WO2022142471A1

WO2022142471A1 - Method and apparatus for acquiring environmental temperature by mobile terminal, mobile terminal, and medium

Info

Publication number: WO2022142471A1
Application number: PCT/CN2021/118088
Authority: WO
Inventors: 宋轩; 陈达寅; 张浩然
Original assignee: 南方科技大学
Priority date: 2020-12-31
Filing date: 2021-09-14
Publication date: 2022-07-07
Also published as: CN112834067A

Abstract

A method for acquiring environmental temperature by a mobile terminal, comprising: acquiring a usage state of a mobile terminal and basic temperature measured by a temperature sensor provided in the mobile terminal (S11); and inputting the usage state and the basic temperature into a trained deep learning model so as to predict the temperature of the environment where the mobile terminal is located (S12). By taking the acquired basic temperature as a reference and considering the effect of the heating factor of the mobile terminal on the measurement result, the temperature of the environment where the mobile terminal is located is predicted, so that environmental temperature can be accurately measured using a portable mobile terminal. Also disclosed are an apparatus for acquiring environmental temperature, a mobile terminal, and medium.

Description

Method, device, mobile terminal and medium for obtaining ambient temperature by mobile terminal

technical field

Embodiments of the present invention relate to the technical field of temperature detection, and in particular, to a method, an apparatus, a mobile terminal, and a medium for obtaining an ambient temperature by a mobile terminal.

Background technique

Temperature is a physical quantity that measures the degree of hot and cold of an object. It plays an important role in regulating and controlling the survival, reproduction and many physiological processes in the body. In addition to these conventional requirements for temperature, there are also some people with temperature-sensitive diseases who need to strictly control the body temperature of their environment. Therefore, it is necessary to provide these special groups with an environment that can monitor the surrounding body temperature in real time to ensure their safety. In addition, the pursuit of temperature measurement by ordinary people is getting higher and higher, and they are no longer satisfied with the temperature information obtained from the weather forecast. For example, when people are indoors, especially when using refrigeration or heating equipment, the temperature Information may lose reference value. Therefore, it becomes necessary to know the temperature of the environment at any time.

A current solution is to carry the temperature detection device with you, but it is very inconvenient to carry and there is a risk of forgetting. On the other hand, there are also some thermometer APPs in the current mobile terminals, but they basically rely on the data crawled from the weather website. Most of them display the temperature value of the whole area, which is not much different from the results of the weather forecast. , which is of little significance for the required ambient temperature monitoring.

technical problem

Embodiments of the present invention provide a method, device, mobile terminal, and medium for a mobile terminal to acquire ambient temperature, so as to more accurately measure the temperature in the environment where the user is located through the portable mobile terminal.

technical solutions

In a first aspect, an embodiment of the present invention provides a method for a mobile terminal to obtain an ambient temperature, the method comprising:

Obtain the use state of the mobile terminal and the basic temperature measured by the temperature sensor provided in the mobile terminal;

The usage state and the base temperature are input into the trained deep learning model to predict the ambient temperature where the mobile terminal is located.

In a second aspect, an embodiment of the present invention further provides an apparatus for acquiring an ambient temperature by a mobile terminal, the apparatus comprising:

a data acquisition module for acquiring the use state of the mobile terminal and the basic temperature measured by a temperature sensor provided in the mobile terminal;

A temperature prediction module, configured to input the use state and the basic temperature into the deep learning model after training, so as to predict the ambient temperature where the mobile terminal is located.

In a third aspect, an embodiment of the present invention further provides a mobile terminal, where the mobile terminal includes:

one or more processors;

memory for storing one or more programs;

When the one or more programs are executed by the one or more processors, the one or more processors implement the method for obtaining an ambient temperature for a mobile terminal provided by any embodiment of the present invention.

