CN113777938B - Household appliance control method, device, equipment and storage medium - Google Patents
Household appliance control method, device, equipment and storage medium Download PDFInfo
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- CN113777938B CN113777938B CN202111017559.1A CN202111017559A CN113777938B CN 113777938 B CN113777938 B CN 113777938B CN 202111017559 A CN202111017559 A CN 202111017559A CN 113777938 B CN113777938 B CN 113777938B
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B15/00—Systems controlled by a computer
- G05B15/02—Systems controlled by a computer electric
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B19/00—Programme-control systems
- G05B19/02—Programme-control systems electric
- G05B19/418—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS], computer integrated manufacturing [CIM]
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B2219/00—Program-control systems
- G05B2219/20—Pc systems
- G05B2219/26—Pc applications
- G05B2219/2642—Domotique, domestic, home control, automation, smart house
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]
Abstract
The invention discloses a household appliance control method, a household appliance control device, household appliance control equipment and a storage medium, and belongs to the technical field of household appliances. The intelligent household appliance control system is characterized in that the vehicle is connected with the household appliances, the position of the vehicle is monitored in real time, the position information of the intelligent household appliances is obtained, the distance information of the vehicle and the intelligent household appliances is obtained according to the position of the vehicle and the position of the intelligent household appliances, the intelligent household appliances are determined through a distance household appliance control model, and the household appliances which need to be started are determined; the control instructions are generated and transmitted through the preset strategy, the control of the household appliances is realized according to the control instructions, the intelligent household appliances which are required to be started by the user can be calculated according to the distance household appliance control model, the control instructions are generated, the household appliances are controlled, the user does not need to operate, and the use effect of the user is improved.
Description
Technical Field
The present invention relates to the field of home appliances, and in particular, to a home appliance control method, apparatus, device, and storage medium.
Background
Along with the improvement of the living standard of people, the demands of people on the intellectualization and the high efficiency of household appliances are higher and higher, various intelligent household appliances are connected through wireless routers and mobile phones at present and controlled, but users can only open or close the household appliances through control instruction remote control, the household appliances cannot be automatically opened according to the habit of the users, the intelligent degree can not meet the demands of the users, the network transmission signal conversion is poor in the instruction transmission process of the remote control intelligent household appliances, and the problems of high error rate, high time delay and poor scheduling capability of the instruction network transmission are caused by weak identification capability.
The foregoing is provided merely for the purpose of facilitating understanding of the technical solutions of the present invention and is not intended to represent an admission that the foregoing is prior art.
Disclosure of Invention
The invention mainly aims to provide a household appliance control method, a household appliance control device, household appliance control equipment and a storage medium, and aims to solve the technical problem that the household appliance cannot be controlled in a self-adaptive mode according to user habits in the prior art.
In order to achieve the above object, the present invention provides a home appliance control method, comprising the steps of:
acquiring current position information of a vehicle and position information of intelligent household appliances;
determining the distance information between the vehicle and the intelligent household appliance according to the current position information and the position information of the intelligent household appliance;
determining the intelligent household appliances through a distance household appliance control model according to the distance information to obtain target intelligent household appliances;
generating a control instruction of the target intelligent household appliance by adopting a preset strategy;
and controlling the target intelligent household appliance based on the control instruction.
Optionally, the determining of the intelligent home appliance according to the distance information through a distance home appliance control model, before obtaining the target intelligent home appliance, further includes:
acquiring the frequency information for controlling the intelligent household appliances;
performing feature extraction on the frequency information and the distance information based on an initial convolutional neural network model, and generating an intelligent household appliance feature map;
and performing feature training on the intelligent household appliance feature map through the initial convolutional neural network model to obtain a distance household appliance control model.
Optionally, the initial convolutional neural network model includes a convolutional layer, an activation layer, and a pooling layer;
the feature extraction is carried out on the frequency information and the distance information based on the initial convolutional neural network model, and an intelligent household appliance feature map is generated, and the method comprises the following steps:
inputting the data set of the frequency information and the distance information into a convolution layer of an initial convolution neural network model for convolution to obtain a convolution result matrix;
inputting the convolution result matrix to an activation layer, and obtaining a feature map according to an activation function;
and inputting the feature map to a pooling layer for feature extraction to obtain an intelligent household appliance feature map.
Optionally, the inputting the data set of the frequency information and the distance information to a convolution layer of an initial convolution neural network model, and performing convolution to obtain a convolution result matrix includes:
inputting the data set of the frequency information and the distance information into a convolution layer of an initial convolution neural network model, and acquiring a convolution kernel matrix and a bias matrix;
convolving according to the data set, the convolution kernel matrix and the offset matrix to obtain iteration layer matrix data;
and convolving the convolution kernel matrix and the offset matrix according to the iteration layer matrix data to obtain a convolution result matrix.
Optionally, the feature training is performed on the intelligent home appliance feature map through the initial convolutional neural network model to obtain a distance home appliance control model, which includes:
convolving according to the intelligent household appliance characteristic diagram to obtain candidate window data;
comparing the coincidence ratio of the candidate window data and preset target window data;
acquiring candidate target window data when the coincidence ratio of the candidate window data and the preset target window data is a preset value;
and performing feature training on the candidate target window data through a window regression loss function of the initial convolutional neural network model to obtain a distance household appliance control model.
