Control Engineering Practice最新文献_第2页

Burn-through point prediction and control based on multi-cycle dynamic spatio-temporal feature extraction 基于多周期动态时空特征提取的烧穿点预测与控制

IF 5.4 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Control Engineering Practice

Pub Date : 2024-11-21 DOI: 10.1016/j.conengprac.2024.106165

Xiaoxia Chen , Chengshuo Liu , Hanzhong Xia , Zhengwei Chi

Burn-Through Point (BTP) is a critical state in the sintering process, and maintaining a stable BTP is crucial for ensuring the quality of sintered products. However, the complex mechanistic relationships during the sintering process make it challenging to extract meaningful correlations between data, leading to suboptimal performance of prediction-based control methods. To address this issue, this paper proposed a BTP prediction method based on multi-period dynamic spatio-temporal extraction. Building upon this, a comprehensive fuzzy controller based on historical and future state recognition is introduced to achieve stable BTP. Firstly, a time series alignment method based on multi-cycle partitioning is proposed. The Fast Fourier Transform (FFT) operations is introduced to identify hidden data patterns within the observation sequence. Time series alignment is achieved by weighted time delay through fuzzy curve analysis applied to different data patterns. Temporal features are extracted along the temporal dimension using multi-scale 2D convolution, while the graph learning module generates the graph structure by introducing an attentional mechanism to capture the inter-variable dependencies in the learning window. Next, the spatial feature extraction module uses the outputs of the above two modules as inputs to further capture potential spatial features in the time series. Finally, the comprehensive fuzzy controller, by recognizing historical and future states, provides recommendations for the current sintering process speed, stabilizing the sintering process towards the desired operating states. According to the simulation results on actual datasets, this method not only exhibits high predictive accuracy but also effectively maintains control over BTP within a fluctuation range with a mean square error of 0.0109.

烧穿点（BTP）是烧结过程中的一个关键状态，保持稳定的 BTP 对确保烧结产品质量至关重要。然而，烧结过程中复杂的机理关系使得提取数据间有意义的相关性变得十分困难，从而导致基于预测的控制方法无法达到最佳性能。针对这一问题，本文提出了一种基于多周期动态时空提取的 BTP 预测方法。在此基础上，引入了基于历史和未来状态识别的综合模糊控制器，以实现稳定的 BTP。首先，提出了一种基于多周期分区的时间序列排列方法。引入快速傅立叶变换（FFT）运算来识别观测序列中隐藏的数据模式。通过对不同数据模式进行模糊曲线分析，利用加权时间延迟实现时间序列对齐。使用多尺度二维卷积法沿时间维度提取时间特征，而图形学习模块则通过引入注意机制生成图形结构，以捕捉学习窗口中的变量间依赖关系。接下来，空间特征提取模块将上述两个模块的输出作为输入，进一步捕捉时间序列中潜在的空间特征。最后，综合模糊控制器通过识别历史和未来状态，为当前烧结工艺速度提供建议，使烧结工艺稳定在所需的运行状态。根据实际数据集的模拟结果，该方法不仅具有很高的预测精度，还能有效地将 BTP 控制在波动范围内，均方误差为 0.0109。

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引用次数: 0

A resilient consensus algorithm with inputs for the distributed monitoringof cyber-physical systems 带输入的弹性共识算法，用于网络物理系统的分布式监控

IF 5.4 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Control Engineering Practice

Pub Date : 2024-11-21 DOI: 10.1016/j.conengprac.2024.106166

Sabato Manfredi , David Angeli , Ciro Tortora

Recent advancements in multi-agent systems (MASs) have led to the development of numerous algorithms for achieving specific objectives, such as consensus. However, security remains a major challenge in MAS consensus, particularly addressing the adversarial behavior of malicious agents. This paper explores the extension of Mean-Subsequence-Reduced (MSR) algorithm-type mechanisms for resilient dynamic consensus in the presence of input reference signals. We provide necessary and sufficient conditions for resilient dynamic consensus without relying on the presence of trusted agents. Additionally, we experimentally validate the proposed algorithm and related conditions over a small cyber–physical system used for temperature monitoring. Furthermore, we propose and experimentally validate a fault-detection and recovery algorithm to achieve a resilient dynamic average consensus of regular agents.

