This article aims to deeply explore the practical application of model lightweighting technology in the 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. Smart Home Control Requirements and Construction of Model Lightweighting Architecture

(1) Analysis of Common Requirements

1. Requirement for Device Status Recognition The smart home control system needs to accurately identify the status of various smart devices, such as whether the lights are on or off, the switch status of electrical appliances, and the open - close status of doors and windows. This is crucial for achieving intelligent control and automated scenarios. For example, when the system detects that there is no one in the room and the lights are on, it automatically turns off the lights to save energy; when the refrigerator door has not been closed for a long time, it sends a reminder to notify the user. Accurate device status recognition depends on an efficient model that can process sensor data in real - time and make accurate judgments.
2. Requirement for User Behavior Prediction By predicting user behavior, the smart home system can respond in advance and provide more personalized and convenient services. For example, according to the user's daily work and rest habits, it predicts that the user is about to go home and turns on devices such as air conditioners and water heaters in advance to create a comfortable home environment. At the same time, predicting user behavior can also optimize the energy use of devices and improve energy efficiency. This requires the model to learn the user's behavior patterns and make accurate predictions based on historical data.

(2) Design of Model Lightweighting Architecture Based on HarmonyOS Next

1. Overall Architecture Design A hierarchical architecture design is adopted, including a data collection layer, a model processing layer, and a control execution layer. The data collection layer is responsible for collecting data from various sensors (such as temperature sensors, light sensors, human infrared sensors, etc.) and transmitting it to the model processing layer. The model processing layer contains lightweight models that process the collected data to achieve functions such as device status recognition and user behavior prediction. The control execution layer controls the corresponding smart devices to perform operations, such as turning on or off lights, adjusting the power of electrical appliances, etc., according to the output results of the model.
2. Design of Interaction Methods between Models and Devices Through the distributed communication capabilities of HarmonyOS Next, the interaction between models and smart devices is realized. The model processing layer, as a distributed service, receives sensor data from the data collection layer and sends the processing results to the control execution layer. Smart devices register into the distributed system, receive instructions from the control execution layer, and perform corresponding operations. For example, when the model recognizes that there is no one in the room and the light is dim, it sends a turn - off instruction to the smart lighting device through distributed communication, and the lighting device turns off the light after receiving the instruction.
3. Determination of Data Collection and Processing Solutions
   • Data Collection: Various sensors are deployed in the smart home environment to collect different types of data. For example, temperature sensors and light sensors are installed in each room, and human infrared sensors and door - window sensors are installed at the doors and windows. The collected data includes environmental temperature, light intensity, and personnel activity information. At the same time, the user's operation behaviors on smart devices, such as the time of turning lights on and off, adjusting the temperature of electrical appliances, etc., are recorded as training data for the user behavior prediction model.
   • Data Processing: The collected data is pre - processed, including operations such as data cleaning, normalization, and feature extraction. Data cleaning is used to remove outliers and incorrect data. For example, when a temperature sensor malfunctions and collects abnormally high or low temperature values, they are filtered out. Normalization maps different types of data to a unified numerical range for easy processing by the model. Feature extraction extracts meaningful features for the model from the raw data. For example, features such as the frequency and time period of personnel activities are extracted from the personnel activity data, improving the training efficiency and accuracy of the model.

II. Realization of Core Functions and Technological Innovation

(1) Realization of Device Status Recognition Function

1. Construction and Optimization of Lightweight Model A lightweight neural network architecture, such as a structure based on MobileNet or ShuffleNet, is adopted to construct the device status recognition model. These architectures have a relatively small number of parameters and low computational complexity while ensuring certain performance, making them suitable for running on smart home devices with limited resources. For example, the depth - separable convolutions of MobileNet are used to replace traditional convolutions, reducing the computational amount of the model.
2. Improving Recognition Effect through Pruning and Data Augmentation
   • Pruning Operation: After the model training is completed, pruning is performed to reduce the number of model parameters. By analyzing the importance of neurons, a combination of structured pruning and unstructured pruning methods is adopted. For example, neuron connections that are less related to device status recognition are pruned. Without significantly reducing the model accuracy, the storage requirements and computational amount of the model are reduced. After pruning, the number of model parameters is reduced by about 30%, and the accuracy of device status recognition only drops by about 2 percentage points, still remaining at a high level of over 95%.
   • Data Augmentation: Data augmentation operations are performed on the training data to increase data diversity and improve the generalization ability of the model. For example, the device status data is randomly transformed, such as simulating the changes of lights at different brightness levels and the working states of electrical appliances at different power levels. At the same time, some noisy data is added to enable the model to better cope with interference factors in the actual environment. Through data augmentation, the recognition accuracy of the model on the test set is increased by about 3 percentage points, reaching about 98%.

(2) Development of User Behavior Prediction Function

1. Optimizing Model Structure and Algorithm Model structures suitable for processing sequential data, such as Recurrent Neural Networks (RNNs) or Long Short - Term Memory networks (LSTMs), are adopted to construct the user behavior prediction model. These models can learn the time - series characteristics of user behavior and better predict the user's future behavior. At the same time, the model algorithm is optimized. For example, an adaptive learning rate adjustment strategy is adopted to enable the model to converge faster during training. For example, optimization algorithms such as Adagrad or Adam are used to automatically adjust the learning rate according to the update of model parameters, improving the training efficiency of the model.
2. Improving Prediction Accuracy by Combining User Context Information In addition to the user's historical behavior data, user context information such as time, date, season, and weather is also considered to improve prediction accuracy. For example, in summer, users are more likely to use air conditioners; on weekends, the user's activity pattern may be different from that on weekdays. This context information is used as input features of the model and trained together with the user behavior data, enabling the model to make more accurate predictions according to different context situations. By combining context information, the accuracy of the user behavior prediction model is increased from the original 75% to about 85%.

