作者机构：
[Zhaoyu Liu; Caidie Yi; Ziyi Zhu; Zengyang He; Yiye Wu; Chengjuan Yang] School of Management, Wuhan Polytechnic University, Wuhan, Hubei, China
会议名称：
2025 IEEE International Conference on Electronics, Energy Systems and Power Engineering (EESPE)
会议时间：
17 March 2025
会议地点：
Shenyang, China
会议论文集名称：
2025 IEEE International Conference on Electronics, Energy Systems and Power Engineering (EESPE)
关键词：
Fiscal prediction;Adaptive-Lasso regression;Grey neural network;Combined prediction model
摘要：
The widespread application of data mining technology and the rapid development of machine learning techniques provide a simple and efficient method for predicting local fiscal revenue. The current main model for fiscal revenue prediction involves using data mining techniques for reasonable selection and analysis of data, followed by training neural networks to construct prediction models. This paper proposes a fiscal revenue prediction model based on data mining and grey neural networks, selecting 12 influencing factors that affect fiscal revenue. The Adaptive-Lasso method is used for variable coefficient estimation, and least angle regression is employed to solve the problem, eliminating some variables with lesser impact. The remaining variables are then subjected to GM (1,1) grey prediction to obtain their predicted values, and the prediction accuracy is evaluated with a grading system. Finally, historical data is used to train a BP neural network, constructing a grey neural network combined prediction model, where the grey predicted values are substituted into the trained grey neural network to yield future fiscal revenue predictions. Experimental results indicate that due to the high fault tolerance and adaptability of neural networks, the predicted values fit well with the actual values, with the two curves nearly overlapping. The grey neural network prediction results constructed in this paper are highly reliable.
作者：
Shuqing Sun;Hongwei Li;Sulan Li;Feifan Feng;Zhiwei Liu
作者机构：
[Zhiwei Liu] Wuhan Polytechnic University, School of Civil Engineering and Architecture, Wuhan, Hubei, China [email protected];[Sulan Li] Jianghan University, State Key Laboratory of Precision Blasting, Wuhan, Hubei, China [email protected];[Shuqing Sun; Hongwei Li; Feifan Feng] Hohai University, College of Civil and Transportation Engineering, Nanjing, Jiangsu, China [email protected]
会议名称：
SCSD '25: Proceedings of the 2025 International Conference on Smart City and Sustainable Development
摘要：
This study used a controlled interruption time series to analyze the impact of Chica-go's COVID-19 policies on traffic safety, including stay-home orders, cautious reopening, gradual return, second wave of restrictions, restrictions lifed, and the new normal. The heterogeneity of policy effects among different population groups is also consid-ered. The results showed that: 1) Stay-at-home order and cautious opening policy had adverse effects on the casualty rate, and the effect of cautious opening policy was more significant;The stay-at-home order led to a spike in injury rates only in the first few weeks, while the rate continued to rise during the cautious reopening, suggesting that the policy could have long-term adverse effects. 2) The reduction in mortality rates under the new normal phase was the most significant among all the reopening stages, and by the end of 2021, overall mortality rates returned to levels seen in previous years. 3) Males faced higher risks during lockdowns and initial reopening, whereas females experi-enced increased casualties during commercial or entertainment venue reopenings. 4) Casualty rates for cyclists and pedestrians were less affected by the blockade and initial reopening. However, pedestrian casualty rates increased during the reopening phase, especially after the reopening of the recreational and commercial sectors. 5) Young people had lower casualty rates during the reopening phase, but their rise was more pronounced during the lockdown. In terms of casualty rate, the elderly are less sensi-tive to policy interventions. These findings highlight the need to consider phased changes in travel patterns and demographic differences when formulating traffic management strategies at different stages of a pandemic.
