CN116149198A - Household appliance remote control system based on Internet of things - Google Patents
Household appliance remote control system based on Internet of things Download PDFInfo
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- CN116149198A CN116149198A CN202310255250.9A CN202310255250A CN116149198A CN 116149198 A CN116149198 A CN 116149198A CN 202310255250 A CN202310255250 A CN 202310255250A CN 116149198 A CN116149198 A CN 116149198A
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- 238000010801 machine learning Methods 0.000 claims abstract description 37
- 230000007613 environmental effect Effects 0.000 claims abstract description 34
- 238000013480 data collection Methods 0.000 claims abstract description 15
- 238000012795 verification Methods 0.000 claims abstract description 4
- 230000006870 function Effects 0.000 claims description 16
- 238000000034 method Methods 0.000 abstract description 4
- 230000000007 visual effect Effects 0.000 abstract 1
- 238000005516 engineering process Methods 0.000 description 3
- 230000006399 behavior Effects 0.000 description 2
- 239000012141 concentrate Substances 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 238000010438 heat treatment Methods 0.000 description 2
- 238000005057 refrigeration Methods 0.000 description 2
- 238000005406 washing Methods 0.000 description 2
- 238000004378 air conditioning Methods 0.000 description 1
- 238000013528 artificial neural network Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000012706 support-vector machine Methods 0.000 description 1
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B15/00—Systems controlled by a computer
- G05B15/02—Systems controlled by a computer electric
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B19/00—Programme-control systems
- G05B19/02—Programme-control systems electric
- G05B19/418—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B2219/00—Program-control systems
- G05B2219/20—Pc systems
- G05B2219/26—Pc applications
- G05B2219/2642—Domotique, domestic, home control, automation, smart house
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]
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Abstract
The invention discloses a household appliance remote control system based on the Internet of things, which relates to the technical field of remote control, wherein basic information of a household appliance is collected in advance by arranging a household appliance information collection module; an identity recognition module is arranged to carry out identity verification on users in families; setting a historical operation data collection module to collect operation habits of each family member on the household appliances in real time; the set model training module trains a corresponding machine learning model for predicting the target state of the machine according to the historical operation data of each family member; setting a real-time information collection module to collect environmental conditions of household appliances in real time when family members use the remote control terminal; the method comprises the steps that a push-to-push module is arranged, a machine learning model is used for predicting operation setting of household members on household appliances, and visual display is carried out on the household members through a remote control terminal; the problem of safety accidents caused by excessive attraction of the remote operation to the attention of the user is avoided.
Description
Technical Field
The invention belongs to the field of household appliances, relates to a remote control technology, and in particular relates to a household appliance remote control system based on the Internet of things.
Background
The current internet of things technology is sufficient to support a user to remotely control the household appliance in real time, namely, the user sends control information of the household appliance through a remote operation interface, then the control information is transmitted back to a wireless receiving device of the household appliance through a wireless network, and the household appliance executes the corresponding control information; however, the operation of the household appliance is complicated, and when the user performs remote operation, the user often needs to concentrate on a remote operation interface, and the behavior brings inconvenience and even danger when the user performs other activities; for example, focusing on remote control may present a hazard when the user is driving;
for this reason, a home appliance remote control system based on the internet of things is proposed.
Disclosure of Invention
The present invention aims to solve at least one of the technical problems existing in the prior art. Therefore, the household appliance remote control system based on the Internet of things provided by the invention has the advantages that the household appliance remote control system based on the Internet of things automatically pushes the electric appliance operation most suitable for the user to the user, so that the problem of safety accidents caused by excessive attraction of the remote operation to the user is avoided.
