CN114047708B - Household equipment control method and device, electronic equipment and storage medium - Google Patents

Household equipment control method and device, electronic equipment and storage medium Download PDF

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CN114047708B
CN114047708B CN202111294884.2A CN202111294884A CN114047708B CN 114047708 B CN114047708 B CN 114047708B CN 202111294884 A CN202111294884 A CN 202111294884A CN 114047708 B CN114047708 B CN 114047708B
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data
regulated
event
equipment
household equipment
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CN114047708A (en
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梁文德
李绍斌
宋德超
唐杰
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Gree Electric Appliances Inc of Zhuhai
Zhuhai Lianyun Technology Co Ltd
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Gree Electric Appliances Inc of Zhuhai
Zhuhai Lianyun Technology Co Ltd
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    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B15/00—Systems controlled by a computer
    • G05B15/02—Systems controlled by a computer electric
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B19/00—Programme-control systems
    • G05B19/02—Programme-control systems electric
    • G05B19/418—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/045—Combinations of networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B2219/00—Program-control systems
    • G05B2219/20—Pc systems
    • G05B2219/26—Pc applications
    • G05B2219/2642—Domotique, domestic, home control, automation, smart house
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
    • Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
    • Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]

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Abstract

The application relates to a household equipment control method, a household equipment control device, electronic equipment and a storage medium, wherein the method comprises the following steps: acquiring source data related to household equipment, wherein the source data comprises equipment operation data and/or user behavior data, the equipment operation data is used for representing the operation state of the household equipment, and the user behavior data is used for representing the operation behavior of a user on the household equipment; determining an event to be regulated according to the equipment operation data and/or the user behavior data, wherein the event to be regulated is used for representing the fault type of the household equipment or the function type of the household equipment to be regulated; inputting the equipment operation data and/or the user behavior data into a preset model corresponding to an event to be regulated to predict, and obtaining a prediction result, wherein the prediction result is used for representing target parameters to be regulated of household equipment; and adjusting target parameters of the household equipment. Therefore, the household equipment can be controlled according to the equipment operation data and/or the user behavior data, so that the control mode is more intelligent.

Description

Household equipment control method and device, electronic equipment and storage medium
Technical Field
The application relates to the technical field of intelligent home control, in particular to a home equipment control method and device, electronic equipment and a storage medium.
Background
With the development of internet technology, intelligent control of home equipment is becoming more and more popular in people's daily life. At present, the intelligent control of the household equipment is mainly realized by a user sending a control instruction through an Application program (App for short), and the control mode is single. When a performance of the household equipment is not good, the household equipment cannot meet the expected requirement of a user, for example, when the refrigerating performance of an air conditioner is not good, even if the indoor temperature is set to be 27 ℃ by the user, the indoor temperature is difficult to be actually reduced to be 27 ℃, so that the intelligent degree of the control mode of the existing household equipment is low.
Disclosure of Invention
The application provides a household equipment control method, a household equipment control device, electronic equipment and a storage medium, and aims to solve the problem that the control mode of the existing household equipment is low in intelligent degree.
In a first aspect, the present application provides a method for controlling a home device, where the method includes:
acquiring source data related to the household equipment, wherein the source data comprises equipment operation data and/or user behavior data, the equipment operation data is used for representing the operation state of the household equipment, and the user behavior data is used for representing the operation behavior of a user on the household equipment;
determining an event to be regulated according to the equipment operation data and/or the user behavior data, wherein the event to be regulated is used for representing the fault type of the household equipment or the function type of the household equipment to be regulated;
Inputting the equipment operation data and/or the user behavior data into a preset model corresponding to the event to be regulated to predict, so as to obtain a prediction result, wherein the prediction result is used for representing target parameters to be regulated of the household equipment;
And adjusting the target parameters of the household equipment.
Optionally, the determining an event to be regulated according to the device operation data and/or the user behavior data includes:
Comparing each parameter value in the equipment operation data with a corresponding preset reference value respectively;
under the condition that the household equipment is determined to have faults according to the comparison result, determining the fault type of the faults;
And determining the event to be regulated according to the fault type.
Optionally, the determining an event to be regulated according to the device operation data and/or the user behavior data includes:
determining the function type of the household equipment to be adjusted according to the user behavior data and the equipment operation data;
and determining the event to be regulated according to the function type to be regulated of the household equipment.
