1. A method for automatically controlling a cigarette making machine, comprising:

acquiring current cigarette machine use behavior data of a user;

predicting cigarette machine control parameters of the user by combining the cigarette machine use details in the time set by the user and the current cigarette machine use behavior data of the user;

and issuing the predicted control parameters of the cigarette machine to the cigarette machine.

2. The method according to claim 1, wherein the cigarette machine usage specification for the user-set time is obtained by:

using a data warehouse technology ETL to obtain the use details of the cigarette making machines for multiple times within the time set by the user;

and merging the use details of the cigarette machines for multiple times.

3. The method according to claim 1, wherein the plurality of cigarette machine usage profiles within the user-set time, the interval between usage profiles does not exceed a first set time period, and the actual usage time period exceeds a second set time period.

4. The method according to claim 1, wherein the cigarette machine control parameters comprise:

an illumination lamp on-state parameter; and/or

And (4) wind speed gear state parameters.

5. The method according to claim 4, wherein predicting cigarette machine control parameters of a user in combination with cigarette machine usage details within the user-set time and the user's current cigarette machine usage behavior data comprises:

and inputting the cigarette machine use details in the time set by the user and the current cigarette machine use behavior data of the user into a pre-trained first neural convolution network to obtain the prediction parameters of the lighting lamp turn-on state parameters.

6. The method according to claim 4, wherein predicting cigarette machine control parameters of a user in combination with cigarette machine usage details within the user-set time and the user's current cigarette machine usage behavior data comprises:

and inputting the cigarette machine use details in the time set by the user and the current cigarette machine use behavior data of the user into a pre-trained second neural convolution network to obtain the prediction parameters of the wind speed gear state parameters.

7. The method of any of claims 1 to 6, further comprising:

when two to-be-selected prediction results with the same possibility exist in the prediction results of the control parameters of the cigarette making machine, determining the latest occurrence time of the two to-be-selected prediction results;

and taking the prediction result to be selected, wherein the latest occurrence time is close to the current time.

8. An apparatus for automatically controlling a cigarette maker, comprising:

the acquisition unit is used for acquiring the current cigarette machine use behavior data of a user;

the prediction unit is used for predicting the cigarette machine control parameters of the user by combining the cigarette machine use details in the time set by the user and the current cigarette machine use behavior data of the user;

and the issuing unit is used for issuing the predicted control parameters of the cigarette machine to the cigarette machine.

9. A storage medium, characterized in that it stores program instructions which, when executed, perform the method of any one of claims 1 to 7.

10. A server, comprising a processor and a memory, the memory storing program instructions, the processor being configured to perform the method of any one of claims 1 to 7 based on the program instructions.

Background

At present, with the development of the internet of things technology, intelligent household appliances gradually move into each household, and the intellectualization of the household appliances is a trend in the future. The cigarette machine is as the indispensable product of family life, is the important equipment that influences the quality of life of family, how to realize the intellectuality of cigarette machine, becomes the important problem that needs to solve urgently.

Disclosure of Invention

The following presents a simplified summary in order to provide a basic understanding of some aspects of the disclosed embodiments. This summary is not an extensive overview nor is intended to identify key/critical elements or to delineate the scope of such embodiments but rather as a prelude to the more detailed description that is presented later.

The embodiment of the disclosure provides a method, a device, a storage medium and a server for automatically controlling a cigarette making machine, so as to solve the technical problem of realizing the intellectualization of the cigarette making machine.

In some embodiments, there is provided a method for automatically controlling a range hood, comprising:

acquiring current cigarette machine use behavior data of a user;

predicting cigarette machine control parameters of the user by combining the cigarette machine use details in the time set by the user and the current cigarette machine use behavior data of the user;

and issuing the predicted control parameters of the cigarette machine to the cigarette machine.

In some embodiments, the usage specification of the cigarette machine within the user-set time is obtained by:

using ETL to obtain the use details of the cigarette making machine for a plurality of times within the time set by the user;

and merging the use details of the cigarette machines for multiple times.

