CN114493028A - Method and device for establishing prediction model, storage medium and electronic device - Google Patents
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Abstract
The invention discloses a method and a device for establishing a prediction model, a storage medium and an electronic device, wherein the method comprises the following steps: acquiring element information when a target object controls household electrical appliance equipment to execute target operation; partitioning the element information into a target set, wherein the target set at least comprises: a user attribute information set, a time attribute information set, a position attribute information set, a context information set and an intention attribute information set; and establishing a behavior prediction model according to the user attribute information set, the time attribute information set, the position attribute information set, the context information set and the intention attribute information set so as to predict the behavior of the target object through the behavior prediction model.
Description
Technical Field
The invention relates to the field of communication, in particular to a method and a device for establishing a prediction model, a storage medium and an electronic device.
Background
With the continuous development of the smart home industry, people are willing to use smart home devices in more scenes to realize effective and convenient interaction and control between people and devices, and in order to enable people to experience the absence and the absence of the smart home devices, multiple inlets are needed to realize interaction with users, such as mobile phone APP, smart sound boxes, smart televisions and other user side devices; meanwhile, more natural communication between people and equipment needs to be realized, and active and intimate recommendation and service are provided for people.
In the existing data modeling scheme about user behaviors, as shown in fig. 3, the relationship between user characteristics and behaviors and the association relationship between behaviors are mainly analyzed, and a user behavior analysis model is constructed; however, in the existing user behavior data modeling scheme, information collection is insufficient, and only basic information and historical behavior information of a user are collected; and the analysis is not comprehensive, and only the relationship between the user characteristics and the behaviors and the association relationship between the behaviors are analyzed.
Aiming at the problems that the established prediction model cannot accurately predict the user behavior and cannot provide reasonable and effective prediction behaviors for the user and the like caused by insufficient information collection and incomplete incidence relation in the related technology, an effective solution is not provided.
Disclosure of Invention
The embodiment of the invention provides a method and a device for establishing a prediction model, a storage medium and an electronic device, which are used for at least solving the problems that the established prediction model cannot accurately predict user behaviors and cannot provide reasonable and effective predicted behaviors for users due to insufficient information collection and incomplete incidence relation in the related technology.
According to an embodiment of the present invention, there is provided a method for building a prediction model, including: acquiring element information when a target object controls household electrical appliance equipment to execute target operation, wherein the element information at least comprises the following components: basic information of the target object, location information of the home appliance, device information of the home appliance, a target operation performed by the target object control home appliance, behavior information when the target object control home appliance performs the target operation, environment information when the target object control home appliance performs the target operation, and time information when the target object control home appliance performs the target operation; partitioning the element information into a target set, wherein the target set at least comprises: a user attribute information set, a time attribute information set, a position attribute information set, a context information set and an intention attribute information set; and establishing a behavior prediction model according to the user attribute information set, the time attribute information set, the position attribute information set, the context information set and the intention attribute information set so as to predict the behavior of the target object through the behavior prediction model.
In one exemplary embodiment, the partitioning the element information into target sets includes: acquiring at least basic information of the target object, location information of the home appliance, target operation executed by the target object control home appliance, environment information when the target object control home appliance executes the target operation, device information of the home appliance, and time information when the target object control home appliance executes the target operation, through the element information; classifying the basic information of the target object into the user attribute information set; classifying the position information of the household appliance into the position attribute information set; classifying environment information when the target object control household appliance executes target operation, equipment information of the household appliance and behavior information when the target object control household appliance executes the target operation into the context information set; classifying the time information of the target object control household appliance when the target object control household appliance executes the target operation into the time attribute information set; and classifying the target operation executed by the target object control household appliance device into the intention attribute information set.
In one exemplary embodiment, after dividing the element information into target sets, the method further comprises: determining cleaning rules respectively corresponding to the user attribute information set, the time attribute information set, the position attribute information set and the context information set; and respectively cleaning the data classified into the user attribute information set, the time attribute information set, the position attribute information set, the context information set and the intention attribute information set according to the cleaning rule so as to unify the data formats classified into the user attribute information set, the time attribute information set, the position attribute information set, the context information set and the intention attribute information set.
In one exemplary embodiment, the obtaining of the element information when the target object controls the home device to perform the target operation includes: acquiring data information which is sent by a plurality of data sources and used for controlling household electrical appliance equipment to execute target operation, wherein the data information is used for indicating the element information; carrying out duplicate removal and coverage processing on the data information to obtain processed data information; and analyzing the processed data information, and acquiring element information when the target object controls the household appliance to execute target operation.
In one exemplary embodiment, building a behavior prediction model from the set of user attribute information, the set of time attribute information, the set of location attribute information, the set of context information, and the set of intent attribute information includes: establishing an incidence relation among the user attribute information set, the time attribute information set, the position attribute information set, the context information set and the intention attribute information set; and establishing a behavior prediction model through the incidence relation among the user attribute information set, the time attribute information set, the position attribute information set, the context information set and the intention attribute information set.
