CN114691752A - Usage intention prediction method and apparatus, storage medium, and electronic apparatus - Google Patents

Usage intention prediction method and apparatus, storage medium, and electronic apparatus Download PDF

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CN114691752A
CN114691752A CN202210240965.2A CN202210240965A CN114691752A CN 114691752 A CN114691752 A CN 114691752A CN 202210240965 A CN202210240965 A CN 202210240965A CN 114691752 A CN114691752 A CN 114691752A
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spatial information
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intention
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赵仕军
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Qingdao Haier Technology Co Ltd
Haier Smart Home Co Ltd
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Qingdao Haier Technology Co Ltd
Haier Smart Home Co Ltd
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Priority to PCT/CN2022/099875 priority patent/WO2023168853A1/en
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Abstract

The application discloses a method and a device for predicting use intention, a storage medium and an electronic device, which relate to the technical field of smart families, wherein the method for predicting use intention comprises the following steps: acquiring first spatial information and second spatial information of target equipment, wherein the first spatial information is used for indicating the geographical position of the location of the target equipment, and the second spatial information is used for indicating the characteristics of the space of the target equipment in the home area; determining a target use environment of the target device according to the first spatial information and the second spatial information; determining a device operation event matched with the target use environment from a database of the cloud end of the Internet of things; and predicting the use intention of the target object to the target device according to the device operation event. The problems that the prediction of the use intention cannot be carried out by associating time and space, the prediction mode is single and the like in the prior art can be solved.

Description

Usage intention prediction method and apparatus, storage medium, and electronic apparatus
Technical Field
The present application relates to the field of smart homes, and in particular, to a method and an apparatus for predicting usage intention, a storage medium, and an electronic apparatus.
Background
With the continuous development of artificial intelligence and interconnection technology, intelligent applications increasingly sink to various scenes and devices, such as intelligent automobiles capable of driving automatically and intelligent homes capable of being served intelligently. The prediction of the user's intention of using the device according to the user's usage habits is an important interactive mode in various scenarios.
However, in the related art, the method for the habit of using the home appliance by the user is basically in the prediction of the time series, but it is known that the data sparsity of the habit of using the home appliance by the home user is high, and it is difficult to obviously improve the intention ability by the prediction of the time series alone.
In the related art, an effective technical scheme is not provided aiming at the problems that the prediction of the use intention cannot be carried out by associating time and space, the prediction mode is single and the like.
Disclosure of Invention
The embodiment of the invention provides a method and a device for predicting use intention, a storage medium and an electronic device, which are used for at least solving the problems that the use intention cannot be predicted by correlating time and space, the prediction mode is single and the like in the related technology.
According to an embodiment of the present invention, there is provided a usage intention prediction method including: acquiring first spatial information and second spatial information of target equipment, wherein the first spatial information is used for indicating the geographical position of the location of the target equipment, and the second spatial information is used for indicating the characteristics of the space of the target equipment in the home area; determining a target use environment of the target device according to the first spatial information and the second spatial information; determining an equipment operation event matched with a target use environment from a database of the cloud of the Internet of things; and predicting the use intention of the target object to the target device according to the device operation event.
In one exemplary embodiment, determining a target usage environment of a target device based on first spatial information and second spatial information includes: inputting the first spatial information into a spatial prediction model to obtain a first environmental characteristic of the target device, wherein the spatial prediction model is trained through machine learning by using a plurality of groups of data, and each group of data in the plurality of groups of data comprises: the first spatial information of the target device and the environmental characteristics corresponding to the first spatial information of the target device; and determining the target use environment of the target device according to the first environment characteristic and the second spatial information.
In one exemplary embodiment, the determining, from a database in the cloud of the internet of things, a device operation event matching the target usage environment includes: determining time sequence information of the target device used by the target object; matching a plurality of time sequence equipment operation events from a database of the Internet of things cloud based on the time sequence information; determining a use environment corresponding to each time sequence equipment operation event in a plurality of time sequence equipment operation events to obtain a plurality of use environments; the plurality of usage environments are compared to a target usage environment to determine device operational events matching the target usage environment.
In one exemplary embodiment, comparing the plurality of usage environments to a target usage environment to determine device operational events matching the target usage environment includes: determining the similarity between each use environment in a plurality of use environments and a target use environment; and under the condition that the similarity accords with a preset threshold, determining that the current time sequence equipment operation event is an equipment operation event matched with the target use environment.
In one exemplary embodiment, predicting the use intention of the target object for the target device according to the device operation event comprises the following steps: acquiring operation preference of a target object under the condition that a plurality of device operation events matched with the target use environment exist, wherein the operation preference is used for indicating the target object to use the target device to perform the operation event with the largest number of times; determining a plurality of degrees of matching of a plurality of device operation events with operation preferences; and arranging the plurality of equipment operation events according to the sizes of the plurality of matching degrees, and selecting the equipment operation event with the maximum matching degree to predict the use intention of the target object on the target equipment.
