CN111077786A - Intelligent household equipment control method and device based on big data analysis - Google Patents
Intelligent household equipment control method and device based on big data analysis Download PDFInfo
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Abstract
The invention discloses intelligent home equipment control based on big data analysis, relates to the technical field of intelligent home, and can improve the accuracy of control instruction output so as to improve the control experience of a user. The method comprises the following steps: acquiring historical instruction data of each user, and constructing a dedicated pre-judging model of each user, wherein the pre-judging model comprises a health state pre-judging model and a non-health state pre-judging model; acquiring fuzzy operation information input by a current user, and identifying the control intention of the current user; carrying out weighted assignment on three indexes, namely the identified control intention, the control preference identified by using a pre-judging model corresponding to the current user and the collected physical health data of the current user; when the sum of the weighted assignments of the three indexes is larger than a preset threshold value, outputting the identified control intention as a control instruction; and when the sum of the weighted assignments of the three indexes is less than a preset threshold value, outputting the control preference identified by the pre-judging model as a control instruction. The device is applied with the method.
Description
Technical Field
The invention relates to the technical field of intelligent home, in particular to an intelligent home equipment control method and device based on big data analysis.
Background
Along with the rapid development of smart homes, various smart home devices enter thousands of households, for example, smart lighting devices, smart televisions, smart refrigerators, smart air conditioners and the like, the existing distribution network process of the smart home devices is complicated, the control mode is single, and poor user experience is caused.
Disclosure of Invention
The invention aims to provide a method and a device for controlling intelligent household equipment based on big data analysis, which can improve the accuracy of control instruction output and further improve the control experience of a user.
In order to achieve the above object, an aspect of the present invention provides a smart home device control method based on big data analysis, including:
acquiring historical instruction data of each user, and constructing a dedicated pre-judging model of each user, wherein the pre-judging model comprises a health state pre-judging model and a non-health state pre-judging model;
acquiring fuzzy operation information input by a current user, and identifying the control intention of the current user;
carrying out weighted assignment on three indexes, namely the identified control intention, the control preference identified by using a pre-judging model corresponding to the current user and the collected physical health data of the current user;
when the sum of the weighted assignments of the three indexes is larger than a preset threshold value, outputting the identified control intention as a control instruction;
and when the sum of the weighted assignments of the three indexes is less than a preset threshold value, outputting the control preference identified by the pre-judging model as a control instruction.
Preferably, the method for acquiring the physical health data of the current user comprises the following steps:
regularly downloading current user physical health data from a medical data platform; or,
the current user manually updates his own physical health data at regular intervals.
Preferably, the method for collecting the historical instruction data of each user and constructing the exclusive prejudgment model of each user comprises the following steps:
storing historical instruction data of each user in a corresponding database according to a healthy state and an unhealthy state in a classified manner, and classifying and summarizing control data of each user in each time period of each day under two states by regularly counting operation information of each user in a preset time period;
based on the total number of days of a preset time period and control data of each user in each time period in each day, a probability algorithm is adopted to obtain a health state pre-judging model of each user in a health state and a non-health state pre-judging model in a non-health state, and the health state pre-judging model/the non-health state pre-judging model can reflect the control preference of the user in each time period by taking the day as a unit.
Preferably, the method for acquiring the operation information input by the current user and identifying the control intention of the current user comprises the following steps:
acquiring fuzzy operation information sent by a current user by using an operation terminal, wherein the fuzzy operation information comprises one type or multiple types of voice information, gesture information and data information;
when a user sends out various types of operation information, acquiring different types of fuzzy operation information in stages based on a time sequence, converting and combining the fuzzy operation information semantics of each stage in sequence to obtain the control intention of the current user, and if the control intention of the current user fails to be identified, reminding the current user to input the operation information again within a preset time period.
Preferably, the method for adapting and networking each smart home device and the operation terminal in the area comprises the following steps:
the operation terminal shoots the intelligent household equipment to be adapted and extracts appearance characteristics;
searching networking configuration information corresponding to the intelligent household equipment to be adapted from a server based on the appearance characteristics;
and after the to-be-adapted intelligent household equipment is activated and is in a networking state, automatically executing an adaptation networking process of the operation terminal and the to-be-adapted intelligent household equipment according to the networking configuration information.
