These features are crucial for identifying power consumption patterns and trends, and provide high-quality input data for subsequent intelligent predictions to ensure the accuracy of predictions. The key power consumption time series feature data is reasonably divided into data sets to generate model training sets and test sets. This division ensures the effectiveness and stability of model training, avoids data overfitting, and provides reliable test data for model optimization. By training the training set using a recursive neural network (RNN) algorithm, the dynamic change characteristics of the power consumption time series can be captured to generate a preliminary model for power consumption prediction. By optimizing and iterating the power consumption prediction pre-model through the model test set, the accuracy and generalization ability of the prediction model can be further improved. The process of continuous iterative optimization enables the model to adapt to the changes in power consumption under different devices and usage scenarios, thereby improving the applicability and prediction accuracy of the model. By importing the device power consumption fragment data set into the final 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, ensuring the energy efficiency optimization of the device in different working modes. By extracting the power consumption time series characteristics and combining recursive neural network for training and optimization, the model can more accurately identify the law of power consumption changes. Especially for complex time series data, RNN can better capture long-term dependencies and improve 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 requirements of the device, it can provide a reasonable basis for power distribution, equipment scheduling, etc., avoid excessive power consumption, and improve energy efficiency. Through intelligent prediction of device power consumption, the system can better adapt to the power consumption changes during the use of the device, and perform real-time adjustment and optimization, which not only improves the operating 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: Obtaining total power data of the VR box device;

[0035] Step S242: marking the total power data of the VR box device as a low power state based on the standby mode, and performing minimum power resource allocation on the device power consumption fragment data set based on the low power state to generate standby mode power allocation data;

[0036] Step S243: Screening the operation data of the working modules in the charging and discharging mode for the total power data of the VR box device based on the charging and discharging mode, and generating the operation screening data of the working modules in the charging and discharging mode, wherein the operation screening data of the working modules in the charging and discharging mode 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;

[0037] Step S244: marking 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 performing hierarchical power control on the device power consumption segment data set based on the medium power state to generate charge and discharge mode power distribution data;

[0038] Step S245: filtering the operation data of the working module of the wireless screen projection mode on the total power data of the VR box device based on the wireless screen projection mode, and generating the operation screening data of the working module of the wireless screen projection mode, wherein the operation screening data of the working module of the wireless screen projection mode 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;

[0039] Step S246: marking a high power state according to the data transmission module operation data, the screen refresh module operation data and the graphics processing module operation data, and adaptively allocating power to the device power consumption segment data set using the power high load control formula based on the high power state, thereby generating wireless screen projection mode power allocation data;

[0040] Step S247: Integrate the power strategy through the standby mode power distribution data, the charge-discharge mode power distribution data, and the wireless projection mode power distribution data to generate a VR box power distribution strategy.

[0041] The present invention performs detailed power allocation for different working modes (standby mode, charging and discharging mode, wireless projection mode), and 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 the minimum power consumption through low-power state marking; in charging and discharging mode, the system provides reasonable power for the device through hierarchical power control; in wireless projection mode, through high-power state marking and using power high-load control formula, it ensures that the device can obtain appropriate power supply when high power demand occurs. This allocation strategy can effectively optimize the overall energy efficiency of VR box devices. The system marks the power state of the device in different working modes (low power, medium power, high power state) and adjusts the power allocation according to these states. This accurate power state identification helps to ensure that the device obtains reasonable power support in different behavior modes and avoids excessive consumption or unnecessary power waste. By finely screening and allocating power to 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 performed according to the power consumption requirements of different modules. For example, in charging and discharging mode, the power consumption requirements of different modules are different, and the system will allocate power in the medium power state according to actual needs. In the wireless projection mode, the system further performs adaptive power allocation according to high power requirements to ensure efficient operation of the device. By optimizing the power distribution in different modes, the system can effectively reduce power waste and extend the use time of the device. In particular, the low-power and medium-power control strategies in standby mode and charge-discharge mode can significantly reduce unnecessary power consumption. The power high-load regulation formula is used to provide adaptive power distribution for the wireless projection mode to ensure that power can be reasonably allocated even when the device is heavily loaded (such as when graphics processing, screen refresh and other tasks are heavy) to prevent power shortages or device overheating. By integrating the power distribution data of standby mode, charge-discharge mode and wireless projection mode, the system can generate a comprehensive power distribution strategy that can comprehensively consider the different working requirements and power consumption of the device to ensure that the VR box device can achieve optimal power management and efficient operation in all working modes. This power distribution strategy is not only suitable for device management in different working modes, but also can flexibly adjust power distribution in various practical application scenarios. For example, when a long period of standby is required, the system will prioritize entering a low-power state; when high-definition video needs to be played or screen projection is required, the system can automatically adjust the power supply according to real-time power consumption requirements to ensure smooth operation.

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

[0043]

[0044] Where P aIt represents the adaptive power allocation power in the wireless screen projection mode, T represents the running time of the entire wireless screen projection mode, α represents the priority coefficient of the transmission module power consumption in the total power, and P TX (t) represents the data transmission power that changes with time t, β represents the control coefficient of the graphics processing module, P GPU (t) represents the instantaneous power consumption change rate of the graphics processing module at time t, P CPU (t) represents the sinusoidal modulation term of the processor power consumption that changes with time t, γ represents the CPU power consumption weight coefficient, ω represents the frequency parameter, It is expressed 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 high-load power control formula. The purpose of the formula is to adaptively control the power of the system in the wireless projection mode. Each item in the formula corresponds to the power consumption contribution of different hardware modules, and finally a total adaptive power allocation (Pa) is obtained. This power allocation takes into account the power consumption and change rate of different hardware modules (such as transmission module, GPU, CPU, RAM), and dynamically adjusts the power allocation according to their priority to keep the system running efficiently. Where α is the priority coefficient of the power consumption of the transmission module in the total power. It determines the proportion of the transmission module in the power allocation. A larger α value means that the transmission module will obtain more power first to ensure that data transmission is not restricted. P TX (t) is the power consumption of data transmission that changes with time. The power consumption of the transmission module changes with time, which is usually related to factors such as data flow and transmission frequency. The transmission module requires a certain amount of power to maintain the stability of data transmission. β is the control coefficient of the graphics processing module. It controls the weight of the influence of the GPU power consumption change rate on power distribution. The size of the β value determines the instantaneous change rate of GPU power consumption. Importance in power regulation. A larger β will make GPU power consumption changes have a greater impact on power distribution. is the instantaneous rate of change of GPU power consumption at time t, reflecting the change of graphics processing load. If the GPU load changes greatly, this item will generate a large power regulation demand to ensure that the GPU is always within a reasonable power consumption range. CPU (t) is the processor power modulation term that changes with time t. The power consumption of the CPU changes dynamically during the calculation process, which is usually related to the system load and the running tasks. This power consumption is modulated by a sine wave to simulate the periodic changes of the CPU load, such as the alternation between peak load period and low load period. γ is the CPU power consumption weight coefficient, which determines the priority of CPU power consumption in power distribution. A larger γ value indicates that the system will give priority to changes in CPU power consumption and maintain CPU load balance. It is the total RAM power consumption from the initial time to the current time t. The change of RAM power consumption will change with the system memory usage, especially when processing big data or complex tasks, the power consumption of memory will fluctuate greatly. This item reflects the cumulative power consumption of RAM through integration. As a storage module, RAM requires a higher power supply to ensure the stability of data reading and writing during long-term operation. By monitoring its power consumption, power can be allocated to RAM in a timely manner to avoid system performance degradation due to insufficient memory resources. When using the conventional power high-load control formula in the field, the adaptive power allocation power in the wireless projection mode can be obtained. By applying the power high-load control formula provided by the present invention, the adaptive power allocation power in the wireless projection 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 or performance bottlenecks caused by too much or too little power in a single module. Using sinusoidal wave modulation to control CPU power consumption and calculating the accumulation of RAM power consumption by integration can better predict and adjust power consumption changes, reduce power volatility, and thus improve system stability. This formula optimizes the overall power efficiency of the system by dynamically adjusting the power consumption and change rate of each hardware module. Through priority coefficients, instantaneous power consumption change rate, modulation terms and integral calculations, it can adjust power distribution according to the actual needs of the system, ensure smooth and efficient operation of each module, and avoid excessive power consumption and performance bottlenecks.

