CN110362184A - A kind of method and wearable device of controlling electronic devices - Google Patents

Abstract

The embodiment of the invention provides a kind of method of controlling electronic devices and wearable devices, it is related to field of artificial intelligence, this method comprises: using time gathered in advance and physiological characteristic parameter as training sample, acquisition sleep judgment models are trained by the way of supervised learning, therefore after wearable device acquisition current time and the current physiological characteristic parameter of user, the current hypnagogic state of user can be determined using sleep judgment models.When the hypnagogic state of user is hypnagogic state, and the current operating status of electronic equipment is open state, shutdown signal is sent to electronic equipment and so that making electronic equipment not influences user's sleep, while saving electric energy so that electronic equipment switches to closed state.

Description

A method for controlling an electronic device and a wearable device

technical field

The embodiments of the present invention relate to the technical field of artificial intelligence, and in particular to a method for controlling an electronic device and a wearable device.

Background technique

With the development of computer technology, various electronic devices have become an indispensable part of people's daily life. In life, many people like to sleep while listening to music with headphones, or play soft music with speakers to sleep, and the music remains on after falling asleep. Some people fall asleep while watching TV, but the TV has not been turned off, and some people forget to turn off the lights and fall asleep. These electronic devices are always on after the user falls asleep, which not only affects the user's sleep quality, but also wastes electricity.

Contents of the invention

Since some electronic devices are always on after the user falls asleep, which not only affects the sleep quality of the user, but also wastes electric energy, the embodiments of the present invention provide a method for controlling an electronic device and a wearable device.

In one aspect, an embodiment of the present invention provides a method for controlling an electronic device, including:

Obtain the current time and the user's current physiological characteristic parameters;

Inputting the current time and the physiological characteristic parameters into a sleep judgment model to determine the user's falling asleep state, the sleep judgment model uses pre-collected time and physiological characteristic parameters as training samples, and uses supervised learning to train the obtained model;

The operating state of the electronic device is controlled according to the sleeping state of the user.

Optionally, the inputting the current time and the physiological characteristic parameters into a sleep judgment model to determine the user's falling asleep state includes:

When the current time is within a time interval and the physiological characteristic parameter is within a numerical range, it is determined that the user's falling asleep state is a falling asleep state, and the time interval and the numerical interval are generated by the sleep judgment model of;

When the current time is not within the time interval, or the physiological characteristic parameter is not within the value range, it is determined that the falling asleep state of the user is not falling asleep.

Optionally, the controlling the operating state of the electronic device according to the sleeping state of the user includes:

When the falling asleep state of the user is the falling asleep state and the current operating state of the electronic device is the on state, sending a shutdown signal to the electronic device so that the electronic device is switched to the off state;

When the sleeping state of the user is not falling asleep and the current operating state of the electronic device is in the on state, no shutdown signal is sent to the electronic device so that the electronic device remains in the on state.

Optionally, the sleep judgment model is updated in the following manner:

Collect the judgment results of user feedback to update the training samples;

The updated sleep judgment model is obtained by retraining based on the updated training samples.

Optionally, the electronic device includes a multimedia device.

On the one hand, an embodiment of the present invention provides a wearable device, including:

An acquisition module, configured to acquire the current time and the current physiological characteristic parameters of the user;

A processing module, configured to input the current time and the physiological characteristic parameters into a sleep judgment model to determine the state of falling asleep of the user. The sleep judgment model uses pre-collected time and physiological characteristic parameters as training samples and adopts supervision The model obtained by training in the way of learning;

A control module, configured to control the operating state of the electronic device according to the sleeping state of the user.

Optionally, the processing module is specifically configured to:

When the current time is within a time interval and the physiological characteristic parameter is within a numerical range, it is determined that the user's falling asleep state is a falling asleep state, and the time interval and the numerical interval are generated by the sleep judgment model of;

When the current time is not within the time interval, or the physiological characteristic parameter is not within the value range, it is determined that the falling asleep state of the user is not falling asleep.

