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Rocky slopes are vulnerable to landslides and mudslides, which pose a major threat to human life and property. Research is being conducted to improve the adhesion between soil and minerals by mineral-solubilizing bacteria to manage slopes scientifically and develop novel methods for slope greening. From the soil of Nanjing Mufu Mountain’s weathered rock walls, we isolated various soil mineral-solubilizing soil bacteria. During the soil bacterial solubilization test, we discovered that some soil bacteria could enhance the adherence of soil to minerals; therefore, we selected three soil bacteria (NL-7, NL-8, and NL-11) with higher performance for further investigation. Controlled experiments were used to investigate the effects of soil bacteria on soil characteristics (soil moisture content, soil pH, and soil exchangeable metal content) and soil adhesion to minerals. According to the findings, soil bacteria can improve the soil’s adhesion to minerals, improve the soil’s capacity to hold water, regulate soil pH, and solubilize and release exchangeable calcium, magnesium, sodium, and potassium ions. A structural equation modeling analysis was performed to thoroughly examine the relationship between soil characteristics and soil adherence to minerals. The analysis findings showed that soil moisture had the greatest total and direct positive impact on soil adherence to minerals. The most significant indirect impact of soil pH on soil adhesion to minerals is mainly caused by the exchangeable sodium and magnesium ions. Additionally, soil exchangeable sodium ions can only indirectly affect the adhesion of soil to minerals, which is accomplished by controlling soil exchangeable magnesium ions. Therefore, mineral-solubilizing soil bacteria primarily work by enhancing the soil’s water retention capacity to improve the soil’s adherence to minerals. Our study on the effect of mineral-solubilizing bacteria on the adhesion of soil and minerals demonstrates the significant potential of mineral-solubilizing bacteria in spray seeding greening, which will provide data and theoretical support for the formation, application, and promotion of mineral-solubilizing bacteria greening methods and gradually form a new set of scientific and efficient greening methods with Chinese characteristics.

Clustering technology and boundary point detection technology and its application in intrusion detection system are introduced in this paper from three aspects, which are the application of clustering analysis, boundary detection and clustering analysis in Intrusion Detection System. The data processing and the requirement of clustering algorithm for intrusion detection system are introduced in detail. Analyzed the result of the experiment environment and experiment, further validation of this project is based on the improved NPRIM algorithm applied to intrusion detection is effective and feasible. 1. Boundary point detection technology The cluster boundary point is a point that has two or more clustering characteristics between the cluster and the cluster. The study of clustering boundary points is an important branch of clustering analysis, which plays an important role in disease prevention, biology, image retrieval, virtual reality, and improving clustering accuracy. Since Chenyi Xia first proposed the boundary point detection algorithm (BORDER) in 2006, researchers have proposed some boundary detection algorithms. In order to describe these algorithms, the algorithm is divided into four categories: density based boundary detection algorithm, grid based boundary detection algorithm and angle based boundary detection algorithm. 1.1. Boundary point detection algorithm based on density Based on the density of the boundary point detection algorithm is the use of clustering near the boundary of the uneven distribution of data objects to extract the characteristics of the clustering boundary point. On the noisy data set, the algorithm can separate the boundary point from the noise region, especially the uniform data set. BRIM is a typical boundary detection algorithm in this algorithm. In order to solve the existing problems of BORDER algorithm, BRIM is a density based boundary point detection algorithm, which can effectively detect the boundary of clustering in noisy data sets. The algorithm first according to the data object plus or minus the number of data points difference within half a neighborhood to calculating the boundary of points, and then the boundary is greater than the boundary degrees threshold  marked point boundary point.