多代理系统（MAS）的最新进展促使人们开发了许多算法来实现共识等特定目标。然而，安全性仍然是多代理系统共识的一大挑战，尤其是在解决恶意代理的对抗行为方面。本文探讨了平均序列降低（MSR）算法类型机制的扩展，以便在存在输入参考信号的情况下达成弹性动态共识。我们提供了弹性动态共识的必要条件和充分条件，而无需依赖可信代理的存在。此外，我们还在一个用于温度监测的小型网络物理系统上对所提出的算法和相关条件进行了实验验证。此外，我们还提出并通过实验验证了一种故障检测和恢复算法，以实现常规代理的弹性动态平均共识。

引用次数: 0

3DoF-KF HMPC: A Kalman filter-based Hybrid Model Predictive Control Algorithm for Mixed Logical Dynamical Systems 3DoF-KF HMPC：基于卡尔曼滤波器的混合逻辑动态系统混合模型预测控制算法

IF 5.4 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Control Engineering Practice

Pub Date : 2024-11-21 DOI: 10.1016/j.conengprac.2024.106171

Owais Khan , Mohamed El Mistiri , Sarasij Banerjee , Eric Hekler , Daniel E. Rivera

This paper presents the formulation, design procedure, and application of a hybrid model predictive control (HMPC) scheme for hybrid systems that is embedded in a mixed logical dynamical (MLD) framework. The proposed scheme adopts a three degrees-of-freedom (3DoF) tuning method to accomplish precise setpoint tracking and ensure robustness in the face of disturbances (both measured and unmeasured) and uncertainty. Furthermore, the HMPC algorithm employs setpoint and disturbance anticipation to proactively enhance controller performance and potentially reduce control effort. Slack variables in the objective function prevent the mixed-integer quadratic problem from becoming infeasible. The effectiveness of the proposed algorithm is demonstrated through its application in three distinct case studies, which include control of production–inventory systems, time-varying behavioral interventions for physical activity, and management of epidemics/pandemic prevention. These case studies indicate that the HMPC algorithm can effectively manage hybrid dynamics, setpoint tracking and disturbance rejection in diverse and demanding circumstances, while tuned to perform well in the presence of nonlinearity and uncertainty.

本文介绍了嵌入混合逻辑动力学（MLD）框架的混合系统混合模型预测控制（HMPC）方案的制定、设计过程和应用。所提出的方案采用三自由度（3DoF）调整方法来实现精确的设定点跟踪，并确保在面对干扰（测量到的和未测量到的）和不确定性时的鲁棒性。此外，HMPC 算法还采用了设定点和干扰预测，以主动提高控制器性能，并有可能减少控制工作量。目标函数中的松弛变量可防止混合整数二次方问题变得不可行。通过在三个不同案例研究中的应用，证明了所提算法的有效性，这三个案例研究包括生产-库存系统的控制、针对体育活动的时变行为干预以及流行病/大流行病预防管理。这些案例研究表明，HMPC 算法可以在各种苛刻的环境下有效地管理混合动力学、设定点跟踪和干扰抑制，同时还能在非线性和不确定性的情况下保持良好的性能。

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引用次数: 0

Ride-pooling Electric Autonomous Mobility-on-Demand: Joint optimization of operations and fleet and infrastructure design 拼车电动自主按需移动：联合优化运营、车队和基础设施设计

IF 5.4 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Control Engineering Practice