作者机构：
[Qiyao Luo; Yongqing Qian] School of Electrical and Electronic Engineering, Wuhan Polytechnic University, Wuhan, China
会议名称：
2025 6th International Conference on Artificial Intelligence and Electromechanical Automation (AIEA)
会议时间：
01 August 2025
会议地点：
Hefei, China
会议论文集名称：
2025 6th International Conference on Artificial Intelligence and Electromechanical Automation (AIEA)
关键词：
Foggy weather;BIFPN;MANet;Dysample
摘要：
Due to reduced visibility and other reasons, the probability of accidents involving vehicles and pedestrians increases in foggy weather. In this paper, we present a real-time and efficient vehicle-pedestrian detection model for foggy images. In the backbone network, we added Mixed Aggregation Network (MANet) to replace C2f module to achieve stronger feature extraction. In the neck network, we introduced the Bidirectional Feature Pyramid Network (BIPFN) structure and used dysample for upsampling to achieve fast and efficient feature fusion in the model. In the loss function, we introduced Wise-IoU, which enables the model to focus more on samples with general quality, thereby improving overall performance. Our experiments were performed on the Real-world Task-Driven Testing Set (RTTS). Compared with the YOLOv10 model, our model has improved the detection accuracy by 3.5 %.
作者机构：
[Bairu Xiao; Feng Xu; Zhangyueer Yan; Junkun Zhang; Siyue Zhu] Wuhan Polytechnic University, School of Civil Engineering and Architecture, Wuhan, Hubei, China [email protected]
会议名称：
SCSD '25: Proceedings of the 2025 International Conference on Smart City and Sustainable Development
摘要：
The emergence of new quality productive forces has served as robust impetus for the transformation and upgrading of the construction industry. To systematically investigate the synergistic effects arising from the integration of artificial intelligence (AI) with construction practices, this study categorizes AI applications into three phases: design, construction, and operation and maintenance (O&M). In the design phase, Building Information Modeling (BIM) and generative AI are synergistically employed to optimize architectural solutions. During the construction phase, intelligent construction robots, 5G-enabled tower cranes and other automated equipments are integrated with BIM and Augmented Reality (AR) platforms. This combination increases managerial and operational efficiency. For the O&M phase, a digital twin platform supported by Internet of Things (IoT) networks and AI-driven predictive analytics enables preventive equipment maintenance and energy consumption optimization. Taking Xiong'an New Area as a representative case study, this research demonstrates that the symbiotic integration of AI technologies with construction substantial improvements across project lifecycles, operational efficiency, and safety performance. These advancements provide both practical evidence and technical pathways to support future urban renewal initiatives.
作者机构：
Hubei Key Laboratory of Digital Finance Innovation, Hubei University of Economics, 430205, Wuhan, Hubei, China;School of Information Engineering, Hubei University of Economics, 430205, Wuhan, Hubei, China;[Zhi Yang] College of Electronics and Information Engineering, Sichuan University, 610065, Chengdu, China;[Yanfei Zhu] School of Foreign Languages, Sun Yat-sen University, 510275, Guangzhou, China;[Libin Lu] School of Mathematics and Computer Science, Wuhan Polytechnic University, 430023, Wuhan, China
会议名称：
Brain Informatics: 17th International Conference, BI 2024, Bangkok, Thailand, December 13–15, 2024, Proceedings, Part II
摘要：
Early diagnosis of mild cognitive impairment (MCI) is crucial for the effective treatment and intervention of neurodegenerative diseases. Effective connectivity is one kind of brain network, which is helpful for analyzing the pathogenic mechanism of MCI. It is challenging to model causal relationships between brain regions from multimodal imaging data. This study proposes a new method for brain network causality modeling based on the structure-guided spatiotemporal diffusion model (SSDM), aiming to improve the accuracy of MCI diagnosis. By utilizing the advanced diffusion models, we introduced structural connectivity to guide the transformer-based network to learn topological and spatiotemporal features, which can better remove uncorrelated noise and improve effective connectivity estimation. The proposed model can not only generate temporal features of brain regions with individual differences but also construct discriminable effective connectivities. Experiments on the ADNI dataset demonstrate the effectiveness of our model, showing a certain improvement in diagnostic accuracy compared with competing methods. In addition, by analyzing the effective connectivities, our model predicts abnormal brain connections that are highly correlated with MCI. Overall, the framework proposed in this paper provides insights into the potential neurobiological mechanisms of MCI, which may promote early intervention strategies.
