CN113774639A - Intelligent clothes airing machine control method, device and system and storage medium - Google Patents
Intelligent clothes airing machine control method, device and system and storage medium Download PDFInfo
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- CN113774639A CN113774639A CN202111154314.3A CN202111154314A CN113774639A CN 113774639 A CN113774639 A CN 113774639A CN 202111154314 A CN202111154314 A CN 202111154314A CN 113774639 A CN113774639 A CN 113774639A
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- D—TEXTILES; PAPER
- D06—TREATMENT OF TEXTILES OR THE LIKE; LAUNDERING; FLEXIBLE MATERIALS NOT OTHERWISE PROVIDED FOR
- D06F—LAUNDERING, DRYING, IRONING, PRESSING OR FOLDING TEXTILE ARTICLES
- D06F58/00—Domestic laundry dryers
- D06F58/32—Control of operations performed in domestic laundry dryers
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- D—TEXTILES; PAPER
- D06—TREATMENT OF TEXTILES OR THE LIKE; LAUNDERING; FLEXIBLE MATERIALS NOT OTHERWISE PROVIDED FOR
- D06F—LAUNDERING, DRYING, IRONING, PRESSING OR FOLDING TEXTILE ARTICLES
- D06F34/00—Details of control systems for washing machines, washer-dryers or laundry dryers
- D06F34/04—Signal transfer or data transmission arrangements
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- D—TEXTILES; PAPER
- D06—TREATMENT OF TEXTILES OR THE LIKE; LAUNDERING; FLEXIBLE MATERIALS NOT OTHERWISE PROVIDED FOR
- D06F—LAUNDERING, DRYING, IRONING, PRESSING OR FOLDING TEXTILE ARTICLES
- D06F34/00—Details of control systems for washing machines, washer-dryers or laundry dryers
- D06F34/14—Arrangements for detecting or measuring specific parameters
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- D—TEXTILES; PAPER
- D06—TREATMENT OF TEXTILES OR THE LIKE; LAUNDERING; FLEXIBLE MATERIALS NOT OTHERWISE PROVIDED FOR
- D06F—LAUNDERING, DRYING, IRONING, PRESSING OR FOLDING TEXTILE ARTICLES
- D06F57/00—Supporting means, other than simple clothes-lines, for linen or garments to be dried or aired
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F3/00—Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
- G06F3/01—Input arrangements or combined input and output arrangements for interaction between user and computer
- G06F3/011—Arrangements for interaction with the human body, e.g. for user immersion in virtual reality
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Abstract
The invention discloses an intelligent clothes airing machine control method which comprises the steps of obtaining interaction data generated when a plurality of users operate an intelligent clothes airing machine and forming a data set; then acquiring a data set and carrying out data analysis on the data in the data set by adopting a preset data analysis method to obtain a user behavior habit model; and acquiring interactive data generated when the user operates the intelligent clothes airing machine currently in real time, and matching the interactive data with the user behavior habit model to predict the next operation of the user on the intelligent clothes airing machine. According to the intelligent clothes airing machine, the operation of the next step on the intelligent clothes airing machine is automatically recommended to the user according to the current operation of the user, and good use experience is brought to the user. The invention also discloses a control device, a system and a storage medium for the intelligent clothes airing machine.
Description
Technical Field
The invention relates to an intelligent clothes airing machine control, in particular to an intelligent clothes airing machine control method, device and system and a storage medium.
Background
Along with the improvement of living standard, intelligent household appliances are more and more popular in families, and the intellectualization of the intelligent household appliances enables the life of people to be more convenient and more intelligent. Especially for the intelligent clothes airing machine, the manual operation of the non-intelligent clothes airing machine is solved, and the hands of people are liberated. At present, the intelligent clothes airing machine is generally operated through a certain preset control logic, and a user operates the intelligent clothes airing machine through an operation remote controller according to an operation instruction. When the user need operate intelligent airing machine at every turn, all need carry out corresponding research to the remote controller to look for the operation button and carry out corresponding operation, it is not intelligent enough to the operation of intelligent airing machine, brings not good use experience for the user.
