CN114047708A - 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 PDFInfo
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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 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 needing to be regulated; inputting equipment operation data and/or user behavior data into a preset model corresponding to an event to be adjusted for prediction to obtain a prediction result, wherein the prediction result is used for representing a target parameter to be adjusted of the household equipment; and adjusting the 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
Technical Field
The present application relates to the field of smart home control technologies, and in particular, to a home device control method and apparatus, an electronic device, and a storage medium.
Background
With the development of internet technology, intelligent control of household equipment is more and more popular in daily life of people. At present, intelligent control of home 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 certain performance of the household equipment is not good, the household equipment can not meet the expected requirements of the user, for example, when the refrigeration performance of the air conditioner is not good, even if the indoor temperature set by the user is reduced to 27 ℃, the indoor temperature is difficult to be actually reduced to 27 ℃, and therefore, 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 and device, electronic equipment and a storage medium, and aims to solve the problem that the intelligent degree of the existing household equipment control mode is low.
In a first aspect, the present application provides a home device control method, 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 needing to be regulated;
inputting the equipment operation data and/or the user behavior data into a preset model corresponding to the event to be adjusted for prediction to obtain a prediction result, wherein the prediction result is used for representing a target parameter to be adjusted of the household equipment;
and adjusting the target parameters of the household equipment.
Optionally, the determining an event to be adjusted 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;
determining the fault type of the fault under the condition that the household equipment is determined to have the fault according to the comparison result;
and determining the event to be regulated according to the fault type.
Optionally, the determining an event to be adjusted according to the device operation data and/or the user behavior data includes:
determining the function type of the household equipment which needs 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 required to be regulated by 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 adjusted for prediction to obtain a prediction result includes:
acquiring a plurality of associated parameters in a preset model corresponding to the event to be adjusted, wherein the associated parameters are parameters having an associated relationship with the event to be adjusted, and the associated 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 correlation parameter with the preset reference range corresponding to each correlation parameter;
and determining the relevant parameters meeting target conditions in the plurality of relevant parameters as the prediction results, wherein the target conditions are that the actual parameter values of the relevant parameters exceed the corresponding preset reference ranges.
Optionally, the obtaining of a plurality of associated parameters in a preset model corresponding to the event to be adjusted includes:
determining a plurality of associated parameters in a preset model corresponding to the event to be adjusted 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 associated relation between the event to be adjusted and the associated parameters.
Optionally, before the determining an event to be adjusted 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 the event to be adjusted according to the source data comprises:
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 household device, the method further includes:
and counting the prediction result and the adjustment record corresponding to the prediction result, and sending the statistical result to a user terminal so that the user terminal can display the statistical result.
In a second aspect, the present application provides a household appliance control apparatus, the apparatus comprising:
the acquisition module is used for 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;
the first determining module is used for determining an event to be adjusted according to the equipment operation data and/or the user behavior data, wherein the event to be adjusted is used for representing the fault type of the household equipment or the function type of the household equipment needing to be adjusted;
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 adjusted for prediction to obtain a prediction result, and the prediction result is used for representing a target parameter to be adjusted 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, which includes a processor, a communication interface, a memory and a communication bus, wherein 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 configured to implement the steps of the home equipment control method according to any 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, source data related to the home equipment is acquired, where the source data includes equipment operation data and/or user behavior data, the equipment operation data is used for representing an operation state of the home equipment, and the user behavior data is used for representing an operation behavior of a user on the home 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 needing to be regulated; inputting the equipment operation data and/or the user behavior data into a preset model corresponding to the event to be adjusted for prediction to obtain a prediction result, wherein the prediction result is used for representing a target parameter to be adjusted of the household equipment; and adjusting the target parameters of the household equipment. By the method, the event to be adjusted of the household equipment can be analyzed according to the acquired equipment operation data and/or the user behavior data, the fault type possibly existing in the household equipment and/or the function type needing to be adjusted of the household equipment are determined, and automatic control is performed according to the fault type possibly existing in the household equipment and/or the function type needing to be adjusted of the household equipment, 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 present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without inventive exercise.