作者机构：
[Yu Zhai; Jianjun Yang; Xiao Rang; Zean Wang; Houchang Pei; Shaoyun Song] College of Mechanical Engineering, Wuhan Polytechnic University, Wuhan, China
会议名称：
2025 5th International Conference on Artificial Intelligence and Industrial Technology Applications (AIITA)
会议时间：
28 March 2025
会议地点：
Xi'an, China
会议论文集名称：
2025 5th International Conference on Artificial Intelligence and Industrial Technology Applications (AIITA)
关键词：
Steel material defects detection;YOLOv7;Attention mechanism;Data enhancement
摘要：
The detection of steel material defects is vital for enhancing product quality, safety, and reliability. However, conventional deep learning approaches, such as YOLOv7 and SSD, suffer from slow detection speed and suboptimal accuracy. To address these issues, we present an enhanced YOLOv7 algorithm for steel defect detection. Our approach optimizes the YOLOv7 model by integrating ResNet Channel Attention Connection modules into the backbone network to improve feature extraction capabilities. Furthermore, a self-Coordinate Attention mechanism is introduced to enhance detection accuracy. Additionally, we modify downsampling using the unfold model to improve the detection of small objects. Experimental results demonstrate the effectiveness of our proposed enhanced YOLOv7 model in accurately identifying steel defects. Compared to the original model, we achieved an 11.4% increase in mean average precision (mAP) and reduced training time by 2.795 hours.
摘要：
Data-driven applications need advanced predictive modeling to manage nonlinear relationships and high-dimensional datasets. To address these challenges, this research presents a novel hybrid optimization model integrating an Improved Zebra Optimization Algorithm (IZOA) with a Back Propagation (BP) neural network to enhance predictive performance in complex datasets. The IZOA addresses inherent limitations in traditional optimization methods by employing a Logistic chaotic initialization technique that increases population diversity. Furthermore, a Golden Sine optimization strategy is incorporated to balance exploration and exploitation effectively. The model’s architecture leverages the strengths of both IZOA and BP neural networks, allowing for refined local adjustments while maintaining global search capabilities. Experimental evaluations demonstrate that the IZOA-BP model significantly outperforms conventional approaches, achieving superior metrics such as a Root Mean Squared Error (RMSE) of 2.271, Mean Absolute Error (MAE) of 0.891, and an R² value of 0.935. These advancements highlight the model’s robustness in capturing nonlinear interactions and adapting to high-dimensional data, positioning the IZOA-BP framework as a transformative tool for various predictive applications.
摘要：
In order to relieve the pressure of manual drug dispensing in hospitals and address the problem of medical powder spraying due to the negative pressure inside the bottles during manual dispensing. This conference paper presents a robotic system used to accomplish the task of in vitro diagnostic (IVD) dispensing for medical freeze dried bottles which is sealed with rubber stopper. This device is mainly composed of four modules: the cap-processing module, the transportation module, the needle-puncturing module, and the injection module. The function of the robotics system is to open the cap of the freeze-dried bottle, insert the needle, inject the corresponding reagent, and tighten the cap, thereby completing the drug preparation process. It is expected that the reagent preparation time will be shortened within two minutes per bottle of medicine, while the accuracy of reagent preparation is controlled within 5% of the error. Experiments are conducted for the performance evaluation of the proposed robot. Results show that, with the liquid injection accuracy reaching 97.6% and the reagent preparation time reaching about 51 seconds.
