CN116719969B - Intelligent home interaction data analysis method and system based on Internet of things - Google Patents

Intelligent home interaction data analysis method and system based on Internet of things Download PDF

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CN116719969B
CN116719969B CN202311004866.5A CN202311004866A CN116719969B CN 116719969 B CN116719969 B CN 116719969B CN 202311004866 A CN202311004866 A CN 202311004866A CN 116719969 B CN116719969 B CN 116719969B
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CN116719969A (en
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钱志云
邓亦超
李华林
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Arrow Intelligence Technology Zhangjiagang Co ltd
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Abstract

The embodiment of the application provides an intelligent home interaction data analysis method and system based on the Internet of things, which combine image content characteristics in selected interaction images according to an interaction image relation network and combine network construction relations between interaction content items and the interaction images, so that a deep learning generation model can learn characteristic correlation among the selected interaction images according to the interaction image relation network, and further accurately output interaction content correlation degree between a reference newly-added interaction image and each second template function interaction image in an intelligent home function interaction image set, so as to judge the operation of loading the reference newly-added interaction image into the intelligent home function interaction image set, and further improve the reliability of updating the reference newly-added interaction image into the intelligent home function interaction image set.

Description

Intelligent home interaction data analysis method and system based on Internet of things
Technical Field
The application relates to the technical field of the Internet of things, in particular to an intelligent home interaction data analysis method and system based on the Internet of things.
Background
Smart home is an implementation of Internet of things under the influence of the Internet. The intelligent home is connected with various devices (such as audio and video equipment, a lighting system, curtain control, air conditioner control, a security system, a digital cinema system, an audio and video server, a video cabinet system, network household appliances and the like) in the home through the internet of things technology, and various functions and means such as household appliance control, lighting control, telephone remote control, indoor and outdoor remote control, anti-theft alarm, environment monitoring, heating ventilation control, infrared forwarding and programmable timing control are provided. In the use process of the intelligent home, the Internet of things user can conduct demand feedback through intelligent home function interaction, for example, the Internet of things system can interact with the Internet of things user in the form of intelligent home function interaction images, and compared with the text interaction mode, the Internet of things system can be more vivid and more vivid, so that the user can quickly know the function items of the intelligent home. The more rich the intelligent home function interaction images are, the better the interaction experience between the intelligent home function interaction images and the users of the Internet of things is. Therefore, when the intelligent home function interaction image set is expanded, how to effectively improve the reliability of updating the reference newly-added interaction image to the intelligent home function interaction image set is a technical problem to be solved in the technical field.
Disclosure of Invention
In order to at least overcome the defects in the prior art, the application aims to provide an intelligent home interaction data analysis method and system based on the Internet of things.
In a first aspect, the application provides an intelligent home interaction data analysis method based on the internet of things, which is applied to an internet of things system, and the method comprises the following steps:
acquiring a reference newly-added interaction image and a first template function interaction image in an intelligent home function interaction image set, determining the reference newly-added interaction image as a selected interaction image, wherein the reference newly-added interaction image is used for representing an interaction image which is automatically generated by a cloud or generated by a development user and is to be newly added to the intelligent home function interaction image set, the intelligent home function interaction image set is an interaction image database used for carrying out image interaction with an intelligent home user, the interaction image is used for representing flow display content of a conversation between the internet of things system and the user based on a consultation request of the user, and the first template function interaction image is used for representing an inquiry interaction image set of the internet of things system when the internet of things system carries out conversation with an internet of things registration object;
configuring an interactive image relationship network based on the selected interactive image; the interactive image relation network comprises at least two network members for establishing association attributes, wherein the at least two network members comprise an interactive function item member for the selected interactive image and an interactive content item member for an interactive content item in the selected interactive image, the interactive function item member is used for representing a member identifier corresponding to any one intelligent home control function, and the interactive content item member is used for representing a member identifier corresponding to any one intelligent home control content;
Generating interactive image prediction data corresponding to each interactive function item member in the interactive image relation network based on network composition data of the interactive image relation network and image content characteristics corresponding to each network member in the interactive image relation network by using a deep learning generation model; the interactive image prediction data reflects the interactive content association degree between the selected interactive image corresponding to the interactive function item member and each second template functional interactive image in the intelligent home functional interactive image set, the functional interactive direction of the second template functional interactive image is opposite to the functional interactive direction of the first template functional interactive image, the functional interactive direction comprises an active functional interactive direction and a passive functional interactive direction, the second template functional interactive image is used for representing an answer interactive image set of the Internet of things system when the Internet of things system carries out a session with an Internet of things registration object, and the image content characteristics corresponding to each network member are used for representing the content characteristics of the flow display content;
and judging the operation of loading the reference newly-added interactive image into the intelligent home function interactive image set based on the interactive image prediction data of the interactive function item members of the reference newly-added interactive image in the interactive image relation network.
In a possible implementation manner of the first aspect, the configuring the interactive image relation network based on the selected interactive image includes at least one of:
generating corresponding interactive content item members for each target interactive content item in each selected interactive image respectively;
constructing a member association link of a first link attribute between an interaction content item member corresponding to the target interaction content item and an interaction function item member corresponding to a selected interaction image in which the target interaction content item is positioned;
generating corresponding window interaction content item members for each window interaction content item in each selected interaction image respectively;
constructing a member association link of a second link attribute between a window interaction content item member corresponding to the window interaction content item and an interaction function item member corresponding to a selected interaction image where the window interaction content item is located;
for a target selected interactive image in the selected interactive images, determining a matched selected interactive image of the target selected interactive image from each selected interactive image;
and constructing a member association link of a third link attribute between the interactive function item member corresponding to the target selected interactive image and the interactive function item member corresponding to the matched selected interactive image.
