CN110793208A - Configuration parameter determination method and device for water heater - Google Patents

Configuration parameter determination method and device for water heater Download PDF

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CN110793208A
CN110793208A CN201810865619.7A CN201810865619A CN110793208A CN 110793208 A CN110793208 A CN 110793208A CN 201810865619 A CN201810865619 A CN 201810865619A CN 110793208 A CN110793208 A CN 110793208A
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water heater
parameters
configuration parameters
configuration
neural network
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肖龙
刘星
郑威
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Gree Electric Appliances Inc of Zhuhai
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Gree Electric Appliances Inc of Zhuhai
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    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F24—HEATING; RANGES; VENTILATING
    • F24H—FLUID HEATERS, e.g. WATER OR AIR HEATERS, HAVING HEAT-GENERATING MEANS, e.g. HEAT PUMPS, IN GENERAL
    • F24H9/00—Details
    • F24H9/20—Arrangement or mounting of control or safety devices
    • F24H9/2007—Arrangement or mounting of control or safety devices for water heaters
    • F24H9/2014—Arrangement or mounting of control or safety devices for water heaters using electrical energy supply
    • 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

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Abstract

The invention discloses a method and a device for determining configuration parameters of a water heater. Wherein, the method comprises the following steps: acquiring contextual parameters, wherein the contextual parameters are basis for generating configuration parameters of the water heater, and the configuration parameters are basis for running the water heater; converting each parameter in the context parameters into an input of a neural network model, wherein the neural network model is obtained by training data, and each set of training data comprises: the scene parameters and the corresponding configuration parameters under the scene parameters; obtaining the output of the neural network model; converting the output into configuration parameters and sending the configuration parameters to the water heater. The invention solves the technical problems of low intellectualization degree of the water heater and low user experience caused by more complicated use in the prior art.

Description

Configuration parameter determination method and device for water heater
Technical Field
The invention relates to the field of smart home, in particular to a method and a device for determining configuration parameters of a water heater.
Background
The water heater is an important ring in an intelligent home system, and is a device for increasing the temperature of cold water into hot water within a certain time through various physical principles, so that great convenience is provided for the life of people, for example, in hot summer, a user can sweat due to sports or other activities, and needs to take a shower after returning home, and the direct use of cold water is very unfavorable for the body of people. In addition, many people have a certain rule for taking a shower, for example, some people are used to take a shower in the morning, some people are used to take a shower in the evening, and the like, however, the conventional water heater does not have a memory function, various contextual parameters when a user turns on the water heater cannot be identified, the configuration of the water heater under various contextual parameters cannot be identified, and the user needs to reset the water heater through a function key before using the water heater each time. The water heater in the above-mentioned correlation technique uses the mode relatively loaded down with trivial details, has wasted user's part time, and then has reduced user's experience.
Aiming at the problems that the water heater in the related technology has low intelligent degree and the user experience is low due to the fact that the water heater is complex to use, an effective solution is not provided at present.
Disclosure of Invention
The embodiment of the invention provides a method and a device for determining configuration parameters of a water heater, which are used for at least solving the technical problems of low intelligentization degree of the water heater and low user experience caused by more complicated use in the related technology.
According to an aspect of an embodiment of the present invention, there is provided a method for determining configuration parameters of a water heater, including: acquiring contextual parameters, wherein the contextual parameters are basis for generating configuration parameters of a water heater, and the configuration parameters are basis for running the water heater; converting each parameter in the context parameters into an input of a neural network model, wherein the neural network model is obtained by training data, and each set of training data comprises: the scene parameters and the corresponding configuration parameters under the scene parameters; obtaining an output of the neural network model; converting the output into the configuration parameter and sending the configuration parameter to the water heater.
Optionally, the obtaining the neural network model through training of the training data includes: converting each parameter in the scene parameters in each set of training data into a numerical value; respectively taking the obtained numerical value corresponding to each scene parameter as an input layer node of the neural network model; converting each of the configuration parameters in the training data to a numerical value; taking the obtained data corresponding to each configuration parameter as an output layer node of the neural network model; and training according to the input layer nodes and the output layer nodes to obtain the neural network model.
Optionally, the training data is part of historical data collected from a plurality of users.
Optionally, another part of the historical data is used as verification data, wherein the verification data is used for verifying the neural network model.
Optionally, the obtaining the neural network model by training according to the input layer node and the output layer node includes: determining whether an error exists in an output node; and under the condition that the output node has errors, adjusting parameters from the hidden layer node closest to the output layer node, and repeating the steps until the output conforms to the training data, wherein the hidden layer comprises one or more layers, each hidden layer comprises at least one hidden layer node, and each hidden layer node is a calculation mode which comprises parameters capable of adjusting the calculation.
Optionally, the adjusting of the parameter from the hidden layer node closest to the output layer node comprises: acquiring the error of each hidden layer node; and under the condition that the error meets a preset condition, determining that the hidden layer node has an error, and adjusting the parameters of the hidden layer node.
According to another aspect of the embodiment of the present invention, there is also provided a method for determining configuration parameters of a water heater, including: acquiring a situation parameter corresponding to a water heater, wherein the situation parameter is a basis for generating a configuration parameter of the water heater, and the configuration parameter is a basis for running the water heater; sending the scene parameters to a server; receiving configuration parameters from a server; and operating according to the configuration parameters.
Optionally, before receiving the configuration parameters from the server, the method for determining the configuration parameters of the water heater further includes: receiving a request message, wherein the request message is generated by a user of the water heater by triggering a key on the water heater, and the request message represents a request for obtaining configuration parameters from the server.
Optionally, the obtaining of the contextual parameter corresponding to the water heater includes at least one of: acquiring the scene parameters through a mobile terminal; and acquiring the scene parameters through the data recorded by the water heater.
Optionally, operating according to the configuration parameters comprises: the method comprises the steps that configuration parameters from a mobile terminal are received, wherein a server sends the configuration parameters to the mobile terminal, and when the mobile terminal receives indication information for determining that the configuration parameters are used for controlling the operation of a water heater, the configuration parameters are sent to the water heater.
According to another aspect of the embodiment of the present invention, there is also provided a method for determining configuration parameters of a water heater, including: receiving configuration parameters from a server, wherein the configuration parameters are basis for running of a water heater, and the situation parameters are basis for generating the configuration parameters of the water heater; receiving indication information from a user; and determining whether the operation of the water heater is configured according to the configuration parameters or not according to the indication information.
Optionally, before receiving the configuration parameters from the server, the method for determining the configuration parameters of the water heater further includes: receiving a request message, wherein the request message is generated by a user of the water heater by triggering a key on the water heater, and the request message represents a request for obtaining configuration parameters from the server.
Optionally, before determining whether to configure the operation of the water heater according to the configuration parameter according to the indication information, the method for determining the configuration parameter of the water heater further includes: and sending the scene parameters to the water heater.
Optionally, determining whether to configure the operation of the water heater according to the configuration parameter according to the indication information includes: under the condition that the operation of the water heater is determined to be configured according to the configuration parameters based on the indication information, the configuration parameters are sent to the water heater; and sending an update message to the server under the condition that the operation of the water heater is determined not to be configured according to the configuration parameters based on the indication information, wherein the update message is used for indicating the server to update the configuration parameters.
According to another aspect of the embodiment of the present invention, there is also provided a configuration parameter determining apparatus for a water heater, including: the system comprises a first obtaining unit, a second obtaining unit and a control unit, wherein the first obtaining unit is used for obtaining contextual parameters, the contextual parameters are basis for generating configuration parameters of a water heater, and the configuration parameters are basis for running the water heater; a first determining unit, configured to convert each of the context parameters into an input of a neural network model, where the neural network model is obtained by training data, and each set of training data includes: the scene parameters and the corresponding configuration parameters under the scene parameters; a second obtaining unit, configured to obtain an output of the neural network model; and the first sending unit is used for converting the output into the configuration parameters and sending the configuration parameters to the water heater.
Optionally, the first determining unit includes: the first conversion subunit is used for converting each parameter in the scene parameters in each group of training data into a numerical value; the first determining subunit is configured to use the obtained numerical value corresponding to each context parameter as an input layer node of the neural network model; a second conversion subunit, configured to convert each parameter in the configuration parameters in the training data into a numerical value; the second determining subunit is used for taking the obtained data corresponding to each configuration parameter as an output layer node of the neural network model; and the first acquisition subunit is used for training according to the input layer nodes and the output layer nodes to obtain the neural network model.
Optionally, the training data is part of historical data collected from a plurality of users.
