Based on large eddy simulation， different sub-lattice turbulence models are used to simulate the flow field of a peach cavity hydrodynamic coupling under braking condition. The Q-criterion vortex recognition method is used to extract the three-dimensional multi-scale vortex structure inside the turbine. The unsteady multi-scale vortex spatiotemporal evolution law， energy transfer and loss mechanism inside the turbine is analysed based on the vortex dynamics theory. In order to verify and evaluate the accuracy and reliability of the simulation results， the velocity field and vorticity field are extracted based on flow field visualization experiment by particle image velocimetry. From the perspective of the qualitative identification of three-dimensional vortex structure spatiotemporal characteristics and the quantitative extraction of two-dimensional flow field parameters： WMLE S-Ω model simulation can provide rich information of three-dimensional multi-scale vortex of the near-wall region on the blade，the small-scale vortex can be captured accurately. WALE model simulation can accurately identify small-scale vortices in the corner area where the blade and the outer ring intersect. The simulation results of the two-dimensional flow field in the mainstream area by WMLES S-Ω model tend to be true， the secondary flow phenomenon in the corner area can be presented accurately. The numerical distribution of the two-dimensional flow field simulation in the near-wall region on the blade by WMLES model is consistent with experimental values. The research results can provide certain theoretical and technical guidance for the flow field simulation of hydrodynamic coupling.

A fault diagnosis method for ultra precision machine tool spindle based on multi state time series predictive learning was proposed， and the simulation test of the method was completed. By constructing a DAFDC-RNN model （Dual-stage attention and full dimension convolution based recurrent neural network） introduces attention， full-dimensional convolution and time attention mechanisms to generate the correlation between spindles in the process of running state. The output of the model is the predicted value of spindle operating failure. The EMD-AR analysis method （Empirical mode decomposition-auto regressive） decomposes the first-order components in the time domain signal， and excludes the generation of a new signal until it is no longer decomposed. After decomposition， the signal is calculated and solved， and the fault diagnosis result is obtained. The experimental results show that the fault prediction accuracy of the proposed method can reach 0.95~1.0， and the time-consuming can be controlled within 10 ms. The amplitude fluctuation of the normal signal decomposed by the spindle running signal decomposition under the research method is basically consistent with the actual results.

In order to solve the problem that it is difficult to manually clean the pollutants and residual bait attached to the bulkhead of aquaculture， an underwater robot which can automatically clean the wall is developed. The robot is a design scheme of rotary brush cleaning， thrust adsorption and wheel mobile mechanism. By analyzing the cleaning rate and cleaning energy consumption of the experimental brush plate， the design and layout of the brush plate are determined， and the matching between the robot moving speed and the brush plate rotating speed is completed. The results show that when the rotating speed of the brush disk is constant， increasing the number of bristles or reducing the moving speed of the robot can improve the cleaning rate； The cleaning energy consumption can be reduced when the robot is arranged with a small size brush plate. Through the test， the robot can stick to the wall and navigate stably. When the moving speed of the robot is 0.2 m/s and the rotating speed of the brush disc is 200 r/min， the cleaning effect is remarkable， and the cleaning efficiency is 388 m²/h， which can meet the requirements of the actual working conditions.

Based on Hertz contact theory， the contact model of contact wheel and workpiece is simplified to analyze the principle of ultrasonic vibration belt grinding， and the motion characteristics of ultrasonic assisted abrasive particles are analyzed. By dividing grinding force into cutting deformation force and friction force， the influence of ultrasonic vibration on cutting deformation force and friction force is analyzed. The results show that the proposed model has high prediction accuracy and provides effective reference and theoretical basis for subsequent practical machining guidance.

In order to solve the defects of traditional node importance degree method in highway network layout， a new arterial highway network layout method was proposed. Firstly， the actual road network is transformed into a topological network composed of nodes and sections， and the nodes hierarchy is divided based on node importance indexes such as comprehensive transportation index and urban development potential. After that， the assignment is based on the route demand index such as the service level of the comprehensive transportation corridors， the improved Floyd algorithm is used to explore the path with the greatest demand among important nodes. Finally， according to the sharing of road transport in the comprehensive transport， determine the main transport lines of the road， and connect the cities along the line， eventually form the main arterial highway network. The scope of study is the region consisting of Harbin， Qiqihar， Daqing， Suihua. Compared with the current road network， the node connectivity increases by 1.32%， the road network density increases by 1.91%， and the time spent in regional transportation is saved by 5.3%.

