This paper systematically reviews the various research results of in-depth learning applied in the field of industrial fault diagnosis， in order to fill the gaps in the evolution route， key technology direction and practical application of existing reviews. During the combing process， it can be observed that the development of deep learning in this field has gradually extended from the construction of basic network architecture to the development of hybrid architecture. The current research direction is focused on the innovative development of technology directions such as attention mechanism， transfer learning and generative adversarial networks. In the actual landing process， the industrial scene puts forward multiple requirements for the model， including the difficulty of data acquisition， the difficulty of model logic interpretation， and the need for the calculation speed to match the pace of industrial production. This paper further prospects the future directions of automated machine learning， multimodal fusion， physical information fusion， etc.， which can facilitate the follow-up researchers to carry out related work and promote the integration of intelligent diagnosis technology into the independent operation and maintenance mode.

The practical drag reduction effect of negative-pressure surfaces on real vehicles was investigated， RANS simulations based on the DrivAer vehicle model were adopted to comparatively analyse the aerodynamic drag and flow field characteristics of the negative-pressure configuration model and the smooth configuration model， and the inner mechanism of drag reduction was analyzed based on the three-dimensional structures of recirculation zones and recirculating vortices. The results show that non-smooth surfaces might not be capable of producing drag reduction over the full Reynolds number range. At low Reynolds number， a drag reduction of 2.1% is achieved， but at high Reynolds number， the drag increase by non-smooth surface itself could not be compensated by the drag reduction through weakening recirculating vortices in the rear of the vehicle， resulting in an increase in the overall drag.

Aiming at the problem of few samples in the fault diagnosis of rotating machinery， a fault diagnosis method for planetary gearboxes based on a diffusion model is proposed. The time-frequency diagrams of fault samples are used as the representation of fault features for fault classification. Firstly， the continuous wavelet transform is used to convert the one-dimensional vibration signals of the gearbox into time-frequency diagrams as training samples. Secondly， the diffusion model is optimized and improved to enable it to fully learn the fault features of the training samples and generate high-quality fault samples. Finally， a classifier based on a dense convolutional network is constructed， which combines the attention mechanism to enhance the performance of the classifier. The performance of the classifier is tested using a test set containing multiple fault types. The results show that the diagnostic accuracy of the proposed method is close to that under the condition of sufficient samples， reaching over 99%， and experimental comparisons with existing methods have been carried out. In addition， ablation experiments on the optimization settings of the wavelet transform method and the classifier are conducted to verify their effectiveness.

A control scheme using non-singular fast sliding mode control and a Nonlinear Extended State Observer （NESO） is proposed to solve the problem of reduced trajectory tracking performance caused by load swinging during transport in quadrotor load transportation systems.First，the dynamic model of the quadrotor system is built using Lagrange-Euler equations.The system is divided into three parts： position control， attitude control， and swing angle suppression.Controllers are designed for each part.NESO is used to estimate and compensate for uncertainties，disturbances，and noise in real time.Simulations show that this method reduces the maximum load swing angle by about 30%，and significantly cuts swing duration and load oscillation times compared to methods without swing control.It also improves the trajectory tracking accuracy of position and attitude controls.This scheme balances robustness and efficiency through decoupled and coordinated control，offering a practical solution for quadrotor transportation systems.

To address the limitations of single-channel vibration signals in comprehensively characterizing gearbox fault features， this paper proposes a data-level fusion method called MFTDCN is proposed.This method effectively integrates information from multiple channels to obtain richer features compared to single vibration signals.The approach begins by applying Principal Component Analysis （PCA） to multi-channel vibration signals to reduce them into three components. These components are then transformed into three time-frequency images using Short-Time Fourier Transform （STFT）. An improved cosine similarity measure， based on Hoyer sparsity， is employed to calculate weights by combining sparsity and similarity metrics. These weights are used to perform weighted fusion of the three time-frequency images， resulting in a single RGB image， which is then used as input for the diagnostic model. The diagnostic model combines Deformable Convolution Networks （DCN） with Transformer architectures to extract features and classify faults. Experimental results on the gearbox data set of Southeast University and the data set jointly published by Beijing University of Technology and Beijing Jiaotong University confirm the effectiveness and reliability of the proposed method.

