Temperature control device and temperature control method for tablet personal computer

Abstract

The invention provides a temperature control device and method for a tablet personal computer, and belongs to the technical field of temperature control. The device fuses collected sensing data into a high-dimensional feature vector, and inputs the high-dimensional feature vector and the posture of the tablet personal computer into a thermal field prediction model adopting a convolutional long-short-term memory network; predicting temperature data of each area of the tablet computer, and outputting a predicted thermal field distribution thermodynamic diagram; according to temperature distribution in the thermal field distribution thermodynamic diagram, a target function is defined in combination with the multiple target parameters, and the target function dynamically distributes the weight of each target parameter according to the use environment of the tablet personal computer; and generating a Pareto optimal solution set by adopting a multi-objective optimization algorithm, selecting an optimal temperature control strategy suitable for the current application scene from a plurality of candidate temperature control strategies through a fuzzy logic decision, and adjusting hardware parameters of the tablet computer according to the optimal temperature control strategy. Through accurate thermal field prediction, temperature fluctuation is reduced, and the stability of equipment in a high-load state is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of temperature control of intelligent terminal devices, and particularly to a temperature control device and method for a tablet computer. Background Art

[0002] There are many technical bottlenecks in the existing tablet computer temperature control systems. Traditional temperature control strategies usually rely on fixed thresholds (for example, when the chip temperature reaches 80°C, frequency reduction is triggered), which leads to large performance fluctuations and response lags. In addition, traditional solutions usually only collect single-point temperature data and cannot accurately predict local heat diffusion paths, easily resulting in false triggers or temperature control failures. More complicatedly, it is difficult to dynamically adjust the balance among heat dissipation, performance, noise, and energy consumption. For example, the high-speed operation of the fan can effectively cool down, but it increases power consumption and noise. In addition, existing solutions lack adaptation to the user application scenarios and fail to automatically adjust the temperature control strategy for different applications (such as games, video playback, reading).

[0003] In the existing technologies, such as temperature control methods based on the PID algorithm (P represents Proportional, I represents Integral, D represents Differential) and methods for predicting temperature using neural networks, although there are breakthroughs, they still fail to solve the problem of multi-variable coupling, and the computational complexity is relatively high, making it difficult to run in real time. Summary of the Invention

[0004] In view of this, the purpose of the embodiments of the present invention is to provide a temperature control device and method for a tablet computer to solve at least one of the above technical problems.

[0005] To achieve the above purpose, in the first aspect, a temperature control device for a tablet computer is provided. The temperature control device includes: A perception data acquisition unit for acquiring perception data of the tablet computer; A data preprocessing and fusion unit for preprocessing the acquired perception data and fusing the preprocessed perception data into a high-dimensional feature vector; A thermal field prediction unit for inputting the high-dimensional feature vector and the posture of the tablet computer into a thermal field prediction model using a convolutional long short-term memory network, and predicting temperature data of each region of the tablet computer through the thermal field prediction model and generating a thermal field distribution heat map; An objective function definition unit for defining an objective function according to the temperature distribution in the thermal field distribution heat map and combining multiple objective parameters. The objective function dynamically assigns weight coefficients to each objective parameter according to the usage environment of the tablet computer; A temperature control decision-making engine, which is used to generate a Pareto optimal solution set by using a multi-objective optimization algorithm according to the objective function and the weight coefficient. The Pareto optimal solution set contains multiple candidate temperature control strategies; select the optimal temperature control strategy suitable for the current application scenario from the multiple candidate temperature control strategies through fuzzy logic decision-making, and transmit the optimal temperature control strategy to the execution unit; An execution unit, which is used to adjust the hardware parameters of the tablet computer according to the optimal temperature control strategy.

[0006] In a second aspect, an embodiment of the present invention provides a temperature control method for a tablet computer. The temperature control method includes: Collect the perception data of the tablet computer; Preprocess the collected perception data, and fuse the preprocessed perception data into a high-dimensional feature vector; Input the high-dimensional feature vector and the posture of the tablet computer into a thermal field prediction model using a convolutional long short-term memory network, and predict the temperature data of each area of the tablet computer through the thermal field prediction model and generate a thermal field distribution heat map; According to the temperature distribution in the thermal field distribution heat map, and in combination with multiple target parameters, define an objective function, and the objective function dynamically allocates the weight coefficient of each target parameter according to the usage environment of the tablet computer; According to the objective function and the weight coefficient, use a multi-objective optimization algorithm to generate a Pareto optimal solution set. The Pareto optimal solution set contains multiple candidate temperature control strategies; select the optimal temperature control strategy suitable for the current application scenario from the multiple candidate temperature control strategies through fuzzy logic decision-making, and transmit the optimal temperature control strategy to the execution unit; Adjust the hardware parameters of the tablet computer according to the optimal temperature control strategy.

[0007] The above technical solution has the following beneficial effects: Through accurate thermal field prediction, the present invention reduces temperature fluctuations, thereby improving the stability of the tablet computer under high load conditions. In high-performance application scenarios, it can reduce the frequency of CPU downclocking, enhance the stability of the frame rate, and improve the temperature control accuracy and overall performance.

[0008] By dynamically adjusting the weights, the present invention achieves lower overall power consumption and is more efficient in power consumption control compared to traditional temperature control solutions. Moreover, the processing delay of the algorithm is effectively controlled, ensuring the smoothness of the user experience and not being affected by the temperature control decision-making delay. Through a complete strategy library, it can automatically adapt to different application scenarios, provide broader support, and ensure the best temperature control effect in various environments.

[0009] The present invention adopts a "prediction, decision-making, execution" closed-loop temperature control architecture, avoiding the limitations of traditional threshold control. It uses a lightweight Conv-LSTM thermal field prediction model to achieve high-precision thermal diffusion modeling on mobile devices for the first time. In addition, the multi-objective Pareto optimal real-time solution algorithm of the present invention effectively solves the problem of multi-variable coupling such as temperature, performance, and noise, and realizes scenario-adaptive personalized temperature control through a software-defined mechanism. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Figure 1 is a functional block diagram of a temperature control device for a tablet computer according to an embodiment of the present invention; Figure 2 is a functional block diagram of a data preprocessing and fusion unit according to an embodiment of the present invention; Figure 3 is a functional block diagram of a thermal field prediction unit according to an embodiment of the present invention; Figure 4 is a functional block diagram of a perception data acquisition unit according to an embodiment of the present invention; Figure 5 is a functional block diagram of another temperature control device for a tablet computer according to an embodiment of the present invention; Figure 6 is a functional block diagram of a scenario recognition and adaptive strategy switching unit according to an embodiment of the present invention; Figure 7 is a functional block diagram of a real-time inference and control execution unit according to an embodiment of the present invention; Figure 8 is a functional block diagram of an anomaly diagnosis and model fine-tuning unit according to an embodiment of the present invention; Figure 9 is a functional block diagram of a temperature control execution and feedback adjustment unit according to an embodiment of the present invention; Figure 10 is a flowchart of a temperature control method for a tablet computer according to an embodiment of the present invention; Figure 11 is a functional block diagram of a computer-readable storage medium according to an embodiment of the present invention; Figure 12 is a functional block diagram of an electronic device according to an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0011] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0012] The objective of the embodiments of the present invention is to solve the defects in the prior art through a software-defined temperature control method based on dynamic thermal field modeling and multi-objective optimization. The specific objectives include: accurately identifying the temperature distribution trend through a real-time thermal field prediction algorithm; designing a lightweight multi-objective decision-making model to dynamically balance temperature, performance, power consumption, and user experience; and constructing an adaptive control strategy library based on the user application scenario to achieve personalized temperature control.

[0013] Embodiment 1 As Figure 1 shown, the embodiments of the present invention provide a temperature control device for a tablet computer, which includes: A perception data acquisition unit for acquiring the perception data of the tablet computer, where the perception data includes the temperature data of multiple regions of the tablet computer, the temperature and humidity data of the surrounding environment, and the power consumption of the tablet computer during operation.

[0014] Specifically, in the tablet computer, the temperature data is acquired through a high-precision thermocouple array sensing system, and the thermocouple array is distributed in the CPU (Central Processing Unit), GPU (Graphics Processing Unit), battery, screen driver chip, and the holding area of the shell to acquire the temperature data of each key part. The temperature data acquisition can be performed in real time at a frequency of 10 Hz to ensure accurate thermal information is obtained. At the same time, the ambient temperature and humidity sensor will synchronously record the temperature and humidity data of the surrounding environment to provide necessary environmental parameters for subsequent temperature prediction and temperature control decisions. In addition, the power consumption monitoring module will also monitor the power consumption of the tablet computer in real time to facilitate the evaluation of the load situation of the tablet computer.

[0015] A data preprocessing and fusion unit for preprocessing the acquired perception data and fusing the preprocessed perception data into a high-dimensional feature vector.

