The lightweight design of box-type double-girder overhead crane main girders is important for improving load-carrying capacity, reducing energy consumption, and enhancing transportation efficiency. However, the structural optimization of crane main girders involves multiple constraints and strong nonlinearity, which often leads to slow convergence and premature stagnation when using traditional optimization methods. To address these issues, a multi-strategy improved dung beetle optimization algorithm (MSIDBO) is proposed for the lightweight design of overhead crane main girders. First, the search mechanism and inherent limitations of the standard dung beetle optimization (DBO) algorithm are analyzed. Subsequently, several enhancement strategies are introduced, including hybrid chaotic population initialization; reflective boundary handling; adaptive quantum jump updating; adaptive hybrid updating; and a staged control strategy for search intensity. These strategies are designed to enhance population diversity and achieve a better balance between global exploration and local exploitation. The performance of MSIDBO was evaluated on 29 CEC2017 benchmark functions. The results show that MSIDBO generally converges faster on 25 functions and reaches the global optimum on 24 functions among the compared algorithms. Finally, based on mechanical analysis and design specifications of overhead crane main girders, a constrained structural optimization model is established. The lightweight design optimization is carried out, and finite element simulations were conducted using ANSYS Workbench to verify the effectiveness and engineering feasibility of the optimized design. The results show that the proposed MSIDBO algorithm exhibits enhanced stability and convergence performance, achieving a weight reduction of 19.4% in the main girder under the specified design configuration, meeting satisfying strength and safety requirements.

Human body measurement involves large-scale measurement. The acquisition of three-dimensional spatial coordinate data is a complex process. The errors which are generated in each stage of the process can potentially affect the final measurement data. Therefore, the accuracy of measurement remains one of the key technical issues that influence the development of the three-dimensional human body scanner. On the basis of analyzing the parameters to be calibrated of the entire measurement system, calibration methods for the parameters of angle a and angle β were established. After analyzing errors of the laser human body scanning system, a mechanical error correction algorithm for the system was established. Then, a mechanical error correction experiment using a standard cylinder was designed, and the overall effect was analyzed. The correctness of the mechanical error correction algorithm was verified, which made the scanner more accurate. To further verify the accuracy and reliability of the measurement result when the system used human bodies as measured objects, a comparative experiment was designed. The results of the comparative experiment demonstrated that the absolute error of the system for 3D measurement of a large-sized human body is less than 2 mm, and the relative error is less than 1%, which can meet the needs of fields such as clothing design and production.