Optionally, the inputting the device operation data and/or the user behavior data into a preset model corresponding to the event to be regulated to predict, to obtain a prediction result, includes:
Acquiring a plurality of association parameters in a preset model corresponding to the event to be regulated, wherein the plurality of association parameters are a plurality of parameters with association relation with the event to be regulated, and the plurality of association parameters are contained in the equipment operation data and/or the user behavior data;
Acquiring actual parameter values corresponding to each associated parameter in the plurality of associated parameters from the equipment operation data and/or the user behavior data;
Comparing the actual parameter value corresponding to each associated parameter with a preset reference range corresponding to each associated parameter;
And determining the associated parameters meeting target conditions in the plurality of associated parameters as the prediction result, wherein the target conditions are that the actual parameter values of the associated parameters exceed the corresponding preset reference ranges.
Optionally, the acquiring a plurality of association parameters in a preset model corresponding to the event to be adjusted includes:
Determining a plurality of association parameters in a preset model corresponding to the event to be regulated based on a preset knowledge graph, wherein the preset knowledge graph is established based on a deep learning model, and the preset knowledge graph is used for representing the association relation between the event to be regulated and the association parameters.
Optionally, before the determining an event to be regulated according to the source data, the method further includes:
verifying the source data to obtain first intermediate data;
Cleaning the first intermediate data to obtain second intermediate data;
The determining an event to be regulated according to the source data comprises the following steps:
and determining the event to be regulated according to the second intermediate data.
Optionally, after the adjusting the parameter of the target parameter of the home device, the method further includes:
And counting the prediction results and adjustment records corresponding to the prediction results, and sending the statistics results to a user terminal so that the user terminal displays the statistics results.
In a second aspect, the present application provides a home appliance control apparatus, the apparatus comprising:
The system comprises an acquisition module, a control module and a control module, wherein the acquisition module is used for acquiring source data related to the household equipment, the source data comprises equipment operation data and/or user behavior data, the equipment operation data is used for representing the operation state of the household equipment, and the user behavior data is used for representing the operation behavior of a user on the household equipment;
The first determining module is used for determining an event to be regulated according to the equipment operation data and/or the user behavior data, wherein the event to be regulated is used for representing the fault type of the household equipment or the function type of the household equipment to be regulated;
The prediction module is used for inputting the equipment operation data and/or the user behavior data into a preset model corresponding to the event to be regulated to perform prediction, so as to obtain a prediction result, wherein the prediction result is used for representing target parameters to be regulated of the household equipment;
and the adjusting module is used for adjusting the target parameters of the household equipment.
In a third aspect, an electronic device is provided, including a processor, a communication interface, a memory, and a communication bus, where the processor, the communication interface, and the memory complete communication with each other through the communication bus;
A memory for storing a computer program;
And the processor is used for realizing the steps of the household equipment control method according to any one embodiment of the first aspect when executing the program stored in the memory.
In a fourth aspect, a computer readable storage medium is provided, on which a computer program is stored, which when being executed by a processor, implements the steps of the home device control method according to any one of the embodiments of the first aspect.
In the embodiment of the application, the source data related to the household equipment is obtained, wherein the source data comprises equipment operation data and/or user behavior data, the equipment operation data is used for representing the operation state of the household equipment, and the user behavior data is used for representing the operation behavior of a user on the household equipment; determining an event to be regulated according to the equipment operation data and/or the user behavior data, wherein the event to be regulated is used for representing the fault type of the household equipment or the function type of the household equipment to be regulated; inputting the equipment operation data and/or the user behavior data into a preset model corresponding to the event to be regulated to predict, so as to obtain a prediction result, wherein the prediction result is used for representing target parameters to be regulated of the household equipment; and adjusting the target parameters of the household equipment. By the method, the events to be regulated of the household equipment can be analyzed according to the acquired equipment operation data and/or user behavior data, the possible fault types of the household equipment and/or the functional types of the household equipment to be regulated are determined, and automatic control is performed on the possible fault types of the household equipment and/or the functional types of the household equipment to be regulated, so that the control mode of the household equipment is more intelligent.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and together with the description, serve to explain the principles of the invention.
In order to more clearly illustrate the embodiments of the invention or the technical solutions of the prior art, the drawings which are used in the description of the embodiments or the prior art will be briefly described, and it will be obvious to a person skilled in the art that other drawings can be obtained from these drawings without inventive effort.
