基于AIOS的智能产品生态 AIOS-Powered Intelligent Product Ecosystem

三才智能·家的三大本能 Three Intelligent Cores · Home's Three Instincts

星璇所感望舒所循曜衡所断
Sensor HubLearning UnitDecision Engine

一个会思考的家,从感知到决策,全程在你手中 A home that thinks - from perception to decision, everything under your control

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星璇所感 Sensor Hub

本地实时感知 Local Real-time Sensing
毫秒级响应 Millisecond Response
50 ms 以内完成事件触发 Event triggering within 50ms
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精准感知 Precise Sensing
分布式多模态传感器 Distributed Multimodal Sensors
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Xun自研协议 Self-developed Protocol
机制自研,隐私自控 Mechanism self-developed, privacy self-controlled
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望舒所循 Learning Unit

私域节律学习 Private Domain Learning
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自适应学习 Adaptive Learning
30 天自适应作息曲线 30-day adaptive routine curve
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联邦学习 Federated Learning
在私域内多设备共享经验 Multi-device experience sharing within private domain
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可插拔模型 Pluggable Models
支持第三方算法热插拔 Support for third-party algorithm hot-swapping
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曜衡所断 Decision Engine

边缘智能决策 Edge Intelligence Decision
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峰谷调度 Peak-Valley Scheduling
统计计算最优运行时段 Statistical calculation of optimal operation time
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低功耗推理 Low-Power Inference
端侧芯片推理 Edge-side chip inference
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断网可用 Offline Available
本地决策不中断 Local decision-making without interruption
100,000+
行源代码
Lines of Source Code
20+
个核心模块 Core Modules
100%
测试覆盖率 Test Coverage
≤ 1s
响应时间 Response Time
100+
个工具函数 Utility Functions
🚀 深入了解AIOS技术架构 🚀 Explore AIOS Architecture

基于AIOS的产品矩阵 AIOS-Powered Product Matrix

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循星智能管家 SidebyStar Smart Steward

基于AIOS事件驱动架构的个性化家庭中枢,集成视觉识别、语音识别,支持多模态融合、语音交互、设备联动、智能学习,持续进化 Personalized home hub based on AIOS event-driven architecture, integrating vision recognition, supporting multimodal fusion, voice interaction, device coordination, and intelligent learning.

可解释AI 个性化智能中枢
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循星环境感知系统 SidebyStar Environmental Sensing System

基通过自研分布式非传统意义多模态传感器获取数据,自研模型处理多模态数据,形成随时间成长、贯穿空间结构的“世界状态认知系统” Based on self-developed distributed non-traditional multi-modal sensors, the world state cognition system is formed by self-developed models processing multi-modal data.

分布式多模态传感器 自研模型 世界状态认知
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循星个性化学习引擎 SidebyStar Personalized Learning Engine

通过最优化理论与深度学习并行建模系统性自演化机制,逐步形成“拟合个人的认知行为世界模型”并能与其他模块协商鲁棒处理事件流 Through the parallel modeling of optimization theory and deep learning, the system's self-evolution mechanism is gradually formed, and the "cognitive behavior world model of the individual" is gradually formed, and the event stream can be robustly processed through negotiation with other modules.

自演化机制 拟合世界模型 协商事件流
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循星语音交互平台 SidebyStar Voice Interaction Platform

集成身份识别模块,结合人脸识别和声纹识别,确保安全交互 Identity recognition module combines face recognition and voiceprint for secure interaction.

本地语音助手 无障碍交互 多语言支持
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循星性能监控中心 SidebyStar Performance Monitoring Center

基于AIOS实时监控系统,提供模型自诊断、行为日志记录、执行轨迹分析。UI界面展示系统性能、AI服务状态、事件流量的全方位监控数据 Based on AIOS real-time monitoring system, providing model self-diagnosis, behavior logging, and execution trace analysis. UI interface displays comprehensive monitoring data for system performance, AI service status, and event traffic.

模型自诊断 UI监控 行为日志
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循星开发者工具包 SidebyStar Developer Toolkit

完整的AIOS开发工具链,支持Zigbee/BLE/Matter/Modbus协议适配,XunProto自研协议开发,安全沙箱策略模块,权限校验和加密传输 Complete AIOS development toolchain, supporting Zigbee/BLE/Matter/Modbus protocol adaptation, XunProto custom protocol development, security sandbox policy module, permission validation and encrypted transmission.

协议适配层 安全沙箱 权限校验 加密传输

技术理念 Technology Philosophy

以大脑为核心的智能循环系统 Brain-Centric Intelligent Circular System

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感知Sensing

多模态环境感知理解Multimodal Environmental Understanding

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推理Reasoning

分布式推理统一决策Distributed Reasoning Unified Decision

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决策Decision

个性化情感支持Personalized Emotional Support

六大核心技术能力 Six Core Technical Capabilities

  • 🌐 多模态环境感知理解:融合视觉、听觉、触觉等多维度信息Multimodal Environmental Understanding: Fusing visual, auditory, tactile multi-dimensional information
  • 🧠 分布式推理统一决策:本地边-端协同的智能推理架构Distributed Reasoning Unified Decision: Edge-side collaborative intelligent reasoning architecture
  • ❤️ 个性化情感支持:基于用户行为的情感计算与响应Personalized Emotional Support: Emotional computing and response based on user behavior
  • 🎛️ 高自由度设备控制:全场景智能设备协调管理High-Freedom Device Control: Full-scenario intelligent device coordination management
  • 👤 用户状态建模长期记忆:持续学习用户偏好与习惯User State Modeling Long-term Memory: Continuous learning of user preferences and habits
  • 🔄 自适应权重更新机制:动态优化系统响应策略Adaptive Weight Update Mechanism: Dynamic optimization of system response strategies