In this study， Mo₂C/CoCrFeNiMn high-entropy alloy composite coating was successfully prepared for the first time by induction cladding technology. The effects of Mo₂C particles on the phase composition， microstructure， microhardness， room temperature and high temperature wear properties of the composite coating were studied. The results show that in addition to the FCC phase， there are Mo₂C phase and precipitated carbide phase in the composite coating. The addition of Mo₂C particles has the effect of grain refinement， second phase strengthening and solid solution strengthening. At the same time， it also promotes the formation of small angle grain boundaries and geometric necessary dislocations， and improves the microhardness of the coating. The mechanical mixed layer （MML） formed on the worn surface at room temperature and the oxidized enamel layer formed on the worn surface at 600 ℃ play a physical barrier role， which enhances the wear resistance of the coating.

Considering that a large number of CAV and HV will mix in the future， an agglomeration lane-changing model considering drivers' driving styles was proposed to enable CAV vehicles to achieve local agglomeration， promote traffic flow homogenization， and enhance traffic operation efficiency. The influence of driving styles in agglomeration was investigated， and the effects of aggressive， conservative， and autonomous vehicle driving styles on the three agglomeration lane-changing models CDA， CVA， and NOA were quantitatively analyzed. The agglomeration lane-changing model is based on the NGSIM database， and vehicle driving styles were classified using principal component analysis and K-means analysis， and finally simulated using Matlab to analyze the road capacity under the three lane-changing models. The research results indicate that the application of the lane-changing model and the increase in the penetration rate of aggressive drivers effectively improved road capacity， with the two roads studied showing a maximum increase of more than 7% in capacity. The increase in the proportion of conservative drivers improved road traffic flow stability and safety levels， while the increase in CAV penetration rate significantly improved road traffic flow stability and safety levels while also improving road capacity.

This paper， based on fundamental distortion theory， derives theoretical formulas for calculating the minimum diaphragm stiffness of steel box girders under different diaphragm spacing conditions. On this basis， using the principle of energy conservation， the applicable range of the minimum diaphragm stiffness formula is clarified. Finite element analysis is used to study the impact of varying diaphragm spacing on the distortion behavior of conventional steel box girder sections. This analysis addresses and resolves the issue present in design codes， where excessively thick diaphragms are required for small spacings， providing clear theoretical guidance for the rational placement of diaphragms in conventional steel box girders. Using the "world's longest-span steel-concrete composite girder bridge under construction" as a case study， a large-scale reduced model test was designed to investigate the impact of diaphragm stiffness variations on the deformation of steel box girders under torsional conditions. 3D scanning technology and finite element methods were employed to analyze the test results， and the research shows that once the diaphragm stiffness provides basic constraints， further changes in stiffness have a very limited effect on the distortion behavior of the box girder.

In order to predict the unconfined compressive strength of Pisha Sandstone Cement-soil at different curing ages and different basalt fiber dosage， Basalt Fiber Pisha Sandstone Cement-soil mixed with basalt fiber of different mass fractions（0%~0.6%） was prepared， and the unconfined compressive strength test and nuclear magnetic resonance test were carried out. The results show that the fiber dosage of 0.15%~0.3% can significantly increase the unconfined compressive strength， and the optimal dosage is 0.15%， and the unconfined compressive strength has a linear relationship with the secant modulus. With the increase of curing age， unconfined compressive strength increased and porosity decreased. With the increase of fiber dosage， unconfined compressive strength increased first and then decreased， and the porosity decreased first and then increased. Grey correlation analysis showed that the bound fluid saturation and the proportion of small pores were highly correlated with unconfined compressive strength. Based on this， a GM（1，N） grey model was established to predict unconfined compressive strength， and the average relative error of the estimated value was less than 9.27%. The research results provide a reference for the application of Pisha Sandstone in subgrade engineering.

