Multi-object tracking is an important branch in the field of computer vision. Owing to the rapid development of computer hardware and deep learning technology, significant progress has been made in deep learning-based multi-object tracking, yielding remarkable results. To promote the research progress in the field of visual multi-object tracking, a comprehensive review of recent innovative outcomes was conducted to discuss the current state of research advancements.On the basis of introducing the background and application scenarios of multi-object tracking, the research progress was discussed in four aspects: tracking by detection,joint detecting and tracking,transformer-based tracking,referring multi-object tracking. Common benchmark datasets and evaluation metrics for multi-tracking algorithms were summarized, and a comparative analysis of the algorithms mentioned was conducted on these datasets. Ultimately, exploring the prospective evolution of deep learning-based visual multi-object tracking, three future research directions were proposed for scholars actively engaged in this field.

The catenary system, which is regarded as a critical component of the high-speed rail traction power supply system, is deemed essential for the normal operation of high-speed trains. It has been demonstrated by previous earthquake disasters that the catenary system is susceptible to varying degrees of damage under seismic effects. The seismic research progress of the catenary system was systematically reviewed from four aspects: the dynamics modeling and inherent dynamic characteristics of the catenary system, the seismic damage characteristics and common types of failures, the seismic response of the catenary system and its influencing factors, and an overview of the current state of earthquake resistance research, which includes a comparative analysis of the seismic design standards and regulations for catenary systems in different countries and regions. By summarizing the relevant research, prospects for future research directions are provided.

For the group decision-making based on generalized grey target with mixed attributes, a group information aggregation method based on an improved power-weighted averaging operator was proposed. This method takes into account the interrelationships among decision-makers and among attributes, reducing the distortion of uncertain information and simplifying the computational process. First, the mixed attribute data are uniformly measured and transformed. Then, the comprehensive weighted G-S(Gini-Simpson) index was calculated by evaluating each expert’s values relative to the target centers of all experts, to determine the objective weight of experts. Next, the differences among experts are further calculated by the comprehensive weighted G-S index. Finally, a novel information aggregation method was constructed based on the proposed improved power-weighted averaging operator to aggregate group decision-making information with mixed attributes. The effectiveness and feasibility of the proposed method are verified by a case analysis.

Low level wind shear is an important factor affecting aircraft flight safety. Based on the observations from a three-dimensional scanning wind lidar at Baiyun Airport, Guangzhou during March 2023, the measurements from the wind lidar were preprocessed firstly. Then, TSSI (two-step identification method for wind shear) was proposed, which combined TDSI (two-dimensional synthetic wind shear identification) method with an adaptive window and the temporal wind shear identification method. The wind shear results recognized by the TSSI and TDSI methods were compared, and the evolutions of wind shear were analyzed. The main conclusions are as follows. Data preprocessing effectively removes isolated points and radial fluctuations observed by wind lidar, and fills in the missing data. The TSSI method is conducive to early warning of wind shear. During the observation period at Baiyun Airport, Guangzhou in March 2023, a total of 25 wind shear processes are identified by the TSSI. Among them, 21 cases are warned ahead of the TDSI, with an average warning time of 3~5 minutes, and TSSI also has a good alarm recognition function for both time and space dimension wind shear. Most of the identified wind shear processes occur around noon (e.g. 11:00-15:00) and last for about 15 minutes. The wind shear position is greatly influenced by the background wind field. The TSSI method proposed in this study can identify low-level wind shear earlier and more comprehensively, which is helpful to improve the accuracy of wind shear warning and provide guarantees for aircraft flight safety.

Located in the south of the Central Asian orogenic belt, the southern Beishan belt in Gansu Province is a key area for studying the tectonic evolution of the Central Asian orogenic belt. Its late Paleozoic tectonic setting has been controversial for a long time. In order to further explore the late Paleozoic tectonic evolution of the southern Beishan belt,the geochronology and geochemical characteristics of the Changshan monzogranite body were analyzed. The analysis results show that the LA-ICP-MS zircon U-Ph weighted average age of the Changshan monzogranite is (291.1±1.5) Ma, and the emplacement of the plutons occurred in the early Permian. Geochemical datas show that the plutons are high potassium calc-alkaline and peraluminous series rocks. The results show that SiO₂ ranges from 72.07% to 72.94%, K₂O ranges from 4.93% to 5.10%, and the contents of K₂O>Na₂O, A1₂O₃ ranges from 13.52% to 13.97%. The curves of chondrite-normalized REE are obviously right inclined, and the LREE are relatively enriched (LREE/HREE are 10.96~14.98), δEu are 0.78~0.92, with weak negative Eu anomaly. Trace elements are relatively enriched in LILE (large ion lithophile elements), depleted HFSE (high field strength elements), and significantly depleted in high field strength Elements Nb, Sm, Y. According to the regional tectonic setting, petrological and geochemical characteristics, the Changshan adamellite plutons are considered to be the product by post-collisional magmatic activity, reflecting the completion of the collision collage on the southern margin of the Central Asian orogenic belt in the early Permian.

The two important characteristics of microseismic are: tiny and shear rupture. The resulting monitoring characteristics are significantly different from those used to monitor natural earthquakes and artificial seismic exploration sources. Microseismic and its monitoring characteristics are the cornerstone of the development, application, and judgment of microseismic monitoring methods. First, different monitoring methods were investigated, suggestions were puts forward for their development and scope of application, and the reasons why some methods have not improved much were exploved. Among them, the most important ones are: when the number of microseismic, positive and negative initial motion, and signal-to-noise ratio are not easy known, it is necessary to conduct large-scale trial calculations and statistically investigate the combination of focal mechanisms with a high probability, so as to complete reasonable migration stacking. Mathematical statistics in the denoising should be used throughout all steps of detection, and so on. From the perspective of probability and mathematical statistics, following the characteristics of microseismic and its monitoring, the results show that microseismic monitoring has to be based on the fact of low signal-to-noise ratio, summarizes and improves the principle and denoising of VS(vector scanning). In the process of VS processing and interpretation automation, a large number of mathematical statistics are implemented to confirm the noise coherence parameters and analyze the microseismic activity. It makes up also for the defect that the vertical accuracy of ground monitoring is poor and cannot confirm the vertical height of the stimulation rock volume. VS has formed a relatively complete ground monitoring system after more than 20 years of research and development. Probability and mathematical statistics are important concepts and tools to ensure the success of the development and application of microseismic monitoring methods.

