CN112834067A - Method and device for acquiring environment temperature by mobile terminal, mobile terminal and medium - Google Patents
Method and device for acquiring environment temperature by mobile terminal, mobile terminal and medium Download PDFInfo
- Publication number
- CN112834067A CN112834067A CN202011633454.4A CN202011633454A CN112834067A CN 112834067 A CN112834067 A CN 112834067A CN 202011633454 A CN202011633454 A CN 202011633454A CN 112834067 A CN112834067 A CN 112834067A
- Authority
- CN
- China
- Prior art keywords
- mobile terminal
- temperature
- preset
- acquiring
- ambient temperature
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01K—MEASURING TEMPERATURE; MEASURING QUANTITY OF HEAT; THERMALLY-SENSITIVE ELEMENTS NOT OTHERWISE PROVIDED FOR
- G01K11/00—Measuring temperature based upon physical or chemical changes not covered by groups G01K3/00, G01K5/00, G01K7/00 or G01K9/00
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
- G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
Abstract
The embodiment of the invention discloses a method and a device for a mobile terminal to acquire an ambient temperature, the mobile terminal and a medium. The method comprises the following steps: acquiring the use state of the mobile terminal and the basic temperature measured by a temperature sensor arranged in the mobile terminal; and inputting the use state and the basic temperature into the trained deep learning model so as to predict the environment temperature of the mobile terminal. According to the technical scheme provided by the embodiment of the invention, the influence of the self-heating factor of the mobile terminal on the measurement result is considered by taking the obtained basic temperature as the reference, the environment temperature of the mobile terminal is predicted, and the accurate measurement of the environment temperature by using the portable mobile terminal is realized, so that the requirement of people on the measurement of the environment temperature at any time is met.
Description
Technical Field
The embodiment of the invention relates to the technical field of temperature detection, in particular to a method and a device for acquiring ambient temperature by a mobile terminal, the mobile terminal and a medium.
Background
The temperature is a physical quantity for measuring the cold and hot degree of an object, and has important regulation and control functions on the living and the multiplication of organisms and a plurality of physiological processes in the body. In addition to these conventional requirements for temperature, there is a need for more strict control of the sensible temperature of the environment for some people with temperature-sensitive diseases. Therefore, an environment capable of monitoring the ambient sensible temperature in real time is required to be provided for the special people so as to ensure the safety of the special people. In addition, the pursuit of temperature measurement by ordinary people is also increasing, and temperature information obtained from weather forecast, such as when people are indoors, especially in the case of using cooling or heating equipment, may lose reference value, cannot be satisfied. Therefore, it becomes necessary to be able to know the temperature of the environment at any time.
One current solution is to carry the temperature detection device with them, but it is very inconvenient to carry and has a risk of forgetting. On the other hand, some thermometers APP exist in the current mobile terminal, but basically depend on data crawled from a weather website, the displayed temperature value of the whole region is mostly not much different from the result of weather forecast, and the significance is not great for the required environmental temperature monitoring.
Disclosure of Invention
The embodiment of the invention provides a method and a device for acquiring an environment temperature by a mobile terminal, the mobile terminal and a medium, so that the temperature in the environment where a user is located can be measured more accurately by the portable mobile terminal.
In a first aspect, an embodiment of the present invention provides a method for a mobile terminal to obtain an ambient temperature, where the method includes:
acquiring the use state of a mobile terminal and the basic temperature measured by a temperature sensor arranged in the mobile terminal;
and inputting the use state and the basic temperature into a trained deep learning model so as to predict the environment temperature of the mobile terminal.
In a second aspect, an embodiment of the present invention further provides an apparatus for acquiring an ambient temperature by a mobile terminal, where the apparatus includes:
the mobile terminal comprises a data acquisition module, a data processing module and a data processing module, wherein the data acquisition module is used for acquiring the use state of the mobile terminal and the basic temperature measured by a temperature sensor arranged in the mobile terminal;
and the temperature prediction module is used for inputting the use state and the basic temperature into the trained deep learning model so as to predict the environment temperature of the mobile terminal.
In a third aspect, an embodiment of the present invention further provides a mobile terminal, where the mobile terminal includes:
one or more processors;
a 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 acquiring the ambient temperature of the mobile terminal provided by any embodiment of the 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, where the computer program, when executed by a processor, implements the method for acquiring an ambient temperature by a mobile terminal provided in any embodiment of the present invention.
