I. Analysis of the Core Requirements of ML-Enabled PCBAs in Smart Home Scenarios

　　As smart homes evolve from passive response to proactive service, PCBAs supporting machine learning (ML) need to address the pain points of traditional PCBAs: weak scenario adaptability, homogeneous interactions, and reliance on manual configuration. These core requirements focus on:

　　Adaptive learning of user habits: ML algorithms must analyze user behavior data (such as usage time, device preferences, and environmental parameters) to automatically adapt device operating modes (e.g., adjusting lighting color temperature before returning home or gradually lowering air conditioning temperature during sleep). Learning accuracy must be ≥90%, eliminating the need for repetitive manual configuration.

　　Precise recognition of complex scenarios: ML can integrate multimodal data (ambient temperature and humidity, human presence, device status, and user operations) to distinguish between "real needs" and "false triggers" (e.g., distinguishing between a user "passing by the living room" and "stopping by to watch a movie" to avoid accidental activation of audio and video equipment). Scene recognition accuracy must be ≥95%.

　　On-device ML Low Inference Latency: Smart home interactions require real-time responses (e.g., instant device state adjustment when a user approaches). PCBAs must support on-device offline ML inference with an inference latency of ≤100ms to avoid network failures or delays caused by reliance on the cloud.

　　Multi-Device Collaborative Decision-Making: Build a device-linked decision-making engine based on ML (e.g., automatically closing windows and turning on dehumidification in the "Rainy Day + User Returns Home" scenario). Support for coordinated control of 10+ devices with a decision response time of ≤300ms, adapting to whole-home smart scenarios.

　　Low-Power ML Operation: Most smart home devices (e.g., wireless sensors and battery-powered controllers) require long-term standby operation. PCBAs must optimize ML inference power consumption (≤80mA during inference, ≤15μA during standby) and adapt to battery operation (e.g., two AA batteries can support over one year of standby and ML learning).

　　II. Core Technology Architecture of Smart Home PCBAs Supporting Machine Learning

　　1. Multimodal Data Acquisition Module (Based on ML Algorithm Input)

　　Sensor Selection and Data Fusion Design:

　　The PCBA must be equipped with multimodal sensors to provide rich input data for ML algorithms and adapt to the data requirements of different scenarios:

　　Environmental Perception Sensors: Temperature and Humidity Sensors (such as the SHT30, with an accuracy of ±0.3°C/±2% RH), Light Sensors (such as the BH1750, with a range of 0-65535 lux), and Air Quality Sensors (such as the SGP30, detecting VOCs and CO₂). These sensors are connected via an I²C interface with an adjustable sampling frequency of 1Hz-10Hz.

　　Human and Behavior Sensors: Millimeter-wave Radar Sensors (such as the A111, detecting human presence and movement, with a range of 0.5-10 meters), PIR Human Sensors (such as the HC-SR501, supplementing static human detection), and Gesture Sensors (such as the VL53L8CX, providing action intention data). SPI/UART interface, with data output format compatible with ML feature extraction;

　　Device status sensors: Current sensors (such as the ACS712, to detect device power consumption and determine operating status) and position sensors (such as rotary encoders, to determine curtain opening/closing degree), acquired through an ADC interface with sampling accuracy exceeding 12 bits;

　　Data preprocessing circuits (such as filtering and normalization) are designed to uniformly encapsulate multimodal data in a "timestamp + feature value" format to ensure consistent data input to ML algorithms.

　　Data storage and cache optimization:

　　Integrated low-power Flash memory (such as the W25Q64, 64MB capacity) is used to store historical behavior data (such as 7-30 days of user operation records), supporting categorized storage by "scenario type" (such as "sleep scenario" and "cooking scenario");

　　Built-in SRAM (e.g., 64KB-256KB) serves as a temporary cache for ML inference, avoiding the increased power consumption and latency caused by frequent Flash reads. Data read and write speeds of ≥10MB/s meet the requirements of real-time ML feature extraction.

　　2. ML Inference Hardware and Algorithm Deployment Module (Core Computing Power Support)

　　ML Master Controller Chip Selection (Tiered Computing Power Solution):

　　Select differentiated master controllers based on the ML scenario complexity, balancing computing power and power consumption:

　　Lightweight ML scenarios (e.g., single-device habit learning): Utilize an MCU with an NPU (e.g., the STM32H750VB, with an NPU computing power of 0.5TOPS and support for INT8/FP16 precision). Suitable for gesture intent recognition and simple habit analysis (e.g., learning when a user turns on the lights). Operating power consumption ≤30mA, standby power ≤5μA.