In an exemplary embodiment, after predicting the usage intention of the target object with the target device according to the device operation event, the method further includes: obtaining a feedback result of the target object on the predicted use intention; and when the feedback result is that the execution is allowed, indicating that the currently predicted use intention meets the use requirement of the target object, and ending the prediction flow of the use intention.
In an exemplary embodiment, after predicting the usage intention of the target object with the target device according to the device operation event, the method further includes: the predicted successful use intention, the target object, the first spatial information and the second spatial information are subjected to associated binding; and uploading the correlation binding result to a database at the cloud end of the Internet of things to generate a usage intention database of the target object.
According to another embodiment of the present invention, there is provided a usage intention prediction apparatus including: the system comprises an acquisition module, a processing module and a display module, wherein the acquisition module is used for acquiring first spatial information and second spatial information of target equipment, the first spatial information is used for indicating the geographical position of the location of the target equipment, and the second spatial information is used for indicating the characteristics of the space of the target equipment in the home area; the first determining module is used for determining a target use environment of the target device according to the first spatial information and the second spatial information; the second determination module is used for determining a device operation event matched with the target use environment from a database of the cloud end of the Internet of things; and the prediction module is used for predicting the use intention of the target object to the target equipment according to the equipment operation event.
In an exemplary embodiment, the first determining module is further configured to input the first spatial information into a spatial prediction model to obtain the first environmental characteristic of the target device, where the spatial prediction model is trained through machine learning by using multiple sets of data, and each set of data in the multiple sets of data includes: the first spatial information of the target device and the environmental characteristics corresponding to the first spatial information of the target device; and determining the target use environment of the target device according to the first environment characteristic and the second spatial information.
In an exemplary embodiment, the second determining module is further configured to determine timing information of the target device used by the target object; matching a plurality of time sequence equipment operation events from a database of the Internet of things cloud based on the time sequence information; determining a use environment corresponding to each time sequence equipment operation event in a plurality of time sequence equipment operation events to obtain a plurality of use environments; the plurality of usage environments are compared to a target usage environment to determine device operational events matching the target usage environment.
In an exemplary embodiment, the second determining module is further configured to determine a similarity between each of the multiple usage environments and the target usage environment; and under the condition that the similarity accords with a preset threshold, determining that the current time sequence equipment operation event is an equipment operation event matched with the target use environment.
In an exemplary embodiment, the predicting module is further configured to, in a case that there are a plurality of device operation events matching the target usage environment, obtain an operation preference of the target object, where the operation preference is used to indicate that the target object uses the target device to perform an operation event with a maximum number of times; determining a plurality of degrees of match of a plurality of device operational events to operational preferences; and arranging the plurality of equipment operation events according to the sizes of the plurality of matching degrees, and selecting the equipment operation event with the maximum matching degree to predict the use intention of the target object on the target equipment.
In an exemplary embodiment, the apparatus further comprises: the feedback module is used for acquiring a feedback result of the target object on the predicted use intention; and when the feedback result is that the execution is allowed, indicating that the currently predicted use intention meets the use requirement of the target object, and ending the prediction flow of the use intention.
In an exemplary embodiment, the apparatus further includes: the association module is used for associating and binding the successfully predicted use intention, the target object, the first spatial information and the second spatial information; and uploading the correlation binding result to a database at the cloud end of the Internet of things to generate a usage graph database of the target object.
According to a further embodiment of the present invention, there is also provided a storage medium having a computer program stored therein, wherein the computer program is arranged to perform the steps of any of the method embodiments described above when executed.
According to yet another embodiment of the present invention, there is also provided an electronic device, including a memory in which a computer program is stored and a processor configured to execute the computer program to perform the steps in any of the above method embodiments.
According to the invention, first spatial information and second spatial information of the target device are obtained, wherein the first spatial information is used for indicating the geographical position of the location of the target device, and the second spatial information is used for indicating the characteristics of the space of the target device in the home area; determining a target use environment of the target device according to the first spatial information and the second spatial information; determining a device operation event matched with the target use environment from a database of the cloud end of the Internet of things; and predicting the use intention of the target object to the target device according to the device operation event. That is to say, by determining various kinds of spatial information of the target device, the target usage environment of the target device is determined, then the device operation event corresponding to the target usage environment is determined from the database, the usage intention of the target object using the target device is determined based on the device operation event, and the time corresponding to the set operation event and the space corresponding to the target device are coupled and associated, so that the predicted usage intention is more accurate.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and together with the description, serve to explain the principles of the application.
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly described below, and it is obvious for those skilled in the art to obtain other drawings without inventive exercise.