Optionally, the generating the control instruction of the target intelligent home appliance by adopting a preset policy includes:
acquiring an initial control instruction of the target intelligent household appliance;
acquiring a time delay component and a code element sequence of a transmission network according to the initial control instruction;
obtaining the channel characteristic quantity of the initial control instruction according to the time delay component and the code element sequence;
demodulating the channel characteristic quantity according to a characteristic optimization separation algorithm;
and generating a control instruction of the target intelligent household appliance according to the demodulation result.
Optionally, after the controlling the target intelligent home appliance based on the control instruction, the method further includes:
acquiring information of other intelligent household appliances which are in the same Internet of things with the target intelligent household appliance, and monitoring safety information of the other intelligent household appliances;
and when the safety information is abnormal information, transmitting the abnormal information to the vehicle.
In addition, in order to achieve the above object, the present invention also proposes a home appliance control device including:
the acquisition module is used for acquiring the current position information of the vehicle and the position information of the intelligent household appliance;
the determining module is used for determining the distance information between the vehicle and the intelligent household appliance according to the current position information and the position information of the intelligent household appliance;
the determining module is further used for determining the intelligent household appliances through a distance household appliance control model according to the distance information to obtain target intelligent household appliances;
the generation module is used for generating a control instruction of the target intelligent household appliance by adopting a preset strategy;
and the control module is used for controlling the target intelligent household appliance based on the control instruction.
In addition, to achieve the above object, the present invention also proposes a home appliance control device including: a memory, a processor, and a home appliance control program stored on the memory and executable on the processor, the home appliance control program configured to implement the steps of the home appliance control method as described above.
In addition, in order to achieve the above object, the present invention also proposes a storage medium having stored thereon a home appliance control program which, when executed by a processor, implements the steps of the home appliance control method as described above.
The invention obtains the current position information of the vehicle and the position information of the intelligent household appliance; determining the distance information between the vehicle and the intelligent household appliance according to the current position information and the position information of the intelligent household appliance; determining the intelligent household appliances through a distance household appliance control model according to the distance information to obtain target intelligent household appliances; generating a control instruction of the target intelligent household appliance by adopting a preset strategy; controlling the target intelligent household appliance based on the control instruction; the method comprises the steps of interconnecting a vehicle and an intelligent household appliance, monitoring the position of the vehicle in real time, acquiring the position information of the intelligent household appliance, acquiring the distance information between the vehicle and the intelligent household appliance according to the position of the vehicle and the position of the intelligent household appliance, determining the intelligent household appliance through a distance household appliance control model, and determining the household appliance to be started; the control instructions are generated and transmitted through the preset strategy, the control of the household appliances is realized according to the control instructions, the intelligent household appliances which are required to be started by the user can be calculated according to the distance household appliance control model, the control instructions are generated, the household appliances are controlled, the user does not need to operate, and the use effect of the user is improved.
Drawings
Fig. 1 is a schematic structural diagram of a home appliance control device in a hardware running environment according to an embodiment of the present invention;
FIG. 2 is a schematic flow chart of a first embodiment of a method for controlling a household appliance according to the present invention;
FIG. 3 is a flowchart of a second embodiment of the home electronic control method of the present invention;
FIG. 4 is a schematic flow chart of a third embodiment of a method for controlling a household appliance according to the present invention;
fig. 5 is a schematic control diagram between a vehicle end and a home appliance end of a third embodiment of the home appliance control method of the present invention;
fig. 6 is a block diagram of a first embodiment of the home control device of the present invention.
The achievement of the objects, functional features and advantages of the present invention will be further described with reference to the accompanying drawings, in conjunction with the embodiments.
Detailed Description
It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
Referring to fig. 1, fig. 1 is a schematic structural diagram of a home appliance control device in a hardware running environment according to an embodiment of the present invention.
As shown in fig. 1, the home appliance control device may include: a processor 1001, such as a central processing unit (Central Processing Unit, CPU), a communication bus 1002, a user interface 1003, a network interface 1004, a memory 1005. Wherein the communication bus 1002 is used to enable connected communication between these components. The user interface 1003 may include a Display, an input unit such as a Keyboard (Keyboard), and the optional user interface 1003 may further include a standard wired interface, a wireless interface. The network interface 1004 may optionally include a standard wired interface, a Wireless interface (e.g., a Wireless-Fidelity (Wi-Fi) interface). The Memory 1005 may be a high-speed random access Memory (Random Access Memory, RAM) Memory or a stable nonvolatile Memory (NVM), such as a disk Memory. The memory 1005 may also optionally be a storage device separate from the processor 1001 described above.
It will be appreciated by those skilled in the art that the structure shown in fig. 1 does not constitute a limitation of the home control device, and may include more or fewer components than shown, or may combine certain components, or a different arrangement of components.
As shown in fig. 1, an operating system, a network communication module, a user interface module, and a home appliance control program may be included in the memory 1005 as one type of storage medium.
