The complexity of multichannel data, the intricate temporal dynamics, and the diverse frequency characteristics of time series pose significant challenges for self-supervised representation learning. To address these issues, we present the Teacher Student Score (TSS) framework, a novel contrastive learning approach for multidimensional time series representations. This framework introduces two key innovations. First, we present time-channel-frequency consistency (TCF-C) approach of time, channel, and frequency-based contrastive representations and incorporate it into contrastive learning framework. This technique utilizes a weighting mechanism to prioritize self-supervised tasks that emphasize consistency across these dimensions. Second, we propose a Score Network with Adaptive Augmentation Aggregator (AAA) module. This module dynamically combines augmented strategies to create a unified augmented representation, enhancing the efficacy of augmentation in contrastive learning. We evaluate our method on UEA datasets against eight state-of-the-art methods, and the results show that TSS achieves significant improvements over existing SOTAs of self-supervised learning for time series classification.