Optionally, the control module is specifically used for:

When the falling asleep state of the user is the falling asleep state and the current operating state of the electronic device is the on state, sending a shutdown signal to the electronic device so that the electronic device is switched to the off state;

When the sleeping state of the user is not falling asleep and the current operating state of the electronic device is in the on state, no shutdown signal is sent to the electronic device so that the electronic device remains in the on state.

Optionally, an update module is also included;

The update module is specifically used for:

Collect the judgment results of user feedback to update the training samples;

The updated sleep judgment model is obtained by retraining based on the updated training samples.

Optionally, the electronic device includes a multimedia device.

On the one hand, an embodiment of the present invention provides a computer device, including a memory, a processor, and a computer program stored in the memory and operable on the processor, and the method for controlling an electronic device is implemented when the processor executes the program A step of.

On the one hand, an embodiment of the present invention provides a computer-readable storage medium, which stores a computer program executable by a computer device, and when the program is run on the computer device, the computer device executes a method for controlling an electronic device A step of.

In the embodiment of the present invention, the pre-collected time and physiological characteristic parameters are used as training samples, and the supervised learning method is used to train to obtain the sleep judgment model. Therefore, after the wearable device collects the current time and the user's current physiological characteristic parameters, it uses sleep judgment The model can determine the user's current sleep state, and then control the operation state of the electronic device according to the user's sleep state, so that the operation state of the electronic device matches the user's sleep state, so that the electronic device does not affect the user's sleep and saves power.

Description of drawings

In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings that need to be used in the description of the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention. For Those of ordinary skill in the art can also obtain other drawings based on these drawings without any creative effort.

FIG. 1 is a schematic diagram of an application scenario provided by an embodiment of the present invention;

FIG. 2 is a schematic flowchart of a method for controlling an electronic device provided by an embodiment of the present invention;

FIG. 3 is a schematic flowchart of a method for controlling an electronic device provided by an embodiment of the present invention;

FIG. 4 is a schematic structural diagram of a wearable device provided by an embodiment of the present invention;

FIG. 5 is a schematic structural diagram of a computer device provided by an embodiment of the present invention.

Detailed ways

In order to make the object, technical solution and beneficial effects of the present invention more clear, 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 here are only used to explain the present invention, not to limit the present invention.

The method for controlling an electronic device in the embodiment of the present invention can be applied to the application scenario shown in FIG. A portable device for the user's clothing or accessories, such as smart watches, smart glasses, smart headbands, smart clothing, smart accessories, etc. The electronic device 102 may be a multimedia device, a mobile terminal device, a smart home device, etc., such as a sound box, a smart phone, a TV, a lamp, and the like. The wearable device 101 is connected to the electronic device 102 through a wireless network. After the user wears the wearable device 101, the wearable device 101 collects the user's current physiological characteristic parameters, such as heart rate, respiratory rate, blood pressure, etc., and the wearable device 101 simultaneously obtains the current time. The wearable device 101 inputs the current time and the user's current physiological characteristic parameters into the sleep judgment model to determine the user's current falling asleep state. In a specific implementation, the trained sleep judgment model may be imported into the wearable device 101 in advance, or the sleep judgment model may be saved in a cloud server. When the wearable device 101 needs to judge the user's falling asleep state, it sends the collected current time and the user's current physiological characteristic parameters to the cloud server, and the sleep judgment model in the cloud server judges the user's sleep status according to the current time and the user's current physiological characteristic parameters. The user's falling asleep state, and then send the user's falling asleep state to the wearable device 101. The wearable device 101 controls the operating state of the electronic device according to the user's falling asleep state. For example, if the electronic device is set as a multimedia device, if the user's falling asleep state is not falling asleep and the multimedia device is in the open state, then no shutdown signal is sent to the multimedia device. device to keep the multimedia device turned on. If the sleeping state of the user is the falling asleep state and the multimedia device is in the on state, then send an off signal to the multimedia device so that the multimedia device switches to the off state.