In the home appliance control device shown in fig. 1, the network interface 1004 is mainly used for data communication with a network server; the user interface 1003 is mainly used for data interaction with a user; the processor 1001 and the memory 1005 in the home electric control device of the present invention may be provided in the home electric control device, and the home electric control device invokes a home electric control program stored in the memory 1005 through the processor 1001 and executes the home electric control method provided by the embodiment of the present invention.
An embodiment of the present invention provides a home appliance control method, referring to fig. 2, fig. 2 is a schematic flow chart of a first embodiment of a home appliance control method of the present invention.
In this embodiment, the home appliance control method includes the following steps:
step S10: and acquiring the current position information of the vehicle and the position information of the intelligent household appliance.
The execution body of the present embodiment may be a host controller that is installed on an intelligent home appliance and is used to control the intelligent home appliance, or may be another controller that can realize control of the home appliance, which is not limited in this embodiment.
In this embodiment, the current position information of the vehicle may be obtained by positioning the vehicle in real time in a GPS positioning manner, or may be obtained by other satellite positioning manners, which is not limited in this embodiment;
it should be understood that the location information of the smart home appliances refers to location information of the smart home appliances located in the home, and accordingly, a sensor may be installed in the home to acquire location information of each home appliance in the home.
Step S20: and determining the distance information between the vehicle and the intelligent household appliance according to the current position information and the position information of the intelligent household appliance.
When the intelligent home appliance end obtains the position of the vehicle, the distance information between the vehicle and the intelligent home appliance is calculated according to the position information of the vehicle and the position information of the home appliance in the house.
Step S30: and determining the intelligent household appliances through the distance household appliance control model according to the distance information to obtain the target intelligent household appliances.
The distance household appliance control model refers to a neural network training model, and the obtained data set is subjected to neural network training through the neural network training to obtain target data in the data set, and the existing data can be predicted according to the trained neural network; the target intelligent home appliance refers to a home appliance which needs to be controlled according to habit of a user, for example, if the intelligent home appliance which needs to be controlled is determined to be an air conditioner according to distance information and a distance home appliance control model, the air conditioner is the target intelligent home appliance.
In this embodiment, control instructions for remotely controlling intelligent home appliances by 1000 vehicles are counted in advance, and the control instructions comprise 5 control instruction signals of an air conditioner, a hall lamp, a bathtub, a television and a sweeping robot. And packaging 1000 cases of acquired data into a data set as a training set of a neural network training model, wherein the training set comprises the times of remotely controlling each intelligent household appliance and the distance between a vehicle and the intelligent household appliance when each intelligent household appliance is correspondingly started. Furthermore, the training set is not limited to the 5 control instruction signals counted above, and the statistical data of intelligent household appliances such as a water heater, a dish washer and the like can be added to be updated to the training set, so that the variety and the number of the intelligent household appliances are controlled and different demands of users can be met.
It should be understood that the trained target data is obtained according to the distance home appliance control model, at this time, the trained target data is compared according to the distance information of the vehicle and the intelligent home appliance, when the distance information meets the distance information in the target data, the intelligent home appliance corresponding to the distance information in the target data is determined, and the intelligent home appliance is used as the target intelligent home appliance. For example, when the distance information between the vehicle and the intelligent home appliance is 100m from the vehicle, the target data can be searched according to the distance information of 100m, and if the hall lamp is turned on corresponding to 100m in the target data, the hall lamp is determined to be the target intelligent home appliance.
Step S40: and generating a control instruction of the target intelligent household appliance by adopting a preset strategy.
It should be understood that the preset strategy refers to a transmission network channel model strategy, and the network transmission channel modulation and demodulation processing is realized by adopting a characteristic optimization separation method of intelligent household appliance control instructions.
In specific implementation, the intelligent household appliance control instruction is generated by performing interference suppression on the transmission of the control signal and performing balanced control on the load of the transmission of the control signal through a transmission network channel model strategy. For example, if the target intelligent home appliance is a hall lamp according to the determination, the generated intelligent home appliance control instruction is a control instruction for turning on the hall lamp.
Step S50: and controlling the target intelligent household appliance based on the control instruction.
In this embodiment, the host controller controls the corresponding intelligent home appliance according to the acquired control instruction, including opening or closing the home appliance. For example, if the received control instruction is to start the air conditioner, the air conditioner can be started according to the control instruction to start the air conditioner, the mode and the temperature of the air conditioner can be adjusted according to the related temperature information, and the starting temperature of the air conditioner can also be adjusted according to the temperature setting habit of the user. If the received control instruction is to turn on the hall lamp, the hall lamp can be turned on according to the current weather condition sensed by the sensor and the brightness of the hall lamp can be adjusted.
The embodiment obtains the current position information of the vehicle and the position information of the intelligent household appliance; determining the distance information between the vehicle and the intelligent household appliance according to the current position information and the position information of the intelligent household appliance; determining the intelligent household appliances through a distance household appliance control model according to the distance information to obtain target intelligent household appliances; generating a control instruction of the target intelligent household appliance by adopting a preset strategy; controlling the target intelligent household appliance based on the control instruction; the method comprises the steps of interconnecting a vehicle and an intelligent household appliance, monitoring the position of the vehicle in real time, acquiring the position information of the intelligent household appliance, acquiring the distance information between the vehicle and the intelligent household appliance according to the position of the vehicle and the position of the intelligent household appliance, determining the intelligent household appliance through a distance household appliance control model, and determining the household appliance to be started; the control instructions are generated and transmitted through the preset strategy, the control of the household appliances is realized according to the control instructions, the intelligent household appliances which are required to be started by the user can be calculated according to the distance household appliance control model, the control instructions are generated, the household appliances are controlled, the user does not need to operate, and the use effect of the user is improved.
