Muscle dysfunction, as one of the common extrapulmonary manifestations of COPD (chronic obstructive pulmonary disease), limits the exercise capacity, cardiorespiratory health, and quality of life of COPD patients, leading to poor prognosis. PBMT (photobiomodulation therapy) an emerging adjunctive treatment for COPD-related muscle dysfunction, has been widely promoted and utilized in clinical practice. It positively affects muscle inflammation, alleviates muscle fatigue, improves muscle metabolism, enhances muscle endurance, and accelerates muscle healing. The comprehensive review of the rehabilitation mechanisms and current application status of PBMT (photobiomodulation therapy) in addressing COPD (chronic obstructive pulmonary disease)-related muscle dysfunction, both domestically and internationally, is conducted to offer insights and guidance for the application of PBMT in the rehabilitation treatment of COPD-related muscle dysfunction.

The issue of soil erosion in the Loess Plateau was addressed, with a comprehensive analysis of the application and evolution of vegetation restoration technologies for loess slopes. The text highlights that vegetation restoration serves as an effective method to mitigate soil erosion and rejuvenate ecological functions. Vegetation restoration not only enhances soil stability but also boosts the ecological quality of slopes and contributes to the sustainable development of ecosystems. Considering the geographical characteristics of the Loess Plateau and the factors contributing to soil erosion, a review of the research history and current progress of vegetation restoration techniques in China was conducted. Detailed discussions on methods such as spray seeding and slope coverage restoration, each characterized by distinct benefits and drawbacks, were included. These techniques were selectively implemented based on specific site conditions and environmental characteristics, and were continuously refined to address any arising challenges during their application. Furthermore, the document outlined future directions for advancing vegetation restoration technology, stressing that a thoughtful integration of plant species selection, soil matrix enhancement, and technological innovation is essential for improving the ecological restoration outcomes on slopes. The adoption of modern technological tools like remote sensing and artificial intelligence for monitoring and managing geological hazards was recommended to enhance the effectiveness and accuracy of ecological restoration efforts. Finally, an interdisciplinary approach was advocated to spur innovative developments in slope management and ecological restoration technologies, with the goal of achieving ecological sustainability in the Loess Plateau.

In order to evaluate the quality of professional athletes in martial arts, the camera array based measurement and multi-view geometry were combined to develop a refined recognition method of human movements under the constraints of human parametric model, and a quantitative evaluation method system of martial arts movements was established based on the obtained joint position and angle information, and the technical movements of athletes of different levels in the five-step boxing event were measured and evaluated. The results show that the method developed in this paper can effectively realize the identification and quality evaluation of athletes’ movements in Wushu events, and the research results can also be extended to other competitive sports and public health, so as to provide support for scientific training and sports rehabilitation.

Aerodynamic principles and the assumption of axial inextensibility of a two-dimensional flexible plate were used to derive a nonlinear theoretical model of flag flutter, investigate and analyze the coupled motion characteristics of flexible flag and airflow in nature and wind energy collection fields, and examine the effects of length, mass ratio, and wind speed on its motion characteristics. The flag oscillation process in the wind was numerically simulated using the bidirectional fluid-structure coupling method and the overlapping mesh methodology, from which the features of the surrounding flow field and the motion behavior of the flag inside it were determined. The findings indicate that while swing displacement increases and subsequently declines with wind speed, the crucial flutter wind speed lowers as flag length increases. The chirp frequency decreases as the mass ratio increases, and the Strahl number is less affected. With the predefined dimensions of the flag, at low wind speeds, both the displacement and frequency of the swing are low. However, when the wind speed exceeds the critical vibration threshold, a significant vibration phenomenon occurs. Changes in surrounding pressure and velocity are caused by the flag-encircling vortex as it progresses through phases of formation, shedding, and disappearing. Numerical simulation techniques based on the overlapping mesh methodology successfully address the deformation problem of flexible flags. Theoretical and numerical simulations can be verified and analyzed with this method.

The physical properties lower limits of oil-gas charging in tight sandstone reservoirs are identified through a large number of core test and analysis data. The Chang8 reservoir types, pore-throat structure, and physical properties were clarified. Methods such as oil-gas occurrence, displacement pressure, physical property statistics, oil recovery index, and minimum pore-throat radius were employed to determine the current reservoir physical properties lower limit. By integrating the period of hydrocarbon accumulation and pore evolution, the critical physical properties during oil-gas charging were inverted. The results show that the reservoir types of Chang 8 are mainly feldspar sandstone and lithic feldspar sandstone in Fuxian area, with complex pore-throat relationship. These reservoirs are classified as tight reservoirs with low to extra-low porosity and extra-low to ultra-low permeability. It is preferred that the physical properties lower limits of the current reservoir are 7.0% and 0.15 mD, respectively. For inversion of oil-gas charging in Phase I (192.5~152.0 Ma), the lower limits of physical properties are 20.8% and 7.37 mD, respectively, for oil-gas charging in Phase II (152.0~126.0 Ma), the lower limits of physical properties are 8.2% and 0.22 mD. For oil-gas charging in Phase III (65.0~36.5 Ma), the lower limits of physical properties are basically consistent with the current lower limits of physical properties. The research findings provide an significant geological basis for the evaluation of reservoir and the prediction of favorable in the study area.

The total organic carbon content in shale reservoirs is a crucial parameter for assessing hydrocarbon generation potential and shale gas enrichment. Accurate prediction of TOC(total organic carbon) is essential for oil and gas exploration and development. Conventional linear regression methods are limited in their predictive accuracy due to the complex nonlinear relationships among regional and well logging data. To address this issue, a prediction model based on Adaboost-WOA-BP was proposed for predicting TOC content. This model integrates WOA(whale optimization algorithm) optimized Backpropagation neural networks as weak learners within the Adaboost framework to construct a strong learner. Use of optimal natural gamma, density, acoustic time difference, and other sensitive logging parameters associated with TOC content calculation as inputs for the prediction model. Compared to conventional linear regression, BP neural networks and WOA-BP neural networks, the Adaboost-WOA-BP model demonstrates higher predictive accuracy, achieving a 95% match between predicted and measured TOC values.

