Energy and AI最新文献_第7页

Identification of cell chemistries in lithium-ion batteries: Improving the assessment for recycling and second-life 锂离子电池的化学成分鉴定：改进对回收和二次使用的评估

IF 9.6

Energy and AI Pub Date : 2025-01-01 DOI: 10.1016/j.egyai.2024.100468

Christopher Wett , Jörg Lampe , Dominik Görick , Thomas Seeger , Bugra Turan

{"title":"Identification of cell chemistries in lithium-ion batteries: Improving the assessment for recycling and second-life","authors":"Christopher Wett , Jörg Lampe , Dominik Görick , Thomas Seeger , Bugra Turan","doi":"10.1016/j.egyai.2024.100468","DOIUrl":"10.1016/j.egyai.2024.100468","url":null,"abstract":"<div><div>Recycling and second life of lithium-ion batteries are vital for lowering the growing resource demand of sectors like mobility or home energy storage. However, an often-overlooked issue is the sometimes-unknown cell chemistry of batteries entering the end-of-life. In this work, a machine learning based approach for the identification of lithium-ion battery cathode chemistries is presented. First, an initial measurement boundary determination is introduced. Using the Python Battery Mathematical Modelling (PyBaMM) framework, synthetical partial open circuit voltage (OCV) charge and discharge curves are generated with an electrochemical single particle model for three different cathode chemistries and the initial state of charge and state of health values as well as the initial capacities are varied. The dV/dQ characteristics are chosen as features and four machine learning algorithms are trained on different lengths of OCV curves. The trade-off between achievable accuracy and the number of OCV steps showed that an increasing accuracy correlates with a higher step number. While extremely small charge and discharge capacities per step did not yield sufficient testing accuracies, capacities starting from 0.2 Ah per step up to 0.6 Ah per step showed increasingly good results with an accuracy of up to 89.3 % for 0.5 Ah and 15 OCV steps. Additionally, the approach was validated by classifying experimental data. The results especially demonstrate the effectiveness of the approach to distinguish between lithium iron phosphate (LFP) and lithium nickel manganese cobalt (NMC) cells.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"19 ","pages":"Article 100468"},"PeriodicalIF":9.6,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143155830","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A power extraction approach with load state modification for energy disaggregation 基于负荷状态修正的能量提取方法

IF 9.6

Energy and AI Pub Date : 2025-01-01 DOI: 10.1016/j.egyai.2024.100461

Yusen Zhang , Feng Gao , Kangjia Zhou , Shuquan Wang , Hanzhi Wang

{"title":"A power extraction approach with load state modification for energy disaggregation","authors":"Yusen Zhang , Feng Gao , Kangjia Zhou , Shuquan Wang , Hanzhi Wang","doi":"10.1016/j.egyai.2024.100461","DOIUrl":"10.1016/j.egyai.2024.100461","url":null,"abstract":"<div><div>Energy disaggregation is a technology that disassembles the energy consumption from the entire house into load-level contributions. One of the foundational tasks for this technology is to accurately ascertain the truth electrical energy consumption of the target load. However, current energy disaggregation methods find it difficult to accurately predict the actual operating power of appliances when there are significant differences in the data distribution of appliances across various scenarios due to the diversity in manufacturers, usage times, and operating conditions. In this study, we propose a power extraction approach with load state modification to capture accurate load operating power with minimal influence from usage scenarios. To be specific, the on/off state sequence of appliances is first predicted leveraging existing energy disaggregation methods, and two state modification methods based on non-operating time and operating time of appliances are respectively proposed to modify the erroneous states in sequence. Subsequently, the power extraction approach calculates the operational power of target appliance based on the amplitude of fluctuations within the aggregated energy consumption caused by its state changes. Furthermore, a removing signal spikes method is proposed to improve the accuracy of the extracted power value. We conducted extensive experiments on a public dataset, demonstrating that the proposed method can significantly improve the accuracy of state-of-the-art solution. The average of mean absolute error across commonly used appliances during on state were reduced by 44.75 % and 32.07 % respectively in the UK-DALE and REFIT datasets.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"19 ","pages":"Article 100461"},"PeriodicalIF":9.6,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143155835","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Corrigendum to “Constraint-incorporated deep learning model for predicting heat transfer in porous media under diverse external heat fluxes” [Energy and AI 18 (2024) 100425] “用于预测不同外部热通量下多孔介质传热的约束结合深度学习模型”的勘误表[能源与人工智能18 (2024)100425]