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

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

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

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

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

[0051] The present invention can refine complex power management tasks into multiple small tasks (power management task data) by splitting the power management task of the VR box working mode data. This process makes power management more accurate and controllable, and provides a reliable data basis for subsequent scheduling and processing. After generating the power management task data, the system assigns the task to different computing units through a distributed task scheduling method. This scheduling method can effectively utilize the computing resources of the system, so that the power management tasks are processed in parallel, thereby improving the efficiency of the power scheduling of the entire VR box device, reducing the response time, and improving the processing capacity of the system. Micro-task parallel processing enables multiple power scheduling tasks to be carried out simultaneously, which not only significantly improves the processing efficiency of power scheduling, but also can quickly respond to changes in power demand in different modes when the device is running, ensuring that the device always maintains the optimal power support under different workloads. By analyzing the power scheduling data of the device power supply, the power consumption usage in the power scheduling process can be grasped in real time, which provides accurate data support for the subsequent optimization of the power management strategy, ensuring that the device can operate with the lowest energy consumption in different working modes, and adjusting the power distribution in time to avoid power shortage or waste. By performing high-frequency power adjustment feedback on the power scheduling power consumption data (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 the system with refined power regulation to ensure that the power distribution can remain in the best state when the equipment load changes or emergencies occur, thereby avoiding the impact of insufficient power supply or unreasonable distribution on equipment performance. Through the above power scheduling process, the VR box device can maintain a stable power supply under different working modes and load conditions, avoid overload or insufficient power, and enhance the stability and long-term reliability of the device, which is especially important for application scenarios that require continuous high power consumption (such as video playback, screen projection, etc.). By using the high-frequency power scheduling feedback mechanism, the system can adjust according to the real-time power scheduling power consumption usage and further optimize the power distribution. The intelligent scheduling system can not only improve the operating efficiency of the equipment, but also adapt to the changes in power demand in different usage scenarios, and provide a more flexible and intelligent power management solution. By optimizing power scheduling and feedback, power waste and excessive consumption are avoided, which 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 frequency of charging.

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

[0053] Divide the power management task data into equipment power tasks to generate power management microtask data; perform microtask load analysis on the power management microtask data to generate power management microtask load data;

[0054] Defining power management optimization targets for power management microtask load data to generate power management optimization target data; formulating task scheduling strategies for power management microtask load data based on the power management optimization target data to generate power management task scheduling strategies;

[0055] Perform task mapping on power management microtask data to generate power management task mapping data; perform resource allocation on power management microtask data through power management task scheduling strategy to generate power management resource allocation data; perform distributed dynamic task migration on power management task mapping data according to power management resource allocation data to generate power management scheduling microtask data set.

[0056] The present invention generates power management microtask data by dividing power management task data, and disassembles complex power management tasks into small, independent microtasks. This process makes task management more refined, facilitates subsequent optimization and scheduling, and provides a clear data structure for distributed task processing. Load analysis is performed on the power management microtask data to evaluate the computing requirements, resource consumption and running priority of each microtask, thereby generating power management microtask load data. This analysis provides a quantitative basis for subsequent task scheduling, helps to reasonably allocate resources, avoid task resource competition or overload, and improve the overall efficiency of the system. Through the analysis of the power management microtask load data, the system can set clear power management optimization target data. The optimization target data helps to formulate a task scheduling strategy to ensure that each microtask can be efficiently scheduled and executed according to the optimization target. For example, task resources are reasonably allocated, power consumption peaks are reduced, and equipment working efficiency is optimized, and a power management task scheduling strategy is finally generated. After the power management microtask data is task mapped, the system performs appropriate resource allocation on the task according to the resource allocation strategy. This process can ensure that each microtask can allocate the most appropriate resources according to its own needs, avoid resource waste, and ensure efficient execution of tasks. By dynamically migrating the power management task mapping data, intelligent migration and load balancing of tasks between multiple computing units can be achieved. This distributed dynamic task migration can dynamically adjust the execution location of tasks according to the current system status and load conditions, thereby improving the flexibility and adaptability of task scheduling and ensuring the optimal configuration 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 equipment can make adaptive adjustments at any time according to changes in power demand during operation. This real-time and high efficiency significantly improves the operation stability of the equipment and the response speed of power management. Through intelligent power management task scheduling strategies, excessive power consumption can be avoided and the system burden can be reduced. The execution of each microtask can be completed at the most appropriate time and on the most appropriate device, thereby effectively saving power, improving energy 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 equipment faces changes or high loads, it can maintain smooth operation through fine power resource management to avoid 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 equipment, thereby reducing equipment wear and extending service life. Especially for equipment that needs long-term stable operation, optimizing power management can significantly improve the sustainability of equipment.

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

[0058] Step S41: performing power management energy efficiency evaluation on high-frequency power electric power dispatching feedback record data to generate power management energy efficiency evaluation data;

[0059] Step S42: Optimizing the VR box power distribution strategy through the power management energy efficiency evaluation data to generate a VR box power distribution optimization strategy;

[0060] Step S43: Use the VR box power distribution optimization strategy to perform management optimization verification on the high-frequency power scheduling feedback record data, and generate VR box power management optimization verification data to perform efficient power management operations for the multi-device compatible wireless projection VR box.

[0061] The present invention can fully understand the power efficiency of the VR box under different working conditions by performing energy efficiency evaluation on high-frequency power dispatch feedback record data. The energy efficiency evaluation data provides a reliable basis for further optimizing power dispatch and helps to discover potential problems in power management, such as power waste or unreasonable resource allocation. By optimizing the VR box power distribution strategy based on power management energy efficiency evaluation data, more efficient power dispatching can be achieved. The optimized power distribution strategy can adjust the power distribution plan according to equipment requirements and energy efficiency evaluation results, thereby reducing power consumption waste, extending equipment operation time, and reducing energy consumption while meeting performance requirements. Using the optimized power distribution strategy to manage and optimize the high-frequency power dispatch feedback record data can ensure that the optimized strategy is effective in practical applications. This verification process can not only further verify the effectiveness and accuracy of the optimization strategy, but also simulate the power dispatch behavior under different working conditions to ensure that the VR box can achieve efficient power management in different environments. Through power management optimization verification, it can ensure that the VR box can still maintain efficient power management operations when multiple devices are compatible with wireless projection. This optimization solution can deal with power conflicts or power consumption problems that occur when multiple devices are running at the same time, ensuring that each device can operate efficiently within a reasonable power consumption range, thereby improving system stability and user experience. After optimizing the power distribution strategy and management verification, power consumption waste can be minimized. When multiple devices are running at the same time, the optimized power management can ensure the efficiency of battery use and reduce overcharging or over-discharging, thereby extending the service life of the device. The entire process can achieve efficient power scheduling operations through comprehensive evaluation and strategy optimization of power management energy efficiency, thereby optimizing the working mode of the VR box, whether it is standby, playback or wireless projection mode, it can maintain maximum energy efficiency. At the same time, with the efficient compatibility of multiple devices, the optimized power management strategy ensures the stable operation of the system under high load conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] Figure 1A schematic diagram of the steps of an efficient power management method for a multi-device compatible wireless screen projection VR box;

[0063] Figure 2 for Figure 1 Detailed implementation steps of step S2 in the flowchart;

[0064] Figure 3 for Figure 1 Detailed implementation steps of step S3 in FIG.

