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We present Lunar-R1, an 8B-parameter reasoning model tailored for lunar science and mission operations, developed through continued pre-training, supervised fine-tuning, and reinforcement learning. To optimize the accuracy-efficiency trade-off, we introduce Latent Difficulty Perception (LDP), a lightweight mechanism that leverages final-layer hidden states to estimate query difficulty and regulates Chain-of-Thought generation length via difficulty-aware reward modeling. We construct Lunar-QA, an expert-verified benchmark derived from authentic lunar mission materials, and further evaluate on domain-specific tasks spanning astronomy and astrodynamics. Experimental results demonstrate that LDP reduces average token consumption by 38.9% while improving reasoning accuracy. Profiling on an RK3588 NPU yields 0.54 J/token energy efficiency and 380 ms time-to-first-token latency, validating feasibility for lunar surface edge deployment.