In some embodiments, the user sets multiple cigarette machine usage details within a time, the interval between each usage detail does not exceed a first set time period, and the actual usage time period exceeds a second set time period.

In some embodiments, the range hood control parameters include:

an illumination lamp on-state parameter; and/or

And (4) wind speed gear state parameters.

In some embodiments, the predicting the cigarette machine control parameters of the user by combining the cigarette machine use details in the user-set time and the current cigarette machine use behavior data of the user comprises:

and inputting the cigarette machine use details in the time set by the user and the current cigarette machine use behavior data of the user into a pre-trained first neural convolution network to obtain the prediction parameters of the lighting lamp turn-on state parameters.

In some embodiments, the predicting the cigarette machine control parameters of the user by combining the cigarette machine use details in the user-set time and the current cigarette machine use behavior data of the user comprises:

and inputting the cigarette machine use details in the time set by the user and the current cigarette machine use behavior data of the user into a pre-trained second neural convolution network to obtain the prediction parameters of the wind speed gear state parameters.

In some embodiments, the method provided by the embodiments of the present disclosure further includes:

when two to-be-selected prediction results with the same possibility exist in the prediction results of the control parameters of the cigarette making machine, determining the latest occurrence time of the two to-be-selected prediction results;

and taking the prediction result to be selected, wherein the latest occurrence time is close to the current time.

In some embodiments, the disclosed embodiments provide an apparatus for automatically controlling a cigarette making machine, comprising:

the acquisition unit is used for acquiring the current cigarette machine use behavior data of a user;

the prediction unit is used for predicting the cigarette machine control parameters of the user by combining the cigarette machine use details in the time set by the user and the current cigarette machine use behavior data of the user;

and the issuing unit is used for issuing the predicted control parameters of the cigarette machine to the cigarette machine.

In some embodiments, the apparatus further comprises a merging unit configured to:

using a data warehouse technology ETL to obtain the use details of the cigarette making machines for multiple times within the time set by the user;

and merging the use details of the cigarette machines for multiple times.

In some embodiments, the user sets multiple cigarette machine usage details within a time, the interval between each usage detail does not exceed a first set time period, and the actual usage time period exceeds a second set time period.

In some embodiments, the range hood control parameters include:

an illumination lamp on-state parameter; and/or

And (4) wind speed gear state parameters.

In some embodiments, the prediction unit is configured to:

and inputting the cigarette machine use details in the time set by the user and the current cigarette machine use behavior data of the user into a pre-trained first neural convolution network to obtain the prediction parameters of the lighting lamp turn-on state parameters.

In some embodiments, the prediction unit is configured to:

and inputting the cigarette machine use details in the time set by the user and the current cigarette machine use behavior data of the user into a pre-trained second neural convolution network to obtain the prediction parameters of the wind speed gear state parameters.

In some embodiments, the apparatus provided by the embodiments of the present disclosure further includes a selection unit, configured to:

when two to-be-selected prediction results with the same possibility exist in the prediction results of the control parameters of the cigarette making machine, determining the latest occurrence time of the two to-be-selected prediction results;

and taking the prediction result to be selected, wherein the latest occurrence time is close to the current time.

In some embodiments, the present disclosure provides a storage medium storing program instructions that, when executed, perform a method provided by an embodiment of the present disclosure.

In some embodiments, the present disclosure provides a server comprising a processor and a memory, the memory storing program instructions, the processor configured to execute a method provided by an embodiment of the present disclosure based on the program instructions. The method, the device, the storage medium and the server for automatically controlling the range hood provided by the embodiment of the disclosure can realize the following technical effects:

the cigarette machine control parameter prediction method based on the cigarette machine use details in the user set time can predict cigarette machine control parameters of the user by combining the current cigarette machine use behavior data of the user, can combine the historical use data of the cigarette machine, realizes intelligent control of the cigarette machine, and improves the intelligence of the cigarette machine.

The foregoing general description and the following description are exemplary and explanatory only and are not restrictive of the application.