[0065] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0066] The technical method of the present invention is described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within the scope of protection of the present invention.

[0067] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented 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 herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another. For example, without departing from the scope of the exemplary embodiments, a first unit may be referred to as a second unit, and similarly, a second unit may be referred to as a first unit, and the term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0069] To achieve this, please refer to Figures 1 to 3 , an efficient power management method for a multi-device compatible wireless projection VR box, the method comprising the following steps:

[0070] Step S1: acquiring device power consumption data; performing VR box working mode analysis on the device power consumption data to generate VR box working mode data, wherein the VR box working mode data includes a standby mode, a charging and discharging mode, and a wireless projection mode; performing power consumption dynamic feature extraction on the device power consumption data to obtain power consumption mode dynamic feature data; performing mode parameter mapping based on the power consumption mode dynamic feature data to generate a power consumption mode mapping table;

[0071] Step S2: Intelligently predict the device power consumption of the power consumption mode mapping table to generate device power consumption prediction data; perform transmission distance power consumption weight allocation on the VR box working mode data based on the device power consumption prediction data to generate transmission power consumption weight allocation data; generate power allocation strategies for standby mode, charging and discharging mode, and wireless projection mode based on the transmission power weight allocation data to obtain the VR box power supply power allocation strategy;

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

[0073] Step S4: Perform power management energy efficiency evaluation on the high-frequency power dispatch feedback record data to generate power management energy efficiency evaluation data; perform management optimization verification on the VR box power distribution strategy through the power management energy efficiency evaluation data to generate VR box power management optimization verification data to perform efficient power management operations for multi-device compatible wireless projection VR boxes.

[0074] The present invention collects the power consumption data of the device and analyzes different working modes (standby, playback, and screen projection), which helps to deeply understand the power requirements of the device in each mode and lay the foundation for subsequent power consumption optimization. By extracting dynamic features and mapping mode parameters, the changing law of 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 battery efficiency. The power consumption is weighted based on the transmission distance to ensure that the power consumption of the device is reasonably distributed during wireless screen projection, and unnecessary power waste caused by the long signal transmission distance is avoided. By optimizing the power distribution of each mode, the power consumption in different working modes is ensured to be reasonable, the overall power efficiency of the system is improved, and the use time of the device is extended. The efficient generation of power supply power scheduling data is achieved through distributed parallel processing, and multiple tasks can be processed simultaneously, reducing the power consumption bottleneck of the device when multi-tasking is running, and improving processing efficiency. The high-frequency power consumption power usage is analyzed and feedback records are generated, which helps to monitor power consumption changes in real time, optimize power usage strategies, and avoid unnecessary energy waste. By evaluating the energy efficiency of power management, potential problems in power distribution can be identified and improved to ensure efficient use of power resources and improve the overall energy efficiency of the VR box. The power management strategy is optimized and verified based on the evaluation data, so that power management is continuously improved and optimized to ensure high performance and long battery life in a multi-device compatible wireless projection environment. Therefore, the present invention improves the efficiency and comprehensiveness of power management of wireless projection VR boxes through accurate power consumption prediction, intelligent power distribution strategy and efficient power scheduling optimization.

[0075] In the embodiment of the present invention, reference Figure 1 The above is a schematic diagram of the steps of a high-efficiency power management method for a multi-device compatible wireless projection VR box of the present invention. In this example, the high-efficiency power management method for a multi-device compatible wireless projection VR box includes the following steps:

[0076] Step S1: acquiring device power consumption data; performing VR box working mode analysis on the device power consumption data to generate VR box working mode data, wherein the VR box working mode data includes a standby mode, a charging and discharging mode, and a wireless projection mode; performing power consumption dynamic feature extraction on the device power consumption data to obtain power consumption mode dynamic feature data; performing mode parameter mapping based on the power consumption mode dynamic feature data to generate a power consumption mode mapping table;

[0077] In an embodiment of the present invention, the power consumption data of the VR box in actual operation is obtained through the sensors and monitoring modules in the device. Data collection should cover the power consumption of the device in standby, playback and wireless projection modes. The collected data includes key parameters such as power consumption changes over time, current and voltage, to ensure that the power consumption characteristics of the device can be fully reflected. The collected data is cleaned and preprocessed to remove abnormal data, fill in missing data, and sort according to the time axis to form a complete power consumption time series data. According to the power consumption characteristics and the operating state of the device, the characteristics of the standby, playback and wireless projection modes are defined. Each mode has specific power consumption mode characteristics, such as low power consumption in standby mode, medium power consumption in charging and discharging mode, and high power consumption in wireless projection mode. By analyzing the time series characteristics of power consumption data, such as the peak value, duration, fluctuation amplitude of power consumption, etc., it is determined which working mode the device is in. VR box working mode data is generated by classifying and labeling the power consumption data in different modes. This data records the power consumption characteristics and time periods of each mode for subsequent analysis and optimization. Dynamic feature extraction of power consumption data is performed through statistical methods and signal processing techniques, such as sliding average, Fourier transform, etc. The main extracted features include the average value, peak value, fluctuation frequency, growth rate, etc. of power consumption, which reflect the changes in power consumption of the device in different modes. The extracted features are summarized as dynamic feature data of power consumption mode. This data provides the basis for subsequent power consumption mode mapping and power scheduling optimization. Parameterize each feature in the dynamic feature data of power consumption mode. For example, for standby mode, map out the low power consumption range parameters; for charging and discharging mode and wireless projection mode, map out the characteristic parameters of medium power consumption and high power consumption. Determine the key parameters such as the upper and lower limits of power consumption, power consumption fluctuation range and power load of each mode, and establish mode parameter mapping. Arrange the mapped parameters 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 allocation strategy in subsequent steps.

[0078] Step S2: Intelligently predict the device power consumption of the power consumption mode mapping table to generate device power consumption prediction data; perform transmission distance power consumption weight allocation on the VR box working mode data based on the device power consumption prediction data to generate transmission power consumption weight allocation data; generate power allocation strategies for standby mode, charging and discharging mode, and wireless projection mode based on the transmission power weight allocation data to obtain the VR box power supply power allocation strategy;

[0079] In the embodiment of the present invention, the dynamic feature data in the power consumption mode mapping table is used as input, including key parameters such as average power consumption, power consumption fluctuation, upper and lower limits of load under different working modes. A recursive neural network (RNN) or a long short-term memory network (LSTM) model is used to better capture the time series characteristics 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 under 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 allocation to ensure the reasonable allocation of power and the efficient use of resources. Considering the influence of the transmission distance, the device power consumption prediction data and the transmission distance are weighted and allocated. For longer transmission distances, a higher power consumption weight is allocated to ensure the stability of transmission; for shorter distance transmissions, a lower power consumption weight is allocated to optimize power resources. Using the power consumption prediction data and the weight allocation algorithm, the power consumption weight allocation data is generated to record 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, which provides input support for the power resource scheduling of each mode. According to the transmission power consumption weight allocation data, power is allocated to each working mode. The specific steps are as follows: Based on the low power consumption requirements, the minimum power resources are allocated to maintain the lowest operating state of the device. According to the power consumption weights in the charging and discharging modes and the transmission requirements of the equipment, medium power is allocated to ensure smooth playback of audio and video. In the case of long transmission distances and high loads, higher power resources are allocated to ensure smooth and stable projection images. For each mode, the allocation strategy is dynamically adjusted to optimize the efficiency of power resource use based on real-time power consumption prediction data and mode switching. The power allocation schemes under all modes are integrated into a complete set of power supply power allocation strategies so that the strategy can be automatically called for power management during device operation.

