Human-sensing bath heater and control method thereof

Abstract

The invention relates to a human-sensing bath heater and a control method thereof. The problems that in the prior art, a bath heater is difficult to carry out automatic linkage control adjustment according to the use scene requirement of a user, and the customized requirement of the user cannot be met are solved. The system comprises a hardware layer, a software layer and a cloud service layer. The hardware layer comprises a human body sensor, a temperature and humidity sensor, a control module, a heating module, an illumination module, a communication module and a power supply module. The software layer receives sensor data processed by the hardware layer and executes core control logic and scene logic; and the cloud service layer stores user configuration data, push notifications and statistical energy consumption data. The method has the advantages that automatic configuration and scene control of the bath heater are achieved, the requirement for scene custom linkage of a user is met, and the use effect is good.

Description

Technical Field

[0001] This invention relates to the field of intelligent device control technology, specifically to a human-sensor bathroom heater and its control method. Background Technology

[0002] Bathroom heaters come in various types, including light-based and fan-based models, and often integrate general lighting and ventilation functions. Currently, in most bathroom heater products, the heater and switch are located in separate ceiling and wall positions, respectively. The switch directly connects to and controls the various functions of the heater, which is not only inconvenient for installation and replacement but also makes it difficult to customize the product to meet individual user needs, resulting in a low level of intelligence. Furthermore, traditional bathroom heaters struggle to automatically adjust and control according to user scenarios, leading to a poor user experience, failing to meet customized needs, and being detrimental to water conservation.

[0003] To address the shortcomings of existing technologies, people have conducted long-term explorations and proposed various solutions. For example, Chinese patent literature discloses an intelligent bathroom heater system [CN111322662B], which includes a first control module and at least one functional module, the at least one functional module including a heating module; a control panel is installed on the wall, and the control panel is equipped with a second control module and at least one functional switch, the at least one functional switch and the second control module are electrically connected, the second control module receives the switch control signal generated by the at least one functional switch and converts the switch control signal into a corresponding control command; the second control module and the first control module are communicatively connected, and the first control module controls the corresponding functional module to perform the corresponding function according to the control command issued by the second control module.

[0004] The above solution has solved to some extent the problems of inconvenient installation and replacement of bathroom heater switches and difficulty in intelligent control in the existing technology. However, the solution still has many shortcomings, such as: difficulty in automatic linkage control and adjustment according to user usage scenario needs, poor user experience, inability to meet user customization needs, and is not conducive to water conservation. Summary of the Invention

[0005] The purpose of this invention is to address the above-mentioned problems by providing a human-sensing bathroom heater and its control method.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: a human-sensing bathroom heater, comprising a hardware layer, a software layer, and a cloud service layer. The hardware layer includes a human body sensor, a temperature and humidity sensor, a control module, a heating module, a lighting module, a communication module, and a power supply module. The software layer receives sensor data processed by the hardware layer and executes core control logic and scene logic. The cloud service layer stores user configuration data, push notifications, and statistical energy consumption data.

[0007] In the aforementioned human-sensor bathroom heater, the human body sensor is a millimeter-wave radar sensor with a detection distance of 1-4m and a detection angle of 120°; the control module is a low-power MCU; the heating module is a PTC ceramic heating element; the lighting module is an LED panel light; the communication module integrates a WiFi module; the power module inputs AC220V and outputs DC12V / 5V, and is an isolated power supply.

[0008] The above-mentioned human-sensor bathroom heater provides a control method, including the following steps: S1. Human body detection; S2, linkage control; S3, Automatic Scene Configuration; S4, cloud storage.

[0009] In step S1, the human body detection process uses the hardware layer sensing data received by the software layer for processing. The processing steps include: S11. Filtering algorithm: Perform mean filtering / Kalman filtering on the raw data of the millimeter-wave radar sensor to remove environmental interference; S12. Human presence detection: Avoids false triggering through dynamic and static dual detection logic; The dynamic detection type is: human movement; The static detection type is: the human body is stationary, but the radar detects micro-movements.