To solve the queuing overflow problem of single-berth bus stations at route density points during peak traffic hours under green wave control conditions， a bus speed guidance model is proposed. Define the concept of the bus group to clarify the object of bus group speed guidance scheme. By issuing the speed command at the speed decision point， the front and rear adjacent buses are guided to adjust the headway， and the bus priority strategy is realized. The model is simulated by Vissim software. The simulation results show that after applying the model proposed， the average overlapping delay at stop time of the upstream bus station and the downstream bus station is reduced by 41.02% and 68.68%， respectively. The average stops of bus decreased by 14.46%. The model proposed in this paper can effectively solve the queuing overflow problem of single-berth bus stations in the line overlap interval and improve the section operation efficiency of buses.

In order to divide the associated road chains of urban road network and accurately predict the traffic operation state， calculate the importance of each road section and the shortest distance path length to represent the spatial characteristics of the road section， this paper proposes the density peak clustering algorithm to identify the correlation road chain set with strong temporal and spatial correlation of traffic flow. A traffic flow prediction model is constructed by the long short-term memory neural network based on road chain groups division（RCGD-LSTMNN），and takes the spatiotemporal two-dimensional matrix of all sections in the same road chain as the model input. In Beijing road network， for example， the fourth ring road in the backbone network is divided into eight associated road chain group， the model accuracy can reach more than 95%， and is superior to the traditional LSTM and BP model predicted results， show that the presented model has good applicability and accuracy of stability， applicable to different spatial and temporal patterns of road traffic state forecasting chain group.

To improve the attractiveness of low-carbon travel way of public transportation， "preferential transfer" strategy is optimized in this paper. The travel time and cost accounts perceived by passengers are split， and the passengers' decision-making processes are described based on mental account theory. The passenger corridor super network is constructed， and the generalized time cost of passenger multi-mode travels are quantified. Considering the difference of transfer travel-chain， passenger ticket discount method is designed and the travel cost is quantified. On this basis， a bi-level programming optimization model of discount rate is proposed. The upper layer aims at maximizing operators' revenue， and the lower layer is a balanced distribution model of multi-mode transportation system. Genetic algorithm and successive average method are used to solve the model， and the sensitivity analysis of the impact of passenger perceived risk on multi-mode selection is carried out. The results show that time account presenting risk pursuit of passengers can increase the demand of transfer trip， while expense account can reduce and passengers are more sensitive to the changes in time accounts. When the transfer discount rate is 0.043 and 0.017 yuan/min respectively in off-peak and peak hours， the transfer ratio can be increased and the income of bus enterprises can be increased at the same time.

In order to solve low signal control efficiency caused by traffic load difference at the intersection entrance， a multi-objective optimization method of traf?c signals based on dandelion algorithm is proposed. A model of delay unevenness between intersection lanes is established using Theil index to quantify the delay difference of vehicles within and between lanes in the same phase of signal cycle. Taking the cycle vehicle delay and the cycle delay unbalance degree as the objective function， a multi-objective optimization model of traf?c signals is established， and a multi-objective dandelion optimization algorithm is proposed to solve the timing optimization model. Two simulation environments are established taking the intersection of Changzhou Road and Yangzhou Road in Jiaozhou city， comparison and analysis are proposed on traffic efficiency of signal schemes designed by the method in this paper， Webster and NSGA-Ⅱ algorithm. VISSIM simulation shows that the vehicle delay and queue length are effectively reduced， which effectively improves the traffic efficiency of the intersection.

Aiming at the requirements of intelligent vehicle virtual test technology for the construction of driving scenario， 59 cases of lane change behavior data were obtained by threshold method and manual verification method. The effects of differentiated scenario type elements and continuous scenario elements on risk perception coefficient were analyzed. After dimensionality reduction， 4 types of scenario elements significantly related to the risk degree of lane change scene were obtained： ego-vehicle longitudinal speed， relative longitudinal speed， longitudinal distance and front vehicle cut in duration. The k-means algorithm based on hierarchical clustering optimization was used to cluster and obtain 4 types of urban road cut-in scenarios. With the help of PreScan， the automatic driving virtual test scene database based on natural driving data was constructed.