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

[0081] In an embodiment of the present invention, the working modes (standby mode, charging and discharging mode, wireless projection mode) of the VR box are divided into multiple power management task units according to the power supply and power allocation strategy. For example, the charging and discharging mode can be further subdivided into tasks such as audio and video decoding, data transmission, and screen refresh. The corresponding power resource requirements are allocated to each task unit, and the tasks are split into microtasks that can be processed in parallel to generate a device power scheduling microtask data set. The distributed computing framework (such as edge computing or task parallel processing mechanism) is used to process each microtask in parallel to ensure that each task unit runs efficiently within the allocated power budget. In parallel processing, the power consumption of task execution is monitored in real time to ensure that the power limit of the power allocation strategy is not exceeded. The power consumption of each microtask is collected, and the device power supply and power scheduling data is integrated and generated as the basic data for the subsequent high-frequency power consumption analysis. The high-frequency power consumption data under each working mode is extracted from the device power supply and power scheduling data, with particular attention to the tasks that are frequently run in the charging and discharging mode and the wireless projection mode (such as screen refresh, data transmission, and graphics processing). The high-frequency power consumption data is analyzed in time series using the sliding window technology to identify the power consumption characteristics during the peak period. Perform power fluctuation analysis to identify the main factors affecting power consumption (such as transmission bandwidth and power load changes) and generate high-frequency power consumption in each mode. Record the analysis results as high-frequency power dispatch 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 equipment.

[0082] Step S4: Perform power management energy efficiency evaluation on the high-frequency power dispatch feedback record data to generate power management energy efficiency evaluation data; perform management optimization verification on the VR box power distribution strategy through the power management energy efficiency evaluation data to generate VR box power management optimization verification data to perform efficient power management operations for multi-device compatible wireless projection VR boxes.

[0083] In an embodiment of the present invention, detailed information on power consumption of each working mode (standby, playback, screen projection) in high-frequency tasks is extracted from high-frequency power dispatch 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 allocation strategy. Combined with power fluctuation analysis, determine whether the high-frequency power consumption is within the expected range, and identify potential power waste or over-allocation points. The analysis results are integrated into power management energy efficiency evaluation data to provide key input for the next step of strategy optimization. The energy efficiency evaluation data includes parameters such as the average energy efficiency ratio, power consumption peak, and fluctuation frequency in each mode. Based on the energy efficiency evaluation data, the power allocation weight and power state threshold in the VR box power allocation strategy are optimized. The power allocation of high-frequency tasks in each mode is recalibrated to ensure that each mode obtains appropriate power resources only under its workload requirements. The optimized power management parameters, scheduling priority and power allocation ratio are integrated into a new power allocation strategy. The power distribution optimization strategy will perform 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 projection and multi-device compatibility. Apply the optimization strategy to each working mode of the VR box to simulate power management in multi-device connection and wireless projection scenarios. Record the actual power consumption data in each mode and compare it with the power usage before optimization to evaluate the effectiveness of the optimization strategy and generate 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 feedback basis for further optimization of subsequent power management.

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

[0085] Step S11: using the power consumption monitoring module in the VR box to collect power consumption parameters of the connected device to obtain device power consumption collection data, wherein the device power consumption collection module includes internal device power consumption collection data and external connection device power consumption collection data;

[0086] Step S12: performing VR box working mode analysis on the internal device power consumption collection data and the external connection device power consumption collection data to generate VR box working mode data, wherein the VR box working mode data includes a standby mode, a charging and discharging mode, and a wireless screen projection mode;

[0087] Step S13: performing data preprocessing on the device power consumption collection data to generate standard device power consumption collection data, wherein the data preprocessing includes data cleaning, data denoising, missing value filling and data standardization; performing mode classification on 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: extracting power consumption dynamic features from the power consumption mode classification data to obtain power consumption mode dynamic feature data; and performing mode parameter mapping based on the power consumption mode dynamic feature data to generate a power consumption mode mapping table.

[0089] In the embodiment of the present invention, by configuring the power consumption monitoring module, it is connected to various devices in the VR box, including internal devices (such as VR helmets, batteries, display screens) and external connection 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 external connection device power consumption acquisition data for subsequent analysis. Monitor the working state of the VR box. Common working modes include standby mode, charging and discharging mode, wireless projection mode, etc. Use the power consumption data to match the system log, and determine the current working mode by calculating the power consumption change trend. Generate VR box working mode data according to the power consumption change pattern for subsequent power consumption analysis and classification. Perform data preprocessing on the collected device power consumption data: remove duplicate and erroneous power consumption data to ensure data accuracy. Use filtering algorithms (such as low-pass filtering) to remove noise caused by environmental factors or acquisition devices. Interpolate and fill missing power consumption data to avoid the impact of missing values ​​on 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 power consumption characteristics in different modes. Use timing 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, peak values, frequencies, etc. Analyze the power consumption fluctuation patterns of the device in different modes, such as lower power consumption in standby mode and higher power consumption in charging and discharging mode. Based on the extracted dynamic features, 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. The table contains the power consumption change patterns and corresponding parameters in different modes to help further optimize the power consumption management of the device.

[0090] Preferably, performing VR box operation mode analysis on the internal device power consumption collection data and the external connection device power consumption collection data includes:

[0091] Perform power consumption collection and discrimination on the power consumption collection data of the internal device and the power consumption collection data of the externally connected device, and when only the power consumption collection data of the internal device is collected, perform a first mode mark to generate a standby mode;

[0092] When the power consumption data collected by the internal device and the power consumption data collected by the externally connected device are collected at the same time, the power consumption fluctuation analysis is performed on the power consumption data collected by the externally connected device to generate the power consumption fluctuation analysis data; based on the power consumption fluctuation analysis data, the power consumption fluctuation spectrum is converted to the power consumption fluctuation spectrum diagram of the power consumption data collected by the externally connected device;

[0093] Perform fluctuation frequency stability analysis on the power consumption fluctuation spectrum diagram to generate power consumption fluctuation stability data and power consumption fluctuation instability data; perform second mode marking on the internal device power consumption collection data and the external connection device power consumption collection data based on the power consumption fluctuation stability data to obtain the charging and discharging mode;

[0094] Based on the power consumption fluctuation instability data, the internal device power consumption collection data and the external connection device power consumption collection data are marked with a third mode to obtain the wireless screen projection mode.

