Za Kan

To meet the precision spraying of poultry house immunization robots, an immunization sprayer based on microporous atomization is designed to ensure uniform and efficient atomization of immunological reagents. The optimal driving frequency is determined through modal analysis of the piezoelectric atomization plate, which is 113 kHz. Using the resonant frequency-driven droplet generation model, the simulation of single-pore droplet formation is realized, and the initial droplet velocity and diameter are extracted as boundary conditions for input into the droplet diffusion model. This reveals the diffusion and evolution rules of the atomization field, along with the distribution characteristics of velocity and particle size. The atomization diffusion distance in a dynamically stable state is 68.36 cm, with a diffusion range of 32°. Within 15–55 cm from the atomizer, over 90% of droplets have an adequate particle size of 30–200 μm. Practical testing with the VisiSize P15 droplet size measuring instrument showed that the diffusion angle of the stable atomization morphology is approximately 30°, with a diffusion distance of 65 cm. The simulation results show an absolute relative error of less than 7%, while the droplet size distribution has an absolute relative error of less than 10%. For the segmented characteristics of droplet velocity attenuation, the absolute relative error is less than 18%, with an absolute error of less than 0.15 m/s. These results demonstrate that the overall atomization and diffusion effect meet the requirements for immunization spraying.

To reduce the impurity and loss rates during agricultural film fragment separation, an integrated airflow and mechanical separation approach was devised, and the airflow-rotating disc separation device with air suction mechanism and curved disc rotating screening mechanism was designed. The processes of throwing, collision and slipping were analyzed, considering film compressibility. The factors affecting the separation performance were identified as feeding quantity, airflow velocity and disc rotational velocity. Subsequently, a Box-Behnken response surface experiment was conducted, using fan rotational velocity, disc rotational velocity, and feeding quantity as factors, with impurity removal rate and film leakage rate as the evaluation indices. The multiple regression equations were established to depict the relationships between the factors and the indices, followed by the response surface analysis. The optimal parameter combination was a fan rotational velocity of 1450 r/min, a disc rotational velocity of 54 r/min, and a feeding quantity of 130 kg/h, attaining an impurity removal rate of 96.51% and a film leakage rate of 21.92%. The discrepancies between the verification values and the prediction values were 4.61% and 5.30% respectively, signifying the equations’ reliable predictability. This research lays the groundwork for the design of film fragment separation apparatuses.

Influence law on the effect of shredding characteristics of residual film in the mixture of mechanically recovered residual films and impurities in cotton fields

Addressing the issues of the sand cat swarm optimization algorithm (SCSO), such as its weak global search ability and tendency to fall into local optima, this paper proposes an improved strategy called the multi-strategy integrated sand cat swarm optimization algorithm (MSCSO). The MSCSO algorithm improves upon the SCSO in several ways. Firstly, it employs the good point set strategy instead of a random strategy for population initialization, effectively enhancing the uniformity and diversity of the population distribution. Secondly, a nonlinear adjustment strategy is introduced to dynamically adjust the search range of the sand cats during the exploration and exploitation phases, significantly increasing the likelihood of finding more high-quality solutions. Lastly, the algorithm integrates the early warning mechanism of the sparrow search algorithm, enabling the sand cats to escape from their original positions and rapidly move towards the optimal solution, thus avoiding local optima. Using 29 benchmark functions of 30, 50, and 100 dimensions from CEC 2017 as experimental subjects, this paper further evaluates the MSCSO algorithm through Wilcoxon rank-sum tests and Friedman’s test, verifying its global solid search ability and convergence performance. In practical engineering problems such as reducer and welded beam design, MSCSO also demonstrates superior performance compared to five other intelligent algorithms, showing a remarkable ability to approach the optimal solutions for these engineering problems.

This study aimed to enhance the efficiency of tiger nut combine harvesters by reducing impurity and loss rates during processing. Scholars focused on analyzing the composition and suspension speed of the bean mixture, leading to the development of a wind-screen impurity-removal method. The wind-screen-type bean-separation device was designed with a cross-flow fan, louver screen, frame, and driving mechanism. Theoretical analysis was employed to discuss the motion characteristics and behavior of the sieve body and material, thereby revealing the screening dynamics of tiger nuts and impurities. Factors such as crank radius, crank speed, and fan speed were identified as crucial for optimizing separation performance. Initial single-factor tests helped narrow down the range of influencing factors. Subsequently, a three-factor, three-level Box–Behnken test was conducted with crank radius, crank speed, and fan speed as variables and impurity rate and loss rate as evaluation indexes. This led to the establishment of a multiple regression equation linking these factors to the evaluation indexes. Through response surface analysis and multi-objective optimization using the regression model, the optimal operational parameters for the device were determined: crank radius of 45 mm, crank speed of 497 r/min, and the fan speed of 1100 r/min. Theoretical calculations predicted an impurity rate of 2.42% and a loss rate of 0.51%. Verification tests confirmed these findings, showing an average impurity rate of 2.53% and a loss rate of 0.56%, which met the mechanized harvesting standards for tiger nuts. Overall, this study introduces a novel method and technical framework for effectively separating tiger nuts from impurities, thereby advancing the mechanization of tiger nut harvesting processes.

