CN110865666A - Temperature control method, temperature control device, storage medium and electronic equipment - Google Patents
Temperature control method, temperature control device, storage medium and electronic equipment Download PDFInfo
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Abstract
The application discloses a temperature control method, a device, a storage medium and an electronic device, wherein the temperature control method comprises the following steps: acquiring historical temperature data of a sensor and historical hardware parameters corresponding to the historical temperature data; taking historical temperature data and historical hardware parameters as training samples of a training model, and training the training model to obtain a trained temperature control model; obtaining a temperature control strategy according to the trained temperature control model, wherein the temperature control strategy comprises at least two target temperatures, and each target temperature corresponds to a hardware target parameter; acquiring temperature data of a sensor; and adjusting the parameters of the hardware according to the temperature data and the temperature control strategy. According to the method and the device, the training model is trained through historical temperature data and historical hardware parameters to obtain the temperature control strategy, the hardware parameters are adjusted in a segmented mode according to the current temperature data of the hardware and the temperature control strategy, bad feedback caused by direct adjustment of the hardware parameters is avoided, and therefore the temperature control effect is improved.
Description
Technical Field
The present application relates to the field of electronic technologies, and in particular, to a temperature control method and apparatus, a storage medium, and an electronic device.
Background
With the continuous development of the terminal, the functions of the terminal are more and more comprehensive. The user can realize a plurality of functions such as a call function, a camera function, a recording function, a navigation function, a shopping function and the like through the terminal. This is convenient for the user to use and at the same time, it is easy to cause the terminal temperature to be too high. In the related art, the terminal has a single temperature control mode and a poor temperature control effect.
Disclosure of Invention
The embodiment of the application provides a temperature control method, a temperature control device, a storage medium and an electronic device, which can enable a temperature control strategy to be more fit with the current state of the electronic device, and improve the temperature control effect.
In a first aspect, an embodiment of the present application provides a temperature control method, including:
acquiring historical temperature data of a sensor and historical hardware parameters corresponding to the historical temperature data;
taking the historical temperature data and the historical hardware parameters as training samples of a training model, and training the training model to obtain a trained temperature control model;
obtaining a temperature control strategy according to the trained temperature control model, wherein the temperature control strategy comprises at least two target temperatures, and each target temperature corresponds to a hardware target parameter;
acquiring temperature data of the sensor;
and adjusting the parameters of the hardware according to the temperature data and the temperature control strategy.
In a second aspect, an embodiment of the present application further provides another temperature control method, including:
acquiring temperature data of a sensor;
acquiring a temperature control strategy, wherein the temperature control strategy comprises at least two target temperatures, each target temperature corresponds to a hardware target parameter, the historical temperature data of a sensor and the historical hardware parameters corresponding to the historical temperature data are acquired, the historical temperature data and the historical hardware parameters are used as training samples of a training model, the training model is trained to obtain a trained temperature control model, and the temperature control strategy is obtained according to the trained temperature control model;
and adjusting the parameters of hardware corresponding to the sensor according to the temperature data and the temperature control strategy.
In a third aspect, an embodiment of the present application provides a temperature control apparatus, including:
the system comprises a first acquisition module, a second acquisition module and a control module, wherein the first acquisition module is used for acquiring historical temperature data of a sensor and historical hardware parameters corresponding to the historical temperature data;
the training module is used for taking the historical temperature data and the historical hardware parameters as training samples of a training model, and training the training model to obtain a trained temperature control model;
the strategy module is used for obtaining a temperature control strategy according to the trained temperature control model, wherein the temperature control strategy comprises at least two target temperatures, and each target temperature corresponds to a hardware target parameter;
the second acquisition module is used for acquiring the temperature data of the sensor;
and the adjusting module is used for adjusting the parameters of the hardware according to the temperature data and the temperature control strategy.
In a fourth aspect, a storage medium is provided in this application, and a computer program is stored thereon, and when the computer program runs on a computer, the computer is caused to execute the temperature control method provided in any of the embodiments of the application.
In a fifth aspect, an electronic device provided in an embodiment of the present application includes a processor and a memory, where the memory has a computer program, and the processor is configured to execute the temperature control method provided in any embodiment of the present application by calling the computer program.
According to the temperature control scheme, the training model is trained through historical temperature data and historical hardware parameters to obtain a temperature control strategy, the temperature control strategy comprises at least two target temperatures and hardware target parameters corresponding to the target temperatures, the hardware parameters are adjusted according to the temperature control strategy and the current temperature data of hardware, bad feedback caused by direct adjustment of the hardware parameters is avoided, and therefore the temperature control effect is improved.
Drawings
The technical solutions and advantages of the present application will become apparent from the following detailed description of specific embodiments of the present application when taken in conjunction with the accompanying drawings.
Fig. 1 is a schematic flow chart of a temperature control method according to an embodiment of the present disclosure.
Fig. 2 is a schematic flow chart of a temperature control method according to an embodiment of the present disclosure.
Fig. 3 is a schematic flow chart of a temperature control method according to an embodiment of the present application.
Fig. 4 is a schematic structural diagram of a temperature control device according to an embodiment of the present application.
Fig. 5 is a schematic structural diagram of a first electronic device according to an embodiment of the present application.
Fig. 6 is a schematic structural diagram of a second electronic device according to an embodiment of the present application.
Detailed Description
The following description is based on illustrated embodiments of the application and should not be taken as limiting the application with respect to other embodiments that are not detailed herein. The term "module" as used herein may be considered a software object executing on the computing system. The various modules, engines, and services herein may be considered as objects of implementation on the computing system.
The embodiment of the application provides a temperature control method, and an execution main body of the temperature control method can be the temperature control device provided by the embodiment of the application or an electronic device integrated with the temperature control device. The electronic device may be a smart phone, a tablet computer, a Personal Digital Assistant (PDA), or the like.
The following is a detailed description of the analysis.
An embodiment of the present application provides a temperature control method, please refer to fig. 1, where fig. 1 is a first flowchart of the temperature control method provided in the embodiment of the present application, and the temperature control method may include the following steps:
101. historical temperature data of the sensor and historical hardware parameters corresponding to the historical temperature data are obtained.