Fig. 1 is a schematic flow chart of a home device control method provided in an embodiment of the present application;
fig. 2 is a schematic diagram of a specific implementation process of a home device control method provided in an embodiment of the present application;
fig. 3 is a schematic structural diagram of a household equipment control device provided in the 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
In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some embodiments of the present application, but not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
Referring to fig. 1, fig. 1 is a schematic flow chart of a home device control method provided in the embodiment of the present application. As shown in fig. 1, the household equipment control method includes the following steps:
Specifically, the household devices may include, but are not limited to, air conditioners, humidifiers, refrigerators, televisions, lamps, air purifiers, and the like. The home equipment can report the running data of the home equipment through the Communication module, wherein the Communication module can be any one of a Wireless Fidelity (Wi-Fi) module, a bluetooth module, a Near Field Communication (NFC) module and the like. Different household devices can select corresponding communication protocols to report the running data of the household devices through the types of the communication modules of the household devices. The device operation data may include device basic information (such as model subdivision feature codes, device identifiers, and the like), device control information (such as information about function type setting, parameter setting, and the like of a user), device state information (such as various index parameters in an operation process), device fault information (such as early warning information, fault codes, and the like), and of course, may also include external environment information (such as indoor and outdoor temperature, humidity, and the like) acquired by the home device through its own sensor. 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 category are generated, the host program of the household equipment can report the information and the data through the communication module.
Certainly, the home equipment can also be connected with a user terminal, so that a user can remotely control the home equipment through an App on the user terminal. Because a defined embedded event, such as a page loading event, a control clicking event, an application initialization event, etc., can be preset in the App, when a user uses the App, the App can collect user behavior data through a Software Development Kit (SDK for short) of the embedded event.
It should be noted that, the home device control method in the embodiment of the present application may be executed by a 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, and the present application is not limited specifically. 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 a buried 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 household equipment control method is executed by the server, the server can receive equipment operation data sent by the household equipment in real time through the communication module, and simultaneously receive user behavior data acquired by the user terminal through a buried point of the server, so that the server 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.
Specifically, the event to be adjusted refers to an event that needs to be adjusted for a certain fault type of the home equipment or a certain function type that needs to be adjusted for the home equipment. For example, when the event to be adjusted is an air-conditioning refrigeration fault event, the associated parameters causing the air-conditioning refrigeration fault and the parameter values thereof need to be adjusted; when the event to be regulated is an event of turning on the intelligent air function, household equipment participating in the regulation of the indoor air temperature and humidity, such as air conditioners, humidifiers and the like, needs to be turned on, and the operation parameter values of the equipment are regulated.
As an implementation manner, the operation state of the device may be analyzed according to the device operation data, and whether some critical values, early warning values, or failure values are generated in the device operation data is determined, so as to determine the event to be adjusted. 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 the fault existing in the household equipment can be found in time and processed in time, and the household equipment is prevented from being irreparably damaged.
As another implementation, the user requirement may also be determined by analyzing the user behavior data, and the home equipment may be intelligently controlled according to the user requirement. For example, if it is known from the analysis of the user behavior data that the user frequently uses a certain function and sets a certain parameter value, the user behavior data may be used to simulate the user's preference to perform automatic event processing, for example, if the user frequently uses the sleep function of the air conditioner and sets a higher temperature of 28 degrees, the function and parameter may be preferentially set for the user in the event of starting the air conditioner.
Of course, comprehensive analysis can be performed based on the user behavior data and the equipment operation data, and the difference between the equipment operation data and the user requirements is judged, so that a certain function of the household equipment is automatically adjusted. For example, it is assumed that the current outdoor temperature is 40 ℃ and the indoor temperature is 35 ℃ as can be known through equipment operation data analysis, and the air conditioner cooling temperature set by the recent habit of the user is 27 ℃ as can be known through user behavior data analysis, and since the current indoor temperature and the outdoor temperature are both higher than 27 ℃, the air conditioner is automatically turned on and the temperature is set to 27 ℃ for the user after the user App has authorized the use of the automatic temperature control setting.
