CN110308661A

CN110308661A - Smart machine control method and device based on machine learning

Info

Publication number: CN110308661A
Application number: CN201910490889.9A
Authority: CN
Inventors: 樊思远
Original assignee: Midea Group Co Ltd
Current assignee: Midea Group Co Ltd
Priority date: 2019-06-06
Filing date: 2019-06-06
Publication date: 2019-10-08
Anticipated expiration: 2039-06-06
Also published as: CN110308661B

Abstract

The smart machine control method and device based on machine learning that the embodiment of the invention provides a kind of obtain the location information and current operating temporal information of mobile terminal if method includes: the application management program starting detected on mobile terminal；By location information and current operating temporal information input into usual smart machine identification model, the identification information of usual smart machine is exported；Application management program is controlled according to the identification information of usual smart machine, so that application management program shows the control interface for remotely operating usual smart machine.Mode of the embodiment of the present invention based on machine learning, start the control interface of corresponding smart machine automatically according to user's habit, the trouble for the smart machine to be controlled is found so as to save user from the multiple smart machines stored in application management program, so that the operation of user becomes easy, directly and quickly the smart machine to be controlled can be controlled, to improve user experience.

Description

Smart machine control method and device based on machine learning

Technical field

The present invention relates to field of intelligent control technology, and in particular to a kind of smart machine control method based on machine learning And device.

Background technique

With the development of technology of Internet of things, smart home device is also gaining popularity.Intelligence in user's office or family Home equipment also increases constantly, and the human-computer interaction interface that the APP of installation on mobile terminals can be used in user is remotely grasped Make each smart home device, to obtain more automations and intelligent scene control function.

Currently, the human-computer interaction interface of installation APP on mobile terminals usually present in the following manner it is to be controlled Smart home device: (1) all smart home devices of user are listed；(2) according to the classification of smart home device or online/ Off-line state, classification display smart home device.

For two kinds of presentation modes above-mentioned, have the following problems: when user need to some home equipment into When row control, the home equipment controlled needed for needing to recognize in entire smart home device list, this will cause user behaviour Make it is complex and cumbersome, it is bad so as to cause user experience.

Summary of the invention

For the problems of the prior art, the embodiment of the present invention provides a kind of smart machine controlling party based on machine learning Method and device.

In order to solve the above technical problems, the embodiment of the present invention the following technical schemes are provided:

In a first aspect, the embodiment of the invention provides a kind of smart machine control method based on machine learning, comprising:

If detecting the application management program starting on mobile terminal, obtains the location information of the mobile terminal and work as Preceding operating time information；

By the location information and the current operating temporal information input into usual smart machine identification model, output The identification information of usual smart machine；

The application management program is controlled according to the identification information of the usual smart machine, so that the application Management program shows the control information for remotely operating the usual smart machine；

Wherein, the usual smart machine identification model is according to historical operating data, based on machine learning algorithm training It obtains；The historical operating data includes that the current operating temporal foregoing description mobile terminal utilizes the application management journey Any smart machine of ordered pair completes location information where the mobile terminal locates when long-range control, operating time information and described The identification information of one smart machine.

Further, it is identified by the location information and the current operating temporal information input to usual smart machine In model, before the identification information for exporting usual smart machine, the smart machine control method based on machine learning is also wrapped It includes: establishing the usual smart machine identification model；

It is wherein, described to establish the usual smart machine identification model, comprising:

Detecting the mobile terminal using the application management program to the long-range control of any smart machine completion every time When processed, the mark letter of location information where the mobile terminal locates, operating time information and any smart machine is obtained Breath, the location information where the mobile terminal locates and operating time information that will acquire as sample input data, and, by institute The identification information of any smart machine is stated as sample output data, machine learning algorithm is based on, carries out model training, obtain institute State usual smart machine identification model.

Further, the smart machine control method based on machine learning, further includes:

During establishing the usual smart machine identification model, detecting the mobile terminal using institute every time When stating application management program to the long-range control of any smart machine completion, the operation content to any smart machine is also obtained Information；Correspondingly, the location information where the mobile terminal locates that will acquire and operating time information as sample input data, And using the identification information of any smart machine and the operation content information as sample output data, it is based on machine Learning algorithm carries out model training, obtains the usual smart machine identification model；

Correspondingly, described to identify the location information and the current operating temporal information input to usual smart machine In model, when exporting the identification information of usual smart machine, corresponding operation content information is also exported；

Correspondingly, the identification information according to the usual smart machine controls the application management program, When so that the application management program showing the control information for remotely operating the usual smart machine, also make the control It include the corresponding operation content information in information processed.

Further, the location information for obtaining the mobile terminal, specifically includes:

Obtain the wireless signal strength indicating RSSI value conduct for the default specified smart machine that the mobile terminal listens to The location information of the mobile terminal；Wherein, the default specified smart machine is fixed smart machine and quantity is greater than or waits In 2；

Correspondingly, described mobile whole detecting every time during establishing the usual smart machine identification model When long-range control is completed to any smart machine using the application management program in end, the institute that the mobile terminal listens to is obtained The RSSI value for stating default specified smart machine is monitored as location information where the mobile terminal locates, and by the mobile terminal The RSSI value and operating time information of the default specified smart machine arrived as sample input data, and, will be described The identification information of one smart machine is based on machine learning algorithm as sample output data, carries out model training, obtains described used With smart machine identification model.

The location information that the mobile terminal is presently in is obtained by the positioning software installed on the mobile terminal；

Or, obtaining the location information that the mobile terminal is presently in by global position system GPS locator；

Or, obtaining the position that the mobile terminal is presently in by the multiple pyroelectric infrared sensor nodes arranged in advance Confidence breath；

Or, obtaining the position that the mobile terminal is presently in by the image processing algorithm based on computer machine vision Information.

Second aspect, the embodiment of the invention also provides a kind of smart machine control device based on machine learning, comprising:

Module is obtained, if obtaining the mobile terminal for detecting that the application management program on mobile terminal starts Location information and current operating temporal information；

Processing module, for knowing the location information and the current operating temporal information input to usual smart machine In other model, the identification information of usual smart machine is exported；

Control module, for being controlled according to the identification information of the usual smart machine to the application management program System, so that the application management program shows the control information for remotely operating the usual smart machine；

Further, the smart machine control device based on machine learning, further includes: model construction module；

Wherein, the model construction module, is specifically used for:

Further, the model construction module is during establishing the usual smart machine identification model, every time When detecting that the mobile terminal completes long-range control to any smart machine using the application management program, also acquisition pair The operation content information of any smart machine；Correspondingly, the mobile terminal institute that the model construction module will acquire Location information and operating time information as sample input data, and, by the identification information of any smart machine With the operation content information as sample output data, it is based on machine learning algorithm, carries out model training, is obtained described usual Smart machine identification model；

Correspondingly, the processing module is by the location information and the current operating temporal information input to usual intelligence In energy equipment identification model, when exporting the identification information of usual smart machine, corresponding operation content information is also exported；

Correspondingly, the control module according to the identification information of the usual smart machine to the application management program It is controlled, when so that the application management program showing the control information for remotely operating the usual smart machine, also So that including the corresponding operation content information in the control information.