The embodiment of the invention provides a method for acquiring an environment temperature by a mobile terminal. According to the method for acquiring the ambient temperature by the mobile terminal, which is provided by the embodiment of the invention, the ambient temperature of the mobile terminal is predicted by taking the acquired basic temperature as a reference and considering the influence of the self-heating factor of the mobile terminal on the measurement result, so that the portable mobile terminal is utilized to accurately measure the ambient temperature, and the requirement of people on measuring the ambient temperature at any time is met.
Drawings
Fig. 1 is a flowchart of a method for acquiring an ambient temperature by a mobile terminal according to an embodiment of the present invention;
fig. 2 is a flowchart of a method for acquiring an ambient temperature by a mobile terminal according to a second embodiment of the present invention;
fig. 3 is a schematic structural diagram of an apparatus for acquiring an ambient temperature by a mobile terminal according to a third embodiment of the present invention;
fig. 4 is a schematic structural diagram of a mobile terminal according to a fourth embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting of the invention. It should be further noted that, for the convenience of description, only some of the structures related to the present invention are shown in the drawings, not all of the structures.
Before discussing exemplary embodiments in more detail, it should be noted that some exemplary embodiments are described as processes or methods depicted as flowcharts. Although a flowchart may describe the steps as a sequential process, many of the steps can be performed in parallel, concurrently or simultaneously. In addition, the order of the steps may be rearranged. The process may be terminated when its operations are completed, but may have additional steps not included in the figure. The processes may correspond to methods, functions, procedures, subroutines, and the like.
Example one
Fig. 1 is a flowchart of a method for acquiring an ambient temperature by a mobile terminal according to an embodiment of the present invention. The embodiment is applicable to the case of measuring the ambient temperature by using a mobile terminal, and the method can be executed by the device for acquiring the ambient temperature by using the mobile terminal provided by the embodiment of the invention, the device can be realized by hardware and/or software, and can be generally integrated in the mobile terminal, and the mobile terminal can be but not limited to a mobile phone, a tablet computer, an intelligent wearable device and the like. As shown in fig. 1, the method specifically comprises the following steps:
and S11, acquiring the use state of the mobile terminal and the basic temperature measured by the temperature sensor arranged in the mobile terminal.
The usage status of the mobile terminal may include various variables that may affect readings of a temperature sensor disposed in the mobile terminal, such as whether the mobile terminal is being charged, a current power of the mobile terminal, an occupancy rate of a Central Processing Unit (CPU), an occupancy rate of a memory, screen brightness, and the like, and may also be viewed from a direction of software, such as whether a user is using the mobile terminal to make a call, play a game, or watch a video, and may also be viewed from a direction of combining software and hardware, such as whether the user is playing a game in a charged state or watching a video with higher screen brightness. The usage status obtained in this embodiment specifically includes which variables can be determined according to the training condition of the deep learning model used subsequently. Optionally, the usage status includes one or a combination of at least two of an occupancy rate of a central processor of the mobile terminal, a screen activation status, sensor data, and battery information. Where the sensor data may include proximity sensor data and the like, the battery information may include battery health, charge level, voltage, and whether charging is occurring, and the like.
In a mobile terminal, it is common to carry one or more temperature sensors that can be called by some application within the mobile terminal and return a temperature reading. However, since the temperature sensor is installed inside the mobile terminal, and various hardware may be affected by specific operating conditions and emit heat when the mobile terminal is operating, the temperature sensor is also generally used for monitoring the temperature inside the mobile terminal, so as to ensure that the temperature inside the mobile terminal can be maintained within a relatively safe range. Therefore, the temperature measured by the temperature sensor arranged in the mobile terminal can be acquired as the basic temperature for analyzing the current environment temperature, so that the environment temperature of the mobile terminal, namely the environment temperature of the user of the mobile terminal, can be finally determined by combining the influence of the use state of the mobile terminal on the temperature, and the user can conveniently know the environment temperature at any time.
And S12, inputting the use state and the basic temperature into the trained deep learning model to predict the environment temperature of the mobile terminal.
After the use state and the basic temperature of the mobile terminal are obtained, the use state and the basic temperature can be input into the trained deep learning model to predict and obtain the current environment temperature of the mobile terminal. The deep learning model used may be, but is not limited to, a convolutional neural network, and the like, and this embodiment is not particularly limited. The method provided by the embodiment can be used through the application program installed in the mobile terminal, and when the user opens the corresponding application program, the use state and the basic temperature of the mobile terminal can be acquired once according to a certain period (which can be 5 seconds), and the environmental temperature is predicted once.
