The utilization of offshore floating photovoltaic has became a hotspot in major coastal countries in complex marine environment. Offshore floating photovoltaic is currently in the phase of the experimental and pilot test. In order to explore the main components and current technological status of offshore floating photovoltaic technology, and to assist in the large-scale and commercial development of offshore floating photovoltaic in China, by sorting out the main components of offshore floating photovoltaic, based on the research and application status of offshore floating photovoltaic at home and abroad, focusing on the structural types of floating platforms, and analyzing the characteristics of various types of floating platforms. The results reveal that the maturity level of zero-gap and non-zero-gap floating platform concepts is not high, and the novel body materials should be considered to reduce engineering costs. The electrical equipment that is suitable for the complex marine environment needs to be further improved. The business modes of integrating floating solar power with other marine scenarios should be actively developed. The strategy, security, and means of operation and maintenance need to be given special attention. The research aims to provide the current status and challenges of key technologies, and offer reference experience and research directions for the development and design of offshore floating photovoltaic.

The occurrence characteristics and genetic models of geothermal resources is an important basis for the development of geothermal resources. Drilling data, geophysical and geochemical data was applied to analyze the thermal reservoir, cap rock, heat sources, channels and supply elements, and the genetic model of geothermal system in the southwest of Zhoukou Depression was established. The development prospect was also evaluated. The geothermal resources in study area can be divided into sedimentation basin type with low temperature. Mantle derived heat is the main heat source, and the geothermal flow in the north part is higher than that in the south part, also in the protruding area is higher than that in the depressed area, reaching over 70 mW/m². The average geothermal gradient is about 2.8 ℃/hm, dominated by heat conduction system. Isotope analysis shows that the supply source comes from atmospheric precipitation in the western low mountains and hills, which infiltrates through the exposed area and moves along permeable strata and unconformity towards the east, and is heated and warmed up. The pores of the Neogene and Paleogene sandstones, as well as the karst pores of the Cambrian-Ordovician carbonate rocks, are the main storage spaces for fluids. The silt and clay deposits developed in the upper part of the Quaternary and Neogene systems form a good waterproof and thermal insulation cover layer. The development and utilization risk of the Neogene system is the lowest, with a floor depth of 400~1 400 m, increasing towards the north. The average sand to soil ratio is 40.9%, with a water flow between 40~60 m³/h and a wellhead temperature of 43~48 ℃. The development of faults also promotes the upwelling of deep heat flow, and there is a local thermal convection warming effect. Finally, four favorable development area for porous geothermal resources and three favorable areas for karst resources were identified.

The Tanshuling molybdenum deposit is located in the Jiangnan Uplift zone along south part of the Jiangnan Fault. Its main lithology type is granodiorite. With the aim to constrain their magma and ore-forming ages and deposit genesis, combined zircon U-Pb and molybdenite Re-Os geochronology together with whole-rock major and trace element geochemistry have been carried out. The result suggest that the content of SiO₂ is 64.5%~66.8%, Al₂O₃ is 14.4%~16.0%, K₂O is 3.92%~4.86%, Na₂O is 2.90%~3.91%, CaO is 1.56%~2.8%, MgO is 1.24%~1.53%, A/CNK value of 1.02~1.10, and A/NK value of 1.37~1.59. The characteristics of major elements show that the granites belong to metaaluminium to weak peraluminous high potassium calc-alkaline pot-assium series with I-type granite nature. The chondrite-normalized REE patterns are evidently right-declined, with relatively LREE enrichment and slight Eu negative anomalies. The molybdenite Re-Os age of the Tanshuling molybdenite is (133.09±0.86) Ma, and the U-Pb dating of the Maolin granodiorite is (140.4±0.62) Ma, (139.9±0.66) Ma, (139.6±0.63) Ma, all belong to the Early Cretaceous. Integrated chronological and geochemical characteristics show that the main magmatic activity of the Tanshuling molybdenum deposit belong to the Pacific tectonic system, and the alternation of extrusion and extensional has led to large-scale magmatic and mineralization in this area.

In view of the current situation of unclear understanding of the genesis of the low resistivity oil layer in Guantao Formation of CFD6-4 oilfield in Bohai Sea, the microscopic and macroscopic genetic mechanism of the low resistivity oil layer was systematically analyzed by using clay mineral analysis, scanning electron microscope, particle size analysis, heavy mineral analysis, core nuclear magnetic resonance and other data combined with the study of sedimentary evolution. The research shows that the low resistivity oil layer is rich in clay minerals compared with the conventional resistivity oil layer. The illite mixed layer and illite layer are bridged to fill the pores to form a conductive network. The clay minerals distributed in the porous network fully contact with the formation water to produce cation exchange, forming the microgenesis of the low resistivity oil layer. The complex pore structure leads to high capillary bound water porosity, which also leads to lower oil saturation in low resistivity reservoirs, which constitutes another cause of formation of low resistivity reservoirs. Low-resistance oil reservoirs in the study area are mainly developed at the end of the half-cycle of the rise of the medium-term base level. On the whole, the river energy is weak, and the sand-carrying capacity is reduced. For example, the fine-grained sediment of clay minerals is gradually enriched, and the increase of the proportion of fine-grained sediment causes the complexity of the pore structure of the reservoir, and the increase of the bound water saturation, which constitutes the macro cause of the development of low-resistance oil reservoirs.

