In response to the problem of vibration caused by the large length/diameter ratio of the boring bar during deep hole boring， which affects the processing quality and efficiency， three vibration control methods， passive control， semi-active control， and active control， were summarized. The specific structures， vibration reduction mechanisms， characteristics， shortcomings， and development trends of the three methods have been sorted out. Comprehensive analysis shows that the structure， materials， and control methods of vibration damping boring bars are currently the focus of research. With the continuous development of structural design， material science， vibration reduction mechanism， control theory， big data， artificial intelligence and other technologies， the research on vibration damping boring bars is gradually becoming diversified， integrated， and intelligent. Meanwhile， intelligence is a new development direction for vibration damping boring bars.

In order to improve the driving stability of the narrow vehicle on a bend， a tilt-driving mechanism is designed according to balancing the gravity torque and the centrifugal torque generated by vehicle tilting to avoid vehicle roller. The kinematics model of the tilt-driving mechanism is established by vector polygon method， which analyzes the relationship between tilt driving and tilt executing parameters. An active tilt reverse three wheels prototype vehicle with the tilting-drive mechanism is developed. And the turning and tilting experiment of S-track is conducted. Taking the theoretical tilt execution parameters as the target parameters， the theoretical value of motor is solved and used as the input. The theoretical and experimental data of motor and vehicle tilt parameters are compared and analyzed. Finally， the experiment results show that the experimental data are consistent with the theoretical data and the root mean square error of tilt angle is 0.89°. It verifies that the design scheme of the tilt-driving mechanism is feasible and the kinematics analysis is correct. At the same time， the vehicle tilt-driving control is verified to be reliable.

In order to reasonably evaluate the impact of various factors on the charging load， this paper introduced the concept of user portrait， and generated a typical user portrait that could describe the charging behavior of users through the construction and extraction of the characteristics of vehicle charging behavior data. At the same time， it is found that the shape of load curve can be adjusted by adjusting the proportion of different types of users. Through practical examples， the effects of user behavior characteristics and attribute characteristics on key grid indicators such as charging load form， peak valley time and load rate are comprehensively analyzed， so as to reasonably guide users to charge in order， provide basis for power grid planning and capacity expansion considering electric vehicle charging load.

In this paper， the simulation conditions of pitching motion are determined， and the external flow field of the whole vehicle under various conditions is simulated and analyzed. The aerodynamic characteristics under various pitching conditions and the interaction between aerodynamic components are explored. The reasons for the differences of aerodynamic result between various pitching conditions are obtained by analyzing the changes of flow field around the vehicle body. By analyzing the simulation data， it can be seen that the aerodynamic negative lift of the car under all pitching conditions is mainly provided by the body， front wing and tail wing， while the front and rear wheels mainly provide aerodynamic lift. The aerodynamic drag of the car is mainly generated by the body and tail wing， and the sum of the two accounts for more than 75%. The sum of the aerodynamic drag of the front and rear wheels accounts for only about 10%. When the car body attitude changes in pitch， the changes of front wing， tail wing， ground clearance and the interaction between aerodynamic components lead to the change of the aerodynamic characteristics of the whole car. The results show that under the condition of pitch angle -1.0°， the aerodynamic lift drag ratio of the whole vehicle is the largest， the aerodynamic efficiency of the car is the highest， the absolute value of the total aerodynamic negative lift is the largest， the total aerodynamic resistance is relatively small， and the aerodynamic pitch torque is the smallest. Under this condition， the performance of the car is the best.

In order to solve the matching and functional problems caused by the welding deformation of B-pillar in BIW （Body-In-White）， the whole welding process of B-pillar area is simulated based on the finite element （FEM） method， and the welding deformation of B-pillar is predicted， also the prediction accuracy is evaluated by means of test and measurement. The results show that the B-pillar welding deformation simulation based on FEM method has good prediction accuracy. Two welding pre-deformation control methods are proposed， one is compensated pre-bending with constant cross section， and the other is pre-bending with changed cross section. The pre-deformation is calculated by multi-objective optimization model. The two pre-deformation control methods are verified by FEM simulation. The results show that， both the two pre-deformation control methods can achieve the body size's tolerance meeting the design requirements， and the control accuracy of pre-bending with changed cross section is better than the method of compensated pre-bending with constant cross section.