To achieve the above objective, an embodiment according to a first aspect of the present invention provides a home appliance remote control system based on internet of things, including a home appliance information collection module, an identity recognition module, a historical operation data collection module, a model training module, a real-time information collection module, a push-to-push module, and a remote control module; wherein, each module is connected by an electric and/or wireless network mode;
the household appliance information collection module is mainly used for collecting basic information of household appliances in advance;
the basic information of the household appliances comprises the types, the models and the function settings of the household appliances;
the household appliance information collection module sends the collected basic information of the household appliances to the historical operation data collection module;
the identity recognition module is mainly used for carrying out identity verification on users in families;
the identity recognition module determining the current user identity comprises the following steps:
step S1: each family member inputs identity information and the corresponding relation of identity identification characteristics on a remote control terminal;
step S2: before each family member uses the remote control terminal to remotely control the household appliance, the user inputs the identity mark characteristic, and the remote control terminal searches the family member identity corresponding to the identity mark characteristic;
the historical operation data collection module is mainly used for collecting operation habits of each family member on household appliances in real time;
the mode of collecting the operation habits of family members to the household appliances by the historical operation data collection module is as follows:
when each family member uses a remote control terminal to operate the household appliance, an identity recognition module is used for determining the identity of an operator, and the operation result executed by the operator and the current environmental condition are recorded; the real-time functional state is parameters of various functions of the current household appliance, such as air conditioner refrigeration or heating, set temperature data and the like; the current environmental conditions comprise current date, season, time, air humidity and other environmental parameters related to the functions of the household appliances;
the historical operation data collection module sends the collected operation habits of each family member on the household appliances to the model training module;
the model training module is mainly used for training a corresponding machine learning model for predicting the target state of the machine according to the historical operation data of each family member;
the model training module trains a machine learning model of the operation of the predictive execution comprising the steps of:
step P1: classifying the collected historical operation data according to family members and the operated household appliances;
step P2: for each family member and household appliance, quantizing historical operation data of the household appliance by the member, merging the quantized historical operation data into a digital vector form, taking the digital vector as input of a machine learning model, taking a target state of the household appliance as output, and training the machine learning model;
step P3: setting a prediction accuracy threshold in advance according to actual experience, and stopping training when the prediction accuracy of the trained machine learning model is greater than the prediction accuracy threshold;
the model training module sends all the machine learning models after training to the one-key pushing module;
the real-time information collection module is mainly used for collecting the environmental conditions of the household appliances in real time when the household members use the remote control terminal;
the real-time information collection module collects environmental conditions of the household appliances in real time, wherein the environmental conditions comprise current date, season, time, air humidity and environmental conditions affecting the current operation functions of the appliances; the environment conditions affecting the current operation function of the electric appliance are collected by a plurality of physical sensors assembled on the household appliance, and the collected physical data are sent to the remote control terminal through the wireless signal sending device;
the real-time information collection module sends the environmental conditions of the household appliances collected in real time to the one-key pushing module;
the one-key pushing module is mainly used for predicting operation setting of household members on household appliances by using a machine learning model and visually displaying the operation setting to the household members through a remote control terminal;
the one-key pushing module predicts the operation setting mode of family members on the household appliances as follows:
the family member is marked as p, the household appliance is marked as e, and a machine learning model for operating the household appliance e by using a remote control terminal for the family member p is marked as Mpe; the remote control terminal recognizes the identity information of the current family member p through the identity recognition module, and then obtains a machine learning model Mpe of the family member p and the corresponding household appliance e; quantifying the real-time environmental conditions, merging the real-time environmental conditions into a digital vector form, and inputting the digital vector into a machine learning model Mpe to obtain a predicted target state of the household appliance; the remote control terminal calculates the operation steps required by the target state according to the current state of the household appliance, and displays the operation steps to the family member p through the display interface of the remote control terminal;
the remote control module is mainly used for remotely controlling the household appliances through the remote control terminal after a user sees operation display;
the remote control module for remotely controlling the household appliances comprises the following steps:
step Q1: after clicking an operation key through a display interface of the remote control terminal, the remote control terminal sends a corresponding operation step instruction to the household appliance in a wireless network mode;
step Q2: the control circuit board arranged on the household appliance body receives the operation instruction sent by the remote control terminal, and operates the household appliance according to the corresponding operation instruction.