Pub Date : 2024-11-20 DOI: 10.1016/j.conengprac.2024.106169

Fabio Paparella, Karni Chauhan, Luc Koenders, Theo Hofman, Mauro Salazar

This paper presents a modeling and design optimization framework for an Electric Autonomous Mobility-on-Demand system that allows for ride-pooling, i.e., multiple users can be transported at the same time towards a similar direction to decrease vehicle hours traveled by the fleet at the cost of additional waiting time and delays caused by detours. In particular, we first devise a multi-layer time-invariant network flow model that jointly captures the position and state of charge of the vehicles. Second, we frame the time-optimal operational problem of the fleet, including charging and ride-pooling decisions as a mixed-integer linear program, whereby we jointly optimize the placement of the charging infrastructure. Finally, we perform a case-study using Manhattan taxi-data. Our results indicate that jointly optimizing the charging infrastructure placement allows to decrease overall energy consumption of the fleet and vehicle hours traveled by approximately 1% compared to a heuristic placement. Most significantly, ride-pooling can decrease such costs considerably more, and up to 45%. Finally, we investigate the impact of the vehicle choice on the energy consumption of the fleet, comparing a lightweight two-seater with a heavier four-seater, whereby our results show that the former and latter designs are most convenient for low- and high-demand areas, respectively.

本文提出了一种电动自主按需移动系统的建模和设计优化框架，该系统允许拼车，即多个用户可以同时被送往相似的方向，以减少车队的行车时间，但代价是额外的等待时间和绕行造成的延误。具体而言，我们首先设计了一个多层时变网络流模型，该模型可联合捕捉车辆的位置和充电状态。其次，我们将车队的时间最优运营问题（包括充电和拼车决策）设计为混合整数线性程序，并据此共同优化充电基础设施的布局。最后，我们利用曼哈顿出租车数据进行了案例研究。结果表明，与启发式布局相比，联合优化充电基础设施布局可将车队的总体能耗和车辆行驶小时数降低约 1%。最重要的是，合乘可以大大降低这些成本，最高可达 45%。最后，我们研究了车辆选择对车队能耗的影响，比较了轻型双座车和重型四座车，结果表明前者和后者的设计分别最适合低需求和高需求地区。

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引用次数: 0

Anomaly detection using invariant rules in Industrial Control Systems 在工业控制系统中使用不变规则进行异常检测

IF 5.4 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Control Engineering Practice

Pub Date : 2024-11-19 DOI: 10.1016/j.conengprac.2024.106164

Qilin Zhu , Yulong Ding , Jie Jiang , Shuang-Hua Yang

Industrial Control Systems (ICS) are intelligent control systems that integrate computing, physical processes, and communication to manage critical infrastructures such as power grids, oil and gas processing facilities, and water treatment plants. In recent years, ICS have been increasingly targeted by malicious attacks, causing severe consequences. Anomaly detection systems utilized in ICS are crucial in safeguarding ICS from potential threats by sending out an alert upon detecting any network attacks. However, existing methods for ICS anomaly detection often suffer from limitations. Supervised machine learning methods encounter the issue of imbalanced positive and negative samples, while residual-based anomaly detection methods face challenges in detecting stealthy attacks. This paper presents an unsupervised anomaly detection method for ICS using association rule mining techniques. Utilizing the proposed variation-driven predicate generation strategy, the method incorporates temporal features of sensor readings into the generated predicates, achieving the mining of invariant rules that take into account the temporal dependencies among physical variables. This approach allows for a more comprehensive exploration of the invariant patterns maintained in the dynamic processes of systems. Through experiments conducted on two public datasets, the method demonstrates high detection efficiency, meeting the real-time demands of online detection. Experimental results showcase its notable efficacy in anomaly detection, with a substantial enhancement in the recall rate. Furthermore, the method’s capability to promptly issue warnings enables it to detect multiple attacks with low latency.

工业控制系统（ICS）是集计算、物理过程和通信于一体的智能控制系统，用于管理电网、油气处理设施和水处理厂等关键基础设施。近年来，ICS 越来越多地成为恶意攻击的目标，造成了严重后果。ICS 中使用的异常检测系统在检测到任何网络攻击时都会发出警报，这对于保护 ICS 免受潜在威胁至关重要。然而，现有的 ICS 异常检测方法往往存在局限性。有监督的机器学习方法会遇到正负样本不平衡的问题，而基于残差的异常检测方法在检测隐形攻击方面面临挑战。本文利用关联规则挖掘技术，提出了一种针对综合监控系统的无监督异常检测方法。该方法利用所提出的变异驱动谓词生成策略，将传感器读数的时间特征纳入生成的谓词中，从而挖掘出考虑到物理变量之间时间依赖性的不变规则。这种方法可以更全面地探索系统动态过程中保持的不变模式。通过在两个公共数据集上进行的实验，该方法展示了很高的检测效率，满足了在线检测的实时需求。实验结果表明，该方法在异常检测方面效果显著，召回率大幅提高。此外，该方法还能及时发出警告，从而以较低的延迟检测到多种攻击。