FIG. 1 is a diagram of a hardware environment for a method of prediction of usage intent according to an embodiment of the present application;
FIG. 2 is a flow chart of a prediction method of usage intent according to an embodiment of the present invention;
FIG. 3 is a schematic illustration of usage intent by spatiotemporal prediction according to an alternative embodiment of the present invention;
FIG. 4 is a block diagram (one) of the structure of a prediction apparatus for usage intention according to an embodiment of the present invention;
fig. 5 is a block diagram (ii) illustrating a configuration of a prediction apparatus using intention according to an embodiment of the present invention.
Detailed Description
In order to make the technical solutions better understood by those skilled in the art, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only partial embodiments of the present application, but not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
It should be noted that the terms "first," "second," and the like in the description and claims of this application and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the application described herein are capable of operation in sequences other than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
According to an aspect of an embodiment of the present application, there is provided a prediction method of usage intention. The prediction method of the use intention is widely applied to full-House intelligent digital control application scenes such as intelligent homes (Smart Home), intelligent homes, intelligent Home equipment ecology, intelligent House (Intelligent House) ecology and the like. Alternatively, in the present embodiment, the above-described method for predicting usage intention may be applied to a hardware environment formed by the terminal device 102 and the server 104 as shown in fig. 1. As shown in fig. 1, the server 104 is connected to the terminal device 102 through a network, and may be configured to provide a service (e.g., an application service) for the terminal or a client installed on the terminal, set a database on the server or independent of the server, and provide a data storage service for the server 104, and configure a cloud computing and/or edge computing service on the server or independent of the server, and provide a data operation service for the server 104.
The network may include, but is not limited to, at least one of: wired networks, wireless networks. The wired network may include, but is not limited to, at least one of: a wide area network, a metropolitan area network, which may include, but is not limited to, at least one of: WIFI (Wireless Fidelity), bluetooth. Terminal equipment 102 can be and not be limited to PC, the cell-phone, the panel computer, intelligent air conditioner, intelligent cigarette machine, intelligent refrigerator, intelligent oven, intelligent kitchen range, intelligent washing machine, intelligent water heater, intelligent washing equipment, intelligent dish washer, intelligent projection equipment, the intelligent TV, intelligent clothes hanger, intelligent (window) curtain, intelligence audio-visual, smart jack, intelligent stereo set, intelligent audio amplifier, intelligent new trend equipment, intelligent kitchen guarding's equipment, intelligent bathroom equipment, the intelligence robot of sweeping the floor, the intelligence robot of wiping the window, intelligence robot of mopping the floor, intelligent air purification equipment, intelligent steam ager, intelligent microwave oven, intelligent kitchen guarding, intelligent clarifier, intelligent water dispenser, intelligent lock etc..
In the present embodiment, a method for predicting usage intention is provided, and fig. 2 is a flowchart of a method for predicting usage intention according to an embodiment of the present invention, where the flowchart includes the following steps:
step S202, acquiring first spatial information and second spatial information of target equipment, wherein the first spatial information is used for indicating the geographical position of the location of the target equipment, and the second spatial information is used for indicating the characteristics of the space of the target equipment in the home area;
optionally, the first spatial information may be provided by a target object bound to the target device, or may be located by a GPS module or a GPS function carried by the target device; the second spatial information can be confirmed by combining the position of the equipment with the room spatial information or according to the spatial information selected and filled by the target object, and therefore the method and the device are not limited too much.
Step S204, determining a target use environment of the target device according to the first spatial information and the second spatial information;
step S206, determining an equipment operation event matched with the target use environment from a database of the Internet of things cloud;
it should be noted that the device operation event includes time, action, and attached information of the device executing the operation event, the device, and a target object included in the device as event core elements, and then unifies information.
And step S208, predicting the use intention of the target object to the target device according to the device operation event.
Through the steps, first spatial information and second spatial information of the target device are obtained, wherein the first spatial information is used for indicating the geographical position of the location of the target device, and the second spatial information is used for indicating the characteristics of the space where the target device is located in the home area; determining a target use environment of the target device according to the first spatial information and the second spatial information; determining a device operation event matched with the target use environment from a database of the cloud end of the Internet of things; and predicting the use intention of the target object to the target device according to the device operation event. That is to say, by determining various kinds of spatial information of the target device, the target usage environment of the target device is determined, then the device operation event corresponding to the target usage environment is determined from the database, the usage intention of the target object using the target device is determined based on the device operation event, and the time corresponding to the set operation event and the space corresponding to the target device are coupled and associated, so that the predicted usage intention is more accurate.