Drawings

One or more embodiments are illustrated by way of example in the accompanying drawings, which correspond to the accompanying drawings and not in limitation thereof, in which elements having the same reference numeral designations are shown as like elements and not in limitation thereof, and wherein:

figure 1 is a schematic diagram of a method for automatically controlling a cigarette making machine provided by an embodiment of the present disclosure;

figure 2 is a schematic diagram of another method for automatically controlling a cigarette machine provided by an embodiment of the present disclosure;

figure 3 is a schematic diagram of another method for automatically controlling a cigarette machine provided by an embodiment of the present disclosure;

figure 4 is a schematic view of an apparatus for automatically controlling a cigarette maker according to an embodiment of the present disclosure;

fig. 5 is a schematic diagram of a server provided by the embodiments of the present disclosure.

Detailed Description

So that the manner in which the features and elements of the disclosed embodiments can be understood in detail, a more particular description of the disclosed embodiments, briefly summarized above, may be had by reference to the embodiments, some of which are illustrated in the appended drawings. In the following description of the technology, for purposes of explanation, numerous details are set forth in order to provide a thorough understanding of the disclosed embodiments. However, one or more embodiments may be practiced without these details. In other instances, well-known structures and devices may be shown in simplified form in order to simplify the drawing.

The terms "first," "second," and the like in the description and in the claims, and the above-described drawings of embodiments of the present disclosure, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It should be understood that the data so used may be interchanged under appropriate circumstances such that embodiments of the present disclosure described herein may be made. Furthermore, the terms "comprising" and "having," as well as any variations thereof, are intended to cover non-exclusive inclusions.

The term "plurality" means two or more unless otherwise specified.

In the embodiment of the present disclosure, the character "/" indicates that the preceding and following objects are in an or relationship. For example, A/B represents: a or B.

The term "and/or" is an associative relationship that describes objects, meaning that three relationships may exist. For example, a and/or B, represents: a or B, or A and B.

The embodiment of the disclosure provides a method, a device, a storage medium and a server for automatically controlling a cigarette making machine, so as to solve the technical problem of realizing the intellectualization of the cigarette making machine.

In some embodiments, as shown in figure 1, the present disclosure provides a method for automatically controlling a cigarette making machine, comprising:

s101, a server acquires current cigarette machine use behavior data of a user;

s102, the server predicts cigarette machine control parameters of the user by combining cigarette machine use details in the time set by the user and current cigarette machine use behavior data of the user;

s103, the server issues the predicted cigarette machine control parameters to the cigarette machine.

The smoke machine is a kitchen appliance for purifying the kitchen environment. The kitchen ventilator is arranged above a kitchen stove, can rapidly pump away waste burnt by the stove and oil smoke harmful to human bodies generated in the cooking process, and exhaust the oil smoke out of a room, and simultaneously condenses and collects the oil smoke, thereby reducing pollution, purifying air and having the safety guarantee effects of gas defense and explosion prevention.

This is disclosed obtains user's current cigarette machine and uses the action data through the camera at cigarette machine equipment side, uses the particulars to the cigarette machine in the user's settlement time according to big data again and combs, uses the action data combination with current cigarette machine, comes to predict user's cigarette machine control parameter, can combine the historical data of cigarette machine, realizes the intelligent prediction of cigarette machine operation, effectively realizes the intellectuality of cigarette machine.

For example, the current cigarette machine using behavior data of the user is to open the cigarette machine, prediction is carried out according to the cigarette machine using details in the time set by the user, the subsequent actions of the user can be predicted to be to open the illumination and the low-speed gear, at the moment, the control parameters of the illumination and the low-speed gear can be automatically output, the user does not need to manually carry out subsequent control, and therefore intelligent control of the cigarette machine is achieved.

In some embodiments, as shown in fig. 2, the usage specification of the cigarette machine within the user-set time is obtained by:

s201, a server acquires the use details of the cigarette making machine for multiple times within the time set by the user by using an Extract-Transform-Load (ETL) technology;

s202, the server combines the use details of the cigarette making machines for multiple times.