Disclosure of Invention
In order to overcome the defects of the prior art, one of the purposes of the invention is to provide an intelligent clothes airing machine control method, which can automatically recommend the next operation for a user according to the current operation of the user, and bring good use experience to the user.
The second purpose of the invention is to provide an intelligent clothes airing machine control device, which can automatically recommend the next operation for a user according to the current operation of the user and bring good use experience to the user.
The invention further aims to provide an intelligent clothes airing machine control system which can automatically recommend the next operation to a user according to the current operation of the user and bring good use experience to the user.
The fourth objective of the present invention is to provide a storage medium, which can automatically recommend the next operation to the user according to the current operation of the user, and bring a good use experience to the user.
One of the purposes of the invention is realized by adopting the following technical scheme:
an intelligent clothes airing machine control method comprises the following steps:
a data acquisition step: acquiring interactive data generated when a plurality of users operate the intelligent clothes airing machine and forming a data set;
and (3) data analysis step: acquiring the data set and carrying out data analysis on interactive data in the data set by adopting a preset data analysis method to obtain a user behavior habit model;
a prediction step: and acquiring interactive data generated when the user operates the intelligent clothes airing machine currently in real time, and matching the interactive data with the user behavior habit model to predict the next operation of the user on the intelligent clothes airing machine.
Further, the interactive data comprises one or more combinations of voice interactive data, remote control interactive data and APP interactive data; the voice interaction data are interaction data generated when a user operates the intelligent clothes airing machine in a voice mode; the remote control interactive data refers to interactive data generated when a user operates the intelligent clothes airing machine through remote control equipment; the APP interactive data refer to interactive data generated when the user operates the intelligent clothes airing machine through the APP bound by the intelligent clothes airing machine.
Further, the interactive data comprises operation data information, operation time and operation environment data generated by the intelligent clothes airing machine when a user operates the intelligent clothes airing machine;
the user behavior habit model comprises an incidence relation between the operation behavior of the user on the intelligent clothes airing machine and environmental factors, an incidence relation between the operation behavior of the user on the intelligent clothes airing machine and time factors and an operation change trend of the user on the intelligent clothes airing machine in a cycle period.
Further, the predetermined data analysis method includes one or more of a combination of a periodicity analysis method and a correlation analysis method.
Further, the data analysis step specifically includes: preprocessing the data in the data set, and then performing data analysis on the preprocessed data according to the preset data analysis method to obtain a user behavior habit model; the preprocessing comprises one or more of data cleaning, data integration, data conversion and data specification combination; wherein:
the data cleaning is carried out to remove interactive data containing invalid data;
the data integration is used for uniformly storing the interactive data acquired from a plurality of data sources;
the data conversion is to carry out normalization processing on each interactive data so as to convert the data formats of a plurality of interactive data into the same data format;
the data protocol creates new attributes or deletes irrelevant attributes for a plurality of interactive data in an attribute merging mode.
Further, the method also comprises the updating step: and adding interactive data which are obtained in real time and generated when the user operates the intelligent clothes airing machine currently into a data set, and performing a data analysis step to update the user behavior habit model.
Further, the predicting step further comprises: and controlling the intelligent clothes airing machine to perform corresponding actions according to the predicted operation of the user on the intelligent clothes airing machine in the next step.
The second purpose of the invention is realized by adopting the following technical scheme:
an intelligent airing machine controlling means includes:
the data acquisition module is used for acquiring interactive data generated when a plurality of users operate the intelligent clothes airing machine and forming a data set;
the data analysis module is used for acquiring the data set and carrying out data analysis on the interactive data in the data set by adopting a preset data analysis method to obtain a user behavior habit model;
and the prediction module is used for acquiring interactive data generated when the user operates the intelligent clothes airing machine currently in real time, and matching the interactive data with the user behavior habit model to predict the operation of the user on the intelligent clothes airing machine next step.
The third purpose of the invention is realized by adopting the following technical scheme:
the invention provides an intelligent clothes airing machine control system which comprises a memory, a processor and a computer program stored on the memory and running on the processor, wherein the computer program is an intelligent clothes airing machine control program, and the steps of the intelligent clothes airing machine control method adopted by one of the purposes of the invention are realized when the processor executes the intelligent clothes airing machine control program.
