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关键词： 微博热搜数据   突发事件   网络舆情   政府治理   语义网络分析  

摘要： 当前我国正处于社会矛盾日益凸显的快速转型时期,各类突发事件引起的网络舆情已成为影响社会稳定的重要因素,深入分析我国突发事件网络舆情热搜话题的分布及多维度演化对于舆情引导及治理有着重要意义.通过Python爬取 2023 年 1 月 1 日—2023年12 月31 日期间的微博热搜数据,包括文本、分类、热度等数据字段,经过中文情感分类模型等文本分析方法进行筛选最终获得 14841 条有效数据,利用HanLP 分词、AipNLP 情感计算等工具进行微博热搜文本处理,使用Tomotopy工具构建LDA主题模型推断出突发事件网络舆情的主题分布情况,随后结合文本挖掘、情感计算、语义分析等方法从热度、情感和主题三个方面对突发事件网络舆情的演化过程进行系统性挖掘和可视化分析.研究发现,2023年我国突发事件网络舆情主要有自然灾害、社会治安、人身安全、国际冲突等四大主题,突发事件网络舆情的生成、催化、扩散三个维度分别对公众认知、政府治理、平台监管等方面具有重要启示作用.

关键词： 网络信息技术   智慧体育课堂   数据分析   智能感知  

摘要： 在当前网络信息技术迅猛发展的时代背景下，智慧教育已逐渐崭露头角，成为推动教育改革与创新的关键力量。其中，智慧体育课堂的构建对于全面提升学生体质健康水平、显著增强体育教学效果而言，意义非凡。本文从网络信息技术及智慧体育课堂内涵出发，分析网络信息技术在初中体育教学中的应用现状，进而探讨网络信息技术下智慧体育课堂构建的技术路径，以期借助技术手段实现体育教学的智能化、个性化和高效化，为初中体育智慧课堂的实践应用提供理论指导和技术支持。

关键词： Nuclear industry  

摘要： Ultrasonic guided wave detection technology has the advantages of high efficiency, low cost, and convenient detection, and is widely used in pipeline damage detection. However, the propagation of ultrasonic guided wave in the pipeline is affected by the environment such as temperature and load, which seriously interferes with the extraction and recognition of damage information. Therefore, a machine learning pipeline damage identification method based on particle swarm optimization-bidirectional gated recurrent unit-attention mechanism (PSO-BiGRU-Attention) model was proposed in this paper. The model effectively establishes a mapping between raw ultrasonic guided wave data and the state of the pipeline, thereby enhancing the capability of the feature extraction layer to discern damage characteristics, mitigating environmental interferences, and accurately detecting authentic damage signals. Taking the test bench of a circulating water-cooling pipeline in nuclear industry as the experimental object, the pipeline damage identification experiment under the condition of temperature and load changes was carried out. Through the experimental analysis, it is verified that the model can effectively realize the pipeline damage identification, and the recognition accuracy is better than other models such as recurrent neural network, long short-term memory, bidirectional gated recurrent unit, etc., which proves the effectiveness and superiority of the proposed method in this paper. © 2024 Chinese Vibration Engineering Society. All rights reserved.

关键词： Partitions (building)  

摘要： In addressing the paradigm of acquiring monitoring data in practical engineering for building structures and the limitations of current data-driven algorithms, a building structure damage identification algorithm based on a multichannel one-dimensional convolutional neural network (ID CNN) was proposed. This method merges feature extraction from vibration data of various building floors, implicitly utilizes multi-channel ID CNN to extract topological relationships between monitoring points, and extracts high-dimensional damage features from signals to achieve effective identification of building structural damage. Validity of the proposed approach was verified using the IASC-ASCE benchmark model. Comparative analysis with traditional single-channel ID CNN demonstrates significant improvement in identification accuracy, with a predictive accuracy reaching 0. 989. Furthermore, the introduction of monitoring mechanisms during model training greatly enhances training efficiency. Comparative analysis indicates that the proposed multi-channel model architecture converges faster during training and exhibits more stable extraction of structural damage features. © 2024 Chinese Vibration Engineering Society. All rights reserved.

关键词： 民族传统体育   赛事网络治理   利益相关者  

摘要： 民族传统体育赛事肩负着文化传承与传播的重要使命，在文化传承和族群认同的促进中发挥着关键作用。本文运用文献资料法、专家访谈法、网络分析法等，剖析京津冀一体化视域下民族传统体育赛事的利益相关者，并进行利益网络分析，得出：民族传统体育赛事的利益相关主体是指个体集合，它具有权利能力和自主行动能力，是受到利益影响驱使形成的一个利益关联体，利益相关者由核心利益相关者、蛰伏利益相关者、边缘利益相关者构成。赛事治理中出现了利益网络凝聚性不强、益网络中介角色缺失、利益网络等级性明显的特征突出等关键问题，提出了赛事利益网络治理路径：利益相关者参与主体的多元化、构建赛事的协同治理机制、以文化自信为依托的赛事回归，以期提高京津冀一体化视域下民族传统体育赛事利益网络治理水平，更好地发挥赛事的综合效益，发挥区域经济的优势，弘扬中华优秀传统文化。

关键词： Data fusion  

摘要： The safe and stable operation of hydropower units is very important for the safety of power stations and regional power grids. An intelligent early warning method for hydropower units based on Kernel Principal Component Analysis (KPCA) and Long Short-Term Memory (LSTM) is proposed in this paper, which realizes that trend early war function based on multi-channel vibration signal fusion. Firstly, the multi-channel vibration signals of hydropower units are fused, and KPCA is used to compress the data to reduce the amount of information processing in the process of early warning. Furthermore, the early warning threshold is set according to the steady-state operation condition of the unit by the multivariate statistical process control method, and the early warning function based on data fusion is realized. Then, the prediction model of vibration fusion data is constructed based on LSTM, and the prediction function of future operation data is realized. In that end, the effectiveness of the propose method is verified by the given case with a R2 coefficient exceeding 0.97, and realize the state monitoring and alarm prediction. Compared to the KPCA-RNN and KPCA-Informer models, the proposed model demonstrates the best performance on the same experimental data. © 2024 Chinese Vibration Engineering Society. All rights reserved.