Energy management method and system for intelligent micro display terminal

Abstract

The invention relates to an energy management method and system for an intelligent micro display terminal. The method comprises the following steps: acquiring operation state parameters of the micro display terminal; inputting the operation state parameters into a preset energy consumption evaluation model, and calculating the operation state parameters based on the preset energy consumption evaluation model to obtain a current energy consumption level; calling a corresponding energy control strategy according to the current energy consumption level, and dynamically correcting execution parameters of the energy control strategy based on the state change of the environment where the micro display terminal is located and the user interaction behavior change to obtain a corrected energy control strategy; adjusting operation parameters of the micro display terminal according to the corrected energy control strategy; and acquiring response indexes of the micro display terminal after the operation parameters are adjusted, and feeding back the response indexes to a preset energy consumption evaluation model to optimize the decision process of the next round of energy control strategy. The method has the effect of improving the reliability of the equipment.

Description

Technical Field

[0001] The present application relates to the technical field of smart terminal energy management, and in particular to an energy management method and system for a smart micro display terminal. Background Art

[0002] With the widespread adoption of smart wearable devices, micro-monitoring terminals, and lightweight information prompt modules, micro-display terminals, as core interactive interfaces, require a continuous and stable power supply, which is crucial for improving user experience and device reliability. However, in practice, micro-display terminals are often deployed in scenarios with insufficient lighting, complex environments, or limited space. These scenarios include indoor low-light environments, industrial protective equipment, and enclosed work areas. This makes it difficult for traditional power supply methods that rely on solar energy or fixed-capacity batteries to achieve long-term, stable operation.

[0003] In existing technologies, two approaches are commonly used to alleviate the energy bottleneck of micro-terminals: one is to extend battery life by increasing battery capacity, and the other is to reduce energy consumption through power consumption control technology at the static circuit level. However, the former will significantly increase the weight and volume of the device, undermining the lightweight and portable characteristics of the device; the latter, due to the lack of an environmental adaptive mechanism, cannot respond in real time when the usage scenario is changing or the user behavior is unstable, making it difficult to ensure the dynamic balance of the power supply system. In addition, although some solutions have introduced policy control modules, they are often triggered based on fixed thresholds, lack the ability to comprehensively model the terminal's operating status, environmental status, and user behavior, and have not formed a feedback loop, making it difficult to continuously optimize energy efficiency.

[0004] The above-mentioned existing technical solutions have the following defects: under conditions of frequent changes in operating status or long-term deployment, it is difficult for the terminal to accurately judge the energy consumption level and effectively call the strategy, the energy control strategy cannot be dynamically corrected according to environmental fluctuations and user behavior, and the adjustment of operating parameters lacks real-time feedback basis, which seriously affects the reliability of the equipment and the user's interactive experience. Therefore, there is room for improvement. Summary of the Invention

[0005] In order to improve the reliability of the equipment, the present application provides an energy management method and system for an intelligent micro display terminal.

[0006] The above-mentioned invention objective of this application is achieved through the following technical solutions: An energy management method for an intelligent micro display terminal, the energy management method for an intelligent micro display terminal comprising: Obtaining the operating status parameters of the micro display terminal; Inputting the operating status parameters into a preset energy consumption evaluation model, and calculating the operating status parameters based on the preset energy consumption evaluation model to obtain a current energy consumption level; Invoking a corresponding energy control strategy according to the current energy consumption level, and dynamically revising execution parameters of the energy control strategy based on changes in the environmental state of the micro display terminal and changes in user interaction behavior to obtain a revised energy control strategy; adjusting the operating parameters of the micro display terminal according to the revised energy control strategy; The response index of the micro display terminal after adjusting the operating parameters is collected, and the response index is fed back to the preset energy consumption evaluation model to be used for optimizing the decision-making process of the next round of energy control strategy.

[0007] By adopting the above technical solution, by obtaining the operating status parameters of the micro display terminal, the current operating load, battery status and environmental influencing factors of the device can be accurately perceived, thereby providing real and reliable input data for subsequent energy consumption evaluation; by inputting the operating status parameters into the energy consumption evaluation model for calculation, the current power consumption level can be intelligently classified, thereby providing an accurate basis for policy control; by calling the energy control strategy according to the current energy consumption level and dynamically correcting it in combination with the environmental status and user behavior, personalized energy management strategy matching can be achieved, thereby improving the adaptability and responsiveness of the control strategy; by adjusting the terminal operating parameters and collecting feedback results and returning them to the model, a closed-loop optimization mechanism for energy control can be constructed, thereby achieving continuous adaptive adjustment of the policy effect and improving the overall energy utilization efficiency and system stability.

[0008] In one example, the present application may be further configured as follows: obtaining the operating status parameters of the micro display terminal includes: Detecting the difference between the current screen brightness of the micro display terminal and the ambient brightness; Monitoring the real-time rate of change of the battery charge of the micro display terminal; Collecting the signal strength, connection status and data transmission frequency of the communication module of the micro display terminal; Statistics are collected on the number of user operations, dwell time, and complexity of operation paths within a unit of time.

[0009] By adopting the above technical solution, by detecting the difference between the screen brightness and the ambient brightness, the degree of matching between the screen power consumption and the lighting conditions can be evaluated, thereby providing a perceptual basis for brightness adjustment; by monitoring the battery charge change rate, the terminal energy consumption trend can be reflected, thereby triggering policy correction in a timely manner; by collecting the status and transmission frequency of the communication module, the communication power consumption load can be quantified, thereby rationally planning communication resources; by counting user interaction data, the impact of usage behavior on energy consumption can be dynamically analyzed, thereby supporting behavior-driven policy adjustments.

[0010] In one example, the present application may be further configured as follows: before inputting the operating status parameters into a preset energy consumption evaluation model, the energy management method for an intelligent micro display terminal further includes: Constructing a training data set based on the state parameters and corresponding energy consumption data collected by multiple micro display terminals during historical operation; Normalizing the state parameters in the training data set to construct an input feature vector in a unified format; Inputting the input feature vector into a lightweight neural network model for training, wherein the neural network model includes an input layer, at least one hidden layer and an output layer for outputting a corresponding energy consumption level label; By minimizing the loss function between the predicted energy consumption level and the actual energy consumption level marked in the training data, the parameters of the neural network model are optimized and trained to generate the preset energy consumption evaluation model.