Preferably, the intelligent household equipment to be adapted is photographed at the operation terminal, and the method further comprises the following steps before extracting the appearance characteristics:
according to the operation of a user, code scanning information of the intelligent home equipment is obtained and/or adaptation scheme information of the intelligent home equipment input by the user is received, and the intelligent home equipment is adapted to the corresponding intelligent home equipment according to the code scanning information and/or the adaptation scheme information; and extracting and storing configuration information and appearance information of the intelligent household equipment in an associated manner.
Preferably, after outputting the control instruction of the smart home device to be controlled to make the smart home device to be controlled in the response state, the method further includes:
and synchronously updating the image display frame of the intelligent household equipment on the display interface of the operation terminal based on the working state of the intelligent household equipment.
Compared with the prior art, the intelligent household equipment control method based on big data analysis has the following beneficial effects:
the intelligent household equipment control method based on big data analysis provided by the invention constructs a pre-judging model exclusive to each user by acquiring historical instruction data of each user in family members in advance, specifically comprises the steps of constructing a health state pre-judging model based on the historical instruction data corresponding to each user in a health state, constructing a non-health state pre-judging model based on the historical instruction data corresponding to each user in a non-health state, then after acquiring fuzzy operation information input by the current user, if the current user belongs to one member of the family members, calling the corresponding health state pre-judging model/non-health state pre-judging model to pre-judge the control preference of the current user according to the health state of the current user after identifying the control intention of the current user, and when the weighted assignment sum of the three indexes is larger than the weighted assignment sum of the three indexes by weighting and adjusting the results of the three indexes, and outputting the identified control intention as a control instruction, and outputting the control preference identified by the pre-judgment model as the control instruction when the sum of the weighted assignments of the three indexes is less than a preset threshold value.
The operation information comprises one type or multiple types of voice information, gesture information and data information, compared with a mode of inputting the operation information singly in the prior art, the method has more flexible controllability for the user, and in addition, the pre-judging model is used for pre-judging the control preference of the intelligent household equipment controlled by the user at each time period in one day, so that the operation preference of the user can be reflected with high probability, the control instruction output accuracy is improved, and the user can be ensured to obtain better user experience.
Another aspect of the present invention provides an intelligent home device control apparatus based on big data analysis, which is applied to the intelligent home device control method based on big data analysis mentioned in the above technical solution, and the apparatus includes:
the system comprises an acquisition unit, a judgment unit and a judgment unit, wherein the acquisition unit is used for acquiring historical instruction data of each user and constructing a dedicated pre-judgment model for each user, and the pre-judgment model comprises a health state pre-judgment model and a non-health state pre-judgment model;
the identification unit is used for acquiring fuzzy operation information input by a current user and identifying the control intention of the current user;
the evaluation unit is used for carrying out weighted evaluation on three indexes, namely the identified control intention, the control preference identified by the pre-judging model corresponding to the current user and the collected physical health data of the current user;
and the judging unit is used for outputting the identified control intention as a control instruction when the sum of the weighted assignments of the three indexes is greater than a preset threshold value, and outputting the control preference identified by the pre-judging model as the control instruction when the sum of the weighted assignments of the three indexes is less than the preset threshold value.
Compared with the prior art, the beneficial effects of the intelligent household equipment control device based on big data analysis provided by the invention are the same as the beneficial effects of the intelligent household equipment control method based on big data analysis provided by the technical scheme, and the detailed description is omitted here.
A third aspect of the present invention provides a computer-readable storage medium, where a computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, the steps of the smart home device control method based on big data analysis are executed.
Compared with the prior art, the beneficial effects of the computer-readable storage medium provided by the invention are the same as those of the intelligent home equipment control method based on big data analysis provided by the technical scheme, and are not repeated herein.
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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 specification, illustrate embodiments of the invention and together with the description serve to explain the invention and not to limit the invention. In the drawings:
fig. 1 is a schematic flow chart of a smart home device control method based on big data analysis according to an embodiment of the present invention;
fig. 2 is a block diagram of a structure of an intelligent home device control apparatus based on big data analysis in the second embodiment of the present invention.
Detailed Description
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in detail below. It is to be understood that the described embodiments are merely exemplary of the invention, and not restrictive of the full scope of the invention. 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.