In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium on which a computer program is stored, and when the program is executed by a processor, implements the method for obtaining an ambient temperature by a mobile terminal provided by any embodiment of the present invention.

beneficial effect

An embodiment of the present invention provides a method for a mobile terminal to obtain an ambient temperature. First, the use state of the mobile terminal and the basic temperature measured by a temperature sensor set in the mobile terminal are obtained, and then the use state and the basic temperature are input into a trained model. Deep learning model to predict the ambient temperature where the mobile terminal is located. The method for obtaining the ambient temperature of the mobile terminal provided by the embodiment of the present invention takes the obtained basic temperature as the benchmark, and considers the influence of the heating factor of the mobile terminal on the measurement result, so as to predict the ambient temperature where the mobile terminal is located, so as to realize The portable mobile terminal is used to accurately measure the ambient temperature, so as to meet people's demand for measuring the ambient temperature at any time.

Description of drawings

1 is a flowchart of a method for a mobile terminal to acquire an ambient temperature according to Embodiment 1 of the present invention;

2 is a flowchart of a method for a mobile terminal to acquire an ambient temperature according to Embodiment 2 of the present invention;

3 is a schematic structural diagram of an apparatus for obtaining an ambient temperature by a mobile terminal according to Embodiment 3 of the present invention;

FIG. 4 is a schematic structural diagram of a mobile terminal according to Embodiment 4 of the present invention.

Embodiments of the present invention

The present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention, but not to limit the present invention. In addition, it should be noted that, for the convenience of description, the drawings only show some but not all structures related to the present invention.

Before discussing the exemplary embodiments in greater detail, it should be mentioned that some exemplary embodiments are described as processes or methods depicted as flowcharts. Although the flowchart depicts the steps as a sequential process, many of the steps may be performed in parallel, concurrently, or concurrently. Furthermore, the order of the steps can be rearranged. The process may be terminated when its operation is complete, but may also have additional steps not included in the figures. The processes may correspond to methods, functions, procedures, subroutines, subroutines, and the like.

Example 1

FIG. 1 is a flowchart of a method for a mobile terminal to acquire an ambient temperature according to Embodiment 1 of the present invention. This embodiment is applicable to the case where a mobile terminal is used to measure the ambient temperature, and the method may be executed by the apparatus for acquiring the ambient temperature by the mobile terminal provided in the embodiment of the present invention, and the apparatus may be implemented by hardware and/or software. The implementation can generally be integrated in a mobile terminal, and the mobile terminal can be, but is not limited to, a mobile phone, a tablet computer, and a smart wearable device. As shown in Figure 1, it specifically includes the following steps:

S11. Acquire a usage state of the mobile terminal and a basic temperature measured by a temperature sensor set in the mobile terminal.

The usage status of the mobile terminal may include various variables that may affect the readings of the temperature sensors set in the mobile terminal, such as whether the mobile terminal is being charged, the current power level of the mobile terminal, a central processing unit (Central Processing Unit, CPU) The occupancy rate, memory occupancy rate, screen brightness, etc., can also be viewed from the direction of the software, such as whether the user is using the mobile terminal to make calls, play games, or watch videos. Depending on the direction, for example, whether the user is playing games while charging or watching videos with higher screen brightness, etc. Which variables are specifically included in the usage status acquired in this embodiment can be determined according to the training situation of the subsequently used deep learning model. Optionally, the usage status includes one or a combination of at least two of the occupancy rate of the central processing unit of the mobile terminal, the screen activation status, sensor data, and battery information. The sensor data may include proximity sensor data, etc., and the battery information may include battery health, power, voltage, whether it is being charged, and the like.

In a mobile terminal, one or more temperature sensors are usually built-in, and the temperature sensor can be called by some applications in the mobile terminal and returns the temperature degree. However, since the temperature sensor is installed inside the mobile terminal, and when the mobile terminal is running, various hardware will be affected by the specific operating conditions and emit heat, so the temperature sensor is also usually used to monitor the temperature inside the mobile terminal to ensure that the mobile terminal The internal temperature can be maintained in a relatively safe range. Therefore, the temperature measured by the temperature sensor provided in the mobile terminal can be obtained as the basic temperature for analyzing the current ambient temperature, so as to finally determine the ambient temperature where the mobile terminal is located in combination with the influence of the use state of the mobile terminal on the temperature, that is, the mobile terminal The ambient temperature where the user of the terminal is located is convenient for the user to know at any time.

