According to characters of marine ecology domain knowledge, a marine ecology knowledge organization model was proposed. Referring to engineering field literature and the device-function knowledge representation theory that the "function" concept was used to describe marine ecology functional process; a viewpoint of device-function was fixed, a domain upper ontology for marine ecosystem was presented, and then marine ecological conceptual model and marine ecology OWL ontology were constructed. By extending OWL-DL, OWL-Process model oriented function-process was proposed, and then marine ecology function-process ontology instance was constructed. Based on constructed marine ecology ontology repository, marine ecological knowledge management system was developed. The ontology application system provides marine ecology knowledge query and crisis early warning functions; and it also verifies the validity, rationality and feasibility of constructed marine ecology ontology.

Construction， mining， exploration and other industries have mandatory regulations on helmet wearing in production. The helmet wearing detection algorithms have been widely used in the above industries， but the existing algorithms have problems such as too many parameters， high complexity and poor real-time performance. Therefore， a lightweight safety helmet wearing detection algorithm YOLO v8-s-LE was proposed on the basis of YOLO v8 （You Only Look Once v8）. Firstly， the LAD （Light Adaptive-weight Downsampling） method was designed，so that compared with the original YOLO v8 algorithm， the proposed algorithm reduced floating-point computation significantly. Then， the efficient multi-scale convolution C2f_EMC （C2f_Efficient Multi-Scale Conv） method was used to extract multi-scale feature information， which increased the depth of the network effectively， made the neural network take into account both shallow and deep semantic information， and further improved the expression ability of the algorithm for feature information. Experimental results show that compared with YOLO v8-s algorithm on the public dataset SHWD （Safety Helmet Wearing Dataset）， the proposed algorithm has the parameters reduced by 77%， the floating-point computation reduced by 73%， and the precision reached 92.6%， verifying that the algorithm takes into account the requirements of accuracy and real-time performance， and is more suitable for the deployment and application in actual production environments.