It should be noted that for each event to be adjusted, there is a preset model corresponding to it. After the event to be adjusted is determined, the required preset model can be determined according to the type of the event to be adjusted. In this way, the preset model can analyze the event to be adjusted according to the currently acquired device operation data and/or user behavior data, so as to determine the target parameter to be adjusted.
And 104, adjusting target parameters of the household equipment.
In this step, a corresponding instruction can be automatically issued to the home equipment according to a preset adjustment scheme of each target parameter, so as to adjust the target parameters of the home equipment. For example, the air conditioner refrigeration efficiency is poor, and the reason is mainly that the factor 1 value is low through the analysis of a preset model, so that a corresponding instruction for increasing 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 home device control method is executed by the user terminal, the user terminal may directly send a corresponding instruction to the home device, so as to adjust the target parameter of the home device; 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 adjust the target parameters of the home equipment.
In this embodiment, the event to be adjusted of the home equipment may be analyzed according to the acquired equipment operation data and/or the user behavior data, the function of the home equipment, which may have the invisible fault and/or needs to be adjusted, may be determined, and the function of the home equipment, which may have the invisible fault and/or needs to be adjusted, may be automatically and intelligently controlled, so that the control mode of the home equipment is more intelligent.
Further, the step 102 of determining the event to be adjusted 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;
determining the fault type of the fault under the condition that the household equipment is determined to have the fault according to the comparison result;
and determining an event to be regulated according to the fault type.
In an embodiment, the event to be adjusted is determined by the device operation data, and specifically, since the device operation data reported by the home device includes information such as device state information and device failure information, it is possible to determine whether a certain type of failure exists in the home device by analyzing various parameter values in the device operation data. For example, after receiving an air conditioner starting control instruction, it is detected that the indoor fan continuously blows out large cold air from the starting time, but the air supply amount is not adjusted to be large after the indoor fan is started for a period of time, so that the air conditioner can be determined to have a cold air prevention fault. For another example, when the compression frequency of the air conditioner compressor reaches the warning value (preset by the user), it may be determined that the air conditioner has a compressor failure.
In this embodiment, the operation state of the device may be analyzed according to the device operation data, and it is determined whether some critical values, early warning values, or failure values are generated in the device operation data, so as to determine an event to be adjusted. 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 the fault existing in the household equipment can be found and processed in time, and the household equipment is prevented from being irreparably damaged.
Further, the step 102 of determining the event to be adjusted 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 required to be regulated by the household equipment.
In one embodiment, the event to be adjusted is determined by user behavior data and device operating data. Specifically, the function type of the home equipment which needs 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 which needs to be adjusted. For example, if it is known through the device operation data analysis that the current outdoor temperature is 40 ℃ and the indoor temperature is 35 ℃, and it is known through the user behavior data analysis that the air conditioner cooling temperature set by the user in recent habit is 27 ℃, since the current indoor temperature and the outdoor temperature are both higher than 27 ℃, after receiving the automatic temperature control setting instruction sent by the user from the App, the air conditioner can be automatically turned on and the temperature set to 27 ℃ for the user. Therefore, the using habits of the user can be analyzed according to the user behavior data, and the household equipment is intelligently controlled according to the using habits of the user. For another example, it is assumed that the user behavior data analysis indicates that the cooling temperature set by the user is 27 ℃, and the equipment operation data analysis indicates that the outlet air temperature of the air conditioner is still 30 ℃ 2 hours after the user sets the cooling temperature, and at this time, it can be determined that the air conditioner has a cooling fault. Therefore, the difference between the user setting parameter and the actual household operation parameter can be compared, and the possible faults of the household equipment can be subjected to early warning analysis.