Referring to fig. 1, the present embodiment provides a smart home device control method based on big data analysis, including:
acquiring historical instruction data of each user, and constructing a dedicated pre-judging model of each user, wherein the pre-judging model comprises a health state pre-judging model and a non-health state pre-judging model; acquiring fuzzy operation information input by a current user, and identifying the control intention of the current user; carrying out weighted assignment on three indexes, namely the identified control intention, the control preference identified by the pre-judging model corresponding to the current user and the collected physical health data of the current user; when the sum of the weighted assignments of the three indexes is larger than a preset threshold value, outputting the identified control intention as a control instruction; and when the sum of the weighted assignments of the three indexes is less than a preset threshold value, outputting the control preference identified by the pre-judging model as a control instruction.
In the method for controlling smart home devices based on big data analysis provided by this embodiment, a pre-determination model dedicated to each user is constructed by collecting historical instruction data of each user in a family member in advance, which specifically includes constructing a health state pre-determination model based on the historical instruction data corresponding to each user in a health state, constructing a non-health state pre-determination model based on the historical instruction data corresponding to each user in a non-health state, and then after obtaining fuzzy operation information input by a current user, if the current user belongs to one of the family members, after identifying a control intention of the current user, calling the corresponding health state pre-determination model/non-health state pre-determination model according to the health state of the current user to pre-determine the control preference of the current user, and adjusting the results of the three indexes by weighting, when the sum of weighted assignments of the three indexes is greater than the sum of weighted assignments of the three indexes, and outputting the identified control intention as a control instruction, and outputting the control preference identified by the pre-judgment model as the control instruction when the sum of the weighted assignments of the three indexes is less than a preset threshold value.
The operation information comprises one type or multiple types of voice information, gesture information and data information, and compared with a mode of inputting the operation information in a single mode in the prior art, the method has more flexible controllability for the user.
For example, the assignment weight of the control intention of the current user is identified as A, the assignment weight of the control preference identified by the correlation model is B, the assignment weight of the physical health data of the current user is C, the preset threshold value is D, when the sum of A, B, C is less than D, the control preference result is output as a control instruction, otherwise, the identified control intention result is output as a control instruction.
Illustratively, the method for acquiring the physical health data of the current user in the above embodiment includes:
regularly downloading current user physical health data from a medical data platform; or, the current user manually updates the physical health data of the current user regularly. The medical data platform may be a self-built database platform, or may be a public medical database platform, which is not limited in this embodiment. The manual updating of the own body health data means that the relevant body health data is input through operating a terminal networking server.
In order to further ensure the accuracy of the output of the control command, the above embodiment further includes, after the outputting the control command:
after receiving the control instruction, the intelligent home equipment to be controlled actively sends a secondary confirmation prompt of the current control instruction to the user so as to obtain the follow-up authorization of the user; after the user successfully authorizes the current control instruction, the intelligent household equipment to be controlled executes corresponding control operation; and when the authorization of the current control instruction by the user fails, the intelligent household equipment to be controlled reminds the current user to input the operation information again, and the process is executed again to obtain the control instruction.
When the method is specifically implemented, the accuracy of control instruction output can be ensured through a secondary confirmation reminding mechanism, and the step can be omitted in the actual operation process, so that the disturbance rate to a user is reduced. In addition, after the authorization of the current control instruction fails, the current user can be reminded to input the operation information again, the control intention of the current user is identified again, and the steps are repeated until the control instruction is output again.
In the above embodiment, before collecting the historical instruction data of each user and constructing the exclusive pre-judgment model for each user, the method further includes:
each intelligent household device in the area is adaptively networked with an operation terminal; inputting biological characteristics of each user through an operation terminal, and constructing a database which corresponds to each user and is used for storing historical instruction data; the biometric features include one or more of voiceprint features, face features, password features. The biological characteristics are used for identifying the current user identity of the input operation information, and can be accurately matched with the corresponding exclusive pre-judging model through comparison of the biological characteristics.
The method for adapting and networking each intelligent household device and the operation terminal in the area specifically comprises the following steps:
the operation terminal shoots the intelligent household equipment to be adapted and extracts appearance characteristics; searching networking configuration information corresponding to the intelligent household equipment to be adapted from the server based on the appearance characteristics; and after the to-be-adapted intelligent household equipment is activated and is in a networking state, automatically executing an adaptation networking process of the operation terminal and the to-be-adapted intelligent household equipment according to the networking configuration information.
