Intelligent shower system based on large language model and control method thereof

Abstract

The application provides an intelligent shower system and control method based on a large language model, comprising a main control module, a large language model interaction module, a multi-modal environment perception module, an actuator module, a human-computer interaction module and a cloud service platform, user physiological data, shower room environment data, user behavior data and user needs are obtained through the multi-modal environment perception module and the human-computer interaction module, then the large language model interaction module reasons according to the user physiological data, the shower room environment data, the user behavior data and the user needs, to further analyze and understand the user needs, and generates a natural language response with context understanding ability and a structured instruction containing a control intention, to make the main control module drive the actuator module to adjust, to realize adjusting the shower equipment according to the user needs in real time.

Description

Technical Field

[0001] This invention relates to the field of smart homes, and in particular to a smart shower system and control method based on a large language model. Background Technology

[0002] With the rapid development of IoT and AI technologies, traditional home products are gradually evolving towards smart technology. As an indispensable part of daily life, the smart upgrade of showers has enormous market potential and user value.

[0003] Currently, the main functions of smart shower products on the market include thermostatic control, which maintains a stable water temperature through mechanical or electronic thermostatic valves to avoid sudden changes in temperature; preset modes, which allow users to select several pre-set shower modes via a panel or remote control; music playback, which integrates a Bluetooth speaker, allowing users to play music from their mobile phones while showering; and basic lighting, which is equipped with simple monochrome or color-changing LED lights to create ambiance.

[0004] However, existing smart shower products often have pre-set functions that cannot be adjusted according to the user's real-time needs. They also cannot understand the user's complex and ambiguous intentions and lack the ability to analyze user commands. Summary of the Invention

[0005] The purpose of this invention is to address the problems that existing smart shower products often have preset functions that cannot be adjusted according to the user's real-time needs, and that they cannot understand the user's complex and ambiguous intentions and lack the ability to analyze user commands. Therefore, this invention proposes a smart shower device and control method based on a large language model.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: A smart shower system based on a large language model includes: The main control module is used to transmit the received data to the large language model interaction module, and generate drive signals to control the actuator module based on the structured control instructions generated by the large language model interaction module. The large language model interaction module, connected to the main control module, is used to receive and process data from the main control module, generate natural language responses with context understanding capabilities, and output structured instructions containing control intentions. The multimodal environment perception module, connected to the main control module, is used to collect user physiological data, shower room environment data, and user behavior data. The actuator module, connected to the main control module, is used to receive drive signals from the main control module and perform operations such as water temperature adjustment, water volume adjustment, nozzle mode adjustment, fragrance release, light control, and audio playback. The human-computer interaction module, connected to the main control module, is used to receive voice, touch or gesture input from the user and output voice, visual or tactile feedback to the user. The cloud service platform is connected to the main control module via a wireless network and is used to provide large language model computing services, store user data, and perform data analysis and model optimization.

[0007] Furthermore, the large language model interaction module includes: The local semantic understanding submodule is used to perform preliminary intent recognition and slot filling on the user's original input commands, and generate a standardized semantic framework. The cloud-based large language model engine receives standardized semantic frameworks and data from the multimodal environment perception module, and generates natural language responses through its vast knowledge base and reasoning capabilities. The instruction parsing and generation submodule is used in natural language responses to parse out executable and precise control instruction sequences and send them to the main control module.

[0008] Furthermore, the multimodal environment perception module includes: A non-contact physiological sensor array is used to collect the user's heart rate, heart rate variability, body surface temperature, and respiratory rate. An environmental sensor array is used to collect ambient temperature, humidity, ambient light, and water quality parameters in the shower room. A 3D vision sensor is used to identify the user's posture, range of motion, and position in the shower room through skeletal joint tracking technology, in order to determine the user's status and enable gesture interaction.

