In order to improve the detection accuracy of road network traffic incidents and reduce the false alarm rate， a real-time automatic detection method of road network traffic incidents based on Graph Spatial-Temporal Pattern Learning Network （GSTPL） is proposed. Firstly， the traffic incident detection problem in the road network is abstracted into a graph structure anomaly detection task； a traffic spatial-temporal fusion graph representation method is designed to filter the road network graph node with strong spatial-temporal dependence and same pattern regularity as the input. Then， the graph spatial-temporal convolution and graph embedding layer are introduced to extract the spatial-temporal pattern features， and the multi-component input and fusion prediction structure are constructed to fuse traffic pattern rules in different time dimensions， and realize stable forecasts of graph node parameters. An abnormal state evaluation method is designed， and the final incident detection result is given by learning of the prediction error distribution and combining with the current detection data. Two real road networks datasets were used for validation experiments， and the proposed algorithm was compared with several typical traffic incident detection algorithms. The comparison results show that the proposed GSTPL has higher detection accuracy， lower false alarm rate and shorter average detection time. When the acceptable false positive rate is 5% and 10%， the detection rate of traffic incidents can reach more than 91% and 96% respectively.

A one-dimensional GT-POWER model was built with a 1.5 T VVT in-cylinder direct-injection hydrogen internal combustion engine， and the effects of inlet and exhaust valve timing changes on the engine's air exchange process， power performance and economy were studied. When the engine speed is 2 700 r/min， the lean combustion strategy is adopted to keep the pulse width of hydrogen injection consistent under the full-load condition， and the inlet valve timing is advanced and the exhaust valve timing is delayed， the intake mass flow rate is increased， the effective thermal efficiency is increased from 40.8% to 41.07%， and the effective gas consumption is decreased. The engine speed is 5 500 r/min， the excess air coefficient is the same under the full-load condition， the inlet and exhaust valve delay strategy can increase the gas mass in the cylinder， reduce the pump gas loss， increase the power by 12.7% and slightly reduce the break specific gas consumption. The influence of exhaust valve timing on the power performance and economy of in-cylinder direct-injection hydrogen combustion engine is more significant than that of intake valve.

In order to solve the problem that it is difficult to measure the key mechanical parameters of GH600 nickel-based alloy in variable temperature environment， an ipsilateral detection method and opposite-side centering detection method based on the energy distribution characteristics of surface wave and longitudinal wave is proposed. By coupling the laser ultrasonic detection system with the high and low temperature devices made in the laboratory， the velocities of the surface wave and longitudinal wave are obtained， and the mechanical parameters of the alloy at -90~1 000 ℃ are theoretically deduced. The experimental results show that the elastic modulus and shear modulus decrease and the Poisson ratio increases with the increase of temperature， and the difference between the measured values and the reference values is small. The experiments validate that the laser ultrasonic system can effectively gauge the performance of components within challenging environments.

Concerning the rheological problems affecting the quality of composite filament in metal fused filament fabrication， Firstly， self-made three types of 17-4PH stainless steel powder/polymer composite filaments with different high filling rate were studied in this paper， rapid prototyping equipment was used to conduct molding research on green samples， and the formability conditions of the composite filaments were canalized. Secondly， the pressure drop of the molten composite filament with different metal powder filling ratio during the shaping process was measured by the self-constructed experimental platform， and the test results of the relevant rheological properties were analyzed. Thirdly， an rheological property model of the molten material was established， and the theoretical analysis on the corresponding characteristic parameters was completed. Through the comparison between the theoretical and experimental results， the correctness of the theoretical model was verified， and the mechanism of the rheological properties of molten materlals were elucidated. Finally， a sensitivity analysis was carried out on the analytical model to investigate the influence of processing parameters on rheological property. The results show that the self-made composite filament can be used to form good-quality green samples； the rheological property parameters of the molten material gradually increase with the increasing filling ratio of the metal powder； the theoretical and experimental results are in good agreement， validating the correctness of the analytical model； the nozzle diameter has the most significant effect on the melt flow behavior of the molten material in the discussed parameter ranges， followed by the building speed， while the effect of the molten temperature is relatively weak.