The fourth purpose of the invention is realized by adopting the following technical scheme:
a storage medium which is a computer-readable storage medium having stored thereon a computer program which is an intelligent clothes airing machine control program, which when executed by a processor, realizes the steps of an intelligent clothes airing machine control method as one of the objects of the present invention.
Compared with the prior art, the invention has the beneficial effects that:
according to the intelligent clothes airing machine control method, interactive data generated when a user controls the intelligent clothes airing machine are collected, wherein the interactive data can comprise various types according to different control modes, such as voice interactive data, remote control interactive data and APP interactive data, and the collected data are subjected to data analysis to form a user behavior habit model; the user behavior habit model stores the incidence relation between the operation behavior of the user on the intelligent clothes airing machine and time, environment data and the like; by constructing the user behavior habit model, the operation of the user on the intelligent clothes airing machine next step can be pre-judged according to the current operation of the user on the intelligent clothes airing machine, so that the user can quickly operate the intelligent clothes airing machine, and the user experience is improved.
Drawings
Fig. 1 is a flow chart of a control method of an intelligent clothes airing machine provided by the invention;
fig. 2 is a flow chart of model updating in the intelligent clothes airing machine control method provided by the invention.
Detailed Description
The present invention will be further described with reference to the accompanying drawings and the detailed description, and it should be noted that any combination of the embodiments or technical features described below can be used to form a new embodiment without conflict.
Example one
According to the method and the system, the incidence relation among the operation mode, the operation time, the operation environment data and the like of the user when the user operates the intelligent clothes airing machine is obtained by performing data analysis on the interactive data generated when the user operates the intelligent clothes airing machine, and then the user behavior habit model is constructed, so that the next operation behavior of the user can be obtained through prejudgment according to the operation of the user on the intelligent clothes airing machine, the intelligent recommendation of the user on the operation of the intelligent clothes airing machine is realized, and the use experience of the user is improved.
An intelligent clothes airing machine control method is shown in figures 1-2 and comprises the following steps:
and step S1, acquiring a plurality of interactive data generated when the user operates the intelligent clothes airing machine and forming a data set.
Each interactive data is a data record generated when a user operates and controls the intelligent clothes airing machine. The data records in this embodiment may include operation data, operation time, operation environment data, and the like of the intelligent clothes airing machine.
Preferably, the intelligent clothes drying machine generates different interaction data due to different control modes of the intelligent clothes drying machine. Wherein the interactive data comprises one or a combination of types of: voice interaction data, remote control interaction data, and APP (application) interaction data. The voice interaction data refers to data records generated when a user operates the intelligent clothes airing machine in a voice mode. The remote control interactive data refers to data records generated when a user operates the intelligent clothes airing machine through remote control equipment. The APP interactive data refers to data records generated when the user operates the intelligent clothes airing machine through the intelligent APP bound by the intelligent clothes airing machine. According to the intelligent clothes airing machine, data analysis is carried out by collecting data records generated when the user operates the intelligent clothes airing machine, so that the behavior habits of the user can be analyzed.
In particular, the operational data of the intelligent laundry machine may be the execution data of the intelligent laundry machine, which may indicate the operational behavior of the user at the time. For example, for voice interaction data:
when the user controls the intelligent clothes airing machine to descend, the execution data in the intelligent clothes airing machine may include a series of data of execution actions such as receiving voice data, analyzing and matching the voice data, generating a control instruction, sending the control instruction to a motor of the intelligent clothes airing machine, controlling a rod of the intelligent clothes airing machine to move downwards by the motor, stopping the movement when the rod of the intelligent clothes airing machine moves to a corresponding limit position, and the like.
More specifically, when storing the data set, the data may be stored in a cloud, a database, or the like.
And step S2, acquiring a data set and performing data analysis on a plurality of interactive data in the data set by adopting a preset data analysis method to construct and obtain a user behavior habit model.