作者机构：
[Hua Yang; Zhonger Li; Zhan Shu; Junda Liu; Ming Zhao; Mingzhi Mu] College of Mathematics and Computer Science, Wuhan Polytechnic University, Wuhan, China
会议名称：
2025 8th International Conference on Advanced Electronic Materials, Computers and Software Engineering (AEMCSE)
会议时间：
09 May 2025
会议地点：
Nanjing, China
会议论文集名称：
2025 8th International Conference on Advanced Electronic Materials, Computers and Software Engineering (AEMCSE)
摘要：
To enhance the accuracy and convergence speed of electricity load forecasting, this paper proposes an optimized electricity load forecasting model that integrates a Modified Frilled Lizard Optimization (MFLO) algorithm with a Backpropagation (BP) neural network. The MFLO addresses the traditional challenges of slow convergence and local optima by incorporating Lévy flight mechanisms and self-weight factors, which enhance global search capabilities. By integrating information entropy into fitness adjustments, the model improves the quality of initial solutions, leading to better predictions. The MFLO-BP model demonstrates significant performance improvements, achieving a Mean Absolute Percentage Error (MAPE) of 1.09%, a Root Mean Square Error (RMSE) of 152.5, and an $\mathbf{R}^{2}$ value of 0.97, outperforming conventional BP models with MAPE of 1.57% and RMSE of 205.4. Our findings contribute to more effective energy management strategies and offer a robust framework for evolving energy demands.
作者机构：
[Siyuan Peng; Dan Lyu] School of Econmics, Wuhan Polytechnic University, Wuhan, Hubei, China [email protected]
会议名称：
DEAI '25: Proceedings of the 2nd Guangdong-Hong Kong-Macao Greater Bay Area International Conference on Digital Economy and Artificial Intelligence
摘要：
Based on the Howard-Sheth model and brand equity theory, this study takes Benjarong rice as a case study, and applies big data technologies such as LDA theme model and co-occurrence analysis to analyse the dynamic mechanism of imported agricultural product brands influencing consumption decisions. The study found that: (1) consumers' brand sentiment has a significant positive skewed distribution (71.7% positive evaluation), and the synergistic driving effect of origin marking and cultural symbols is prominent; (2) consumer decision-making focuses on the three dimensions of brand effect, quality assurance and service experience, and the co-occurrence intensity of “brand-quality” reaches 0.68, which verifies the two-way reinforcement path of “information processing-quality perception” in the Howard-Sheth model; (3) the service experience is enhanced by the brand equity theory through the LDA theme model and big data analysis, and analyzes the dynamic mechanism of imported agricultural products brand influence. (3) Service experience reflects the risk buffer function through the 12.8% increase in customer churn rate. Accordingly, a three-dimensional competition model is constructed: geographical indication certification and cultural narrative synergistically strengthen the brand premium, blockchain traceability breaks the quality and trust dilemma, and cold chain logistics and environmentally friendly packaging design constructs a trust safety valve. The research results provide big data decision-making support for breaking through the monopoly of imported brands and realising the transformation of “blue ocean value”.
作者：
Yunhan Yang;Fangxiu Wang;Yaxiao Zhang;Chaokang Ren;Xuan Sun
作者机构：
[Yunhan Yang; Fangxiu Wang; Yaxiao Zhang; Chaokang Ren; Xuan Sun] School of Mathematics and Computer Science, Wuhan Polytechnic University, Wuhan, China
会议名称：
2025 21st International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)
会议时间：
26 July 2025
会议地点：
Hohhot, China
会议论文集名称：
2025 21st International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)
摘要：
This study addresses the limitation of existing calculators for triple integrals cannot compute definite integrals, this paper proposes a method to transform the definite integral input mode into a triple integral input mode. First, this paper introduces the input modes and their characteristics of calculating definite integrals and triple integrals respectively. Then, a specific method for transforming the definite integral input mode into an equivalent triple integral input mode is designed, which can effectively achieve the conversion between the two. Finally, the specific steps and procedures for calculating definite integrals using the improved triple integral calculator are proposed. The experimental results show that the improved triple integral calculator can successfully calculate definite integrals, and the calculation results are accurate and reliable. This research provides a new idea for the calculation of definite integrals and broadens the application scope of triple integral calculators.