Pavement runoff could enter the karst aquifer system through sinkholes, karst windows, karst pools in karst areas, which could influence the karst water environment quality. Consequently, it is necessary to study the hydrochemical environment characteristics of pavement runoff in karst areas. Pavement runoff of Yaji, Qingshuiqiao and Baizhujing were sampled, characteristics of hydrochemical compounds and their influencing factors, hydrochemical environment quality were analyzed using multiple statistical method, Nemerow index method and comprehensive pollution index method. External influencing factor has small influences on the common hydrochemical ions, while has great influences on these trace elements. The compounds influencing the water environment of pavement runoff were nutrient compounds (NH₃-N, TP, COD_Mn), landscape compound (suspended solids) and metal compounds (Mn, Hg and TFe) by analyzing the concentrations of hydrochemical compounds of pavement runoff. These compounds have close relationships with pavement behaviors, surrounding vegetations, traffic flow and came from fuel, lube, slop oil, gasoline, worn tyre and vegetations. Four main factors with the cumulative variance contribution rate of 97.99% were extracted from the monitoring dataset using the factor analysis method. It could be known from the four main factors that carbonates weathering was the main source of hydrochemical compounds of pavement runoff, the second was the particles of atmospheric and pavement influencing the SS of pavement runoff, the third was the human activities including pavement behaviors and protective measures of surrounding vegetations. Hydrochemical environment quality of Qingshuiqiao, Yaji and Baizhujing decreased in turn by using the Nemerow index method and comprehensive pollution index method. Hydrochemical environment quality of Baizhujing were poorest, which had potential risks for the water ecological environment, the pavement runoff could be reused for the surrounding vegetations through reasonable measurements. The results could not only provide scientific instructions for the treatment measures of pavement runoff, but also provide scientific evidences for the reasonable exploitation and utilization of karst water resources.

Machine learning models, widely applied in landslide susceptibility assessment due to their powerful feature extraction capabilities, are continuously evolving in their algorithms to address the common issue of low accuracy. The GCNN (group convolutional neural network) model was introduced into landslide susceptibility assessment, and its results were compared with those of various common machine learning models to comprehensively evaluate the adaptability of these models in this field. Taking Hebei Province as the research area, 16 influencing factors were selected from three aspects: triggering factors, pregnant disaster environment, and susceptible bodies. GCNN model and other common machine learning models—CNN (convolutional neural network), Logistic (logistic regression), RF (random forest), and SVM (support vector machine)—were constructed to build corresponding susceptibility assessment models. The research area is divided into four categories of landslide susceptibility zones, and the accuracy of the zoning is comprehensively evaluated. The study indicates that compared with the other four machine learning models, the GCNN model achieves higher scores in various confusion matrix indicators and is more suitable for landslide susceptibility zoning. The resulting zoning of landslide susceptibility is consistent with the actual occurrence of landslide points, indicating a more accurate delineation of landslide-prone areas.

Acoustic wave is one of the important means to realize the fast and accurate transmission of downhole information. The drill string serving as information transmission channel has obvious periodic pipe structure characteristics. In order to study the frequency spectrum quality of drill string channel in actual working conditions, a multilevel evaluation method of acoustic frequency spectrum based on improved radar chart was provided. The influence of axial tension stress on the frequency spectrum characteristics of acoustic wave in pipe structure was studied and analyzed by the method, and the criteria for judging the frequency spectrum characteristics were established. Practice shows that, the existence of tensile stress has a significant impact on the acoustic frequency spectrum and passband characteristics. With the increase of tensile stress, the evaluation index of frequency spectrum characteristic increases first and then decreases, and there exists a peak point with the best frequency spectrum characteristic. This method can comprehensively reflect the influence of different factors on the frequency spectrum characteristics of acoustic wave in drill string channel, and comprehensively evaluate the distortion degree of frequency spectrum characteristics from the multiple perspectives. It provides a basis for optimizing carrier frequency and designing the installation position of repeater when dealing with multi-factors interference in the field application of downhole information acoustic transmission technology.

The sedimentary environment of Cardium formation in western Canada basin is the shallow sea coastal face. The conventional oil and gas reservoir was formed from the conglomerate and sandstone deposited on the coastal muddy seafloor. The unconventional tight oil reservoir was formed from the low-permeable argillaceous sandstone around the conglomerate. Faced with the coexistence of different types of resources, the development of SINOPEC oversea blocks with benefit needs to be realized. Firstly, the member A of Cardium formation was divided into three types according to the core observation and experiment test. Then the development features of different types of reservoirs were compared based on the production dynamic data. Thirdly, the geological knowledge was verified via single well theoretical models. Finally, the development potential of different types of reservoirs in the interest block were evaluated. The development strategies of different types of reservoirs were put forward. The results show that the member A of Cardium formation can be classified into type I conglomerate reservoir, type Ⅱ conglomerate reservoir and type III tight sandstone reservoir according to the lithology differences. From type I to type Ⅲ, the reservoir physical properties gradually weaken. The development way changes from vertical well development to multistage fracturing horizontal well development. The productivity controlling factors gradually complicates. Currently, type I and II reservoir in the interest block have a limited potential, which can be further released through reperforations on some old wells. Type III reservoir has a certain potential, which can be effectively released through deployment of large numbers of horizontal wells.

The enhancement of oil recovery by altering the salinity and ion composition of injected water has become a focal point of numerous studies. However, there is relatively less attention given to techniques combining surfactants with the quality injected water. To investigate the synergistic effects of different cations and surfactants on recovery efficiency, micro-scale displacement experiments were conducted to simulate the displacement process, along with experiments measuring interfacial tension and viscoelasticity modulus at the oil-water interface. Results from the micro-scale displacement experiments show that 10 000 mg/L NaCl solution and 50 000 mg/L CaCl₂ solution exhibite the best oil recovery efficiencies, reaching 64.51% and 59.27% respectively. After adding surfactants, the efficiency improved further with 10 000 mg/L NaCl +0.2% dodecyl dimethyl ammonium betaine solution and 50 000 mg/L CaCl₂+0.2% hexadecyl trimethyl ammonium bromide solution achieving the highest recovery rates at 87.28% and 80.92% respectively. Results from the interfacial tension and viscoelasticity modulus experiments indicated that when anionic and nonionic surfactants were added to NaCl and CaCl₂ solutions, the interfacial tension reached the magnitude of 10^-1 (m·N)/m. However, with the addition of amphoteric and cationic surfactants, the interfacial tension decreased to the magnitude of 10^-2~10^-3 (m·N)/m, accompanied by a significant decrease in viscoelasticity modulus. This study explores the mechanisms of the synergistic effects of different cations and surfactants on the displacement process, considering factors such as interfacial tension, viscoelasticity modulus, and wettability, and microscale oil displacement behaviors thus providing a comprehensive analysis of the relationship between multiple factors and recovery efficiency.