In one exemplary embodiment, determining the target usage environment of the target device according to the first spatial information and the second spatial information includes: inputting the first spatial information into a spatial prediction model to obtain a first environmental characteristic of the target device, wherein the spatial prediction model is trained through machine learning by using a plurality of groups of data, and each group of data in the plurality of groups of data comprises: the first spatial information of the target device and the environmental characteristics corresponding to the first spatial information of the target device; and determining the target use environment of the target device according to the first environment characteristic and the second spatial information.
For example, the part of the spatial prediction of the target device includes two parts, a space of a geographical location (corresponding to the first spatial information in the embodiment of the present invention), a city province, and the like; a space of a home room (corresponding to second space information in the embodiment of the present invention), a bedroom, a living room, a washing room, a kitchen, a balcony, and the like;
alternatively, the prediction for the spatial portion may employ a GNN graph convolution network type model. And by utilizing the graph neural network, information of other provinces and cities is also referred in, and the prediction precision is further improved. After the graph convolution network is introduced, the accuracy of the result is improved no matter fine-grained prediction is performed on counties and districts or coarse-grained prediction is performed on province and city levels.
Alternatively, the model used for the portion of the timing prediction is not limited to RNN, CNN, etc. type models (LSTM, etc.).
In one exemplary embodiment, the determining, from a database in the cloud of the internet of things, a device operation event matching the target usage environment includes: determining time sequence information of the target device used by the target object; matching a plurality of time sequence equipment operation events from a database of the Internet of things cloud based on the time sequence information; determining a use environment corresponding to each time sequence equipment operation event in a plurality of time sequence equipment operation events to obtain a plurality of use environments; the plurality of usage environments are compared to a target usage environment to determine device operational events matching the target usage environment.
In one exemplary embodiment, comparing the plurality of usage environments to a target usage environment to determine device operational events matching the target usage environment includes: determining the similarity between each use environment in a plurality of use environments and a target use environment; and under the condition that the similarity accords with a preset threshold, determining that the current time sequence equipment operation event is an equipment operation event matched with the target use environment.
It can be understood that, when determining the device operation event corresponding to the target usage environment, in order to better fit the usage habit of the target object, the time sequence information of the target object using the target device is determined, that is, the target object likes to use the target device in which time period, so that a plurality of time sequence device operation events are matched in the database at the cloud end of the internet of things, the usage environment corresponding to the time sequence device operation events is determined, the usage environment and the target usage environment are compared, the device operation event matching with the target usage environment is determined from the plurality of time sequence device operation events, and the usage intention of the target object using the target device is predicted by integrating the time sequence and the spatial information.
In one exemplary embodiment, predicting the use intention of the target object for the target device according to the device operation event comprises the following steps: acquiring operation preference of a target object under the condition that a plurality of device operation events matched with the target use environment exist, wherein the operation preference is used for indicating the target object to use the target device to perform the operation event with the largest number of times; determining a plurality of degrees of matching of a plurality of device operation events with operation preferences; and arranging the plurality of equipment operation events according to the sizes of the plurality of matching degrees, and selecting the equipment operation event with the maximum matching degree to predict the use intention of the target object on the target equipment.
In brief, in order to improve the accuracy of the prediction of the use intention and exclude the interference of other redundant options on the prediction result, the use intention of the target object can be predicted by determining the operation preference of the target object, further determining the matching degree of the plurality of device operation events and the corresponding operation preference of the target object, then ranking the plurality of device operation events through the matching degree, and preferentially using the device operation event with the largest matching degree.
In an exemplary embodiment, after predicting the usage intention of the target object with the target device according to the device operation event, the method further includes: obtaining a feedback result of the target object on the predicted use intention; and when the feedback result is that the execution is allowed, indicating that the currently predicted use intention meets the use requirement of the target object, and ending the prediction flow of the use intention.
That is, after the usage intention of the target object is predicted, the accuracy of the prediction of the usage intention of the current time can be determined by obtaining the feedback result of the target object on the predicted usage intention, that is, the predicted usage intention is verified by correlating the feedback result of the target object, so that the predicted usage intention and the usage habit of the target object are ensured to be attached to each other.
In an exemplary embodiment, after predicting the usage intention of the target object with the target device according to the device operation event, the method further includes: the predicted use intention, the target object, the first spatial information and the second spatial information are subjected to associated binding; and uploading the correlation binding result to a database at the cloud end of the Internet of things to generate a usage graph database of the target object.
In order to enable the target object use target equipment to be predicted more quickly, the personalized prediction database belonging to the target object is generated by collecting the successfully predicted use intention and the corresponding target object, the spatial information and the like, the corresponding use intention can be obtained quickly by the database when the target object needs the same directly, and the prediction efficiency of the predicted use intention is improved.
In order to better understand the process of the prediction method with the use intention, the flow of the prediction method with the use intention is described below with reference to several alternative embodiments.