[0009] Furthermore, the actuator module includes: The dynamic water temperature regulation unit consists of a drive motor and a valve body with dual valve cores. It is used to drive the drive motor according to the drive signal of the main control module to adjust the valve core opening of the valve body with dual valve cores, thereby regulating the water temperature. The multi-dimensional water flow control unit consists of multiple independent solenoid valves and piezoelectric ceramic actuators. The multiple independent solenoid valves are located inside the nozzle to control different water outlet arrays to achieve different shower modes. The multiple piezoelectric ceramic actuators are located in some of the water outlets of the nozzle to achieve high-frequency micro-vibration, which produces a massage sensation when the nozzle comes into contact with the human body. The fragrance release unit is used to control the release of different fragrances according to the drive signal from the main control module; The dynamic light and shadow unit consists of a full-color LED light strip and a pattern projector, which can generate dynamic light and shadow effects according to the drive signal of the main control module.

[0010] A control method for the intelligent shower system according to any one of claims 1-4, comprising: Step S1: Loading user identification information and personalized profile; Step S2: Collect user physiological state and environmental data in real time through the multimodal environment perception module; Step S3: Receive natural language commands from the user through the human-computer interaction module; Step S4: The large language model interaction module integrates user identification information, personalized profile, user physiological status, environmental data and user natural language commands to perform deep reasoning and generate personalized shower solutions and natural language interaction content; Step S5: The main control module parses and executes the personalized shower plan, and controls the actuator module to work in coordination; Step S6: During the shower, the multimodal environment perception module and human-computer interaction module continuously monitor user feedback and environmental changes, and the large language model interaction module dynamically adjusts the personalized shower plan in real time. Step S7: After showering, store the data and optimize the user model and recommendation strategy through the cloud service platform based on explicit or implicit user feedback.

[0011] Furthermore, the specific steps for generating the personalized shower plan and natural language interaction content in step S4 include: Step S41: The large language model interaction module infers user needs based on the user's natural language commands and combined with the user's physiological state; Step S42: Generate structured control instructions based on the inferred user needs, and convert the content of the structured control instructions into natural language interactive content to feed back to the user. The content of the structured control instructions includes water temperature setting, water flow mode setting, fragrance selection, music playback, and light tone adjustment.

[0012] Furthermore, in step S6, while the multimodal environment perception module continuously monitors user feedback, it also provides health risk warnings for the user's physical condition. Specific steps include: Step S61: When the multimodal environment perception module detects an abnormal increase in the user's heart rate, a sudden change in body surface temperature, or an abnormal posture, the main control module sends this as high-risk context information to the large language model interaction module. Step S62: The large language model interaction module generates a high-risk warning response. If the user does not respond to the high-risk warning response within the set time, the shower will automatically terminate and the preset emergency contact will be notified.

[0013] Furthermore, the high-risk warning response includes immediately lowering the water temperature to a safe range, reducing water pressure, and issuing friendly but clear health inquiries via voice and flashing lights.

[0014] Furthermore, it also includes the ability for users to describe a virtual scene using natural language, which is then analyzed by a large language model to extract scene elements and drive the actuator module to adjust to the corresponding water temperature, water sound, water flow feel, fragrance, and lighting effects, creating an immersive experience.

[0015] Compared with existing technologies, the advantages of this invention are: 1. This invention acquires user physiological data, shower room environment data, user behavior data, and user needs through a multimodal environment perception module and a human-computer interaction module. Then, a large language model interaction module performs inference based on the user physiological data, shower room environment data, user behavior data, and user needs to further analyze and understand user needs, and generates natural language responses with contextual understanding capabilities and structured instructions containing control intentions. This enables the main control module to drive the actuator module to make adjustments, thereby achieving real-time adjustment of the shower equipment according to user needs.

[0016] 2. This invention uses a multimodal environment perception module to provide real-time health risk warnings for users' physical condition, enabling timely detection of potential health risks and proactive intervention to ensure user safety. Attached Figure Description

[0017] Figure 1 This is a block diagram of the overall architecture of an intelligent shower system based on a large language model proposed in this invention.

[0018] Figure 2 This is a control flowchart for the generation and execution of the personalized shower solution proposed in this invention. Detailed Implementation

[0019] The invention will now be further explained with reference to the accompanying drawings.