S12. Input the usage state and the basic temperature into the deep learning model after training, so as to predict the ambient temperature where the mobile terminal is located.

After the usage state and the basic temperature of the mobile terminal are acquired, the usage state and the basic temperature can be input into the trained deep learning model to predict the current ambient temperature of the mobile terminal. The used deep learning model may be, but not limited to, a convolutional neural network, etc., which is not specifically limited in this embodiment. The method provided in this embodiment can be used by an application program installed in the mobile terminal. When the user opens the corresponding application program, the use status and basic temperature of the mobile terminal can be obtained once according to a certain period (may be 5 seconds). And make a prediction about the ambient temperature.

On the basis of the above technical solution, optionally, after inputting the use state and the basic temperature into the deep learning model after training to predict the ambient temperature where the mobile terminal is located, the method further includes: based on Bluetooth broadcast crowdsourcing, according to the following steps: The reference usage status and reference ambient temperature broadcast by other surrounding mobile terminals are scanned in a preset period; the predicted ambient temperature is corrected according to the reference usage status and the reference ambient temperature.

Specifically, if the usage state of the mobile terminal is relatively saturated, such as maintaining a high CPU usage for a long time, the temperature of the mobile terminal will be too high, thereby greatly affecting the measurement result of the ambient temperature. In this case, the predicted ambient temperature can be corrected by receiving the reference ambient temperature measured by other mobile terminals in the same environment, and at the same time, the reference usage status of other mobile terminals can also be considered to determine the corresponding reference ambient temperature it's usable or not. Exemplarily, if the reference usage states of other mobile terminals are also relatively saturated, the reference value of the corresponding reference ambient temperature may not be large, and if the reference usage states of other mobile terminal devices are relatively idle, then the corresponding reference ambient temperature It is relatively more accurate. At this time, the reference ambient temperature can be used to correct the ambient temperature predicted by the mobile terminal. Similarly, when the mobile terminal is in any state of use, the predicted ambient temperature can be corrected by the available reference ambient temperature or all the scanned reference ambient temperatures. The specific correction process can be as follows: Take the average value of the predicted ambient temperature and all reference ambient temperatures used for correction, or give a certain weight to the predicted ambient temperature and all reference ambient temperatures used for correction, so that the corrected ambient temperature can be finally calculated. It trains a network, predicts a data reliability as the output of the network by analyzing the data sent by other mobile terminals around, and corrects the ambient temperature predicted by the mobile terminal according to the data reliability.

The acquisition of the reference usage status and reference ambient temperature of other mobile terminals can be realized based on Bluetooth broadcast crowdsourcing. The mobile terminal can scan the Bluetooth broadcast packets sent by other surrounding mobile terminals according to a preset period (which can be 20 seconds). , when another mobile terminal enters the effective distance of the mobile terminal's Bluetooth broadcast, the corresponding Bluetooth broadcast packet can be obtained, which includes data such as reference use status and reference ambient temperature encoded into a digital sequence. Correspondingly, the mobile terminal can use the Bluetooth broadcast function of Bluetooth Low Energy (BLE) according to a certain period (may be 10 seconds), and send the latest calibration result and use status of the mobile terminal through Bluetooth. It is sent out in the form of broadcast packets for use by other mobile terminals.

In the technical solution provided by the embodiment of the present invention, the use state of the mobile terminal and the basic temperature measured by the temperature sensor set in the mobile terminal are first obtained, and then the use state and the basic temperature are input into the deep learning model after training to predict Obtain the ambient temperature where the mobile terminal is located. By taking the acquired basic temperature as the benchmark and considering the influence of the mobile terminal's own heating factor on the measurement results, the ambient temperature of the mobile terminal is predicted, and the portable mobile terminal is used to accurately measure the ambient temperature to meet people's needs. The need to measure ambient temperature at any time.

