With the continuous promotion of the “dual carbon” strategic goals and the construction of new power systems, traditional distribution networks are gradually transforming into information-based, digital, and intelligent new distribution systems. To accurately characterize and analyze the characteristics of different types of loads in the distribution network, and support efficient operation and control of the distribution network, a data-driven classification method for typical load curves in the distribution network was proposed. Firstly, based on load data, various classification scenarios of typical loads in the distribution network were analyzed, and performance evaluation indicators for classification scenarios including error rate, accuracy, and confusion matrix were proposed. On this basis, a data-driven load classification method for distribution networks was proposed, which converts 24 dimensional daily load vectors into image data and uses convolutional neural networks to identify load curve images, achieving accurate classification of distribution network load curves. Finally, the accuracy and effectiveness of the proposed method were verified by combining actual distribution network load data, and analyzed and compared with existing methods. The results indicate that the proposed method for classifying typical load curves in power distribution networks has better classification speed and accuracy.

A low-carbon optimal operation model of an integrated energy system that takes demand response and double-layer power-to-gas conversion into account was proposed to increase system energy utilization and lower carbon emissions. Firstly, the optimization model of dual-layer electric-gas multi-energy complementary integrated energy system with high efficiency of hydrogen was established to study the advantages of hydrogen energy in many aspects. Secondly, the demand response model was modeled, which was divided into price type and alternative type according to the characteristics of flexible load. Thirdly, a stepped carbon trading mechanism was introduced to curb the carbon emissions of the system. Finally, taking Nanning Jiangnan industrial park as an example, the model was solved in CPLEX environment of MATLAB, and verified by scene comparison analysis. The results show that the model can fully mobilize the demand side to participate in the system optimization and achieve the effect of energy saving and emission reduction.

In order to study the effect of unsteady flight parameters on the aerodynamic characteristics of simulated butterflies, a flight dynamics model was established with the black-framed blue Morpho butterfly as the research object. Based on the flight principle, the relative coordinates of butterfly wings, body and ground during flight were established, and the kinematic equations of butterfly wings and body during flight were constructed. The aerodynamic characteristics of the simulated butterfly were verified based on the flight principle of the butterfly, and the effects of the change of flutter angle and pitch angle on the lift and drag of the simulated butterfly were studied under the natural environment flow field. The results show that there is a positive correlation between turning angle and lift force, but no correlation with drag. When the flutter angle is less than 120°, the lift is positively correlated, when the flutter angle is greater than 120°, the lift is negatively correlated, and the flutter angle is negatively correlated with the drag. A high pressure area begins to occur at the leading edge of the wings when the downward flapping occurs, and at the edge of the wings when the upward flapping occurs. The research results provide a reference for the control parameters and wing design of flapping wing aircraft, and provide a scientific basis for further optimization of bionic flapping wing flight.

At present, the renovation of old residential areas is in a comprehensive promotion stage. Building a systematic and scientific external space renovation system for old residential areas is of great significance for promoting the renovation of old residential areas, improving the quality of life of residents, and optimizing urban image. A comprehensive transformation system covering five criteria layers and twenty-three subcategories was organized and constructed based on a literature review and keyword clustering analysis. Secondly, the weights of various renovation elements from different perspectives were quantitatively analyzed using a questionnaire survey and analytic hierarchy process. At the standard level, residents pay more attention to facility renovation and improving community service quality, with evaluation weights of 0.256 9 and 0.223 1, respectively. Planning and design management personnel pay more attention to transportation and environmental renovation, with evaluation weights of 0.238 2 and 0.231 7, respectively. The factor layer weights indicate that all entities emphasize the importance of landscape greening, activity space quality, environmental sanitation facilities, and facade renovation, which should be given special attention in the external space renovation of old residential areas.

China’s tight oil reservoirs have distinctive characteristics, including thin interbedded layers with alternate distribution in the longitudinal direction and strong reservoir heterogeneity. In order to maximize productivity and economic benefits, a development approach was commonly employed, involving a well network with layered fracturing for the simultaneous development of multiple layers. However, existing productivity models for fractured directional wells are only applicable to single-layer development and do not consider inter-layer interference, making them unsuitable for predicting well productivity of multi-layer development. In order to improve the accuracy of productivity prediction, the flow field nearby the fractured directional well is divided into the main fracture region, the stimulated reservoir volume region, and the un-stimulated reservoir volume region. Considering the effects of flow patterns in different regions and stress sensitivity, and introducing a disturbance coefficient, a non-steady-state productivity prediction model for multi-layer fractured directional well in tight oil reservoirs was established. After validating the model accuracy, the influence of fracture half-length, fracture conductivity, threshold pressure gradient, stress sensitivity and reservoir heterogeneity on the productivity of fractured directional well was further investigated. The results indicate that the threshold pressure gradient, stress sensitivity and longitudinal heterogeneity significantly affect the productivity of fractured directional well. The larger the threshold pressure gradient, and the more significant the stress sensitivity and longitudinal heterogeneity, the lower the productivity of fractured directional wells. With the gradual increase in fracture half-length, fracture conductivity, and matrix permeability, the productivity of fractured directional wells increases, but each factor has its optimal range. The ranking of factors affecting productivity is as follows: matrix permeability, fracture conductivity, fracture half-length, threshold pressure gradient, longitudinal heterogeneity, stress sensitivity.

