With the construction of a new type of power system with new energy as the main body under the "double carbon" target, the challenges of natural disasters such as wildfires, icing and typhoons in the production, transmission and distribution of electric energy are more severe. Therefore, a fire point identification algorithm based on the world's first operational dawn-dusk orbit meteorological satellite Fengyun-3E satellite (FY-3E) was proposed, which was suitable for mountain fire monitoring in transmission line corridors, the adverse effects of large solar zenith angle observation conditions and satellite perspective differences on the accurate acquisition of infrared channel detection data were eliminated. Cloud information extraction and cloud pixel fire point extraction under complex atmospheric observation conditions were realized, which reduced fire point false alarms and missed alarms. The analysis of mixed pixel linear spectrum separation method shows that the fire point detection sensitivity of FY-3E mid-infrared channel is 4 times higher than that of geostationary meteorological satellite. The effectiveness of the proposed algorithm and the superiority of FY-3E in fire detection sensitivity, spatial range accuracy and positioning accuracy were verified. Compared with geostationary meteorological satellites, the fire location accuracy can be increased by more than one time, and it can effectively detect the fire time in advance by the transmission line operation and maintenance department, and guide the relevant departments to take timely measures to reduce the impact of wildfires on power grid operation.

To clarify the mechanism of water injection damage in low-permeability reservoirs of the Longdong Oilfield, the Chang 3 reservoir was selected as the research subject. A comprehensive analysis method was developed, starting from the intrinsic factors of the reservoir to the external engineering factors, to analyze the mechanism of reservoir water injection damage. X-ray diffraction (XRD), cast thin sections, and scanning electron microscopy (SEM) were used to analyze the reservoir’s rock physical properties and pore structure. Experimental methods combining visual microfluidics with nuclear magnetic resonance(NMR) were employed to analyze the damage patterns of externally injected water on low-permeability reservoirs. The results show that the intrinsic factors causing blockage in the Chang 3 reservoir are related to its low porosity and low permeability, with pore throat diameters all less than${20\mu }\mathrm{m}$, leading to poor reservoir properties and high liquid flow resistance in the reservoir. The composition of clay minerals mainly includes kaolinite and illite, which are velocity-sensitive minerals prone to fine particle migration and reservoir blockage. The external engineering factors causing blockage are the incompatibility of injected water with formation water, resulting in the formation of scale particle. These scale particles and clay particles can undergo a blockage-breakthrough process at the pore throat passages, causing fluctuating increases in injection pressure. Moreover, the injected water can carry scale and clay particles into the deep parts of the reservoir, where they accumulate and exacerbate blockage, significantly reducing the sweep efficiency of water flooding. The research results have clarified the pattern of water injection damage in the Chang 3 reservoir, providing theoretical guidance for water flooding development in oil fields.

In order to study the reasonable sealing length of bedding gas drainage boreholes, based on the gas-air dual gas and the negative pressure attenuation effect of boreholes, a three-dimensional borehole drainage model was established by finite element simulation software to monitor and analyzed the gas pressure of coal seam in the sealing section. Through the field test of 20915 haulage roadway, the gas drainage effects of different sealing lengths were investigated. The results show that under the negative pressure attenuation effect, the negative pressure of borehole drainage is exponential function distribution, and the reduction of gas pressure in coal seams at different positions of borehole is different, and the closer to the sealing position, the smaller the reduction of gas pressure. The peak gas pressure in the sealing section is proportional to the sealing length. According to the field test, the gas drainage concentration decreases to about${10}\%$after 130 days when the sealing distance is${10}\mathrm{\;m}$. The sealing length of${20}\mathrm{\;m}$maintains at about${30}\%$, and the extraction concentration of the borehole with sealing length of${30}\mathrm{\;m}$maintains at above${60}\%$after 130 days.

