CN110831116A - Household appliance network distribution method, storage medium and terminal - Google Patents
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Abstract
The invention discloses a household appliance network distribution method, a storage medium and a terminal, wherein the household appliance network distribution method comprises the following steps: acquiring image data of a target household appliance; determining the model of the target household appliance by using a household appliance identification model based on the image data; determining distribution network guidance information of the target household appliance according to the model of the target household appliance based on the corresponding relation between the known models of various household appliances and the corresponding distribution network guidance information, wherein the distribution network guidance information comprises relevant information of how to configure the broadcast communication function of the target household appliance; and when the communication signal broadcasted by the target household appliance is received, the target household appliance is distributed with a network. The method and the device can quickly inquire the distribution network guidance information of the target household appliance, and solve the problem that a user searches the distribution network guidance information corresponding to a certain type of household appliance from a mall, which is time-consuming and labor-consuming.
Description
Technical Field
The invention belongs to the technical field of smart home, and particularly relates to a household appliance network distribution method, a storage medium and a terminal.
Background
With the rapid development of the internet of things, intelligent household appliances such as household appliances with networking functions are purchased by many families at present, and because the household appliances with the networking functions not only provide convenience for the life of people, but also are convenient for the management and control of all the intelligent household appliances of the whole family by people. For example, each intelligent household appliance can not only perform data interaction with other intelligent household appliances through networking, but also realize data interaction with the terminal through the cloud end, so that the user can check even the intelligent household appliances in the remote control home through the terminal even if the user is not at home. In daily life, because the types of intelligent household appliances are many, and different types of intelligent household appliances need different distribution network guidance methods, users face the following problems: after purchasing different kinds of intelligent household appliances, how to distribute the network to the different kinds of intelligent household appliances is unknown.
In the prior art, there are two solutions: firstly, the user downloads the distribution network guide information from the mall, and as the types of the intelligent household appliances in the mall are many and can be increased continuously, the user searches the distribution network guide information corresponding to the household appliances of a certain model from the mall, which is time-consuming and labor-consuming. And secondly, a set of distribution network specifications is matched for each intelligent household appliance or a section of distribution network guidance video is provided, but the distribution network is a complex process, so that a user is often difficult to independently complete the distribution network according to the distribution network specifications and the distribution network guidance video, and meanwhile, as a seller, various intelligent household appliance merchants need to match a set of distribution network specifications for each product or provide a section of distribution network guidance video, so that the time and the labor are wasted, and good help is not provided for the user distribution network.
There is a need for a method, a storage medium, and a terminal for configuring a network for a home appliance.
Disclosure of Invention
The invention aims to solve the technical problems that in the prior art, a user searches distribution network guidance information corresponding to a certain type of household appliance from a mall, the time and the labor are wasted, and the user is difficult to independently complete distribution network according to a distribution network specification and a distribution network guidance video.
In order to solve the problems, the invention provides a household appliance network distribution method, a storage medium and a terminal.
In a first aspect, the present invention provides a network distribution method for home appliances, including the following steps:
acquiring image data of a target household appliance;
determining the model of the target household appliance by using a household appliance identification model based on the image data;
determining distribution network guidance information of the target household appliance according to the model of the target household appliance based on the corresponding relation between the known models of various household appliances and the corresponding distribution network guidance information, wherein the distribution network guidance information comprises relevant information of how to configure the broadcast communication function of the target household appliance;
and when the communication signal broadcasted by the target household appliance is received, the target household appliance is distributed with a network.
According to an embodiment of the present invention, preferably, the image data of the target home appliance is obtained by:
when a household appliance distribution network request is received, the camera device is controlled to be continuously started for a preset time length so as to collect multi-frame image data of a target household appliance.
According to an embodiment of the present invention, preferably, the appliance identification model is a convolutional neural network model obtained by inputting image data of a plurality of known appliances into a convolutional neural network for training.
According to an embodiment of the present invention, preferably, determining the model of the target appliance by using an appliance identification model based on the image data includes:
determining a possible model and a probability of the possible model of the target appliance based on the image data using an appliance identification model;
judging whether the probability of the possible models is greater than or equal to a preset threshold value:
when the probability of the possible model is larger than or equal to a preset threshold value, taking the possible model as the model of the target household appliance;
and when the probability of the possible models is smaller than a preset threshold value, discarding the possible models.
