Spatio-temporal Graph Convolutional Network Research Articles

Dynamic Perception-Based Vehicle Trajectory Prediction Using a Memory-Enhanced Spatio-Temporal Graph Network

In the realm of intelligent transportation systems, accurately predicting vehicle trajectories is paramount for enhancing road safety and optimizing traffic flow management. Addressing the impacts of complex traffic environments and efficiently modeling the diverse behaviors of vehicles are the key challenges at present. To achieve precise prediction of vehicle trajectories, it is essential to fully consider the dynamic changes in traffic conditions and the long-term dependencies of time-series data. In response to these challenges, we propose the Memory-Enhanced Spatio-Temporal Graph Network (MESTGN), an innovative model that integrates a Spatio-Temporal Graph Convolutional Network (STGCN) with an attention-enhanced Long Short-Term Memory (LSTM)-based sequence to sequence (Seq2Seq) encoder–decoder structure. MESTGN utilizes STGCN to capture the complex spatial dependencies between vehicles and reflects the interactions within the traffic network through road traffic data and network topology, which significantly influences trajectory prediction. Additionally, the model focuses on historical vehicle trajectory data points using an attention-weighted mechanism under a traditional LSTM prediction architecture, calculating the importance of critical trajectory points. Finally, our experiments conducted on the urban traffic dataset ApolloSpace validate the effectiveness of our proposed model. We demonstrate that MESTGN shows a significant performance improvement in vehicle trajectory prediction compared with existing mainstream models, thereby confirming its increased prediction accuracy.

Behaviour recognition of housed sheep based on spatio-temporal information

ABSTRACT Livestock behavior is related to the healthy breeding and welfare level. Therefore, monitoring the behavior of sheep is helpful to predict the health status of sheep and thus safeguard the production performance of sheep. Taking semi-housed sheep as the research object, a behavior identification method for housed sheep based on spatio-temporal information is proposed. Firstly, video acquisition of housed sheep is carried out, and sheep detection and tracking is implemented based on the YOLOv5 coupled with a Deep-SORT algorithm, which detects sheep from the flock and labels their identity information; then, sheep posture estimation is done based on the Alphapose algorithm, which estimates the keypoints in the skeleton; finally, the detected keypoints are inputted into the trained spatio-temporal graph convolutional network model, and the spatio-temporal graph constructed from the keypoints and edge information is used for sheep behavior recognition. In the study, two keypoints selection proposals were made to implement sheep behavior recognition for non-contacted detection of ruminating, lying down and other behaviors. The results show the feasibility of sheep behavior recognition based on spatio-temporal information in semi-housed farming. The method provides a new solution idea for intelligent monitoring of sheep behavior and health assessment.

Predicting Vessel Trajectories Using ASTGCN with StemGNN-Derived Correlation Matrix

This study proposes a vessel position prediction method using attention spatiotemporal graph convolutional networks, which addresses the issue of low prediction accuracy due to less consideration of inter-feature dependencies in current vessel trajectory prediction methods. First, the method cleans the vessel trajectory data and uses the Time-ratio trajectory compression algorithm to compress the trajectory data, avoiding data redundancy and providing feature points for vessel trajectories. Second, the Spectral Temporal Graph Neural Network (StemGNN) extracts the correlation matrix that describes the relationship between multiple variables as a priori matrix input to the prediction model. Then the vessel trajectory prediction model is constructed, and the attention mechanism is added to the spatial and temporal dimensions of the trajectory data based on the spatio-temporal graph convolutional network at the same time as the above operations are performed on different time scales. Finally, the features extracted from different time scales are fused through the full connectivity layer to predict the future trajectories. Experimental results show that this method achieves higher accuracy and more stable prediction results in trajectory prediction. The attention-based spatio-temporal graph convolutional networks effectively capture the spatio-temporal correlations of the main features in vessel trajectories, and the spatio-temporal attention mechanism and graph convolution have certain interpretability for the prediction results.