In order to quickly identify the location of the leakage point and the leak aperture in the coal mine, a model was proposed for identifying and locating the leak aperture by using the pressure and flow signals generated when the water supply pipeline leaked. Modal energy entropy and genetic algorithm combined with envelope entropy were used to optimize the parameters of variational mode decomposition (VMD), and then VMD was used to denoise the pressure signal. Convolutional neural network (CNN) was used to extract the deep feature sequence of pressure and flow signal, and the long short-term memory network (LSTM) was used to extract the time sequence of deep feature sequence to identify and locate the leak aperture. The experimental results show that compared with Kalman filter, mean value filter and low-pass filter, the variational modal decomposition with optimized parameters has higher root-mean-square error (RMSE), mean absolute error (MAE), signal-to-noise ratio (SNR) and normalized cross correlation (NCC), which indicates that it can effectively reduce noise components and retain effective signals. Compared with LSTM, the MAE, mean absolute percentage error (MAPE) and RMSE of CNN-LSTM in leak location decrease by 65.97%, 61.22% and 59.11%. In the identification of leak aperture, MAE decreases by 12.04%, MAPE decreases by 22.45%, and RMSE decreases by 3.29%, which proves that CNN-LSTM can make full use of the spatial and temporal characteristics of pipeline pressure and flow signals to identify the leak location and aperture, and its detection effect is more accurate and stable than LSTM.

In response to the issues of parameter homogenization and insufficient rationality in the support design of the mining roadway in the Lingtai mining area, it is highly significant to conduct classification research on the mining roadway and propose differentiated support strategies. Firstly, based on a systematic analysis of the geological conditions of the surrounding rock and the matching relationship with the support system in the mining roadway of the Lingtai mining area, six classification indicators were determined. Secondly, a mining roadway classification method based on mixed data clustering was proposed, extracting the informational characteristics of numerical and categorical indicators and introducing a penalty competition mechanism to dynamically optimize the number of clusters during the classification process. Subsequently, the proposed method was applied to classify 37 segments of the Lingtai mining area's mining roadways into four categories, validating the feasibility of the method. Finally, a field test was conducted in the D segment of the 2502 transport roadway in Shaozhai Coal Mine. The results indicate that the maximum convergence of the roof and floor of the test roadway segment is 109 mm, and the maximum convergence of the two sides is 113 mm, with a small range of surrounding rock damage. The dynamic classification and differentiated support method for mining roadways can achieve stable control of the surrounding rock in the roadways.

The piers of sea-crossing bridge are always subject to the scouring effect of flow and waves. The flow environment around the piers is complex. There is a risk of foundation erosion, endangering the safety of the bridge. Based on computational fluid dynamics (CFD) and the open-source software OpenFOAM, three-dimensional numerical simulations of the flow field around the inclined oblong pier were conducted with different inclination angles and length-to-width ratios$\left({L/D}\right)$. The results demonstrate that under the influence of wave and current, a symmetrically distributed vortex is formed behind the pier and undergoes periodic changes. The wake vortex constantly strengthens and moves backward as the trough approaches the pier. It reaches the maximum than gradually reduces and dissipates before the crest reaches the pier. With an increased downstream inclination angle, the pier tends to be streamlined, resulting the decreasing of wake vortex intensity and the horizontal load of pier. As the length-to-width ratio$\left({L/D}\right)$increases, the tail vortex area behind the bridge pier decreases and the horizontal load increases. Within the range of$L/D = 1$to 3 and a range of$-{30}^{\circ }$to${30}^{\circ }$, the minimal load on the pier is achieved when$L/D$is 1 and inclination angle is${10}^{\circ }$.

Given the prevalent issue of aging assets within power grid companies and the subpar management of aging equipment, a decision-making approach was introduced for optimizing the decommissioning of aging assets, taking into account the influence of transmission and distribution tariffs. Firstly, in accordance with the policy framework for reforming transmission and distribution tariffs, an accounting model for transmission and distribution tariffs for provincial power grids operating at varying voltage levels was developed. Secondly, the decommissioning planning model for over-age assets was established with the optimization objective of achieving the highest return throughout the investment cycle. The decision variables of time and scale for decommissioning were utilized in this model. Through a process of rolling optimization and feedback correction, the model ensured that the scale of decommissioning for overage assets in each stage aligns with the desired value, thereby meeting the premise of system reliability. Finally, the efficacy of the model and its ability to enhance investment efficiency were demonstrated through the examination of the${110}\mathrm{{kV}}$voltage level grid in a specific province.