According to the embodiment of the present invention, preferably, when the number of the possible models with the probability greater than or equal to the preset threshold is single, determining the distribution network guidance information of the target household appliance includes the following steps:
and searching distribution network guide information corresponding to the possible models in a corresponding relation table of the models of various known household appliances and the corresponding distribution network guide information according to the possible models, and taking the searched distribution network guide information as the distribution network guide information of the target household appliance.
According to the embodiment of the present invention, preferably, when the number of the possible models with the probability greater than or equal to the preset threshold is multiple, determining the distribution network guidance information of the target household appliance includes the following steps:
selecting one possible model from a plurality of possible models as the model of the target household appliance;
and searching distribution network guide information corresponding to the model of the target household appliance in a corresponding relation table of the model of various known household appliances and the corresponding distribution network guide information according to the model of the target household appliance, wherein the searched distribution network guide information is used as the distribution network guide information of the target household appliance.
According to the embodiment of the present invention, preferably, when receiving the communication signal broadcasted by the target appliance, the network distribution method for the target appliance includes the following steps:
when a communication signal broadcasted by the target household appliance is received, sending network login information to the target household appliance so that the target household appliance can be connected to a network through the network login information;
and when receiving the household appliance identification information sent by the target household appliance through the network, binding the household appliance identification information with the corresponding household appliance distribution network authority information, and storing the bound household appliance identification information and the household appliance distribution network authority information to the cloud.
According to the embodiment of the present invention, preferably, when receiving the communication signal broadcasted by the target appliance, the network distribution method for the target appliance further includes the following steps:
and storing the network login information sent to the target household appliance every time the target household appliance is connected with a network, so that the target household appliance can be automatically matched, connected and accessed to the network according to the stored network login information.
In a second aspect, the present invention provides a storage medium, on which a computer program is stored, wherein the computer program, when executed by a processor, implements the steps of the household appliance network distribution method as described above.
In a third aspect, the present invention provides a terminal, which includes an image capturing device, a storage medium, and a processor, where the memory stores a computer program, and the computer program, when executed by the processor, implements the steps of the above-mentioned household appliance network distribution method.
Compared with the prior art, one or more embodiments in the above scheme can have the following advantages or beneficial effects:
1) the household appliance network distribution method is applied, the model of the target household appliance is determined by using a household appliance identification model based on the image data, the network distribution guide information of the target household appliance is determined according to the model of the target household appliance based on the corresponding relation between the known models of various household appliances and the corresponding network distribution guide information, the network distribution guide information of the target household appliance is selected from the network distribution guide information of various household appliances by an AI (artificial intelligence) technology, the network distribution guide information of the target household appliance can be quickly inquired, the difficulty of inquiring the network distribution guide information of the target household appliance is reduced for a user, and the problem that the user wastes time and labor when searching the network distribution guide information corresponding to the household appliance of a certain model from a mall is solved;
2) by applying the household appliance network distribution method, when a communication signal broadcasted by the target household appliance is received, network login information is sent to the target household appliance, so that the target household appliance can be connected to the network through the network login information, and pre-stored household appliance identity identification information is sent after the target household appliance is connected to the network; the household appliance identity identification information is received, the household appliance identity identification information and the household appliance distribution network authority information are bound, the bound household appliance identity identification information and the household appliance distribution network authority information are stored to the cloud, a user can be assisted to realize household appliance distribution network, the distribution network difficulty of the user on the household appliance is reduced, and the problem that the user is difficult to independently complete distribution network according to a distribution network specification and a distribution network guidance video is solved.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by the practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention. In the drawings:
fig. 1 shows a flowchart of a household appliance network distribution method according to an embodiment of the present invention;
fig. 2 shows a flowchart of a network distribution method for a second household appliance according to an embodiment of the present invention;
fig. 3 shows a flowchart of a network distribution method for three home appliances according to an embodiment of the present invention;
fig. 4 shows an application flowchart of a network distribution method for three home appliances in the embodiment of the present invention.