Enhancing human behavior recognition with spatiotemporal graph convolutional neural networks and skeleton sequences

ObjectivesThis study aims to enhance supervised human activity recognition based on spatiotemporal graph convolutional neural networks by addressing two key challenges: (1) extracting local spatial feature information from implicit joint connections that is unobtainable through standard graph convolutions on natural joint connections alone. (2) Capturing long-range temporal dependencies that extend beyond the limited temporal receptive fields of conventional temporal convolutions.MethodsTo achieve these objectives, we propose three novel modules integrated into the spatiotemporal graph convolutional framework: (1) a connectivity feature extraction module that employs attention to model implicit joint connections and extract their local spatial features. (2) A long-range frame difference feature extraction module that captures extensive temporal context by considering larger frame intervals. (3) A coordinate transformation module that enhances spatial representation by fusing Cartesian and spherical coordinate systems.FindingsEvaluation across multiple datasets demonstrates that the proposed method achieves significant improvements over baseline networks, with the highest accuracy gains of 2.76%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\%$$\\end{document} on the NTU-RGB+D 60 dataset (Cross-subject), 4.1%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\%$$\\end{document} on NTU-RGB+D 120 (Cross-subject), and 4.3%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\%$$\\end{document} on Kinetics (Top-1), outperforming current state-of-the-art algorithms. This paper delves into the realm of behavior recognition technology, a cornerstone of autonomous systems, and presents a novel approach that enhances the accuracy and precision of human activity recognition.

Complex-Value Spatiotemporal Graph Convolutional Neural Networks and Its Applications to Electric Power Systems AI

Complex-Value Spatiotemporal Graph Convolutional Neural Networks and Its Applications to Electric Power Systems AI

Dynamic spatial aware graph transformer for spatiotemporal traffic flow forecasting

Accurately predicting traffic flow is a crucial upstream technique in intelligent transportation systems for future travel plans, the efficiency of urban transport, and the regulation of transport departments, etc. The mainstream spatiotemporal graph convolutional neural networks are usually based on prior knowledge to predefine adjacency matrix graphs for spatial dependencies of the road network. However, modeling spatial correlation statically limits these models to accurately predict traffic flow, since the spatial correlations of road segments change over time. To address these issues, we propose a spatiotemporal gated transformer network with a graph latent information learning structure, termed GL-STGTN, for spatiotemporal traffic flow forecasting. First, we propose a graph latent information learning structure to dynamically learn the spatial dependencies for road network conditions from global and local learning perspectives. Second, we design a spatiotemporal gated transformer network (STGTN) block, which consists of a localized geographically aware block to extract the local embedding of spatial correlations and a temporal-aware enlarged block to extract local temporal dependencies. The learned spatial and temporal feature embeddings are further aggregated in a spatial multi-head attention module and a temporal multi-head attention module, respectively. In the end, a spatiotemporal fusion layer fuses the spatial and temporal information from the stacked STGTN blocks. Experiments on two public real-world benchmark datasets show that our model outperforms six state-of-the-art models for multi-step traffic flow forecasting.

Congestion-aware Spatio-Temporal Graph Convolutional Network Based A* Search Algorithm for Fastest Route Search

The fastest route search, which is to find a path with the shortest travel time when the user initiates a query, has become one of the most important services in many map applications. To enhance the user experience of travel, it is necessary to achieve accurate and real-time route search. However, traffic conditions are changing dynamically, especially the frequent occurrence of traffic congestion may greatly increase travel time. Thus, it is challenging to achieve the above goal. To deal with it, we present a congestion-aware spatio-temporal graph convolutional network based A* search algorithm for the task of fastest route search. We first identify a sequence of consecutive congested traffic conditions as a traffic congestion event. Then, we propose a spatio-temporal graph convolutional network that jointly models the congestion events and changing travel time to capture their complex spatio-temporal correlations, which can predict the future travel time information of each road segment as the basis of route planning. Further, we design a path-aided neural network to achieve effective origin-destination (OD) shortest travel time estimation by encoding the complex relationships between OD pairs and their corresponding fastest paths. Finally, the cost function in the A* algorithm is set by fusing the output results of the two components, which is used to guide the route search. Our experimental results on the two real-world datasets show the superior performance of the proposed method.

FeSTGCN: A frequency-enhanced spatio-temporal graph convolutional network for traffic flow prediction under adaptive signal timing

FeSTGCN: A frequency-enhanced spatio-temporal graph convolutional network for traffic flow prediction under adaptive signal timing

Intelligent Analysis of E-commerce Reviews based on Complex-Valued Spatio-temporal Graph Convolutional Neural Network Optimized Tyrannosaurus Optimization Algorithm