Further, the acquisition module, is specifically used for:

Correspondingly, the model construction module exists every time during establishing the usual smart machine identification model When detecting that the mobile terminal completes long-range control to any smart machine using the application management program, the shifting is obtained Described preset that dynamic terminal monitoring arrives specifies the RSSI value of smart machine as location information where the mobile terminal locates, and will The RSSI value and operating time information for the default specified smart machine that the mobile terminal listens to input number as sample According to, and, using the identification information of any smart machine as sample output data, it is based on machine learning algorithm, carries out mould Type training obtains the usual smart machine identification model.

Further, the acquisition module, is specifically used for:

The location information for obtaining the mobile terminal, specifically includes:

The third aspect the embodiment of the invention also provides a kind of electronic equipment, including memory, processor and is stored in On reservoir and the computer program that can run on a processor, the processor are realized when executing described program such as first aspect institute The step of stating the smart machine control method based on machine learning.

Fourth aspect, the embodiment of the invention also provides a kind of non-transient computer readable storage mediums, are stored thereon with Computer program is realized when the computer program is executed by processor as described in relation to the first aspect based on the smart machine of machine learning The step of control method.

As shown from the above technical solution, smart machine control method provided in an embodiment of the present invention based on machine learning and Device is based on machine learning mode, according to user's habit (where when region is in being accustomed to operating that intelligence is set It is standby) start the control interface of corresponding smart machine automatically, so as to save user stored from application management program it is multiple The trouble for the smart machine to be controlled is found in smart machine, so that the operation of user becomes easy, it can directly quickly Ground controls the smart machine to be controlled, to improve user experience.

Detailed description of the invention

In order to more clearly explain the embodiment of the invention or the technical proposal in the existing technology, to embodiment or will show below There is attached drawing needed in technical description to be briefly described, it should be apparent that, the accompanying drawings in the following description is the present invention Some embodiments for those of ordinary skill in the art without creative efforts, can also basis These attached drawings obtain other attached drawings.

Fig. 1 is the flow chart for the smart machine control method based on machine learning that one embodiment of the invention provides；

Fig. 2 is the schematic diagram for the smart machine in predeterminated position habit operation that one embodiment of the invention provides；

Fig. 3 is a kind of schematic diagram for control interface that one embodiment of the invention provides；

Fig. 4 is the schematic diagram for another control interface that one embodiment of the invention provides；

Fig. 5 is the schematic diagram for another control interface that one embodiment of the invention provides；

Fig. 6 is the CNN model schematic that one embodiment of the invention provides；

Fig. 7 is the structural schematic diagram for the smart machine control device based on machine learning that one embodiment of the invention provides；

Fig. 8 is the structural schematic diagram for the electronic equipment that one embodiment of the invention provides.

Specific embodiment

In order to make the object, technical scheme and advantages of the embodiment of the invention clearer, below in conjunction with the embodiment of the present invention In attached drawing, the technical scheme in the embodiment of the invention is clearly and completely described, it is clear that described embodiment is A part of the embodiment of the present invention, instead of all the embodiments.Based on the embodiments of the present invention, those of ordinary skill in the art Every other embodiment obtained without creative efforts, shall fall within the protection scope of the present invention.

The present inventor after research alone and based on the big data analysis to user operation habits data statistics, use by discovery Family, which controls the location of specific intelligent appliance and user and time using mobile terminal remote, has certain correlation.Example Such as, certain user group is controlled in the sofa position in parlor between workaday 21 points to 22 points or before smart television with mobile phone When intelligent appliance, there is biggish probability operation for controlling smart television, and at 23 points to 1 point of next day near the bed in bedroom When, then there is biggish probability operation for controlling intelligent air condition.

Based on the above cognition, this application provides the smart machine control methods based on machine learning, by believing position Breath and current operating temporal information, using machine learning algorithm well known to neural network etc., can train to obtain as input data The high-precision user that user's specific intelligence equipment to be controlled (or specific operation function) can be exported is accustomed to prediction model (namely the subsequent described usual smart machine identification model), to be accustomed to automatic obtain to corresponding smart machine according to user Control.

Based on the above analysis, specific embodiment will be passed through below to the smart machine provided by the present application based on machine learning Explanation is explained in detail in the working principle and the course of work of control method.

Fig. 1 shows the flow chart of the smart machine control method based on machine learning of one embodiment of the invention offer, Referring to Fig. 1, the smart machine control method provided in an embodiment of the present invention based on machine learning, comprising:

Step 101: if detecting the application management program starting on mobile terminal, obtaining the position of the mobile terminal Information and current operating temporal information.

In the present embodiment, the location information can be absolute location information (such as GPS geographical position coordinates), can also be with (movement end such as can be determined according to the range information of mobile terminal and indoor several default fixed points for relative position information The relative position information of end indoors).

In the present embodiment, the current operating temporal information, which refers to, is detecting the application management program on mobile terminal Corresponding temporal information when starting.The temporal information can refer to work Time of Day and time at weekend, can also refer to the noon It with, can also refer to any time period in one day time in the evening, can also refer to other while include date information and clock information Temporal information, can also be the temporal information of other various forms, the present embodiment is not construed as limiting this.

In the present embodiment, the application management program refers to the APP for being managed control to smart machine, these APP is typically mounted on mobile terminal, is remotely operated corresponding smart machine for user and is used.

In the present embodiment, the step of location information for obtaining the mobile terminal can detect mobile terminal On application management program starting when execute automatically, can also detect on mobile terminal application management program starting when by User triggers execution manually.

In the present embodiment, the mobile terminal can refer to smart phone, intelligent remote controller etc..The smart machine can be with Refer to various household appliances or office equipment, such as refrigerator, TV, air-conditioning, washing machine.

Step 102: the location information and the current operating temporal information input to usual smart machine are identified into mould In type, the identification information of usual smart machine is exported.

In this step, the usual smart machine identification model is according to current operating temporal foregoing description mobile terminal Location information where the mobile terminal locates, behaviour when completing long-range control to any smart machine using the application management program Make temporal information and the identification information of any smart machine these historical operating datas, trained based on machine learning algorithm It arrives.Therefore, after step 101 gets location information and current operating temporal information, by the location information and described work as Preceding operating time information is input in usual smart machine identification model as input data, is set so as to export usual intelligence Standby identification information.