Fig. 1 is a schematic flow chart of a control method of home equipment according to an embodiment of the present application;
fig. 2 is a schematic diagram of a specific implementation process of a home device control method according to an embodiment of the present application;
fig. 3 is a schematic structural diagram of a home appliance control device according to an embodiment of the present application;
fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present application more apparent, the technical solutions of the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application, and it is apparent that the described embodiments are some embodiments of the present application, but not all embodiments of the present application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
Referring to fig. 1, fig. 1 is a schematic flow chart of a home device control method according to an embodiment of the present application. As shown in fig. 1, the home equipment control method includes the following steps:
step 101, acquiring source data related to household equipment, wherein the source data comprises equipment operation data and/or user behavior data, the equipment operation data is used for representing the operation state of the household equipment, and the user behavior data is used for representing the operation behavior of a user on the household equipment.
Specifically, the home devices may include, but are not limited to, air conditioners, humidifiers, refrigerators, televisions, lights, air purifiers, and the like. The home equipment can report the equipment operation data of the home equipment through a Communication module, wherein the Communication module can be any one of a wireless fidelity (WIRELESS FIDELITY, wi-Fi for short) module, a Bluetooth module, a near field Communication (NEAR FIELD Communication, NFC for short) module and the like. Different household devices can select corresponding communication protocols to report own device operation data through the types of the communication modules of the household devices. The device operation data may include device basic information (such as machine type subdivision feature codes, device identifiers, etc.), device control information (such as information of function type setting, parameter setting, etc. of a user), device status information (such as various index parameters in the operation process), device fault information (such as early warning information, fault codes, etc.), and of course, external environment information (such as indoor and outdoor temperature, humidity, etc.) acquired by home devices through own sensors. When the whole household equipment is started or closed, new control information is received, the equipment state is changed, and parameters belonging to a predefined fault scope are generated, the host program of the household equipment can report the information and the data through the communication module.
Of course, the home equipment can also be connected with the user terminal, so that the user can remotely control the home equipment through an App on the user terminal. Because the App can preset defined embedded point events, such as page loading events, control clicking events, application initializing events and the like, when a user is using the App, the App can collect user behavior data through a software development kit (Software Development Kit, abbreviated as SDK) of the embedded point events.
It should be noted that, the method for controlling the home device in the embodiment of the present application may be executed by the user terminal connected to the home device, or may be executed by other electronic devices, such as a server, connected to the home device and the user terminal together, which is not particularly limited in the present application. When the household equipment control method is executed by the user terminal, the user terminal can receive equipment operation data sent by the household equipment in real time through the communication module and acquire user behavior data through the embedded point of the user terminal, so that the user terminal can acquire the equipment operation data and the user behavior data, and execute subsequent steps through the acquired equipment operation data and/or the user behavior data. When the control method of the household equipment is executed by the server, the server can receive the equipment operation data sent by the household equipment in real time through the communication module, and simultaneously receive the user behavior data acquired by the user terminal through the embedded point of the server, so that the server can acquire the equipment operation data and the user behavior data, and execute the subsequent steps through the acquired equipment operation data and/or the user behavior data.
Step 102, determining an event to be regulated according to the equipment operation data and/or the user behavior data, wherein the event to be regulated is used for representing the fault type of the household equipment or the function type of the household equipment to be regulated.
Specifically, the event to be regulated refers to an event that needs to be regulated for a certain fault type of the home equipment or a certain function type of the home equipment that needs to be regulated. For example, when the event to be regulated is an air-conditioning refrigeration fault event, the associated parameters and parameter values thereof causing the air-conditioning refrigeration fault need to be regulated; when the event to be regulated is an event of turning on the intelligent air function, home equipment such as an air conditioner, a humidifier and the like participating in indoor air temperature and humidity regulation needs to be turned on, and the operation parameter values of the equipment are regulated.
As an implementation manner, the device operation state may be analyzed according to the device operation data, and whether some critical values, early warning values or fault values are generated in the device operation data is determined, so as to determine the event to be regulated. Because the critical values, the early warning values or the fault values are possibly precursors of the household equipment in a sub-health state or a fault state, the data are screened out, so that the faults of the household equipment can be found out in time and processed in time, and the irreparable damage to the household equipment is avoided.
As another implementation mode, the user behavior data can be analyzed to determine the user requirements, and the household equipment can be intelligently controlled according to the user requirements. For example, if it is known through analysis of the user behavior data that a user frequently uses a function and sets a parameter value, an automatic event processing can be performed according to the preference of the user through simulation of the user behavior data, for example, the user frequently uses the sleep function of the air conditioner and sets a higher temperature of 28 degrees, and then the function and the parameter may be set for the user preferentially in an air conditioner starting event.