[0016] In this embodiment, based on the temperature data of multiple regions of the tablet computer, the humidity data and temperature data of the surrounding environment, and the power consumption during the operation of the tablet computer collected, these data will be preprocessed. The main operations of the preprocessing include denoising, data synchronization, and time alignment. After data preprocessing, all the perception data will be fused into a high-dimensional feature vector for subsequent thermal field prediction and decision-making engines. The information after data fusion can not only provide the current temperature distribution of the tablet computer, but also comprehensively reflect the overall thermal state of the tablet computer in combination with environmental factors and the current power consumption of the tablet computer.

[0017] As Figure 2 shown, in some embodiments, the data preprocessing and fusion unit specifically includes: a data preprocessing subunit, a data fusion subunit, and a data storage and caching subunit.

[0018] The data preprocessing subunit is used to perform denoising, data synchronization, time alignment, standardization and normalization on the collected perception data to obtain the preprocessed perception data.

[0019] Specifically, first of all, the temperature data collected from multiple areas of the tablet computer, the temperature data and humidity data of the surrounding environment, and the power consumption data during the operation of the tablet computer will be affected by factors such as sensor accuracy, environmental interference or data transmission, resulting in the appearance of noise. In order to ensure the accuracy of the data, these data need to be denoised. Denoising methods include: moving average method, which averages each set of data by sliding window to smooth out fluctuations in a short period of time; median filtering method, which removes outliers or extreme data by taking the median in the data window; Kalman filtering method, which recursively processes the data and suppresses noise by combining the prediction model and actual observation data. Through denoising, the noise interference in the data is reduced, making the data cleaner and more accurate, and can more truly reflect the actual situation of the monitored object, thereby improving the quality and reliability of the data. For example, the high-frequency noise introduced by factors such as electromagnetic interference when the sensor collects data is removed to make the data curve smoother.

[0020] Secondly, during the data collection process, the temperature, humidity, power consumption and other information from different sensors will have different sampling time points. In order to ensure that all data can be compared and integrated at the same time, the data needs to be synchronized. Synchronization methods include: interpolation and resampling. Interpolation refers to interpolating data with different sampling times and unifying them to a common time base. For missing data between time points, linear interpolation or spline interpolation can be used for estimation; resampling refers to adjusting all data to the same time interval, such as resampling sensor data with low sampling frequency to 10Hz to keep it consistent with other high-frequency data. Through data synchronization, data from all sensors can be processed within the same time window to ensure the accuracy of subsequent analysis.

[0021] Furthermore, in addition to synchronizing data from different sources, time alignment also needs to ensure that the time order between data points is exactly matched. Data timestamps are inconsistent due to processing delays or transmission delays of different sensors. Specific practices include: timestamp matching, attaching accurate timestamps to each data point and sorting the data by timestamps; filling operations, if data is missing at certain moments, interpolation or estimation methods can be used to fill in the missing values so that each time point has complete temperature, humidity, and power consumption data. Through data synchronization and time alignment, data from different sources and different sampling frequencies are made consistent and comparable in time. This enables subsequent fusion and analysis of multi-source data to be carried out within an accurate time frame, avoiding incorrect analysis results caused by time differences. For example, for data from multiple sensors, after processing, it can be ensured that their values at the same time point can correspond, facilitating the comprehensive analysis of the overall state of the device at a certain moment.

[0022] Finally, the data is standardized and normalized. To eliminate the dimensional differences of different data sources, data such as temperature, humidity, and power consumption are standardized or normalized. For example, temperature is in degrees Celsius (°C), power consumption is in watts (W), and humidity is expressed as a percentage (%). After these data are standardized and normalized, they can be unified to the same dimension, facilitating subsequent analysis and processing. The processing methods include: standardization, which subtracts the mean of each feature and divides by the standard deviation, making the data have zero mean and unit variance; normalization, which scales the data between 0 and 1 using the min-max normalization method. Through standardization and normalization processing, the dimensional differences between different feature data are eliminated, making the data have the same weight and influence in subsequent analysis and modeling, helping to improve the accuracy and stability of the algorithm, and also facilitating data visualization and comparison. For example, after standardizing or normalizing data of different physical quantities such as temperature, voltage, and current, they can be analyzed and processed on the same scale, and no single feature's large numerical range will dominate the entire analysis process.

[0023] The data fusion subunit is used to fuse the preprocessed perception data to generate a high-dimensional feature vector.

[0024] In this embodiment, after all the preprocessing steps are completed, the data from each sensor will be fused next to generate a high-dimensional feature vector. The purpose of data fusion is to integrate information from different sources (such as temperature, humidity, power consumption, etc.) into a unified representation for subsequent use by the thermal field prediction and temperature control decision-making engine. The specific method is as follows: Feature concatenation, which combines the data of each dimension such as temperature, humidity, and power consumption into a high-dimensional feature vector by concatenation. The dimension of each high-dimensional feature vector corresponds to a specific sensor data; Weighted fusion, which assigns weights to each feature data according to different importance levels, and fuses different data into a unified feature vector through weighted summation. For example, different weights can be assigned to the temperature and power consumption data according to the accuracy and importance of different sensors.

[0025] The fused high-dimensional feature vector not only contains the temperature distribution information of each area of the tablet computer, but also combines the ambient temperature and humidity and the power consumption data during the operation of the tablet computer, and can comprehensively reflect the overall thermal state of the tablet computer, providing effective input for subsequent thermal field prediction and decision-making engine.

[0026] The data storage and caching subunit is used to store the preprocessed and fused data in a temporary cache or database for subsequent use by the analysis, prediction, and decision-making engine. This storage process ensures the integrity and accessibility of the data, allowing the subsequent system to quickly access historical data and perform calculations.

[0027] In this embodiment, through the preprocessing and fusion of the sensed data, the multi-dimensional data such as the temperature data of multiple areas of the tablet computer, the temperature and humidity data of the surrounding environment, and the power consumption data are denoised, data synchronized, time-aligned, standardized, etc., and finally fused into a high-dimensional feature vector. This process ensures that the subsequent thermal field prediction and temperature control decision-making engine can make temperature control decisions based on accurate and complete input data, thereby effectively improving the temperature control accuracy and performance of the tablet computer.

[0028] The thermal field prediction unit is used to input the high-dimensional feature vector and the posture of the tablet computer into a thermal field prediction model using a convolutional long short-term memory network, predict the temperature data of each area of the tablet computer, and output the predicted thermal field distribution heat map.

[0029] The thermal field prediction model of this embodiment uses a lightweight Convolutional Long Short-Term Memory (Conv-LSTM) model for thermal field prediction. This model takes the temperature sensor data collected in real time, the power consumption of the tablet computer, the ambient temperature and humidity, and the posture of the tablet computer (for example, the posture information of the tablet computer obtained by the gyroscope sensor, which is used to reflect the physical state of the tablet computer in different application scenarios, such as the holding angle, etc.) as inputs to predict the heat diffusion process and can perform real-time calculations on the tablet computer with low latency. The thermal field prediction model can effectively capture the dependencies in space and time, predict the temperature changes in each area of the tablet computer, and then output the predicted thermal field distribution heat map.

[0030] As Figure 3 shown, in some embodiments, the thermal field prediction unit specifically includes: a data input subunit, a first processing subunit, a second processing subunit, an output subunit, a lightweight optimization subunit, and a model evaluation and verification subunit.

[0031] The data input subunit is used to take the temperature data collected in real time, the power consumption of the tablet computer, the ambient temperature and humidity data, and the posture data of the tablet computer as the inputs of the Conv-LSTM model. Before these input data enter the Conv-LSTM model, they need to go through the preprocessing and fusion in the previous steps to ensure that the data formats are consistent and meet the requirements of the model.

[0032] The first processing subunit is used to process the input data through convolution operations to extract the spatial features of the temperature distribution in each area. The first part of the Conv-LSTM model is the convolutional layer, and the role of the convolutional layer is to extract the local features of the temperature distribution from the spatial dimension. This convolutional layer processes the input data through convolution operations to extract the spatial features of the temperature distribution in each area. For example, for the temperature sensor data, the convolutional layer will identify and capture the spatial structure of the temperature change, such as the heat distribution difference between the CPU area and the battery area.

[0033] The convolution operation formula is: ； where y is the output of the convolutional layer, W is the convolutional kernel weight, x is the input data, represents the convolution operation, and b is the bias term. Through convolution processing, spatial feature maps can be obtained, and these feature maps will provide effective information for the next LSTM processing.

[0034] The second processing subunit uses the time series information of historical temperature data, the power consumption of the tablet computer, and environmental humidity data to predict the temperature changes in each area of the tablet computer within a preset future time. The second part of the Conv-LSTM is the LSTM layer, and the LSTM layer can effectively capture the dependencies in the time series. The role of the LSTM layer is to use time series information such as past temperature data, power consumption data, and environmental humidity data to predict the temperature changes in each area within a preset future time (e.g., 30 seconds). LSTM can remember long-term historical information and adjust the weights according to the changes in the time series, so as to accurately predict the future thermal field changes.