In order to improve the accuracy of subtle fault identification, an artificial intelligence subtle fault prediction method based on the seismic data was developed. By making sample labels based on subtle fault interpretation results, a sample label library based on interpretation results was built. The subtle fault modeling and forward methods for subtle faults were developed, and a label library based on model forward was built. The special neural network for identifying subtle faults was developed, which can directly generate attribute data for subtle faults. In addition, the seismic preprocessing approaches such as removing strong seismic events and structure oriented smoothing filtering were added to improve the original seismic data. Multi-attribute fusion based on principal component was used to reflect multi-scale faults. Finally, the prediction results were verified through three steps, forming the subtle fault prediction workflow with artificial intelligence. This study demonstrates good application in the Daniudi gas field in the Ordos Basin. Compared with conventional attributes, the number of subtle fault identification has increased by 30%, and the resolution and continuity of the subtle faults have significantly improved. The prediction results are consistent with seismic data, well data, and conventional seismic attributes. Based on the subtle faults identification, the new understandings of regional structure are revealed: the dominant orientation of subtle faults is northwest, the most subtle faults are relatively concentrated in the western and northeastern parts of the survey, and the fractures between faults are also very dense. Some subtle faults are distributed along the boundary of the high-quality reservoir, perhaps related to the development of reservoirs. The prediction results contribute to evaluating high-quality reservoirs in the Lower Paleozoic and well deployment, and have application prospects for similar areas.

The deep-sea is rich in polymetallic nodules resources that are promising for mining, but the process of mining poses a number of environmental problems, in particular the migratory diffusion of plumes formed by sediments disturbance. To evaluate the environmental impact of sediment setting behavior during polymetallic nodule mining, seafloor surface sediments and seawater from the Beijing Pioneer polymetallic nodule mining area in the northwestern Pacific Ocean were employed. Sedimentation experiments were conducted to investigate the deposition characteristics of the suspended sediments. The results indicate that the deep-sea sediments are primarily composed of viscous particles, with median particles sizes ranging from 2.57 to 4.47 μm, and maximum particle size of 66.9 μm. The relationship between settling velocity and particle size was analyzed, showing that the settling velocity of viscous particles increased due to flocculation effects. After 5 hours of sedimentation, the median particle size of suspended sediments decreased to below 0.1 μm, and the maximum optical density has observed at 14 minutes after the onset of sedimentation. A positive correlation is observed between the mass concentration and optical density of the suspension. By fitting the temporal variation of sediment mass concentration, the settling pattern is determined. The mass concentration reached its peak 14 minutes after the onset of settling, and decreased to 2.46% of the initial value after 5 hours, indicating that 97.54% of the total mass has settled. These findings provide supporting data for environmental impact assessments related to plume behavior during deep-sea polymetallic nodule mining.

To investigate the mechanism of lncRNA (long chain non coding RNA) Rpph1 activating cytopyrosis through AMP-AMPK(activated protein kinase)/Nrf2(nuclear factor E2 related factor 2) signaling pathway, then to promote podocyte injury in DN (diabetes nephropathy). HGPC(Human glomerular podocytes) were cultured in vitro and randomly divided into control group, model group, lncRNA Rpph1 over-expression group, low-expression group, and empty vector group. HGPC were incubated with 5 mmol/L D-glucose as control group, while the other three groups were incubated with 30 mmol/L D-glucose to establish DN model. Liposome transfection method was used to co-incubate stable plasmids carrying Rpph1 over-expression, low-expression, and empty vector with HGPC. qRT-PCR was used to detect lncRNA Rpph1 expression, Western blot was used to detect p-AMPK/AMPK and Nrf2 proteins, as well as the expression levels of cytopyrosis related proteins including NLRP3(Nod like receptor thermal domain associated protein 3), caspase-1, and GSDMD-N. MTT assay was used to detect cell survival rate. Flow cytometry was used to detecte apoptosis rate. Compared with control group, the expression level of lncRNA Rpph1 in model group significantly increased (P<0.05). The expression levels of p-AMPK/AMPK, Nrf2, NLRP3, caspase-1, and GSDMD-N proteins significantly increased in model group (P<0.05). The survival rate of model group cells significantly reduced, while apoptosis rate increased in model group (P<0.05). Compared with model group and empty vector group, lncRNA Rpph1, p-AMPK/AMPK, Nrf2,NLRP3, caspase-1, and GSDMD-N proteins in lncRNA Rpph1 over-expression group significantly increased, and cell survival rate significantly reduced, apoptosis rate increased (P<0.05). The expression levels of lncRNA Rpph1, p-AMPK/AMPK, Nrf2, NLRP3, caspase-1, and GSDMD-N proteins significantly decreased in lncRNA Rpph1 low-expression group, and cell survival rate significantly increased, apoptosis rate reduced (P<0.05). In all, High expression of lncRNA Rpph1 in DN may activate cytopyrosis and promote podocyte injury by AMPK/Nrf2 signaling pathway.

The effects of different diversion schemes on air flow and heat transfer in a countercurrent drying tower were studied by numerical simulation of maize drying process based on porous medium model. The influence of the angle box arrangement on the temperature field and velocity field in the tower was studied by numerical simulation and experiment. The results show that the cross arrangement of corner boxes can improve heat and mass transfer efficiency, reduce heat loss and solve the problem of uneven drying. At the same time, the increase of the inlet speed can also improve the uneven temperature distribution and improve the drying effect. It can be seen that the two key factors, the arrangement of the corner box and the inlet speed, should be fully considered in the design of the counter-current drying tower for corn drying. The optimization of drying tower structure can improve the overall drying efficiency and corn quality. The conclusion of this paper provides a useful reference for corn drying industry to reduce the cost and improve the quality of corn.

Based on the actual needs of large drilling spacing, and frequent long-distance relocation of drilling rigs in coal mine underground construction, as well as the need for remote control function, a research idea of compact layout and modular design of each unit of rubber wheel directional drilling rig was adopted to solve key technical development problems, such as independent walking rubber wheel chassis, multi power output units, hydraulic system, and electrical control system. After whole machine was processed and assembled, load testing was simulated on the drilling performance test bench of the National Safety Production Inspection Center. The experiment shows that all functional parameters of the drilling rig meet the design requirements. The ZDY3500JDK rubber tyre directional drilling rig developed for coal mine meets demand for long-distance autonomous relocation underground, greatly improving the production and transportation efficiency of mine, and providing reliable equipment support for drilling construction operations in large and medium-sized mines with trackless rubber tyre transportation.

Underground coal mining faces hazards like gas explosions, coal dust explosions, and fires, underscoring the need for safety robotics based on 3D reconstruction. While essential for precise navigation and detection in complex environments, traditional methods fall short in data quality, accuracy, and cost. To address this, a 3D reconstruction method called 2DGS-DbTrans for underground coal mine tunnels was proposed, which is based on pure vision-based 3D reconstruction technology. To improve image resolution, a Transformer module was designed to enhance the input images, consisting of two core components: the multi-head depth convolutional axis attention mechanism and the deep convolutional gated network. In the processing workflow, sparse point clouds were first generated using Colmap, and the underground mine tunnel environment was represented by 2D Gaussian surfaces, where each Gaussian surface contains the coordinates, color, size, and orientation information of the objects. In addition, two loss functions were defined: the color loss function and the road smoothness loss function. Experimental results show that the 2DGS-DbTrans method outperforms other methods in 3D reconstruction of underground coal mine tunnels.