Of course, comprehensive analysis can be performed based on the user behavior data and the device operation data, and a certain function of the household device can be automatically adjusted by judging the difference between the device operation data and the user demand. For example, it is assumed that the current outdoor temperature is 40 ℃ and the indoor temperature is 35 ℃ through the analysis of the equipment operation data, and that the cooling temperature of the air conditioner which is set by the recent habit of the user is 27 ℃ through the analysis of the user behavior data, and the current indoor temperature and the outdoor temperature are higher than 27 ℃, so that after the user App is authorized to use the automatic temperature control setting, the air conditioner is automatically turned on for the user and the temperature is set to 27 ℃.
And 103, inputting the equipment operation data and/or the user behavior data into a preset model corresponding to the event to be regulated to predict, so as to obtain a prediction result, wherein the prediction result is used for representing the target parameters to be regulated of the household equipment.
It should be noted that, for each event to be adjusted, there is a preset model corresponding to the event to be adjusted. After determining the event to be regulated, a required preset model can be determined according to the type of the event to be regulated. In this way, the preset model can analyze the event to be regulated according to the currently acquired equipment operation data and/or user behavior data, so as to determine the target parameter to be regulated.
And 104, adjusting target parameters of the household equipment.
In the step, corresponding instructions can be automatically issued to the household equipment according to the preset adjustment scheme of each target parameter so as to realize the adjustment of the target parameters of the household equipment. For example, the refrigerating efficiency of the air conditioner is poor, and the reason is mainly that the factor 1 value is low through the analysis of the preset model, so that a corresponding instruction for improving the factor 1 value can be automatically issued, or the user is prompted to perform corresponding processing through the APP.
It should be noted that, when the control method of the home equipment is executed by the user terminal, the user terminal may directly send a corresponding instruction to the home equipment, so as to implement adjustment of the target parameters of the home equipment; when the home equipment control method is executed by the server, the server can send corresponding instructions to the home equipment and/or the user terminal so as to realize adjustment of target parameters of the home equipment.
In this embodiment, the event to be regulated of the home equipment may be analyzed according to the acquired equipment operation data and/or user behavior data, so as to determine a possible hidden fault of the home equipment and/or a function to be regulated of the home equipment, and automatically and intelligently control the possible hidden fault of the home equipment and/or the function to be regulated of the home equipment, thereby enabling a control mode of the home equipment to be more intelligent.
Further, the step 102 of determining the event to be regulated according to the device operation data and/or the user behavior data includes:
Comparing each parameter value in the equipment operation data with a corresponding preset reference value respectively;
under the condition that the household equipment is determined to have faults according to the comparison result, determining the fault type of the faults;
and determining an event to be regulated according to the fault type.
In an embodiment, the event to be regulated is determined by the device operation data, and specifically, since the device operation data reported by the home device includes information such as device status information and device fault information, it is determined whether a certain type of fault exists in the home device by analyzing each parameter value in the device operation data. For example, after receiving an air conditioner start control instruction, detecting that the indoor fan continuously blows out larger cold air from the start time, rather than turning on for a period of time before turning on to increase the air supply quantity, thereby determining that the air conditioner has cold air protection faults. For another example, when the compression frequency of the air conditioner compressor reaches an early warning value (preset by a user), it may be determined that the air conditioner has a compressor failure.
In this embodiment, the device operation state may be analyzed according to the device operation data, to determine whether some critical values, early warning values or fault values are generated in the device operation data, so as to determine the event to be regulated. Because the critical values, the early warning values or the fault values can be precursors of the household equipment in a sub-health state or a fault state, the data are screened out, so that faults of the household equipment can be found out in time and processed, and irreparable damage to the household equipment is avoided.
Further, the step 102 of determining the event to be regulated according to the device operation data and/or the user behavior data includes:
according to the user behavior data and the equipment operation data, determining the function type of the household equipment to be adjusted;
And determining an event to be regulated according to the function type required to be regulated by the household equipment.