Intelligent analysis of E-commerce reviews based on multimodal social media big data using applies the benefit of innovative artificial intelligence method, especially deep learning algorithms, to study and realize web-based product consider and social network data connected to e-commerce. In this manuscript, Intelligent Analysis of E-commerce reviews based on Complex-Valued Spatio-temporal Graph Convolutional Neural Network Optimized Tyrannosaurus Optimization Algorithm (IAER-CVSGCNN-TOA)is proposed for Intelligent Analysis of E-commerce Reviews. The input data are collected from E-commerce big Dataset. Then data are pre-processing utilizing federated neural collaborative filtering (FNCF) to remove the noise and clean the data. The pre-processed data is provided to the CVSGCNN is used for intelligent analysis of e-commerce reviews. In general, CVSGCNN does no express adapting optimization approaches to determine optimal parameters to ensure accurate prediction. Hence, proposed to utilize the Tyrannosaurus Optimization Algorithm enhancement CVSGCNN for intelligent analysis of e-commerce reviews. The proposed IAER-CVSGCNN-TOA method is implemented on python. Then performance of proposed technique is analyzed with other existing techniques. The proposed technique attains 30.26%, 28.70% and 46.25% higher accuracy, 20.53%, 32.60%, and 53.43% higher precision, 25.23%, 23.15%, and 22.33%higher recall, 25.75%, 44.30% and 53.70%higher specificity comparing with the existing methods such as a Retracted: Online Troll Reviewer Detection Utilizing Deep Learning Methods (IAER-CNN), Developing an Intelligent System and DL Algorithms for Sentiment Analysis of E-Commerce Product Reviews(IAER-LSTM), Intelligent Perception System of Big Data Decision in Cross-Border e-Commerce Depend on Data Fusion(IAER-DFN) respectively.

Passenger spatiotemporal distribution prediction in airport terminals based on physics-guided spatio-temporal graph convolutional network and its effect on indoor environment prediction

The airport as an important transportation hub plays a leading role in promoting sustainable cities and new-type urbanization. To boost safe, environmental-friendly and technologically advanced airports, the passenger travel behavior as a core that decides the resource allocation, system tuning and capacity dispatching, must be grasped. Previous research in passenger distribution prediction focused on physics-based methods or only mining temporal dynamics. In this work, a refined passenger distribution prediction was modeled based on a learning-based method embedding physical prior knowledge, and then its effects on indoor environment prediction were analyzed. Among them, based on insect intelligent building architecture, a virtual spatial graph was defined in Guangzhou Baiyun International Airport Terminal 2, then a Wi-Fi positioning system was constructed; Next, a physics-guided spatio-temporal graph convolutional network, considering both the spatial dependencies and the passenger arrival pattern extracted from cost-free flight schedules, was developed for domestic and international passenger distribution predictions with R2 over 0.87 and 0.76 respectively; Lastly, the contributions of predicted occupant densities to the indoor environment prediction were evaluated with results showing that the average R2 for indoor temperature, relative humidity and CO2 concentration prediction was enhanced by 0.4 %∼91.5 %, 0.2 %∼29.7 % and 0.4 %∼45.4 % respectively as the prediction horizon broadening.

Simultaneous forecasting of wind speed for multiple stations based on attribute-augmented spatiotemporal graph convolutional network and tree-structured parzen estimator

The global increase in energy demand and environmental concerns have made the development of wind energy increasingly important. Accurate wind speed prediction is crucial for maximizing the benefits of wind energy. In order to further improve the accuracy of multi-site wind speed prediction, this study adopts an evolutionary algorithm-based deep learning model that fully considers the spatiotemporal relationships among multiple station's wind speed data. Firstly, the mutual information (MI) method is used to select variables with stronger correlations to wind speed as auxiliary input factors. Then, an improved version of Attribute-Augmented Spatiotemporal Graph Convolutional Network (IASTGCN) is employed to process data from multiple stations, taking into account both temporal and spatial factors. Additionally, an MI-based wind speed data relationship matrix between multiple stations is calculated to replace the original distance relationship matrix in the model, enabling the model to better capture and utilize the relationships between stations. Next, the Tree-structured Parzen Estimator (TPE) is used to optimize the hyperparameters of the model. This ultimately achieves multi-site multi-step wind speed prediction. Experimental results demonstrate that the proposed model outperforms baseline models and models that only consider either temporal or spatial factors in various scenarios, exhibiting better predictive performance.

