This paper presents a multimodal world-state method for task planning and anomaly recovery in low-Earth-orbit (LEO) satellite missions, where heterogeneous observations, tightly coupled hard constraints, and costly global replanning make closed-loop autonomy difficult. The method aligns imagery, telemetry, orbital context, and mission semantics at each decision instant to form a unified world-state layer with modality-availability masks. It then propagates hard constraints over a dynamic task-resource-environment-action graph and uses a learned transition model to assess future reachability. Anomalies and their impact paths are localized through prediction-observation residuals, after which recovery is restricted to the affected temporal window. For training, an offline MILP solver subject to the same hard constraints generates teacher trajectories; behavior-cloning pretraining is followed by closed-loop interaction and advantage-weighted updates in the digital twin. On a self-built LEO digital-twin benchmark, the method achieves task success, recovery success, and constraint satisfaction rates of 89.8%, 81.6%, and 96.3%, respectively, outperforming the strongest baseline by 6.7, 12.2, and 3.3 percentage points while maintaining 59 ms online latency. These results support the four proposed mechanisms within the digital-twin setting.