[0035] The output subunit is used to output the predicted thermal field distribution heat map. After the Conv-LSTM model undergoes convolution and LSTM processing, it will generate a predicted thermal field distribution heat map. This heat map shows the temperature change trends in each area of the tablet computer within the next 30 seconds, specifically reflecting the temperature distribution in different areas (such as the CPU, GPU, screen, shell, etc.). The thermal field distribution heat map can represent different temperature ranges through different colors, which is convenient for the subsequent decision-making engine to identify which areas have higher temperatures and which areas need to enhance heat dissipation.

[0036] The lightweight optimization subunit is used to perform lightweight processing on the convolutional long short-term memory network.

[0037] Specifically, in order to enable the Conv-LSTM model to run efficiently on the tablet computer and ensure the inference speed of the model, the model parameters need to be lightweight processed. This includes parameter compression, quantization, and knowledge distillation. Parameter compression refers to reducing the number of model parameters through pruning techniques and removing unimportant parameters; quantization refers to converting floating-point parameters to low-precision representations (e.g., converting 32-bit floating-point numbers to 8-bit integers) to reduce storage and computational overhead; knowledge distillation refers to training a small model to imitate the behavior of a large model, thereby obtaining a smaller and more efficient model. Through these optimizations, the size of the Conv-LSTM model will be controlled within 500KB and can run on the NPU (Neural Processing Unit) of the tablet computer with a latency of less than 5ms, ensuring real-time performance.

[0038] The model evaluation and verification subunit is used to evaluate and verify the convolutional long short-term memory network.

[0039] In practical applications, to ensure the effectiveness and accuracy of the thermal field prediction model, the Conv-LSTM model is evaluated and verified. This process includes cross-validation and accuracy evaluation. Cross-validation means dividing the data into multiple subsets and training and validating the thermal field prediction model multiple times to ensure the generalization ability of the model. Accuracy evaluation means evaluating the prediction accuracy of the model by comparing the predicted thermal field distribution with the actually measured temperature data.

[0040] Through the above processing, the Conv-LSTM model in this embodiment can, within a 5-second time window, predict the temperature changes in each area of the tablet computer in the next 30 seconds based on the real-time collected temperature, power consumption, ambient temperature and humidity, and posture data, and generate a thermal field distribution heat map. This model can not only simulate the heat diffusion process with high precision but also run in real time on the tablet computer. The embodiment of the present invention realizes high-precision thermal field modeling on the mobile side and avoids the limitations of traditional methods.

[0041] The objective function definition unit is used to define an objective function according to the temperature distribution in the thermal field distribution heat map and in combination with multiple objective parameters. The objective function dynamically assigns weights to each objective parameter according to the usage environment of the tablet computer; the objective parameters include but are not limited to fan power consumption, CPU downclocking amplitude, and noise generated during the operation of the tablet computer.

[0042] In this embodiment, after obtaining the thermal field prediction result, the system enters the multi-objective optimization decision-making stage. At this time, the system will consider multiple objective parameters, and each objective parameter is closely related to the operating conditions of the tablet computer, such as temperature, fan power consumption, CPU downclocking amplitude, sound interference generated during the operation of the tablet computer, etc. For example, reducing the temperature requires higher fan power consumption, and increasing the fan power consumption will cause an increase in noise. Therefore, it is necessary to balance these factors by optimizing the objective function. The objective function can be expressed as: ; where is the predicted maximum temperature (°C), that is, the highest temperature reached by the tablet computer in the next period of time; is the fan power consumption, indicating the power consumed by the fan operation; is the CPU downclocking amplitude (Hz), that is, the new frequency to which the CPU is reduced from the original frequency; Noise is the noise level (dB), indicating the noise generated by the tablet computer in the current operating state. Dynamically adjust these weight coefficients according to different application scenarios, so as to optimize the temperature control strategy and balance the conflicts between different objectives.

[0043] In the above objective function, the weight coefficients α, β, γ, and δ are used to adjust the relative importance of each objective to ensure that the system can make appropriate optimization decisions based on different application scenarios and requirements. By adjusting the weight coefficients, performance and temperature can be balanced in different working modes (such as high-performance mode, balanced mode, and silent mode) to achieve the optimal temperature control strategy.

[0044] In this embodiment, the priority of temperature control is different in different application scenarios. For example, when performing high-intensity computing (such as gaming or video rendering), more attention is paid to the balance between performance and temperature, while in a quiet environment (such as reading mode), more attention is paid to reducing noise and power consumption. Therefore, the weight coefficients (α, β, γ, δ) need to be dynamically adjusted according to the application scenario.

[0045] In high-performance mode, temperature control is more relaxed, but performance requirements are higher, so the weight coefficient α (temperature) is lower, β (fan power consumption) and γ (CPU frequency reduction range) are higher to maximize performance. In this embodiment, high-performance mode refers to a mode for scenarios with high performance requirements for tablet computers, such as 3D games, video rendering, etc. In this mode, the tablet computer will maximize performance such as 3D games and video rendering through a series of optimization measures. For example, the tablet computer will allocate more hardware resources, such as the computing power of the CPU and GPU, to high-performance applications that are running. For example, when running a 3D game, the system will allow the CPU and GPU to run at full capacity to provide a smoother game screen and a higher frame rate. In addition, in order to meet high-performance requirements, the device will allow hardware such as the CPU to be overclocked for a short time. That is, the CPU is allowed to run at a speed higher than its default frequency, thereby improving data processing capabilities and enabling applications to complete complex computing tasks more quickly, such as special effects processing in video rendering, scene modeling and rendering in 3D games, etc. Although temperature control is relatively loose in high-performance mode, in order to ensure the stability of the device when running under high load, heat dissipation measures will be strengthened. For example, the fan will run at a higher speed to dissipate the heat generated by the CPU, GPU and other hardware as quickly as possible to prevent device performance degradation or failure due to excessive temperature.

[0046] In balanced mode (multi-tasking, 5G download), a balance needs to be maintained between performance and temperature, and the weight coefficients α, β, and γ need to be moderate to ensure a balance between temperature and performance while reducing fan power consumption and noise.

[0047] In silent mode (reading, standby), priority is given to reducing noise and power consumption, the weight coefficient δ (noise) will be higher, and α (temperature) and β (fan power consumption) will be lower.

[0048] The weight coefficients in this embodiment can be dynamically adjusted according to the actual scenario, so as to optimize the balance of each objective.

[0049] In an alternative embodiment, the objective function is: ; Where is the predicted maximum temperature, representing the maximum temperature reached by each area in the tablet computer, is the fan power consumption, representing the power consumption required for system heat dissipation, is the CPU frequency reduction amplitude, representing the degree of CPU frequency reduction to reduce heat generation, is the noise level, representing the noise generated during the operation of the tablet computer, BE (Battery Energy) represents the remaining battery capacity of the tablet computer, and the goal is to maximize the battery energy, so a minus sign is used in front to indicate that this objective needs to be maximized. TG (Thermal Gradient) is the thermal gradient, representing the temperature difference between different areas of the tablet computer. The goal is to minimize the thermal gradient to ensure uniform temperature distribution of the tablet computer and avoid local overheating. MU (Memory Usage) is the memory usage, representing the amount of memory used during the operation of the tablet computer, and the goal is to minimize the memory usage.

[0050] The embodiment of the present invention solves the problem of multi-variable coupling in temperature control by dynamically adjusting the weights and adjusting the temperature control strategy in real time according to different application scenarios.

[0051] A temperature control decision engine, which is used to generate a Pareto optimal solution set by using a multi-objective optimization algorithm according to the defined objective function and the dynamically adjusted weight coefficients. The Pareto optimal solution set contains multiple candidate temperature control strategies; select the optimal temperature control strategy suitable for the current application scenario from multiple candidate temperature control strategies through fuzzy logic decision-making, and transmit the optimal temperature control strategy to the execution unit.

[0052] In order to obtain the best temperature control strategy, the system will solve a multi-objective optimization problem based on the defined objective function and the dynamically adjusted weight coefficients. To this end, the system uses an improved NSGA-II (Non dominated sorting genetic algorithm -II, second-generation non-dominated sorting genetic algorithm) multi-objective genetic algorithm to solve it. Within a calculation time of 10ms, the algorithm generates a Pareto optimal solution set, listing multiple control strategies that meet various constraints. Among these strategies, fuzzy logic is further analyzed to select the final temperature control strategy that best suits the current application scenario and needs. Through this multi-objective optimization, it can ensure that while maintaining temperature control, performance is maximized, power consumption is reduced, and noise is reduced. Among them, the use of an improved NSGA-II (second-generation non-dominated sorting genetic algorithm) multi-objective genetic algorithm to solve the problem specifically includes the following steps: Step 5.1: Initialize the population. In the NSGA-II multi-objective genetic algorithm, the population must be initialized first. Initializing the population means generating multiple candidate solutions, each of which represents a temperature control strategy. Each solution consists of a set of genes, which represent various parameters in the control strategy, such as fan speed, CPU frequency, voltage, etc. In the initialization stage, the solutions in the population can be obtained by random generation to ensure the diversity of the population. The size of the initialized population is set to a certain number, such as 100 candidate solutions.