In one exemplary embodiment, establishing an association relationship between the set of user attribute information, the set of time attribute information, the set of location attribute information, the set of context information, and the set of intent attribute information includes: determining a first association relation between the user attribute information set and the intention attribute information set according to the basic information of the target object and behavior information of the target object when the household appliance is controlled by the target object to execute target operation; determining a second association relation between the time attribute information set and the intention attribute information set according to the time information when the target object control household appliance executes the target operation and the behavior information when the target object control household appliance executes the target operation; determining a third association relation between the position attribute information set and the intention attribute information set according to the position information of the household appliance and behavior information of the target object when the household appliance is controlled to execute target operation; and determining a fourth association relationship between the context information set and the intention attribute information set by the target object control household appliance, the target operation executed by the target object control household appliance, the environment information when the target object control household appliance executes the target operation and the behavior information when the target object control household appliance executes the target operation.
In an exemplary embodiment, after the behavior prediction model is built according to the user attribute information set, the time attribute information set, the location attribute information set, the context information set, and the intention attribute information set, the method further comprises: acquiring the current behavior of the target object, and inputting the current behavior into the prediction model; and predicting the behavior to be executed of the target object according to the user attribute information set, the time attribute information set, the position attribute information set, the context information set and the intention attribute information in the prediction model, and controlling the target equipment to execute corresponding operation according to the behavior to be executed.
According to another embodiment of the present invention, there is also provided a device for creating a prediction model, including: an obtaining module, configured to obtain element information when a target object controls an electrical home appliance to perform a target operation, where the element information at least includes: basic information of the target object, location information of the home appliance, device information of the home appliance, a target operation performed by the target object control home appliance, behavior information when the target object control home appliance performs the target operation, environment information when the target object control home appliance performs the target operation, and time information when the target object control home appliance performs the target operation; a classification module configured to classify the element information into a target set, wherein the target set at least includes: a user attribute information set, a time attribute information set, a position attribute information set, a context information set and an intention attribute information set; and the establishing module is used for establishing a behavior prediction model according to the user attribute information set, the time attribute information set, the position attribute information set, the context information set and the intention attribute information set so as to predict the behavior of the target object through the behavior prediction model.
According to another aspect of the embodiments of the present invention, there is also provided a computer-readable storage medium, in which a computer program is stored, wherein the computer program is configured to execute the above method for establishing a prediction model when running.
According to another aspect of the embodiments of the present invention, there is also provided an electronic apparatus, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the above method for building a prediction model through the computer program.
In the embodiment of the present invention, element information of a target object controlling a home appliance device to perform a target operation is obtained, where the element information at least includes: basic information of the target object, location information of the home appliance, device information of the home appliance, a target operation performed by the target object control home appliance, behavior information when the target object control home appliance performs the target operation, environment information when the target object control home appliance performs the target operation, and time information when the target object control home appliance performs the target operation; partitioning the element information into a target set, wherein the target set at least comprises: a user attribute information set, a time attribute information set, a position attribute information set, a context information set and an intention attribute information set; establishing a behavior prediction model according to the user attribute information set, the time attribute information set, the position attribute information set, the context information set and the intention attribute information set so as to predict the behavior of the target object through the behavior prediction model; by adopting the technical scheme, the problems that the established prediction model cannot accurately predict the user behavior and cannot provide reasonable and effective predicted behavior for the user due to insufficient information collection and incomplete incidence relation are solved.
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The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the invention and together with the description serve to explain the invention without limiting the invention. In the drawings:
fig. 1 is a block diagram of a hardware configuration of a computer terminal of a method for building a prediction model according to an embodiment of the present invention;
FIG. 2 is a flow chart of a method of building a predictive model according to an embodiment of the invention;
FIG. 3 is a flow chart of a prior art method of building a predictive model;
FIG. 4 is a schematic diagram of a method of building a predictive model according to an embodiment of the invention;
FIG. 5 is a flow chart of a method of building a predictive model according to an alternative embodiment of the invention;
fig. 6 is a block diagram of a device for creating a prediction model according to an embodiment of the present invention.
Detailed Description
In order to make the technical solutions of the present invention better understood, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the 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 invention.
It should be noted that the terms "first," "second," and the like in the description and claims of the present invention 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 invention 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.
The method provided by the embodiment of the application can be executed in a mobile terminal, a computer terminal or a similar operation device. Taking the operation on a computer terminal as an example, fig. 1 is a hardware structure block diagram of a computer terminal of a method for establishing a prediction model according to an embodiment of the present invention. As shown in fig. 1, the computer terminal may include one or more (only one shown in fig. 1) processors 102 (the processors 102 may include, but are not limited to, a processing device such as a microprocessor MCU or a programmable logic device FPGA) and a memory 104 for storing data, and in an exemplary embodiment, may also include a transmission device 106 for communication functions and an input-output device 108. It will be understood by those skilled in the art that the structure shown in fig. 1 is only an illustration and is not intended to limit the structure of the computer terminal. For example, the computer terminal may also include more or fewer components than shown in FIG. 1, or have a different configuration with equivalent functionality to that shown in FIG. 1 or with more functionality than that shown in FIG. 1.