[0020] like Figure 1 The present invention provides an intelligent shower system based on a large language model, comprising: The main control module is used to transmit the received data to the large language model interaction module, and generate drive signals to control the actuator module based on the structured control instructions generated by the large language model interaction module.

[0021] The large language model interaction module, connected to the main control module, is used to receive and process data from the main control module, generate natural language responses with contextual understanding capabilities, and output structured instructions containing control intentions.

[0022] The multimodal environment perception module, connected to the main control module, is used to collect user physiological data, shower room environment data, and user behavior data.

[0023] The actuator module, connected to the main control module, is used to receive drive signals from the main control module and perform operations such as water temperature adjustment, water volume adjustment, nozzle mode adjustment, fragrance release, light control, and audio playback.

[0024] The human-computer interaction module, connected to the main control module, is used to receive user voice, touch or gesture input, and output voice, visual or tactile feedback to the user.

[0025] The cloud service platform is connected to the main control module via a wireless network and is used to provide large language model computing services, store user data, and perform data analysis and model optimization.

[0026] The main control module is typically composed of a high-performance microprocessor or microcontroller (such as ARM Cortex-A series chips). It is responsible for running the core control logic and lightweight AI algorithms on the device side and managing communication with all other modules (such as via UART, I2C, SPI, WiFi, Bluetooth, etc.).

[0027] In this implementation, the main control module processes the real-time data stream from the perception module, performs preliminary filtering and fusion, and transmits it to the large language model interaction module. When the main control module receives the structured control instructions from the large language model interaction module, it translates them into drive signals that the underlying actuators can recognize and transmits them to the actuators. At the same time, it coordinates the timing of the actions of each actuator to ensure that the system works together.

[0028] The large language model interaction module includes a local semantic understanding submodule, a cloud-based large language model engine, and an instruction parsing and generation submodule. The local semantic understanding submodule is deployed on the shower device and is responsible for processing the user's initial voice commands. It uses a lightweight natural language understanding model (NLU) to perform wake word detection, speech recognition, and basic intent recognition on the commands, reducing the latency and reliance on cloud-based interaction.

[0029] The cloud-based large language model engine is deployed on a cloud service platform. It receives standardized semantic frameworks, real-time sensing data (such as heart rate, ambient temperature and humidity), user history preference profiles, and dialogue context from various modules of the shower equipment. It uses its model with hundreds of billions or even trillions of parameters to perform deep reasoning and generate natural language responses with contextual understanding capabilities.

[0030] The instruction parsing and generation submodule resides in the cloud or on the shower device. It is responsible for extracting structured and precise control instruction sequences from the natural language responses with contextual understanding capabilities generated by the large language model engine in the cloud. An example of such control instruction sequences is shown below: json { "action_sequence": [ {"target": "water_temperature", "value": 41, "unit": "celsius"}, {"target": "shower_head_mode", "value": "pulse_massage"}, {"target": "water_flow_focus", "value": "upper_back"}, {"target": "aroma_release", "scent": "pine", "intensity": 0.7}, {"target": "audio_play", "content_type": "preset", "preset_id": "forest_breeze"}, {"target": "lighting_color", "value": "#90EE90"} / / Light green ] } The multimodal environmental sensing module includes a non-contact physiological sensor group, an environmental sensor group, and a 3D vision sensor, all of which are arranged on the shower equipment.

[0031] The non-contact physiological sensor group consists of a millimeter-wave radar sensor and an infrared thermal imaging sensor. The millimeter-wave radar sensor can penetrate water mist and steam to accurately detect the micro-movements of the human body, thereby calculating heart rate, respiratory rate, and even heart rate variability without any wearable devices, effectively protecting user privacy and not collecting any optical images. The infrared thermal imaging sensor, in low-resolution mode (such as 8x8 pixels), is only used to measure the user's body surface temperature distribution and determine their cold and hot comfort zone, thus also avoiding privacy leaks.

