Oil and gas drilling and production wellsites are complex and have many types of potential safety hazards, in order to improve the accuracy of the identification of potential safety hazards in wellsites, an oil and gas drilling and production wellsite potential safety hazard identification method based on improved YOLOv5 was proposed. Firstly, in order to solve the problem that the background of the picture was complex and the recognition difficulty increased, the SimAM attention mechanism was introduced in the backbone network; secondly, in order to solve the problem that the scales of the types of hidden hazards were different and there were multiple scales in one picture, the original feature fusion was replaced by adaptive spatial fusion of features (ASFF). Lastly, the hidden hazard recognition effect of the improved model was validated by comparing the model with other models. The results show that the improved YOLOv5 model improves the average accuracy value of recognition by 10.4%, and has a better recognition effect on the safety hazards of oil and gas drilling and production well sites. In order to solve the limitation of video monitoring and identification of oil and gas drilling and production wellsite safety hazards, a set of intelligent wearable device was developed, which effectively improved the portability of the identification of wellsite safety hazards.

Dust deposition can affect the normal operation of equipment. To accurately and efficiently detect dust on equipment and formulate a scientific cleaning strategy, a lightweight dust deposition detection method based on Fast-UNet was proposed. By effectively pruning UNet and adopting max pooling and bilinear interpolation for down-sampling and up-sampling operations, the parameter redundancy was reduced, and a compact basic network was obtained. The lightweight Ghost Module was used to replace the ordinary convolution in the basic network, further reducing the complexity of the network. An convolutional block attention module(CBAM) that integrated channel and spatial attention was embedded in the encoding process, which made the network pay more attention to the target area while introducing minimal parameters. Experiments on a simulated dust deposition dataset show that, compared with the original model, Fast-UNet reduces the number of parameters by 99.6%, decreases computational complexity by 98.7%, achieves an inference speed of 94.18 frames per second, and maintains a recognition accuracy of 91.17%. Compared with five other mainstream segmentation models, Fast-UNet also demonstrates advantages in both accuracy and speed. This method meets the needs of dust detection for both accuracy and efficiency, providing a technical reference for dust quantitative analysis.

The advantages of renewable wind energy lead to a rapid growth in the scale of wind power, while lightning strike accidents on wind farm delivery systems have a significant impact on the new power system. The traditional lightning strike warning method requires high data types and sample sizes, and lacks consideration of relative location as well as the distribution of lightning density. A lightning strike warning method for wind farm delivery systems based on the stepped lightning strike probability calculation method was proposed. Firstly, the data of lightning points around a wind farm in Hainan, China in 2020 were analyzed, and the Monte Carlo method was used to find the center of mass of the clusters as well as the density of lightning points to fit the trajectory of the thunderclouds. Then, based on the relative position of the movement trajectory and transmission line, the stepped lightning strike probability calculation method was combined to calculate the value of the lightning strike probability in a short period of time. Finally, the simulation was combined with the operation monitoring data of a wind farm in Hainan from 2020 to 2022. The results show that the relative error of the proposed method is within 15%, and the impact of the difference in the density of lightning points on the warning accuracy is effectively reduced, which ensures the safety of the wind farm delivery system.

In response to the current situation of relying on manual alignment of the optical path in existing velocity interferometer system for any reflector(VISAR) devices, and to meet the future demand for remote automated control, a new method for automatic alignment of the optical path was proposed. The complementary metal oxide semiconductor(CMOS) of this method was measured indirectly, and the pixel deviation of the light spot was used as a system input. Coefficient matrix transformation and discrete fuzzy feedback control methods were used to quickly eliminate the errors. Based on the modules such as vision and motion in the Windows control and automation technology(TwinCAT), each of which was run in a different real-time kernel, the communication link between the vision and motion control modules was eliminated, and fast real-time closed-loop control was realized. After the experimental verification of shock wave velocity measurement, the remote “one-button” automatic alignment was realized. The system can shorten the alignment time to 2 s and improve the alignment accuracy to 4.5 μm. The problem of inefficient manual adjustment of the existing device was solved, and the accuracy and stability of the system were improved.

To explore the factors affecting customers' evaluation of fresh logistics service quality, a logistics service quality evaluation model was proposed and established based on sentiment analysis of online reviews and latent Dirichlet allocation (LDA). A convolutional neural network (CNN) model integrating a multi-head self-attention mechanism and bidirectional long short-term memory network (BiLSTM) was constructed for sentiment analysis of online comments. Additionally, LDA topic model was carried out for positive and negative comments after classification. The key factors affecting the evaluation of fresh product logistics service quality were obtained by exploring the focus of customers' demand for fresh product logistics service. The sentiment analysis based on CNN-BiLSTM-Attention was implemented through Python programming, and the results of sentiment analysis on online comments were compared with those of support vector machine (SVM), CNN, BiLSTM, and CNN-BiLSTM. The comparison results show that, compared with the classification results of other models, the CNN-BiLSTM-Attention model is superior in accuracy, precision, recall rate, F₁, and other indexes, effectively improving the accuracy of text emotion classification. The research results demonstrate that researching the factors affecting the logistics service quality of fresh e-commerce based on online review data can help e-commerce enterprises better improve logistics efficiency and service quality from the perspective of consumer demand.