Referring to fig. 3, fig. 3 is a flowchart of a second embodiment of a home appliance control method according to the present invention.
Based on the first embodiment, the home appliance control method of the present embodiment further includes, before the step S30:
step S21: and acquiring the frequency information for controlling the intelligent household appliance.
It should be understood that the number of times information for controlling the smart home appliances refers to the counted number of times for controlling the smart home appliances to be turned on or off. For example, the number of times of controlling the air conditioner to be turned on is 10 times, the number of times of controlling the television to be turned on is 8 times, etc., which is not limited in this embodiment.
In a specific implementation, 1000 cases of collected data are packed into a data set x, where x ε ((C, d) C )，(L，d L )，(B，d B )，(T，d T )，(R，d R ) C, L, B, T, R represents the number of times each home appliance is controlled, for example, C represents the number of times air conditioner is controlled; d, d C 、d L 、d B 、d T 、d R Respectively represent the distance between the vehicle and the home appliance when the home appliance is started, e.g. d C Indicating the distance between the air conditioner and the vehicle when the air conditioner is turned on, then (C, d C ) Indicating the number of times the air conditioner is turned on and the distance between the vehicle and the air conditioner when the air conditioner is turned on.
Step S22: and carrying out feature extraction on the frequency information and the distance information based on the initial convolutional neural network model, and generating an intelligent household appliance feature map.
It should be noted that the initial convolutional neural network model includes a convolutional layer, an activation layer, and a pooling layer; the convolution layer is used for carrying out convolution feature extraction on data information such as frequency information, distance information and the like to obtain local features of the data information, so that the operand can be reduced; the active layer is mainly used for retaining main characteristics of data obtained by convolution, and simultaneously reducing parameters and calculated amount; the pooling layer is mainly used for carrying out secondary feature extraction on the features of the obtained data information to obtain a more accurate feature map, namely a target intelligent household appliance feature map, wherein the feature extraction comprises 7 convolution layers, 7 activation layers and 2 pooling layers.
It should be understood that the smart home feature map includes a distance of the control home and a category of the control home, for example, when the distance between the vehicle and the smart home is 100m, the controlled smart home is a hall light; when the distance between the vehicle and the intelligent home appliance is 500m, the intelligent home appliance to be controlled is an air conditioner or the like, and the present embodiment is not limited thereto.
In a specific implementation, inputting the set x of data of the number of times information for controlling the smart home and the position information of the vehicle and the smart home into the convolution layer to perform convolution includes:
in the method, in the process of the invention,for the input of the last iteration layer, +.>For convolution kernel matrix, ++>For biasing matrix +.>For the output of the current convolutional layer, i is the ith of the inputs, j is the jth of the inputs, M j The number of inputs is l, the number of layers. When an initial convolution, i.e., a first layer convolution, is performed, training is performedThe set x is input to the first convolution layer, i=1, j=1, l=1, i.e. calculated +.>The convolution of the first convolution layer is calculated to obtain +.>When the second layer convolution is performed, the result of the last convolution, i.e., the convolution result of the last iteration layer, is input +.>Output->And obtaining a convolution result matrix according to the convolution of the data set.
From the output of the convolutional layer, there is an active layer calculation formula:
in the method, in the process of the invention,and inputting a convolution result matrix of the convolution layer into the activation layer for activation to obtain an activation function for the input of the activation layer, and calculating and activating the function according to 7 activation layers to obtain a feature map.
The calculation formula of the pooling layer is as follows:
in the method, in the process of the invention,for the input of the current pooling layer, down () is a downsampling function.
And carrying out pooling, namely secondary feature extraction, on the obtained feature map according to the pooling layer to obtain the intelligent household appliance feature map.
Step S23: and performing feature training on the intelligent household appliance feature map through the initial convolutional neural network model to obtain a distance household appliance control model.
It should be understood that the feature training of the intelligent household appliance feature map through the initial convolutional neural network model refers to feature training according to a window regression loss function of the initial convolutional neural network model, that is, the distance household appliance control model is obtained through feature training of window data.
In specific implementation, the target smart home electrogram is convolved to obtain data of a candidate window, and the candidate window is positioned in the center of the convolution kernel.
It should be appreciated that the candidate window consists of 9 different sized windows, the sizes of the 9 windows being {32×64}, {64×32}, {32×32}, {64×128}, {128×64}, {64×64, 128×256, 256×128, {128×128}, respectively, for a total of 9 proportional different area sized windows.
It should be noted that, comparing the data of the candidate window with the target window in the training set, and judging whether the data of the candidate window can participate in the training according to the comparison result, wherein the overlap ratio IOV of the candidate window and the target window in the training set is available:
in the formula, p is a binary label, if p=1, the candidate window contains the target, at this time, the candidate window corresponds to the positive label, if p=0, the candidate window does not contain the target and is the background, at this time, the candidate window corresponds to the negative label, and if p=non used, the candidate window does not contain the target and the background, at this time, the candidate window does not contribute and does not participate in training the label.