[0095] In an embodiment of the present invention, the working mode of the device is determined based on the collected power consumption data. Only the power consumption data of the internal device is collected, that is, only the data of the device in the VR box (such as the head display, battery, and display screen) is collected, and there is no power consumption data of the external device. If only the power consumption data of the internal device is collected, it is inferred that the device is in standby mode. At this time, the power consumption of the device is low and is basically idle. The mode is marked as standby mode, and the power consumption value is recorded for subsequent analysis. At the same time, the power consumption data of the internal device and the externally connected device are collected, indicating that the device interacts with the external device (such as a controller, a wireless receiver, a sensor, etc.), and the power consumption changes significantly. The power consumption fluctuation analysis is performed on the power consumption collection data of the external device. By calculating the time series changes of the power consumption, the fluctuation characteristics, such as the periodic fluctuation of the power consumption, are extracted. Algorithms (such as Fourier transform or autocorrelation analysis) are used 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, the power consumption fluctuation of the externally connected device is converted into a spectrum to generate a power consumption fluctuation spectrum diagram. This figure shows the frequency and intensity of power consumption changes over time. Perform stability analysis on the generated power consumption fluctuation spectrum to evaluate whether the power consumption fluctuation frequency is stable. The stability of the fluctuation is determined by calculating the standard deviation and peak value of the frequency. If the frequency is stable, power consumption fluctuation stability data is generated; if the frequency fluctuates greatly, power consumption fluctuation instability data is generated. Based on the power consumption fluctuation stability data, if the power consumption fluctuation frequency is stable, the device is considered to be in the charging and discharging mode, and the second mode mark is performed, i.e., the charging and discharging mode. If the power consumption fluctuation is unstable, further analysis is performed to enter the next stage of wireless screen projection mode determination. If the power consumption fluctuation spectrum shows significant instability and the fluctuation amplitude is large, this usually indicates that the device is in a state of frequent interaction with external devices, such as wireless screen projection 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. The third mode mark is performed based on the instability data of power consumption fluctuation, that is, the device is considered to be in the wireless screen projection mode. During the wireless screen projection process, the device will establish a connection with the external device through wireless protocols such as Wi-Fi or Bluetooth. At this point, the power consumption of internal and external devices should change in a specific pattern, such as an increase in the power consumption of internal devices (due to data processing and display requirements during wireless transmission) and fluctuations in the power consumption of external devices (display devices entering the receiving state, etc.). After detecting that the device has entered the wireless projection mode, a specific identifier (such as a tag or a specific power consumption threshold) is used to mark the period as "wireless projection mode". Based on the instability of power consumption fluctuations, a power consumption threshold is defined. When the threshold is exceeded, the device is considered to have entered the wireless projection mode. At this point, the VR box usually emits large power consumption fluctuations, especially when content is transmitted or data is received, a mark for the wireless projection mode is generated, and relevant power consumption data is recorded.

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

[0097] Step S21: performing time series sorting on the power consumption mode mapping table to generate a power consumption feature time series sorting data set; performing time series segmentation on the power consumption feature time series sorting data set to generate power consumption time series segmentation data; performing sliding window setting on the power consumption time series segmentation data to generate a time series power consumption fragment data set;

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

[0099] Step S23: Analyze the power demand of the device in the standby mode, the charging and discharging mode, and the wireless projection mode based on the device power consumption prediction data to generate device power demand data; perform transmission distance power consumption weight allocation based on the device power demand data to generate transmission power consumption weight allocation data;

[0100] Step S24: Generate power distribution strategies for the standby mode, the charging and discharging mode, and the wireless projection mode based on the transmission power weight allocation data to obtain a VR box power distribution strategy.

[0101] In an embodiment 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 feature time series sorting data set is generated, which contains the power consumption characteristics in each mode and the corresponding timestamps. The time series data set is time-segmented, and the data is divided into multiple time periods according to different cycles of the device working mode, such as standby, playback and wireless projection mode, etc., to generate power consumption time series segmentation data, and the power consumption data in 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 size. The power consumption data in the window will be used as a complete fragment for prediction, and a time series power consumption fragment data set will be generated, where each fragment contains a continuous power consumption data, which is convenient for capturing the power consumption fluctuation pattern. Time series data prediction models such as long short-term memory network (LSTM) and convolutional neural network (CNN) can be selected. Model training is performed using the time series power consumption fragment data set to learn the law of power consumption data changing over time. After the training is completed, the trained model is used to predict the future device power consumption and generate device power consumption prediction data, that is, the estimated value of power consumption in the future period. Based on the predicted power consumption data, analyze the power requirements in standby mode, charging and discharging mode, and wireless projection mode. Calculate the power consumption and duration of the device in each mode, obtain the total power demand in different working modes, and generate device power demand 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 demand, weight the transmission power consumption of different working modes. If the device is far away from the transmission source (such as Wi-Fi or Bluetooth), more power is required to maintain signal transmission. Generate transmission power consumption weight allocation data based on power demand data and transmission distance. Design a power allocation strategy based on power consumption data and transmission weight allocation. Ensure that power consumption in different working modes can be effectively allocated to avoid excessive battery power consumption. In standby mode, prioritize the low power consumption of the device; in playback and wireless projection modes, reasonably allocate more power to support data transmission and high-intensity computing. Based on the above analysis, generate a VR box power distribution strategy, which will automatically adjust power distribution in different working modes to optimize the power efficiency of the device.

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

[0103] Step S221: extract key power consumption timing characteristics from the device power consumption fragment data set to obtain key power consumption timing characteristic data, wherein the key power consumption timing characteristic extraction includes power consumption moving average value extraction and power consumption change rate extraction;

[0104] Step S222: Divide the key power consumption timing characteristic data into data sets to generate a model training set and a model test set; perform model training on the model training set using a recursive neural network algorithm to generate a power consumption prediction pre-model; perform model optimization iteration on the power consumption prediction pre-model using the model test set to generate a power consumption prediction model;

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

[0106] In an embodiment of the present invention, a moving average of the device power consumption data is calculated through a sliding window. The moving average can smooth the power consumption data, remove short-term fluctuations, and highlight long-term power consumption trends. 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 moving average of power consumption, which can help capture power consumption trends over long time periods. Calculate the rate of change of device power consumption to analyze the rate of change of device power consumption over time. Calculate the percentage change of device power consumption between adjacent time points to obtain the power consumption change rate, which reflects the fluctuation of power consumption in different time periods. For example: Where P I and P I+1Represent the power consumption values ​​at time points I and I+1 respectively. Extract the moving average and the rate of change as key timing features, and generate key power consumption timing feature data. These feature data can help capture the law of power consumption fluctuations and provide input for subsequent model training. Divide the key power consumption timing feature data set 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 a cross-validation method is used. Randomly shuffle the data and divide it to ensure that the data in the training set and the test set can cover the power consumption characteristics under different working modes. Build an RNN model, select a suitable network structure (such as LSTM or GRU), and use the model training set as input data for training. Define the loss function (such as mean square error) and perform back propagation. Adjust the model parameters through optimization algorithms such as gradient descent to minimize the prediction error. During the training process, use optimizers (such as Adam, SGD, etc.) to optimize parameters to obtain a preliminary model that can accurately predict the power consumption of the device. Input the test set into the trained power consumption prediction model and calculate the prediction error of the model on the test set. Adjust and optimize the model according to the results of the test set. For example, by adjusting the model's hyperparameters (such as learning rate, number of network layers, etc.), the model's prediction ability can be improved. Through multiple rounds of optimization iterations, a high-precision power consumption prediction model is finally obtained, which can accurately predict the power consumption of devices in different modes. The processed device power consumption fragment data set is input into the trained power consumption prediction model. The model performs inference based on the input feature data and generates power consumption prediction results. The model outputs predicted device power consumption data, which reflects the power consumption of the device in 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: Obtaining total power data of the VR box device;

[0109] Step S242: marking the total power data of the VR box device as a low power state based on the standby mode, and performing minimum power resource allocation on the device power consumption fragment data set based on the low power state to generate standby mode power allocation data;

[0110] Step S243: Screening the operation data of the working modules in the charging and discharging mode for the total power data of the VR box device based on the charging and discharging mode, and generating the operation screening data of the working modules in the charging and discharging mode, wherein the operation screening data of the working modules in the charging and discharging mode 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: marking 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 performing hierarchical power control on the device power consumption segment data set based on the medium power state to generate charge and discharge mode power distribution data;

