Dynamic Scheduling and Resource Optimization Algorithm for Union Activities by Integrating Transformer and...

To address the problem of reduced organizational efficiency caused by frequent resource allocation conflicts and delayed scheduling responses in managing trade union activities, this paper proposes a dynamic scheduling algorithm that integrates Transformer and PPO (Proximal Policy Optimization). In the specific implementation, a unified scheduling scenario modeling structure is first designed to convert activity, personnel, and resource states into tensor inputs, thereby achieving multidimensional constraint integration. Next, the Transformer multi-head attention mechanism is used to encode the time series of historical activity requests and resource status, extract multi-dimensional spatiotemporal features, and enhance the perception of conflict risks. Subsequently, based on the encoding results and the PPO strategy network, scheduling actions are generated from the current state to enhance the strategy's adaptability to complex environments. Finally, through the pruning update and the advantage function correction mechanism, the strategy's stability during iteration and improved scheduling performance are guaranteed. Experiments have shown that when the task density is 1000, the scheduling algorithm's average decision time is 0.72s and its average response delay is 1.59s, indicating high response speed and decision-making efficiency. Across seven activity types and complexity levels, the resource conflict rate is 0.05-0.12; the average resource utilization rate is 0.75-0.86; and the scheduling stability index is 0.8-0.91, effectively reducing frequent resource allocation conflicts and achieving high scheduling stability. Under high-concurrency conditions, the resource balance index and the strategy transfer robustness index are 0.88 and 0.85, respectively, indicating good adaptability to task concurrency loads.