The memory 104 may be used to store a computer program, for example, a software program and a module of an application software, such as a computer program corresponding to the method for building a prediction model in the embodiment of the present invention, and the processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, so as to implement the method described above. The memory 104 may include high speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include memory located remotely from the processor 102, which may be connected to a computer terminal over a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The transmission device 106 is used for receiving or transmitting data via a network. Specific examples of the network described above may include a wireless network provided by a communication provider of the computer terminal. In one example, the transmission device 106 includes a Network adapter (NIC), which can be connected to other Network devices through a base station so as to communicate with the internet. In one example, the transmission device 106 may be a Radio Frequency (RF) module, which is used for communicating with the internet in a wireless manner.
In this embodiment, a method for building a prediction model is provided, which is applied to the computer terminal, and fig. 2 is a flowchart of a method for building a prediction model according to an embodiment of the present invention, where the flowchart includes the following steps:
step S202, acquiring element information when the target object controls the home appliance device to perform the target operation, wherein the element information at least includes: basic information of the target object, location information of the home appliance, device information of the home appliance, a target operation performed by the target object control home appliance, behavior information when the target object control home appliance performs the target operation, environment information when the target object control home appliance performs the target operation, and time information when the target object control home appliance performs the target operation;
step S204, dividing the element information into a target set, wherein the target set at least comprises: a user attribute information set, a time attribute information set, a position attribute information set, a context information set and an intention attribute information set;
step S206, establishing a behavior prediction model according to the user attribute information set, the time attribute information set, the position attribute information set, the context information set and the intention attribute information set, so as to predict the behavior of the target object through the behavior prediction model.
Through the steps, element information of the target object controlling the household electrical appliance equipment to execute the target operation is obtained, wherein the element information at least comprises the following steps: basic information of the target object, location information of the home appliance, device information of the home appliance, a target operation performed by the target object control home appliance, behavior information when the target object control home appliance performs the target operation, environment information when the target object control home appliance performs the target operation, and time information when the target object control home appliance performs the target operation; partitioning the element information into a target set, wherein the target set at least comprises: a user attribute information set, a time attribute information set, a position attribute information set, a context information set and an intention attribute information set; according to the method and the device for predicting the behavior of the target object, a behavior prediction model is established according to the user attribute information set, the time attribute information set, the position attribute information set, the context information set and the intention attribute information set, so that the behavior of the target object is predicted through the behavior prediction model, the problems that the established prediction model cannot accurately predict the behavior of the user and cannot provide reasonable and effective predicted behaviors for the user due to insufficient information collection and incomplete association relation in the related technology are solved, the relation among the tuples is analyzed through comprehensively collecting the relevant information of the target object and the behavior, a brand new prediction model is established, and more effective and accurate behavior prediction is achieved.
In one exemplary embodiment, the partitioning the element information into target sets includes: acquiring at least basic information of the target object, location information of the home appliance, target operation executed by the target object control home appliance, environment information when the target object control home appliance executes the target operation, device information of the home appliance, and time information when the target object control home appliance executes the target operation, through the element information; classifying the basic information of the target object into the user attribute information set; classifying the position information of the household appliance into the position attribute information set; classifying environment information when the target object control household appliance performs target operation, equipment information of the household appliance, and behavior information when the target object control household appliance performs target operation into the context information set; classifying the time information of the target object control household appliance when the target object control household appliance executes the target operation into the time attribute information set; and classifying the target operation executed by the target object control household appliance device into the intention attribute information set.
That is, the relevant information such as user information, family information, location information, environment information, device information, and user behavior is collected on a device-by-device basis. The related information not only comprises user basic information such as user age, birthday and body fat, but also continuous or historical behavior information such as current behavior, previous behavior of the current behavior, next behavior of the current behavior and the like; but also family information, environment information, equipment state information, and the like. And analyzing and sorting the collected information of each element. Dividing all element information into five tuple sets; the system comprises a user attribute information set, a time attribute information set, a position attribute information set, a context information set and an intention attribute information set. And designing a uniform data model for each set, wherein the uniform data model comprises a uniform storage format, uniform codes, uniform units and the like.
In an exemplary embodiment, after the element information is divided into a target set, determining a user attribute information set, a time attribute information set, a position attribute information set and a context information set, wherein intention attribute information sets respectively correspond to cleaning rules; and respectively cleaning the data classified into the user attribute information set, the time attribute information set, the position attribute information set, the context information set and the intention attribute information set according to the cleaning rule so as to unify the data formats classified into the user attribute information set, the time attribute information set, the position attribute information set, the context information set and the intention attribute information set.
In one exemplary embodiment, the obtaining of the element information when the target object controls the home device to perform the target operation includes: acquiring data information which is sent by a plurality of data sources and used for controlling household electrical appliance equipment to execute target operation, wherein the data information is used for indicating the element information; carrying out duplicate removal and coverage processing on the data information to obtain processed data information; and analyzing the processed data information, and acquiring element information when the target object controls the household appliance to execute target operation.