As an optional embodiment, an intention identification method for smart home user habits based on space-time prediction is provided, geographic position and home room space information are added on the basis of time sequence prediction, graph neural network prediction is performed on the geographic position and home room space information, prediction results of the geographic position and home room space information are fused, time and space correlation information is effectively incorporated into prediction information, the capability of the home user for using the use intention of the home appliance is improved, and therefore the accuracy of prediction is improved. The capability of predicting the use intention of the user in the smart home to use the smart household appliance is improved.
Alternatively, FIG. 3 is a schematic diagram of usage intent through spatiotemporal prediction according to an alternative embodiment of the present invention; specifically, the corresponding time sequence data is obtained by determining the event that the user uses the household appliance at each moment every day; and further acquiring spatial correlation data, and fusing the time sequence data and the spatial correlation data to obtain a space-time prediction result.
Alternatively, the time-series data and the spatial correlation data can be as shown in table 1 below, for example, the water consumption time, the water consumption amount, and the water temperature prediction of the gas water heater on the next day can be predicted by the data in table 1.
TABLE 1 prediction of hot-fired zero cold water usage
Serial number Date Time Position of Room Device Movement of Amount of water
1 2020-01-01 08：20 Qingdao (Qingdao) Kitchen cabinet Gas water heater Using water 100
2 2020-01-20 09：21 Qingdao (Qingdao) Kitchen cabinet Gas water heater Using water 150
3 2020-01-25 14：20 Qingdao (Qingdao) Kitchen cabinet Gas water heater Using water 200
4 2020-01-28 20：25 Qingdao (Qingdao) Kitchen cabinet Gas water heater Using water 100
5 2020-02-01 16：36 Qingdao island Kitchen cabinet Gas water heater Using water 150
6 2020-03-01 21：21 Qingdao (Qingdao) Kitchen cabinet Gas water heater Using water 200
7 2020-04-10 22：00 Qingdao (Qingdao) Kitchen cabinet Gas water heater Using water 300
8 2020-04-10 06：34 Qingdao island Parlor Air conditioner Starting up
Optionally, the location information is provided by the user or obtained by gps; the room information is confirmed by the position information of the equipment, and the user selects and fills the room in the home; the time, action and accessory information, equipment and users are used as event core elements and are uniformly provided by a suite of the Internet of things.
Optionally, the spatio-temporal prediction use intention may exist in a manner of tool software, and the following conditions need to be ensured when the obtained data is predicted: data noise is reduced, the influence of information loss is reduced, and the quality of data is ensured; the method includes the steps that various dimensions such as trend, period, burst and the like are contained in a time sequence, and the applicability of data is guaranteed; the limitation that the traditional prediction model can only perform single-point prediction is broken through in the spatial dimension, and the correlation influence can be accurately predicted and utilized in the spatial structure.
As an alternative embodiment, when performing time series prediction by time series data, the model used by the time series prediction section is not limited to RNN, CNN, or the like type model (LSTM, or the like); when spatial prediction is performed by spatial correlation data, a space including two parts, for example, (1) a space of a geographical location, a city province, and the like; (2) the space of a family room, a bedroom, a living room, a washing room, a kitchen, a balcony and the like. The prediction for the spatial portion may employ a GNN graph convolution network type model. And by utilizing the graph neural network, information of other provinces and cities is also referred in, and the prediction precision is further improved. After the graph convolution network is introduced, the accuracy of the result is improved no matter fine-grained prediction is performed on counties and districts or coarse-grained prediction is performed on province and city levels. Further, the temporal regularity information of the adjacent stations needs to be further superimposed to perform information aggregation in space.
For example, there is often a relationship between an a-user's home and its neighboring homes-as neighboring home appliance usage increases. In this case, when the family daily water consumption is predicted in time sequence and the expected increase of the family daily water consumption of the adjacent families on the spatial layer is observed, the accuracy rate of the equipment water consumption or other behaviors of the family on the next day can be predicted to increase, and thus the spatial association relationship of the sites is merged into the model. For nodes with larger relevance, the priority of the relevance relation is higher in prediction.
In summary, by the above prediction method, a method of predicting habit intentions based on fusion of geographic location information and time information is avoided, for example, an xgboost method is used, in which the geographic location information and the time information are only two-dimensional information of a table model and have no correlation with each other. The method based on space-time prediction in the above-mentioned optional embodiments of the present invention also adds temporal and spatial coupling and correlation to improve the prediction capability of the user's usage habits. Geographic position and family room space information are added on the basis of time sequence prediction, graph neural network prediction is carried out on the geographic position and the family room space information, prediction results of the geographic position and the family room space information are fused, and therefore the prediction accuracy is improved.
Through the above description of the embodiments, those skilled in the art can clearly understand that the method according to the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but the former is a better implementation mode in many cases. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium (such as ROM/RAM, magnetic disk, optical disk) and includes instructions for enabling a terminal device (such as a mobile phone, a computer, a server, or a network device) to perform the prediction of the usage intention according to the embodiments of the present invention.