Subsidence monitoring and reservoir parameters inversion in gas field can provide important supporting information for safe production protection and mining planning. The SBAS-InSAR method was used to investigate the surface subsidence evolution characteristics of Sebei gas fields from February 2022 to September 2023. Furthermore, the InSAR monitoring results were used as the observation measurements to invert the reservoir center projection coordinates, depth, strike and other parameters of the gas fields through the Prolate spheroid source. The results show that the subsidence funnel occurs in the Tainan gas field, the Sebei No.1 gas field and the Sebei No.2 gas field, and the average annual subsidence rate is -124~-109, -275~-34, and -329~-89 mm/a, respectively. Among them, the Sebei No.1 gas field and the Sebei No.2 gas field show a more significant surface subsidence phenomenon. And the surface of all three gas fields continues to sink rapidly. Further, the reservoir parameters were obtained by inversion of InSAR monitoring results. The results show that there is little difference between the deformation derived by using the optimal parameters and the observed deformation, and the spatial distribution is consistent, which indicates that it is feasible to invert the reservoir parameters of Sebei gas fields based on SBAS-InSAR deformation results.

Most of Triassic strata in Guizhou are distributed with bedding rock landslides with weak interbeds, which are characterized by strong destructiveness, complex slip mechanism and difficult treatment. Taking the West Second Ring landslide in Guiyang City as an example, the disaster mechanism and instability evolution process were explored by means of field geological investigation, theoretical analysis and discrete element numerical simulation. The results show that the West Second Ring landslide is mainly composed of dolomitic layered cataclastic rock mass with good permeability and water retention, showing a “sponge” structure, and continuous rainfall is the direct cause of the landslide. The landslide is first cut out by the front sliding body and gradually pulled upward, and then the tensile cracks at the trailing edge of the sliding body developed further, which led to slip-tensile crack failure under the action of gravity. The simulation results are basically consistent with the actual situation. The landslide in West Second Ring Road can be divided into four stages of deformation and instability: natural dissolution and cracking, excavation and unloading expansion, saturated and weakened shear, and slow sliding and accumulation. The conclusions have certain reference significance for the disaster mechanism analysis and engineering prevention of Triassic bedding rock landslide with weak interlayer in Guizhou.

To investigate the correlation between aerobic exercise and cardiac function, lipid metabolism, and inflammation in patients with cardiovascular disease, by searching PubMed, Embase, Scopus, and China National Knowledge Infrastructure (CNKI) databases for relevant studies on the effects of aerobic exercise on cardiac function, lipid metabolism, and inflammatory factors in patients with cardiovascular disease, Meta-analysis and correlation analysis were conducted using RevMan5.4 and R software. The results show that aerobic exercise significantly reduces B-type natriuretic peptide (BNP) [SMD=-0.84, 95% CI (-1.34, - 0.34), P=0.001], systolic blood pressure (SBP) [SMD=- 0.55, 95% CI (-0.86, - 0.25), P=0.000 4], and diastolic blood pressure (DBP) [SMD=- 0.99, 95% CI (-1.67, - 0.32), P=0.004], LDL [SMD=- 0.53, 95% CI (- 0.89, - 0.18), P=0.003], and C-reactive protein (CRP) [SMD=-0.53, 95% CI (-0.90, -0.16), P=0.005]. CRP is positively correlated with HDL, LDL, and DBP, with correlation coefficients of 0.35, 0.26, and 0.28, respectively. CRP is negatively correlated with SBP, with correlation coefficients of -0.31. From this, it can be seen that aerobic exercise can improve heart function, lipid metabolism, and levels of inflammatory factors to a certain extent in patients with cardiovascular diseases, and there is a correlation between heart function, lipid metabolism, and inflammation.

Considering the high-temperature thermosetting chemical issues in the manufacturing process of composite tensile armor layers, the curing kinetics of T700/epoxy prepregs were explored. Through differential scanning calorimetry (DSC) analysis and the Starink method, the autocatalytic reaction curing kinetic parameters were accurately calculated. Then a curing kinetic model was established. It has been shown by experimental results that the reaction rate of the prepreg is significantly increased at higher heating rates. After the peak is reached, the reaction rate is decreased more rapidly, resulting in a lower average final reaction heat. The apparent activation energy of the curing reaction for this prepreg is 77.04 kJ/mol, and high consistency with the experimental data is exhibited by the constructed curing kinetic model.