In bolt connection， excessive pre-tightening force will make bolts fail in case of accidental overload. Traditional method of bolt pre-tightening analysis considers that the parameters are determined， but the parameters are random in actual working conditions， which causes larger analysis errors. To solve this problem， considering the influence of random factors， a finite element analysis model of bolt pre-tightening failure behavior is proposed in this paper， the reliability analysis is carried out by judging whether the stress value at the thread of bolt exceeds the allowable stress value， and the reliability sensitivity analysis is calculated to evaluate the influence of various parameters on the failure phenomenon in bolt connection. The results show that the change of bolt diameter has the greatest influence on the phenomenon of bolt static strength failure， and the Poisson's ratio of material has the second influence on it. The reliability of bolt connection will increase with the increase of bolt tooth angle， screw pitch and Poisson's ratio of material， and decrease with the increase of bolt diameter， material density and elastic modulus.

In order to improve the accuracy of analysis of ball joint with clearance， the coupling characteristics between the non-linear contact stiffness of the contact bodies and the mechanism in the impact were considered， and the contact-impact characteristics of the ball joint was studied under the conditions of different restitution coefficients and initial impact speed. The results show that the contact stiffness and hysteresis damping coefficient avoid the situation that remains constant during the real contact process； the improved model describes the impact characteristics of the spherical joint more currently； the peak contact force and deformation is greater with increase of coefficient of restitution， the longer time it takes to reach the peak and shorter time to complete an impact； the contact-impact force and deformation increase rapidly with the increase of initial velocity， and the time to complete the impact decreases. The accuracy of obtaining the impact characteristics of the spherical joint provides a mechanical basis for the precise control of vehicle handling stability.

The flow field in torque converter is numerically simulated based on computational fluid dynamics， and the external characteristic curves are drawn from the two perspectives of whether cavitation is considered or not. The accuracy of simulation results is verified by experiment. In order to deeply analyze the cavitation of torque converter for the influence law of internal and external characteristic， Q criterion of vortex identification method is used to extract the internal three-dimensional vortex structure characteristic. The dynamic evolution law of cavitation characteristic on each blade surface under low-speed ratio conditions is analyzed and clarified， and the cavitation phenomenon on stator blade is mainly studied， and the influence mechanism of cavitation phenomenon on spatiotemporal structure distribution of flow field and energy loss is analyzed and revealed. The results show that： ① The numerical simulation considering cavitation is in good agreement with the experimental results， and the prediction error of external characteristics is less than 3%； ② There is a large error between the torque coefficient of the pump and the test data in the numerical simulation results without considering cavitation， and the error is more than 20% on the braking condition， simulation results are seriously distorted； ③ When pump speed is 2000 r/min， the condition of 0.4 speed ratio is the critical point of cavitation disappearance， with the reduction of the speed ratio， cavitation phenomenon and its influence become more obvious. The research results can provide theoretical guidance for high precision numerical simulation of torque converter.

To avoid the failure of orbit insertion caused by thrust decline， a mission capability evaluation and trajectory reconstruction method is proposed based on the local collocation method and neural network. First， the local collocation method is used to solve the fuel-optimal launch ascent problem， leading to the establishment of the trajectory database for the target and secondary rescue orbit insertion scenarios with various thrust decline percentages. Then， the mapping between the thrust decline percentage and the mission capability is learned from the trajectory database by using the neural network. Thus， the mission capability evaluation and decision can be made in real time when the thrust declines. As last， the online trajectory reconstruction is carried out using the local collocation method to perform the orbit insertion. Numerical simulation results show that the proposed method can evaluate the mission capability and reconstruct the ascent trajectory when the thrust of the launch vehicle declines， thereby improving the success rate of the launch mission.