Based on the application scenario diagram shown in FIG. 1, an embodiment of the present invention provides a flow of a method for controlling an electronic device. The flow of the method can be executed by a wearable device, as shown in FIG. 2, including the following steps:

Step S201, acquiring the current time and the current physiological characteristic parameters of the user.

Specifically, the current physiological characteristic parameters of the user include heart rate, breathing rate, blood pressure, etc., and these physiological characteristic parameters can determine whether the user is falling asleep. For example, when a person falls asleep, the heart rate and breathing rate will slow down and become even, and the blood pressure will also stabilize. After the user wears the portable device, the portable device collects and records the user's physiological characteristic parameters in real time. In addition, there are regularities in the time when users fall asleep. For example, some people go to bed at 22:00-6:00, and some people go to bed at 23:00-7:00. Therefore, it can be judged whether the user is asleep by combining the current time and the time the user is used to sleeping. .

Step S202, inputting the current time and physiological characteristic parameters into the sleep judgment model to determine the user's falling asleep state.

Specifically, the sleep judgment model is a model obtained by using pre-collected time and physiological characteristic parameters as training samples and training in a supervised learning manner. The sleep judgment model may be a supervised learning model such as a linear regression model, a nonlinear regression model, a decision tree model, a logistic regression model, or a Support Vector Machine (Support Vector Machine, SVM for short) model. After the sleep judgment model is used to judge the falling asleep state, the judgment results fed back by the user can be collected to update the training samples, and then retraining based on the updated training samples to obtain the updated sleep judgment model. By updating the training samples and repeating the training, the accuracy of the sleep judgment model is continuously improved.

After training the sleep judgment model, you can directly import the sleep judgment model into the wearable device, or save the sleep judgment model in the cloud server. When the wearable device judges the user's falling asleep state, it will compare the current time and the user's current time through the wireless network. The physiological characteristic parameters of the user are sent to the cloud server, and the sleep judgment model in the cloud server judges the user's sleep state based on the current time and the user's current physiological characteristic parameters.

Step S203, controlling the running state of the electronic device according to the sleeping state of the user.

Specifically, the electronic device may be a multimedia device, a mobile terminal device, a smart home device, etc., such as a sound box, a smart phone, a TV, an electric lamp, and the like. The running state includes an on state, an off state, a sleep state, and the like.

In the embodiment of the present invention, the pre-collected time and physiological characteristic parameters are used as training samples, and the supervised learning method is used to train to obtain the sleep judgment model. Therefore, after the wearable device collects the current time and the user's current physiological characteristic parameters, it uses sleep judgment The model can determine the current sleep state, and then control the operation state of the electronic device according to the user's sleep state, so that the operation state of the electronic device matches the user's sleep state, so that the electronic device does not affect the user's sleep and saves power.

Optionally, in the above step S202, when using the sleep judgment model to judge whether the user falls asleep, the following judgment rules may be used:

When the current time is within the time interval, and the physiological characteristic parameters are within the numerical range, determine that the user's falling asleep state is the asleep state; when the current time is not within the time interval, or the physiological characteristic parameters are not within the numerical range, determine the user The falling asleep state is not falling asleep, and the time interval and value interval are generated by the sleep judgment model.

In a specific implementation, the time interval is the time period when the user falls asleep, and the value range is the value range of the physiological characteristic parameters when the user falls asleep. When the sleep judgment model is obtained through big data training, the time interval and the value range can be obtained. When the sleep judgment model is used to judge whether the user is falling asleep, the sleep judgment model generates a time interval and a numerical interval. If the current time is within the time interval and the physiological characteristic parameters are within the numerical range, it means that the current time is the time when the user is used to falling asleep, and If the physiological characteristic parameters of the user meet the physiological characteristic parameters when falling asleep, the user is likely to be in a state of falling asleep. If the current time is not within the time interval, or the physiological characteristic parameters are not within the value range, it means that the current time is not the time when the user is used to falling asleep, or the user's physiological characteristic parameters do not meet the physiological characteristic parameters when falling asleep, and the user is likely to be in the not asleep. The user's falling asleep state is judged by combining time and physiological characteristic parameters, thereby improving the accuracy of judgment.