During specific implementation, the user can treat the adaptation smart home devices and take a picture, in order to acquire the appearance characteristics of the intelligent home devices to be adapted, match with the appearance information of the pre-stored smart home devices through the appearance characteristic information, find the pre-stored appearance information with the appearance characteristics, because the pre-stored appearance information is associated with the corresponding configuration information of the smart home devices, at least including the model information or the parameter setting of the intelligent home devices to be adapted in the configuration information, the model information is extracted to replace the manual input process, the user can enable the operation terminal to be rapidly adapted to the smart home devices without knowing the model or the specific connection mode of the smart home devices, and the adaptation process is greatly simplified.
Will treat the adaptation smart home devices at operation terminal and shoot, before extracting appearance characteristics, still include: according to the operation of a user, code scanning information of the intelligent home equipment is obtained and/or adaptation scheme information of the intelligent home equipment input by the user is received, and the intelligent home equipment is adapted to the corresponding intelligent home equipment according to the code scanning information and/or the adaptation scheme information; and extracting and storing configuration information and appearance information of the intelligent household equipment in an associated manner.
In the technical scheme, for the intelligent household equipment adapted for the first time, an associated storage process of appearance information and configuration information is carried out, specifically, the configuration information of the intelligent household equipment adapted for the first time can be obtained in a mode of scanning a two-dimensional code/bar code of the intelligent household equipment, the configuration information is read to be used for completing adaptation with the intelligent household equipment, the appearance information of the intelligent household equipment adapted for the first time is correspondingly input, data exchange can be carried out after the operation terminal is adapted with the intelligent household equipment, the appearance information of the intelligent household equipment can also be obtained from a cloud server under the condition that the condition allows, the appearance information and the configuration information are stored in an associated mode, and next adaptation is facilitated. In addition, under the condition that code scanning cannot be carried out, the user can manually input configuration information and can also complete the first-time adaptation process. After the first adaptation to the intelligent household equipment is completed through the process, the process of reconnection is simpler and quicker.
Preferably, the use frequency and the use sequence of each smart home device controlled by the user are periodically acquired, and the identifiers of the smart home devices are sequentially arranged on the display interface of the operation terminal according to the use frequency and the use sequence, wherein the identifiers of the smart home devices comprise icons and/or character lists, and the user clicks the identifiers to enter the control interface of the corresponding smart home devices.
During specific implementation, the interaction conditions of the operation terminal and the intelligent home devices are counted, and the icons of the intelligent home devices in the internet of things system are distributed according to the use habits of users, so that the icon arrangement of each intelligent home device on the display interface of the operation terminal is more reasonable and humanized. Particularly, under the condition that the number of the intelligent home devices is large, the sequencing of the commonly used intelligent home devices is advanced, and the intelligent home devices are weighted and sequenced by combining the relevance, the use frequency and the use sequence among the intelligent home devices. For example, the operation sequence of the user on the smart home device is as follows: the air conditioner is started first, then the television is started, then the water dispenser is controlled to start heating, and meanwhile the use frequency of the intelligent household equipment is higher, so that the intelligent household equipment is ranked on the display interface of the operation terminal as the air conditioner, the television and the water dispenser. The intelligent home equipment remote control system is displayed in a text list or entity-shaped icons or user-defined icons on a display interface of the operation terminal, and a user can click the responding intelligent home equipment to enter a corresponding control interface for remote control operation or other settings, so that the operation process of the user is facilitated, and the use experience is improved.
Specifically, the method for acquiring historical instruction data of each user and constructing the exclusive prejudgment model of each user in the above embodiment includes:
storing historical instruction data of each user in a corresponding database according to a healthy state and an unhealthy state in a classified manner, and classifying and summarizing control data of each user in each time period of each day under two states by regularly counting operation information of each user in a preset time period; based on the total number of days of a preset time period and control data of each user in each time period in each day, a probability algorithm is adopted to obtain a health state pre-judging model of each user in a health state and a non-health state pre-judging model in a non-health state, and the health state pre-judging model/the non-health state pre-judging model can reflect the control preference of the user in each time period by taking the day as a unit.