Detailed Description
The following detailed description of the embodiments of the present invention will be provided with reference to the drawings and examples, so that how to apply the technical means to solve the technical problems and achieve the technical effects can be fully understood and implemented. It should be noted that, as long as there is no conflict, the embodiments and the features of the embodiments of the present invention may be combined with each other, and the technical solutions formed are within the scope of the present invention.
Example one
In order to solve the technical problems in the prior art, the embodiment of the invention provides a household appliance network distribution method.
Referring to fig. 1, the household appliance network distribution method according to the embodiment of the present invention includes the following steps:
s110, inputting known image data of various household appliances into a convolutional neural network for training to obtain a convolutional neural network model as a household appliance identification model;
s120, judging whether a household appliance distribution network request is received: if yes, go to step S130; if not, no response is given;
s130, controlling the camera device to be continuously started for a preset time length so as to collect multi-frame image data of the target household appliance;
s140, determining the model of the target household appliance by using a household appliance identification model based on the image data;
s150, determining distribution network guide information of the target household appliance according to the model of the target household appliance based on the corresponding relation between the known models of various household appliances and the corresponding distribution network guide information, wherein the distribution network guide information comprises relevant information of how to configure the broadcast communication function of the target household appliance;
s160, determining whether the communication signal broadcasted by the target appliance is received: if yes, carrying out distribution network on the target household appliance; if not, no response is given.
Example two
In order to solve the technical problem in the prior art, an embodiment of the present invention provides a network distribution method for home appliances based on the first embodiment, where the network distribution method for home appliances in the first embodiment of the present invention is further improved in step S140, step S150, and step S160, and in this embodiment, the number of the possible models with a probability greater than or equal to a preset threshold is a single model.
Referring to fig. 2, the household appliance network distribution method according to the embodiment of the present invention includes the following steps:
s210, inputting known image data of various household appliances into a convolutional neural network for training to obtain a convolutional neural network model as a household appliance identification model;
s220, judging whether a household appliance distribution network request is received: if yes, go to step S230; if not, no response is given;
s230, controlling the camera device to be continuously started for a preset time length so as to acquire multi-frame image data of the target household appliance;
s241, determining the possible model and the probability of the possible model of the target household appliance by using a household appliance identification model based on the image data;
s242, judging whether the probability of the possible models is greater than or equal to a preset threshold value: if so, taking the possible model as the model of the target household appliance; if not, discarding the possible models;
s251, according to the single possible model with the probability greater than or equal to the preset threshold, searching distribution network guidance information corresponding to the possible model in a correspondence table between models of various known household appliances and corresponding distribution network guidance information, and using the searched distribution network guidance information as distribution network guidance information of the target household appliance, where the distribution network guidance information includes information on how to configure a broadcast communication function of the target household appliance;
s261, determining whether the communication signal broadcasted by the target appliance is received: if yes, go to step S262; if not, no response is given;
s262, sending network login information to the target household appliance, so that the target household appliance can be connected to a network through the network login information;
s263, when receiving the household appliance identification information sent by the target household appliance through the network, binding the household appliance identification information with the corresponding household appliance distribution network permission information, and storing the bound household appliance identification information and the household appliance distribution network permission information to a cloud end;
and S264, storing the network login information sent to the target household appliance every time the target household appliance is connected with the network, so that the target household appliance can be automatically connected and connected to the network according to the stored network login information.
The following describes, by way of example, a case where the model of the target appliance is a single model;
in step S241, based on the image data, a possible model and a probability of the possible model of the target appliance, for example, an appliance of model a, are determined by using an appliance identification model, where the probability is 90%; b type household appliances, the probability is 40%; type C household appliances, the probability is 10%;
in step S242, it is determined whether the probability of the possible model is greater than or equal to a preset threshold, for example, 50%: if so, taking the possible model as the model of the target household appliance; and if not, discarding the possible models, so that only the A-type household appliance is used as the model of the target household appliance.
In step S251, according to the possible model, network distribution guidance information corresponding to the possible model is searched in a correspondence table between models of various known household appliances and corresponding network distribution guidance information, and the searched network distribution guidance information is used as the network distribution guidance information of the target household appliance, so that the target household appliance can broadcast a communication signal based on the network distribution guidance information. In this case, only one model is the model of the target appliance, and when it is determined in step S242 that only the appliance of model a is the model of the target appliance, the distribution network guidance information interface corresponding to the appliance of model a may be directly skipped, where the distribution network guidance information may be displayed in the form of a graphic description and/or an audio-video.
