Methane exceedance events in underground coal mines are often associated with progressive multisensor abnormal evolution processes involving operational, ventilation, environmental, and methane-drainage subsystems. Existing studies primarily focus on methane concentration prediction and provide limited interpretability regarding how abnormal evolution patterns emerge before threshold exceedance. To address this limitation, this study proposes an interpretable event-driven multisensor risk-evolution analysis framework for methane early warning. Methane exceedance events are first extracted from multisensor monitoring data, and a continuous multisensor risk representation together with a persistence-based trigger mechanism is developed to identify sustained abnormal evolution prior to methane exceedance. Event-specific temporal dependency networks are then constructed using lagged dependency analysis to characterize multisensor interaction structures within event windows. Representative evolution paths and recurrent critical variables are further identified to reveal interpretable abnormal evolution patterns. Experiments conducted on a real underground coal mine monitoring dataset containing 784 methane exceedance events demonstrate that the proposed framework achieved the highest early-warning performance among all compared baselines. Under the Any-Target-Sensor criterion, the framework attained an effective warning rate of 0.848 and significantly outperformed benchmark methods in event-level McNemar tests (p < 0.001). The results further indicate that methane exceedance events are generally associated with structured multisensor abnormal evolution processes rather than isolated methane fluctuations, providing an interpretable system-level perspective for methane early warning.

In the platform economy, the pricing mechanisms are typically determined unilaterally by the platform, leading to increasingly prominent issues such as unreasonable commission rules and insufficient protection of drivers’ rights and interests. The commission rate is a key regulatory parameter for balancing revenue distribution between network freight platforms and carriers, directly shaping their strategic behaviors and influencing the industry’s sustainable development. This study aims to adopt the commission rate as the core indicator of platform revenue distribution. Through equilibrium analysis and numerical simulation, it develops a revenue-maximization model grounded in both individual rationality and collective rationality. Based on this framework, it identifies the commission ranges that achieve Pareto improvements at different stages of platform development, thereby optimizing the platform’s revenue distribution strategy. The findings show that, under collective rationality, the effort levels of both platforms and carriers exceed those under individual rationality. When the commission rate falls within a specific range, the collective-revenue-maximization strategy enhances the benefits of both parties and achieves Pareto improvement. The optimal commission rate increases progressively as the platform develops and is strongly associated with the marginal effects of effort levels on order volume and the logistics service cost coefficient. The results offer theoretical guidance and practical insights for the scientific design of commission mechanisms in network freight platforms.