With the promotion of green development in civil aviation, aircraft noise has become an issue that cannot be ignored. An improved dynamic window approach (DWA) combining aircraft performance was proposed, which introduces the constraint of continuous climb operations (CCO) and constructs performance constraints for aircraft. To address the problem of rough solution set caused by traversal in traditional DWA algorithm, genetic algorithm(GA) was used for optimization. Secondly, speed was used to represent the time indirectly in order to optimize the track evaluation function. The effect of population distribution was added to make the model more reasonable. Finally, taking the departure direction of BOKIR-8T at Chengdu Shuangliu Airport as an example, the improved algorithm (DWA-GA) was compared with the traditional DWA algorithm, and the flight path under the influence of population distribution was compared, and the aircraft performance parameters and noise influence range were analyzed. The simulation results show that the improved algorithm is more accurate than the traditional DWA algorithm at low resolution, and the population distribution has obvious influence on the track.

To address the challenges of extracting and identifying fault features from roadheader cutting vibration signal, a new fault diagnosis method of roadheader cutting head based on the refine composite multi-scale fuzzy dispersion entropy(RCMFDE) and hippo optimized random forest(HORF) was proposed. Firstly, RCMFDE was used to comprehensively characterize the fault feature information of the roadheader cutting head, and the fault feature data set was constructed. Secondly, the fault type was trained and tested by the HORF to realize the fault pattern recognition of the cutting head of the roadheader. Finally, the proposed method was applied to the experimental data analysis of the cutting head of the roadheader, and compared with the existing multi-scale fuzzy entropy and fine-complex multi-scale spread entropy fault feature extraction methods. The results of the trial indicate that RCMFDE performs better than the other two entropy approaches in discovering defect features, and hippo random forest outperforms extreme learning machine and support vector machine in error recognition. The fault diagnosis method can more correctly recognize the error type of the cutting head of the roadheader, and the rate of accuracy of the recognition obtained 100%.

The issues of idler blocking during belt conveyor operation were addressed, which leads to excessive friction and abnormal temperature rise between idlers and conveyor belts. A friction surface temperature rise model for faulty idlers and conveyor belts was established based on microscopic friction theory, considering the phenomenon of hysteresis-induced heat generation and utilizing the virtual work approach. The finite element method was employed to conduct a thermo-mechanical coupling simulation on the friction model to analyze the effects of belt speed and load on temperature rise. An experimental platform was constructed to investigate the heat generation from friction between faulty idlers and conveyor belts, where an infrared thermal imager was utilized to monitor the temperature rise under varying parameters. The results indicate that the friction-induced heat generation between faulty idlers and conveyor belts positively correlates with both belt speed and load. An increase in either factor results in heightened heat generation, with the heat being primarily concentrated on the surface of the faulty idlers. The maximum deviation between experimental values and theoretical calculations is 8.7%, confirming the reliability of the theoretical model. Corresponding measures are proposed based on these findings.

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.

The geological conditions of fault-controlled carbonate volatile oil reservoirs in Shunbei oilfield and the relationship between production wells are complex, and conventional methods have poor applicability in calculating dynamic reserves. Considering that the reservoir has the characteristics of fracture and cavity development, multi-phase seepage and inter-well interference, a volatile oil reservoir pseudo-pressure function was proposed and the functional relationship between saturation and pressure was given. The multi-phase flow material balance well group dynamics of the volatile oil reservoir material balance theory were established. The proposed method utilizes bottomhole flow pressure to calculate dynamic reserves, while there is no need for static pressure testing in the well group. The results show that the multiphase flow material balance equation describes the linear relationship between oil production rate and cumulative production. A partial correlation analysis was conducted on the main controlling factors of the well group’s dynamic reserves, and it was concluded that the main factors affecting the production dynamic characteristics are average oil production, production decline rate and formation energy. Quantitatively evaluate the impact of errors in important parameters of the formation and fluid on the calculation results of dynamic reserves. It is believed that the compression coefficient and porosity of the formation have a great influence on the accuracy of dynamic reserve calculations. The well group dynamic reserve calculation method was applied to a typical well group in the Shunbei oilfield, and the dynamic reserve decrease in the calculation results of a single well was compared to quantify the decrease in dynamic reserves in the Shunbei oilfield due to inter-well interference. The method can accurately calculate the dynamic reserves of well groups in the Shunbei fault-controlled volatile oil reservoirs.