That is, the user behavior related information is collected through different device terminals, such as APP, AI, multiple screens, and the like, and associated systems, such as a user center, an IOT domain model, a home model, and the like. The collected information includes, but is not limited to, user information, family information, location information, environment information, device information, and associated information such as user behavior. And according to the information analysis, carrying out duplicate removal and coverage processing on the collected information.
In one exemplary embodiment, building a behavior prediction model from the set of user attribute information, the set of time attribute information, the set of location attribute information, the set of context information, and the set of intent attribute information includes: establishing an incidence relation among the user attribute information set, the time attribute information set, the position attribute information set, the context information set and the intention attribute information set; and establishing a behavior prediction model through the incidence relation among the user attribute information set, the time attribute information set, the position attribute information set, the context information set and the intention attribute information set.
Establishing the association relationship among the user attribute information set, the time attribute information set, the location attribute information set, the context information set and the intention attribute information set in a specific manner as follows: determining a first association relation between the user attribute information set and the intention attribute information set according to the basic information of the target object and behavior information of the target object when the household appliance is controlled by the target object to execute target operation; determining a second association relation between the time attribute information set and the intention attribute information set according to the time information when the target object control household appliance executes the target operation and the behavior information when the target object control household appliance executes the target operation; determining a third association relation between the position attribute information set and the intention attribute information set according to the position information of the household appliance and behavior information of the target object when the household appliance is controlled to execute target operation; and determining a fourth association relationship between the context information set and the intention attribute information set by the target object control household appliance, the target operation executed by the target object control household appliance, the environment information when the target object control household appliance executes the target operation and the behavior information when the target object control household appliance executes the target operation.
That is, the association relationship between the respective sets is established by:
1) transverse analysis: and transversely pulling through information such as basic information of the target object, position information of the household appliance, equipment information of the household appliance, target operation executed by the target object control household appliance, behavior information when the target object control household appliance executes the target operation, environment information when the target object control household appliance executes the target operation, time information when the target object control household appliance executes the target operation and the like, and analyzing the relationship between each element information and the user behavior. For example, the relationship between 'the number of family' and the user behavior attribute 'turn on the air conditioner' in the family attribute;
2) longitudinal analysis:
A) analyzing different behavior attributes: performing correlation analysis on different user behavior attributes according to the time sequence of behavior occurrence; such as the correlation between the 'turn on air conditioner' and 'turn on water heater' activities.
B) Same behavior attribute analysis: and performing association analysis of multiple historical behaviors aiming at the same user behavior. For example, zhangsan is 7 pm on 1 month, 1 day: 05 'turn on air conditioner'; at 1 month, 2 days, 7 pm: 15 'turn on the air conditioner', and analyze the association relationship of the historical behaviors. It should be noted that the above-mentioned numbers are only for better understanding of the embodiments of the present invention, and the embodiments of the present invention are not limited thereto.
In an exemplary embodiment, after a behavior prediction model is established according to the user attribute information set, the time attribute information set, the location attribute information set, the context information set and the intention attribute information set, the current behavior of the target object is obtained, and the current behavior is input into the prediction model; and predicting the behavior to be executed of the target object according to the user attribute information set, the time attribute information set, the position attribute information set, the context information set and the intention attribute information in the prediction model, and controlling the target equipment to execute corresponding operation according to the behavior to be executed.
That is to say, the next step behavior of the target object is predicted according to the user attribute information set, the time attribute information set, the position attribute information set, the context information set and the intention attribute information in the prediction model, and the predicted behavior message of the prediction model is pushed to the target object according to the information pushing rule.
In order to better understand the process of the method for establishing the prediction model, the following describes a flow of the method for establishing the prediction model with reference to an optional embodiment, but is not limited to the technical solution of the embodiment of the present invention.
In this embodiment, a method for building a prediction model is provided, and fig. 4 is a schematic diagram of a method for building a prediction model according to an embodiment of the present invention, as shown in fig. 4, specifically as follows:
user data/equipment data/behavior log data/and the like are obtained from a GIO layer or an AI equipment end and input into an operational data warehouse ODS, so that the ODS processes the data, and a data warehouse DW for sending the processed data is used for classifying the processed data and distributing the classified data into different tuples of people, time, place, context, intention and the like; and sending the classified data to a data mart DM established by taking a certain application as a starting point, and analyzing the classified data by the DM through a related algorithm to obtain a corresponding knowledge graph, a prediction model and the like.