[0011] By adopting the above technical solutions and building a training data set based on historical data, it is possible to cover energy consumption behaviors in various usage scenarios, thereby improving the generalization ability of the model; by constructing input feature vectors in a unified format through normalization processing, the consistency and computational efficiency of the model input can be improved; by using lightweight neural networks for training, effective learning of energy consumption levels can be completed without increasing the burden on the equipment; by minimizing prediction errors for parameter optimization, the model's prediction accuracy for energy consumption levels can be improved, thereby improving the rationality of subsequent strategy matching.

[0012] In one example, the present application may be further configured as follows: inputting the operating status parameter into a preset energy consumption evaluation model, and calculating the operating status parameter based on the preset energy consumption evaluation model to obtain the current energy consumption level includes: Performing data preprocessing on the operating status parameters to obtain preprocessed operating status parameters, wherein the data preprocessing includes missing value filling, dimension normalization, and time window grouping; Constructing the pre-processed operating state parameters into a time series feature vector and inputting it into the preset energy consumption evaluation model; The energy consumption assessment model is used to infer the time series feature vector and output a corresponding energy consumption level label, which is used to identify the power consumption risk level of the current micro display terminal, and finally the current energy consumption level is obtained.

[0013] By adopting the above technical solution, by filling in missing values, normalizing and grouping the operating status parameters into time windows, the data integrity and consistency of the model's temporal input can be improved, thereby enhancing the stability of the model; by constructing a time series feature vector input model, the state change trend can be captured more accurately, thereby improving the timeliness of energy consumption level prediction; by outputting energy consumption level labels through model reasoning, the current power consumption risk level can be quickly identified, thereby assisting in the dynamic selection of control strategies.

[0014] In one example, the present application may be further configured as follows: calling a corresponding energy control strategy according to the current energy consumption level, and dynamically modifying execution parameters of the energy control strategy based on changes in the environmental state of the micro display terminal and changes in user interaction behavior, wherein the modified energy control strategy includes: Selecting a target control strategy from a preset multi-level energy control strategy set based on the current energy consumption level, the target control strategy including control instructions for display regulation, communication regulation, and task scheduling; Monitoring the ambient brightness change data and user operation behavior data of the micro display terminal, and matching and judging the ambient brightness change data and user operation behavior data with corresponding policy adjustment trigger conditions respectively; When the ambient brightness change data or the user operation behavior data meets the policy adjustment trigger condition, the parameter configuration items in the target control policy are modified based on the preset adjustment rules, and the modification includes adjusting the display brightness target value, the communication module duty cycle, and the execution priority of the background task; The revised energy control strategy is generated for use in subsequent steps to adjust the operating parameters of the micro display terminal.

[0015] By adopting the above technical solution and selecting the target strategy based on the current energy consumption level, it is possible to ensure that the control instructions match the actual operating status of the device, thereby avoiding invalid adjustments; by matching the changes in ambient brightness with user behavior data to trigger conditions, behavior-driven strategy adaptation can be achieved, thereby improving the system's intelligent response; by executing control parameter correction operations, the display brightness, communication cycle and task scheduling parameters can be dynamically adjusted to optimize power consumption distribution and user experience; by generating a corrected control strategy for subsequent steps, it is possible to ensure that the adjustment actions are consistent and continuous, thereby forming a stable strategy execution link.

[0016] In one example, the present application may be further configured as follows: adjusting the operating parameters of the micro display terminal according to the revised energy control strategy includes: Adjusting the brightness output value and refresh rate of the display module of the micro display terminal according to the parameter configuration items in the revised energy control strategy; adjusting the transmission power, wake-up period, and data transmission interval of the communication module of the micro display terminal according to the communication control instructions in the revised energy control strategy; According to the task scheduling configuration items in the revised energy control strategy, the execution priority, delay threshold and maximum allowable execution time of the background task are set.

[0017] By adopting the above technical solutions, by adjusting the brightness output and refresh frequency of the display module, the screen power consumption can be effectively reduced, thereby extending the terminal's battery life; by adjusting the transmission power and data sending frequency of the communication module, the communication energy consumption can be dynamically controlled, thereby improving the overall communication energy efficiency ratio; by configuring the priority and maximum execution time of background tasks, the resource occupation of non-critical tasks can be suppressed, thereby freeing up processing resources to support the stable operation of core functions.

[0018] In one example, the present application may be further configured as follows: collecting the response index of the micro display terminal after adjusting the operating parameters and feeding the response index back to the preset energy consumption evaluation model includes: Collecting the adjusted power consumption change, screen response delay, communication stability score, and user interaction response frequency of the micro display terminal to construct a response indicator set; Normalizing the response indicator set to generate a feedback vector; The feedback vector is used to update the parameter configuration of the preset energy consumption assessment model to improve the accuracy and dynamic response capability of energy consumption level prediction.

[0019] By adopting the above technical solution, by collecting key response indicators such as power consumption changes and screen response delays, the actual effects of operating parameter adjustments can be comprehensively evaluated, thereby verifying the effectiveness of the control strategy; by standardizing the response indicators to generate feedback vectors, multi-dimensional feedback information can be integrated into a unified structure to facilitate model processing; by using feedback vectors to update the parameter configuration of the energy consumption assessment model, the model's assessment accuracy under different environmental and behavioral conditions can be improved, thereby realizing the self-evolution of the strategy recommendation and prediction mechanism.

[0020] The second object of the present invention is achieved through the following technical solutions: An energy management system for an intelligent micro display terminal, the energy management system for an intelligent micro display terminal comprising: A parameter acquisition module is used to obtain the operating status parameters of the micro display terminal; a calculation module, configured to input the operating status parameters into a preset energy consumption evaluation model, calculate the operating status parameters based on the preset energy consumption evaluation model, and obtain a current energy consumption level; a strategy determination module, configured to call a corresponding energy control strategy according to the current energy consumption level, and dynamically modify execution parameters of the energy control strategy based on changes in the environmental state of the micro display terminal and changes in user interaction behavior, thereby obtaining a modified energy control strategy; a parameter adjustment module, configured to adjust the operating parameters of the micro display terminal according to the revised energy control strategy; The feedback module is used to collect the response index of the micro display terminal after adjusting the operating parameters, and feed the response index back to the preset energy consumption evaluation model to optimize the decision-making process of the next round of energy control strategy.