[0032] The environmental sensor suite includes high-precision temperature / humidity sensors, ambient light sensors, and water quality sensors (TDS, pH value), etc., for environmental data acquisition.

[0033] A 3D vision sensor (optional, requires user authorization) uses a structured light or ToF camera. When the user explicitly agrees and enables advanced gesture interaction or posture analysis functions, it is used for skeletal joint tracking. It can recognize user actions such as rubbing and rinsing to determine whether the user is ready to rinse, or realize complex gesture control (such as drawing circles to adjust the water volume). All raw image data is processed on the device, and only the coordinates of the skeletal joints are extracted and uploaded to ensure privacy and security.

[0034] The actuator module includes a dynamic water temperature adjustment unit, a multi-dimensional water flow control unit, a fragrance release unit, and a dynamic light and shadow unit.

[0035] The dynamic water temperature control unit consists of a drive motor and a valve body with dual valve cores, which is installed on the shower equipment. The drive motor drives the valve body with dual valve cores, and in conjunction with a high-response temperature sensor, adjusts the water temperature. At the same time, the main control module runs a model predictive PID algorithm to adjust the valve core opening in advance according to changes in water flow rate, achieving a temperature control accuracy of ±0.1°C and a sub-second response speed, completely eliminating water temperature fluctuations.

[0036] In the model-predictive PID algorithm, the water temperature model is T(t+1) = a*T(t) + b*u(t) + c*v(t), where T represents temperature, u represents valve opening, v represents water flow velocity, and a, b, and c are model parameters.

[0037] The multi-dimensional water flow control unit consists of multiple independent solenoid valves and piezoelectric ceramic actuators. The multiple independent solenoid valves are located inside the spray head to control different arrays of water outlets, thereby adjusting the shower mode of different spray heads. The multiple piezoelectric ceramic actuators are located in some of the water outlets of the spray head to achieve high-frequency micro-vibration, which generates a massage sensation when the spray head comes into contact with the human body.

[0038] The fragrance release unit uses inkjet printer-like technology and has multiple replaceable fragrance capsules (such as lavender, mint, ocean, and forest). It uses a micropump to precisely control the mixing ratio and release rate of different fragrances to achieve a complex top, middle, and base note fragrance experience.

[0039] The dynamic light and shadow unit consists of a full-color LED light strip and a pattern projector, which can generate dynamic light and shadow effects according to the drive signal of the main control module.

[0040] The human-computer interaction module consists of a high signal-to-noise ratio microphone array (supporting far-field voice wake-up and noise reduction), a waterproof touchscreen, and a 3D visual gesture recognition module as its inputs, and a high-quality waterproof speaker (for playing music and providing voice feedback), an LED screen, and a waterproof linear motor as its outputs.

[0041] The system described above uses the main control module to drive multiple execution units, such as water temperature adjustment, water volume adjustment, shower head mode, fragrance release, lighting control, and audio playback, in a synchronous or sequential manner, based on the structured instructions containing control intentions output by the large language model interaction module. This makes the execution modules no longer isolated and separate, but rather work together as a whole around the same user shower scenario, forming a unified, coherent, and immersive smart shower experience.

[0042] The present invention also provides a control method for an intelligent shower system, comprising: Step S1: Load user identification information and personalized profile.

[0043] Step S2: Collect user physiological state and environmental data in real time through the multimodal environment perception module.

[0044] Step S3: Receive the user's natural language commands through the human-computer interaction module.

[0045] Step S4: The large language model interaction module integrates user identification information, personalized profile, user physiological status, environmental data and user natural language commands to perform deep reasoning and generate personalized shower solutions and natural language interaction content.

[0046] Step S5: The main control module parses and executes the personalized shower solution, and controls the actuator module to work together.

[0047] Step S6: During the shower, the multimodal environment perception module and human-computer interaction module continuously monitor user feedback and environmental changes, and dynamically adjust the personalized shower plan in real time through a large language model.

[0048] Step S7: After showering, store the data and optimize the user model and recommendation strategy through the cloud service platform based on explicit or implicit user feedback.

