Remote control system for household appliances based on internet of things

Abstract

The application discloses a household appliance remote control system based on Internet of Things and relates to the technical field of remote control. The household appliance information collection module is arranged to collect the basic information of household appliances in advance; the identity recognition module is arranged to verify the identity of users in the family; the historical operation data collection module is arranged to collect the operation habits of each family member on household appliances in real time; the model training module is arranged to train the corresponding machine learning model for predicting the target state of machines according to the historical operation data of each family member; the real-time information collection module is arranged to collect the environmental conditions of household appliances in real time when the family members use the remote control terminal; and the one-key push module is arranged to predict the operation settings of the family members on household appliances by using the machine learning model and to visually display the operation settings to the family members through the remote control terminal, so that the problem of safety accidents caused by the excessive attraction of remote operation to the attention of users is avoided.

Description

Technical Field

[0001] This invention belongs to the field of home appliances and relates to remote control technology, specifically a remote control system for home appliances based on the Internet of Things. Background Technology

[0002] Current IoT technology is sufficient to support users to remotely control home appliances in real time. This means that users send control information to home appliances through a remote operation interface, and then transmit the control information to the wireless receiver of the home appliances through a wireless network. The home appliances then execute the corresponding control information. However, operating home appliances is relatively cumbersome. When operating them remotely, users often need to focus on the remote operation interface. This behavior can cause inconvenience or even danger when users are performing other activities. For example, focusing on remote control while driving may be dangerous.

[0003] To address this, a remote control system for home appliances based on the Internet of Things (IoT) is proposed. Summary of the Invention

[0004] This invention aims to at least solve one of the technical problems existing in the prior art. To this end, this invention proposes an Internet of Things (IoT)-based remote control system for home appliances. This IoT-based remote control system automatically pushes the most suitable appliance operations to the user, avoiding safety accidents caused by excessive user attention being drawn to remote operation.

[0005] To achieve the above objectives, an Internet of Things-based remote control system for home appliances is proposed according to an embodiment of the first aspect of the present invention, comprising 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 one-click push module, and a remote control module; wherein the modules are connected to each other via electrical and / or wireless network means.

[0006] The household appliance information collection module is mainly used to collect basic information about household appliances in advance.

[0007] The basic information of the household appliances includes the category, model, and functional settings of the household appliances;

[0008] The home appliance information collection module sends the collected basic information of the home appliances to the historical operation data collection module;

[0009] The identity recognition module is mainly used to verify the identity of users in the household.

[0010] The identity recognition module determines the current user's identity through the following steps:

[0011] Step S1: Each family member enters their identity information and the corresponding relationship of their identity identification features on the remote control terminal;

[0012] Step S2: Before each family member uses the remote control terminal to remotely control home appliances, the user enters an identity identifier, and the remote control terminal retrieves the family member identity corresponding to that identifier.

[0013] The historical operation data collection module is mainly used to collect the operating habits of each family member on household appliances in real time.

[0014] The historical operation data collection module collects family members' operating habits of home appliances in the following way:

[0015] When each family member operates home appliances using a remote control terminal, the identity recognition module is used to identify the operator and record the operation results performed by the operator and the current environmental conditions; the real-time functional status refers to the parameters of various functions of the current home appliances, such as air conditioning cooling or heating, and the set temperature data; the current environmental conditions include the current date, season, time, air humidity, and other environmental parameters related to the functions of each home appliance.

[0016] The historical operation data collection module will send the collected operating habits of each family member for home appliances to the model training module;

[0017] The model training module is mainly used to train a corresponding machine learning model to predict the target state of the machine based on the historical operation data of each family member.

[0018] The model training module trains a machine learning model for predicting the operations to be performed, including the following steps:

[0019] Step P1: Categorize the collected historical operation data by family members and the household appliances operated;

[0020] Step P2: For each family member and household appliance, quantify the historical operation data of that family member for that household appliance, merge it into a digital vector, use the digital vector as input to the machine learning model, and use the target state of the household appliance as output to train the machine learning model.

[0021] Step P3: Set a prediction accuracy threshold in advance based on practical experience. When the prediction accuracy of the trained machine learning model exceeds the prediction accuracy threshold, stop training.