[0010] In step S12, based on the valid trigger signal, a stable "manned / unmanned" status is output. The algorithm logic is as follows: When a valid trigger is detected, the system switches to "occupied" state and starts the timeout timer (timer=timeout). - The timer decrements every second, and the "occupied" state is maintained when timer>0; If timer=0 and no new triggers occur, switch to "No One"; The formula is: state = { Someone: Effectively triggered ∨timer>0 No one: timer=0 ∧ No new triggers }; Symbol explanation: timeout: The timeout period.

[0011] In step S12, compensation is performed for motion detection of stationary human bodies to prevent stationary human bodies from being mistakenly identified as "no one" due to the lack of trigger signals; the algorithm logic is as follows: The timeout period is dynamically adjusted based on the number of triggers per unit of time; the more frequent the activity, the longer the timeout. The formula is: timeout={ base_timeout+5A: A>0 indicates an activity. max(base_timeout / 2,10): A=0 and t_static<300s short-term static state. }; Symbol explanation: A: Number of valid triggers per unit of time; base_timeout: Base timeout period; t_static: Duration of stillness.

[0012] In step S2, the linkage control includes wireless linkage control and offline linkage control; wherein: S21. Wireless linkage control: Visualize the configuration of human-sensing scenarios through the WIFI module, and distinguish between three scenario logics: human entry, human presence, and human departure. S22, Offline Linkage Control: Implements a 24-hour effective mechanism to execute any function as required.

[0013] In step S3, after the device is configured with the network, the network time is obtained, and automatic scenario configuration is performed in conjunction with the already connected app. The automatic scenario configuration includes: ①Human-sensing lighting: Sensing from 18:00 to 21:59, lighting will turn on if a person is present for 0 seconds and if a person leaves for 30 seconds. Switch control is supported. ②Human-sensing blower: Sensing for 5 seconds when a person is present and 30 seconds when a person leaves to activate the blower. The blower is set to the highest setting and features a rotating blade system. It supports on / off control. ③ Human-sensing ventilation: Sensing from 10:00 to 18:00, ventilation is activated after 10 seconds when a person is present and after 30 seconds when a person leaves. The ventilation setting is the highest level and can be controlled by an on / off switch. ④ Human-sensored deodorization: Sensing from 10:00 to 18:00, deodorization is activated after 20 seconds when a person is present and after 30 seconds when a person leaves. The oscillating leaf circulation function is supported and can be controlled by an on / off switch. ⑤ Human-sensing night light: Factory-activated 24-hour sensing. After successful network pairing, the sensor will activate from 22:00 to 6:00. It will trigger when a person is present for 0 seconds (cannot be changed) and when a person leaves for 30 seconds. It supports switch control.

[0014] In step S4, the cloud storage stores the user-defined configuration and pushes device status and statistical energy consumption data through the mobile terminal.

[0015] In step S4, the configuration data stored in the cloud provides a selection type after the device connects to the network, specifically including: A. Coverage and execution in cloud-based scenarios; B. Maintain the current offline configuration status; C. Upload the current offline configuration and keep the device running.

[0016] Compared with existing technologies, the advantages of this invention are as follows: it achieves intelligent control of automatic human presence detection, linkage lighting, and heating modules, with a high degree of automation; secondly, it requires no manual operation, turning on when someone arrives and turning off when they leave, with customizable delays, thus optimizing the user experience; it also effectively avoids unused circuit breakers, reduces standby power consumption, saves energy, and achieves the goal of energy conservation and efficiency improvement; at the same time, it supports integration into the home smart ecosystem, realizing multi-scenario control, improving the user experience, and meeting users' personalized customization needs. Attached Figure Description

[0017] Figure 1 This is a block diagram showing the overall structure and connections of the present invention; Figure 2 This is a schematic diagram of the hardware layer functional connections of the present invention; Figure 3 This is a schematic diagram of the linkage control of the present invention; The diagram shows: Hardware layer 1, Human body sensor 11, Temperature and humidity sensor 12, Control module 13, Heating module 14, Lighting module 15, Communication module 16, Power supply module 17, Software layer 2, Cloud service layer 3. Detailed Implementation

[0018] The present invention will now be described in further detail with reference to the accompanying drawings and specific embodiments.