Oil booms play a vital role in dealing with oil spills on water. Aiming at the single problem of existing oil booms in fast-flowing rivers, a new type of double-layer mesh fence was designed for oil spill recovery in fast-flowing rivers based on the parameters of traditional oil booms. Based on the mainstream CFD (computational fluid dynamics) software FLUENT, a two-dimensional numerical flume model was established to simulate the transient distribution of oil and water under the action of the new oil containment boom with VOF (volume of fluid) as the computational model, and the volume of oil intercepted was used as the monitoring data to explore the direction of optimizing the oil containment performance of the oil containment boom. Monitoring data was used to explore the optimization direction of oil containment performance of the oil containment boom. The results show that the optimised oil containment boom has a grid radius of 30 mm and a porosity of 0.3. Compared with the traditional boom, the new oil containment capacity of the new oil containment boom is better than that of the traditional boom, which provides an effective reference for the design of oil containment boom for fast-flowing rivers.

In order to detect the abnormal working conditions such as overpressure and leakage, that may occur in pipelines and installations in the process of natural gas regional production, the current industrial control and alarm systems cannot accurately reflect the real state of the equipment, and the single-parameter early warning has a higher rate of error judgement, which is insufficient in practicality. A collaborative prediction and warning method for process parameters related to upstream and downstream stations in a natural gas production area was tested. Aiming at the characteristics of natural gas region with many stations, complex production process and diverse monitoring data, firstly, the parameters of each station were downgraded to extract the key process parameters of each station. Then, the key parameters are evaluated and grouped by correlation, and a multivariate nonlinear lasso regression prediction model was established with the highly correlated parameters in the same group as the independent variables. At the same time, a long and short-term memory prediction model was established for the key parameters, and a comparison analysis of the prediction results was performed to determine the dynamic prediction and early warning of natural gas production. Comparative analysis of the prediction results of the two models was used to determine the dynamic thresholds for coordinated early warning of regional production. The results show that the method can not only effectively reduce the misjudgment of single-value anomalies, but also locate the anomalous stations and points, which is of high practical value.

To address the issues of low accuracy in rolling bearing life prediction and the difficulty of constructing health indicators, a bearing remaining life prediction model based on ASFF (adaptively spatial feature fusion) and AAKR (auto associative kernel regression) combined with CNN (convolutional neural networks) and BILSTM (bi-directional long-short term memory networks) was proposed. Firstly, the multidimensional features were extracted in the time domain, frequency domain, and time-frequency domain, and the sensitive features were screened using monotonicity and trend. Secondly, the sensitive features were feature fused using ASFF-AAKR to construct the health indicators. Finally, the health indicators were inputted into CNN and BILSTM to realize the life prediction of rolling bearings. The results show that the constructed life prediction model is better than other models, and the method has lower error and higher life prediction accuracy.

It will raise their feedwater temperature and then improve their operating economy to add No.0 HPH (high-pressure heater) in thermal power units. A steam-water distribution model for a thermal system coupled with No.0 HPH was established based on the mass and energy conservation laws. And a variable-condition calculation method for its heat economics was also proposed. Furthermore, a derivative characterization approach was developed to represent the heat economics impact of No.0 HPH extracted steam flowrate, which realize the quantitative evaluation before and after coupling with No.0 HPH. Taking a 600 MW unit for example to carry out calculations and analysis, it indicates that the index calculation error of the method proposed in this paper is less than 0.15%, which has high precision and can be used for thermal economic evaluation of large thermal power units coupled with No. 0 HPH. Furthermore, after coupling No.0 HPH, the feedwater temperature can be lifted by 19.1~32.3 ℃, the cycle heat efficiency raised by 0.18%~0.2%, and the heat consumption rate decreased by 29.3~34.6 kJ/(kW·h). Moreover, the sacrificial internal work will be even smaller, and the heat consumption decrease be even larger if the turbine load was smaller.

An analysis was conducted on the circulating flow process of coolant in the cooling water jacket of motorcycle single cylinder engines, twin cylinder engines, and four cylinder engines. The analysis of coolant flow velocity and water jacket wall heat transfer coefficient were conducted. Based on the analysis of the circulating flow path of coolant, the optimization of the cooling water jacket structure was studied. The results show that the coolant flow rate on the exhaust side and nose bridge area of a single cylinder engine is relatively low, and there is a zero flow rate area in the nose bridge area. The distribution of coolant flow rate in the two cylinders of a twin cylinder engine is uneven, and the coolant flow rate in the middle area of the connection between the two cylinders is relatively low. There is also an issue of uneven coolant flow rate in each cylinder of a four cylinder engine. By analyzing the flow path of coolant circulation, the flow direction and function of the coolant on the water holes on each cylinder gasket are clarified. Based on the design criteria that require key cooling in high-temperature areas, the layout of the water holes on the cylinder gasket and the local flow area of the water jacket are optimized for three types of engine cooling water jackets. After optimization, the coolant flow rate in the high-temperature areas such as the exhaust side and nose bridge area of the three types of engine cooling water jackets reached the requirement of not less than 1.5 m/s, and the difference in coolant flow rate between each cylinder is reduced. Through engine thermal balance verification, the optimization of the cooling water jacket structure for three types of engines can effectively reduce the temperature of the cylinder head spark plug gasket and reduce the temperature difference between the spark plug gaskets of each cylinder, verifying the effectiveness of the water jacket structure optimization design.

Radiation dose monitoring using thermoluminescent detectors is currently one of the main methods of personal or environmental dose monitoring in China. In order to solve the problem of uniformity screening before the use of thermoluminescent detector, and the complexity of the measurement process. Test dose and thermoluminescent peak counts normalization were used to optimize the measurement process of thermoluminescent detectors. With the simple irradiation device, the same batch of thermoluminescent dosemeters for radiation environment monitoring were measured using the optimized and general measurement processes. The relative error of the dose values of the two was within ±5%, which satisfied the accuracy requirements of thermoluminescent dosemeters in the process of monitoring the dose of ionizing radiation to the individual or the environment. The results show that the optimized thermoluminescent dosimetry process reduces detector uniformity performance requirements and improves the applicability of the process. It provides a high-precision, high-efficiency and low-cost measurement method for personal or environmental dose monitoring in China.