ETL is used to describe the process of extracting (extract), converting (transform), and loading (load) data from a source to a destination. The term ETL is more commonly used in data warehouses, but its objects are not limited to data warehouses.

In practical application, after the use details of the cigarette machines for multiple times in the time set by the user are processed by using a data warehouse technology, the use details of the cigarette machines for multiple times in the time set by the user can be combined. The time set by the user here may be a time predefined by a person skilled in the art, and may be a time set by the user within a week or a month, or other forms, and the application is not limited thereto.

The cigarette machine used for the combination was used as a list and was screened as follows. For example, in the two-time cigarette making machine use details, the time of closing the cigarette making machine using details last time is not more than 300s from the time of opening the cigarette making machine using details next time. And the actual use time of the cigarette machine is more than or equal to 60 seconds, so that the cigarette machine is an effective cigarette machine use detail. Such machines use the particulars to enable the merger. And performing prediction processing according to the merged cigarette machine usage particulars.

In practical application, in some embodiments, the cigarette machine usage details of a plurality of times within the user-set time are separated by no more than a first set time period, and the actual usage time period exceeds a second set time period. The cigarette machine may include the following information fields in the specification: identification, Media Access Control Address, time, on-off state, lighting state, wind speed gear and delay state. The on-off state, the lighting state, the wind speed gear and the time delay state are all running states of the cigarette machine equipment.

In some embodiments, the range hood control parameters include:

an illumination lamp on-state parameter; and/or

And (4) wind speed gear state parameters.

In some embodiments, the predicting the cigarette machine control parameters of the user by combining the cigarette machine use details in the user-set time and the current cigarette machine use behavior data of the user comprises:

and the server inputs the cigarette machine use details in the time set by the user and the current cigarette machine use behavior data of the user into a pre-trained first neural convolution network to obtain the prediction parameters of the lighting lamp turn-on state parameters.

In some embodiments, the predicting the cigarette machine control parameters of the user by combining the cigarette machine use details in the user-set time and the current cigarette machine use behavior data of the user comprises:

and the server inputs the cigarette machine use details in the time set by the user and the current cigarette machine use behavior data of the user into a pre-trained second neural convolution network to obtain the prediction parameters of the wind speed gear state parameters.

In some embodiments, if the habit of using the cigarette machine daily is to turn on the lighting after starting and then turn on the low-level air exhaust, when the automatic control function of the cigarette machine provided by the embodiment of the disclosure is used, the lighting function and the low-level air exhaust function are automatically started after the starting is detected. If the user adjusts the use habit within a period of time, for example, after the user starts the cigarette machine within a period of time, the high-grade air exhaust function is turned on, and the illumination function is not turned on, the prediction result is correspondingly updated in the subsequent prediction process, that is, after the behavior of starting the cigarette machine is detected, the high-grade air exhaust function is automatically turned on, and the illumination function is not turned on any more. Whether the lighting function is started or not is determined according to the prediction parameters of the lighting lamp starting state parameters, and the selection of the air exhaust gear is determined according to the prediction parameters of the wind speed gear state parameters. The prediction parameters of the lighting lamp starting state parameters can be determined through the first convolutional neural network, and the prediction parameters of the wind speed gear state parameters can be determined through the second convolutional neural network. The first convolutional neural network is obtained through training according to historical data of starting of the illuminating lamp, and the second convolutional neural network is obtained through training according to historical data of wind speed gears. When the habit of turning on the lighting lamp by the user is changed, a new first convolution neural network can be obtained by retraining the first convolution neural network, and in the subsequent prediction, the new first convolution neural network is used for prediction. When the use habit of the user on the wind speed gear is changed, the new second convolutional neural network can be obtained by retraining the second convolutional neural network, and in the subsequent prediction, the new second convolutional neural network is used for prediction.

