Hazardous Material Escort Robot
RACEMIND
Product Overview
RACEMIND is RACETEC's self-developed general-purpose foundation large model. Its name combines "Race" (speed, forward momentum) and "Mind" (intelligence), embodying the spirit of forging ahead and leading with wisdom. As the foundation model of RACETEC's AI capability system, RACEMIND serves as the core AI engine powering all of RACETEC's intelligent terminal products.
RACEMIND is deeply optimized for the OpenHarmony operating system, delivering excellent inference efficiency and resource utilization in the native OpenHarmony environment. The model covers core AI capabilities including natural language understanding, multimodal perception, knowledge reasoning, and decision planning, effectively supporting the AI needs of RACETEC's scenario-specific intelligent terminal products.
RACEMIND is deeply optimized for the OpenHarmony operating system, delivering excellent inference efficiency and resource utilization in the native OpenHarmony environment. The model covers core AI capabilities including natural language understanding, multimodal perception, knowledge reasoning, and decision planning, effectively supporting the AI needs of RACETEC's scenario-specific intelligent terminal products.
Technical Architecture
RACEMIND adopts the Transformer++ architecture combined with a Mixture of Experts (MoE) strategy, optimizing inference efficiency while maintaining high accuracy. Key technical features include:
1
Model Architecture
Transformer++ based MoE architecture, supporting hundreds of billions of parameters
2
Adaptation Optimization
Deeply optimized for the OpenHarmony runtime environment, supporting NNE (Neural Network Engine) accelerated inference
3
Multimodal Fusion
Unified visual, speech, and text multimodal encoders for cross-modal understanding and generation
4
Continuous Learning
Supports online incremental training, continuously learning and optimizing from terminal data
5
Device-Cloud Collaboration
Supports flexible switching between on-device lightweight inference and full cloud inference
6
Federated Learning
Supports privacy-preserving federated learning framework to ensure data security
1
Model Architecture
Transformer++ based MoE architecture, supporting hundreds of billions of parameters
2
Adaptation Optimization
Deeply optimized for the OpenHarmony runtime environment, supporting NNE (Neural Network Engine) accelerated inference
3
Multimodal Fusion
Unified visual, speech, and text multimodal encoders for cross-modal understanding and generation
4
Continuous Learning
Supports online incremental training, continuously learning and optimizing from terminal data
5
Device-Cloud Collaboration
Supports flexible switching between on-device lightweight inference and full cloud inference
6
Federated Learning
Supports privacy-preserving federated learning framework to ensure data security
Natural Language Understanding
Chinese semantic understanding, intent recognition, multi-turn dialogue, text summarization, sentiment analysis
Multimodal Perception
Image recognition, video understanding, speech recognition and synthesis, intelligent document parsing
Knowledge Reasoning
Industry knowledge Q&A, logical reasoning, causal inference, commonsense reasoning
Decision Planning
Task planning, path planning, resource scheduling, emergency decision-making
Security & Compliance
Content safety review, privacy protection, adversarial attack defense, model watermarking
Deployment Architecture Diagram
RACEMIND adopts a hybrid deployment architecture of "cloud-based training + edge-cloud collaborative inference"
Application Scenarios
Full-Stack Intelligent Government Terminal
Provides AI capabilities including intelligent voice interaction, automated document generation, and policy knowledge Q&A
Hazardous Material Escort Robot
Environmental perception and decision-making, path planning, risk early warning, automatic obstacle avoidance
OpenClaw All-in-One Terminal
Intelligent recognition and grasping, visual guidance, adaptive control
