The influence of hydrothermal fluids on fluid-rock interactions and hydrocarbon generation in basins is of great significance. The mineralogical and geochemical characteristics of hydrothermal activity in the black shale of the Wufeng Formation-Longmaxi Formation in southeastern Sichuan was investigated using advanced techniques such as large field splicing scanning electron microscopy, mineral quantitative analysis, X-ray diffraction, isotopes, and electron probes. The results show that non-metallic minerals such as barium ice feldspar, calcite, apatite, and barite, as well as metal minerals like sphalerite, pyrite, galena, and chalcopyrite, exhibit distinct characteristics of hydrothermal activity. The in-situ Sr isotope ratio of barite ranges from 0.719 76 to 0.723 94, with an average of 0.722 37. The carbonate mineral content is exceptionally high, up to 60%. Based on previous research, mineralogical and geochemical indicators for hydrothermal fluid activity in the Wufeng Formation-Longmaxi Formation of southeastern Sichuan were established, suggesting that hydrothermal fluids have detrimental effects on reservoir space.

In the context of large-scale integration of wind and solar power into the grid, power system dispatch strategies faced unprecedented challenges. The volatility and randomness of wind and photovoltaic power generation significantly impacted system stability and controllability. To accurately characterize the spatiotemporal correlation of wind-solar power output and construct a practically valuable scenario set, a method for generating spatiotemporal correlated scenarios for wind-solar complementary systems was proposed, based on a coupled SGMM (seasonal Gaussian mixture model) and MCopula (mixed Copula function). Initially, the SGMM was constructed to capture the temporal correlation among wind-solar output variables. Then, the mixed Copula function was employed to describe the spatial correlation among variables. Based on the comprehensive modeling of spatiotemporal correlations, a series of uncertainty scenario sets reflecting these characteristics was generated using the Copula conditional distribution function and inverse transform sampling technique. The simulation results confirmed the effectiveness and reliability of the proposed method. The generated scenario sets not only reflected the spatiotemporal correlation characteristics and annual variation trends of wind-solar output but also better matched the historical actual sequences in terms of distance, providing strong decision-making support for power system dispatch. New perspectives and tools were offered for quantifying uncertainties in wind-solar complementary systems, which had profound theoretical and practical significance for optimizing power system dispatch strategies, reducing uncertainty risks, promoting the efficient utilization of renewable energy, and advancing the sustainable development of power systems.

It is a new problem for the prevention and control of non-point source pollution to adsorb pollutants by colloids and assist them to quickly migrate from soil to water during the rainfall-runoff process. Southwest Guizhou Province is one of the important ecological barrier areas in the Pearl River Basin, but some areas sow corn in the middle and late April and enter the rainy season in May. The destruction of soil structure, the significant increase of precipitation and the typical karst landforms in the region lead to high water environmental risks in sloping farmland areas. Undisturbed soil samples are collected from newly ploughed yellow soil slope farmland in karst area. These samples undergo simulated rainfall infiltration experiments. The purpose is to investigate the dynamic release rule of colloids under different rainfall intensities, including the change characteristics of colloid concentration, particle size distribution and its content level with the increase of accumulated rainfall. The results show as follows. The colloid concentration increased with the increase of rainfall intensity and cumulative infiltration, and the study further revealed that the difference of released soil colloid concentration under three rainfall intensities showed three different stages: the colloid concentration is not significantly different when the rainfall intensity is 25 mm/h and 40 mm/h in the 0~100 mm stage, but is significantly higher than 10 mm/h. The colloid concentration in the 100~250 mm stage is quite different under three rainfall intensities. When the accumulated rainfall is more than 250 mm, the difference between them is very small. When the rainfall intensity is 40 mm/h, the characteristic statistics of colloidal particle size show obvious two-stage and sudden drop characteristics. At first, the average particle size of effluent increases with the increase of rainfall intensity, and then decreases slightly with the further increase of rainfall. When the rainfall intensity is 25 mm/h and 10 mm/h, it presents a gradual change characteristic. The change of colloid content with different particle sizes shows a trend of stability, increase and decrease respectively. The research innovatively reveals the stage characteristics of the outflow concentration difference of soil colloids at different flow rates, and quantifies the change trend of colloids with different particle sizes with the cumulative infiltration from the meso-scale, which will provide a reference for further evaluating the regional water environmental risk and driving mechanism in this period.