The user behavior habit model in this embodiment refers to a correlation relationship model, a periodic relationship model, and the like between the user behavior habit and the operation time, the operation environment, and the like. The method specifically comprises the following steps: the relationship between the next operation behavior of the user on the intelligent clothes airing machine and the current operation behavior of the user on the intelligent clothes airing machine and environmental factors, the relationship between the next operation behavior of the user on the intelligent clothes airing machine and the current operation behavior of the user on the intelligent clothes airing machine and time factors, the operation change trend of the user on the intelligent clothes airing machine in a cycle period and the like.
In the embodiment, the correlation analysis is performed on the operation behavior, the operation time, the operation environment and the next operation behavior of the user when the user operates the intelligent clothes airing machine each time, so as to obtain the correlation between the next operation behavior of the user and the current operation behavior, operation time, operation environment and the like of the user. Therefore, the current operation behavior, operation time and operation environment of the user on the intelligent clothes airing machine can be obtained according to the interaction data of the user, and the current operation behavior, operation time and operation environment are analyzed and matched with the user behavior habit model in the system, so that the next operation behavior of the user on the intelligent clothes airing machine can be obtained, the action of the intelligent clothes airing machine can be controlled rapidly, and the user experience is improved. Specific examples thereof are: when data analysis yields: for a certain family user, when the user lifts the clothes drying rod of the intelligent clothes drying machine to dry clothes in the wet or rainy weather between 9 and 10 in the morning, the user can start the drying operation; on the contrary, when the weather is clear between 9 and 10 points in the morning, the user does not perform any operation on the intelligent clothes drying machine after lifting the clothes drying rod of the intelligent clothes drying machine.
Therefore, when the user is obtained between 9-10 points in the morning and in wet weather or rainy days, the system can predict the operation behavior of the user on the intelligent clothes airing machine next step after the user lifts the clothes airing rod of the intelligent clothes airing machine currently, namely, the drying operation is started on the intelligent clothes airing machine, so that the drying operation can be quickly and automatically controlled on the intelligent clothes airing machine, and the user experience is improved. Therefore, the operation behaviors of the user on the intelligent clothes airing machine each time can be collected, the data analysis is carried out on the operation behaviors to obtain the incidence relation between the current operation behaviors, time, environment and the like of the user and the next operation behaviors of the user, namely, a user behavior habit model is built, so that the next operation behaviors can be recommended for the user quickly, and the user experience is improved.
That is, the interactive data generated by the intelligent clothes airing machine when the user operates the intelligent clothes airing machine each time is collected, and the collected interactive data is analyzed in a data correlation or a periodic manner to obtain the user behavior habit model, so that the operation behavior of the user can be identified later, the operation data of the user in the next step can be judged in advance in time, and the intelligent clothes airing machine is convenient for the user to use.
The preset data analysis method comprises a periodicity analysis method and a correlation analysis method. And analyzing the interactive data by adopting a data analysis method so as to prejudge the operation of the user on the intelligent clothes airing machine.
And step S3, acquiring interactive data generated when the user operates the intelligent clothes airing machine currently in real time, and matching the interactive data with the user behavior habit model to predict the next operation of the user on the intelligent clothes airing machine.
For example, the current operation behavior, operation time, operation environment data and the like of the user are obtained according to the interaction data obtained in real time, and then the current operation behavior, operation time, operation environment data and the like are matched with the data in the constructed user behavior habit model one by one so as to obtain the operation of the user on the intelligent clothes airing machine in the next step, and the operation is recommended to the user.
In addition, as more than one matching result is possible, a plurality of matching results can be analyzed, and the matching result with the highest possibility is recommended to the user.
According to the method and the system, the next operation behavior of the user is predicted according to the current operation behavior of the user according to the behavior habit model of the user pre-established in the system, so that the user can quickly control the intelligent clothes airing machine.
And step S4, recommending the operation behavior to the user when the operation behavior of the user on the intelligent clothes airing machine next step is predicted, and controlling the intelligent clothes airing machine to execute corresponding actions according to the operation behavior.