Step 103: the application management program is controlled according to the identification information of the usual smart machine, so that The application management program shows the control information for remotely operating the usual smart machine.

In the present embodiment, identification information (the also referred to as identification of smart machine of the smart machine is obtained in step 102 Code) after, the identification information of the smart machine obtained according to step 102 controls the application management program, so that described Application management program shows the control information for remotely operating the smart machine, and controlling information here can be for for long-range It operates the control button of the smart machine, may be the control instruction for remotely operating the smart machine, it can be with For the control interface etc. comprising control button or control instruction.

For example, referring to fig. 2, it is assumed that find that user's habit controls sky in the noon in position A1 according to historical data 1 is adjusted, and is accustomed to controlling TV in position A1 the time in the evening, then according to these historical operating datas, is based on machine learning algorithm It can train to obtain usual smart machine identification model.If being obtained when detecting the application management program starting on mobile terminal The location information of the mobile terminal taken is position A1 and the current operating time (time of starting application management program) is noon It is time, then the location of mobile terminal information (position A1) and current operating temporal information (noon such as 12:30) is defeated Enter into the usual smart machine identification model, corresponding usual intelligence is then exported by the usual smart machine identification model The identification information of energy equipment (air-conditioning 1), the identification information for being then based on air-conditioning 1 control the application management program, make The control information that shows of the application management program as shown in figure 3, its Fig. 3 and subsequent Fig. 4 and Fig. 5 are with control interface For be illustrated.Referring to Fig. 3 it is found that shown on the application management program in the position with current operating temporal user It is accustomed to the control interface of the smart machine (air-conditioning 1) of operation, consequently facilitating user controls air-conditioning 1 accordingly.Here, in It can specifically be set between the period of the day from 11 a.m. to 1 p.m with, the general noon refers to 12. -14 points, and refer to 18. -22 the time in the evening the time in the evening Point.

For another example, it is assumed that find that user's habit carries out air-conditioning 2 in position A3 in summer the time in the evening according to historical data Control, therefore, if the location information of the mobile terminal of acquisition is when detecting the application management program starting on mobile terminal Position A3 and current operating time are in summer time in the evening, then by the location of mobile terminal information (position A3) and work as Preceding operating time information 20 points of June 22 (in summer such as) is input to the usual smart machine identification model time in the evening In, the identification information of corresponding usual smart machine (air-conditioning 2) is then exported by the usual smart machine identification model, then The application management program is controlled, so that the control interface that the application management program is shown is as shown in Figure 4.Referring to figure 4 it is found that show the smart machine (sky for being accustomed to operation with current operating temporal user in the position on the application management program Adjust 2) control interface (it is shown in the control interface various for controlling the virtual keys of air-conditioning 2, such as can choose mode, Set temperature, control power supply opening and closing etc.), consequently facilitating user controls air-conditioning 2 accordingly.

In addition, it should be noted that, being not limited to Fig. 3 or Fig. 4 in the control interface that the application management program is shown Shown in only comprising a kind of or a smart machine control interface, can also for it is as shown in Figure 5 include multiple smart machines Control interface.For example, it is assumed that user is accustomed in summer in position A1 while operating air conditioner 1 and TV the time in the evening, then According to these historical operating datas, can train to obtain usual smart machine identification model based on machine learning algorithm.If examining When measuring the application management program starting on mobile terminal, the location information of the mobile terminal of acquisition is position A1 and current behaviour Make the time is summer time in the evening, then can believe the location of mobile terminal information (position A1) and current operating temporal Breath () is input in the usual smart machine identification model time in the evening, then defeated by the usual smart machine identification model The identification information of corresponding usual smart machine (air-conditioning 1 and TV) out, is then based on the identification information of air-conditioning 1 and TV to institute Application management program is stated to be controlled so that the control interface that the application management program is shown as shown in figure 5, consequently facilitating with Family controls air-conditioning 1 and TV accordingly.It should be noted that the quantity of smart machine is not due in general family Less than 10, therefore, using this control method provided in this embodiment, user can be saved and stored from application management program Multiple smart machines in find the smart machine to be controlled trouble so that user can directly be seen that the intelligence to be controlled is set Standby control interface, thus improve user experience.

In the present embodiment, it should be noted that usual smart machine identification model recited above is according to current behaviour (user's history remotely controls smart machine by the application management program historical operating data before making the time Data) generated based on machine learning algorithm training.Wherein, it is set based on the machine learning algorithm training generation usual intelligence When standby identification model, generally by mobile terminal using application management program to smart machine progress history control when where position Information and corresponding operating time information make the identification information of the corresponding smart machine controlled as sample input data For sample output data, initial machine learning model is trained after meeting the model condition of convergence, is generated described usual Smart machine identification model.In the present embodiment, model training can be carried out using CNN or RNN machine learning model.In addition, It should be noted that generating the usual smart machine identification mould being based on machine learning algorithm training according to historical operating data When type, the historical operating data of use is all newest historical operating data, for example, it may be nearest one week, nearest one month, Nearest three months or the nearest half a year closer historical operating data of equidistant current operating temporal, in order to improve described used With the recognition accuracy of smart machine identification model, it can relatively accurately identify user in corresponding position and operation Time most thinks the smart machine of control.

It should be noted that the present embodiment according to user habit start the control interface of corresponding smart machine automatically when, Not only consider user where region habit operate what smart machine, and also contemplate user where area When domain is in being accustomed to that smart machine operated, to automatically and accurately provide user's intelligence currently to be controlled for user The control interface of energy equipment, thus eliminate user and search from the more smart machine stored in application management program in the position It sets and in the trouble of the common smart machine of current time, to improve user experience.

In addition, it should be noted that, the smart machine control method based on machine learning provided by the present embodiment may be used also With for user where region habit operate what smart machine control, namely relative to being recited above in Hold, when to the usual smart machine identification model input data, need to only input the location information where mobile terminal, Without inputting current operating temporal information.Correspondingly, when being trained to the usual smart machine identification model, sample number It also no longer needs in comprising this content of temporal information.For example, referring to fig. 2, it is assumed that user is within the past time Get used to always controlling air-conditioning 1 and TV in position A1, then it, can be with based on machine learning algorithm according to these historical operating datas Training obtains usual smart machine identification model, then when detecting that mobile terminal is currently located at position A1, then can will move The location of dynamic terminal information A1 is input in the usual smart machine identification model, then by the usual smart machine Identification model exports the identification information of corresponding usual smart machine (air-conditioning 1 and TV), is then based on the mark of air-conditioning 1 and TV Know information to control the application management program, so that comprising useful in the control interface that the application management program is shown The smart machine air-conditioning 1 of operation and the control interface of TV are accustomed in family in the position, consequently facilitating user to air-conditioning 1 and TV into The corresponding control of row.