Further, in step 103, inputting the device operation data and/or the user behavior data into a preset model corresponding to the event to be adjusted for prediction, so as to obtain a prediction result, where the prediction result includes:
acquiring a plurality of associated parameters in a preset model corresponding to an event to be regulated, wherein the associated parameters are parameters having an associated relation with the event to be regulated, and the associated parameters are contained in equipment operation data and/or 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 correlation parameter with the preset reference range corresponding to each correlation parameter;
and determining the relevant parameters meeting the target conditions in the plurality of relevant parameters as prediction results, wherein the target conditions are that the actual parameter values of the relevant parameters exceed the corresponding preset reference ranges.
Specifically, the preset model may be any Deep learning model, such as a Convolutional network model (CNN), a Deep Generation Model (DGM), a Generative Adaptive Network (GAN), and the like, which is not limited in this embodiment.
The preset model can determine a plurality of associated parameters associated with the event to be adjusted through pre-training, so that after the source data of the household equipment are obtained, the associated parameters and actual parameter values corresponding to the associated parameters are selected from the source data, the actual parameter values of the associated parameters are compared with the preset reference range corresponding to the associated parameters, the associated parameters of which the actual parameter values do not belong to the corresponding preset reference range are determined, and the associated parameters are used as target parameters to determine the prediction result.
In this embodiment, model analysis can be performed on each event to be adjusted, a target parameter to be adjusted of the home equipment is accurately determined, and the target parameter is automatically adjusted, so that intelligent control of the home equipment is realized.
Further, the step of obtaining a plurality of associated parameters in the preset model corresponding to the event to be adjusted includes:
determining a plurality of associated parameters in a preset model corresponding to the event to be adjusted 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 associated relation between the event to be adjusted and the associated parameters.
Specifically, in the preset model, a deep learning model can be modeled and trained according to human experience and historical data, and a knowledge graph is established in advance. The knowledge graph can be used for representing the association relationship between the event to be regulated and the associated parameters. For example, assuming that the event to be adjusted is an air conditioner refrigeration fault type, in the preset model, the following can be queried according to the knowledge map: from a relation chain between the product-problem-cause-solution scheme, a relation parameter (also called a factor) associated with the air conditioner refrigeration fault can be determined through the relation chain, and if the relation parameter associated with the air conditioner refrigeration fault comprises the compressor compression frequency and the fan rotating speed, the actual parameter values of the compressor compression frequency and the fan rotating speed can be further compared with a preset reference range, so that whether the reason causing the air conditioner refrigeration fault is that the compressor compression power is too small or the fan rotating speed is too small is determined, and a prediction result is obtained.
In the embodiment, the knowledge graph of the event to be adjusted is established through the deep learning model, and then the target parameter to be adjusted is accurately determined according to the analysis of the associated parameters in the knowledge graph, so that the accuracy and the intelligent degree of the control of the household equipment are improved. Meanwhile, the deep learning and training of the preset model mainly adjusts the numerical range of the existing analysis factors continuously through a large amount of practical data of a user, for example, a refrigeration problem factor 1 is initially determined to be poor in operation state at 10-20, but 12-22 also generate a larger problem through simulation of a large amount of real data, the preset model is updated, and the event analysis model is more and more accurate and intelligent through the learning and training of a large amount of data. That is to say, the preset model can also continue to perform learning training based on the acquired device operation data and/or user behavior data, and the intelligence degree of the preset model is continuously improved.
Further, before determining the event to be adjusted according to the source data in step 102, the household device control method further includes:
verifying the source data to obtain first intermediate data;
cleaning the first intermediate data to obtain second intermediate data;
the step 102 of determining the event to be adjusted according to the source data includes:
and determining the event to be adjusted according to the second intermediate data.
Specifically, the validation process may include screening the accuracy and integrity of the source data, and if a fault report message is obtained, it is necessary to check the fault ID and whether the parameters are accurate and complete. The cleaning process is to convert the verified data into standard data conforming to a preset model according to a preset rule, and perform desensitization, compression and other processing, for example, the same is fault information, the expression form in reporting is a binary string or a hexadecimal string, and the like, and the data format and parameters in the preset model need to be translated into, and necessary parameters are extracted and analyzed from the data format and parameters.