The electronic device is often provided with a plurality of sensors, such as temperature sensors, and historical temperature data can be obtained through the temperature sensors, for example, the temperature sensors can be arranged on corresponding hardware, such as the temperature sensors can be arranged on the outer surface of the corresponding hardware or in the corresponding hardware, the temperature sensors are used for detecting temperature data of the corresponding hardware during operation, recording temperature data generated when the corresponding hardware operates in a historical time period, obtaining historical temperature data, and recording hardware parameter records of the corresponding hardware during operation, and the hardware parameters can be hardware performance parameters, such as load parameters, frequency parameters, memory utilization rate parameters and the like of the hardware. Wherein, the hardware in the electronic device may include: a Central Processing Unit (CPU), an image processor (GPU), a microprocessor (DSP), an embedded neural Network Processor (NPU), a circuit board (PCB), etc., and the sensor may include: the temperature sensor of the CPU is used for correspondingly detecting the temperature of the CPU, the temperature sensor of the GPU is used for correspondingly detecting the temperature of the GPU, the temperature sensor of the NPU is used for correspondingly detecting the temperature of the NPU, the temperature sensor of the DPS is used for correspondingly detecting the temperature of the DPS, and the temperature sensor of the PCB is used for correspondingly detecting the temperature of the PCB.
102. And taking the historical temperature data and the historical hardware parameters as training samples of the training model, and training the training model to obtain the trained temperature control model.
The historical temperature data of the sensor and the historical hardware parameters of the corresponding hardware are used as samples of a training model, the training model is trained, for example, a time recursive Neural Network model (LSTM) or a Recurrent Neural Network (RNN) can be used as the training model, a required training model can be selected according to the hardware environment or the software environment of the electronic equipment, the historical temperature data is modeled through one of the two Neural Network models to obtain output data, the historical hardware parameters are used as verification data of the output data, the parameters in the training model are continuously adjusted, the training model is trained, and finally the trained temperature control model is obtained. In addition, other training models can be selected for training to obtain the corresponding temperature control model.
103. And obtaining a temperature control strategy according to the trained temperature control model, wherein the temperature control strategy comprises at least two target temperatures, and each target temperature corresponds to a hardware target parameter.
The trained temperature control model can output reasonable hardware parameters according to input temperature data, for example, the temperature data of a CPU temperature sensor is input, reasonable CPU frequency can be output, when the input temperature data is enough, a plurality of corresponding CPU frequencies are obtained, the one-to-one correspondence relationship between the temperature data and the hardware parameters is fitted into a step-type fitting function, a plurality of temperature ranges and corresponding hardware parameter ranges are obtained, each temperature range corresponds to one hardware parameter range, a target temperature is determined from each temperature range, a hardware target parameter is determined from the corresponding hardware parameter range, and the mapping relationship between the target temperature and the hardware target parameter is obtained, namely one target temperature corresponds to one hardware target parameter.
As shown in Table 1
| Target temperature | Hardware target parameter |
| First target temperature | First hardware target parameter |
| Second target temperature | Second hardware target parameter |
| Third target temperature | Third hardware target parameter |
| Fourth target temperature | Fourth hardware object parameter |
TABLE 1
The temperature control strategy was obtained according to table 1.
104. Temperature data of the sensor is acquired.
The real-time temperature data of the temperature sensor is obtained, if the hardware parameter of the temperature control strategy corresponds to the performance parameter of the CPU, the temperature data of the CPU temperature sensor is obtained in real time, and the CPU temperature sensor reflects the heat generated when the CPU runs.
105. And adjusting the parameters of the hardware according to the temperature data and the temperature control strategy.
And comparing the temperature data of the sensor acquired in real time with the target temperature in the temperature control strategy, and adjusting the parameters of the corresponding hardware according to the comparison result, for example, adjusting the parameters of the corresponding hardware to the target parameters of the hardware so as to realize the control of the hardware temperature.
In the embodiment of the application, historical temperature data and historical hardware parameters are used as samples for model training, the training model is trained to obtain a trained temperature control model, a temperature control strategy is obtained according to the temperature control model, the temperature control strategy reflects the corresponding relation between the temperature change of hardware during use and the hardware parameters, when the temperature of a sensor corresponding to the hardware is detected to meet the temperature condition in the temperature control strategy, the hardware parameters are adjusted to the hardware parameters in the temperature control strategy according to the corresponding relation between the temperature change and the hardware parameters, and poor feedback caused by direct adjustment of the hardware parameters is avoided, so that the temperature control effect is improved.
Referring to fig. 2, fig. 2 is a second flow chart of a temperature control method according to an embodiment of the present disclosure, where the temperature control method may include:
201. historical temperature data of the sensor and historical hardware parameters corresponding to the historical temperature data are obtained.
The electronic device is often provided with a plurality of sensors, such as temperature sensors, and historical temperature data can be obtained through the temperature sensors, for example, the temperature sensors can be arranged on corresponding hardware, the temperature sensors are used for detecting temperature data of the corresponding hardware during operation, recording the temperature data generated when the corresponding hardware operates in a historical time period to obtain historical temperature data, and simultaneously recording hardware parameter records of the corresponding hardware during operation, and the hardware parameters can be hardware performance parameters, such as load parameters, frequency parameters, memory utilization rate parameters and the like of the hardware.
Wherein, the hardware in the electronic device may include: a Central Processing Unit (CPU), an image processor (GPU), a microprocessor (DSP), an embedded neural Network Processor (NPU), a circuit board (PCB), etc., and the sensor may include: the temperature sensor of the CPU is used for correspondingly detecting the temperature of the CPU, the temperature sensor of the GPU is used for correspondingly detecting the temperature of the GPU, the temperature sensor of the NPU is used for correspondingly detecting the temperature of the NPU, the temperature sensor of the DPS is used for correspondingly detecting the temperature of the DPS, and the temperature sensor of the PCB is used for correspondingly detecting the temperature of the PCB.
202. And taking the historical temperature data and the historical hardware parameters as training samples of the training model, and training the training model to obtain the trained temperature control model.
For example, in order to obtain a corresponding relationship between the frequency of the CPU and the temperature of the CPU, historical temperature data of the CPU temperature sensor may be used as input data of a training model, historical temperature data of the CPU temperature sensor may be input into the training model, output data may be obtained, corresponding CPU frequency parameters may be used as verification data of the training model, parameters of the training model may be adjusted, the training model may be continuously trained, and when there are enough historical temperature data and historical hardware parameters, parameters of the training model may be continuously adjusted, and a trained temperature control model may be obtained.
