Real-time dynamic optimal path planning is critical for facilitating rapid evacuation of personnel in indoor fire scenarios. However， due to the rapid development and high dynamics of indoor fires， the existing evacuation path planning algorithms cannot achieve efficient dynamic path planning in multi-source multi-sink environments. To address this issue， an indoor fire emergency evacuation path planning method based on an improved D^*Lite algorithm was proposed， namely Rectangular Constrained Shared Map D^*Lite （RCSM-D^*Lite） algorithm. First， a search rectangle constraint was introduced to reduce the number of traversed nodes. Second， to account for the rapid spread of fire， a dual-layer search mechanism was adopted to ensure that evacuees maintain a safe distance from hazardous fire regions. Finally， a virtual node was introduced to unify exits， and a shared environment map was incorporated to share information such as map layout， fire hazard levels and other fire dynamics， thereby improving algorithm efficiency. Path planning experiments with 20 persons were carried out on a 40×160 map. The results show that the improved D^*Lite algorithm reduces the time computation by 72.1% compared with the original D^*Lite algorithm. Simulation results under dynamic fire scenarios verify that this algorithm allows evacuees to keep a safe distance from hazardous fire regions and reach exits safely， providing support for indoor emergency evacuation simulation and planning.

In the 3D point cloud semantic segmentation algorithm based on deep learning， to enhance the fine-grained ability to extract local features and learn the long-range dependencies between different local neighborhoods， a neural network based on attention mechanism and global feature optimization was proposed. First， a Single-Channel Attention （SCA） module and a Point Attention （PA） module were designed in the form of additive attention. The former strengthened the resolution of local features by adaptively adjusting the features of each point in a single channel， and the latter adjusted the importance of the single-point feature vector to suppress useless features and reduce feature redundancy. Second， a Global Feature Aggregation （GFA） module was added to aggregate local neighborhood features to capture global context information， thereby improving semantic segmentation accuracy. The experimental results show that the proposed network improves the mean Intersection?over?Union （mIoU） by 1.8 percentage points compared with RandLA-Net （Random sampling and an effective Local feature Aggregator Network） on the point cloud dataset S3DIS， and has good segmentation performance and good adaptability.