When the operation behavior of the user on the intelligent clothes airing machine in the next step is predicted, the intelligent clothes airing machine can be controlled to execute corresponding actions. Meanwhile, in order to avoid the situation that the operation behavior of the intelligent clothes drying machine is inconsistent with the actual operation behavior of the user, the operation behavior is recommended to the user before the intelligent clothes drying machine is controlled to execute the action, for example, the operation behavior is recommended to the user in a voice or APP mode, and if the user does not operate or confirms the operation, the intelligent clothes drying machine can be controlled; and if the user does not confirm the operation behavior, controlling the intelligent clothes airing machine to execute corresponding actions or not adopting any action according to other operation behaviors selected by the user, and keeping the intelligent clothes airing machine in a standby state.
Further, step S2 includes: and preprocessing a plurality of interactive data in the data set. The efficiency of subsequent data processing is improved by preprocessing a plurality of interactive data in the data set.
Further, the preprocessing mode can include one or more modes of data cleaning, data integration, data conversion and data reduction. In this embodiment, the multiple modes of preprocessing may be sequentially executed in sequence, or executed out of order, and may be specifically set according to the requirement of data processing. In addition, one or more combinations of multiple data processing modes can be selected, for example, data cleaning, data integration, data conversion and data protocol processing can be performed on multiple pieces of voice interaction data in the data set in sequence, and one, two or three combinations of data cleaning, data integration, data conversion and data protocol can be selected arbitrarily to process multiple pieces of interaction data in the data set.
More specifically, data cleansing is used to remove invalid data in each piece of interaction data. Namely: incomplete data (such as data with missing values), inconsistent data, abnormal data and the like can exist in interactive data generated by a user when the user operates the intelligent clothes airing machine, and the data can cause the execution efficiency of data mining modeling and even can cause deviation of a mining result. Therefore, invalid data is removed through data cleaning, so that the modeling accuracy of subsequent data and the execution efficiency of data modeling are improved.
Data integration is the process of consolidating multiple data sources into a coherent data store, such as a data warehouse. In data integration, the expression forms of real-world entities from a plurality of data sources are different and possibly not matched, and entity identification problems and attribute redundancy problems are considered so that source data is converted, refined and integrated at the lowest layer. That is, multiple interactive data from multiple different data sources are stored in the same data warehouse. In order to ensure the accuracy of model establishment, for example, when a plurality of intelligent clothes drying machines exist in a family, the embodiment can acquire interactive data from the plurality of intelligent clothes drying machines; or, to facilitate data storage, a plurality of storage channels are generally used to store a large amount of interactive data, and therefore, the interactive data of the plurality of storage channels are subjected to data integration processing.
The data conversion is mainly used for carrying out normalized processing on the data and converting the data into a proper form so as to meet the requirements of mining tasks and algorithms. For example, the same or changed data format such as the average number of times of use per day is changed to the number of times of use per day, which is more suitable for the thinking habit of people. Because the thinking habit of people is different from the data recording mode of the machine, a plurality of interactive data can be converted according to a certain rule so as to be more in line with the thinking habit of people.
And the data reduction is used for creating a new attribute dimension through attribute combination or directly reducing the data dimension through deleting irrelevant attributes, so that the data mining efficiency is improved, and the calculation cost is reduced. The goal of attribute reduction is to find the smallest subset of attributes and to ensure that the probability distribution of the new data subset is as close as possible to the probability distribution of the original data set. For example, to show the frequency distribution of the average use of the clothes drying machine by the user, the data distribution can be approximated in a box separation mode, and the data in a certain interval is calculated for analysis, so that the purpose of compressing the data is achieved.
Preferably, the preset data analysis method in step S2 may include a periodicity analysis method and/or a correlation analysis method. The periodic analysis method is to find out whether a certain variable shows a certain periodic variation trend along with the variation of the viewing angle. The time scale is selected from time periods such as year, quarter, month, week, day, and hour. For example, a certain periodic trend of the operation of the intelligent clothes drying machine by the user in a certain time period is obtained by searching the times, operation behaviors, operation time and the like of controlling the intelligent clothes drying machine when the user operates the intelligent clothes drying machine by voice, remote control and the like in the certain time period through a periodic analysis method. Therefore, when the operation behavior of the user for operating the intelligent clothes airing machine at a certain time is obtained, the next operation of the user on the intelligent clothes airing machine can be obtained through prejudgment according to the periodic trend. For example, in a day, a user generally performs a lifting operation after the user stays for a period of time after descending the intelligent clothes airing machine between 9 pm and 10 am, and the like, so that the user can start timing after the user descends the intelligent clothes airing machine and recommend the lifting operation of the intelligent clothes airing machine to the user after the timing time is reached.