[0019] like Figure 1-3 As shown, a human-sensing bathroom heater includes a hardware layer 1, a software layer 2, and a cloud service layer 3. The hardware layer 1 includes a human body sensor 11, a temperature and humidity sensor 12, a control module 13, a heating module 14, a lighting module 15, a communication module 16, and a power supply module 17. The software layer 2 receives sensor data from the hardware layer 1, processes the sensor data, and executes core control logic and scene logic. The cloud service layer 3 stores user configuration data, push notifications, and statistical energy consumption data.

[0020] The overall logic is "sensor perception → algorithm decision → module control → cloud linkage", thereby realizing automatic human body detection and linkage control, intelligent control of heating module 14, no manual operation required, reducing energy consumption, and supporting access to the home smart ecosystem to realize scenario-based control.

[0021] The function of temperature and humidity sensor 12 is to automatically turn on ventilation when the bathroom humidity is >80%.

[0022] The human-sensor bathroom heater is equipped with a function display panel and a status display panel, which makes it easy for users to manually adjust and observe the working status.

[0023] The system includes a millimeter-wave radar sensor, which is unaffected by temperature and light, and can detect static human bodies. The detection range is 1-4 meters, and the detection angle is 120°. The control module 13 is a low-power MCU used to process sensor data and execute control logic. The heating module 14 uses a PTC ceramic heating element and supports power adjustment. The lighting module 15 is an LED panel light with adjustable brightness and supports warm / white light switching. The communication module 16 integrates a WiFi module, supports the MQTT protocol, and is used to connect to a smart home platform and handle human-sensing scene logic. The power module 17 inputs AC220V and outputs DC12V / 5V, and is an isolated power supply. All components meet home appliance safety standards.

[0024] A method for controlling a human-sensor bathroom heater includes the following steps: S1. Human body detection; S2, linkage control; S3, Automatic Scene Configuration; S4, cloud storage.

[0025] In step S1, the human body detection process involves processing the sensor data received from hardware layer 1 via software layer 2. The processing steps include: S11, Filtering Algorithm: Perform mean filtering / Kalman filtering on the raw data of the millimeter-wave radar sensor to remove environmental interference such as swaying curtains or passing pets; S12. Human presence detection: Avoids false triggering through dynamic and static dual detection logic; The dynamic detection type is: human movement; The static detection type is: the human body is stationary, but the radar detects micro-movements.

[0026] In step S12, based on the valid trigger signal, a stable "manned / unmanned" status is output. The algorithm logic is as follows: When a valid trigger is detected, the system switches to "occupied" state and starts the timeout timer (timer=timeout). - The timer decrements every second, and the "occupied" state is maintained when timer>0; If timer=0 and no new triggers occur, switch to "No One"; The formula is: state = { Someone: Effectively triggered ∨timer>0 No one: timer=0 ∧ No new triggers }; Symbol explanation: timeout: The timeout period.

[0027] Recommended duration: 5~300 seconds. Scene adjustable: 5~10 seconds for corridor, 60~300 seconds for bedroom.

[0028] In step S12, compensation is performed for motion detection of stationary human bodies to prevent stationary human bodies from being mistakenly identified as "no one" due to the lack of trigger signals; the algorithm logic is as follows: The timeout period is dynamically adjusted based on the number of triggers per unit of time and the activity level; the more frequent the activity, the longer the timeout. The formula is: timeout={ base_timeout+5A: A>0 indicates an activity. max(base_timeout / 2,10): A=0 and t_static<300s short-term static state. }; Symbol explanation: A: Activity level: Number of effective triggers per unit of time, unit: times / minute; base_timeout: Recommended base timeout is 60 seconds; t_static: Duration of stillness, in seconds.

[0029] In step S2, the linkage control includes wireless linkage control and offline linkage control; wherein: S21. Wireless linkage control: Visualize the configuration of human-sensing scenarios through the WIFI module, and distinguish between three scenario logics: human entry, human presence, and human departure. S22, Offline Linkage Control: Implements a 24-hour activation mechanism, allowing the execution of any function as needed. For example, the default function is lighting.