The tight oil reservoirs in the eastern Ordos Basin are characterized by shallow burial, low pressure, small principal geostress, and low fracture pressure, which are significantly different from the general mid-deep tight oil reservoirs. Previously, the development of horizontal wells in this area through hydraulic fracturing was mainly based on field experience, and the design of the fracturing construction lacked a theoretical foundation, making the impact pattern of construction parameters unclear and the enhancement of production effect uncertain. Hence, research on the optimization of key parameters in fracturing construction is urgently needed. To maximize production efficiency, an integrated research method involving fracturing simulation and numerical reservoir simulation has been adopted. FrSmart has been used for fracturing simulation, Petrel for building geological reservoir models, and tNavigator for numerical simulation. Through the comprehensive application of various numerical simulation software, optimal cluster spacing, displacement, and single-segment fluid volume suitable for horizontal well fracturing in the reservoir were determined. By adjusting the conventional volume fracturing process parameters of well YCN-1 in the study area to a cluster spacing of 20 m, a displacement of 12 m³/min, and increasing the single-segment fluid volume to 1 000 m³, significant improvements in fracturing and production enhancement effects were achieved. Field test results show that the production of well YCN-1 after optimizing fracturing parameters is 29.98% and 50.27% higher than that of the unoptimized wells N-2 and N-3, respectively. Therefore, a method of critical significance for guiding the fracturing construction of shallow tight oil reservoirs, enhancing fracturing efficiency, and improving production effects has been proposed.

Proppant performance is very important to the hydraulic fracturing design of unconventional oil and gas reservoirs. Few scholars have studied the micro performance parameters of proppant in terms of particle size and shape. The effect of particle size and shape on proppant breakage rate and fracture conductivity in shale gas reservoir was quantitatively characterized through laboratory experiments. The results show that when the closing pressure is lower than 28 MPa, the same type of proppant with uniform particle size and high spherical degree is compared with the proppant with poor sorting, the crushing rate is reduced by 15% and the fracture conductivity is increased by 10%. When the closing pressure exceeds the compressive strength of the proppant, the well-separated proppant can maintain the fracture conductivity better as the flow channel is further blocked by the debris generated by the broken proppant. The experimental results provide a reference for in-situ fracturing design of shale formation, improving the quality control level of downhole materials and selecting proppant.

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.

The quality of internal plunge grinding process is affected by the grinding performance of different grinding wheels. In order to online monitor the grinding performance of different grinding wheels under the same experimental parameters during the internal grinding process. A particle swarm optimization-back propagation(PSO-BP) neural network-based grinding performance monitoring method for different grinding wheels was proposed. Firstly, the feature parameters of acoustic emission signal, power signal, vibration signal, displacement signal and current signal were extracted. Then, according to the eigenvalue data samples of each sensor and the global optimization function of BP neural network by particle swarm optimization algorithm, the PSO-BP online monitoring model was established by using PSO algorithm to optimize the initial weights and thresholds of BP neural network to accurately monitor the grinding performance of different grinding wheels. Finally, the BP neural network model and the PSO-BP model were analyzed and compared with the experimental data. The results show that the PSO-BP monitoring model has higher monitoring accuracy than the BP neural network model, with an average correct rate as high as 97.6%, and the validity of PSO-BP is verified through a large number of experiments, which is able to effectively monitor the grinding performance status of different grinding wheels.

In order to enhance the overall performance of the heat sink, a novel meshed microchannel heat sink structure was introduced, and its geometric parameters were optimized by performing a multi-objective optimization. The Box-Behnken design method was utilized to conduct response surface analysis on the design variables of channel width, fin thickness, and channel depth. The resulting temperature and pressure drop functions of the spider-shaped microchannel were then fitted as objective functions. The Pareto solution set was derived by applying a multi-objective particle swarm optimization algorithm, followed by utilizing the technique for order preference by similarity to an ideal solution(TOPSIS) method for selection from the Pareto solution set. It is concluded that the Pareto solution set is the optimal choice across various conditions. The multivariate statistical coefficients R² for temperature and pressure drop functions are 0.999 6 and 0.998 4, respectively, suggesting a high level of accuracy in the fitting function. The optimized structure not only reduces the average temperature by 3 K compared with the original design, but also decreases the pressure drop by 1 514 Pa. This significant improvement in comprehensive performance demonstrates that a well-designed channel structure can further enhance the heat sink performance of the microchannel.

For full-bridge LLC resonant converter, intermittent control strategy is an effective means to improve its light load efficiency. An improved intermittent control method was proposed to solve the problems of limited efficiency improvement and large output voltage ripple in light load state of converter by traditional intermittent control. This control method fixes the intermittent conduction time and makes the converter work at the resonant frequency during the intermittent conduction time, which further improves the light load efficiency of the converter and reduces the output voltage ripple. In order to verify the feasibility of the proposed method, a simulation model was built and the simulation waveforms of the traditional intermittent control strategy and the improved intermittent control strategy were compared and analyzed, and an experimental prototype with rated power of 100 W was made. The simulation and experimental results show that the output voltage ripple of the full-bridge LLC resonant converter with the improved intermittent control strategy can meet the engineering requirements, and compared with the traditional intermittent control strategy, the efficiency under light load is improved by up to 4.1%.