In some implementations, as shown in fig. 3, the method provided by the embodiments of the present disclosure further includes:

s301, when two to-be-selected prediction results with the same possibility exist in the prediction results of the cigarette machine control parameters, the server determines the latest occurrence time of the two to-be-selected prediction results;

s302, the server obtains the prediction result to be selected, wherein the latest occurrence time of the prediction result is close to the current time.

In practical applications, if the probability of two candidate prediction results in the neural convolutional network is the same, one of the two results needs to be selected as the candidate prediction result to be output. In practical application, the selection can be performed according to the latest occurrence time of the two results, and if the latest occurrence time of the result a is 1 day before and the latest occurrence time of the result B is 2 hours before, the result B is taken as the candidate prediction result to be output. And if the latest occurrence time of the result A is 2 days before and the latest occurrence time of the result B is 1 week before, taking the result A as the candidate prediction result to be output.

Before the automatic control of the cigarette machine is carried out according to the method provided by the embodiment of the disclosure, the method can also detect whether the user opens the automatic control function of the cigarette machine, when the user does not open the automatic control function of the cigarette machine, the prompt whether the automatic control function of the cigarette machine is opened or not can be output, and after the prompt whether the automatic control function of the cigarette machine is opened or not is output, the response that the user confirms the opening of the automatic control function of the cigarette machine is received, the automatic control function of the cigarette machine is started.

In some embodiments, if the habit of using the cigarette machine daily is to turn on the lighting after starting and then turn on the low-level air exhaust, when the automatic control function of the cigarette machine provided by the embodiment of the disclosure is used, the lighting function and the low-level air exhaust function are automatically started after the starting is detected. If the user adjusts the use habit within a period of time, for example, after the user starts the cigarette machine within a period of time, the high-grade air exhaust function is turned on, and the illumination function is not turned on, the prediction result is correspondingly updated in the subsequent prediction process, that is, after the behavior of starting the cigarette machine is detected, the high-grade air exhaust function is automatically turned on, and the illumination function is not turned on any more. Whether the lighting function is started or not is determined according to the prediction parameters of the lighting lamp starting state parameters, and the selection of the air exhaust gear is determined according to the prediction parameters of the wind speed gear state parameters. The prediction parameters of the lighting lamp starting state parameters can be determined through the first convolutional neural network, and the prediction parameters of the wind speed gear state parameters can be determined through the second convolutional neural network. The first convolutional neural network is obtained through training according to historical data of starting of the illuminating lamp, and the second convolutional neural network is obtained through training according to historical data of wind speed gears. When the habit of turning on the lighting lamp by the user is changed, a new first convolution neural network can be obtained by retraining the first convolution neural network, and in the subsequent prediction, the new first convolution neural network is used for prediction. When the use habit of the user on the wind speed gear is changed, the new second convolutional neural network can be obtained by retraining the second convolutional neural network, and in the subsequent prediction, the new second convolutional neural network is used for prediction.

In some embodiments, as shown in figure 4, embodiments of the present disclosure provide an apparatus for automatically controlling a cigarette making machine, comprising:

the acquisition unit 401 is used for acquiring current cigarette machine use behavior data of a user;

the prediction unit 402 is used for predicting the cigarette machine control parameters of the user by combining the cigarette machine use details in the time set by the user and the current cigarette machine use behavior data of the user;

and the issuing unit 403 is used for issuing the predicted cigarette machine control parameters to the cigarette machine.

The smoke machine is a kitchen appliance for purifying the kitchen environment. The kitchen ventilator is arranged above a kitchen stove, can rapidly pump away waste burnt by the stove and oil smoke harmful to human bodies generated in the cooking process, and exhaust the oil smoke out of a room, and simultaneously condenses and collects the oil smoke, thereby reducing pollution, purifying air and having the safety guarantee effects of gas defense and explosion prevention.

This is disclosed obtains user's current cigarette machine and uses the action data through the camera at cigarette machine equipment side, uses the particulars to the cigarette machine in the user's settlement time according to big data again and combs, uses the action data combination with current cigarette machine, comes to predict user's cigarette machine control parameter, can combine the historical data of cigarette machine, realizes the intelligent prediction of cigarette machine operation, effectively realizes the intellectuality of cigarette machine.