[0053] Step 5.2: Calculate the fitness and evaluate the objective function. After generating the initial population, the system needs to evaluate the fitness of each candidate solution. The calculation of fitness depends on the objective function defined previously, which comprehensively considers multiple factors such as temperature, fan power consumption, CPU frequency reduction range and noise. For each candidate solution, its fitness value is evaluated according to this objective function. The solution with a lower fitness value indicates that the solution performs better in multi-objective optimization and can better balance temperature, performance, power consumption and noise. Among them, the formula for calculating the fitness value is closely related to the objective function. According to the definition of the objective function, the calculation of fitness can be expressed by the following formula: ; The lower the fitness value (Fitness), the better the candidate solution is in terms of balancing multiple objectives such as temperature control, power management, performance maintenance, and noise control, so the solution is better. In multi-objective optimization, the objective function is minimized, that is, the fitness value is minimized by adjusting the weight coefficients and solution parameters to obtain an ideal temperature control strategy. In this formula, the adjustment of the weight coefficients (α, β, γ, δ) determines the relative importance of different objectives. According to the needs of different scenarios, the fitness value reflects the comprehensive performance of the temperature control strategy in meeting different objectives (lowering temperature, reducing noise, and reducing power consumption).

[0054] Step 5.3: Non-dominated sorting, the NSGA-II algorithm sorts the individuals in the population by non-dominated sorting. The purpose of non-dominated sorting is to find the dominance relationship based on the performance of individuals in the multi-objective space. An individual A dominates individual B if and only if individual A is not inferior to individual B in all objectives and is superior to individual B in at least one objective. After non-dominated sorting, the individuals in the population are divided into multiple levels (i.e., Pareto fronts). The first level contains the optimal solutions that dominate all other individuals, the second level contains those solutions that are dominated by the first-level individuals but are no longer dominated by other solutions, and so on. The sorted population provides a basis for priority selection for subsequent operations of the algorithm.

[0055] Step 5.4: Crowding calculation and selection. After completing the non-dominated sorting, the NSGA-II algorithm will perform crowding calculation, that is, measure the distribution of each solution on the Pareto front. Crowding reflects the diversity between solutions. The lower the crowding of the solution, the sparser the area where the solution is located and the greater the diversity. Through crowding calculation, the system can give priority to those individuals that are far away from other solutions on the Pareto front, so as to maintain the diversity of the population and avoid early convergence to a local solution. Solutions with higher crowding will be eliminated, while those solutions that occupy a larger area in the multi-objective space will be retained.

[0056] Step 5.5: Crossover and mutation operations. After selection, the NSGA-II algorithm will use crossover and mutation operations to generate new candidate solutions. The crossover operation refers to the generation of new individuals by selecting two individuals for crossover (i.e. exchanging some genes). The crossover operation helps to explore new solution spaces and generate more potential solutions. The mutation operation refers to the generation of new individuals by randomly changing the value of a gene. The mutation operation is conducive to introducing new features, increasing the diversity of the solution space, and preventing the algorithm from falling into a local optimum.

[0057] The new solution after the crossover and mutation operations will be merged with the current population, and the non-dominated sorting and crowding calculation will be re-performed to select a new parent population.

[0058] Step 5.6: Generate a Pareto optimal solution set. Through a series of selection, crossover, mutation, and evaluation, the NSGA-II algorithm will eventually generate a Pareto optimal solution set. This solution set contains multiple candidate temperature control strategies, each of which balances different objectives, and no other strategy can surpass them on all objectives at the same time. These optimal solutions will represent different temperature control strategies and can be used in subsequent decision-making processes.

[0059] Step 5.7: Fuzzy logic selects the final strategy. After generating multiple Pareto optimal solutions, fuzzy logic decision-making is used to select the temperature control strategy that best suits the current application scenario. Fuzzy logic allows smooth transitions between multiple optimization solutions based on preset rules and current usage status (such as user behavior, workload, environmental conditions, etc.) to select the optimal solution. For example, Rule 1: If the current temperature of the tablet is high, a strategy with a lower temperature prediction is preferred; Rule 2: If the current mode requires low noise, a strategy with lower noise is preferred; Rule 3: If the battery power is low, a strategy with lower power consumption is preferred. Through fuzzy logic rules, the system can select the temperature control strategy that best meets current needs from multiple candidate solutions.

[0060] Step 5.8: Execution of the final temperature control strategy. The final temperature control strategy selected by fuzzy logic will be passed to the execution module to adjust the hardware parameters of the tablet computer, such as fan speed, CPU frequency, voltage, etc., to ensure that the tablet computer maintains a balance between temperature, power consumption and noise while meeting user needs.

[0061] The embodiment of the present invention comprehensively considers multiple factors such as temperature, fan power consumption, CPU frequency reduction range and noise through the objective function, and dynamically adjusts the weight coefficient according to different application scenarios. By solving the multi-objective optimization problem, a set of Pareto optimal solutions is generated, and then the optimal temperature control strategy is selected through fuzzy logic, and finally the optimal temperature control decision is achieved to balance the conflicts between various goals and ensure the temperature, performance and user experience of the tablet computer. By using the Pareto optimal solution set generated by the multi-objective genetic algorithm, the system can select the best solution from multiple temperature control strategies. The application of decision engine and fuzzy logic ensures real-time and accuracy, and enables the system to select the most appropriate strategy according to different needs in a dynamic environment.

[0062] The execution unit is used to adjust the hardware parameters of the tablet computer according to the optimal temperature control strategy.

[0063] The temperature control strategy selected by fuzzy logic will be passed to the execution module for actual temperature control. The execution module will adjust the fan speed, CPU frequency, voltage and other hardware parameters according to the temperature control strategy to ensure that the tablet computer's temperature, noise and power consumption are maintained within the appropriate range while ensuring performance.

[0064] like Figure 4 As shown, in some embodiments, the sensing data acquisition unit specifically includes: a thermocouple array, an environmental temperature and humidity data acquisition subunit, a current and voltage monitoring subunit, and a data transmission and integration subunit.

[0065] A thermocouple array is used to collect temperature data of multiple areas of the tablet computer in real time, and the multiple areas include any one of a processor area, a battery area, a screen driver chip area, and a housing grip area; First, high-precision thermocouple arrays are placed in multiple key parts of the tablet. These arrays include multiple thermocouple sensors, which are distributed in the following locations: the CPU / GPU area, which is used to monitor the temperature changes of the core processor and graphics chip in real time; the battery area, which ensures that the battery operates within a safe temperature range; the screen driver chip area, which ensures the stable operation of the display system; and the outer shell grip area, which is used to sense the temperature of the user's hand contact position to prevent the user experience from being affected by overheating of the body.

[0066] These thermocouple arrays collect temperature information from various parts in real time to ensure that all key temperature-sensitive areas of the tablet are covered.

[0067] In addition, in order to ensure the real-time and accuracy of temperature data, the temperature data acquisition frequency is set to 10 Hz, that is, data is collected 10 times per second. This frequency can track temperature changes in detail without placing too much burden on system performance. The data of each sensor will be recorded in chronological order to ensure that subsequent analysis and model prediction can be processed based on high-precision time data.

[0068] The environmental temperature and humidity data acquisition subunit is used to synchronously collect the temperature and humidity data of the surrounding environment of the tablet computer; in this embodiment, in addition to the temperature data of the internal hardware, changes in the ambient temperature and humidity will also affect the temperature of the tablet computer. Therefore, the tablet computer is also equipped with an environmental temperature and humidity sensor for synchronously recording the temperature and humidity data of the surrounding environment. These sensors can detect changes in temperature and humidity in the surrounding environment of the tablet computer, and transmit the data to the temperature control system in real time, providing environmental parameters for subsequent temperature prediction and temperature control decisions. The collection frequency of environmental temperature and humidity data also needs to be synchronized with the temperature data in order to accurately reflect the impact of the environment on the tablet computer temperature control system.

[0069] The power consumption monitoring subunit is used to monitor the power consumption of the tablet computer in real time. In order to comprehensively evaluate the thermal state of the tablet computer, this embodiment also needs to collect the power consumption data of the tablet computer when it is running. To this end, a power consumption monitoring subunit is configured in the tablet computer to monitor the battery voltage, current and power consumption of other hardware of the tablet computer in real time. The power consumption monitoring subunit will record the power consumption data of the CPU, GPU, display and other major hardware components, and feed it back to the temperature control device in real time. The real-time collection of power consumption data is conducive to evaluating the load condition of the tablet computer, because under high load conditions, the tablet computer will generate more heat and require more heat dissipation measures to maintain normal operating temperature.

[0070] The data transmission and integration subunit is used to transmit the collected temperature, ambient temperature and humidity, and power consumption to the central processing unit. The central processing unit integrates the temperature, ambient temperature and humidity, and power consumption into a unified data stream and stores it.