Optionally, another part of the historical data is used as verification data, wherein the verification data is used for verifying the neural network model.
Optionally, the first obtaining subunit includes: the first determining module is used for determining whether an error exists in an output node; and the adjusting module is used for adjusting parameters from the hidden layer node closest to the output layer node under the condition that the output node has errors, and repeating the operation until the output is consistent with the training data, wherein the hidden layer comprises one or more layers, each hidden layer comprises at least one hidden layer node, and each hidden layer node is a calculation mode which comprises parameters capable of adjusting calculation.
Optionally, the adjusting module includes: the acquisition submodule is used for acquiring the error of each hidden layer node; and the adjusting submodule is used for determining that the hidden layer node has an error and adjusting the parameters of the hidden layer node under the condition that the error meets a preset condition.
According to another aspect of the embodiment of the present invention, there is also provided a configuration parameter determining apparatus for a water heater, including: the third obtaining unit is used for obtaining a contextual parameter corresponding to a water heater, wherein the contextual parameter is a basis for generating a configuration parameter of the water heater, and the configuration parameter is a basis for running the water heater; a second sending unit, configured to send the context parameter to a server; a first receiving unit, configured to receive configuration parameters from a server; and the operation unit is used for operating according to the configuration parameters.
Optionally, the configuration parameter determining device of the water heater further includes: a second receiving unit, configured to receive a request message before receiving the configuration parameters from the server, where the request message is a request message generated by a user of the water heater by triggering a key on the water heater, and the request message indicates a request to acquire the configuration parameters from the server.
Optionally, the third obtaining unit includes at least one of: the second acquiring subunit is used for acquiring the scene parameters through the mobile terminal; and the third acquisition subunit is used for acquiring the scene parameters according to the data recorded by the water heater.
Optionally, the operation unit includes: the receiving subunit is used for receiving the configuration parameters from the mobile terminal, wherein the server sends the configuration parameters to the mobile terminal, and when the mobile terminal receives the indication information for determining that the configuration parameters are used for controlling the operation of the water heater, the server sends the configuration parameters to the water heater.
According to another aspect of the embodiment of the present invention, there is also provided a configuration parameter determining apparatus for a water heater, including: the third receiving unit is used for receiving configuration parameters from the server, wherein the configuration parameters are basis for running of the water heater, and the situation parameters are basis for generating the configuration parameters of the water heater; a fourth receiving unit, configured to receive indication information from a user; and the second determining unit is used for determining whether the operation of the water heater is configured according to the configuration parameters according to the indication information.
Optionally, the configuration parameter determining device of the water heater further includes: a fourth receiving unit, configured to receive a request message before receiving the configuration parameters from the server, where the request message is a request message generated by a user of the water heater by triggering a key on the water heater, and the request message indicates a request to acquire the configuration parameters from the server.
Optionally, the configuration parameter determining device of the water heater further includes: and the third sending unit is used for sending the contextual parameters to the water heater before determining whether the operation of the water heater is configured according to the configuration parameters according to the indication information.
Optionally, the second determining unit includes: the first sending subunit is used for sending the configuration parameters to the water heater under the condition that the operation of the water heater is determined to be configured according to the configuration parameters based on the indication information; and a second sending subunit, configured to send, to the server, an update message when it is determined, based on the indication information, that the operation of the water heater is not configured according to the configuration parameters, where the update message is used to instruct the server to update the configuration parameters.
According to another aspect of the embodiment of the present invention, there is also provided a storage medium including a stored program, wherein the program executes the configuration parameter determination method for a water heater described in any one of the above.
According to another aspect of the embodiment of the present invention, there is also provided a processor, configured to execute a program, where the program executes the method for determining the configuration parameters of the water heater described in any one of the above.
In the embodiment of the invention, the situation parameters are obtained, wherein the situation parameters are the basis for generating the configuration parameters of the water heater, and the configuration parameters are the basis for operating the water heater; then converting each parameter in the scene parameters into the input of a neural network model, wherein the neural network model is obtained by training data, and each set of training data comprises: the scene parameters and the corresponding configuration parameters under the scene parameters; and obtaining the output of the neural network model; the method for determining the configuration parameters of the water heater can realize the purposes of converting the contextual parameters set by a user into the input of the neural network model, calculating the input according to the neural network model to obtain the output parameters and converting the output parameters into the configuration parameters of the water heater, so that the user can obtain the configuration parameters of the water heater meeting the requirements of the user only by setting the contextual parameters without setting each function of the water heater by using a function key of the water heater, the technical effect of the use process of the water heater is simplified, and the technical problems of low intelligentization degree of the water heater in the related technology and low user experience caused by more complexity in use are solved.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the invention and together with the description serve to explain the invention without limiting the invention. In the drawings:
FIG. 1 is a system block diagram of a configuration parameter determination method for a water heater according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of different time granularities of context parameters according to an embodiment of the invention;
FIG. 3 is a system block diagram of an alternative water heater configuration parameter determination method according to an embodiment of the present invention;
FIG. 4 is a flow chart of a method of configuration parameter determination for a water heater according to an embodiment of the present invention;
FIG. 5 is a schematic diagram of input parameter classes and output parameter classes of a neural network model according to an embodiment of the present invention;
FIG. 6 is a block diagram of a neural network model according to an embodiment of the present invention;
FIG. 7 is a flow diagram of neural network model training according to an embodiment of the present invention;
FIG. 8 is a flow chart of a neural network algorithm according to an embodiment of the present invention;
FIG. 9 is a flow chart of an alternative water heater configuration parameter determination method according to an embodiment of the present invention;
FIG. 10 is a preferred flow chart of a configuration parameter determination method for a water heater according to an embodiment of the present invention;
FIG. 11 is a schematic diagram of a configuration parameter determination device for a water heater according to an embodiment of the present invention;
FIG. 12 is a schematic diagram of an alternative water heater configuration parameter determining arrangement according to an embodiment of the present invention;
FIG. 13 is a preferred schematic diagram of a configuration parameter determination device for a water heater according to an embodiment of the invention.
Detailed Description
In order to make the technical solutions of the present invention better understood, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that the terms "first," "second," and the like in the description and claims of the present invention and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are capable of operation in sequences other than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
For convenience of description, some nouns or terms appearing in the embodiments of the present invention will be described in detail below.
A neural network: also called artificial neural network or connection model, it is an algorithmic mathematical model simulating animal neural network behavior characteristics and performing distributed parallel information processing. The network achieves the aim of processing information by adjusting the mutual connection relationship among a large number of nodes in the network depending on the complexity of the system.
BP neural Network (Back Propagation neural Network, BPNN for short): the method is a multi-layer feedforward neural network trained according to an error back propagation algorithm.
Long Short Term Memory network (long Short-Term Memory, abbreviated LSTM): is a time-recursive neural network suitable for processing and predicting important events with relatively long intervals and delays in a time sequence.
Convolutional Neural Network (CNN): the artificial neuron is a feedforward neural network, can respond to peripheral units and can perform large-scale image processing.
Recurrent Neural Network (RNN): is a generic term for both temporal recurrent neural networks and structural recurrent neural networks. The inter-neuron connections of a temporal recurrent neural network form a directed graph, while a structural recurrent neural network recursively constructs a more complex deep network using a similar neural network structure.
The configuration parameter determining method of the water heater provided by the embodiment of the invention can be applied to various types of water heaters. In the embodiment of the present invention, the type of the water heater is not particularly limited. For example, electric water heaters, gas water heaters, solar water heaters, magnetic water heaters, air water heaters, heating water heaters, and the like.
In addition, in the embodiment of the invention, the basic structure of the neural network model, the number of nodes of the input layer and the output layer, the number of layers of the hidden layer, the number of nodes of the hidden layer, the initial weight of the neural network and the like can be preliminarily determined according to the operation data of the water heater and the included rules of the operation data of different set temperatures, environment temperatures, operation time periods and the like. It should be noted that, in the embodiment of the present invention, the kind of the neural network is not specifically limited, and may include but is not limited to: long and short term memory networks LSTM, convolutional neural networks CNN, recurrent neural networks RNN, BP neural networks, and the like.