In some embodiments, the hardware type causing the electronic device to generate heat may be various, for example, a CPU, a GPU, an NPU, a DSP, a PCB, and the like, and the heat generation cause may be caused by only one hardware or may be caused by a plurality of hardware, so when training a model, historical temperature data of a corresponding sensor and corresponding historical hardware parameters may be obtained as a training sample of the model according to a requirement, a corresponding temperature control model is obtained, and then parameters of the corresponding hardware are adjusted, and the temperature control model reflects a relationship between temperatures at which the plurality of hardware operates and the corresponding hardware parameters.
It should be noted that, the training model may be integrated in the electronic device, or may be integrated in the server, taking the example that the training model is integrated in the electronic device, when the user uses the electronic device, the temperature data generated by the sensor and the corresponding hardware parameter are recorded and processed to be used as a training sample of the training model, and when the user uses the electronic device for a longer time, the obtained temperature control model and the obtained temperature control strategy are more suitable for the use state of the electronic device, thereby improving the temperature control effect. Taking the example that the training model is integrated in the server, the model can be trained by taking the sensor temperature data collected on the platform big data and the corresponding hardware parameters as the sample of model training, and the temperature control model is obtained by training.
203. And obtaining a first temperature control strategy according to the trained temperature control model, wherein the first temperature control strategy comprises a plurality of target temperatures and a plurality of hardware target parameters, the plurality of target temperatures are a plurality of incremental temperature thresholds, and each temperature threshold corresponds to one hardware target parameter.
The first temperature control strategy may be understood as a mapping relationship between temperature data obtained according to a temperature control model and hardware parameters, where the mapping relationship includes a plurality of incremental temperature thresholds and corresponding hardware target parameters, and taking hardware as a CPU as an example, the first temperature control strategy includes a plurality of different temperature thresholds and a CPU frequency parameter corresponding to each temperature threshold, as shown in table 2:
| target temperature | Hardware target parameter |
| 45° | 1.8GHz |
| 55° | 1.7GHz |
| 65° | 1.5GHz |
| 75° | 1.0GHz |
TABLE 2
In some embodiments, the first temperature control strategy may be obtained through a fitting function of a temperature control model, specifically, a target fitting function is obtained from the temperature control model, a plurality of temperature ranges and a hardware parameter range corresponding to each temperature range are obtained according to the target fitting function; calculating the temperature range of each section to obtain a plurality of target temperature thresholds; and calculating the hardware parameter range corresponding to each section of temperature range to obtain a plurality of hardware target parameters, wherein each hardware target parameter corresponds to a target temperature threshold. For example, the fitting function is a stepped fitting function, a stepped CPU temperature range and a corresponding CPU frequency range are obtained, a target temperature threshold value is obtained by calculating each temperature range through an algorithm, and a hardware target parameter is obtained by calculating the corresponding CPU frequency range, one target temperature threshold value corresponds to one hardware target parameter, as shown in table 2, the target temperature obtained through the temperature control model is 45 °, and the corresponding hardware target parameter is 1.8 GHz.
The numerical value in each temperature range can be calculated through a mean algorithm or a median algorithm to obtain the corresponding target temperature, and the corresponding hardware parameter range is calculated through the mean algorithm or the median algorithm to obtain the target hardware parameter.
204. Temperature data of the sensor is acquired.
If the hardware corresponding to the first temperature control strategy is a CPU, and the electronic device acquires temperature data of the CPU temperature sensor during operation, it can be understood that acquiring the CPU temperature sensor data is a real-time continuous process.
In some embodiments, the hardware corresponding to the temperature control strategy obtained by the temperature control model is a GPU, and the electronic device obtains temperature data of a GPU temperature sensor during operation.
205. When the temperature data is in an ascending trend and the temperature value in the temperature data is greater than a first temperature threshold value in the plurality of incremental temperature threshold values, adjusting the parameter of the hardware to a first hardware target parameter corresponding to the first temperature threshold value.
The temperature data of the sensor is obtained in real time, calculation and analysis are carried out on the temperature data, when it is analyzed that the temperature change trend detected by the sensor is an ascending trend, the temperature value in the temperature data is analyzed, when the temperature value of the temperature data is larger than a first temperature threshold value, the hardware parameter is adjusted to a first hardware parameter, it can be understood that the temperature value used for being compared with the first temperature threshold value can be the maximum temperature value in the obtained temperature data or the average temperature value in the temperature data, and the temperature value used for comparison can be selected according to specific conditions. Taking the adjustment of the CPU frequency as an example, the temperature data of the CPU temperature sensor during the operation of the electronic device is obtained in real time, and when it is detected that the temperature of the CPU is in an increasing trend, the obtained temperature data of the CPU temperature sensor reaches the first temperature threshold value of 45 °, and the frequency of the CPU is reduced to 1.8 GHZ.
206. And continuously acquiring temperature data of the sensor, and if the temperature data is in an ascending trend and the temperature value in the temperature data is greater than a second temperature threshold value in the incremental temperature threshold values, adjusting the hardware parameter to a second hardware target parameter, wherein the first temperature threshold value is smaller than the second temperature threshold value.
The temperature data of the sensor is continuously acquired in real time, the temperature data fed back by the temperature sensor is monitored, hardware parameters of corresponding hardware are conveniently adjusted according to the fed back temperature data, after the hardware parameters are adjusted to the first hardware parameters, if the temperature of the sensor is still rising and rises to a second temperature threshold value, the hardware parameters are adjusted to second hardware target parameters, and the first hardware parameters are larger than the second hardware target parameters.
Taking the continuous adjustment of the CPU frequency parameter as an example, the temperature of the CPU temperature sensor is obtained in real time, if the temperature of the CPU is continuously increased to 55 degrees of the second temperature threshold, the frequency parameter of the CPU is adjusted from 1.8GHz to 1.7 GHz.
It can be understood that if the temperature of the CPU is still rising, the frequency of the CPU is adjusted to 1.5GHz when the temperature value in the temperature data acquired by the CPU sensor reaches 65 °, and if the temperature of the CPU is still rising, the frequency of the CPU is directly adjusted to 1.0GHz when the temperature value in the temperature data acquired by the CPU sensor reaches 75 °.