In an embodiment, after the source data is acquired, the source data may be verified first, then the verified data is cleaned, and then the event to be adjusted is determined according to the cleaned data, 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 parameter of the home device, the home device control method further includes:
and counting the prediction result and the adjustment record corresponding to the prediction result, and sending the statistical result to the user terminal so that the user terminal can display the statistical result.
In an embodiment, after the prediction result is obtained through the preset model and the target parameter of the home equipment is adjusted, the prediction result and the adjustment record corresponding to the prediction result can be counted, the counting result is sent to the user terminal, and the App on the user terminal is used for displaying, so that the user can intuitively master the running state of the home equipment. The statistical result display mode can be displayed by using visual chart data.
In practical application, the household equipment control method can be completed through a data acquisition module and a program, a deep learning-based data analysis and problem solving model and an App-end data display program. Wherein, data acquisition module and procedure: reporting relevant 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 end; data analysis and problem solving model based on deep learning: embedding points and establishing an event analysis and response model according to the reported data; data display program of App end: and displaying the event statistical record and the processing record at the App end. The specific execution process of the household equipment control method is shown in fig. 2:
202, verifying and cleaning equipment operation data and user behavior data and determining the type of an event to be regulated;
and step 205, continuously performing deep learning and training on the preset model through data acquisition, and improving the intelligent degree of the model.
Through the mode, various sub-health data of the household equipment can be subjected to embedded point analysis and intelligent response, so that the service life of the household equipment is prolonged, a better operation state is adjusted, and a better service experience is provided for a user; in addition, by performing embedded point analysis on data and information reported by the home equipment, giving a response and adjustment scheme through a preset model and automatically executing, the intelligent control of the home equipment is improved, and meanwhile, intuitive display and statistics are given to a user through an App; moreover, the intelligence degree of the data model can be continuously improved through deep learning and training of the event model.
Referring to fig. 3, fig. 3 is a schematic structural diagram of a household equipment control apparatus provided in an embodiment of the present application, and as shown in fig. 3, the household equipment control apparatus 300 includes:
the acquiring module 301 is configured to acquire source data related to the home equipment, where the source data includes equipment operation data and/or user behavior data, the equipment operation data is used to represent an operation state of the home equipment, and the user behavior data is used to represent an operation behavior of a user on the home equipment;
a first determining module 302, 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 represent a fault type of the home device or a function type that needs to be adjusted by the home device;
the prediction module 303 is configured to input the device operation data and/or the user behavior data into a preset model corresponding to the 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 household device;
and the adjusting module 304 is used for adjusting the target parameters of the household equipment.
Optionally, the first determining module 302 includes:
the first comparison submodule 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 of the household equipment is determined according to the comparison result;
and the second determining submodule is used for determining the event to be adjusted 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 which needs to be adjusted according to the user behavior data and the equipment operation data;
and the fourth determining submodule is used for determining the event to be adjusted according to the function type which needs to be adjusted by the household equipment.
Optionally, the prediction module 303 comprises:
the first obtaining sub-module is used for obtaining a plurality of associated parameters in a preset model corresponding to the event to be adjusted, wherein the associated parameters are parameters in an associated relation with the event to be adjusted, and the associated parameters are contained in equipment operation data and/or user behavior data;
the second obtaining submodule is used for obtaining actual parameter values corresponding to all the associated parameters in the associated parameters from the equipment operation data and/or the user behavior data;
the second comparison submodule is used for comparing the actual parameter value corresponding to each correlation parameter with the preset reference range corresponding to each correlation parameter;
and the fifth determining submodule is used for determining the correlation parameters meeting the target conditions in the correlation parameters as the prediction results, wherein the target conditions are that the actual parameter values of the correlation parameters exceed the corresponding preset reference ranges.
Optionally, the first obtaining sub-module is specifically configured to:
determining a plurality of associated parameters in a preset model corresponding to the event to be adjusted 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 associated relation between the event to be adjusted and the associated parameters.
Optionally, the household device control apparatus 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 the event to be adjusted according to the second intermediate data.