In order to analyze the influence of uncertain factors on power system, PCA (polynomial chaos approximation) method, which is both fast and accurate, is widely used in probabilistic power flow calculation. The polynomial chaotic approximation method requires that the probability density function of the random input variable is known, and the random input variable must satisfy the independent condition. A probabilistic power flow method based on DDPCA (data driven polynomial chaos approximation) was proposed for the known random input variables which are historical data. First, DDPCA selects the optimal orthogonal polynomial according to the historical data, and then determines the Gaussian sample considering the nonlinear correlation of random input variables, and then computes the weights with Monte Carlo integral. Then, a small amount of power flow was calculated based on Gaussian samples, and the approximation coefficient was solved according to the power flow results and weights, and then the statistical characteristics of the random output variables were obtained. The proposed method was compared with the point estimation method, and the effectiveness of the proposed method was verified by the results of three examples.

To solve the problem of large steady-state error and poor parameter robustness of MPFC system predicted by traditional model of PMSM (permanent magnet synchronous motor), a multi-voltage vector selection method based on stator flux prediction error vector analysis was proposed. Firstly, the multi-voltage vector selection criteria for determining the region where the flux error vector is located were established by dividing the sectors according to the axis in the two-phase stationary coordinate system. Then, the predicted value of stator flux and the value function of stator flux in two-phase stationary coordinate system were used to calculate the action time of each voltage vector. In addition, a discrete sliding mode stator flux observer considering the mismatch of resistance and inductance parameters was designed, which further improves the parameter robustness of the system. Finally, the effectiveness and feasibility of the proposed predictive stator flux control method are verified by simulation and experiments. The proposed method still has good steady-state performance under the condition of system parameter mismatch, and significantly reduces the stator flux and electromagnetic torque ripple.

With the increasing penetration of distributed power sources in the distribution network, active distribution has become the mainstream direction of the power grid in the future. Microgrids and active stations that are slightly smaller than their scale will gradually increase. Whether these subsystems can be used as energy to schedule becomes the key to improving the economy and stability of power grid operation. Therefore, aiming at the overall optimal scheduling of microgrid and active station area with distributed power supply, a scheduling model solution method based on improved target cascade method was proposed, which mainly takes the optimal benefits generated by different stakeholders as the final scheduling target, and adopts the opportunity constraint description for the processing of uncertain factors of wind and solar. The upper layer is the distribution network and the optimal target of the distribution network, and the lower layer is the microgrid and active station area with the ability to participate in the scheduling. Based on the modeling of distribution network and microgrid, the improved target cascade method was introduced, and the interactive power was used as a shared variable to equivalent the generator and virtual load, so as to realize the decoupling and independent optimization of the upper and lower layers. The comparison of the experimental results shows that the target cascade method with the balance coefficient can obtain better results in the number of iterations, convergence performance, anti-interference performance and overall economic evaluation.

To address the voltage fluctuations caused by the integration of renewable energy into the distribution network, a voltage coordinated control method based on MPC (model predictive control) was proposed to ensure safe operation. Aiming at the uncertainty of wind and photovoltaic output, the AP-K-Medoids clustering algorithm was proposed to generate and reduce output scenarios, and a model was established with the optimization objective of minimizing system network loss. Adopting multi time scale voltage control through the integration of on load voltage regulating transformers, capacitor banks, static reactive power compensators, wind solar reactive power output, and energy storage charging and discharging coordination. Long term scale optimization control solves the output of each device in the system through multi-step rolling optimization, with wind solar output and load demand prediction as the premise. On the basis of short-term and long-term rolling optimization, the increment of output is solved. The optimization control model is a non convex and nonlinear model, which utilizes a second-order cone programming model to solve nonlinear problems. Using an improved IEEE33 node distribution network system for case analysis, the research results demonstrate the feasibility of the proposed voltage optimization control strategy.

Aiming at the technical problems of "untimely perception, poor transmission and difficult equipment deployment" in the monitoring and early warning of mountain disasters in the complex environment of the Qinghai-Tibetan Plateau, a UAV-throwing monitoring device, LoRa networking and edge computing gateway, as well as other embedded hardware and software equipment applicable to deformation and micro-motion monitoring of high-level and high-risk mountain disasters were developed, and focused on the research of the system low-power adaptive data acquisition algorithm and RF frequency adaptive technology, were developed the self-organised network routing algorithm based on LoRa and Beidou RDSS, as well as the multimodal communication intelligent switching technology, so as to solve the problems of timeliness of data perception in complex environments and the problems of low-power consumption and environmental adaptability. The results show that the developed system had a good on-site pilot application effect, which meeting the requirements for long-term monitoring of mountain disasters in alpine mountainous areas, and the average packet loss rate of data transmission in extreme environments is 2.328 8 percent, providing new technologies and methods for disaster prevention and mitigation in the construction and operation of major projects in alpine and complex mountainous areas.

Aiming at the problems that the large-scale pre-training language model faces when dealing with news headlines, such as huge parameters, inefficient use of contextual semantic features and circular convolution neural network’s neglect of the importance of initial input elements, a news headline classification method that combines ERNIE(enhanced representation through knowledge integration) of mixture-of-expert model and recurrent convolution neural network with attention mechanism were proposed. Firstly, the text was encoded with the help of MoE’s improved ERNIE technology, and then the text was classified with attention RCNN (recurrent convolutional neural networks)on the basis of preserving the word order and characteristics of the text. In order to improve the classification ability, RCNN was improved by calculating the input fusion context weight. In the process of calculating the weights of experts in MoE, Gumbel-Softmax was selected as a new gating function to improve the traditional Softmax function, so as to better control the smoothness. According to the experimental results, it is found that compared with the traditional classification methods, the classification method proposed in this study shows significant advantages and greatly reduces the number of parameters. On this basis, the F₁ value is increased by 0.51% compared with the traditional model. After the ablation experiment, the feasibility of this classification method in the classification task has been confirmed.

Ship targets in remote sensing images have multi-scale characteristics, changeable backgrounds, and complex meteorological characteristics, which lead to low accuracy, false detection, and missed detection of small target ships. In response to the above situation, an improved small-target ship detection model based on YOLOv5s was proposed. First, in order to solve the problems of scale changes and background variability in ship detection, the ASFF(adaptive spatial feature fusion) module was introduced. Secondly, in order to reduce the calculation amount and parameter amount of the detection network, the BoTNet attention mechanism was introduced, and then in order to improve the overall network to improve the detection accuracy, the EIoU border loss function was used, and finally the Slim-neck network was introduced to ensure the overall lightweight of the network. Experiments show that on the main data set LEVIR-Ship, compared with the benchmark YOLOv5s, mAP@0.5 increased by 7.1% to 81.3%, the number of parameters is reduced by 0.44 M, the calculation amount is reduced by 0.6GFLOPs, and the weight was reduced by 0.9 M. The proposed method performs better in various key indicators and achieves high-precision small target ship detection in complex environments. Comparative experiments are conducted on the verification data set McShips. The experiments show that the proposed method still performs better, verifying the universal applicability of the proposed method.