In the present embodiment, it should be noted that the training data used when being trained based on machine learning algorithm The source of collection can there are many modes, the present embodiment to be not construed as limiting to this, such as may is that A: coming solely from the use gathered in advance The historical operating data at family；B: coming solely from default training dataset, which is inventor according to adopting in advance Collection multiple other users use habit data and generate, for example, can according to the user purchase when register age, Gender, the user correspond to the register informations such as the household electrical appliances type that family is bought to determine matched training dataset for the user； C: on the basis of default training dataset, the operating habit data set that further training obtains of the user is utilized.

As shown from the above technical solution, smart machine control method provided in an embodiment of the present invention, based on machine learning Mode starts corresponding intelligence according to user's habit (where when region is in being accustomed to that smart machine operated) automatically Can equipment control interface, found from the multiple smart machines stored in application management program so as to saving user and be intended to control The trouble of the smart machine of system can directly and quickly set the intelligence to be controlled so that the operation of user becomes easy It is standby to be controlled, to improve user experience.

It should be noted that smart machine control method provided in this embodiment can be by mobile terminal execution, it can also be with It is executed, can also be executed jointly in a manner of information exchange by mobile terminal and server by server.

For example, in one implementation, smart machine control method provided in this embodiment can be held by mobile terminal Row.In this implementation, the training of the usual smart machine identification model is completed by mobile terminal.Meanwhile it is mobile Terminal obtains the position letter of mobile terminal itself when detecting thereon for controlling the application management program starting of smart machine Breath and current operating time information, then by the location information and the current operating temporal information input to described usual In smart machine identification model, and obtain from the output end of the usual smart machine identification model mark of corresponding smart machine Information is known, to control according to the identification information of the smart machine the application management program, so that the application Management program shows the control interface or control command for remotely operating the smart machine, consequently facilitating using the movement The user of terminal sees the control interface (or control command) for being accustomed to the smart machine of operation in the position at the first time, thus just It is controlled in corresponding smart machine, thus eliminates and searched from the more smart machine stored in application management program The trouble of smart machine is commonly used in the position.Furthermore, it is necessary to specified otherwise, smart machine controlling party provided in this embodiment Method is different from some show on mobile terminal application management program according to location information of mobile terminal and is located near mobile terminal Or the control method of the smart machine information list of surrounding, because the central inventive thought of the present embodiment is to be accustomed to according to user Start the control interface of corresponding smart machine for user, rather than is to be provided near the position according to the position of user for user Smart machine control interface.Finally, it is emphasized that the present embodiment is intended to embody according to user's habit (in what position Region is set in when being accustomed to that smart machine operated) start this core of the control interface of corresponding smart machine hair automatically Bright thought.

For another example, in one implementation, smart machine control method provided in this embodiment can be executed by server. In this implementation, the training of the usual smart machine identification model is completed by server.Correspondingly, server is logical Cross certain way detect on mobile terminal application management program starting when, obtain the mobile terminal location information and Then current operating temporal information sets the location information and the current operating temporal information input to the usual intelligence In standby identification model, and believe from the mark that the output end of the usual smart machine identification model obtains corresponding smart machine Then breath passes through the application management program of certain way on mobile terminals to installation according to the identification information of the smart machine It is controlled, so that the application management program shows control interface or control life for remotely operating the smart machine It enables.

It should be noted that in this implementation, server can be detected in several ways and is used on mobile terminal Whether the application management program of control smart machine starts, for example, such processing logic can be preset: in advance described Monitoring software is installed, which can be communicated with the server, when the monitoring software monitors on mobile terminal When on to mobile terminal for controlling the application management program starting of smart machine, prompt information just is sent to the server. It wherein, further include the current location information and current operating temporal of the mobile terminal for thering is monitoring software to obtain in the prompt information Information.For another example, it can also realize by other means, logic is handled as can preset: when mobile terminal detects When on to mobile terminal for controlling the application management program starting of smart machine, Xiang Suoshu server sends prompt information.Its In, it further include the current location information and current operating temporal information by acquisition for mobile terminal in the prompt information.

Similarly, server obtains the intelligence in the output end of the usual smart machine identification model from server local After the identification information of equipment, the application management program of installation on mobile terminals can also be controlled by the monitoring software System, so that the application management program shows the control interface or control command for remotely operating the smart machine.Alternatively, The identification information of the smart machine can be sent to mobile whole by server after the identification information for obtaining the smart machine End, controls the application management program by mobile terminal, so that the application management program is shown for remotely operating The control interface or control command of the smart machine.

In this implementation, similarly, server generates the usual intelligence in the mode training based on machine learning When equipment identification model, the sample data from acquisition for mobile terminal training is needed, when the sample data includes: history Between location information of middle user when remotely being controlled by the application management program smart machine, operating time information with And the identification information of the corresponding smart machine operated.Server when from these sample datas of the acquisition for mobile terminal, Monitoring software the relevant technologies recited above can also be used.For example, monitoring answering on the mobile terminal using monitoring software Whether remote control operation is completed to smart machine with management program, and is monitoring the application management journey on the mobile terminal Ordered pair smart machine complete remote control operation when, obtain this time remotely control corresponding location information, operating time information with And the identification information of the smart machine operated, and these information that will acquire are sent to server, so that server can To complete the training process of the usual smart machine identification model in server local.

For another example, in one implementation, smart machine control method provided in this embodiment can by mobile terminal and Server is executed jointly in a manner of information exchange.In this implementation, the training of the usual smart machine identification model Work is completed by server.Correspondingly, it is detected by mobile terminal for controlling whether the application management program of smart machine starts, And when detecting starting, the location information and current operating temporal information of mobile terminal itself are obtained, then by the position Information and the current operating temporal information are sent to server, so that server is by the location information and the current operation Temporal information is input in the usual smart machine identification model, then by the usual smart machine identification model output pair The identification information for the smart machine answered, server is after the identification information for obtaining the smart machine, by the smart machine Identification information is sent to mobile terminal, by mobile terminal according to the identification information of the smart machine to the application management program Controlled so that the application management program show for remotely operating the smart machine control interface (or control life It enables).

Further, content based on the above embodiment, in the present embodiment, the step 101 or step 102 it Before, the smart machine control method based on machine learning, further includes:

Step 100: establishing the usual smart machine identification model.

