CN113777938A - 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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- 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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- 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 method comprises the steps of monitoring the position of a vehicle and acquiring the position information of intelligent household appliances in real time by interconnecting the vehicle and the household appliances, acquiring the distance information between the vehicle and the intelligent household appliances according to the position of the vehicle and the position of the intelligent household appliances, determining the intelligent household appliances according to the distance information through a distance household appliance control model, and determining the household appliances needing to be started; the control command is generated and transmitted through a preset strategy, the control of the household appliances is realized according to the control command, the intelligent household appliances needing to be started by a user can be calculated according to the distance household appliance control model, the control of the household appliances is realized by generating the control command, the user does not need to operate, and the using effect of the user is improved.
Description
Technical Field
The present invention relates to the field of household appliance technologies, and in particular, to a method, an apparatus, a device, and a storage medium for controlling a household appliance.
Background
Along with the improvement of living standards of people, the requirements of people on intellectualization and high efficiency of household appliances are higher and higher, various intelligent household appliances can be connected and controlled through a wireless router and a mobile phone at present, but a user can only open or close the household appliances through control instruction remote control and cannot automatically open the household appliances according to user habits, the intellectualization degree cannot meet the requirements of the user, 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 instruction network transmission caused by poor recognition capability are solved.
The above is only for the purpose of assisting understanding of the technical aspects of the present invention, and does not represent an admission that the above 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 adaptively 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 an intelligent household appliance;
determining distance information between the vehicle and the intelligent household appliances according to the current position information and the position information of the intelligent household appliances;
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, according to the distance information, of the intelligent household appliance through the distance household appliance control model further includes:
acquiring frequency information for controlling the intelligent household electrical 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 carrying out characteristic training on the intelligent household appliance characteristic diagram through the initial convolutional neural network model to obtain a distance household appliance control model.
Optionally, the initial convolutional neural network model comprises a convolutional layer, an activation layer, and a pooling layer;
the number information and the distance information are subjected to feature extraction 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 sets 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 into an activation layer, and obtaining a characteristic diagram according to an activation function;
and inputting the characteristic diagram into a pooling layer for characteristic extraction to obtain an intelligent household appliance characteristic diagram.
Optionally, the inputting the data set of the number information and the distance information into a convolution layer of an initial convolutional 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;
performing convolution according to the data set, the convolution kernel matrix and the bias matrix to obtain iterative layer matrix data;
and performing convolution according to the iteration layer matrix data, the convolution kernel matrix and the bias matrix to obtain a convolution result matrix.
Optionally, the intelligent household appliance feature map is subjected to feature training through the initial convolutional neural network model to obtain a distance household appliance control model, including:
performing convolution according to the intelligent household appliance characteristic diagram to obtain candidate window data;
comparing the coincidence degree of the candidate window data and preset target window data;
when the coincidence degree of the candidate window data and the preset target window data is a preset value, acquiring candidate target window data;
and performing characteristic 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 household appliance by using 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 appliance based on the control instruction, the method further includes:
acquiring information of other intelligent household appliances 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, sending the abnormal information to the vehicle.
In order to achieve the above object, the present invention also provides 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 appliances according to the current position information and the position information of the intelligent household appliances;
the determining module is further used for determining the intelligent household appliances through the distance household appliance control model according to the distance information to obtain target intelligent household appliances;
the generating 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 further provides 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 being configured to implement the steps of the home appliance control method as described above.
In order to achieve the above object, the present invention further provides a storage medium having a home appliance control program stored thereon, wherein the home appliance control program, when executed by a processor, implements the steps of the home appliance control method as described above.
The method comprises the steps of obtaining current position information of a vehicle and position information of an intelligent household appliance; determining distance information between the vehicle and the intelligent household appliances according to the current position information and the position information of the intelligent household appliances; 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 that the vehicle is interconnected with 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 between 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 distance information is determined through a distance household appliance control model, and the household appliances needing to be started are determined; the control command is generated and transmitted through a preset strategy, the control of the household appliances is realized according to the control command, the intelligent household appliances needing to be started by a user can be calculated according to the distance household appliance control model, the control of the household appliances is realized by generating the control command, the user does not need to operate, and the using effect of the user is improved.
Drawings
Fig. 1 is a schematic structural diagram of a home appliance control device in a hardware operating environment according to an embodiment of the present invention;
FIG. 2 is a schematic flow chart of a first embodiment of the household appliance control method of the present invention;
FIG. 3 is a schematic flow chart of a second embodiment of the household appliance control method of the present invention;
FIG. 4 is a schematic flow chart of a third embodiment of the household appliance control method of the present invention;
fig. 5 is a schematic control diagram between a vehicle end and a home appliance end according to a third embodiment of the home appliance control method of the present invention;
fig. 6 is a block diagram of the first embodiment of the home appliance control device according to the present invention.
The implementation, functional features and advantages of the objects of the present invention will be further explained with reference to the accompanying drawings.