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引用次数: 0

A constrained instrumental variable method for identification of industrial robots 用于识别工业机器人的受限工具变量法

IF 5.4 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Control Engineering Practice

Pub Date : 2024-11-19 DOI: 10.1016/j.conengprac.2024.106168

Fabio Ardiani , Alexandre Janot , Mourad Benoussaad

Robot identification is a prolific topic with a long history of results spanning decades. In recent years, there has been a renewal of interest in this problem mainly due to a rapid increase in robotic hardware platforms capable of accurate model-based control. The standard approach exploits the inverse dynamic model’s linearity to dynamic parameters and uses the linear least-squares (LS) estimation. Since we identify robots with closed-loop procedures, a correlation between errors remains, and we should prefer the Instrumental Variable (IV) method over the LS estimation. Thanks to the increase in computational power, recent works suggest inserting physical constraints to ensure the physical plausibility of estimates. These works have emphasized the usefulness of these physical constraints, but few papers consider their insertion into IV methods, the consistency and optimality of estimates, and the effect of constraints on estimates not addressed. This paper presents a new constrained IV approach that uses physical constraints. It consists of two nested iterative algorithms: an outer one that is a standard IV method and an inner one that accounts for the constraints solved by a Gauss–Newton algorithm. Besides, the conditions to obtain consistent and optimal estimates are emphasized. Experimental results and comparisons with other methods carried out with the TX40 robot show the feasibility and effectiveness of such an IV method: we can identify 60 physically consistent parameters in less than one minute.

机器人识别是一个多产的课题，其成果由来已久，时间跨度长达数十年。近年来，人们对这一问题重新产生了兴趣，主要原因是能够进行基于模型的精确控制的机器人硬件平台迅速增加。标准方法是利用反动态模型与动态参数的线性关系，并使用线性最小二乘（LS）估计。由于我们使用闭环程序识别机器人，误差之间仍存在相关性，因此我们应优先采用工具变量（IV）法，而不是 LS 估算法。由于计算能力的提高，最近的研究建议插入物理约束，以确保估计值的物理可信性。这些著作强调了这些物理约束的有用性，但很少有论文考虑在 IV 方法中插入物理约束、估计值的一致性和最优性以及约束对估计值的影响等问题。本文提出了一种使用物理约束的新约束 IV 方法。它由两个嵌套迭代算法组成：一个外层算法是标准 IV 方法，另一个内层算法通过高斯-牛顿算法求解约束条件。此外，还强调了获得一致和最优估计的条件。使用 TX40 机器人进行的实验结果和与其他方法的比较显示了这种 IV 方法的可行性和有效性：我们可以在一分钟内确定 60 个物理上一致的参数。

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引用次数: 0

Hybrid self-learning model for the prediction and control of sintering furnace temperature 预测和控制烧结炉温度的混合自学模型

IF 5.4 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Control Engineering Practice