In order to increase the post-earthquake repairability of frame structures and to reduce the earthquake residual displacement of buildings， the strengthening scheme of adding self-centering viscous dampers （SCVD） was proposed. The mechanical model of SCVD was established. The proposed model was validated through a suite of SCVD mechanical performance tests. The damper force characteristics of SCVD under three initial pressures were analyzed. The seismic resistant simplified calculation method for installation of viscous damper in single-degree-of-freedom frame structures was proposed and validated through history analysis. The results show that， under the same initial pressure， with the increasing of loading frequency， the yielding damping force， peak damping force， energy dissipation and effective damping ratio increased. Under the same loading frequency and displacement amplitude， with the increasing of initial pressure， the yielding damping force， peak damping force and energy dissipation increased. Compared with normal FVD mechanical property， the damping force of SCVD increased， the energy dissipation increased not much. The time history analysis of steel frame structure indicated that among the two damper layout scheme， the damper force of SCVD was bigger than that of fluidic viscous damper （FVD）， The frame displacement and residual displacement of FVD scheme were larger than that of SCVD scheme. The time history analysis showed that with the increasing of structure period， the displacement demand increased， the base shear of SCVD frame structure was nearly the same with FVD frame scheme， the displacement demand of FVD scheme was larger than that of SCVD scheme. The SCVD can be used for seismic strengthening of frame structures in high seismic intensity area， which has better aseismic performance compared with those of FVD.

A structural optimization design method based on improved adaptive genetic algorithm was proposed for the optimal design of prestressed ultrahigh performance concrete-ordinary concrete （UHPC-NC） composite beams.In order to minimize the cost of prestressed UHPC-NC composite beams， a mathematical model of prestressed UHPC-NC composite beams was established by taking the sectional parameters of the composite beams as the optimization variables and combining the constraints of bearing capacity and deformation.The optimization example shows that the improved adaptive genetic algorithm has better optimization ability and convergence performance.The optimal design parameters of prestressed UHPC-NC composite beams were searched by using adaptive genetic algorithm. The optimization results show that the optimized section parameters of prestressed UHPC-NC composite beams are reasonable and meet the requirements of constraint conditions.

In order to solve the problem of Whale Optimization Algorithm （WOA） in its lacking global search capability in the later stage， this paper puts forward an improved whale optimization algorithm （IWOA） based on cloud adaptive inertial weights and differential evolution. Besides， considering the uncertain factors of some uncertainty factors of engineering structures， such as external loads and material strength， the system reliability of structures is also used in the optimization design as a constraint condition to optimize the minimum weight of the structure. In addition， related optimization models are built to optimize the calculating examples. The results show that the improved whale optimization algorithm is featured by fast convergence speed， better solution accuracy and stability in the process of reliability-based optimization design. Therefore， it turns out to be an effective optimization design method.

In view of the problem that the number of pavement crack images cannot meet basic needs for deep learning， according to using generative adversary network to expand the data-set， a pavement segmentation algorithm based on the U-Net network is proposed. Firstly， the data-set was initially expanded by traditional image generation， according to the principle of generative adversary network， a algorithm of pavement crack segmentation based on semantic segmentation was proposed， which was used to expand the data-set again. Secondly， based on the U-Net， an algorithm of pavement crack segmentation based on semantic segmentation was proposed， which increased the number of network layers and added Batch Normalization and dropout layer. Finally， the semantic segmentation model of pavement cracks was used to extract cracks in the expanded data image， and compared with the traditional detection algorithm and the existing mainstream segmentation algorithm FCN. The results show that the segmentation accuracy of the algorithm is better than other two algorithms， which more precisely segments pavement crack images and avoids error detection when background pixel is complicated. The mean pixel accuracy and mean intersection over union of the algorithm are 92.43% and 83.43%， respectively. In the practical scene application， it has better detection effect and stronger generalization performance.

In order to study the stressing performance of steel-steel fiber reinforced concrete composite bridge deck， especially the influence of steel fiber reinforced concrete on the concrete cracking performance of the bridge deck in the negative moment region of the composite bridge deck. Two orthotropic composite bridge decks were designed and manufactured， one of which was a conventional C60 concrete composite bridge deck and other was a steel fiber reinforced concrete composite bridge deck. The static tests of two composite bridge deck were carried out. The experiment tested the deformation of two bridge decks under different loads， the strain of each member on different sections， the width of concrete cracks， the failure mode and the ultimate load carrying capacity. The existing standard formula for flexural bearing capacity of composite bridge decks was amended， and a recommended formula for flexural bearing capacity considering the residual strength of steel fiber reinforced concrete after cracking was proposed. The tests show that the initial cracking load of the steel-steel fiber reinforced concrete composite bridge deck are 3.5 times higher than those of the conventional C60 concrete composite bridge deck respectively. Using steel fiber reinforced concrete instead of conventional C60 concrete as the composite bridge deck can greatly improve the cracking resistance of the concrete in the negative moment region of the orthotropic composite bridge deck. At the same time， the proposed formula has high accuracy and can effectively predict the flexural bearing capacity of the orthotropic steel-SFRC composite bridge deck， providing a theoretical reference for practical engineering applications.