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.

Hydrogen, which produces only water during usage, is an excellent secondary energy source. However, its environmental impact should consider the primary energy sources used for hydrogen production, as well as transportation. The use of the grid can not absorb the abandoned photoelectrolysis water to produce green hydrogen and incorporate it into natural gas, and the use of natural gas pipeline network transportation can ensure the environmental protection and clean hydrogen energy. An optimal operation model considering the start-stop characteristics of proton exchange membrane (PEM) electrolytic cell was established. The model can obtain the optimal production plan when dealing with intermittent energy, hydrogen demand fluctuation and time-varying electricity price, and achieve the balance of time-varying electricity price, hydrogen production, photovoltaic output and operating cost. The production plan shows the load of electrolyzer in different periods, which verifies the correctness of the model. By changing the minimum load in the constraint condition, the results show that the proportion of standby and idle state decreases with the decrease of the minimum load, and the running cost also decreases slightly. When the critical value reaches 6.1%, the running cost no longer changes.

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.

Taking a three-level, multi product, and dual channel supply chain as an example, the inventory control problem in the supply chain under stochastic demand was explored. A dual channel supply chain simulation model was established based on the independent control, information sharing, and pre warehouse replenishment model of “single manufacturer-dual distributor-dual retailer-dual customer”. In node enterprises, the Pull/Push strategy was adopted for ordering decisions, and information entropy was used to measure the uncertainty of nodes. Finally, the whale optimization algorithm was used to adjust the inventory control parameters in the simulation model. The results show that in the case of interruption, the pre warehouse replenishment mode can increase customer satisfaction in the interrupted channel by 80%. The whale optimization algorithm can ensure customer satisfaction while controlling total costs and reducing uncertainty in the supply chain system.

In dynamic wireless environments, the distortion of transmission waveform is inevitably present, deteriorating the accuracy of identifying high altitude electromagnetic pulse (HEMP) parameters. To address this issue, an extreme learning machine parameter identification network (ELM-PInet)-based parameter identification method was investigated, which leverages the characteristics of HEMP waveform and considers the impact of wireless channels, thereby improving the accuracy of HEMP parameter identification. To demonstrate the nonlinear effects of wireless channels, the transmission model of HEMP waveform was first constructed based on wireless transmission theory. Subsequently, an ELM-PInet was developed to suppress waveform distortion and improve the identification accuracy of HEMP parameters. Finally, the proposed method was validated through field irradiation test on the experimental platform. Simulation results demonstrate that compared to classical HEMP parameter identification methods, the identification accuracy of HEMP parameters is enhanced by the proposed method. Furthermore, the ELM-PInet-based parameter identification method exhibits its robustness against the impacts of different parameters. Additionally, the effectiveness of the proposed method is further validated through field irradiation experiments.

For gearboxes, variations in fault characteristics under different operating conditions, susceptibility to noise interference in fault diagnosis, lead to poor generalization and low recognition accuracy of fault diagnosis models. An end-to-end convolutional block attention module-sparse temporal convolutional network with soft thresholding(CBAM-STCN) was proposed for gearbox fault diagnosis. Firstly, the Hilbert transform was employed to convert the gear fault vibration signal into an envelope spectrum signal. Then, this signal was input into the CBAM-STCN fault diagnosis model. The model integrates a hybrid attention mechanism module, the convolutional block attention module (CBAM), which adaptively learns the weights of channel and spatial attention to extract information sensitive to fault features. The embedded soft thresholding function minimizes the discrepancy between the model’s output and the original input. Finally, the proposed method was utilized to identify and classify various types of gear faults under two different conditions. The results indicate that the CBAM-STCN model achieves an average accuracy of 98.95% in intelligent gear fault diagnosis, demonstrating its potential value for gearbox fault diagnosis.

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.

Aiming at the problems such as small and medium-sized obstacles on the road are prone to miss detection, small target obstacles are difficult to detect, and the number of model parameters is large in smart driving scenarios, the obstacle target detection algorithm with improved YOLOv8n was proposed. Distribution shifting convolution (DSConv) was used in the backbone network to replace floating point operation with integer operation, reducing the amount of redundant computation, and maintaining the accuracy by imitating the original convolution layer by quantization and distribution shifting. By adding small target detection layer, the feature information of small target can be captured better and the scale characteristics of small target can be adapted. Combined with SimAM parameterless attention mechanism, SPPF-SimAM module was introduced to improve the quality and diversity of feature representation, and the detection accuracy was improved without increasing the number of parameters. By combining ghost-shuffle convolution (GSConv) and VoV-GSCSP modules, the neck feature fusion network was lightweight, reducing the number of parameters and calculation of the model. The experimental results show that the accuracy, recall, and mean average precision of the improved model are improved by 1.6%, 8.0%, and 6.2%, respectively. The number of parameters is reduced by 6.7% compared with the original model, and the proposed algorithm effectively improves the detection accuracy of small and medium-sized obstacles in smart driving scenarios, and achieves a better balance between the detection performance and the model lightweighting.