In a possible implementation manner of the first aspect, for a target selected interaction image of the selected interaction images, determining a matching selected interaction image of the target selected interaction image from the selected interaction images includes:
generating image content characteristics corresponding to the selected interactive images respectively by using a convolutional neural network;
for each selected interaction image, determining a feature distance between the image content features of the selected interaction image and the image content features of the target selected interaction image, as a feature distance between the selected interaction image and the target selected interaction image;
and acquiring the selected interaction image with the characteristic distance meeting the set distance requirement between the selected interaction image and the target selected interaction image, and determining the selected interaction image as a matched selected interaction image of the target selected interaction image.
In a possible implementation manner of the first aspect, the method further includes:
acquiring an Internet of things collaborative function interaction image corresponding to the first template function interaction image, and determining the interaction image as the selected interaction image;
said configuring an interactive image relationship network based on said selected interactive image, comprising:
And constructing a member association link of a fourth link attribute between the interactive function item member corresponding to the first template function interactive image and the interactive function item member corresponding to the Internet of things collaborative function interactive image corresponding to the first template function interactive image.
In a possible implementation manner of the first aspect, the generating, using a deep learning generation model, based on network composition data of the interactive image relationship network and image content features corresponding to each network member in the interactive image relationship network, interactive image prediction data corresponding to each interactive function item member in the interactive image relationship network includes:
generating a model according to sample deep learning, and generating initial interactive image prediction data corresponding to each interactive function item member in the interactive image relation network based on network composition data of the interactive image relation network and initial image content characteristics corresponding to each network member in the interactive image relation network;
based on initial interactive image prediction data of interactive function item members of the interactive image relation network for the first template functional interactive image and a second template functional interactive image corresponding to the first template functional interactive image, updating network weight information of the sample deep learning generation model to generate a target deep learning generation model, updating initial image content characteristics respectively corresponding to each network member in the interactive image relation network, and generating target image content characteristics respectively corresponding to each network member in the interactive image relation network;
Generating target interactive image prediction data corresponding to each interactive function item member in the interactive image relation network based on network composition data of the interactive image relation network and target image content characteristics corresponding to each network member in the interactive image relation network respectively according to the target deep learning generation model;
judging the operation of loading the reference new interactive image into the intelligent home function interactive image set based on the interactive image prediction data of the interactive function item members of the reference new interactive image in the interactive image relation network, wherein the judging comprises the following steps:
and judging the operation of loading the reference newly-added interactive image into the intelligent home function interactive image set based on target interactive image prediction data of the interactive function item members of the reference newly-added interactive image in the interactive image relation network.
In a possible implementation manner of the first aspect, the generating, using a deep learning generation model, based on network composition data of the interactive image relationship network and image content features corresponding to each network member in the interactive image relationship network, interactive image prediction data corresponding to each interactive function item member in the interactive image relationship network includes:
Decomposing the interactive image relationship network into a plurality of decomposed interactive image relationship networks based on link attributes of member associated links included in the interactive image relationship network;
for each decomposition interaction image relation network, generating decomposition generation image content characteristics corresponding to each network member in the decomposition interaction image relation network based on network composition data of the decomposition interaction image relation network and loading image content characteristics corresponding to each network member in the decomposition interaction image relation network according to the graph self-encoder;
generating, for each network member in the interactive image relationship network, a generated image content feature of the network member based on a decomposition of the network member in each of the decomposed interactive image relationship networks;
and generating interactive image prediction data corresponding to each interactive function item member in the interactive image relation network based on the generated image content characteristics corresponding to each interactive function item member in the interactive image relation network according to the fully connected output network in the deep learning generation model.
In a possible implementation manner of the first aspect, the initial image content characteristics of the network members in the interactive image relation network are determined according to the following steps:
for the interactive function item members of the selected interactive image in the interactive image relation network, determining the image content characteristics of the selected interactive image by utilizing a convolutional neural network, and determining the initial image content characteristics of the interactive function item members;
for the interactive content item members of the interactive image relation network for the target interactive content item in the selected interactive image, determining the image content characteristics of the target interactive content item according to the convolutional neural network, and determining the initial image content characteristics of the interactive content item members;
and randomly initializing window interactive content item members of window interactive content items in the selected interactive images in the interactive image relation network to obtain image content characteristics of the window interactive content items, and determining initial image content characteristics of the window interactive content item members.
In a possible implementation manner of the first aspect, the acquiring the reference newly added interactive image includes:
And acquiring a basic interaction image loaded when the Internet of things registration object and the Internet of things interaction application perform session interaction, and determining the basic interaction image as the reference newly-added interaction image.
In a possible implementation manner of the first aspect, the determining, based on the interactive image prediction data of the interactive function item member for the reference newly added interactive image in the interactive image relationship network, the operation of loading the reference newly added interactive image into the smart home function interactive image set includes:
generating a target interaction content association degree and a target second template function interaction image corresponding to the target interaction content association degree based on the interaction content association degree between the reference newly-added interaction image and each second template function interaction image in the intelligent home function interaction image set, which is included in the interaction image prediction data;
if the relevance of the target interactive contents is larger than a threshold value, configuring the mapping relation between the reference newly added interactive image and the target second template functional interactive image,
uploading the mapping contact to a development server, and acquiring indication information sent by the development server;
And if the indication information reflects that the mapping relation is a normal mapping relation, loading the reference newly-added interactive image and the mapping relation into the intelligent home function interactive image set.
In a second aspect, an embodiment of the present application further provides an internet of things system, where the internet of things system includes a processor and a machine-readable storage medium, where a computer program is stored in the machine-readable storage medium, and the computer program is loaded and executed in conjunction with the processor to implement the intelligent home interaction data analysis method based on the internet of things in the first aspect.