In order to apply remote sensing technology to uranium exploration in the Mouding area of Yunnan Province, based on ASTER (advanced spaceborne thermal emission and reflection radiometer) remote sensing data, the interference removal and PCA (principal component analysis) method was used to extract the Al-OH, Mg-OH, {\mathrm{CO}}_3^{2-} and iron-stained alteration information in the study area. The lineaments in the study area were automatically extracted by PCA and LINE model in PCI Geomatica software, and the density map of lineaments was created. Finally, combined with geological data, the relationship between uranium mineralization and alteration and linear structures in the study area was analyzed, and a favorable mineralization area was delineated. This study can provide some ideas for subsequent exploration in the area, and also provide some reference for remote sensing technology in mineral exploration in vegetation-covered areas.

In order to improve the engineering quality problems such as pavement cracking caused by excessive subgrade deformation caused by road use of collapsible loess, alkali activated sustainable material industrial solid waste GGBS(ground granulated blastfurnace slag was used to reinforce and improve the collapsible loess. The influence of different dosage of curing agent on the basic physical properties, mechanical characteristics, permeability and collapsibility of the solidified loess was discussed, and the improvement mechanism of the curing agent was expounded from the microstructure. The results show that the liquid plastic limit of solidified soil increases and the plasticity index decreases. The optimum moisture content decreases first and then increases with the increase of the content of curing agent. The corresponding maximum dry density increases first and then decreases. The maximum dry density of 10% of the content is 1.80 g/cm³. The strength of loess is improved by the curing agent. The strength increases linearly with the increase of the content. The content of 20% curing agent can increase to 2.3 MPa, while the C_BR value of 6% curing agent can increase to 8.3%. The permeability coefficient decreases with the increase of the content of the curing agent. When the content of the curing agent is 10%, the permeability coefficient can be reduced to below 10^-7 m/s, and the collapsibility coefficient decreases with the increase of the content of the curing agent. When the content of the curing agent reaches 6%, the solidified soil becomes non collapsible soil. The comprehensive performance shows that 10% of the content is the optimal amount for road use. In terms of microscopic morphology, the curing agent has changed the contact mode of loess particles. The loess particles have changed from point-surface contact to surface-surface contact. The pore size has changed from middle pore and macropore to middle pore, and the number has decreased. Macropores are filled. Compared with remolded loess, the macropores and mesopores in 10% stabilized soil have decreased from 33.0% and 31.5% to 3.9% and 14.8%, respectively, 29.1% and 16.7%, The fractal dimension of pore distribution decreases from 1.12 to 0.96. Through alkali activated GGBS to solidify collapsible loess, the collapsibility of loess is improved, and its performance has a good road use prospect. This study can provide theoretical basis and practical reference for the consolidation of collapsible loess.

Due to the special double salient pole structure of switched reluctance motor, there will be large Torque ripple during operation. In order to reduce the peak current and torque ripple during phase commutation, a novel TSF (torque sharing function) control method is proposed. Firstly, considering the relation between torque and inductance, the exchange was divided into two subintervals by inductance boundary points, and different TSF curves were designed in different intervals. Secondly, with the increase of motor speed and load, the fixed overlap Angle will reduce the efficiency of the motor, and an online overlap Angle optimization control strategy was proposed. Finally, the simulation and experimental results show that compared with the traditional cubic torque distribution function, the torque ripple and current peak value of the proposed method are reduced by 3.6%, 12%, and 1.1 A, 3.2 A respectively at a load of 5 N·m and a speed of 500 r/min and 1 000 r/min respectively. The proposed method can effectively reduce torque ripple and current peak value.

