# Technical Architecture and Technical Logic
### **1.1** Underlying Technical Architecture Design Floa's core architecture is dual-driven by "Modular Microservices" and "Real-Time Digital Human Engine". \ Based on our self-developed digital human engine and private large model cluster, we focus on overcoming key challenges including real-time interaction of digital humans, secure rights confirmation of Web3 assets, and ecological expansion efficiency. **The overall technology stack is divided into five layers:** #### **1.1.1** Infrastructure Layer: Ensuring Real-Time Interaction & Data Security * **Computing Resources**: Adopt a three-tier architecture of "Hybrid Cloud + Edge Nodes + Rendering Accelerators". To meet the real-time driving of digital humans' facial expressions, movements, and voices, we have deployed edge rendering nodes across multiple global regions, optimizing end-to-end interaction latency to less than 100ms. * **Storage Solutions**: Implement hierarchical processing based on data characteristics. Core assets (e.g., digital human avatars, motion libraries) are stored on IPFS with on-chain certification; training and interaction data use a distributed file system; we have specially designed a "Digital Asset Repository" to support high-concurrency, millisecond-level resource calls. * **Security Mechanisms**: Build a three-layer protection system—smart contracts undergo third-party audits, data transmission is encrypted throughout, and each digital human identity is uniquely authenticated and copyright-anchored via blockchain, ensuring Web3-native security for assets and data. #### **1.1.2** Core Technology Layer: Deep Integration of Large Models & Digital Human Engine This layer serves as Floa's intelligent core, enabling a closed loop from "Perception" to "Generation" and then to "**Decision-Making**". \ We have conducted in-depth optimizations based on open-source architectures, with the key improvement lying in the collaborative reasoning efficiency between large models and the digital human engine. **Below is a code snippet illustrating our interaction logic.** ``` python # Core Interaction Engine: End-to-End Generation from Text to Digital Human Performance class FLOAAgentCore: def __init__(self, model_path: str, renderer_config: dict): # Load the large model, using bfloat16 precision to balance performance and overhead self.tokenizer = AutoTokenizer.from_pretrained(model_path) self.llm = AutoModelForCausalLM.from_pretrained( model_path, torch_dtype=torch.bfloat16, device_map="auto" ) # Initialize the digital human rendering engine self.face_renderer = FaceRenderer(renderer_config["face"]) self.motion_controller = MotionController(renderer_config["motion"]) def generate_response(self, user_input: str) -> tuple: # 1. Generate text responses (with integrated context management) prompt = self._build_prompt(user_input) response_text = self._generate_text(prompt) # 2. Parallelly drive digital human performance (a key FLOA optimization) emotion = self._predict_emotion(response_text) #Lightweight sentiment analysis motion_sequence = self._generate_motion(emotion, response_text) # 3. Compose rendering data streams render_data = { "facial": self.face_renderer.render(emotion), "motion": self.motion_controller.execute(motion_sequence) } return response_text, render_data ``` #### Our Core Optimizations: **Model Collaboration**: Collaborate with leading model teams to customize a "Digital Human Multimodal Enhancement Layer" on the foundation model. This layer unifies the reasoning of speech, semantic, and visual signals, significantly enhancing interaction naturalness. **Decision Engine**: Integrate rule-based engines with RLHF strategies. It not only handles task planning but also dynamically adjusts digital humans' interactive performance (e.g., tone, microexpressions), synchronizing "task execution" and "emotional interaction". **Tool Framework**: The built-in API gateway has integrated 30+ services, with a dedicated ecological open interface layer designed. In the future, via key management and rate limiting, we will safely open digital human capabilities to developers. #### 1.1.3 Agent Capability Layer: Scalable Digital Human Skill System * **Basic Capabilities**: Offer out-of-the-box voice/text conversation, real-time avatar driving, and basic task automation (e.g., schedule management, information retrieval). * **Advanced Capabilities**: Gradually unlocked through training, including multi-agent collaboration (virtual teams), commercial scenario customization (brand endorsement, virtual live streaming), and Web3 asset integration (NFT management, on-chain transactions). * **Personalized Customization**: Support full-dimensional customization from appearance (character modeling, outfits) and skills (model fine-tuning) to interaction styles (tone, expression preferences). #### **1.1.4** Ecological Interaction Layer: Multi-terminal & Cross-platform Adaptation * **User Interfaces**: Compatible with Web, mobile DApps, and VR/AR devices, ensuring consistent digital human rendering across terminals. Provide a low-code editor for users to quickly create exclusive digital humans. * **Developer Interfaces**: Gradually opened in phases: V1.5 (Basic Capability APIs) → V2.0 (Large Model Collaboration APIs) → V3.0 (Complete SDK & Developer Platform). * **Cross-ecosystem Integration**: Seamlessly integrate with Web3 wallets, exchanges, and traditional SaaS services (e.g., WeChat Work, Slack), enabling cross-scenario interoperability of digital human identities. #### **1.1.5** Incentive & Governance Layer: Building a Value Closed Loop * **Smart Contracts**: Implement token incentives for training contributions, NFT-based rights confirmation for digital human assets, and support rights circulation such as NFT staking and trading. * **Decentralized Governance**: Plan to introduce a DAO mechanism, allowing core NFT holders to participate in community governance of API standards, copyright norms, and incentive policies.