IF 9.6

Energy and AI Pub Date : 2025-01-01 DOI: 10.1016/j.egyai.2024.100460

Ziling Guo, Hui Wang, Huangyi Zhu, Zhiguo Qu

引用次数: 0

Optimal capacity planning with economic emission considerations in isolated solar-wind-diesel microgrid using combined arithmetic-golden jackal optimization 考虑经济排放的孤立型太阳能-风能-柴油微电网容量规划

IF 9.6

Energy and AI Pub Date : 2025-01-01 DOI: 10.1016/j.egyai.2025.100469

Sujoy Barua , Adel Merabet , Ahmed Al-Durra , Tarek El Fouly , Ehab F. El-Saadany

{"title":"Optimal capacity planning with economic emission considerations in isolated solar-wind-diesel microgrid using combined arithmetic-golden jackal optimization","authors":"Sujoy Barua , Adel Merabet , Ahmed Al-Durra , Tarek El Fouly , Ehab F. El-Saadany","doi":"10.1016/j.egyai.2025.100469","DOIUrl":"10.1016/j.egyai.2025.100469","url":null,"abstract":"<div><div>This study aims to optimize an isolated solar-wind-diesel microgrid to reduce reliance on diesel generators, lower operational costs, and mitigate environmental pollution in remote areas. In this optimization, arithmetic optimization algorithm and golden jackal optimization are combined for achieving optimal capacity planning, considering economic and emission dispatch factors. This combination enhances the optimization by considering the balance in exploration and exploitation offered by the arithmetic operators of the arithmetic optimization algorithm and the dynamic adjustment by the adaptive search of the golden jackal optimization. Performance analysis is conducted by simulating and comparing three scenarios of only diesel generators, solar-wind-diesel and solar-wind with low number of diesel generators. The results demonstrate significant cost savings using the solar-wind-diesel microgrid under the proposed combined optimization compared to the arithmetic optimization algorithm and golden jackal algorithm and conventional metaheuristic optimization based on genetic algorithms.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"19 ","pages":"Article 100469"},"PeriodicalIF":9.6,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143155417","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Digital twin-centered hybrid data-driven multi-stage deep learning framework for enhanced nuclear reactor power prediction 增强核反应堆功率预测的数字双中心混合数据驱动多阶段深度学习框架