The correlation analysis method is to analyze two or more variable elements with correlation so as to measure the degree of closeness of correlation between two factors, and a certain relation or probability needs to exist between the elements with correlation to perform the correlation analysis. And carrying out correlation analysis on a plurality of interactive data in the data set by using a correlation analysis method so as to analyze the strength of linear correlation degree between continuous variables, and further obtain the relation between the times when the user controls the intelligent clothes airing machine, the environmental data, the temperature and the humidity. Such as: when the temperature is higher or the weather is clear, a user generally only descends the intelligent clothes drying machine, ascends the intelligent clothes drying machine after drying clothes, and dries the clothes in a natural air drying mode without drying the clothes in a drying mode; on the contrary, when the temperature is low or the weather is rainy, the user needs to descend the intelligent clothes airing machine, air the clothes, then ascend the intelligent clothes airing machine and start the drying function.
Further, the present embodiment further includes step S5, adding the interaction data, which is obtained in real time and generated when the user operates the intelligent clothes airing machine currently, into the data set; step S2 is then performed to update the user behavior habit model. The user behavior habit model is updated in real time, so that the prediction result is more accurate.
Example two
Based on the first control method of the intelligent clothes airing machine provided by the embodiment, the invention also provides another embodiment, and the control device of the intelligent clothes airing machine comprises the following modules:
the data acquisition module is used for acquiring interactive data generated when a plurality of users operate the intelligent clothes airing machine and forming a data set;
the data analysis module is used for acquiring the data set and carrying out data analysis on the interactive data in the data set by adopting a preset data analysis method to obtain a user behavior habit model;
and the prediction module is used for acquiring interactive data generated when the current intelligent clothes airing machine of the user operates in real time, and matching the interactive data with the user behavior habit model to predict the operation of the user on the intelligent clothes airing machine next step.
Example two
Based on the first embodiment, the invention provides an intelligent clothes airing machine control method, and the invention further provides another embodiment, the intelligent clothes airing machine control system comprises a memory, a processor and a computer program which is stored on the memory and runs on the processor, the computer program is an intelligent clothes airing machine control program, and the processor executes the intelligent clothes airing machine control program to realize the following steps:
a data acquisition step: acquiring interactive data generated when a plurality of users operate the intelligent clothes airing machine and forming a data set;
and (3) data analysis step: acquiring the data set and carrying out data analysis on interactive data in the data set by adopting a preset data analysis method to obtain a user behavior habit model;
a prediction step: and acquiring interactive data generated when the user operates the intelligent clothes airing machine currently in real time, and matching the interactive data with the user behavior habit model to predict the next operation of the user on the intelligent clothes airing machine.
Further, the interactive data comprises one or more combinations of voice interactive data, remote control interactive data and APP interactive data; the voice interaction data are interaction data generated when a user operates the intelligent clothes airing machine in a voice mode; the remote control interactive data refers to interactive data generated when a user operates the intelligent clothes airing machine through remote control equipment; the APP interactive data refer to interactive data generated when the user operates the intelligent clothes airing machine through the APP bound by the intelligent clothes airing machine.
Further, the interactive data comprises operation data information, operation time and operation environment data generated by the intelligent clothes airing machine when a user operates the intelligent clothes airing machine;
the user behavior habit model comprises the relationship between the operation behavior of the user on the intelligent clothes airing machine and environmental factors, the relationship between the operation behavior of the user on the intelligent clothes airing machine and time factors and the operation change trend of the user on the intelligent clothes airing machine in a cycle period.
Further, the predetermined data analysis method includes one or more of a combination of a periodicity analysis method and a correlation analysis method.