[0049] Explicit user feedback specifically refers to evaluations, instructions, or preference modifications that users actively provide through the human-computer interaction module using natural language, buttons, touch screens, voice, etc. Implicit user feedback specifically refers to physiological, behavioral, and environmental changes collected by the multimodal environment perception module, which allows the large language model interaction module to infer user comfort, satisfaction, or needs.

[0050] The control method described above is illustrated in three different scenarios as follows: Example 1: Typical Daily Use Scenario User A (registered profile) enters the shower room. The 3D sensor confirms the user's identity through skeletal recognition, and the main control module loads their preference profile.

[0051] Steps S1 & S2: The system detected user A's heart rate as 75 bpm, slightly rapid breathing, and normal body temperature using millimeter-wave radar. The ambient temperature was 24°C.

[0052] Step S3: User A says, "Xiao Yu, I'm under a lot of work pressure today, and I have a bit of a headache." The microphone of the human-computer interaction module captures speech, the local semantic understanding submodule identifies the intent as RELAX_RELIEF, and fills the slot [symptom: headache], [cause: work stress].

[0053] Step S4: The semantic framework, along with real-time physiological data and user A's historical data (showing his preference for warmer water temperatures and woody scents), is sent to the cloud-based large language model engine.

[0054] The large language model reasoned: "The user complained of high stress and headaches. They need to soothe their nerves and relieve tension. Warm water helps with relaxation, but too hot water may worsen headaches. Gentle water massage is better than strong stimulation. The aroma of lavender and chamomile has been shown to help relieve stress and headaches. Soft lighting and soothing music can enhance the effect." The generated response and instructions are as follows: • Natural Language Response: “It sounds like you need to relax. I suggest using 38°C warm water, a gentle rain shower mode, and a soothing lavender scent, along with some calming nature music to help relax your scalp and neck. Shall we begin now?” • Control command sequence: json { "water_temperature": 38, "water_pressure": "low", "shower_mode": "gentle_rain", "aroma_mix": [{"scent": "lavender", "ratio": 1.0}], "music_playlist": "calm_nature_sounds", "lighting_color": "soft_purple", "lighting_brightness": 30 } After user A agrees, the system proceeds to step S5, where the main control module drives each actuator to work according to instructions.

[0055] Step S6: Five minutes after the shower begins, the millimeter-wave radar detects that User A's heart rate has dropped to 65 bpm and their breathing has become deeper. The system takes this as positive feedback. User A says, "The water temperature could be a little bit hotter." The system understands this through dialogue, fine-tunes the water temperature to 39°C, and responds, "The temperature has been increased slightly for your comfort." Step S7: After the shower, the system records the effectiveness of this plan (a decrease in heart rate, relaxed breathing, and user-initiated temperature adjustment are considered positive feedback), and updates user A's profile, reinforcing the association between "high stress and headache" and "38-39°C warm water, gentle mode, and lavender fragrance".

[0056] Example 2: Health Risk Warning Scenario User B (an elderly person) is taking a shower.

[0057] Steps S2 & S6: The millimeter-wave radar of the multimodal environment perception module suddenly detected that user B's heart rate suddenly increased from 72 bpm to 110 bpm, and at the same time the 3D sensor detected that his body posture swayed and remained still for more than 10 seconds.

[0058] The main control module immediately identified this as a high-risk event context and urgently sent it to the large language model interaction module.

[0059] Step S4: The large language model receives a high-risk signal and generates the highest priority response and instruction: • Natural Language Response: (Through the speaker, in a clear, concerned but calm tone) "We have detected that you may be feeling unwell and have automatically lowered the water temperature and pressure. Are you feeling alright? If you need help, please say 'I need help' or gently clap your hands. If there is no response within 10 seconds, I will contact your family." • Control command sequence: json { "emergency_override": true, "water_temperature": 30, / / Rapidly reduce to a safe temperature "water_pressure": "off", / / Turn off water flow "audio_alert": "health_warning", "lighting_mode": "slow_blink_amber" / / Amber light flashes slowly as a warning. } Step S6: If user B responds "I'm fine" within 10 seconds, indicating they only slipped, the system will resume the shower but maintain a lower water temperature and close monitoring. If there is no response, the system will execute emergency protocols, sending alert information and location data to the mobile app of a pre-set emergency contact (such as a child) via the cloud service platform.