IF 9.6

Energy and AI Pub Date : 2025-01-01 DOI: 10.1016/j.egyai.2024.100450

James Daniell , Kazuma Kobayashi , Ayodeji Alajo , Syed Bahauddin Alam

{"title":"Digital twin-centered hybrid data-driven multi-stage deep learning framework for enhanced nuclear reactor power prediction","authors":"James Daniell , Kazuma Kobayashi , Ayodeji Alajo , Syed Bahauddin Alam","doi":"10.1016/j.egyai.2024.100450","DOIUrl":"10.1016/j.egyai.2024.100450","url":null,"abstract":"<div><div>The accurate and efficient modeling of nuclear reactor transients is crucial for ensuring safe and optimal reactor operation. Traditional physics-based models, while valuable, can be computationally intensive and may not fully capture the complexities of real-world reactor behavior. This paper introduces a novel hybrid digital twin-focused multi-stage deep learning framework that addresses these limitations, offering a faster and more robust solution for predicting the final steady-state power of reactor transients. By leveraging a combination of feed-forward neural networks with both classification and regression stages, and training on a unique dataset that integrates real-world measurements of reactor power and controls state from the Missouri University of Science and Technology Reactor (MSTR) with noise-enhanced simulated data, our approach achieves remarkable accuracy (96% classification, 2.3% MAPE). The incorporation of simulated data with noise significantly improves the model’s generalization capabilities, mitigating the risk of overfitting. Designed as a digital twin supporting system, this framework integrates real-time, synchronized predictions of reactor state transitions, enabling dynamic operational monitoring and optimization. This innovative solution not only enables rapid and precise prediction of reactor behavior but also has the potential to revolutionize nuclear reactor operations, facilitating enhanced safety protocols, optimized performance, and streamlined decision-making processes. By aligning data-driven insights with the principles of digital twins, this work lays the groundwork for adaptable and scalable solutions for advanced reactors.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"19 ","pages":"Article 100450"},"PeriodicalIF":9.6,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143155832","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Ignition delay prediction for fuels with diverse molecular structures using transfer learning-based neural networks 基于迁移学习的神经网络对不同分子结构燃料的点火延迟预测

IF 9.6

Energy and AI Pub Date : 2025-01-01 DOI: 10.1016/j.egyai.2024.100467

Mo Yang, Dezhi Zhou

{"title":"Ignition delay prediction for fuels with diverse molecular structures using transfer learning-based neural networks","authors":"Mo Yang, Dezhi Zhou","doi":"10.1016/j.egyai.2024.100467","DOIUrl":"10.1016/j.egyai.2024.100467","url":null,"abstract":"<div><div>In this study, a transfer learning-based neural network approach to predict ignition delays for a variety of fuels is proposed to meet the demand for accurate combustion analysis. A comprehensive dataset of ignition delays was generated using a random sampling technique across different temperatures and pressures, focusing on hydrocarbon fuels with 1–4 carbon atoms. Two machine learning models, an artificial neural network and a graph convolutional network, are trained on this dataset, and their prediction performance was evaluated. A transfer learning framework was subsequently developed, enabling the models trained on smaller molecules (1–3 carbon atoms) to predict ignition delays for larger molecules (4 carbon atoms) with minimal additional data. The proposed framework demonstrated reliable and high prediction accuracy, achieving a high level of reliability for fuels with limited experimental measurements. This approach offers significant potential to streamline the prediction of ignition delays for novel fuels, reducing the dependence on resource-intensive experiments and complex simulations while contributing to the advancement of clean and efficient energy technologies.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"19 ","pages":"Article 100467"},"PeriodicalIF":9.6,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143155418","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

RL-ADN: A high-performance Deep Reinforcement Learning environment for optimal Energy Storage Systems dispatch in active distribution networks 主动配电网中储能系统优化调度的高性能深度强化学习环境

IF 9.6

Energy and AI Pub Date : 2025-01-01 DOI: 10.1016/j.egyai.2024.100457

Shengren Hou , Shuyi Gao , Weijie Xia , Edgar Mauricio Salazar Duque , Peter Palensky , Pedro P. Vergara