Compared with the prior art, the invention has the beneficial effects that:
according to the invention, basic information of household appliances is collected in advance, then the household members are distinguished according to the identity identification characteristics of each household member, habit history data of each household member on the electric appliance state of each household appliance under different environmental conditions is collected in advance, the habit history data is used as input of a machine learning model, the machine learning model of each household member on the proper state of each household appliance under the specific environmental conditions is trained, when the household member controls the household appliance through a remote control terminal, the current environmental condition data is collected in real time and is input into the corresponding machine learning model, the most proper electric appliance state is obtained, operation steps required by the electric appliance are calculated according to the electric appliance state, and are displayed to the household members, and after the display, the household members control the remote control terminal to send remote operation instructions to the household appliance for remote operation; the electric appliance operation most suitable for the user is automatically pushed to the user, so that the problem of safety accidents caused by excessive attraction of the user to the remote operation is avoided.
Drawings
Fig. 1 is a schematic diagram of the present invention.
Detailed Description
The technical solutions of the present invention will be clearly and completely described in connection with the embodiments, and it is obvious that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The current internet of things technology is sufficient to support a user to remotely control the household appliance in real time, namely, the user sends control information of the household appliance through a remote operation interface, then the control information is transmitted back to a wireless receiving device of the household appliance through a wireless network, and the household appliance executes the corresponding control information; however, the operation of the household appliance is complicated, and when the user performs remote operation, the user often needs to concentrate on a remote operation interface, and the behavior brings inconvenience and even danger when the user performs other activities; for example, focusing on remote control may present a hazard when the user is driving;
as shown in fig. 1, the household appliance remote control system based on the internet of things comprises a household appliance information collection module, an identity recognition module, a historical operation data collection module, a model training module, a real-time information collection module, a one-key pushing module and a remote control module; wherein, each module is connected by an electric and/or wireless network mode;
the household appliance information collection module is mainly used for collecting basic information of household appliances in advance;
in a preferred embodiment, the basic information of the household appliance includes, but is not limited to, the type, model and function setting of the household appliance; preferably, the categories include, but are not limited to, washing machines, air conditioners, refrigerators, televisions, and the like; the model comprises the brand of the household appliance and the model of the appliance under the brand; the functions are different according to the types of the household appliances;
the household appliance information collection module sends the collected basic information of the household appliances to the historical operation data collection module;
the identity recognition module is mainly used for carrying out identity verification on users in families;
when the user can understand, the use habits of different family members in the same family on the household appliances are different, so that the current user identity is required to be determined before setting recommendation is performed on the user;
in a preferred embodiment, the identity module determining the current user identity comprises the steps of:
step S1: each family member inputs identity information and the corresponding relation of identity identification characteristics on a remote control terminal; preferably, the identification features include, but are not limited to, fingerprints, faces, and tone features;
step S2: before each family member uses the remote control terminal to remotely control the household appliance, the user inputs the identity mark characteristic, and the remote control terminal searches the family member identity corresponding to the identity mark characteristic;
the historical operation data collection module is mainly used for collecting operation habits of each family member on household appliances in real time;
in a preferred embodiment, the historical operation data collection module collects operation habits of household members on household appliances in the following manner:
when each family member uses a remote control terminal to operate the household appliance, an identity recognition module is used for determining the identity of an operator, and the operation result executed by the operator and the current environmental condition are recorded; preferably, the operation result is a real-time functional state of the household appliance after the operation of the household appliance by an operator; the real-time functional state is parameters of various functions of the current household appliance, such as air conditioner refrigeration or heating, set temperature data and the like; further, the current environmental conditions include, but are not limited to, current date, season, time, air humidity, and other environmental parameters related to the function of the respective household appliance; for example: the weight of clothes in the washing machine, the air humidity which has influence on the air conditioning function and the like;
the historical operation data collection module sends the collected operation habits of each family member on the household appliances to the model training module;
the model training module is mainly used for training a corresponding machine learning model for predicting the target state of the machine according to the historical operation data of each family member;
in a preferred embodiment, the model training module trains a machine learning model of the operation predicted to be performed comprising the steps of:
step P1: classifying the collected historical operation data according to family members and the operated household appliances;