Detailed Description
It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Referring to fig. 1, fig. 1 is a schematic structural diagram of a home appliance control device in a hardware operating 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 (CPU), a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. Wherein a communication bus 1002 is used to enable connective communication between these components. The user interface 1003 may include a Display screen (Display), an input unit such as a Keyboard (Keyboard), and the optional user interface 1003 may also 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 Random Access Memory (RAM) Memory, or may be a Non-Volatile Memory (NVM), such as a disk Memory. The memory 1005 may alternatively be a storage device separate from the processor 1001.
Those skilled in the art will appreciate that the configuration shown in fig. 1 is not intended to be limiting, and may include more or fewer components than those shown, or some components may be combined, or a different arrangement of components.
As shown in fig. 1, a memory 1005, which is a storage medium, may include an operating system, a network communication module, a user interface module, and a home appliance control program.
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 of the home appliance control device according to the present invention may be provided in the home appliance control device, and the home appliance control device calls the home appliance control program stored in the memory 1005 through the processor 1001 and executes the home appliance control method according to the embodiment of the present invention.
An embodiment of the present invention provides a home appliance control method, and referring to fig. 2, fig. 2 is a flowchart illustrating a first embodiment of a home appliance control method according to the present invention.
In this embodiment, the household 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.
It should be noted that the execution main body in this embodiment may be a host controller installed on the intelligent home appliance for controlling the intelligent home appliance, or may be another controller capable of implementing home appliance control, and this embodiment is not limited thereto.
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 in other satellite positioning manners, which is not limited in this embodiment;
it should be understood that the location information of the smart home appliance refers to location information of the smart home appliance in a house, and accordingly, a sensor may be installed in the house to acquire location information of each home appliance in the house.
Step S20: and determining the distance information between the vehicle and the intelligent household appliances according to the current position information and the position information of the intelligent household appliances.
When the intelligent household appliance acquires the position of the vehicle, distance information between the vehicle and the intelligent household appliance is calculated according to the position information of the vehicle and the position information of the household appliance in the house.
Step S30: and determining the intelligent household appliances through a 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, the obtained data set is subjected to neural network training through the neural network training, target data in the data set can be obtained, and the existing data can be predicted according to the trained neural network; the target intelligent household appliance refers to a household appliance which needs to be controlled according to the habit of a user, for example, the intelligent household appliance which needs to be controlled is determined to be an air conditioner according to the distance information and the distance household appliance control model, and the air conditioner is the target intelligent household appliance.
In this embodiment, the control instructions of 1000 vehicles for remotely controlling the intelligent household appliance are counted in advance, and the control instructions include 5 control instruction signals of an air conditioner, a hall lamp, a bathtub, a television and a floor sweeping robot. And 1000 collected data are packed 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 above 5 statistical control command signals, and statistical data of intelligent household appliances such as water heaters and dish washers can be added and updated to the training set, so that the types and the number of the intelligent household appliances can be controlled, and different requirements of users can be met.
It should be understood that trained target data is obtained according to the distance household appliance control model, the trained target data is compared according to distance information between the vehicle and the intelligent household appliance, when the distance information meets the distance information in the target data, the intelligent household appliance corresponding to the distance information in the target data is determined, and the intelligent household appliance is used as the target intelligent household appliance. For example, when the distance information between the vehicle and the intelligent household appliance is that the vehicle is 100m away from the intelligent household appliance, 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 household 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 modulation and demodulation processing of the network transmission channel is realized by adopting a characteristic optimization separation method of an intelligent household appliance control instruction.
In specific implementation, the transmission of the control signal is subjected to interference suppression through a transmission network channel model strategy, and the load of the transmission of the control signal is subjected to balanced control, so that an intelligent household appliance control instruction is generated. For example, if the target intelligent household appliance is a hall lamp according to the determination, the generated intelligent household appliance control command is a control command 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 household appliance according to the acquired control instruction, including turning on or turning off the household appliance. For example, if the received control instruction is to turn on the air conditioner, the air conditioner can be turned on according to the control instruction to turn on the air conditioner, the mode and the temperature of the air conditioner can be adjusted according to the relevant temperature information, and the turn-on 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 distance information between the vehicle and the intelligent household appliances according to the current position information and the position information of the intelligent household appliances; 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 that the vehicle is interconnected with 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 between 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 distance information is determined through a distance household appliance control model, and the household appliances needing to be started are determined; the control command is generated and transmitted through a preset strategy, the control of the household appliances is realized according to the control command, the intelligent household appliances needing to be started by a user can be calculated according to the distance household appliance control model, the control of the household appliances is realized by generating the control command, the user does not need to operate, and the using effect of the user is improved.
Referring to fig. 3, fig. 3 is a flowchart illustrating a household appliance control method according to a second embodiment of the present invention.
Based on the first embodiment, before step S30, the method for controlling a home appliance according to this embodiment further includes:
step S21: and acquiring the frequency information for controlling the intelligent household electrical appliance.
It should be understood that the number information for controlling the intelligent appliance refers to the number of times the intelligent appliance is controlled to be turned on or off, which is counted. For example, the number of times of controlling the air conditioner to be turned on is 10, the number of times of controlling the television to be turned on is 8, and the like, which is not limited in this embodiment.