Optionally, the household device control apparatus 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 statistical result to the user terminal so that the user terminal can display the statistical result.
It should be noted that the home device control apparatus 300 may implement the steps of the home device control method provided in any one of the foregoing method embodiments, and can achieve the same technical effects, which are not described in detail herein.
As shown in fig. 4, an embodiment of the present application provides an electronic device, which includes 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 complete mutual communication through the communication bus 414,
a memory 413 for storing a computer program;
in an embodiment of the present application, when the processor 411 is configured to execute a program stored in the memory 413, the method for controlling a home device according to any one of the foregoing method embodiments 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 needing to be regulated;
inputting equipment operation data and/or user behavior data into a preset model corresponding to an event to be adjusted for prediction to obtain a prediction result, wherein the prediction result is used for representing a target parameter to be adjusted of the household equipment;
and adjusting the target parameters of the household equipment.
The embodiment of the present application further provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the steps of the home equipment control method provided in any one of the foregoing method embodiments.
It is noted that, in this document, relational terms such as "first" and "second," and the like, may be 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. Also, 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 an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
The foregoing are merely exemplary embodiments of the present invention, which enable those skilled in the art to understand or practice the present 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 (10)
1. A household equipment control method is characterized by comprising the following steps:
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 needing to be regulated;
inputting the equipment operation data and/or the user behavior data into a preset model corresponding to the event to be adjusted for prediction to obtain a prediction result, wherein the prediction result is used for representing a target parameter to be adjusted of the household equipment;
and adjusting the target parameters of the household equipment.
2. The method of claim 1, wherein determining an event to be adjusted based on the device operational data and/or the user behavior data comprises:
comparing each parameter value in the equipment operation data with a corresponding preset reference value respectively;
determining the fault type of the fault under the condition that the household equipment is determined to have the fault according to the comparison result;
and determining the event to be regulated according to the fault type.
3. The method of claim 1, wherein determining an event to be adjusted based on the device operational data and/or the user behavior data comprises:
determining the function type of the household equipment which needs 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 required to be regulated by the household equipment.
4. The method according to claim 1, wherein the inputting the device operation data and/or the user behavior data into a preset model corresponding to the event to be adjusted for prediction to obtain a prediction result comprises:
acquiring a plurality of associated parameters in a preset model corresponding to the event to be adjusted, wherein the associated parameters are parameters having an associated relationship with the event to be adjusted, and the associated 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 correlation parameter with the preset reference range corresponding to each correlation parameter;
and determining the relevant parameters meeting target conditions in the plurality of relevant parameters as the prediction results, wherein the target conditions are that the actual parameter values of the relevant parameters exceed the corresponding preset reference ranges.
5. The method according to claim 4, wherein the obtaining of the plurality of associated parameters in the preset model corresponding to the event to be adjusted comprises:
determining a plurality of associated parameters in a preset model corresponding to the event to be adjusted 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 associated relation between the event to be adjusted and the associated parameters.
6. The method of claim 1, wherein prior to said determining an event to adjust from said source data, said method further comprises:
verifying the source data to obtain first intermediate data;
cleaning the first intermediate data to obtain second intermediate data;
the determining the event to be adjusted according to the source data comprises:
and determining the event to be regulated according to the second intermediate data.
7. The method according to claim 1, wherein after the adjusting the parameter of the target parameter of the household device, the method further comprises:
and counting the prediction result and the adjustment record corresponding to the prediction result, and sending the statistical result to a user terminal so that the user terminal can display the statistical result.
8. A home equipment control apparatus, comprising:
the acquisition module is used for 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;
the first determining module is used for determining an event to be adjusted according to the equipment operation data and/or the user behavior data, wherein the event to be adjusted is used for representing the fault type of the household equipment or the function type of the household equipment needing to be adjusted;
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 adjusted for prediction to obtain a prediction result, and the prediction result is used for representing a target parameter to be adjusted of the household equipment;
and the adjusting module is used for adjusting the target parameters of the household equipment.