It is in this step, described to establish the usual smart machine identification model, comprising:

In the present embodiment, it should be noted that the usual smart machine identification model is the mistake of a dynamic training Journey and the usual smart machine identification model are not unalterable, but according to constantly occurring to smart machine The long-range controlling behavior location information, operating time information and the corresponding smart machine that are included identification information, be based on Machine learning algorithm obtains after constantly training to model progress.It is also the usual smart machine identification model according to recently User's operation behavior can constantly update so that improving the recognition accuracy of the usual smart machine identification model. In the present embodiment, to keep the usual smart machine identification model that can be continuously updated, when detecting the shifting every time When dynamic terminal completes long-range control to any smart machine using the application management program, it is required to obtain the mobile terminal The identification information of the location information at place, operating time information and any smart machine, and the movement that will acquire Location information and operating time information where terminal as sample input data, and, by the mark of any smart machine Information is known as sample output data, is then based on machine learning algorithm, is carried out model training, obtains the usual smart machine Identification model.

For example, with reference to Fig. 2, it is assumed that in 1-3 month, user gets used to mobile terminal and carries out remotely in the position A1 to TV Control, but found since April, user's mobile terminal accustomed to using remotely controls TV in the position A2 (may It is that the owner of family goes on business, that carry out family lodging is other friends, and friend habit remotely controls TV in position A2 System), therefore, according to the operation behavior several times of the friend, so that it may be based on machine learning algorithm, carry out constantly training more to model Newly, so that the recognition accuracy of usual smart machine identification model improves, gradually meet the habit of active user.

In the present embodiment, when carrying out model training by way of machine learning, CNN or RNN model can be used. It is illustrated by taking CNN model as an example below with reference to Fig. 6, it should be noted that Fig. 6 is a schematic model, wherein only simple Two convolutional layers and two pond layers are illustrated, in practical applications, the number of convolutional layer and pond layer is generally greater than 2 It is a.Specifically, the structure of CNN model specifically includes that an input layer, n convolutional layer, n pond layer, m full articulamentums, one A output layer；Wherein, the input of the input layer is defeated for the sample of the location information comprising mobile terminal and operating time information Enter data, input layer is connected with convolutional layer C1；The convolutional layer C1 contains the convolution kernel that k1 size is a1 × a1, described defeated The sample input data for entering layer obtains k1 characteristic pattern by convolutional layer C1, and then obtained characteristic pattern is sent to pond layer P1；The pond layer P1 carries out pond to the characteristic pattern that the convolutional layer C1 is generated with the sample size of b1 × b1, obtains corresponding K1 sampling after characteristic pattern, then obtained characteristic pattern is sent to next convolutional layer C2；The n convolutional layer and pond Layer constantly extracts the Sampling characters of sample input data profound level to being sequentially connected with, the last one pond layer Pn with entirely Articulamentum F1 is connected, wherein convolutional layer Ci contains the convolution kernel that ki size is ai × ai, and the sample size of pond layer Pj is Bj × bj, Ci indicate that i-th of convolutional layer, Pj indicate j-th of pond layer；The full articulamentum F1 is the last one described pond layer One-dimensional layer made of the pixel mapping of the resulting all kn characteristic patterns of Pn, each pixel represent the one of the full articulamentum F1 A neuron node, F1 layers of all neuron nodes are connect entirely with the neuron node of next full articulamentum F2；Through m A full articulamentum is sequentially connected with, the last one full articulamentum Fm is connect entirely with the output layer；The output layer output packet The sample output data of identification information containing usual smart machine.In the present embodiment, believed using the position comprising mobile terminal The sample input data of breath and operating time information, and the sample output data of the identification information comprising usual smart machine, base In machine learning algorithm, above-mentioned CNN model is trained until above-mentioned CNN model is restrained, and then is obtained described usual Smart machine identification model.

For example, when carrying out model training based on machine learning algorithm, it is assumed that by the note to historical operating data Record, the sample data for training pattern got are as shown in table 1 below.

Table 1

For upper table 1, it is assumed that position Ax is the sofa in family, first time period 12:00-14:00, described the Two periods were 19:00-22:00, and smart machine a is air-conditioning, smart machine b is washing machine, and smart machine c is TV, because Whatsoever the time on the sofa, opens air-conditioning when being all accustomed to opening TV, and there was only hotter at noon to user, And it can just be taken using laundry washer when only at night.As it can be seen that using above-mentioned sample data to model training after, can Allow to the obtained usual smart machine identification model of training and the recognition result for more matching user demand is provided, and then can be with The control interface of more matching user habit is provided for user, so as to improve user experience.In addition, it should be noted that, Upper table 1 is intended merely to facilitate one of citing to illustrate, and data volume will be far longer than institute in above-mentioned table 1 when reality carries out sample training The content shown.

Further, content based on the above embodiment, in the present embodiment, the smart machine based on machine learning Control method further include:

It should be noted that the present embodiment is on the basis of above-described embodiment, to increase in the corresponding operation of display Hold this content of information, so that the content that control interface is shown more matches the demand of user, to further save Operating time of user, and then improve user experience.

As an example it is assumed that user's habit sees the program in TV in noon (first time period) habit in position Ax 1 (such as midday news), and be accustomed to seeing program 2 (such as football match) in (second time period) habit in position Ax the time in the evening.Cause This, when user is currently located at position Ax, and current time be at night, when starting application management program, the application management program Not only show the control interface for remotely operating TV, be also used to show the triggering to program 2 or control button, in order to Family fast implements the starting to program 2.

In the present embodiment, the operation content information can be control model, the operating mode of smart machine, work frequency The information such as road, operational detail, the present embodiment are not construed as limiting this.For example, can be series channel, electricity for TV The channel informations such as shadow channel, music channel, or specific programme information.In addition, for refrigerator, in the operation Holding to be the operating mode of refrigerator, as battery saving mode (being relatively specific for working day) and normal mode (are relatively specific for week End).In addition, the operation content can be refrigeration mode, heating mode, the dehumidification mode etc. of air-conditioning for air-conditioning, into One step can also be the specific set temperature etc. under refrigeration mode.For the concrete meaning of the operation content, the present embodiment No longer illustrate one by one.

In the present embodiment, when carrying out model training based on machine learning algorithm, it is assumed that by historical operating data Record, the sample data for training pattern got is as shown in table 2 below.

Table 2

For upper table 2, it is assumed that position Ax is the sofa in family, first time period 12:00-14:00, described the Two periods were 19:00-22:00, and smart machine a is TV, because time (first time period) habit sees section to user at noon Mesh 1 (such as midday news), and time (second time period) habit sees program 2 (such as football match) at night, it is seen then that using above-mentioned After sample data is to model training, enables to the usual smart machine identification model that can be trained to provide more matching and use The recognition result of family demand, and then the control interface of more matching user habit can be provided for user, so as to improve use Family experience.In addition, it should be noted that, upper table 2 is intended merely to facilitate one of citing to illustrate, it is practical to carry out number when sample training To be far longer than content shown in above-mentioned table 2 according to amount and data content.