[0021] By adopting the above technical solution, by setting up a parameter acquisition module, a calculation module and a strategy determination module, it is possible to complete the operating status perception and power consumption level judgment, thereby providing a data basis for energy control decision-making; by setting up a parameter adjustment module, it is possible to accurately execute operating parameter updates according to the revised control strategy, thereby dynamically regulating the terminal power consumption; by setting up a feedback module, it is possible to realize the collection and feedback of control results, thereby building an energy management closed-loop system and improving control accuracy and system adaptability.

[0022] In summary, this application has the following beneficial technical effects: 1. By acquiring the operating status parameters of the micro display terminal, it is possible to accurately perceive the device's current operating load, battery status, and environmental factors, thereby providing reliable input data for subsequent energy consumption assessment. By inputting the operating status parameters into the energy consumption assessment model for calculation, it is possible to achieve intelligent classification of the current power consumption level, thus providing an accurate basis for policy control. 2. By calling the energy control strategy according to the current energy consumption level and dynamically correcting it in combination with the environmental status and user behavior, personalized energy management strategy matching can be achieved, thereby improving the adaptability and responsiveness of the control strategy; by adjusting the terminal operating parameters and collecting feedback results and returning them to the model, a closed-loop optimization mechanism for energy control can be established, thereby achieving continuous adaptive adjustment of the strategy effect and improving the overall energy utilization efficiency and system stability. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 This is a flow chart of an energy management method for an intelligent micro display terminal in one embodiment of the present application; Figure 2 This is a flowchart for implementing step S10 in an energy management method for an intelligent micro display terminal in one embodiment of the present application; Figure 3 This is a flowchart for implementing step S20 in an energy management method for an intelligent micro display terminal in one embodiment of the present application; Figure 4 This is a flowchart for implementing step S20 in an energy management method for an intelligent micro display terminal in one embodiment of the present application; Figure 5 This is a flowchart for implementing step S30 in an energy management method for an intelligent micro display terminal in one embodiment of the present application; Figure 6 This is a flowchart for implementing step S40 in an energy management method for an intelligent micro display terminal in one embodiment of the present application; Figure 7 This is a flowchart for implementing step S50 in an energy management method for an intelligent micro display terminal in one embodiment of the present application; Figure 8 This is a principle block diagram of an energy management system for an intelligent micro display terminal in one embodiment of the present application. DETAILED DESCRIPTION

[0024] The present application is further described in detail below with reference to the accompanying drawings.

[0025] In one embodiment, if Figure 1 As shown, the present application discloses an energy management method for an intelligent micro display terminal, which specifically includes the following steps: S10: Obtaining the operating status parameters of the micro display terminal.

[0026] Specifically, the operation status update task is executed periodically by scheduling the operation parameter collection process, the recorded current screen brightness value is used as the brightness status input, the battery power percentage, current output value and discharge rate provided in the power management unit are used as the power status input, and the channel occupancy rate, connection status identifier and current data packet transmission frequency recorded in the communication stack are read as communication load input. In addition, the number of operation clicks, the type of triggered interactive controls and the interaction interval duration in the last 10 seconds are extracted in real time from the device user interaction log as user behavior indicator input. After the collection is completed, it is integrated into a structured parameter set through the status cache module and transmitted to the energy consumption assessment module for subsequent analysis and processing.

[0027] S20: Inputting the operating status parameters into a preset energy consumption evaluation model, calculating the operating status parameters based on the preset energy consumption evaluation model, and obtaining a current energy consumption level.

[0028] Specifically, after the state parameter set is passed in, the parameter standardization processing flow is first started, and the maximum and minimum normalization transformations are performed on all numerical type parameters. The Z-score method is used to process the nonlinear distribution parameters to improve data consistency. The processed parameters are filled into the feature vector buffer in a preset order to form a complete input vector. The input vector is sent to the energy consumption assessment model deployed locally on the device. The energy consumption assessment model is constructed with a multi-layer perception structure. The hidden layer in the model is used to calculate the state weight and mode mapping relationship and generate an energy consumption score label in the output layer. The label is mapped to a discrete level value as the current energy consumption level output by comparing with the preset energy consumption level threshold table. The level information is written into the state control context for subsequent policy process reference. For example, if the output result falls in the "0.6-0.8" range, it is judged to be a medium-level energy consumption state. The level label is then passed as an input item of the control strategy matching module.

[0029] S30: calling the corresponding energy control strategy according to the current energy consumption level, and dynamically correcting the execution parameters of the energy control strategy based on the changes in the environmental state of the micro display terminal and the changes in the user's interactive behavior to obtain a corrected energy control strategy.

[0030] Specifically, after receiving the current energy consumption level label, the energy consumption level-strategy mapping table is immediately searched and the corresponding energy control strategy template is loaded as the initial control strategy. The control strategy includes a parameter setting framework for multiple module control items. Then, the environmental perception module is scheduled to collect the current external brightness sensor value and the lighting intensity data at the device location. Combined with the user's current operation type, operation rhythm and stay time interval recorded in the behavior monitoring module as input features, a set of strategy correction rule functions are executed to dynamically replace and adjust the display module brightness setting value, communication module active cycle and task scheduling delay parameters in the original strategy template. The adjusted control strategy is reorganized into a parameter execution queue and written into the control strategy buffer pool as a revised energy control strategy for use in the running parameter update process. For example, the screen refresh rate is reduced from the default value of 60Hz to 45Hz, and the background task running time window is compressed from 30 seconds to 20 seconds. Finally, a revised energy control strategy that adapts to the current state is output.

[0031] S40: Adjusting the operating parameters of the micro display terminal according to the revised energy control strategy.