Further, the data analysis step specifically includes: preprocessing the data in the data set, and then performing data analysis on the preprocessed data according to the preset data analysis method to obtain a user behavior habit model; the preprocessing comprises one or more of data cleaning, data integration, data conversion and data specification combination; wherein:
the data cleaning is carried out to remove interactive data containing invalid data;
the data integration is used for uniformly storing the interactive data acquired from a plurality of data sources;
the data conversion is to carry out normalization processing on each interactive data so as to convert the data formats of a plurality of interactive data into the same data format;
the data protocol creates new attributes or deletes irrelevant attributes for a plurality of interactive data in an attribute merging mode.
Further, the method also comprises the updating step: and adding interactive data which are obtained in real time and generated when the user operates the intelligent clothes airing machine currently into a data set, and performing a data analysis step to update the user behavior habit model.
Further, the predicting step further comprises: and controlling the intelligent clothes airing machine to perform corresponding actions according to the predicted operation of the user on the intelligent clothes airing machine in the next step.
EXAMPLE III
Based on the first embodiment provided by the present invention, the present invention further provides an embodiment, a storage medium, where the storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium, where the computer program is an intelligent clothes airing machine control program, and when being executed by a processor, the intelligent clothes airing machine control program implements the following steps:
a data acquisition step: acquiring interactive data generated when a plurality of users operate the intelligent clothes airing machine and forming a data set;
and (3) data analysis step: acquiring the data set and carrying out data analysis on interactive data in the data set by adopting a preset data analysis method to obtain a user behavior habit model;
a prediction step: and acquiring interactive data generated when the user operates the intelligent clothes airing machine currently in real time, and matching the interactive data with the user behavior habit model to predict the next operation of the user on the intelligent clothes airing machine.
Further, the interactive data comprises one or more combinations of voice interactive data, remote control interactive data and APP interactive data; the voice interaction data are interaction data generated when a user operates the intelligent clothes airing machine in a voice mode; the remote control interactive data refers to interactive data generated when a user operates the intelligent clothes airing machine through remote control equipment; the APP interactive data refer to interactive data generated when the user operates the intelligent clothes airing machine through the APP bound by the intelligent clothes airing machine.
Further, the interactive data comprises operation data information, operation time and operation environment data generated by the intelligent clothes airing machine when a user operates the intelligent clothes airing machine;
the user behavior habit model comprises the relationship between the operation behavior of the user on the intelligent clothes airing machine and environmental factors, the relationship between the operation behavior of the user on the intelligent clothes airing machine and time factors and the operation change trend of the user on the intelligent clothes airing machine in a cycle period.
Further, the predetermined data analysis method includes one or more of a combination of a periodicity analysis method and a correlation analysis method.
Further, the data analysis step specifically includes: preprocessing the data in the data set, and then performing data analysis on the preprocessed data according to the preset data analysis method to obtain a user behavior habit model; the preprocessing comprises one or more of data cleaning, data integration, data conversion and data specification combination; wherein:
the data cleaning is carried out to remove interactive data containing invalid data;
the data integration is used for uniformly storing the interactive data acquired from a plurality of data sources;
the data conversion is to carry out normalization processing on each interactive data so as to convert the data formats of a plurality of interactive data into the same data format;
the data protocol creates new attributes or deletes irrelevant attributes for a plurality of interactive data in an attribute merging mode.
Further, the method also comprises the updating step: and adding interactive data which are obtained in real time and generated when the user operates the intelligent clothes airing machine currently into a data set, and performing a data analysis step to update the user behavior habit model.
Further, the predicting step further comprises: and controlling the intelligent clothes airing machine to perform corresponding actions according to the predicted operation of the user on the intelligent clothes airing machine in the next step.
Example four
Based on the first embodiment, the invention further provides an embodiment, namely an intelligent clothes airing machine, which is used for executing the steps of the control method of the intelligent clothes airing machine provided by the first embodiment.
The above embodiments are only preferred embodiments of the present invention, and the protection scope of the present invention is not limited thereby, and any insubstantial changes and substitutions made by those skilled in the art based on the present invention are within the protection scope of the present invention.
