信息安全风险分析及其对应急管理的启示

突发事件应急管理是当前政府和研究机构都关注的重要问题.本文在前期研究的基础上,通过对信息安全风险分析中的基本理念,安全标准、风险分析的一般模型、方法和工具的分别介绍,并将信息安全风险分析与突发事件应急管理进行比较分析,希望已有的信息安全风险方面的研究成果能够扩展到更广泛领域的突发事件应急管理中,对突发事件应急管理的研究和实际应用有所帮助.     

基于共词网络的群智知识挖掘方法——在应急决策中应用

为了汇聚分布在社会网络中的群体智慧以合力化解特大公共安全突发事件重大风险，提出了一种基于共词网络的群智知识挖掘方法，并将其有效运用到突发事件应急管理当中.首先，提出了一种改进的共词网络构建方法，用以实现社会网络公众群智知识的可视化分析，从中获取应急决策属性参数信息；同时，为了确保数据质量、提高群智知识挖掘的有效性，综合数据影响力以及数据涵盖的主题信息两方面因素，构建了群智知识价值测度模型；而后，综合专家属性偏好与公众群体智慧，提出了一种基于群智知识的动态属性信息生成办法.最后，以中国新冠肺炎疫情特大公共安全事件应急决策为例，对本文所提方法进行了应用.     

自然异动下关键基础设施网络风险研究

关键基础设施保护是突发事件应急管理的重要对象构成,确定风险和保护排序是其保护机制的基本依据。在自然异动约束下,本文将信息扩散理论的超越概率引入风险分析模型,研究跨区域网络级联失效造成的风险,提出了确定关键基础设施网络构件的风险等级及保护排序的方法。以覆冰灾害下电网为例,研究了电网中不同支路在覆冰灾害下面临风险程度,与基本脆弱点进行对比,验证以异动风险作为保护参考的合理性。     

基于社交媒体数据的应急决策大群体群智知识挖掘及其价值测度方法研究

本文针对突发事件下的社交媒体数据体量庞大,但能够辅助应急决策的大群体群智知识分布无序且价值难以测度的问题,提出了一种应急决策大群体群智知识挖掘及其价值测度方法。首先,基于社交媒体博文数据,依据特定应急决策知识框架,采用TF-IDF(term frequency-inverse document trequency)法和改进后的词汇链算法构建了应急决策大群体群智知识挖掘方法;然后,提出了博文热度计算方法,在此基础上根据知识的语义重要性计算博文包含的群智知识总价值,并使用Shapley函数对群智知识价值进行分配;最后,以“新冠疫情”事件为案例对方法进行应用,并通过对比分析说明了方法的有效性和优越性。     

基于事件链的知识导航模型研究

当前突发公共事件应急管理研究已经成为热点问题.本文从应急管理知识需求出发,研究突发事件对象要素属性关系特点,提出了基于事件链的知识导航模型.该模型由资源模型、知识本体模型、应急处置模型、事件对象模型及事件链模型组成.通过对不同模型间的关系研究,向用户提供方便、快捷、适时的知识导航功能,最后给出应用实例.     

地铁突发事件中风险沟通对公众风险认知影响的研究

突发事件发生时,公众对突发事件风险的主观判断和客观风险之间往往存在偏差,会严重影响突发事件的恢复进程。作为应急管理部门和公众之间的信息桥梁,风险沟通对公众风险认知有直接的影响。本文在地铁突发事件的背景下,研究风险沟通对公众风险认知的影响机理。     

基于免疫系统的非常规突发事件风险识别模型

非常规突发事件的风险识别是应急管理的核心科学问题之一,由于理论研究和实践应用中常常面临“无真可仿”的局面,使得风险识别成为亟待解决的难题.本文综合运用免疫学理论、复杂适应系统涌现理论和计算实验技术,提出了风险识别器抗体向非常规突发事件抗原进化的理论,建立了风险识别的亲和度、抗原清除效果、风险识别器数量和能量等识别标准体系及其模型,采用识别亚非常规突发事件和各类事件组合关系以及它们的涌现来无限接近地认识非常规突发事件的研究思路,通过计算实验和算例分析,验证了研究成果的科学性和可行性.     

基于信度规则库的突发事件链式演变推理方法北大核心CSCDCSSCI

突发事件演变规律复杂,在分析突发事件链式效应时存在数据信息类型多样,参考信息不确定的问题。针对这一问题,提出了一种基于信度规则库的推理方法。该方法首先从知识管理的视角出发,结合模型的共性知识构造信度规则库,然后将多种不确定性数据类型转换成统一的信度结构,最后通过证据推理方法融合信度规则对突发事件链式演变进行推理,为应急决策提供支持。文末,通过地震耦合暴雨引发泥石流和滑坡的示例验证了该方法的实用性和有效性。     

基于风险因素的区域水安全评价模糊模型研究

针对现有水安全评价理论、方法的不足,提出从风险分析角度评价区域水安全的构想.在评价方法上,兼顾因素风险等级与风险重要性,并给出了以模糊语义变量描述的分级标准;通过将模糊语义变量假设为三角模糊数,构建了风险等级模糊隶属度函数,进而建立了区域水安全评价的模糊分析模型.研究表明,从风险分析的角度,运用模糊原理评价区域水安全状况,理论上是可行的,评价结果是可信的.     