In the exploration and exploitation of oil and gas, artificial intelligence models are extensively employed in the prediction of formation pore pressure. Among them, single models tend to encounter problems such as overfitting or unstable prediction outcomes, leaving room for improvement in aspects like prediction accuracy and generalization ability. To enhance the prediction accuracy of formation pore pressure, a CNN-Attn neural network-based formation pore pressure prediction model was established by virtue of deep learning technology. In this research, five types of logging and while-drilling data were optimally selected, and the linear correlation between the data and formation pore pressure was verified using the Pearson correlation coefficient method. Through the optimization of the structure of the one-dimensional CNN, the model can effectively capture the local characteristics of the data and, when combined with the self-attention mechanism, strengthen the model’s ability to capture global dependencies, thereby elevating the model’s expressiveness and comprehension. To validate the prediction accuracy of this model, two wells in the Bayan block were subjected to prediction. The average absolute errors of the prediction results were both less than 1 MPa, the root mean square errors were both less than 1 MPa, the average relative errors were both less than 1.3%, and the determination coefficients were both greater than 0.9, with higher accuracy compared to the BP, CNN, and LSTM models. This model has improved the prediction accuracy of formation pore pressure and provided data support for drilling safety.

Fire flooding is a primary method used to enhance heavy oil recovery, often replacing steam stimulation. However, it encounters challenges such as low sweep efficiency and delayed effective times in heavy oil development. A new fire flooding PGI (pulse gas injection) technology was used to solve these problems. The combustion sweep effect of fire flooding can be improved by adjusting the working system of the gas injector. Based on the geological characteristics of the Hongqian 1 heavy oil reservoir in Xinjiang, the feasibility and mechanism of PGI were elucidated through numerical simulations. The influence of geological and engineering factors was studied on the development effect of PGI, and the consequences are applied to the fire flooding field. The combustion front can be controlled by the peak-valley value stage of PGI, accelerating the uniform movement of the combustion front and improving the oil displacement efficiency. Specifically, PGI can reduce the effective time by approximately 600 days and increase combustion sweep by more than 30%. This technique is particularly suitable for medium heterogeneous reservoirs with a vertical permeability contrast of less than 15 and crude oil viscosities ranging from 2 000 to 10 000 mPa·s. The optimal pulse amplitude ranges between 1.5 and 2, with a recommended step length of 30 days. When applied to the Hongqian 1 fire flooding industrial area in Xinjiang, daily oil production increased by 0.5 to 2.8 t for the well group, and the air-oil ratio decreased by 34%. PGI can achieve a better production increase effect for field production.

To investigate the spontaneous imbibition mechanism of shale reservoirs to fracturing fluid, the marine shale of Longmaxi Formation in Sichuan Basin and the continental shale of Yanchang Formation in Ordos Basin were taken as the research object, and the actual fracturing fluid was taken as the imbibition fluid. The spontaneous imbibition characteristics and control mechanism of marine and continental shales on fracturing fluid were studied by the spontaneous imbibition test system and the characterization methods of mineral composition and microscopic pore. The results show that the spontaneous imbibition curves of the two groups of shale increase rapidly and then decrease slowly with the increase of imbibition time, and the decrease of imbibition is mainly due to the decrease of shale weight caused by the dissolution of soluble minerals. TOC (total organic carbon) and quartz content of the two groups of shale exhibit a negative correlation with the maximum imbibition, while clay minerals, carbonate and pore volume exhibit a positive correlation with the maximum imbibition. Although the clay mineral content of Yanchang Formation shale is higher than that of Longmaxi Formation shale, its maximum imbibition is relatively low, which may be due to the more developed pore structure of Longmaxi Formation shale. The adsorption of hydrophilic minerals in shale and the capillary action of microscopic pores are the main driving forces for the spontaneous imbibition of fracturing fluid, and the influence of microscopic pores is stronger than that of hydrophilic minerals. For the flowback problem of fracturing fluid in shale gas wells, a better fracturing fluid ratio scheme can be proposed by adjusting the chemical composition of fracturing fluid and combining the spontaneous imbibition test results of fracturing fluid to meet the actual engineering needs. The research results can provide theoretical references for efficient development of shale gas.

Aiming at the thermoacoustic oscillation in the combustion process of a new generation of gas turbine, several time-domain analysis methods of high frequency data signals were retested and compared by constructing a complex network model. The results show that the two complex network models, node strength and network diameter, can give an earlier warning of thermoacoustic oscillations than the traditional time-domain analysis methods(root mean square and time kurtosis). Coupling prediction effect and data processing time, the node strength is preferred to construct the complex network model. Finally, the method was applied to the experimental data analysis of multi-nozzle micro-mixing burners, and it was found that the characteristic turning point of the method is about 2.3 s ahead of limit cycle analysis and the characteristic point of statistical analysis.

The voltage fluctuations in the high-voltage DC bus of the train traction converter have a significant impact on the output power quality of the traction system. Therefore, it is necessary to improve the response speed of the intermediate stage isolated DC/DC converter to reduce the power coupling between the high-voltage stage and the low-voltage stage. Taking the isolated DC/DC converter as the research object, an unbiased model predictive control and sampling noise suppression strategy was proposed to address its inherent problems of high sensitivity to circuit parameters and susceptibility to sampling noise. Firstly, the operation principle of the dual-bridge series resonant converter and the causes of the steady-state errors were analyzed, and a feedback correction method based on recursive least square algorithm was designed to eliminate the steady-state error. Then, the introduction of noise suppression coefficient reduces the sensitivity of the control variable to the control target through a simple and effective method. Furthermore, the virtual current was utilized in predictive model instead of the actual current sampling value, and it further reduces the system costs. Finally, an experimental platform was built to verify the improvement of the proposed strategy in both steady-state and dynamic performance.

The issue of distributed PV(photovoltaic) integration capacity allocation in distribution networks was addressed, which focuses on the photovoltaic integration capacity configuration based on load-storage coordination optimization. A coordinated regulation model for load-storage systems, incorporating energy storage, dispatchable load, and interruptible load, was first established. Based on this model, the constraints of load-storage regulation capabilities were considered. The optimization model for the configuration of distributed PV integration in the distribution network was developed with the objective of maximizing the capacity of distributed PV and the net investment and operational profit of the distribution network. Simulation results show that through the coordinated regulation of distributed PV with energy storage, dispatchable loads, and interruptible loads, significant improvements in the integration of distributed PV into the grid can be achieved.

