With the increasing complexity of industrial chains, intelligent monitoring methods for industrial chain data have become essential for economic security and high-quality industrial development. Industrial chain data has temporal and heterogeneous characteristics, requiring intelligent systems that can forecast trends and adjust monitoring strategies in response to anomalies. Current trend prediction methods struggle to model complex heterogeneous interactions between variables, while existing adaptive monitoring approaches lack sensitivity to abnormal trends and cannot efficiently sample multiple variables synchronously. To address these challenges, we propose: (1) HGAN-TPMLP, a trend prediction algorithm combining heterogeneous graph representation learning with a Patch-based MLP structure and temporal causal convolution, improving both short-term feature sensitivity and long-range forecasting; (2) HTAAM, an adaptive monitoring method including HGAN-GRU for trend anomaly detection and EATSA for time-synchronized adaptive sampling based on anomaly scores. Experiments demonstrate that our methods significantly improve prediction accuracy on industrial chain data while reducing unnecessary data transmission and enhancing anomaly coverage, making them well-suited for complex industrial environments.