Understanding the evolution law of rail service performance is of great significance for reducing the operation and maintenance costs of heavy-duty railway rails. Due to the complex and variable operating environment of rail tracks, which makes it difficult to construct scientifically effective damage evolution indicators to reflect objective development patterns, a method based on t-SNE(t-distributed stochastic neighbor embedding) was proposed for constructing the evolution law of corrugation damage. Firstly, the time-domain, frequency-domain, statistical, and entropy features were extracted from the original rail corrugation vibration signal. The random forest algorithm was then used to rank the features by importance, and the top-ranked features were selected to construct the feature vector. Dimensionality reduction was performed using t-SNE and other methods, and it is found that t-SNE demonstrates superior performance. The final temporal damage degradation index is obtained through Euclidean distance metric and median filtering for smoothing. The results indicate that this method provides good discrimination, anti-interference capability, and practical applicability for damage stages classification.

In the manufacturing process of wind turbine blades, gluing is an important part of ensuring the structural strength and tightness of the blades. In view of the low efficiency, uneven gluing, low quality of glue line and glue overflow on the surface of parts caused by the manual sealing and gluing operations commonly used in the gluing process of wind turbine blades, a kind of intelligent rubber shoe for wind turbine blade gluing with servo motor and reducer as driving components was developed, and the gluing process was introduced in detail. ANSYS analysis was carried out on the key components, and after simulation analysis, it was found that the structure of the intelligent rubber shoe could meet expectations. After experiments, it is verified that the shape of the rubber road is full, the two sides are smooth and flat, and the width and thickness of the rubber road meet expectations. The use of rubber shoes can not only reduce the work intensity of workers, but also reduce the use of 10% of the glue amount when gluing, and improve the efficiency and quality of gluing.

Photovoltaic power generation has an important place in the energy sector. In order to accurately quantify the uncertainty and fluctuation range of PV(photovoltaic) power and to improve the comprehensiveness of interval forecasts, a probabilistic prediction method for PV power intervals based on feature mining with improved TCN-BiGRU was proposed. First, the maximum information coefficient and symbolic transfer entropy causal analysis were utilized to screen the meteorological features, remove redundant information, and construct global horizontal radiation trend features, seasonal features, and weather clustering features to provide more effective information. Subsequently, the TCN-BiGRU model was improved by combining the temporal pattern attention mechanism and quantile regression methods to construct a combined model for interval prediction. Finally, the probabilistic prediction results are generated using the KDE method of empirical bandwidth selection with scatter measure semi-polar optimization. The proposed method is analyzed by real PV plant data, which verifies the high reliability and applicability of the proposed method in PV power interval probability prediction.

The key factor to control the shape of salt cavern cavity is to control the depth of the interface between solvent and brine, and the accurate monitoring of the interface is the prerequisite for its control. The current two-phase media interface monitoring technology has the disadvantages of poor real-time performance and low precision. A special algorithm for two-phase media monitoring in cavity well was proposed. Distributed optical fiber was used to collect the temperature data in the vertical direction of the cavity, and the original temperature curves were respectively processed by local weighted regression analysis and Kalman filtering. Through the difference calculation of the treated curve, the region with the most obvious temperature difference was identified, and the interface position of the two-phase medium was preliminatively determined. Then, the temperature curve in the initial location area was processed by convolutional smoothing filtering, the weighted rate of change of the convolutional smoothing filter curve was calculated, the maximum value of the temperature change rate was found, and the specific location of the interface of the two-phase medium was finally determined. The algorithm was used to monitor the interface of two phase media under actual conditions. The experimental results show that the error of the measured data obtained by this system is controlled within 0.3 m compared with that obtained by neutron logging method. Compared with the traditional optical fiber monitoring technology, the proposed algorithm has the advantages of simple operation, high measurement accuracy and high reliability.