[0022] The model training module sends all trained machine learning models to the one-click push module;

[0023] The real-time information collection module is mainly used to collect the environmental conditions of home appliances in real time when family members use the remote control terminal.

[0024] The real-time information collection module collects the environmental conditions of the household appliances in real time, including the current date, season, time, air humidity, and environmental conditions that affect the function of the currently operating appliances. The environmental conditions that affect the function of the currently operating appliances are collected by several physical sensors installed on the household appliances, and then the collected physical data is sent to the remote control terminal through a wireless signal transmitting device.

[0025] The real-time information collection module sends the environmental conditions of the home appliances collected in real time to the one-click push module;

[0026] The one-click push module is mainly used to use machine learning models to predict the operation settings of family members for home appliances and to display them visually to family members through a remote control terminal.

[0027] The one-click push module predicts family members' operation settings for home appliances in the following way:

[0028] Family members are labeled as p, and household appliances are labeled as e. The machine learning model for family member p operating household appliance e using a remote control terminal is labeled Mpe. The remote control terminal identifies the identity information of the current family member p through the identity recognition module, and then obtains the machine learning model Mpe of family member p and the corresponding household appliance e. The real-time environmental conditions are then quantified and merged into a digital vector form, and the digital vector is input into the machine learning model Mpe to obtain the predicted target state of the household appliance. The remote control terminal calculates the operation steps required to reach the target state based on the current state of the household appliance, and displays the operation steps to family member p through the display interface of the remote control terminal.

[0029] The remote control module is mainly used by users to remotely control home appliances through a remote control terminal after seeing the operation display.

[0030] The remote control module remotely controls home appliances by including the following steps:

[0031] Step Q1: After family member p clicks the operation button on the display interface of the remote control terminal, the remote control terminal sends the corresponding operation instructions to the home appliance via wireless network;

[0032] Step Q2: The control circuit board on the home appliance receives the operation instructions sent by the remote control terminal and operates the home appliance according to the corresponding operation instructions.

[0033] Compared with the prior art, the beneficial effects of the present invention are:

[0034] This invention pre-collects basic information about household appliances, distinguishes family members based on their identification characteristics, and further pre-collects historical data on each family member's habitual behavior with each appliance under different environmental conditions. This historical data is then used as input to a machine learning model to train a model that determines the appropriate state for each appliance under specific environmental conditions. When a family member controls a household appliance via a remote control terminal, the current environmental condition data is collected in real time and input into the corresponding machine learning model to obtain the most suitable appliance state. Based on this state, the required operating steps for the appliance are calculated and displayed to the family member. After display, the family member can control the remote control terminal to send remote operation commands to the appliance. By automatically pushing the most suitable appliance operation to the user, the invention avoids safety accidents caused by excessive user attention during remote operation. Attached Figure Description

[0035] Figure 1 This is a schematic diagram of the present invention. Detailed Implementation

[0036] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0037] Current IoT technology is sufficient to support users to remotely control home appliances in real time. This means that users send control information to home appliances through a remote operation interface, and then transmit the control information to the wireless receiver of the home appliances through a wireless network. The home appliances then execute the corresponding control information. However, operating home appliances is relatively cumbersome. When operating them remotely, users often need to focus on the remote operation interface. This behavior can cause inconvenience or even danger when users are performing other activities. For example, focusing on remote control while driving may be dangerous.

[0038] like Figure 1 As shown, the IoT-based remote control system for home appliances includes 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 one-click push module, and a remote control module; wherein, the various modules are connected to each other via electrical and / or wireless network means.

[0039] The household appliance information collection module is mainly used to collect basic information about household appliances in advance.

[0040] In a preferred embodiment, the basic information of the home appliance includes, but is not limited to, the category, model, and functional settings of the home appliance; preferably, the category includes, but is not limited to, washing machines, air conditioners, refrigerators, and televisions; the model includes the brand of the home appliance and the model number of that appliance within that brand; the functions vary depending on the category of the home appliance.

[0041] The home appliance information collection module sends the collected basic information of the home appliances to the historical operation data collection module;

[0042] The identity recognition module is mainly used to verify the identity of users in the household.

[0043] It is understandable that different family members in the same household have different habits of using home appliances. Therefore, before making settings recommendations to users, it is necessary to determine the current user's identity.