By adopting the technical scheme in any aspect, a first template function interaction image in the reference newly added interaction image and the intelligent home function interaction image set is firstly obtained as a selected interaction image; then, configuring an interactive image relation network based on each selected interactive image, wherein the interactive image relation network comprises at least two network members for establishing association attributes, the at least two network members comprise interactive function item members for the selected interactive image and interactive content item members for interactive content items in the selected interactive image, thereby generating interactive image prediction data respectively corresponding to each interactive function item member in the interactive image relation network based on network composition data of the interactive image relation network and image content characteristics respectively corresponding to each network member in the interactive image relation network by utilizing a deep learning generation model, and the interactive image prediction data reflects the association degree of the interactive content between the selected interactive image corresponding to the interactive function item member and each second template functional interactive image in the intelligent home functional interactive image set; finally, based on the interactive image prediction data of the interactive function item members aiming at the reference newly added interactive image in the interactive image relation network, loading the reference newly added interactive image into the intelligent home function interactive image set. Therefore, image content characteristics in each selected interactive image are combined according to the interactive image relation network, and network construction relation between the interactive content items and the interactive images is combined, so that a deep learning generation model can learn characteristic correlation between the selected interactive images according to the interactive image relation network, and further accurately output interactive content correlation degree between a reference newly-added interactive image and each second template function interactive image in the intelligent home function interactive image set, so that operation of loading the reference newly-added interactive image into the intelligent home function interactive image set is judged, and reliability of updating the reference newly-added interactive image into the intelligent home function interactive image set is improved.
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For a clearer description of the technical solutions of the embodiments of the present application, reference will be made to the accompanying drawings, which are needed to be activated in the embodiments, and it should be understood that the following drawings only illustrate some embodiments of the present application and should not be considered as limiting the scope, and that other related drawings can be obtained by those skilled in the art without the inventive effort.
Fig. 1 is a flow chart of an intelligent home interaction data analysis method based on the internet of things, which is provided by the embodiment of the application;
fig. 2 is a schematic block diagram of network composition data of an internet of things system for implementing the intelligent home interaction data analysis method based on the internet of things according to an embodiment of the present application.
Detailed Description
The following description is presented to enable one of ordinary skill in the art to make and use the application and is provided in the context of a particular application and its requirements. It will be apparent to those having ordinary skill in the art that various changes can be made to the disclosed embodiments and that the general principles defined herein may be applied to other embodiments and applications without departing from the principles and scope of the application. Therefore, the present application is not limited to the described embodiments, but is to be accorded the widest scope consistent with the claims.
Referring to fig. 1, the application provides an intelligent home interaction data analysis method based on the internet of things, which comprises the following steps of.
Step S101: and acquiring a first template function interaction image in the reference newly-added interaction image and the intelligent home function interaction image set, and determining the first template function interaction image as a selected interaction image.
When the intelligent home function interaction image set is subjected to interaction image expansion, a reference newly-added interaction image and a first template function interaction image in the intelligent home function interaction image set can be acquired, and the selected interaction image is determined. In some alternative embodiments, the interactive image expansion of the smart home function interactive image set may be performed every preset time period, for example, the interactive image expansion is performed once every 1 week, so that a new interactive image may be continuously referred to, so that the call is convenient when the interactive image expansion is performed on the smart home function interactive image set.
The reference newly added interaction image is used for representing an interaction image which is automatically generated by a cloud or generated by a development user and is to be newly added to an intelligent home function interaction image set, the intelligent home function interaction image set is an interaction image database used for carrying out image interaction with an intelligent home user, and the interaction image is used for representing flow display content of a session between the internet of things system and the user based on a consultation request of the user. For example, in the process of using the intelligent door lock, the user needs to consult how to connect the intelligent door lock to the bluetooth gateway for linkage control, or needs to consult how to configure the door lock verification information of the intelligent door lock in the related APP, so that the above consultation request can be sent to the internet of things system, for example, in the form of text interaction, video interaction and voice interaction. After receiving the consultation request, the internet of things system issues an interactive image to the user, wherein the interactive image can comprise flow display contents of how to connect the intelligent door lock to the Bluetooth gateway for linkage control or flow display contents of how to configure door lock verification information of the intelligent door lock in the related APP, and compared with text description in the traditional scheme, the interactive image can be more vivid, so that the user can quickly know functional items of the intelligent home. Therefore, in this embodiment, after the reference new interactive image is generated, the reference new interactive image and the first template function interactive image in the smart home function interactive image set may be determined together as the selected interactive image, so as to facilitate the subsequent discrimination of the operation of loading the reference new interactive image into the smart home function interactive image set.
In this embodiment, the reference newly added interactive image is a candidate interactive image called when the interactive image expansion is performed on the intelligent home function interactive image set, that is, the interactive image loaded into the intelligent home function interactive image set can be selected from the reference newly added interactive image, so that the interactive image expansion is performed on the intelligent home function interactive image set.
For example, a basic interaction image loaded when the internet of things registration object and the internet of things interaction application perform session interaction can be acquired, and the basic interaction image is determined to be a reference newly-added interaction image.
In this embodiment, the smart home function interaction image set may include an active function interaction image set (may include a first template function interaction image) and a passive function interaction image set (may include a second template function interaction image), for example, the active function interaction image set may be a query interaction image set when the internet of things system performs a session with the internet of things registered object, and the passive function interaction image set may be an answer interaction image set when the internet of things system performs a session with the internet of things registered object.
Step S102: configuring an interactive image relationship network based on the selected interactive image; at least two network members establishing association attributes are included in the interactive image relationship network, the at least two network members including an interactive function item member for the selected interactive image and an interactive content item member for an interactive content item in the selected interactive image.
Still referring to the above example, assuming that how the selected interactive image connects the intelligent door lock to the bluetooth gateway for performing the flow display content of the coordinated control, the interactive function item members may include, but are not limited to, functions of a lamp coordinated control function (such as a coordinated opening of a part of indoor lights after the door lock is opened), a curtain coordinated control function (such as a coordinated closing of a curtain after the door lock is opened), and the like, and the interactive content item members may be understood as content items in the flow display content, for example, content items in the flow display content of the lamp coordinated control function, such as a light number, a light brightness, a light sequence, and the like. And association attributes are established between at least two network members, for example, the lamp linkage control function has association binding relation with content items such as the quantity of lamps, the brightness of lamps, the sequence of lamps and the like.