Optionally, in the above step S203, when controlling the operating state of the electronic device according to the sleeping state of the user, the embodiment of the present invention at least provides the following implementation manners:

When the user's falling asleep state is the falling asleep state and the current operating state of the electronic device is the on state, a shutdown signal is sent to the electronic device so that the electronic device switches to the off state. When the sleeping state of the user is not falling asleep and the current operating state of the electronic device is in the on state, no shutdown signal is sent to the electronic device so that the electronic device remains in the on state.

Specifically, when the electronic device receives the shutdown signal, it may first gradually reduce the volume or brightness, and then shut down. When the electronic device is switched to the off state, the wearable device can stop obtaining the current time and collecting the user's current physiological characteristic parameters, thereby reducing the power consumption of the wearable device and improving the battery life of the wearable device.

In order to better explain the embodiment of the present invention, a method for controlling an electronic device provided by the embodiment of the present invention is described below in combination with a specific implementation scenario. The method is executed by a wearable device, as shown in FIG. 3 , the method includes the following steps :

In step S301, the wearable device obtains the current time and the current physiological characteristic parameters of the user.

The physiological characteristic parameters are heart rate, respiratory rate, blood pressure, and the like.

Step S302, inputting the current time and physiological characteristic parameters into the sleep judgment model.

Step S303, judging whether the current time and physiological characteristic parameters satisfy the falling asleep condition, if yes, execute step S304, otherwise execute step S305.

The condition for falling asleep is that the current time is within the time interval, and the physiological characteristic parameters are within the numerical range, and the time interval and numerical interval are generated by the sleep judgment model.

Step S304, sending a shutdown signal to the electronic device, so that the electronic device switches to the shutdown state.

Step S305, not sending a shutdown signal to the electronic device, so that the electronic device remains in an on state.

In step S306, the judgment result fed back by the user is collected to update the training samples, and the updated training samples are used for retraining to update the sleep judgment model.

The above process is described below in combination with specific implementation scenarios:

Exemplarily, the electronic device is set to be a smart phone, and the user turns on a music player in the smart phone, and then uses a smart earphone to listen to music to fall asleep. The smart earphone collects the user's physiological characteristic parameters such as heart rate X1, respiratory frequency X2, blood pressure X3 and the current time T1, and then inputs the physiological characteristic parameters and current time into the sleep judgment model, when the physiological characteristic parameters such as heart rate X1, respiratory frequency X2, blood pressure X3 When it is within the numerical range [xm, xn] and the current time T1 is within the time interval [tp, tq], the sleep judgment model outputs the state of falling asleep. The smart earphone sends a shutdown signal to the smart phone, and after the smart phone receives the shutdown signal, it gradually reduces the volume of the music played until it is turned off, or it is turned off directly. When the physiological characteristic parameters such as heart rate X1, respiratory rate X2, blood pressure X3 are not within the value range [xm, xn], or the current time T1 is not within the time interval [tp, tq], the sleep judgment model outputs the state of not falling asleep, and the intelligent The earphone continues to collect time and physiological characteristic parameters of the user.

Exemplarily, the electronic device is set as a stereo, and the user turns on the stereo to play music to fall asleep. The wearable device collects the user's physiological characteristic parameters such as heart rate X1, respiratory rate X2, blood pressure X3 and the current time T1, and then inputs the physiological characteristic parameters and the current time into the sleep judgment model. When the physiological characteristic parameters such as heart rate X1, respiratory rate X2, and blood pressure X3 are within the value range [xm, xn], and the current time T1 is within the time interval [tp, tq], the sleep judgment model outputs the state of falling asleep. The wearable device sends a shutdown signal to the speaker. After receiving the shutdown signal, the speaker gradually reduces the volume of the music until it is turned off, or it is turned off directly. When the physiological characteristic parameters such as heart rate X1, respiratory rate X2, blood pressure X3 are not within the value range [xm, xn], or the current time T1 is not within the time interval [tp, tq], the sleep judgment model outputs the state of not falling asleep, which can be The wearable device continues to collect time and physiological characteristic parameters of the user.