摘要：
This article focuses on the synchronization problem of a class of delayed memristive neural networks (DMNNs) with inertial terms. Firstly, a synchronization scheme for delay inertial memristive neural networks (DIMNNs) is proposed by using variable substitution and incorporating intermittent adaptive control strategy. On this basis, a new Lyapunov-Krasovsky function is constructed and combined with advanced inequality techniques to derive a set of verifiable algebraic criteria for ensuring complete synchronization of the drive-response system. Finally, the correctness of the theoretical results was verified through rigorous numerical simulations, and the practical feasibility of the proposed scheme was demonstrated.
期刊：
Proceedings of SPIE - The International Society for Optical Engineering,2025年13539:102 ISSN：0277-786X
通讯作者：
Mao, ZY
作者机构：
[Wang, Fangxiu; Mao, Ziyang; Chen, Zunyu; Chen, GuoXing; An, Jiaqi] Wuhan Polytech Univ, Sch Math & Comp Sci, Wuhan, Hubei, Peoples R China.
通讯机构：
[Mao, ZY ] W;Wuhan Polytech Univ, Sch Math & Comp Sci, Wuhan, Hubei, Peoples R China.
会议名称：
16th International Conference on Graphics and Image Processing-ICGIP-Annual
会议时间：
NOV 08-10, 2024
会议地点：
Nanjing University of Science and Technology School of Computer Science an, Nanjing, PEOPLES R CHINA
会议主办单位：
Nanjing University of Science and Technology School of Computer Science an
会议论文集名称：
Proceedings of SPIE
关键词：
Surface integral;Double integral;Input mode
摘要：
To address the issue that existing double integral calculators cannot calculate surface integrals, a method of converting surface integral input modes into double integral input modes is proposed. First, nine input modes for calculating double integrals are presented. Next, a method for converting surface integral input modes into double integral input modes is designed. Finally, a method for calculating surface integrals using a double integral calculator is designed. Experimental results show that the improved double integral calculator can calculate surface integrals.
作者机构：
[Yang, Jinlei; Guan, Lu; Huang, Ronghua; Bao, Yongcheng] Jiangsu Integr Transport Technol Co Ltd, Nanjing 211100, Peoples R China.;[Guan, Lu] Wu Han Polytech Univ, Wuhan 430048, Peoples R China.;[Chen, Leilei] Southeast Univ, Key Lab Safety & Risk Management Transport Infras, Nanjing 210018, Peoples R China.
会议名称：
9th International Conference on Intelligent IoT as a Service
会议时间：
OCT 27-29, 2023
会议地点：
Nanjing, PEOPLES R CHINA
会议主办单位：
[Huang, Ronghua;Guan, Lu;Bao, Yongcheng;Yang, Jinlei] Jiangsu Integr Transport Technol Co Ltd, Nanjing 211100, Peoples R China.^[Guan, Lu] Wu Han Polytech Univ, Wuhan 430048, Peoples R China.^[Chen, Leilei] Southeast Univ, Key Lab Safety & Risk Management Transport Infras, Nanjing 210018, Peoples R China.
会议论文集名称：
Lecture Notes of the Institute for Computer Sciences Social Informatics and Telecommunications Engineering
关键词：
IoT and 3D Visualization;Road maintenance base;Intelligent application
摘要：
In the field of "new infrastructure" intelligent management and control, the Internet of Things and 3D visualization of these two technologies are developing more and more rapidly, and complement each other, which has also aroused the keen attention of road maintenance base managers. This paper will take a road maintenance base as the research object, to explore the practical application of Internet of Things 3D visualization technology in this particular scene. According to the needs of the whole project, through real-time monitoring and data collection of each business data in the intelligent road maintenance base, we analyze the different operational efficiency, energy consumption and output analysis under the traditional mode and the new mode of applying IoT 3D visualization technology, so as to provide a new solution for the intelligent operation and control of the road maintenance base in the future by using IoT 3D visualization technology.