Upon acquisition of the selected interactive image, an interactive image relationship network may be configured based on the acquired selected interactive image and the interactive content items in the selected interactive image, which may express an association between the interactive image and the interactive content items.
An interactive image relationship network is a directed network graph that includes a plurality of network members and at least one member association link. The interactive image relation network can comprise at least two network members for establishing association attributes, wherein the at least two network members comprise an interactive function item member aiming at a selected interactive image and an interactive content item member aiming at an interactive content item in the selected interactive image, a member association link can be arranged between the interactive function item member and the interactive content item member, and a member association link can also be arranged between the interactive function item member and the interactive function item member.
In the process of configuring the interactive image relation network, corresponding interactive content item members can be respectively generated for each target interactive content item in the selected interactive image, so that a member association link of a first link attribute is constructed between the interactive content item member corresponding to the target interactive content item and the interactive function item member corresponding to the selected interactive image in which the target interactive content item is located.
For example, after each selected interactive image is acquired, a corresponding interactive function item member may be generated for each selected interactive image. Then extracting target interactive content items from each selected interactive image, thereby respectively generating corresponding interactive content item members for each target interactive content item. If the same target interactive content item is parsed from different selected interactive images, a corresponding interactive content item member may be constructed for the target interactive content item. When the interactive image relation network is configured, a member association link with a first link attribute can be constructed between an interactive content item member corresponding to a target interactive content item and an interactive function item member corresponding to a selected interactive image where the target interactive content item is located, and the member association link with the first link attribute characterizes that an association relation exists between the connected interactive content item member and the connected interactive function item member.
Or in the process of configuring the interactive image relation network, corresponding window interactive content item members can be generated for each window interactive content item in the selected interactive image respectively, so that a member association link with a second link attribute is constructed between the window interactive content item member corresponding to the window interactive content item and the interactive function item member corresponding to the selected interactive image in which the window interactive content item is located.
For example, after each selected interactive image is acquired, a corresponding interactive function item member may be generated for each selected interactive image. If the same window interactive content item is parsed from different selected interactive images, i.e. window interactive content items for the same entity are extracted from different selected interactive images, a corresponding window interactive content item member may be constructed for the window interactive content item. When the interactive image relation network is configured, a member association link of a second link attribute is constructed between a window interactive content item member corresponding to the window interactive content item and an interactive function item member corresponding to the selected interactive image where the window interactive content item is located, and the member association link of the second link attribute is used for indicating that an association relation exists between the window interactive content item member connected with the member association link and the interactive function item member connected with the member association link.
Or in the process of configuring the interactive image relation network, a matching selected interactive image of the target selected interactive image can be determined from all the selected interactive images according to the target selected interactive image in the selected interactive images, so that a member association link of a third link attribute is constructed between the interactive function item member corresponding to the target selected interactive image and the interactive function item member corresponding to the matching selected interactive image.
For example, corresponding interactive function item members may be generated for each selected interactive image, respectively, each selected interactive image may be used as a target selected interactive image, and a matching selected interactive image of the target selected interactive image may be determined from other selected interactive images other than the target selected interactive image. For example, a selected interaction image that matches the image characteristics of the target selected interaction image may be determined, as is a matching selected interaction image of the target selected interaction image. Therefore, a member association link of a third link attribute can be constructed between the interactive function item member corresponding to the target selected interactive image and the interactive function item member corresponding to the matched selected interactive image of the target selected interactive image, and the member association link of the third link attribute is used for representing that the two connected interactive function item members have association relations.
In an alternative embodiment, a matching selected interaction image of the target selected interaction image may be selected: determining the image content characteristics corresponding to each selected interactive image respectively by using a convolutional neural network; for each selected interaction image, calculating a feature distance between the image content feature of the selected interaction image and the image content feature of the target selected interaction image, determining the feature distance between the selected interaction image and the target selected interaction image, thereby generating a selected interaction image with the feature distance meeting the set distance requirement, and determining the matching selected interaction image of the target selected interaction image.
For example, feature extraction may be performed on each selected interactive image, and image content features corresponding to each selected interactive image may be generated. When determining the matching selected interactive image of the target selected interactive image, for each selected interactive image except the target selected interactive image, a cosine feature distance between the image content features of the selected interactive image and the image content features of the target selected interactive image may be calculated, and the feature distance between the selected interactive image and the target selected interactive image is determined.
Thus, N selected interaction images with the smallest feature distance from the target selected interaction image can be determined, and a matching selected interaction image for the target selected interaction image can be determined.
In other alternative embodiments, the embodiment may further acquire, as the selected interaction image, an interaction image of the collaborative function of the internet of things corresponding to the interaction image of the first template function. Therefore, a member association link of the fourth link attribute can be constructed between the interactive function item member corresponding to the first template function interactive image and the interactive function item member corresponding to the Internet of things collaborative function interactive image corresponding to the first template function interactive image. For example, corresponding interactive function item members are constructed for each selected interactive image, including constructing corresponding interactive function item members for the first template functional interactive image and constructing corresponding interactive function item members for the internet of things collaborative function interactive image corresponding to the first template functional interactive image, and for the interactive function item members corresponding to the first template functional interactive image and the interactive function item members corresponding to the internet of things collaborative function interactive image corresponding to the first template functional interactive image, a member association link of a fourth link attribute is constructed between the elements.