9. An electronic device is characterized by comprising a processor, a communication interface, a memory and a communication bus, wherein the processor and the communication interface are used for realizing mutual communication by the memory through the communication bus;
a memory for storing a computer program;
a processor for implementing the steps of the home device control method according to any one of claims 1 to 7 when executing the program stored in the memory.
10. 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 home device control method according to any one of claims 1 to 7.
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Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103019158A (en) * | 2011-09-20 | 2013-04-03 | 朗德华信（北京）自控技术有限公司 | System and method for managing and controlling green building facilities and equipment based on cloud computing |
| CN104965492A (en) * | 2015-04-30 | 2015-10-07 | 广东美的制冷设备有限公司 | Detection method of household appliances, detection device of household appliances and terminal |
| CN109946544A (en) * | 2019-03-29 | 2019-06-28 | 广东美的制冷设备有限公司 | Household electrical appliances fault detection method and device |
| CN110456647A (en) * | 2019-07-02 | 2019-11-15 | 珠海格力电器股份有限公司 | A kind of intelligent home furnishing control method and intelligent home control device |
| CN111578444A (en) * | 2019-02-19 | 2020-08-25 | 珠海格力电器股份有限公司 | Air conditioner fault prediction method and device, storage medium and air conditioner |
| CN111737431A (en) * | 2020-06-19 | 2020-10-02 | 海尔优家智能科技（北京）有限公司 | Equipment exception processing method and device, storage medium and electronic device |
| CN112036444A (en) * | 2020-07-31 | 2020-12-04 | 海尔优家智能科技（北京）有限公司 | Fault detection method and system for household equipment |
| CN112696798A (en) * | 2020-12-07 | 2021-04-23 | 美的集团股份有限公司 | Parameter setting method of air conditioner, air conditioning apparatus, and computer-readable storage medium |
| CN112902406A (en) * | 2019-12-04 | 2021-06-04 | 佛山市云米电器科技有限公司 | Parameter setting method, device and computer readable storage medium |
| CN113339955A (en) * | 2021-05-31 | 2021-09-03 | 青岛海信日立空调系统有限公司 | Air conditioner control system |
-
2021
- 2021-11-03 CN CN202111294884.2A patent/CN114047708A/en active Pending
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103019158A (en) * | 2011-09-20 | 2013-04-03 | 朗德华信（北京）自控技术有限公司 | System and method for managing and controlling green building facilities and equipment based on cloud computing |
| CN104965492A (en) * | 2015-04-30 | 2015-10-07 | 广东美的制冷设备有限公司 | Detection method of household appliances, detection device of household appliances and terminal |
| CN111578444A (en) * | 2019-02-19 | 2020-08-25 | 珠海格力电器股份有限公司 | Air conditioner fault prediction method and device, storage medium and air conditioner |
| CN109946544A (en) * | 2019-03-29 | 2019-06-28 | 广东美的制冷设备有限公司 | Household electrical appliances fault detection method and device |
| CN110456647A (en) * | 2019-07-02 | 2019-11-15 | 珠海格力电器股份有限公司 | A kind of intelligent home furnishing control method and intelligent home control device |
| CN112902406A (en) * | 2019-12-04 | 2021-06-04 | 佛山市云米电器科技有限公司 | Parameter setting method, device and computer readable storage medium |
| CN111737431A (en) * | 2020-06-19 | 2020-10-02 | 海尔优家智能科技（北京）有限公司 | Equipment exception processing method and device, storage medium and electronic device |
| CN112036444A (en) * | 2020-07-31 | 2020-12-04 | 海尔优家智能科技（北京）有限公司 | Fault detection method and system for household equipment |
| CN112696798A (en) * | 2020-12-07 | 2021-04-23 | 美的集团股份有限公司 | Parameter setting method of air conditioner, air conditioning apparatus, and computer-readable storage medium |
| CN113339955A (en) * | 2021-05-31 | 2021-09-03 | 青岛海信日立空调系统有限公司 | Air conditioner control system |
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