On the basis of the above technical solution, optionally, after inputting the use state and the basic temperature into the trained deep learning model to predict the ambient temperature of the mobile terminal, the method further includes: based on Bluetooth broadcast crowdsourcing, scanning reference use states and reference environment temperatures broadcast by other surrounding mobile terminals according to a preset period; the predicted ambient temperature is corrected based on the reference use state and the reference ambient temperature.
Specifically, if the usage state of the mobile terminal is saturated, for example, if the usage state of the mobile terminal is kept at a high CPU occupancy level for a long time, the temperature of the mobile terminal may be too high, and the measurement result of the environmental temperature may be greatly affected. In this case, the predicted ambient temperature may be corrected by receiving the reference ambient temperatures measured by other mobile terminals in the same environment, and at the same time, whether the corresponding reference ambient temperatures are available may be determined in consideration of the reference usage states of the other mobile terminals. For example, 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, the corresponding reference ambient temperature is relatively more accurate, and at this time, the reference ambient temperature may be used to correct the ambient temperature predicted by the mobile terminal. Similarly, the predicted environment temperature in any use state of the mobile terminal may be corrected by an available reference environment temperature, or may be corrected by all scanned reference environment temperatures, and the specific correction process may be to take an average value of the predicted environment temperature and all reference environment temperatures used for correction, or to give a certain weight to the predicted environment temperature and all reference environment temperatures used for correction, so as to finally calculate the corrected environment temperature, or may be to train a network, to predict a data reliability obtained by analyzing data transmitted from other surrounding mobile terminals, as an output of the network, and to correct the environment temperature predicted by the mobile terminal according to the data reliability, and so on.
The acquisition of the reference use state and the reference ambient temperature of other mobile terminals can be realized based on bluetooth broadcast crowdsourcing, the mobile terminal can scan bluetooth broadcast packets sent by other surrounding mobile terminals once according to a preset period (which can be 20 seconds), and when the other mobile terminals enter an effective distance of bluetooth broadcast of the mobile terminal, the corresponding bluetooth broadcast packets can be acquired, wherein the corresponding bluetooth broadcast packets include data such as the reference use state and the reference ambient temperature which are coded into a digital sequence. Correspondingly, the mobile terminal can send out the latest correction result and the use state of the mobile terminal in the form of a Bluetooth broadcast packet according to a certain period (which can be 10 seconds) through the Bluetooth broadcast function of the Bluetooth Low Energy (BLE) itself, so as to be used by other mobile terminals.
According to the technical scheme provided by the embodiment of the invention, the use state of the mobile terminal and the basic temperature measured by the temperature sensor arranged in the mobile terminal are firstly obtained, and then the use state and the basic temperature are input into the trained deep learning model so as to predict the environment temperature of the mobile terminal. By taking the obtained basic temperature as a reference and considering the influence of the self heating factor of the mobile terminal on the measurement result, the environment temperature of the mobile terminal is predicted, and the accurate measurement of the environment temperature by using the portable mobile terminal is realized, so that the requirement of people on the measurement of the environment temperature at any time is met.
Example two
Fig. 2 is a flowchart of a method for acquiring an ambient temperature by a mobile terminal according to a second embodiment of the present invention. The technical scheme of the embodiment is further refined on the basis of the technical scheme, and optionally, a specific deep learning model training method is provided to ensure accurate prediction of the environment temperature. Specifically, in this embodiment, before inputting the usage state and the basic temperature into the trained deep learning model to predict the ambient temperature of the mobile terminal, the method further includes: acquiring training sample data, wherein each training sample data comprises a corresponding basic temperature of a preset mobile terminal marked by a preset environment temperature in a preset use state; and training the deep learning model according to the training sample data to obtain the trained deep learning model. Correspondingly, as shown in fig. 2, the method specifically includes the following steps:
and S21, acquiring training sample data, wherein each training sample data comprises a corresponding basic temperature of the preset mobile terminal marked by the preset environment temperature in a preset use state.