In order to investigate the influence pattern of residual stress on material indentation response， this paper investigates the mechanical behavior and cyclic deformation characteristics of materials under residual stress in indentation experiments using finite element simulation and cyclic indentation method， the influence of material mechanical property parameters and residual stress on the cyclic indentation behavior of materials is revealed. The variation of material mechanical properties with yield stress， hardening index and residual stress was obtained by single factor indentation experiments， and the dynamic behavior of materials with different yield stress， hardening index and residual stress under cyclic loading is also studied. The results show that the indentation depth and plastic displacement decrease with the increase of yield stress， hardening index and residual stress. In the cycle， the indentation depth and plastic displacement increase and stabilize faster with the increase of cycle weeks.

In order to investigate the effect of high temperature on cement mortar modified with waterborne epoxy resin（WER）， this study analyzed the mechanical properties， damping capacity， structure and composition of high-early-strength cement mortar modified with different WER contents（0%， 2%， 3%， and 4%） after being treated at different temperatures（25， 100， 150， 200， and 300 °C）. The results showed that WER delayed the hydration reaction of cement， reduced the early strength of cement mortar. However， with the continuing hydration of cement and WER cures to form a film， the polymer film connects the hydration products to formed a three-dimensional network structure and constituted dispersed damping units to enhance the flexural strength， bond strength and damping capacity of cement mortar. Especially when the dosage of WER was 3%， cement mortar performed better and met the early-strength requirements of cement-based rapid repair materials. With the increase of temperature， the macroscopic performance of cement mortar deteriorated. The better high temperature resistance and hydrophilicity of WER helped alleviate the deterioration in mechanical strength and damping capacity of cement mortar to improve its thermal stability， this pattern is particularly especially within the range below 200 °C.

Corrosion-damage can easily cause local elements distortion， leading to numerical instability in the nonlinear topology optimization of structures. Existing suppression methods are mainly applied to elastic structures undergoing large deformation， but these methods suffer from the issues such as low efficiency and limited applicability， making them difficult to meet the requirements of 3D load paths search for corrosion-damaged structures. Therefore， a method for structural 3D load paths topology search based on distortion control of corrosion-damaged elements is proposed in this paper. Firstly， this method employs the concept of adaptive scaling of local stiffness to control the distortion of corrosion-damaged elements. Their local stiffness is scaled at different proportions based on a defined distortion degree of corrosion-damaged elements， while restraining element distortion and optimization errors. Secondly， a mathematical expression of nonlinear topology optimization of load paths search in corrosion-damaged structures is given. By taking into account the effects of material deterioration and bond degradation， a sensitivity calculation formula for corrosion-damaged elements is derived based on the adjoint method. Finally， numerical examples are conducted for verification， demonstrating that the proposed method in this paper can reasonably generate 3D load paths for corrosion-damaged structures， revealing the development law of load paths in corroded RC beams.

This study aims to determining the safe distance between the exit of the semi-underground hub interchange ramp tunnel and the secondary diversion point， so as to optimize line design and conserve land. In this study， the theoretical safe distances were calculated based on the decomposition of driving behavior and kinematic equations. Data were gathered from 39 drivers at four different distances through driving simulation tests. Speed standard deviation， brake pedal force， and lane offset served as selected indicators via the MW test. Factor analysis was employed to construct a driving safety risk calculation model and quantify the risk scores under different distance. The results show that the theoretical safety distance was 164.8 meters， and the distance with the lowest driving safety risk was 250 meters.

Aiming at the problem of tidal supply-demand imbalance of shared bicycles， a built environment indicator system was constructed from the three dimensions of socio-demographic， transportation design， and land use. Based on the Agglomerative Hierarchical Clustering model， typical time-aggregation patterns of shared bicycles were obtained from clustering. The nonlinear relationship and the threshold effect between them were revealed by using the Random Forest and SHapley Additive exPlanations models. Ultimately， Kunming was an example to prove that. The results show that the temporal aggregation modes of shared bicycles mainly show the first-in-last-out mode and the first-out-last-in mode， accounting for 91.4% of the sum of all modes. There is a significant nonlinear relationship between the built environment and the temporal aggregation modes of shared bicycles. The temporal aggregation modes of shared bicycles are primarily influenced by the density of the bus line network， proximity of parks and attractions， proximity of metro stations， and POI density of companies. When the proximity of metro stations is less than 0.5 km， there is an elevating effect on the probability of showing the first-in-last-out mode. When the POI density of companies is less than 25 per cell， there is a depressing effect on the probability of showing the first-in-last-out mode.