In an alternative embodiment, the interactive image relationship network includes an interactive function item member, an interactive content item member, and a window interactive content item member; each interaction function item member aims at a selected interaction image, and the selected interaction image can be any one of a reference newly-added interaction image, a first template function interaction image and an Internet of things cooperative function interaction image; each interactive content item member is directed to a target interactive content item parsed from the selected interactive image; each window interactive content item member is directed to a window interactive content item parsed from the selected interactive image. The interactive image relation network comprises member association links of a first link attribute, a second link attribute, a third link attribute and a fourth link attribute; the member association links of the first link attribute are used for associating the interactive content item members and the interactive function item members, and the target interactive content items corresponding to the interactive content item members belong to selected interactive images corresponding to the interactive function item members; the member association links of the second link attribute are used for associating window interaction content item members with interaction function item members, and window interaction content items corresponding to the window interaction content item members belong to selected interaction images corresponding to the interaction function item members; the member association links of the third link attribute are used for associating two interactive function item members, and the image content characteristics of the selected interactive images corresponding to the two interactive function item members are matched; the member association links of the fourth link attribute are also used for associating two interactive function item members, wherein the image content characteristics of the selected interactive images corresponding to the two interactive function item members are matched, but the function groups of the internet of things where the two selected interactive images are located are different.
Step S103: generating interactive image prediction data corresponding to each interactive function item member in the interactive image relation network based on network composition data of the interactive image relation network and image content characteristics corresponding to each network member in the interactive image relation network by using a deep learning generation model; and the interactive image prediction data reflects the interactive content association degree between the selected interactive image corresponding to the interactive function item member and each second template function interactive image in the intelligent home function interactive image set.
In this embodiment, the purpose of the deep learning generation model is to obtain the association degree of the interaction content between the selected interaction image corresponding to the interaction function item member and each second template function interaction image in the intelligent home function interaction image set, for example, the association degree of the interaction content between the selected interaction image corresponding to the lamp linkage control function and each second template function interaction image in the intelligent home function interaction image set. And the image content characteristics corresponding to each network member are used for representing the content characteristics of the flow display content, such as the content characteristics of the flow display content of the lamp linkage control function.
After the interactive image relation network is configured, a deep learning generation model can be utilized to generate interactive image prediction data corresponding to each interactive function item member in the interactive image relation network based on network composition data of the interactive image relation network and image content characteristics corresponding to each network member in the interactive image relation network. The interactive image prediction data can represent the interactive content association degree between the selected interactive image corresponding to the interactive function item member and each second template functional interactive image in the intelligent home functional interactive image set.
In an alternative embodiment, a model may be generated according to sample deep learning, and initial interactive image prediction data corresponding to each interactive function item member in the interactive image relationship network is generated based on network composition data of the interactive image relationship network and initial image content characteristics corresponding to each network member in the interactive image relationship network; then, based on initial interactive image prediction data of interactive function item members of an interactive image of a first template function in an interactive image relation network and a second template function interactive image corresponding to the interactive image of the first template function, updating network weight information of a sample deep learning generation model to generate a target deep learning generation model, updating initial image content characteristics respectively corresponding to each network member in the interactive image relation network to generate target image content characteristics respectively corresponding to each network member in the interactive image relation network, and generating target interactive image prediction data respectively corresponding to each interactive function item member in the interactive image relation network based on network composition data of the interactive image relation network and the target image content characteristics respectively corresponding to each network member in the interactive image relation network according to the target deep learning generation model.
Because the mapping relation between each first template functional interaction image and each second template functional interaction image in the intelligent home functional interaction image set is configured in advance, when the sample deep learning generation model is trained, the mapping relation between the first template functional interaction images and the second template functional interaction images can be directly utilized to determine the data to be learned; updating network weight information of a sample deep learning generation model according to the data to be learned, and simultaneously updating initial image content characteristics respectively corresponding to each network member in the input interactive image relation network to generate target deep learning generation model and target image content characteristics respectively corresponding to each network member in the interactive image relation network; finally, the target deep learning generation model is utilized to generate interactive image prediction data corresponding to each interactive function item member in the interactive image relation network based on the target image content characteristics corresponding to each network member in the interactive image relation network.
In an alternative embodiment, the initial image content characteristics corresponding to each network member in the interactive image relationship network may be generated by: for the interactive function item member of the selected interactive image in the interactive image relation network, determining the image content characteristics of the selected interactive image by utilizing a convolutional neural network, and determining the initial image content characteristics of the interactive function item member; for an interactive content item member of a target interactive content item in a selected interactive image in the interactive image relation network, determining the image content characteristics of the target interactive content item by utilizing a convolutional neural network, and determining the initial image content characteristics of the interactive content item member; and randomly initializing window interactive content item members of window interactive content items in the selected interactive images aiming at the interactive image relation network to obtain image content characteristics of the window interactive content items, and determining the image content characteristics as initial image content characteristics of the window interactive content item members.
In an alternative embodiment, the two-dimensional array of relationships between vertices of the network composition data for the interactive image relationship network and the target image content features respectively corresponding to each network member in the interactive image relationship network may be input into a target deep learning generation model, and target interactive image prediction data respectively corresponding to each interactive function item member in the interactive image relationship network output by the target deep learning generation model may be obtained. In an alternative embodiment, the interactive image expansion can be performed on the intelligent home function interactive image set according to the target interactive image prediction data of the interactive function item members for the reference newly added interactive image in the interactive image relation network, that is, whether the reference newly added interactive image can be loaded into the intelligent home function interactive image set or not is judged.
In an alternative embodiment, the deep learning generation model (including the sample deep learning generation model and the target deep learning generation model described above) may include a graph self-encoder and a fully connected output network. When the deep learning generation model is operated, firstly, generating generated image content characteristics corresponding to each network member in the interactive image relation network based on network composition data of the interactive image relation network and loading image content characteristics corresponding to each network member in the interactive image relation network according to a graph self-encoder; and generating interactive image prediction data corresponding to each interactive function item member in the interactive image relation network based on the generated image content characteristics corresponding to each interactive function item member in the interactive image relation network according to the fully connected output network.
In an alternative implementation manner, the loading image content features of the network members described above refer to image content features input into the deep learning generation model, which may be the initial image content features of the foregoing embodiment, or the target image content features of the foregoing embodiment, that is, the image content features input into the deep learning generation model may all be used as the loading image content features. The generated image content features of the network members refer to the image content features of the network members output from the encoder in the deep learning generation model.
