基于海上风电功率数据的多任务符号序列生成统一建模框架OA

With the continuous expansion of offshore wind power installed capacity,the number of diverse tasks derived from homogeneous data has significantly increased.To facilitate information sharing and mutual enhancement across tasks,this paper proposes a symbolic sequence generative framework(SSGF)that uses a unified modeling paradigm to represent and model multiple tasks.The framework designs a universal symbolic system,encoding task types,input data,and target outputs into standardized token sequences,and introduces a"next token prediction"training mechanism to transform diverse tasks into a unified sequence generation problem.Based on this,a three-layer decoder-only model is constructed as the underlying architecture,featuring fully shared parameters and consistent inference paths,enabling mul-ti-task collaborative training and inference within the same neural network.By applying the proposed method to offshore wind power data,this paper achieves unified modeling for both wind power prediction and abnormal data imputation tasks,.The mean squared error of the wind power prediction task is decreased by 3.18％compared to training separately,and the imputation accuracy of the abnormal data imputation task is increased by approximately 1.72％.This method can be used to not only eliminate structural fragmentation issues in conventional methods,but also significantly reduce model design and deployment costs,providing a feasible pathway for building a unified,efficient,and scalable intelligent sens-ing framework.