In this embodiment, a method for building a prediction model is provided, and fig. 5 is a flowchart of a method for building a prediction model according to an alternative embodiment of the present invention, as shown in fig. 5, specifically as follows:
step S501: starting;
step S502: collecting user behavior related information through different equipment terminals, such as APP, AI, multiple screens and the like, and associated systems, such as a user center, an IOT field model, a family model and the like, and collecting information including but not limited to three-party information, such as user information, family information, address information, weather and the like, equipment information, user behavior and other associated information;
step S503: analyzing information;
specifically, 1) data horizontal analysis:
and (4) transversely pulling through associated information such as user information, family information, position information, environment information, equipment information, user behaviors and the like, and analyzing the relationship between each element information and the user behaviors. For example, the relationship between 'the number of family' and the user behavior attribute 'turn on the air conditioner' in the family attribute;
2) longitudinal analysis of data
A) Analyzing different behavior attributes: performing correlation analysis on different user behavior attributes according to the time sequence of behavior occurrence; such as the correlation between the 'turn on air conditioner' and 'turn on water heater' activities.
B) Same behavior attribute analysis: and performing association analysis of multiple historical behaviors aiming at the same user behavior. For example, zhangsan is 7 pm on 1 month, 1 day: 05 'turn on air conditioner'; at 1 month, 2 days, 7 pm: 15 'turn on the air conditioner', and analyze the association relationship of the historical behaviors.
Step S504: information standardization;
the method specifically comprises the following steps: 3.1 information Classification
According to information analysis, grouping and classifying various data, and performing duplication removal and coverage treatment; a user attribute information set U, a time attribute information set T, a location attribute information set a, and a context attribute information set L (corresponding to the user attribute information set, the time attribute information set, the location attribute information set, and the context information set in the above embodiments) are obtained, respectively.
3.2 tuple information normalization
And designing a unified data model for the classified data, wherein the unified data model comprises standardized processing such as unified storage format, unified coding, unified units and the like. For example, the 'date' format is unified as '2021-10-20'; the 'city' codes are unified as 'city', 'water consumption' units are unified as 'L', etc.
3.3 behavioral information standardization
And performing behavior abstraction and classification processing aiming at the same behavior events from different data sources to form behavior information with uniform codes. For example, the AI terminal acquires a behavior event as 'open a living room air conditioner', extracts location tuple information as 'living room', and obtains a behavior event attribute of a context tuple as 'open an air conditioner'; for example, if the APP side acquires the function page as an 'air conditioner detail page', the execution operation is 'on', and the behavior event attribute combined into the context tuple is 'on air conditioner'.
Step S505: establishing a quintuple model according to the incidence relation among the information in the quintuple;
the quintuple model includes: 1) the user: user ID information, and user profile information.
2) Time: a time series of behaviors; the information comprises user behavior time stamps, the belonged year, month, day, hour and the like of the behavior time.
3) Position: behavioral location address information; a space containing a user behavior, such as 'living room'; and the information comprises province, city, county, district and the like of the behavior.
4) Context: front-to-back order behavior, front-to-back order behavior status, user or net portrait, weather, air quality, etc.
5) Intention is: the data predicts subsequent behavior information.
Step S506: and (3) predicting user behaviors: predicting the next action of the user according to the user tuple, the time tuple, the position tuple and the context tuple in the quintuple, and storing the predicted user action information into an intention tuple;
step S507: predicting user behavior push: pushing the predicted behavior message of the prediction model to the user according to the information pushing rule;
step S508: and (6) ending.
It should be noted that: u: a user attribute information set; u (x) represents a certain user attribute, and u (u _1, u _2, …, u _ k) represents the 1 st to k th attributes of the user. For example, the user contains attributes of age, gender, occupational information, etc.
T: a set of time attribute information; t (x) represents a time attribute of a certain user behavior, and t (t _1, t _2, …, t _ k) represents 1 st to k th attributes of time. Such as attributes of the year, month, day, hour, etc. to which the user behavior belongs.
A: a set of location attribute information; b (x) a location attribute representing a certain user behavior, b (b _1, b _2, …, b _ k) 1 st to k-th attributes of a location. Such as the province, city, county, room, etc., to which the user behavior belongs.
L: a set of context attribute information; l (x) represents a context attribute of a certain user behavior, and l (l _1, l _2, …, l _ k) represents the 1 st to k-th features of the context. Such as attributes of the user's previous behavior, current weather, current device power-on status, etc.
I: an intent attribute information set; l (x) represents a certain user intention attribute, and l (l _1, l _2, …, l _ k) represents the 1 st to k th attributes of the user intention. Such as opening curtains, turning on lights, increasing wind speed, etc.
F: a quintuple attribute information set; f _ x (u _1, t _1, a _1, l _1, i _1, …) indicates that user l _1 and corresponding user behavior time attribute 1 is t _1, address location attribute 1 is a _1, and context attribute 1 is l _ 1; the fifth tuple i _1 is obtained from the first four tuples. For example, f _1(u _1, t _1, a _1, l _1, i _1) indicates that the user: 'Zhang three', the corresponding user behavior attribute is '2021-10-20', the location attribute is 'living room', the context attribute is 'too cold', and the predicted user behavior intention is 'turn on air conditioner'.