[0044] In a preferred embodiment, the identity recognition module determines the current user's identity by including the following steps:

[0045] Step S1: Each family member enters their identity information and the corresponding relationship of identity identification features on the remote control terminal; preferably, the identity identification features include, but are not limited to, fingerprints, facial features, and voice characteristics;

[0046] Step S2: Before each family member uses the remote control terminal to remotely control home appliances, the user enters an identity identifier, and the remote control terminal retrieves the family member identity corresponding to that identifier.

[0047] The historical operation data collection module is mainly used to collect the operating habits of each family member on household appliances in real time.

[0048] In a preferred embodiment, the historical operation data collection module collects family members' operating habits of household appliances in the following way:

[0049] When each family member operates home appliances using a remote control terminal, an identity recognition module is used to identify the operator and record the operation results performed by that operator and the current environmental conditions. Preferably, the operation results are the real-time functional status of the home appliances after the operator has operated them. The real-time functional status refers to the parameters of various functions of the home appliances, such as air conditioning cooling or heating, and the set temperature data. Furthermore, the current environmental conditions include, but are not limited to, the current date, season, time, air humidity, and other environmental parameters related to the functions of each home appliance, such as the weight of clothes in the washing machine and air humidity that affects the air conditioning function.

[0050] The historical operation data collection module will send the collected operating habits of each family member for home appliances to the model training module;

[0051] The model training module is mainly used to train a corresponding machine learning model to predict the target state of the machine based on the historical operation data of each family member.

[0052] In a preferred embodiment, the model training module trains a machine learning model for predicting the operations to be performed, comprising the following steps:

[0053] Step P1: Categorize the collected historical operation data by family members and the household appliances operated;

[0054] Step P2: For each family member and household appliance, quantify the historical operation data of that family member for that household appliance, merge it into a digital vector, use the digital vector as input to the machine learning model, and use the target state of the household appliance as output to train the machine learning model.

[0055] Step P3: Set a prediction accuracy threshold in advance based on practical experience. When the prediction accuracy of the trained machine learning model is greater than the prediction accuracy threshold, stop training. It can be understood that the number of trained machine learning models is the number of family members multiplied by the number of household appliances. Preferably, the machine learning model can be a deep neural network, a deep belief network, or a support vector machine model.

[0056] The model training module sends all trained machine learning models to the one-click push module;

[0057] The real-time information collection module is mainly used to collect the environmental conditions of home appliances in real time when family members use the remote control terminal.

[0058] In a preferred embodiment, the real-time information collection module collects environmental conditions of the household appliance in real time, including but not limited to the current date, season, time, air humidity, and environmental conditions affecting the function of the appliance. Furthermore, the environmental conditions affecting the function of the appliance are collected by several physical sensors mounted on the household appliance, and the collected physical data is then transmitted to a remote control terminal via a wireless signal transmitting device. It is understood that the physical sensors are selected and installed according to the actual needs of the household appliance, and the physical data sensed by these sensors represents the current environmental conditions.

[0059] The real-time information collection module sends the environmental conditions of the home appliances collected in real time to the one-click push module;

[0060] The one-click push module is mainly used to use machine learning models to predict the operation settings of family members for home appliances and to display them visually to family members through a remote control terminal.

[0061] In a preferred embodiment, the one-click push module predicts the operation settings of family members for home appliances in the following way:

[0062] Family members are labeled as p, and household appliances are labeled as e. The machine learning model for family member p operating household appliance e using a remote control terminal is labeled Mpe. The remote control terminal identifies the identity information of the current family member p through the identity recognition module, and then obtains the machine learning model Mpe of family member p and the corresponding household appliance e. The real-time environmental conditions are then quantified and merged into a digital vector form, and the digital vector is input into the machine learning model Mpe to obtain the predicted target state of the household appliance. The remote control terminal calculates the operation steps required to reach the target state based on the current state of the household appliance, and displays the operation steps to family member p through the display interface of the remote control terminal.

[0063] The remote control module is mainly used by users to remotely control home appliances through a remote control terminal after seeing the operation display.

[0064] In a preferred embodiment, the remote control module remotely controls household appliances by comprising the following steps:

[0065] Step Q1: After family member p clicks the operation button on the display interface of the remote control terminal, the remote control terminal sends the corresponding operation instructions to the home appliance via wireless network;

[0066] In another preferred embodiment, family members can also click the modify button to modify the operation instructions; furthermore, family members can also control the remote control terminal to send operation instructions to home appliances via voice.