The theory and performance of the BP neural network algorithm are mature, compared with the traditional algorithm, the BP neural network algorithm saves a large amount of calculation, and also ensures certain accuracy, and the algorithm model can be adjusted only by adjusting parameters, namely, the number of nodes of an input layer, a hidden layer, an output layer and the number of hidden layers can be adjusted according to the requirements in practical application. In the later adjustment and maintenance of the traditional algorithm model, the change cost is huge. In addition, a BP neural network is used for fitting a curve to the discrete points, so that the purpose of prediction is achieved. Generally, past values are associated with future values in an observation, and when the past observation is used as an input of the BP network, the future values are given as an output of the BP network. From a mathematical perspective, the BP neural network model becomes a nonlinear function of the input and output. In other words, the time prediction method based on the BP neural network model is to fit a prediction function with the BP neural network model and then predict a future value.
Therefore, in the embodiment of the present invention, a detailed description is given by taking a BP neural network model as an example of a network structure for obtaining configuration parameters of a water heater under predetermined contextual parameters.
Fig. 1 is a system structure diagram of a configuration parameter determining method for a water heater according to an embodiment of the present invention, and as shown in fig. 1, the configuration parameter determining method may include a user side and a server side. Wherein, the user side includes: the system comprises a water heater and a mobile terminal (for example, a mobile phone), wherein the water heater can directly send scene parameters to a database of a server side, the mobile terminal can also be used for sending the scene parameters to an intelligent home server, and the intelligent home server can send the received scene parameters to the database; the database sends the received scene parameters to the machine learning system, the machine learning system converts the scene parameters into numerical values, the numerical values obtained after conversion are used as input of the neural network model, calculation is carried out according to the neural network model to obtain output numerical values, the numerical values are converted into configuration parameters, the configuration parameters are used as configuration parameters of the water heater, meanwhile, the configuration parameters are sent to the intelligent home server, the intelligent home server sends the configuration parameters to the database, and the database records the configuration parameters; the intelligent home server can also send the configuration parameters to the mobile terminal and the water heater, wherein under the condition that the configuration parameters are sent to the water heater, a user of the water heater receives the water heater to operate according to the configuration parameters by default; in addition, in case of transmitting the configuration parameters to the mobile terminal, the user may determine whether to allow the water heater to operate with the configuration parameters through the mobile terminal, and the water heater operates according to the received configuration parameters.
The input of the machine learning system may include factors (i.e., context parameters) affecting various configuration parameters of the water heater: the time period of use of the water heater (typically corresponding to: morning, midnight) week, weather, current temperature, etc.
Fig. 2 is a schematic diagram of different time granularities of scenario parameters according to an embodiment of the present invention, and as shown in fig. 2, the time period may be 120 days, 35 days, 14 days, 7 days, 3 days, or less, for example, 1 day; week: monday, tuesday, wednesday, thursday, friday, saturday, sunday; the time attribute is as follows: working days and rest days; festival and holiday: mid-autumn long holiday, national celebration long holiday; specific time points are as follows: 0: 00-6:00, 6:00-9:00, 9:00-16:00, 16:00-19:00, 19:00-0: 00. The water heater collects the factors influencing the configuration parameters in a gathering mode, the operation records of the water heater by the user in a plurality of time periods are selected as sample data, the neural network model is learned and trained, the weight between the neural network structure and the neural network nodes is adjusted through an error back propagation algorithm, the neural network model is enabled to fit the relationship between the configuration parameters of the water heater and other contextual parameters such as date, and finally the neural network model can accurately fit the corresponding relationship between the user behaviors of the water heater and the contextual parameters.
In addition, the input data may not be a single parameter, but may be a one-dimensional or multi-dimensional scene parameter.
Table 1 shows specific functions of the water heater in each mode, and table 1 shows each operation mode of the water heater and functions corresponding to each operation mode.
TABLE 1
Figure BDA0001750861020000081
It should be noted that, in the configuration parameter determining method of a water heater provided in the present invention, in the case that the processing capabilities of the water heater and the mobile terminal are sufficiently strong, the function of the machine learning system may be directly executed by the water heater or the mobile terminal.
Fig. 3 is a system structure diagram of an alternative configuration parameter determining method for a water heater according to an embodiment of the present invention, and as shown in fig. 3, a smart phone, an intelligent device, and a server may be used as devices for running an artificial intelligence algorithm (i.e., a neural network algorithm). Firstly, the water heater with the communication function can send the contextual parameters to any one or more of the smart phone, the smart device and the server, then the smart phone, the smart device or the server which receive the contextual parameters carries out calculation to obtain configuration parameters, the configuration parameters are sent to the water heater, and under the condition that a user allows the water heater to be configured according to the configuration parameters, the water heater can operate according to the obtained configuration parameters.
It should be noted that, when the water heater sends the context parameters to the smart phone, the smart device, and the server at the same time, the configuration parameters obtained by the smart phone, the smart device, and the server respectively may be evaluated by using a predetermined method (for example, a random forest algorithm), and the water heater may be configured by using the configuration parameter with the highest evaluation value. Of course, other ways of determining the optimal configuration parameter from the plurality of configuration parameters may be used to configure the water heater.
The following embodiments of the present invention will be described in detail with reference to a machine learning system on a server side.
Example 1
In accordance with an embodiment of the present invention, there is provided a method embodiment of a method for configuration parameter determination of a water heater, it is noted that the steps illustrated in the flowchart of the drawings may be performed in a computer system such as a set of computer executable instructions and that, although a logical order is illustrated in the flowchart, in some cases the steps illustrated or described may be performed in an order different than that illustrated herein.
Fig. 4 is a flowchart of a configuration parameter determining method of a water heater according to an embodiment of the present invention, and as shown in fig. 4, the configuration parameter determining method of the water heater includes the following steps:
step S402, obtaining the situation parameters, wherein the situation parameters are the basis for generating the configuration parameters of the water heater, and the configuration parameters are the basis for the operation of the water heater.
The context parameters may include, but are not limited to, the following: a parameter for indicating a time period, a parameter for indicating a week, a parameter for indicating weather, a parameter for indicating a set temperature, and a parameter for indicating a mode.
In addition, the configuration parameters may include, but are not limited to: standard mode, energy saving mode, fast mode, set temperature, reserved time.
Step S404, converting each parameter in the context parameters into an input of a neural network model, where the neural network model is obtained by training data, and each set of training data includes: context parameters and corresponding configuration parameters under the context parameters.
Step S406, an output of the neural network model is obtained.
For example, fig. 5 is a schematic diagram of input parameter types and output parameter types of a neural network model according to an embodiment of the present invention, as shown in fig. 5, input parameters (the input parameters are converted from context parameters) (e.g., time period, week, weather, time point … … pattern n) are input to the neural network model, and output parameters (e.g., user behavior 1, user behavior 2, user behavior 3, user behavior 4 … … user behavior n) are calculated through a neural network algorithm in an implicit layer in the neural network model.
It should be noted that the "time period, week, weather, time point … … pattern n" is converted into a corresponding numerical value before being calculated by the neural network algorithm of the hidden layer; the user behavior 1, the user behavior 2, the user behavior 3, the user behavior 4 … …, and the user behavior n are configuration parameters configured for the water heater by each user, respectively, where the configuration parameters are obtained by converting the configuration parameters according to the output parameters.
And step S408, converting the output into configuration parameters and sending the configuration parameters to the water heater.
In the above embodiment, the contextual parameters may be obtained, where the contextual parameters are bases for generating configuration parameters of the water heater, and the configuration parameters are bases for operating the water heater; and converting each parameter in the scene parameters into an input of a neural network model, wherein the neural network model is obtained by training data, and each set of training data comprises: the scene parameters and the corresponding configuration parameters under the scene parameters; simultaneously obtaining the output of the neural network model; and converting the output into configuration parameters, and sending the configuration parameters to the water heater to realize the configuration of the water heater. Compared with the prior art, the traditional water heater does not have self-learning capability, cannot automatically generate configuration parameters meeting the requirements of a user according to contextual parameters set by the user, and is complicated in use process due to the fact that the function keys of the water heater are required to be repeatedly set when the user needs to use the water heater every time, and the user experience is reduced. The method for determining the configuration parameters of the water heater can realize the purposes of converting the contextual parameters set by a user into the input of the neural network model, calculating the input according to the neural network model to obtain the output parameters and converting the output parameters into the configuration parameters of the water heater, so that the user can obtain the configuration parameters of the water heater meeting the requirements of the user only by setting the contextual parameters without setting all functions of the water heater by using function keys of the water heater, the technical effect of the use process of the water heater is simplified, and the technical problems of low intelligentization degree of the water heater in the related technology and low user experience caused by more complicated use are solved.