Specifically, a series of different preset environment temperatures and different preset use states of the preset mobile terminal can be set, the different preset use states are used for carrying out experiments under different preset environment temperatures, the basic temperature measured by the temperature sensor arranged in the preset mobile terminal under each condition is acquired respectively, and the preset use states marked by the preset environment temperatures and the corresponding basic temperatures are used as training sample data. The preset environment temperature can be the temperature of a low-temperature air-conditioning room, a hot outdoor environment, a comfortable indoor environment and the like, and the preset use state can be the state acquired by utilizing the mobile terminal to make a call, play a game or watch a video, and charge and play the game or watch the video.
Optionally, obtaining training sample data includes: acquiring a corresponding basic temperature of a preset mobile terminal in a preset use state; receiving a preset environment temperature uploaded by a real-time electronic thermometer; and marking the corresponding basic temperature of the preset mobile terminal in the preset use state according to the preset environment temperature and the timestamp. Specifically, after the preset mobile terminal is placed in a certain environment, the corresponding basic temperature may be obtained by measuring with a temperature sensor disposed in the preset mobile terminal, and meanwhile, if some hardware parameters in the preset mobile terminal need to be used as variables in the preset use state, the corresponding basic temperature may be obtained through a built-in sensor or an Application Programming Interface (API) provided in the mobile terminal. Then, for the preset environmental temperature, a real-time electronic thermometer may be placed at a preset distance from the preset mobile terminal, the electronic thermometer may record the air temperature of each timestamp, and may be saved in its storage medium in Comma-Separated Values (CSV) format, and then may upload the recently generated file at a preset time. After receiving the preset ambient temperature uploaded by the real-time electronic thermometer, the corresponding basic temperature of the preset mobile terminal in the preset use state can be matched with the preset ambient temperature according to the timestamp, and the corresponding basic temperature of the preset mobile terminal in the preset use state marked by the preset ambient temperature can be obtained, so that training sample data for training can be obtained.
And S22, training the deep learning model according to the training sample data to obtain the 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 the trained deep learning model. Specifically, the training process of the deep learning model can be realized on a computer, that is, the real-time electronic thermometer can upload the generated file to the computer, and the preset mobile terminal can also upload the preset use state and the corresponding basic temperature which are set or obtained to the computer, so that the model training process is realized through the computer, and the training performance is improved. Correspondingly, after the training of the model is completed through the computer, the trained deep learning model can be deployed to the mobile terminal through the computer, so that the mobile terminal can acquire the ambient temperature at any time.
And S23, acquiring the use state of the mobile terminal and the basic temperature measured by the temperature sensor arranged in the mobile terminal.
And S24, inputting the use state and the basic temperature into the trained deep learning model to predict the environment temperature of the mobile terminal.
On the basis of the foregoing technical solution, optionally, before training the deep learning model according to training sample data to obtain a trained deep learning model, the method further includes: respectively determining a correlation coefficient between each variable in a preset use state and a preset environment temperature; and screening the variables in the preset using state according to the correlation coefficient.
Specifically, various variables that may affect the measurement of the ambient temperature in the preset mobile terminal may be obtained as much as possible, but because some of the variables may have a non-close relationship with the temperature, the difficulty of the model building process and the difficulty of the model training process may be increased due to too many variables, so that the training effect is deteriorated, and when the correction is performed based on the bluetooth broadcast crowdsourcing, the process of transmitting information by using the bluetooth broadcast may be affected. Therefore, the advantages and the disadvantages are balanced, the variables can be screened once through the correlation coefficient between each variable and the preset environment temperature before training, and a deep learning model is built and trained by using the screened variables. Optionally, the correlation coefficient is a pearson correlation coefficient, which is also called a pearson product moment correlation coefficient, and is a linear correlation coefficient, which is used to reflect the linear correlation degree between two variables, and the value of the linear correlation coefficient is between-1 and 1, and a value closer to 1 indicates a more positive correlation, and a value closer to-1 indicates a more negative correlation. After the correlation coefficient of each variable is calculated, the variable with a higher degree of correlation may be selected, and may be specifically determined by comparing with a preset degree of correlation.
According to the technical scheme provided by the embodiment of the invention, the accurate prediction of the environment temperature is ensured by providing the specific deep learning model training method, meanwhile, various acquired variables in the preset use state can be screened according to the correlation coefficient, and then the deep learning model is constructed and trained by using the screened variables, so that on the basis of ensuring the training effect of the model, the difficulty of model construction and training is reduced, and the training efficiency of the model is improved.
