Dust removal control method for gas water heater and gas water heater

Abstract

The invention discloses a dust removal control method for a gas water heater and the gas water heater. The dust removal control method for the gas water heaters comprises the following steps that S1, through communication connection between an Internet of Things module and a plurality of locally distributed gas water heaters, rotating speed data of fans of the plurality of gas water heaters at different time points are collected in real time, acquiring local wind power grade, wind speed, PM2.5 concentration and visibility information through a mobile phone terminal of a user, and sending the information to the Internet of Things module; s2, arranging and storing the rotating speed data, the wind power level, the wind speed, the PM2.5 concentration and the visibility information by the Internet of Things module to form a data packet, and sending the data packet to a central control system of the gas water heater; and S3, the central control system of the gas water heater judges whether dust removal operation is executed or not according to the data packet, and if the dust removal operation is executed, the central control system instructs a fan to operate. According to the dust removal control method for the gas water heater, the automatic dust removal effect can be achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of household appliances, and particularly to a dust removal control method for a gas water heater and a gas water heater. Background Art

[0002] In the field of intelligent household gas water heaters, as the usage time increases, dust, particulate matter, etc. in the air will enter the interior of the gas water heater, especially adhering to the blower, affecting the normal operation of the blower and the performance of the water heater.

[0003] To address the problems of dust and particulate matter, some existing gas water heaters are provided with simple filters at the air inlet to block the entry of larger particulate dust. However, such filters can only block some larger particles, and have limited filtering effect on fine particles. Another part of high-end water heaters may be equipped with a regular maintenance reminder function to remind users to clean and maintain the water heater, but this requires users to take the initiative to operate and cannot handle the particulate pollution problem in a timely manner.

[0004] Therefore, there is an urgent need for a dust removal control method for a gas water heater to solve the above problems. Summary of the Invention

[0005] The present invention aims to solve at least one of the problems existing in the related art to some extent. For this purpose, the present invention proposes a dust removal control method for a gas water heater, which collects local environmental data through a mobile phone and collects the rotation speed data of multiple local blowers at different time points through an Internet of Things module, realizes the fusion of multi-source data, and uses the blower to achieve an automatic dust removal effect. These different types of data complement each other, can more comprehensively reflect the operating environment and state of the blower, and improve the judgment accuracy of the dust removal requirements of the blower. Moreover, the mobile phone end and the Internet of Things module can collect relevant data in real time, enabling the system to quickly respond to environmental changes and timely adjust the dust removal operation of the blower, further improving the dust removal control accuracy.

[0006] The above object is achieved by the following technical solutions:

[0007] A dust removal control method for a gas water heater includes the following steps:

[0008] S1: Communicate with multiple gas water heaters distributed locally through an Internet of Things module, and collect the rotation speed data of multiple blowers arranged inside the gas water heater at different time points in real time. Obtain the local wind force level, wind speed, PM2.5 concentration, and visibility information through the user's mobile phone end and send it to the Internet of Things module;

[0009] S2: The IoT module organizes, stores the rotational speed data, the wind force level, the wind speed, the PM2.5 concentration, and the visibility information to form a data packet and sends it to the central control system of the gas water heater;

[0010] S3: The central control system of the gas water heater determines whether to perform a dust removal operation according to the data packet. If the dust removal operation is to be performed, the central control system instructs the blower to operate to generate an air flow to discharge the dust from the inside of the gas water heater.

[0011] Optionally, in S1, obtaining the local wind force level, wind speed, PM2.5 concentration, and visibility information through the mobile phone and sending them to the IoT module includes the following steps:

[0012] S11: An application program related to the gas water heater is set on the mobile phone;

[0013] S12: Use the built-in GPS positioning system of the mobile phone to obtain the longitude and latitude information where the mobile phone is located;

[0014] S13: The application program accesses the meteorological data service platform of the local meteorological department through a network connection and sends a meteorological data query request containing the longitude and latitude information to the meteorological data service platform;

[0015] S14: The meteorological data service platform sends the corresponding wind force level and wind speed information to the application program according to the longitude and latitude information;

[0016] S15: The application program receives the wind force level and wind speed information and sends them to the central control system through a wireless network.

[0017] Optionally, in S1, communicating with multiple gas water heaters distributed locally through the IoT module and collecting the rotational speed data of the blowers of multiple gas water heaters at different time points includes the following steps:

[0018] The IoT module communicates with the central control system through Bluetooth, WiFi, or the IoT communication protocol. A rotational speed sensor is set inside the blower. The rotational speed sensor measures the rotational speed information of the blower in real time and sends it to the central control system. The central control system sends the rotational speed information to the IoT module.

[0019] Optionally, when the connection between the IoT module and the central control system is interrupted, the IoT module reconnects to the central control system and continues to receive the rotational speed information of the blower after the connection is restored.