Pavement spectrum measurement method based on the inertial coordinate datum and multi-sensor coupling system， the measuring system platform is installed on the vehicle to obtain the pavement spectrum. The pavement roughness is calculated by using the instantaneous acceleration， pitch angle， real-time displacement， and geographic information which obtained by the measurement platform during vehicle driving. Pavement roughness will stimulate the vehicle motion of pitch and roll， which brings errors to the collection of pavement parameters. Based on inertial coordinate datum， multi-sensor and actuator coupling method is adopted in this paper. Based on the PID control algorithm， to implement closed-loop control for the stepper motor combined with gyroscope， acceleration sensor and angle sensor data， the influence of vehicle pitch， roll and vertical motion on the attitude of laser displacement sensor are corrected in real time to achieve accurate measurement of pavement spectrum. It can be clearly seen from the power spectral density images of different types of pavement displacement obtained by the measuring platform， that the proposed method in this paper is correct and superior.

In order to improve the microstructure and mechanical properties on surface of magnesium alloys， the surface modification of Mg-3.4Al-0.8Zn-0.4Sn magnesium alloy was carried out by friction stir processing. By using a pin-less tool， setting a series of travel speeds and rotation speeds， analyzing the thermal cycle process， observing the macroscopic morphology of the surface modification zone， characterizing microstructure and measuring microhardness， the influence of process parameters on the surface modification layer was studied. The results show that， the surface modification zone can be divided into four layers including stir layer， rotation flow layer， transition layer and themo-mechanical affected layer. As the travel speed increases， the time and the peak temperature of the thermal cycle， the thickness of the surface modification layer and the average grain size decrease， while the microhardness increases. By contrast， as the rotation speed increases， the peak temperature of the thermal cycle， the thickness of the surface modification layer and the average grain size increase， while the microhardness decreases. The modification layer obtained at each parameter showed significant grain refinement and microhardness increment compared to the base material.

In this paper， a garnet-type Li₇La₃Zr₂O₁₂ powder was synthesized through the solid-phase reaction route， an electric field-assisted fast hot-press sintering process was used to prepare a cubic phase lithium ion conductive Li₇La₃Zr₂O₁₂ solid electrolyte at a low temperature of 850 ℃， and the influence of sintering temperature on phase composition， microscopic morphology， density and lithium ion conductivity was systematically studied in the range of 820~900 ℃. The results show that when the sintering process conditions are set to a low temperature of 850 ℃， a constant pressure of 25 MPa， and a holding time of 15 minutes， a pure cubic Li₇La₃Zr₂O₁₂ structure can be obtained， the crystal grains are arranged tightly， the measured relative density is 93%， and the total ion conductivity has reached 1.14×10^-4 S cm^-1.

Desert Sand engineered cementitious composite （DS-ECC） was prepared with MuSu desert sand and Tengger desert sand as fine aggregates， respectively. When the pre-tension strain failures is 1%， 1.5% and 2%， the crack characteristics， self-healing tensile properties and self-healing products in 3 d， 7 d and 28 d of DS-ECC specimens were studied under air and wet/dry cycles. The results show that DS-ECC specimens prepared with the two kinds of desert sands have good self-healing effect， which is more obvious under wet-dry cycle condition than that of the air conditions； the all maximum reduction of residual cracks occurs at 3 days， the reduction of residual cracks in DS-ECC specimens prepared with Mu Su desert sand and Tengger desert sand is 89.3% and 80.9%， respectively； The ultimate tensile strain of DS-ECC specimens with cracks after self-healing is mostly higher than that of without cracks， but the final strength is reduced in some extent， compared with air conditions， the ultimate tensile strain of DS-ECC specimen under wet-dry cycles is larger after self-healing， and the final strength is reduced； DS-ECC self-healing and matrix mixed are mainly SiO₂， Ca（OH）₂ and CaCO₃. Considering the self-healing effect of DS-ECC and the tensile properties after self-healing， the DS-ECC specimens prepared from Mu Us desert sand have better self-healing properties.