In a specific implementation, 1000 collected data are packed into a data set x, wherein x is an element of ((C, d)C)，(L，dL)，(B，dB)，(T，dT)，(R，dR) C, L, B, T, R respectively represent the number of times of controlling each home appliance, for example, C represents the number of times of controlling an air conditioner; dC、dL、dB、dT、dREach representing the distance of the vehicle from the appliance when the appliance is turned on, e.g. dCIndicating 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 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.
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 extracting convolution characteristics of data information such as frequency information, distance information and the like to obtain local characteristics of the data information, so that the calculation amount can be reduced; the active layer mainly retains the main characteristics of data obtained by convolution and can reduce 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, and the feature extraction comprises 7 convolution layers, 7 activation layers and 2 pooling layers.
It should be understood that the smart appliance characteristic map includes the distance of the controlled appliance and the category of the controlled appliance, for example, when the distance between the vehicle and the smart appliance is 100m, the controlled smart appliance is a hall lamp; 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, which is not limited in this embodiment.
In a specific implementation, inputting a set x of data of number information for controlling the intelligent household appliance and position information of the vehicle and the intelligent household appliance into the convolutional layer for convolution includes:
in the formula (I), the compound is shown in the specification,
as an input to the last iteration layer,
in order to convolve the kernel matrix with the desired pattern,
in order to be a bias matrix, the bias matrix,
is the output of the current convolutional layer, i is the ith of the input, j is the jth of the input, MjFor the number of inputs, l is the number of layers. When the initial convolution, i.e. the first layer convolution, is performed, the training set x is input to the first convolution layer, i is 1, j is 1, and l is 1, that is, the training set x is obtained by calculation
The convolution calculation of the first convolutional layer is obtained
When performing the second layer convolution, the result of the last convolution, i.e. the convolution result of the last iteration layer, is input
Output of
And obtaining a convolution result matrix according to the convolution of the data set.
From the convolution layer output, there is the equation for the active layer:
in the formula (I), the compound is shown in the specification,
and inputting the convolution result matrix of the convolutional layer into the active layer for activation to obtain an activation function, and calculating and activating the function according to 7 active layers to obtain a characteristic diagram.
The formula for the pooling layer is:
in the formula (I), the compound is shown in the specification,
for the input of the current pooling layer, down () is a down-sampling function.
And performing pooling, namely a secondary characteristic extraction step, on the obtained characteristic graph according to the pooling layer to obtain the intelligent household appliance characteristic graph.
Step S23: and carrying out characteristic training on the intelligent household appliance characteristic diagram 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, a distance household appliance control model is obtained by performing feature training on window data.
In specific implementation, data of a candidate window is obtained by performing convolution on a target intelligent household electrical appliance graph, and the candidate window is located in the center of a convolution kernel.
It should be understood that the candidate window is composed of 9 windows of different sizes, the sizes of the 9 windows are {32 × 64}, {64 × 32}, {32 × 32}, {64 × 128}, {128 × 64}, {64 × 64, 128 × 256, 256 × 128, and {128 × 128}, respectively, for a total of 9 windows of proportionally different area sizes.
It should be noted that, comparing the data of the candidate window with the target window in the training set, and determining whether the data of the candidate window can participate in the training according to the comparison result, where the degree of coincidence IOV between the candidate window and the target window in the training set is:
in the formula, p is a binary label, if p is 1, the candidate window includes the object, which corresponds to a positive label, if p is 0, the candidate window does not include the object and is a background, which corresponds to a negative label, and if p is not used, the candidate window does not include the object and the background, which does not contribute to training the label.
In specific implementation, the coincidence degree of the candidate window data and the preset window data is compared, the preset window data refers to data of a target window in a training set, if the coincidence degree 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 the candidate target window data, and the candidate target window data is subjected to feature training according to a window regression loss function.
The window regression has a starting point (x) in the training set corresponding to the true target window*，y*) Width and height (w) of the real target window*，h*) Corresponding to the starting point (x) of the candidate target windowa，ya) Width and height (w) of the real target windowa，ha) The width and height (w, h) of the real target window correspond to the starting point (x, y) of the predicted target window. And (3) calculating a windowed regression loss function:
in the formula (I), the compound is shown in the specification,
has tx＝(x-xa)/wa，ty＝(y-ya)/ha，
Function of regression loss R when windowregAnd → 0, finishing training of the initial convolutional neural network model to obtain the distance household appliance control model.
The embodiment controls the frequency information of the intelligent household electrical appliance by obtaining; 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; carrying out 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 method comprises the steps of inputting the number information of the intelligent household appliances and the distance information between a vehicle and the intelligent household appliances into an initial convolutional neural network model to carry out convolutional layer, activation layer and pooling layer calculation, extracting the characteristics of the input data information for multiple times, generating an intelligent household appliance characteristic diagram according to the extracted characteristics, carrying out convolution on the intelligent household appliance characteristic diagram to extract candidate window data, training the candidate window data to obtain a distance household appliance control model, extracting a more accurate intelligent household appliance characteristic diagram through neural network training, and determining the household appliances to be controlled more quickly and accurately according to the distance household appliance control model.
Referring to fig. 4, fig. 4 is a flowchart illustrating a method for controlling a home appliance according to a third embodiment of the present invention.