[0071] All collected data such as temperature, ambient temperature and humidity, power consumption, etc. will be transmitted to the central processing unit (CPU) through the built-in communication bus or sensor interface. These data will be integrated into a unified data stream for subsequent temperature prediction and temperature control decision engines. During the transmission process, in order to ensure the accuracy and integrity of the data, the system needs to verify the data during the transmission process and handle the transmission delay or packet loss problems to ensure real-time performance.

[0072] In addition, in order to ensure the integrity of the data and the needs of subsequent analysis, the collected temperature, ambient temperature and humidity, and power consumption data will be stored in a dedicated storage module. The data storage module needs to be able to efficiently and securely save a large amount of real-time data, while having fast access capabilities to enable historical data backtracking or analysis when needed.

[0073] The tablet computer of this embodiment can collect temperature data of each key part, ambient temperature and humidity information, and system power consumption data in real time. Through accurate data collection and processing, it is ensured that the temperature control system can perform subsequent thermal field prediction, decision generation, and temperature control execution based on real and accurate data. These data provide necessary inputs for the entire temperature control system and ensure the efficient operation of the system.

[0074] like Figure 5 As shown, in some embodiments, the temperature control device may also include: a scene recognition and adaptive strategy switching unit, which is used to identify the working mode of the tablet computer by analyzing the application process list, touch event frequency and screen brightness change rate of the tablet computer, and automatically switch the temperature control strategy according to the preset strategy library according to the working mode of the tablet computer.

[0075] like Figure 6 As shown, in some embodiments, the scene recognition and adaptive strategy switching unit specifically includes: an information collection subunit, a touch event frequency monitoring subunit, a screen brightness change rate detection subunit, a temperature control strategy dynamic adjustment subunit and a real-time monitoring and mode switching subunit.

[0076] The information collection subunit is used to collect information about currently running applications by monitoring the application process list.

[0077] This embodiment first collects information about currently running applications by monitoring the application process list. The load of these applications will affect the performance requirements of the tablet computer. For example, when the user runs high-intensity games or video rendering, the CPU and GPU resource consumption is high, and the system will identify it as high-performance mode; when running daily office software or light-load applications such as browsers, the system will identify it as balanced mode. By identifying different application processes, the system can dynamically determine the current workload and provide a basis for the subsequent selection of temperature control strategies.

[0078] The touch event frequency monitoring subunit is used to determine the interaction strength of the tablet computer by monitoring the frequency of touch events, and to determine the working mode of the tablet computer according to the interaction strength. The working modes include high-performance mode, silent mode and balanced mode.

[0079] This embodiment determines the interaction intensity of the device by monitoring the frequency of touch events. If the frequency of touch events is high, it means that the user is actively operating the tablet computer, such as playing games or performing high-intensity activities such as video editing. In this case, the system will think that the tablet computer is in high-performance mode and needs to increase processing power; on the contrary, low-frequency touch events indicate that the tablet computer is in a relatively static state, such as reading or standby, and the tablet computer will be identified as silent mode. In this step, the touch frequency is an important factor that can accurately identify the user's usage needs.

[0080] The screen brightness change rate detection subunit is used to detect the screen brightness change rate of the tablet computer, and further confirm the working mode of the tablet computer according to the screen brightness change rate.

[0081] The screen brightness change rate is another indicator that helps the system determine the application scenario. In high-load scenarios (such as games, video playback, etc.), the screen brightness will change more frequently, while in low-load scenarios (such as static reading, standby, etc.), the screen brightness changes less. By monitoring the changes in screen brightness, it is possible to further confirm whether it is in high-performance mode or silent mode. For example, when the brightness changes rapidly, it is determined to be a high-load scenario, and then the temperature control strategy is adjusted to high-performance mode; if the brightness changes less, it is determined that the tablet is in a low-load state and enters silent mode.

[0082] The application scenario identification subunit is used to identify the current application scenario based on application information, the frequency of touch events and the rate of change of screen brightness.

[0083] After collecting and analyzing the application process, touch event frequency, and screen brightness change rate, the data is integrated to identify the current application scenario. For example, in high-load applications (such as 3D games or video rendering), the system will automatically identify it as high-performance mode, which requires more heat dissipation and performance improvement; when multitasking or browsing the web, the system enters balanced mode, dynamically adjusting the fan speed and CPU frequency to balance temperature and performance; and in low-load applications such as reading and standby, the system will identify it as silent mode, turn off the fan and control the temperature by reducing the frequency.

[0084] The temperature control strategy dynamic adjustment subunit is used to automatically select and switch the appropriate temperature control strategy from the preset temperature control strategy library according to the current application scenario.

[0085] In this embodiment, the temperature control strategy is automatically selected and switched from the preset strategy library according to the identified scenario. In high-performance mode, the fan is started at high speed first, and the CPU is allowed to be overclocked for a short time to ensure sufficient processing power under high load; in balanced mode, the voltage and frequency of the CPU / GPU and the fan speed are dynamically adjusted to balance the relationship between temperature and performance; in silent mode, the fan is turned off, and the CPU frequency is reduced to reduce power consumption and reduce noise, thereby improving user comfort.

[0086] The real-time monitoring and mode switching subunit is used to continuously monitor the working state of the tablet computer, and adjust the working mode of the tablet computer in real time according to the working state, and dynamically adjust the temperature control strategy of the tablet computer according to the working mode.

[0087] During the operation of the tablet, the tablet's working status is continuously monitored, and the temperature control strategy is continuously adjusted according to the working status (such as CPU load, temperature changes, touch frequency, etc.). If the user switches from a high-performance application to a low-load application, it automatically switches to silent mode or balanced mode; on the contrary, when the load increases, it switches to high-performance mode in time to enhance heat dissipation and performance. Through this dynamic adjustment, the best temperature control effect can be maintained.

[0088] Specifically, the external environment temperature and humidity of the tablet computer will change. Even if the temperature control strategy has been adjusted in a specific mode, if the ambient temperature suddenly rises, the heat dissipation will be affected. At this time, it is necessary to adjust the strategy in real time according to the new temperature change to maintain the best temperature control effect. For example, in a high temperature environment in summer, the balanced mode can maintain a good balance between temperature and performance, but as the ambient temperature rises, it is necessary to further adjust the fan speed or reduce the CPU frequency to prevent overheating.

[0089] Although the mode switching and temperature control strategy are adjusted according to the application switching situation, the workload of the device is constantly changing in actual operation. Taking the balanced mode as an example, when running some light office software, the system will dynamically adjust the voltage, frequency and fan speed according to real-time data such as CPU load. Because even in a low-load office scenario, different operations such as opening multiple documents or performing complex graphics rendering will cause different CPU loads, and it is necessary to constantly adjust the strategy to adapt to these subtle changes and achieve the best balance between temperature and performance.

[0090] In addition, the ways and scenarios in which users use devices are diverse and uncertain. Through continuous monitoring and adjustment, we can ensure that no matter how users use the device, whether it is playing games for a long time, watching videos, or occasionally performing simple browsing operations, the device can provide stable performance and a comfortable user experience in different situations. For example, if a user temporarily opens a large software during use, the system can monitor the load changes in time and switch to high-performance mode to enhance heat dissipation and performance, avoid frequency reduction caused by freezes or overheating, and give users a smooth experience.

[0091] Moreover, the hardware performance and life of tablet computers are closely related to temperature. Continuous monitoring and adjustment of temperature control strategies can prevent the device from being overheated or overcooled for a long time, thereby protecting the hardware and extending the life of the device. For example, it can prevent the CPU from experiencing electron migration due to long-term high temperature, which affects its performance and stability. At the same time, a reasonable temperature control strategy can also enable the device to maintain good performance under different loads and give full play to the potential of the hardware.

[0092] The embodiment of the present invention can automatically switch the temperature control strategy according to the changes in the application scenario by identifying different application scenarios (such as high-performance mode, balanced mode, and silent mode). This scenario-adaptive mechanism realizes personalized and flexible adjustment in the tablet computer temperature control, adjusts the temperature control strategy according to the actual use requirements and environment, and improves the user experience.

[0093] like Figure 5 As shown, in some embodiments, the temperature control device may also include: a real-time reasoning and control execution unit, which is used to adjust the temperature control strategy according to the predicted thermal field distribution thermogram, and record the user's behavior habits and learn the usage preferences in different time periods, and preload the temperature control strategy suitable for the user's current needs according to the behavior habits and usage preferences.

[0094] This embodiment will assign the computing tasks of the thermal field prediction model and the decision engine to the device's CPU (central processing unit) and NPU (neural network processing unit). This heterogeneous computing can accelerate the temperature control decision-making process and ensure real-time performance. With the support of the inference engine, the system can not only adjust the temperature control strategy according to the predicted temperature trend, but also record the user's behavior habits and learn usage preferences in different time periods. In this way, the system can preload the temperature control strategy that best suits the user's current needs and achieve personalized temperature control.

[0095] like Figure 7 As shown, in some embodiments, the real-time reasoning and control execution unit specifically includes: a computing task allocation subunit, an inference engine, a recording and learning subunit, a personalized temperature control strategy preloading subunit, and a real-time feedback and strategy adjustment subunit.