To improve the nighttime safety level of the unsignalized highway intersections that near villages， and reduce the likelihood of crashes between vehicles， bicycles and pedestrians， we used the attention distribution theory and combined with Logistics regression to construct the quantitative model of visual search ability. We selected the pixel brightness of drivers' visual field pictures， gaze area， one-time Markov stationary distribution and cumulative fixation time percentage as the attention distribution indicators. A simulation experiment scenario was built based on simulated driver， and the eye movement data of drivers under six speeds and four lighting schemes were collected to verify the model. The results show that the overall correct percentage of classification of the model is 93.45%. The maximum speed limit of highway intersections near villages should be set at 50 km/h， and the average road surface illuminance should not be less than 10 lx.

Aiming at the problem that there is a single factor local solution limitation in the planning of charging piles， a distributed planning algorithm for urban vehicles considering the path time-consuming is proposed. From the two dimensions of space and time， the vehicle's charging load requirements are analyzed， and the charging peak time is obtained as one of the planning reference conditions. Collect the queuing data of electric vehicle charging on urban roads through GIS technology， establish the objective function with the shortest path time， calculate the queuing probability value and the current power peak value in different time periods of the day， derive the power steady state equation， and solve the load The objective function， the threshold for the minimum power peak and the minimum probability of queuing， are used as planning goals. The power path and driving path calculation is implemented for road lines and underground power lines， and the optimal solution is continuously iteratively searched until the target value is met and the calculation is completed. Experiments show that the number of iterations required by the proposed algorithm to achieve the objective function is 275 times. And after the charging piles are planned and selected by this method， it can ensure that the power distribution of each node is even， the planning scheme is in line with the actual operation situation， the adaptability is strong， and the calculation amount required by the algorithm is small.

In order to improve the reliability and risk resistance of the express delivery network, the article constructs a basic model of the express delivery network from the perspective of multimodal transportation. At the same time, considering the possible hub failure problem of the hub and spoke express delivery network, the optimal backup node is selected for the hub node, and a backup hub model for express delivery network design is proposed. Study the changes in network structure, total cost, and express handling volume of hub nodes after hub failure, and analyze the impact on adjacent hubs and paths. Case analysis shows that the failure of hub nodes can cause changes in the characteristics of express delivery networks, transportation routes, and transportation modes; Enabling a backup hub will lead to an increase in the express handling volume of hub nodes, increasing the pressure on the hub to handle express delivery and potentially causing congestion in the backup hub.

In order to grasp the impact of strong crosswind on the driving safety of vehicles in the bridge-tunnel connection section， CARSIM is applied to simulate the vehicle dynamics and calibrate the parameters of wind-vehicle-road coupling simulation model. Through the analysis of different combinations of model， vehicle speed， wind speed and wind angle for a total of 120 simulation conditions， the relationship between vehicle lateral deviation distance and reaction time under various conditions is derived. Orthogonal experiments and ANOVA are used to find out The significant factors influencing the lateral stability of the vehicle were identified using orthogonal experiments and ANOVA. The test results show that the factors affecting the driving stability of the car are wind angle， vehicle speed， vehicle type and wind level in descending order； the larger the windward area of the body section and the wider the width of the car， the more obvious the effect of the side wind， and the safety reaction time is only 2.7 s for cars and 2.3 s for SUV under 120 km/h speed， 9-level wind and 90° wind angle open loop. The results of the study provide a reference for the safety evaluation and control strategy design of self-driving vehicles in the side wind area.

Based on the actual project， through the field monitoring by acquiring soil temperature and micro-strain around prefabricated box culverts， different numbers of temperature sensors and vibrating string strain gauges are buried around the culvert， and with the help of finite element software analysis， the temperature field distribution and frost heaving characteristics of the soil around the box culvert is explored. Firstly， the temperature and micro-strain of the soil around the box culvert were monitored for 12 months. Secondly， based on the unsteady heat transfer theory solved by enthalpy field， the corresponding numerical model is established by finite element software and the monitoring data are compared and analyzed to analyze the variation law of soil around the prefabricated box culvert， and finally draw some conclusions. The results show that the soil temperature around the culvert shows seasonal variation under the combined influence of thermal radiation and air convection. the quilt-effect caused by transient heating leads to a trend that temperature decreases first and then increases with soil depth. Affected by frost heaving force， the maximum stress position of box culvert is at the hinge and the maximum uplift displacement is at the top of the culvert.