{"title":"RL-ADN: A high-performance Deep Reinforcement Learning environment for optimal Energy Storage Systems dispatch in active distribution networks","authors":"Shengren Hou , Shuyi Gao , Weijie Xia , Edgar Mauricio Salazar Duque , Peter Palensky , Pedro P. Vergara","doi":"10.1016/j.egyai.2024.100457","DOIUrl":"10.1016/j.egyai.2024.100457","url":null,"abstract":"<div><div>Deep Reinforcement Learning (DRL) presents a promising avenue for optimizing Energy Storage Systems (ESSs) dispatch in distribution networks. This paper introduces RL-ADN, an innovative open-source library specifically designed for solving the optimal ESSs dispatch in active distribution networks. RL-ADN offers unparalleled flexibility in modeling distribution networks, and ESSs, accommodating a wide range of research goals. A standout feature of RL-ADN is its data augmentation module, based on Gaussian Mixture Model and Copula (GMC) functions, which elevates the performance ceiling of DRL agents, achieving an average performance improvement of 21.43%, 1.08%, 2.76%, by augmenting five-year, one-year and three-month data, respectively. Additionally, RL-ADN incorporates the Tensor Power Flow solver, significantly reducing the computational burden of power flow calculations during training without sacrificing accuracy, maintaining voltage magnitude with an average error not exceeding 0.0001%. The effectiveness of RL-ADN is demonstrated using distribution networks with size varying, showing marked performance improvements in the adaptability of DRL algorithms for ESS dispatch tasks. Furthermore, RL-ADN achieves a tenfold increase in computational efficiency during training, making it highly suitable for large-scale network applications. The library sets a new benchmark in DRL-based ESSs dispatch in distribution networks and it is poised to advance DRL applications in distribution network operations significantly. RL-ADN is available at: <span><span>https://github.com/ShengrenHou/RL-ADN</span><svg><path></path></svg></span> and <span><span>https://github.com/distributionnetworksTUDelft/RL-ADN</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"19 ","pages":"Article 100457"},"PeriodicalIF":9.6,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143155415","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

DGL-STFA: Predicting lithium-ion battery health with dynamic graph learning and spatial–temporal fusion attention DGL-STFA：基于动态图学习和时空融合注意的锂离子电池健康预测

IF 9.6

Energy and AI Pub Date : 2025-01-01 DOI: 10.1016/j.egyai.2024.100462

Zheng Chen , Quan Qian

{"title":"DGL-STFA: Predicting lithium-ion battery health with dynamic graph learning and spatial–temporal fusion attention","authors":"Zheng Chen , Quan Qian","doi":"10.1016/j.egyai.2024.100462","DOIUrl":"10.1016/j.egyai.2024.100462","url":null,"abstract":"<div><div>Accurately predicting the State of Health (SOH) of lithium-ion batteries is a critical challenge to ensure their reliability and safety in energy storage systems, such as electric vehicles and renewable energy grids. The intricate battery degradation process is influenced by evolving spatial and temporal interactions among health indicators. Existing methods often fail to capture the dynamic interactions between health indicators over time, resulting in limited predictive accuracy. To address these challenges, we propose a novel framework, Dynamic Graph Learning with Spatial–Temporal Fusion Attention (DGL-STFA), which transforms health indicator time-series data into time-evolving graph representations. The framework employs multi-scale convolutional neural networks to capture diverse temporal patterns, a self-attention mechanism to construct dynamic adjacency matrices that adapt over time, and a temporal attention mechanism to identify and prioritize key moments that influence battery degradation. This combination enables DGL-STFA to effectively model both dynamic spatial relationships and long-term temporal dependencies, enhancing SOH prediction accuracy. Extensive experiments were conducted on the NASA and CALCE battery datasets, comparing this framework with traditional time-series prediction methods and other graph-based prediction methods. The results demonstrate that our framework significantly improves prediction accuracy, with a mean absolute error more than 30% lower than other methods. Further analysis demonstrated the robustness of DGL-STFA across various battery life stages, including early, mid, and end-of-life phases. These results highlight the capability of DGL-STFA to accurately predict SOH, addressing critical challenges in advancing battery health monitoring for energy storage applications.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"19 ","pages":"Article 100462"},"PeriodicalIF":9.6,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143155416","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Probabilistic forecasting of renewable energy and electricity demand using Graph-based Denoising Diffusion Probabilistic Model 基于图去噪扩散概率模型的可再生能源和电力需求概率预测