[0112] Step S245: filtering the operation data of the working module of the wireless screen projection mode on the total power data of the VR box device based on the wireless screen projection mode, and generating the operation screening data of the working module of the wireless screen projection mode, wherein the operation screening data of the working module of the wireless screen projection mode 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;

[0113] Step S246: marking a high power state according to the data transmission module operation data, the screen refresh module operation data and the graphics processing module operation data, and adaptively allocating power to the device power consumption segment data set using the power high load control formula based on the high power state, thereby generating wireless screen projection mode power allocation data;

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

[0115] In an embodiment of the present invention, the total power data of the device is obtained from the power management system of the VR box device, and these data include the real-time power consumption, power fluctuation and power consumption of the device in each working mode. A power meter, sensor or data acquisition system is used for real-time monitoring, and the acquired data should include the power changes in standby mode, charging and discharging mode and wireless projection mode. In 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 standby mode and mark it as a low power state. Based on the low power state in standby mode, the device power consumption fragment data set is analyzed and the minimum power resource is allocated to it. For example, by reducing the display brightness, turning off non-essential modules or reducing the sensor frequency, the power consumption of the device in standby mode is minimized. The power allocation strategy of each module is recorded to ensure minimum power consumption. Identify and filter out the active working modules in the charging and discharging mode, which include audio and video decoding modules, data transmission modules, screen control modules, etc. The actual operation data of these modules are monitored by power sensors and marked as "charge and discharge mode working module operation data". The power consumption data of each module is screened and classified to ensure that only the module data with significant power consumption in the charging and discharging mode is retained. In the charging and discharging mode, modules such as audio and video decoding, data transmission, and screen control consume medium power. Use power consumption data and module performance indicators to mark the charging and discharging mode in medium power state. Based on the medium power state, hierarchical power control is performed on the device power consumption fragment data set. Appropriate power resources are allocated to different modules (such as audio and video decoding, data transmission, screen control, etc.). Power consumption can be controlled by adjusting decoding accuracy, reducing data transmission rate, adjusting screen refresh rate, etc. to ensure that the power consumption of each module in the charging and discharging mode meets the optimization requirements. Record the power allocation strategy of each module to ensure power consumption control of the device in the charging and discharging mode. In the wireless screen projection mode, the data transmission module, screen refresh module, and graphics processing module usually consume high power. Filter the power consumption data of these modules through the power monitoring device. Mark the data of these modules as "wireless screen projection mode working module operation data". Filter and record the power consumption data of each module to ensure that the power demand 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 high. According to the power consumption data, these modules are marked in high power state. Based on the power consumption requirements of each module, the power high load control formula is used for adaptive power allocation. This formula will be adjusted according to the actual power consumption of each module, system requirements and battery status. Based on the high power requirements of the device in wireless projection mode, the power consumption allocation is dynamically adjusted to ensure smooth projection operation while avoiding excessive power consumption. The power allocation strategy of each module in wireless projection mode is recorded to ensure power optimization.Summarize the power distribution data of standby mode, charging and discharging mode, and wireless projection mode, and combine the power requirements of each mode to form a comprehensive power distribution strategy. Use weighted average method or optimization algorithm to integrate the power distribution strategies of each mode according to the power consumption priority and resource allocation requirements in different modes. Finally, a complete power distribution strategy is generated to ensure that the power consumption of VR box devices in different working modes is reasonably controlled, extend the use time of the device and optimize battery performance.

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

[0117]

[0118] Where P a It represents the adaptive power allocation power in the wireless screen projection mode, T represents the running time of the entire wireless screen projection mode, α represents the priority coefficient of the transmission module power consumption in the total power, and P TX (t) represents the data transmission power that changes with time t, β represents the control coefficient of the graphics processing module, P GPU (t) represents the instantaneous power consumption change rate of the graphics processing module at time t, P CPU (t) represents the sinusoidal modulation term of the processor power consumption that changes with time t, γ represents the CPU power consumption weight coefficient, ω represents the frequency parameter, It is expressed as 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 high-load power control formula. The purpose of the formula is to adaptively control the power of the system in the wireless projection mode. Each item in the formula corresponds to the power consumption contribution of different hardware modules, and finally a total adaptive power allocation (Pa) is obtained. This power allocation takes into account the power consumption and change rate of different hardware modules (such as transmission module, GPU, CPU, RAM), and dynamically adjusts the power allocation according to their priority to keep the system running efficiently. Where α is the priority coefficient of the power consumption of the transmission module in the total power. It determines the proportion of the transmission module in the power allocation. A larger α value means that the transmission module will obtain more power first to ensure that data transmission is not restricted. P TX (t) is the power consumption of data transmission that changes with time. The power consumption of the transmission module changes with time, which is usually related to factors such as data flow and transmission frequency. The transmission module requires a certain amount of power to maintain the stability of data transmission. β is the control coefficient of the graphics processing module. It controls the weight of the influence of the GPU power consumption change rate on power distribution. The size of the β value determines the instantaneous change rate of GPU power consumption. Importance in power regulation. A larger β will make GPU power consumption changes have a greater impact on power distribution. is the instantaneous rate of change of GPU power consumption at time t, reflecting the change of graphics processing load. If the GPU load changes greatly, this item will generate a large power regulation demand to ensure that the GPU is always within a reasonable power consumption range. CPU (t) is the processor power modulation term that changes with time t. The power consumption of the CPU changes dynamically during the calculation process, which is usually related to the system load and the running tasks. This power consumption is modulated by a sine wave to simulate the periodic changes of the CPU load, such as the alternation between peak load period and low load period. γ is the CPU power consumption weight coefficient, which determines the priority of CPU power consumption in power distribution. A larger γ value indicates that the system will give priority to changes in CPU power consumption and maintain CPU load balance. It is the total RAM power consumption from the initial time to the current time t. The change of RAM power consumption will change with the system memory usage, especially when processing big data or complex tasks, the power consumption of memory will fluctuate greatly. This item reflects the cumulative power consumption of RAM through integration. As a storage module, RAM requires a higher power supply to ensure the stability of data reading and writing during long-term operation. By monitoring its power consumption, power can be allocated to RAM in a timely manner to avoid system performance degradation due to insufficient memory resources. When using the conventional power high-load control formula in the field, the adaptive power allocation power in the wireless projection mode can be obtained. By applying the power high-load control formula provided by the present invention, the adaptive power allocation power in the wireless projection 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 or performance bottlenecks caused by too much or too little power in a single module. Using sinusoidal wave modulation to control CPU power consumption and calculating the accumulation of RAM power consumption by integration can better predict and adjust power consumption changes, reduce power volatility, and thus improve system stability. This formula optimizes the overall power efficiency of the system by dynamically adjusting the power consumption and change rate of each hardware module. Through priority coefficients, instantaneous power consumption change rate, modulation terms and integral calculations, it can adjust power distribution according to the actual needs of the system, ensure smooth and efficient operation of each module, and avoid excessive power consumption and performance bottlenecks.