Based on the health monitoring information of the in-service bridge， it is very important to reasonably analyse the structural reliability for the safety and serviceability assessment. In this paper， the time series analysis method， the moving average method and the cubical smoothing algorithm with five-point approximation are used to reduce the noise of monitoring information （extreme stress signal）. Considering that some time series is not enough to predict with a single dynamic linear model， these time series need to be predicted by a combination of multiple dynamic linear models. This study introduces multi-process model and establishes a multi-process Bayesian dynamic linear model to predict and analyse the extreme stresses； Further， the time-dependent reliability of bridge members is predicted by combining multi-process Bayesian dynamic linear model （BDLM） and first-order second moment （FOSM） reliability method. The research results of this paper will provide the theoretical basis for structural reliability prediction.

In order to study the low temperature mechanical properties of expansive soil in the alpine deep seasonal permafrost region， the consolidation undrained shear tests under low confining pressure in different freezing temperatures， moisture content and freeze-thaw cycles were carried out. The results show that the stress-strain curves of the frozen expansive soil change from hardening to softening with the decrease of freezing temperature and initial water content. The effect of freeze-thaw cycles intensifies the softening effect and promotes the sensitivity of confining pressure. When the freezing temperature is within the frozen zone， the shear process of frozen expansive soil can be divided into four stages with the increase of axial strain： elastic stage， plastic shear shrinkage stage， plastic dilatancy stage and failure stage. The elastic modulus of frozen expansive soil increases with decreasing temperature and presents a good linear correlation. The statistical damage model of frozen expansive soil considering the influence of temperature was deduced， and the law of macroscopic mechanical properties deterioration of frozen expansive soil was revealed.

Measuring cup to disk ratio（CDR） by segmenting optic disc（OD） and optic cup（OC） is an effective method for diagnosis of glaucoma. Compared with OD segmentation，OC segmentation still faces difficulties in segmentation accuracy. In this paper，a deep learning architecture MS-HRNET is proposed for joint segmentation of OC and OD. It is an improved architecture based on HRNET. By adding multi-scale input to HRNET，information loss during feature extraction can be compensated. Combined with multi-scale spatial and channel attention mechanism，deep image features are extracted. By adding a side output layer，the early training of the network is guided. Experimental results show that the proposed model performs better than the existing OC and OD segmentation methods on Drishti-GS1 and REFUGE datasets.

In order to effectively filter the noise in terminal ciphertext data， a segmentation wavelet noise reduction algorithm for large-scale IoT terminal ciphertext data is proposed. The continuous spectral components and line spectral components of different types of data are extracted by the periodogram estimation feature extraction method， and the noise features of the ciphertext data are obtained. The maximum projection of noise data in the scale space is used to construct the energy matching criterion， and the structured wavelet filter bank is used to establish the optimal energy matching wavelet consistent with the signal energy. Through the waveform matching criterion， the optimal waveform matching wavelet which is the same as the signal waveform is established by using the optimization function to complete the segmented wavelet de-noising of the ciphertext data of the large-scale IOT terminal. The experimental test results show that the proposed algorithm can effectively reduce the delay of data noise reduction， and can also obtain satisfactory noise reduction effect.

Due to the fast divergence of error in pedestrian track estimation， the PDR positioning trust time is short and the accumulated error is large. A pedestrian track estimation technology based on TrAdaBoost algorithm is proposed. In this method， outdoor pedestrian movement information is collected offline by GPS， and the TrAdaBoost algorithm in transfer learning is used to screen out the most suitable pedestrian movement features， which are transferred to indoor PDR location to correct pedestrian steps and realize pedestrian track calculation. The experimental results show that the pedestrians step roughly distribution within the scope of the 65—75 cm， and after correcting the positioning of the trajectory match degree is high， and real location will not be because of the accumulation of inertial sensor error and appear serious deviation， the average error compared with pure PDR localization algorithm has fallen dramatically， location accuracy is within 2 m of probability of 80%. Therefore， this method reduces the divergence speed of PDR， prolonging the credibility time of PDR location， and improving the credibility of the location results within a short distance.