The promotion of self-consistent energy systems in highway service areas is a crucial initiative for achieving a dual-carbon strategy. However， the assessment of the resilience and energy efficiency of self-consistent energy systems in service areas remains a pressing challenge. This paper proposes an approach for assessing self-consistent energy systems in highway service areas. We analyzed the structure and characteristics of self-consistent energy systems， considering key indicators such as reliability， availability， maintainability， and safety. Subsequently， we established an assessment model and designed assessment strategies. In addition， the weights of various indicators were determined using the entropy weight-TOPSIS method， while the weights of system attributes were determined using an enhanced AHP-VIKOR method. We established a novel multi-criteria comprehensive assessment framework. Through analysis of comparison， the validity and rationality of the method system are verified by computing a self-consistent energy system in a service area as an example.

The impact of various external factors such as holidays， weather conditions， and epidemic prevention policies was considered in this paper， based on the passenger arrivals date at Beijing Capital International Airport between March 2021 and May 2023， and seven major high-speed railway stations from December 2021 to July 2023， a hybrib Transformer model integrating convolutional neural networks， long short-term memory networks is proposed for hab-to-station passenger flow prediction， analyzing the time-varying patterns of passenger flow volume at arrival stations under the influence of multiple factors. The empirical results reveal that the hybrib Transformer model outperforms conventional machine learning and deep learning models in prediction accuracy， achieving over 80% across all stations， and nearing 90% for six of them， this evidences the model's robust applicability.

In order to investigate the spatiotemporal instability on pedestrian accident injury severities， a random parameters approach with heterogeneity in means and variances was constructed to analyze the heterogeneity of pedestrian accident injury severities. Using the pedestrian traffic accident data， 21 influencing factors were selected， the temporal instability and spatial transferability of the model were assessed using the log-likelihood ratio test， while the average elasticity coefficient was used to analyze the influence of each factor on three types of accidents： severe injury， minor injury and no injury. The results revealed that the influencing factors of pedestrian accident severities have significant spatiotemporal instability. The influence of the four factors， namely driver escape， physical separation， dirt shoulder and signal control on the severity of the accident varies with the positive and negative of the year， and there was temporal instability in the three years. The influence of pedestrian crossing the road and dark lights at night on severe injury accidents varies positively or negatively across different regions， and there was spatial instability in urban and rural areas. The effects of improper pedestrian behavior and nighttime darkness with lighting on pedestrian accident injury severities vary positively or negatively with both year and region， displaying significant spatiotemporal instability. Moreover， the random parameters， as well as the heterogeneity in means and variances， also exhibit significant spatiotemporal instability in urban and rural areas across different years.

To more accurately estimate the natural frequency of vibration in irregular building structures， a method for estimating the natural frequency of vibration in irregular building structures is studied. This article uses the finite element method to solve the problem of difficulty in accurately estimating the natural frequency of vibration in irregular building structures. This method meticulously divides the building structure into multiple elements and calculates the mass， stiffness， and damping of each element. Then， the mass matrix， stiffness matrix， and damping matrix of these elements are assembled according to the node connection relationship to obtain the global mass matrix， stiffness matrix， and damping matrix of the entire structure. This global matrix effectively describes the mechanical properties of the entire building structure and is used to simulate the dynamic response of the structure under external excitation. Based on the above global matrix， this paper further establishes a dynamic equation to describe structural vibration， thereby achieving the estimation of the natural frequency of irregular building structure vibration. The experimental results show that the maximum error value of the estimated natural frequency using this method is only 0.02 Hz， demonstrating extremely high accuracy.

To enhance the durability of reinforced concrete through mix design， a whale optimization algorithm-back propagation neural network model with a topology structure of 6-14-2 is designed. The model dataset comprises 100 2sets of data， with60×2 sets used for model establishment and 40*2 sets for model validation. By comparing the predictive performance of the backpropagation neural network model with the whale optimization algorithm-back propagation neural network model， it is evident that the whale optimization algorithm significantly improves the predictive ability of the back propagation neural network model. The whale optimization algorithm-back propagation neural network model predicts the mean values of T₁ performance indicators as follows： R²=0.90， RMSE=33.92， MAPE=0.06， MAE=27.31； the mean value of T₂ performance indicators as follows： R²=0.90， RMSE=29.75， MAPE=0.04， MAE=23.81. Therefore， the whale optimization algorithm-back propagation neural network model can effectively predict the durability of reinforced concrete.