[0096] The computing task allocation subunit is used to allocate the computing tasks of the field prediction model and the decision engine to the processing units of the device, including the CPU and the NPU.

[0097] In the real-time reasoning stage, the thermal field prediction model and decision engine need to be calculated. Since these two tasks involve complex calculations, the system uses heterogeneous computing to distribute these computing tasks to the CPU and NPU of the tablet. NPU (neural network processing unit) is good at processing a large number of parallel calculations, suitable for performing thermal field prediction and deep learning model calculations, and can accelerate the temperature prediction process. The CPU is responsible for processing the control logic and other computing tasks in the decision engine to ensure that the system can make comprehensive and rapid temperature control decisions. This task allocation optimizes computing resources, improves the system's response speed, and ensures that real-time reasoning can be completed within 10ms.

[0098] The inference engine is used to perform inference calculations based on real-time collected temperature data, power consumption, and temperature and humidity data to predict future thermal field distribution.

[0099] The inference engine is used to perform inference calculations based on inputs such as real-time collected temperature data, power consumption, and environmental information to predict future thermal field distribution. Through the inference engine, the predicted results of the thermal field distribution can be updated in real time, and the temperature control strategy can be adjusted. For example, if the predicted temperature trend is too high, the fan will be started in advance or the CPU frequency will be reduced to prevent the tablet from overheating. The inference engine supports low-latency operation to ensure that temperature control decisions can respond quickly in actual use of the tablet and avoid adverse effects of temperature fluctuations on the user experience.

[0100] The recording and learning subunit records the user's behavior habits and learns the usage preferences in different time periods.

[0101] In order to achieve personalized temperature control, it is necessary to record the user's behavior habits and learn the usage preferences in different time periods. By analyzing historical usage data, such as the user's operating habits, application scenarios, temperature control strategy preferences, etc., a user model is established. On this basis, the user's temperature control needs can be inferred, and the most suitable temperature control strategy can be preloaded in different application scenarios. For example, at night, users prefer to use the low-noise silent mode, while during the day or when playing games, a high-performance mode is more needed. In this way, the most appropriate temperature control strategy can be intelligently selected according to the user's habits and preferences to improve the user experience.

[0102] The preloading subunit of the personalized temperature control strategy is used to preload the personalized temperature control strategy based on the learning of user behavior habits.

[0103] This embodiment can preload personalized temperature control strategies by learning user behavior habits so that they can be quickly applied when the user starts using the tablet computer. By analyzing usage patterns in different time periods, it is possible to predict user needs and load temperature control strategies suitable for the current usage scenario in advance. For example, when the system detects that the user is entering a high-load application (such as a game or video editing), it can automatically enable high-performance mode; in a quiet environment, it will switch to silent mode, turn off the fan and reduce the CPU frequency. The implementation of preloading strategies not only improves the temperature control efficiency, but also reduces the delay caused by strategy switching, making the temperature control response more sensitive and personalized.

[0104] In addition, in some embodiments, a real-time feedback and strategy adjustment subunit may be included to monitor the temperature changes and user behavior of the tablet computer in real time. When executing the temperature control strategy, the temperature changes and user behavior of the tablet computer are monitored in real time to ensure the effectiveness of the temperature control strategy. If the temperature is monitored in real time to be out of the set range or abnormal, the temperature control strategy will be automatically adjusted. For example, if the temperature rises too quickly, the fan speed will be temporarily increased or the CPU frequency will be adjusted to prevent the device from overheating. In addition, the temperature control strategy will be continuously adjusted and optimized based on user feedback and usage patterns to ensure that it can always adapt to user needs and changes in the status of the tablet computer.

[0105] The embodiments of the present invention can achieve rapid temperature control decisions and perform personalized optimization based on user behavior habits through real-time reasoning and control execution. Through heterogeneous computing, thermal field prediction and decision-making tasks are assigned to the CPU and NPU, ensuring the efficiency and real-time performance of the system; at the same time, by learning user behavior and preloading personalized temperature control strategies, the user experience is further improved. The real-time feedback mechanism enables the temperature control strategy to flexibly respond to changes in device status, ensuring that the temperature control effect of the tablet computer in various application scenarios is always in the best state.

[0106] like Figure 5As shown, in some embodiments, the temperature control device may also include: an abnormal diagnosis and model fine-tuning unit, which is used to continuously monitor the actual temperature of each area of ​​the tablet computer and compare the actual temperature with the predicted temperature. When the difference between the monitored actual temperature and the predicted temperature exceeds a set threshold, the thermal field prediction model is dynamically adjusted according to the use environment and load changes of the tablet computer, and the temperature control strategy is adjusted in real time according to the adjusted thermal field prediction model.

[0107] In this embodiment, scene recognition is performed according to the current application scenario of the tablet computer. By analyzing the application process list, the touch event frequency, and the screen brightness change rate, the use status of the tablet computer can be identified. For example, when performing high-intensity games or video rendering, it will be identified as high-performance mode, and when reading or on standby, it will be identified as silent mode. After the scene is identified, the temperature control strategy is automatically switched according to the preset strategy library. Specifically, in high-performance mode, high-speed fan operation is enabled first, allowing the CPU to be overclocked for a short time to improve performance. In balanced mode, the voltage and frequency of the CPU or GPU, as well as the fan speed, are dynamically adjusted to ensure a balance between temperature and performance. In silent mode, the temperature is controlled only by frequency reduction, and the fan is turned off to reduce power consumption and noise.

[0108] The abnormal temperature self-diagnosis function in this embodiment can detect the difference between the predicted temperature and the actual temperature in real time, and trigger the online fine-tuning function of the thermal field prediction model. This makes it possible to adjust the thermal field prediction model according to environmental changes and usage status, thereby improving temperature control accuracy and system robustness.

[0109] like Figure 8 As shown, in some embodiments, the abnormal diagnosis and model fine-tuning unit specifically includes: a temperature detection subunit, an abnormal diagnosis subunit, an online fine-tuning subunit, a verification and evaluation subunit, and a real-time adjustment of the temperature control strategy subunit.

[0110] The temperature detection subunit is used to continuously monitor the actual temperature data of the tablet computer and compare the monitored actual temperature data with the predicted temperature data. When the difference between the monitored actual temperature data and the predicted temperature data exceeds a set temperature error threshold, the abnormal diagnosis subunit is triggered.

[0111] This embodiment continuously monitors the data of each temperature sensor of the tablet computer and compares the actual temperature with the predicted temperature. When the difference between the actual temperature and the predicted temperature exceeds the set threshold (for example, 3°C), the system triggers the abnormal temperature detection mechanism. This mechanism ensures that the temperature control system can perceive the deviation of the temperature prediction in real time to avoid overheating of the tablet computer or failure of temperature control. The goal of anomaly detection is to quickly respond to temperature control problems by accurately comparing the predicted and actual temperatures, thereby ensuring the reliability of the temperature control system.

[0112] The abnormality diagnosis subunit is used to evaluate the accuracy of the current thermal field prediction model according to the difference between the monitored actual temperature data and the predicted temperature data, and to analyze the reasons for exceeding the set temperature error threshold.

[0113] In this embodiment, once the system detects a temperature anomaly, it will start the anomaly diagnosis mechanism. This mechanism first evaluates the current accuracy of the temperature prediction model and analyzes the cause of the temperature prediction error. It checks the status of the temperature sensor to confirm whether there is a sensor failure or data anomaly, and also analyzes the impact of the current environment and load conditions on the temperature. This process is conducive to locating the root cause of the temperature control failure, providing a basis for subsequent model fine-tuning, and ensuring that adaptive adjustments can be made under various environmental changes.

[0114] The online fine-tuning subunit is used to adjust the thermal field prediction model according to the reasons, so that the thermal field prediction can accurately reflect the changing trend of the current environment and load, and obtain the fine-tuned thermal field prediction model.

[0115] When the system recognizes that there is a significant deviation between the predicted temperature and the actual temperature, the system automatically starts the online fine-tuning mechanism. The online fine-tuning mechanism adjusts the parameters of the thermal field prediction model so that the model can more accurately reflect the changes in the current environment and load. This process includes optimizing model weights, short-term incremental training, and data updates. Through this fine-tuning, the system can improve the accuracy of temperature prediction, ensure that the temperature control strategy can accurately respond to real-time temperature changes, and avoid overheating or temperature control failure. Among them, optimizing model weights means that when the online fine-tuning mechanism is started, the weights of the thermal field prediction model will be optimized first. Specifically, based on the error between the actual temperature and the predicted temperature, the gradient descent method or other optimization algorithms are used to adjust the weights of the model to make the prediction results more accurate. The optimization of weights will adjust the impact of each parameter in the model on the prediction results, ensuring that the model can better adapt to the current load conditions and environmental conditions. This process is the core step of fine-tuning, which can help the model reduce errors and improve prediction accuracy. Short-term incremental training refers to short-term incremental training after optimizing weights. This stage of training uses new temperature data, especially data obtained from the current actual environment and load, to further adjust the parameters of the model. Unlike traditional full training, incremental training only uses new data for updates, avoiding the computational cost of completely retraining the model. The purpose of incremental training is to enable the model to quickly adapt to new temperature patterns while avoiding overfitting existing data, thereby maintaining the generalization ability of the model. Data update means that during the fine-tuning process, the training data is also updated in real time. This means that the system will update its data set based on new temperature data, environmental variables, and load conditions, and incorporate this new data into the model training process. Data updates ensure that the model can always make predictions based on the latest environmental changes, thereby improving the model's ability to predict future temperature changes. By continuously receiving and processing new data, the tablet can adjust the temperature control strategy in a timely manner to avoid temperature control failures caused by outdated data.