[0032] Specifically, according to the revised energy control strategy's instruction structure, the mapping relationship between target modules and target parameter items is sequentially read, and the corresponding set values ​​are submitted to the driver layer call entry point to complete the parameter implementation operation. Specifically, the brightness register value of the display control interface is set to the new brightness value defined in the strategy, and the refresh rate interface function is called to adjust the screen redraw period. The original timed transmission interval parameter in the communication scheduler is replaced with the newly configured period and the low-power protocol stack is restarted. In addition, the execution threshold and resource allocation time slice of all low-priority tasks in the background task allocation table are dynamically updated to the delayed start parameters in the current strategy. This process forms a configuration snapshot of the adjusted version in the running status record area for easy backtracking and comparison. For example, executing the brightness level adjustment command in the display control interface to reduce the brightness from 80% to 55%, setting the low-power Bluetooth mode in the communication interface and adjusting the data transmission period from 10 seconds to 30 seconds, and adjusting the execution priority of some non-core services in the task scheduling module and setting the delayed start time window to 15 seconds. All operation parameter changes are executed in the order of the timestamps in the strategy until they are completed.

[0033] S50: collecting response indicators of the micro display terminal after adjusting the operating parameters, and feeding the response indicators back to the preset energy consumption evaluation model to be used for optimizing the decision-making process of the next round of energy control strategy.

[0034] Specifically, within the preset observation window after the operation parameter adjustment is completed, the operation feedback acquisition module is called to periodically read the average current and actual power consumption values ​​recorded by the device power management chip, detect the completion and trigger delay time of the screen refresh action to calculate the user operation response delay, evaluate the current handshake success rate, packet loss rate and data throughput of the communication module as communication stability indicators, and synchronously record the user's operation frequency and stay time changes in the adjusted time period. All response indicators will be assembled into response vectors according to the set dimensions, and after normalization, they will form feedback input data, which will be used as auxiliary reference input before the next energy consumption level evaluation calculation and written into the energy consumption evaluation model, so that the model can dynamically adjust the feature weights or enable new evaluation paths according to the feedback effect, thereby achieving strategy self-optimization and improving energy consumption prediction accuracy.

[0035] By adopting the above technical solution, by obtaining the operating status parameters of the micro display terminal, the current operating load, battery status and environmental influencing factors of the device can be accurately perceived, thereby providing real and reliable input data for subsequent energy consumption evaluation; by inputting the operating status parameters into the energy consumption evaluation model for calculation, the current power consumption level can be intelligently classified, thereby providing an accurate basis for policy control; by calling the energy control strategy according to the current energy consumption level and dynamically correcting it in combination with the environmental status and user behavior, personalized energy management strategy matching can be achieved, thereby improving the adaptability and responsiveness of the control strategy; by adjusting the terminal operating parameters and collecting feedback results and returning them to the model, a closed-loop optimization mechanism for energy control can be constructed, thereby achieving continuous adaptive adjustment of the policy effect and improving the overall energy utilization efficiency and system stability.

[0036] In one embodiment, if Figure 2 As shown, in step S10, the operating status parameters of the micro display terminal are obtained, which specifically include: S11: Detecting the difference between the current screen brightness of the micro display terminal and the ambient brightness.

[0037] Specifically, by accessing the currently effective brightness setting value in the display control module as the display output benchmark, and synchronously calling the external environment perception interface to obtain the ambient light intensity value read by the close-range light sensor, the difference between the two is calculated to obtain the relative deviation between the current screen and ambient brightness. During the calculation process, the built-in light intensity conversion table is called to convert the original sensor data into standard light units to improve the comparison accuracy. The difference result is temporarily cached in the operating parameter data area for subsequent model evaluation input. If the difference is too high, it may cause the strategy to lower the brightness level to reduce energy consumption. If the difference is too low, it may prompt that the brightness needs to be increased to ensure user readability.

[0038] S12: Monitor the real-time change rate of the battery power of the micro display terminal.

[0039] Specifically, the continuous battery percentage snapshot values ​​recorded in the battery status reading service are called, and the battery change between the two readings is calculated by setting a time interval window and divided by the time interval to obtain the current battery decline rate. This rate is marked as the energy consumption trend indicator for the current period, and the timestamp of this round of battery measurement is recorded for reference in the next round of calculation. The change rate will be used as one of the input features of energy consumption evaluation to participate in energy consumption level judgment. When a rapid increase in the decline rate is detected, the strategy optimization module will be triggered to switch to the energy consumption protection strategy in advance to slow down the battery attenuation process.

[0040] S13: Collecting the signal strength, connection status and data transmission frequency of the communication module of the micro display terminal.

[0041] Specifically, the communication layer interface is first called to obtain the signal receiving strength indicator RSSI corresponding to the current connection channel as a connection quality parameter, and then the connection management stack is checked to see if there is an active session identifier to confirm whether the current connection status is valid. When the valid connection condition is confirmed, the number of data packet transmissions and the total transmission time in the past period are counted to calculate the transmission frequency index. The three indicators are combined to form the current communication load state vector and written into the operating parameter cache. This vector will serve as an important input item when evaluating the impact of communication power consumption. When the signal strength is lower than the set threshold or the transmission frequency increases abnormally, it will guide the strategy layer to adjust the working mode or wake-up cycle of the communication module to optimize power consumption.

[0042] S14: Count the number of user operations, dwell time, and complexity of operation paths within a unit of time.

[0043] Specifically, the interaction log analysis module is turned on to parse the touch event stream, and the total number of valid touch events in the set time window is counted as the operation frequency indicator. At the same time, the page residence time after each event is recorded and the average residence time is calculated as the interaction continuity parameter. At the same time, it is analyzed whether the user's jump path between multiple interfaces involves multi-level pages, complex branches or repeated rollbacks to quantify the path complexity. The set of operation behavior parameters is organized into a user interaction feature vector and sent to the operation status parameter set. This feature will be used to judge the current user activity and interaction complexity to assist the policy decision module in evaluating the behavioral adaptability of the power consumption control strategy.

[0044] In one embodiment, if Figure 3 As shown, before step S20, that is, before inputting the operating status parameters into the preset energy consumption evaluation model, the energy management method of the intelligent micro display terminal further includes: S201: Constructing a training data set based on state parameters and corresponding energy consumption data collected by multiple micro display terminals during historical operation.

[0045] Specifically, during the training preparation stage, historical operation log files are extracted from multiple micro-display terminals in turn, and the status parameter information recorded therein, such as display brightness settings, communication module activity status, battery power changes, and user interaction frequency, are parsed respectively. The current consumption data in the corresponding time period is extracted from the power consumption monitoring module as the energy consumption label. The timestamp alignment method is used to establish an association between each set of state parameters and their corresponding actual energy consumption values. The continuous state segments are divided into standard training samples through the sliding window method. Finally, a training data set containing a large number of historical state features and labeled energy consumption data pairs is constructed for model learning.