The wear of modular expansion joints was essentially regarded as the cumulative result of continuous intercomponent interactive responses under external loads， with wind loading and traffic flow being identified as the primary external drivers of their motion. To enable precise wear analysis， a coupled "load-bridge-expansion joint" analysis system that integrates the evolutionary processes of the driving loads was established. First， the two operational scenarios， namely the wind environment and the traffic flow evolution around the bridge-expansion joint structure， were independently simulated by separate programs. For the purpose of expansion joint response analysis， a main-direction vehicle wind load estimation method and a unified simplified aerodynamic interference treatment were proposed， forming the wind-field and traffic-flow evolution scenarios along with their load-correlated association patterns. Subsequently， same-scale bridge and expansion joint models were connected through multi-point constraint couplings. By means of programmatic cyclic calls， the operational scenarios and their load-correlated processes were linked with the response analysis of the same-scale bridge-expansion joint model， and an integrated wind-traffic-bridge-expansion joint analysis system was developed， which provided a computational platform for wear analysis of the expansion joint. Finally， a cable-stayed bridge equipped with a modular expansion joint was selected， and wear parameter analyses under combined wind and evolving traffic flows were conducted based on prescribed wear indices. The results indicate that the design parameters of the sliding bearings and compression bearings inside the displacement box can be uniformly configured， whereas those of the shear spring and the sliding and compression bearings between the upper and lower beams require differentiated designs. Wear inside the displacement box increases with wind speed on the windward side but decreases on the leeward side， while the wear of the shear spring and the bearings between the upper and lower beams decreases as wind speed rises. The sum of bi-directional traffic flow densities plays a controlling role in wear： the greater the total density， the more severe the wear， and the more uniform the traffic distribution， the greater the wear. Compared with wind speed variations， traffic flow exerts a more significant influence on the wear of the expansion joint.

To investigate the application of engineering geopolymer composites （EGC） in strengthening existing concrete components， a study was conducted using concrete as the substrate and EGC as the overlay. Two types of bonding interface treatments， chiseling and grooving， were employed， with various parameter levels specified for each treatment. Direct shear tests were performed to evaluate the bonding interface performance between EGC and concrete. According to the experimental data， it can be observed that as the interface roughness increases， the interface shear strength also increases. Although all bonded interfaces ultimately fail in EGC shear failure under ultimate shear states， different interface configurations exhibit distinct failure modes. The study further revealed that the load-displacement curves of the specimens exhibited similarities， characterized by ascending and descending segments. Specimens with higher interface roughness displayed a gentler descending curve after reaching the ultimate load. Using the experimental data， a nonlinear fitting method was employed to develop a shear strength calculation formula for the NC-EGC interface， which aligned well with the experimental results. Furthermore， the shear toughness of the NC-EGC bonding specimens was assessed through shear load-displacement curve analysis and a toughness evaluation approach tailored for fiber-reinforced concrete. The results showed that the toughness prior to the peak load was 139% and 234% of the C60 concrete control group， demonstrating the outstanding toughening ability of EGC.

To address the safety hazards in driving blind spot scenarios， a collaborative obstacle avoidance planning algorithm for vehicles based on a collision risk field model was proposed. Considering the impact of vehicle-to-vehicle communication continuity， a communication continuity risk field model is constructed. This risk field was incorporated into the dynamic path planning algorithm， and an adaptive obstacle avoidance safety cost function based on the communication continuity impact factor was designed. Simulation results demonstrate that the proposed risk model-based obstacle avoidance planning algorithm can accurately reflect dynamic risks in driving blind spots， enhancing the safety of autonomous vehicles in extreme situations.

To investigate the variation law of driving comfort on expressway tunnel group sections， real vehicle tests were carried out to collect the tunnel section length， regional luminance， and drivers' real-time heart rate.The relationships among tunnel spacing， environmental luminance difference， and heart rate growth rate were analyzed. Based on the mechanism by which tunnel spacing affects the downstream heart rate response， the concept of the tunnel group psychological effect was proposed. Driving comfort was characterized by heart rate growth rate， and a comfort model was developed under the coupling of luminance difference and tunnel spacing. The mechanism underlying the tunnel group psychological effect was also explored. The results show that as tunnel spacing increases， the heart rate growth rate first rises and then falls. A longer tunnel spacing intensifies the driving load in downstream tunnels and reduces comfort. The heart rate growth rate increases with the environmental luminance difference. The coupling of tunnel spacing and luminance difference significantly affects driving comfort. A greater luminance difference corresponds to a wider tunnel spacing range in which the psychological effect is likely to occur.