The pressure-and multi-touch-based smartphone user interface uses multiple interaction techniques to improve input efficiency and reduce finger fatigue. With wide application prospect， it can enhance natural interaction and using experience between human and smartphone， which makes it simple to use. For interactions on pressure-and multi-touch-based smartphone user interface， aiming at the problems of lacking relevant graphic user interface paradigms and the limits of the existing paradigms， a new interface paradigm called LWHP for interaction on the mobile devices， and an algorithm predicting users' input intent and optimizing the division for pressure space using Bayesian method are presented. In LWHP paradigm， L means Layer， W means Widget， H means Hierarchical Menu， and P means Pressure+Gesture. Compared with WIMP paradigm， LWHP paradigm is designed on the desk metaphor， which extends the desktop metaphor from two dimensions to three dimensions， which decreases users' cognitive and learning load by imitating daily scenes and real working environment. Considering the different habit between individuals when interacting by pressure， the division of pressure spaces should be finer-adjusted according to their previous input. The optimizing algorithm collects the pressure signals， the properties of user behavior（Movement Time and Number of Crossings）， and the environmental information， and uses Bayesian method to interpret and predict users' input， which improves the accuracy and enhances natural interaction. At the end， a pressure-based album application designed with LWHP paradigm is described to illustrate how the new paradigm is applied and the advantages of these pressure-based components.

The poor performance of time series scheduling for multi-source big data on a trusted cloud computing platform can increase platform transmission energy consumption and operating costs， and decrease the utilization rate of multi-source big data. In order to enable the data within the platform to be reasonably scheduled according to task objectives， a trusted cloud computing platform multi-source big data time series scheduling algorithm is proposed. This method first constructs a chaotic time series model to mine the multi-source big data on the trusted cloud computing platform， and then optimizes the data using the wavelet threshold denoising method. The optimized multi-source big data is then combined with the massive parallel Bayesian factorization decomposition method. Based on the time series scheduling strategy output by this method， the time series scheduling of multi-source big data on a trusted cloud computing platform is realized. Experimental results show that the maximum acceleration ratio achieved by this method is 97.2%， the total power of resource scheduling is only 2300 kW， and the load balance deviation does not exceed 0.2.

The quality of face feature extraction results affects the accuracy of face recognition. At present， the feature extraction methods of face images still have the problems of low extraction accuracy and low efficiency. In order to solve the problems in the methods， a scale extraction method of face image living feature transformation based on deep learning algorithm is proposed. The deep learning method is used to denoise the face image. Based on this， Gabor wave filter is used to decompose the face signal and input it into the deep subspace model to extract the feature transform scale. Based on PSO （particle swarm optimization）， the scale extraction of face image living feature transformation is completed. The experimental results show that the proposed face image feature extraction method has higher accuracy， faster recognition speed and better overall application effect.

To solve the denoising problem from visible light remote sensing image， a denoising method for remote sensing image based on Curvelet transformation and Goodness of Fit test is proposed. The method first decomposes the remote sensing image by Curvelet theory to get the decomposition coefficients. Then the method normalizes Curvelet coefficients， and tests the normalized Curvelet coefficients locally by using the goodness of fit test. The real signal coefficients are obtained after the goodness of fit test， and the coefficients are inversely normalized to obtain the inversely normalized Curvelet coefficients. Finally， Curvelet coefficients are inversely transformed to obtain the denoised remote sensing image. The denoising algorithm is compared with Wavelet threshold denoising algorithm， Curvelet threshold denoising algorithm， Discrete wavelet transform and Goodness of Fit test denoising algorithm and Curvelet cyclic translation denoising algorithm. Experimental results show that this algorithm is better than the above algorithms in the indexes of peak signal-to-noise ratio and structural similarity.

In order to solve the problems of low detection integrity and poor detection effectiveness， a moving target detection method for video SAR Based on machine vision is proposed. Firstly， the moving target pixels are matched by Gaussian mixture model to obtain the target region of video SAR moving image. Secondly， the shadow generated by moving target is removed by HSV model and reflectivity algorithm. Finally， the processed target region is input into Yolo algorithm to complete the final video SAR moving target detection. Experimental results show that the proposed algorithm has high detection integrity， high detection rate， low false detection rate and better detection effectiveness.

