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Oilfield Sewage System Scheduling is a complicated, large-scale, nonlinear system problem with multiple variables. The complexity of the sewage system pipeline network connection grows along with the ongoing building of oilfield stations, and the shortcomings of the sewage system water quantity scheduling program based on human experience decision-making become increasingly apparent. The key to solving this problem is to realize the digital and intelligent scheduling of sewage systems. Taking the sewage system of an oil production plant in Daqing oilfield as the research object, the water scheduling model of the sewage system is established in this paper. Aiming at the complex nonlinear characteristics of the model, the Levy flight speed updating operator, the adaptive stochastic offset operator, and the Brownian motion selection optimization operator are established by taking advantage of the particle swarm optimization (PSO) and the cuckoo search (CS) algorithm. Based on these operators, a hybrid PSO-CS algorithm is proposed, which jumps out of the local optimum and has a strong global search capability. Comparing PSO-CS with other algorithms on the CEC2022 test set, it was found that the PSO-CS algorithm ranked first in all 12 test functions, proving the excellent solving performance of the PSO-CS algorithm. Finally, the PSO-CS is applied to solve a water scheduling model for the sewage system of an oil production plant in Daqing Oilfield. It is found that the scheduling plan optimized by PSO-CS has a 100% water supply rate to the downstream water injection station, and the total energy consumption of the scheduling plan on the same day is reduced from 879.95 × 106 m5/d to 712.84 × 106 m5/d, which is a 19% reduction in energy consumption. The number of water balance stations in the sewage station increased by 7, which effectively improved the water resource utilization rate of the sewage station. [ABSTRACT FROM AUTHOR]

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Unmanned tractors under ploughing conditions suffer from body tilting, violent shaking and limited hardware resources, which can reduce the detection accuracy of unmanned tractors for field obstacles. We optimize the YOLOv8 model in three aspects: improving the accuracy of detecting tilted obstacles, computational reduction, and adding a visual ranging mechanism. By introducing Funnel ReLU, a self-constructed inclined obstacle dataset, and embedding an SE attention mechanism, these three methods improve detection accuracy. By using MobileNetv2 and Bi FPN, computational reduction, and adding camera ranging instead of LIDAR ranging, the hardware cost is reduced. After completing the model improvement, comparative tests and real-vehicle validation are carried out, and the validation results show that the average detection accuracy of the improved model reaches 98.84% of the mAP value, which is 2.34% higher than that of the original model. The computation amount of the same image is reduced from 2.35 billion floating-point computations to 1.28 billion, which is 45.53% less than the model computation amount. The monitoring frame rate during the movement of the test vehicle reaches 67 FPS, and the model meets the performance requirements of unmanned tractors under normal operating conditions. [ABSTRACT FROM AUTHOR]

When performing hydrological model parameter calibration, equifinality inevitably reduces the simulation and prediction ability of hydrological models. To lessen the impact of equifinality, a novel algorithmic improvement framework is proposed in this paper. This framework allows the parameters to be searched hierarchically in order of sensitivity size and shrinks the original ranges of the parameters before the final search. The shuffled complex evolution (SCE_UA) algorithm, which is the most popular method for addressing hydrological model calibration issues, is improved using this new framework yielding HSRS_SCE algorithm, which stands for the SCE_UA algorithm with hierarchical search (HS) and range shrinkage (RS). A 26-dimensional parametric calibration problem is constructed and solved in this study utilizing 12 schemes based on the HSRS_SCE algorithm with various parameters and a control scheme based on the SCE_UA algorithm. The results show that the HSRS_SCE algorithm can not only produce calibrated parameter results significantly superior to those of the traditional scheme (p < 0.05) but also produce objective function values 26.1% better than those of the traditional scheme and reduce the search time through parallel computing. [ABSTRACT FROM AUTHOR]

When marine deep-towed multichannel seismic data are processed, the description of the receiving array geometry significantly impacts the quality of the imaging profile. Therefore, achieving a highly precise description of the receiving array geometry is very important for the fine imaging of such data. While basic particle swarm optimization (PSO) is known for its ease of implementation and efficiency, it often exhibits a low convergence accuracy. Consequently, the PSO algorithm is improved by modifying the inertia weight and incorporating Gaussian mutation. In combination with the actual motion of the towing streamer during surveys, a strategy for inheriting particle positions is introduced. When each seismic shot is solved sequentially, the results from the previous shot can serve as the initial particle positions for the next shot. The results indicate that this strategy achieves superior fitness values and outperforms the basic PSO algorithm. This method exhibits simplicity, rapid optimization, and a favorable solution quality, thereby offering a valuable approach to deep-towed array geometry inversion. It enhances the efficiency of deep-towed seismic data processing and serves as a reference for similar applications. [ABSTRACT FROM AUTHOR]