A large number of nonlinear components are used in the AC microgrid of photovoltaic grid-connected system which is equipped with a certain capacity of energy storage devices. When there is a large number of nonlinear loads in the microgrid system, the current waveform of the microgrid system is prone to distortion, resulting in harmonic pollution. In order to reduce the interference of grid-connected microgrid system on the receiving grid, based on the LCL grid-connected inverter structure, PI+ repeated control compound control strategy was adopted to realize the tracking and control of command current on the basis of meeting the tracking speed and accuracy, which can effectively suppress harmonic current and compensate reactive power. Based on the composite control, it is considered that when the photovoltaic output power and load power change, the multi-function grid-connected inverter under the composite control can still realize dual functions when the researched microgrid and the grid transmit different power in different modes. Finally, the simulation results show that the strategy realizes harmonic compensation and reactive power compensation, and transmits power to the grid at the same time.

Qiandongnan is the largest and best-preserved Miao settlement area in China, holding significant ethnic cultural heritage. Traditional settlements form an essential part of this heritage. Studying their spatial characteristics and influencing factors is crucial for the sustainable development and protection of cultural heritage in this region. By comprehensively utilizing ArcGIS spatial analysis, boundary morphology index, spatial syntax, and geographic detector methods, the spatial characteristics of settlements were deconstructed from the perspective of regional pattern and case feature analysis, and their influencing factors were explored. The results indicate that the spatial distribution of traditional Miao settlements in Qiandongnan is characterized by significant agglomeration and hierarchy. The highest nuclear density is at the intersection of Leishan, Taijiang and Jianhe. The overall spatial pattern shows a “dense in the southwest and central-south, sparse in the northeast” distribution. Constrained by natural geography, the settlements are mainly distributed in the Qingshui River and Duliu River valleys at altitudes of 500~1 000 m, with undulations of 10~20 m, gradients of 2°~5°, and sunny slopes of 90°~270°. The settlements’ spatial structure exhibits a “clustered” distribution with finger-like external boundaries, and the center shows differentiated traffic flow within and at the edges of the settlements. The geodetector study reveals that Miao traditional settlements are regional spatial carriers of a natural-economic-social complex system. The natural geographic environment fundamentally shapes spatial patterns, the social environment guides and controls internal spatial organization and evolution, and economic development decisively influences spatial development and protection. The study enhances the understanding of this complexity, which is vital for appreciating Miao culture, developing strategies for protecting and developing this cultural heritage, and implementing rural revitalization strategies.

To solve the problem of deterioration of concrete performance caused by low temperature in cold regions. Based on the theory of nanomaterials to improve the properties of concrete, the effect of nano-silica on the properties of concrete was studied from the macro and micro scales. The results of the compressive strength test show that the compressive strength of ordinary concrete is attenuated by about 10% under low temperature curing. After being mixed with nano silica, the compressive strength of concrete is increased by about 20%, and the optimal dosage is 2%. The improvement mechanism of nano-silica on concrete properties was explored through microscopic test data such as mercury intrusion, X-ray diffraction and scanning electron microscopy. The results show that nano-silica can promote cement hydration at room temperature and low temperature curing, consume calcium hydroxide generated by hydration, and produce more hydrated calcium silicate and hydrated calcium aluminate gel, thereby reducing the porosity of concrete, optimizing the microstructure of concrete, and improving the performance of concrete. Compared with the room temperature environment, the improvement effect of nano-silica on concrete at low temperature is slightly reduced, but it completely overcomes the adverse effects of low temperatures on the performance of ordinary concrete.

In order to solve the problem of poor forecasting effect due to the large number of influencing factors of aviation material consumption and small amount of sample data. A prediction model for aircraft spare parts demand based on principal component analysis (PCA), improved particle swarm optimization (IPSO), and least squares support vector machine (LSSVM) was proposed. Firstly, the principal component analysis method was used to screen the main influencing factors of aviation spare parts, and then the improved particle swarm optimization algorithm was used to optimize the least square support vector machine parameter combination, and finally the selection results and optimization parameter combination were used to complete the PCA-IPSO-LSSVM aviation spare parts demand prediction model training. The results show that compared with the other four prediction models, the PCA-IPSO-LSSVM model has the highest prediction accuracy, and the RMSE and MRE of the test set are 3.24 and 4.23%, respectively, indicating that the model has good prediction precision and fitting effect.

In order to address the issues of low accuracy and high missed detection rates in existing pavement crack detection algorithms, an improved pavement crack detection algorithm based on YOLOv8n, named YOLO-CD (YOLO-crack detection), has been proposed. The scale sequence feature fusion (SSFF) module and triple feature encoder (TFE) module from the ASF-YOLO architecture were utilized by the YOLO-CD algorithm to enhance the detection performance for multi-scale cracks and the perception capability of target features. Additionally, the coordinate attention(CA) mechanism was introduced at the end of the backbone network and in the neck network, with positional information embedded into channel attention, thereby strengthening the extraction capability of crack features. Furthermore, an additional P2 small object detection layer was added on top of the original three output layers of YOLOv8n, increasing the multi-scale receptive field of the network, allowing both global and local context information to be captured simultaneously, thereby improving the detection capability for small cracks in complex scenes. The original YOLOv8n detection head was replaced by the DyHead detection head, achieving the integration of scale, spatial, and task attention mechanisms, and further enhancing the network’s detection performance for cracks. Experimental results show that in the self-built PD-Dataset, the mAP50 of the improved YOLO-CD algorithm is increased by 4.1% compared to the original YOLOv8n algorithm. In the public dataset RDD2020, the mAP50 of the improved YOLO-CD algorithm is increased by 1.5% compared to the original YOLOv8n algorithm. Moreover, the algorithm’s detection speed is found to reach 89.9 frames/s, meeting the real-time requirements of pavement crack detection.