EXAMPLE III
In order to solve the technical problems in the prior art, an embodiment of the present invention provides a network distribution method for home appliances based on the second embodiment, where in the present embodiment, the number of the possible models with the probability greater than or equal to the preset threshold is multiple.
Referring to fig. 3, the household appliance network distribution method according to the embodiment of the present invention includes the following steps:
s310, inputting known image data of various household appliances into a convolutional neural network for training to obtain a convolutional neural network model as a household appliance identification model;
s320, judging whether a household appliance distribution network request is received: if yes, go to step S130; if not, no response is given;
s330, controlling the camera device to be continuously started for a preset time length so as to acquire multi-frame image data of the target household appliance;
s341, based on the multi-frame image data, determining the possible model and the probability of the possible model of the target household appliance by using a household appliance identification model;
s342, judging whether the probability of the possible models is greater than or equal to a preset threshold value: if so, taking the possible model as the model of the target household appliance; if not, discarding the possible models;
s351, selecting one possible model from a plurality of possible models with the probability greater than or equal to a preset threshold value as the model of the target household appliance;
s352, according to the model of the target household appliance, searching distribution network guide information corresponding to the model of each type of known household appliance in a corresponding relation table of the model of each type of known household appliance and the corresponding distribution network guide information, wherein the searched distribution network guide information is used as the distribution network guide information of the target household appliance, and the distribution network guide information comprises relevant information on how to configure the broadcast communication function of the target household appliance;
s361, determining whether the communication signal broadcasted by the target appliance is received: if yes, go to step S362; if not, no response is given;
s362, sending network login information to the target household appliance, so that the target household appliance can access the network through the network login information;
s363, when receiving the home appliance identification information sent by the target home appliance through the network, binding the home appliance identification information with the corresponding home appliance distribution network permission information, and storing the bound home appliance identification information and home appliance distribution network permission information to a cloud;
and S364, storing the network login information sent to the target household appliance every time the target household appliance is connected with the network, so that the target household appliance can be automatically matched, connected and connected to the network according to the stored network login information.
In step S310, deep learning is initially promoted in image recognition, but in a few years, deep learning is promoted in various fields of machine learning. Deep learning today performs very well in many areas of machine learning. The deep learning not only breaks through the bottleneck of the image classification technology, but also breaks through the bottleneck of the object identification technology. Convolutional neural networks are one of the representative algorithms for deep learning. In this step, known image data of various home appliances are input to a convolutional neural network for training to obtain a convolutional neural network model as a home appliance identification model, which is specifically realized as follows:
firstly, pictures are collected, household appliances are photographed under different light environments according to different types, images containing the household appliances are obtained, and the trained model has better environmental adaptability;
secondly, cutting the images containing the household appliances according to the types of the household appliances to obtain household appliance images of different types of the household appliances;
thirdly, preprocessing the household appliance images of different household appliance models, such as automatically cutting by using a tool of the Tansorflow, rotating the images by an interpolation method, adjusting the colors of the images, and encrypting and decrypting the images;
fourthly, the preprocessed household appliance images of different household appliance models are sorted into batch to be used as the input of the convolutional neural network;
fifthly, training is carried out by using a convolutional neural network, an inclusion-V3 model can be used, wherein the inclusion-V3 is divided into 46 layers and consists of 11 inclusion modules, each inclusion module uses filters with different sizes, and then the obtained matrixes are spliced;
and sixthly, training the Incep-V3 algorithm, and taking the trained convolutional neural network as a household appliance identification model.
When the convolutional neural network is used for training, a transfer learning technology can be used for accelerating the training speed, and a neural network model with a good effect can be trained in a short time by using a small amount of data.
