Practical Application of Model Conversion and Data Processing in the Smart Home Control System of HarmonyOS Next

This article aims to deeply explore the practical application of model conversion and data processing technologies in building a smart home control system based on the Huawei HarmonyOS Next system (up to API 12 as of now), and summarize it based on actual development experience. It mainly serves as a vehicle for technical sharing and communication. There may be mistakes and omissions. Colleagues are welcome to put forward valuable opinions and questions so that we can make progress together. This article is original content, and any form of reprint must indicate the source and the original author.

I. Requirements and Technology Selection for the Smart Home System

(1) Analysis of Functional Requirements

1. Requirements for Device Status Monitoring The smart home control system needs to monitor the status of various smart devices in real time, such as the on/off status of lights, the switch status of electrical appliances, the open/closed status of doors and windows, and the data from temperature and humidity sensors. Through the monitoring of these device statuses, the system can promptly understand the situation of the home environment, providing a basis for subsequent intelligent control decisions. For example, when it is detected that the indoor temperature is too high, the system can trigger the air conditioner to automatically turn on the cooling mode.
2. Requirements for Intelligent Control Decisions Based on the data monitored from device statuses, as well as user settings and habits, the system needs to make intelligent control decisions. For example, according to different time periods and the activities of people in the room, the system can automatically adjust the brightness of lights and the operating status of electrical appliances. If there are people in the living room at night, the system can automatically turn on the living room lights and adjust the brightness to an appropriate level; if there is no one in the room for a long time, the system can automatically turn off unnecessary electrical appliances to achieve energy conservation and convenient home control.
3. Requirements for Data Visualization To facilitate users to intuitively understand the operating status of home devices and environmental information, the system needs to provide a data visualization function. Through mobile applications or smart control panels, information such as device status data, energy consumption, and changes in environmental parameters can be displayed in the form of charts and graphs. For example, users can view the electricity consumption trend chart of their home in the past week through a mobile application to adjust their electricity usage habits reasonably.

(2) Decision-making on Technology Selection

1. Selection of Deep Learning Frameworks According to the characteristics of HarmonyOS Next and the requirements of the smart home system, select an appropriate deep learning framework for model development. If the system has high requirements for the real-time performance of the model and needs to run on devices with limited resources, lightweight frameworks such as TensorFlow Lite or PyTorch Mobile may be good choices. These frameworks are optimized for mobile and embedded devices and can reduce resource consumption while ensuring a certain level of performance. For example, when developing a device status prediction model, using the TensorFlow Lite framework can take advantage of its efficient model compression and inference capabilities to quickly process device status data and make predictions.
2. Determination of Model Conversion and Data Processing Technical Solutions For model conversion, select the corresponding HarmonyOS Next model conversion tool according to the selected deep learning framework. For example, if the TensorFlow framework is used, the OMG offline model conversion tool may be used (assuming it is mentioned in the document). In terms of data processing, determine the technical solutions for operations such as data cleaning, normalization, and preprocessing. For example, the data collected by sensors may contain some outliers or noise, and data cleaning technology can be used to remove these abnormal data to ensure the accuracy of the data. Then, according to the input requirements of the model, perform normalization or standardization processing on the data to make the data within an appropriate numerical range and improve the training and inference effects of the model.

(3) Data Architecture Design

1. Data Collection Architecture In the smart home environment, various sensors and smart devices are deployed to collect data. For example, temperature and humidity sensors, light sensors, human infrared sensors, smart sockets, and other devices are installed in each room, and the collected data is transmitted to the smart home control center through wireless communication technologies such as Wi-Fi, Bluetooth, or ZigBee. The data collection architecture should ensure the stable collection and transmission of data, taking into account factors such as the rational layout of sensors, the stability of communication protocols, and the setting of data collection frequency. For example, for temperature and humidity sensors, according to the size and usage of the room, reasonably determine the installation location to accurately reflect the temperature and humidity conditions in the room; at the same time, set an appropriate data collection frequency to avoid excessive collection, which may lead to resource waste, or too low a collection frequency that cannot reflect environmental changes in a timely manner.
2. Data Transmission Architecture Design an efficient data transmission architecture to ensure that data can be quickly and reliably transmitted from sensors to the control center or cloud server (if cloud processing is required). Select an appropriate communication protocol, such as MQTT (Message Queuing Telemetry Transport), which has the characteristics of being lightweight, low-power, and reliable for data transmission, making it very suitable for data transmission between smart home devices. During the data transmission process, consider the encryption and security of the data to prevent data from being stolen or tampered with. For example, use the SSL/TLS encryption protocol to encrypt the transmitted data to protect user privacy and the security of the home system.
3. Data Storage Architecture According to the type and usage frequency of the data, select an appropriate data storage method. For data with high real-time requirements that are often queried and processed, such as the current status data of devices, an in-memory database (such as Redis) can be used for storage to achieve fast data read and write operations. For historical data, such as device status change records and energy consumption data, a relational database (such as MySQL) or a non-relational database (such as MongoDB) can be used for storage to facilitate data analysis and statistics. At the same time, consider data backup and recovery strategies to prevent data loss. For example, regularly back up the database and store it in different physical locations or cloud storage services.