EXAMPLE III
Fig. 3 is a schematic structural diagram of an apparatus for acquiring an ambient temperature by a mobile terminal according to a third embodiment of the present invention, where the apparatus may be implemented by hardware and/or software, and may be generally integrated in the mobile terminal. As shown in fig. 3, the apparatus includes:
the data acquisition module 31 is configured to acquire a use state of the mobile terminal and a basic temperature measured by a temperature sensor arranged in the mobile terminal;
and the temperature prediction module 32 is used for inputting the use state and the basic temperature into the trained deep learning model so as to predict the environment temperature of the mobile terminal.
According to the technical scheme provided by the embodiment of the invention, the use state of the mobile terminal and the basic temperature measured by the temperature sensor arranged in the mobile terminal are firstly obtained, and then the use state and the basic temperature are input into the trained deep learning model so as to predict the environment temperature of the mobile terminal. By taking the obtained basic temperature as a reference and considering the influence of the self heating factor of the mobile terminal on the measurement result, the environment temperature of the mobile terminal is predicted, and the accurate measurement of the environment temperature by using the portable mobile terminal is realized, so that the requirement of people on the measurement of the environment temperature at any time is met.
On the basis of the foregoing technical solution, optionally, the apparatus for acquiring the ambient temperature by the mobile terminal further includes:
the data scanning module is used for inputting the use state and the basic temperature into the trained deep learning model so as to predict the ambient temperature of the mobile terminal, and scanning the reference use state and the reference ambient temperature broadcasted by other mobile terminals around according to a preset period based on Bluetooth broadcast crowdsourcing;
and the temperature correction module is used for correcting the predicted environment temperature according to the reference use state and the reference environment temperature.
On the basis of the foregoing technical solution, optionally, the apparatus for acquiring the ambient temperature by the mobile terminal further includes:
the system comprises a sample acquisition module, a data processing module and a data processing module, wherein the sample acquisition module is used for acquiring training sample data before inputting a use state and a basic temperature into a trained deep learning model so as to predict the environment temperature of the mobile terminal, and each training sample data comprises a basic temperature which is marked by a preset environment temperature and corresponds to the preset mobile terminal in the preset use state;
and the model training module is used for training the deep learning model according to the training sample data so as to obtain the trained deep learning model.
On the basis of the above technical solution, optionally, the sample obtaining module includes:
the temperature acquisition unit is used for acquiring the corresponding basic temperature of the preset mobile terminal in a preset use state;
the temperature receiving unit is used for receiving the preset environment temperature uploaded by the real-time electronic thermometer;
and the data calibration unit is used for marking the corresponding basic temperature of the preset mobile terminal in the preset use state according to the preset environment temperature and the timestamp.
On the basis of the foregoing technical solution, optionally, the apparatus for acquiring the ambient temperature by the mobile terminal further includes:
the correlation coefficient determining module is used for respectively determining the correlation coefficient between each variable in the preset using state and the preset environment temperature before the deep learning model is trained according to the training sample data to obtain the trained deep learning model;
and the variable screening module is used for screening the variables in the preset use state according to the correlation coefficient.
On the basis of the above technical solution, optionally, the correlation coefficient is a pearson correlation coefficient.
On the basis of the above technical solution, optionally, the usage state includes one or a combination of at least two of an occupancy rate of a central processing unit of the mobile terminal, a screen activation state, sensor data, and battery information.
The device for acquiring the ambient temperature by the mobile terminal provided by the embodiment of the invention can execute the method for acquiring the ambient temperature by the mobile terminal provided by any embodiment of the invention, and has corresponding functional modules and beneficial effects of the execution method.
It should be noted that, in the embodiment of the apparatus for acquiring an ambient temperature by a mobile terminal, each included unit and module are only divided according to functional logic, but are not limited to the above division as long as the corresponding function can be implemented; in addition, specific names of the functional units are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present invention.
Example four
Fig. 4 is a schematic structural diagram of a mobile terminal according to a fourth embodiment of the present invention, which shows a block diagram of an exemplary mobile terminal suitable for implementing the embodiment of the present invention. The mobile terminal shown in fig. 4 is only an example, and should not bring any limitation to the functions and the 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 the processors 41 in the mobile terminal may be one or more, 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 manners, and the connection by the bus is taken as an example in fig. 4.
