High-precision vehicle trajectory data is crucial for the realization of intelligent transportation systems. However, existing vehicle trajectory sensing technologies are limited by the range of data collection, making it challenging to obtain full-period and full-area vehicle trajectory data, which cannot meet the demands for trajectory tracking accuracy and real-time performance in practical applications. Considering the characteristics of vehicle trajectory data across radar scenarios, a cross-device vehicle trajectory tracking method was proposed based on radar data. Firstly, trajectory data was filtered based on the lower bound of the confidence interval, and the position and velocity of vehicle trajectories were smoothed and denoised using Kalman filtering. Next, the trajectory timestamp, position coordinates, speed, direction, and lane number from the radar detection area were used as model inputs, while the position information of non-overlapping areas was used as output to construct a non-overlapping area trajectory prediction model based on long short-term memory (LSTM). Subsequently, a fast dynamic time warping (DTW) algorithm based on trajectory condition constraints and search area limitations was proposed to compute trajectory similarity, enabling cross-radar device vehicle trajectory tracking. Finally, verification was conducted using overlapping and non-overlapping scenario data from radar detection on highways. Experimental results show that the trajectory tracking accuracy in overlapping scenarios reaches 92.2%, and the trajectory matching accuracy in non-overlapping areas reaches 90.3%.

Container terminal yard as the hub of the operation area on both land and sea sides, the space allocation will directly affect the overall operation efficiency of the terminal. In order to reasonably allocate the space of U-shaped yard to improve the efficiency of container loading, two blocks sharing one intelligent guided vehicle(IGV) lane were formed into a group. From the perspective of reducing lane congestion, considering the constraints of high and low workloads of adjacent operation areas within the block groups, and with the goal of minimizing the total loading operation time and balancing the number of containers in the block groups, the first stage of the export container allocation model was constructed, and an immunity compensation mechanism and Metropolis criterion were introduced. And an improved adaptive genetic algorithm was designed by introducing immune compensation mechanism and Metropolis criterion. Based on the allocation results of the first stage, a priority descending balanced stockpiling strategy was established to ensure the continuous operation of the yard crane, and the second stage of the container allocation model was constructed, and a heuristic algorithm was designed to solve the problem. The design of large-scale case experiments shows that the algorithm is able to give effective export container stockpiling area delineation and container space allocation scheme, and the performance of the algorithm is verified by different scale comparison experiments, which shows that the algorithm has a faster convergence speed and is able to optimize 10% of the target value, which improves the overall operational efficiency of the U-shaped automated terminal.

Uplift piles, in accordance with their structural properties, effectively sustain the structural uplift loads and have emerged as an efficacious solution to address the anti-floating issue. The precise determination of the internal forces within uplift piles is crucial for comprehending their load-bearing characteristics. Nevertheless, the tensile capacity of concrete is relatively feeble. Once the load attains a specific magnitude, its elastic modulus will decline, rendering the traditional axial force calculation methods inapplicable. By leveraging the optical frequency domain reflectometry(OFDR) strain measurement technology and conducting indoor model tests of uplift piles, the strain distribution and evolution patterns of both steel bars and concrete during the pulling process were analyzed. The alterations in the elastic modulus of concrete throughout the tension-failure process were thereby obtained. A method for optimizing the axial force calculation, which exploits the relationship curve between the concrete strain and elastic modulus, was put forward. This enables the accurate acquisition of the axial force of the pile body and its subsequent application in practical engineering projects. The test results indicate that under the condition of small loads, the OFDR technology can identify the locations where concrete cracks emerge based on the strain curve of the pile body. In the event of pile body failure under large loads, the elastic modulus of concrete can be rectified using the relationship curve between strain and elastic modulus. Compared with traditional calculation methods, the relative error of the axial force throughout the entire process can be confined within 5%. The viability of this approach has been corroborated in actual engineering endeavors, and the optimized axial force calculation exhibits enhanced precision.