It should be noted that the above first temperature control strategy is only exemplary, and the frequency parameters corresponding to different types of CPUs are also different, so that the obtained hardware target parameters are also correspondingly different.
In some embodiments, a second temperature control strategy is obtained, where the second temperature control strategy includes a plurality of target temperatures and a plurality of hardware target parameters, the target temperatures are decreasing temperature thresholds, each temperature threshold corresponds to a hardware target parameter, temperature data of the sensor is obtained, and when the temperature data is in a decreasing trend and a temperature value in the temperature data is smaller than a third temperature threshold in the decreasing temperature thresholds, the hardware parameter is adjusted to a third hardware target parameter corresponding to the third temperature threshold; and continuously acquiring temperature data of the sensor, and if the temperature data is in a descending trend and the temperature value in the temperature data is smaller than a fourth temperature threshold value in the plurality of decreasing temperature threshold values, adjusting the hardware parameter to a fourth hardware target parameter, wherein the third temperature threshold value is larger than the fourth temperature threshold value.
Wherein the second temperature control strategy may be such as described in table 3:
| target temperature | Hardware target parameter |
| 45° | 1.2GHz |
| 35° | 1.6GHz |
| 25° | 1.8GHz |
| 20° | 2.0GHz |
TABLE 3
After the temperature of the CPU is decreased to 45 ° of the third temperature threshold, the frequency parameter of the CPU can be adjusted by obtaining the second temperature control strategy, and the frequency parameter of the CPU is raised, thereby increasing the operation speed of the CPU, for example, after the frequency of the CPU is adjusted to 1.0GHz by the first temperature control strategy, the temperature of the CPU is decreased to 45 °, and the temperature of the CPU is also decreased continuously, and the frequency parameter of the CPU is raised to 1.2GHz, thereby increasing the operation speed of the GPU; after the frequency parameter of the CPU is increased to 1.2GHz, the temperature of the CPU is continuously reduced, and when the temperature of the CPU is detected to be reduced to 35 degrees, the frequency parameter of the CPU is continuously increased from 1.2CHz to 1.6GHz, so that the operation speed of the CPU is further increased; if the temperature of the CPU is continuously reduced after the frequency parameter of the CPU is increased to 1.6GHz, when the temperature of the CPU is detected to be reduced to 25 ℃, the frequency parameter of the CPU is continuously increased from 1.6CHz to 1.8GHz, and the operation speed of the CPU is further increased; if the temperature of the CPU is continuously reduced after the frequency parameter of the CPU is increased to 1.8GHz, when the temperature of the CPU is detected to be reduced to 20 degrees, the frequency parameter of the CPU is continuously increased from 1.8CHz to 2.0GHz, and the operation speed of the CPU is further increased. In this embodiment, the frequency of the CPU is adjusted by the first temperature control strategy and the second temperature control strategy, and the operation speed of the CPU can be ensured on the premise of ensuring the temperature of the CPU.
In some embodiments, for model training, the training model includes an input layer, a plurality of hidden layers, and an output layer, the plurality of hidden layers are connected in a full connection manner, historical temperature data is used as input data of the input layer, historical hardware parameters are used as verification data of the output layer, and the training model is trained according to the input layer, the plurality of hidden layers, and the output layer. The hidden layers can comprise an excitation function and a pooling layer and are used for preventing the training model from being over-fitted, the accuracy of the obtained temperature control strategy is not high, the user experience is not good, the activation function is used for carrying out nonlinear transformation on input data, and then the transformed output data is used as the input data to be transmitted to the next layer. The common activation functions comprise a Binary Step function, a linear function, a Sigmoid function, a tanh function, a ReLU function, a Leaky ReLU function and a Softmax function, the pooling layer can ensure the invariance of translation and rotation of the training model, main characteristics are kept, parameters and calculated quantity are reduced, over-fitting of the training model is prevented, and the generalization capability of the model is improved.
In the embodiment of the application, take the performance parameter of adjusting CPU as an example, CPU is as electronic equipment's main heat production hardware, can be through the frequency of adjusting CPU in order to reduce CPU's temperature, the lower the frequency of CPU operation, the lower the heat of production, for the user, just turn down CPU's frequency when CPU temperature rises to artifical customization temperature threshold, can produce bad feedback, can discover electronic equipment "heat again and block" if the user, this application trains the training model through historical temperature data and historical hardware parameter, obtain the control by temperature change strategy, according to the cascaded adjustment hardware parameter of control by temperature change strategy, avoid the bad feedback that direct adjustment hardware parameter caused, thereby improve the temperature control effect.
Referring to fig. 3, fig. 3 is a third flow chart of a temperature control method according to an embodiment of the disclosure.
301. Acquiring temperature data of a sensor;
acquiring temperature data of a temperature sensor in real time, wherein the temperature sensor may include: the temperature sensor comprises a CPU temperature sensor, a GPU temperature sensor, a DSP temperature sensor, an NPU temperature sensor, a PCB temperature sensor and the like, for the CPU temperature sensor, the temperature value recorded by the CPU temperature sensor can be obtained in real time, and the temperature value reflects the heat generated when the CPU runs.
302. And acquiring a temperature control strategy, wherein the temperature control strategy comprises at least two target temperatures, each target temperature corresponds to a hardware target parameter, the historical temperature data of the sensor and the historical hardware parameters corresponding to the historical temperature data are acquired, the historical temperature data and the historical hardware parameters are used as training samples of a training model, the training model is trained to obtain a trained temperature control model, and the temperature control strategy is obtained according to the trained temperature control model.
The method comprises the steps of obtaining a corresponding temperature control strategy, for example, a temperature control strategy of a CPU corresponding to a CPU temperature sensor, wherein the CPU temperature control strategy is obtained through a temperature control model, specifically, training the training model by obtaining historical temperature data recorded by the CPU temperature sensor and frequency parameters of the historical CPU corresponding to the historical temperature data, the historical temperature data and the frequency parameters of the historical CPU as training samples of the training model to obtain the trained temperature control model, and obtaining the temperature control strategy according to the temperature control model, wherein the temperature control strategy comprises target temperature and corresponding hardware target parameters, and the temperature control strategy is as the temperature control strategy.