Embodiment 2

FIG. 2 is a flowchart of a method for a mobile terminal to acquire an ambient temperature according to Embodiment 2 of the present invention. The technical solution of this embodiment is further refined on the basis of the above technical solution, and optionally, a specific deep learning model training method is provided to ensure accurate prediction of the ambient temperature. Specifically, in this embodiment, before inputting the usage state and the basic temperature into the deep learning model after training to predict the ambient temperature where the mobile terminal is located, the method further includes: acquiring training sample data, where each training sample data includes The preset basic temperature corresponding to the preset use state of the mobile terminal marked with the preset ambient temperature; the deep learning model is trained according to the training sample data to obtain the trained deep learning model. Correspondingly, as shown in Figure 2, the specific steps may include the following:

S21. Acquire training sample data, where each training sample data includes a base temperature corresponding to a preset mobile terminal marked with a preset ambient temperature in a preset use state.

Specifically, it is possible to set a series of different preset ambient temperatures and preset different preset usage states of the mobile terminal, and perform experiments using different preset usage states under different preset ambient temperatures, and obtain the respective conditions for each case. The basic temperature measured by the temperature sensor set in the mobile terminal is preset, so that each preset usage state marked with a preset ambient temperature and the corresponding basic temperature are used as training sample data. The preset ambient temperature can be the temperature of a low-temperature air-conditioned room, a hot outdoor, a more comfortable indoor room, etc., and the preset use state can be using a mobile terminal to make calls, play games or watch videos, and play while charging The state collected during games or watching videos while charging.

Optionally, acquiring training sample data includes: acquiring a preset basic temperature corresponding to a mobile terminal in a preset use state; receiving a preset ambient temperature uploaded by a real-time electronic thermometer; The corresponding basic temperature of the mobile terminal in the preset use state is marked. Specifically, after placing the preset mobile terminal in a certain environment, the corresponding basic temperature can first be obtained by measuring the temperature sensor set in the preset mobile terminal. At the same time, if necessary, some hardware parameters in the preset mobile terminal As a variable in the preset use state, it can be obtained through a built-in sensor or a built-in application programming interface (Application Programming Interface, API). Then for the preset ambient temperature, a real-time electronic thermometer can be placed at a preset distance from the preset mobile terminal. Values, CSV) in its storage medium, after which the most recently generated file can be uploaded at a preset time. After receiving the preset ambient temperature uploaded by the real-time electronic thermometer, the basic temperature corresponding to the preset mobile terminal in the preset use state can be matched with the preset ambient temperature according to the time stamp, and then each preset ambient temperature can be obtained. The marked preset mobile terminal corresponds to the base temperature in the preset use state, so as to obtain training sample data for training.

S22. Train the deep learning model according to the training sample data to obtain a trained deep learning model.

After the training sample data is obtained, the deep learning model can be trained by using the training sample data to obtain a trained deep learning model. Specifically, the training process of the deep learning model can be implemented on a computer, that is, the above-mentioned real-time electronic thermometer can upload the generated file to the computer, and the preset mobile terminal can also set or obtain the preset use state and the corresponding The basic temperature is uploaded to the computer, so that the model training process is realized through the computer, so as to improve the training performance. Correspondingly, after the training of the model is completed by the computer, the trained deep learning model can be deployed in the mobile terminal by the computer, so as to realize the acquisition of the ambient temperature by the mobile terminal at any time.

S23. Acquire the use state of the mobile terminal and the basic temperature measured by the temperature sensor set in the mobile terminal.

S24. Input the usage state and the basic temperature into the deep learning model after training, so as to predict the ambient temperature where the mobile terminal is located.

On the basis of the above technical solution, optionally, before the deep learning model is trained according to the training sample data to obtain the trained deep learning model, the method further includes: respectively determining each variable in the preset use state and the preset Set the correlation coefficient between ambient temperatures; filter the variables in the preset use state according to the correlation coefficient.