Pub Date : 2024-11-18 DOI: 10.1016/j.conengprac.2024.106159

Yuanshen Dai , Ning Chen , Zhijiang Shao

Ternary cathode materials are important components of lithium-ion batteries. However, the sintering process during manufacturing is challenging to control due to the inaccessibility of key dynamic variables and the frequent fluctuations in operating conditions. These lead to high energy consumption and inconsistent product quality. In this paper, we propose a hybrid self-learning prediction model and control method for sintering furnace temperature based on both first-principle and process data. Firstly, a mechanism model with temperature time delay is established based on energy flow analysis in the furnace. To capture the tail gas temperature dynamic in the mechanism model, a Ventingformer-based prediction data-driven model is proposed. In this model, a memory updating technique and an autoregressive module based on the Transformer framework are developed to identify long-time dependencies and respond to variations in input sequences. Then, a hybrid self-learning modeling framework is designed. Based on the established hybrid model, a multiscale objective function-based nonlinear model predictive control (MSCF-NMPC) method is proposed to achieve precise tracking control of the internal temperature in the furnace. A multiscale objective function with short-term cost in terms of energy consumption and tracking accuracy as well as long-term cost in terms of energy loss is constructing in the control optimization problem. Finally, the proposed hybrid self-learning model and MSCF-NMPC method are verified using the actual process data from a sintering furnace, demonstrating the effectiveness of the proposed method. The results offer practical guidance for industrial applications.

三元正极材料是锂离子电池的重要组成部分。然而，由于无法获得关键的动态变量以及操作条件的频繁波动，生产过程中的烧结工艺难以控制。这导致了高能耗和产品质量不稳定。本文提出了一种基于第一原理和过程数据的烧结炉温度混合自学习预测模型和控制方法。首先，基于炉内能量流分析，建立了温度时间延迟的机理模型。为了在机理模型中捕捉尾气温度动态，提出了基于 Ventingformer 的预测数据驱动模型。在该模型中，开发了基于变压器框架的记忆更新技术和自回归模块，以识别长期依赖关系并对输入序列的变化做出响应。然后，设计了一个混合自学习建模框架。基于建立的混合模型，提出了一种基于多尺度目标函数的非线性模型预测控制（MSCF-NMPC）方法，以实现对炉内温度的精确跟踪控制。在控制优化问题中，构建了一个多尺度目标函数，其中包括能耗和跟踪精度方面的短期成本以及能量损失方面的长期成本。最后，利用烧结炉的实际工艺数据对所提出的混合自学习模型和 MSCF-NMPC 方法进行了验证，证明了所提方法的有效性。这些结果为工业应用提供了实际指导。

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引用次数: 0

Physical-anchored graph learning for process key indicator prediction 用于工艺关键指标预测的物理锚定图学习

IF 5.4 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Control Engineering Practice

Pub Date : 2024-11-16 DOI: 10.1016/j.conengprac.2024.106167

Mingwei Jia , Lingwei Jiang , Bing Guo , Yi Liu , Tao Chen

Data-driven soft sensors in the process industry, whilst intensively investigated, struggle to handle unforeseen disruptions and operating changes not covered in the training data. Incorporating physical knowledge, such as mass/energy balances and reaction mechanisms, into a data-driven model is a potential remedy. In this study, a physical-anchored graph learning (PAGL) soft sensor is proposed, integrating process variable causality and mass balances. Knowledge-derived causality is further supplemented by mining dependencies from data. PAGL uses causality and mass balance as physical anchors to predict key indicators and evaluate whether the prediction logic aligns with physical principles, ensuring physical consistency in inference. The case study on wastewater treatment demonstrates PAGL's interpretability and reliability, maintaining physical consistency instead of acting as a black box.

过程工业中的数据驱动型软传感器虽然得到了深入研究，但在处理训练数据中未涵盖的意外中断和操作变化时却显得力不从心。将质量/能量平衡和反应机理等物理知识纳入数据驱动模型是一种潜在的补救措施。本研究提出了一种物理锚定图学习（PAGL）软传感器，将过程变量因果关系和质量平衡整合在一起。通过从数据中挖掘依赖关系，进一步补充了知识衍生的因果关系。PAGL 将因果关系和质量平衡作为物理锚来预测关键指标，并评估预测逻辑是否符合物理原理，从而确保推理的物理一致性。有关废水处理的案例研究证明了 PAGL 的可解释性和可靠性，它保持了物理一致性，而不是充当黑箱。

引用次数: 0

Predictive sliding mode control for flexible spacecraft attitude tracking with multiple disturbances 预测性滑动模式控制用于具有多重干扰的灵活航天器姿态跟踪

IF 5.4 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Control Engineering Practice