II. Implementation of Model Conversion and Data Processing

(1) Detailed Explanation of the Model Conversion Process

1. Conversion Steps and Code Example Taking the TensorFlow Model as an Example Suppose we have developed a model for device status prediction using TensorFlow. The following is the process of converting it into a format that can be run on HarmonyOS Next (simplified version):

import tensorflow as tf
from tensorflow.python.tools import freeze_graph
from tensorflow.python.tools import optimize_for_inference_lib

# Load the original model
model_path = 'device_status_prediction_model.pb'
graph = tf.Graph()
with graph.as_default():
    od_graph_def = tf.compat.v1.GraphDef()
    with tf.io.gfile.GFile(model_path, 'rb') as fid:
        serialized_graph = fid.read()
        od_graph_def.ParseFromString(serialized_graph)
        tf.import_graph_def(od_graph_def, name='')

# Define input and output nodes
input_tensor = graph.get_tensor_by_name('input:0')
output_tensor = graph.get_tensor_by_name('output:0')

# Prepare the calibration dataset (assuming the calibration dataset has been obtained)
calibration_data = get_calibration_data()

# Perform model quantization (if needed)
with tf.compat.v1.Session(graph=graph) as sess:
    # Freeze the model
    frozen_graph = freeze_graph.freeze_graph_with_def_protos(
        input_graph_def=graph.as_graph_def(),
        input_saver_def=None,
        input_checkpoint=None,
        output_node_names='output',
        restore_op_name=None,
        filename_tensor_name=None,
        output_graph='frozen_model.pb',
        clear_devices=True,
        initializer_nodes=None
    )
    # Optimize the model
    optimized_graph = optimize_for_inference_lib.optimize_for_inference(
        input_graph_def=frozen_graph,
        input_node_names=['input'],
        output_node_names=['output'],
        placeholder_type_enum=tf.float32.as_datatype_enum
    )
    # Quantize the model (here, use TFLiteConverter for quantization, assuming quantization is supported)
    converter = tf.lite.TFLiteConverter.from_session(sess, [input_tensor], [output_tensor])
    converter.optimizations = [tf.lite.Optimize.DEFAULT]
    converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8]
    converter.inference_input_type = tf.uint8
    converter.inference_output_type = tf.uint8
    tflite_model = converter.convert()
    # Save the quantized model
    with open('harmonyos_device_status_prediction_model.tflite', 'wb') as f:
        f.write(tflite_model)

In this example, first, the original TensorFlow model is loaded, and then the input and output nodes are defined, and the calibration dataset is prepared (if quantization is required). Then, through a series of steps, including freezing the model and optimizing the model, finally, the TFLiteConverter is used for quantization operations (if needed), and the quantized model is saved in the .tflite format for deployment on HarmonyOS Next devices.

(2) Implementation of Data Processing Functions

1. Code Display of Data Cleaning, Normalization, and Preprocessing The following is a simple data processing code snippet for processing sensor data in the smart home system (assuming it is temperature and humidity data, using the Python language and the numpy library):

import numpy as np

# Assume that data is a two-dimensional array, where each row represents the temperature and humidity data collected at a certain time point, and the first column is the temperature and the second column is the humidity
data = np.array([[25.5, 50], [26.2, 52], [30.1, 55], [24.8, 48], [200.0, 60], [25.8, 51]])  # Among them, [200.0, 60] is abnormal data

# Data cleaning - remove outliers (here, assume that the temperature exceeding 50 degrees is an outlier)
clean_data = data[np.where(data[:, 0] <= 50)]

# Data normalization - normalize the temperature and humidity to the range of 0 to 1
min_temp = np.min(clean_data[:, 0])
max_temp = np.max(clean_data[:, 0])
min_humidity = np.min(clean_data[:, 1])
max_humidity = np.max(clean_data[:, 1])
normalized_data = np.zeros_like(clean_data)
normalized_data[:, 0] = (clean_data[:, 0] - min_temp) / (max_temp - min_temp)
normalized_data[:, 1] = (clean_data[:, 1] - min_humidity) / (max_humidity - min_humidity)

# Data preprocessing - other preprocessing operations, such as smoothing, can be added (omitted here)

In this example, first, data cleaning is carried out to remove the data points with abnormal temperatures. Then, the temperature and humidity data are normalized respectively, mapping them to the range of 0 to 1, providing a suitable data format for subsequent model training or inference.

(3) Process Optimization Measures

1. Adjust Model Parameters According to Device Resources In the smart home control system, the resource situations of different devices may vary. For example, some smart sensor devices may have limited memory and computing power, while smart central control devices may have relatively stronger resources. Adjust the model parameters according to the device resource situation. For devices with limited resources, the number of model layers or the number of neurons can be reduced to lower the complexity of the model. For example, in the device status prediction model, if it is deployed on a smart sensor, the model structure can be simplified, and only the key feature extraction layers and prediction layers can be retained to reduce the storage requirements and computational amount of the model, ensuring that the model can run stably on the device.
2. Adopt Incremental Data Processing to Improve Real-time Performance In the smart home scenario, data is generated in real time, and adopting incremental data processing can improve the real-time performance of the system. For example, for the data collected by sensors, there is no need to wait until a batch of data is completely collected before processing it. Instead, an incremental method can be adopted, and whenever new data arrives, it is processed immediately. In terms of model training, online learning or incremental learning methods can be used. As new data continues to arrive, the model can update its parameters in a timely manner to adapt to environmental changes. For example, when the smart electricity meter collects new electricity consumption data, it can be immediately used to update the device energy consumption prediction model, enabling the model to reflect the changes in the home electricity consumption situation in real time and providing a more accurate basis for intelligent control decisions.