In one embodiment, the event to be adjusted is determined by user behavior data and device operational data. Specifically, the function type of the home equipment to be adjusted can be determined according to the user behavior data and the equipment operation data, so that the event to be adjusted is determined according to the function type of the home equipment to be adjusted. For example, it is assumed that the current outdoor temperature is 40 ℃ and the indoor temperature is 35 ℃ as determined by the analysis of the device operation data, and the air conditioner cooling temperature which is set by the recent habit of the user is 27 ℃ as determined by the analysis of the user behavior data, and the current indoor temperature and the outdoor temperature are higher than 27 ℃, so that after receiving the automatic temperature control setting instruction sent by the user from the App, the air conditioner can be automatically turned on for the user and the temperature is set to 27 ℃. Therefore, the user using habit can be analyzed according to the user behavior data, and the household equipment is intelligently controlled according to the user using habit. For another example, it is assumed that the cooling temperature set by the user is 27 ℃ as determined by the user behavior data analysis, and the cooling failure of the air conditioner can be determined as the outlet air temperature of the air conditioner is 30 ℃ after the cooling temperature is set by the user for 2 hours as determined by the equipment operation data analysis. Therefore, the difference between the user setting parameters and the actual household operation parameters can be compared, and the possible faults of household equipment can be subjected to early warning analysis.
Further, the step 103 of inputting the device operation data and/or the user behavior data into a preset model corresponding to the event to be regulated to predict, to obtain a prediction result, includes:
acquiring a plurality of association parameters in a preset model corresponding to an event to be regulated, wherein the plurality of association parameters are a plurality of parameters with association relation with the event to be regulated, and the plurality of association parameters are contained in equipment operation data and/or user behavior data;
Acquiring actual parameter values corresponding to each associated parameter in a plurality of associated parameters from equipment operation data and/or user behavior data;
Comparing the actual parameter value corresponding to each associated parameter with a preset reference range corresponding to each associated parameter;
And determining the associated parameters meeting the target conditions in the plurality of associated parameters as prediction results, wherein the target conditions are that the actual parameter values of the associated parameters exceed the corresponding preset reference ranges.
Specifically, the preset model may be any deep learning model, such as a convolutional network model (Convolutional Neural Networks, abbreviated as CNN), a deep generation model (DEEP GENERATIVE Models, abbreviated as DGM), a generated countermeasure network (GENERATIVE ADVERSARIAL Networks, abbreviated as GAN), and the embodiment is not limited specifically.
The preset model can determine a plurality of associated parameters associated with the event to be regulated through pre-training, so that after source data of household equipment are acquired, the associated parameters and actual parameter values corresponding to the associated parameters can be selected from the source data, the actual parameter values of the associated parameters are compared with preset reference ranges corresponding to the associated parameters, and associated parameters, of which the actual parameter values do not belong to the corresponding preset reference ranges, are determined, and a prediction result is determined by taking the associated parameters as target parameters.
In this embodiment, model analysis can be performed for each event to be adjusted, so that target parameters to be adjusted of the home equipment can be accurately determined, and the target parameters can be automatically adjusted, thereby realizing intelligent control of the home equipment.
Further, the step of obtaining a plurality of associated parameters in a preset model corresponding to the event to be regulated includes:
And determining a plurality of association parameters in a preset model corresponding to the event to be regulated based on a preset knowledge graph, wherein the preset knowledge graph is established based on a deep learning model, and the preset knowledge graph is used for representing the association relation between the event to be regulated and the association parameters.
Specifically, in the preset model, the deep learning model can be modeled and trained according to artificial experience and historical data, and a knowledge map is established in advance. The knowledge graph can be used for representing the association relation between the event to be regulated and the association parameter. For example, assuming that the event to be regulated is an air-conditioning refrigeration fault type, the preset model can be queried according to a knowledge graph: from a relation chain between products, problems, reasons and solutions, a relation parameter (also called a factor) related to the refrigeration fault of the air conditioner can be determined, and if the relation parameter related to the refrigeration fault of the air conditioner includes a compressor compression frequency and a fan rotating speed, an actual parameter value of the compressor compression frequency and the fan rotating speed can be further compared with a preset reference range, so that whether the reason for causing the refrigeration fault of the air conditioner is that the compression power of the compressor is too small or the fan rotating speed is too small can be determined, and a prediction result is obtained.
In the embodiment, a knowledge graph of an event to be regulated is established through a deep learning model, and then target parameters to be regulated are accurately determined according to analysis of associated parameters in the knowledge graph, so that accuracy and intelligent degree of control of household equipment are improved. Meanwhile, the deep learning and training of the preset model mainly comprises the steps of continuously adjusting the numerical range of the existing analysis factors, such as refrigeration problem factor 1, which is initially determined to be worse in running state at 10-20, and updating the preset model after a large amount of real data simulation and generating larger problems at 12-22, wherein the event analysis model is more accurate and intelligent through the learning and training of a large amount of data. That is, the preset model may further perform learning training based on the acquired device operation data and/or user behavior data, so as to continuously improve the intelligentization degree of the preset model.