Exemplarily, the electronic device is set to be a TV, and the user falls asleep watching TV. The wearable device collects the user's physiological characteristic parameters such as heart rate X1, respiratory rate X2, blood pressure X3 and the current time T1, and then inputs the physiological characteristic parameters and the current time into the sleep judgment model. When the physiological characteristic parameters such as heart rate X1, respiratory rate X2, and blood pressure X3 are within the value range [xm, xn], and the current time T1 is within the time interval [tp, tq], the sleep judgment model outputs the state of falling asleep. The wearable device sends a shutdown signal to the TV, and the TV turns off directly after receiving the shutdown signal. When the physiological characteristic parameters such as heart rate X1, respiratory rate X2, blood pressure X3 are not within the value range [xm, xn], or the current time T1 is not within the time interval [tp, tq], the sleep judgment model outputs the state of not falling asleep, which can be The wearable device continues to collect time and physiological characteristic parameters of the user.

Exemplarily, the electronic device is set as a light, and the user turns on the light to fall asleep. The wearable device collects the user's physiological characteristic parameters such as heart rate X1, respiratory rate X2, blood pressure X3 and the current time T1, and then inputs the physiological characteristic parameters and the current time into the sleep judgment model. When the physiological characteristic parameters such as heart rate X1, respiratory rate X2, and blood pressure X3 are within the value range [xm, xn], and the current time T1 is within the time interval [tp, tq], the sleep judgment model outputs the state of falling asleep. The wearable device sends a turn-off signal to the light. After receiving the turn-off signal, the light gradually reduces the brightness until it turns off, or turns off directly. When the physiological characteristic parameters such as heart rate X1, respiratory rate X2, blood pressure X3 are not within the value range [xm, xn], or the current time T1 is not within the time interval [tp, tq], the sleep judgment model outputs the state of not falling asleep, which can be The wearable device continues to collect time and physiological characteristic parameters of the user.

In the embodiment of the present invention, the pre-collected time and physiological characteristic parameters are used as training samples, and the supervised learning method is used to train to obtain the sleep judgment model. Therefore, after the wearable device collects the current time and the user's current physiological characteristic parameters, it uses sleep judgment The model can determine the current sleep state, and then control the operation state of the electronic device according to the user's sleep state, so that the operation state of the electronic device matches the user's sleep state, so that the electronic device does not affect the user's sleep and saves power.

Based on the same technical concept, an embodiment of the present invention provides a wearable device, as shown in FIG. 4, the wearable device 400 includes:

An acquisition module 401, configured to acquire the current time and the current physiological characteristic parameters of the user;

The processing module 402 is configured to input the current time and the physiological characteristic parameters into a sleep judgment model to determine the user's falling asleep state. The sleep judgment model uses pre-collected time and physiological characteristic parameters as training samples, adopts The model obtained by training in the way of supervised learning;

A control module 403, configured to control the operating state of the electronic device according to the user's falling asleep state.

Optionally, the processing module 402 is specifically configured to:

When the current time is within a time interval and the physiological characteristic parameter is within a numerical range, it is determined that the user's falling asleep state is a falling asleep state, and the time interval and the numerical interval are generated by the sleep judgment model of;

When the current time is not within the time interval, or the physiological characteristic parameter is not within the value range, it is determined that the falling asleep state of the user is not falling asleep.