IF 9.6

Energy and AI Pub Date : 2025-01-01 DOI: 10.1016/j.egyai.2024.100459

Amir Miraki , Pekka Parviainen , Reza Arghandeh

{"title":"Probabilistic forecasting of renewable energy and electricity demand using Graph-based Denoising Diffusion Probabilistic Model","authors":"Amir Miraki , Pekka Parviainen , Reza Arghandeh","doi":"10.1016/j.egyai.2024.100459","DOIUrl":"10.1016/j.egyai.2024.100459","url":null,"abstract":"<div><div>Renewable energy production and the balance between production and demand have become increasingly crucial in modern power systems, necessitating accurate forecasting. Traditional deterministic methods fail to capture the inherent uncertainties associated with intermittent renewable sources and fluctuating demand patterns. This paper proposes a novel denoising diffusion method for multivariate time series probabilistic forecasting that explicitly models the interdependencies between variables through graph modeling. Our framework employs a parallel feature extraction module that simultaneously captures temporal dynamics and spatial correlations, enabling improved forecasting accuracy. Through extensive evaluation on two real-world datasets focused on renewable energy and electricity demand, we demonstrate that our approach achieves state-of-the-art performance in probabilistic energy time series forecasting tasks. By explicitly modeling variable interdependencies and incorporating temporal information, our method provides reliable probabilistic forecasts, crucial for effective decision-making and resource allocation in the energy sector. Extensive experiments validate that our proposed method reduces the Continuous Ranked Probability Score (CRPS) by 2.1%–70.9%, Mean Absolute Error (MAE) by 4.4%–52.2%, and Root Mean Squared Error (RMSE) by 7.9%–53.4% over existing methods on two real-world datasets.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"19 ","pages":"Article 100459"},"PeriodicalIF":9.6,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143155833","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Prognostic machine learning models for thermophysical characteristics of nanodiamond-based nanolubricants for heat pump systems 用于热泵系统的纳米金刚石基纳米润滑剂热物理特性的预测机器学习模型

IF 9.6

Energy and AI Pub Date : 2024-12-01 DOI: 10.1016/j.egyai.2024.100453

Ammar M. Bahman , Emil Pradeep , Zafar Said , Prabhakar Sharma

{"title":"Prognostic machine learning models for thermophysical characteristics of nanodiamond-based nanolubricants for heat pump systems","authors":"Ammar M. Bahman , Emil Pradeep , Zafar Said , Prabhakar Sharma","doi":"10.1016/j.egyai.2024.100453","DOIUrl":"10.1016/j.egyai.2024.100453","url":null,"abstract":"<div><div>Lubricants for compressor oil significantly enhance the energy efficiency and performance of heat pump (HP) systems. This study compares prognostic machine learning (ML) models designed to predict the thermal conductivity and viscosity of nanolubricants used in HP compressors. Nanodiamond (ND) nanoparticles were mixed in Polyolester (POE) oil at volume concentrations ranging from 0.05 to 0.5 vol.% and temperatures ranging from 10 to 100°C. The data collected from the experimental research were used to build prognostic models using modern supervised ML techniques, including Gaussian process regression (GPR) and boosted regression tree (BRT). The GPR model demonstrated superior performance compared to the BRT model, achieving coefficient of correlation (R) values of 0.9996 and 0.9991 for thermal conductivity and viscosity, respectively. The reliability of the GPR and BRT models was further validated through comprehensive validation, sensitivity analysis, and extrapolation assessment using both empirical and unseen dataset references from the literature. When validated against an empirical correlation, the ML models exhibited a mean absolute error (MAE) of 0.17% for thermal conductivity and below 8% for viscosity. Additionally, when the GPR-based model was extended up to 120°C, the parametric analysis confirmed the reliability and accuracy of thermal conductivity and viscosity within a relative error of 5%. Furthermore, in the extrapolation analysis, despite changes in oil grade and nanolubricant concentrations, the GPR-based model showed a maximum absolute error (AE) of 19% compared to non-trained experimental data. Overall, the developed ML models can aid in designing and optimizing ND/POE nanolubricants for HP applications, achieving desired performance parameters while remaining economically viable and reducing the need for time-consuming laboratory-based testing.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"18 ","pages":"Article 100453"},"PeriodicalIF":9.6,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143131781","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0