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.

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.

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.

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.

In order to meet the automatic safety needs of the assembly of fixed threaded hole bolts for underground coal mine roadway pipelines, a flexible operation control algorithm for bolt tightening based on admittance method was studied for the six-degree-of-freedom robotic arm equipped with six-dimensional force/torque sensor. The main contents include the theoretical research of gravity compensation algorithm based on six-point positioning method and admittance-based bolt tightening compliance control algorithm. A bolt fastening experimental system based on the robotic arm was set up, and the assembly experiment of fixed threaded hole bolts was carried out. The results indicate a decreasing trend in the forces and torques experienced during assembly, demonstrating the effectiveness and safety of the compliant operation control algorithm based on the admittance method.

Reclaimed asphalt pavement (RAP) has been utilized in numerous road projects. However, considering safety and service life, further in-depth investigation is required into the road performance of high dosage RAP hot-mix/warm-mix recycled asphalt mixtures under traffic loads. To investigate this, indoor tests and mathematical analysis were employed to examine the impact of design parameters, including the waste cooking oil (WCO) content, RAP dosage, and mixing method, on the road performance of recycled asphalt mixtures. The results indicate that in most cases, the VMA of R-100RAP mixes exceeds 14%, and the VV exceeds 3%. Moreover, both the VV and VMA of asphalt mixtures with 60% RAP content, 10% WCO, and 1.0% asphalt fall within specified ranges. Additionally, the split strength ratios of the majority of R-100RAP, R-80RAP, W-80RAP, and R-60RAP mixes surpass 80%, indicating excellent resistance to water damage. The addition of WCO proves beneficial in enhancing the water damage resistance of recycled asphalt mixtures. Although the rutting resistance of the W-80RAP asphalt mixture was inferior to that of the recycled asphalt mixtures containing R-80RAP and R-60RAP, all three exhibited commendable rutting resistance. When comparing the performance of W-80RAP and R-60RAP, it is observed that W-80RAP exhibits a smaller phase angle and a larger stiffness value, indicating better aging resistance. In order to maximise performance and facilitate the effective building of high dosage RAP hot-mix/warm-mix recycled asphalt mixes, the research provides a theoretical framework.

The issue of comfort in subway stations is typically analyzed using the predicted mean vote (PMV) and the predicted percent dissatisfied (PPD) indices. Based on the PMV-PPD comfort indices calculation model, the weight proportions of different environmental parameters on comfort were studied. The PMV calculation model was improved by considering the spatiotemporal characteristics of passengers’ clothing and activity levels. The PPD calculation model was enhanced by taking into account the impact of drastic environmental temperature changes on comfort. The environmental comfort of public areas in stations was then analyzed using the improved PMV-PPD calculation model. On this basis, the feasibility of predicting environmental comfort using long short-term memory (LSTM) networks was explored. The research results indicate that the weight proportions of metabolic rate, air temperature, clothing thermal resistance, and humidity on environmental comfort are 0.558, 0.260, 0.113, and 0.069, respectively. At a given time, the maximum differences in PMV and PPD at different monitoring points on the platform are approximately 15% and 60%, respectively. The improved PMV-PPD calculation model is found to be more universally applicable compared to the traditional PMV-PPD calculation model. The neural network is shown to accurately predict PMV and PPD values, with a maximum error of 8% for PMV and 14% for PPD between the actual and predicted values.