To address the issues of slow convergence speed and susceptibility to local optima in ELM (electromagnetism-like mechanism) algorithm for fault location problems in distribution networks, BELM (binary electromagnetism-like mechanism) algorithm was proposed. First, the Sobol sequence was introduced to initialize the population to ensure the quality of the initial population. Second, based on the fitness value, the population was divided into a high-quality population and an ordinary population, and an optimal particle guidance strategy and a local search strategy based on the XOR operation were adopted for these two sub-populations, respectively, the former guides the high-quality particles to the potentially optimal location to accelerate the convergence speed, the latter performs global exploration and enriches the diversity of the population by exchanging information with the elite particles. Finally, the search efficiency of the algorithm was further improved by the improvement of the combined force calculation and particle movement rules. The simulation results show that compared with other algorithms, the proposed algorithm demonstrates superior accuracy and rapid convergence in locating faults within distribution networks.

To address the issue of fault signal transient characteristics being easily affected by noise, leading to misidentification of feeders in single-phase ground faults within resonant grounded systems, a line selection method was proposed that combines parameter-optimized VMD (variational mode decomposition) and improved D-S (dempster-shafer) evidence theory for fault feature fusion. First, to tackle the challenge of selecting the penalty factor Alpha and decomposition level K parameters in VMD, NRBO (Newton-Raphson-based optimizer) is introduced to adaptively determine these parameters under different noise environments. Next, three fault features—kurtosis, polarity, and transient energy—was fused, and the Jousselme distance was incorporated into D-S evidence theory to prevent conflicting results caused by noise interference on fault features. This approach provides the probability of fault occurrence on each feeder, allowing for accurate fault feeder identification. Finally, Simulink simulation results demonstrate that the method can accurately identify the fault feeder across various noise levels and fault scenarios. Compared to other parameter optimization algorithms, it achieves faster convergence, and the introduction of Jousselme distance further enhances the reliability of fault feeder identification.

In order to improve the prediction accuracy of lithology affected by imbalanced geological data, an ECA-MSCB ResNet model was proposed. The model integrates ECA (efficient channel attention) and MSCB (multi-scale convolutional block) into the traditional ResNet architecture to achieve efficient extraction and representation of lithological data features. For the issue of imbalanced lithology categories, prior probability-balanced logit bias was introduced during model training, and the focal loss function was modified to enhance the recognition of minority lithology classes. Experimental results show that the model based on ECA-MSCB ResNet performs well on the imbalanced geological lithology dataset, achieving an average prediction accuracy improvement of approximately 7.45% compared to the original ResNet model and 27.33% compared to the random forest method. Notably, the recognition of minority lithology classes improves by an average of 17.9%. Furthermore, the model demonstrates strong lithology classification ability on public datasets, achieving an F₁-score of 75.77%. In addition, the recognition accuracy of the proposed model outperformed both traditional and mainstream methods. The ECA-MSCB ResNet method holds significant application value in the field of imbalanced geological lithology recognition.

Dual-mode communication based on wireless and PLC (power line communication) can complement each other, and is widely used in smart metering and power Internet of Things. For the application of OFDM (orthogonal frequency division multiplexing) dual-mode communication system in new energy low-latency service access and other applications, an OFDM subcarrier diversity combination and power adaptive allocation algorithm considering the non-ideal channel estimation was proposed. Firstly, an adaptive optimal power allocation model was established with the constraints of service data volume and transmission power and the goal of minimizing the total delay. Then, on the basis of diversity grouping, whale optimization algorithm and threshold recovery were used to optimize power allocation, to achieve the compromise between algorithm complexity and performance. The simulation results show that the proposed algorithm can reduce the average transmission delay and stability while satisfying constraints such as rate, providing enhanced performance guarantees for real-time data acquisition in applications such as distribution equipment status monitoring.

In order to solve constrained optimization problems, a dingo optimization algorithm with ε constrained method and crisscross strategy (εCDOA) was proposed. The algorithm first introduces the crisscross strategy in dingo optimization algorithm after all individuals select one hunting strategy and update their positions, so as to improve the global and local search capabilities of the obtained algorithm, which also help the algorithm jump out of the local optimum. Then, according to ε constrained method, the equality constraints were transformed into the inequality constraints, and the ε level comparison method was used instead of fitness value comparison to evaluate the qualities of the dingoes. Finally, based on the individuals’ constraint violations, the population is divided into two subgroups according to adaptive ε values. The individuals’ survival rates were calculated using the survival strategy of each subgroup, and the individuals with low survival rates were updated. The results of numerical experiments on 19 standard constrained optimization problems in CEC 2006 show that algorithm εCDOA has better optimization performance than four comparative algorithms such as dingo optimization algorithm with ε constrained method. For three classical engineering design problems, the design schemes given by algorithm εCDOA are obviously better than those given by other algorithms.

The growing sophistication of deepfake speech poses significant security threats to ASV(automatic speaker verification) systems. Current anti-spoofing models based on CNNs(convolutional neural networks) are constrained by inadequate global feature extraction and limited generalization capability against unseen spoofing attacks. To address these challenges, a novel network architecture integrating CT-DSCNet(channel-temporal attention mechanisms with depthwise separable convolutions) was proposed. Building upon the RawNet2 framework, the developed model incorporates dual-domain attention modules to enhance discriminative feature representation while suppressing irrelevant acoustic artifacts. Furthermore, depthwise separable convolutional residual blocks were strategically implemented to optimize computational efficiency and real-time processing capabilities. Comprehensive evaluations were conducted across three benchmark datasets: ASVspoof2019 LA, ASVspoof2021 DF, and FMFCC-A. Experimental results demonstrate state-of-the-art performance with EER(equal error rate) of 1.53% on ASVspoof2019 LA, representing a 70.58% relative improvement over baseline systems. Notably, the proposed architecture exhibits superior cross-dataset generalization, achieving a 25.35% lower EER on the FMFCC-A evaluation set compared with conventional approaches. These findings validate the effectiveness of the hybrid attention-convolution design in advancing spoofing detection robustness and domain adaptability.