[0046] S202: Normalize the state parameters in the training data set to construct an input feature vector in a unified format.

[0047] Specifically, the numerical distribution characteristics of each state parameter in the training data set are analyzed, and continuous variables such as brightness, power, transmission frequency, etc. are linearly compressed using maximum and minimum normalization or logarithmic scaling. Categorical variables such as connection status or operation mode are one-hot encoded to construct an input vector format. After the processing is completed, the state parameters in all samples are arranged and combined according to the preset dimensions to form an input vector of uniform length. Each input vector serves as the feature part of a training sample and together with the corresponding energy consumption level label constitutes the data structure required for neural network training. The entire training set is cached in the model training interface module for subsequent calculation calls.

[0048] S203: Input the input feature vector into a lightweight neural network model for training. The neural network model includes an input layer, at least one hidden layer and an output layer, which is used to output a corresponding energy consumption level label.

[0049] Specifically, a multi-layer feedforward neural network structure with an input layer, a single hidden layer and an output layer is constructed, in which the number of neurons in the input layer is consistent with the dimension of the input vector after normalization. The hidden layer uses ReLU or Sigmoid activation function to process nonlinear mapping relationships. The output layer adopts a Softmax structure to support the classification output of multiple categories of energy consumption level labels. The input vectors of all samples in the training set are fed into the neural network in batches for forward propagation calculation, and the predicted output generated by each round of iteration is compared with the actual energy consumption level label to record the error results. The overall design of the model structure is lightweight, compressible and suitable for embedded terminal deployment, ensuring a balance between training efficiency and subsequent inference speed.

[0050] S204: Optimizing and training the parameters of the neural network model by minimizing the loss function between the predicted energy consumption level and the actual energy consumption level marked in the training data to generate a preset energy consumption assessment model.

[0051] Specifically, during each round of training, the cross entropy loss value between the predicted level distribution output by the neural network and the actual energy consumption level label is calculated as the result of the loss function of the current round, and the gradient descent algorithm or its improved algorithm such as the Adam optimizer is used to backpropagate and update the network parameters. The iteration is continued until the loss value converges to the set threshold or reaches the maximum number of training rounds. The final converged network weights and structural configuration are saved as the initial version of the energy consumption assessment model. The model will be used locally in the terminal as a lightweight prediction tool to evaluate the energy consumption level corresponding to the operating status in real time, providing data support for subsequent energy control strategy decisions.

[0052] In one embodiment, if Figure 4As shown, in step S20, the operating state parameters are input into a preset energy consumption evaluation model, and the operating state parameters are calculated based on the preset energy consumption evaluation model to obtain the current energy consumption level, which specifically includes: S21: Perform data preprocessing on the operating status parameters to obtain preprocessed operating status parameters. The data preprocessing includes missing value completion, dimension normalization, and time window grouping.

[0053] Specifically, after receiving the operating status parameters, the data integrity of each dimension is first checked. If missing items are detected, the interpolation algorithm is called to fill in the missing items with the mean value of the historical similar scenario or the trend difference of the previous and next data points. After filling, all continuous parameters are uniformly mapped to the numerical range of 0 to 1 for normalization to eliminate the dimensional differences between different physical quantities. At the same time, the timestamp information is used as the main axis to slice and group all state parameters according to continuous fixed time windows. In each group of windows, a sequence of state data points arranged in the sampling order is retained to form a preprocessed data set with a clear structure, unified dimensions and time order for subsequent vectorization processing and model input.

[0054] S22: The pre-processed operating status parameters are constructed as a time series feature vector and input into a preset energy consumption evaluation model.

[0055] Specifically, time series segments of various operating status parameters are extracted from each time window in turn, and the values ​​of each parameter in the time dimension are combined into sub-vectors in a preset order. The sub-vectors are then spliced ​​according to the dimension to form a high-dimensional time series feature vector. This vector structure can reflect the changing trend and combination characteristics of the operating parameters of the equipment over a continuous period of time. The feature vector is encapsulated as an evaluation input sample and input into the energy consumption evaluation model entrance for evaluation reasoning. The model supports sequence input structure analysis and time series pattern recognition, and can identify state evolution patterns related to power consumption anomalies or energy surges from the input features for judging the energy consumption level.

[0056] S23: Use the energy consumption assessment model to infer the time series feature vector and output the corresponding energy consumption level label. The energy consumption level label is used to identify the power consumption risk level of the current micro display terminal, and finally obtain the current energy consumption level.

[0057] Specifically, after receiving the time series feature vector, the energy consumption assessment model performs feature mapping, time series aggregation and classification mapping operations in sequence. In the feature mapping stage, each dimension of the feature is embedded in the model representation space through the input layer. In the time series aggregation stage, global vector synthesis is performed according to the feature weights of different time points. In the classification mapping stage, the synthesized feature vector is divided into predefined energy consumption level intervals according to the positional relationship of the synthesized feature vector and a corresponding level label is generated. The label is used to indicate the power consumption risk level corresponding to the current time window. The risk level can be divided into different categories such as high, medium and low to reflect the energy consumption trend and risk level in the current state. The level label will be written into the current execution context and serve as the core judgment basis for subsequent policy matching logic.

[0058] In one embodiment, if Figure 5 As shown, in step S30, the corresponding energy control strategy is called according to the current energy consumption level, and the execution parameters of the energy control strategy are dynamically modified based on the changes in the environmental state of the micro display terminal and the changes in the user's interactive behavior, to obtain a modified energy control strategy, which specifically includes: S31: Selecting a target control strategy from a preset multi-level energy control strategy set based on the current energy consumption level, where the target control strategy includes control instructions for display regulation, communication regulation, and task scheduling.

[0059] Specifically, after receiving the output current energy consumption level label, the energy control strategy set stored in the local database is immediately retrieved. The strategy set presets multiple hierarchical control schemes according to different level labels. Each scheme contains default parameter configuration structures of multiple control dimensions, including the brightness and refresh rate adjustment instruction set of the display module, the transmission power and connection cycle control instruction set of the communication module, and the task execution order and resource scheduling strategy in the processing module. The strategy structure matching the current level label is selected as the target control strategy and loaded into the control cache for subsequent correction processing. If the current level is in an intermediate critical state, fuzzy strategy synthesis is executed to mix the two adjacent level parameter templates to construct a transition strategy structure.