In an alternative embodiment, the interaction image relationship network may be broken up into a plurality of broken-up interaction image relationship networks based on link attributes of member association links included in the interaction image relationship network; then, for each of the decomposed interactive image relationship networks, generating decomposed generated image content features of each of the network members in the decomposed interactive image relationship network based on the network composition data of the decomposed interactive image relationship network and the loaded image content features respectively corresponding to each of the network members in the decomposed interactive image relationship network by using the graph self-encoder in the deep learning generation model, thereby generating generated image content features of each of the network members in the decomposed interactive image relationship network based on the decomposed generated image content features of the network members.
In an alternative embodiment, assuming that the interaction image relationship network includes member association links of a first link attribute, a second link attribute, a third link attribute and a fourth link attribute, the interaction image relationship network may be first decomposed into a first decomposed interaction image relationship network including member association links of the first link attribute, a second decomposed interaction image relationship network including member association links of the second link attribute, a third decomposed interaction image relationship network including member association links of the third link attribute and a fourth decomposed interaction image relationship network including member association links of the fourth link attribute based on the link attributes of the member association links, and the corresponding relationship two-dimensional array between vertices reflecting the structures thereof may be configured for the first decomposed interaction image relationship network, the second decomposed interaction image relationship network, the third decomposed interaction image relationship network and the fourth decomposed interaction image relationship network, respectively. And then, for each decomposition interaction image relation network, inputting the two-dimensional array of the relationship between the vertexes corresponding to the decomposition interaction image relation network and the loaded image content characteristics corresponding to each network member in the decomposition interaction image relation network into a graph self-encoder in a deep learning generation model, and obtaining the decomposition generated image content characteristics corresponding to each network member in the decomposition interaction image relation network output by the graph self-encoder. Further, for each network member in the interactive image relationship network, the decomposed image content features of the network member in the first decomposed interactive image relationship network, the second decomposed interactive image relationship network, the third decomposed interactive image relationship network, and the fourth decomposed interactive image relationship network are fused to generate the generated image content features of the network member.
Step S104: and judging the operation of loading the reference newly-added interactive image into the intelligent home function interactive image set based on the interactive image prediction data of the interactive function item members of the reference newly-added interactive image in the interactive image relation network.
In an alternative implementation manner, after the deep learning generation model is utilized to generate the interactive image prediction data corresponding to each interactive function item member in the interactive image relation network, the interactive image prediction data of the interactive function item member for the reference newly added interactive image can be obtained from the interactive image prediction data, and based on the interactive image prediction data of the interactive function item member for the reference newly added interactive image, whether the reference newly added interactive image has stronger relevance with a certain second template function interactive image or not is generated, so that whether the reference newly added interactive image can be loaded into the intelligent home function interactive image set or not is judged.
For example, when it is determined that the association degree of the interaction content between the selected interaction image corresponding to the lamp linkage control function and each second template function interaction image in the intelligent home function interaction image set is greater than the set association degree, it indicates that the reference newly-added interaction image related to the lamp linkage control function has a stronger association with a certain second template function interaction image, and then the reference newly-added interaction image can be loaded into the intelligent home function interaction image set.
In an alternative embodiment, the target interaction content association degree and the target second template function interaction image corresponding to the target interaction content association degree may be generated based on the interaction content association degree between the reference newly-added interaction image and each second template function interaction image in the smart home function interaction image set included in the interaction image prediction data; if the association degree of the target interaction content is larger than the threshold value, the mapping relation between the reference newly-added interaction image and the target second template functional interaction image can be configured, and the reference newly-added interaction image and the mapping relation are loaded into the intelligent home functional interaction image set.
In an alternative embodiment, the interactive image prediction data corresponding to the reference newly added interactive image includes the interactive content association degree between the reference newly added interactive image and each second template function interactive image, so that the maximum interactive content association degree can be determined from the interactive image prediction data as a target interactive content association degree, and the second template function interactive image corresponding to the target interactive content association degree is taken as a target second template function interactive image. In an alternative embodiment, whether the relevance of the target interaction content is greater than a threshold value can be judged, if so, it is indicated that a stronger relevance exists between the reference newly-added interaction image and the second template functional interaction image, so that the mapping relation between the reference newly-added interaction image and the second template functional interaction image can be configured, and the reference newly-added interaction image and the mapping relation are loaded into the intelligent home functional interaction image set.
In an alternative implementation manner, before loading the reference newly added interactive image and the mapping connection into the intelligent home function interactive image set, uploading the mapping connection to a development server, and acquiring indication information sent by the development server; and if the indication information reflects that the mapping relation is a normal mapping relation, loading the reference newly added interactive image and the mapping relation into the intelligent home function interactive image set.
Fig. 2 schematically illustrates an internet of things system 100 that may be used to implement various embodiments described in the present application.
For one embodiment, fig. 2 illustrates an internet of things system 100, the internet of things system 100 having a plurality of processors 102, a control module (chipset) 104 coupled to one or more of the processor(s) 102, a memory 106 coupled to the control module 104, a non-volatile memory (NVM)/storage 108 coupled to the control module 104, a plurality of input/output devices 110 coupled to the control module 104, and a network interface 112 coupled to the control module 104.
Processor 102 may include a plurality of single-core or multi-core processors, and processor 102 may include any combination of general-purpose or special-purpose processors (e.g., graphics processors, application processors, baseband processors, etc.). In some alternative embodiments, the internet of things system 100 can be used as a server device such as a gateway in the embodiments of the present application.
In some alternative implementations, the internet of things system 100 may include a plurality of computer readable media (e.g., memory 106 or NVM/storage 108) having instructions 114 and a plurality of processors 102 combined with the plurality of computer readable media configured to execute the instructions 114 to implement the modules to perform the actions described in this disclosure.
For one embodiment, the control module 104 may include any suitable interface controller to provide any suitable interface to one or more of the processor(s) 102 and/or any suitable device or component in communication with the control module 104.
The control module 104 may include a memory controller module to provide an interface to the memory 106. The memory controller modules may be hardware modules, software modules, and/or firmware modules.