作者机构：
[Yuxuan Liu] Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Hong Kong SAR, China;[Chenguang Zhang] Wuhan Polytechnic University, Wuhan, China;[Guoyang Zhao; Fulong Ma; Weiqing Qi; Ming Liu; Jun Ma] Robotics and Autonomous Systems Thrust, The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China
会议名称：
2025 IEEE International Conference on Robotics and Automation (ICRA)
会议时间：
19 May 2025
会议地点：
Atlanta, GA, USA
会议论文集名称：
2025 IEEE International Conference on Robotics and Automation (ICRA)
摘要：
Traffic sign is a critical map feature for navigation and traffic control. Nevertheless, current methods for traffic sign recognition rely on traditional deep learning models, which typically suffer from significant performance degradation considering the variations in data distribution across different regions. In this paper, we propose TSCLIP, a robust fine-tuning approach with the contrastive language-image pre-training (CLIP) model for worldwide cross-regional traffic sign recognition. We first curate a cross-regional traffic sign benchmark dataset by combining data from ten different sources. Then, we propose a prompt engineering scheme tailored to the characteristics of traffic signs, which involves specific scene descriptions and corresponding rules to generate targeted text descriptions. During the TSCLIP fine-tuning process, we implement adaptive dynamic weight ensembling (ADWE) to seamlessly incorporate outcomes from each training iteration with the zero-shot CLIP model. This approach ensures that the model retains its ability to generalize while acquiring new knowledge about traffic signs. To the best knowledge of authors, TSCLIP is the first contrastive language-image model used for the worldwide cross-regional traffic sign recognition task. The project website is available at: https://github.com/guoyangzhao/TSCLIP.
作者机构：
[Lisa Tang] Wuhan Polytechnic University, Wuhan, Hubei, China [email protected];[Dan Chen] Changjiang Institute of Technology, Wuhan, Hubei, China [email protected];[Wei Chen; Lifeng Zhang; Junrong Tang; Xiaoli Xu] Hubei Communications Technical College, Wuhan, Hubei, China [email protected]
会议名称：
ICIIS '25: Proceedings of the 2025 2nd International Conference on Innovation Management and Information System
摘要：
In today's highly competitive global economic environment, technological innovation is regarded as one of the engines driving the sustainable development of countries and organizations. This article takes the scientific and technological innovation data of 31 provinces (municipalities, autonomous regions, etc.) in China as the research object, and uses a non radial network DEA model based on SBM to measure the efficiency of scientific and technological innovation. The results show that: (1) the scientific and technological innovation efficiency of the 31 provinces is generally at a medium to low level, and the development between provinces is extremely uneven. (2) Most provinces focus more on the stage of scientific research output and neglect the stage of economic benefits transformation, resulting in lower overall operational efficiency. (3) Most provinces have low overall efficiency due to poor pure technical efficiency; A few provinces have insufficient resource investment. Through the above empirical analysis, corresponding policy recommendations are proposed for improving innovation efficiency in various provinces of China, promoting sustainable social development.