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

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

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

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

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

[0125] In an embodiment of the present invention, the working mode data of the VR box is divided into different subtasks according to tasks, such as standby mode, charging and discharging mode, wireless screen projection mode, etc. In each working mode, a specific power management task is split out, such as enabling the low-power module when the device is in standby mode, enabling the medium-power module in the charging and discharging mode, etc. Each subtask should include the corresponding power requirements and execution time, as well as the modules and functions involved. For example, in the charging and discharging mode, audio and video decoding, data transmission and screen control modules are the main tasks, while the standby mode mainly involves shutting down non-essential modules. According to the split task data, the detailed information of each power management task, such as execution time, power requirements, modules involved, task priority, etc., is recorded to form a power management task data set. Based on the hardware and software capabilities of the device, a distributed task scheduling algorithm is used to assign power management tasks to different processing units or computing nodes. The power requirements, priorities and dependencies between tasks of the task are considered during the scheduling process. Each task is split into more detailed microtasks to form a device power scheduling microtask data set. Each microtask will be assigned to a different computing node to ensure that each task can be completed within the specified time while optimizing power consumption. Distribute the microtask data set to multiple processing units and process each microtask in parallel. Use a distributed computing framework (such as Hadoop, Spark or other edge computing platforms) to coordinate the execution of microtasks and ensure that multiple tasks are performed synchronously on different computing nodes. During the parallel processing process, track the execution of each microtask in real time and generate power scheduling data based on the task completion. The final device power scheduling data should include the completion status, power consumption and scheduling results of each subtask. According to the device power scheduling data, analyze the power consumption in each working mode. For example, the power consumption in standby mode is low, the power consumption in charging and discharging mode is medium, and the power consumption in wireless projection mode is high. Combine the power consumption and working mode of each microtask to generate power scheduling power usage data. By analyzing the power consumption fluctuations, identify abnormal states of excessive or low power consumption. Based on the results of the power consumption analysis, generate power scheduling power usage data, including power consumption in each working mode, microtask consumption and overall power consumption trend. Use the real-time feedback mechanism to frequently adjust the device power according to the power consumption anomalies or peaks in the power scheduling power usage data. For example, if the power consumption is too high for a short period of time during the operation of the equipment, the system will adjust the power distribution strategy to reduce power consumption and ensure stable operation of the equipment. The detailed data of each power adjustment is recorded, including the time of adjustment, the amplitude of adjustment, the change of power consumption, the feedback response time, etc., to form high-frequency power dispatch feedback record data, which will provide a basis for further optimization of power dispatch.

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

[0127] Divide the power management task data into equipment power tasks to generate power management microtask data; perform microtask load analysis on the power management microtask data to generate power management microtask load data;

[0128] Defining power management optimization targets for power management microtask load data to generate power management optimization target data; formulating task scheduling strategies for power management microtask load data based on the power management optimization target data to generate power management task scheduling strategies;

[0129] Perform task mapping on power management microtask data to generate power management task mapping data; perform resource allocation on power management microtask data through power management task scheduling strategy to generate power management resource allocation data; perform distributed dynamic task migration on power management task mapping data according to power management resource allocation data to generate power management scheduling microtask data set.

[0130] In an embodiment of the present invention, the power management task data is divided into a plurality of power management microtasks 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. Each microtask corresponds to the power management operation of a specific module of the device, for example: turning off irrelevant modules in standby mode and adjusting the power of audio and video decoding in charge and discharge mode. Each microtask is defined in detail, including the power requirements, execution time, module functions, etc. of the microtask, to generate a power management microtask data set. The power management microtask data is load analyzed to evaluate the occupation of device resources by each microtask during execution. For example, audio and video decoding in charge and discharge mode occupies higher computing resources, while tasks in standby mode consume almost no resources. The resource requirements of each microtask are analyzed based on the hardware characteristics of the device (such as CPU, GPU, memory, etc.), and the power management microtask load data is generated. According to the power resource constraints of the device and the requirements in different working modes, the power management optimization goals are defined, such as minimizing power consumption, improving device performance, and extending battery life. Set multiple optimization goals and determine their priorities so that these goals can be comprehensively considered during task scheduling, generate power management optimization target data, and clarify the priority and resource allocation direction of each microtask in scheduling. Based on the power management optimization target data, formulate task scheduling strategies for power management microtask load data. For example, in standby mode, give priority to reducing modules with higher power consumption, while in charge and discharge mode, give priority to ensuring the smooth operation of the decoding module. The task scheduling strategy should consider the dependencies, priorities, and resource conflicts between tasks, formulate the optimal resource allocation and task execution order, and generate power management task scheduling strategies. Task mapping is performed on power management microtask data according to the hardware architecture and resource allocation of the device. 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 modules with lower power consumption. Generate power management task mapping data and record in detail the mapping relationship between each task and device resources. Based on the power management task scheduling strategy, resources are reasonably allocated to each microtask. Resource allocation should be based on task priority, resource requirements, and available hardware resources. For tasks with heavier loads, more computing resources and power can be allocated, and vice versa. Generate power management resource allocation data and record the resource allocation details of each microtask. During task execution, monitor the execution status of the task and the real-time power consumption of the device. If the execution of a 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 tasks will be remapped to suitable computing resources to ensure smooth execution of the task while reducing power consumption.Through power management resource allocation and dynamic task migration, a power management scheduling microtask dataset is generated to ensure that tasks can be efficiently executed on different resource units, avoid resource conflicts and ensure the reasonable allocation of power.

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

[0132] Step S41: performing power management energy efficiency evaluation on high-frequency power electric power dispatching feedback record data to generate power management energy efficiency evaluation data;

[0133] Step S42: Optimizing the VR box power distribution strategy through the power management energy efficiency evaluation data to generate a VR box power distribution optimization strategy;

[0134] Step S43: Use the VR box power distribution optimization strategy to perform management optimization verification on the high-frequency power scheduling feedback record data, and generate VR box power management optimization verification data to perform efficient power management operations for the multi-device compatible wireless projection VR box.

[0135] In an embodiment of the present invention, power management related information, including power consumption data, scheduling strategy execution status, task load status, etc., is extracted from high-frequency power dispatch feedback record data. The feedback data is sorted and classified in units of time to ensure that each historical record of high-frequency power adjustment can be tracked in detail. Design energy efficiency evaluation indicators, such as power efficiency (EE), power consumption optimization amplitude, ratio of task execution time to power consumption, etc. Analyze the high-frequency power dispatch feedback data and calculate the energy efficiency score corresponding to each feedback record. For example, based on the rate of change of power consumption, the load of the adjusted system and the change of power consumption, evaluate whether the system has achieved the expected energy saving effect. 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 identification of performance bottlenecks and optimization space. Based on the energy efficiency evaluation data generated in step S41, optimization goals are set, including maximizing power efficiency, reducing power consumption while maintaining VR experience fluency, ensuring power load balance during peak hours, etc. Determine the energy efficiency optimization goals under different modes (such as standby, playback, projection, etc.), and set appropriate weights for each mode. Design targeted optimization strategies based on the problems and bottlenecks found in the evaluation data analysis. For example, for modes with excessively high power consumption, the working status of certain modules can be adjusted, or power resources can be reallocated. Use optimization algorithms (such as dynamic programming, heuristic algorithms, etc.) to tune the power allocation strategy to ensure that the optimized strategy can reduce power consumption and improve the overall energy efficiency of the device. Generate a VR box power distribution optimization strategy based on the optimization goals and algorithm results. This strategy will combine the needs of multiple working modes to ensure the reasonable allocation of power resources under different loads and maximize the energy efficiency performance of the system. Apply the generated power management optimization strategy 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, check whether the strategy effectively reduces power consumption and optimizes power distribution. Verify in multiple devices and usage scenarios to ensure that the optimization strategy can adapt to different load conditions and power consumption requirements. Synchronously verify the power management requirements of different devices (such as VR boxes, wireless projection devices, etc.) to ensure that the strategy has good compatibility and adaptability. Generate VR box power management optimization verification data based on the data in the verification process. This data records the effect of the optimization strategy, including the power consumption changes and load balance of each device after optimization, and compares it with the original state. Through the analysis of optimization verification data, the power management strategy is further adjusted and optimized to ensure that it can achieve the best power distribution and energy efficiency performance in a multi-device compatible wireless projection environment. This optimization strategy ultimately provides a theoretical and practical basis for the power management of multi-device compatible wireless projection VR boxes, ensuring system stability, energy saving, and extending the use time of the device.