[0067] Step Q2: The control circuit board on the home appliance receives the operation instructions sent by the remote control terminal, and operates the home appliance according to the corresponding operation instructions to achieve the target state of the home appliance.

[0068] The above embodiments are only used to illustrate the technical methods of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical methods of the present invention without departing from the spirit and scope of the technical methods of the present invention.

Claims

1. A remote control system for household appliances based on the Internet of Things, characterized in that, The system 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 push module and a remote control module, wherein the modules are connected through electrical and / or wireless network; The household appliance information collection module is configured to collect the basic information of the household appliances in advance and send the collected basic information of the household appliances to the historical operation data collection module; The identity recognition module is configured to verify the identity of the users in the family; The historical operation data collection module is configured to collect the operation habits of each family member on the household appliances in real time and send the collected operation habits of each family member on the household appliances to the model training module; The model training module is configured to train the machine learning model for predicting the target state of the machine according to the historical operation data of each family member and send all the trained machine learning models to the one-key push module; The real-time information collection module is configured to collect the environmental conditions of the household appliances in real time when the family members use the remote control terminal and send the collected environmental conditions of the household appliances to the one-key push module; The model training module for training the machine learning model for predicting the operation comprises the following steps: Step P1: classifying the collected historical operation data according to the family members and the operated household appliances; Step P2: for each family member and household appliance, quantifying the historical operation data of the member on the household appliance, merging the data into a digital vector, taking the digital vector as the input of the machine learning model and taking the target state of the household appliance as the output, and training the machine learning model; Step P3: setting a prediction accuracy threshold according to the actual experience in advance, and stopping the training when the prediction accuracy of the trained machine learning model is greater than the prediction accuracy threshold; The one-key push module is configured to predict the operation settings of the family members on the household appliances using the machine learning model and visually display the prediction to the family members through the remote control terminal; The remote control module is configured to remotely control the household appliances through the remote control terminal; The one-key push module predicts the operation settings of the family members on the household appliances in the following manner: Mark the family members as p and the household appliances as e, then mark the machine learning model for the family member p to operate the household appliance e as Mpe, the remote control terminal identifies the identity information of the current family member p through the identity recognition module, obtains the machine learning model Mpe of the family member p and the corresponding household appliance e, quantifies the real-time environmental conditions, merges the data into a digital vector, inputs the digital vector into the machine learning model Mpe, obtains the predicted target state of the household appliance, calculates the operation steps required to reach 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. 2.The IoT-based remote control system of home appliances according to claim 1, wherein, The basic information of the household appliances includes the category, model and function settings of the household appliances. 3.The IoT-based remote control system of home appliances according to claim 1, wherein, The identity recognition module determines the current user identity, comprising the following steps: Step S1: Each family member enters the identity information and the corresponding relationship between the identity information and the identity features on the remote control terminal; Step S2: The user inputs the identity features, and the remote control terminal searches the identity of the family member corresponding to the identity features. 4.The IoT-based remote control system of home appliances according to claim 1, wherein, The historical operation data collection module collects the operation habits of the family members on the household appliances, comprising the following steps: When each family member uses the remote control terminal to operate the household appliances, the identity recognition module determines the identity of the operator, and records the operation results performed by the operator and the current environmental conditions; the real-time function state is the parameters of each function of the current household appliances, and the current environmental conditions include the current date, season, time, air humidity, and the environmental parameters related to each function of the household appliances. 5.The IoT-based remote control system of home appliances according to claim 1, wherein, The real-time information collection module collects the environmental conditions of the household appliances in real time, including the current date, season, time, air humidity, and the environmental conditions affecting the current operation of the appliances. 6.The IoT-based remote control system of home appliances according to claim 1, wherein, The remote control module remotely controls the household appliances, comprising the following steps: Step Q1: After the family member p clicks the operation button on the display interface of the remote control terminal, the remote control terminal sends the corresponding operation step instructions to the household appliances through the wireless network mode; Step Q2: The control circuit board arranged on the household appliances receives the operation instructions sent by the remote control terminal, and operates the household appliances according to the corresponding operation instructions.

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