BCI (brain computer interface) is one of the important research methods in the fields of brain cognition, brain medicine and brain-like research,where the precise implantation of microelectrodes is a necessary prerequisite and important guarantee. With the rapid development of robotics, machine vision and artificial intelligence, surgical robots are gradually used in brain-computer interface implantation surgery. To meet the demand for precise implantation of microelectrodes in the somatosensory and cerebral motor cortex of SD(Sprague-Dawley) rats, a vision-guided precision implantation system for brain-computer interface microelectrodes was presented. Based on the method of machine vision to identify the key points of the rat skull, the coordinate system was established to obtain the point cloud information of the rat skull, to realize the high-precision identification and localization of the target points, and to guide the actuator to complete the electrode implantation operation. Through model analysis and animal experiments, it has been demonstrated that the implantation system can accurately identify surgical targets on the subjects, guide the actuator to swiftly penetrate the skull, and accurately and stably implant the electrodes into the target area, which effectively improves the accuracy of microelectrode implantation.

In order to improve the accuracy and efficiency of inventory counting in the process of monitoring and auditing biological assets, a biological asset detection model YOLOSC incorporating the attention mechanism and loss function optimization was proposed. Firstly, the SENet attention mechanism was introduced into the backbone network of the YOLOv5s model to enhance the ability of extracting the key features in the pictures of the biological assets. Secondly, the CIoU was adopted as the regression of the detection frames with the loss function to enhance the regression speed and localization accuracy of the detection frame during the training process. Finally, a biological asset datasets was constructed for targeted training of the proposed model to enhance the model detection effect. The experimental results show that compared with the YOLOv5model, the precision, recall, F₁ value and AP of YOLOSC are improved by 2.3%, 2.1%, 2.7% and 1.6%, respectively, which proves the effectiveness of the proposed biological asset detection model YOLOSC.

In response to the low detection accuracy and high model complexity of existing road damage detection algorithms in complex environments, a lightweight road damage detection algorithm named LDC-YOLOv5 (lightweight deformable convolution YOLOv5) was proposed based on YOLOv5.To address the complexity of real road surface damages, a lightweight feature extraction module was designed using Deformable Conv (deformable convolution) and Depthwise Conv (depthwise convolution) to replace the C3 module in the original network backbone, enabling convolutional kernels to focus on irregular crack damages and enhancing feature extraction for damage detection. To reduce algorithm complexity in the feature fusion stage, a lightweight feature fusion module was constructed using GhostConv to replace the C3 module in the original network neck, lowering network parameters and complexity. Additionally, to prevent missed detections caused by uneven lighting and shadow obstruction, a lightweight attention mechanism, TripletAttention, was introduced in the backbone network to improve the algorithm's understanding of damage information and context. Experiments conducted on the IEEE open dataset RDD2022 and the Kaggle open dataset Road Damage demonstrate that, compared to YOLOv5s, the proposed LDC-YOLOv5 achieves a 1.4% and 4.2% improvement in mAP50 on the two datasets, respectively, with only 67.6% of the model parameters of YOLOv5s.

Aiming at the shortcomings of low-quality face recognition algorithms based on unified feature space, such as poor robustness to low-quality faces and limited feature representation capability, a low-quality face image recognition algorithm based on knowledge distillation was proposed. First, the ResNeXt network was used as the backbone feature extraction network, and the two-channel attention module was introduced to construct a teacher-student knowledge distillation framework with an attention mechanism. Secondly, the output features of the teacher network were adopted as labeled knowledge, and the effective recognition features were passed to the student network. And the attention graph features were adopted as the intermediate layer knowledge to solve the lack of single knowledge information in the output layer, and the feature knowledge was enriched by combining two kinds of knowledge distillation to ensure the diversity of knowledge information in the teacher network model. Then, the weighted average of labeled knowledge distillation loss, attention graph distillation loss, and recognition loss were fused as the total network loss function to ensure that the student network model has a better learning ability. Finally, tested under different quality images in AgeDB-30 and CPLFW test sets, the results of the ablation experiments show that compared to the generic face recognition model without distillation, the model with two types of knowledge distillation gains 2.25%, 11.33%, 24.64% and 2.8%, 10.58%, 27.85% improvement in recognition accuracy, respectively. Comparative experiments show that the algorithm proposed in this paper also obtains different degrees of improvement in accuracy compared to other mainstream algorithms.

In order to address issues such as high noise, low brightness, and blurred details in low-light conditions, a new algorithm named UMDCEAD-NET, integrating zero-reference depth curves for low-light image enhancement and denoising, was developed. The algorithm's design was initially centered around a feature extraction network, employing a U-Net architecture as the backbone network. To enhance the feature extraction capabilities and preserve more detailed image information, Mobile-Net was integrated into the downsampling phase of the U-Net backbone. Subsequently, to address the issue of inadequate lighting and pixel-level image degradation, the extracted features underwent iteration using depth curve estimation (LE-curve), in conjunction with depth separable convolution, which served to reduce the network model's parameter count. Furthermore, five non-reference loss functions were engineered to bolster the algorithm's generalization capabilities and its retention of detail under varying lighting conditions. The enhanced image was then subjected to noise reduction in tandem with AD-NET(attentional denoising network), thereby diminishing the noise and aligning the image more closely with human visual perception. Experimental outcomes demonstrated that the proposed algorithm achieved an average PSNR (peak signal-to-noise ratio) of 22.29 on the public dataset Zero-DCE, which exceeded the performance of the Zero-DCE++ algorithm by 32%. Additionally, on the public dataset LOL, the algorithm attained an average PSNR of 21.15, outperforming the SGZ algorithm by 3%. These results indicate that the algorithm effectively mitigates noise in enhanced images, enriching the detail information in both dark and bright regions, and significantly improving image quality compared to other mainstream algorithms.