[0060] S32: monitoring the ambient brightness change data and the user operation behavior data of the micro display terminal, and matching and judging the ambient brightness change data and the user operation behavior data with corresponding policy adjustment trigger conditions respectively.

[0061] Specifically, the external light intensity value returned by the terminal environment sensor is periodically read and a continuous time series is constructed to analyze the current brightness change trend. At the same time, behavioral labels such as the current interaction event frequency, the number of interface jumps, and the user stay period are recorded in the behavior monitoring process. The environmental brightness change data and the user behavior data are aligned according to the time window and compared with the preset strategy trigger condition threshold. If the brightness change exceeds the upper and lower fluctuation range or the user behavior frequency change exceeds the set threshold, a matching flag is generated to start the strategy correction process. A context-aware mechanism is also introduced in the matching process to avoid false triggering.

[0062] S33: When the ambient brightness change data or user operation behavior data meets the policy adjustment triggering conditions, the parameter configuration items in the target control policy are modified based on the preset adjustment rules. The modification includes adjusting the display brightness target value, the communication module working cycle, and the execution priority of the background task.

[0063] Specifically, after determining that any input data meets the adjustment trigger conditions, the strategy correction function group is immediately called in. The function group calculates the correction factor based on the current brightness deviation and the intensity of the user operation behavior, and applies the correction factor to each module parameter field in the current target control strategy structure to generate a new configuration value. For example, the original set screen brightness target value is reduced by 10% or the communication wake-up cycle is extended from 10 seconds to 30 seconds, and the level of low-frequency tasks in the task scheduling priority table is reduced from 2 to 4 to reduce the resource preemption level. All correction parameters are dynamically generated without affecting the continuity of the main function and immediately written into the target control strategy structure to form a corrected strategy candidate version for execution and call.

[0064] S34: Generate a revised energy control strategy for subsequent steps to adjust operating parameters of the micro display terminal.

[0065] Specifically, the control strategy for completing parameter item correction is encapsulated in the form of a strategy instance structure and numbered as the current round correction strategy. The packaged content includes the respective control instruction sequences and adjustment values ​​of the display module, communication module and scheduling module. The corresponding instructions will be marked as "updated" for the running parameter control process to identify the execution scope, and the generated correction strategy instance will be written into the energy control strategy buffer queue waiting for the parameter adjustment module to call. If the difference between the current correction strategy and the previous round strategy is lower than the set sensitivity threshold, the invalid change flag will be skipped and only the round will be refreshed to improve the control efficiency.

[0066] In one embodiment, if Figure 6 As shown, in step S40, the operating parameters of the micro display terminal are adjusted according to the revised energy control strategy, specifically including: S41: adjusting the brightness output value and refresh rate of the display module of the micro display terminal according to the parameter configuration items in the revised energy control strategy.

[0067] Specifically, the brightness target value and refresh frequency setting value in the current correction strategy are read, the brightness value is directly mapped to the driving brightness control channel and the original brightness register configuration is overwritten. At the same time, the frequency adjustment instruction in the image output refresh process is executed to reset the frame output rhythm. Before the brightness adjustment, the delay compensation function is called to record the degree of matching between the current brightness and the user's ambient light value as a benchmark reference. During the refresh rate adjustment process, the double buffering mechanism of the screen is synchronously closed and the image update rhythm is rebuilt to adapt to the new frame rate configuration, ensuring that the adjustment process is smooth and flicker-free and does not interfere with the main display content drawing task being executed.

[0068] S42: According to the communication control instructions in the revised energy control strategy, the transmission power, the wake-up period and the data transmission interval of the communication module of the micro display terminal are adjusted.

[0069] Specifically, the communication control field in the current policy structure is parsed to extract the transmission power level, periodic wake-up time interval and data transmission interval parameters, the transmission power value is rewritten to the power configuration register of the RF driver interface and a recalibration action is triggered to update the transmission field strength reference, and the wake-up period value is filled in the low-power communication scheduling timer to control the active time period of the module. In the data sending part, the data buffer scheduling is rearranged according to the interval setting, the packet start instruction of non-critical data is delayed and similar small packets are merged to improve channel utilization and reduce power consumption. After all parameter modifications are completed, the communication reload entry application configuration is called uniformly to take effect and enter a new round of communication status maintenance.

[0070] S43: According to the task scheduling configuration items in the revised energy control strategy, the execution priority, delay threshold, and maximum allowable execution time of the background task are set.

[0071] Specifically, the task management parameter set is extracted from the correction control strategy, the corresponding policy field is matched according to the task identifiers in the currently running task pool, and the task execution priority flag is updated. In terms of delay control, a delay timer is added to start low-priority tasks so that they are not scheduled for execution before the set time. In the maximum time limit control, the maximum tolerable execution time is set for each task and an interrupt detection process is embedded to actively terminate the execution of timed-out tasks. All configuration items are written into the current scheduling table and the task scheduling is rescheduled to form a new priority queue structure for the next scheduling cycle call, ensuring that processing resource allocation prioritizes high efficiency and the operational stability of core business logic tasks.

[0072] In one embodiment, if Figure 7As shown, in step S50, the response index of the micro display terminal after adjusting the operating parameters is collected and the response index is fed back to the preset energy consumption evaluation model, which specifically includes: S51: Collect the power consumption change, screen response delay, communication stability score and user interaction response frequency of the micro display terminal after adjustment, and build a response indicator set.

[0073] Specifically, after the parameter adjustment operation is completed, the response monitoring logic is started to capture the energy consumption and performance change signals within the target observation period, and the current current curve change trend is read from the power tracking service to calculate the power consumption difference. The average response time between the user operation trigger and the interface feedback is extracted from the interface event queue to construct the screen response delay index. The channel stability score is extracted from the communication status report and combined with the packet loss rate to construct the connection reliability index. The number of touches and the active period are counted from the operation behavior tracking record to generate an interactive response frequency index. The above indicators are packaged into a response indicator set according to a predefined structure for feedback analysis and processing.

[0074] S52: Normalize the response indicator set to generate a feedback vector.

