To address the efficiency limitations of vehicle--UAV collaborative operations in large-scale, high-frequency inspections of flexible DC transmission lines, this paper proposes a path--energy coordinated bi-level nested optimization framework, targeting the inherent strong coupling among vehicle path planning, UAV task scheduling, and battery resource allocation. First, a unified multi-objective constrained optimization model is formulated at the system level, integrating vehicle parking decisions, UAV operational radius, and path-dependent energy consumption into a single decision framework to characterize the intrinsic coupling between routing and energy allocation. Second, a dual-layer collaborative optimization structure is developed, where closed-loop interaction between outer-layer routing decisions and inner-layer energy scheduling enables the co-evolution and joint convergence of vehicle paths, UAV task assignments, and battery resource distribution. A path-feature-based UAV energy demand prediction mechanism is further incorporated to enhance the foresight and feasibility of energy scheduling. Finally, simulation studies based on representative flexible DC transmission line scenarios are conducted. The results demonstrate that the proposed framework consistently outperforms existing decoupled or partially coupled optimization strategies in system-level metrics, including total operation time, vehicle travel distance, and battery utilization efficiency, while exhibiting robust performance and practical deployability under expanded inspection scales and varying UAV endurance conditions.