303. And adjusting the parameters of the hardware corresponding to the sensor according to the temperature data and the temperature control strategy.
And comparing the temperature data of the sensor acquired in real time with the target temperature in the temperature control strategy, and adjusting the parameters of the corresponding hardware according to the comparison result, for example, adjusting the parameters of the corresponding hardware to the target parameters of the hardware so as to realize the control of the hardware temperature. If the temperature value recorded by the CPU temperature sensor is larger than the first temperature threshold value in the target temperature, the frequency parameter of the CPU is adjusted to the first hardware target parameter corresponding to the first temperature threshold value, if the frequency of the CPU is adjusted, the temperature of the CPU is still rising, and when the temperature of the CPU rises to the second temperature threshold value, the frequency of the CPU is adjusted to the second hardware parameter corresponding to the second temperature threshold value, wherein the first hardware parameter is larger than the second hardware target parameter, and the hardware parameter can be understood as the frequency parameter of the CPU.
In the embodiment of the application, the temperature control strategy is obtained through the acquired sensor temperature data and the acquired temperature control strategy, the hardware parameters are gradually adjusted in a stepped mode, bad feedback caused by direct adjustment of the hardware parameters is avoided, and the temperature control effect is improved.
In some embodiments, the sensor may be a casing temperature sensor of the electronic device, temperature data recorded by the casing temperature sensor may reflect a temperature change of the casing, that is, the casing temperature may be visually perceived by a user as the temperature of the electronic device, and when a temperature value in the temperature data is greater than a target temperature in a corresponding temperature control policy, a corresponding hardware parameter may be adjusted according to a correspondence between the target temperature in the temperature control policy and the target hardware parameter, for example, a brightness parameter of the display screen, a frame rate parameter of a display screen, a power of the speaker, a transceiving power of the radio frequency signal, and the like may be adjusted.
Specifically, when the temperature of the shell temperature sensor is detected to be greater than a first threshold value in a corresponding temperature control strategy obtained through the temperature control model, the brightness parameter of the display screen is adjusted to the first parameter, the temperature of the shell temperature sensor is monitored in real time, and when the temperature is greater than a second threshold value, the brightness parameter of the display screen is adjusted to the second parameter, so that the screen brightness of the display screen is adjusted in a stepped mode, and the user experience is improved.
Or when the temperature of the shell temperature sensor is detected to be larger than a first threshold value in a corresponding temperature control strategy obtained through the temperature control model and the loudspeaker is in a working state, adjusting the brightness parameter of the display screen to the first parameter, adjusting the power parameter of the loudspeaker to the third parameter, monitoring the temperature of the shell temperature sensor in real time, adjusting the brightness parameter of the display screen to the second parameter when the temperature is larger than the second threshold value, adjusting the power parameter of the loudspeaker to the fourth parameter, adjusting the screen brightness of the display screen and the hardware parameter of the loudspeaker in a stepped mode, comparing the screen brightness of the display screen and the hardware parameter of the loudspeaker with the hardware parameter directly adjusted in a cutting mode, comparing bad feedback brought to a user, and improving the user experience.
Referring to fig. 4, fig. 4 is a schematic structural diagram of a temperature control device according to an embodiment of the present disclosure. The temperature control device 400 may include: a first obtaining module 401, a training module 402, a strategy module 403, a second obtaining module 404, and an adjusting module 405.
The system comprises a first acquisition module, a second acquisition module and a control module, wherein the first acquisition module is used for acquiring historical temperature data of a sensor and historical hardware parameters corresponding to the historical temperature data;
the training module is used for taking the historical temperature data and the historical hardware parameters as training samples of a training model, and training the training model to obtain a trained temperature control model;
the strategy module is used for obtaining a temperature control strategy according to the trained temperature control model, wherein the temperature control strategy comprises at least two target temperatures, and each target temperature corresponds to a hardware target parameter;
the second acquisition module is used for acquiring the temperature data of the sensor;
and the adjusting module is used for adjusting the parameters of the hardware according to the temperature data and the temperature control strategy.
In some embodiments, the policy module 403 may also be configured to: obtaining a first temperature control strategy, wherein the first temperature control strategy comprises a plurality of target temperatures and a plurality of hardware target parameters, the target temperatures are a plurality of incremental temperature thresholds, and each temperature threshold corresponds to one hardware target parameter.
In some embodiments, the adjustment module 405 may also be configured to: when the temperature data is in an ascending trend and the temperature value in the temperature data is greater than a first temperature threshold value in the plurality of incremental temperature threshold values, adjusting the parameter of the hardware to a first hardware target parameter corresponding to the first temperature threshold value.
In some embodiments, the adjusting module 405, after adjusting the parameter of the hardware to the first hardware target parameter, may further be configured to: and continuously acquiring temperature data of the sensor, and if the temperature data is in an ascending trend and the temperature value in the temperature data is greater than a second temperature threshold value in the incremental temperature threshold values, adjusting the hardware parameter to a second hardware target parameter, wherein the first temperature threshold value is smaller than the second temperature threshold value.
In some embodiments, the policy module 403 may also be configured to: and obtaining a second temperature control strategy, wherein the second temperature control strategy comprises a plurality of target temperatures and a plurality of hardware target parameters, the target temperatures are decreasing temperature thresholds, and each temperature threshold corresponds to one hardware target parameter.
In some embodiments, the adjustment module 405 may also be configured to: and when the temperature data is in a descending trend and the temperature value in the temperature data is smaller than a third temperature threshold value in the plurality of decreasing temperature threshold values, adjusting the parameter of the hardware to a third hardware target parameter corresponding to the third temperature threshold value.
In some embodiments, the adjusting module 405, after adjusting the parameter of the hardware to the third hardware parameter, may further be configured to: and continuously acquiring temperature data of the sensor, and if the temperature data is in a descending trend and the temperature value in the temperature data is smaller than a fourth temperature threshold value in the plurality of decreasing temperature threshold values, adjusting the hardware parameter to a fourth hardware target parameter, wherein the third temperature threshold value is larger than the fourth temperature threshold value.