The memory 42 is used as a computer-readable storage medium for storing 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 data acquisition module 31 and the temperature prediction module 32 in the device for acquiring the ambient temperature by the mobile terminal). The processor 41 executes various functional applications and data processing of the mobile terminal by executing software programs, instructions and modules stored in the memory 42, that is, implements the method for acquiring the ambient temperature of the mobile terminal described above.
The memory 42 may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store data created according to the use of the mobile terminal, and the like. Further, the memory 42 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 non-volatile solid state storage device. In some examples, memory 42 may further include memory located remotely from processor 41, which may be connected to the mobile terminal through a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The input device 43 may be used to acquire a basic temperature measured by a temperature sensor, and to generate key signal inputs related to user settings and function control of the mobile terminal, and the like. The output device 44 includes a display screen or the like, and is used to display the finally predicted ambient temperature or the like to the user.
EXAMPLE five
An embodiment of the present invention further provides a storage medium containing computer-executable instructions, which when executed by a computer processor, are configured to perform a method for acquiring an ambient temperature of a mobile terminal, where the method includes:
acquiring the use state of the mobile terminal and the basic temperature measured by a temperature sensor arranged in the mobile terminal;
and inputting the use state and the basic temperature into the trained deep learning model so as to predict the environment temperature of the mobile terminal.
The storage medium may be any of various types of memory devices or storage devices. The term "storage medium" is intended to include: mounting 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, Lanbas (Rambus) RAM, etc.; non-volatile memory such as flash memory, magnetic media (e.g., hard disk or optical storage); registers or other similar types of memory elements, etc. 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 the program instructions to the computer for execution. The term "storage medium" may include two or more storage media that may reside in different locations, such as in different computer systems that are connected by a network. The storage medium may store program instructions (e.g., embodied as a computer program) that are executable by one or more processors.
Of course, the storage medium containing the computer-executable instructions provided by the embodiments of the present invention is not limited to the method operations described above, and may also perform related operations in the method for acquiring the ambient temperature by the mobile terminal provided by any embodiment of the present invention.
A computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
Program code embodied on a computer readable medium may be transmitted using any appropriate 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, it is obvious for those skilled in the art that the present invention can be implemented by software and necessary general hardware, and certainly, can also be implemented by hardware, but the former is a better embodiment in many cases. Based on such understanding, the technical solutions of the present invention may be embodied in the form of a software product, which may be stored in a computer-readable storage medium, such as a floppy disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a FLASH Memory (FLASH), a hard disk or an optical disk of a computer, and includes instructions for enabling a mobile terminal (which may be a mobile phone, a tablet computer, or an intelligent wearable device) to execute the methods according to the embodiments of the present invention.
It is to be noted that the foregoing is only illustrative of the preferred embodiments of the present invention and the technical principles employed. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, although the present invention has been described in greater detail by the above embodiments, the present invention is not limited to the above embodiments, and may include other equivalent embodiments without departing from the spirit of the present invention, and the scope of the present invention is determined by the scope of the appended claims.
Claims (10)
1. A method for acquiring ambient temperature by a mobile terminal is characterized by comprising the following steps:
acquiring the use state of a mobile terminal and the basic temperature measured by a temperature sensor arranged in the mobile terminal;
and inputting the use state and the basic temperature into a trained deep learning model so as to predict the environment temperature of the mobile terminal.
2. The method for acquiring the ambient temperature by the mobile terminal according to claim 1, wherein after the inputting the usage status and the base temperature into the trained deep learning model to predict the ambient temperature of the mobile terminal, the method further comprises:
based on Bluetooth broadcast crowdsourcing, scanning reference use states and reference environment temperatures broadcast by other surrounding mobile terminals according to a preset period;
correcting the predicted ambient temperature based on the reference usage state and the reference ambient temperature.
3. The method for acquiring the ambient temperature by the mobile terminal according to claim 1, wherein before the inputting the usage status and the base temperature into the trained deep learning model to predict the ambient temperature of the mobile terminal, the method further comprises:
acquiring training sample data, wherein each training sample data comprises a corresponding basic temperature of a preset mobile terminal marked by a preset environment temperature in a preset use state;
and training the deep learning model according to the training sample data to obtain the trained deep learning model.
4. The method according to claim 3, wherein the obtaining training sample data comprises:
acquiring a corresponding basic temperature of the preset mobile terminal in the preset use state;
receiving the preset environment temperature uploaded by the real-time electronic thermometer;
and marking the corresponding basic temperature of the preset mobile terminal in the preset use state according to the preset environment temperature and the timestamp.