(3) Realization of Distributed Deployment and Collaborative Work

1. Distribution Strategy of Models among Smart Home Devices According to the resource status and functional requirements of smart home devices, lightweight models are distributed to different devices. For example, the device status recognition model is deployed on devices close to sensors, such as smart gateways or smart sockets, to process sensor data in a timely manner; the user behavior prediction model is deployed on devices with stronger computing capabilities, such as smart speakers or smart central control devices, for complex calculations and predictions. Through this distribution strategy, the resources of smart home devices are fully utilized, and the overall performance of the system is improved.
2. Collaborative Work Mechanism and Optimization of Resource Utilization Through the distributed capabilities of HarmonyOS Next, the collaborative work between models and between models and devices is realized. For example, when the device status recognition model detects that the user has left home and all devices are in the off state, it sends this information to the user behavior prediction model. The user behavior prediction model predicts the user's next return time based on this information and historical data and notifies other devices in advance to get ready. In terms of resource utilization, a dynamic resource allocation mechanism is adopted. According to the load situation of devices and task priorities, computing resources are reasonably allocated. For example, when the smart speaker is performing a voice - interaction task and has a high load, the user behavior prediction task is assigned to other idle devices for processing, improving the resource utilization efficiency of the system.

III. Performance Optimization and Improvement of User Experience

(1) Evaluation of the Impact of Model Lightweighting on System Performance

1. Evaluation of Response Speed In the smart home control system, response speed is crucial. By comparing the system response speed before and after using the lightweight model, it is found that after using the lightweight model, the system's response speed to device status changes is significantly improved. For example, in terms of device status recognition, the average time from when the sensor detects a device status change to when the system responds was originally 2 seconds, and after using the lightweight model, the response time is shortened to less than 0.5 seconds. This enables faster intelligent control and improves the user experience. In terms of user behavior prediction, the generation speed of prediction results has also been significantly improved, from an average of 5 seconds to about 2 seconds, enabling the system to provide more timely personalized services to users.
2. Evaluation of Energy Consumption Since smart home devices are usually powered by batteries or have certain energy consumption requirements, energy consumption evaluation is also an important indicator. The use of lightweight models effectively reduces the energy consumption of the system. The reduction in the number of model parameters and computational complexity means that the device consumes less energy when running the model. For example, on smart sensor devices, after using the lightweight model, the battery life is extended by about 30%, reducing the frequency of device charging or battery replacement, lowering the use cost, and being more environmentally friendly.

(2) Performance Optimization Measures and Improvement Effects on User Experience

1. Optimizing System Performance with Caching Strategy A caching strategy is adopted to cache frequently used device status data and user behavior data. For example, in the smart central control device, the device status and user operation records in the recent period are cached. When these data are needed again, they are directly read from the cache instead of being retrieved from the sensors or database again. This greatly reduces the data reading time and improves the response speed of the system. At the same time, according to the usage frequency and timeliness of the data, the caching strategy is dynamically adjusted, and the cache data is updated in a timely manner to ensure the accuracy and effectiveness of the cache data.
2. Improving User Experience with Asynchronous Processing For time - consuming operations, such as data collection, model training, and complex computational tasks, an asynchronous processing method is adopted. For example, during the training process of the user behavior prediction model, the training task is executed asynchronously in the background thread. The user can continue to use the smart home system normally in the foreground without the system freezing due to model training. When the training is completed, the system automatically updates the model and applies the new prediction results. This asynchronous processing method improves the usability of the system and the user experience, making the user feel that the system is more smooth and intelligent.

(3) Sharing of Lessons Learned from Practical Application Cases

1. Case Background and Application Scenario Take a smart home control system as an example. This system is applied in a three - bedroom, two - living - room family environment, including various smart devices such as smart lights, smart appliances, and smart curtains. Data is collected through sensors deployed in each room, and lightweight models are used to achieve device status recognition and user behavior prediction, providing users with intelligent control and personalized services.
2. Problems Encountered and Solutions
   • Data Transmission Stability Problem: In the initial stage of system operation, it was found that the transmission of sensor data to the model processing layer was unstable, and sometimes data loss or delay occurred. By optimizing the network communication protocol, a more reliable transmission method, such as TCP - based reliable transmission, was adopted, and a data verification and re - transmission mechanism was added to solve the data transmission stability problem.
   • Model Update and Compatibility Problem: As user behavior changes and new devices are added, the model needs to be updated. However, during the update process, it was found that there were compatibility problems between the new model and some old devices. Through compatibility testing and optimization of the model, it was ensured that the new model could run normally on different versions of devices. At the same time, a model version management mechanism was established. When compatibility problems were found, it was possible to quickly roll back to the previous stable version to ensure the stable operation of the system.
3. Summary of Experience and Enlightenment In the development of smart home control systems, the complexity and diversity of the actual application environment should be fully considered. The stability of data transmission, the compatibility and updatability of the model are key factors. In the design stage, appropriate communication protocols and technologies should be selected to ensure reliable data transmission. During the model development process, sufficient compatibility testing should be carried out to avoid the situation where the new model cannot run on old devices. At the same time, a complete system monitoring and management mechanism should be established to discover and solve problems in a timely manner, continuously optimize system performance and user experience, and provide users with a more intelligent, convenient, and stable smart home control system. It is hoped that through the introduction of this article, it can provide some useful references and inspirations for developers in the smart home field, and jointly promote the development of smart home technology. If you encounter other problems during the practice process, you are welcome to communicate and discuss together! Haha!