Pub Date : 2024-11-16 DOI: 10.1016/j.conengprac.2024.106160

Quan-Zhi Liu, Liu Zhang, Yang Xiao, Le Zhang, Guo-Wei Fan

This study addresses the challenge of achieving high-precision attitude control in flexible spacecraft subjected to multiple disturbances (MD). A predictive sliding mode (PSM) control method is proposed to tackle this issue. First, a second-order fully actuated (SOFA) system model for the attitude control of flexible spacecraft is established. Subsequently, sliding mode variables are introduced to enhance the robustness of the closed-loop system. Then, a Diophantine equation and sliding mode variables are applied to establish an incremental second-order fully actuated (ISOFA) sliding mode predictive model. A sliding mode reference is designed using a double power function to eliminate jitter. Based on the designed sliding mode predictive model, the multi-step ahead predictions are developed to optimize attitude tracking performance and suppress MD. Furthermore, the control performance and stability of the system are analyzed. Finally, a series of simulation results demonstrate the effectiveness of the proposed method.

本研究探讨了在受多重干扰（MD）影响的柔性航天器中实现高精度姿态控制的难题。针对这一问题，提出了一种预测滑动模态（PSM）控制方法。首先，建立了用于柔性航天器姿态控制的二阶全致动（SOFA）系统模型。随后，引入滑模变量以增强闭环系统的鲁棒性。然后，应用 Diophantine 方程和滑动模式变量建立了增量二阶全致动（ISOFA）滑动模式预测模型。使用双幂函数设计滑动模式参考，以消除抖动。基于所设计的滑模预测模型，开发了多步超前预测，以优化姿态跟踪性能并抑制 MD。此外，还分析了系统的控制性能和稳定性。最后，一系列仿真结果证明了所提方法的有效性。

引用次数: 0

Spatial–temporal cooperative guidance with no-fly zones avoidance 时空协同制导，避开禁飞区

IF 5.4 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Control Engineering Practice

Pub Date : 2024-11-15 DOI: 10.1016/j.conengprac.2024.106162

Kai Zhao , Jia Song , Yang Liu

This paper proposes a three-dimensional spatial–temporal cooperative guidance law for striking maneuvering targets, with consideration of no-fly zones avoidance. A fixed-time convergent integral sliding mode guidance law based on a second-order system consensus protocol is proposed to ensure the consistency of the remaining distance and radial relative velocity, instead of using estimates of time-to-go based on small angle assumptions. In the elevation and azimuth directions, to mitigate excessive guidance commands during the initial phase, a nonlinear sliding surface and a finite-time reaching law are designed to meet impact angle constraints. In addition, considering the stagnation points escape in the process of no-fly zones avoidance an integrated cooperation and obstacle avoidance guidance law is proposed, which effectively avoids no-fly zones, accelerates the convergence speed of cooperative consistency, and reduces terminal errors. Using Lyapunov’s theory, this paper theoretically proves the fixed-time and finite-time convergence characteristics of the proposed algorithm. Simulation results indicate that the miss distance and terminal elevation and azimuth angle errors of the proposed algorithm are 55.04%, 27.5%, and 81.75% of those of the comparison algorithm, respectively.

本文提出了一种用于打击机动目标的三维时空协同制导法则，并考虑了禁飞区规避问题。本文提出了一种基于二阶系统共识协议的固定时间收敛积分滑模制导法则，以确保剩余距离和径向相对速度的一致性，而不是使用基于小角度假设的到达时间估计值。在仰角和方位角方向，为减少初始阶段过多的制导指令，设计了非线性滑动面和有限时间到达法，以满足撞击角约束。此外，考虑到避开禁飞区过程中的停滞点逃逸，提出了合作与避障一体化制导法则，有效地避开了禁飞区，加快了合作一致性的收敛速度，减少了终端误差。本文利用李亚普诺夫理论，从理论上证明了所提算法的定时收敛特性和有限时间收敛特性。仿真结果表明，所提算法的失误距离、终端仰角和方位角误差分别是对比算法的 55.04%、27.5% 和 81.75%。

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引用次数: 0