[0075] Specifically, each indicator in the response indicator set is matched with the preset standard range of the parameter category to which it belongs, and the original numerical value is converted into a standard scale value between 0 and 1 using the maximum and minimum interval normalization method. At the same time, binary or ternary encoding is used to supplement the input dimension for categorized indicators such as communication connection status. After all normalization processes are completed, the standardized values ​​are spliced ​​in sequence to form a feedback feature vector with a fixed structure, and the vector collection period and the corresponding adjustment strategy version number are marked for subsequent correlation model input and result correction. The full vector will be passed to the energy consumption model as feedback input to support the next stage of strategy optimization judgment.

[0076] S53: Using the feedback vector to update the parameter configuration of the preset energy consumption assessment model to improve the accuracy and dynamic response capability of energy consumption level prediction.

[0077] Specifically, the feedback adaptation function is called to receive the standardized feedback vector and calculate the deviation index between it and the previous round of prediction results. The deviation result is used as a weight update factor to act on the response weights of some input feature channels in the model. If a persistent deviation trend is detected, the lightweight model self-tuning entrance is triggered to re-evaluate the parameter distribution and adjust the normalization mapping rules or classification threshold parameters. The weight balance and feature re-evaluation and allocation operations are completed without changing the model structure and main parameters, so that the model can more accurately judge the current power consumption level to which it belongs and respond to mutation risks in a timely manner when similar data is input in the next round. Finally, the updated parameter configuration is saved to the current model version instance and replaces the original model for the next inference call.

[0078] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0079] In one embodiment, an energy management system for an intelligent micro display terminal is provided, and the energy management system for the intelligent micro display terminal corresponds one-to-one to the energy management method for an intelligent micro display terminal in the above embodiment. Figure 8 As shown, the energy management system of the intelligent micro display terminal includes a parameter acquisition module, a calculation module, a strategy determination module, a parameter adjustment module and a feedback module. The detailed description of each functional module is as follows: A parameter acquisition module is used to obtain the operating status parameters of the micro display terminal; A calculation module is used to input the operating status parameters into a preset energy consumption evaluation model, calculate the operating status parameters based on the preset energy consumption evaluation model, and obtain the current energy consumption level; A strategy determination module is used to call the corresponding energy control strategy according to the current energy consumption level, and dynamically modify the execution parameters of the energy control strategy based on changes in the environmental state of the micro display terminal and changes in user interaction behavior to obtain a modified energy control strategy; A parameter adjustment module, used to adjust the operating parameters of the micro display terminal according to the revised energy control strategy; The feedback module is used to collect the response indicators of the micro display terminal after adjusting the operating parameters, and feed the response indicators back to the preset energy consumption evaluation model to optimize the decision-making process of the next round of energy control strategy.

[0080] Optionally, the parameter acquisition module includes: The brightness difference acquisition submodule is used to detect the difference between the current screen brightness of the micro display terminal and the ambient brightness; The power change rate monitoring submodule is used to monitor the real-time change rate of the battery power of the micro display terminal; The communication acquisition submodule is used to collect the signal strength, connection status and data transmission frequency of the communication module of the micro display terminal; The statistical operation submodule is used to count the number of user operations, dwell time and complexity of operation paths within a unit of time.

[0081] Optionally, the energy management system of the intelligent micro display terminal further includes: A training set acquisition module is used to construct a training data set based on state parameters and corresponding energy consumption data collected by multiple micro display terminals during historical operation; Construct a feature vector module to normalize the state parameters in the training data set and construct an input feature vector in a unified format; A model training module, configured to input the input feature vector into a lightweight neural network model for training. The neural network model includes an input layer, at least one hidden layer, and an output layer, configured to output a corresponding energy consumption level label. The model generation module is used to optimize the parameters of the neural network model by minimizing the loss function between the predicted energy consumption level and the actual energy consumption level marked in the training data, and generate a preset energy consumption evaluation model.

[0082] Optionally, the calculation module includes: The preprocessing submodule is used to perform data preprocessing on the operating status parameters to obtain the preprocessed operating status parameters. The data preprocessing includes missing value filling, dimension normalization, and time window grouping. The input model submodule is used to construct the pre-processed operating status parameters into a time series feature vector and input it into the preset energy consumption evaluation model; The inference submodule is used to use the energy consumption assessment model to infer the time series feature vector and output the corresponding energy consumption level label. The energy consumption level label is used to identify the power consumption risk level of the current micro display terminal and finally obtain the current energy consumption level.

[0083] Optionally, the determination strategy module includes: A strategy selection submodule is used to select a target control strategy from a preset multi-level energy control strategy set based on the current energy consumption level. The target control strategy includes control instructions for display regulation, communication regulation, and task scheduling. The judgment submodule is used to monitor the ambient brightness change data and user operation behavior data of the micro display terminal, and match the ambient brightness change data and user operation behavior data with the corresponding policy adjustment trigger conditions; The policy adjustment submodule is used to modify the parameter configuration items in the target control policy based on preset adjustment rules when the ambient brightness change data or user operation behavior data meets the policy adjustment trigger conditions. The modification includes adjusting the display brightness target value, the communication module duty cycle, and the execution priority of the background task; The strategy generation submodule is used to generate a revised energy control strategy for subsequent steps to adjust the operating parameters of the micro display terminal.

[0084] Optionally, the parameter adjustment module includes: A brightness adjustment submodule, configured to adjust the brightness output value and refresh rate of the display module of the micro display terminal according to the parameter configuration items in the revised energy control strategy; a communication adjustment submodule, configured to adjust the transmission power, wake-up period, and data transmission interval of the communication module of the micro display terminal according to the communication control instructions in the revised energy control strategy; The task configuration adjustment submodule is used to set the execution priority, delay threshold and maximum allowable execution time of the background task according to the task scheduling configuration items in the revised energy control strategy.

[0085] Optionally, the feedback module includes: The response acquisition submodule is used to collect the power consumption change, screen response delay, communication stability score and user interaction response frequency of the micro display terminal after adjustment, and build a response indicator set; Generate feedback vector submodule, which is used to standardize the response indicator set and generate feedback vector; The model adjustment submodule is used to use the feedback vector to update the parameter configuration of the preset energy consumption assessment model to improve the accuracy and dynamic response capability of energy consumption level prediction.