For example, the historical instruction data of the family member users in the health state and the non-health state in the last half year are counted, the control data of each user in each time period in two states in the last 180 days are summarized, then the control objects of each user in each time period in two states in the last 180 days are counted, then the control object with the highest probability of the user in each time period in the health state in the one day and the control object with the highest probability of the user in the non-health state in the one day are calculated through a probability algorithm, the control objects with the highest probabilities in the two states are respectively summarized, then the health state prejudgment model and the non-health state prejudgment model are correspondingly obtained, the same steps are adopted to respectively construct the two prejudgment models for each member user, because the exclusive prejudgment models in different states of each member user can be most fit with the operation habits of the user, therefore, the control preference of different time periods can be accurately distinguished by combining the exclusive prejudgment model with the current health data of the user.
Further, the method for acquiring the operation information input by the current user and identifying the control intention of the current user in the above embodiments includes:
acquiring fuzzy operation information sent by a current user by using an operation terminal, wherein the fuzzy operation information comprises one type or multiple types of voice information, gesture information and data information; when a user sends out various types of operation information, acquiring different types of fuzzy operation information in stages based on a time sequence, converting and combining the fuzzy operation information semantics of each stage in sequence to obtain the control intention of the current user, and if the control intention of the current user fails to be identified, reminding the current user to input the fuzzy operation information again in a preset time period.
In specific implementation, a combined input mode of various types of operation information can be adopted, for example, a user says "turn on a desk lamp", at this time, after receiving the gesture, the user does not know the brightness of which gear to turn on, at this time, a question is issued to the user, the user can make a gesture of "two fingers" after receiving the gesture, after acquiring the gesture made by the user, the intelligent desk lamp to be controlled is matched with a pre-stored gesture instruction, after the matching is successful, the gesture instruction of the user is recognized as "second-gear brightness", at this time, the "turn on the desk lamp" after semantic conversion is combined with the "second-gear brightness" recognized by the gesture instruction, and the control intention of the current user, namely "turn on the desk lamp", and adjust the control intention to the second-gear brightness.
Optionally, the method further includes, after outputting a control instruction of the to-be-controlled smart home device, that the to-be-controlled smart home device is in a response state:
and synchronously updating the image display frame of the intelligent household equipment on the display interface of the operation terminal based on the working state of the intelligent household equipment.
For example, when the air conditioner is changed from the off state to the on and swinging state, the image display picture of the intelligent household equipment on the display interface of the operation terminal is synchronously updated, so that the image display picture synchronously displays that the air conditioner is in the on and swinging state, the display approaching lattice is increased, and the user experience is improved.
Example two
Referring to fig. 2, the present embodiment provides an intelligent home device control apparatus based on big data analysis, including:
the system comprises an acquisition unit, a judgment unit and a judgment unit, wherein the acquisition unit is used for acquiring historical instruction data of each user and constructing a dedicated pre-judgment model for each user, and the pre-judgment model comprises a health state pre-judgment model and a non-health state pre-judgment model;
the identification unit is used for acquiring fuzzy operation information input by a current user and identifying the control intention of the current user;
the evaluation unit is used for carrying out weighted evaluation on three indexes, namely the identified control intention, the control preference identified by the pre-judging model corresponding to the current user and the collected physical health data of the current user;
and the judging unit is used for outputting the identified control intention as a control instruction when the sum of the weighted assignments of the three indexes is greater than a preset threshold value, and outputting the control preference identified by the pre-judging model as the control instruction when the sum of the weighted assignments of the three indexes is less than the preset threshold value.
Compared with the prior art, the beneficial effects of the intelligent home equipment control device based on big data analysis provided by the embodiment are the same as the beneficial effects of the intelligent home equipment control method based on big data analysis provided by the embodiment, and are not repeated herein.
EXAMPLE III
The embodiment provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the smart home device control method based on big data analysis are executed.
Compared with the prior art, the beneficial effects of the computer-readable storage medium provided by the embodiment are the same as those of the smart home device control method based on big data analysis provided by the above technical scheme, and are not described herein again.
It will be understood by those skilled in the art that all or part of the steps in the method for implementing the invention may be implemented by hardware instructions related to a program, the program may be stored in a computer-readable storage medium, and when executed, the program includes the steps of the method of the embodiment, and the storage medium may be: ROM/RAM, magnetic disks, optical disks, memory cards, and the like.
The above description is only for the specific embodiments of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present invention, and the changes or substitutions should be covered within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the appended claims.