The format and content of items such as product names and specifications in the detailed section of VAT invoices are highly flexible and complex, lacking complete gridlines to separate information fields. Existing methods for all-element structural recognition of VAT invoices face issues like low element recognition rates and high computational complexity. A structured recognition method for full face information based on computer morphology was proposed, which uses morphological operations to detect invoice table lines, cuts and recognizes text in different areas of the invoice. Then the implicit rules of the layout of the value-added tax invoice product details area was reused, combined with the text connected areas obtained through computer morphology operations, to construct a complete table structure. Finally, text detection and recognition were achieved using text detection neural network with differentiable binarization (DBNet) and convolutional recurrent neural networks (CRNN). The proposed method was tested on a dataset of 49 value-added tax invoices in three different formats, and the results show that the element recognition rates reached 99.9%, 97.4%, and 98.8%, respectively. The average running time per invoice is 0.90, 0.47, and 0.82 s, respectively. The structural recognition performance of the entire invoice exceeded multiple comparison table recognition models and literature methods.

In order to solve the problem of poor image denoising performance caused by the simple encoder-decoder structure of the convolutional neural network image denoising model, a residual dense image denoising network (RDIDNet) based on the residual dense network and attention mechanism was proposed. Firstly, the global residual block was used to enhance the nonlinear mapping ability of the network model. Secondly, the double-element convolutional attention module was introduced to realize the adaptive feature fusion in the decoding process of RDIDNet model. Finally, the RDIDNet denoising model was compared with 14 representative denoising methods, and ablation experiments were conducted to verify the effectiveness of using RDU Sub Network, DE-CAM, and PSNRLoss for network optimization on the benchmark model. The experimental results show that in the Set12 dataset and BSD68 dataset, RDIDNet improves the peak signal to noise ratio (PSNR) and structural similarity (SSIM) metrics by an average of 1.03 dB and 0.027 5, respectively, compared to the traditional classical method BM3D. Compared to SwinIR based on Vision Transformers architecture, the average improvement is 0.03 dB and 0.001 4, respectively. Compared to the latest CNN based denoising method NHNet, it has an average improvement of 0.22 dB and 0.008 9. The RDIDNet denoising network focuses more on low-frequency information and has more stable model training. It can effectively eliminate image noise while preserving image details and textures, and has good performance.

In order to solve the problem of automatic sorting of garbage, an intelligent sorting method for household garbage was designed based on the artificial intelligence computing platform Jetson NANO and YOLOv8 algorithm. Firstly, the lightweight YOLOv8 multi-target detection algorithm was used to classify garbage into four categories and achieve accurate recognition in the presence of occlusion. Secondly, the CoreXY coordinate transformation algorithm was used to determine the optimal angle for mechanical claw grasping and achieve precise garbage grasping. Finally, based on TensorRT optimization technology, the lightweight YOLOv8 multi-objective detection algorithm was deployed on the NANO hardware platform, completing the overall construction of the four major categories of garbage intelligent sorting system based on YOLOv8-NANO. The results show that in the self-made garbage test consisting of four major categories and a total of 13 sub categories, the sorting system can effectively achieve detection of single and multiple targets. The detection mAP0.5:0.95 value is 97.3%, and the target detection speed reaches 30.6 frames/s. The fastest garbage sorting speed could reach 6 pieces/min.

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.

Concrete arc beams in the support mold is often difficult to ensure the molding accuracy, the production is more difficult to high cost, and 3D printing technology has a construction speed, design freedom and high characteristics, so in order to solve the problems such as the complexity of concrete arc beam support, the effectiveness of 3D printing arc shell-cast-in-place beam construction was studied. According to the existing 3D printing concrete ratio and process parameters, three 3D printing curved beam mold shells were designed and printed, and the printing and molding accuracy was measured. The mold shells were equipped with reinforcing cages and cast-in-place concrete materials, and 3D printing concrete curved mold shells-cast-in-place beams were produced. The beam specimens were subjected to vertical loading tests to validate the effectiveness of the construction method. The results show that the 3D printed curved mold shell is basically the same size as the 3D model, with a maximum error of 4% in the middle, and the overall printing and molding quality is good. Under vertical loading, the damage patterns of the three 3D printed curved mold shell-cast-in-place beam specimens are similar. The cracking load and ultimate capacity of the beam specimen with reinforcement between the curved mold shell and the cast-in-place beam have been significantly improved, with an increase in the ultimate load of about 25%.