In step S304, the training data to obtain the neural network model may include: converting each parameter in the scene parameters in each set of training data into a numerical value; respectively taking the obtained numerical value corresponding to each scene parameter as an input layer node of the neural network model; converting each parameter in the configuration parameters in the training data into a numerical value; taking the obtained data corresponding to each configuration parameter as an output layer node of the neural network model; and training according to the input layer nodes and the output layer nodes to obtain a neural network model.
For example, table 2 shows the correspondence between each context parameter and input parameter 1 of the neural network model (where the input parameter is converted from the context parameter), the output value of the neural network model, and the configuration parameter. Here, "0" indicates the standard mode, "1" indicates the energy saving mode, and "2" indicates the fast mode.
TABLE 2
Context parameters Inputting parameters Output parameter Configuring parameters
Monday, sunny, ambient temperature: 30 ℃ and 20:00 1、1、30、4 3、4 1. Setting the temperature: 35 deg.C
Tuesday, sunny day, ambient temperature: 30 ℃ at 21:00 2、1、30、5 3、7 1. Setting the temperature: 36 deg.C
Wednesday, rain, ambient temperature: 27 ℃ at 20:00 3、2、27、4 3、7 1. Setting the temperature: 36 deg.C
Thursday, rain, ambient temperature: 20 ℃ at 20:00 4、2、20、4 3、8 1. Setting the temperature: 39 deg.C
Friday, sunny, ambient temperature: 30 ℃ and 20:00 5、1、30、4 3、7 1. Setting the temperature: 36 deg.C
Saturday, sunny, ambient temperature: 30 ℃ and 20:00 6、1、30、4 2、4 0. Setting the temperature: 35 deg.C
Sunday, sunny, ambient temperature: 36 ℃ and 23:00 7、1、36、9 5、6 2. Setting the temperature: 38 deg.C
In table 2, when the scene parameters set by the user are "sunday, sunny, and ambient temperature: 36 ℃ and 23:00 ", the input parameters of the neural network model are 7, 1, 36 and 9", the output parameters are 5 and 6, and the configuration parameters of the corresponding water heater are 2, the set temperature: 38 c ", i.e. the water heater is operated in fast mode to a water temperature of 38 c.
Fig. 6 is a block diagram of a neural network model according to an embodiment of the present invention, and as shown in fig. 6, the neural network model may include: an input layer, a hidden layer, and an output layer, wherein the input layer includes a plurality of input layer nodes (e.g., X1, X2 … … Xn-1, Xn), the hidden layer includes a plurality of hidden layer nodes, and likewise, the output layer includes a plurality of output layer nodes (e.g., Y1, Y2). It should be noted that the hidden layer may include a plurality of layers, where, when the number of layers of the hidden layer is large, the water heater is configured based on the configuration parameters obtained by the neural network model, and the obtained hot water may better meet the requirements of the user, but the requirement on the processing capacity of the device running the neural network model is also high, and the processing process is also relatively complex; on the contrary, under the condition that the number of hidden layers is small, the water heater is configured based on the configuration parameters obtained by the neural network model, and the obtained hot water may have a large difference from the requirement of a user, but the requirement on the processing capacity of equipment for operating the neural network model is relatively low, and the processing process is relatively simple.
As an alternative embodiment of the present invention, the training data is a part of historical data collected from a plurality of users.
The plurality of users may include users whose similarity to the water heater meets a certain condition, for example, a predetermined number of data for training the neural network model may be obtained from the plurality of servers, and the data may be filtered in order to make the obtained data more targeted, so that training data more similar to a habit of the water heater user using the water heater may be obtained, and efficiency of training the neural network model may be improved. Specifically, the user whose similarity to the user of the water heater meets a certain condition, which is obtained by using the collaborative filtering algorithm, that is, the user whose habit of using the water heater meets a certain condition with the user of the water heater is obtained by using the collaborative filtering algorithm; in addition, the similarity of the situation parameters, which are obtained according to the Bayesian algorithm and set by the water heater, and the situation parameters can meet certain conditions. Wherein the data collected from the plurality of users each comprises: context parameters and configuration parameters set by a user under the context parameters.
It should be noted that a part of the training data is mainly used as an input parameter of an input layer of the neural network model.
Preferably, the training data may be collected by recording the daily user operation (i.e. configuration parameters) of the water heater and the corresponding situation parameters of the operation record. Specific collection methods include, but are not limited to: running parameters of the water heater in a laboratory simulation environment, running parameters of the water heater when the water heater is used by an actual user are collected through the technology of the Internet of things, and the like.
In addition, another part of the historical data is used as verification data, wherein the verification data is used for verifying the neural network model.
As an optional embodiment of the present invention, the obtaining of the neural network model by training according to the input layer nodes and the output layer nodes may include: determining whether an error exists in an output node; and under the condition that the output node has an error, adjusting parameters from the hidden layer node closest to the output layer node, and analogizing once until the output conforms to the training data, wherein the hidden layer comprises one or more layers, each hidden layer comprises at least one hidden layer node, and each hidden layer node is a calculation mode which comprises parameters capable of adjusting the calculation.
For example, the first scenario parameter set by the user is: monday, sunny, ambient temperature: 30 ℃ and 20:00, wherein the temperature of Monday, sunny and environment can be: converting the temperature of 30 ℃ and the temperature of 20:00 'into numerical values of 1, 30 and 4', then using the numerical values of 1, 30 and 4 obtained after conversion as input parameters of a neural network model, and calculating the neural network model according to the input parameters to obtain output parameters of 3 and 4; the second scenario parameter set by the user is: tuesday, sunny day, ambient temperature: 30 ℃ and 21:00, wherein the temperature of the mixture can be adjusted from tuesday, sunny day and ambient temperature: converting the temperature of 30 ℃ and the temperature of 21:00 ' into numerical values of 2, 1, 30 and 5 ', then using the numerical values of 2, 1, 30 and 5 obtained after conversion as input parameters of a neural network model, and calculating the neural network model according to the input parameters to obtain output parameters of 3 and 7 '; the third scenario parameter set by the user is: wednesday, rain, ambient temperature: 27 ℃ and 20:00, and the temperature of the Wednesday, rain and environment can be: the 27 ℃ and the 20: 00' are converted into numerical values of 3, 2, 27 and 4, then the numerical values of 3, 2, 27 and 4 obtained after conversion are used as input parameters of a neural network model, and the neural network model calculates according to the input parameters to obtain output parameters of 3 and 9. As can be seen from table 2, the output parameters corresponding to the scenario parameter one and the scenario parameter three are correct, and the output parameters corresponding to the scenario parameter three are not consistent with the theoretical result, that is, there is an error in the output node of the neural network model.
At this time, the neural network model needs to be adjusted, and specifically, the hidden layer node closest to the output layer node may be used to adjust the parameters, and so on until the output matches the training data.
Preferably, in the above embodiment, the adjusting of the parameter from the hidden layer node closest to the output layer node may include: acquiring the error of each hidden layer node; and under the condition that the error meets a preset condition, determining that the hidden layer node has the error, and adjusting the parameters of the hidden layer node.
That is, traversal is started from the hidden layer closest to the output layer node to obtain the error of each hidden layer node in each hidden layer, and when the error is greater than the error threshold, the hidden layer node is determined to have an error, and then the parameter of the hidden layer node is adjusted.
That is, under the condition that the error is greater than the error threshold, the hidden layer nodes are adjusted in a predetermined manner, where the adjustment manner may be to adjust a weight or a parameter of a function corresponding to the neural network algorithm of each hidden layer node, and is not specifically limited herein; in addition, when the error is smaller than the error threshold, the trained neural network model can be applied to the method for determining the configuration parameters of the water heater in the embodiment of the invention.
An alternative neural network model training approach of the present invention is described in detail below.
Firstly, inputting input parameters into input layer nodes of a neural network model, wherein the hidden layer nodes of almost each hidden layer have corresponding weight values and bias values, the input parameters pass through each function in the neural network algorithm when flowing through the hidden layer nodes and the output layer nodes from the input layer, and then the initialized weight values and bias values are calculated according to each function in the neural network algorithmCalculating the actual output a (x) of the neural network model, namely a (x) 1/(1+ e)-z) Wherein Z ═ Wk*x+bι. Next, it is determined whether the expected output y (x) and the actual output a (x) of the neural network model satisfy the requirement of output accuracy, i.e., | y (x) -a (x) Y<E, where e is the target minimum error. If the accuracy requirement is met, finishing the training, and if the accuracy requirement is not met, updating the weight W of the neural network model according to the following modekOffset b fromι。
The specific updating method is as follows: c (ω, b) is an error energy function (taking a standard deviation function as an example), n is a total number of training data, and the summation is performed on the total training data, specifically, the error energy function can be determined by a first formula, wherein the first formula is:
Figure BDA0001750861020000131
then, updating the weight of each hidden layer by using a second formula, wherein the second formula is as follows:
Figure BDA0001750861020000132
and simultaneously, updating the bias value of each hidden layer by using a third formula, wherein the third formula is as follows:
Figure BDA0001750861020000133
wherein, ω isκAs an initial weight value, the weight value,
Figure BDA0001750861020000134
is the partial derivative of the error energy function to the weight, bιIn order to be the initial bias, the bias is,partial derivatives of bias as a function of error energy
Figure BDA0001750861020000136
Until the output error of the network is less than e, the value of (d) can be obtained by a chain-derivative rule.