Claims (10)
1. The intelligent clothes airing machine control method is characterized by comprising the following steps:
a data acquisition step: acquiring interactive data generated when a plurality of users operate the intelligent clothes airing machine and forming a data set;
and (3) data analysis step: acquiring the data set and carrying out data analysis on interactive data in the data set by adopting a preset data analysis method to obtain a user behavior habit model;
a prediction step: and acquiring interactive data generated when the user operates the intelligent clothes airing machine currently in real time, and matching the interactive data with the user behavior habit model to predict the next operation of the user on the intelligent clothes airing machine.
2. The intelligent clothes airing machine control method according to claim 1, wherein the interaction data comprises one or more of voice interaction data, remote control interaction data and APP interaction data in combination; the voice interaction data are interaction data generated when a user operates the intelligent clothes airing machine in a voice mode; the remote control interactive data refers to interactive data generated when a user operates the intelligent clothes airing machine through remote control equipment; the APP interactive data refer to interactive data generated when the user operates the intelligent clothes airing machine through the APP bound by the intelligent clothes airing machine.
3. The intelligent clothes airing machine control method according to claim 1, wherein the interaction data includes operation data information, operation time and operation environment data generated by the intelligent clothes airing machine when a user operates the intelligent clothes airing machine;
the user behavior habit model comprises an incidence relation between the operation behavior of the user on the intelligent clothes airing machine and environmental factors, an incidence relation between the operation behavior of the user on the intelligent clothes airing machine and time factors and an operation change trend of the user on the intelligent clothes airing machine in a cycle period.
4. The intelligent clothes horse control method according to claim 1, wherein the preset data analysis method comprises one or more of a combination of a periodicity analysis method and a correlation analysis method.
5. The intelligent clothes airing machine control method according to claim 1, wherein the data analysis step specifically includes: preprocessing the data in the data set, and then performing data analysis on the preprocessed data according to the preset data analysis method to obtain a user behavior habit model; the preprocessing comprises one or more of data cleaning, data integration, data conversion and data specification combination; wherein:
the data cleaning is carried out to remove interactive data containing invalid data;
the data integration is used for uniformly storing the interactive data acquired from a plurality of data sources;
the data conversion is to carry out normalization processing on each interactive data so as to convert the data formats of a plurality of interactive data into the same data format;
the data protocol creates new attributes or deletes irrelevant attributes for a plurality of interactive data in an attribute merging mode.
6. The intelligent clothes airing machine control method according to claim 1, further comprising the updating step of: and adding interactive data which are obtained in real time and generated when the user operates the intelligent clothes airing machine currently into a data set, and performing a data analysis step to update the user behavior habit model.
7. The intelligent clothes airing machine control method according to claim 1, wherein the predicting step further includes: and controlling the intelligent clothes airing machine to perform corresponding actions according to the predicted operation of the user on the intelligent clothes airing machine in the next step.
8. An intelligent clothes airing machine control device, characterized by including:
the data acquisition module is used for acquiring interactive data generated when a plurality of users operate the intelligent clothes airing machine and forming a data set;
the data analysis module is used for acquiring the data set and carrying out data analysis on the interactive data in the data set by adopting a preset data analysis method to obtain a user behavior habit model;
and the prediction module is used for acquiring interactive data generated when the user operates the intelligent clothes airing machine currently in real time, and matching the interactive data with the user behavior habit model to predict the operation of the user on the intelligent clothes airing machine next step.
9. An intelligence airing machine control system, includes memory, treater and the computer program that stores on the memory and operate on the treater, the computer program is intelligence airing machine control program, its characterized in that: the processor, when executing the intelligent clothes airing machine control program, implements the steps of an intelligent clothes airing machine control method according to any one of claims 1-7.
10. A storage medium which is a computer-readable storage medium having a computer program stored thereon, the computer program being an intelligent clothes airing control program, characterized in that: the intelligent clothes airing machine control program when executed by the processor implements the steps of an intelligent clothes airing machine control method according to any one of claims 1-7.
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