[0020] Optionally, when multiple connections between the IoT module and the central control system are unsuccessful, the central control system issues an alarm for the gas water heater to disconnect from the network to remind the user to perform a manual network connection.

[0021] Optionally, S3 includes the following steps:

[0022] S31: The machine learning algorithm unit of the central control system uses the LSTM algorithm to establish a fan dust removal prediction model;

[0023] S32: The central control system acquires the data packet;

[0024] S32: The data processing unit of the central control system performs missing value processing, outlier processing, and normalization processing on the rotational speed data, wind force level, wind speed, PM2.5 concentration, and visibility information in the data packet;

[0025] S33: Input the data packet information into the fan dust removal prediction model;

[0026] S34: The fan dust removal prediction model outputs a dust removal probability K. If K > 0, then proceed to S35. If K < 0, then it is determined that no dust removal operation needs to be performed and return to S32;

[0027] S35: The central air control system instructs the fan to perform corresponding dust removal operations according to the dust removal probability K.

[0028] Optionally, S35 includes the following steps:

[0029] When 0 < K < 0.3, then the central control system issues an inertial dust removal instruction to the fan, the fan operates at S1 speed, and the fan operates for T1 minutes per hour;

[0030] When 0.3 ≤ K < 0.6, then the central control system issues a centrifugal dust removal instruction to the fan, the fan operates at S2 speed, and the fan operates for T2 minutes per hour;

[0031] When 0.6 ≤ K < 1, then the central control system issues a filtration dust removal instruction to the fan, the fan operates at S3 speed, and the fan operates for T3 minutes per hour;

[0032] S3 < S1 < S2, T3 > T1 > T2.

[0033] Optionally, S1 is 1200 rpm, S2 is 1500 rpm, S3 is 1000 rpm, T1 is 20 min, T2 is 15 min, and T3 is 25 min.

[0034] On the other hand, the present invention provides a gas water heater that operates using the above-described dust removal control method for a gas water heater.

[0035] Compared with the prior art, the present invention has at least the following beneficial effects:

[0036] The dust removal control method for a gas water heater provided by the present invention collects local environmental data through a mobile phone and collects the rotational speed data of multiple local fans at different time points through an Internet of Things module, realizing the fusion of multi-source data and using the fans to achieve an automatic dust removal effect. These different types of data complement each other, can more comprehensively reflect the operating environment and state of the fans, and improve the accuracy of judging the dust removal requirements of the fans. Moreover, the mobile phone end and the Internet of Things module can collect relevant data in real time, enabling the system to quickly respond to environmental changes and timely adjust the dust removal operations of the fans, further improving the dust removal control accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 is a step diagram of the dust removal control method for a gas water heater provided by a specific embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0038] The following embodiments are used to illustrate the present invention, but the present invention is not limited by these embodiments. Modifying the specific implementation manner of the present invention or equivalently replacing some technical features without departing from the spirit of the present invention's solution shall all be covered within the scope of the technical solution claimed by the present invention.

[0039] Please refer to Figure 1 , the present invention provides a dust removal control method for a gas water heater, including the following steps:

[0040] S1: Communicate with multiple gas water heaters distributed locally through an Internet of Things module, and collect the rotational speed data of multiple fans installed inside the gas water heaters at different time points in real time. Obtain the local wind force level, wind speed, PM2.5 concentration, and visibility information through the user's mobile phone and send it to the Internet of Things module;

[0041] S2: The Internet of Things module organizes, stores the rotational speed data, wind force level, wind speed, PM2.5 concentration, and visibility information to form a data packet and sends it to the central control system of the gas water heater;

[0042] S3: The central control system of the gas water heater determines whether to perform a dust removal operation based on the data packet. If a dust removal operation is to be performed, the central control system instructs the fan to operate to generate an air flow to discharge the dust from inside the gas water heater.

[0043] The dust removal control method for gas water heaters provided by the present invention collects local environmental data through a mobile phone and collects the rotational speed data of multiple local fans at different time points through an Internet of Things module, realizing the fusion of multi-source data and using the fans to achieve an automatic dust removal effect. These different types of data complement each other, can more comprehensively reflect the operating environment and status of the fans, and improve the judgment accuracy of the dust removal requirements of the fans. Moreover, the mobile phone end and the Internet of Things module can collect relevant data in real time, enabling the system to quickly respond to environmental changes and timely adjust the dust removal operation of the fans, further improving the dust removal control accuracy.

[0044] Optionally, in S1, obtaining the local wind level, wind speed, PM2.5 concentration, and visibility information through the mobile phone end and sending them to the Internet of Things module includes the following steps:

[0045] S11: Set an application related to the gas water heater on the mobile phone;

[0046] S12: Use the built-in GPS positioning system of the mobile phone to obtain the longitude and latitude information of the location where the mobile phone is located;

[0047] S13: The application accesses the meteorological data service platform of the local meteorological department through a network connection and sends a meteorological data query request containing the longitude and latitude information to the meteorological data service platform;

[0048] S14: The meteorological data service platform sends the corresponding wind level and wind speed information to the application according to the longitude and latitude information;

[0049] S15: The application receives the wind level and wind speed information and sends them to the central control system through a wireless network.