In order to solve the problem of low strength in the preparation of alkali-activated slag cementitious materials from industrial solid waste carbide slag， this study used the simplex center of gravity design method to explore the changes of fluidity， setting time and compressive strength when sodium silicate/sodium metaaluminate cooperated with carbide slag to activate slag， and analyzed its enhancement mechanism. The results show that the optimum ratio of the optimized activator is that the content of sodium silicate is 39%~59%， the content of sodium aluminate is 5%~25%， and the content of carbide slag is more than 26% and less than 47%. Both sodium silicate and sodium aluminate can enhance the alkalinity of the system， accelerate the dissolution of aluminosilicate components， and promote the formation of hydration products such as C-（A）-S-H. The double effect of sodium aluminate hydrolysis will accelerate the polymerization of aluminum oxide tetrahedron and improve the early strength of the matrix. Sodium silicate provides sufficient silicon oxygen tetrahedron for the system， which ensures the sustainable development of the later strength. After the hydrolysis of carbide slag， it combines with CO₃^2- and ［Al（OH）₄］^- in the solution to form calcium carbonate and hydrated calcium carbonate， which makes the structure more dense. When sodium silicate/sodium aluminate cooperates with carbide slag to activate slag， the system has higher activity， better mechanical strength and working performance， and the microstructure tends to be gelled and densified.

Combining the feature that railway self-centering bridge piers require a pedestal at the pier bottom due to the stringent lateral stiffness demand， a study on the mechanical behavior of railway self-centering bridge piers with an orderly sacrificial mechanism is conducted， in which unbonded energy-dissipating bars are arranged in the pedestal and unbonded prestressing tendons are arranged in the pier body. By discussing the influence of the configuration form of unbonded reinforcements on the seismic performance， a hybrid configuration form of reinforcements to enhance the seismic performance of self-centering piers is proposed. The analysis of the process of change in the mechanical behaviors of reinforcements under the action of lateral cyclic loads is carried out. The factors influencing the hysteresis characteristics of self-centering pier and relationship between the key seismic performance indicators were investigated. The results showed that the high-strength reinforcements are arranged in a single row， the sudden drop in loading capacity caused by reinforcements fracture can lead to the failure of bridge piers. Based on the limited strain variability of reinforcements with different grades， mixing low-grade steel bars（HRB400） with high-strength steel bars（HTRB630） in a staggered arrangement can achieve orderly sacrifice of different grades of reinforcements and the continuous regulation of the compression area of the pedestal， and improve the deformation and bearing capacity of the pier. The reinforcement ratio of prestressing reinforcements affects the post-yield stiffness and lateral strength of self-centering pier. Initial prestressing values， reinforcement ratios， and vertical axial forces affect lateral stiffness. The distance ratio of reinforcements affects the failure of displacement of piers. The three main seismic performance indicators of the self-centering piers have significant positive correlations， i.e.， the load carrying capacity decreases， the energy dissipation capacity and the residual displacement also decrease， whereas the maximum displacement and the residual displacement do not show a positive correlation.

In order to alleviate the pressure of urban transportation， in order to further improve the transportation efficiency. This paper designs evaluation indexes according to the theory of complex transportation network nodes， combines the multi-objective decision-making method of hierarchical analysis （AHP-RatioMOORA）， ranks the importance of subway stations and calculates the weights， and selects the location of subway logistics and distribution hubs according to the importance. In order to solve the traditional optimization algorithm in the complex constraints there are convergence difficulties， global search can be poor， this paper through the A* algorithm to improve the structure， the establishment of a modular optimization algorithm， the subway truck express intermodal task analysis modeling， combined with a variety of constraints of the improvement of the objective function of the task of the case study analysis. The results of the case study show that the method proposed in this paper has a significant improvement compared with other optimization algorithms in the subway truck express intermodal transport task， the shortest logistics transportation distance after optimization is reduced by 13.21% on average， the logistics order completion time is shortened by 11.73% on average， and the algorithm running time is shortened by 24.79% on average.