Optionally, the control module 403 is specifically configured to:

When the falling asleep state of the user is the falling asleep state and the current operating state of the electronic device is the on state, sending a shutdown signal to the electronic device so that the electronic device is switched to the off state;

When the sleeping state of the user is not falling asleep and the current operating state of the electronic device is in the on state, no shutdown signal is sent to the electronic device so that the electronic device remains in the on state.

Optionally, an update module 404 is also included;

The updating module 404 is specifically used for:

Collect the judgment results of user feedback to update the training samples;

The updated sleep judgment model is obtained by retraining based on the updated training samples.

Optionally, the electronic device includes a multimedia device.

Based on the same technical concept, an embodiment of the present invention provides a computer device, as shown in FIG. 5 , including at least one processor 501, and a memory 502 connected to at least one processor. The processor is not limited in the embodiment of the present invention. As for the specific connection medium between 501 and memory 502, the bus connection between processor 501 and memory 502 in FIG. 5 is taken as an example. The bus can be divided into address bus, data bus, control bus and so on.

In the embodiment of the present invention, the memory 502 stores instructions executable by at least one processor 501, and at least one processor 501 can execute the steps included in the aforementioned method for controlling an electronic device by executing the instructions stored in the memory 502.

Among them, the processor 501 is the control center of the computer equipment, which can use various interfaces and lines to connect various parts of the computer equipment, by running or executing instructions stored in the memory 502 and calling data stored in the memory 502, thereby controlling electronic equipment. Optionally, the processor 501 may include one or more processing units, and the processor 501 may integrate an application processor and a modem processor. The tuner processor mainly handles wireless communication. It can be understood that the foregoing modem processor may not be integrated into the processor 501 . In some embodiments, the processor 501 and the memory 502 can be implemented on the same chip, and in some embodiments, they can also be implemented on independent chips.

The processor 501 may be a general processor, such as a central processing unit (CPU), a digital signal processor, an application specific integrated circuit (Application Specific Integrated Circuit, ASIC), a field programmable gate array or other programmable logic devices, discrete gates or transistors Logic devices and discrete hardware components can implement or execute the methods, steps and logic block diagrams disclosed in the embodiments of the present invention. A general purpose processor may be a microprocessor or any conventional processor or the like. The steps of the methods disclosed in connection with the embodiments of the present invention may be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules in the processor.

The memory 502, as a non-volatile computer-readable storage medium, can be used to store non-volatile software programs, non-volatile computer-executable programs and modules. The memory 502 may include at least one type of storage medium, such as flash memory, hard disk, multimedia card, card-type memory, random access memory (Random Access Memory, RAM), static random access memory (Static Random Access Memory, SRAM), Programmable Read Only Memory (PROM), Read Only Memory (ROM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Magnetic Memory, Disk, discs and more. Memory 502 is, but is not limited to, any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer. The memory 502 in the embodiment of the present invention may also be a circuit or any other device capable of implementing a storage function, and is used for storing program instructions and/or data.

Based on the same technical concept, an embodiment of the present invention provides a computer-readable storage medium, which stores a computer program executable by a computer device, and when the program is run on the computer device, the computer device executes a control electronic The steps of the method of the device.

Those skilled in the art should understand that the embodiments of the present invention may be provided as methods or computer program products. Accordingly, the present invention can take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) having computer-usable program code embodied therein.

The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It should be understood that each procedure and/or block in the flowchart and/or block diagram, and a combination of procedures and/or blocks in the flowchart and/or block diagram can be realized by computer program instructions. These computer program instructions may be provided to a general purpose computer, special purpose computer, embedded processor, or processor of other programmable data processing equipment to produce a machine such that the instructions executed by the processor of the computer or other programmable data processing equipment produce a An apparatus for realizing the functions specified in one or more procedures of the flowchart and/or one or more blocks of the block diagram.

These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing apparatus to operate in a specific manner, such that the instructions stored in the computer-readable memory produce an article of manufacture comprising instruction means, the instructions The device realizes the function specified in one or more procedures of the flowchart and/or one or more blocks of the block diagram.