In addition, after one part of the historical data is used as training data to train the neural network model, the other part of the historical data is used as verification data to verify the trained neural network model. And when the verification error does not meet the requirement, continuously training the neural network model with the error by using the mode until the obtained error is smaller than the error threshold value, and finishing the training.
FIG. 7 is a flowchart of neural network model training according to an embodiment of the present invention, as shown in FIG. 7, including the following steps:
and S701, starting.
S702, acquiring training data from a database of a server side, and inputting the training data into a neural network model.
And S703, training the neural network model by using the training data, and judging whether the error between the obtained neural network model and the theoretical neural network model is smaller than an error threshold value. If the determination result is yes, S704 is executed; otherwise, the process returns to S703.
S704, obtaining the scene parameters from the database, and inputting the scene parameters into the neural network model.
And S705, sending the configuration parameters obtained according to the neural network model to a control subprogram of the water heater.
And S706, ending.
FIG. 8 is a flow chart of a neural network algorithm according to an embodiment of the present invention, as shown in FIG. 8, including the steps of:
s801, initializing the weight value and the offset value.
S802, inputting training data and calculating the output of each hidden layer.
S803, an error of the output of each layer is calculated.
S804, local gradients are calculated.
S805, adjusting the weight and the offset value of each output layer node of the output layer.
S806, adjusting the weight and bias value of each hidden layer node of each hidden layer.
S807, whether the error is smaller than the error threshold value is judged. In the case of yes, S808 is performed; otherwise, return to S802.
And S808, ending.
In the embodiment of the invention, the situation parameters of the water heater can be uploaded to the intelligent device, the mobile terminal and the server when the water heater with wireless communication runs. The intelligent device, the mobile terminal and the server input the scene parameters into the trained neural network model, and after configuration parameters with similarity to the operation rule of the user meeting certain conditions are determined, the configuration parameters are sent to the water heater, and the water heater operates according to the configuration parameters. It should be noted that, in the embodiment of the present invention, the intelligent device is not specifically limited, and may include but is not limited to: the wireless communication module, the router, the server, the smart phone and the like can also directly integrate the neural network algorithm into the controller of the water heater without additionally connecting an intelligent device.
Example 2
According to another aspect of the embodiment of the present invention, there is further provided a method for determining configuration parameters of a water heater, fig. 9 is a flowchart of an optional method for determining configuration parameters of a water heater according to the embodiment of the present invention, as shown in fig. 9, the method for determining configuration parameters of a water heater may include:
step S902, obtaining a contextual parameter corresponding to the water heater, wherein the contextual parameter is a basis for generating a configuration parameter of the water heater, and the configuration parameter is a basis for running the water heater.
The context parameters may include, but are not limited to, the following: a parameter for indicating a time period, a parameter for indicating a week, a parameter for indicating weather, a parameter for indicating a set temperature, and a parameter for indicating a mode.
In addition, the configuration parameters may include, but are not limited to: standard mode, energy saving mode, fast mode, set temperature, reserved time.
Step S904, the scene parameters are sent to the server.
Step S906, receiving the configuration parameters from the server.
Step S908, operating according to the configuration parameters.
In the above embodiment, the contextual parameters corresponding to the water heater may be acquired, and then the contextual parameters are sent to the server and operated according to the received configuration parameters from the server. Compared with the prior art, the traditional water heater does not have self-learning capability, cannot automatically generate configuration parameters meeting the requirements of a user according to contextual parameters set by the user, and is complicated in use process due to the fact that the function keys of the water heater are required to be repeatedly set when the user needs to use the water heater every time, and the user experience is reduced. The method for determining the configuration parameters of the water heater can realize the purposes of converting the contextual parameters set by a user into the input of the neural network model, calculating the input according to the neural network model to obtain the output parameters and converting the output parameters into the configuration parameters of the water heater, so that the user can obtain the configuration parameters of the water heater meeting the requirements of the user only by setting the contextual parameters without setting all functions of the water heater by using function keys of the water heater, the technical effect of the use process of the water heater is simplified, and the technical problems of low intelligentization degree of the water heater in the related technology and low user experience caused by more complicated use are solved.
As an optional embodiment of the present invention, before receiving the configuration parameters from the server, the method for determining the configuration parameters of the water heater may further include: and receiving a request message, wherein the request message is generated by a user of the water heater by triggering a key on the water heater, and the request message represents a request for obtaining the configuration parameters from the server.
For example, when a user of the water heater touches a key of the water heater, it indicates that the water heater sends a request message for recommending the configuration parameters to the server, and at this time, the server obtains the configuration parameters of the water heater based on the neural network model according to the contextual parameters input by the user, and the obtained configuration parameters can be executed after the confirmation by the user of the water heater or directly executed by default.
It should be noted that the "one-key setting" is a physical key on the water heater or a software button on the interface of the mobile terminal, and the name of the "one-key setting" is not limited to be used for the "one-key setting", and may also be called as "intelligent setting", "quick setting", or the like. The function of the device is used for rapidly acquiring the parameter setting of the water heater. The set water heater parameters are derived from the habit of the user learned by the machine and the change of factors such as meteorological conditions detected by the water heater, and are not fixed parameters.
As an optional embodiment of the present invention, in step S902, the obtaining of the contextual parameter corresponding to the water heater may include at least one of: acquiring scene parameters through a mobile terminal; and acquiring scene parameters through data recorded by the water heater.
For example, on one hand, the water heater may obtain different context parameters according to its own storage module; in another aspect, the user may send the contextual parameters to the water heater through a mobile terminal (e.g., a cell phone) in his possession.
Additionally, operating according to the configuration parameters may include: and receiving the configuration parameters from the mobile terminal, wherein the server sends the configuration parameters to the mobile terminal, and when the mobile terminal receives the indication information for determining to use the configuration parameters to control the operation of the water heater, the server sends the configuration parameters to the water heater.
For example, the water heater can directly receive configuration parameters from a server side; in addition, the server can also send the configuration parameters to the mobile terminal, and when the user determines to agree to configure the water heater with the configuration parameters through the mobile terminal, the configuration parameters are sent to the target water heater, and then the water heater operates with the configuration parameters.
Example 3
According to another aspect of the embodiment of the present invention, there is also provided a method for determining configuration parameters of a water heater, fig. 10 is a preferred flowchart of the method for determining configuration parameters of a water heater according to the embodiment of the present invention, as shown in fig. 10, the method for determining configuration parameters of a water heater may include:
step S1002, receiving configuration parameters from a server, wherein the configuration parameters are basis for operation of the water heater, and the situation parameters are basis for generating the configuration parameters of the water heater.
The context parameters may include, but are not limited to, the following: a parameter for indicating a time period, a parameter for indicating a week, a parameter for indicating weather, a parameter for indicating a set temperature, and a parameter for indicating a mode.
In addition, the configuration parameters may include, but are not limited to: standard mode, energy saving mode, fast mode, set temperature, reserved time.
In step S1004, instruction information from the user is received.
And step S1006, determining whether the operation of the water heater is configured according to the configuration parameters according to the indication information.
In the above embodiment, the configuration parameters from the server may be received, and the indication information from the user may be received, and it is determined whether to configure the operation of the water heater according to the configuration parameters according to the indication information. Compared with the prior art, the traditional water heater does not have self-learning capability, cannot automatically generate configuration parameters meeting the requirements of a user according to contextual parameters set by the user, and is complicated in use process due to the fact that the function keys of the water heater are required to be repeatedly set when the user needs to use the water heater every time, and the user experience is reduced. The method for determining the configuration parameters of the water heater can realize the purposes of converting the contextual parameters set by a user into the input of the neural network model, calculating the input according to the neural network model to obtain the output parameters and converting the output parameters into the configuration parameters of the water heater, so that the user can obtain the configuration parameters of the water heater meeting the requirements of the user only by setting the contextual parameters without setting all functions of the water heater by using function keys of the water heater, the technical effect of the use process of the water heater is simplified, and the technical problems of low intelligentization degree of the water heater in the related technology and low user experience caused by more complicated use are solved.