In order to advance the analysis and mining of TCM(traditional Chinese medicine) text data and achieve intelligent extraction and processing of knowledge, the BIO(begin, inside, outside) sequence labeling method, the BiLSTM-CRF model, and manually defined rules were adopted to complete the knowledge extraction task. Utilizing the Py2neo library in Python 3.6 and the Neo4j database, a spleen and stomach disease knowledge graph was constructed based on Neo4j, and a TCM spleen and stomach disease named entity recognition system was developed using the Flask framework. The results show that the BiLSTM-CRF model achieves high performance and good generalization ability on the test set, with accuracy, precision, recall, and F₁ scores of 96.19%, 86.64%, 88.82%, and 87.71%, respectively. The constructed knowledge graph includes eight types of node labels, such as prescriptions or patent medicines, Chinese medicines, and clinical manifestations, as well as ten types of relationships. It supports the querying and discovery of nodes and relationships among Western medical diagnosis, TCM syndromes, and TCM treatment principles for spleen and stomach diseases. It is concluded that the BiLSTM-CRF model demonstrates excellent generalizability in named entity recognition of TCM spleen and stomach disease. It exhibits outstanding performance in handling complex text structures and domain-specific terminology, providing strong support for the research on knowledge extraction and knowledge graph construction in Traditional Chinese Medicine for spleen and stomach diseases.

Bolts are the key to the stable connection of high-altitude equipment, but they are prone to abnormalities such as loosening under the influence of various factors, threatening the safety of the equipment. Currently, bolt detection methods based on deep learning are faced with the problems of class imbalance and label missing. Existing deep-learning-based bolt detection methods suffer from class imbalance and missing labels. A HDWL(historical dynamic weighted loss) model based on semi-supervised pseudo-label learning was proposed. By dynamic weighted orthogonality and class-adaptive fair punishment, the model classification was evaluated with historical data. Adaptive punishment was introduced to prevent overfitting and focus more on hard-to-classify samples, boosting model performance. Experiments showed that the HDWL model achieved significantly higher accuracy than other methods, with advantages in minority-class training and feature focus.

Recently, SSVEP(steady-state visual evoked potential-based BCI(brain-computer interface) researches have achieved significant development. However, the practical application of BCIs are still limited by several factors, one of which is the visual stimulus source. Most SSVEP-BCI systems rely on monitors, which are not portable and thus restrict the practical use in daily life. VR glasses, as wearable and portable devices, can provide realistic and immersive stimulus sources, which do not rely on monitors. Thus, they offer significant potentials for BCI applications. The VR(virtual reality) technology was introduced to display VR-SSVEP visual stimuli in 3D environment and enables subjects to immersively engage in BCI. The performance of 3D and 2D visual stimuli based on VR-SSVEP were compared in this study. The experimental results demonstrate that the performance of 3D visual stimuli is better than that of 2D visual stimuli. The average classification accuracy of 3D stimuli reaches 90.10%, which is 7.08% higher than 2D stimuli. Additionally, a 2-second stimulation duration achieves an optimal information transfer rate. This study confirms that 3D visual stimuli can effectively enhance SSVEP recognition performance, which indicates a practical use of the system and provides a novel approach for applying VR devices to the SSVEP paradigm.

To address the issues of distortion and poor segmentation results in weld defect image segmentation, the crack and porosity welding defect images in the rim production process were taken as the research object. An improved particle swarm optimization algorithm based on simulated annealing is proposed for the three-threshold image segmentation of welding defects. First, a three-dimensional Otsu model is constructed using the grayscale value, average grayscale value, and median grayscale value of the image. Next, an adaptive inertia weight and asymmetric learning factor were introduced and integrated into the SA strategy to enhance the algorithm’s solving efficiency and ability to escape local optima. Finally, the SA-IPSO algorithm was used to optimize the three-dimensional Otsu model to obtain the optimal threshold and corresponding defect segmentation image. Various algorithms and models are employed to segment welding defect images. The results show that for crack and porosity defect images, the proposed improved algorithm outperforms the comparison algorithms in terms of peak signal-to-noise ratio and structural similarity evaluation metrics. The proposed method accelerates algorithm convergence while preventing distortion in segmentation results, thereby improving segmentation accuracy.

Microwave assisted cutter breaking has a strong application prospect, in order to deeply analyse the auxiliary effect of microwave irradiation on cutter breaking, the TBM scale cutter breaking test after microwave irradiation was carried out. Firstly, the rock was irradiated by microwave using different parameters, and the influence law of different microwave parameters on the surface temperature of the rock was studied. After the rock is back to room temperature, the damaged rock was taken as the basis to carry out the TBM scale cutter rotary rock breaking experiment, to study the influence of microwave irradiation time and power on the cutter thrust, cutter torque, rock ballast weight, cutter wear and the specific energy of rock breaking. The results show that: with the increase of microwave irradiation time and irradiation power, the rock surface temperature increases, the rock heating rate increases, the highest temperature of the rock surface is 172.6 ℃. The cutter thrust is fluctuating in the breaking rock, the rock below the cutter is crushed to powder, the cutter side produces block ballast. With the increase of microwave irradiation time and microwave power, the cutter thrust decreases, the disc torque decreases, the weight of the rock ballast increases, the amount of cutter abrasion decreases, and the specific energy of rock breaking decreases. The best microwave irradiation time should be more than 40 s and the microwave power should be more than 7 kW for the test of Chifeng basalt, which provides a certain experimental basis for the microwave-assisted cutter rock-breaking.

Relying on a deep foundation pit project of a metro station in a dual soil-rock stratum, the deformation patterns of the supporting structure and key sections of the foundation pit during different construction stages were obtained through on-site measured data. Based on the presence of adjacent buildings, a 3D numerical model for dual soil-rock deep foundation pits was established. After the excavation and dewatering construction of the deep foundation pit, the deformation and stress of the retaining structure on both the side adjacent to and away from the buildings, surface subsidence, and changes in the axial force of the supports were analyzed. On this basis, a sensitivity analysis was conducted on the influencing parameters of foundation pit deformation and stress, and the variation patterns were summarized and fitted. The study results indicate that the excavation of dual-element deep foundation pits in soil and rock exhibits significant spatial and temporal effects. In the time dimension, this is manifested as rapid development in the lateral displacement of pile bodies and surface settlement in soil layers, while the development rate slows in rock layers. In the spatial dimension, this is manifested as the corner effect of the pit. Changes in spacing and pile diameter essentially alter the overall stiffness of the retaining structure and the magnitude of external soil and water pressure borne by an individual pile. When the pile diameter is less than 1.0 m, the force sensitivity of deformation in the adjacent building side of the foundation pit significantly increases. The properties of rock and soil masses vary greatly at the soil-rock interface in a dual soil-rock formation, where over-excavation, support spacing, and changes in prestress are more pronounced. The research findings can provide valuable insights for similar soil-rock dual-element deep foundation pit engineering projects.