On low-resource corpus， the mainstream translation quality estimation models have poor performance. Meanwhile， the sentence embedding strategy is naive. In view of reasons mentioned above， a Korean-Chinese translation quality estimation based on cross-lingual pretraining model is proposed. Firstly， a cross-lingual sentence embedding method is proposed by drawing on the idea of attention. The method can effectively fuse the cross-layer information and token positions of the pre-trained model. Second， a cross-lingual pretraining model is introduced to the task as a way to alleviate the few-shot caused by the low-resource of Korean. Finally， the regression is performed on the sentence embedding vectors， so that the Korean-Chinese translation quality estimation can be completed. Experimental results show that the method can effectively improve the performance of the Korean-Chinese translation quality estimation task. Compared with QuEst++， Bilingual Expert， and TransQuest， the dominant models for quality estimation tasks， Pearson correlation coefficients improved by 0.226， 0.156， and 0.034， and Spearman correlation coefficients improved by 0.123， 0.038， and 0.026， respectively.

In order to solve the problem of high cost of labeling named entity data and difficulty in obtaining large amounts of labeled data in the biomedical field，this article proposes a two-stage learning framework to realize BioNER under low resources. In the first stage， Word2Vec and BERT are used as the basic model to pre-train and fine-tune to obtain the word embedding representation in a specific field； In the second stage， the generated word embedding representations are input to the neural network composed of BiLSTM and CRF and then used for the training of the final task. This paper conducts experiments on the Yidu-S4k dataset， and even in the case of a small number of labels， the results show that the algorithm in this paper achieves an accuracy of 80.94% and has great performance.

In view of spectral leakage and aliasing in the wideband signal channelization in the field of communication and radio astronomy. The effects of different window functions such as Hanning windows， Hamming windows and Kaiser windows on the performance of the finite impulse response （FIR） digital filter were simulated and analyzed. Based on the Hamming window FIR digital filter designed and implemented the critical sampling polyphase filter banks， and the spectral characteristics of the critical sampling polyphase filter banks were studied. The sub-bands division of wideband signals were realized by using the astronomical observation baseband data， and the filtering performance characteristics of the critical sampling polyphase filter banks and FIR digital filter banks were analyzed. Employed the critical sampling polyphase filter banks realized the multichannel output of wideband baseband data， and the frequency spectrum of the sub-channels was analyzed and corrected， which correspond to the original signal.

Aiming at the problem of inaccurate positioning of intelligent vehicles due to the loss of GPS signals on complex urban roads， a factor graph optimization model is established to perform data fusion on LiDAR （Light detection and ranging） and Inertial Measurement Unit（IMU）， and the lidar-inertial odometry （LiDAR-inertial odometry， LIO） vehicle positioning method under the tightly-coupled framework is proposed， which can estimate vehicle state information in real time； a loop-closure detection algorithm based on point cloud histograms is proposed. The similarity of the point cloud determines whether the vehicle has reached the same position， and then combines the information from the last time that the vehicle passed the position to correct the current state of the vehicle， reducing the accumulation of positioning errors. The test results on the KITTI dataset show that the loop-closure detection module can effectively reduce the error accumulation of the LIO， and the LIO with the loop-closure detection module has excellent positioning accuracy.

To effectively improve the active safety of autonomous vehicles， a single parent genetic algorithm based active collision avoidance method for autonomous vehicles is proposed. Construct a dynamic model for autonomous vehicles using normalized azimuth evaluation functions. Based on the predicted behavior trajectory， pre braking is taken in advance， and a single point gene replication strategy is introduced to implement genetic operations with the goal of minimizing the path from the initial point to the endpoint， in order to obtain the optimal solution for autonomous vehicle active collision avoidance. The simulation results show that this method can plan a reliable turning collision avoidance path under emergency conditions， with stable vehicle state parameters， small path deviation， and strong robustness to different vehicle speeds and turning paths.

