基于动态数据驱动的煤矿瓦斯异常涌出风险预警系统设计*

为解决煤矿瓦斯异常涌出风险预警过程中过度依赖系统模型,不能动态实时修正预测模型,预测、预报精准度和可靠度不高的难题,基于动态数据驱动技术,搭建瓦斯异常涌出风险预警系统架构,探讨动态数据驱动的瓦斯涌出监测曲线拟合、动态预警模型选择和修正、预警系统研发等关键性技术,开发基于动态数据驱动的瓦斯异常涌出风险预警系统软件。结果表明:动态数据驱动技术在煤矿瓦斯异常涌出风险预警方面具有强大的信息处理和问题求解能力,可实现仿真系统与实际系统间的动态响应和控制功能,并实时反馈修正,使预测结果更加精确、可靠,设计研发的预警系统可在矿井受瓦斯异常涌出威胁时发出可靠的预警信号。     

Evaluation of the k-nearest neighbor method for forecasting the influent characteristics of wastewater treatment plant

The k-nearest neighbor (k-NN) method was evaluated to predict the influent flow rate and four water qualities, namely chemical oxygen demand (COD), suspended solid (SS), total nitrogen (T-N) and total phosphorus (T-P) at a wastewater treatment plant (WWTP). The search range and approach for determining the number of nearest neighbors (NNs) under dry and wet weather conditions were initially optimized based on the root mean square error (RMSE). The optimum search range for considering data size was one year. The square root-based (SR) approach was superior to the distance factor-based (DF) approach in determining the appropriate number of NNs. However, the results for both approaches varied slightly depending on the water quality and the weather conditions. The influent flow rate was accurately predicted within one standard deviation of measured values. Influent water qualities were well predicted with the mean absolute percentage error (MAPE) under both wet and dry weather conditions. For the seven-day prediction, the difference in predictive accuracy was less than 5% in dry weather conditions and slightly worse in wet weather conditions. Overall, the k-NN method was verified to be useful for predicting WWTP influent characteristics.     

高分辨率数据驱动的流域非点源污染输出风险评估方法

近年来,非点源污染已成为我国部分水库水质恶化的主要原因.以潘家口水库流域为例,引入动态降水因子和地形因子改进经典的输出风险模型,结合高分辨率的卫星反演降水产品(GPM)和高分六号卫星影像,建立高分辨率数据驱动的非点源污染输出风险评估模型,开展潘家口水库流域的非点源污染输出风险时空分布特征研究.结果表明,研究区2018年非点源污染输出风险较高,其中氮元素污染输出高风险和较高风险区约占流域总面积的70.6%,磷元素污染输出无高风险区,较高风险区约占流域总面积的21.9%.分析流域非点源污染输出风险时空分布特征,发现4～9月潘家口水库流域非点源污染输出风险呈现先增后减趋势,在7月和8月最高,与流域降水时空分布一致;结合土地利用分布特征分析,流域上游以耕地为主,城市集中在流域下游,受农业生产和人类活动的影响,这些区域的非点源污染输出风险较高.针对非点源污染输出风险时空分布特征,应制定合理的农业施肥方式,规划非点源污染“源-汇”景观布局以及建设植被缓冲带.     

Digital data sources and methods for conservation culturomics

Ongoing loss of biological diversity is primarily the result of unsustainable human behavior. Thus, the long-term success of biodiversity conservation depends on a thorough understanding of human–nature interactions. Such interactions are ubiquitous but vary greatly in time and space and are difficult to monitor efficiently at large spatial scales. However, the Information Age also provides new opportunities to better understand human–nature interactions because many aspects of daily life are recorded in a variety of digital formats. The emerging field of conservation culturomics aims to take advantage of digital data sources and methods to study human–nature interactions and thus to provide new tools for studying conservation at relevant temporal and spatial scales. Nevertheless, technical challenges associated with the identification, access, and analysis of relevant data hamper the wider adoption of culturomics methods. To help overcome these barriers, we propose a conservation culturomics research framework that addresses data acquisition, analysis, and inherent biases. The main sources of culturomic data include web pages, social media, and other digital platforms from which metrics of content and engagement can be obtained. Obtaining raw data from these platforms is usually desirable but requires careful consideration of how to access, store, and prepare the data for analysis. Methods for data analysis include network approaches to explore connections between topics, time-series analysis for temporal data, and spatial modeling to highlight spatial patterns. Outstanding challenges associated with culturomics research include issues of interdisciplinarity, ethics, data biases, and validation. The practical guidance we offer will help conservation researchers and practitioners identify and obtain the necessary data and carry out appropriate analyses for their specific questions, thus facilitating the wider adoption of culturomics approaches for conservation applications.     

基于Weibull分布的测控装备寿命预测方法研究

目的利用寿命预测技术定期对测控装备的状态进行评估,挖掘装备潜在使用价值,降低出现故障的风险,为装备的更新维护提供参考依据。方法分析测控装备的特点,针对性地选用基于数据驱动的寿命预测技术,并重点分析基于Weibull分布的测控装备的寿命预测方法。结果通过数学公式推导,给出了采用最大似然估计法对二参数Weibull分布进行参数估计时的求解方法。结论对测控装备的寿命预测和可靠性评估提供科学的参考依据。     

海域水质模型参数动态反演方法研究初探

海域水质模型长周期数值模拟中,模型参数全时段统一赋值的方法忽略了参数随时间动态变化的物理特性,降低了模型的可靠性,增加了海域水质模型验证工作的难度.本文建立了将数据驱动模型和水质模型有机结合的参数动态反演的新方法:以水质模型多参数设计工况的数值模拟,构建海域内部观测点污染物浓度响应解集,并将解集划分为若干时段;应用基于人工神经网络的数据驱动模型归纳建立观测点每一时段内污染物浓度同多个模型参数之间的非线性关系;将实测资料带入关系中,进行模型参数随时间变化的动态反演.以渤海海域水质模型为例,采用“孪生”实验验证参数动态反演新方法的可行性,结果表明该方法是有效的,能够保证模拟周期内较高的数值精度,提高了模型的准确性.     

基于PSO-SVM的工艺装置液位监测方法

生产装置的"假液位"现象严重影响了工业生产过程的正常运行。为此,运用软测量思想建立数学模型,监测直接测量结果,防止假液位的发生,保障生产安全。通过比较机理建模法与基于数据驱动建模法的优劣,提出采用PSO-SVM法建立数学模型,并以SBR泄料槽液位的监测为例进行分析。以集管进料压力P_1、泄料槽入口温度T_A丁二烯分离系统压力p_O这3个量作为辅助变量来预测泄料槽液位L。结果表明,该模型预测值与实际值符合良好,具有较强的预测性能,能够较好地对SBR泄料槽液位进行监测,有效避免SBR泄料槽假液位的产生。     

基于数据驱动贝叶斯网络的内河船舶交通事故分析

伴随着我国内河航运的蓬勃发展,内河船舶的行驶安全受到了广泛的关注.将贝叶斯网络引入内河船舶交通事故分析,探究不同事故类型与航道、气候、船舶等方面影响因素之间的关系.首先,以芜湖海事局20132020年间上报的219条内河航运事故船舶数据为基础,抽取样本并使用贝叶斯可视化软件Netica训练得出内河船舶交通事故的贝叶斯网...