Further, content based on the above embodiment, it is described to obtain the movement eventually in a kind of optional embodiment The location information at end, specifically includes:

The location information that the mobile terminal is presently in is obtained by the positioning software installed on the mobile terminal.

In the present embodiment, the location information that the mobile terminal is presently in can be by pacifying on the mobile terminal The positioning software of dress directly acquires.It should be noted that be generally in room by the location information obtained in this present embodiment or The more specific location information of Office Area, therefore it is required that the positioning accuracy of positioning software wants sufficiently high, can distinguish in room or do Different zones position is minimum requirements in public area.For example, being subject to the rooms of 90 square meters, the positioning accuracy of positioning software needs full Foot can distinguish parlor position and bedroom position is minimum requirements.

In addition, the location information that the mobile terminal is presently in can also be by global position system GPS (Global Positioning System) locator acquisition.In addition, the location information that the mobile terminal is presently in can also pass through people Body infrared sensor (pyroelectric infrared sensor) obtains.Further, it is also possible to pass through computer machine visual correlation technology (such as base In the image processing algorithm of computer machine vision) it is positioned, since the contents of the section can be using current comparative maturity Location technology, therefore I will not elaborate.It should be noted that the positioning result obtained in this way is typically all one straight Connect positioning result, namely what the location information obtained referred to is exactly absolute location information that the mobile terminal is presently in.

Further, content based on the above embodiment, it is described to obtain the movement in another optional embodiment The location information of terminal, specifically includes:

In the present embodiment, when obtaining the location information of the mobile terminal, there is no obtained using positioning software The absolute position of mobile terminal, but specify smart machine as location reference point using presetting in room, then according to movement Terminal monitoring to the wireless signal strength indicating RSSI value of the default specified smart machine determine the position of the mobile terminal It sets.

In the present embodiment, the default specified smart machine is fixed smart machine and quantity is greater than or equal to 2, it It requires that quantity is greater than or equal to 2 is to guarantee positioning accuracy.For example, 3 default specified smart machines can be set, by It is more accurate in three-point fix, therefore pass through the setting of 3 default specified smart machines, it is ensured that positioning accuracy.

It is set it should be noted that being provided with multiple intelligence with wireless communication function in general user family or in office Standby (smart home device, intelligent appliance), the smart machine including fixed position and non-fixed position smart machine.Its In, after the smart machine of fixed position refers to that air-conditioning, refrigerator etc. one is set, usually will not shift position easily intelligent family Electricity is suitable as the reference point of positioning mobile terminal.Correspondingly, the small intelligents equipment such as intelligent sound box is due to when in use Setting position may be often replaced, therefore is not suitable for as a reference point.For example, the default specified smart machine can be with For refrigerator, television set, air-conditioning 1, air-conditioning 2, air-conditioning 3 and washing machine in Fig. 2, these are fixed into the smart machine of position as described in The wireless signal strength of default specified smart machine, the default specified smart machine that can be listened to according to mobile terminal refers to Show that RSSI value accurately determines the position of the mobile terminal.Here, the position of the mobile terminal can be understood as the position of user It sets.

It should be noted that the wireless signal strength indicating RSSI value mentioned in the present embodiment can be WiFi signal intensity Indicating RSSI value may be bluetooth signal intensity indicating RSSI value.

In addition, in the present embodiment, the monitoring of RSSI value can also be used in combination with infrared sensor of the human body, for example, Can be after infrared sensor of the human body detect human body, then RSSI value is obtained, to reduce power consumption and operand.

It should be noted that mobile terminal can be in promiscuous mode when preset monitored specifies the RSSI value of smart machine Under, read the data packet of each smart machine.Alternatively, all smart machines under the user account is ordered to be broadcasted for measuring RSSI The beacon frame of value, to obtain the RSSI value of default specified smart machine.Wherein, the default specified smart machine either By user's manual setting, it is also possible to what mobile terminal was identified by the identification code of smart machine.

For example, user utilizes smart phone (or intelligent remote controller) to start intelligent device management APP in the room When, after APP logins user account, smart phone monitors (such as promiscuous mode) fixed intelligent appliance-ice by WiFi module Beacon frame that case, television set, air-conditioning 1, air-conditioning 2, air-conditioning 3, washing machine are issued (standard beacon frame or with beacon function Customized normal data frame), acquire the RSSI value of smart phone position.It should be noted that if smart phone is logical The RSSI value that bluetooth module monitors each intelligent appliance is crossed, then can not influence the normal use of mobile phone WiFi network.

It should be noted that although present embodiment has references to RSSI Indoor Position Techniques Based on Location Fingerprint, but the present embodiment without Offline sample phase and real-time positioning stage need to be divided into, each operation of user is all recorded in foundation of the database as positioning (fingerprint location method must be sampled offline in advance, and only record RSSI value in offline sample phase, in real-time positioning stage RSSI value be only used for positioning, which can not be recorded in database), significantly reduce the learning cost of user.This Outside, present embodiment is without location coordinate and access point layout is established, without the positioning accurate of fingerprint location technology requirement Degree, therefore reduce operand and required precision.

In addition, in a preferred embodiment, it, can also basis before the RSSI value of acquisition mobile terminal position The SSID or network segment of the current GPS location of mobile terminal or the Wi-Fi hotspot of connection carry out the group of smart machine Filtering (for example, the intelligent appliance for being placed on office is group, office, the intelligent appliance for being placed on family is group, family), So that leaving the smart machine group being adapted to current scene in APP.For example, filtering out and doing when mobile terminal is located at house Gong Shi group filters out group, family when mobile terminal is located at office.The advantages of this processing, is: facilitating user manual The default specified smart machine is set.In addition, the default specified smart machine can be the intelligence bound with user account Equipment, or the smart machine that do not bound with user account.