Claims (9)
1. The intelligent household equipment control method based on big data analysis is characterized by comprising the following steps:
acquiring historical instruction data of each user, and constructing a dedicated pre-judging model of each user, wherein the pre-judging model comprises a health state pre-judging model and a non-health state pre-judging model;
acquiring fuzzy operation information input by a current user, and identifying the control intention of the current user;
carrying out weighted assignment on three indexes, namely the identified control intention, the control preference identified by the pre-judging model corresponding to the current user and the collected physical health data of the current user;
when the sum of the weighted assignments of the three indexes is larger than a preset threshold value, outputting the identified control intention as a control instruction;
and when the sum of the weighted assignments of the three indexes is less than a preset threshold value, outputting the control preference identified by the pre-judging model as a control instruction.
2. The method of claim 1, wherein the method for obtaining the physical health data of the current user comprises:
regularly downloading current user physical health data from a medical data platform; or,
the current user manually updates his own physical health data at regular intervals.
3. The method of claim 2, wherein collecting historical instructional data for each user, and wherein constructing a predictive model specific to each user comprises:
storing historical instruction data of each user in a corresponding database according to a healthy state and an unhealthy state in a classified manner, and classifying and summarizing control data of each user in each time period of each day under two states by regularly counting operation information of each user in a preset time period;
based on the total number of days of a preset time period and control data of each user in each time period in each day, a probability algorithm is adopted to obtain a health state pre-judging model of each user in a health state and a non-health state pre-judging model in a non-health state, and the health state pre-judging model/the non-health state pre-judging model can reflect the control preference of the user in each time period by taking the day as a unit.
4. The method of claim 3, wherein the operation information input by the current user is acquired, and the method for identifying the control intention of the current user comprises the following steps:
acquiring fuzzy operation information sent by a current user by using an operation terminal, wherein the fuzzy operation information comprises one type or multiple types of voice information, gesture information and data information;
when a user sends out various types of operation information, acquiring different types of fuzzy operation information in stages based on a time sequence, converting and combining the fuzzy operation information semantics of each stage in sequence to obtain the control intention of the current user, and if the control intention of the current user fails to be identified, reminding the current user to input the fuzzy operation information again in a preset time period.
5. The method according to claim 4, wherein the method for adapting and networking each smart home device and the operation terminal in the area comprises the following steps:
the operation terminal shoots the intelligent household equipment to be adapted and extracts appearance characteristics;
searching networking configuration information corresponding to the intelligent household equipment to be adapted from a server based on the appearance characteristics;
and after the to-be-adapted intelligent household equipment is activated and is in a networking state, automatically executing an adaptation networking process of the operation terminal and the to-be-adapted intelligent household equipment according to the networking configuration information.
6. The method according to claim 5, wherein the operation terminal photographs the smart home device to be adapted, and before extracting the appearance features, the method further comprises:
according to the operation of a user, code scanning information of the intelligent home equipment is obtained and/or adaptation scheme information of the intelligent home equipment input by the user is received, and the intelligent home equipment is adapted to the corresponding intelligent home equipment according to the code scanning information and/or the adaptation scheme information; and extracting and storing configuration information and appearance information of the intelligent household equipment in an associated manner.
7. The method according to any one of claims 1 to 6, wherein after outputting the control instruction of the smart home device to be controlled to make the smart home device to be controlled in the response state, the method further comprises:
and synchronously updating the image display frame of the intelligent household equipment on the display interface of the operation terminal based on the working state of the intelligent household equipment.
8. The utility model provides an intelligent household equipment controlling means based on big data analysis which characterized in that includes:
the system comprises an acquisition unit, a judgment unit and a judgment unit, wherein the acquisition unit is used for acquiring historical instruction data of each user and constructing a dedicated pre-judgment model for each user, and the pre-judgment model comprises a health state pre-judgment model and a non-health state pre-judgment model;
the identification unit is used for acquiring fuzzy operation information input by a current user and identifying the control intention of the current user;
the evaluation unit is used for carrying out weighted evaluation on three indexes, namely the identified control intention, the control preference identified by the pre-judging model corresponding to the current user and the collected physical health data of the current user;
and the judging unit is used for outputting the identified control intention as a control instruction when the sum of the weighted assignments of the three indexes is greater than a preset threshold value, and outputting the control preference identified by the pre-judging model as the control instruction when the sum of the weighted assignments of the three indexes is less than the preset threshold value.
9. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method according to any one of the claims 1 to 7.
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