In the embodiment of the invention, each element information is collected from a plurality of user sides, including but not limited to user information, family information, position information, environment information, equipment information, user behavior and other associated information; analyzing the information characteristics of each element and the relation between each element and the behavior; constructing a quintuple data model of user behaviors; therefore, the relation situation of the user and the behavior can be analyzed more comprehensively, and more accurate user behavior prediction information can be obtained.
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 solutions 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 execute the method according to the embodiments of the present invention.
In this embodiment, a device for building a prediction model is further provided, and the device for building a prediction model is used to implement the foregoing embodiments and preferred embodiments, which have already been described and are not described again. 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. 6 is a block diagram of a device for creating a prediction model according to an embodiment of the present invention; as shown in fig. 6, includes:
an obtaining module 62, configured to obtain element information when the target object controls the home appliance to perform the target operation, where the element information at least includes: basic information of the target object, location information of the home appliance, device information of the home appliance, a target operation performed by the target object control home appliance, behavior information when the target object control home appliance performs the target operation, environment information when the target object control home appliance performs the target operation, and time information when the target object control home appliance performs the target operation;
a classification module 64, configured to classify the element information into a target set, where the target set at least includes: a user attribute information set, a time attribute information set, a position attribute information set, a context information set and an intention attribute information set;
an establishing module 66, configured to establish a behavior prediction model according to the user attribute information set, the time attribute information set, the location attribute information set, the context information set, and the intention attribute information set, so as to predict a behavior of the target object through the behavior prediction model.
By the device, element information of the target object controlling the household electrical appliance equipment to execute the target operation is obtained, wherein the element information at least comprises the following components: basic information of the target object, location information of the home appliance, device information of the home appliance, a target operation performed by the target object control home appliance, behavior information when the target object control home appliance performs the target operation, environment information when the target object control home appliance performs the target operation, and time information when the target object control home appliance performs the target operation; partitioning the element information into a target set, wherein the target set at least comprises: a user attribute information set, a time attribute information set, a position attribute information set, a context information set and an intention attribute information set; according to the method and the device, a behavior prediction model is established according to the user attribute information set, the time attribute information set, the position attribute information set, the context information set and the intention attribute information set, so that the behavior of the target object is predicted through the behavior prediction model, the problems that the established prediction model cannot accurately predict the behavior of the user and cannot provide reasonable and effective predicted behaviors for the user due to insufficient information collection and incomplete association relation in the related technology are solved, and further the relation among tuples is analyzed through comprehensively collecting the relevant information of the target object and the behavior, a brand new prediction model is established, and more effective and accurate behavior prediction is achieved.
In an exemplary embodiment, the classification module is further configured to obtain, through the element information, at least basic information of the target object, location information of the home device, a target operation performed by the target object control home device, environment information when the target object control home device performs the target operation, device information of the home device, and time information when the target object control home device performs the target operation; classifying the basic information of the target object into the user attribute information set; classifying the position information of the household appliance into the position attribute information set; classifying environment information when the target object control household appliance performs target operation, equipment information of the household appliance, and behavior information when the target object control household appliance performs target operation into the context information set; classifying the time information of the target object control household appliance when the target object control household appliance executes the target operation into the time attribute information set; and classifying the target operation executed by the target object control household appliance device into the intention attribute information set.
In an exemplary embodiment, the obtaining module is further configured to determine a cleaning rule corresponding to the user attribute information set, the time attribute information set, the location attribute information set, and the context information set, respectively; and respectively cleaning the data classified into the user attribute information set, the time attribute information set, the position attribute information set, the context information set and the intention attribute information set according to the cleaning rule so as to unify the data formats classified into the user attribute information set, the time attribute information set, the position attribute information set, the context information set and the intention attribute information set.
In an exemplary embodiment, the obtaining module is further configured to obtain data information, which is sent by multiple data sources and used by the target object to control the home appliance device to perform a target operation, where the data information is used to indicate the element information; carrying out duplicate removal and coverage processing on the data information to obtain processed data information; and analyzing the processed data information, and acquiring element information when the target object controls the household appliance to execute target operation.
In an exemplary embodiment, the establishing module is further configured to establish an association relationship between the user attribute information set, the time attribute information set, the location attribute information set, the context information set, and the intention attribute information set; and establishing a behavior prediction model through the incidence relation among the user attribute information set, the time attribute information set, the position attribute information set, the context information set and the intention attribute information set.
In an exemplary embodiment, the establishing module is further configured to determine a first association relationship between the user attribute information set and the intention attribute information set according to the basic information of the target object and behavior information of the target object when controlling an electrical home appliance to perform a target operation; determining a second association relation between the time attribute information set and the intention attribute information set according to the time information when the target object control household appliance executes the target operation and the behavior information when the target object control household appliance executes the target operation; determining a third association relation between the position attribute information set and the intention attribute information set according to the position information of the household appliance and behavior information of the target object when the household appliance is controlled to execute target operation; and determining a fourth association relationship between the context information set and the intention attribute information set by the target object control household appliance, the target operation executed by the target object control household appliance, the environment information when the target object control household appliance executes the target operation and the behavior information when the target object control household appliance executes the target operation.