Based on the first embodiment, the step S40 of the household appliance control method in this embodiment specifically includes:
step S41: and acquiring an initial control instruction of the target intelligent household appliance.
It should be understood that the initial control instruction refers to that when a target intelligent household appliance to be controlled is determined through the distance household appliance control model and the distance information between the vehicle and the intelligent household appliance, a corresponding control instruction is generated according to the target intelligent household appliance, for example, when the target intelligent household appliance to be controlled is determined to be a bathtub, the initial control instruction generated at the vehicle end is to control the opening of the bathtub.
Step S42: and acquiring a time delay component and a code element sequence of the transmission network according to the initial control instruction.
In the specific implementation, in order to avoid the problems of poor signal conversion and weak identification capability of the control instruction network for remotely controlling the household appliances, the transmission control of the vehicle-end control instruction is realized by adopting a self-adaptive spread spectrum sequence detection method, and the dynamic migration code element modulation output of the initial control instruction transmission network with the intelligent household appliances is as follows:
it should be understood that, in the process of network transmission of the initial control command, there are interference to transmission caused by surrounding signals and external environments such as buildings, which affects the transmission speed of the network; in order to reduce the influence of external environment interference on control instruction transmission, a cascade filtering method is adopted to suppress the interference of the intelligent household appliance control transmission instruction, and the time delay component of the control instruction transmission network is obtained 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 the instruction transmission process, in order to improve the processing capability of the network on the instruction data, the bandwidths of the network device and the server may be expanded, that is, the load of the initial control instruction transmission of the intelligent appliance is controlled in a balanced manner, and an intelligent appliance control instruction transmission network delay control model is constructed as follows:
in the formula: g (U ∞)μk·∑k) Representing the degree of associative coupling between the control instruction sets; u represents a time delay characteristic component; u. ofkAnd the code element sequence represents the transmission of the intelligent household appliance control command. Therefore, a channel automatic optimization control model for intelligent household appliance control instruction transmission is established, error compensation is carried out by combining a spread spectrum technology of the channel, and a spread spectrum sequence distributed adjustment model for intelligent household appliance control instruction transmission is established.
It should be understood that, according to the dynamic migration code element modulation output of the initial control instruction transmission network of the intelligent household appliance, the optimal solution distribution for controlling the instruction transmission of the intelligent household appliance is obtained as follows:
where i is the ith input, j is the jth input,
and v is the modulation frequency, S is smooth of the window regression loss function, and f is a calculation formula in the activation function. And calculating to obtain a code element sequence transmitted by the intelligent household appliance control instruction according to the optimal solution.
It should be noted that, for the multipath interference characteristic quantity in the channel, the autocorrelation matching filtering method is used to perform interference suppression, and the obtained spectrum distribution characteristic quantity transmitted by the intelligent household appliance control instruction is:
in the formula:
it should be noted that, according to the constructed load balancing scheduling model of the intelligent household appliance control instruction transmission network, the spatial distribution information of the code elements of the intelligent household appliance control instruction transmission network is analyzed, the interference suppression of the intelligent household appliance control instruction is realized by adopting a time-frequency characteristic decomposition and matched filtering method, the short-time energy output by the intelligent household appliance control instruction is obtained, that is, the channel characteristic quantity E of the intelligent household appliance control instruction is obtained according to the frequency spectrum distribution characteristic quantity transmitted by the intelligent household appliance control instructionjComprises the following steps:
step S44: and demodulating the channel characteristic quantity according to a characteristic optimization separation algorithm.
In the specific implementation, the input signal is sampled at a nyquist rate of at least 2 times, and the received signal spectrum beyond the nyquist rate to the frequency of f-N/MT is equalized, so that the optimal design of the intelligent household appliance control instruction transmission is realized, and an optimal transfer function is obtained:
in the formula, N represents the spectrum length of the network transmission signal of the intelligent household appliance control instruction, J is the signal sampling frequency, the multipath interference characteristic quantity in the channel is subjected to interference suppression by adopting an autocorrelation matching filtering method, the intelligent household appliance control instruction channel characteristic quantity is extracted, and the characteristic optimization separation method of the intelligent household appliance control instruction is adopted to realize the channel modulation and demodulation processing of the network transmission.
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 command of the target intelligent appliance refers to a control command of the target intelligent appliance obtained according to an optimized result by detecting the generated transmission network of the initial control command and automatically optimizing the transmission channel of the initial control command. And controlling the target intelligent household appliance based on the control instruction.
In a specific implementation, as shown in fig. 5, the vehicle end uses a vehicle as a mobile device, and the vehicle end as a terminal, and incorporates a wireless power technology, and the home appliance is turned on or off by operating the vehicle. And the household appliance end receives the position of the vehicle in real time, determines the distance between the vehicle and the intelligent household appliance according to the position of the vehicle, determines the intelligent household appliance to be controlled based on the initial convolutional neural network model and generates an initial control instruction, obtains a final control instruction for controlling the intelligent household appliance by optimizing a transmission network of the initial control instruction, and controls the intelligent household appliance. Accordingly, since many unexpected situations may occur when the household appliances are remotely controlled to be turned on or off, for example, a short circuit may be caused when the operating voltage of the household appliances is too high and the user cannot timely know the conditions of the household appliances in the house, a house security detection sensor, such as a smoke sensor, an infrared sensor, a camera, etc., may be installed in the house and connected to the house host controller.
