Wood material is the foundation of wood structure architecture, and its production technology and equipment technology decide the development and upgrading of modern wood structure architecture. Aiming at the problems of low automation degree, low material utilization rate, low production efficiency and high labor costs in the process of traditional wood processing, we explore the integration and innovation of the traditional wood processing industry and modern industrial Internet information technology on the basis of studying the properties of Betula (Betula costata) solid wood materials, wood comprehensive utilization rate, wood structure component development and processing technology requirements, and form an intelligent, automatic and industrial production mode for building materials. Through technology and methods such as mechanical design, automation technology, machine vision, deep learning, optimization algorithm, electronic design automation, computer aided manufacturing, etc., the key technologies of intelligent automatic optimization of wood materials were studied, and intelligent automatic production lines of Betula species identification, log optimization sawing, solid timber longitudinal multiblade sawing, sawn timber quality detection and solid timber optimizing cross-cuts are built. Based on the machine vision method, features are extracted; a tree species, defect classification and recognition model database is established; an image processing algorithm with high recognition accuracy, as well as fast processing speed and high robustness are studied; non-destructive testing and classification methods of machine vision are optimized; key problems of online rapid classification, detection and optimization of sawing are solved and production quality and processing efficiency are improved. Finally, the timber defect detection accuracy and Betula timber yield are analyzed, and the comprehensive utilization value of optimized sawing timber is compared with the comprehensive utilization value of manually marking sawing timber. The processing cost and efficiency of Betula sawing timber with an intelligent automatic production line are calculated. The test results show that the average detection accuracy of timber defect type, size and location is 89.69%, 89.69%, 92.25% and 82.29%, respectively, and the detection stability is high. By adopting intelligent automatic detection, classification and optimization sawing production line of wood, the comprehensive utilization value of optimized sawing timber is 14.13% higher than that of manual marking sawing timber, and 16,089.29 m³ more building materials can be processed annually. In the process of intelligent automatic wood processing, the intelligent detection system is used to detect defects, improve production performance and production efficiency and reduce labor costs. Compared with the traditional wood processing process, the method studied in this paper is improved to optimize the production line processing performance and processing technology. The research and development of an intelligent automatic production system for solid wood can promote the application and development of an automatic industrial production mode for sawn timber for the wood structure construction industry, deepen the integration of artificial intelligence technology, Internet technology and the whole wood processing industry and lead the upgrading of building materials for wood structures to an intelligent manufacturing production mode.

To improve wood structure processing efficiency, a palletizing robot suitable for loading and unloading glued laminated timber (GLT) has been developed. The robot comprises a six-axis connecting rod mechanism and a sponge sucker as a grasping actuator, which can enable the intelligent automatic loading and unloading and palletizing operations for small-sized GLT. Matlab robotics was used to construct the kinematic model of the GLT loading and unloading robot. Based on Matlab and Monte Carlo methods, the robot workspace was simulated and analyzed to determine the scope of the robot workspace. Using the high-order quintic and sixtic polynomial curve interpolation method, the trajectory of wood structure parts in the process of loading and unloading operations was planned, respectively, under the two conditions of staying and not staying. Tests verified that the simulation results of the pose of the end-effector were consistent with the actual pose of the robot. The robot’s working range could be analyzed intuitively and effectively. The robot’s operation trajectory planning provides data support and a parameter basis for the automatic control and program design of a loading, unloading and palletizing robot.

Bamboo is widely used as an excellent engineering material in construction and furniture. The harvesting equipment and the bamboo application process need to be integrated better. According to the application requirements of the original bamboo structure building, the mechanism and practical experience of bamboo harvesting were analyzed using field experiments in bamboo forests and the finite element method. The operation stability and the side-face planeness of the saw were the direct factors affecting bamboo cutting quality. Further, the indirect factors were the clamp saw effect on the cutting surface, the hollow structure and diaphragm of bamboo, the density and moisture, and the swarf pocket.

This paper proposes a non-contact, online, rapid, and non-destructive measurement method of Pinus densiflora S.et Z. (Pinus densiflora) dimension lumber based on an algorithm of maximizing lumber recovery by minimizing the enclosure rectangle fitting area. The method takes the full influence of multiple factors, such as the difficulty in measuring large-size dimension lumber, conveying deflection, etc. into account. Image splicing, object and background segmentation, and lumber size measurements are carried out. Compared to other current algorithms, it can decrease the measurement errors and improve measurement efficiency. The problems of slow image segmentation, large-size lumber measurement, and conveying deflection are solved. Through measurement tests and error analysis, the measurement accuracy of the lumber size can reach 0.8 mm/m. This method meets the requirements of high-speed lumber production and lays the foundation for the development of a lumber detection and evaluation system.

The objective of this study was to advance sustainable forestry development through the creation of mechanical equipment, taking into account forestry operational methods. A suspended automatic feeding and retracting excavation device for tree pits was engineered, and its interaction with soil was investigated by integrating the Discrete Element Method (DEM) with Multi-Flexible Body Dynamics (MFBD). Based on simulation results, the research explored the impact mechanisms of the machine on soil transportation, working load, and fatigue lifespan of the spiral blades for different terrains and operating conditions. The coupling simulation method demonstrated the potential for designing and testing forestry equipment in specific operating environments, reducing time and resource consumption for field testing. Terrain significantly influenced soil disturbance variability, while the effect of operating direction was minor. Operational parameters should consider soil and water conservation, favoring the formation of fish-scale pits. Field tests in forested areas validate the practicality of the apparatus, providing valuable insights for the operation and equipment design of earth augers in hilly regions.