Further, in the step 102, before determining the event to be regulated according to the source data, the home equipment control method further includes:
verifying the source data to obtain first intermediate data;
Cleaning the first intermediate data to obtain second intermediate data;
step 102, determining an event to be regulated according to the source data, including:
and determining an event to be regulated according to the second intermediate data.
Specifically, the verification process may include screening the accuracy and integrity of the source data, and if a piece of fault report information is obtained, checking whether the fault ID and the carried parameter are accurate and complete is required. The above cleaning process refers to converting the checked data into standard data conforming to a preset model according to a preset rule, and performing desensitization, compression and other processes, for example, the data is also fault information, the expression form in reporting is binary character string or hexadecimal character string, and the like, which needs to be translated into data format and parameters in the preset model, and necessary parameters are extracted and analyzed from the data format and parameters.
In an embodiment, after the source data is obtained, the source data may be verified, and then the verified data may be cleaned, and then the event to be adjusted may be determined according to the cleaned data, and the event may be marked and transmitted to the corresponding preset model. Therefore, the data input into the preset model can be ensured to be accurate and effective, and the accuracy of the prediction result is improved.
Further, after the step 104 of adjusting the target parameters of the home device, the home device control method further includes:
And counting the prediction results and adjustment records corresponding to the prediction results, and sending the statistics results to the user terminal so that the user terminal displays the statistics results.
In an embodiment, after the prediction result is obtained through the preset model and the target parameter of the home equipment is adjusted, statistics can be performed on the prediction result and the adjustment record corresponding to the prediction result, the statistics result is sent to the user terminal, and the App on the user terminal is used for displaying, so that the running state of the home equipment can be intuitively mastered by the user. The statistical result display mode can be displayed in a visual chart data mode.
In practical application, the household equipment control method can be completed through three parts of a data acquisition module and a program, a data analysis and problem solving model based on deep learning and a data display program at an App end. Wherein, data acquisition module and procedure: reporting related data of equipment operation through a WI-FI communication module; meanwhile, the data acquisition mode also comprises a user behavior embedded point program of the App terminal; data analysis and problem solving model based on deep learning: burying points according to the reported data, and establishing an event analysis and response model; app-end data presentation program: and displaying the event statistics record and the processing record at the App end. The specific implementation process of the home equipment control method is shown in fig. 2:
Step 201, the home equipment reports equipment operation data through a communication module, and an application program collects and reports user behavior data through a buried point;
Step 202, verifying, cleaning and determining the type of an event to be regulated on equipment operation data and user behavior data;
Step 203, performing prediction analysis on different event types to be adjusted according to different preset models to obtain a prediction result;
step 204, responding, adjusting and issuing instructions according to the prediction result;
step 205, continuously performing deep learning and training on a preset model through data acquisition, and improving the intelligent degree of the model.
By the method, various sub-health data of the household equipment can be subjected to buried point analysis and intelligent response, so that the service life of the household equipment is prolonged, the better running state is adjusted, and better service experience is given to a user; in addition, the embedded point analysis is carried out on the data and the information reported by the household equipment, a response and adjustment scheme is given through a preset model, the response and adjustment scheme is automatically executed, the intelligent control of the household equipment is improved, and meanwhile, visual display and statistics are given to a user through an App; moreover, the intelligent degree of the data model can be continuously improved through the deep learning and training of the event model.
Referring to fig. 3, fig. 3 is a schematic structural diagram of a home appliance control device according to an embodiment of the present application, and as shown in fig. 3, the home appliance control device 300 includes:
The acquiring module 301 is configured to acquire source data related to a home device, where the source data includes device operation data and/or user behavior data, the device operation data is used to characterize an operation state of the home device, and the user behavior data is used to characterize an operation behavior of a user on the home device;
the first determining module 302 is configured to determine an event to be adjusted according to the device operation data and/or the user behavior data, where the event to be adjusted is used to characterize a fault type of the home device or a function type of the home device to be adjusted;
the prediction module 303 is configured to input device operation data and/or user behavior data into a preset model corresponding to an event to be adjusted to perform prediction, so as to obtain a prediction result, where the prediction result is used to represent a target parameter to be adjusted of the home equipment;
The adjusting module 304 is configured to adjust a target parameter of the home device.