In this embodiment, a device for predicting usage intention is also provided, and the device is used to implement the foregoing embodiments and preferred embodiments, and the description of the device that has been already made is omitted. As used below, the term "module" may be a combination of software and/or hardware that implements a predetermined function. Although the means described in the embodiments below are preferably implemented in software, an implementation in hardware, or a combination of software and hardware is also possible and contemplated.
Fig. 4 is a block diagram (a) of a prediction apparatus of usage intention according to an embodiment of the present invention, as shown in fig. 4, the apparatus including:
(1) an obtaining module 42, configured to obtain first spatial information and second spatial information of a target device, where the first spatial information is used to indicate a geographical location where the target device is located, and the second spatial information is used to indicate a feature of a space where the target device is located in a home area;
(2) a first determining module 44, configured to determine a target usage environment of the target device according to the first spatial information and the second spatial information;
(3) a second determining module 46, configured to determine, from a database in the cloud of the internet of things, a device operation event matching the target usage environment;
(4) and the prediction module 48 is used for predicting the use intention of the target object for the target device according to the device operation event.
By the device, first spatial information and second spatial information of the target device are obtained, wherein the first spatial information is used for indicating the geographical position of the location of the target device, and the second spatial information is used for indicating the characteristics of the space where the target device is located in the home area; determining a target use environment of the target device according to the first spatial information and the second spatial information; determining a device operation event matched with the target use environment from a database of the cloud end of the Internet of things; and predicting the use intention of the target object to the target device according to the device operation event. That is to say, by determining various kinds of spatial information of the target device, the target usage environment of the target device is determined, then the device operation event corresponding to the target usage environment is determined from the database, the usage intention of the target object using the target device is determined based on the device operation event, and the time corresponding to the set operation event and the space corresponding to the target device are coupled and associated, so that the predicted usage intention is more accurate.
In an exemplary embodiment, the first determining module is further configured to input the first spatial information into a spatial prediction model to obtain the first environmental characteristic of the target device, where the spatial prediction model is trained through machine learning by using multiple sets of data, and each set of data in the multiple sets of data includes: the first spatial information of the target device and the environmental characteristics corresponding to the first spatial information of the target device; and determining the target use environment of the target device according to the first environment characteristic and the second spatial information.
For example, the part of the spatial prediction of the target device includes two parts, a space of a geographical location (corresponding to the first spatial information in the embodiment of the present invention), a city province, and the like; a space of a home room (corresponding to second space information in the embodiment of the present invention), a bedroom, a living room, a washing room, a kitchen, a balcony, and the like;
alternatively, the prediction for the spatial portion may employ a GNN graph convolution network type model. And by utilizing the graph neural network, information of other provinces and cities is also referred in, and the prediction precision is further improved. After the graph convolution network is introduced, the accuracy of the result is improved no matter fine-grained prediction is performed on counties and districts or coarse-grained prediction is performed on province and city levels.
Alternatively, the models used for the portion of the temporal prediction are not limited to RNN, CNN, etc. type models (LSTM, etc.).
In an exemplary embodiment, the second determining module is further configured to determine timing information of the target device used by the target object; matching a plurality of time sequence equipment operation events from a database of the Internet of things cloud based on the time sequence information; determining a use environment corresponding to each time sequence equipment operation event in a plurality of time sequence equipment operation events to obtain a plurality of use environments; the plurality of usage environments are compared to a target usage environment to determine device operational events matching the target usage environment.
In an exemplary embodiment, the second determining module is further configured to determine a similarity between each of the multiple usage environments and the target usage environment; and under the condition that the similarity accords with a preset threshold, determining that the current time sequence equipment operation event is an equipment operation event matched with the target use environment.
It can be understood that, when determining the device operation event corresponding to the target usage environment, in order to better fit the usage habit of the target object, the time sequence information of the target object using the target device is determined, that is, the target object likes to use the target device in which time period, so that a plurality of time sequence device operation events are matched in the database at the cloud end of the internet of things, the usage environment corresponding to the time sequence device operation events is determined, the usage environment and the target usage environment are compared, the device operation event matching with the target usage environment is determined from the plurality of time sequence device operation events, and the usage intention of the target object using the target device is predicted by integrating the time sequence and the spatial information.
In an exemplary embodiment, the predicting module is further configured to, in a case that there are a plurality of device operation events matching the target usage environment, obtain an operation preference of the target object, where the operation preference is used to indicate an operation event that is performed by the target object the most times using the target device; determining a plurality of degrees of matching of a plurality of device operation events with operation preferences; and arranging the plurality of equipment operation events according to the sizes of the plurality of matching degrees, and selecting the equipment operation event with the maximum matching degree to predict the use intention of the target object on the target equipment.
