Traffic safety is a critical factor for the sustainable development of urban transportation systems. This study investigates the impact of historical accident information on the prediction of future traffic accident risks, as well as the interaction between this information and other features, such as driver violations and vehicle attributes. Using a comprehensive dataset of traffic accidents involving passenger vehicles in a western Chinese city, we developed two predictive models: Model 1, which is based on vehicle information and driver violations, and Model 2, which integrates historical accident data. The results indicate that the inclusion of historical accident information significantly enhances the predictive performance of the model, particularly in terms of AUC (Area Under the Curve) and AP (Average Precision) values. Furthermore, through feature importance analysis and SHAP (SHapley Additive exPlanations) value evaluation, this study reveals the interaction effects between historical accident data and other features, and how these interactions influence model decisions. The findings suggest that historical accident data play a positive role in predicting future accident risk, with varying effects on risk mitigation. These insights provide a scientific basis for developing strategies to ensure the sustainable development of urban transportation systems.

Peanuts are susceptible to defects such as insect damage, breakage, germinant, and mildew, leading to varying qualities of peanuts. The disparity in peanut kernel quality results in significant differences in their prices and economic value. Conducting real-time, accurate, and non-destructive quality inspections of peanut kernels can effectively increase the utilization rate and commercial value of peanuts. Manual inspections are inefficient and subjective, while photoelectric sorting is costly and less precise. Therefore, this study proposes a peanut kernel quality detection algorithm based on an enhanced YOLO v5 model. Compared to other models, this model is practical, highly accurate, lightweight, and easy to integrate. Initially, YOLO v5s was chosen as the foundational training model through comparison. Subsequently, the original backbone network was replaced with a lightweight ShuffleNet v2 network to improve the model’s ability to differentiate features among various types of peanut kernels and reduce the parameters. The ECA (Efficient Channel Attention) mechanism was introduced into the C3 module to enhance feature extraction capabilities, thereby improving average accuracy. The CIoU loss function was replaced with the alpha-IoU loss function to boost detection accuracy. The experimental results indicated that the improved model, SEA-YOLOv5, achieved an accuracy of 98.8% with a parameter count of 0.47 M and an average detection time of 11.2 ms per image. When compared to other detection models, there was an improvement in accuracy, demonstrating the effectiveness of the proposed peanut kernel quality detection model. Furthermore, this model is suitable for deployment on resource-limited embedded devices such as mobile terminals, enabling real-time and precise detection of peanut kernel quality.

To improve its cleaning performance, the peanut sheller’s air–screen cleaning device was used as the research object, and a numerical simulation was carried out. A scheme was proposed to optimize the installation angle of the screen surface of the air–screen cleaning device, and the influence of the airflow velocity of the sieve surface before and after the structural improvement of the machine on the airflow distribution of the whole chamber was evaluated. At the same time, the computational fluid dynamics–discrete element method (CFD–DEM) of coupling simulation was used to carry out single-factor simulation tests on the improved air–screen cleaning device, analyze the movement trajectory, velocity, and displacement of the extrudates in the cleaning chamber, characterize the separation law of extrudates, and determine the parameter ranges for each factor. In addition, based on the central composite Box–Behnken design, an orthogonal simulation test was carried out with three factors, including fan speed, amplitude, and vibration frequency, and the influence of each factor on the kernel loss rate and impurity rate was investigated. Furthermore, the influencing factors were optimized, and the optimal parameter combination was obtained; when the fan speed was 1682.72 r/min, the amplitude was 3.74 mm, and the vibration frequency was 492.86 Hz, it was more conducive to the cleaning of kernels. Finally, the accuracy of the simulation and optimization methods was verified via the machine test, and the optimal parameter combination was obtained: at a fan speed of 1680 r/min, an amplitude of 3.7 mm and a vibration frequency of 490 Hz were achieved, and the kernel loss rate and impurity rate were 2.01% and 2.42%, 3.71 and 4.42 percentage points lower than those before optimization, respectively.

Two-stage harvesting is the main method used for the mechanized harvesting of peanuts in China, in which the pickup device is a core part of the combine harvester. In order to solve the problem of pod loss caused by “stack” and “loss picking” from peanut plants when using a traditional spring-finger cylinder pickup device, an optimal spring-finger cylinder peanut pickup mechanism was designed and its picking properties were tested. Based on the picking characteristics and picking force analysis when considering a peanut plant windrow, the ideal picking attitude and swing rule of the spring-finger were determined, and the cam slide, as the core element of the spring-finger cylinder peanut pickup device, was optimized. A mathematical model of the cam cylinder center line was established according to the swing law of the four picking stations utilizing the spring-finger. MATLAB and ADAMS were used to establish a simulation of the pickup mechanism, and kinematics and dynamics simulation analyses of the pickup mechanism were carried out. According to the design results, a prototype was constructed and a running pickup test was carried out. The peanut plant picking experiments indicated that the phenomenon of peanut plant stacking had significantly disappeared. Furthermore, through response surface analysis, the optimal working parameters of the picking device were obtained as follows: the forward speed V_m was 48.0 m/min, the rotational speed N was 50 r/min, and the ground height H was −16.8 mm. The picking rate of peanut plants was 99.21% and the pod loss rate was 1.79% under two harvesting conditions, with a peanut plant moisture content of 15% to 17%. This study provides technical support for the future design of picking devices for two-stage peanut pickup harvesters.

Two-stage harvesting is the main method for performing the mechanized harvesting of peanuts, and the picking device is a core part of the combined harvester in China. In order to solve the problem of pod loss caused by the "stacking", "impact" and "throwing up" of peanut plants by a traditional cam-slide spring-finger cylinder picking device, the shovel-finger and cylinder peanut-picking device was developed and used in a picking-up performance test based on the study of peanut-plant strip-picking characteristics. The mathematical model of the mechanism was established by analyzing the structure of the mechanism and the peanut-plant strip-picking characteristics, and the parameters of the mechanism were optimized using the objective function method. The prototype was developed and tested. The experiments in which the prototype was used to collect peanut plants indicated that the phenomenon of peanut plants being stacked and thrown disappeared. Through a response surface analysis and prototype test, the optimal working parameters of the picking device were obtained: the forward speed V was 48.0 m/minute, the rotational speed N was 45.3 r/minute and the ground height H was -18 mm. The peanut-plant picking rate was 98.9% and the fruit loss rate was 2.8% under two different harvesting conditions for which the peanut-plant moisture content was 15~17%.