[0060] Example 3: Creative Scene Experience User C said, "I want to take a shower like the feeling after an adventure under a waterfall in a rainforest." Steps S3 & S4: This instruction is highly creative and ambiguous. The Large Language Model uses its vast world knowledge to deconstruct it: • "Tropical rainforest": means warm, humid, with the smell of vegetation, and the sounds of insects and birds.

[0061] • "Waterfall": means a large volume of water, strong impact, coolness, and a roaring sound.

[0062] • "After an expedition": This implies exhaustion, a need to rejuvenate, and possibly the smell of sweat and dirt (but a shower is for cleansing, so the concept needs to be reversed).

[0063] A large language model generates a fusion scheme: • Natural Language Response: "Received! Creating an adventure atmosphere of a rainforest waterfall for you. The initial water temperature simulates the temperature in the forest, followed by a cool 'waterfall' impact, with the air filled with the mixed scents of earth, green leaves, and wild orchids, and the sounds of the rainforest in the background." • Control command sequence: json { "scenario_mode": "rainforest_waterfall", "water_temperature_sequence": [ {"time_from_start": 0, "value": 36}, {"time_from_start": 120, "value": 32}, / / Simulates the cooling sensation of a waterfall after 2 minutes. {"time_from_start": 180, "value": 35} ], "water_pressure_sequence": [ {"time_from_start": 0, "value": "medium"}, {"time_from_start": 120, "value": "high"}, / / Waterfall impact {"time_from_start": 180, "value": "pulse_massage"} / / Relaxation button after the adventure Mo ], "aroma_sequence": [ {"time_from_start": 0, "scent_mix": [{"petrichor": 0.5}, {"green_ leaf": 0.5}]}, / / Soil and green leaves {"time_from_start": 90, "scent_mix": [{"orchid": 0.8}, {"green_leaf": 0.2}]} / / Turn into floral fragrance ], "audio_soundscape": "jungle_waterfall_ambience", "lighting_effect": "dappled_green_light" / / Simulates light spots filtering through leaves } The above embodiments fully demonstrate how the present invention combines the cognitive intelligence of a large language model with a sophisticated electromechanical control system.

[0064] As is known from common technical knowledge, this invention can be implemented through other embodiments that do not depart from its spirit or essential characteristics. Therefore, the disclosed embodiments described above are merely illustrative and not exhaustive. All modifications within the scope of this invention or its equivalents are included in this invention.

Claims

1. An intelligent shower system based on a large language model, characterized in that, include: The main control module is used to transmit the received data to the large language model interaction module, and generate drive signals to control the actuator module based on the structured control instructions generated by the large language model interaction module. The large language model interaction module, connected to the main control module, is used to receive and process data from the main control module, generate natural language responses with context understanding capabilities, and output structured instructions containing control intentions. The multimodal environment perception module, connected to the main control module, is used to collect user physiological data, shower room environment data, and user behavior data. The actuator module, connected to the main control module, is used to receive drive signals from the main control module and perform operations such as water temperature adjustment, water volume adjustment, nozzle mode adjustment, fragrance release, light control, and audio playback. The human-computer interaction module, connected to the main control module, is used to receive voice, touch or gesture input from the user and output voice, visual or tactile feedback to the user. The cloud service platform is connected to the main control module via a wireless network and is used to provide large language model computing services, store user data, and perform data analysis and model optimization.

2. The intelligent shower system based on a large language model according to claim 1, characterized in that: The large language model interaction module includes: The local semantic understanding submodule is used to perform preliminary intent recognition and slot filling on the user's original input commands, and generate a standardized semantic framework. The cloud-based large language model engine receives standardized semantic frameworks and data from the multimodal environment perception module, and generates natural language responses through its vast knowledge base and reasoning capabilities. The instruction parsing and generation submodule is used in natural language responses to parse out executable and precise control instruction sequences and send them to the main control module.