Preferably, before receiving the configuration parameters from the server, the method for determining the configuration parameters of the water heater may further include: and receiving a request message, wherein the request message is generated by a user of the water heater by triggering a key on the water heater, and the request message represents a request for obtaining the configuration parameters from the server.
For example, when a user of the water heater touches a key of the water heater, it indicates that the water heater sends a request message for recommending the configuration parameters to the server, and at this time, the server obtains the configuration parameters of the water heater based on the neural network model according to the contextual parameters input by the user, and the obtained configuration parameters can be executed after the confirmation by the user of the water heater or directly executed by default.
It should be noted that the "one-key setting" is a physical key on the water heater or a software button on the interface of the mobile terminal, and the name of the "one-key setting" is not limited to be used for the "one-key setting", and may also be called as "intelligent setting", "quick setting", or the like. The function of the device is used for rapidly acquiring the parameter setting of the water heater. The set water heater parameters are derived from the habit of the user learned by the machine and the change of factors such as meteorological conditions detected by the water heater, and are not fixed parameters.
As an optional embodiment of the present invention, before determining whether to configure the operation of the water heater according to the configuration parameter according to the indication information, the method for determining the configuration parameter of the water heater may further include: and sending the scene parameters to the water heater.
That is, under the condition that the user of the water heater determines that the water heater needs to be used, the user can select to use the mobile terminal to send the contextual parameters to the water heater, and then the water heater can send the contextual parameters to the server. The mode can enable a user to remotely control the operation of the water heater through the mobile terminal in the case of arriving at home.
Preferably, the determining whether to configure the operation of the water heater according to the configuration parameters according to the indication information may include: under the condition that the operation of the water heater is determined to be configured according to the configuration parameters based on the indication information, the configuration parameters are sent to the water heater; and sending an updating message to the server under the condition that the operation of the water heater is determined not to be configured according to the configuration parameters based on the indication information, wherein the updating message is used for indicating the server to update the configuration parameters.
For example, the server may send the configuration parameters to the mobile terminal before sending them to the water heater, and when the user of the water heater determines, through the mobile terminal, to configure the water heater with the configuration parameters, the mobile terminal sends the configuration parameters to the water heater, which operates with the configuration parameters.
Example 4
The embodiment of the present invention further provides a device for determining configuration parameters of a water heater, and it should be noted that the device for determining configuration parameters of a water heater according to the embodiment of the present invention may execute the method for determining configuration parameters of a water heater according to the embodiment of the present invention. The configuration parameter determining apparatus of the water heater will be described below.
Fig. 11 is a schematic diagram of a configuration parameter determining apparatus of a water heater according to an embodiment of the present invention, and as shown in fig. 11, the configuration parameter determining apparatus of the water heater includes: a first acquisition unit 1101, a first determination unit 1103, a second acquisition unit 1105, and a first transmission unit 1107. The configuration parameter determining device of the water heater will be described in detail below.
The first obtaining unit 1101 is configured to obtain a contextual parameter, where the contextual parameter is a basis for generating a configuration parameter of the water heater, and the configuration parameter is a basis for operating the water heater.
A first determining unit 1103, configured to convert each of the context parameters into an input of a neural network model, where the neural network model is obtained by training data, and each set of training data includes: context parameters and corresponding configuration parameters under the context parameters.
A second obtaining unit 1105, configured to obtain an output of the neural network model.
A first sending unit 1107 is used to convert the output into configuration parameters and send the configuration parameters to the water heater.
In the above embodiment, the first obtaining unit may be used to obtain the contextual parameters, where the contextual parameters are bases for generating configuration parameters of the water heater, and the configuration parameters are bases for operating the water heater; then, each parameter in the scene parameters is converted into an input of a neural network model by using a first determining unit, wherein the neural network model is obtained by training through training data, and each set of training data comprises: the scene parameters and the corresponding configuration parameters under the scene parameters; acquiring the output of the neural network model by using a second acquisition unit; and converting the output into configuration parameters by using the first sending unit, and sending the configuration parameters to the water heater. Compared with the prior art, the traditional water heater does not have self-learning capability, cannot automatically generate configuration parameters meeting the requirements of a user according to contextual parameters set by the user, and is complicated in use process due to the fact that the function keys of the water heater are required to be repeatedly set when the user needs to use the water heater every time, and the user experience is reduced. The configuration parameter determining device of the water heater provided by the embodiment of the invention can realize the purposes of converting the contextual parameters set by a user into the input of the neural network model, calculating the input according to the neural network model to obtain the output parameters and converting the output parameters into the configuration parameters of the water heater, so that the user can obtain the configuration parameters of the water heater meeting the requirements of the user only by setting the contextual parameters without setting each function of the water heater by using a function key of the water heater, the technical effect of the use process of the water heater is simplified, and the technical problems of low intelligentization degree of the water heater in the related technology and low user experience caused by more complicated use are solved.
As an alternative embodiment of the present invention, the first determining unit may include: the first conversion subunit is used for converting each parameter in the scene parameters in each group of training data into a numerical value; the first determining subunit is used for respectively taking the obtained numerical value corresponding to each scene parameter as an input layer node of the neural network model; the second conversion subunit is used for converting each parameter in the configuration parameters in the training data into a numerical value; the second determining subunit is used for taking the obtained data corresponding to each configuration parameter as an output layer node of the neural network model; and the first acquisition subunit is used for training according to the input layer nodes and the output layer nodes to obtain a neural network model.
As an alternative embodiment of the present invention, the training data is a part of historical data collected from a plurality of users.
As an alternative embodiment of the present invention, another part of the historical data is used as verification data, wherein the verification data is used for verifying the neural network model.
As an alternative embodiment of the present invention, the first obtaining subunit may include: the first determining module is used for determining whether an error exists in an output node; and the adjusting module is used for adjusting parameters from the hidden layer node closest to the output layer node under the condition that the output node has errors, and repeating the operation until the output accords with the training data, wherein the hidden layer comprises one or more layers, each hidden layer comprises at least one hidden layer node, and each hidden layer node is a calculation mode which comprises parameters capable of adjusting calculation.
As an alternative embodiment of the present invention, the adjusting module may include: the acquisition submodule is used for acquiring the error of each hidden layer node; and the adjusting submodule is used for determining that the hidden layer node has an error and adjusting the parameters of the hidden layer node under the condition that the error meets a preset condition.
Example 5
The embodiment of the present invention further provides a device for determining configuration parameters of a water heater, and it should be noted that the device for determining configuration parameters of a water heater according to the embodiment of the present invention may execute the method for determining configuration parameters of a water heater according to the embodiment of the present invention. The following describes a configuration parameter determining apparatus for a water heater according to an embodiment of the present invention.
Fig. 12 is a schematic diagram of an alternative configuration parameter determining device for a water heater according to an embodiment of the present invention, as shown in fig. 12, the configuration parameter determining device for a water heater includes: a third acquiring unit 1201, a second transmitting unit 1203, a first receiving unit 1205 and an executing unit 1207. The configuration parameter determining device of the water heater will be described in detail below.
A third obtaining unit 1201, configured to obtain a contextual parameter corresponding to the water heater, where the contextual parameter is a basis for generating a configuration parameter of the water heater, and the configuration parameter is a basis for operating the water heater.
A second sending unit 1203, configured to send the contextual parameter to the server, where the contextual parameter is a basis for generating a configuration parameter of the water heater, and the configuration parameter is a basis for operating the water heater.
A first receiving unit 1205 is used to receive the configuration parameters from the server.
And an operation unit 1207, configured to operate according to the configuration parameter.