In an exemplary embodiment, the apparatus further includes a prediction module, configured to obtain a current behavior of the target object, and input the current behavior into the prediction model; and predicting the behavior to be executed of the target object according to the user attribute information set, the time attribute information set, the position attribute information set, the context information set and the intention attribute information in the prediction model, and controlling the target equipment to execute corresponding operation according to the behavior to be executed.
An embodiment of the present invention further provides a storage medium including a stored program, wherein the program executes any one of the methods described above.
Alternatively, in the present embodiment, the storage medium may be configured to store program codes for performing the following steps:
s1, acquiring element information when the target object controls the home device to perform the target operation, wherein the element information at least includes: basic information of the target object, location information of the home appliance, device information of the home appliance, a target operation performed by the target object control home appliance, behavior information when the target object control home appliance performs the target operation, environment information when the target object control home appliance performs the target operation, and time information when the target object control home appliance performs the target operation;
s2, dividing the element information into a target set, wherein the target set at least comprises: a user attribute information set, a time attribute information set, a position attribute information set, a context information set and an intention attribute information set;
s3, establishing a behavior prediction model according to the user attribute information set, the time attribute information set, the position attribute information set, the context information set and the intention attribute information set, so as to predict the behavior of the target object through the behavior prediction model.
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.
Optionally, 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.
Optionally, in this embodiment, the processor may be configured to execute the following steps by a computer program:
s1, acquiring element information when the target object controls the home device to perform the target operation, wherein the element information at least includes: basic information of the target object, location information of the home appliance, device information of the home appliance, a target operation performed by the target object control home appliance, behavior information when the target object control home appliance performs the target operation, environment information when the target object control home appliance performs the target operation, and time information when the target object control home appliance performs the target operation;
s2, dividing the element information into a target set, wherein the target set at least comprises: a user attribute information set, a time attribute information set, a position attribute information set, a context information set and an intention attribute information set;
s3, establishing a behavior prediction model according to the user attribute information set, the time attribute information set, the position attribute information set, the context information set and the intention attribute information set, so as to predict the behavior of the target object through the behavior prediction model.
Optionally, in this embodiment, the storage medium may include, but is not limited to: various media capable of storing program codes, 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.
Optionally, the specific examples in this embodiment may refer to the examples described in the above embodiments and optional implementation manners, and this embodiment is 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 by a general purpose computing device, they may be centralized on a single computing device or distributed across a network of multiple computing devices, and alternatively, they may be implemented by program code executable by a computing device, such that they may be stored in a storage device and executed by a computing device, and in some cases, the steps shown or described may be performed in an order different than that described herein, or they may be separately fabricated into individual integrated circuit modules, or multiple ones of them may be fabricated into a single integrated circuit module. Thus, the present invention is not limited to any specific combination of hardware and software.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. A method for building a prediction model is characterized by comprising the following steps:
acquiring element information when a target object controls household electrical appliance equipment to execute target operation, wherein the element information at least comprises the following components: basic information of the target object, location information of the home appliance, device information of the home appliance, a target operation performed by the target object control home appliance, behavior information when the target object control home appliance performs the target operation, environment information when the target object control home appliance performs the target operation, and time information when the target object control home appliance performs the target operation;
partitioning the element information into a target set, wherein the target set at least comprises: a user attribute information set, a time attribute information set, a position attribute information set, a context information set and an intention attribute information set;
and establishing a behavior prediction model according to the user attribute information set, the time attribute information set, the position attribute information set, the context information set and the intention attribute information set so as to predict the behavior of the target object through the behavior prediction model.
2. The method of claim 1, wherein the partitioning the element information into a target set comprises:
acquiring at least basic information of the target object, location information of the home appliance, target operation executed by the target object control home appliance, environment information when the target object control home appliance executes the target operation, device information of the home appliance, and time information when the target object control home appliance executes the target operation, through the element information;
classifying the basic information of the target object into the user attribute information set;
classifying the position information of the household appliance into the position attribute information set;
classifying environment information when the target object control household appliance executes target operation, equipment information of the household appliance and behavior information when the target object control household appliance executes the target operation into the context information set;
classifying the time information of the target object control household appliance when the target object control household appliance executes the target operation into the time attribute information set;
and classifying the target operation executed by the target object control household appliance into the intention attribute information set.
3. The method of claim 1, wherein after partitioning the element information into a target set, the method further comprises:
determining cleaning rules respectively corresponding to the user attribute information set, the time attribute information set, the position attribute information set and the context information set;
and respectively cleaning the data classified into the user attribute information set, the time attribute information set, the position attribute information set, the context information set and the intention attribute information set according to the cleaning rule so as to unify the data formats classified into the user attribute information set, the time attribute information set, the position attribute information set, the context information set and the intention attribute information set.