In order to deeply understand the spatio-temporal distribution patterns of forest fires and reduce the adverse effects of forest fires on the ecological environment and human activities, the key parameters and dynamics of multi-source satellite for fire point identification was established using data from 8 domestic and foreign meteorological satellites and based on the classic context method. The satellite monitoring buffer zone radius verification method was used to verify the authenticity of forest hot spots retrieved from multi-source satellites, and the real forest hot spot data during the fire prevention period in 2021—2022 were used to analyze the spatio-temporal characteristics of forest fires. The results show as follows. The accuracy of satellite fire spot monitoring is 84.42%, and the fire point classification accuracy is 89.90%. The established inversion method is reasonable and reliable. The spatial distribution of forest fires in Hunan is “more in the southwest and less in the northeast”. At the same time the high-incidence areas are mainly distributed in southern Hunan, and the second-highest-incidence area is western Hunan. In summary, the risk of forest fires during the autumn prevention period is much greater than that during the spring prevention period. During the extreme high temperature and drought in 2022, forest fires were mainly distributed in the southern Hunan region and the Hengshao Basin. From the perspective of process distribution, the distribution of forest fires can be divided into four stages. The number of forest fires in the first three stages showed a significant increase trend, and in the third stage, the number of forest fires increased significantly. Fire risk is the most serious. In the fourth stage, due to the dual impact of precipitation and the province's fire ban, the risk of forest hot spots was significantly reduced.

In order to simulate the seismic damage process of RC (reinforced concrete) columns precisely and effectively, the method of seismic damage analysis of reinforced concrete members was established by combining the uniaxial damage constitutive model of concrete and steel bar with the flexibility-based fiber beam-column element model. The quasi-static test of reinforced concrete column was simulated and analyzed. It is found that the simulation model can simulate the degradation process of stiffness and bearing capacity of component with good accuracy. The shaking table test of reinforced concrete column under biaxial loading was simulated and analyzed. The results show that the simulation model can simulate the nonlinear dynamic behavior and damage distribution of component with good accuracy. Furthermore, the established model can effectively simulate the evolution process of seismic damage of components, and describe weak parts of components, and has high computational efficiency and solution accuracy, which can be used to analyze the collapse process of buildings and bridge structures under earthquake action.

In order to study the effect of temperature on the shear strength of fully weathered mudstone with different saturations,fully weathered mudstone specimens from a typical seasonal frozen region were used as the object of study.The GDS dynamic triaxial test system and the GDS unsaturated test system were used to conduct indoor triaxial tests on specimens with different saturations to investigate the trends of the shear strength parameters of fully weathered mudstone in the seasonal frozen region under different temperatures and surrounding pressures, and to compare and analyse the shear strength parameters of the two different saturations of the fully weathered mudstone were compared and analysed. The results show that the fully weathered mudstone is strongly influenced by saturation and temperature.The cohesion of both soil samples increases with decreasing temperature, and the internal friction angle increases and then decreases. The cohesion of the unsaturated mudstone specimens is consistently greater than that of the saturated mudstone specimens when the temperature and test system conditions are consistent. Saturation has a low effect on the internal friction angle of the specimens, which peaks at -5 ℃ and 0 ℃for the two soil samples respectively.

During the geothermal development of dry hot rock, the high temperature rock mass is subjected to repeated cold and thermal cycles. It leads to the rupture of thermal reservoirs and the change of physical and mechanical properties. In order to further explore the mechanism of the influence of temperature and cooling-heating cycle on rock characteristics, the granite specimens subjected to different high temperature nodal heat treatment were treated with natural cooling, fresh water cooling and seawater cooling respectively. The physical and mechanical indexes and microstructure were studied. The damage constitutive equations of granite under uniaxial compression with three cooling cycles were established. The results show as follows. With the increase of temperature and cycle times, the mass loss rate is in the order of freshwater cooling > natural cooling > seawater cooling, but at 600 ℃, serious particle breakup and shedding cause the mass loss of seawater cooling rock sample to exceed that of natural cooling. The elastic modulus, compressive strength and tensile strength are decreasing. The damage of water cooling to high temperature rock is greater than that of natural cooling. The damage effect of high temperature is more obvious than that of cycle times. The micro-cracks of seawater cooling rock sample are more developed. The damage variables consider the effects of temperature and cycle times, and add the damage coefficient to consider the damage effects of freshwater cooling and seawater cooling. The uniaxial compressive stress-strain curves combined with damage analysis under load are compared with the experimental results in a high degree of fitting, which reflects the rationality of the model.

With the development of the concept of composite materials, in order to further explore the mechanical properties of composite modified recycled concrete under the coupling effect of NS (nano-SiO₂) modified recycled coarse aggregate and PVA(polyvinyl alcohol) fibers, slump, cubic compression, axial compression, splitting tensile and flexural tests were carried out to study the working performance and mechanical performance changes of modified recycled coarse aggregate concrete with increasing PVA fiber content under different substitution rates. The results show that the slump of concrete increases with the increase of fiber volume. The damage of concrete is brittle, and the damage pattern of recycled concrete mixed with fiber is better. When the fiber volume content is 0.05 vol% and 0.10 vol%, the cubic compressive strength, ultimate bearing capacity, splitting tensile strength, folding strength and static elastic modulus will decrease under different regeneration and replacement rates, but all the strengths will exceed and increase when the fiber volume content is 0.15 vol%. PVA fiber will reduce the ultimate compressive bearing capacity and have different positive and negative effects on the peak strain. It is recommended to add PVA fiber with a volume content of 0.1 vol%. If PVA fiber is needed, it is recommended to use it when the regeneration and replacement rate is less than 30 wt%. In addition, it is found that the modified reclaimed coarse aggregate has good performance and can effectively replace natural aggregate or be mixed with natural aggregate in practical engineering.

In order to study the effect of basalt fiber on the durability of recycled concrete under the erosion of salt solution, the durability of recycled concrete specimens with different basalt fiber contents after salt-dry-wet cycle coupling erosion was studied. A comprehensive durability index D value was established to evaluate the durability of recycled concrete based on the entropy weight method. The effects of dry-wet cycle period of salt solution and basalt fiber content on D value were analyzed. A GM (1,1) mean model was constructed to reveal the time-varying law of the D-value of recycled concrete, and the predicted life of recycled concrete under different conditions was obtained. The results show that the D value can reflect the influence of different salt solution dry-wet cycle cycles and basalt fiber content on the durability of recycled concrete. As the salt solution's dry-wet cycle increased, the D value of the specimen gradually decreased, indicating a severe change. However, adding basalt fiber to the recycled concrete can effectively enhance its D value and durability. When the content of basalt fiber is 1.0%, the durability of recycled concrete is the best. The GM(1,1) model can more accurately predict the time-varying pattern of D values of recycled concrete under coupled salt-dry-wet cycle erosion when the amount of data is small.

