To address the inadequacy of multimodal data fusion and complexities in dynamic constraint optimization for satellite mission requirement decision-making, an intelligent decision model is designed to enhance automation and accuracy. The proposed Retrieval-Augmented Generation (RAG)-based optimization model for satellite mission planning comprises: ① An input layer receiving multimodal data such as user requirement texts and geospatial coordinates, etc. ; ② A processing layer integrating Transformer-architecture Large Language Model ( LLM) with vector databases to enable semantic retrieval and knowledge augmentation; ③ A constraint verification module in the output layer generating feasible solutions; ④ A feedback layer dynamically updating the knowledge base. Experimental validation demonstrates 90% decision accuracy—achieving 20% and 9.8% absolute accuracy improvements over conventional Rule-Based Expert Systems ( RBES) and Machine Learning Models ( MLM ), respectively. The model significantly enhances adaptability in satellite mission decision-making, enables efficient resource allocation under dynamic constraints, and exhibits substantial engineering applicability.
| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |