Advanced information technologies are driving revolutionary transformations in production and lifestyles. The rapid evolution of artificial intelligence (AI) has prompted academia to reconceptualize human-machine collaboration paradigms. In mining operations, leveraging AI-empowered cyber-physical systems (CPS) to transition on-site operators toward high-value decision-making roles, such as remote monitoring and strategic optimization, is critical for enhancing operational efficacy. Nevertheless, two fundamental challenges persist in smart mining development: inadequate autonomy of individual engineering machinery and inefficient human-machine interaction in complex scenarios. To address these gaps, this study proposes an evolutionary pathway for intelligent mining equipment based on an industrial intelligence transformation framework. Focusing on truck-excavator collaborative operations, we construct a task-driven interaction model and employ semi-tensor product (STP) theory for semi-quantitative analysis. This approach validates the rationality of designed workflows and behavioral logic, while proposing a future methodological framework for human-machine collaboration analytics.