In specific implementation, comparing the coincidence ratio of the candidate window data and the preset window data, wherein the preset window data refers to the data of the target window in the training set, if the coincidence ratio of the candidate window data and the preset window data is 1, the candidate window data can participate in feature training, the candidate window data is used as candidate target window data, and the candidate target window data is subjected to feature training according to a window regression loss function.
The window is regressed with the start point (x) of the training set corresponding to the real target window * ，y * ) The width and height (w * ，h * ) Corresponds to the starting point (x a ，y a ) The width and height (w a ，h a ) The width and height (w, h) of the real target window correspond to the starting point (x, y) of the predicted target window. Windowed regression loss function calculation:
in the method, in the process of the invention,has t x ＝(x-x a )/w a ，t y ＝(y-y a )/h a ，/>
When window regression loss function R reg And when the distance is 0, the initial convolutional neural network model training is completed, and the distance household appliance control model is obtained.
The embodiment controls the intelligent household appliance by acquiring the frequency information; performing feature extraction on the frequency information and the distance information based on an initial convolutional neural network model, and generating an intelligent household appliance feature map; performing feature training on the intelligent household appliance feature map through the initial convolutional neural network model to obtain a distance household appliance control model; the number of times information of the intelligent household appliances and the distance information between the vehicle and the intelligent household appliances are input into an initial convolutional neural network model to calculate a convolutional layer, an activation layer and a pooling layer, characteristics of the input data information are extracted for multiple times, an intelligent household appliance characteristic diagram is generated according to the extracted characteristics, candidate window data are extracted through convolution of the intelligent household appliance characteristic diagram, a distance household appliance control model is obtained through training the candidate window data, a more accurate intelligent household appliance characteristic diagram is extracted through neural network training, and the household appliances needing to be controlled can be determined more quickly and accurately according to the distance household appliance control model.
Referring to fig. 4, fig. 4 is a flowchart of a third embodiment of a home appliance control method according to the present invention.
Based on the above-mentioned first embodiment, the step S40 of the home appliance control method of this embodiment specifically includes:
step S41: and acquiring an initial control instruction of the target intelligent household appliance.
It should be understood that, when determining the target intelligent home appliance to be controlled according to the distance information between the vehicle and the intelligent home appliance and the distance information between the vehicle and the intelligent home appliance, the initial control instruction is generated by the vehicle end to control the opening of the bathtub.
Step S42: and acquiring a time delay component and a code element sequence of a transmission network according to the initial control instruction.
In a specific implementation, in order to avoid the problem of poor conversion and weak recognition capability of a control command network of a remote control household appliance, the transmission control of a vehicle end control command is realized by adopting a self-adaptive spread spectrum sequence detection method, and the modulation output of a dynamic migration code element of an initial control command transmission network of an intelligent household appliance is as follows:
it should be understood that in the transmission process of the initial control command network, surrounding signals and external environments such as buildings interfere with the transmission, and the transmission speed of the network is affected; in order to reduce the influence of external environment interference on the transmission of control instructions, a cascade filtering method is adopted to perform interference suppression of intelligent household appliance control transmission instructions, and the delay components of the obtained control instruction transmission network are as follows:
step S43: and obtaining the channel characteristic quantity of the initial control instruction according to the time delay component and the code element sequence.
It should be understood that in order to improve the processing capability of the network to the instruction data in the instruction transmission process, the bandwidth of the network device and the server may be expanded, that is, the load of the initial control instruction transmission of the intelligent household appliance is controlled in a balanced manner, and the delay control model of the intelligent household appliance control instruction transmission network is constructed as follows:
wherein: g (U|) μk ·∑ k ) Representing the degree of associative coupling between the sets of control instructions; u represents a time delay characteristic component; u (u) k A symbol sequence representing the transmission of intelligent appliance control instructions. Therefore, an automatic channel optimization control model for intelligent household appliance control instruction transmission is constructed, error compensation is carried out by combining a channel spread spectrum technology, and a spread spectrum sequence distributed adjustment model for intelligent household appliance control instruction transmission is constructed.
It should be understood that, according to the dynamic migration code element modulation output of the intelligent household appliance initial control instruction transmission network, the optimal solution distribution for controlling the intelligent household appliance instruction transmission is obtained as follows:
where i is the ith input, j is the jth input,symbol sequence v is modulation frequency, S is the signal length of the window regression loss function, and f is the calculation formula in the activation function. And calculating according to the optimal solution to obtain a code element sequence transmitted by the intelligent household appliance control instruction.
The method is characterized in that the multipath interference characteristic quantity in the channel is subjected to interference suppression by adopting an autocorrelation matched filtering method, and the obtained spectrum distribution characteristic quantity of intelligent household appliance control instruction transmission is as follows:
wherein:
the method is characterized in that according to a constructed intelligent household appliance control instruction transmission network load balancing scheduling model, spatial distribution information of intelligent household appliance control instruction transmission network code elements is analyzed, interference suppression of the intelligent household appliance control instructions is realized by adopting a time-frequency characteristic decomposition and matched filtering method, short-time energy output by the intelligent household appliance control instructions is obtained, namely channel characteristic quantity E of the intelligent household appliance control instructions is obtained according to spectrum distribution characteristic quantity transmitted by the intelligent household appliance control instructions j The method comprises the following steps:
step S44: and demodulating the channel characteristic quantity according to a characteristic optimization separation algorithm.