When the outdoor mobile robot detects obstacles in the environment including sloping roads， the traditional RANSAC ground point cloud removal algorithm will be unable to remove the slope point clouds， resulting in the subsequent identification of the slope as obstacles. In view of this situation， this paper proposes an Adjacent Laser Points Algorithm to detect and eliminate slope point clouds， and detect obstacles based on Euclidean clustering. In this scheme， the 3D laser point cloud is preprocessed， and the ground point cloud including the slope is segmented according to the geometric relationship between the adjacent lines of the 3D lidar， and then downsampled by voxel filtering， the segmented obstacle point cloud is clustered based on KDTree， and the size and direction of the outer bounding frame are calculated by PCA principal component analysis to detect the obstacle. The experimental results show that the obstacle detection algorithm proposed in this paper can effectively segment the slope road surface in the environment， and can avoid identifying the slope road surface as an obstacle in the clustering process， which provides the basis for the robot autonomous walking obstacle avoidance strategy.

Aiming at the problems of fixed network topology， low dimensionality， uneven data distribution and low correlation， the training effect of intrusion detection model in industrial distributed environment is poor. In this paper， Fedformer， a federated deep learning algorithm based intrusion detection model for industrial Internet of Things （IOT）， is proposed. Firstly， the encoder structure of Transformer network model is introduced and improved， and the convolutional neural network and gated cyclic unit are integrated， and the intrusion detection model for industrial IOT is constructed by using the attention mechanism. Secondly， the detection model is integrated with the federated learning framework， which allows multiple industrial IOT to jointly build a comprehensive intrusion detection model. Under the premise of protecting the privacy of local data， the detection accuracy of industrial IOT network attacks is improved and the false positive rate is reduced. Experimental results show that the detection accuracy of Fedformer in the industrial network environment is 98.09%， and the false positive rate is reduced to 8.31%.

This study proposed an integrated neuropeptide prediction algorithm. This study integrated nine feature descriptors and five machine learning algorithms in order to generate 45 baseline learning models for predictive training of neuropeptides. The first layer performs feature selection on these 45 baseline models to select the features with good performance. While the second layer selects eight basic learning models based on the accuracy of the baseline model and the sum of Pearson correlation coefficients. The third layer inputs the output of these learners into logical regression， Extreme Gradient Boosting （XGBoost）， and other classifiers for the final step selection to train the final model， and uses the output as the final prediction result. The final accuracy on the test dataset is 0.9169， which is higher than existing models.

The precision funding process of the student funding system is susceptible to issues such as redundant data and false data， resulting in poor accuracy of precision funding. Therefore， a study on the precision funding method of the student funding system based on the improved Apriori algorithm is proposed. This method first collects student consumption data and uses an extended tree like knowledge base to clean damaged and redundant data in the consumption data， avoiding the impact of such data on the precise funding process. Secondly， the outlier detection method based on normal distribution is used to obtain the poor students' label data. Finally， the improved Apriori algorithm is used to obtain the association rules between students' consumption and family economic status， which provides the basis for the identification of poor students and completes the precise funding of the student funding system. The experimental results show that the proposed method has high identification accuracy， long running time and low space complexity for poor students.

In order to improve the effectiveness of fire risk warning， a fire risk intelligent perception warning method is proposed with the support of big data technology. Build a big data analysis platform and a fire risk intelligent perception early warning platform， and combine the two platforms to establish a fire risk early warning index system and early warning model. The fuzzy mathematics method is used to spread the single-valued samples to the fire risk points， output the warning signal vector， obtain the fire risk early warning signal， and realize the intelligent perception and early warning of the fire risk. The experimental results show that the false alarm rate of this method is low， and the false alarm rate is always lower than 6%. The actual fire occurrence times are consistent with the early warning times， which verifies its early warning effect.

In order to better capture the changes of steganography to the statistical characteristics of images， improve the detection rate of steganographic images and solve the problem of feature mapping， a steganalysis method combining fusion features and feature mapping is proposed to extract fusion features and capture more comprehensively the perturbation of the steganographic algorithm to the statistical characteristics of the carrier image. And a feature map combined with PCA is proposed to solve the problem of direct projection when the number of images is less than the feature dimension. The fused features are then subjected to approximate mapping combined with PCA for steganalysis. Experiments show that this method can effectively improve the detection rate of steganographic images.