It should be noted that all safety information of the target intelligent household appliance and other intelligent household appliances in the same internet of things with the target intelligent household appliance can be monitored through the sensor and the camera, when the safety information of the intelligent household appliance is abnormal information, the abnormal information can be sent to a vehicle in time, and when the smoke sensor detects that the smoke concentration of a house is abnormal, smoke alarm information and camera information are transmitted to the host controller, and the alarm information is sent to the vehicle end; when the infrared sensor detects that the temperature of the house is abnormal, infrared alarm information and camera information are transmitted to the host controller, and the alarm information is sent to the vehicle end; the smoke signal, the infrared signal and the like are transmitted to the vehicle through real-time monitoring, so that unsafe events such as fire, burglary and the like are effectively avoided.
In the embodiment, the initial control instruction of the target intelligent household appliance is obtained; 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 a demodulation result; the transmission channel of the control command of the control target intelligent household appliance is optimized by obtaining the time delay component and the code element sequence of the transmission command network, the conversion and identification capabilities of the control command network transmission signal of the remote control household appliance are improved, the error rate and the time delay of the command network transmission are low, the scheduling capability of the network transmission is improved, the household appliance control equipment can receive the control command for controlling the intelligent household appliance more quickly and accurately, all safety information in a house is monitored through the safety detection sensor, unsafe events are 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 appliance 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 a vehicle and location information of an intelligent household appliance.
And the determining module 20 is configured to determine distance information between the vehicle and the intelligent household appliance according to the current location information and the location information of the intelligent household appliance.
The determining module 20 is further configured to determine the intelligent household appliance through the distance household appliance control model according to the distance information, so as to obtain the target intelligent household appliance.
And the generating module 30 is configured to generate a control instruction of the target intelligent household appliance by using a preset strategy.
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 distance information between the vehicle and the intelligent household appliances according to the current position information and the position information of the intelligent household appliances; 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 that the vehicle is interconnected with 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 between 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 distance information is determined through a distance household appliance control model, and the household appliances needing to be started are determined; the control command is generated and transmitted through a preset strategy, the control of the household appliances is realized according to the control command, the intelligent household appliances needing to be started by a user can be calculated according to the distance household appliance control model, the control of the household appliances is realized by generating the control command, the user does not need to operate, and the using effect of the user is improved.
In an embodiment, the determining module 20 is further configured to obtain information on the number of times of controlling the intelligent 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 carrying out characteristic training on the intelligent household appliance characteristic diagram 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 number 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 into an activation layer, and obtaining a characteristic diagram according to an activation function; and inputting the characteristic diagram into a pooling layer for characteristic extraction to obtain an intelligent household appliance characteristic diagram.
In an embodiment, the determining module 20 is further configured to input the data set of the number 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; performing convolution according to the data set, the convolution kernel matrix and the bias matrix to obtain iterative layer matrix data; and performing convolution according to the iteration layer matrix data, the convolution kernel matrix and the bias matrix to obtain a convolution result matrix.
In an embodiment, the determining module 20 is further configured to perform convolution according to the intelligent household appliance feature map to obtain candidate window data; comparing the coincidence degree of the candidate window data and preset target window data; when the coincidence degree of the candidate window data and the preset target window data is a preset value, acquiring candidate target window data; and performing characteristic 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 intelligent 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 acquire information of other intelligent home appliances in the same internet of things as the target intelligent home appliance, and monitor security information of the other intelligent home appliances; and when the safety information is abnormal information, sending the abnormal information to the vehicle.
In addition, to achieve the above object, the present invention further provides 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 being configured to implement the steps of the home appliance control method as described above.
Since the home appliance control device adopts all technical solutions of all the embodiments, at least all the beneficial effects brought by the technical solutions of the embodiments are achieved, and no further description is given here.
In addition, an embodiment of the present invention further provides a storage medium, where the storage medium stores a home appliance control program, and the home appliance control program, when executed by a processor, implements the steps of the home appliance control method described above.
Since the storage medium adopts all technical solutions of all the embodiments, at least all the beneficial effects brought by the technical solutions of the embodiments are achieved, and no further description is given here.
It should be understood that the above is only an example, and the technical solution of the present invention is not limited in any way, and in a specific application, a person skilled in the art may set the technical solution as needed, and the present invention is not limited thereto.
It should be noted that the above-described work flows are only exemplary, and do not limit the scope of the present invention, and in practical applications, a person skilled in the art may select some or all of them to achieve the purpose of the solution of the embodiment according to actual needs, and the present invention is not limited herein.
In addition, the technical details that are not described in detail in this embodiment may refer to the home appliance control method provided in any embodiment of the present invention, and are not described herein again.
Further, it is to 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. The term "comprising", without further limitation, means that the element so defined is not excluded from the group of processes, methods, articles, or systems that include the element.
The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.
Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner. Based on such understanding, the technical solution of the present invention or portions thereof that contribute to the prior art may be embodied in the form of a software product, where the computer software product is stored in a storage medium (e.g. Read Only Memory (ROM)/RAM, magnetic disk, optical disk), and includes several instructions for enabling a terminal device (e.g. a mobile phone, a computer, a server, or a network device) to execute the method according to the embodiments of the present invention.
The above description is only a preferred embodiment of the present invention, and not intended to limit the scope of the present invention, and all modifications of equivalent structures and equivalent processes, which are made by using the contents of the present specification and the accompanying drawings, or directly or indirectly applied to other related technical fields, are included in the scope of the present invention.
Claims (10)
1. A home appliance control method, comprising:
acquiring current position information of a vehicle and position information of an intelligent household appliance;
determining distance information between the vehicle and the intelligent household appliances according to the current position information and the position information of the intelligent household appliances;
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.
2. The method of controlling home appliances according to claim 1, wherein the determining the smart home appliance through the distance home appliance control model according to the distance information further includes, before obtaining the target smart home appliance:
acquiring frequency information for controlling the intelligent household electrical 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 carrying out characteristic training on the intelligent household appliance characteristic diagram through the initial convolutional neural network model to obtain a distance household appliance control model.
3. The appliance control method of claim 2, wherein the initial convolutional neural network model comprises a convolutional layer, an activation layer, and a pooling layer;
the number information and the distance information are subjected to feature extraction 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 sets 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 into an activation layer, and obtaining a characteristic diagram according to an activation function;
and inputting the characteristic diagram into a pooling layer for characteristic extraction to obtain an intelligent household appliance characteristic diagram.
4. The household appliance control method according to claim 3, wherein the step of inputting the data sets of the number information and the distance information into a convolution layer of an initial convolutional neural network model and 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;
performing convolution according to the data set, the convolution kernel matrix and the bias matrix to obtain iterative layer matrix data;
and performing convolution according to the iteration layer matrix data, the convolution kernel matrix and the bias matrix to obtain a convolution result matrix.
5. The household appliance control method according to claim 2, wherein the 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 comprises:
performing convolution according to the intelligent household appliance characteristic diagram to obtain candidate window data;
comparing the coincidence degree of the candidate window data and preset target window data;
when the coincidence degree of the candidate window data and the preset target window data is a preset value, acquiring candidate target window data;
and performing characteristic 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.
6. The appliance control method according to any one of claims 1 to 5, wherein the generating of the control command of the target intelligent appliance using 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.
7. The appliance control method according to any one of claims 1 to 5, wherein after the controlling the target smart appliance based on the control command, further comprising:
acquiring information of other intelligent household appliances 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, sending the abnormal information to the vehicle.
8. A home appliance control device, 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 appliances according to the current position information and the position information of the intelligent household appliances;
the determining module is further used for determining the intelligent household appliances through the distance household appliance control model according to the distance information to obtain target intelligent household appliances;
the generating 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.
9. An appliance control device, 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 being configured to implement the home appliance control method according to any one of claims 1 to 7.
10. A storage medium having a home appliance control program stored thereon, the home appliance control program realizing the home appliance control method according to any one of claims 1 to 7 when executed by a processor.