step P2: for each family member and household appliance, quantizing historical operation data of the household appliance by the member, merging the quantized historical operation data into a digital vector form, taking the digital vector as input of a machine learning model, taking a target state of the household appliance as output, and training the machine learning model;
step P3: setting a prediction accuracy threshold in advance according to actual experience, and stopping training when the prediction accuracy of the trained machine learning model is greater than the prediction accuracy threshold; it can be appreciated that the number of machine learning models trained is the number of family members multiplied by the number of home appliances; preferably, the machine learning model may be a deep neural network, a deep belief network or a support vector machine model;
the model training module sends all the machine learning models after training to the one-key pushing module;
the real-time information collection module is mainly used for collecting the environmental conditions of the household appliances in real time when the household members use the remote control terminal;
in a preferred embodiment, the environmental conditions in which the household appliances are located that are collected in real time by the real-time information collection module include, but are not limited to, the current date, season, time, air humidity, and environmental conditions affecting the function of the currently operated appliance; further, environmental conditions affecting the current function of operating the electric appliance are collected by a plurality of physical sensors assembled on the household appliance, and the collected physical data are sent to the remote control terminal through the wireless signal sending device; it can be understood that the physical sensor is selected and installed according to the actual requirement of the household appliance, and the physical data sensed by the sensor is the current environmental condition;
the real-time information collection module sends the environmental conditions of the household appliances collected in real time to the one-key pushing module;
the one-key pushing module is mainly used for predicting operation setting of household members on household appliances by using a machine learning model and visually displaying the operation setting to the household members through a remote control terminal;
in a preferred embodiment, the one-button push module predicts the operation setting of the household member on the household appliance in such a way that:
the family member is marked as p, the household appliance is marked as e, and a machine learning model for operating the household appliance e by using a remote control terminal for the family member p is marked as Mpe; the remote control terminal recognizes the identity information of the current family member p through the identity recognition module, and then obtains a machine learning model Mpe of the family member p and the corresponding household appliance e; quantifying the real-time environmental conditions, merging the real-time environmental conditions into a digital vector form, and inputting the digital vector into a machine learning model Mpe to obtain a predicted target state of the household appliance; the remote control terminal calculates the operation steps required by the target state according to the current state of the household appliance, and displays the operation steps to the family member p through the display interface of the remote control terminal;
the remote control module is mainly used for remotely controlling the household appliances through the remote control terminal after a user sees operation display;
in a preferred embodiment, the remote control module remotely controls the home appliance, comprising the steps of:
step Q1: after clicking an operation key through a display interface of the remote control terminal, the remote control terminal sends a corresponding operation step instruction to the household appliance in a wireless network mode;
in another preferred embodiment, the family member may also click a modify key to modify the operation instruction; furthermore, the family members can control the remote control terminal to send operation instructions to the household appliances in a voice mode;
step Q2: the control circuit board arranged on the household appliance body receives the operation instruction sent by the remote control terminal, and operates the household appliance according to the corresponding operation instruction so as to achieve the target state of the household appliance.
The above embodiments are only for illustrating the technical method of the present invention and not for limiting the same, and it should be understood by those skilled in the art that the technical method of the present invention may be modified or substituted without departing from the spirit and scope of the technical method of the present invention.
Claims (8)
1. The household appliance remote control system based on the Internet of things is characterized by comprising a household appliance information collection module, an identity recognition module, a historical operation data collection module, a model training module, a real-time information collection module, a one-key pushing module and a remote control module; wherein, each module is connected by an electric and/or wireless network mode;
the household appliance information collection module is used for collecting basic information of household appliances in advance; the collected basic information of the household appliances is sent to a historical operation data collection module;
the identity recognition module is used for carrying out identity verification on users in families;
the historical operation data collection module is used for collecting the operation habit of each family member on the household appliances in real time; the collected operation habits of each family member on the household appliances are sent to a model training module;
the model training module is used for training a corresponding machine learning model for predicting the target state of the machine according to the historical operation data of each family member; transmitting all the trained machine learning models to a one-key pushing module;
the real-time information collection module is used for collecting the environmental conditions of the household appliances in real time when the household members use the remote control terminal; the environmental conditions of the household appliances collected in real time are sent to a push-to-push module;
the one-key pushing module is used for predicting the operation setting of household members on the household appliances by using the machine learning model and visually displaying the operation setting to the household members through the remote control terminal;
the remote control module is used for a user to remotely control the household appliances through the remote control terminal.