The following describes, by way of example, a case where the model of the target appliance is multiple models;
in step S341, based on the appliance identification model, determining a possible model and a probability of the possible model corresponding to the target appliance, for example, a D-model appliance, according to the image data of the target appliance, where the probability is 90%; type E home appliances, the probability is 80%; type F household appliances, the probability is 30%;
in step S342, it is determined whether the probability of the possible model is greater than or equal to a preset threshold, for example, 50%: if so, taking the possible model as the model of the target household appliance; if not, discarding the possible models, so that two models of D-type household appliances and E-type household appliances are used as the models of the target household appliances;
in step S351, a model is determined from the possible models as the model of the target appliance, for example, the target appliance purchased by the user is a D model, and the selection instruction input by the user is the D model, in which case the D model is the model of the target appliance.
Referring to fig. 4, the application process of the household appliance power distribution method in the embodiment of the present invention executed in the mobile phone app by taking the electric cooker as an example is as follows:
the user takes pictures of various household appliances including the electric cooker to obtain pictures of the various household appliances;
the user classifies the photos of various household appliances and acquires the classified photos of the household appliances;
training the classified household appliance pictures by using a convolutional neural network, and outputting a trained household appliance identification model;
the user clicks the mobile phone app, the camera is automatically started, the user shoots the electric cooker to be distributed with the network, and the mobile phone app transmits video stream data shot by the camera to the trained household appliance identification model in the shooting process of the camera;
the method comprises the steps that when shooting time of a mobile phone app reaches a preset time period, for example, 5s, a rice cooker model list matched with video stream data is popped up, wherein the preset time period is set for shooting the video stream data, multi-frame images of household appliances to be distributed are obtained, each frame image is identified based on a household appliance identification model, the multi-frame images are identified and verified for multiple times based on the household appliance identification model, the multi-frame images are identified and verified for multiple times, the multi-frame images of the rice cooker to be distributed can be identified, and accuracy of matching the rice cooker models is improved;
selecting a corresponding electric cooker model from the electric cooker model list by a user according to the model of the electric cooker to be distributed with the network;
the mobile phone app jumps to a distribution network guidance information interface corresponding to the electric cooker model selected by the user according to the electric cooker model selected by the user;
a user presses a reset key of the electric cooker according to the distribution network guidance information to trigger the electric cooker to broadcast a hot spot signal;
when the mobile phone app receives the thermoelectric signal broadcasted by the electric cooker, prompting a user to click the next step;
when the user clicks the 'next step', the mobile phone app displays the SSID number and the password of the WIFI stored in the mobile phone app, wherein the password of the WIFI can be manually input by the user or directly acquired from the mobile phone;
when a complete account number and a complete password of WIFI are displayed on the mobile phone app interface, prompting a user to click 'next';
when the user clicks the 'next step', the mobile phone app sends the SSID number and the password of the WIFI to the electric cooker;
the electric cooker logs in the SSID number and the password of the WIFI, is connected with the WIFI, and sends the identification information of the electric cooker to the mobile phone app through the WIFI (a local area network consisting of a mobile phone, the electric cooker and a router);
when the mobile phone app receives the identification information of the electric cooker, a plurality of selectable use position options of the electric cooker, such as a kitchen, a living room, a bedroom and the like, are displayed;
after the user selects the use position of the electric cooker, the mobile phone app binds the identification information of the electric cooker with the use position and stores the identification information in the mobile phone app, so that the user can conveniently manage the position distribution of household appliances in a family;
the mobile phone app also binds the identity identification information of the electric cooker with the account and the password for logging in the mobile phone app, and uploads the identity identification information to the cloud end, so that when a user replaces a mobile phone, the user still can be in the distribution network of the electric cooker as long as the mobile phone app for the household appliance distribution network is downloaded and the account and the password of the mobile phone app are logged in;
when the use position of the electric cooker is changed, the mobile phone app provides the SSID number and the password of the connectable WIFI within the preset range of the electric cooker again according to the detected WIFI, and the electric cooker is newly provided with a network;
the electric cooker stores the SSID number and the password of the WIFI when the network is distributed each time, so that when the using position of the electric cooker is changed, if the SSID number and the password of the WIFI covering the position are stored in the electric cooker, the network switching can be automatically realized.