Seismic soil liquefaction can lead to soil instability and slip, resulting in irreversible and severe damage to bridges. The seismic response of curved bridges in liquefied lateral extension sites is a major concern due to the complex stress state. Three representative far-field seismic waves were selected and applied to a four-span continuous curved bridge from 12 different directions. The maximum tilt angle of the site was set to be the same as the seismic input angle in order to investigate the seismic response behavior of the curved bridge in the liquefaction expansion site and conduct a comparative analysis. The results show that as the far site seismic input gradually changes from${0}^{\circ }$to${180}^{\circ }$, the pile top bending moment of the curved bridge decreases gradually, with the side piers experiencing larger bending moments compared to the secondary center piers and the center piers. When the seismic input wave changes gradually from${180}^{\circ }$to${360}^{\circ }$, the pile top bending moment gradually increases, with the middle pier and the second pier experiencing higher bending moments than the side piers. The maximum bending moment at the bottom of the pier alternates between the middle pier and the second pier as the seismic input angle changes, with the second pier experiencing a significantly higher number of occurrences of the largest bending moment compared to the middle pier. The relative displacement between the pier and beam and the ground shaking input angle exhibits a cyclic trend of initially increasing and then decreasing. Therefore, it is recommended that the location of a bridge project susceptible to ground vibration should be determined based on the type of ground vibration, and corresponding anti-liquefaction measures should be implemented accordingly.

In response to the common forms of specimens used for interface shear performance testing, these specimens fail to effectively reflect the actual situation of shear stress on the interface. An optimized improvement scheme for the traditional Z-type specimen was proposed. By employing the optimized Z-type specimen, experimental studies on the shear strength at the interface between ultra-high performance concrete (UHPC) and normal concrete (NC) were conducted, examining the influence of shear reinforcement ratio and interface roughness on the interface shear strength. The research results indicate that the failure occurs on the NC side or partially on the interface and partially on the NC side, which is characteristic of typical brittle failure. The shear strength of the interface increases with the increase of roughness, but when the roughness is larger than the value of 1.8, the effect of increasing interface roughness to enhance the interface shear strength is not very significant. Ribbed reinforcement as shear reinforcement can fully exert the anchoring effect and the effect of improving the interface shear strength is more evident than that of smooth-round reinforcement. A calculation formula for the interface shear capacity of UHPC and NC, which can consider the contribution of shear reinforcement and interface roughness, has been established. The theoretical calculation values are in good agreement with the experimental results, which can provide reference for the engineering design of composite components with UHPC and NC interfaces.

The accurate detection of coke overflow in high-dust environments is a pivotal challenge in achieving intelligent coke loading. A method was proposed to address this issue for the intelligent detection of coke loading overflow, which was based on dark channel prior knowledge and the ResNet network. Firstly, a video collector was used to obtain video information of the coke loading scene, and the original time-series video image frames were processed to obtain the region of interest between the discharge port and the loader. Secondly, the prior knowledge method of dark channels was employed to process the regions of interest. Enhancing the contrast between the target areas and irrelevant areas within the regions of interest, thereby mitigating the effects of dust on subsequent detection models. Moreover, the problem of overflow detection was transformed into a binary classification task by labeling the regions of interest based on the actual loading of coke. Finally, the ResNet network was utilized for modeling, enabling the completion of model training and experimentation during the loading process of newly acquired coke. The experimental results demonstrate that the proposed method exhibits promising performance on new data, achieving an overall accuracy of 86.81%. Specifically, the accuracy, recall, and F1 score for the overflow class are 84. 12%, 90.74%, and 0.8730, respectively. Furthermore, the application of the dark channel prior algorithm in data processing results in a notable increase in the recall rate of the overflow class by 3.31%.