A novel spatiotemporal graph convolutional network framework for functional connectivity biomarkers identification of Alzheimer’s disease

BackgroundFunctional connectivity (FC) biomarkers play a crucial role in the early diagnosis and mechanistic study of Alzheimer’s disease (AD). However, the identification of effective FC biomarkers remains challenging. In this study, we introduce a novel approach, the spatiotemporal graph convolutional network (ST-GCN) combined with the gradient-based class activation mapping (Grad-CAM) model (STGC-GCAM), to effectively identify FC biomarkers for AD.MethodsThis multi-center cross-racial retrospective study involved 2,272 participants, including 1,105 cognitively normal (CN) subjects, 790 mild cognitive impairment (MCI) individuals, and 377 AD patients. All participants underwent functional magnetic resonance imaging (fMRI) and T1-weighted MRI scans. In this study, firstly, we optimized the STGC-GCAM model to enhance classification accuracy. Secondly, we identified novel AD-associated biomarkers using the optimized model. Thirdly, we validated the imaging biomarkers using Kaplan–Meier analysis. Lastly, we performed correlation analysis and causal mediation analysis to confirm the physiological significance of the identified biomarkers.ResultsThe STGC-GCAM model demonstrated great classification performance (The average area under the curve (AUC) values for different categories were: CN vs MCI = 0.98, CN vs AD = 0.95, MCI vs AD = 0.96, stable MCI vs progressive MCI = 0.79). Notably, the model identified specific brain regions, including the sensorimotor network (SMN), visual network (VN), and default mode network (DMN), as key differentiators between patients and CN individuals. These brain regions exhibited significant associations with the severity of cognitive impairment (p < 0.05). Moreover, the topological features of important brain regions demonstrated excellent predictive capability for the conversion from MCI to AD (Hazard ratio = 3.885, p < 0.001). Additionally, our findings revealed that the topological features of these brain regions mediated the impact of amyloid beta (Aβ) deposition (bootstrapped average causal mediation effect: β = -0.01 [-0.025, 0.00], p < 0.001) and brain glucose metabolism (bootstrapped average causal mediation effect: β = -0.02 [-0.04, -0.001], p < 0.001) on cognitive status.ConclusionsThis study presents the STGC-GCAM framework, which identifies FC biomarkers using a large multi-site fMRI dataset.

A fish appetite assessment method based on improved ByteTrack and spatiotemporal graph convolutional network

The appetite of fish significantly influences aquaculture efficiency and fish welfare. However, accurately assessing fish appetite has posed a challenging problem. Currently, the study of fish feeding behaviour relies primarily on the overall information obtained from images of fish schools, often overlooking the distinctive behavioural traits of individual fish. Analysing the behaviour of individual fish is hindered by challenges such as intraclass variation and cross-occlusion within real aquaculture environments. To address these challenges, this paper introduces a novel method for assessing appetite based on individual fish behaviour. The ByteTrack model was improved to enable stable tracking of each fish within a school under complex conditions. Additionally, this paper employs the spatiotemporal graph convolutional neural network (ST-GCN) to extract the movement characteristics of individual fish, facilitating accurate appetite assessment. The experimental results demonstrate that the proposed method achieves 98.47% accuracy in appetite assessment, surpassing the performance of other state-of-the-art methods. This paper provides a new opportunity and effective means for analysing fish behaviour and appetite in intricate environments.

A novel dynamic spatio-temporal graph convolutional network for wind speed interval prediction

It is crucial to predict the wind speed for the utilization of renewable wind energy and the operation of transmission lines with increased capacity. The intermittency and stochastic fluctuations of wind speed pose a significant challenge for the high-quality wind speed prediction, and a novel wind speed interval prediction (WSIP) model is constructed in this study by employing the residual estimation (RE)-oriented dynamic spatio-temporal graph convolutional network (DSTGCN) approach. Firstly, a dynamic adjacency matrix is designed to obtain time-varying global spatial weight allocations among each wind speed node. Then, the spatio-temporal features are extracted by using gated recurrent units (GRUs) and GCNs to construct the wind speed graph networks. Moreover, the RE-oriented strategy incorporating the pinball loss is designed to provide a guidance the parameter training of the constructed model, thus eliminating the quantile crossings problem. As a result, the deterministic point prediction of the wind speed is expanded to the quantile-based probabilistic interval prediction. Finally, the experimental results are presented to demonstrate the validity and superiority of proposed scheme in both qualitative and quantitative performance.