Specifically, first of all, you can obtain as many variables as possible that may affect the measurement of ambient temperature in the preset mobile terminal, but because there may be some variables that are not closely related to temperature, and too many variables will also lead to model building. The difficulty of the process and the model training process increases, which makes the training effect worse. When correcting based on Bluetooth broadcast crowdsourcing, it will also affect the process of using Bluetooth broadcast to send information. Therefore, to weigh the pros and cons, you can screen the variables by the correlation coefficient between each variable and the preset ambient temperature before training, and use the filtered variables to build a deep learning model and train it. Among them, the optional correlation coefficient is the Pearson correlation coefficient. The Pearson correlation coefficient is also called the Pearson product moment correlation coefficient. It is a linear correlation coefficient and is used to reflect the degree of linear correlation between two variables. Its value Between -1 and 1, the closer to 1, the more positive correlation, and the closer to -1, the more negative correlation. After the correlation coefficient of each variable is calculated, a variable with a higher degree of correlation can be selected, which can be determined by comparing with a preset degree of correlation.

The technical solution provided by the embodiment of the present invention ensures the accurate prediction of the ambient temperature by providing a specific deep learning model training method. The variables are screened, and then the deep learning model is constructed and trained using the filtered variables. On the basis of ensuring the training effect of the model, the difficulty of model construction and training is also reduced, and the training efficiency of the model is improved.

Embodiment 3

3 is a schematic structural diagram of an apparatus for obtaining an ambient temperature by a mobile terminal according to Embodiment 3 of the present invention. The apparatus may be implemented by hardware and/or software, and may generally be integrated in a mobile terminal. As shown in Figure 3, the device includes:

The data acquisition module 31 is used for acquiring the use state of the mobile terminal and the basic temperature measured by the temperature sensor provided in the mobile terminal;

The temperature prediction module 32 is configured to input the usage state and the basic temperature into the deep learning model after training, so as to predict the ambient temperature where the mobile terminal is located.

On the basis of the above technical solution, optionally, the device for acquiring the ambient temperature by the mobile terminal further includes:

The data scanning module is used to input the usage status and basic temperature into the deep learning model after training to predict the ambient temperature where the mobile terminal is located, based on Bluetooth broadcast crowdsourcing, and scan the broadcast of other mobile terminals around it according to a preset period. The reference use state and the reference ambient temperature;

The temperature correction module is used to correct the predicted ambient temperature according to the reference usage state and the reference ambient temperature.

The sample acquisition module is used to acquire training sample data before inputting the usage state and the basic temperature into the trained deep learning model to predict the ambient temperature where the mobile terminal is located, and each training sample data includes a preset ambient temperature the corresponding basic temperature of the marked preset mobile terminal in the preset use state;

The model training module is used to train the deep learning model according to the training sample data to obtain the trained deep learning model.

On the basis of the above technical solutions, the optional sample acquisition module includes:

a temperature acquisition unit, configured to acquire the basic temperature corresponding to the preset mobile terminal in the preset use state;

The temperature receiving unit is used to receive the preset ambient temperature uploaded by the real-time electronic thermometer;

The data calibration unit is used to mark the preset basic temperature of the mobile terminal corresponding to the preset use state according to the time stamp according to the preset ambient temperature.

The correlation coefficient determination module is used to respectively determine the correlation coefficient between each variable in the preset use state and the preset ambient temperature before training the deep learning model according to the training sample data to obtain the trained deep learning model ;

The variable screening module is used to screen the variables in the preset usage state according to the correlation coefficient.

On the basis of the above technical solution, optionally, the correlation coefficient is a Pearson correlation coefficient.

Based on the above technical solution, optionally, the use state includes one or a combination of at least two of the occupancy rate of the central processing unit of the mobile terminal, the screen activation state, sensor data, and battery information.

The apparatus for obtaining an ambient temperature by a mobile terminal provided by the embodiment of the present invention can execute the method for obtaining an ambient temperature by a mobile terminal provided in any embodiment of the present invention, and has functional modules and beneficial effects corresponding to the execution method.

It is worth noting that, in the above-mentioned embodiment of the apparatus for obtaining the ambient temperature by the mobile terminal, the included units and modules are only divided according to functional logic, but are not limited to the above-mentioned division, as long as the corresponding functions can be realized, that is, Yes; in addition, the specific names of the functional units are only for the convenience of distinguishing from each other, and are not used to limit the protection scope of the present invention.