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114383282A (en) * | 2021-12-16 | 2022-04-22 | 青岛海尔空调器有限总公司 | Control method and control system of air conditioner, electronic device and storage medium |
| CN114448965A (en) * | 2021-12-22 | 2022-05-06 | 天翼云科技有限公司 | Method, device and system for managing big data assembly and readable storage medium |
| CN116876950A (en) * | 2023-09-05 | 2023-10-13 | 山东智赢门窗科技有限公司 | Intelligent door and window control system and method, computer equipment and storage medium |
| CN116991089A (en) * | 2023-09-28 | 2023-11-03 | 深圳市微琪思网络有限公司 | Intelligent control method and system for electric iron based on wireless connection |
Citations (30)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN201114174Y (en) * | 2007-03-02 | 2008-09-10 | 李东江 | Wireless interdynamic multimedia administrative system for food and rink service based on ZigBee protocol |
| WO2010045954A1 (en) * | 2008-10-22 | 2010-04-29 | Telecom Italia S.P.A. | Method for setting the operation of a routing node of an asynchronous wireless communication network, network node and communication network implementing the method |
| AU2013205479A1 (en) * | 2012-09-13 | 2014-03-27 | Cognian Technologies Ltd | Systems, methods and devices for networking over a network |
| CN104536346A (en) * | 2014-12-18 | 2015-04-22 | 珠海格力电器股份有限公司 | Intelligent device control method and system |
| CN104597761A (en) * | 2014-12-31 | 2015-05-06 | 珠海格力电器股份有限公司 | Control method and device of intelligent household appliances |
| CN104881009A (en) * | 2015-04-30 | 2015-09-02 | 广东美的制冷设备有限公司 | Household electrical appliance control method and apparatus |
| CN105759620A (en) * | 2016-02-04 | 2016-07-13 | 智车优行科技（北京）有限公司 | Intelligent vehicle and intelligent equipment control method based on intelligent vehicle |
| CN105791374A (en) * | 2014-12-26 | 2016-07-20 | 北京奇虎科技有限公司 | Smart home vehicle-mounted control system |
| CN107018049A (en) * | 2017-03-09 | 2017-08-04 | 广东美的制冷设备有限公司 | Home appliance distribution methods, devices and systems |
| CN107065627A (en) * | 2017-06-02 | 2017-08-18 | 昆山锐芯微电子有限公司 | The control method of smart machine, apparatus and system, storage medium |
| CN107247962A (en) * | 2017-05-23 | 2017-10-13 | 华中科技大学 | A kind of real-time electrical appliance recognition and system based on sliding window |
| CN108076214A (en) * | 2016-11-10 | 2018-05-25 | 三星电子株式会社 | Electronic equipment and the data transmission method performed in the electronic equipment |
| CN108599975A (en) * | 2018-01-31 | 2018-09-28 | 宋星 | a kind of intelligent building management system |
| CN109086912A (en) * | 2018-07-11 | 2018-12-25 | 东北大学 | A kind of intelligent appliance user's behavior prediction method and apparatus based on LSTM |
| CN109167711A (en) * | 2018-11-02 | 2019-01-08 | 珠海格力电器股份有限公司 | A kind of control method of household electrical appliances, device, storage medium and server |
| CN109212996A (en) * | 2018-11-13 | 2019-01-15 | 奇瑞汽车股份有限公司 | Intelligent home equipment control method and device |
| CN110147067A (en) * | 2019-06-26 | 2019-08-20 | 东莞育商智能科技有限公司 | Using distance as the method and system of remote auto control equipment |
| CN110262413A (en) * | 2019-05-29 | 2019-09-20 | 深圳市轱辘汽车维修技术有限公司 | Intelligent home furnishing control method, control device, car-mounted terminal and readable storage medium storing program for executing |
| CN110308661A (en) * | 2019-06-06 | 2019-10-08 | 美的集团股份有限公司 | Smart machine control method and device based on machine learning |
| CN110460377A (en) * | 2019-07-25 | 2019-11-15 | 浙江大学 | A kind of device carrying out LED light communication using driving circuit switching harmonics |
| KR20190134891A (en) * | 2018-05-08 | 2019-12-05 | 현대자동차주식회사 | METHOD FOR MATCHING IoT DEVICE OF VEHICLE TERMINAL AND VEHICLE TERMINAL THEREFOR |
| US20190383865A1 (en) * | 2019-07-11 | 2019-12-19 | Lg Electronics Inc. | Intelligent method for controlling home appliance, apparatus and intelligent computing device for controlling home appliance |
| CN110716446A (en) * | 2019-11-29 | 2020-01-21 | 的卢技术有限公司 | Method and system for controlling home based on Internet of vehicles |
| US20200167834A1 (en) * | 2017-12-28 | 2020-05-28 | Google Llc | Intelligent identification and provisioning of devices and services for a smart home environment |
| CN111340210A (en) * | 2020-02-18 | 2020-06-26 | 深圳市腾讯信息技术有限公司 | User position identification model construction method and user position identification method |
| CN111435237A (en) * | 2019-12-30 | 2020-07-21 | 合肥移顺信息技术有限公司 | Electric appliance control method and device, electronic equipment and storage medium |
| CN111880488A (en) * | 2020-06-29 | 2020-11-03 | 海尔优家智能科技（北京）有限公司 | Method, device and equipment for acquiring position of household appliance |
| CN112446405A (en) * | 2019-09-04 | 2021-03-05 | 杭州九阳小家电有限公司 | User intention guiding method for home appliance customer service and intelligent home appliance |
| US20210112389A1 (en) * | 2019-10-09 | 2021-04-15 | Nokia Technologies Oy | Access mechanism for single purpose operation |
| CN113205807A (en) * | 2021-04-06 | 2021-08-03 | 珠海格力电器股份有限公司 | Voice equipment control method and device, storage medium and voice equipment |
-
2021
- 2021-08-31 CN CN202111017559.1A patent/CN113777938B/en active Active
Patent Citations (31)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN201114174Y (en) * | 2007-03-02 | 2008-09-10 | 李东江 | Wireless interdynamic multimedia administrative system for food and rink service based on ZigBee protocol |