In brief, in order to improve the accuracy of the prediction of the use intention and exclude the interference of other redundant options on the prediction result, the use intention of the target object can be predicted by determining the operation preference of the target object, further determining the matching degree of the plurality of device operation events and the corresponding operation preference of the target object, then ranking the plurality of device operation events through the matching degree, and preferentially using the device operation event with the largest matching degree.
Optionally, fig. 5 is a block diagram (ii) of a structure of a device for predicting usage intention according to an embodiment of the present invention, where the device may further include: a feedback module 50 and an association module 52.
In an exemplary embodiment, the apparatus further includes: the feedback module is used for acquiring a feedback result of the target object on the predicted use intention; and when the feedback result is that the execution is allowed, indicating that the currently predicted use intention meets the use requirement of the target object, and ending the prediction flow of the use intention.
That is, after the usage intention of the target object is predicted, the accuracy of the prediction of the usage intention of the current time can be determined by obtaining the feedback result of the target object on the predicted usage intention, that is, the predicted usage intention is verified by correlating the feedback result of the target object, so that the predicted usage intention and the usage habit of the target object are ensured to be attached to each other.
In an exemplary embodiment, the apparatus further comprises: the association module is used for associating and binding the successfully predicted use intention, the target object, the first spatial information and the second spatial information; and uploading the correlation binding result to a database at the cloud end of the Internet of things to generate a usage graph database of the target object.
In order to enable the target object use target equipment to be predicted more quickly, the personalized prediction database belonging to the target object is generated by collecting the successfully predicted use intention and the corresponding target object, the spatial information and the like, the corresponding use intention can be obtained quickly by the database when the target object needs the same directly, and the prediction efficiency of the predicted use intention is improved.
In the description of the present invention, it is to be understood that the terms "center", "upper", "lower", "front", "rear", "left", "right", and the like, indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, and are only for convenience in describing the present invention and simplifying the description, but do not indicate or imply that the device or assembly referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus, should not be construed as limiting the present invention. Furthermore, the terms "first" and "second" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
In the description of the present invention, it should be noted that, unless otherwise explicitly specified or limited, the terms "mounted," "connected," and "connected" are to be construed broadly, e.g., as meaning either a fixed connection, a removable connection, or an integral connection; can be mechanically or electrically connected; the two components can be directly connected or indirectly connected through an intermediate medium, and the two components can be communicated with each other. When an element is referred to as being "secured to" or "disposed on" another element, it can be directly on the other element or intervening elements may also be present. When a component is referred to as being "connected" to another element, it can be directly connected to the other element or intervening elements may also be present. The specific meaning of the above terms in the present invention can be understood in specific cases to those skilled in the art.
It should be noted that the above modules may be implemented by software or hardware, and for the latter, the following may be implemented, but not limited to: the modules are all positioned in the same processor; alternatively, the modules are respectively located in different processors in any combination.
Embodiments of the present invention also provide a storage medium having a computer program stored therein, wherein the computer program is arranged to perform the steps of any of the above method embodiments when executed.
In an exemplary embodiment, in the present embodiment, the storage medium may be configured to store a computer program for executing the steps of:
s1, acquiring first spatial information and second spatial information of the target device, wherein the first spatial information is used for indicating the geographical position of the location of the target device, and the second spatial information is used for indicating the characteristics of the space of the target device in the home area;
s2, determining the target usage environment of the target device according to the first spatial information and the second spatial information;
s3, determining a device operation event matched with the target use environment from a database of the Internet of things cloud;
and S4, predicting the use intention of the target object to the target device according to the device operation event.
In an exemplary embodiment, in the present embodiment, the storage medium may include, but is not limited to: various media capable of storing computer programs, such as a usb disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic disk, or an optical disk.
Embodiments of the present invention also provide an electronic device comprising a memory having a computer program stored therein and a processor arranged to run the computer program to perform the steps of any of the above method embodiments.
In an exemplary embodiment, the electronic apparatus may further include a transmission device and an input/output device, wherein the transmission device is connected to the processor, and the input/output device is connected to the processor.
In an exemplary embodiment, in the present embodiment, the processor may be configured to execute the following steps by a computer program:
s1, acquiring first spatial information and second spatial information of the target device, wherein the first spatial information is used for indicating the geographical position of the location of the target device, and the second spatial information is used for indicating the characteristics of the space of the target device in the home area;
s2, determining the target use environment of the target device according to the first spatial information and the second spatial information;
s3, determining a device operation event matched with the target use environment from a database of the Internet of things cloud;
and S4, predicting the use intention of the target object to the target device according to the device operation event.
In an exemplary embodiment, for specific examples in this embodiment, reference may be made to the examples described in the above embodiments and optional implementation manners, and details of this embodiment are not described herein again.
