In the context of rapidly growing logistics demand, traditional warehouse management methods are inadequate in meeting contemporary efficiency and accuracy requirements. The present study proposes the development of an intelligent warehouse visualization platform, the objective of which is to address issues such as high labor dependency, opaque inventory, and operational inefficiencies. The construction of a virtual warehouse environment was undertaken using Unity3D, with the aim of simulating real-world zones. These comprised storage areas, automatic guided vehicle (AGV) pathways, and operational spaces. The platform incorporates radio frequency identification devices (RFID) for item tracking and a role-based access system, enabling real-time monitoring and management of inbound, inventory, and outbound processes. In order to optimize AGV path planning, the proposed algorithm incorporates a dynamic weighted heuristic, a five-neighborhood search, a bidirectional search, and Bézier curve-based smoothing. The efficacy of these enhancements has been demonstrated through a reduction in searched nodes, computation time, and path length, while simultaneously enhancing smoothness. As demonstrated by simulations conducted in Unity3D, the optimized algorithm exhibits a reduction in search nodes of 59.19%, in time of 45.41%, and in path length of 18%, in comparison with the conventional A-star algorithm. The platform offers a safe, efficient, and scalable solution for enterprise training and operational simulation, contributing valuable insights for intelligent warehouse upgrading.

Prolonged operation of cotton picker poses significant risks of work-related musculoskeletal disorders (WMSDs), primarily driven by non-ergonomic cab layouts that fail to accommodate the unique “left-hand steering, right-hand lever” operational mode. Traditional optimization methods, relying on general digital human models or isolated surface electromyography (sEMG) measurements, often lack the physiological fidelity and computational efficiency for high-dimensional, personalized design. To address this interdisciplinary challenge in agricultural engineering and ergonomics, this study proposes a novel AMS-RF-DBO framework that integrates high-fidelity biomechanical simulation with intelligent optimization. A driver–cabin biomechanical model was developed using the AnyBody Modeling System (AMS) and validated against sEMG data (ICC = 0.695). This model generated a dataset linking cab layout parameters to maximum muscle activation (MA). Using steering wheel and control lever coordinates (X, Y, Z) as inputs, a Random Forest (RF) regression model demonstrated strong performance (R² = 0.91). Optimization with the Dung Beetle Optimizer (DBO) algorithm yielded an optimal configuration: steering wheel (L1 = 434 mm, H1 = 738 mm, θ = 32°) and control lever (L2 = 357 mm, H2 = 782 mm, M = 411 mm), reducing upper-limb MA from 3.82% to 1.47% and peak muscle load by 61.5%. This study not only provides empirical support for ergonomic cab design in cotton pickers to reduce operator fatigue and health risks but also establishes a replicable technical paradigm for ergonomic optimization of other specialized agricultural machinery.

Automatic container-number recognition in land-port yards is challenged by clutter, illumination changes, scale variation, surface wear, and character distortion. This paper proposes a two-stage detection–cropping–recognition framework comprising an improved YOLO11 detector and a lightweight convolutional recurrent neural network (CRNN). The detector integrates C3k2 with channel-split multiscale aggregation (C3k2-CSMA), a hierarchical efficient feature fusion pyramid network (HEFFPN), selective boundary aggregation (SBA), and a shared enhanced detection head (SED-Head) with detail-enhanced convolution (DEConv) to improve small-target representation, feature fusion, and boundary localization. The recognizer combines MobileNetV3 with bidirectional long short-term memory (Bi-LSTM) and is trained using connectionist temporal classification (CTC) loss and L2 regularization. The detector achieved 97.3% mean average precision at an intersection-over-union threshold of 0.5 (mAP@0.5), 97.1% recall, 55 frames per second (FPS), and a model size of 12.2 MB. The recognizer achieved 97.6% exact-match accuracy, a model size of 6.2 MB, and an inference time of 8.9 ms/crop. The complete system achieved 96.8% end-to-end exact-match accuracy, a total model size of 18.4 MB, and a total inference time of 27.1 ms/image, demonstrating a practical accuracy–efficiency balance for the evaluated complex land-port scenes.