Fig. 7 shows the structural representation of the smart machine control device based on machine learning of one embodiment of the invention offer Figure, referring to Fig. 7, the smart machine control device provided in an embodiment of the present invention based on machine learning, comprising: acquisition module 21, Processing module 22 and control module 23, in which:

Module 21 is obtained, if obtaining described mobile whole for detecting that the application management program on mobile terminal starts The location information and current operating temporal information at end；

Processing module 22, for by the location information and the current operating temporal information input to usual smart machine In identification model, the identification information of usual smart machine is exported；

Control module 23, for being controlled according to the identification information of the usual smart machine to the application management program System, so that the application management program shows the control information for remotely operating the usual smart machine；

Further, content based on the above embodiment, in the present embodiment, the smart machine based on machine learning Control device, further includes: model construction module；

Wherein, the model construction module, is specifically used for:

Further, content based on the above embodiment, in the present embodiment, the model construction module is described in the foundation During usual smart machine identification model, every time detect the mobile terminal using the application management program to appoint When one smart machine completes long-range control, the operation content information to any smart machine is also obtained；Correspondingly, the mould The type location information where the mobile terminal locates that will acquire of building module and operating time information as sample input data, with And using the identification information of any smart machine and the operation content information as sample output data, it is based on engineering Algorithm is practised, model training is carried out, obtains the usual smart machine identification model；

Further, content based on the above embodiment, in a kind of optional embodiment, the acquisition module, specifically For:

Further, content based on the above embodiment, in another optional embodiment, the acquisition module, tool Body is used for:

Since the smart machine control device provided in this embodiment based on machine learning can be used for executing above-mentioned second Smart machine control method described in a embodiment based on machine learning, working principle is similar with beneficial effect, therefore herein It is no longer described in detail, particular content can be found in the introduction of above-described embodiment.

Based on identical inventive concept, further embodiment of this invention provides a kind of electronic equipment, referring to Fig. 8, the electricity Sub- equipment specifically includes following content: processor 601, memory 602, communication interface 603 and communication bus 604；

Wherein, the processor 601, memory 602, communication interface 603 are completed each other by the communication bus 604 Communication；The communication interface 603 is for realizing between the relevant devices such as each modeling software and intelligent manufacturing equipment module library Information transmission；

The processor 601 is used to call the computer program in the memory 602, and the processor executes the meter The Overall Steps that the above-mentioned smart machine control method based on machine learning is realized when calculation machine program, for example, the processor is held Following step is realized when the row computer program: if detecting the application management program starting on mobile terminal, obtaining institute State the location information and current operating temporal information of mobile terminal；The location information and the current operating temporal information is defeated Enter into usual smart machine identification model, exports the identification information of usual smart machine；According to the usual smart machine Identification information controls the application management program, so that the application management program is shown for remotely operating described be used to With the control information of smart machine；Wherein, the usual smart machine identification model is to be based on machine according to historical operating data Learning algorithm training obtains；The historical operating data includes that the current operating temporal foregoing description mobile terminal utilizes institute State location information where the mobile terminal locates when application management program completes long-range control to any smart machine, operating time The identification information of information and any smart machine.

It should be noted that the electronic equipment mentioned in the present embodiment can be mobile terminal, or cloud service Device.

Based on identical inventive concept, further embodiment of this invention provides a kind of non-transient computer readable storage medium Matter is stored with computer program on the computer readable storage medium, which realizes above-mentioned when being executed by processor The Overall Steps of smart machine control method based on machine learning, for example, when the processor executes the computer program It realizes following step: if detecting the application management program starting on mobile terminal, obtaining the position letter of the mobile terminal Breath and current operating temporal information；The location information and the current operating temporal information input to usual smart machine are known In other model, the identification information of usual smart machine is exported；According to the identification information of the usual smart machine to the application Management program is controlled, so that the application management program shows that the control for remotely operating the usual smart machine is believed Breath；Wherein, the usual smart machine identification model is to be obtained according to historical operating data based on machine learning algorithm training 's；The historical operating data includes that the current operating temporal foregoing description mobile terminal utilizes the application management program pair Any smart machine completes location information, operating time information and any intelligence where the mobile terminal locates when long-range control The identification information of energy equipment.

In addition, the logical order in above-mentioned memory can be realized and as independence by way of SFU software functional unit Product when selling or using, can store in a computer readable storage medium.Based on this understanding, of the invention Technical solution substantially the part of the part that contributes to existing technology or the technical solution can be with software in other words The form of product embodies, which is stored in a storage medium, including some instructions use so that One computer equipment (can be personal computer, server or the network equipment etc.) executes each embodiment institute of the present invention State all or part of the steps of method.And storage medium above-mentioned includes: USB flash disk, mobile hard disk, read-only memory (ROM, Read- Only Memory), random access memory (RAM, Random Access Memory), magnetic or disk etc. are various can be with Store the medium of program code.

The apparatus embodiments described above are merely exemplary, wherein described, unit can as illustrated by the separation member It is physically separated with being or may not be, component shown as a unit may or may not be physics list Member, it can it is in one place, or may be distributed over multiple network units.It can be selected according to the actual needs In some or all of the modules realize the purpose of the embodiment of the present invention.Those of ordinary skill in the art are not paying wound In the case where the labour for the property made, it can understand and implement.

Through the above description of the embodiments, those skilled in the art can be understood that each embodiment can It realizes by means of software and necessary general hardware platform, naturally it is also possible to pass through hardware.Based on this understanding, on Stating technical solution, substantially the part that contributes to existing technology can be embodied in the form of software products in other words, should Computer software product may be stored in a computer readable storage medium, such as ROM/RAM, magnetic disk, CD, including several fingers It enables and using so that a computer equipment (can be personal computer, server or the network equipment etc.) executes each implementation Smart machine control method based on machine learning described in certain parts of example or embodiment.

In the description of the present invention, it should be noted that the orientation or positional relationship of the instructions such as term " on ", "lower" is base In orientation or positional relationship shown in the drawings, it is merely for convenience of description of the present invention and simplification of the description, rather than indication or suggestion Signified device or element must have a particular orientation, be constructed and operated in a specific orientation, therefore should not be understood as to this The limitation of invention.Unless otherwise clearly defined and limited, term " installation ", " connected ", " connection " shall be understood in a broad sense, example Such as, it may be fixed connection or may be dismantle connection, or integral connection；It can be mechanical connection, be also possible to be electrically connected It connects；It can be directly connected, the connection inside two elements can also be can be indirectly connected through an intermediary.For this For the those of ordinary skill in field, the specific meanings of the above terms in the present invention can be understood according to specific conditions.

It should also be noted that, herein, relational terms such as first and second and the like are used merely to one Entity or operation are distinguished with another entity or operation, without necessarily requiring or implying between these entities or operation There are any actual relationship or orders.Moreover, the terms "include", "comprise" or its any other variant are intended to contain Lid non-exclusive inclusion, so that the process, method, article or equipment including a series of elements is not only wanted including those Element, but also including other elements that are not explicitly listed, or further include for this process, method, article or equipment Intrinsic element.In the absence of more restrictions, the element limited by sentence "including a ...", it is not excluded that There is also other identical elements in process, method, article or equipment including the element.