For example, the current cigarette machine using behavior data of the user is to open the cigarette machine, prediction is carried out according to the cigarette machine using details in the time set by the user, the subsequent actions of the user can be predicted to be to open the illumination and the low-speed gear, at the moment, the control parameters of the illumination and the low-speed gear can be automatically output, the user does not need to manually carry out subsequent control, and therefore intelligent control of the cigarette machine is achieved.

In some embodiments, the apparatus further comprises a merging unit 404 configured to:

using a data warehouse technology ETL to obtain the use details of the cigarette making machines for multiple times within the time set by the user;

and merging the use details of the cigarette machines for multiple times.

ETL is used to describe the process of extracting (extract), converting (transform), and loading (load) data from a source to a destination. The term ETL is more commonly used in data warehouses, but its objects are not limited to data warehouses.

In practical application, after the use details of the cigarette machines for multiple times in the time set by the user are processed by using a data warehouse technology, the use details of the cigarette machines for multiple times in the time set by the user can be combined. The time set by the user here may be a time predefined by a person skilled in the art, and may be a time set by the user within a week or a month, or other forms, and the application is not limited thereto.

The cigarette machine used for the combination was used as a list and was screened as follows. For example, in the two-time cigarette making machine use details, the time of closing the cigarette making machine using details last time is not more than 300s from the time of opening the cigarette making machine using details next time. And the actual use time of the cigarette machine is more than or equal to 60 seconds, so that the cigarette machine is an effective cigarette machine use detail. Such machines use the particulars to enable the merger. And performing prediction processing according to the merged cigarette machine usage particulars.

In practical application, in some embodiments, the cigarette machine usage details of a plurality of times within the user-set time are separated by no more than a first set time period, and the actual usage time period exceeds a second set time period. The cigarette machine may include the following information fields in the specification: identification, Media Access Control Address, time, on-off state, lighting state, wind speed gear and delay state. The on-off state, the lighting state, the wind speed gear and the time delay state are all running states of the cigarette machine equipment.

In some embodiments, the user sets multiple cigarette machine usage details within a time, the interval between each usage detail does not exceed a first set time period, and the actual usage time period exceeds a second set time period.

In some embodiments, the range hood control parameters include:

an illumination lamp on-state parameter; and/or

And (4) wind speed gear state parameters.

In some embodiments, the prediction unit 402 is configured to:

and inputting the cigarette machine use details in the time set by the user and the current cigarette machine use behavior data of the user into a pre-trained first neural convolution network to obtain the prediction parameters of the lighting lamp turn-on state parameters.

In some embodiments, the prediction unit 402 is configured to:

and inputting the cigarette machine use details in the time set by the user and the current cigarette machine use behavior data of the user into a pre-trained second neural convolution network to obtain the prediction parameters of the wind speed gear state parameters.

In some embodiments, if the habit of using the cigarette machine daily is to turn on the lighting after starting and then turn on the low-level air exhaust, when the automatic control function of the cigarette machine provided by the embodiment of the disclosure is used, the lighting function and the low-level air exhaust function are automatically started after the starting is detected. If the user adjusts the use habit within a period of time, for example, after the user starts the cigarette machine within a period of time, the high-grade air exhaust function is turned on, and the illumination function is not turned on, the prediction result is correspondingly updated in the subsequent prediction process, that is, after the behavior of starting the cigarette machine is detected, the high-grade air exhaust function is automatically turned on, and the illumination function is not turned on any more. Whether the lighting function is started or not is determined according to the prediction parameters of the lighting lamp starting state parameters, and the selection of the air exhaust gear is determined according to the prediction parameters of the wind speed gear state parameters. The prediction parameters of the lighting lamp starting state parameters can be determined through the first convolutional neural network, and the prediction parameters of the wind speed gear state parameters can be determined through the second convolutional neural network. The first convolutional neural network is obtained through training according to historical data of starting of the illuminating lamp, and the second convolutional neural network is obtained through training according to historical data of wind speed gears. When the habit of turning on the lighting lamp by the user is changed, a new first convolution neural network can be obtained by retraining the first convolution neural network, and in the subsequent prediction, the new first convolution neural network is used for prediction. When the use habit of the user on the wind speed gear is changed, the new second convolutional neural network can be obtained by retraining the second convolutional neural network, and in the subsequent prediction, the new second convolutional neural network is used for prediction.