To study the mechanism of mix-mode fracture and damage evolution in concrete， and analyze the influence of the mesoscale structural properties， a phase field regularized cohesive zone model （PF-CZM） was used to perform numerical simulations. The results indicated that the PF-CZM can accurately model the mesoscale fracture behavior of concrete， which is independent from the phase field scale parameter and meshing size. In the process of mix-mode fracture， damage and cracking occur at the notch tip， leading to concrete softening， cracks to expand at an angle， and eventual failure. Reducing the aggregate volume content can improve the cracking resistance. Optimizing the interfacial properties also contributes to these improvements， but the effect is weaker compared to a decrease in aggregate volume fraction.

Aiming at the bearing characteristics of slotted pile under vertical load， the difference of load bearing performance and failure mode of soil around pile of ordinary pile and slotted pile were analyzed through laboratory model test and a discrete element simulation analysis model respectively， and the influence of pile-forming technology on shear strength of soil at the side of slotted pile was also discussed. The results show that the ultimate bearing capacity of notched pile is 1.4 times that of the ordinary pile， and the material utilization rate is 1.9 times that of the ordinary pile. The pile end resistance sharing ratio of slotted pile and ordinary pile is 30.3 % and 64.9 %， which mainly shows the bearing characteristics of the end bearing friction pile and friction end bearing pile. There are obvious end bearing effect and soil squeezing effect at the slot， the side friction of pile is greatly affected by the shear force between soils， and the plastic failure surface from the top of pile to the end of pile is continuously fluctuating. Considering the design of the structure parameters of the slotted pile， the phenomenon of stress superposition or independent load failure between the slots should be avoided， and the optimal ratio between slot distance and pile diameter h/D is 1.06-1.46. The earth pressure theory of Terzaghi and Peck and the stress path method are used to show that The shear strength of soil on the side of the pile is significantly improved by extrusion and expansion process for forming holes.

Facing the nonlinear， high noisy， and unstable characteristics of highway slope displacement monitoring data lead to insufficient accuracy of slope displacement prediction， this paper proposes a regression prediction algorithm that optimizes the variational mode decomposition（VMD） and extreme gradient boosting（XGBoost） for the processing and prediction of highway slope displacement data. Firstly， the particle swarm optimization is used to find the optimal number of decomposition layers and the penalty factor of VMD， and then the slope displacement data are subjected to VMD to obtain the trend displacement， periodic displacement and random fluctuation displacement features. Secondly， other monitoring data are added as regression prediction feature variables， and the Shapley additive explanation（SHAP） is used to interpret the importance of the input feature variables， and then the important features are screened and inputted into XGBoost model for prediction， and the grid search is used to determine the optimal parameters of the XGBoost model. Finally， the applicability and robustness of the slope displacement decomposition method and regression prediction model proposed in this paper are verified based on actual case analysis.The results show that in the real cases of this paper， for slope displacement data decomposition， VMD has stronger applicability than empirical modal decomposition（EMD） and ensemble empirical modal decomposition（EEMD）； for slope displacement prediction， the prediction accuracy of XGBoost is improved by 4.37% and 0.41% compared with the extreme learning machine（ELM） and support vector machine（SVM）， respectively. The regression model proposed in this paper has high prediction accuracy and strong robustness. Meanwhile， it is shown that the slope displacement characteristic variables （periodici displacement， random fluctuation displacement and trend displacement） extracted by VMD have a greater degree of SHAP importance for slope displacement prediction. The method proposed in this paper can provide some ideas for highway slope displacement prediction and safety warning research.

On the basis of linear elasticity hypothesis and force method， an analytic theory for static analysis of pin-bar assemblies is developed from the incremental form of equilibrium equations， where the effect of initial internal forces， the compatibility equations and constitutive equations are taken into account. Then the displacements and the increments of axial force are decoupled by using linear algebra and Moore-Penrose generalized inverse theory. The basic formulas proposed finally consists of two parts — generalized equilibrium equations and generalized compatibility equations， both of which have square coefficient matrices of full rank being transposed with each other. In other words， they are formally consistent with the basic equations using in traditional force method， and will degenerate into the latter ones in dealing with the statically and kinematically determinate structures. Therefore， the proposed theory can be regarded as an extended version of the traditional force method considering the stiffening effect of initial internal forces， which is applicable to any pin-bar assemblies with small deformation and linear elasticity static structural analysis. Its calculation accuracy is increasing with the increment of prestress level and structural stiffness.