[0116] The verification and evaluation subunit is used to use actual temperature data to verify whether the fine-tuned thermal field prediction model can accurately predict the temperature and provide the correct temperature control strategy.

[0117] After fine-tuning the model, verify and evaluate the new model to ensure its effectiveness. Use new temperature data, especially data containing abnormal temperature changes, to verify whether the fine-tuned model can accurately predict the temperature and provide correct temperature control decisions. The evaluation process includes comparing with the unfine-tuned model to analyze whether the error after fine-tuning has been significantly improved to ensure that the model can run stably in the long term and avoid overfitting or other unnecessary errors.

[0118] The real-time adjustment of the temperature control strategy subunit is used to adjust the temperature control strategy in real time according to the thermal field prediction model verified by the verification and evaluation subunit.

[0119] The fine-tuned model will be used to adjust the temperature control strategy in real time. When the predicted temperature is more accurate, temperature fluctuations can be identified earlier and measures can be taken. For example, when the temperature is predicted to be too high, a higher speed fan will be enabled in advance or the CPU frequency will be lowered to prevent the tablet from overheating. When the temperature is low, the fan power consumption and noise will be reduced, thereby saving energy and improving the user experience. With this real-time adjustment, the system can respond more accurately and quickly when the temperature changes.

[0120] In this embodiment, the temperature prediction model is continuously monitored and adjusted to ensure that the temperature control strategy always maintains high accuracy. Through the online fine-tuning mechanism, it can quickly adapt to different usage environments and load changes to avoid temperature runaway or overheating of the tablet computer. This mechanism enhances the adaptability of the temperature control system, enabling it to maintain stable operation under different conditions and improve the overall user experience.

[0121] like Figure 5 In some embodiments, the temperature control device may further include: a temperature control execution and feedback adjustment unit, which is used to execute hardware control tasks according to the optimal temperature control strategy to adjust the temperature of the tablet computer in real time; wherein the hardware control tasks include but are not limited to controlling the speed of the fan, adjusting the frequency of the processor, and adjusting the screen brightness.

[0122] In this embodiment, once the optimized temperature control strategy is determined, the system will perform related hardware control tasks. For example, the temperature of the tablet computer can be adjusted in real time by controlling the speed of the fan, adjusting the frequency of the CPU or GPU, or adjusting the screen brightness. At this time, the temperature changes are continuously monitored, and the strategy is dynamically adjusted according to the real-time feedback to ensure that the temperature of the tablet computer is always kept within a safe range. Through this closed-loop control, a stable temperature control effect can be maintained under various usage conditions to ensure that the user experience is not affected.

[0123] like Figure 9 As shown, in some embodiments, the temperature control execution and feedback adjustment unit specifically includes: an execution optimization temperature control strategy subunit, a real-time temperature change monitoring subunit, a dynamic adjustment temperature control strategy subunit, a closed-loop control and feedback optimization subunit, and a user experience monitoring and adjustment subunit.

[0124] The optimized temperature control strategy execution subunit is used to implement the optimal temperature control strategy through hardware control tasks.

[0125] In this embodiment, once the optimized temperature control strategy is determined, it will be implemented through hardware control tasks. These control tasks include adjusting the fan speed, adjusting the frequency of the CPU or GPU, adjusting the screen brightness, etc. Through these means, measures can be taken quickly to affect the temperature of the tablet computer. For example, the fan speed can be increased as needed to increase the heat dissipation capacity, or the fan speed can be reduced to reduce power consumption and noise; the frequency of the CPU or GPU can be dynamically adjusted according to the change of temperature, thereby controlling the heat output of the system and reducing the risk of overheating; the adjustment of the screen brightness is also part of the temperature control, especially in low load conditions, by reducing the screen brightness to reduce power consumption.

[0126] The real-time temperature change monitoring subunit is used to continuously monitor the temperature change trend of the tablet computer.

[0127] This embodiment will continuously monitor the temperature changes of the tablet computer to ensure the effectiveness of the temperature control strategy. This includes the collection and analysis of real-time data provided by the temperature sensor. The temperature change data will be transmitted to the central processing unit (CPU) for processing and real-time feedback of the current temperature status. For example, the CPU temperature (T CPU )、GPU temperature(T GPU )、Battery temperature(T Battery ) and other data will be used to evaluate the overall thermal status of the tablet and decide whether the temperature control strategy needs to be adjusted. Based on this data, the system will continuously evaluate whether to increase or decrease the cooling measures.

[0128] The temperature control strategy dynamic adjustment subunit is used to dynamically adjust the temperature control strategy of the tablet computer according to the temperature change trend of the tablet computer.

[0129] In this embodiment, the system dynamically adjusts the temperature control strategy based on the temperature data monitored in real time. For example, if the system detects that the temperature of a certain area (such as the CPU or GPU) is too high (exceeding the safety threshold), it will temporarily increase the fan speed or reduce the CPU frequency to quickly reduce the temperature; if the temperature is low, the fan speed will be reduced to reduce power consumption and increase the battery life of the tablet. The adjusted temperature control strategy will take effect immediately based on the feedback data to ensure that the temperature of the tablet is always kept within a safe range to avoid overheating or performance limitations.

[0130] The closed-loop control and feedback optimization subunit is used to evaluate the temperature control strategy according to the current temperature data of the tablet computer after each adjustment of the temperature control strategy, obtain the temperature control effect of the temperature control strategy, and fine-tune the temperature control strategy according to the temperature control effect.

[0131] After implementing the temperature control strategy, the system will enter the closed-loop control stage for continuous feedback and adjustment. After each strategy adjustment, the temperature control system will re-evaluate the current temperature and the effectiveness of the strategy. If the temperature fails to reach the expected target, the strategy will be fine-tuned based on the feedback. This closed-loop control mechanism ensures that the temperature control strategy can respond flexibly under various usage conditions. For example, when the user switches from a high-intensity application to a light application, the temperature control system will adjust the fan speed and CPU frequency accordingly to achieve the best temperature control effect. In this process, the temperature control strategy will be continuously optimized based on the tablet's long-term operating data and environmental changes to further improve the stability and accuracy of the temperature control system.

[0132] In addition, in some embodiments, a user experience monitoring and adjustment subunit is also included, which is used to continuously monitor the user's experience and ensure that the temperature control strategy minimizes the impact on user operations. This embodiment will continuously monitor the user's experience to ensure that the temperature control system minimizes the impact on user operations. For example, indicators such as the noise level, response speed, and battery life of the device are monitored to ensure that the user experience is not significantly affected during the temperature control process. If excessive noise or performance degradation (for example, frequent frequency reduction causes freezes) is detected, it will adjust the strategy accordingly to seek the best solution to balance performance and temperature control. Through these feedback mechanisms, not only can the stable operation of the tablet be maintained, but the user experience can also be optimized to ensure that users can get a smooth experience under different load conditions.

[0133] The temperature control execution and feedback adjustment of this embodiment is a continuous process. The temperature control strategy is dynamically adjusted according to the actual temperature data monitored in real time to ensure that the tablet computer can maintain a safe temperature in various usage environments. Through closed-loop control, the temperature control strategy can be optimized according to real-time feedback, and while ensuring the temperature control effect, the performance is maximized, power consumption and noise are reduced, thereby ensuring that the user experience is not affected.

[0134] Embodiment 2 like Figure 10 As shown, the present invention also provides a temperature control method for a tablet computer, the temperature control method comprising the following steps: S11: collecting perception data of the tablet computer, wherein the perception data includes temperature data of multiple areas of the tablet computer, temperature and humidity data of the surrounding environment, and power consumption data during operation of the tablet computer; S12: preprocessing the collected perception data, and fusing the preprocessed perception data into a high-dimensional feature vector; S13: inputting the high-dimensional feature vector and the posture of the tablet computer into a thermal field prediction model using a convolutional long short-term memory network, predicting temperature data of each area of ​​the tablet computer through the thermal field prediction model and generating a thermal field distribution thermogram; S14: defining an objective function according to the temperature distribution in the thermal field distribution thermogram and in combination with a plurality of objective parameters, wherein the objective function dynamically allocates a weight coefficient of each objective parameter according to the use environment of the tablet computer; the objective parameters include fan power consumption, central processing unit frequency reduction amplitude, and noise generated by the tablet computer during operation; S15: according to the objective function and the dynamically adjusted weight coefficient, a Pareto optimal solution set is generated by using a multi-objective optimization algorithm, wherein the Pareto optimal solution set includes multiple candidate temperature control strategies; an optimal temperature control strategy suitable for the current application scenario is selected from the multiple candidate temperature control strategies through fuzzy logic decision making, and the optimal temperature control strategy is transmitted to the execution unit; S16: adjusting the hardware parameters of the tablet computer according to the optimal temperature control strategy.