5. The method according to claim 3, wherein before the training the deep learning model according to the training sample data to obtain the trained deep learning model, the method further comprises:
respectively determining a correlation coefficient between each variable in the preset using state and the preset environment temperature;
and screening the variables in the preset using state according to the correlation coefficient.
6. The method for acquiring the ambient temperature by the mobile terminal according to claim 5, wherein the correlation coefficient is a Pearson correlation coefficient.
7. The method for acquiring the ambient temperature by the mobile terminal according to claim 1, wherein the usage status comprises one or a combination of at least two of occupancy rate of a central processing unit of the mobile terminal, screen activation status, sensor data and battery information.
8. An apparatus for acquiring ambient temperature by a mobile terminal, comprising:
the mobile terminal comprises a data acquisition module, a data processing module and a data processing module, wherein the data acquisition module is used for acquiring the use state of the mobile terminal and the basic temperature measured by a temperature sensor arranged in the mobile terminal;
and the temperature prediction module is used for inputting the use state and the basic temperature into the trained deep learning model so as to predict the environment temperature of the mobile terminal.
9. A mobile terminal, comprising:
one or more processors;
a memory for storing one or more programs;
when executed by the one or more processors, cause the one or more processors to implement a method for acquiring ambient temperature by a mobile terminal as recited in any of claims 1-7.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out a method of acquiring an ambient temperature for a mobile terminal according to any one of claims 1 to 7.
Priority Applications (2)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011633454.4A CN112834067A (en) | 2020-12-31 | 2020-12-31 | Method and device for acquiring environment temperature by mobile terminal, mobile terminal and medium |
| PCT/CN2021/118088 WO2022142471A1 (en) | 2020-12-31 | 2021-09-14 | Method and apparatus for acquiring environmental temperature by mobile terminal, mobile terminal, and medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011633454.4A CN112834067A (en) | 2020-12-31 | 2020-12-31 | Method and device for acquiring environment temperature by mobile terminal, mobile terminal and medium |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN112834067A true CN112834067A (en) | 2021-05-25 |
Family
ID=75925911
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202011633454.4A Pending CN112834067A (en) | 2020-12-31 | 2020-12-31 | Method and device for acquiring environment temperature by mobile terminal, mobile terminal and medium |
Country Status (2)
| Country | Link |
|---|---|
| CN (1) | CN112834067A (en) |
| WO (1) | WO2022142471A1 (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113419868A (en) * | 2021-08-23 | 2021-09-21 | 南方科技大学 | Temperature prediction method, device, equipment and storage medium based on crowdsourcing |
| CN113720494A (en) * | 2021-08-09 | 2021-11-30 | Oppo广东移动通信有限公司 | Temperature detection method, device, equipment and storage medium |
| WO2022142471A1 (en) * | 2020-12-31 | 2022-07-07 | 南方科技大学 | Method and apparatus for acquiring environmental temperature by mobile terminal, mobile terminal, and medium |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117309195B (en) * | 2023-11-30 | 2024-04-12 | 中控技术股份有限公司 | Detection method and device of temperature measuring instrument and electronic equipment |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20030064749A1 (en) * | 2001-10-02 | 2003-04-03 | Nokia Corporation | Mobile telephone featuring accelerated ambient temperature measurement module |
| CN103196574A (en) * | 2013-03-20 | 2013-07-10 | 北京小米科技有限责任公司 | Method and device for measuring environment temperatures |
| CN104583725A (en) * | 2012-07-02 | 2015-04-29 | 盛思锐股份公司 | Portable electronic device |
| CN104729748A (en) * | 2013-12-19 | 2015-06-24 | 罗伯特·博世有限公司 | Method for determining the ambient temperature of a mobile device |
| CN109642827A (en) * | 2016-09-02 | 2019-04-16 | 株式会社资生堂 | Temperature Estimation System, temperature projectional technique and record have the record media of temperature estimation program |