　　Medium-complex ML scenarios (e.g., multi-device collaborative decision-making): Utilize an edge computing chip (e.g., the RK3308, with a quad-core Cortex-A35 and a 1TOPS NPU). Support multimodal data fusion inference (e.g., combining temperature and humidity, human body trajectory, and device status judgment). Operating power consumption ≤80mA. Support for deploying lightweight ML frameworks on Linux systems.

　　Complex ML scenarios (such as whole-home smart decision-making): Use a high-computing SoC (such as the ESP32-S3, paired with a 2TOPS NPU and TensorFlow Lite Micro support). This is suitable for user profiling and long-term behavior prediction (such as predicting a user's weekend movie preferences). It requires an operating power consumption of ≤100mA and supports WiFi 6/Zigbee dual-mode communication.

　　The main control chip must support hardware acceleration (such as an NPU or DSP). This improves ML inference efficiency by 10-50 times compared to pure CPUs, reducing power consumption and latency.

　　ML Algorithm Deployment and Optimization:

　　Algorithm Framework Compatibility: Supports lightweight on-device ML frameworks such as TensorFlow Lite Micro and ONNX Runtime. Model file size is ≤1MB (compatible with Flash storage). Model quantization (INT8 quantization, accuracy loss ≤5%) is supported, reducing computing power and storage usage.

　　Core Algorithm Modules:

　　Habit Learning Algorithm: Uses a time series neural network (LSTM) to analyze user operation time series data (e.g., "Turn on the living room lights at 8:00 PM every day"). The learning cycle is 3-7 days, with a habit recognition accuracy of ≥92%. Dynamic updates are supported (adapting within 72 hours to changes in user habits).

　　Scene Classification Algorithm: Based on a convolutional neural network (CNN), it integrates multimodal data to distinguish over 10 common home scenarios (e.g., "cooking," "sleeping," and "watching a movie") with a scene recognition latency of ≤80ms and a misclassification rate of ≤3%.

　　Decision Optimization Algorithm: Uses reinforcement learning (Q-Learning) to optimize device linkage strategies (e.g., "user returns home"). In certain scenarios, frequently used devices are prioritized and redundant devices are disabled. The decision iteration cycle is 1 hour per time, improving energy utilization by 15%-20%.

　　Model Update Mechanism: Supports OTA (Over-The-Air) firmware upgrades, updating ML models via WiFi/Zigbee transmission (incremental updates, data size ≤ 500KB). The update process does not interrupt normal device operation.

　　3. Intelligent Control and Multi-Device Linkage Module (ML Decision Output)

　　Adaptive Control Interface:

　　The PCBA reserves a configurable control interface to dynamically adjust outputs based on ML decisions:

　　Low-Voltage Intelligent Control: Integrated with 8 PWM outputs (12-bit precision, 1kHz-10kHz frequency) for adjusting LED brightness/color temperature and motor speed (e.g., curtains, fans), supporting "gradual control" output from ML algorithms (e.g., linearly reducing light brightness during sleep);

　　High-Voltage Device Control: Connects to AC 220V devices (e.g., air conditioners, range hoods) via a relay driver chip (e.g., ULN2003). ML decisions output control signals via GPIO, with a relay response time of ≤10ms and support for "delayed start" (e.g., starting the air conditioner 5 minutes before the user returns home);

　　Analog Control: Integrated with 4 DAC outputs (12-bit precision) for controlling analog devices (e.g., traditional thermostats), suitable for retrofitting older home appliances with ML. The algorithm can output continuous adjustment values (e.g., gradually lowering the air conditioner temperature from 26°C to 24°C).

　　Multi-protocol Interconnected Communication:

　　Integrated multi-mode communication modules (Wi-Fi 6 + Bluetooth 5.3 + Zigbee 3.0) support cross-protocol transmission of ML decision commands:

　　Short-range Interconnection: Bluetooth 5.3 is used for direct device connection within 10 meters (e.g., ML recognizes "user approaches a desk lamp" and directly controls the lamp to turn on), with communication latency ≤ 50ms;

　　Multi-device Networking: Zigbee 3.0 mesh networking is used for multi-device collaboration within 200 meters (e.g., ML recognizes "cooking scene" and connects the range hood, kitchen lights, and exhaust fan), supporting 50+ nodes and command synchronization latency ≤ 200ms;

　　Cloud Collaboration: Wi-Fi 6 is used to upload ML learning data (e.g., user habit logs) and retrieve cloud-based optimization models (e.g., updating scenario decision parameters with seasonal changes), with uplink speeds ≥ 10Mbps and downlink speeds ≥ 20Mbps.

　　4. Low-Power ML Operation Optimization (Long-Term Standby)

　　Dynamic Computing Power Scheduling:

　　Designing an "on-demand wake-up" ML inference mechanism:

　　Sleep Phase: Only low-power sensors (such as PIR) and RTC remain operational, with power consumption ≤15μA and no ML inference.