Due to the obvious unbalance of communication data set， the difficulty of classification is greatly increased. Therefore， a time series fuzzy segmentation classification algorithm is proposed. Using the principal component analysis method， the feature vector with the largest eigenvalue is obtained， the time series of the data is established according to the interval number theory， and the segmentation target of the time series of the communication data is characterized by the Langley distance measure function between the data and the class. The classification result of the fuzzy segmentation is obtained according to the judgment relationship between the difference of the fuzzy classification matrix and the convergence condition. The experimental results verify that the data quantity of the three classification cases is always at the corresponding ideal level， with high accuracy and low error and failure.

The number of railway signal switch machines is large， the working environment is bad， and many factors lead to the high frequency of equipment failure. In order to realize the accurate fault diagnosis of railway switch machine， a fault diagnosis method of switch machine based on dynamic weight particle swarm optimization and adaptive neural fuzzy network is proposed by analyzing the vibration signals generated during the switch machine operation. Firstly， the set empirical mode decomposition algorithm was used to decompose the vibration signals into several intrinsic mode functions and screen them. Then， the improved time-domain multi-scale spread entropy algorithm was used to extract the eigenentropy of IMFs， and then input the optimized ANFIS model to learn the fault diagnosis. Finally， it is compared with a variety of diagnostic model algorithms and learning algorithms. The experimental results show that the proposed method can effectively diagnose the fault of the switch machine， and has certain reference significance for the intelligent fault diagnosis of the switch machine and related research in the future.

In order to meet the requirements of corn direct harvest with high moisture content in Huang Huai Hai area and solve the problems of grain damage and high non threshing rate in the process of direct harvest of existing corn series， combined with the characteristics of corn harvest in Huang Huai Hai area， a plate tooth threshing drum with spiral distribution of threshing tooth plate and threshing concave plate and a plate tooth longitudinal axial flow threshing device combined with circular tube grid threshing concave plate were designed. The rotating speed and diameter of the drum are determined. The stress of the grain in the threshing process of the newly developed plate tooth threshing element is analyzed. It is concluded that the grain crushing rate of the plate tooth threshing is lower than that of the nail tooth threshing. The length and height of the plate tooth are designed， and the main factors affecting the threshing performance are determined. Through the single factor test of 4YZ-6 corn grain harvester， the variation range of drum speed， threshing clearance and plate tooth length is obtained when threshing performance is good. The orthogonal experiment was carried out with the drum speed， threshing clearance and plate tooth length as the experimental factors. The regression equation and significance of the drum speed， threshing clearance and plate tooth length on the grain crushing rate and non threshing rate were obtained， and the experimental factors were analyzed by response surface analysis. The optimal parameter combination is determined： drum speed 350 r/min， threshing clearance 53 mm and plate tooth length 100 mm. Under this condition， the grain crushing rate of corn ear was 2.73%， and the non de purification rate was 0.41%， which met the relevant national standards.

Seedlings transplanting， a critical process of vegetable production， can be mechanized to reduce labor intensity， improve production efficiency and ensure work quality. The process of taking and dropping seedlings is an extremely important link in vegetable transplanting. In view of the problems of existing automatic seedling picking and throwing device， such as complex structure and severely damaging to roots system， a kind of picking and dropping mechanism with a stem clipping type was proposed and analyzed for vegetable plug seedling. The mechanism was consisted of a seedling clamping mechanism，a lifting mechanism and a shift mechanism. Herein， a kinematics model of plug seedling in the process of seedling picking and throwing was established. Based on the mechanical property test of plug seedlings and the mechanical analysis of seedling clamping process， the clamping height of the stalks at different distances from the center of the plug seedling was determined. In order to study the influence of different picking and throwing frequency on the effect of seedling throwing， the kinetic analysis of the process of picking and throwing seedlings was carried out， and then we obtained the motion force equation between the pot seedlings and the seedling clip during the picking and throwing process， and thus the spacing of seedling clamp and the height of seedling throwing were determined. A trial was conducted by using chili seedlings with an average seedling height of 152.24 mm. Here， the success rate of seedling taking and dropping， the broken rate of substrates， and the damage rate of seedling were set as evaluation indicators. The experimental results showed that the best efficiency was obtained when the picking and dropping frequency was 90 plant/min， meanwhile， the success rate of picking seedlings and casting seedlings， the broken rate of substrates， the damage rate of seedling were 96.8%， 96.8%， 4.0%， and 1.6%， respectively. The acceptable results indicated that this finding has potential to provide sound technical support to improve the automation level of a plug-seedling transplanter in agricultural production.