To study the influence of hydrothermal coupling curing at different temperatures on the creep characteristics of concrete， uniaxial compression test， multistage creep test， and scanning electron microscope （SEM） test of concrete after hydrothermal coupling curing at 20， 40， 60， 80 ℃ were conducted. The results show that the strength of concrete under different temperatures in hydrothermal coupling curing is significantly higher than that of normal curing in the 20 days of curing. The maximum increase rate of concrete strength under the hydrothermal coupling curing at 60 ℃ is 54.8%. Then the strength of concrete decreases at different temperatures hydrothermal coupling curing， and the strength is lower than the normal curing after curing 60 days. With the increasing hydrothermal coupling temperature， the creep performance of concrete first increases and then decreases. The creep properties of concrete were significantly improved after the hydrothermal coupling at 60 ℃， and the creep strength was increased by 22.24%， the creep time was extended by 23.95 hours， the instantaneous strain and creep strain were decreased by 6.4% and 32%， respectively， compared with normal curing. However， after experiencing hydrothermal coupling at 80 ℃， it will have a greater negative effect on the concrete creep properties. Based on the test results， the Burgers creep model agrees well with the creep test data.

In order to study the dynamic characteristics of assembled steel-concrete composite beam bridges considering the influence of discontinuous shear connection caused by cluster-group bolts， a theoretical formula for the self-vibration characteristics of this type of composite girder bridges based on the Euler beam theory and Rayleigh-Ritz method was proposed. A coupled model of the assembled composite beam bridge with multi-point discontinuous shear constraints was established to analyze the effects of road roughness， vehicle speed， vehicle mass， and clustering degree on the dynamic response of the composite beam bridge under vehicle loading and the impact characteristics. The results indicate that as the clustering degree increases and the length of the shear-free connection zone becomes longer， the bridge stiffness and natural frequency decrease. An increase in road roughness leads to a higher impact coefficient. The influence of vehicle speed on dynamic response shows nonlinearity， and when the vehicle speed increases from 100 km/h to 120 km/h， the impact coefficient suddenly increases by 68.10%， significantly affecting the dynamic performance. A greater vehicle mass results in a larger impact coefficient and stronger dynamic response. For clustering degrees ranging from 0 to 0.31， the increase in maximum dynamic displacement is slightly higher than the increase in static displacement， leading to a decrease of 5.25% in the impact coefficient. The effect of clustering degree on the impact coefficient is not significant.

Since calculating the exact similarity between two graphs is usually an NP-hard problem， the tradeoff between precision and speed needs to be addressed. In this paper， a pooling-based graph neural network method is proposed， which effectively integrates the coarse-grained interaction features of the graph data and the fine-grained interaction features of the nodes between the subgraphs， and further reduce the computational cost while ensuring the accuracy. The experimental results show that the proposed method has good performance on real graph data sets. Compared with existing methods， the proposed method not only improves the accuracy but also improves the computational efficiency.

In order to improve customer satisfaction， massive Web multi-attribute data queries are conducted by integrating user interests. Based on the attributes of the data， obtain the topic vector of Web multi-attribute data， and consider the forgetting factor to dynamically manage user interests， in order to more accurately reflect the current interests of users. On this basis， cosine similarity is used to calculate the similarity between Web multi-attribute data feature vectors and user interest topic vectors， and Borda counting method is used to integrate the initialization query list and personalized query list to obtain the final query list that comprehensively considers user interests， achieving a personalized data query experience. The experimental results show that the algorithm can achieve Web multi-attribute data queries and has the ability to handle ambiguous queries. And as the number of queries increases， user satisfaction approaches 100%. This article demonstrates that the algorithm can further understand the needs of users based on their query content， and provide users with more accurate query results.