Pipeline leakage is a major cause of urban road collapse accidents. Understanding the evolution process and catastrophic mechanisms of road subsidence is crucial for preventing such safety incidents. Focusing on sewage pipelines in Beijing municipal roads, this study employs DEM-CFD(discrete element method-computational fluid dynamics) coupled flow-solid approach. Microscopic model parameters were calibrated based on laboratory experiments to simulate deformation and cavity evolution in sandy soil layers under various pipeline leakage locations and burial depths. Key parameters, including particle displacement, soil compactness, and medium flow, were analyzed during cavity formation. The results indicate that leakage at the top and middle of the pipeline leads to the formation of a funnel-shaped cavity as water and soil are lost. Without traffic load, the road surface exhibits negligible settlement. By analyzing particle displacement and compactness variations, the soil deformation was divided into stable, loose, and cavity zones, and an elliptical partition model was established for the loose zone. Based on the particle loss rate, the progressive failure process of the soil was classified into three stages: particle migration, rapid loss, and gradual convergence. In terms of cavity formation time, subsidence extent, particle loss rate, and total particle loss, leakage at the pipeline’s middle section yielded the highest values, followed by the top section, with the lowest at the bottom section. However, bottom leakage resulted in the largest loose zone. These findings provide theoretical support for detecting and identifying underground risks associated with urban road collapse disasters.

As a key equipment and a major source of energy consumption in a building, chiller plant, if it fails, it will not only affect the normal operation of the system, but also cause serious energy waste. In order to improve the reliability of chiller system operation. A multi-strategy IDBO(improved dung beetle optimization algorithm) combined with a HKELM(hybrid kernel extreme learning machine) fusion fault diagnosis model was constructed to achieve accurate diagnosis of early faults in chiller systems. The model firstly employs hybrid kernel functions to improve the learning ability and generalization of KELM(kernel-extreme learning machine). Secondly, Bernoulli mapping, adaptive inertia factor, and Levy flight fusion dynamic weight coefficients strategies were used to improve the DBO(dung beetle optimization) algorithm in order to balance the global exploration performance of the DBO algorithm. Finally, the effectiveness of the IDBO algorithm was verified by benchmark functions, and the HKELM hyperparameters are optimized using the IDBO algorithm to construct a data-driven model for early fault diagnosis of chiller units. Through relevant training simulations and experimental validation, the accuracy of the proposed IDBO-HKELM model for early fault diagnosis of chillers is improved to 99.71%, which is an obvious advantage over other algorithms.

The soil covering of urban pipe jacking is shallow, and the surrounding pipelines are densely distributed and close to pipe jacking. In the process of pipe jacking construction, various factors may lead to gas pipeline fracture and gas leakage, resulting in gas, mainly gas, spreading in the soil and entering the pipe jacking, which brings potential great harm to the safety of pipe jacking construction. Based on the main-pipeline project of Maojiawan sewage treatment plant, the diffusion mechanism of gas in soil and pipe jacking and the construction ventilation technology were studied by theoretical analysis and numerical calculation. The results show as follows. With the increase of time, the diffusion flux of gas in soil first increases and then becomes stable. The farther away the pipe jacking is from the leakage hole, the lower the gas emission amount on the face, and the longer the time required for gas diffusion to the pipe jacking. The gas concentration on the opposite side of the air duct near the face of the pipe jacking is relatively high, and the gas is easy to gather. With the increase of air supply volume in the air duct, strong air flow is generated near the face, and the gas concentration near the face continues to decrease, but the rate of gas concentration reduction decreases. When the air supply volume in the air duct is 131 m³/min, the highest gas concentration in the pipe jacking is 4.57%, which is lower than the lower limit of gas explosion. With the increase of the distance between the air duct tuyere and the face, the maximum gas concentration in the pipe jacking first decreases and then increases. When the distance between the air duct tuyere and the face is 4 m, the maximum gas concentration in the pipe jacking reaches the minimum value of 4.41%. Based on this, a more reasonable ventilation parameter for pipe jacking construction is proposed.

To quantitatively evaluate the protective effect of the mangrove wave dissipating zone on the near coastline, based on the demonstration area of the Taishan Town Bay Mangrove Project in Guangdong Province, a combination method of on-site observation, theoretical analysis, model experiments, and numerical analysis was used to study the interaction process between the mangrove forests and waves. The verification and analysis of the wave dissipating characteristics of the mangrove forests were carried out from the perspectives of directional statistical characteristics and annual extreme value statistical characteristics, thus achieving a summary of the wave dissipating mechanism and statistical characteristics of the wave dissipating effect of the mangrove forests. The research results indicate that the basic theoretical system of vegetation wave dissipation based on the rigid column group flow theory has strong applicability to mangrove vegetation types, and the relevant wave dissipation theories can be adapted and integrated with numerical calculation models based on wave energy spectra. The simulation of vegetation wave dissipation process was carried out using physical model experiments, and the results were compared with the numerical simulation results using vegetation wave dissipation empirical formula, fully demonstrating the reliability of the vegetation wave dissipation empirical formula adopted in this study. The numerical simulation results show that mangrove forests can significantly reduce the frequency of large waves in various directions from a statistical perspective. The wave dissipation effect of mangrove forests increases significantly with the increase of wave height in different recurrence periods in the direction of strong waves. In the case of inconsistent normal wave direction and strong wave direction, the wave dissipation rate does not show a significant increase trend with the increase of wave height in different recurrence periods.

The flow pattern and its characteristics in the flood discharge stilling basin with step-down floors play dominant roles in effectively dissipating the discharged flooding water energy, guaranteeing the safety of the hydraulic structure and its downstream river bank stability as well as navigable flow conditions. A three-dimensional numerical model for water-air two-phase flows with strong nonlinearity, involving large free surface deformation and complicated solid-wall boundary conditions was established, and was applied to analyze flow characteristics in Xiangjiaba flood discharge stilling Basin with step-down floors. RANS(Reynolds-averaged Navier-Stokes) equations, Realizable k-ε turbulence model and VOF free surface tracking method were used in the developed numerical model. The model was firstly validated through comparisons of the simulated results and measured data for the flow patterns as well as the time-averaged pressure at the dam surface induced by the flood discharge jet. It is then applied to simulate the 3-D flow structure and fluctuation characteristics in the stilling basin with step-down floors induced by flood discharge from dam under the same discharge amount but different flood discharge scenarios. The results show that, for the flood discharge stilling basin with high and low step-down floors,under the same flood discharge condition, different scenarios of dam discharge gate open mode significantly affects the three-dimensional structure and characteristics of discharged flow in the dissipative pool induced by flood jets. The combined discharge from dam surface and middle holes results in the strong turbulent mixing of submerged multi-layer and multi-jet flows, effectively dissipating the energy of the discharged water and reducing the water surface fluctuations. The established numerical model can better reproduce the high-speed flooding jets and its turbulent motion in the dissipative pool associated with flood water discharge.