When detecting uneven deformation of highway subgrade and pavement， the anchor box size of RPN is fixed and relies on rounding operations， resulting in inaccurate alignment between candidate boxes and real deformation areas， especially with poor adaptability to multi-scale deformation. Therefore， a method for detecting uneven deformation of highway subgrade and pavement based on Faster R-CNN algorithm is proposed. Construct a deformation detection network structure based on the Faster R-CNN algorithm， and introduce SE attention mechanism in the feature extraction network module to adaptively adjust the feature channel weights， thereby more accurately capturing the subtle features of uneven deformation and generating a feature map of uneven deformation of highway subgrade and pavement； The regional recommendation network module is based on the uneven deformation feature map of highway subgrade and pavement， and generates a fixed size and more accurate candidate region feature map of uneven deformation of highway subgrade and pavement by introducing multi-scale algorithms； The object detection network module uses ROI Pooling technology to process each candidate area box in the feature map of the uneven deformation candidate area of the highway subgrade and pavement one by one， and classifies them through a fully connected layer to distinguish different types of deformation， thereby achieving uneven deformation detection of the highway subgrade and pavement. The experimental results show that this method exhibits excellent feature representation ability， which can keenly capture the subtle features of uneven deformation of highway subgrade and pavement， and accurately enhance the key information of uneven deformation features； And it has demonstrated extremely high accuracy in the task of detecting uneven deformation of highway subgrade and pavement， with the output of various types of deformation quantities basically matching the actual quantity， indicating that its detection effect is good and its reliability is stronger.

The different routes traffic flow was calculated and the travel cost function of the expressway was established through analyzing the operation expenditure of expressway and the travel choice willingness of truck drivers. The function is a two-level optimization model， and the upper layer planning selects the idea of road network managers pursuing maximizing the surplus of the network users and takes the financial indexes of the road operating enterprises as the constraints condition， the lower layer planning chooses the user equilibrium model under elastic demand to describe the travel choice behavior of road users. The genetic simulated annealing algorithm was used to solve the model. Finally， using the example verifies that the optimization scheme proposed by the thesis can improve the truck flow of the highway by 20.67%， and the toll revenue is improved by 5.88%， and the truck toll fee is reduced by 135600 yuan/day. It shows that the differentiated optimization of truck charging scheme researched in the thesis has a good application effect in practical implementation.

Aiming at the problems of poor cross-view adaptability and insufficient model training stability existing in current gait recognition technologies， a dual-channel spatiotemporal convolutional generative adversarial network （GAN） model that incorporates style encoding was proposed. The model employs a dual-channel spatiotemporal convolutional network to extract complex spatiotemporal and gait features from gait images， while a style encoder is introduced to minimize the semantic discrepancies between the generated images and the original ones， thereby enhancing the stability of the generator. Experimental results on the CASIA-B dataset demonstrate that， compared to GaitGAN， the proposed model achieves recognition rate improvements of 23%， 28%， and 22% at three representative gait angles of 54°， 90°， and 126°， respectively. Additionally， the method shows strong applicability on the OU-MVLP large-scale cross-view gait database， achieving an average recognition accuracy of 89.1%， significantly improving cross-view gait recognition performance.

This paper introduces DEBI-HPA， an adaptive container auto-scaling method for cloud services that integrates Bi-directional Long Short-Term Memory （BiLSTM）， extreme value theory， and dynamic utilization ratios. DEBI-HPA dynamically adjusts the number of Pods based on workload changes， accurately estimates extreme load levels， and adaptively sets target utilization ratios， thereby enhancing service quality， resource utilization， and cost efficiency. It employs predictive scaling during low-load periods to anticipate growth and reactive scaling under high loads to ensure efficient resource allocation. Experimental results demonstrate that DEBI-HPA outperforms Kubernetes' native Horizontal Pod Autoscaler （HPA） by increasing resource utilization by up to 30% and reducing costs by up to 47%. Compared to other predictive scaling methods like ARIMA-HPA and LSTM-HPA， DEBI-HPA improves resource utilization by approximately 18% and lowers costs by about 37%. Additionally， DEBI-HPA achieves effective scaling with common prediction models， reducing dependency on model accuracy. This method offers a novel and robust solution for container auto-scaling in cloud environments.