作者机构：
[Sen Guo] School of Mathematics and Computer Science, Wuhan Polytechnic University, HuBei, 430048, China
会议名称：
2025 2nd International Conference on Digital Image Processing and Computer Applications (DIPCA)
会议时间：
25 April 2025
会议地点：
Xi'an, China
会议论文集名称：
2025 2nd International Conference on Digital Image Processing and Computer Applications (DIPCA)
关键词：
U-Net;attention mechanism;multi-scale feature fusion;street scene
摘要：
This study proposes a street scene segmentation model based on attention and multi-level feature enhancement to address the problem of multi-scale object omission in traditional U-Net semantic segmentation of complex street scenes. Firstly, by integrating the scSE attention mechanism module, a collaborative mechanism of channel weight mapping and spatial saliency screening is established in the feature extraction stage to enhance the ability to focus on semantically sensitive areas; Then, an improved atrous pyramid module (ASPP) is embedded in the encoding and decoding architecture, utilizing parallel convolution with differential dilation rate to capture multi-level receptive field features and achieve dynamic fusion of local details and global semantics; Finally, by combining the mixed loss function of cross entropy and Dice coefficient, the gradient contributions of the majority and rare classes are balanced through adjustable weights to alleviate the prediction bias caused by long tail distribution. Experiments on the Cityscapes benchmark dataset showed that the proposed model MIOU improved by 3.93% compared to the original U-Net.
摘要：
To enhance the accuracy of PM2.5 concentration predictions amidst inherent randomness and complexity, this paper introduces a novel prediction method called the Integrated Black-winged Kite Algorithm with Backpropagation (IBKA-BP). This approach improves the traditional Backpropagation (BP) neural network by optimizing its weights and thresholds, effectively addressing common issues such as slow convergence and the tendency to get trapped in local optima. Comparative analyses of prediction errors demonstrate that the IBKA-BP model outperforms other advanced PM2.5 concentration prediction models. Notably, it achieves a Mean Absolute Error (MAE) of 5.51, a Root Mean Square Error (RMSE) of 7.29, and an $\mathbf{R}^{2}$ value of $\mathbf{0. 9 3 6 4}$, indicating superior predictive accuracy. These findings confirm that the IBKA-BP model significantly enhances PM2.5 concentration prediction, offering a robust framework for future applications in air quality assessment.
作者机构：
[Zhao, YuDan; Ni, Ying; Xia, Peng] Wuhan Polytech Univ, Sch Elect & Elect Engn, Wuhan 430023, Hubei, Peoples R China.;[Zeng, Wu; Zeng, W] Wuhan Polytech Univ, Sch Math & Comp Sci, Wuhan 430023, Hubei, Peoples R China.;[Tan, RuoChen] Univ Calif San Diego, Comp Sci & Engn, San Diego, CA 92093 USA.
会议名称：
1st International Artificial Intelligence Conference-IAIC
会议时间：
NOV 25-27, 2023
会议地点：
Nanjing, PEOPLES R CHINA
会议主办单位：
[Zhao, YuDan;Ni, Ying;Xia, Peng] Wuhan Polytech Univ, Sch Elect & Elect Engn, Wuhan 430023, Hubei, Peoples R China.^[Zeng, Wu] Wuhan Polytech Univ, Sch Math & Comp Sci, Wuhan 430023, Hubei, Peoples R China.^[Tan, RuoChen] Univ Calif San Diego, Comp Sci & Engn, San Diego, CA 92093 USA.
会议论文集名称：
Communications in Computer and Information Science
关键词：
Data Visualization;Hydrological Data;Digital Twin
摘要：
In the context of digital transformation, cities and enterprises are striving to build a digital industrial chain, cultivate a digital ecosystem, and support high-quality economic development. Therefore, the use of visualization technology to assist decision-makers in rational planning has become a hot spot. Taking wuhan city as an example, combined with 3D modeling technology, it is aimed at smart cities and based on digital twins to create multiple scenarios for hydrological data application services and improve hydrological information services. First, we collected the data released by the china hydrology and water resources station; then, we visualized the hydrological data of the yangtze river Hankou station by using methods such as view juxtaposition and 3D interaction; after that, we constructed a 3D scene based on the real scene of the yangtze river Hankou basin, and used algorithms to the water body model is optimized; finally, the interaction between data and scenes is designed, various functions are realized by using high-level programming language design, and the water level changes in the flood season are simulated to help analyze and understand data more clearly, and assist decision makers in making decisions.