[0030] In step S3, after the device is configured with the network, it obtains the network time and performs automatic scenario configuration in conjunction with the connected apps, such as Mi Home, Huawei Smart Life, WeChat Mini Programs, and other IoT platforms. Automatic scenario configuration includes: ①Human-sensing lighting: Sensing from 18:00 to 21:59, lighting will turn on if a person is present for 0 seconds and if a person leaves for 30 seconds. Switch control is supported. ②Human-sensing blower: Sensing for 5 seconds when a person is present and 30 seconds when a person leaves to activate the blower. The blower is set to the highest setting and features a rotating blade system. It supports on / off control. ③ Human-sensing ventilation: Sensing from 10:00 to 18:00, ventilation is activated after 10 seconds when a person is present and after 30 seconds when a person leaves. The ventilation setting is the highest level and can be controlled by an on / off switch. ④ Human-sensored deodorization: Sensing from 10:00 to 18:00, deodorization is activated after 20 seconds when a person is present and after 30 seconds when a person leaves. The oscillating leaf circulation function is supported and can be controlled by an on / off switch. ⑤ Human-sensing night light: Factory-activated 24-hour sensing. After successful network pairing, the sensor will activate from 22:00 to 6:00. It will trigger when a person is present for 0 seconds (cannot be changed) and when a person leaves for 30 seconds. It supports switch control.

[0031] When connected to the internet, the device automatically controls itself according to preset scenarios, improving the automatic experience.

[0032] In step S4, the cloud storage stores the user-defined configuration and pushes device status and statistical energy consumption data through the mobile terminal.

[0033] User-stored configurations can be accessed with a single click from the cloud at any time, without the need for cumbersome setup.

[0034] In step S4, the configuration data stored in the cloud provides a selection type after the device connects to the network, specifically including: A. Coverage and execution in cloud-based scenarios; B. Maintain the current offline configuration status; C. Upload the current offline configuration and keep the device running.

[0035] After connecting to the network, it provides users with multiple working modes to choose from, improving the flexibility of use.

[0036] In summary, the principle of this embodiment is as follows: human body and temperature and humidity data are sensed by sensors such as millimeter-wave radar at the hardware layer, and the "manned / unmanned" status is determined by the filtering algorithm, dynamic and static dual detection logic and dynamic timeout adjustment algorithm at the software layer. Then, wireless or offline linkage control of modules such as heating and lighting is executed according to the status. At the same time, multi-time period and multi-scenario functions are automatically configured based on network time and access IoT platform. Finally, the cloud service layer stores user configuration, pushes device status and counts energy consumption. The whole process supports manual adjustment and cloud / offline configuration switching, realizing intelligent control and energy consumption optimization without manual operation.

[0037] The specific embodiments described herein are merely illustrative of the spirit of the invention. Those skilled in the art to which this invention pertains may make various modifications or additions to the described specific embodiments or use similar methods to substitute them, without departing from the spirit of the invention or exceeding the scope defined by the appended claims.

[0038] Although this document frequently uses terms such as hardware layer 1, human body sensor 11, temperature and humidity sensor 12, control module 13, heating module 14, lighting module 15, communication module 16, power supply module 17, software layer 2, and cloud service layer 3, the possibility of using other terms is not excluded. These terms are used merely for the convenience of describing and explaining the essence of this invention; interpreting them as any additional limitation would contradict the spirit of this invention.

Claims

1. A human-sensing bathroom heater, comprising a hardware layer (1), a software layer (2), and a cloud service layer (3), characterized in that, The hardware layer (1) includes a human body sensor (11), a temperature and humidity sensor (12), a control module (13), a heating module (14), a lighting module (15), a communication module (16), and a power supply module (17); the software layer (2) receives sensor data from the hardware layer (1) and executes core control logic and scene logic; the cloud service layer (3) stores user configuration data, push notifications, and statistical energy consumption data.

2. The human-sensory bathroom heater according to claim 1, characterized in that, The human body sensor (11) is a millimeter-wave radar sensor with a detection distance of 1-4m and a detection angle of 120°; the control module (13) is a low-power MCU; the heating module (14) is a PTC ceramic heating element; the lighting module (15) is an LED panel light; the communication module (16) integrates a WiFi module; the power supply module (17) inputs AC220V, outputs DC12V / 5V, and is an isolated power supply.