In view of the problems of lack of edge feature information and insufficient multi-level feature fusion in the existing amouflage object detection（COD） methods， a novel camouflaged object detection network MSPEFI-Net based on mixed-scale perception and edge feature interaction was proposed. MSPEFI-Net consists of a mixed-scale perception module（MSP），an edge feature interaction module（EFI）and a global feature aggregation module（GFA）.MSP extracts different scale features with different convolutional kernels，and fuses cross-scale features to achieve mixed-scale perception，so that MSPEFI-Net can effectively expand the receptive field and fully mine the local detail features of the object to ensure the integrity of feature information.EFI can interact and integrate high-level semantic information with some low-level detailed information to mine and supplement edge semantic information.GFA aggregates cross-level features from context local information and edge information to effectively integrate camouflage object features and its edge features，and generates a full prediction plot. Experimental results prove that the S_{\alpha }、F_{\beta }^w、F_{\beta }、E_{\phi } and \mathrm{M}\mathrm{A}\mathrm{E} of MSPEFI-Net on the public datasets CAMO，CHAMELEON，COD10K and NC4K are superior to 12 comparison methods and show better detection performance of MSPEFI-Net.Ablation experiments also verify the effectiveness of each module to improve the performance of MSPEFI-Net.

In response to the issue of low computational performance of edge computing systems when dealing with discrete-continuous hybrid decision-making problems in vehicular network edge computing scenarios， a distributed multi-stage decision-making strategy for dynamic offloading of vehicular network edge computing tasks based on deep reinforcement learning is proposed. By dividing the task processing procedure into two sub-problems： offloading decisions and resource allocation， an algorithm framework capable of handling both discrete and continuous action domains was designed based on the D3DQN network and TD3 network. In order to further enhance effective search in the action space and improve algorithm performance， an optimization and dynamic priority update mechanism for search in the action space was designed. Finally， based on the above algorithm framework and optimization mechanisms， a multi-stage decision-making algorithm for dynamic offloading of vehicle network edge computing tasks was developed and implemented. This algorithm can effectively solve discrete-continuous hybrid decision-making problems in vehicle network edge computing scenarios， utilize edge computing system resources， and ensure that vehicles can complete the offloading of computing tasks with minimal delay and energy consumption， according to the current network status and task size. Simulation results show that， compared with baseline offloading methods， the proposed method has faster convergence and lower latency energy consumption. It can make better use of the computing resources of the edge system to provide computing services for vehicle edge task requests， thus further enhancing the quality of service for users.

Three-dimensional scene reconstruction from a set of two-dimensional images remains a significant and challenging research task in computer vision. Existing algorithms struggle to effectively model the relationships between images captured at different times， resulting in poor rendering quality for large-scale environments. To address this challenge， this paper proposes a neural surfels fusion splatting algorithm for real-time scene reconstruction. The algorithm progressively builds and updates scene models from streaming input data， incorporating neural surfel representations based on 2D Gaussian primitives and a fusion mechanism based on Gated Recurrent Units （GRU）. First， the algorithm achieves perspective-correct rendering by integrating Gaussian surfel splatting into the rendering pipeline and employing explicit ray-splat intersections， thereby enhancing depth consistency and geometric fidelity across multiple viewpoints. Then， to further improve the quality of reconstructed surfaces， the method combines depth pulling and normal consistency regularization， promoting smoother surface reconstruction and facilitating high-quality mesh extraction. The algorithm's performance was evaluated on two large-scale indoor/outdoor scene reconstruction datasets： DeepBlending and Tanks&Temples. Extensive experimental results demonstrate that the algorithm outperforms existing methods in both quantitative and qualitative assessments.

