The underground coal mine production of the fully mechanized mining face exists many problems, such as poor operating environment, high accident rate and so on. Recently, the intelligent autonomous coal mining is gradually replacing the traditional mining process. The artificial intelligence technology is an active research area and is expect to identify and warn the underground abnormal conditions for intelligent longwall mining. It is inseparable from the construction of datasets, but the downhole dataset is still blank at present. This work develops an image dataset of underground longwall mining face (DsLMF+), which consists of 138004 images with annotation 6 categories of mine personnel, hydraulic support guard plate, large coal, towline, miners’ behaviour and mine safety helmet. All the labels of dataset are publicly available in YOLO format and COCO format. The availability and accuracy of the datasets were reviewed by experts in coal mine field. The dataset is open access and aims to support further research and advancement of the intelligent identification and classification of abnormal conditions for underground mining.

The synthesis of a benzenethiol‐derivatized porphyrin for flat‐lying self‐assembly on gold substrates is described. Acetyl protected thiol is not stable enough in Pd‐catalyzed reactions. While acrylate derivatives protected thiol group shows good tolerance in Pd‐catalyzed borylations and Suzuki‐Miyaura coupling reactions and no catalyst poisoning was observed.

In view of the insufficient characteristics and depth acquisition difficulties encountered in the process of uniocular vision measurement, the posture measurement scheme of tunneling equipment based on uniocular vision was proposed in this study. The positioning process of coal mine tunneling equipment based on monocular vision was proposed to extract the environmental features and match the features, and the RANSAC algorithm was used to eliminate the pair of mismatching points. This was done to solve the optimized matching pair and realize the pose estimation of the camera. The pose solution model based on the triangulation depth calculation method was proposed, and the PNP solution method was adopted based on the three-dimensional spatial point coordinates so as to improve the visual measurement accuracy and stability and lay the foundation for the 3D reconstruction of the roadway. This was done to simulate the downhole environment to build an experimental verification platform for monocular visual positioning. The experimental results showed that the position measurement accuracy of the uniocular visual roadheader positioning method within 60 mm and 1.3° could realize the accurate registration of the point cloud in the global coordinate system. The time required for the whole monocular visual positioning was only 179 ms, so it had good real-time performance.

Pose measurement of coal mine excavation equipment is an important part of roadway excavation. However, in the underground mining roadway of coal mine, there are some influencing factors such as low illumination, high dust and interference from multiple equipment, which lead to the difficulty in the position and pose measurement of roadheader with low measurement accuracy and poor stability. A combination positioning method based on machine vision and optical fiber inertial navigation is proposed to realize the position and pose measurement of roadheader and improve the accuracy and stability of the position and pose measurement. The visual measurement model of arm roadheader is established, and the optical fiber inertial navigation technology and the spatial coordinate transformation method are used. Finally, the Kalman filter fusion algorithm is used to fuse the two kinds of data to get the accurate roadheader pose data, and the inertia is compensated and corrected. Underground coal mine experiments are designed to verify the performance of the proposed method. The results show that the positioning error of the roadheader body using this method is within 40 mm, which meets the positioning accuracy requirements of roadway construction. This method compensates for the shortcomings of low accuracy and poor reliability of single vision measurement, single inertial navigation measurement and single odometer measurement.