To address the complex structures of historical buildings in Mindong, lack of historical reference materials, and heavy workload of traditional surveying and mapping methods in the field, a 3D reconstruction method of historical building based on a wearable mobile scanning system for data acquisition was studied. First, a wearable mobile scanning system is used to scan historical buildings in three dimensions. The data are fused to obtain high-precision three-dimensional point cloud data of historical buildings. Second, based on the processed point cloud, a 2D map of historical buildings is accurately drawn using point cloud slicing. Finally, three-dimensional reconstruction of historical buildings is realized using a 2D atlas of historical buildings and modern modeling technology. The accuracy of the three-dimensional reconstruction model was analyzed using a fitting algorithm and structural geometric relationship to verify the effectiveness and accuracy of the method. An old house in Mindong, Republic of China, was taken as the research object. Experimental analysis revealed that (1) a wearable mobile scanning system is suitable for high-quality point cloud data acquisition of buildings with complex geometric features, solving the problems of low efficiency and poor coverage of traditional surveying and mapping results; and (2) the method realizes 2D results rendering and 3D reconstruction of historical buildings based on the wearable mobile scanning system data with good accuracy, providing a reference for the protection and repair of historical buildings and health assessment.

Digital twins (DTs) have seen widespread application across industries, enabling deep integration of cyber–physical systems. However, previous research has largely focused on domain-specific DTs and lacks a universal, cross-industry modeling framework, resulting in high development costs and low reusability. To address these challenges, this study proposes a DT modeling method based on hierarchical decoupling and topological connections. First, the system is decomposed top–down into three levels—system, subsystem, and component—through hierarchical functional decoupling, reducing system complexity and supporting independent component development. Second, a method for constructing component-level DTs using standardized information sets is introduced, employing the JSON-LD language to uniformly describe and encapsulate component information. Finally, a topological connection mechanism abstracts the relationships between components into an adjacency matrix and assembles components and subsystems bottom–up using graph theory, ultimately forming the system-level DT. The effectiveness of the proposed method was validated using a typical surface water purification system as a case study, where the system was decomposed into four functional subsystems and 12 types of components. Experimental results demonstrate that the method efficiently enables automated integration of DTs from standardized components to subsystems and the complete system. Compared with conventional monolithic modeling approaches, it significantly reduces system complexity, supports efficient component development, and accelerates system integration. For example, when the number of components exceeds 300, the proposed method generates topology connections 44.69% faster than direct information set traversal. Consequently, this approach provides a novel and effective solution to the challenges of low reusability and limited generality in DT models, laying a theoretical foundation and offering technical support for establishing a universal cross-industry DT modeling framework.

To address the complexity of roof load calculations in ancient buildings and the of limitations single-method safety assessments, a static safety evaluation approach for timber frames-based on a wearable mobile scanning system (BMMS)-is proposed. First, the BMMS is used to acquire high-precision three-dimensional point cloud data of ancient buildings for digital reconstruction. Next, the roof structure is analyzed in conjunction with regional architectural characteristics, where roof loads and component strengths are determined according to relevant standards. This allows for verification of individual member bearing capacity and subsequent determination of the total roof system load. Finally, based on the modulus of elasticity of wood, the stiffness characteristics of mortise-tenon joints, and the total roof load obtained through the aforementioned process, a finite element model of the timber framework is established. Structural safety is evaluated through static analysis, leading to the formulation of targeted reinforcement measures. The results show that roof components remain within their bearing capacity limits, confirming the rationality of roof dead and live load values. This demonstrates the feasibility of using BMMS-based 3D reconstruction to analyze roof systems, determine force models, and verify bearing capacities. In addition, the effectiveness of the safety assessment method was verified by combining simplified theoretical analysis with finite element analysis. The results showed that while the structure had weaknesses such as melon columns, eaves purlins, through-columns, short beams, and mortise and tenon joints, these weaknesses were all within safety limits, demonstrating good overall stability.

Precipitation on remote offshore islands is characterized by non-stationarity and strong stochasticity. However, rainwater harvesting systems require accurate precipitation forecasts to ensure effective water storage and discharge and a stable freshwater supply. Here, we propose an intelligent framework integrating precipitation signal decomposition, a hybrid neural network for predicting daily precipitation over the next 30 days, and digital twin technology to optimize water storage and discharge strategies for rainwater collection pools. For signal preprocessing, variational mode decomposition decomposes the original daily precipitation series into multiple intrinsic mode functions, a chaotic sparrow search optimization algorithm performs global parameter optimization, and singular spectrum analysis is applied twice to denoise high-frequency mode components. The hybrid prediction model combines a convolutional neural network to extract local temporal features, bidirectional long short-term memory to capture bidirectional long-term dependencies, and a multilayer perceptron for nonlinear mapping and regression output. Finally, this model is integrated into a digital-twin-enabled decision framework to automatically implement water storage control through water balance calculations and threshold comparisons. The proposed method achieves a test mean absolute error of 2.49 mm, a root mean square error of 5.72 mm, and an R² of 0.83; digital twin control significantly reduces overflow losses and, during typical typhoon months, maintains the water level in the rainwater collection pool below the warning threshold. In summary, we present a simulation-based pathway for optimizing small-scale intelligent rainwater harvesting systems, which offers a practical pathway to enhance the sustainability of freshwater resources on remote offshore islands by improving the efficiency of rainwater utilization and reducing dependence on external water supplies.