Memory 106 may be used to load and store data and/or instructions 114 for, for example, internet of things system 100. For one embodiment, memory 106 may comprise any suitable volatile memory, such as, for example, a suitable DRAM. In some alternative embodiments, memory 106 may comprise a double data rate type four synchronous dynamic random access memory.
For one embodiment, the control module 104 may include a plurality of input/output controllers to provide interfaces to the NVM/storage 108 and the input/output device(s) 110.
For example, NVM/storage 108 may be used to store data and/or instructions 114. NVM/storage 108 may include any suitable non-volatile memory (e.g., flash memory) and/or may include any suitable non-volatile storage(s).
NVM/storage 108 may include storage resources that are physically part of the device on which the internet of things system 100 is installed, or which may be accessible by the device may not be necessary as part of the device. For example, NVM/storage 108 may be accessed via input/output device(s) 110 in connection with a network.
Input/output device(s) 110 may provide an interface for internet of things system 100 to communicate with any other suitable device, and input/output device 110 may include a communication component, a pinyin component, a sensor component, and the like. The network interface 112 may provide an interface for the internet of things system 100 to communicate in accordance with a plurality of networks, and the internet of things system 100 may communicate wirelessly with a plurality of components of a wireless network based on any of a plurality of wireless network standards and/or protocols, such as accessing a wireless network in accordance with a communication standard, such as WiFi, 2G, 3G, 4G, 5G, etc., or a combination thereof.
For one embodiment, one or more of the processor(s) 102 may be packaged together with logic of a plurality of controllers (e.g., memory controller modules) of the control module 104. For one embodiment, one or more of the processor(s) 102 may be packaged together with logic of multiple controllers of the control module 104 to form a system in package. For one embodiment, one or more of the processor(s) 102 may be integrated on the same die with logic of multiple controllers of the control module 104. For one embodiment, one or more of the processor(s) 102 may be integrated on the same die with logic of multiple controllers of the control module 104 to form a system-on-chip.
In various embodiments, the internet of things system 100 may be, but is not limited to: a desktop computing device or a mobile computing device (e.g., a laptop computing device, a handheld computing device, a tablet, a netbook, etc.), and the like. In various embodiments, the internet of things system 100 may have more or fewer components and/or different architectures. For example, in some alternative embodiments, the internet of things system 100 includes multiple cameras, a keyboard, a liquid crystal display screen (including a touch screen display), a non-volatile memory port, multiple antennas, a graphics chip, an application specific integrated circuit, and speakers.
The foregoing has outlined rather broadly the more detailed description of the application in order that the detailed description of the principles and embodiments of the application may be implemented in conjunction with the detailed description of the application that follows, the examples being merely intended to facilitate an understanding of the method of the application and its core concepts; meanwhile, as those skilled in the art will have variations in the specific embodiments and application scope in accordance with the ideas of the present application, the present description should not be construed as limiting the present application in view of the above.

Claims (9)

1. The intelligent home interaction data analysis method based on the Internet of things is characterized by being applied to an Internet of things system, and comprises the following steps:
acquiring a reference newly-added interaction image and a first template function interaction image in an intelligent home function interaction image set, determining the reference newly-added interaction image as a selected interaction image, wherein the reference newly-added interaction image is used for representing an interaction image which is automatically generated by a cloud or generated by a development user and is to be newly added to the intelligent home function interaction image set, the intelligent home function interaction image set is an interaction image database used for carrying out image interaction with an intelligent home user, the interaction image is used for representing flow display content of a conversation between the internet of things system and the user based on a consultation request of the user, and the first template function interaction image is used for representing an inquiry interaction image set of the internet of things system when the internet of things system carries out conversation with an internet of things registration object;
Configuring an interactive image relationship network based on the selected interactive image; the interactive image relation network comprises at least two network members for establishing association attributes, wherein the at least two network members comprise an interactive function item member for the selected interactive image and an interactive content item member for an interactive content item in the selected interactive image, the interactive function item member is used for representing a member identifier corresponding to any one intelligent home control function, and the interactive content item member is used for representing a member identifier corresponding to any one intelligent home control content;
generating interactive image prediction data corresponding to each interactive function item member in the interactive image relation network based on network composition data of the interactive image relation network and image content characteristics corresponding to each network member in the interactive image relation network by using a deep learning generation model; the interactive image prediction data reflects the interactive content association degree between the selected interactive image corresponding to the interactive function item member and each second template functional interactive image in the intelligent home functional interactive image set, the functional interactive direction of the second template functional interactive image is opposite to the functional interactive direction of the first template functional interactive image, the functional interactive direction comprises an active functional interactive direction and a passive functional interactive direction, the second template functional interactive image is used for representing an answer interactive image set of the Internet of things system when the Internet of things system carries out a session with an Internet of things registration object, and the image content characteristics corresponding to each network member are used for representing the content characteristics of the flow display content;
Judging the operation of loading the reference newly added interactive image into the intelligent home function interactive image set based on the interactive image prediction data of the interactive function item members of the reference newly added interactive image in the interactive image relation network;
the configuring of the interactive image relation network based on the selected interactive image comprises at least one of the following:
generating corresponding interactive content item members for each target interactive content item in each selected interactive image respectively;
constructing a member association link of a first link attribute between an interaction content item member corresponding to the target interaction content item and an interaction function item member corresponding to a selected interaction image in which the target interaction content item is positioned;
generating corresponding window interaction content item members for each window interaction content item in each selected interaction image respectively;
constructing a member association link of a second link attribute between a window interaction content item member corresponding to the window interaction content item and an interaction function item member corresponding to a selected interaction image where the window interaction content item is located;
for a target selected interactive image in the selected interactive images, determining a matched selected interactive image of the target selected interactive image from each selected interactive image;
And constructing a member association link of a third link attribute between the interactive function item member corresponding to the target selected interactive image and the interactive function item member corresponding to the matched selected interactive image.