In a specific implementation, by sampling an input signal at a nyquist rate of at least 2 times and equalizing a received signal spectrum beyond the nyquist frequency to a frequency f=n/MT, an optimal design of intelligent appliance control command transmission is realized, and an optimal transfer function is obtained:
wherein N represents the spectrum length of a signal transmitted by the intelligent household appliance control command network, J represents the signal sampling frequency, the multipath interference characteristic quantity in the channel is subjected to interference suppression by adopting an autocorrelation matched filtering method, the characteristic quantity of the intelligent household appliance control command channel is extracted, and the channel modulation and demodulation processing of network transmission is realized by adopting a characteristic optimization separation method of the intelligent household appliance control command.
Step S45: and generating a control instruction of the target intelligent household appliance according to the demodulation result.
It should be understood that the control instruction of the target intelligent home appliance refers to the control instruction of the target intelligent home appliance obtained according to the optimized result by detecting the transmission network of the generated initial control instruction and automatically optimizing the transmission channel of the initial control instruction. And controlling the target intelligent household appliance based on the control instruction.
In a specific implementation, as shown in fig. 5, a vehicle end uses a vehicle as a mobile device, a vehicle machine end as a terminal, and a wireless power technology is mounted, and the vehicle machine is operated to turn on or off the home appliance. And the home appliance end receives the vehicle position in real time, determines the distance between the vehicle and the intelligent home appliance according to the vehicle position, determines the intelligent home appliance to be controlled based on the initial convolutional neural network model, generates an initial control command, obtains a final control command for controlling the intelligent home appliance by optimizing a transmission network of the initial control command, and controls the intelligent home appliance. Accordingly, since many unexpected situations occur when the home appliance is remotely controlled to be turned on or off, for example, a short circuit is caused due to an excessive operation voltage of the home appliance, and a user cannot know the situation of the home appliance in the house in time, a house safety detection sensor, including a smoke sensor, an infrared sensor, a camera, etc., can be installed in the house and connected to the house host controller.
The intelligent household appliance monitoring system can monitor all safety information of the target intelligent household appliance and other intelligent household appliances which are in the same internet of things with the intelligent household appliance through the sensor and the camera, can timely send abnormal information to a vehicle when the safety information of the intelligent household appliance is abnormal information, and can send smoke alarm information and camera information to a host controller and send alarm information to a vehicle end when a smoke sensor detects that the concentration of the house smoke reaches the abnormality; when the infrared sensor detects that the temperature of the house reaches an abnormality, infrared alarm information and camera information are transmitted to a host controller, and the alarm information is sent to a vehicle end; the smoke signals, infrared signals and the like are sent to the vehicle through real-time monitoring, so that unsafe events such as fire disasters, burglary and the like are effectively avoided.
The embodiment obtains the initial control instruction of the target intelligent household appliance; acquiring a time delay component and a code element sequence of a transmission network according to the initial control instruction; obtaining the channel characteristic quantity of the initial control instruction according to the time delay component and the code element sequence; demodulating the channel characteristic quantity according to a characteristic optimization separation algorithm; generating a control instruction of the target intelligent household appliance according to the demodulation result; the delay component and the code element sequence of the transmission instruction network are obtained to optimize the transmission channel of the control instruction of the control target intelligent household appliance, so that the transmission signal conversion and recognition capability of the control instruction network for remotely controlling the household appliance is improved, the error rate and the delay of the instruction network transmission are low, the scheduling capability of network transmission is improved, the household appliance control equipment can receive the control instruction for controlling the intelligent household appliance more quickly and accurately, all safety information in a residence is monitored through the safety detection sensor, the occurrence of unsafe events is avoided, and the use experience of a user is improved.
Referring to fig. 6, fig. 6 is a block diagram illustrating a first embodiment of a home electric control device according to the present invention.
As shown in fig. 6, a home appliance control device according to an embodiment of the present invention includes:
the acquiring module 10 is configured to acquire current location information of the vehicle and location information of the smart home.
And the determining module 20 is configured to determine distance information between the vehicle and the intelligent home appliance according to the current location information and the location information of the intelligent home appliance.
The determining module 20 is further configured to determine, according to the distance information, an intelligent home appliance through a distance home appliance control model, and obtain a target intelligent home appliance.
The generating module 30 is configured to generate a control instruction of the target intelligent home appliance by adopting a preset policy.
And the control module 40 is used for controlling the target intelligent household appliance based on the control instruction.