Traditional cloud classification methods exhibit limitations such as subjectivity and low efficiency and accuracy. To address these issues, a cloud classification prediction model based on the light gradient boosting machine (LightGBM) was proposed. Firstly, feature variables, including cloud top height, cloud bottom height, cloud layer thickness, average reflectivity factor, liquid water content, and duration obtained through millimeter-wave radar were utilized. A dataset was then constructed by combining these features with classification labels to meet the requirements of the model. This dataset was subsequently used to build a classification model that categorizes clouds into seven types: St, Sc, Cu, As, Ac, Cs, and Cc. The experimental results demonstrate that the model achieves an accuracy of 94.70%, precision of 94.68%, recall of 94.97%, and F₁ of 94.65%. These results indicate superior classification performance compared to other models. Therefore, the constructed LightGBM model shows significant effectiveness in cloud classification and recognition, exhibits strong applicability, and holds promising prospects for the automation of cloud recognition services.

To address the challenges of complex geology, dense urban spaces, high component production requirements, and difficult excavation parameter control in the intelligent construction of ultra-large diameter shield tunnels, a systematic study of key links in the shield construction industry chain was conducted based on the experience and digitalization needs of completed and ongoing projects. building information modeling(BIM), big data, IoT, and artificial intelligence technologies were applied to build a digital architecture and establish a full lifecycle coding system. A data platform was developed to promote the digitalization of design data, intelligent segment production, tunnel intelligent excavation, and lifecycle management. The research shows that the parametric drive greatly improves the efficiency of digital design, optimizes the precision of segment production and drilling, and enhances the ability to control the whole life cycle. It can be seen that digital means effectively solve the technical problems in construction, improve the ability of construction control, and provide a feasible digital solution for the construction of large-diameter shield tunnel.

In order to improve the unidirectional linear resource metabolism pattern within communities and optimize the urban resource circulation system, a green productive regeneration method for urban communities based on image 3D reconstruction and multi-criteria decision making was proposed with the help of drone low-altitude photogrammetry technology and using Agisoft PhotoScan, Cloud Compare, and Yaahp. By constructing a 3D point cloud model of the community's built environment and a spatial production potential evaluation model, the suitability of different spaces for production within the community was analyzed, and regeneration design was carried out. The results show that the regenerated community using this method can meet 43.8% of its vegetable supply demand, process 87.28% of its organic waste, supplement 23.46% of its irrigation water needs, and effectively reduce food mileage carbon emissions and building energy consumption. It is concluded that the community's green productive regeneration can significantly reduce its external resource dependency, thus transforming its resource metabolism pattern.

In the field of unmanned systems, heterogeneous cross-domain collaboration is recognized as an effective solution to current bottlenecks, integrating functional redundancy and complementary capabilities across different platforms. However, traditional rope-hook recovery systems, due to their high stiffness, result in significant instantaneous impact forces during aircraft hooking, which leads to undesired landing attitudes and an inability to land precisely. Furthermore, these systems are less flexible and have slower response times compared to robotic arms in achieving altitude tracking. To address these challenges, admittance control for robotic arm compliance was introduced, with the goal of enhancing landing stability, preventing structural damage caused by excessive lateral forces, and ensuring stability. In addition, the roll compensation process was optimized through a combination of short-time Fourier transform-fast Fourier transform(STFT-FFT) prediction algorithms and active disturbance rejection control, leading to smoother and more stable compensation responses. Simulation tests confirmed the effectiveness of the admittance control strategy and the optimization of the compensation response algorithm, resulting in improvements in both landing stability and system flexibility. This research presents novel methods for unmanned aircraft recovery and offers valuable insights for the development of future unmanned systems.