Dynamic Spatiotemporal Correlation Graph Convolutional Network for Traffic Speed Prediction

Accurate and real-time traffic speed prediction remains challenging due to the irregularity and asymmetry of real-traffic road networks. Existing models based on graph convolutional networks commonly use multi-layer graph convolution to extract an undirected static adjacency matrix to map the correlation of nodes, which ignores the dynamic symmetry change of correlation over time and faces the challenge of oversmoothing during training iterations, making it difficult to learn the spatial structure and temporal trend of the traffic network. To overcome the above challenges, we propose a novel multi-head self-attention gated spatiotemporal graph convolutional network (MSGSGCN) for traffic speed prediction. The MSGSGCN model mainly consists of the Node Correlation Estimator (NCE) module, the Time Residual Learner (TRL) module, and the Gated Graph Convolutional Fusion (GGCF) module. Specifically, the NCE module aims to capture the dynamic spatiotemporal correlations between nodes. The TRL module utilizes a residual structure to learn the long-term temporal features of traffic data. The GGCF module relies on adaptive diffusion graph convolution and gated recurrent units to learn the key spatial features of traffic data. Experimental analysis on a pair of real-world datasets indicates that the proposed MSGSGCN model enhances prediction accuracy by more than 4% when contrasted with state-of-the-art models.

Multi-faceted spatio-temporal network for weather-aware air traffic flow prediction in multi-airport system

As one of the core modules for air traffic flow management, Air Traffic Flow Prediction (ATFP) in the Multi-Airport System (MAS) is a prerequisite for demand and capacity balance in the complex meteorological environment. Due to the challenge of implicit interaction mechanism among traffic flow, airspace capacity and weather impact, the Weather-aware ATFP (Wa-ATFP) is still a nontrivial issue. In this paper, a novel Multi-faceted Spatio-Temporal Graph Convolutional Network (MSTGCN) is proposed to address the Wa-ATFP within the complex operations of MAS. Firstly, a spatio-temporal graph is constructed with three different nodes, including airport, route, and fix to describe the topology structure of MAS. Secondly, a weather-aware multi-faceted fusion module is proposed to integrate the feature of air traffic flow and the auxiliary features of capacity and weather, which can effectively address the complex impact of severe weather, e.g., thunderstorms. Thirdly, to capture the latent connections of nodes, an adaptive graph connection constructor is designed. The experimental results with the real-world operational dataset in Guangdong-Hong Kong-Macao Greater Bay Area, China, validate that the proposed approach outperforms the state-of-the-art machine-learning and deep-learning based baseline approaches in performance. The case study of convective weather scenarios further proves the adaptability of the proposed approach.

Exploring a spatiotemporal hetero graph-based long short-term memory model for multi-step-ahead flood forecasting

Operational flood prevention platforms and systems rely upon the advance notice provided by flood forecasts to formulate efficient measures for flood mitigation. Capturing complex spatial heterogeneity and correlation of hydro-meteorological variables is fundamentally challenging for artificial neural networks. This challenge becomes even more significant when the complexity involved introduces systematic biases and time-lag phenomena in flood forecasts. For the first time, this study proposed a Spatiotemporal Hetero Graph-based Long Short-term Memory (SHG-LSTM) model for multi-step-ahead flood forecasting. The case study focused on the Jianxi basin in China. 25,341 hydro-meteorological data, with a temporal resolution of three hours, collected during flood events were divided into training and test datasets for model construction purpose. The model was fed with 3-h streamflow and precipitation data from 23 gauge stations, covering a time span of the preceding 21 h, for generating flood forecasts at 1 up to 7 horizons. To make a comparative analysis, both LSTM and the Spatiotemporal Graph Convolutional Network (S-GCN) were constructed. This study conducted multiple rounds of model training with varying initial parameters to assess the accuracy, stability, and reliability of the LSTM, S-GCN, and SHG-LSTM models. The results demonstrated that the SHG-LSTM model outperformed LSTM and S-GCN models, with an average reduction in the volume error (VE) of 6.5% and 11.1%, respectively, a decrease in the Mean Absolute Error (MAE) of 6.7% and 8.1%, respectively, and a reduction in the Root Mean Square Error (RMSE) of 5.0% and 12.9%, respectively. Furthermore, the SHG-LSTM model not only could efficiently overcome the under-prediction bottleneck, but also could largely mitigate the time-lag phenomenon in flood forecasts, even during the testing stages. These findings indicate that the proposed SHG-LSTM model can provide a general framework for modelling the spatial heterogeneity and correlation of hydro-meteorological variables and achieve accurate and reliable flood forecasts, thereby enhancing the model’s applicability in flood prevention platforms and systems.