With the rapid development of urban rail transit in China, a large amount of muck is generated during shield tunneling, and its transportation and landfill will cause serious environmental issues. Based on the Suzhou Metro Line 8 section 6, full life cycle assessment method was used to analyze and compare the carbon emissions of conventional disposal and resource utilization of shield muck. Research results indicate that the total amount of muck produced during shield tunneling is 2.64×10⁵ m³ in this project. When traditional burial treatment is carried out for shield muck, the carbon emissions throughout its entire life cycle will reach 3.00×10⁶ carbon emission equivalents. In this case, 5.28×10⁴ m² of land with a 5-meter landfill depth will be occupied by shield muck. If shield muck and industry-byproducts are used for preparing synchronous grouting materials, the 28-day strength of the grouting material can reach 2.5 MPa. Grouting raw material cost by 467 thousand yuan/km will be reduced, and about 4.6×10⁵ carbon emission equivalents per kilometer will be reduced. The findings provide reference for optimizing the resource utilization of shield muck.

The stability of double-limb high pier cannot be ignored in the process of construction. In order to ensure the safety of construction, finite element models were established for three construction stages: bare high pier, large cantilever, and completed bridge to study the stability of double-limb high pier, based on the Miaoluhe bridge of Zhengzhou-Luoyang Expressway. The buckling modes and stability coefficient of double-limb high pier were obtained, and the nonlinear stability analysis of the most unfavorable construction stage was carried out. Finally, the influencing factors on stability such as the alignment deviation, the hole defects, the pier height and the number of crossbeams were analyzed. The results show that under the three construction stages, the double-limb high pier are longitudinal instability, and the stability coefficient of the most unfavorable conditions are 70.092, 33.513, and 55.034.After considering geometric nonlinearity and geometric and material double nonlinearity, the stability of the large cantilever stage has decreased by 15% and 57%, compared to linear analysis. The alignment deviation and the hole defects occurring in the climbing formwork construction of double-limb high piers have an impact on the stability. Therefore, it is necessary to control alignment deviations and repair holes in time. As the height of the pier increases, stability decreases continuously. When one crossbeam is installed between the two limbs, stability improves by 140%. However, further increasing the number of crossbeams does not significantly improve stability. Therefore setting one crossbeam within the range of 50~70 m pier height is the best.

The existing CFRP(carbon fiber reinforced plastic) plate clip-type anchorage exhibits arching deformation during the tensioning process due to compressive forces on the inside wall of the anchor cup. This causes “voids” at the contact interface between the anchor cup and the clip, leading to an uneven distribution of lateral forces within the CFRP plate anchorage section. The sides of the CFRP plate are prone to cracking failure due to stress concentration. An optimized design for arching clip-type CFRP plate anchors was proposed to address this issue. Finite element numerical simulations and static loading tension tests were conducted on clip-type anchors with varying arch heights. The findings show that the primary failure mode of conventional CFRP plate clip-type anchors is initial cracking followed by fragmentation, with an anchoring efficiency of only 68.75%. When the arch height is low, the voids in the anchor cup are not adequately filled, resulting in lower compressive stress and an anchoring efficiency of 56.67%. When the arch height is too high(0.5 mm), stress concentration occurs in the middle section of the CFRP plate anchorage, which increases the anchoring efficiency to 81.25%, but this is still suboptimal. Notably, when the arch height is set to 0.25 mm, anchoring efficiency increases to 90.83%, and the failure mode shifts to explosive failure, indicating that the CFRP material has been fully utilized. The rational adjustment of the clip’s arch height effectively prevents cracking failures in the CFRP plate anchorage due to voids, demonstrating the significant engineering application value of this research.

In order to meet the demand for segment floating prediction in shield construction and the problem of insufficient training data for deep learning models, a set of shield segment floating prediction model was proposed by combining the tunneling mechanism of the shield machine with the process of segment floating.The numerical simulation software was used to simulate the process of segment floating of the shield structure, and using the large amount of numerical simulation data and the engineering field data for the deep learning training, so as to realize the data enhancement of the segment floating prediction model. The prediction model consists of the tube sheet floating process. The prediction model consists of a segment floating prediction model and two auxiliary models, which consider the interaction of active control and passive response parameters. Finally, a typical case study was carried out based on the shield section of the Beijing East 6th Ring Road Rehabilitation Project, and the results show that the prediction accuracy of the model is controlled within 4 mm, which meets the project requirements. The grouting parameters of the shield tail have the greatest influence on the model performance, followed by the digging parameters, and the shield attitude parameters have the smallest influence. Moreover, the training data of the segment floating based on the numerical simulation data can improve the prediction accuracy of the prediction model by 30%, which proves the effectiveness of the data enhancement method. The effectiveness of the data enhancement method is demonstrated. The data enhancement method based on numerical simulation data proposed in the article provides a new idea for the training and optimization of similar deep learning models.

The external human-machine interface is used to enhance communication between autonomous vehicles and road users like pedestrians and cyclists, thus traffic safety and user experience are improved. The recognizability of the external interface is considered the foundation for ensuring effective and understandable signal functions, and it is explored to ensure pedestrians crossing safety. The form of information expression, interface location, and the speed of autonomous vehicles were considered as independent variables, and eye-tracking technology was used to collect eye movement and behavioral data. The identifiability of the interface was evaluated through repeated measures analysis of variance and logistic regression. The results show that the form of interface information, location, and vehicle speed significantly affect identifiability. The light band has the best identifiability; higher recognition efficiency is observed when the vehicle is traveling at a low speed; and the highest recognition efficiency is found when the interface is at P3, while the lowest is noted at P1 and P4.From the perspective of enhancing pedestrian traffic safety, this study provides a reference for the design of the external interface of autonomous vehicle while driving, and helps to improve pedestrian attention and recognition accuracy of the external interface.

In the ASIST system, data on 86 917 abnormal events from 2017 to 2023 are collected as research objects, and an indicator system for abnormal events was established. To ensure the safety of aviation operations, accurate and reliable risk assessment models were developed to analyze abnormal events in depth, thereby achieving effective risk management. Firstly, the principle of catastrophe theory was introduced into the fuzzy inference system, which enables it to better handle complex issues and enhance the accuracy of evaluations. Then, a risk assessment model based on catastrophe theory and fuzzy inference system was developed to assess the risks of abnormal aviation events. Additionally, 56 cases with detailed background information records were selected for instance analysis, and compared with the cloud model, to verify the feasibility and accuracy of the model. Finally, relevant indicators were controlled using fuzzy methods, providing guidance for the safety management work of aviation operations.