| WO2010045954A1 (en) * | 2008-10-22 | 2010-04-29 | Telecom Italia S.P.A. | Method for setting the operation of a routing node of an asynchronous wireless communication network, network node and communication network implementing the method |
| CN102227891A (en) * | 2008-10-22 | 2011-10-26 | 意大利电信股份公司 | Method for setting operation of routing node of asynchronous wireless communication network, network node and communication network implementing method |
| AU2013205479A1 (en) * | 2012-09-13 | 2014-03-27 | Cognian Technologies Ltd | Systems, methods and devices for networking over a network |
| CN104536346A (en) * | 2014-12-18 | 2015-04-22 | 珠海格力电器股份有限公司 | Intelligent device control method and system |
| CN105791374A (en) * | 2014-12-26 | 2016-07-20 | 北京奇虎科技有限公司 | Smart home vehicle-mounted control system |
| CN104597761A (en) * | 2014-12-31 | 2015-05-06 | 珠海格力电器股份有限公司 | Control method and device of intelligent household appliances |
| CN104881009A (en) * | 2015-04-30 | 2015-09-02 | 广东美的制冷设备有限公司 | Household electrical appliance control method and apparatus |
| CN105759620A (en) * | 2016-02-04 | 2016-07-13 | 智车优行科技（北京）有限公司 | Intelligent vehicle and intelligent equipment control method based on intelligent vehicle |
| CN108076214A (en) * | 2016-11-10 | 2018-05-25 | 三星电子株式会社 | Electronic equipment and the data transmission method performed in the electronic equipment |
| CN107018049A (en) * | 2017-03-09 | 2017-08-04 | 广东美的制冷设备有限公司 | Home appliance distribution methods, devices and systems |
| CN107247962A (en) * | 2017-05-23 | 2017-10-13 | 华中科技大学 | A kind of real-time electrical appliance recognition and system based on sliding window |
| CN107065627A (en) * | 2017-06-02 | 2017-08-18 | 昆山锐芯微电子有限公司 | The control method of smart machine, apparatus and system, storage medium |
| US20200167834A1 (en) * | 2017-12-28 | 2020-05-28 | Google Llc | Intelligent identification and provisioning of devices and services for a smart home environment |
| CN108599975A (en) * | 2018-01-31 | 2018-09-28 | 宋星 | a kind of intelligent building management system |
| KR20190134891A (en) * | 2018-05-08 | 2019-12-05 | 현대자동차주식회사 | METHOD FOR MATCHING IoT DEVICE OF VEHICLE TERMINAL AND VEHICLE TERMINAL THEREFOR |
| CN109086912A (en) * | 2018-07-11 | 2018-12-25 | 东北大学 | A kind of intelligent appliance user's behavior prediction method and apparatus based on LSTM |
| CN109167711A (en) * | 2018-11-02 | 2019-01-08 | 珠海格力电器股份有限公司 | A kind of control method of household electrical appliances, device, storage medium and server |
| CN109212996A (en) * | 2018-11-13 | 2019-01-15 | 奇瑞汽车股份有限公司 | Intelligent home equipment control method and device |
| CN110262413A (en) * | 2019-05-29 | 2019-09-20 | 深圳市轱辘汽车维修技术有限公司 | Intelligent home furnishing control method, control device, car-mounted terminal and readable storage medium storing program for executing |
| CN110308661A (en) * | 2019-06-06 | 2019-10-08 | 美的集团股份有限公司 | Smart machine control method and device based on machine learning |
| CN110147067A (en) * | 2019-06-26 | 2019-08-20 | 东莞育商智能科技有限公司 | Using distance as the method and system of remote auto control equipment |
| US20190383865A1 (en) * | 2019-07-11 | 2019-12-19 | Lg Electronics Inc. | Intelligent method for controlling home appliance, apparatus and intelligent computing device for controlling home appliance |
| CN110460377A (en) * | 2019-07-25 | 2019-11-15 | 浙江大学 | A kind of device carrying out LED light communication using driving circuit switching harmonics |
| CN112446405A (en) * | 2019-09-04 | 2021-03-05 | 杭州九阳小家电有限公司 | User intention guiding method for home appliance customer service and intelligent home appliance |
| US20210112389A1 (en) * | 2019-10-09 | 2021-04-15 | Nokia Technologies Oy | Access mechanism for single purpose operation |
| CN110716446A (en) * | 2019-11-29 | 2020-01-21 | 的卢技术有限公司 | Method and system for controlling home based on Internet of vehicles |
| CN111435237A (en) * | 2019-12-30 | 2020-07-21 | 合肥移顺信息技术有限公司 | Electric appliance control method and device, electronic equipment and storage medium |
| CN111340210A (en) * | 2020-02-18 | 2020-06-26 | 深圳市腾讯信息技术有限公司 | User position identification model construction method and user position identification method |
| CN111880488A (en) * | 2020-06-29 | 2020-11-03 | 海尔优家智能科技（北京）有限公司 | Method, device and equipment for acquiring position of household appliance |
| CN113205807A (en) * | 2021-04-06 | 2021-08-03 | 珠海格力电器股份有限公司 | Voice equipment control method and device, storage medium and voice equipment |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114383282A (en) * | 2021-12-16 | 2022-04-22 | 青岛海尔空调器有限总公司 | Control method and control system of air conditioner, electronic device and storage medium |
| CN114448965A (en) * | 2021-12-22 | 2022-05-06 | 天翼云科技有限公司 | Method, device and system for managing big data assembly and readable storage medium |
| CN116876950A (en) * | 2023-09-05 | 2023-10-13 | 山东智赢门窗科技有限公司 | Intelligent door and window control system and method, computer equipment and storage medium |
| CN116876950B (en) * | 2023-09-05 | 2023-12-05 | 山东智赢门窗科技有限公司 | Intelligent door and window control system and method, computer equipment and storage medium |
| CN116991089A (en) * | 2023-09-28 | 2023-11-03 | 深圳市微琪思网络有限公司 | Intelligent control method and system for electric iron based on wireless connection |
| CN116991089B (en) * | 2023-09-28 | 2023-12-05 | 深圳市微琪思网络有限公司 | Intelligent control method and system for electric iron based on wireless connection |
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