It will be apparent to those skilled in the art that the modules or steps of the present invention described above may be implemented in a general purpose computing device, they may be centralized in a single computing device or distributed across a network of multiple computing devices, and in one exemplary embodiment, they may be implemented in program code executable by a computing device, such that they may be stored in a memory device and executed by a computing device, and in some cases, the steps shown or described may be executed in an order different from that described, or they may be separately fabricated as individual integrated circuit modules, or multiple modules or steps therein may be fabricated as a single integrated circuit module. Thus, the present invention is not limited to any specific combination of hardware and software.
The foregoing is only a preferred embodiment of the present application and it should be noted that those skilled in the art can make several improvements and modifications without departing from the principle of the present application, and these improvements and modifications should also be considered as the protection scope of the present application.

Claims (10)

1. A method for predicting usage intention, comprising:
acquiring first spatial information and second spatial information of target equipment, wherein the first spatial information is used for indicating the geographical position of the location of the target equipment, and the second spatial information is used for indicating the characteristics of the space of the target equipment in the home area;
determining a target use environment of the target device according to the first spatial information and the second spatial information;
determining a device operation event matched with the target use environment from a database of the cloud end of the Internet of things;
and predicting the use intention of the target object to the target device according to the device operation event.
2. The method for predicting usage intention according to claim 1, wherein determining the target usage environment of the target device based on the first spatial information and the second spatial information includes:
inputting the first spatial information into a spatial prediction model to obtain a first environmental characteristic of the target device, wherein the spatial prediction model is trained through machine learning by using a plurality of sets of data, and each set of data in the plurality of sets of data includes: the first spatial information of the target device and the environmental characteristics corresponding to the first spatial information of the target device;
and determining the target use environment of the target device according to the first environment characteristic and the second spatial information.
3. The method for predicting usage intention according to claim 1, wherein the step of determining the device operation event matched with the target usage environment from a database in the cloud of the internet of things comprises the steps of:
determining time sequence information of a target object using the target device;
matching a plurality of time sequence equipment operation events from a database of the Internet of things cloud based on the time sequence information;
determining a use environment corresponding to each time sequence equipment operation event in the time sequence equipment operation events to obtain a plurality of use environments;
comparing the plurality of usage environments with the target usage environment to determine device operational events matching the target usage environment.
4. The method for predicting usage intent according to claim 3, wherein comparing the plurality of usage environments with the target usage environment to determine device operation events matching the target usage environment comprises:
determining the similarity of each of the plurality of use environments and the target use environment;
and under the condition that the similarity accords with a preset threshold value, determining that the current time sequence equipment operation event is the equipment operation event matched with the target use environment.
5. The method for predicting the usage intention according to claim 1, wherein predicting the usage intention of the target object with the target device according to the device operation event comprises:
acquiring operation preference of a target object under the condition that a plurality of device operation events matched with the target use environment exist, wherein the operation preference is used for indicating the target object to use the target device to perform the operation event with the largest number of times;
determining a plurality of degrees of matching of a plurality of the device operation events to the operation preferences;
and arranging the equipment operation events according to the matching degrees, and selecting the equipment operation event with the highest matching degree to predict the use intention of the target object to the target equipment.
6. The method for predicting usage intention according to claim 1, wherein after predicting usage intention of a target object with the target device according to the device operation event, the method further comprises:
obtaining a feedback result of the target object on the predicted using intention;
and when the feedback result is that the execution is allowed, indicating that the currently predicted use intention meets the use requirement of the target object, and ending the prediction process of the use intention.
7. The method for predicting usage intention according to claim 1, wherein after predicting usage intention of a target object with the target device according to the device operation event, the method further comprises:
performing association binding on the predicted successful use intention, the target object, the first spatial information and the second spatial information;
and uploading the correlation binding result to a database at the cloud end of the Internet of things, and generating a usage graph database of the target object.
8. An intended use prediction device, comprising:
the system comprises an acquisition module, a processing module and a display module, wherein the acquisition module is used for acquiring first spatial information and second spatial information of target equipment, the first spatial information is used for indicating the geographical position of the location of the target equipment, and the second spatial information is used for indicating the characteristics of the space of the target equipment in the home area;
a first determining module, configured to determine a target usage environment of the target device according to the first spatial information and the second spatial information;
the second determination module is used for determining a device operation event matched with the target use environment from a database of the cloud end of the Internet of things;
and the prediction module is used for predicting the use intention of the target object for the target equipment according to the equipment operation event.
9. A computer-readable storage medium, comprising a stored program, wherein the program when executed performs the method of any of claims 1 to 7.
10. An electronic device comprising a memory and a processor, characterized in that the memory has stored therein a computer program, the processor being arranged to execute the method of any of claims 1 to 7 by means of the computer program.
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