[0135] For detailed implementation details, please refer to Figures 1 to 9 The temperature control device shown.

[0136] Embodiment 3 like Figure 11 As shown, an embodiment of the present invention further provides a computer-readable storage medium, in which program codes for executing the various steps in the embodiment of the present method are stored. When the program codes are executed by a processor, any of the above-mentioned temperature control methods for a tablet computer is implemented. Figures 1 to 9 If the integrated module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing related hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and when the computer program is executed by a processor, the steps of the above-mentioned method embodiment can be implemented.

[0137] Embodiment 4 Figure 12 Schematic diagram of the structure of an electronic device according to an embodiment of the present invention. Figure 12At the hardware level, the electronic device includes a processor, and optionally also includes an internal bus, a network interface, and a memory. Among them, the memory includes internal memory, such as high-speed random access memory (Random-Access Memory, RAM), and also includes non-volatile memory (non-volatile memory), such as at least one disk storage, etc. Of course, the electronic device also includes hardware required for other services. The processor, network interface and memory can be interconnected through an internal bus. The bus can be divided into an address bus, a data bus, a control bus, etc. The memory is used to store programs, and the program may include program code, and the program code includes computer operation instructions. The memory may include internal memory and non-volatile memory, and provides instructions and data to the processor. The processor reads the corresponding computer program from the non-volatile memory into the internal memory and then runs it, and executes Figure 10 The temperature control method of a tablet computer is shown. Each embodiment in this specification is described in a related manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments.

[0138] The above description is only a preferred embodiment of the present invention and is not intended to limit the protection scope of the present invention. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention are included in the protection scope of the present invention.

Claims

1. A temperature control device for a tablet computer, characterized in that: The temperature control device comprises: A perception data collection unit, used for collecting perception data of the tablet computer; A data preprocessing and fusion unit, used to preprocess the collected perception data and fuse the preprocessed perception data into a high-dimensional feature vector; A thermal field prediction unit, used for inputting the high-dimensional feature vector and the posture of the tablet computer into a thermal field prediction model using a convolutional long short-term memory network, predicting the temperature data of each area of ​​the tablet computer through the thermal field prediction model and generating a thermal field distribution thermogram; An objective function definition unit, used to define an objective function according to the temperature distribution in the thermal field distribution thermodynamic map and in combination with a plurality of objective parameters, wherein the objective function dynamically allocates a weight coefficient of each objective parameter according to the use environment of the tablet computer; A temperature control decision engine is used to generate a Pareto optimal solution set using a multi-objective optimization algorithm according to the objective function and the weight coefficient, wherein the Pareto optimal solution set includes multiple candidate temperature control strategies; select an optimal temperature control strategy suitable for the current application scenario from the multiple candidate temperature control strategies through fuzzy logic decision making, and transmit the optimal temperature control strategy to an execution unit; An execution unit is used to adjust the hardware parameters of the tablet computer according to the optimal temperature control strategy.

2. The temperature control device for a tablet computer according to claim 1, characterized in that: The perception data collection unit specifically includes: Thermocouple arrays for real-time temperature data collection in multiple areas; An environmental temperature and humidity data acquisition subunit, used for synchronously acquiring environmental temperature data and environmental humidity data of the surrounding environment of the tablet computer; The power consumption monitoring subunit is used to monitor the power consumption data of the tablet computer in real time.

3. The temperature control device for a tablet computer according to claim 1, characterized in that: The temperature control device also includes: The scene recognition and adaptive strategy switching unit is used to determine the working mode of the tablet computer by analyzing the application process list, the frequency of touch events and the screen brightness change rate of the tablet computer, and automatically switch the temperature control strategy according to the preset temperature control strategy library according to the working mode of the tablet computer.

4. The temperature control device for a tablet computer according to claim 3, characterized in that: The scene recognition and adaptive strategy switching unit specifically includes: The information collection subunit is used to collect information about currently running applications by monitoring the application process list; A touch event frequency monitoring subunit, used to determine the interaction strength of the tablet computer by monitoring the frequency of touch events, and determine the working mode of the tablet computer according to the interaction strength; A screen brightness change rate detection subunit, used to detect the screen brightness change rate of the tablet computer, and further confirm the working mode of the tablet computer according to the screen brightness change rate; An application scenario identification subunit, used to identify a current application scenario according to the application information, the frequency of the touch event and the screen brightness change rate; The temperature control strategy dynamic adjustment subunit is used to automatically select and switch to a suitable temperature control strategy from a preset temperature control strategy library according to the current application scenario.

5. The temperature control device for a tablet computer according to claim 1, characterized in that: The temperature control device also includes: The real-time reasoning and control execution unit is used to adjust the temperature control strategy according to the predicted thermal field distribution thermogram, record the user's behavior habits and learn the usage preferences in different time periods, and preload the temperature control strategy suitable for the user's current needs according to the behavior habits and usage preferences.

6. The temperature control device for a tablet computer according to claim 1, characterized in that: The temperature control device also includes: The abnormal diagnosis and model fine-tuning unit is used to continuously monitor the actual temperature data of each area of ​​the tablet computer and compare the actual temperature data with the predicted temperature data. When the difference between the actual temperature data and the predicted temperature data exceeds a set threshold, the thermal field prediction model is dynamically adjusted according to the use environment and load changes of the tablet computer, and the temperature control strategy is adjusted in real time according to the adjusted thermal field prediction model.

7. The temperature control device for a tablet computer according to claim 6, characterized in that: The abnormality diagnosis and model fine-tuning unit specifically includes: A temperature detection subunit, used for continuously monitoring the actual temperature data of each area of ​​the tablet computer, and comparing the monitored actual temperature data with the predicted temperature data, and triggering the abnormality diagnosis subunit when the difference between the monitored actual temperature data and the predicted temperature data exceeds a set temperature error threshold; an abnormality diagnosis subunit, used to evaluate the accuracy of the current thermal field prediction model according to the difference between the monitored actual temperature data and the predicted temperature data, and to analyze the reason for exceeding a set temperature error threshold; An online fine-tuning subunit, used for adjusting the thermal field prediction model according to the cause, so that the thermal field prediction can accurately reflect the changing trend of the current environment and load, and obtain a fine-tuned thermal field prediction model; A verification and evaluation subunit, used to verify whether the fine-tuned thermal field prediction model can accurately predict the temperature and provide a correct temperature control strategy using the actual temperature data; The temperature control strategy adjustment subunit is used to adjust the temperature control strategy in real time according to the thermal field prediction model verified by the verification and evaluation subunit.

8. The temperature control device for a tablet computer according to claim 1, characterized in that: The temperature control device also includes: The temperature control execution and feedback adjustment unit is used to execute hardware control tasks according to the optimal temperature control strategy to adjust the temperature of the tablet computer in real time.

9. The temperature control device for a tablet computer according to claim 8, characterized in that: The temperature control execution and feedback adjustment unit specifically includes: An optimized temperature control strategy execution subunit is used to implement the optimal temperature control strategy through hardware control tasks; A real-time temperature change monitoring subunit, used for continuously monitoring the temperature data of the tablet computer; A dynamic temperature control strategy adjustment subunit, used for dynamically adjusting the temperature control strategy of the tablet computer according to the temperature data of the tablet computer; The closed-loop control and feedback optimization subunit is used to evaluate the temperature control strategy according to the current temperature data of the tablet computer after each adjustment of the temperature control strategy, obtain the temperature control effect of the temperature control strategy, and fine-tune the temperature control strategy according to the temperature control effect.

10. A temperature control method for a tablet computer, characterized in that: The temperature control method comprises: Collect sensory data from the tablet; Preprocessing the collected perception data, and fusing the preprocessed perception data into a high-dimensional feature vector; The high-dimensional feature vector and the posture of the tablet computer are input into a thermal field prediction model using a convolutional long short-term memory network, and the temperature data of each area of ​​the tablet computer is predicted by the thermal field prediction model to generate a thermal field distribution thermogram; According to the temperature distribution in the thermal field distribution thermodynamic map, a target function is defined in combination with a plurality of target parameters, wherein the target function dynamically allocates a weight coefficient of each target parameter according to the use environment of the tablet computer; According to the objective function and the weight coefficient, a Pareto optimal solution set is generated by a multi-objective optimization algorithm, wherein the Pareto optimal solution set includes multiple candidate temperature control strategies; an optimal temperature control strategy suitable for the current application scenario is selected from the multiple candidate temperature control strategies through fuzzy logic decision making, and the optimal temperature control strategy is transmitted to an execution unit; The hardware parameters of the tablet computer are adjusted according to the optimal temperature control strategy.

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