[0050] Utilizing the wind level and wind speed information released by the local meteorological department has a relatively high accuracy, eliminating the need for the system to measure the wind force and wind speed separately and avoiding an overly large system.

[0051] Optionally, in S1, communicating with multiple gas water heaters distributed locally through the Internet of Things module and collecting the rotational speed data of the fans of multiple gas water heaters at different time points includes the following steps:

[0052] The Internet of Things module communicates with the central control system through Bluetooth, WiFi, or Internet of Things communication protocols. A rotational speed sensor is set inside the fan. The rotational speed sensor measures the rotational speed information of the fan in real time and sends it to the central control system. The central control system sends the rotational speed information to the Internet of Things module. The structure is simple and compact, and the reliability is relatively high.

[0053] Optionally, when the connection between the Internet of Things module and the central control system is interrupted, the Internet of Things module reconnects with the central control system and continues to receive the rotational speed information of the fan after the connection is restored to ensure the stability of the connection.

[0054] Optionally, when multiple connections between the Internet of Things module and the central control system are unsuccessful, the central control system issues an alarm for the gas water heater to disconnect from the network to remind the user to perform a manual network connection to avoid the system being in a disconnected state for a long time.

[0055] Optionally, S3 includes the following steps:

[0056] S31: The machine learning algorithm unit of the central control system uses the LSTM algorithm to establish a fan dust removal prediction model;

[0057] S32: The central control system obtains data packets;

[0058] S32: The data processing unit of the central control system performs missing value processing, outlier processing, and standardization processing on the rotational speed data, wind force level, wind speed, PM2.5 concentration, and visibility information in the data packet;

[0059] S33: Input the data packet information into the fan dust removal prediction model;

[0060] S34: The fan dust removal prediction model outputs the dust removal probability K. If K > 0, then proceed to S35. If K < 0, then it is determined that no dust removal operation needs to be performed and return to S32;

[0061] S35: The central air control system instructs the fan to perform corresponding dust removal operations according to the dust removal probability K.

[0062] Furthermore, in this application, by inputting the preset data packet and the judgment of whether dust removal is required into the LSTM algorithm, the LSTM model is trained. When the prediction probability K output by the LSTM model meets the requirements, the fan dust removal prediction model is established. This is prior art and will not be elaborated here.

[0063] Using the LSTM algorithm to establish a fan dust removal prediction model realizes the intelligent control of the fan dust removal operation. The system can automatically judge according to the environmental data and the fan status without manual intervention by the user, improving the convenience and efficiency of use.

[0064] Optionally, S35 includes the following steps:

[0065] When 0 < K < 0.3, the central control system issues an inertial dust removal instruction to the fan, and the fan runs at speed S1 for T1 minutes per hour;

[0066] When 0.3 ≤ K < 0.6, the central control system issues a centrifugal dust removal instruction to the fan, and the fan runs at speed S2 for T2 minutes per hour;

[0067] When 0.6 ≤ K < 1, the central control system issues a filtering and dust removal instruction to the fan, and the fan operates at speed S3, running for T3 minutes per hour.

[0068] S3 < S1 < S2, T3 > T1 > T2.

[0069] Optionally, S1 is 1200 rpm, S2 is 1500 rpm, S3 is 1000 rpm, T1 is 20 min, T2 is 15 min, and T3 is 25 min.

[0070] Optionally, in S32, the data processing unit of the central control system performs missing value processing on the rotational speed data, wind force level, wind speed, PM2.5 concentration, and visibility information in the data packet by using the multiple imputation method combined with a regression model.

[0071] Optionally, in S32, the data processing unit of the central control system performs outlier processing on the rotational speed data, wind force level, wind speed, PM2.5 concentration, and visibility information in the data packet by using the LOF algorithm (local outlier factor detection method based on density).

[0072] Optionally, in S32, the data processing unit of the central control system normalizes the rotational speed data, wind force level, wind speed, PM2.5 concentration, and visibility information in the data packet, that is, maps the rotational speed data, wind force level, wind speed, PM2.5 concentration, and visibility information into the interval [0, 1]. For example, mapping the rotational speed data into the interval [0, 1], the formula is:

[0073]

[0074] where x speed is the rotational speed data of the current gas water heater fan, x speed_max is the maximum value of the rotational speed data set of the current gas water heater fan, and x speed_min is the minimum value of the rotational speed data set of the current gas water heater fan.