3. The intelligent shower system based on a large language model according to claim 1, characterized in that: The multimodal environment perception module includes: A non-contact physiological sensor array is used to collect the user's heart rate, heart rate variability, body surface temperature, and respiratory rate. An environmental sensor array is used to collect ambient temperature, humidity, ambient light, and water quality parameters in the shower room. A 3D vision sensor is used to identify the user's posture, range of motion, and position in the shower room through skeletal joint tracking technology, in order to determine the user's status and enable gesture interaction.

4. The intelligent shower system based on a large language model according to claim 1, characterized in that: The actuator module includes: The dynamic water temperature regulation unit consists of a drive motor and a valve body with dual valve cores. It is used to drive the drive motor according to the drive signal of the main control module to adjust the valve core opening of the valve body with dual valve cores, thereby regulating the water temperature. The multi-dimensional water flow control unit consists of multiple independent solenoid valves and piezoelectric ceramic actuators. The multiple independent solenoid valves are located inside the nozzle to control different water outlet arrays to achieve different shower modes. The multiple piezoelectric ceramic actuators are located in some of the water outlets of the nozzle to achieve high-frequency micro-vibration, which produces a massage sensation when the nozzle comes into contact with the human body. The fragrance release unit is used to control the release of different fragrances according to the drive signal from the main control module; The dynamic light and shadow unit consists of a full-color LED light strip and a pattern projector, which can generate dynamic light and shadow effects according to the drive signal of the main control module.

5. A control method for the intelligent shower system according to any one of claims 1-4, characterized in that, include: Step S1: Loading user identification information and personalized profile; Step S2: Collect user physiological state and environmental data in real time through the multimodal environment perception module; Step S3: Receive natural language commands from the user through the human-computer interaction module; Step S4: The large language model interaction module integrates user identification information, personalized profile, user physiological status, environmental data and user natural language commands to perform deep reasoning and generate personalized shower solutions and natural language interaction content; Step S5: The main control module parses and executes the personalized shower plan, and controls the actuator module to work in coordination; Step S6: During the shower, the multimodal environment perception module and human-computer interaction module continuously monitor user feedback and environmental changes, and the large language model interaction module dynamically adjusts the personalized shower plan in real time. Step S7: After showering, store the data and optimize the user model and recommendation strategy through the cloud service platform based on explicit or implicit user feedback.

6. The control method according to claim 5, characterized in that: The specific steps for generating personalized shower solutions and natural language interaction content in step S4 include: Step S41: The large language model interaction module infers user needs based on the user's natural language commands and combined with the user's physiological state; Step S42: Generate structured control instructions based on the inferred user needs, and convert the content of the structured control instructions into natural language interactive content to feed back to the user. The content of the structured control instructions includes water temperature setting, water flow mode setting, fragrance selection, music playback, and light tone adjustment.

7. The control method according to claim 5, characterized in that: In step S6, while the multimodal environment perception module continuously monitors user feedback, it also provides health risk warnings for the user's physical condition. Specific steps include: Step S61: When the multimodal environment perception module detects an abnormal increase in the user's heart rate, a sudden change in body surface temperature, or an abnormal posture, the main control module sends this as high-risk context information to the large language model interaction module. Step S62: The large language model interaction module generates a high-risk warning response. If the user does not respond to the high-risk warning response within the set time, the shower will automatically terminate and the preset emergency contact will be notified.

8. The control method according to claim 7, characterized in that: The high-risk warning response includes immediately lowering the water temperature to a safe range, reducing water pressure, and issuing friendly but clear health inquiries via voice and flashing lights.

9. The control method according to claim 5, characterized in that: It also includes the ability for users to describe a virtual scene using natural language, which is then analyzed by a large language model to extract scene elements and drive the actuator module to adjust to the corresponding water temperature, water sound, water flow feel, fragrance, and lighting effects, creating an immersive experience.

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