In some embodiments, the apparatus provided in the embodiments of the present disclosure further includes a selecting unit 405, configured to:

when two to-be-selected prediction results with the same possibility exist in the prediction results of the control parameters of the cigarette making machine, determining the latest occurrence time of the two to-be-selected prediction results;

and taking the prediction result to be selected, wherein the latest occurrence time is close to the current time.

In practical applications, if the probability of two candidate prediction results in the neural convolutional network is the same, one of the two results needs to be selected as the candidate prediction result to be output. In practical application, the selection can be performed according to the latest occurrence time of the two results, and if the latest occurrence time of the result a is 1 day before and the latest occurrence time of the result B is 2 hours before, the result B is taken as the candidate prediction result to be output. And if the latest occurrence time of the result A is 2 days before and the latest occurrence time of the result B is 1 week before, taking the result A as the candidate prediction result to be output.

Before the automatic control of the cigarette machine is carried out according to the method provided by the embodiment of the disclosure, the method can also detect whether the user opens the automatic control function of the cigarette machine, when the user does not open the automatic control function of the cigarette machine, the prompt whether the automatic control function of the cigarette machine is opened or not can be output, and after the prompt whether the automatic control function of the cigarette machine is opened or not is output, the response that the user confirms the opening of the automatic control function of the cigarette machine is received, the automatic control function of the cigarette machine is started.

In some embodiments, if the habit of using the cigarette machine daily is to turn on the lighting after starting and then turn on the low-level air exhaust, when the automatic control function of the cigarette machine provided by the embodiment of the disclosure is used, the lighting function and the low-level air exhaust function are automatically started after the starting is detected. If the user adjusts the use habit within a period of time, for example, after the user starts the cigarette machine within a period of time, the high-grade air exhaust function is turned on, and the illumination function is not turned on, the prediction result is correspondingly updated in the subsequent prediction process, that is, after the behavior of starting the cigarette machine is detected, the high-grade air exhaust function is automatically turned on, and the illumination function is not turned on any more. Whether the lighting function is started or not is determined according to the prediction parameters of the lighting lamp starting state parameters, and the selection of the air exhaust gear is determined according to the prediction parameters of the wind speed gear state parameters. The prediction parameters of the lighting lamp starting state parameters can be determined through the first convolutional neural network, and the prediction parameters of the wind speed gear state parameters can be determined through the second convolutional neural network. The first convolutional neural network is obtained through training according to historical data of starting of the illuminating lamp, and the second convolutional neural network is obtained through training according to historical data of wind speed gears. When the habit of turning on the lighting lamp by the user is changed, a new first convolution neural network can be obtained by retraining the first convolution neural network, and in the subsequent prediction, the new first convolution neural network is used for prediction. When the use habit of the user on the wind speed gear is changed, the new second convolutional neural network can be obtained by retraining the second convolutional neural network, and in the subsequent prediction, the new second convolutional neural network is used for prediction.

In some embodiments, the present disclosure provides a storage medium storing program instructions that, when executed, perform a method provided by an embodiment of the present disclosure.