In the above embodiment, the third obtaining unit may be used to obtain the contextual parameters corresponding to the water heater, where the contextual parameters are bases for generating configuration parameters of the water heater, and the configuration parameters are bases for operating the water heater; the second sending unit 1203 is used for sending the contextual parameters to the server, wherein the contextual parameters are basis for generating configuration parameters of the water heater, and the configuration parameters are basis for running the water heater; simultaneously, receiving configuration parameters from a server by using a first receiving unit; and operating according to the configuration parameters by using the operating unit. Compared with the prior art, the traditional water heater does not have self-learning capability, cannot automatically generate configuration parameters meeting the requirements of a user according to contextual parameters set by the user, and is complicated in use process due to the fact that the function keys of the water heater are required to be repeatedly set when the user needs to use the water heater every time, and the user experience is reduced. The configuration parameter determining device of the water heater provided by the embodiment of the invention can realize the purposes of converting the contextual parameters set by a user into the input of the neural network model, calculating the input according to the neural network model to obtain the output parameters and converting the output parameters into the configuration parameters of the water heater, so that the user can obtain the configuration parameters of the water heater meeting the requirements of the user only by setting the contextual parameters without setting each function of the water heater by using a function key of the water heater, the technical effect of the use process of the water heater is simplified, and the technical problems of low intelligentization degree of the water heater in the related technology and low user experience caused by more complicated use are solved.
As an optional embodiment of the present invention, the configuration parameter determining apparatus for a water heater may further include: and the second receiving unit is used for receiving a request message before receiving the configuration parameters from the server, wherein the request message is generated by triggering a key on the water heater by a user of the water heater, and the request message represents a request for acquiring the configuration parameters from the server.
As an alternative embodiment of the present invention, the third obtaining unit may include at least one of: the second acquiring subunit is used for acquiring the scene parameters through the mobile terminal; and the third acquisition subunit is used for acquiring the scene parameters through the data recorded by the water heater.
As an alternative embodiment of the present invention, the operation unit may include: and the receiving subunit is used for receiving the configuration parameters from the mobile terminal, wherein the server sends the configuration parameters to the mobile terminal, and when the mobile terminal receives the indication information for determining that the configuration parameters are used for controlling the operation of the water heater, the server sends the configuration parameters to the water heater.
Example 6
The embodiment of the present invention further provides a device for determining configuration parameters of a water heater, and it should be noted that the device for determining configuration parameters of a water heater according to the embodiment of the present invention may execute the method for determining configuration parameters of a water heater according to the embodiment of the present invention. The following describes a configuration parameter determining apparatus for a water heater according to an embodiment of the present invention.
Fig. 13 is a preferred schematic diagram of a configuration parameter determining device of a water heater according to an embodiment of the present invention, and as shown in fig. 13, the configuration parameter determining device of the water heater includes: a third receiving unit 1301, a fourth receiving unit 1303 and a second determining unit 1305. The configuration parameter determining device of the water heater will be described in detail below.
The third receiving unit 1301 is configured to receive the configuration parameters from the server, where the configuration parameters are bases for operation of the water heater, and the context parameters are bases for generating the configuration parameters of the water heater.
And a fourth receiving unit 1303 configured to receive the indication information from the user.
A second determining unit 1305, configured to determine whether to configure the operation of the water heater according to the configuration parameter according to the indication information.
In the above embodiment, the third receiving unit may be utilized to receive the configuration parameters from the server, where the configuration parameters are bases for operation of the water heater, and the context parameters are bases for generating the configuration parameters of the water heater; meanwhile, the fourth receiving unit is used for receiving indication information from a user; and determining whether to configure the operation of the water heater according to the configuration parameters by using a second determination unit according to the indication information. Compared with the prior art, the traditional water heater does not have self-learning capability, cannot automatically generate configuration parameters meeting the requirements of a user according to contextual parameters set by the user, and is complicated in use process due to the fact that the function keys of the water heater are required to be repeatedly set when the user needs to use the water heater every time, and the user experience is reduced. The configuration parameter determining device of the water heater provided by the embodiment of the invention can realize the purposes of converting the contextual parameters set by a user into the input of the neural network model, calculating the input according to the neural network model to obtain the output parameters and converting the output parameters into the configuration parameters of the water heater, so that the user can obtain the configuration parameters of the water heater meeting the requirements of the user only by setting the contextual parameters without setting each function of the water heater by using a function key of the water heater, the technical effect of the use process of the water heater is simplified, and the technical problems of low intelligentization degree of the water heater in the related technology and low user experience caused by more complicated use are solved.
As an optional embodiment of the present invention, the configuration parameter determining apparatus for a water heater may further include: and the fourth receiving unit is used for receiving a request message before receiving the configuration parameters from the server, wherein the request message is generated by triggering a key on the water heater by a user of the water heater, and the request message represents a request for acquiring the configuration parameters from the server.
As an optional embodiment of the present invention, the configuration parameter determining apparatus for a water heater may further include: and the third sending unit is used for sending the contextual parameters to the water heater before determining whether the operation of the water heater is configured according to the configuration parameters according to the indication information.
As an alternative embodiment of the present invention, the second determining unit may include: the first sending subunit is used for sending the configuration parameters to the water heater under the condition that the operation of the water heater is determined to be configured according to the configuration parameters based on the indication information; and the second sending subunit is used for sending an updating message to the server under the condition that the operation of the water heater is determined not to be configured according to the configuration parameters based on the indication information, wherein the updating message is used for indicating the server to update the configuration parameters.
The configuration parameter determining device of the water heater comprises a processor and a memory, wherein the first acquiring unit 1101, the first determining unit 1103, the second acquiring unit 1105, the first transmitting unit 1107, the third acquiring unit 1201, the second transmitting unit 1203, the first receiving unit 1205, the running unit 1207, the third receiving unit 1301, the fourth receiving unit 1303, the second determining unit 1305 and the like are stored in the memory as program units, and the processor executes the program units stored in the memory to realize corresponding functions.
The processor comprises a kernel, and the kernel calls a corresponding program unit from the memory. The kernel can be set to be one or more, and the configuration parameters are sent by adjusting the kernel parameters, wherein the configuration parameters are used for controlling the operation of the water heater.
The memory may include volatile memory in a computer readable medium, Random Access Memory (RAM) and/or nonvolatile memory such as Read Only Memory (ROM) or flash memory (flash RAM), and the memory includes at least one memory chip.
According to another aspect of the embodiment of the present invention, there is also provided a storage medium including a stored program, wherein the program executes the configuration parameter determination method for a water heater of any one of the above.
According to another aspect of the embodiment of the present invention, there is also provided a processor, configured to execute the program, where the program executes the method for determining the configuration parameters of the water heater.
The embodiment of the present invention further provides an apparatus, which includes a processor, a memory, and a program stored in the memory and executable on the processor, and when the processor executes the program, the following steps are implemented: acquiring contextual parameters, wherein the contextual parameters are basis for generating configuration parameters of the water heater, and the configuration parameters are basis for running the water heater; converting each parameter in the context parameters into an input of a neural network model, wherein the neural network model is obtained by training data, and each set of training data comprises: the scene parameters and the corresponding configuration parameters under the scene parameters; obtaining the output of the neural network model; converting the output into configuration parameters and sending the configuration parameters to the water heater.
There is also provided in an embodiment of the invention a computer program product adapted to perform a program for initializing the following method steps when executed on a data processing device: acquiring contextual parameters, wherein the contextual parameters are basis for generating configuration parameters of the water heater, and the configuration parameters are basis for running the water heater; converting each parameter in the context parameters into an input of a neural network model, wherein the neural network model is obtained by training data, and each set of training data comprises: the scene parameters and the corresponding configuration parameters under the scene parameters; obtaining the output of the neural network model; converting the output into configuration parameters and sending the configuration parameters to the water heater.
The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.
In the above embodiments of the present invention, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.
In the embodiments provided in the present application, it should be understood that the disclosed technology can be implemented in other ways. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units may be a logical division, and in actual implementation, there may be another division, for example, multiple units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, units or modules, and may be in an electrical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic or optical disk, and other various media capable of storing program codes.
The foregoing is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, various modifications and decorations can be made without departing from the principle of the present invention, and these modifications and decorations should also be regarded as the protection scope of the present invention.

Claims (30)

1. A configuration parameter determination method for a water heater is characterized by comprising the following steps:
acquiring contextual parameters, wherein the contextual parameters are basis for generating configuration parameters of a water heater, and the configuration parameters are basis for running the water heater;
converting each parameter in the context parameters into an input of a neural network model, wherein the neural network model is obtained by training data, and each set of training data comprises: the scene parameters and the corresponding configuration parameters under the scene parameters;
obtaining an output of the neural network model;
converting the output into the configuration parameter and sending the configuration parameter to the water heater.
2. The method of claim 1, wherein training the neural network model with the training data comprises:
converting each parameter in the scene parameters in each set of training data into a numerical value;
respectively taking the obtained numerical value corresponding to each scene parameter as an input layer node of the neural network model;
converting each of the configuration parameters in the training data to a numerical value;
taking the obtained data corresponding to each configuration parameter as an output layer node of the neural network model;
and training according to the input layer nodes and the output layer nodes to obtain the neural network model.
