Multi-body system dynamics intelligent modeling calculation system and method based on natural language

By using a natural language-based intelligent modeling and computation system for multibody system dynamics, and leveraging the Large Language Model (LLM) and Model Context Protocol (MCP), the system addresses the issues of long iteration cycles and high engineering risks in spacecraft multibody system dynamics modeling, achieving rapid and reliable simulation evaluation.

CN121744463APending Publication Date: 2026-03-27HARBIN INST OF TECH
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Patent Information

Application Number
CN202511709321.3
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-11-20
Publication Date
2026-03-27

AI Technical Summary

Technical Problem

Existing technologies for modeling the dynamics of spacecraft multibody systems suffer from long iteration cycles, reliance on human experience, and high maintenance costs. They also make it difficult to quickly assess simulation requirements for extreme conditions and strong nonlinear coupling, leading to increased engineering risks and costs.

Method used

A natural language-based intelligent modeling and computation system for multi-body system dynamics is adopted. It utilizes the Large Language Model (LLM) for semantic understanding and task analysis, and combines text segmentation and embedding with the Model Context Protocol (MCP) to achieve end-to-end coupled computation from natural language to simulation results.

Benefits of technology

It shortens the design iteration cycle, improves the efficiency and breadth of design space exploration, ensures the engineering reliability of simulation results, and reduces engineering risks and costs.

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Abstract

The invention discloses a multi-body system dynamics intelligent modeling calculation system and method based on a natural language, and belongs to the field of overall design of spacecrafts. The problems that in the prior art, when the simulation requirement related to extreme working conditions and strong nonlinear coupling is met, the period is long, manual experience is relied on, and the maintenance cost is high are solved. The system comprises a text segmentation embedding unit used for carrying out text segmentation embedding processing on a multi-body dynamics knowledge base and constructing a vectorized dynamics knowledge context; the large language model unit is used for receiving natural language task description input by a user, performing semantic understanding through LLM (Logistics Language Model) and generating a task analysis result according to dynamic knowledge context learning; and the task processing unit is used for classifying the tasks into inquiry tasks, command and control tasks or construction tasks according to the task analysis result, and calling corresponding modules according to LLM and MCP technologies. The method is used in the field of multi-body system dynamics modeling of spacecrafts.
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