2. The internet of things-based home appliance remote control system according to claim 1, wherein the basic information of the home appliance includes a category, a model number, and a function setting of the home appliance.
3. The internet of things-based home appliance remote control system of claim 1, wherein the identity recognition module determining the current user identity comprises the steps of:
step S1: each family member inputs identity information and the corresponding relation of identity identification characteristics on a remote control terminal;
step S2: the identification characteristic is input by a person, and the remote control terminal retrieves the family membership corresponding to the identification characteristic.
4. The internet of things-based household appliance remote control system according to claim 1, wherein the manner in which the historical operation data collection module collects the operation habits of household members on the household appliances is:
when each family member uses a remote control terminal to operate the household appliance, an identity recognition module is used for determining the identity of an operator, and the operation result executed by the operator and the current environmental condition are recorded; the real-time functional state is the parameter of each function of the current household appliance, and the current environmental condition comprises the current date, season, time, air humidity and the environmental parameters related to the functions of each household appliance.
5. The internet of things-based household appliance remote control system of claim 1, wherein the model training module trains a machine learning model of the operation predicted to be performed comprising the steps of:
step P1: classifying the collected historical operation data according to family members and the operated household appliances;
step P2: for each family member and household appliance, quantizing historical operation data of the household appliance by the member, merging the quantized historical operation data into a digital vector form, taking the digital vector as input of a machine learning model, taking a target state of the household appliance as output, and training the machine learning model;
step P3: and setting a prediction accuracy threshold according to actual experience in advance, and stopping training when the prediction accuracy of the trained machine learning model is greater than the prediction accuracy threshold.
6. The internet of things-based household appliance remote control system according to claim 1, wherein the environmental conditions of the household appliance collected in real time by the real-time information collection module include a current date, a season, a time, an air humidity, and environmental conditions affecting a current function of operating the appliance.
7. The internet of things-based household appliance remote control system according to claim 1, wherein the one-button push module predicts the operation setting of the household appliance by the family member in the following manner:
the family member is marked as p, the household appliance is marked as e, and a machine learning model for operating the household appliance e by using a remote control terminal for the family member p is marked as Mpe; the remote control terminal recognizes the identity information of the current family member p through the identity recognition module, and then obtains a machine learning model Mpe of the family member p and the corresponding household appliance e; quantifying the real-time environmental conditions, merging the real-time environmental conditions into a digital vector form, and inputting the digital vector into a machine learning model Mpe to obtain a predicted target state of the household appliance; the remote control terminal calculates the operation steps required by the target state according to the current state of the household appliance, and displays the operation steps to the family member p through the display interface of the remote control terminal.
8. The remote control system for home appliances based on the internet of things according to claim 1, wherein the remote control module remotely controls the home appliances comprises the steps of:
step Q1: after clicking an operation key through a display interface of the remote control terminal, the remote control terminal sends a corresponding operation step instruction to the household appliance in a wireless network mode;
step Q2: the control circuit board arranged on the household appliance body receives the operation instruction sent by the remote control terminal, and operates the household appliance according to the corresponding operation instruction.
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| CN202310255250.9A CN116149198A (en) | 2023-03-13 | 2023-03-13 | Household appliance remote control system based on Internet of things |
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| CN202310255250.9A CN116149198A (en) | 2023-03-13 | 2023-03-13 | Household appliance remote control system based on Internet of things |
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115527203A (en) * | 2022-10-21 | 2022-12-27 | 中粮工程装备无锡有限公司 | Grain drying remote control method and system based on Internet of things |
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115527203A (en) * | 2022-10-21 | 2022-12-27 | 中粮工程装备无锡有限公司 | Grain drying remote control method and system based on Internet of things |
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