To efficiently identify the opening and closing status of train doors and control the synchronous opening and closing of platform doors, a lightweight MobileNet network and machine vision based image recognition method was proposed to achieve linkage control between high-speed railway platform doors and train doors. A large dataset of train door images was collected from Beijing South Station and preprocessed to serve as the training and testing dataset for the model. The constructed network was trained and optimized using a binary cross-entropy loss function and the Adam optimization algorithm to achieve efficient and accurate recognition of door status. Validation results demonstrate an accuracy rate of over 95% in recognizing train door actions, with recognition time kept within 400 milliseconds. These results meet the current industry application requirements and greatly enhance the automation and intelligence level of the platform door system.

Aiming at the problem of coaxial wheel partial wear of HX high-power electric locomotive in China, the locomotive dynamics model was established based on the dynamics software SIMPACK, and the damage function prediction method based on wear number was used to analyze the influence of different coaxial sequence, wheel diameter difference, curve radius and other conditions on wheel tread damage. The results show that when there is wheel diameter difference in one axle or multiple axles, the influence on tread damage of 2-axle and 3-axle wheels is greater, and the influence on 1-axle wheels is less. For different values of wheel diameter difference, with the increase of wheel diameter difference, the impact of rolling contact fatigue degree on the right wheel tread of 1-axle is small, and the wear degree of the left wheel tread increases. The damage degree of wheel on both sides of 2-axle is reduced; the cracks of 3-axle left wheel are accentuated. Under left curve condition and R400 condition, wheel diameter difference has greater influence on the wear and rolling contact fatigue of each axle. Under right curve condition and R800 condition, wheel diameter difference has more influence on the wear and rolling contact fatigue of each axle. Compared with right curve condition, wheel diameter difference has more influence on wheel tread damage under left curve condition. Compared with curve radius, wheel diameter difference has less influence on wheel tread damage.

When a train set operates at high speeds, significant vibration and noise are generated in the outer door due to external airflow and road surface excitation, which directly impacts ride stability and train operation safety. To address the significant vibration of the thin-walled backplate structure of the outer door at the first five modal frequencies, particle damping was applied to mitigate vibration and noise. Firstly, a discrete element model of the particle damper for the outer door was established to analyze system energy consumption through simulations under various filler parameters. Based on these simulation results, an optimized design of the filler parameters was conducted, and experiments were performed to assess sound insulation with each filler material. The results show that the installation of particle dampers achieves a sound insulation improvement of 5.33 dB, significantly optimizing the radiated noise from the outer doors. These findings demonstrate the effectiveness of particle damping for vibration and noise reduction in the outer door of a moving train set.

With the continuous integration of urban Bridges into a variety of traffic forms, various traffic vehicles and pedestrians interfere with each other, affecting the safety and comfort of pedestrians. To research the pedestrian response on single-level rail-cum-road bridges, a pedestrian-road vehicle-train-bridge coupling vibration model was established. The acceleration difference between the bridge panel and pedestrian SMD model was researched, and the influence of trains and road vehicle passing the bridge in different ways on pedestrians was calculated. The results show that, compared with bridge panel, the amplitude of acceleration fluctuation of pedestrian SMD model is smaller, but the walking pedestrian model may experience abrupt and significant acceleration peaks. Each moving subsystem in the coupling model has a speed-sensitive interval, in which the pedestrian acceleration increases significantly. The further vehicles are away from the bridge center, the greater the acceleration of pedestrians become. When train and road vehicle pass the bridge simultaneously, the peak acceleration of pedestrian caused by them will be superimposed, and the peak acceleration caused by road vehicles increases significantly due to the train.

Road feeling simulation is one of the key technologies for the development of the steer-by-wire system. In order to improve the quality of the road feeling simulation, the road feeling torque of the steer-by-wire system was designed based on the principle of steering resisting torque generation of the traditional steering system and the method of calculating the dynamics model. In order to reduce the torque pulsation of the road feeling motor caused by uncertain system parameters and sensor noise, a vector control strategy for the road feeling motor based on the active disturbance rejection control algorithm was designed, and the control parameters in the active disturbance rejection control algorithm were optimized using the particle swarm optimization algorithm. Based on the steering wheel middle position maneuvering stability test and the steering-effort test, the road feeling simulation effect was verified. The results show that the active disturbance rejection control based on particle swarm optimization algorithm has strong adaptability, and can effectively realize the accurate simulation of road feeling. It can effectively reduce the burden on the driver. The relevant research can provide a reference for the design of the steer-by-wire.

To address the bottleneck issues in vehicle access efficiency for horizontal shifting mechanical parking garages, an access vehicle scheduling optimization model based on the PSO-OBL algorithm was proposed. The model aims to shorten vehicle access operation time and reduce user average waiting time by precisely controlling vehicle access strategies and time management. To enhance the optimization performance and convergence rate of the traditional particle swarm optimization algorithm, an innovative approach incorporating inter-particle collaboration and information exchange mechanisms was embedded into the algorithm framework, along with the integration of an opposition-based learning mechanism for efficient problem-solving. Experimental data indicates that, compared to the traditional particle swarm optimization algorithm, the PSO-OBL algorithm achieves significant improvements in customer average waiting time, average service time, average queue length, and average energy consumption. The findings of this study are expected to provide theoretical support and practical reference for optimizing access efficiency in horizontal shifting mechanical parking garages.

In order to scientifically assess the operational efficiency of airport surface movements, a departure flight taxi time prediction method considering runway and taxiway system configurations was proposed. Firstly, the definition of surface taxi time was provided, and an analysis of the historical operational data at the airport was conducted to identify more accurate factors influencing surface taxi-out time. Based on the conclusions drawn from correlation analysis, a random forest prediction model for departure flight surface taxi time was constructed. The method was illustrated using actual operational data from Shijiazhuang Zhengding Airport and Shenzhen Bao'an Airport, analyzing the characteristics of surface movement time under different runway system configurations. The results indicate that: Apart from taxi distance, the correlation results of factors influencing surface taxi-out time at airports with different runway and taxiway system configurations are generally consistent. When considering the same factors, the prediction of surface taxi time is better for single-runway airports compared to dual-runway airports. Surface taxi time at single-runway airports approaches unimpeded taxi time, while at dual-runway airports, there is a significant difference from unimpeded taxi time. The research findings are of significant importance for enhancing airport surface operation efficiency and achieving energy savings and emissions reduction.