In some embodiments, the policy module 403 is further configured to: obtaining a target fitting function; obtaining a plurality of sections of temperature ranges and a hardware parameter range corresponding to each section of temperature range according to the target fitting function;
calculating the temperature range of each section, and obtaining a target temperature and a plurality of target temperatures in each section of temperature range; and calculating the hardware parameter range corresponding to each section of temperature range, wherein each section of hardware parameter range obtains one hardware target parameter to obtain a plurality of hardware target parameters, and each hardware target parameter corresponds to one target temperature.
In some embodiments, the policy module 403 is further configured to: calculating the mean value of each temperature range; and calculating the mean value of the hardware parameter range corresponding to each temperature range.
In some embodiments, training module 402 is further configured to: the training model comprises an input layer, a plurality of hidden layers and an output layer, wherein the hidden layers are connected in a full-connection mode, historical temperature data are used as input data of the input layer, historical hardware parameters are used as verification data of the output layer, and the training model is trained according to the input layer, the hidden layers and the output layer.
The present application also provides another temperature control device, including:
the temperature acquisition module is used for acquiring temperature data of the sensor;
the temperature control strategy acquisition module is used for acquiring a temperature control strategy, wherein the temperature control strategy comprises at least two target temperatures, each target temperature corresponds to a hardware target parameter, the historical temperature data of a sensor and the historical hardware parameters corresponding to the historical temperature data are acquired, the historical temperature data and the historical hardware parameters are used as training samples of a training model, the training model is trained to obtain a trained temperature control model, and the temperature control strategy is obtained according to the trained temperature control model;
and the hardware adjusting module is used for adjusting the parameters of the hardware corresponding to the sensor according to the temperature data and the temperature control strategy.
It should be noted that the temperature control device provided in the embodiment of the present application and the temperature control method in the foregoing embodiment belong to the same concept, and any method provided in the embodiment of the temperature control method may be operated on the temperature control device, and the specific implementation process thereof is described in detail in the embodiment of the temperature control method, and is not described herein again.
The embodiment of the present application provides a computer-readable storage medium, on which a computer program is stored, and when the stored computer program is executed on a computer, the computer is caused to execute the steps in the temperature control method provided by the embodiment of the present application. The storage medium may be a magnetic disk, an optical disk, a Read Only Memory (ROM), a Random Access Memory (RAM), or the like.
Referring to fig. 5, an electronic device 500 includes a processor 501 and a memory 502. The processor 501 is electrically connected to the memory 502.
The processor 501 is a control center of the electronic apparatus 500, connects various parts of the entire electronic apparatus using various interfaces and lines, performs various functions of the electronic apparatus 500 and processes data by running or loading a computer program stored in the memory 502, and calling data stored in the memory 502.
The memory 502 may be used to store software programs and modules, and the processor 501 executes various functional applications and data processing by running the computer programs and modules stored in the memory 502. The memory 502 may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, a computer program required by at least one function (such as a sound playing function, an image playing function, etc.), and the like; the storage data area may store data created according to use of the electronic device, and the like.
Further, the memory 502 may include high speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other volatile solid state storage device. Accordingly, the memory 502 may also include a memory controller to provide the processor 501 with access to the memory 502.
In this embodiment, the processor 501 in the electronic device 500 loads instructions corresponding to one or more processes of the computer program into the memory 502, and the processor 501 runs the computer program stored in the memory 502, so as to implement various functions as follows:
acquiring historical temperature data of a sensor and historical hardware parameters corresponding to the historical temperature data;
taking the historical temperature data and the historical hardware parameters as training samples of a training model, and training the training model to obtain a trained temperature control model;
obtaining a temperature control strategy according to the trained temperature control model, wherein the temperature control strategy comprises at least two target temperatures, and each target temperature corresponds to a hardware target parameter;
acquiring temperature data of the sensor;
and adjusting the parameters of the hardware according to the temperature data and the temperature control strategy.
Or, the following functions are realized:
acquiring temperature data of a sensor;
acquiring a temperature control strategy, wherein the temperature control strategy comprises at least two target temperatures, each target temperature corresponds to a hardware target parameter, the historical temperature data of a sensor and the historical hardware parameters corresponding to the historical temperature data are acquired, the historical temperature data and the historical hardware parameters are used as training samples of a training model, the training model is trained to obtain a trained temperature control model, and the temperature control strategy is obtained according to the trained temperature control model;
and adjusting the parameters of hardware corresponding to the sensor according to the temperature data and the temperature control strategy.
Referring to fig. 6, fig. 6 is a second schematic structural diagram of an electronic device according to an embodiment of the present disclosure, which is different from the electronic device shown in fig. 5 in that the electronic device further includes: a camera module 603, a display 604, an audio circuit 605, a radio frequency circuit 606, and a power supply 607. The camera module 603, the display 604, the audio circuit 605, the rf circuit 606 and the power supply 607 are electrically connected to the processor 601, respectively.
The camera assembly 603 may include Image Processing circuitry, which may be implemented using hardware and/or software components, and may include various Processing units that define an Image Signal Processing (Image Signal Processing) pipeline. The image processing circuit may include at least: a plurality of cameras, an Image Signal Processor (ISP), a control logic, and an Image memory. Where each camera may include at least one or more lenses and an image sensor. The image sensor may include an array of color filters (e.g., Bayer filters). The image sensor may acquire light intensity and wavelength information captured with each imaging pixel of the image sensor and provide a set of raw image data that may be processed by an image signal processor.
The display 604 may be used to display information entered by or provided to the user as well as various graphical user interfaces, which may be comprised of graphics, text, icons, video, and any combination thereof.
The audio circuit 605 may be used to provide an audio interface between the user and the electronic device through a speaker, microphone.
The rf circuit 606 may be used for transceiving rf signals to establish wireless communication with a network device or other electronic devices through wireless communication, and for transceiving signals with the network device or other electronic devices.
The power supply 607 may be used to power various components of the electronic device 600. In some embodiments, the power supply 607 may be logically coupled to the processor 601 through a power management system, such that the power management system may manage charging, discharging, and power consumption management functions.