| CN110672231A (en) * | 2019-09-29 | 2020-01-10 | 中国人民解放军国防科技大学 | Air temperature measuring method based on mobile phone battery temperature sensor |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112834067A (en) * | 2020-12-31 | 2021-05-25 | 南方科技大学 | Method and device for acquiring environment temperature by mobile terminal, mobile terminal and medium |
-
2020
- 2020-12-31 CN CN202011633454.4A patent/CN112834067A/en active Pending
-
2021
- 2021-09-14 WO PCT/CN2021/118088 patent/WO2022142471A1/en active Application Filing
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20030064749A1 (en) * | 2001-10-02 | 2003-04-03 | Nokia Corporation | Mobile telephone featuring accelerated ambient temperature measurement module |
| CN104583725A (en) * | 2012-07-02 | 2015-04-29 | 盛思锐股份公司 | Portable electronic device |
| CN103196574A (en) * | 2013-03-20 | 2013-07-10 | 北京小米科技有限责任公司 | Method and device for measuring environment temperatures |
| CN104729748A (en) * | 2013-12-19 | 2015-06-24 | 罗伯特·博世有限公司 | Method for determining the ambient temperature of a mobile device |
| CN109642827A (en) * | 2016-09-02 | 2019-04-16 | 株式会社资生堂 | Temperature Estimation System, temperature projectional technique and record have the record media of temperature estimation program |
| CN110672231A (en) * | 2019-09-29 | 2020-01-10 | 中国人民解放军国防科技大学 | Air temperature measuring method based on mobile phone battery temperature sensor |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2022142471A1 (en) * | 2020-12-31 | 2022-07-07 | 南方科技大学 | Method and apparatus for acquiring environmental temperature by mobile terminal, mobile terminal, and medium |
| CN113720494A (en) * | 2021-08-09 | 2021-11-30 | Oppo广东移动通信有限公司 | Temperature detection method, device, equipment and storage medium |
| CN113419868A (en) * | 2021-08-23 | 2021-09-21 | 南方科技大学 | Temperature prediction method, device, equipment and storage medium based on crowdsourcing |
| CN113419868B (en) * | 2021-08-23 | 2021-11-16 | 南方科技大学 | Temperature prediction method, device, equipment and storage medium based on crowdsourcing |
| WO2023024213A1 (en) * | 2021-08-23 | 2023-03-02 | 南方科技大学 | Crowdsourcing-based temperature prediction method and apparatus, and device and storage medium |
Also Published As
| Publication number | Publication date |
|---|---|
| WO2022142471A1 (en) | 2022-07-07 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN112834067A (en) | Method and device for acquiring environment temperature by mobile terminal, mobile terminal and medium | |
| US10545517B2 (en) | Generating and implementing thermodynamic models of a structure | |
| US10514177B2 (en) | Smartvent and atmospheric controller apparatuses, methods and systems | |
| US8399115B2 (en) | System and apparatus for monitoring large battery stacks using wireless sensor networks | |
| EP2944891B1 (en) | Room temperature estimating device, program | |
| US10754918B2 (en) | System for providing remote building efficiency audits | |
| Hribar et al. | Using deep Q-learning to prolong the lifetime of correlated internet of things devices | |
| Breda et al. | Hot or not: Leveraging mobile devices for ubiquitous temperature sensing | |
| Li et al. | The design of remote temperature monitoring system | |
| He et al. | Mobile device batteries as thermometers | |
| WO2017109919A1 (en) | Electronic device, wbgt determination method, and wbgt determination program | |
| US20170242940A1 (en) | System for providing remote building efficiency audits for solar sensitivity | |
| CN109660964A (en) | Communication means, device, equipment and the computer-readable medium of sensor | |
| CN112128925B (en) | Method for intelligently remotely controlling air conditioner, intelligent terminal and storage medium | |
| CN104919477B (en) | The thermal characteristics estimation device in room and the thermal characteristics method of estimation in room | |
| US10229464B2 (en) | System for providing remote building efficiency audits for wind sensitivity | |
| CN109642827A (en) | Temperature Estimation System, temperature projectional technique and record have the record media of temperature estimation program | |
| JP2018155435A (en) | Air conditioner control device and air conditioner control method | |
| Šolić et al. | Toward smart shirt for sensing thermal comfort | |
| Thitisawat et al. | Adaptive outdoor comfort model calibrations for a semitropical region | |
| Abu Gharbyeh | IOT–based weather reporting system | |
| ES2829024T3 (en) | Procedure for determining a thermal diagnosis of a building | |
| CN110782958A (en) | Intelligent sign detection method based on image processing | |
| KR101613363B1 (en) | Method and Apparatus for Logging of Sensing Data in Living Environments | |
| CN112834064B (en) | Temperature detection device |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |