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The lagoon slopes of coral atolls, which connect reef flats to lagoon basins, are key geomorphic zones where sediments are physically sorted and transported. These areas are essential for understanding post-reef sedimentary processes and the broader evolutionary dynamics of atolls. However, the influence of the microtopographic features of lagoon slopes on sediment grain size distribution remains unclear. Sediment grain size is a sensitive indicator of environmental and climatic changes and provides valuable insights into sediment transport patterns and depositional environment reconstructions. The spatial distribution of grain size is influenced by the proximity to sediment sources, material availability, and surrounding geomorphological conditions. This study focused on the lagoon slopes of the Shuquan and Jiaojian Reefs in the Nansha Islands, where surface sediment samples were collected and analyzed for grain size, along with depth data and underwater microtopography. The results indicated the following: (1) Surface sediments in the lagoon slopes of the Shuquan and Jiaojian Reefs are predominantly composed of sand-sized particles, with minimal silt and fine gravel content. Specifically, Shuquan Reef sediments comprised 94.55% sand-sized particles, 4.18% fine gravel, and 0.61% silt; the S1-S1' transect contained 93.96% sand, 4.04% fine gravel, and 1.65% silt; and the S2-S2' transect consisted of 91.73% sand, 6.84% fine gravel, and 1.17% silt. (2) The grain size characteristics of lagoon sands were primarily governed by the proximity to sediment sources and hydrodynamic conditions. A significant negative correlation was observed between median grain size and water depth, with grain size decreasing as depth increased. Most lagoon sands originated from reef flats or lagoon slopes, reflecting the diminishing sorting capacity of oceanic wave energy as it moves toward the lagoon center. The grain size distribution curves of lagoon sands are generally unimodal or bimodal, with a trend toward negative skewness. In deeper waters, the curves became sharper, indicating a transition from low-energy to high-energy depositional environments, where finer particles were replaced by coarser ones. Geomorphological changes within the lagoon, particularly in the transition zone from the lagoon slope to the inner reef flat, played a critical role in the grain size distribution. The bimodal distribution in Shuquan Reef was mainly attributed to coarse particles from lagoon pinnacles, whereas in Jiaojian Reef, the bimodal distribution was influenced by reduced dynamic sorting due to seagrass root stabilization. By comparing these distinct reef systems, this study elucidated the impact of localized geomorphic features, such as pinnacles and seagrass beds, on sediment sorting mechanisms. Understanding the grain size characteristics and distribution patterns of sand in natural microtopographic settings on the lagoon slopes of atolls is crucial for the rational use and scientific management of resources, with significant implications for land reclamation and reef engineering projects.

Currently, the fault warning technology for the cutting part of cantilever roadheader relies on traditional data collection methods. In the operation process of the cutting part of the roadheader, problems such as difficulty in obtaining signals and high noise limit the capability to predict and warn faults in the cutting part of the roadheader. In order to solve the above problems, a fault warning method for the cutting part of cantilever roadheader based on virtual and real fusion data is proposed. The method performs three-dimensional solid modeling of the cutting section of a cantilever roadheader. It uses the automatic dynamic analysis of mechanical systems (ADAMS) to obtain virtual data of the cutting section's mechanical system, constructs its dynamic simulation model to obtain virtual data. The method uses the cosine similarity function to characterize its similarity with real data to verify the credibility of the virtual data. The method uses Bayesian estimation and adaptive complementary weighted fusion methods to perform similarity association and complementary association fusion on virtual and real data, respectively, to obtain virtual and real fusion data. In response to the problem that the learning efficiency of traditional self-organizing mapping (SOM) neural networks is easily affected by the learning rate, a fault warning model based on an improved SOM neural network is established. A monotonic decreasing function about time is introduced to train the SOM neural network, ensuring both the learning rate and the stability of the model. The method inputs the fused data into the fault warning model based on SOM neural network to determine the winning neuron and adjust its weight. The method calculates the distance between the real data and the winning neuron and adjusts its weight to achieve fault warning. The experimental results show that the average operating efficiency of the improved SOM neural network can be improved by 35.84%. The fault warning method for the cutting part of a cantilever roadheader based on virtual and real fusion data can successfully predict the types of single and composite faults, with a prediction accuracy of 83.33%.

The existing boom-type roadheader cutting control adopts a relatively simple control method and the cutting head completes the roadway section cutting at a constant speed. There's no comprehensive consideration of trajectory planning and autonomous speed control. Therefore, it is difficult to achieve high roadway engineering quality under complex geological conditions. In order to solve the above problems, a boom-type roadheader autonomous speed regulation cutting control system is proposed. Firstly, the three-dimensional model of the cutting head and coal seam are established and imported to ABAQUS software for finite element analysis. The relationship between the reaction force on the cutting head and the swing speed of the cutting arm is obtained. Then the relationship between the swing speed of the cutting arm and the acceleration of the cutting head is obtained. The acceleration is stratified by k-means clustering method. Secondly, the collision detection model of the cutting head is established by using the bounding volume hierarchy algorithm. The appropriate cutting trajectory of the rectangular roadway section is planned. The discrete cutting path planning points are generated through multiple discretizations. The inverse kinematics solution of the cutting arm is calculated to obtain the rotation radian, lifting radian and elongation of the cutting arm required for the cutting head to reach the discrete cutting path planning point. The global optimal speed model is used to solve the speed of the cutting head to move to the discrete cutting path planning point. Finally, the acceleration sensor is used to collect the vibration signal of the cutting arm. The target swing speed of the cutting arm is determined according to the acceleration layering result. Through fuzzy PID control, the swing speed of the cutting arm is adjusted to the target swing speed in time and accurately with the change of the cutting head acceleration. The experimental results show that the fuzzy PID control can achieve a relatively fast and non-overshoot swing speed adjustment of the cutting arm. Compared with the constant speed cutting control, the roadway section forming quality using the autonomous speed control cutting control is high. The width specification deviation is reduced by 37%, and the height specification deviation is reduced by 17%. The results meet the requirements of roadway forming quality specified in MT/T 5009-1994 Standard for quality inspection and assessment of coal mine roadway engineering.

Aiming at the problems of high labor intensity, low degree of automation and low positioning accuracy of single way in the construction process of existing coal mine anchor digging machine, a long-distance positioning system suitable for anchor digging machine is developed by using sensor measurement, machine vision, inertial navigation and other technologies. The system consists of anchor digger, inertial positioning subsystem, visual positioning subsystem, total station positioning subsystem and parameter monitoring subsystem. Anchor digger main machine adopts MB670-1 anchor digger; the inertial navigation and positioning subsystem is composed of built-in optical fiber inertial navigation and explosion-proof computer. The visual positioning subsystem includes infrared explosion-proof target, laser pointing device, front explosion-proof industrial camera and explosion-proof computer. The positioning subsystem of total station consists of 360° prism, three laser pointing instrument and rearview prism. Parameter monitoring subsystem includes parameter display, parameter setting and parameter solution module. The system was successfully developed and tested in Dahaize Coal Mine of China National Coal Group. The experiment shows that the long-distance high-precision positioning system of the all-in-one anchor digger has the advantages of flexible operation and highpositioning accuracy, and can realize the positioning and parameter monitoring of the all-in-one anchor digger.