In the embodiment of the present application, the processor 601 in the electronic device 600 loads instructions corresponding to one or more processes of the computer program into the memory 602 according to the following steps, and the processor 601 runs the computer program stored in the memory 602, thereby implementing various functions as follows:
acquiring historical temperature data of a sensor and historical hardware parameters corresponding to the historical temperature data;
taking the historical temperature data and the historical hardware parameters as training samples of a training model, and training the training model to obtain a trained temperature control model;
obtaining a temperature control strategy according to the trained temperature control model, wherein the temperature control strategy comprises at least two target temperatures, and each target temperature corresponds to a hardware target parameter;
acquiring temperature data of the sensor;
and adjusting the parameters of the hardware according to the temperature data and the temperature control strategy.
In some embodiments, when deriving the temperature control strategy, the processor 601 may perform:
obtaining a first temperature control strategy, wherein the first temperature control strategy comprises a plurality of target temperatures and a plurality of hardware target parameters, the target temperatures are a plurality of incremental temperature thresholds, and each temperature threshold corresponds to one hardware target parameter;
when the parameters of the hardware are adjusted according to the temperature data and the temperature control policy, the processor 601 may execute:
when the temperature data is in an ascending trend and a temperature value in the temperature data is greater than a first temperature threshold value in the plurality of incremental temperature threshold values, adjusting the parameter of the hardware to a first hardware target parameter corresponding to the first temperature threshold value;
after adjusting the parameter of the hardware to the first hardware target parameter corresponding to the first temperature threshold, the processor 601 may perform:
and continuously acquiring temperature data of the sensor, and if the temperature data is in an ascending trend and the temperature value in the temperature data is greater than a second temperature threshold value in the incremental temperature threshold values, adjusting the hardware parameter to a second hardware target parameter, wherein the first temperature threshold value is smaller than the second temperature threshold value.
In some embodiments, when deriving the temperature control strategy, the processor 601 may perform:
obtaining a second temperature control strategy, wherein the second temperature control strategy comprises a plurality of target temperatures and a plurality of hardware target parameters, the target temperatures are decreasing temperature thresholds, and each temperature threshold corresponds to one hardware target parameter;
when the parameters of the hardware are adjusted according to the temperature data and the temperature control policy, the processor 601 may perform:
when the temperature data is in a descending trend and the temperature value in the temperature data is smaller than a third temperature threshold value in the plurality of decreasing temperature threshold values, adjusting the parameter of the hardware to a third hardware target parameter corresponding to the third temperature threshold value;
after adjusting the hardware parameter to a third hardware target parameter corresponding to the third temperature threshold, the processor 601 may execute to continue to acquire temperature data of the sensor, and adjust the hardware parameter to a fourth hardware target parameter if the temperature data is in a downward trend and a temperature value in the temperature data is less than a fourth temperature threshold of the plurality of decreasing temperature thresholds, where the third temperature threshold is greater than the fourth temperature threshold.
In some embodiments, when obtaining the temperature control strategy, the processor 601 performs: obtaining a target fitting function;
obtaining a plurality of sections of temperature ranges and a hardware parameter range corresponding to each section of temperature range according to the target fitting function;
calculating the temperature range of each section, and obtaining a target temperature and a plurality of target temperatures in each section of temperature range;
and calculating the hardware parameter range corresponding to each section of temperature range, wherein each section of hardware parameter range obtains one hardware target parameter to obtain a plurality of hardware target parameters, and each hardware target parameter corresponds to one target temperature.
In calculating each temperature range, processor 601 may perform:
calculating the mean value of each temperature range
In calculating the hardware parameter range corresponding to each temperature range, the processor 601 may perform:
and calculating the mean value of the hardware parameter range corresponding to each temperature range.
In some embodiments, when the training model is trained by using the historical temperature data and the historical hardware parameters as training samples of the training model, the processor 601 may perform:
the training model comprises an input layer, a plurality of hidden layers and an output layer, wherein the hidden layers are connected in a full-connection mode, historical temperature data are used as input data of the input layer, historical hardware parameters are used as verification data of the output layer, and the training model is trained according to the input layer, the hidden layers and the output layer.
In some embodiments, processor 601 is also used to implement the following functions:
acquiring temperature data of a sensor;
acquiring a temperature control strategy, wherein the temperature control strategy comprises at least two target temperatures, each target temperature corresponds to a hardware target parameter, the historical temperature data of a sensor and the historical hardware parameters corresponding to the historical temperature data are acquired, the historical temperature data and the historical hardware parameters are used as training samples of a training model, the training model is trained to obtain a trained temperature control model, and the temperature control strategy is obtained according to the trained temperature control model;
and adjusting the parameters of hardware corresponding to the sensor according to the temperature data and the temperature control strategy.
An embodiment of the present application further provides a storage medium, where the storage medium stores a computer program, and when the computer program runs on a computer, the computer is caused to execute the temperature control method in any one of the above embodiments, such as: acquiring historical temperature data of a sensor and historical hardware parameters corresponding to the historical temperature data; taking the historical temperature data and the historical hardware parameters as training samples of a training model, and training the training model to obtain a trained temperature control model; obtaining a temperature control strategy according to the trained temperature control model, wherein the temperature control strategy comprises at least two target temperatures, and each target temperature corresponds to a hardware target parameter; acquiring temperature data of the sensor; and adjusting the parameters of the hardware according to the temperature data and the temperature control strategy.
Or acquiring temperature data of the sensor; acquiring a temperature control strategy, wherein the temperature control strategy comprises at least two target temperatures, each target temperature corresponds to a hardware target parameter, the historical temperature data of a sensor and the historical hardware parameters corresponding to the historical temperature data are acquired, the historical temperature data and the historical hardware parameters are used as training samples of a training model, the training model is trained to obtain a trained temperature control model, and the temperature control strategy is obtained according to the trained temperature control model; and adjusting the parameters of hardware corresponding to the sensor according to the temperature data and the temperature control strategy.
In the embodiment of the present application, the storage medium may be a magnetic disk, an optical disk, a Read Only Memory (ROM), a Random Access Memory (RAM), or the like.
In the foregoing embodiments, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.
It should be noted that, for the temperature control method of the embodiment of the present application, it can be understood by a person skilled in the art that all or part of the process of implementing the temperature control method of the embodiment of the present application can be completed by controlling the relevant hardware through a computer program, where the computer program can be stored in a computer readable storage medium, such as a memory of an electronic device, and executed by at least one processor in the electronic device, and during the execution process, the process of the embodiment of the temperature control method can be included. The storage medium may be a magnetic disk, an optical disk, a read-only memory, a random access memory, etc.