In some embodiments, the present disclosure provides a server comprising a processor and a memory, the memory storing program instructions, the processor configured to execute a method provided by an embodiment of the present disclosure based on the program instructions. The method, the device, the storage medium and the server for automatically controlling the range hood provided by the embodiment of the disclosure can realize the following technical effects:

the cigarette machine control parameter prediction method based on the cigarette machine use details in the user set time can predict cigarette machine control parameters of the user by combining the current cigarette machine use behavior data of the user, can combine the historical use data of the cigarette machine, realizes intelligent control of the cigarette machine, and improves the intelligence of the cigarette machine. As shown in fig. 5, an embodiment of the present disclosure provides a server including a processor (processor)100 and a memory (memory) 101. Optionally, the apparatus may also include a Communication Interface (Communication Interface)102 and a bus 103. The processor 100, the communication interface 102, and the memory 101 may communicate with each other via a bus 103. The communication interface 102 may be used for information transfer. The processor 100 may call logic instructions in the memory 101 to perform the methods of the embodiments described above.

In addition, the logic instructions in the memory 101 may be implemented in the form of software functional units and stored in a computer readable storage medium when the logic instructions are sold or used as independent products.

The memory 101, which is a computer-readable storage medium, may be used for storing software programs, computer-executable programs, such as program instructions/modules corresponding to the methods in the embodiments of the present disclosure. The processor 100 executes functional applications and data processing, i.e. implements the methods in the above embodiments, by executing program instructions/modules stored in the memory 101.

The memory 101 may 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 terminal device, and the like. In addition, the memory 101 may include a high-speed random access memory, and may also include a nonvolatile memory.

The embodiment of the disclosure provides an article (for example, a computer, a mobile phone and the like) comprising the server.

Embodiments of the present disclosure provide a computer-readable storage medium storing computer-executable instructions configured to perform the above-described method.

The disclosed embodiments provide a computer program product comprising a computer program stored on a computer readable storage medium, the computer program comprising program instructions which, when executed by a computer, cause the computer to perform the above-mentioned method.

The computer-readable storage medium described above may be a transitory computer-readable storage medium or a non-transitory computer-readable storage medium.

The technical solution of the embodiments of the present disclosure may be embodied in the form of a software product, where the computer software product is stored in a storage medium and includes one or more instructions to enable a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method of the embodiments of the present disclosure. And the aforementioned storage medium may be a non-transitory storage medium comprising: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes, and may also be a transient storage medium.

The above description and drawings sufficiently illustrate embodiments of the disclosure to enable those skilled in the art to practice them. Other embodiments may incorporate structural, logical, electrical, process, and other changes. The examples merely typify possible variations. Individual components and functions are optional unless explicitly required, and the sequence of operations may vary. Portions and features of some embodiments may be included in or substituted for those of others. Furthermore, the words used in the specification are words of description only and are not intended to limit the claims. As used in the description of the embodiments and the claims, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. Similarly, the term "and/or" as used in this application is meant to encompass any and all possible combinations of one or more of the associated listed. Furthermore, the terms "comprises" and/or "comprising," when used in this application, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. Without further limitation, an element defined by the phrase "comprising an …" does not exclude the presence of other like elements in a process, method or apparatus that comprises the element. In this document, each embodiment may be described with emphasis on differences from other embodiments, and the same and similar parts between the respective embodiments may be referred to each other. For methods, products, etc. of the embodiment disclosures, reference may be made to the description of the method section for relevance if it corresponds to the method section of the embodiment disclosure.

Those of skill in the art would appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software may depend upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the disclosed embodiments. It can be clearly understood by the skilled person that, for convenience and brevity of description, the specific working processes of the system, the apparatus and the unit described above may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.

In the embodiments disclosed herein, the disclosed methods, products (including but not limited to devices, apparatuses, etc.) may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the units may be merely a logical division, and in actual implementation, there may be another division, for example, multiple units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form. The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to implement the present embodiment. In addition, functional units in the embodiments of the present disclosure may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit.

The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. In the description corresponding to the flowcharts and block diagrams in the figures, operations or steps corresponding to different blocks may also occur in different orders than disclosed in the description, and sometimes there is no specific order between the different operations or steps. For example, two sequential operations or steps may in fact be executed substantially concurrently, or they may sometimes be executed in the reverse order, depending upon the functionality involved. Each block of the block diagrams and/or flowchart illustrations, and combinations of blocks in the block diagrams and/or flowchart illustrations, can be implemented by special purpose hardware-based systems that perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.