Spatial-temporal deep learning model based on Similarity Principle for dock shared bicycles ridership prediction

Demand prediction plays a critical role in traffic research. The key challenge of traffic demand prediction lies in modeling the complex spatial dependencies and temporal dynamics. However, there is no mature and widely accepted concept to support the solution of the above challenge. Essentially, a prediction model combined with similar objects in temporal and spatial dimensions could obtain better performance. This paper proposes a concept called the Similarity-based Principle (SP), which is applied to improve the prediction performance of deep learning models in complex traffic scenarios. For the temporal components, the long-term temporal dynamics in contemporaneous historical data for ridership are extracted by the Stacked Autoencoder (SAE) method. For the spatial components, the activity-based spatial geographic information (ABG-information) is used to capture the spatial correlation of the traffic network, which is reflected in the daily activities of humans. Specifically, the SP is applied to a Spatio-temporal Graph Convolutional Neural Network (STGCNN) model. In the case study, the Similarity-based Principle Spatio-temporal Graph Convolutional Neural Network (SP-STGCNN) model predicts demand for bicycle sharing in San Francisco. The results show that the SP effectively improves the model's performance. The prediction accuracy is enhanced by up to 10.34% compared with STGCNN. For spatial relationships, the model using the geographic information attribute performs better than that using the road information attribute and the distance attribute. It is proved that the construction of the Spatio-temporal model-based similarity principle can improve the performance.

Vessel trajectory prediction based on spatio-temporal graph convolutional network for complex and crowded sea areas

In order to improve the navigation ability of vessels and ensure the safety of maritime traffic, vessel trajectory prediction plays a crucial role in the intelligent navigation and collision avoidance. Especially for complex and crowded waters, autonomous vessels must have high situational awareness to detect other vessels, and predict their future trajectories and assess collision risks. In this work, a deep attention-aware spatio-temporal graph convolutional network based on AIS data (DAA-SGCN) is proposed to predict the future trajectories of vessels. It mainly includes three modules: motion information encoding of vessel trajectories, the spatio-temporal feature extraction module and the trajectory prediction module. The LSTM is used to extract the motion features of vessels, the spatio-temporal graph is constructed based on deep attention mechanism. The spatial social interaction features are extracted by ST-GCN, and the temporal correlation are extracted by RT-CNN, to obtain the high-level spatio-temporal features of vessel trajectories. Then, the feature embedding is fed into the trajectory prediction module to predict the future trajectories of vessels. On the basis of a large number of experiments, the prediction performance of the DAA-SGCN compared to the optimal baseline model is improved by about 74% and 69% in ADE and FDE metrics.

Enhancing four-axis machining center accuracy through interactive fusion of spatiotemporal graph convolutional networks and an error-controlled digital twin system

Mitigating thermal errors constitutes a crucial method for enhancing the machining accuracy of four-axis machining centers. At the heart of effective thermal error control lie the thermal error control platform and a resilient thermal error prediction model. It is imperative to note that thermal errors exhibit intricate dynamic and nonlinear spatiotemporal dependencies. However, prevailing thermal error prediction models tend to primarily focus on temporal features or employ simplistic spatiotemporal characteristics, resulting in diminished accuracy and robustness. Furthermore, the present thermal error compensation system is plagued by a lack of user-friendliness, stemming from its suboptimal execution efficiency. In response to the aforementioned challenges, an innovative approach: the interactive fusion spatiotemporal graph convolutional network is proposed. This novel model is specifically designed to capture the intricate dynamic spatiotemporal dependencies inherent in thermal errors. The interactive fusion spatiotemporal graph convolutional network model consists of three essential components: a bilinear temporal convolutional network, a multi-layer spatiotemporal module, and a linear module. These components work in harmony to comprehensively extract both global and local spatiotemporal features. Subsequently, a mapping relationship between thermal errors and compensation components is established, laying the foundation for theoretical advancements in thermal error compensation within the realm of four-axis machining centers. A digital twin system framework tailored for error control is devised, which leverages cloud-edge computing to enable dynamic control and real-time monitoring of thermal errors. To assess the effectiveness of this digital twin system framework and the interactive fusion spatiotemporal graph convolutional network model, a series of rigorous experiments were conducted. The oriented to error-controlled digital twin system coupled with the interactive fusion spatiotemporal graph convolutional network model yielded exceptional machining accuracy, resulting in minimal geometric disparities of [−3.0 μm, 3.0 μm] for the central hole diameter D and [−3.5 μm, 4.0 μm] for the hole distance H.