In the temperature control device according to the embodiment of the present application, each functional module may be integrated into one processing chip, each module may exist alone physically, or two or more modules may be integrated into one module. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode. The integrated module, if implemented as a software functional module and sold or used as a stand-alone product, may also be stored in a computer readable storage medium such as a read-only memory, a magnetic or optical disk, or the like.
The temperature control method, the temperature control device, the storage medium, and the electronic device provided in the embodiments of the present application are described in detail above, and specific examples are applied herein to illustrate the principles and implementations of the present application, and the descriptions of the above embodiments are only used to help understand the method and the core ideas of the present application; meanwhile, for those skilled in the art, according to the idea of the present application, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present application.
Claims (10)
1. A method of temperature control, comprising:
acquiring historical temperature data of a sensor and historical hardware parameters corresponding to the historical temperature data;
taking the historical temperature data and the historical hardware parameters as training samples of a training model, and training the training model to obtain a trained temperature control model;
obtaining a temperature control strategy according to the trained temperature control model, wherein the temperature control strategy comprises at least two target temperatures, and each target temperature corresponds to a hardware target parameter;
acquiring temperature data of the sensor;
and adjusting the parameters of the hardware according to the temperature data and the temperature control strategy.
2. The method of claim 1, wherein the deriving the temperature control strategy comprises:
obtaining a first temperature control strategy, wherein the first temperature control strategy comprises a plurality of target temperatures and a plurality of hardware target parameters, the target temperatures are a plurality of incremental temperature thresholds, and each temperature threshold corresponds to one hardware target parameter;
the adjusting the parameters of the hardware according to the temperature data and the temperature control strategy comprises:
when the temperature data is in an ascending trend and a temperature value in the temperature data is greater than a first temperature threshold value in the plurality of incremental temperature threshold values, adjusting the parameter of the hardware to a first hardware target parameter corresponding to the first temperature threshold value;
after adjusting the parameters of the hardware to the first hardware target parameters, the method further comprises:
and continuously acquiring temperature data of the sensor, and if the temperature data is in an ascending trend and the temperature value in the temperature data is greater than a second temperature threshold value in the incremental temperature threshold values, adjusting the hardware parameter to a second hardware target parameter, wherein the first temperature threshold value is smaller than the second temperature threshold value.
3. The method of claim 1, wherein the deriving the temperature control strategy comprises:
obtaining a second temperature control strategy, wherein the second temperature control strategy comprises a plurality of target temperatures and a plurality of hardware target parameters, the target temperatures are decreasing temperature thresholds, and each temperature threshold corresponds to one hardware target parameter;
the adjusting the parameters of the hardware according to the temperature data and the temperature control strategy comprises:
when the temperature data is in a descending trend and the temperature value in the temperature data is smaller than a third temperature threshold value in the plurality of decreasing temperature threshold values, adjusting the parameter of the hardware to a third hardware target parameter corresponding to the third temperature threshold value;
after adjusting the parameter of the hardware to a third hardware parameter, the method further comprises:
and continuously acquiring temperature data of the sensor, and if the temperature data is in a descending trend and the temperature value in the temperature data is smaller than a fourth temperature threshold value in the plurality of decreasing temperature threshold values, adjusting the hardware parameter to a fourth hardware target parameter, wherein the third temperature threshold value is larger than the fourth temperature threshold value.
4. The method of claim 1, wherein the deriving the temperature control strategy comprises:
obtaining a target fitting function;
obtaining a plurality of sections of temperature ranges and a hardware parameter range corresponding to each section of temperature range according to the target fitting function;
calculating the temperature range of each section, and obtaining a target temperature and a plurality of target temperatures in each section of temperature range;
and calculating the hardware parameter range corresponding to each section of temperature range, wherein each section of hardware parameter range obtains one hardware target parameter to obtain a plurality of hardware target parameters, and each hardware target parameter corresponds to one target temperature.
5. The method of claim 4, wherein the calculating of each temperature range comprises:
calculating the mean value of each temperature range;
the calculating the hardware parameter range corresponding to each section of temperature range comprises the following steps:
and calculating the mean value of the hardware parameter range corresponding to each temperature range.
6. The temperature control method according to claim 1, wherein the training model includes an input layer, a plurality of hidden layers, and an output layer, the hidden layers are connected in a full connection manner, the training model is trained by using the historical temperature data and the historical hardware parameters as training samples of the training model, and obtaining the trained model includes:
and taking the historical temperature data as input data of the input layer, taking the historical hardware parameters as verification data of the output layer, and training the training model according to the input layer, the hidden layers and the output layer.
7. A method of temperature control, comprising:
acquiring temperature data of a sensor;
acquiring a temperature control strategy, wherein the temperature control strategy comprises at least two target temperatures, each target temperature corresponds to a hardware target parameter, the historical temperature data of a sensor and the historical hardware parameters corresponding to the historical temperature data are acquired, the historical temperature data and the historical hardware parameters are used as training samples of a training model, the training model is trained to obtain a trained temperature control model, and the temperature control strategy is obtained according to the trained temperature control model;
and adjusting the parameters of hardware corresponding to the sensor according to the temperature data and the temperature control strategy.
8. A temperature control apparatus, comprising:
the system comprises a first acquisition module, a second acquisition module and a control module, wherein the first acquisition module is used for acquiring historical temperature data of a sensor and historical hardware parameters corresponding to the historical temperature data;
the training module is used for taking the historical temperature data and the historical hardware parameters as training samples of a training model, and training the training model to obtain a trained temperature control model;
the strategy module is used for obtaining a temperature control strategy according to the trained temperature control model, wherein the temperature control strategy comprises at least two target temperatures, and each target temperature corresponds to a hardware target parameter;
the second acquisition module is used for acquiring the temperature data of the sensor;
and the adjusting module is used for adjusting the parameters of the hardware according to the temperature data and the temperature control strategy.
9. A storage medium having stored thereon a computer program, characterized in that, when the computer program is run on a computer, it causes the computer to execute the temperature control method according to any one of claims 1 to 7.
10. An electronic device comprising a processor, a memory, said memory having a computer program, wherein said processor is adapted to perform the temperature control method of any of claims 1 to 7 by invoking said computer program.
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