Detecting and cleaning grain caking on the inner walls of silos is an important task to ensure food safety in storage facilities. However, in response to challenges such as insufficient lighting conditions, small and diverse forms of grain caking, this paper proposes the development and evaluation of a convolutional neural network model for robot vision detection of grain caking. The following improvements to the visual detection algorithm based on YOLOv5 are proposed in this article. Firstly, the Convolutional Block Attention Module (CBAM) and the improved Total Cross Union (CIoU) loss function are introduced to enhance the detection accuracy of grain caking. Secondly, by adding the Retinex Net algorithm with dark light enhancement, the recognition and detection performance under low light conditions can be improved. The improved YOLOv5 algorithm was trained and validated on a custom grain caking dataset. Comparative experiments show that compared with existing detection architectures, the improved algorithm has improved the average accuracy of grain caking detection by 1.8 % to 3.8 %. Finally, the improved algorithm proposed in this article was deployed on a wall climbing robot based on negative pressure adsorption, achieving real-time detection and automatic cleaning of grain caking.

We present a semiglobal stereo matching technique that combines enhanced census transform with unidirectional dynamic programming optimization. This method not only improves matching accuracy but also substantially reduces noise interference by using the 8-domain pixel median instead of the centre pixel in the standard technique and optimizing the initial matching generation value with the 8-path aggregation optimization algorithm. The experimental findings show that this suggested strategy efficiently avoids interference produced by various noises and reduces the false matching rate significantly.

This study systematically investigates settlement sites that record living patterns of ancient humans, aiming to reveal the interactive mechanisms of human–environment relationships. The core issues of landscape archeology research are the surface spatial structure, human spatial cognition, and social practice activities. This article takes the Han Dynasty settlement site in Sanyangzhuang, Neihuang County, Anyang City, Henan Province, as a typical case. It comprehensively uses ArcGIS 10.8 spatial analysis and remote sensing image interpretation techniques to construct spatial distribution models of elevation, slope, and aspect in the study area, and analyzes the process of the Yellow River’s ancient course changes. A regional historical geographic information system was constructed by integrating multiple data sources, including archeological excavation reports, excavated artifacts, and historical documents. At the same time, the sequences of temperature and dry–wet index changes in the study area during the Qin and Han dynasties were quantitatively reconstructed, and a climate evolution map for this period was created based on ancient climate proxy indicators. Drawing on three dimensions of settlement morphology, architectural spatial organization, and agricultural technology systems, this paper provides a deep analysis of the site’s spatial cognitive logic and the ecological wisdom it embodies. The results show the following: (1) The Sanyangzhuang Han Dynasty settlement site reflects the efficient utilization strategy and environmental adaptation mechanism of ancient settlements for land resources, presenting typical scattered characteristics. Its formation mechanism is closely related to the evolution of social systems in the Western Han Dynasty. (2) In terms of site selection, settlements consider practicality and ceremony, which can not only meet basic living needs, but also divide internal functional zones based on the meaning implied by the orientation of the constellations. (3) The widespread use of iron farming tools has promoted the innovation of cultivation techniques, and the implementation of the substitution method has formed an ecological regulation system to cope with seasonal climate change while ensuring agricultural yield. The above results comprehensively reflect three types of ecological wisdom: “ecological adaptation wisdom of integrating homestead and farmland”, “spatial cognitive wisdom of analogy, heaven, law, and earth”, and “agricultural technology wisdom adapted to the times”. This study not only deepens our understanding of the cultural value of the Han Dynasty settlement site in Sanyangzhuang, but also provides a new theoretical perspective, an important paradigm reference, and a methodological reference for the study of ancient settlement ecological wisdom.