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.

Focusing on the task allocation problem in spatial crowdsourcing, with the consideration of the influence of the spatial crowdsourcing workers' service quality on the allocation results, a task allocation strategy with the quality evaluation of worker's service was proposed. Firstly, in each spatio-temporal environment, the evaluation element of spatial crowdsourcing workers was added to establish a multi-objective model that fully considers the service quality and distance cost of the workers. Secondly, the algorithm convergence speed was increased and the global optimization ability was improved by improving the initialization and coding strategy, position movement strategy and neighborhood search strategy of the discrete glowworm swarm optimization algorithm. Finally, the improved algorithm was used to solve the model. Experimental results on the simulated and real datasets show that, compared with other swarm intelligence algorithms, the proposed algorithm can improve the total score of task allocation by 2% to 25% on datasets with different scales. By considering the service quality of workers, the proposed algorithm can effectively improve the efficiency of task allocation and the final total score.