All the information regarding the distribution of karst caves sometimes cannot be fully reflected by means such as drilling, electrical prospecting and seismic exploration. Even with optimized design, it remains difficult to avoid the situation where pile foundations are constructed above karst caves and have to penetrate through them. The current research status of the bearing characteristics of pile foundations penetrating karst caves was analyzed from aspects like theoretical calculation, numerical simulation, indoor experiment, on-site experiment and karst cave filling materials. The following conclusions are obtained. When calculating the bearing capacity of pile foundations penetrating karst caves, the influence of the three-dimensional geometric dimensions of karst caves on the bearing characteristics of pile foundations must be comprehensively taken into account. The accuracy of parameter selection needs to be enhanced, the load transfer mechanism should be thoroughly understood, and theoretical models considering the influence of multiple factors comprehensively should be developed. In on-site experiments, emphasis should be placed on the accuracy and comprehensiveness of data collection. Unified standards and specifications ought to be established to enhance the representativeness, universality and applicability of the experimental results, thereby providing references for similar issues. Indoor models have investigated the influence law of the shape of karst caves on the bearing characteristics of pile foundations, analyzed the interaction mechanism between pile foundations and the roof of karst caves, and disclosed the stress characteristics and failure modes of pile foundations when penetrating karst caves. However, the shapes of simulated karst caves are relatively regular and single, and the influence of groundwater, temperature and the filling of karst caves on the bearing mechanism of pile foundations is rarely considered. The numerical simulation method is capable of simulating the bearing characteristics of pile foundations under complex stress states with different karst cave shapes, different pile types and different construction conditions. Nevertheless, the accuracy of parameter selection and simulation results requires improvement. The establishment of real geometric models of karst caves is a crucial development direction in the future. The research and optimization of karst cave filling materials are rather idealized, and their application under complex geological conditions is rather difficult. They usually exhibit performances that do not meet expectations. The research and development of karst cave filling materials should fully consider the adverse effects of special geological conditions, temperature changes and the chemical corrosion effect of the water environment in karst caves on the properties of filling materials.

In order to better study the spatial distribution of co-seismic landslides of the Luding earthquake, satellite images of Sentinel-2 on July 8 before and October 1 after the 2022 Luding earthquake were acquired, and a study area of 145.6 km² was obtained after cropping. Supervised classification was performed on the two images using the minimum distance method, maximum likelihood method, and neural network method, respectively, and the classification results were verified by calculating the overall accuracy and Kappa coefficient. Finally, the supervised classification results of the neural network method of the two images were selected for comparison to obtain the change detection results, and a total of 2 247 co-seismic landslides were identified in the study area, covering an area of 22.61 km², which accounted for 15.53% of the total study area. In the statistics of the slope direction, it was found that the slope direction of the co-seismic landslides radiated in the vertical direction of the originating fault to both sides, indicating that the co-seismic landslides were influenced by the originating fault. The analysis of elevation distribution pointed out that the landslides were mainly concentrated in the elevation range of 1 000 m to 2 000 m, accounting for more than 90% of the total. The analysis with other factors indicated that the landslide events were mainly concentrated near rivers, roads and on mountain slopes. The results of the principal component analysis indicates that the most important factor influencing the incidence of landslides in the same earthquake is the distribution of topographic deposits.

The in-stage multi-cluster fracturing technology in horizontal wells is a key method for the efficient development of unconventional oil and gas. Due to the influence of reservoir heterogeneity, non-uniform perforation erosion, and fluid competition, the in-stage multiple perforation clusters cannot initiate simultaneously and propagate evenly in Mahu conglomerate Jinlong 2 well block reservoirs. The perforations with the excessive fluid intake can be plugged by injecting the ball-shaped or knot-shaped diverters. The sand carrier fluids can be dynamically distributed. In this way, the fracturing stage can be evenly stimulated and the reservoir productivity can be fully excavated. It is of great value to judge the temporary plugging effect accurately and timely. In this way, the operation scheme can be adjusted and the stimulation effect can be improved. The perforation plugging, the fracture initiation, and the fracture propagation were completely considered by integrating the three methods of diverter in-place pressurization, same displacement pressure boost, and curve superposition. The comprehensive discrimination method of the in-stage multi-cluster temporary plugging effect was generated and the supporting software was compiled to realize online monitoring and discrimination. The method demonstrates more than 85% consistent with the monitoring results of optical fiber and hawk-eye, which provides method and theoretical support for the upgrade and popularization of the temporary plugging technology.