Large Models Driving Evolution of Smart Homes

With the increasingly widespread application of artificial intelligence (AI) and big data technologies, these advancements are driving a profound evolution in the field of smart homes. Large models, equipped with powerful data processing capabilities and precise predictive analytics, provide robust technical support for the intelligence, personalization, and efficiency of smart homes. Coupled with the evolution of technologies such as fiber to the room (FTTR), next-generation Wi-Fi, video imagery, voice recognition, these innovations collectively propel the development of smart homes.

Application Architecture of Large Models in Smart Home Scenarios

Large models typically refer to deep learning models with massive parameters and complex network structures, such as the GPT series and BERT. In smart homes, the core components of these models include voice recognition, natural language processing, and image recognition. These components enable smart home systems to better understand user needs and deliver more intelligent services.

As the implementation of large models deepens, edge-cloud collaboration has emerged as a key development trend in smart home applications (Fig. 1).

Edge-side applications refer to the direct use of computing power from smart home edge-side chips by large models to generate results. However, the immense computing power, storage, and energy consumption required by large models with hundreds of billions of parameters pose high demands on edge-side chips. Moreover, edge-side users have specific requirements for high performance, low latency, and data privacy. Therefore, deploying large models in the cloud combined with edge-side applications is the optimal choice for balancing performance, cost, power consumption, privacy, and speed.

Edge-side large models are more user-aware and can understand user intentions through edge-side learning, enabling them to provide personalized services. The foundational large models in the cloud, with larger parameter counts and broader capabilities, can address more complex issues. When the smart home perception layer detects a user request or household event, the edge-side model can accurately interpret the intent and provide timely, personalized responses. If users need more information, they will be directed to the foundational large model in the cloud for a more in-depth and comprehensive answer. The complementary capabilities of edge-side and cloud-side models create an excellent experience for smart home users.

ZTE’s smart home solution positions the home gateway/router as the network control hub, with various terminal devices such as smart speakers, cameras, and smart locks serving as perception control nodes. This setup makes it an appropriate choice for edge-side AI applications. Additionally, ZTE’s FTTR+Wi-Fi 7 all-optical networking solution, combined with AI computing power, storage capabilities, and smart home IoT applications, provides a solid network foundation for implementing large models in smart homes.

FTTR and Wi-Fi 7 Provide High-Quality Connection Foundation

As next-generation network communication technologies, FTTR and Wi-Fi 7 will offer smart homes users ultra-high bandwidth and ultra-low latency connection capabilities, ensuring reliable connectivity for the efficient application of large model technology.

FTTR technology lays fiber directly into each room, providing comprehensive high-speed network coverage throughout the home. Compared to traditional broadband access methods, FTTR offers higher bandwidth, lower latency, and more stable network performance. It ensures real-time communication and data transmission among smart home devices, delivering a smoother and more efficient experience to users.

As a next-generation wireless networking technology, Wi-Fi 7 boasts higher transmission speeds, lower latency, and improved anti-interference capabilities. It ensures more stable and reliable wireless communication among smart home devices and supports a greater number of devices accessing the network simultaneously, meeting the growing demands of smart home connectivity.

Large Models Empower Smart Home Applications

Large models, by deeply learning from massive user data, can accurately predict user behavior and preferences, enabling smarter and more personalized services for smart home devices.

• User behavior prediction: By collecting and analyzing users’ daily behavior data, large models can predict user needs and actions, allowing smart home devices to adjust proactively. For instance, based on the user’s wake-up time, the smart lighting system can automatically modify brightness and color temperature to create a comfortable wake-up environment.
•  Personalized services: Large models can provide tailored services based on users’ preferences and habits. For instance, the smart music system can automatically play the user’s favorite songs according to their tastes, while the smart appliance system can adjust operational modes to save energy and improve comfort based on the user’s lifestyle.

Smart Locks Empower Home Security Entrance 

As an essential component of smart homes, the security of smart locks is directly related to personal and property safety. Large models can achieve more intelligent and secure lock management through deep learning and the analysis of user behavior and environmental data.

•  Intelligent recognition and verification: Large models can perform deep learning and recognition of users’ biometric features such as fingerprints and facial characteristics, enabling more intelligent and accurate identity verification. Additionally, these models can also integrate users’ behavior patterns with environmental data to verify users’ identities from multiple dimensions, ensuring the lock’s security.
•  Detection and early warning of abnormal behavior: Large models can also perform deep learning and analyze lock usage data to detect abnormal behavior and issue early warnings. For instance, when detecting that the lock is frequently opened or closed within a short time frame, the large model can identify this as abnormal behavior and send alerts, reminding users to pay attention to safety or take appropriate measures.
• Smart control and home interconnection: Large models enable smart locks to interact with other smart devices in the home. Users can control lighting, temperature, security systems, and more through the smart lock, facilitating one-touch home automation. Also, the smart lock can automatically adjust device settings based on user behavior and environmental changes.

Smart Cameras Empower Home Security Monitoring 

Using large model technology, smart cameras can provide more comprehensive and efficient monitoring solutions to ensure home security and privacy.

• Intelligent recognition and monitoring: Large models can analyze images captured by the camera using deep learning technology to intelligently recognize family members and visitors. By learning user’s facial features and behavioral patterns, the system can offer more personalized monitoring services.
• Environmental perception and analysis: When combined with smart cameras, large models can not only recognize faces but also perceive and analyze the environment.
• Behavior pattern learning and prediction: By continuously learning and analyzing users’ daily behaviors, large models can understand family members’ habits and predict potential security issues.
• Detection and early warning of abnormal behavior: Large models can analyze the behavior captured by smart cameras in real-time, promptly identifying abnormal activities such as intrusions by strangers or the movement of objects.

Smart Speakers Empower Home Intelligent Interaction

As the interaction center of a smart home system, the intelligence of smart speakers directly affects the user experience. The application of large models in smart speakers enables more personalized and intelligent voice interaction services through deep learning and analysis of users’ speech habits, preferences, and environmental data.

• Intelligent voice recognition and interaction: Large models can deeply learn from user speech, achieving high-precision voice recognition. By analyzing users’ pronunciation, tone, and speech speed, smart speakers can better understand instructions and needs. Additionally, the large model can provide personalized voice feedback based on users’ preference settings.
• Environmental perception and intelligent response: Large models empower smart speakers to integrate environmental data for intelligent responses. For instance, by analyzing indoor light intensity, a smart speaker can automatically play soft music when the user wakes up or adjust the lighting when the user returns home.
• Smart control and home interconnection: Large models enable smart speakers to control other smart devices in the home. Users can issue voice commands to control lighting, temperature, security systems, and more, achieving seamless home automation.

Large models, as an advanced AI technology, can enhance the interaction capabilities and intelligence of smart home devices, resulting in a revolutionary upgrade in human-machine interaction experiences. With advancements in hardware computing power, the optimization and innovation of AI algorithms, and improvements in multi-modal data quality, smart home systems empowered by large models will achieve true intelligence and personalization.