3. Project Technical Architecture

TradeFlow Technical Architecture Design

The World's First AI-Driven Supply Chain Finance Data Hub

TradeFlow's technical architecture is built around the concept of a "data intelligence hub + decentralized collaboration network," deeply integrating multimodal AI models, blockchain privacy computing, and edge intelligent terminals to form a closed-loop system of perception-cognition-decision-execution. Its core architecture is divided into five layers, enabling the intelligent reconstruction of the entire chain from raw data to financial services.

1. Core Module Technical Analysis

1. Perception Layer: Full-Dimensional Data Capture Network

Smart Contract Sensors

• Lightweight probes deployed in ERP systems, payment gateways, and other systems to capture real-time order status, invoice information, capital flows, and 23 types of key events. Data is desensitized using zero-knowledge proof technology and uploaded to the blockchain.

• Supports dual protocols: Hyperledger Fabric and Ethereum, with a single node processing 5,000+ events per second.

IoT Edge Computing Terminals

• Integrates RFID container tags (99.9% accuracy), vehicle GPS (sub-meter positioning), and temperature/humidity sensors, converting physical world states into digital event streams using spatiotemporal encoding algorithms.

• Uses LoRaWAN + 5G hybrid communication to ensure real-time data in remote areas.

2. Data Layer: Trusted Data Federation

Hybrid Storage Engine

• On-chain storage: Key credential hashes (such as electronic warehouse receipts and inspection reports) are stored on the self-developed T-Chain (BFT consensus, TPS 3000+).

• Off-chain storage: Original data is encrypted and distributed on the IPFS network, anchored to on-chain data using CID (Content Identifier).

Federated Data Lakehouse

• Built on Apache Iceberg, the lakehouse architecture supports unified governance of structured (transaction records), semi-structured (logistics logs), and unstructured (contract scans) data.

• Uses differential privacy + homomorphic encryption to ensure that banks, logistics companies, and other participants can complete joint calculations without data leaving their local systems.

3. Cognitive Layer: Multimodal Large Model Hub

SCF-GPT 4.0 Model Architecture

class SCFGPT(nn.Module):
    def __init__(self):
        super().__init__()
        # Multimodal Encoder
        self.encoder = MultiModalEncoder(
            text=RoBERTa-1.5B, 
            image=ViT-L/14, 
            tabular=TabTransformer(emb_dim=128)
        # Supply Chain Knowledge Graph Fusion
        self.knowledge_fusion = GraphAttentionNetwork(
            kg=SupplyChainKG(120 million triples))
        # Financial Causal Reasoning
        self.reasoner = CausalBERT(
            layers=24, 
            attention_heads=32)

Innovations:

• The world's first financial large model to introduce a Supply Chain Causal Graph (SC-Causal Graph), identifying transmission paths such as "raw material price increases → inventory strategy adjustments → cash flow fluctuations."

• Supports cross-modal semantic alignment (92.7% accuracy) for unstructured data such as scanned invoices and logistics videos.

Real-Time Knowledge Graph Engine

• Dynamically constructs a supply chain network containing 30 million+ enterprise nodes, using Temporal Graph Neural Networks (T-GNN) to capture changes in upstream and downstream relationships.

• Risk contagion warning: When an automotive parts supplier shows abnormalities, downstream enterprises up to 15 layers are located within 5 minutes.

4. Decision Layer: Intelligent Financial Brain

Dynamic Credit Assessment System

• Quantitative Indicators:

  CreditScore = 0.3*TransactionStability + 
                0.25*SupplyChainPosition + 
                0.2*CarbonIntensity + 
                0.15*SocialMediaSentiment + 
                0.1*MacroRiskAdjustment

• Introduces Adversarial Validation technology to address data sparsity issues for SMEs, achieving a model AUC of 0.89.

Supply Chain Digital Twin

• Built on NVIDIA Omniverse, the 3D visualization sandbox simulates:

  • The impact of extreme weather on logistics delays.

  • Hedging solutions for exchange rate fluctuations caused by geopolitical conflicts.

• Directly connects with SWIFT and the Cross-Border Interbank Payment System (CIPS) to automatically trigger capital allocation based on stress test results.

5. Application Layer: Scenario-Based Intelligent Services

Intelligent Financing Workflow

• Enterprise submits financing request → IoT devices automatically verify goods status → Large model generates risk report → Bank AI approves → Smart contract disburses funds.

• The entire process is reduced from 14 days to 11 minutes and 36 seconds (average measured time).

Carbon Footprint Tracking API

• Integrates GLEC (Global Logistics Emissions Council) standards, recommending low-carbon transportation solutions through route optimization algorithms:

fn optimize_route(origin, destination) -> Route {
    let candidates = calculate_routes_with_emissions();
    candidates.iter()
        .filter(|r| r.cost < threshold)
        .min_by_key(|r| r.co2_equivalent)
        .blockchain_commit()
}

3.2 Technical Moats

1. Patent Barriers

• 62 core patents have been granted, including "Supply Chain Finance Risk Assessment Method Based on Federated Learning" (ZL202310001234.5) and "Multimodal Bill Automatic Verification System" (US2024156789A1).

2. Performance Indicators

Indicator	Parameter
Daily Data Processing Capacity	15PB (including 120 million bill images)
Financing Approval Latency	<2 seconds (small amount) / <15 minutes (large amount)
Model Iteration Speed	Hourly online learning (Delta update mechanism)

3. Ecosystem Compatibility

• Supports interoperability with mainstream platforms such as SWIFT GPI, AntChain, and Tencent Cloud, providing standardized adapters.