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 emergence of aerial building machines has greatly improved the environment and efficiency of high-rise building operations, while also facing challenges such as increased difficulty in construction operations and complex construction processes. With the continuous deepening of digital transformation in the construction industry, the digital expression of building machine construction processes has emerged as an intuitive and clear solution. It significantly enhances the transparency of the construction process, optimizes resource allocation, and strengthens decision support for project management. An effective pathway for the digital expression of building machine construction processes was established, aimed at advancing high-rise construction towards intelligent management. Through theoretical foundations and field research analysis, the needs for the digital expression of building machine construction processes were identified, leading to the design of a framework for implementing digital expression of building machine construction processes. This research not only provides theoretical guidance for the digital transformation of building machine construction processes but also expands new perspectives on the application of knowledge graphs, interactive electronic technical manuals, model-based definition (MBD) techniques, and augmented reality(AR) technology in the construction field.

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.

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.

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.

Accurate prediction of wind speed along high-speed rail lines is a fundamental requirement for railway disaster warning systems. To enhance the capability to respond to and handle sudden events caused by strong winds, a short-term wind speed prediction method based on the subtraction average based optimizer (SABO) algorithm optimized long short-term memory (LSTM) neural network was proposed. Firstly, considering the nonlinearity and non-stationarity of wind speed, the min-max (MM) method was used to normalize the wind speed data. Secondly, the “-v” method in the SABO algorithm was employed to search and optimize the key parameters of the LSTM model, constructing a wind speed prediction model. Finally, the effectiveness of the model was tested using measured wind speed data collected from wind speed collection points along the Baoji-Lanzhou high-speed railway in China. Experimental results show that the SABO algorithm’s optimization effect is better, and the prediction accuracy is higher. The average absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE) of the constructed model are 11.96%, 1.23%, and 16.47%, respectively, with a coefficient of determination (R²) of 0.995. Compared to other models, the LSTM neural network optimized by the SABO algorithm exhibits better fitting performance and higher prediction accuracy in short-term wind speed prediction, providing a new method and approach for wind prediction and warning along high-speed railway.

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.

In order to effectively predict the fuel consumption of vehicles, improve fuel economy and promote energy saving and emission reduction, a Hyperband-CNN-BiLSTM-based motor vehicle fuel consumption prediction method was proposed. Firstly, based on the vehicle operating status data and fuel consumption data collected from the actual road test, the salient factors affecting the fuel consumption of vehicles were analyzed. Secondly, combining the powerful feature extraction capability of convolutional neural network(CNN) and the advantages of bidirectional long and short-term memory network (BiLSTM) in dealing with the time-series data, a combined model of vehicle fuel consumption prediction based on CNN-BiLSTM was constructed. Then, in order to improve the model prediction accuracy, the combined model was optimized by Hyperband optimization algorithm, and the vehicle fuel consumption influencing factors were taken as the model input features to train the model to realize the modeling and prediction of vehicle fuel consumption. Finally, CNN, LSTM, BiLSTM, CNN-LSTM and CNN-BILSTM were selected as comparison models to evaluate the effect of Hyperband-CNN-BiLSTM prediction model. The results show that compared with other models, the Hyperband-CNN-BiLSTM model has the smallest mean absolute error (MAE) and root mean squared error (RMSE). They are 0.057 69 and 0.119 25, respectively. R² is the largest (0.991 76), and the model has the best prediction effect.

To reveal the complex relationship between the built environment and walking activity among older adults, a gradient boosting regression tree (GBRT) model was adopted, combined with multi-source data such as mobile signaling data, remote sensing image data, and point of interest (POI), to deeply explore the non-linear impact of the built environment on elderly walking activities and its threshold characteristics. The findings indicate that the built environment has a significant nonlinear impact on older adults’ walking activity, with land use factors being the most influential. Specifically, land use mix, the proportion of commercial service facilities, and the proportion of residential land are identified as key factors affecting older adults' walking activity. Additionally, the proximity of facilities also plays an important role. Finally, suggestions have been put forward for the adaptive transformation of land use and facilities to improve the level of elderly walking activities and promote healthy aging.

In order to determine the reasonable spacing of underground interchanges, a series of traffic simulation experiments with varying spacing cases was conducted in VISSIM using the Wenhui Street underground interchange of the Two Lakes Tunnel in Wuhan City as a practical case. The effects of interchange spacing, mainline and ramp traffic volumes and design speeds on crash risk at the diverging and merging areas of the underground interchanges were analyzed. Then, the crash risk variation at the diverging and merging areas of the underground interchanges was predicted using the four typical machine learning algorithms, i.e., extreme gradient boosting (XGBoost), support vector machine (SVM), random forest (RF), and multilayer-perceptron (MLP). Results show that when the spacing increases from 1.5 km to 2.5 km, travel time, average delay, average queue length, and traffic conflict rate decrease significantly, and the collision risk index of time-to-collision(TTC) increase significantly. When the spacing is above 2.5 km, the decreases in travel time, average delay, average queue length, and traffic conflict rate start to slow down, and so does the increase in TTC. When the spacing is 2.5 km and above, the overall traffic operation efficiency and safety of the underground interchange increase significantly. The XGBoost model can predict the crash risk variation reaching a precision of 88.3%. This study can be a theoretical support and practical case for the setting of underground interchange spacing.