3. The method of claim 2, wherein the training data is part of historical data collected from a plurality of users.
4. The method of claim 3, wherein another portion of the historical data is used as validation data, wherein the validation data is used to validate the neural network model.
5. The method of any one of claims 1 to 4, wherein training the neural network model based on the input layer nodes and the output layer nodes comprises:
determining whether an error exists in an output node;
and under the condition that the output node has errors, adjusting parameters from the hidden layer node closest to the output layer node, and repeating the steps until the output conforms to the training data, wherein the hidden layer comprises one or more layers, each hidden layer comprises at least one hidden layer node, and each hidden layer node is a calculation mode which comprises parameters capable of adjusting the calculation.
6. The method of claim 5, wherein adjusting parameters from an implied layer node closest to the output layer node comprises:
acquiring the error of each hidden layer node;
and under the condition that the error meets a preset condition, determining that the hidden layer node has an error, and adjusting the parameters of the hidden layer node.
7. A configuration parameter determination method for a water heater is characterized by comprising the following steps:
acquiring a situation parameter corresponding to a water heater, wherein the situation parameter is a basis for generating a configuration parameter of the water heater, and the configuration parameter is a basis for running the water heater;
sending the scene parameters to a server;
receiving configuration parameters from a server;
and operating according to the configuration parameters.
8. The method of claim 7, prior to receiving the configuration parameters from the server, further comprising:
receiving a request message, wherein the request message is generated by a user of the water heater by triggering a key on the water heater, and the request message represents a request for obtaining configuration parameters from the server.
9. The method of claim 7, wherein obtaining the corresponding contextual parameter of the water heater comprises at least one of:
acquiring the scene parameters through a mobile terminal;
and acquiring the scene parameters through the data recorded by the water heater.
10. The method of any of claims 7 to 9, wherein operating according to the configuration parameters comprises:
the method comprises the steps that configuration parameters from a mobile terminal are received, wherein a server sends the configuration parameters to the mobile terminal, and when the mobile terminal receives indication information for determining that the configuration parameters are used for controlling the operation of a water heater, the configuration parameters are sent to the water heater.
11. A configuration parameter determination method for a water heater is characterized by comprising the following steps:
receiving configuration parameters from a server, wherein the configuration parameters are basis for running of a water heater, and the situation parameters are basis for generating the configuration parameters of the water heater;
receiving indication information from a user;
and determining whether the operation of the water heater is configured according to the configuration parameters or not according to the indication information.
12. The method of claim 11, prior to receiving the configuration parameters from the server, further comprising:
receiving a request message, wherein the request message is generated by a user of the water heater by triggering a key on the water heater, and the request message represents a request for obtaining configuration parameters from the server.
13. The method of claim 11, prior to determining whether to configure operation of the water heater according to the configuration parameters based on the indication, further comprising:
and sending the scene parameters to the water heater.
14. The method of claim 13, wherein determining whether to configure operation of the water heater according to the configuration parameters based on the indication comprises:
under the condition that the operation of the water heater is determined to be configured according to the configuration parameters based on the indication information, the configuration parameters are sent to the water heater;
and sending an update message to the server under the condition that the operation of the water heater is determined not to be configured according to the configuration parameters based on the indication information, wherein the update message is used for indicating the server to update the configuration parameters.
15. A configuration parameter determining apparatus for a water heater, comprising:
the system comprises a first obtaining unit, a second obtaining unit and a control unit, wherein the first obtaining unit is used for obtaining contextual parameters, the contextual parameters are basis for generating configuration parameters of a water heater, and the configuration parameters are basis for running the water heater;
a first determining unit, configured to convert each of the context parameters into an input of a neural network model, where the neural network model is obtained by training data, and each set of training data includes: the scene parameters and the corresponding configuration parameters under the scene parameters;
a second obtaining unit, configured to obtain an output of the neural network model;
and the first sending unit is used for converting the output into the configuration parameters and sending the configuration parameters to the water heater.
16. The apparatus of claim 15, wherein the first determining unit comprises:
the first conversion subunit is used for converting each parameter in the scene parameters in each group of training data into a numerical value;
the first determining subunit is configured to use the obtained numerical value corresponding to each context parameter as an input layer node of the neural network model;
a second conversion subunit, configured to convert each parameter in the configuration parameters in the training data into a numerical value;
the second determining subunit is used for taking the obtained data corresponding to each configuration parameter as an output layer node of the neural network model;
and the first acquisition subunit is used for training according to the input layer nodes and the output layer nodes to obtain the neural network model.
17. The apparatus of claim 16, wherein the training data is part of historical data collected from a plurality of users.
18. The apparatus of claim 17, wherein another portion of the historical data is used as verification data, wherein the verification data is used to verify the neural network model.
19. The apparatus according to any one of claims 15 to 18, wherein the first acquiring subunit comprises:
the first determining module is used for determining whether an error exists in an output node;
and the adjusting module is used for adjusting parameters from the hidden layer node closest to the output layer node under the condition that the output node has errors, and repeating the operation until the output is consistent with the training data, wherein the hidden layer comprises one or more layers, each hidden layer comprises at least one hidden layer node, and each hidden layer node is a calculation mode which comprises parameters capable of adjusting calculation.
20. The apparatus of claim 19, wherein the adjustment module comprises:
the acquisition submodule is used for acquiring the error of each hidden layer node;
and the adjusting submodule is used for determining that the hidden layer node has an error and adjusting the parameters of the hidden layer node under the condition that the error meets a preset condition.
21. A configuration parameter determining apparatus for a water heater, comprising:
the third obtaining unit is used for obtaining a contextual parameter corresponding to a water heater, wherein the contextual parameter is a basis for generating a configuration parameter of the water heater, and the configuration parameter is a basis for running the water heater;
a second sending unit, configured to send the context parameter to a server;
a first receiving unit, configured to receive configuration parameters from a server;
and the operation unit is used for operating according to the configuration parameters.
22. The apparatus of claim 21, further comprising:
a second receiving unit, configured to receive a request message before receiving the configuration parameters from the server, where the request message is a request message generated by a user of the water heater by triggering a key on the water heater, and the request message indicates a request to acquire the configuration parameters from the server.
23. The apparatus of claim 21, wherein the third obtaining unit comprises at least one of:
the second acquiring subunit is used for acquiring the scene parameters through the mobile terminal;
and the third acquisition subunit is used for acquiring the scene parameters according to the data recorded by the water heater.
24. The apparatus of any one of claims 21 to 23, wherein the operation unit comprises:
the receiving subunit is used for receiving the configuration parameters from the mobile terminal, wherein the server sends the configuration parameters to the mobile terminal, and when the mobile terminal receives the indication information for determining that the configuration parameters are used for controlling the operation of the water heater, the server sends the configuration parameters to the water heater.
25. A configuration parameter determining apparatus for a water heater, comprising:
the third receiving unit is used for receiving configuration parameters from the server, wherein the configuration parameters are basis for running of the water heater, and the situation parameters are basis for generating the configuration parameters of the water heater;
a fourth receiving unit, configured to receive indication information from a user;
and the second determining unit is used for determining whether the operation of the water heater is configured according to the configuration parameters according to the indication information.
26. The apparatus of claim 25, further comprising:
a fourth receiving unit, configured to receive a request message before receiving the configuration parameters from the server, where the request message is a request message generated by a user of the water heater by triggering a key on the water heater, and the request message indicates a request to acquire the configuration parameters from the server.
27. The apparatus of claim 25, further comprising:
and the third sending unit is used for sending the contextual parameters to the water heater before determining whether the operation of the water heater is configured according to the configuration parameters according to the indication information.
28. The apparatus of claim 27, wherein the second determining unit comprises:
the first sending subunit is used for sending the configuration parameters to the water heater under the condition that the operation of the water heater is determined to be configured according to the configuration parameters based on the indication information;
and a second sending subunit, configured to send, to the server, an update message when it is determined, based on the indication information, that the operation of the water heater is not configured according to the configuration parameters, where the update message is used to instruct the server to update the configuration parameters.
29. A storage medium characterized by comprising a stored program, wherein the program executes the configuration parameter determination method for a water heater of any one of claims 1 to 14.
30. A processor, characterized in that the processor is configured to run a program, wherein the program is run to perform the method of determining configuration parameters of a water heater according to any one of claims 1 to 14.
CN201810865619.7A 2018-08-01 2018-08-01 Configuration parameter determination method and device for water heater Pending CN110793208A (en)

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