These computer program instructions can also be loaded onto a computer or other programmable data processing device, causing a series of operational steps to be performed on the computer or other programmable device to produce a computer-implemented process, thereby The instructions provide steps for implementing the functions specified in the flow chart or blocks of the flowchart and/or the block or blocks of the block diagrams.

While preferred embodiments of the invention have been described, additional changes and modifications to these embodiments can be made by those skilled in the art once the basic inventive concept is appreciated. Therefore, it is intended that the appended claims be construed to cover the preferred embodiment as well as all changes and modifications which fall within the scope of the invention.

Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalent technologies, the present invention also intends to include these modifications and variations.

Claims (10)

1. A method for controlling an electronic device, comprising: Obtain the current time and the user's current physiological characteristic parameters; Inputting the current time and the physiological characteristic parameters into a sleep judgment model to determine the user's falling asleep state, the sleep judgment model uses pre-collected time and physiological characteristic parameters as training samples, and uses supervised learning to train the obtained model; The operating state of the electronic device is controlled according to the sleeping state of the user. 2. The method according to claim 1, wherein the inputting the current time and the physiological characteristic parameters into a sleep judgment model to determine the falling asleep state of the user comprises: When the current time is within a time interval and the physiological characteristic parameter is within a numerical range, it is determined that the user's falling asleep state is a falling asleep state, and the time interval and the numerical interval are generated by the sleep judgment model of; When the current time is not within the time interval, or the physiological characteristic parameter is not within the value range, it is determined that the falling asleep state of the user is not falling asleep. 3. The method according to claim 1, wherein the controlling the operating state of the electronic device according to the sleeping state of the user comprises: When the falling asleep state of the user is the falling asleep state and the current operating state of the electronic device is the on state, sending a shutdown signal to the electronic device so that the electronic device is switched to the off state; When the sleeping state of the user is not falling asleep and the current operating state of the electronic device is in the on state, no shutdown signal is sent to the electronic device so that the electronic device remains in the on state. 4. The method according to claim 1, wherein the sleep judgment model is updated in the following manner: Collect the judgment results of user feedback to update the training samples; The updated sleep judgment model is obtained by retraining based on the updated training samples. 5. The method according to any one of claims 1 to 4, wherein the electronic device comprises a multimedia device. 6. A wearable device, characterized in that, comprising: An acquisition module, configured to acquire the current time and the current physiological characteristic parameters of the user; A processing module, configured to input the current time and the physiological characteristic parameters into a sleep judgment model to determine the state of falling asleep of the user. The sleep judgment model uses pre-collected time and physiological characteristic parameters as training samples and adopts supervision The model obtained by training in the way of learning; A control module, configured to control the operating state of the electronic device according to the sleeping state of the user. 7. The wearable device according to claim 6, wherein the processing module is specifically used for: When the current time is within a time interval and the physiological characteristic parameter is within a numerical range, it is determined that the user's falling asleep state is a falling asleep state, and the time interval and the numerical interval are generated by the sleep judgment model of; When the current time is not within the time interval, or the physiological characteristic parameter is not within the value range, it is determined that the falling asleep state of the user is not falling asleep. 8. The wearable device according to claim 6, wherein the control module is specifically used for: When the user's falling asleep state is the falling asleep state and the current operating state of the electronic device is the on state, sending a shutdown signal to the electronic device, so that the electronic device is switched to the off state; When the sleeping state of the user is not falling asleep and the current operating state of the electronic device is in the on state, no shutdown signal is sent to the electronic device, so that the electronic device remains in the on state. 9. A computer device, comprising a memory, a processor, and a computer program stored in the memory and operable on the processor, characterized in that, when the processor executes the program, any one of claims 1 to 5 is realized The steps of the method are claimed. 10. A computer-readable storage medium, characterized in that it stores a computer program executable by a computer device, and when the program is run on the computer device, the computer device executes any one of claims 1-5. steps of the method described above.