The dynamic obstacles during the inspection process of intelligent robots result in discontinuous accessibility conditions for the inspection path， leading to abrupt changes in the feasible domain of the path and difficulty in obtaining feasible solutions for reconstructing the path， thereby reducing the smoothness of turning in the inspection path planning. To this end， an intelligent robot adaptive inspection path grid map neighborhood search algorithm is proposed. Adopting the grid map method to model the inspection environment， and defining the feasible path domain through the boundary condition processing mechanism. Introducing a turning cost function to improve the A^* algorithm， using a grid map feasible region as the inspection environment model， to generate a preliminary smooth inspection path for turns. Aiming at the failure problem caused by path mutations in dynamic environments， a feasible region dynamic reconstruction method for inspection paths based on adaptive large-scale neighborhood search is proposed. The destruction operator is used to remove some inspection tasks and generate incomplete paths， and the repair operator is introduced to reorganize inspection tasks. Combined with the variable neighborhood descent search strategy， the solution space is deeply explored. The local optimization of the preliminary global inspection path is achieved through the adaptive neighborhood operator scoring and selection mechanism. The experimental results show that the algorithm can successfully avoid dynamic and static obstacles in grid maps and achieve intelligent robot inspection path planning. The inspection path length is relatively short， and the path optimization degree， turning smoothness coefficient， global search ability， and environmental adaptability scores are 95.24%， 0.917， 0.764， and 91.45， respectively.

Aiming at the problem of path conflicts among multiple robots running in the same environment during mobile robot cluster operations， which can lead to collisions among robots， this paper studies a trajectory planning algorithm for mobile robot clusters that integrates graph neural networks. Construct a kinematic model of a mobile robot to describe its motion characteristics； Using a fusion graph neural network-based mobile robot cluster operating environment feature extraction model， multi-dimensional features of the mobile robot cluster operating environment are extracted to comprehensively understand environmental information； Combining the artificial potential field method， based on the extracted features， the destination is set as a gravitational source， and the direction of motion that attracts the robot is towards the destination； Set obstacles as repulsive sources， control robots to avoid obstacles， and determine the direction and speed of each robot's movement based on the resultant force. Plan collision avoidance trajectories for mobile robot clusters during operation. The experiment shows that the proposed algorithm can effectively extract multidimensional features of the operating environment of mobile robot clusters， and plan collision free and risk index less than 0.1 running trajectories for multiple mobile robots.

A streaming multi-layer network consists of multiple dynamically interacting network layers （such as physical layer， protocol layer， and application layer）， and its data exhibits temporal cross layer coupling. However， using non sliding window methods will not be able to capture the dynamic spatiotemporal coupling effect of data between multi-layer networks， resulting in high DBI values in data mining. Therefore， a deep mining algorithm for anomalous data in downstream multi-layer networks with dynamic time window constraints is proposed. Using sliding windows to divide the streaming multi-layer network data stream into multiple time windows， and within each time window， combining local anomaly factors （LOFs） to mine suspected anomalous data； In response to the cross layer coupling characteristics of abnormal patterns in multi-layer network data， a Markov chain model is introduced to construct a temporal state transition probability matrix and a spatial cross state transition probability matrix， effectively capturing the dynamic spatiotemporal coupling effect characteristics of abnormal data. This feature is input into a C-LSTM hybrid model to achieve deep mining of abnormal data. The experimental results show that the algorithm can use dynamic time windows to mine suspected abnormal data， and the deep mining results of abnormal data can match the actual label results； DBI values below 0.2 in various types of abnormal data mining can more accurately distinguish between various categories of abnormal data and normal data.

To understand the mechanism of the different wettability of surfaces on the spreading behavior and evaporation process of attached oil film， which effectively further improves the problem of wet wall of fuel spray in compression-ignition engines， various wettability experimental surfaces were prepared using a surface wettability modification technique， and the spreading behavior and evaporation process of the oil film of n-butanol droplets after impacting the wall were investigated using laser-induced fluorescence. The results showed that the hydrophilicity of the laser-etched surface was substantially increased， resulting in an increase in the spreading area and a decrease in the spreading thickness of the attached oil film. While the chemically etched surface with low surface energy modification significantly enhances the hydrophobicity properties. Which weakens the droplet spreading ability， thus reducing the spreading area and increasing the spreading thickness. In addition， under the same experimental conditions， the oil film evaporation rate was the fastest on the laser-etched surface， while the evaporation time on the smooth surface and the chemically etched surface was increased sequentially. The present study results support critical data for the optimal design of combustion chamber material surfaces for internal combustion engines.