The concept of “resilience” is introduced as a new research direction for cities to withstand uncertain risks in the face of complex challenges such as global environmental changes, accelerated urbanization, and frequent epidemics. To explore the spatiotemporal evolution and obstacle factors of urban natural disaster resilience in Shaanxi Province to enhance resilience against natural disasters. The entropy weight-technique for order preference by similarity to ideal solution(TOPSIS) method was used to assess resilience levels across four dimensions: economy, society, infrastructure, and ecological environment. The spatiotemporal evolution characteristics of resilience in Shaanxi Province from 2018 to 2022 were examined using a combination of GIS, Theil index, and center-standard deviation ellipse. An obstacle degree diagnosis model was employed to analyze influencing factors. The results indicate that over time, the overall resilience level against natural disasters in cities, except for Xianyang, shows an upward trend. Overall resilience, social resilience, and infrastructure resilience increase, while ecological environment resilience slightly declines. Spatially, the resilience in Shaanxi Province follows a pattern of “Guanzhong region > Northern Shaanxi region > Southern Shaanxi region.” The major influencing factors are infrastructure resilience and ecological environment resilience, with the length of urban drainage pipelines and greening coverage area identified as the top obstacles restricting urban resilience. The research findings are expected to provide theoretical references for regional natural disaster management and resilient urban planning in Shaanxi Province.

In the field of fingerprint recognition technology, ridge density, as one of the morphological features of fingerprints, has demonstrated increasing research value. Aiming at the problems of time-consuming and labor-intensive existing measurement methods, an algorithm based automated measurement method was proposed. The algorithm first preprocessed fingerprint images, including grayscale conversion, edge detection, noise reduction, and ridge enhancement, to improve image quality and clarity. Subsequently, it strengthened fingerprint features, performed array transformation, determined directional vectors, detects peaks, and finally plotted a grayscale fluctuation diagram to visually present the measurement results. Experimental results show that the automated measurement algorithm performs well in terms of efficiency and accuracy, exhibiting high consistency and significant statistical correlation with manual measurements. This further validates the scientific robustness and effectiveness of the automated measurement method, providing new perspectives and approaches for the automation and intelligence of fingerprint recognition.

In order to explore the concentration level and health risks of polycyclic aromatic hydrocarbons (PAHs) in cabin of new cars, air samples of 15 newly produced passenger cars in the gaseous and particulate phases under normal temperature and high temperature conditions were collected by using particulate samplers in series with polyurethane foam (PUF) sleeves. The contents of 16 priority PAHs in the samples were determined by GC-MS, and the health risk assessment of drivers and passengers was carried out. The results show that the average detection rates of 16 PAHs in the gaseous and particulate phases is 2.17~50.00.Among them, naphthalene (Nap), phenanthrene (Ace), and phenanthrene (Phe) are detected in some sample cars under high and normal temperature conditions, while phenanthrene (Acy) is detected in all sample cars under high and normal temperature conditions, and the rest of the substances are only detected under high temperature conditions. The concentration of PAHs in cabin of cars under high temperature conditions is higher than that under normal temperature conditions, and the concentration in the gas phase is higher than that in the particulate phase. Overall, Nap exhibites the highest concentration. The carcinogenic health risks of Nap, Ace, Phe, and Acy under high temperature conditions range from 3.53×10^-11 to 4.54×10^-9, while under normal temperature conditions, they range from 1.70×10^-11 to 5.16×10^-9. It can be seen that PAHs in the gas phase and particulate phase in cabin of new cars, can be effectively collected by using particulate matter samplers in series with a polyurethane foam (PUF) sleeves for sampling. The detected concentrations of PAHs in cabin of cars, is low, the overall carcinogenic health risk is less than 10^-6, and the carcinogenic risk is low.

To achieve rapid automatic detection and identification of void damage in high-rise composite structures, a bridge tower full-scale model was tested for damage using Zhangjinggao Yangtze River Bridge's composite structure tower. Through numerical simulation of sound field spatial distribution, time-frequency response characteristics comparison analysis, and convolutional neural network(CNN) model training and visualization. An automatic device for void detection of high-rise composite structures and a deep learning detection method based on acoustic signals were proposed. The results demonstrate that the acoustic signal analysis method based on automatic device acquisition can be used as a new approach for automatic detection and identification of void damage in high-rise composite structures. The constructed CNN model can achieve high-precision classification of structural void state, and the recognition accuracy is 96.8%. The automatic device and intelligent detection method enable automatic real-time detection and classification of high-rise composite structures, improving automation and reducing safety risks.

Gas risk assessment of tunnel construction is one of the key issues to ensure the safe construction of tunnels passing through gas sections. Aiming at the three stages of investigation, design and construction in the process of tunnel construction, the interpretative structural model was used to reveal the hierarchical key of gas risk influencing factors in the above three stages. The hierarchical model of risk assessment was established, and the weight calculation method of risk assessment index was defined. Combined with data collection, literature collation and engineering investigation, the assignment standard of risk assessment index was proposed based on membership degree theory, and the gas risk assessment method in tunnel construction process was constructed. Combined with engineering examples, the rationality of the proposed method was verified. The results show that the pregnancy risk environment in the survey stage is the precondition, and the survey disturbance is the inducing factor. The pregnancy risk environment in the design stage is the precondition, and the design factor is the inducing factor. In the construction stage, the pregnancy risk environment is the precondition, the construction disturbance is the inducing factor, and the site management is the root cause. The risk is revealed in the survey stage, the risk is reduced in the design stage, and the safety is ensured in the construction stage. The proposed method is consistent with the on-site disclosure, which verifies the rationality of the proposed method. Through the above research, it can provide a theoretical basis for the risk determination of the tunnel crossing the gas area, and provide a reference for the selection of subsequent engineering measures.