Optionally, the first determining module 302 includes:
the first comparison sub-module is used for comparing each parameter value in the equipment operation data with a corresponding preset reference value respectively;
The first determining submodule is used for determining the fault type of the fault under the condition that the fault exists in the household equipment according to the comparison result;
and the second determining submodule is used for determining an event to be regulated according to the fault type.
Optionally, the first determining module 302 includes:
The third determining submodule is used for determining the function type of the household equipment to be adjusted according to the user behavior data and the equipment operation data;
And the fourth determining submodule is used for determining an event to be regulated according to the function type required to be regulated by the household equipment.
Optionally, the prediction module 303 includes:
the first acquisition sub-module is used for acquiring a plurality of association parameters in a preset model corresponding to the event to be regulated, wherein the plurality of association parameters are a plurality of parameters with association relation with the event to be regulated, and the plurality of association parameters are contained in equipment operation data and/or user behavior data;
the second acquisition sub-module is used for acquiring actual parameter values corresponding to each associated parameter in the plurality of associated parameters from the equipment operation data and/or the user behavior data;
The second comparison sub-module is used for comparing the actual parameter value corresponding to each associated parameter with the preset reference range corresponding to each associated parameter;
And the fifth determining submodule is used for determining the associated parameters meeting the target condition in the plurality of associated parameters as a prediction result, wherein the target condition is that the actual parameter value of the associated parameters exceeds the corresponding preset reference range.
Optionally, the first acquisition submodule is specifically configured to:
And determining a plurality of association parameters in a preset model corresponding to the event to be regulated based on a preset knowledge graph, wherein the preset knowledge graph is established based on a deep learning model, and the preset knowledge graph is used for representing the association relation between the event to be regulated and the association parameters.
Optionally, the home appliance control device 300 further includes:
The verification module is used for verifying the source data to obtain first intermediate data;
the cleaning module is used for cleaning the first intermediate data to obtain second intermediate data;
And the second determining module is used for determining an event to be regulated according to the second intermediate data.
Optionally, the home appliance control device 300 further includes:
And the processing module is used for counting the prediction result and the adjustment record corresponding to the prediction result, and sending the statistics result to the user terminal so that the user terminal displays the statistics result.
It should be noted that, the home equipment control device 300 may implement the steps of the home equipment control method provided in any one of the foregoing method embodiments, and may achieve the same technical effects, which are not described herein in detail.
As shown in fig. 4, an embodiment of the present application provides an electronic device, including a processor 411, a communication interface 412, a memory 413, and a communication bus 414, where the processor 411, the communication interface 412, and the memory 413 perform communication with each other through the communication bus 414,
A memory 413 for storing a computer program;
In one embodiment of the present application, the processor 411 is configured to implement the home device control method provided in any one of the foregoing method embodiments when executing the program stored in the memory 413, where the method includes:
Acquiring source data related to household equipment, wherein the source data comprises equipment operation data and/or user behavior data, the equipment operation data is used for representing the operation state of the household equipment, and the user behavior data is used for representing the operation behavior of a user on the household equipment;
determining an event to be regulated according to the equipment operation data and/or the user behavior data, wherein the event to be regulated is used for representing the fault type of the household equipment or the function type of the household equipment to be regulated;
Inputting the equipment operation data and/or the user behavior data into a preset model corresponding to an event to be regulated to predict, and obtaining a prediction result, wherein the prediction result is used for representing target parameters to be regulated of household equipment;
And adjusting target parameters of the household equipment.
The embodiment of the application also provides a computer readable storage medium, on which a computer program is stored, which when executed by a processor, implements the steps of the home appliance control method provided in any one of the method embodiments described above.