Aiming at the problems of complicated weeding operation among fruit trees and low weed removal rate， based on the idea of reciprocating telescopic motion of mechanism， an automatic obstacle avoidance weeding machine for clearing weeds among fruit trees was designed. The structure and parameters of key components such as working width adjustment mechanism， signal acquisition mechanism， automatic obstacle avoidance mechanism and weeding knife are determined by theoretical analysis of each component of the weeding machine. Using the synergy of the arc-shaped contact rod and the hydraulic system， an automatic obstacle avoidance control system is constructed， which can dynamically control the weeding knife to avoid fruit trees according to the different positions of the contact rod. A virtual prototype model was established in ADAMS， and a single factor simulation experiment was carried out to determine the key factors affecting the weeding and obstacle avoidance between plants. Taking the speed of the obstacle avoidance hydraulic cylinder， the forward speed of the machine and the elastic coefficient of the return spring as the main influencing factors， and the weed removal ratio as the evaluation index， according to the Box-Benhnken experimental design principle， using the three-factor and three-level response surface analysis method， a two The regression model of the weed removal ratio was established through the second regression combination test， and the optimal parameters were obtained as follows： the speed of the obstacle avoidance hydraulic cylinder is 126 mm/s， the forward speed of the machine is 1.57 km/h， and the elastic coefficient is 21. The field verification test was carried out， and the results showed that the average weed removal rate of the designed weeding machine was 92.65%， which met the agronomic requirements of orchard weeding. The research results provide a reference for the further optimization of the interplant weeder in orchards and other crops.

In this study， the recognition test of bok choy and its associated weeds at the seedling stage was carried out， and a novel method based on recognizing vegetables and then indirectly recognizing weeds was proposed. By combining deep learning and image processing technology， this method can effectively reduce the complexity of weed recognition， and at the same time improving the accuracy and robustness of weed recognition. First， a neural network model was used for detecting the bok choy and drawing bounding boxes. The green targets outside the bok choy bounding boxes were marked as weeds， and color features were used to segment them. Besides， an area filter was used for eliminating the noises and extracting weed regions. In order to explore the effects of different deep learning models on bok choy recognition， SSD model， RetinaNet model and FCOS model were selected， and three evaluation metrics of F₁ value， average accuracy and detection speed were used for comparative analysis. The SSD model was the best model for bok choy recognition， with the highest detection speed and excellent recognition rate. Its F₁ value， average accuracy and detection speed in the test set were 95.4%， 98.1% and 31.0 f/s， respectively. The improved MExG index can effectively recognize weeds， and the segmented weeds have complete shapes and clear outlines. Experiment results show that the proposed method for recognizing weeds in vegetable fields is highly feasible and has excellent application effects， which can also provide technical reference for weed recognition in similar crop fields.

The current deep learning-based hydrological prediction models mainly use the same network weights to simulate the spatio-temporal relationship between soil moisture and predictors， which has some limitations in reflecting the spatio-temporal specificity of soil moisture. In this paper， we propose a spatio-temporal prediction model with channel-agnostic and spatial-specific structures. Spatial-specific features are used to extract the spatial characteristics of soil moisture in different regions by adaptively assigning network weights at different spatial locations； channel-agnostic features are used to extract the spatial characteristics of soil moisture and predict in a single grid point channel using one-dimensional convolution. The channel-agnostic feature extracts the temporal characteristics of soil moisture and predict in a single grid point channel using one-dimensional convolution， accurately captures the transformed relationship between soil moisture and its predictor at a single point， and uses lagged and predictor as model inputs for prediction at a single grid point. The experimental results show that the prediction ability of the model in this paper is greatly improved compared with the traditional DL model， and most obviously in the northern region of China， the determination coefficient （R²） of the model is improved by 80% compared with that of CNN.