According to the embodiment of the invention, firstly, the type of the household appliance is identified through an AI technology according to the appearance of the appliance, and the possible appliance type is directly jumped to, so that the process of operating the distribution network by a user is greatly reduced, the user does not need to read a stiff instruction book for operation as long as the AI image identification is started, and the operation is guided through voice and an electronic instruction document, so that the interaction is friendly, and the customer experiences differently.
Example four
In order to solve the above technical problems in the prior art, an embodiment of the present invention further provides a storage medium.
The storage medium of the present embodiment has stored thereon a computer program which, when executed by a processor, implements the steps of the method in the above-described embodiments.
EXAMPLE five
In order to solve the technical problems in the prior art, the embodiment of the invention also provides a terminal.
The terminal of the present embodiment includes an image pickup device, a storage medium, and a processor, where the memory stores thereon a computer program, and the computer program, when executed by the processor, implements the steps of the method in the above embodiments.
Although the embodiments of the present invention have been described above, the above description is only for the convenience of understanding the present invention, and is not intended to limit the present invention. It will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (10)
1. A household appliance network distribution method is characterized by comprising the following steps:
acquiring image data of a target household appliance;
determining the model of the target household appliance by using a household appliance identification model based on the image data;
determining distribution network guidance information of the target household appliance according to the model of the target household appliance based on the corresponding relation between the known models of various household appliances and the corresponding distribution network guidance information, wherein the distribution network guidance information comprises relevant information of how to configure the broadcast communication function of the target household appliance;
and when the communication signal broadcasted by the target household appliance is received, the target household appliance is distributed with a network.
2. The method of claim 1, wherein the image data of the target appliance is obtained by:
when a household appliance distribution network request is received, the camera device is controlled to be continuously started for a preset time length so as to collect multi-frame image data of a target household appliance.
3. The method of claim 1, wherein the appliance identification model is a convolutional neural network model obtained by inputting image data of a plurality of known appliances into a convolutional neural network for training.
4. The method of claim 1, wherein determining the model number of the target appliance using an appliance identification model based on the image data comprises:
determining a possible model and a probability of the possible model of the target appliance based on the image data using an appliance identification model;
judging whether the probability of the possible models is greater than or equal to a preset threshold value:
when the probability of the possible model is larger than or equal to a preset threshold value, taking the possible model as the model of the target household appliance;
and when the probability of the possible models is smaller than a preset threshold value, discarding the possible models.
5. The method of claim 4, wherein when the number of the possible models with the probability greater than or equal to the preset threshold is single, determining the distribution network guidance information of the target household appliance comprises the following steps:
and searching distribution network guide information corresponding to the possible models in a corresponding relation table of the models of various known household appliances and the corresponding distribution network guide information according to the possible models, and taking the searched distribution network guide information as the distribution network guide information of the target household appliance.
6. The method of claim 4, wherein when the number of the possible models with the probability greater than or equal to the preset threshold is multiple, determining the distribution network guidance information of the target household appliance comprises the following steps:
selecting one possible model from a plurality of possible models as the model of the target household appliance;
and searching distribution network guide information corresponding to the model of the target household appliance in a corresponding relation table of the model of various known household appliances and the corresponding distribution network guide information according to the model of the target household appliance, wherein the searched distribution network guide information is used as the distribution network guide information of the target household appliance.
7. The method of claim 1, wherein when the communication signal broadcasted by the target appliance is received, the network distribution is performed on the target appliance, and the method comprises the following steps:
when a communication signal broadcasted by the target household appliance is received, sending network login information to the target household appliance so that the target household appliance can be connected to a network through the network login information;
and when receiving the household appliance identification information sent by the target household appliance through the network, binding the household appliance identification information with the corresponding household appliance distribution network authority information, and storing the bound household appliance identification information and the household appliance distribution network authority information to the cloud.
8. The method of claim 7, wherein the target appliance is configured to be distributed when the communication signal broadcasted by the target appliance is received, further comprising the steps of:
and storing the network login information sent to the target household appliance every time the target household appliance is connected with a network, so that the target household appliance can be automatically matched, connected and accessed to the network according to the stored network login information.
9. A storage medium on which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 8.
10. A terminal comprising an image pick-up device, a storage medium and a processor, characterized in that the memory has stored thereon a computer program which, when executed by the processor, carries out the steps of the method according to any one of claims 1 to 8.
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