It should be noted that in this document, relational terms such as "first" and "second" and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
The foregoing is only a specific embodiment of the invention to enable those skilled in the art to understand or practice the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims (8)

1. A method for controlling household equipment, the method comprising:
acquiring source data related to the household equipment, wherein the source data comprises equipment operation data and/or user behavior data, the equipment operation data is used for representing the operation state of the household equipment, and the user behavior data is used for representing the operation behavior of a user on the household equipment;
determining an event to be regulated according to the equipment operation data and/or the user behavior data, wherein the event to be regulated is used for representing the fault type of the household equipment or the function type of the household equipment to be regulated;
Inputting the equipment operation data and/or the user behavior data into a preset model corresponding to the event to be regulated to predict, so as to obtain a prediction result, wherein the prediction result is used for representing target parameters to be regulated of the household equipment;
adjusting the target parameters of the household equipment;
The step of inputting the equipment operation data and/or the user behavior data into a preset model corresponding to the event to be regulated to predict, so as to obtain a prediction result, includes:
Acquiring a plurality of association parameters in a preset model corresponding to the event to be regulated, wherein the plurality of association parameters are a plurality of parameters with association relation with the event to be regulated, the plurality of association parameters are contained in the equipment operation data and/or the user behavior data, the plurality of association parameters are determined and obtained based on a preset knowledge graph, the preset knowledge graph is established based on a deep learning model, and the preset knowledge graph is used for representing the association relation between the event to be regulated and the association parameters;
Acquiring actual parameter values corresponding to each associated parameter in the plurality of associated parameters from the equipment operation data and/or the user behavior data;
Comparing the actual parameter value corresponding to each associated parameter with a preset reference range corresponding to each associated parameter;
And determining the associated parameters meeting target conditions in the plurality of associated parameters as the prediction result, wherein the target conditions are that the actual parameter values of the associated parameters exceed the corresponding preset reference ranges.
2. The method according to claim 1, wherein said determining an event to be regulated from said device operational data and/or said user behavior data comprises:
Comparing each parameter value in the equipment operation data with a corresponding preset reference value respectively;
under the condition that the household equipment is determined to have faults according to the comparison result, determining the fault type of the faults;
And determining the event to be regulated according to the fault type.
3. The method according to claim 1, wherein said determining an event to be regulated from said device operational data and/or said user behavior data comprises:
determining the function type of the household equipment to be adjusted according to the user behavior data and the equipment operation data;
and determining the event to be regulated according to the function type to be regulated of the household equipment.
4. The method of claim 1, wherein prior to said determining an event to be adjusted based on said source data, the method further comprises:
verifying the source data to obtain first intermediate data;
Cleaning the first intermediate data to obtain second intermediate data;
The determining an event to be regulated according to the source data comprises the following steps:
and determining the event to be regulated according to the second intermediate data.
5. The method of claim 1, wherein after said adjusting the target parameter of the home device, the method further comprises:
And counting the prediction results and adjustment records corresponding to the prediction results, and sending the statistics results to a user terminal so that the user terminal displays the statistics results.
6. A home appliance control apparatus, the apparatus comprising:
The system comprises an acquisition module, a control module and a control module, wherein the acquisition module is used for acquiring source data related to the household equipment, the source data comprises equipment operation data and/or user behavior data, the equipment operation data is used for representing the operation state of the household equipment, and the user behavior data is used for representing the operation behavior of a user on the household equipment;
The first determining module is used for determining an event to be regulated according to the equipment operation data and/or the user behavior data, wherein the event to be regulated is used for representing the fault type of the household equipment or the function type of the household equipment to be regulated;
The prediction module is used for inputting the equipment operation data and/or the user behavior data into a preset model corresponding to the event to be regulated to perform prediction, so as to obtain a prediction result, wherein the prediction result is used for representing target parameters to be regulated of the household equipment;
The adjusting module is used for adjusting the target parameters of the household equipment;
wherein the prediction module comprises:
The first acquisition sub-module is used for acquiring a plurality of association parameters in a preset model corresponding to the event to be regulated, wherein the plurality of association parameters are a plurality of parameters with association relation with the event to be regulated, the plurality of association parameters are contained in the equipment operation data and/or the user behavior data, the plurality of association parameters are determined based on a preset knowledge graph, the preset knowledge graph is established based on a deep learning model, and the preset knowledge graph is used for representing the association relation between the event to be regulated and the association parameters;
The second obtaining sub-module is used for obtaining actual parameter values corresponding to each associated parameter in the plurality of associated parameters from the equipment operation data and/or the user behavior data;
The second comparison sub-module is used for comparing the actual parameter value corresponding to each associated parameter with the preset reference range corresponding to each associated parameter;
And a fifth determining submodule, configured to determine, as the prediction result, an associated parameter that satisfies a target condition in the plurality of associated parameters, where the target condition is that an actual parameter value of the associated parameter exceeds a corresponding preset reference range.
7. The electronic equipment is characterized by comprising a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory are communicated with each other through the communication bus;
A memory for storing a computer program;
a processor for implementing the steps of the home appliance control method according to any one of claims 1 to 5 when executing a program stored in a memory.
8. A computer-readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the home appliance control method of any one of claims 1-5.
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