In order to improve the edges smoothness and clarity of low contrast image， a low contrast image denoising algorithm based on non local self similarity is designed in this paper. Based on the difference between image blocks in low contrast images， a self similarity dataset is constructed. On this basis， the similarity of image blocks is calculated， and a binary identification matrix is used to distinguish noise points and effective pixel， weight coefficients are introduced to perform weighted averaging on the pixels， resulting in a transition image. Therefore， based on the absolute difference sum between the original image and the transition image is calculated to determined whether has completed denoising. If the absolute difference sum is less than or equal to 0， denoising is achieved. The experimental results show that the SSIM of the proposed algorithm has remained around 0.9， and the denoised image is clearer and more realistic. It is shown that the proposed algorithm can better preserve the original information and structural information， making the denoised images more natural and realistic visually.

Using only the information of the last layer of BERT for prediction can result in the loss of some lexical， syntactic and semantic information of the text. Regarding this problem， this paper firstly puts forward the BERTGAT model based on BERT and graph attention networks （GAT）. By making use of the hidden state matrices and attention matrices of multiple intermediate layers of BERT as the node feature matrices and adjacency matrices of the corresponding number of GAT respectively， and adopting a dynamic weighting strategy to weight different GAT layers， and then applying the activation function to determine the similarity between sentences. Secondly， to enable the BERTGAT model to better acquire the language representations between sentence pairs， the contrastive learning approach is introduced on the basis of the BERTGAT model to propose the BERTGAT-Contrastive model， which enhancing the model's ability to recognize semantic similarities among texts. Finally， through experiments conducted on the LCQMC and BQ datasets， the results indicate that the proposed model and its contrastive learning method are highly effective， with notable improvements in accuracy and F₁ score.

Aiming at the problem that existing methods mainly use a single modality of remote sensing images to solve the problem of low similarity of the same class， a remote sensing image classification method based on multimodal learning is proposed. Firstly， The spatial features of the image are corrected. The image encoder is pre-trained using contrastive learning to generate image features and text features are generated using text encoder. Secondly， a feature decoder is introduced to acquire text perception visual features， and a new attention mechanism approach is proposed in the feature fusion stage. Thirdly， a new image encoder is designed to improve the classification accuracy. Finally， the similarity between the support set and the query set is computed for further class prediction. Experiments are conducted on the NWPU-RESISC45， AID and UC Merced datasets. The 5-way 5-shot accuracies of 86.46%， 85.89%， and 80.32% respectively outperform existing methods in few-shot remote sensing image classification.

In the classroom， student state changes are subtle and key features are difficult to capture. Therefore， a real-time collection and dynamic analysis method based on hybrid intelligence is proposed to detect abnormal learning states in a timely manner. By using image enhancement technology to preprocess and collect images， the enhanced images are input into a convolutional neural network. After convolutional processing， deep level feature maps and key feature maps are extracted， and further input into a long short-term memory network to achieve dynamic analysis and recognition of the learning status of classroom students. Through experimental verification， this method can provide real-time feedback on the learning status of students， with good recognition effect and high stability. With the help of this method， students' academic performance can be effectively improved based on the correction of relevant academic personnel， and it has certain application value.

To solve the problem of person posture change， feature fusion loss and blocking interference in video-based person re-identification，a fusion network model of three-dimensional convolution and self-attention mechanism is proposed in this paper. Firstly， short-term motion features and local detail features between adjacent frames are obtained through three-dimensional convolution. Secondly， a cross scale fusion module with multi-branch structure is proposed， multi-stage features of time and semantic dimensions are fused. Fnally， long-term features are captured by a self-attention mechanism. Compared with other methods， this paper proposed network was tested on three commonly used public datasets， the experiments results demonstrate that the proposed network can efficiently utilized the long-term and short-term information of videos so that advanced performances can be obtained.

Due to the constantly changing positions of satellites， UAVs and ground stations， the space space edge computing network link is not fixed， and the network needs to respond quickly to user requests， which requires high throughput and real-time， increasing the difficulty of network resource allocation. In this regard， this research proposes a network resource allocation method for space edge computing based on deep Q network algorithm. Firstly， considering the dynamic nature of network topology and resource heterogeneity， establish a communication model between resources to provide a basic framework for resource allocation； Then， based on the maximum throughput， a resource allocation objective function is designed， and a Markov decision model is used to express the objective function. The resource allocation problem is transformed into a sequential decision problem， which facilitates decision-making in a dynamically changing network environment； Finally， based on the deep Q-network algorithm， the objective function is solved， and through reinforcement learning， the algorithm can learn the optimal resource allocation strategy through interaction with the environment， adapting to the real-time and dynamic nature of the network. The experimental results show that after applying this method， the cumulative return of the network is higher and the average energy consumption of resource tasks is reduced， indicating that this method is more suitable for practical applications.