2. The internet of things-based intelligent home interaction data analysis method according to claim 1, wherein for a target selected interaction image of the selected interaction images, determining a matching selected interaction image of the target selected interaction image from the selected interaction images comprises:
generating image content characteristics corresponding to the selected interactive images respectively by using a convolutional neural network;
for each selected interaction image, determining a feature distance between the image content features of the selected interaction image and the image content features of the target selected interaction image, as a feature distance between the selected interaction image and the target selected interaction image;
and acquiring the selected interaction image with the characteristic distance meeting the set distance requirement between the selected interaction image and the target selected interaction image, and determining the selected interaction image as a matched selected interaction image of the target selected interaction image.
3. The internet of things-based intelligent home interaction data analysis method according to claim 1 or 2, further comprising:
Acquiring an Internet of things collaborative function interaction image corresponding to the first template function interaction image, and determining the interaction image as the selected interaction image;
said configuring an interactive image relationship network based on said selected interactive image, comprising:
and constructing a member association link of a fourth link attribute between the interactive function item member corresponding to the first template function interactive image and the interactive function item member corresponding to the Internet of things collaborative function interactive image corresponding to the first template function interactive image.
4. The method for analyzing intelligent home interaction data based on the internet of things according to claim 1, wherein the generating, by using a deep learning generation model, interaction image prediction data corresponding to each interaction function item member in the interaction image relationship network based on network composition data of the interaction image relationship network and image content characteristics corresponding to each network member in the interaction image relationship network respectively includes:
generating a model according to sample deep learning, and generating initial interactive image prediction data corresponding to each interactive function item member in the interactive image relation network based on network composition data of the interactive image relation network and initial image content characteristics corresponding to each network member in the interactive image relation network;
Based on initial interactive image prediction data of interactive function item members of the interactive image relation network for the first template functional interactive image and a second template functional interactive image corresponding to the first template functional interactive image, updating network weight information of the sample deep learning generation model to generate a target deep learning generation model, updating initial image content characteristics respectively corresponding to each network member in the interactive image relation network, and generating target image content characteristics respectively corresponding to each network member in the interactive image relation network;
generating target interactive image prediction data corresponding to each interactive function item member in the interactive image relation network based on network composition data of the interactive image relation network and target image content characteristics corresponding to each network member in the interactive image relation network respectively according to the target deep learning generation model;
judging the operation of loading the reference new interactive image into the intelligent home function interactive image set based on the interactive image prediction data of the interactive function item members of the reference new interactive image in the interactive image relation network, wherein the judging comprises the following steps:
And judging the operation of loading the reference newly-added interactive image into the intelligent home function interactive image set based on target interactive image prediction data of the interactive function item members of the reference newly-added interactive image in the interactive image relation network.
5. The method for analyzing intelligent home interaction data based on the internet of things according to claim 1 or 4, wherein the generating, by using a deep learning generation model, interaction image prediction data corresponding to each interaction function item member in the interaction image relationship network based on network composition data of the interaction image relationship network and image content characteristics corresponding to each network member in the interaction image relationship network respectively includes:
decomposing the interactive image relationship network into a plurality of decomposed interactive image relationship networks based on link attributes of member associated links included in the interactive image relationship network;
for each decomposition interaction image relation network, generating decomposition generation image content characteristics corresponding to each network member in the decomposition interaction image relation network based on network composition data of the decomposition interaction image relation network and loading image content characteristics corresponding to each network member in the decomposition interaction image relation network according to a graph self-encoder;
Generating, for each network member in the interactive image relationship network, a generated image content feature of the network member based on a decomposition of the network member in each of the decomposed interactive image relationship networks;
and generating interactive image prediction data corresponding to each interactive function item member in the interactive image relation network based on the generated image content characteristics corresponding to each interactive function item member in the interactive image relation network according to the fully connected output network in the deep learning generation model.
6. The internet of things-based intelligent home interaction data analysis method of claim 4, wherein the initial image content characteristics of network members in the interaction image relationship network are determined according to the following steps:
for the interactive function item members of the selected interactive image in the interactive image relation network, determining the image content characteristics of the selected interactive image by utilizing a convolutional neural network, and determining the initial image content characteristics of the interactive function item members;
for the interactive content item members of the interactive image relation network for the target interactive content item in the selected interactive image, determining the image content characteristics of the target interactive content item according to the convolutional neural network, and determining the initial image content characteristics of the interactive content item members;
And randomly initializing window interactive content item members of window interactive content items in the selected interactive images in the interactive image relation network to obtain image content characteristics of the window interactive content items, and determining initial image content characteristics of the window interactive content item members.
7. The method for analyzing intelligent home interaction data based on the internet of things according to claim 1, wherein the step of obtaining the reference newly added interaction image comprises the steps of:
and acquiring a basic interaction image loaded when the Internet of things registration object and the Internet of things interaction application perform session interaction, and determining the basic interaction image as the reference newly-added interaction image.
8. The method for analyzing intelligent home interaction data based on the internet of things according to claim 1, wherein the determining the operation of loading the reference newly added interaction image into the intelligent home function interaction image set based on the interaction image prediction data of the interaction function item member of the reference newly added interaction image in the interaction image relation network includes:
generating a target interaction content association degree and a target second template function interaction image corresponding to the target interaction content association degree based on the interaction content association degree between the reference newly-added interaction image and each second template function interaction image in the intelligent home function interaction image set, which is included in the interaction image prediction data;
If the relevance of the target interactive contents is larger than a threshold value, configuring the mapping relation between the reference newly added interactive image and the target second template functional interactive image,
uploading the mapping contact to a development server, and acquiring indication information sent by the development server;
and if the indication information reflects that the mapping relation is a normal mapping relation, loading the reference newly-added interactive image and the mapping relation into the intelligent home function interactive image set.
9. An internet of things system, comprising a processor and a machine-readable storage medium having stored therein machine-executable instructions loaded and executed by the processor to implement the internet of things-based intelligent home interaction data analysis method of any one of claims 1-8.
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