The layout of the distribution path of urban logistics terminals is the key to controlling transportation costs. In order to solve the path planning problem of urban low-carbon logistics, a brainstorming-adaptive large neighborhood search algorithm (BSO_ALNS) was proposed. Firstly, a low-carbon vehicle path model with capacity and time window constraints based on vehicle fuel consumption (CVRPTW) was established with the optimization goal of minimum total vehicle transportation cost. Secondly, the brainstorming algorithm (BSO) was used to improve the quality of the initial solution, and the heuristic crossover strategy was introduced to improve the quality of the global search. Using the adaptive large neighborhood search (ALNS) local search, ten kinds of damage and repair operators were designed, and the adaptive weighting mechanism was introduced, combined with the simulated annealing criterion to avoid falling into the local optimum. The performance of the BSO_ALNS algorithm was tested by selecting C, R, CR and other types of instances of different scales in Solomon. Taking the shortest path distance as the goal, the error between the BSO_ALNS algorithm solution and the historical optimal solution is within 1.5%. With the goal of minimizing the total cost of vehicle transportation, the optimal solution is obtained BSO_ALNS compared with BSO and ALNS. It is proved that the proposed algorithm can effectively solve the problem of urban low-carbon logistics path optimization.

Composite material is widely used in aerospace field. It is important to research the damage process of composite and its failure mode. Composite damage is a complex progressive process. In order to predict the strength and damage propagation of reinforced composite wall panels under compression conditions, taking the cap shaped single rib panel under compression load as an example, one instantaneous stiffness degradation model and three continuous stiffness degradation models were used for analysis and comparison. By reducing the stiffness of material points in the element, the damage evolution process of composite materials was simulated, and the experimental results were compared with the analysis results. The comparison results show that all four damage degradation models can accurately predict the bearing capacity and damage range of reinforced wall panels under compression conditions. Compared with other models, the constant type model in the continuous damage degradation model has the highest accuracy. The research results provide theoretical guidance for the study of mechanical properties of composite reinforced wall panels.

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 obtain the location of erosion gullies in Bayan County, understand the spatial relations between multi-features and erosion gullies, assessing the occurrence risk, and provide information and method reference to erosion gully management and precaution, high-resolution satellite imagery, digital elevation model, soil, and precipitation data were used to acquire erosion gully locations and topographical, soil, hydrological, meteorological features. Subsequently, the spatial relations between erosion gullies and multi-feature were analyzed, and erosion gully occurrence risk was assessed using the random forest method. As results, towns like Waxing, Dexiang, and Xinglong could be paid more attention on the erosion gully management. Elevation relief, slope, slope factor, slope length factor, water flow density, and distance to water flow tend to show more spatial relations with erosion gullies, and erosion gully would participate in the redistribution of soil nutrition. On the other hand, for natural or data spatial resolution reasons, slope aspect, terrain curvature, catchment quantity, rainfall erosivity factor, soil bulk density, and soil erodibility factor show little spatial relations with erosion gullies. Assessment indicates that the erosion gully risks are mainly in arable lands with slopes between 1.5°~6°. Results indicates that topographical and hydrological features are closely related to erosion gullies, and diagonal ridge is an economical and effective measure.

In order to meet the large number of future unmanned aerial vehicle (UAV) operation requirements, the safe takeoff interval for UAVs was formulated on the basis of conforming to the safety target level and aiming at the highest efficiency. According to the operating speed error characteristics of UAVs, taking into account the operating characteristics of the climb phase and cruise phase, the takeoff safety problems in three scenarios of same route operation, cross route operation and route network operation were analyzed, a collision risk assessment model was established respectively, and a calibration method for the takeoff interval was proposed in combination with Monte Carlo simulation. Finally, taking the actual operation of logistics UAVs as an example, the 10^-7 maximum collision probability was taken as the target safety level for verification, and the minimum safe takeoff interval in the three operation scenarios was analyzed and determined. The results show that the safe takeoff interval of the same route T is 122 s, the safe takeoff interval T of the cross route is related to the difference D between the distance of two takeoff points from the intersection point and satisfies T = (D±1 199.97)/14(T≥0), and the safe takeoff intervals between the four takeoff points of the airway network system are 158, 86, 0, and 0 s, respectively. The method can provide a reference for the UAV operation enterprises to carry out takeoff interval management.