In order to improve the trajectory tracking accuracy and stability of the aircraft in the traction process, taking the four-wheel steering aircraft traction system as the research object, the kinematics model of the aircraft traction system is established, and the four-wheel steering trajectory tracking control method of the tractor based on the model predictive control was proposed. Taking the double lane changing condition as the reference trajectory, the motion control simulation model of the aircraft traction system was built in MATLAB/Simulink, and the four-wheel steering trajectory tracking controller was established by combining the speed of the tractor and the angular distribution relationship of the four wheels. The controller was compared and analyzed with the traditional PID control to derive the superiority of the controller, and the tractor four-wheel steering and front-wheel steering trajectory tracking controllers were simulated and compared and analyzed at the speeds of 1.5 m/s, 3 m/s and 4 m/s, respectively. The designed controller was simulated and verified by changing the initial positional attitude of the aircraft traction system at a speed of 1.5 m/s. The results show that at three different speeds, the airplane lateral error, the heading angle error, and the tractor heading angle error under the four-wheel steering trajectory tracking control of the tractor are smaller than those under the front-wheel steering trajectory tracking control. In the case of initial deviation, the four-wheel steering trajectory tracking controller can enable the aircraft to complete the correction of the initial deviation in time, reduce the trajectory tracking error, and at the same time improve the stability of the aircraft's traction system in the driving process.

With the rapid development of China's civil aviation, the air traffic flow in terminal areas is experiencing a consistent and significant increase. The accurate forecast of short-term air traffic flow is of great significance for the efficient implementation of air traffic flow management. To enhance the accuracy of short-term air traffic flow forecast, a model combining EMD (empirical mode decomposition) and LSTM (long short-term memory) based on data differential processing was proposed. Firstly, the model performed empirical mode decomposition on short-term air traffic flow sequences. Secondly, to improve prediction accuracy, data difference was utilized to stabilize the time series. Finally, the processed sequences were input into the LSTM network model for prediction, and the final short-term traffic prediction value was obtained through data reconstruction. Experimental verification was conducted using the data from Zhengzhou Xinzheng International Airport. The results demonstrate that the model achieves a significant improvement in prediction accuracy, as indicated by the typical indexes RSME, MAE, and R², which are 0.29, 0.08, and 96.40%, respectively. This approach outperforms other methods and provides valuable reference for short-term air traffic flow prediction.

Yuehai is the largest wetland in Yinchuan of poor water quality during spring, with a significant contribution from agricultural non-point source pollution. In order to gain a deeper understanding of the distribution characteristics of nitrogen and phosphorus in the Yuehai Lake and its eutrophication status, water samples from 28 representative sites in Yuehai Lake and its outflow river were collected during the spring irrigation period of 2021.The distribution characteristics and regularities of TN (total nitrogen) and TP (total phosphorus), as well as different forms of nitrogen and phosphorus in Yuehai Lake were analyzed. The differences in nitrogen and phosphorus concentration between Yuehai Lake and its outflowing river were revealed. The results showed that during the spring irrigation period, the TN concentrations at 78% of the sampling points in Yuehai Lake was lower than the Class III surface water standard, and the TN concentration at 57% of the sampling points belonged to the heavily eutrophic type. During the spring irrigation period, the TP concentration at all sampling points was lower than Class III surface water standard, and the TP concentration at 70% of the sampling points belonged to the heavily eutrophic type. There were differences in the sources and transformation processes of different nitrogen fractions in the water of the Yuehai Lake. pH and TDS (total dissolved solids) affect the concentration of nitrogen and phosphorus in the water, and DO(dissolved oxygen) does not have a significant effect on the nitrogen and phosphorus forms in the water of the Yuehai Lake. The distribution characteristics of nitrogen and phosphorus in the outflow river of the Yuehai and the Yuehai water were different, and the differences in nitrogen concentration and non-orthophosphate phosphorus concentration between the Yuehai Lake and its outflow river were not significant in the direction of river and lake water flow (north-south direction). The nitrogen and phosphorus pollutants in the Yuehai converged into the Yellow River along with the rivers out of the Yuehai Lake, and the nitrogen and non-orthophosphate phosphorus in the Yuehai Lake might impact on the water of the Ningxia section of the Yellow River and increase the risk of pollution of the Yellow River, and the control of the nitrogen and non-orthophosphate phosphorus in the Yuehai Lake should be strengthened. This work provides a reference for continuously and accurately improving the water quality in the middle and upper reaches of the Yellow River Basin.

Bismuth ferrite has become an effective semiconductor photocatalyst for the degradation of various wastewaters due to its narrow band gap, high chemical stability, and good visible light response. Pure phase BiFeO₃ nanofibers were prepared by electrospinning method. The optimal degradation conditions for Congo Red were obtained through single factor experiments such as calcination temperature, PVP (polyvinyl pyrrolidone)concentration, collection distance, spinning voltage, and pushing speed. Four factors that significantly affect photocatalytic efficiency were selected for response surface analysis experiments with four factors and three levels. After optimization, the optimal PVP concentration was 12.17 wt%, collection distance was 14.07 cm, and spinning voltage was 12.03 kV The pushing speed is 0.74 μm/s, and under this condition, the efficiency of BiFeO₃ photocatalytic degradation of Congo red can reach 90.43%. The phase analysis and morphology characterization of bismuth ferrite nanofibers were carried out using X-ray diffraction, scanning electron microscopy, Raman spectroscopy, and Fourier transform infrared spectroscopy. The results show that the pure phase BFO nanofibers prepared by electrospinning has a rough surface and obvious particle sensation, presenting a one-dimensional rod-shaped structure with a size of about 300 nm. This nanorod-shaped structure has a larger specific surface area and more active sites, Can improve the photocatalytic degradation efficiency of BFO.

Manual grinding with hand-held tools generates a lot of dust, currently, it is common to use negative pressure trapping device to limit the dust in the confined space, and make the dusty airflow directed through the dust removal device to be purified to solve the problem of dust dissemination. In order to solve the problem of ineffective ventilation and protection facilities in an aluminum grinding workshop, which led to the uncontrolled spread of dust between operations, a structural design of a “U-shaped slit” dust collection device for the grinding process of small aluminum parts was proposed.Finite element simulation and analysis method was used to investigate the dust prevention and control effect of the device. The results show that: when the device on both sides of the slit width is 2 cm, the rear side of the slit width is 5 cm, the air volume is 3 600 m³/h, and the internal airflow channel structure is an asymmetric slanting closure structure, the control surface of the wind speed is more uniform, average wind speed of personnel breathing zone reaches 1.3 m/s, in the same time, the noise of the internal airflow channel is relatively small and the dust particle trapping effect is better. It is concluded that the size of the device's “U-shaped slit” and the internal airflow channel structure design can effectively capture the dust generated during the sanding process, reduce the concentration of dust in the workplace, and provide a reference for the protection of dust in the sanding place.