　　Trigger Phase: Sensors detect "potential demand" (such as a person approaching) and wake up the ML controller for lightweight inference (such as determining whether it is a user action). Power consumption ≤30mA, inference time ≤200ms.

　　Active Phase: After the ML confirms the user demand, full-power inference (such as scene classification and device-linked decision-making) is initiated, with power consumption ≤80mA. Returning to sleep within 30 seconds after the task is completed.

　　Through dynamic scheduling, the average daily power consumption of ML inference is reduced by over 60%.

　　Hardware-Level Power Consumption Optimization:

　　Power Management: A PMIC (such as the TI TPS65023) is used to implement multi-voltage domain power supply. A separate 3.3V voltage is provided to the NPU during ML inference, and the NPU power is turned off during sleep.

　　Clock Control: The main control chip supports dynamic frequency modulation (e.g., the main frequency increases to 400MHz during ML inference and decreases to 32kHz during sleep), and clock power consumption decreases linearly with frequency.

　　Sensor Power Control: The sensor sampling frequency is dynamically adjusted via the I²C/SPI interface (e.g., the temperature and humidity sensor sampling frequency decreases from 10Hz to 0.1Hz when there is no human activity). The average daily sensor power consumption is ≤5μA.

　　III. Smart Home Scenario-Based Application Solutions

　　1. Bedroom Sleep Scenario (Core Requirements: Habit Learning and Active Adjustment)

　　Pain Points: Traditional smart bedrooms require manual "sleep mode" settings and cannot adapt to individual differences such as bedtime and body temperature fluctuations. The sudden increase in light when waking up at night can easily disrupt sleep, requiring automatic adjustment based on user habits.

　　PCBA Adaptation Solution:

　　Data Collection: 7 days of user sleep data are collected using millimeter-wave radar (to detect user tossing frequency), temperature and humidity sensors (to monitor bedroom environment), light sensors (to determine nighttime brightness), and curtain position sensors (to determine curtain opening/closing degree).

　　ML Inference: Utilizing the STM32H750VB (0.5TOPS NPU) to run an LSTM habit learning algorithm, the algorithm analyzes the user's bedtime (e.g., 11:00 PM), the relationship between tossing frequency and temperature (e.g., lowering the temperature when tossing frequently), and the time of nighttime awakening (e.g., 2:00 AM to 4:00 AM) to generate a personalized sleep strategy.

　　Intelligent Control: ML decisions are made via Zigbee to link curtains (gradually closing after falling asleep and opening 10% before waking up at midnight), air conditioning (26°C at bedtime, lowering to 24°C at midnight), and a night light (automatically opening to 10% brightness upon waking up to avoid glare).

　　Optimization Iteration: Daily at 5:00 AM Automatically analyze the previous night's sleep data and fine-tune the strategy (for example, appropriately increasing the night light brightness if the user wakes up more frequently). After three days, habit adaptation accuracy is ≥92%.

　　2. Kitchen Cooking Scenario (Core Requirements: Scene Recognition, Multi-Device Interaction)

　　Pain Points: Cooking with busy hands prevents manual operation of the range hood, lights, and exhaust fan. Different cooking methods (such as stir-frying and stewing) require different device parameters, and traditional PCBAs require manual mode switching.

　　PCBA Adaptation Solution:

　　Data Acquisition: Real-time scene data is collected through current sensors (to detect the operating status of range hoods and induction cookers), air quality sensors (to monitor oil fume concentration), gesture sensors (to obtain user operation intentions), and temperature and humidity sensors (to determine cooking heat).

　　ML Inference: Utilizing the RK3308 (1TOPS NPU) to run a CNN scene classification algorithm, it distinguishes three scenarios: "frying (high oil smoke, high current), stewing (low oil smoke, low current), and food preparation (no oil smoke, low current)." Recognition latency is ≤80ms and accuracy is ≥96%.

　　Intelligent Control: ML decision-making links devices – frying scenarios (range hood high speed, kitchen light high, exhaust fan on), stewing scenarios (range hood low speed, kitchen light medium, exhaust fan off), and food preparation scenarios (range hood standby, kitchen light low).

　　False Trigger Suppression: ML combines multi-dimensional judgments based on "current changes + oil fume concentration + gestures" to avoid False triggering caused by "users passing by the kitchen" or "briefly turning on the device" has a false trigger rate of ≤0.1 times/day.

　　3. Living Room Audiovisual Scenario (Core Requirements: User Preference Learning, Multi-Device Collaboration)

　　Pain Points: Different family members (such as the elderly and children) have different preferences for audiovisual equipment (TV, speakers, lighting). Traditional PCBAs require manual switching of user modes, requiring individual adjustments for each device during viewing, which is cumbersome.