Farmland in the hilly and mountainous regions of southern China is characterized by complex terrain and highly variable operating loads. Conventional diesel-powered tracked harvesters are constrained by high crop losses, excessive impurity rates, frequent blockages, and low overall energy-use efficiency. Distributed electric drive provides a promising solution; however, threshing cylinder blockage, high-frequency load transients, and slope operation make it difficult for conventional energy-management strategies to simultaneously ensure dynamic responses, fuel economy, and battery state of charge (SOC) stability. This study therefore proposes a deep deterministic policy gradient (DDPG)-based reinforcement learning energy-management strategy (RL-EMS) for a range-extended, distributed-drive hybrid tracked combine harvester. First, a full-vehicle dynamic model incorporating eight electric-drive units and strong electromechanical coupling is established. Second, power allocation is formulated as a Markov decision process (MDP), with a multi-objective reward function that accounts for fuel consumption, SOC tracking, and boundary violations; the load-rate-of-change is introduced as a feedforward state. Finally, a supervisory physical layer comprising feasible power projection, safety filtering, and rate limiting is inserted between the policy network output and the physical plant so that the executed command satisfies range extender power, battery SOC, current, and power-slew constraints. Under the standard 1000 s cycle, SOC-corrected energy-equivalent comparison shows that the RL-EMS reduces fuel consumption by 1.5% relative to the adaptive equivalent consumption minimization strategy (A-ECMS) and by 26.6% relative to the constant-torque energy-management strategy (CT-EMS). Under an unseen complex random cycle, the RL-EMS reduces fuel consumption by 5.1% relative to A-ECMS. It also suppresses DC-bus voltage sag during a threshing cylinder blockage transient, demonstrating favorable electromechanical transient response. The proposed method provides a modeling and control reference for the intelligent energy management of range-extended, distributed-drive agricultural machinery.

To assess pitch consistency among Tujia dongdongkui sharing a common tuning target, we measured eight geometric parameters and the frequencies of Do, Re, Mi and Sol in ten bamboo instruments, using C5 (523.3 Hz), D5 (587.3 Hz), E5 (659.3 Hz) and G5 (784.0 Hz) as references, and reanalysed two independent recordings per instrument with a common procedure. From Do to Sol, the between-instrument coefficient of variation increased from 9.63% to 13.00%, while mean absolute reference deviation increased from 124.6 to 174.0 cents. Mean symmetric relative frequency differences between recordings ranged from 0.67% to 1.42%, and the increasing between-instrument dispersion from Do to Sol was retained when A, B or mean A/B frequencies were used. The mean Do–Sol interval based on mean A/B frequencies was 741.0 cents, exceeding the 700-cent reference. Length parameters L1–L4 covaried strongly, and none of 32 geometry–pitch associations met the multiple-testing-adjusted significance threshold. Controlled passive-bore calculations for instrument 5 showed that moving the proximal tonehole coordinate L4 by ±2 mm primarily shifted the input-impedance peak for the Sol fingering; peak shifts for the other three fingerings did not exceed 0.50 Hz. The study separates between-instrument variation, repeated-playing differences and within-instrument interval variation, and combines single-dimension perturbations with measurements to identify a direction for physical validation in manufacturing-consistency assessment.