[0086] For the specific definition of an energy management system for an intelligent micro display terminal, please refer to the definition of an energy management method for an intelligent micro display terminal above, which will not be repeated here. The various modules in the energy management system of the above-mentioned intelligent micro display terminal can be implemented in whole or in part by software, hardware and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0087] Those skilled in the art will clearly understand that for the sake of convenience and brevity in description, only the division of the above-mentioned functional units and modules is used as an example. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the system can be divided into different functional units or modules to complete all or part of the functions described above.

[0088] The above-described embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present application, and should all be included in the scope of protection of the present application.

Claims

1. An energy management method for an intelligent micro display terminal, characterized in that: The energy management method of the intelligent micro display terminal includes: Obtaining the operating status parameters of the micro display terminal; Inputting the operating status parameters into a preset energy consumption evaluation model, and calculating the operating status parameters based on the preset energy consumption evaluation model to obtain a current energy consumption level; Invoking a corresponding energy control strategy according to the current energy consumption level, and dynamically revising execution parameters of the energy control strategy based on changes in the environmental state of the micro display terminal and changes in user interaction behavior to obtain a revised energy control strategy; adjusting the operating parameters of the micro display terminal according to the revised energy control strategy; The response index of the micro display terminal after adjusting the operating parameters is collected, and the response index is fed back to the preset energy consumption evaluation model to be used for optimizing the decision-making process of the next round of energy control strategy.

2. The energy management method of an intelligent micro display terminal according to claim 1, characterized in that: The step of obtaining the operating status parameters of the micro display terminal includes: Detecting the difference between the current screen brightness of the micro display terminal and the ambient brightness; Monitoring the real-time rate of change of the battery charge of the micro display terminal; Collecting the signal strength, connection status and data transmission frequency of the communication module of the micro display terminal; Statistics are collected on the number of user operations, dwell time, and complexity of operation paths within a unit of time.

3. The energy management method of an intelligent micro display terminal according to claim 1, characterized in that: Before inputting the operating status parameters into a preset energy consumption evaluation model, the energy management method for an intelligent micro display terminal further includes: Constructing a training data set based on the state parameters and corresponding energy consumption data collected by multiple micro display terminals during historical operation; Normalizing the state parameters in the training data set to construct an input feature vector in a unified format; Inputting the input feature vector into a lightweight neural network model for training, wherein the neural network model includes an input layer, at least one hidden layer and an output layer for outputting a corresponding energy consumption level label; By minimizing the loss function between the predicted energy consumption level and the actual energy consumption level marked in the training data, the parameters of the neural network model are optimized and trained to generate the preset energy consumption evaluation model.

4. The energy management method of an intelligent micro display terminal according to claim 1, characterized in that: Inputting the operating status parameters into a preset energy consumption evaluation model, and calculating the operating status parameters based on the preset energy consumption evaluation model to obtain the current energy consumption level includes: Performing data preprocessing on the operating status parameters to obtain preprocessed operating status parameters, wherein the data preprocessing includes missing value filling, dimension normalization, and time window grouping; Constructing the pre-processed operating state parameters into a time series feature vector and inputting it into the preset energy consumption evaluation model; The energy consumption assessment model is used to infer the time series feature vector and output a corresponding energy consumption level label, which is used to identify the power consumption risk level of the current micro display terminal, and finally the current energy consumption level is obtained.

5. The energy management method of an intelligent micro display terminal according to claim 1, characterized in that: The energy control strategy corresponding to the current energy consumption level is called, and the execution parameters of the energy control strategy are dynamically modified based on the changes in the environmental state of the micro display terminal and the changes in the user's interactive behavior. The modified energy control strategy includes: Selecting a target control strategy from a preset multi-level energy control strategy set based on the current energy consumption level, the target control strategy including control instructions for display regulation, communication regulation, and task scheduling; Monitoring the ambient brightness change data and user operation behavior data of the micro display terminal, and matching and judging the ambient brightness change data and user operation behavior data with corresponding policy adjustment trigger conditions respectively; When the ambient brightness change data or the user operation behavior data meets the policy adjustment trigger condition, the parameter configuration items in the target control policy are modified based on the preset adjustment rules, and the modification includes adjusting the display brightness target value, the communication module duty cycle, and the execution priority of the background task; The revised energy control strategy is generated for use in subsequent steps to adjust the operating parameters of the micro display terminal.

6. The energy management method of an intelligent micro display terminal according to claim 1, characterized in that: The adjusting the operating parameters of the micro display terminal according to the modified energy control strategy includes: Adjusting the brightness output value and refresh rate of the display module of the micro display terminal according to the parameter configuration items in the revised energy control strategy; adjusting the transmission power, wake-up period, and data transmission interval of the communication module of the micro display terminal according to the communication control instructions in the revised energy control strategy; According to the task scheduling configuration items in the revised energy control strategy, the execution priority, delay threshold and maximum allowable execution time of the background task are set.

7. The energy management method of an intelligent micro display terminal according to claim 1, characterized in that: The collecting of the response index of the micro display terminal after adjusting the operating parameters and feeding the response index back to the preset energy consumption evaluation model includes: Collecting the adjusted power consumption change, screen response delay, communication stability score, and user interaction response frequency of the micro display terminal to construct a response indicator set; Normalizing the response indicator set to generate a feedback vector; The feedback vector is used to update the parameter configuration of the preset energy consumption assessment model to improve the accuracy and dynamic response capability of energy consumption level prediction.

8. An energy management system for an intelligent micro display terminal, characterized in that: The energy management system of the intelligent micro display terminal includes: A parameter acquisition module is used to obtain the operating status parameters of the micro display terminal; a calculation module, configured to input the operating status parameters into a preset energy consumption evaluation model, calculate the operating status parameters based on the preset energy consumption evaluation model, and obtain a current energy consumption level; a strategy determination module, configured to call a corresponding energy control strategy according to the current energy consumption level, and dynamically modify execution parameters of the energy control strategy based on changes in the environmental state of the micro display terminal and changes in user interaction behavior, thereby obtaining a modified energy control strategy; a parameter adjustment module, configured to adjust the operating parameters of the micro display terminal according to the revised energy control strategy; The feedback module is used to collect the response index of the micro display terminal after adjusting the operating parameters, and feed the response index back to the preset energy consumption evaluation model to optimize the decision-making process of the next round of energy control strategy.

Images