As the operational carrier of civil aviation transportation network, the air route network undertakes the important task of ensuring the safe and efficient operation of aircraft. When important waypoints fail due to thunderstorm disturbances, it is easy to chain reaction to adjacent nodes, ultimately leading to a significant decrease in network performance. Aiming at the problem that existing complex network node importance evaluation models do not effectively consider thunderstorm disturbances, the characteristics of thunderstorm disturbances were incorporated into the waypoint importance evaluation system for thunderstorm weather scenarios. The evaluation indicators were weighted using game theory methods, and the TOPSIS(technique for order preference by similarity to an ideal solution) comprehensive evaluation method was improved based on gravity model theory. A node importance evaluation model based on game theory improved TOPSIS method was established, and the K-medoids algorithm was then used to achieve waypoint clustering and grading. Taking flight operations in the Beijing-Tianjin-Hebei region as an example, the importance of air route network nodes in thunderstorm weather scenarios was evaluated. The results show that within the Beijing-Tianjin-Hebei route network, route points in the southern region are more susceptible to thunderstorm weather and are more densely distributed. The route network contains 9 important route points. When important route points in the route network fail due to thunderstorm impact, it will have a significant negative impact on the performance of the route network. The proposed node importance evaluation model based on game theory-improved TOPSIS method can effectively identify important waypoints in the route network during thunderstorm seasons or areas with high thunderstorm incidence, providing effective basis for optimizing the route network structure and resource allocation in thunderstorm scenarios.

FDS(flocculation-dehydration-solidification) coupling process has been proven to significantly enhance the efficiency of resource conversion in engineering waste soil. However, the material fate within the process and the advantages of recycling press-filter filtrate remain to be further investigated. FDS experiments were conducted to analyze the material fate of each component in the flocculant-solidifying agent during the FDS process. Based on the findings, the potential benefits of recycling press-filter filtrate were explored. The results reveal that approximately 18% to 35.57% of Na⁺ and 0.1% to 0.56% of Si elements are detected in the press-filter filtrate, whereas Ca, Mg, and Al elements primarily remained in the filter cake, with proportions close to or equal to 100%. The recycling of highly alkaline press-filter filtrate into the process is found to not only improve the dissociation efficiency of mud and sand but also serve as a “pretreatment” for subsequent FDS stages. Waste soil particles are observed to adsorb residual materials from the filtrate, enabling dynamic adjustments in material dosage according to material transformation patterns and filter cake performance requirements. This approach ensures that materials lost in the filtrate are continuously recycled and utilized, maintaining a dynamic circular process.

In order to explore the influence of environmental lighting on visual fatigue during safety sign identification, 12 mixed lighting environments were designed with illuminance and color temperature as environmental variables. Eye movement data were tested under different conditions by eye-tracking technology and a two-factor ANOVA was conducted.Combined with the test results of reaction speed and comfort, the changes of visual fatigue under different lighting conditions were analyzed.The experimental results show that the change of illumination has a significant effect on visual fatigue, subjects are more prone to visual fatigue in low illumination environment; The change of color temperature has little effect on visual fatigue, but in different mixed lighting environments, the change of color temperature will affect the visual comfort of the subjects, and the fatigue state will change accordingly.The suitable color temperature range for the lighting conditions in the working environment is 2 100~3 500 K, and the illuminance range is 550~900 lx. It is concluded that companies should pay attention to improving the lighting conditions during production, avoid reducing the recognition efficiency of safety signs by workers due to visual fatigue, and ensure the safety and health of workers.

Oil transfer station plays a crucial role in the oil and gas gathering and transportation system of an oilfield, ensuring stable production and continuous supply of oil and gas. However, given the complexity of its process system and the ambiguous uncertainty surrounding fault modes and relationships, a systematic reliability assessment method integrating T-S fuzzy fault trees with BNs(Bayesian networks) was proposed. Firstly, a T-S fuzzy fault tree was established based on T-S gates and their descriptive rules, which is subsequently converted into a Bayesian network model. Secondly, leveraging limited fault samples and general data sources, Bayesian updating estimation was employed to determine the failure rates of basic events, addressing the uncertainty inherent in fault sample data. Lastly, the T-S fault tree and BN model were synergistically utilized for forward reasoning to predict the reliability of the process system and the contribution of basic events, while reverse diagnosis is conducted to pinpoint the key factors causing different fault states of the system. Research conducted on typical oil transfer station process systems has demonstrated that the proposed method can effectively predict system failure rates and diagnose weak links even under conditions of uncertainty in basic data and event relationships. This provides crucial decision support for the optimal design and reliability maintenance of complex oil and gas process systems.

In order to guarantee the safety of UAV operation in low-altitude airspace and promote the rapid development of low-altitude economy, a detection method and resolution strategy for multi-UAV flight conflicts are constructed. Firstly, based on ADS-B(automatic dependent surveillance-broadcast) flight data, an improved FR-IMMCKF(fuzzy reasoning interactive multiple model cubature Kalman filter) algorithm was used to predict the UAV trajectory, and secondly, based on the relative motion status between UAVs, a preliminary screening of the conflict aircraft was carried out, and based on the velocity obstacle method, the vertical detection part was added so as to support the three-dimensional range of conflict detection, and then, the conflict coefficient was introduced as the weight in the flight conflict network, and the conflict status was proposed as the conflict status. Then, the conflict coefficients were introduced as the weights in the flight conflict network, and the conflict state SSM(space model) was proposed to visualize the resolution intervals, and finally, the resolution strategies of height adjustment, heading adjustment and speed adjustment were set up, and the optional resolution intervals of heading and speed were introduced. A low-altitude airspace five UAV flight conflict scenario was constructed for simulation and validation, and the results show that the proposed method is able to give a conflict resolution order and provide a feasible resolution strategy in a complex flight situation.

A critical human factors analysis method for aircraft runway overrun and veer-off event was proposed to address the complex causal relationship of human factors that have not been fully revealed in the investigation of the event. Firstly, improve the human factors analysis and classification system model to identify the root cause of overrun and excursion event, and use the fault tree analysis method to identify the direct cause of runway overrun and veer-off event. Secondly, a human factors investigation decision support model was constructed, and obtained the causal chain of aircraft runway overrun and veer-off event. Then, a Bayesian network was constructed to quantify the importance of the causal factors for the runway overrun and veer-off event caused by human factors. Through predictive reasoning, diagnostic reasoning, and sensitivity analysis, the key causal chain of the runway overrun and veer-off event was obtained. The results indicate that 32 causal chains of overrun and veer-off events is obtained based on the proposed human factors investigation decision support model, comprehensively revealing the human factors and their coupling relationships of the event. Bayesian inference can obtain the key causal chain of “improper action execution/improper information preprocessing → unsafe behavior → runway overrun and veer-off”. The above conclusions are basically consistent with the findings of investigations into aircraft runway overrun and veer-off events, and they hold positive significance for improving the early warning and prevention capabilities of such incidents.