To address the two problems of low detector accuracy and redundant keypoints in detection results， a two-dimensional human pose estimation algorithm was proposed based on adaptive parameterized NMS. Replacing the original detector with CenterNet detector improves the performance of human detection and lays the foundation for subsequent pose estimation. Propose an adaptive parameterized PoseNMS algorithm that introduces sample appearance similarity to achieve sample adaptive measurement adjustment， making the filtering conditions in NMS more flexible. A non-uniform sampling method based on detection confidence was proposed， which ensured the effectiveness of the samples during the training process and achieved the discovery and mining of difficult samples. Verified on three datasets， a 71.9% mAP was achieved on the MSCOCO 2017 dataset under the condition of using the detection results output by the detector in this paper as the subsequent target box. In addition， the algorithm proposed in this paper has also been extensively experimented on MPII and MSCOCO 2015， and the quantitative and visual results show that the proposed method effectively solves the above two problems and achieves more accurate attitude estimation.

To reduce the probability of radar radiation signals being detected by enemy passive detection systems， this paper proposes a low-intercept radar waveform design method that employs phase and frequency joint coding. The method utilizes complementary two-phase codes and chaotic sequences to encode the phase and frequency within the pulse based on linear frequency modulation signals. Numerical simulation results show that the designed waveform exhibits pseudo-randomness in the time-frequency domain and improves the low-identification performance. The signal has a very low peak side lobe level after pulse compression， which demonstrates excellent low intercept performance. Its three-dimensional ambiguity function graph shows an ideal "peg shape" with good distance and speed resolution and anti-interference characteristics.

A passive indoor positioning method based on improved weighted K-nearest neighbor algorithm is proposed to address the problem of low positioning accuracy caused by excessive interference in amplitude and phase construction. In the offline stage， the isolation forest method is adopted， and the wavelet domain denoising and linear transformation method with improved threshold are used to preprocess the collected channel state information. The processed amplitude and phase information is used together as fingerprint data to construct a stable fingerprint database related to the reference point position information. In the online stage， an improved weighted K-nearest neighbor algorithm is proposed to repeatedly match the estimated coordinates. After obtaining the position coordinates in a single match， the algorithm calculates the Euclidean distance of the position coordinates between K-nearest neighbor points， and uses Gaussian transformation to calculate the weight of the K distance values， completing personnel positioning. Experimental simulation tests were conducted in classrooms and halls， and it was found that approximately 81% of the proposed algorithm's testing position error was controlled within 1 meter， which can effectively improve positioning accuracy.

In order to resist Sybil attacks in wireless sensor networks and protect data privacy， this paper proposes a Sybil attack detection strategy for wireless sensor networks integrating interactive reputation and received signal strength ratio（SD-IR&SR） in wireless sensor networks. Introducing reputation maintenance functions and anomaly correction factors in calculating node interaction reputation values to reduce the impact of historical interaction behavior on reputation values. Establish a global reputation value matrix to calculate the average global reputation value of each node， ensuring the fairness of node reputation values. Set nodes with a global reputation average lower than the reputation threshold as untrustworthy nodes， and set up monitoring nodes to use RSSR value comparison method for detection to determine whether the node is a Sybil node. The simulation results show that SD-IR&SR can effectively detect Sybil nodes in the network， in a network where witch nodes account for 50%， it can still maintain a low packet loss rate， ensure data integrity， low latency transmission， and improve the security of wireless sensor networks.

A non-dominated sorted particle swarm genetic algorithm with hybrid global-local search is proposed， by which the vehicle location routing problem can be effectively solved. Both particle swarm optimization and genetic algorithm operators are utilized in the global search to improve convergence speed. The non-dominated sorting genetic algorithm III is employed so that population diversity is maintained. The local search strategy is applied separately to superior and inferior individuals， by which the probability of obtaining better solutions is increased. Additionally， the user orders of the last 1/12 individuals in the population are shuffled so that the overall population quality is enhanced. The proposed algorithm is compared with benchmark algorithms by using the open standard dataset， and it is demonstrated that population quality and diversity are better provided， and an effective solution to the vehicle location routing problem can be supplied.