The embodiment obtains the current position information of the vehicle and the position information of the intelligent household appliance; determining the distance information between the vehicle and the intelligent household appliance according to the current position information and the position information of the intelligent household appliance; determining the intelligent household appliances through a distance household appliance control model according to the distance information to obtain target intelligent household appliances; generating a control instruction of the target intelligent household appliance by adopting a preset strategy; controlling the target intelligent household appliance based on the control instruction; the method comprises the steps of interconnecting a vehicle and an intelligent household appliance, monitoring the position of the vehicle in real time, acquiring the position information of the intelligent household appliance, acquiring the distance information between the vehicle and the intelligent household appliance according to the position of the vehicle and the position of the intelligent household appliance, determining the intelligent household appliance through a distance household appliance control model, and determining the household appliance to be started; the control instructions are generated and transmitted through the preset strategy, the control of the household appliances is realized according to the control instructions, the intelligent household appliances which are required to be started by the user can be calculated according to the distance household appliance control model, the control instructions are generated, the household appliances are controlled, the user does not need to operate, and the use effect of the user is improved.
In an embodiment, the determining module 20 is further configured to obtain frequency information for controlling the smart home appliance; performing feature extraction on the frequency information and the distance information based on an initial convolutional neural network model, and generating an intelligent household appliance feature map; and performing feature training on the intelligent household appliance feature map through the initial convolutional neural network model to obtain a distance household appliance control model.
In an embodiment, the determining module 20 is further configured to input the data set of the frequency information and the distance information to a convolution layer of an initial convolutional neural network model for convolution, so as to obtain a convolution result matrix; inputting the convolution result matrix to an activation layer, and obtaining a feature map according to an activation function; and inputting the feature map to a pooling layer for feature extraction to obtain an intelligent household appliance feature map.
In an embodiment, the determining module 20 is further configured to input the data set of the frequency information and the distance information to a convolution layer of an initial convolutional neural network model, and obtain a convolution kernel matrix and a bias matrix; convolving according to the data set, the convolution kernel matrix and the offset matrix to obtain iteration layer matrix data; and convolving the convolution kernel matrix and the offset matrix according to the iteration layer matrix data to obtain a convolution result matrix.
In an embodiment, the determining module 20 is further configured to perform convolution according to the smart home appliance feature map to obtain candidate window data; comparing the coincidence ratio of the candidate window data and preset target window data; acquiring candidate target window data when the coincidence ratio of the candidate window data and the preset target window data is a preset value; and performing feature training on the candidate target window data through a window regression loss function of the initial convolutional neural network model to obtain a distance household appliance control model.
In an embodiment, the generating module 30 is further configured to obtain an initial control instruction of the target smart home appliance; acquiring a time delay component and a code element sequence of a transmission network according to the initial control instruction; obtaining the channel characteristic quantity of the initial control instruction according to the time delay component and the code element sequence; demodulating the channel characteristic quantity according to a characteristic optimization separation algorithm; and generating a control instruction of the target intelligent household appliance according to the demodulation result.
In an embodiment, the control module 40 is further configured to obtain information of other intelligent home appliances that are in the same internet of things with the target intelligent home appliance, and monitor security information of the other intelligent home appliances; and when the safety information is abnormal information, transmitting the abnormal information to the vehicle.
In addition, to achieve the above object, the present invention also proposes a home appliance control device including: a memory, a processor, and a home appliance control program stored on the memory and executable on the processor, the home appliance control program configured to implement the steps of the home appliance control method as described above.
The home electronic control device adopts all the technical schemes of all the embodiments, so that the home electronic control device has at least all the beneficial effects brought by the technical schemes of the embodiments, and the description is omitted herein.
In addition, the embodiment of the invention also provides a storage medium, wherein the storage medium stores a household appliance control program, and the household appliance control program realizes the steps of the household appliance control method when being executed by a processor.
Because the storage medium adopts all the technical schemes of all the embodiments, the storage medium has at least all the beneficial effects brought by the technical schemes of the embodiments, and the description is omitted here.
It should be understood that the foregoing is illustrative only and is not limiting, and that in specific applications, those skilled in the art may set the invention as desired, and the invention is not limited thereto.
It should be noted that the above-described working procedure is merely illustrative, and does not limit the scope of the present invention, and in practical application, a person skilled in the art may select part or all of them according to actual needs to achieve the purpose of the embodiment, which is not limited herein.
In addition, technical details not described in detail in the present embodiment may refer to the home appliance control method provided in any embodiment of the present invention, and are not described herein again.
Furthermore, it should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or system. Without further limitation, an element defined by the phrase "comprising one does not exclude the presence of other like elements in a process, method, article, or system that comprises the element.
The foregoing embodiment numbers of the present invention are merely for the purpose of description, and do not represent the advantages or disadvantages of the embodiments.
From the above description of the embodiments, it will be clear to those skilled in the art that the above-described embodiment method may be implemented by means of software plus a necessary general hardware platform, but of course may also be implemented by means of hardware, but in many cases the former is a preferred embodiment. Based on such understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art in the form of a software product stored in a storage medium (e.g. Read Only Memory)/RAM, magnetic disk, optical disk) and including several instructions for causing a terminal device (which may be a mobile phone, a computer, a server, or a network device, etc.) to perform the method according to the embodiments of the present invention.
The foregoing description is only of the preferred embodiments of the present invention, and is not intended to limit the scope of the invention, but rather is intended to cover any equivalents of the structures or equivalent processes disclosed herein or in the alternative, which may be employed directly or indirectly in other related arts.