[0136] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0137] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may 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 the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. An efficient power management method for a multi-device compatible wireless projection VR box, characterized in that: The following steps are involved: Step S1: Acquire device power consumption data; Perform VR box working mode analysis on the device power consumption collection data to generate VR box working mode data, where the VR box working mode data includes standby mode, charging and discharging mode, and wireless projection mode; Extracting power consumption dynamic features from the collected data of device power consumption to obtain power consumption mode dynamic feature data; mapping mode parameters based on the power consumption mode dynamic feature data to generate a power consumption mode mapping table; Step S2: performing 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, the transmission distance power consumption weight is allocated to the VR box working mode data to generate transmission power consumption weight allocation data; Based on the transmission power weight allocation data, power allocation strategies are generated for standby mode, charging and discharging mode, and wireless projection mode to obtain the power allocation strategy for the VR box power supply; Step S3: Perform distributed task parallel processing on the VR box working mode data through the VR box power distribution strategy to generate device power scheduling data; Analyze the high-frequency power consumption of the VR box working mode data according to the equipment power dispatch data, and generate high-frequency power dispatch feedback record data; Step S4: performing power management energy efficiency evaluation on the high-frequency power electric power dispatching feedback record data to generate power management energy efficiency evaluation data; The power management energy efficiency evaluation data is used to manage and optimize the power distribution strategy of the VR box, and generate VR box power management optimization verification data to perform efficient power management operations for multi-device compatible wireless projection VR boxes.

2. The efficient power management method for a multi-device compatible wireless projection VR box according to claim 1 is characterized in that: Step S1 includes the following steps: Step S11: using the power consumption monitoring module in the VR box to collect power consumption parameters of the connected device to obtain device power consumption collection data, wherein the device power consumption collection module includes internal device power consumption collection data and external connected device power consumption collection data; Step S12: performing VR box working mode analysis on the internal device power consumption collection data and the external connection device power consumption collection data to generate VR box working mode data, wherein the VR box working mode data includes a standby mode, a charging and discharging mode, and a wireless screen projection mode; Step S13: performing data preprocessing on the device power consumption collection data to generate standard device power consumption collection data, wherein the data preprocessing includes data cleaning, data denoising, missing value filling and data standardization; performing mode classification on the standard device power consumption collection data according to the VR box working mode data to generate power consumption mode classification data; Step S14: extracting power consumption dynamic features from the power consumption mode classification data to obtain power consumption mode dynamic feature data; and performing mode parameter mapping based on the power consumption mode dynamic feature data to generate a power consumption mode mapping table.

3. The efficient power management method for a multi-device compatible wireless screen projection VR box according to claim 2 is characterized in that: The VR box working mode analysis of the internal device power consumption data and the external connected device power consumption data includes: Perform power consumption collection and discrimination on the power consumption collection data of the internal device and the power consumption collection data of the externally connected device, and when only the power consumption collection data of the internal device is collected, perform a first mode mark to generate a standby mode; When the power consumption data collected by the internal device and the power consumption data collected by the externally connected device are collected at the same time, the power consumption fluctuation analysis is performed on the power consumption data collected by the externally connected device to generate the power consumption fluctuation analysis data; based on the power consumption fluctuation analysis data, the power consumption fluctuation spectrum is converted to the power consumption fluctuation spectrum diagram of the power consumption data collected by the externally connected device; Perform fluctuation frequency stability analysis on the power consumption fluctuation spectrum diagram to generate power consumption fluctuation stability data and power consumption fluctuation instability data; perform second mode marking on the internal device power consumption collection data and the external connection device power consumption collection data based on the power consumption fluctuation stability data to obtain the charging and discharging mode; Based on the power consumption fluctuation instability data, the internal device power consumption collection data and the external connection device power consumption collection data are marked with a third mode to obtain the wireless screen projection mode.

4. The efficient power management method for a multi-device compatible wireless screen projection VR box according to claim 1 is characterized in that: Step S2 includes the following steps: Step S21: performing time series sorting on the power consumption mode mapping table to generate a power consumption feature time series sorting data set; performing time series segmentation on the power consumption feature time series sorting data set to generate power consumption time series segmentation data; performing sliding window setting on the power consumption time series segmentation data to generate a time series power consumption fragment data set; Step S22: performing intelligent prediction of device power consumption on the time series power consumption fragment data set to generate device power consumption prediction data; Step S23: Analyze the power demand of the device in the standby mode, the charging and discharging mode, and the wireless projection mode based on the device power consumption prediction data to generate device power demand data; perform transmission distance power consumption weight allocation based on the device power demand data to generate transmission power consumption weight allocation data; Step S24: Generate power distribution strategies for the standby mode, the charging and discharging mode, and the wireless projection mode based on the transmission power weight allocation data to obtain a VR box power distribution strategy.

5. The efficient power management method for a multi-device compatible wireless screen projection VR box according to claim 4 is characterized in that: Step S22 includes the following steps: Step S221: extract key power consumption timing characteristics from the device power consumption fragment data set to obtain key power consumption timing characteristic data, wherein the key power consumption timing characteristic extraction includes power consumption moving average value extraction and power consumption change rate extraction; Step S222: Divide the key power consumption timing characteristic data into data sets to generate a model training set and a model test set; perform model training on the model training set using a recursive neural network algorithm to generate a power consumption prediction pre-model; perform model optimization iteration on the power consumption prediction pre-model using 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 for a multi-device compatible wireless screen projection VR box according to claim 4 is characterized in that: Step S24 includes the following steps: Step S241: Obtaining total power data of the VR box device; Step S242: marking the total power data of the VR box device as a low power state based on the standby mode, and performing minimum power resource allocation on the device power consumption fragment data set based on the low power state to generate standby mode power allocation data; Step S243: Screening the operation data of the working modules in the charging and discharging mode for the total power data of the VR box device based on the charging and discharging mode, and generating the operation screening data of the working modules in the charging and discharging mode, wherein the operation screening data of the working modules in the charging and discharging mode 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: marking 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 performing hierarchical power control on the device power consumption segment data set based on the medium power state to generate charge and discharge mode power distribution data; Step S245: filtering the operation data of the working module of the wireless screen projection mode on the total power data of the VR box device based on the wireless screen projection mode, and generating the operation screening data of the working module of the wireless screen projection mode, wherein the operation screening data of the working module of the wireless screen projection mode 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: marking a high power state according to the data transmission module operation data, the screen refresh module operation data and the graphics processing module operation data, and adaptively allocating power to the device power consumption segment data set using the power high load control formula based on the high power state, thereby generating wireless screen projection mode power allocation data; Step S247: Integrate the power strategy through the standby mode power distribution data, the charge-discharge mode power distribution data, and the wireless projection mode power distribution data to generate a VR box power distribution strategy.

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

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

9. The efficient power management method for a multi-device compatible wireless screen projection VR box according to claim 8 is characterized in that: Distributed task scheduling for power management task data includes: Divide the power management task data into equipment power tasks to generate power management microtask data; perform microtask load analysis on the power management microtask data to generate power management microtask load data; Defining power management optimization targets for power management microtask load data to generate power management optimization target data; formulating task scheduling strategies for power management microtask load data based on the power management optimization target data to generate power management task scheduling strategies; Perform task mapping on power management microtask data to generate power management task mapping data; perform resource allocation on power management microtask data through power management task scheduling strategy to generate power management resource allocation data; perform distributed dynamic task migration on power management task mapping data according to power management resource allocation data to generate power management scheduling microtask data set.

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

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