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To achieve sustainable heritage conservation, preventive conservation has gradually taken precedence over curative conservation, because it can inhibit the damage caused by various environmental factors and maximizes the preservation life of the artifacts. Due to susceptibility to environmental factors, preventive conservation has been used in the conservation of movable wooden artifacts to further protect them. Recently, digital twin technology, as a concept that transcends reality, can be mapped in virtual space to reflect the full lifecycle process of the corresponding entity, which is a superior characteristic that makes it valued and researched for health monitoring and health management of heritages. This paper proposes a health management method mainly for preventive conservation of movable wooden artifacts, integrating digital twin technology into the health management process. Using the Quanzhou Ship as a typical representative, several important components of health management are specifically analyzed, such as the five-dimensional model of the digital twin, the data interaction process of the digital twin, and the identification and assessment of risks. In particular, the process of preventive conservation of the stern based on the digital twin is presented in detail. This method provides a basis for future preventive conservation of movable wooden artifacts and has implications for the use of digital twin technology in the field of heritage conservation, especially for movable wooden artifacts.

The selection of initial value in digital image correlation (DIC) has significant influence on the search efficiency of image subpixel displacement and the algorithmic convergence speed. An accurate and reasonable initial value can reduce the number of iterations of subsequent IC-GN optimization, accelerate the convergence of the results, and avoid the divergence of the algorithm in the iterative process. This paper proposes a full-parameter initial value estimation method based on a regression convolution neural network with multithreaded calculation. The proposed method sequentially uses the integer-pixel estimation based on neighborhood search, the subpixel estimation based on surface fitting and the first-order displacement gradients estimation based on a regressive convolutional neural network to achieve the initial value estimation of inverse compositional Gauss-Newton (IC-GN) iteration. Experimental results show that the iteration times of the proposed method are reduced by about 30% compared with the integer-pixel initial value estimation method. In the process of IC-GN iteration, the computational efficiency of CPU multithreaded calculation is nearly twice higher as that of the single-thread method. It can not only improve the accuracy of the initial value estimation but also has high adaptability, which can adapt to selecting different subset sizes or different speckle patterns. This study provides a reference for the effect of iterative initial value optimization on efficiency and accuracy in DIC.

In full-field 3D displacement measurement, stereo digital image correlation (Stereo-DIC) has strong capabilities. However, as a result of difficulties with stereo camera calibration and surface merging, 360-deg panoramic displacement measurements remain a challenge. This paper proposes a panoramic displacement field measurement method in order to accurately measure the shape and panoramic displacement field of complex shaped objects with natural textures. The proposed method is based on the robust subset-based DIC algorithm and the well-known Zhang’s calibration method to reconstruct the 3D shape and estimate the full-field displacements of a complex surface from multi-view stereo camera pairs. The method is used in the determination of the scale factor of the 3D reconstructed surface and the stitching of multiple 3D reconstructed surfaces with the aid of the laser point cloud data of the object under test. Based on a discussion of the challenges faced by panoramic DIC, this paper details the proposed solution and describes the specific algorithms implemented. The paper tests the performance of the proposed method using an experimental system with a 360-deg six camera setup. The system was evaluated by measuring the rigid body motion of a cylindrical log sample with known 3D point cloud data. The results confirm that the proposed method is able to accurately measure the panoramic shape and full-field displacement of objects with complex morphologies.

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