3. A control method applicable to the human-sensing bathroom heater described in claims 1-2, characterized in that, Includes the following steps: S1. Human body detection: Detects human activity using millimeter-wave radar sensors; S2, Linkage Control: Automatically runs, starts, and stops according to the core control logic; S3. Scene Auto-Configuration: Pre-set the required scene configuration files through the mobile app, and the device will automatically run according to the scene configuration files after connecting to the Internet; S4. Cloud storage: Automatically stores user-preset scenario configuration files. When execution strategies conflict after connecting to the network, the user can choose the execution strategy.

4. The control method according to claim 3, characterized in that, In step S1, the human body detection is processed by the sensing data received by the hardware layer (1) through the software layer (2). The processing steps include: S11. Filtering algorithm: Perform mean filtering / Kalman filtering on the raw data of the millimeter-wave radar sensor to remove environmental interference; S12. Human presence detection: Avoids false triggering through dynamic and static dual detection logic; The dynamic detection type mentioned is: human movement; The static detection type is: the human body is stationary, but the radar detects micro-movements.

5. The control method according to claim 4, characterized in that, In step S12, based on the valid trigger signal, a stable "manned / unmanned" status is output. The algorithm logic is as follows: When a valid trigger is detected, the system switches to "occupied" state and starts a timeout timer (timer = timeout). - The timer decrements every second, and remains in "occupied" state when timer > 0; If timer = 0 and no new triggers occur, switch to "unattended" mode. The formula is: state = { Someone: Effectively triggered ∨ timer > 0 No one is online: timer = 0 ∧ No new triggers. ｝； Symbol explanation: timeout: The timeout period.

6. The control method according to claim 5, characterized in that, In step S12, compensation is performed for motion detection of stationary human bodies to prevent stationary human bodies from being mistakenly identified as "no one" due to the lack of trigger signals; the algorithm logic is as follows: The timeout period is dynamically adjusted based on the number of triggers per unit of time; the more frequent the activity, the longer the timeout. The formula is: timeout={ base_timeout + 5A: A > 0 (There is activity) max(base_timeout / 2, 10): A = 0 and t_static < 300s (short-term static) ｝； Symbol explanation: A: Number of valid triggers per unit of time; base_timeout: Base timeout period; t_static: Duration of stillness.

7. The control method according to claim 3, characterized in that, In step S2, the linkage control includes wireless linkage control and offline linkage control; wherein: S21. Wireless linkage control: Visualize the configuration of human-sensing scenarios through the WIFI module, and distinguish between three scenario logics: human entry, human presence, and human departure. S22, Offline Linkage Control: Implements a 24-hour effective mechanism to execute any function as required.

8. The control method according to claim 7, characterized in that, In step S3, after the device is configured with the network, it obtains the network time and performs automatic scenario configuration in conjunction with the already connected app. The automatic scenario configuration includes: ①Human-sensing lighting: Sensing from 18:00 to 21:59, lighting will turn on if a person is present for 0 seconds and if a person leaves for 30 seconds. Switch control is supported. ②Human-sensing blower: Sensing for 5 seconds when a person is present and 30 seconds when a person leaves to activate the blower. The blower is set to the highest setting and features a rotating blade system. It supports on / off control. ③ Human-sensing ventilation: Sensing from 10:00 to 18:00, ventilation is activated after 10 seconds when a person is present and after 30 seconds when a person leaves. The ventilation setting is the highest level and can be controlled by an on / off switch. ④ Human-sensored deodorization: Sensing from 10:00 to 18:00, deodorization is activated after 20 seconds when a person is present and after 30 seconds when a person leaves. The oscillating leaf circulation function is supported and can be controlled by an on / off switch. ⑤ Human-sensing night light: Factory-activated 24-hour sensing. After successful network pairing, the sensor will activate from 22:00 to 6:

00. It will trigger when a person is present for 0 seconds (cannot be changed) and when a person leaves for 30 seconds. It supports switch control.

9. The control method according to claim 8, characterized in that, In step S4, the cloud storage stores user-defined configurations and pushes device status and statistical energy consumption data to the mobile terminal.

10. The control method according to claim 9, characterized in that, In step S4, the configuration data stored in the cloud provides options for the type of execution once the device is connected to the network, specifically including: A. Coverage and execution in cloud-based scenarios; B. Maintain the current offline configuration status; C. Upload the current offline configuration and keep the device running.

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