[0075] The wind force level, wind speed, PM2.5 concentration, and visibility information are also mapped into the interval [0, 1] through the above formula. This is for the convenience of the fan dust removal prediction model to capture data for use.

[0076] On the other hand, the present invention provides a gas water heater that operates using the above gas water heater dust removal control method.

[0077] The above are only some embodiments of the present invention. For those of ordinary skill in the art, without departing from the inventive concept of the present invention, several variations and improvements can still be made, and these all fall within the protection scope of the present invention.

Claims

1. A dust removal control method for a gas water heater, characterized in that, It includes the following steps: S1: Communicate and connect with multiple locally distributed gas water heaters through the Internet of Things module, and collect in real time the rotational speed data of the fans installed inside the gas water heaters at different time points. Obtain the local wind level, wind speed, PM2.5 concentration, and visibility information through the user's mobile phone and send them to the Internet of Things module; S2: The Internet of Things module sorts, stores the rotational speed data, the wind level, the wind speed, the PM2.5 concentration, and the visibility information to form a data packet and sends it to the central control system of the gas water heater; S3: The central control system of the gas water heater determines whether to perform a dust removal operation according to the data packet. If the dust removal operation is to be performed, the central control system instructs the fan to operate to generate an air flow to discharge the dust from inside the gas water heater.

2. The dust removal control method of the gas water heater according to claim 1, wherein In S1, obtaining the local wind level, wind speed, PM2.5 concentration, and visibility information through the mobile phone and sending them to the Internet of Things module includes the following steps: S11: Set an application program related to the gas water heater on the mobile phone; S12: Use the built-in GPS positioning system of the mobile phone to obtain the longitude and latitude information where the mobile phone is located; S13: The application program accesses the meteorological data service platform of the local meteorological department through a network connection and sends a meteorological data query request containing the longitude and latitude information to the meteorological data service platform; S14: The meteorological data service platform sends the corresponding wind level and wind speed information to the application program according to the longitude and latitude information; S15: The application program receives the wind level and wind speed information and sends them to the central control system through a wireless network.

3. The dust removal control method of the gas water heater according to claim 1, characterized in that, In S1, communicating and connecting with multiple locally distributed gas water heaters through the Internet of Things module and collecting in real time the rotational speed data of the fans of the gas water heaters at different time points includes the following steps: The Internet of Things module communicates and connects with the central control system through Bluetooth, WiFi, or Internet of Things communication protocol. A rotational speed sensor is installed in the fan. The rotational speed sensor measures the rotational speed information of the fan in real time and sends it to the central control system. The central control system sends the rotational speed information to the Internet of Things module.

4. The dust removal control method of the gas water heater according to claim 3, characterized in that, When the connection between the Internet of Things module and the central control system is interrupted, the Internet of Things module reconnects with the central control system and continues to receive the rotational speed information of the fan after the connection is restored.

5. The dust removal control method of the gas water heater according to claim 4, characterized in that, When multiple connections between the Internet of Things module and the central control system are unsuccessful, the central control system issues an alarm for the gas water heater to disconnect the network connection to remind the user to perform a manual network connection.

6. The dust removal control method of the gas water heater according to any one of claims 1-5, characterized in that, S3 includes the following steps: S31: The machine learning algorithm unit of the central control system uses the LSTM algorithm to establish a fan dust removal prediction model; S32: The central control system obtains the data packet; S32: The data processing unit of the central control system performs missing value processing, outlier processing, and normalization processing on the rotational speed data, wind force level, wind speed, PM2.5 concentration, and visibility information in the data packet; S33: Input the data packet information into the fan dust removal prediction model; S34: The fan dust removal prediction model outputs the dust removal probability K. If K > 0, go to S35. If K < 0, it is determined that there is no need to perform dust removal operation and return to S32; S35: The central air control system instructs the fan to perform corresponding dust removal operations according to the dust removal probability K.

7. The dust removal control method of the gas water heater according to claim 6, wherein, S35 includes the following steps: When 0 < K < 0.3, the central control system issues an inertial dust removal instruction to the fan, and the fan operates at S1 speed, and the fan operates for T1 minutes per hour; When 0.3 ≤ K < 0.6, the central control system issues a centrifugal dust removal instruction to the fan, and the fan operates at S2 speed, and the fan operates for T2 minutes per hour; When 0.6 ≤ K < 1, the central control system issues a filtration dust removal instruction to the fan, and the fan operates at S3 speed, and the fan operates for T3 minutes per hour; S3 < S1 < S2, T3 > T1 > T2.

8. The dust removal control method of the gas water heater according to claim 7, characterized in that, S1 is 1200 rpm, S2 is 1500 rpm, S3 is 1000 rpm, T1 is 20 min, T2 is 15 min, and T3 is 25 min.

9. A gas water heater, characterized in that, Operate using the gas water heater dust removal control method according to any one of claims 1-8.

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