Claims (8)
1. A home appliance control method, characterized in that the home appliance control method comprises:
acquiring current position information of a vehicle and position information of intelligent household appliances;
determining the distance information between the vehicle and the intelligent household appliance according to the current position information and the position information of the intelligent household appliance;
determining the intelligent household appliances through a distance household appliance control model according to the distance information to obtain target intelligent household appliances;
generating a control instruction of the target intelligent household appliance by adopting a preset strategy;
controlling the target intelligent household appliance based on the control instruction;
the intelligent household appliance determining is carried out according to the distance information through a distance household appliance control model, and before the target intelligent household appliance is obtained, the intelligent household appliance determining method further comprises the following steps:
acquiring the frequency information for controlling the intelligent household appliances;
performing feature extraction on the frequency information and the distance information based on an initial convolutional neural network model, and generating an intelligent household appliance feature map;
performing feature training on the intelligent household appliance feature map through the initial convolutional neural network model to obtain a distance household appliance control model;
the generating the control instruction of the target intelligent household appliance by adopting the preset strategy comprises the following steps:
acquiring an initial control instruction of the target intelligent household appliance;
acquiring a time delay component and a code element sequence of a transmission network according to the initial control instruction;
obtaining the channel characteristic quantity of the initial control instruction according to the time delay component and the code element sequence;
demodulating the channel characteristic quantity according to a characteristic optimization separation algorithm;
and generating a control instruction of the target intelligent household appliance according to the demodulation result.
2. The appliance control method of claim 1, wherein the initial convolutional neural network model comprises a convolutional layer, an active layer, and a pooling layer;
the feature extraction is carried out on the frequency information and the distance information based on the initial convolutional neural network model, and an intelligent household appliance feature map is generated, and the method comprises the following steps:
inputting the data set of the frequency information and the distance information into a convolution layer of an initial convolution neural network model for convolution to obtain a convolution result matrix;
inputting the convolution result matrix to an activation layer, and obtaining a feature map according to an activation function;
and inputting the feature map to a pooling layer for feature extraction to obtain an intelligent household appliance feature map.
3. The home appliance control method of claim 2, wherein the inputting the data set of the number of times information and the distance information to the convolution layer of the initial convolution neural network model, performing convolution to obtain a convolution result matrix, comprises:
inputting the data set of the frequency information and the distance information into a convolution layer of an initial convolution neural network model, and acquiring a convolution kernel matrix and a bias matrix;
convolving according to the data set, the convolution kernel matrix and the offset matrix to obtain iteration layer matrix data;
and convolving the convolution kernel matrix and the offset matrix according to the iteration layer matrix data to obtain a convolution result matrix.
4. The appliance control method of claim 1, wherein the feature training the smart appliance feature map through the initial convolutional neural network model to obtain a distance appliance control model comprises:
convolving according to the intelligent household appliance characteristic diagram to obtain candidate window data;
comparing the coincidence ratio of the candidate window data and preset target window data;
acquiring candidate target window data when the coincidence ratio of the candidate window data and the preset target window data is a preset value;
and performing feature training on the candidate target window data through a window regression loss function of the initial convolutional neural network model to obtain a distance household appliance control model.
5. The home appliance control method according to any one of claims 1 to 4, wherein after the target smart home appliance is controlled based on the control instruction, further comprising:
acquiring information of other intelligent household appliances which are in the same Internet of things with the target intelligent household appliance, and monitoring safety information of the other intelligent household appliances;
and when the safety information is abnormal information, transmitting the abnormal information to the vehicle.
6. A home appliance control device, characterized by comprising:
the acquisition module is used for acquiring the current position information of the vehicle and the position information of the intelligent household appliance;
the determining module is used for determining the distance information between the vehicle and the intelligent household appliance according to the current position information and the position information of the intelligent household appliance;
the determining module is further used for determining the intelligent household appliances through a distance household appliance control model according to the distance information to obtain target intelligent household appliances;
the generation module is used for generating a control instruction of the target intelligent household appliance by adopting a preset strategy;
the control module is used for controlling the target intelligent household appliance based on the control instruction;
the determining module is also used for obtaining the frequency information for controlling the intelligent household appliances; performing feature extraction on the frequency information and the distance information based on an initial convolutional neural network model, and generating an intelligent household appliance feature map; performing feature training on the intelligent household appliance feature map through the initial convolutional neural network model to obtain a distance household appliance control model;
the generating module is further used for acquiring an initial control instruction of the target intelligent household appliance; acquiring a time delay component and a code element sequence of a transmission network according to the initial control instruction; obtaining the channel characteristic quantity of the initial control instruction according to the time delay component and the code element sequence; demodulating the channel characteristic quantity according to a characteristic optimization separation algorithm; and generating a control instruction of the target intelligent household appliance according to the demodulation result.
7. A home appliance control apparatus, characterized by comprising: a memory, a processor, and a home appliance control program stored on the memory and executable on the processor, the home appliance control program configured to implement the home appliance control method of any one of claims 1 to 5.
8. A storage medium having stored thereon a home appliance control program which when executed by a processor implements the home appliance control method according to any one of claims 1 to 5.
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