Embodiment 4

FIG. 4 is a schematic structural diagram of a mobile terminal according to Embodiment 4 of the present invention, and shows a block diagram of an exemplary mobile terminal suitable for implementing the embodiments of the present invention. The mobile terminal shown in FIG. 4 is only an example, and should not impose any limitations on the functions and scope of use of the embodiments of the present invention. As shown in FIG. 4 , the mobile terminal includes a processor 41, a memory 42, an input device 43 and an output device 44; the number of processors 41 in the mobile terminal may be one or more, and one processor 41 is taken as an example in FIG. 4 , the processor 41 , the memory 42 , the input device 43 and the output device 44 in the mobile terminal may be connected by a bus or in other ways, and the connection by a bus is taken as an example in FIG. 4 .

As a computer-readable storage medium, the memory 42 can be used to store software programs, computer-executable programs and modules, such as program instructions/modules corresponding to the method for acquiring the ambient temperature by the mobile terminal in the embodiment of the present invention (for example, the mobile terminal acquires the The data acquisition module 31 and the temperature prediction module 32 in the device of ambient temperature). The processor 41 executes various functional applications and data processing of the mobile terminal by running the software programs, instructions and modules stored in the memory 42, that is, the above-mentioned method for obtaining the ambient temperature of the mobile terminal is implemented.

The memory 42 may mainly include a stored program area and a stored data area, wherein the stored program area may store an operating system, an application program required for at least one function; the stored data area may store data created according to the use of the mobile terminal, and the like. Additionally, memory 42 may include high speed random access memory, and may also include nonvolatile memory, such as at least one magnetic disk storage device, flash memory device, or other nonvolatile solid state storage device. In some instances, the memory 42 may further include memory located remotely from the processor 41, and these remote memories may be connected to the mobile terminal through a network. Examples of such networks include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

The input device 43 can be used to acquire the basic temperature measured by the temperature sensor, and generate key signal input related to user setting and function control of the mobile terminal, and the like. The output device 44 includes a display screen and other devices, which can be used to display the final predicted ambient temperature and the like to the user.

Embodiment 5

Embodiment 5 of the present invention further provides a storage medium containing computer-executable instructions, where the computer-executable instructions, when executed by a computer processor, are used to execute a method for a mobile terminal to acquire an ambient temperature, and the method includes:

Obtain the usage status of the mobile terminal and the basic temperature measured by the temperature sensor set in the mobile terminal;

The use state and base temperature are input into the trained deep learning model to predict the ambient temperature where the mobile terminal is located.

The storage medium may be any of various types of memory devices or storage devices. The term "storage medium" is intended to include: installation media such as CD-ROM, floppy disk or tape devices; computer system memory or random access memory such as DRAM, DDR RAM, SRAM, EDO RAM, Rambus RAM, etc.; non-volatile memory such as flash memory, magnetic media (eg, hard disk or optical storage); registers or other similar types of memory elements, and the like. The storage medium may also include other types of memory or combinations thereof. In addition, the storage medium may be located in the computer system in which the program is executed, or may be located in a different second computer system connected to the computer system through a network such as the Internet. The second computer system may provide program instructions to the computer for execution. The term "storage medium" may include two or more storage media that may reside in different locations (eg, in different computer systems connected by a network). The storage medium may store program instructions (eg, embodied as a computer program) executable by one or more processors.

Of course, a storage medium containing computer-executable instructions provided by the embodiments of the present invention is not limited to the above-mentioned method operations, and can also execute the mobile terminal provided by any embodiment of the present invention to obtain the ambient temperature related operations in the method.

A computer-readable signal medium may include a propagated data signal in baseband or as part of a carrier wave, with computer-readable program code embodied thereon. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the foregoing. A computer-readable signal medium can also be any computer-readable medium other than a computer-readable storage medium that can transmit, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device .