A methodology combining physical experiments with simulation was employed to acquire contact parameters of sandy soil precisely for planting tiger nuts in the desert area of Xinjiang. The stacking angle under different parameter combinations was applied as a response value. Through the Plackett–Burman test, several factors that have a significant influence were determined. The steepest ascent test was conducted to establish the finest scope of values for these parameters. The stacking angle was considered the response variable, and non-linear tools were used to optimize these parameters for simulation. The findings showed that applying response surface methodology (RSM) resulted in a relative error of 1.24%. In the case of BP-GA, the relative error compared to the physical test value was 0.34%, while for BP, it was 2.18%. After optimization using Wavelet Neural Network (WNN), the relative error was reduced to only 0.15%. Results suggest that WNN outperforms the RSM model, and the sandy soil model and parameters generated using WNN can be effectively utilized for discrete element simulation research.

To tackle problems such as the difficult separation from sand and the high power consumption of tiger nut harvesting in the sandy areas of Xinjiang, a conveying and separating device for tiger nut harvesters was designed. The axial and radial migrations of materials under screw action and the separation process of materials under vibratory action were analyzed dynamically. A simulation analysis was carried out on the conveying and separating process based on EDEM software. The migration trajectories of tiger nuts and sand particles were extracted, the displacement variations of sand particles on the X-axis, Y-axis, and Z-axis were analyzed in the action area of the screen-cleaning spike teeth and the screw action area, respectively, and the conveying and separation law of the tiger nut harvest mixture was clarified. With key parameters such as the screw velocity ratio, amplitude, vibration frequency, and machine operation velocity as test factors, and with the sand removal rate, crushing rate, and power consumption as test evaluation indicators, a four-factor, five-level orthogonal central composite test design was implemented. The test results were analyzed via the regression variance analysis method, and relation models between variable factors and evaluation indicators were constructed. The test results show that under the combined conditions of a screw velocity ratio of 0.88, an amplitude of 4.7 mm, a vibration frequency of 7.5 Hz, and a machine operation velocity of 0.92 km/h, the sand removal rate is 90.40%, the crushing rate is 1.66%, and the power consumption is 2.24 kW in theory. The optimized results were verified by tests. The sand removal rate was 88.92%, the crushing rate was 1.71%, the total power consumption was 2.29 kW, and the errors from the predicted values were 1.6%, 3.0%, and 2.2%, respectively, meeting the requirements for tiger nut harvesting conveyance and separation. This research can provide support for the development of technology and equipment for mechanized harvesting of tiger nuts in the sandy areas of Xinjiang.

Currently, unmanned aerial vehicle hyperspectral images (UAV-HSIs) lack quick, objective, and comprehensive image quality assessment (IQA) methods. Therefore, a multifeature-based fuzzy comprehensive evaluation (FCE) method was proposed in this letter to comprehensively evaluate UAV-HSI quality. To characterize the hyperspectral quality comprehensively, we selected four radiometric features, three spatial features, and two spectral features to construct an indicator set. After analyzing the statistical distribution of the above features of the 23 UAV-HSIs, a fuzzy evaluation threshold table and membership functions were established. To determine the optimal feature weights, the weights obtained using the three methods were tested on a test set of UAV-HSIs constructed with different degrees of noise and blur. The test results showed that the combined weight based on a combination of subjective and objective weight is more robust. The experiment for comprehensive quality assessment of different distortion types and flight heights was done with UAV-HSIs. The results indicated that the comprehensive quality score was in good agreement with the subjective assessment and the objective fact. This comprehensive quality evaluation method can be effectively used for blurred, noisy, overexposed, and different-height UAV-HSIs.

Design and optimization of an airflow-vibration sieving device for the effective cleaning of cyperus esculentus L. harvesting mixture