4. The method for building a prediction model according to claim 1, wherein obtaining element information of the target object controlling the home device to perform the target operation comprises:
acquiring data information which is sent by a plurality of data sources and used for controlling household electrical appliance equipment to execute target operation, wherein the data information is used for indicating the element information;
carrying out duplicate removal and coverage processing on the data information to obtain processed data information;
and analyzing the processed data information, and acquiring element information when the target object controls the household appliance to execute target operation.
5. The method for building a prediction model according to claim 1, wherein building a behavior prediction model from the user attribute information set, the time attribute information set, the location attribute information set, the context information set, and the intention attribute information set comprises:
establishing an incidence relation among the user attribute information set, the time attribute information set, the position attribute information set, the context information set and the intention attribute information set;
and establishing a behavior prediction model through the incidence relation among the user attribute information set, the time attribute information set, the position attribute information set, the context information set and the intention attribute information set.
6. The method for building a prediction model according to claim 5, wherein building an association relationship among the set of user attribute information, the set of time attribute information, the set of location attribute information, the set of context information, and the set of intention attribute information comprises:
determining a first association relation between the user attribute information set and the intention attribute information set according to the basic information of the target object and behavior information of the target object when the household appliance is controlled by the target object to execute target operation;
determining a second association relation between the time attribute information set and the intention attribute information set according to the time information when the target object control household appliance executes the target operation and the behavior information when the target object control household appliance executes the target operation;
determining a third association relation between the position attribute information set and the intention attribute information set according to the position information of the household appliance and behavior information of the target object when the household appliance is controlled to execute target operation;
and determining a fourth association relationship between the context information set and the intention attribute information set by the target object control household appliance, the target operation executed by the target object control household appliance, the environment information when the target object control household appliance executes the target operation and the behavior information when the target object control household appliance executes the target operation.
7. The method of building a prediction model according to claim 1, wherein after building a behavior prediction model based on the set of user attribute information, the set of time attribute information, the set of location attribute information, the set of context information, and the set of intention attribute information, the method further comprises:
acquiring the current behavior of the target object, and inputting the current behavior into the prediction model;
and predicting the behavior to be executed of the target object according to the user attribute information set, the time attribute information set, the position attribute information set, the context information set and the intention attribute information in the prediction model, and controlling the target equipment to execute corresponding operation according to the behavior to be executed.
8. An apparatus for creating a prediction model, comprising:
an obtaining module, configured to obtain element information when a target object controls an electrical home appliance to perform a target operation, where the element information at least includes: basic information of the target object, location information of the home appliance, device information of the home appliance, a target operation performed by the target object control home appliance, behavior information when the target object control home appliance performs the target operation, environment information when the target object control home appliance performs the target operation, and time information when the target object control home appliance performs the target operation;
a classification module configured to classify the element information into a target set, wherein the target set at least includes: a user attribute information set, a time attribute information set, a position attribute information set, a context information set and an intention attribute information set;
and the establishing module is used for establishing a behavior prediction model according to the user attribute information set, the time attribute information set, the position attribute information set, the context information set and the intention attribute information set so as to predict the behavior of the target object through the behavior prediction model.
9. A computer-readable storage medium, comprising a stored program, wherein the program is operable to perform the method of any one 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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| CN115481315A (en) * | 2022-08-30 | 2022-12-16 | 海尔优家智能科技（北京）有限公司 | Method and device for determining recommendation information, storage medium and electronic device |
| WO2023151215A1 (en) * | 2022-02-08 | 2023-08-17 | 青岛海尔科技有限公司 | Prediction model establishment method and device, storage medium, and electronic device |
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| US20150112754A1 (en) * | 2013-10-17 | 2015-04-23 | Ebay Inc. | User intent tracking streams |
| CN110824930B (en) * | 2018-08-08 | 2021-10-08 | 珠海格力电器股份有限公司 | Control method, device and system of household appliance |
| KR102374448B1 (en) * | 2018-11-02 | 2022-03-15 | 서울대학교산학협력단 | Method for predicting intention of user and apparatus for performing the same |
| CN109858970B (en) * | 2019-02-02 | 2021-07-02 | 中国银行股份有限公司 | User behavior prediction method, device and storage medium |
| CN114493028A (en) * | 2022-02-08 | 2022-05-13 | 青岛海尔科技有限公司 | Method and device for establishing prediction model, storage medium and electronic device |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| WO2023151215A1 (en) * | 2022-02-08 | 2023-08-17 | 青岛海尔科技有限公司 | Prediction model establishment method and device, storage medium, and electronic device |
| CN115481315A (en) * | 2022-08-30 | 2022-12-16 | 海尔优家智能科技（北京）有限公司 | Method and device for determining recommendation information, storage medium and electronic device |
| WO2024045501A1 (en) * | 2022-08-30 | 2024-03-07 | 海尔优家智能科技（北京）有限公司 | Recommendation information determination method and apparatus, and storage medium and electronic apparatus |
| CN115481315B (en) * | 2022-08-30 | 2024-03-22 | 海尔优家智能科技（北京）有限公司 | Recommendation information determining method and device, storage medium and electronic device |
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