Finally, it should be noted that the above embodiments are merely illustrative of the technical solutions of the present invention, rather than its limitations；Although Present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that: it still may be used To modify the technical solutions described in the foregoing embodiments or equivalent replacement of some of the technical features； And these are modified or replaceed, technical solution of various embodiments of the present invention that it does not separate the essence of the corresponding technical solution spirit and Range.

Claims

1. a kind of smart machine control method based on machine learning characterized by comprising

If detecting the application management program starting on mobile terminal, obtains the location information of the mobile terminal and currently grasp Make temporal information；

By the location information and the current operating temporal information input into usual smart machine identification model, export usual The identification information of smart machine；

The application management program is controlled according to the identification information of the usual smart machine, so that the application management Program display is used to remotely operate the control information of the usual smart machine；

Wherein, the usual smart machine identification model is to be obtained according to historical operating data based on machine learning algorithm training 's；The historical operating data includes that the current operating temporal foregoing description mobile terminal utilizes the application management program pair Any smart machine completes location information, operating time information and any intelligence where the mobile terminal locates when long-range control The identification information of energy equipment.

2. the smart machine control method according to claim 1 based on machine learning, which is characterized in that by institute's rheme Confidence breath and the current operating temporal information input export the mark of usual smart machine into usual smart machine identification model Before knowing information, the smart machine control method based on machine learning, further includes: establish the usual smart machine identification Model；

When detecting that the mobile terminal completes long-range control to any smart machine using the application management program every time, The identification information of location information where the mobile terminal locates, operating time information and any smart machine is obtained, it will The location information where the mobile terminal locates and operating time information obtained as sample input data, and, will be described The identification information of one smart machine is based on machine learning algorithm as sample output data, carries out model training, obtains described used With smart machine identification model.

3. the smart machine control method according to claim 2 based on machine learning, which is characterized in that described to be based on machine The smart machine control method of device study further include:

During establishing the usual smart machine identification model, detecting that the mobile terminal answers described in utilizing every time When completing long-range control to any smart machine with management program, also obtains and the operation content of any smart machine is believed Breath；Correspondingly, the location information where the mobile terminal locates that will acquire and operating time information as sample input data, with And using the identification information of any smart machine and the operation content information as sample output data, it is based on engineering Algorithm is practised, model training is carried out, obtains the usual smart machine identification model；

Correspondingly, described by the location information and the current operating temporal information input to usual smart machine identification model In, when exporting the identification information of usual smart machine, also export corresponding operation content information；

Correspondingly, the identification information according to the usual smart machine controls the application management program, so that When the application management program shows the control information for remotely operating the usual smart machine, believe the control It include the corresponding operation content information in breath.

4. the smart machine control method according to claim 2 based on machine learning, which is characterized in that the acquisition institute The location information for stating mobile terminal, specifically includes:

Described in the wireless signal strength indicating RSSI value conduct for obtaining the default specified smart machine that the mobile terminal listens to The location information of mobile terminal；Wherein, the default specified smart machine is fixed smart machine and quantity is greater than or equal to 2；

Correspondingly, during establishing the usual smart machine identification model, the mobile terminal benefit is being detected every time When completing long-range control to any smart machine with the application management program, obtain the mobile terminal listen to it is described pre- If specify the RSSI value of smart machine as location information where the mobile terminal locates, and the mobile terminal is listened to The RSSI value and operating time information of the default specified smart machine as sample input data, and, by any intelligence The identification information of energy equipment is based on machine learning algorithm as sample output data, carries out model training, obtains the usual intelligence It can equipment identification model.

5. the smart machine control method according to claim 1 based on machine learning, which is characterized in that the acquisition institute The location information for stating mobile terminal, specifically includes:

Or, obtaining the position letter that the mobile terminal is presently in by the multiple pyroelectric infrared sensor nodes arranged in advance Breath；

Believe or, obtaining the position that the mobile terminal is presently in by the image processing algorithm based on computer machine vision Breath.

6. a kind of smart machine control device based on machine learning characterized by comprising

Module is obtained, if obtaining the position of the mobile terminal for detecting that the application management program on mobile terminal starts Confidence breath and current operating temporal information；

Processing module, for the location information and the current operating temporal information input to usual smart machine to be identified mould In type, the identification information of usual smart machine is exported；

Control module makes for being controlled according to the identification information of the usual smart machine the application management program It obtains the application management program and shows the control information for remotely operating the usual smart machine；

7. the smart machine control device according to claim 6 based on machine learning, which is characterized in that described to be based on machine The smart machine control device of device study, further includes: model construction module；

Wherein, the model construction module, is specifically used for:

8. the smart machine control device according to claim 7 based on machine learning, which is characterized in that the model structure Block is modeled during establishing the usual smart machine identification model, is being detected described in the mobile terminal utilization every time When application management program completes long-range control to any smart machine, also obtains and the operation content of any smart machine is believed Breath；Correspondingly, the location information where the mobile terminal locates and operating time information that the model construction module will acquire are made For sample input data, and, the identification information of any smart machine and the operation content information is defeated as sample Data out are based on machine learning algorithm, carry out model training, obtain the usual smart machine identification model；

Correspondingly, the processing module is set by the location information and the current operating temporal information input to usual intelligence In standby identification model, when exporting the identification information of usual smart machine, corresponding operation content information is also exported；

Correspondingly, the control module carries out the application management program according to the identification information of the usual smart machine Control, when so that the application management program showing the control information for remotely operating the usual smart machine, also makes It include the corresponding operation content information in the control information.

9. the smart machine control device according to claim 7 based on machine learning, which is characterized in that the acquisition mould Block is specifically used for:

Correspondingly, the model construction module is detecting every time during establishing the usual smart machine identification model When being controlled to the mobile terminal using the application management program is long-range to the completion of any smart machine, obtain described mobile whole Hold the RSSI value of the default specified smart machine listened to as location information where the mobile terminal locates, and will be described The RSSI value and operating time information for the default specified smart machine that mobile terminal listens to as sample input data, with And using the identification information of any smart machine as sample output data, it is based on machine learning algorithm, carry out model instruction Practice, obtains the usual smart machine identification model.

10. the smart machine control device according to claim 6 based on machine learning, which is characterized in that the acquisition Module is specifically used for:

11. a kind of electronic equipment including memory, processor and stores the calculating that can be run on a memory and on a processor Machine program, which is characterized in that the processor is realized when executing described program is based on machine as described in any one of claim 1 to 5 The step of smart machine control method of device study.

12. a kind of non-transient computer readable storage medium, is stored thereon with computer program, which is characterized in that the computer The smart machine control method as described in any one of claim 1 to 5 based on machine learning is realized when program is executed by processor The step of.