Program code embodied on a computer readable medium may be transmitted using any suitable medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

From the above description of the embodiments, those skilled in the art can clearly understand that the present invention can be realized by software and necessary general-purpose hardware, and of course can also be realized by hardware, but in many cases the former is a better embodiment . Based on such understanding, the technical solutions of the present invention can be embodied in the form of software products in essence or the parts that make contributions to the prior art, and the computer software products can be stored in a computer-readable storage medium, such as a floppy disk of a computer , Read-Only Memory (ROM), Random Access Memory (RAM), flash memory (FLASH), hard disk or CD, etc., including several instructions to make a mobile terminal (which can be a mobile phone, A tablet computer, or a smart wearable device, etc.) executes the methods described in the various embodiments of the present invention.

Note that the above are only preferred embodiments of the present invention and applied technical principles. Those skilled in the art will understand that the present invention is not limited to the specific embodiments described herein, and various obvious changes, readjustments and substitutions can be made by those skilled in the art without departing from the protection scope of the present invention. Therefore, although the present invention has been described in detail through the above embodiments, the present invention is not limited to the above embodiments, and can also include more other equivalent embodiments without departing from the concept of the present invention. The scope is determined by the scope of the appended claims.

Claims

A method for obtaining ambient temperature by a mobile terminal, comprising:

Obtain the use state of the mobile terminal and the basic temperature measured by the temperature sensor provided in the mobile terminal;

The usage state and the base temperature are input into the trained deep learning model to predict the ambient temperature where the mobile terminal is located.
The method for obtaining an ambient temperature by a mobile terminal according to claim 1, characterized in that, in the deep learning model after inputting the use state and the basic temperature into a trained deep learning model, to determine the environment in which the mobile terminal is located. After the temperature is predicted, it also includes:

Based on Bluetooth broadcast crowdsourcing, scan the reference usage status and reference ambient temperature broadcast by other surrounding mobile terminals according to a preset period;

The predicted ambient temperature is corrected according to the reference usage state and the reference ambient temperature.
The method for obtaining an ambient temperature by a mobile terminal according to claim 1, characterized in that, in the deep learning model after inputting the use state and the basic temperature into a trained deep learning model, to determine the environment in which the mobile terminal is located. Before temperature predictions are made, it also includes:

Acquiring training sample data, where each training sample data includes a base temperature corresponding to a preset mobile terminal marked with a preset ambient temperature in a preset use state;

The deep learning model is trained according to the training sample data to obtain the trained deep learning model.
The method for acquiring ambient temperature by a mobile terminal according to claim 3, wherein the acquiring training sample data comprises:

acquiring the basic temperature corresponding to the preset mobile terminal in the preset use state;

receiving the preset ambient temperature uploaded by the real-time electronic thermometer;

According to the preset ambient temperature, the preset basic temperature corresponding to the mobile terminal in the preset use state is marked according to the time stamp.
The method for obtaining an ambient temperature by a mobile terminal according to claim 3, wherein before the deep learning model is trained according to the training sample data to obtain the trained deep learning model, further include:

respectively determining the correlation coefficient between each variable in the preset use state and the preset ambient temperature;

The variables in the preset usage state are screened according to the correlation coefficient.
The method for obtaining an ambient temperature by a mobile terminal according to claim 5, wherein the correlation coefficient is a Pearson correlation coefficient.
The method for acquiring an ambient temperature by a mobile terminal according to claim 1, wherein the usage status includes one or more of an occupancy rate of a central processing unit of the mobile terminal, a screen activation status, sensor data, and battery information. A combination of at least two.
A device for obtaining ambient temperature by a mobile terminal, comprising:

a data acquisition module for acquiring the use state of the mobile terminal and the basic temperature measured by a temperature sensor provided in the mobile terminal;

A temperature prediction module, configured to input the usage state and the basic temperature into the deep learning model after training, so as to predict the ambient temperature where the mobile terminal is located.
A mobile terminal, comprising:

one or more processors;

memory for storing one or more programs;

When the one or more programs are executed by the one or more processors, the one or more processors implement the method for obtaining an ambient temperature by a mobile terminal according to any one of claims 1-7.
A computer-readable storage medium on which a computer program is stored, characterized in that, when the program is executed by a processor, the method for obtaining an ambient temperature by a mobile terminal according to any one of claims 1-7 is implemented.