基于应对等级的突发事件网络舆情热度分析

突发事件应对等级是应急管理部门采取应对措施的基础,而网络舆情热度是突发事件态势发展的一个重要因素。以应对等级为切入点,构建网络舆情热度评价指标体系,从不同应对等级和事件类型的角度选取8个典型案例,运用GooSeeker等工具进行案例数据挖掘,获得各突发事件的舆情热度值,进一步通过构建突发事件应对等级和舆情热度分析图,发现其存在"一致"和"不一致"的对应关系。研究结果表明：一方面,把突发事件应对等级引入网络舆情热度量表,使舆情热度的评价与政府发布的应对等级建立联系,评价指标更加完善。另一方面,获得不同对应关系下应对等级对舆情热度的指标贡献,对突发事件网络舆情应对提出了更有针对性和科学性的建议。     

DEA控制投影模型及其应用

考虑到输入输出要素的有限取值范围,本文提出了DEA控制投影模型,并给出了在特定情形下非CRS有效单元有现实意义的投影。在工程应用中,本文采用所提出的模型,对182家创新型企业的技术创新能力与竞争力的调查数据进行了分析。研究结果表明,利用该控制投影模型得到的有效性分类不仅与传统CRS模型的有效性分类完全一致,而且其按行业分竞争力投影结果为企业审计竞争力提供了科学的高标,具有较强的工程应用价值。     

A New Multicriteria Risk Mapping Approach Based on a Multiattribute Frontier Concept

Invasive species risk maps provide broad guidance on where to allocate resources for pest monitoring and regulation, but they often present individual risk components (such as climatic suitability, host abundance, or introduction potential) as independent entities. These independent risk components are integrated using various multicriteria analysis techniques that typically require prior knowledge of the risk components’ importance. Such information is often nonexistent for many invasive pests. This study proposes a new approach for building integrated risk maps using the principle of a multiattribute efficient frontier and analyzing the partial order of elements of a risk map as distributed in multidimensional criteria space. The integrated risks are estimated as subsequent multiattribute frontiers in dimensions of individual risk criteria. We demonstrate the approach with the example of Agrilus biguttatus Fabricius, a high‐risk pest that may threaten North American oak forests in the near future. Drawing on U.S. and Canadian data, we compare the performance of the multiattribute ranking against a multicriteria linear weighted averaging technique in the presence of uncertainties, using the concept of robustness from info‐gap decision theory. The results show major geographic hotspots where the consideration of tradeoffs between multiple risk components changes integrated risk rankings. Both methods delineate similar geographical regions of high and low risks. Overall, aggregation based on a delineation of multiattribute efficient frontiers can be a useful tool to prioritize risks for anticipated invasive pests, which usually have an extremely poor prior knowledge base.     

基于SC-RS的我国工业电力用户价值画像模型研究

电力用户价值画像对于提升我国供电企业的营销服务资源配置效能,提高智慧营销管理水平,从而最大化撬动供电企业的综合效益具有重要的现实意义。现有工业电力用户价值画像模型评级指标体系未能反映国家最新能源政策对我国工业企业的发展要求、无法良好应对现实电力用户用电数据的稀疏性,以及对于新用户价值等级预测的准确性存在提高空间。针对以上问题,本文集成数据挖掘技术中的谱聚类算法(Spectral Clustering, SC)与智能算法中的粗糙集理论(Rough Set, RS),构建了一种优化的数据驱动型工业电力用户价值画像模型,简称SC-RS模型。新模型构造围绕“知识萃取-知识推理-知识服务”的逻辑脉络展开,首先,在“知识萃取”部分,结合我国“碳达峰”目标与“碳中和”愿景,构建优化的我国工业电力用户价值评级指标体系,此外,采用谱聚类技术,并联合网格搜索策略,提炼用户价值等级信息情报;然后,在“知识推理”部分,应用粗糙集理论,继承已有三维规则挖掘框架,构建基于ChiMerge离散法与变异系数的行约简、基于系统依赖度的列约简、基于对象确定性因子的格约简,以及基于规则强度的规则提取方案这一拓展的四维规则挖掘模型,生成用户价值等级规则库;最后,在“知识服务”部分,一方面运用用户价值等级信息情报,构造价值决策系统,以及描摹群体用户价值画像,另一方面运用规则库,呈现可理解的价值知识,以及构造价值等级规则软分类器,实现新用户价值等级预测与个体价值画像描摹。为了展示模型的应用路径与具体步骤,采用实际工业电力用户数据,对模型开展实证研究。结果表明,SC-RS模型构建的评级指标体系紧跟我国电力行业最新发展动态,具有较强先进性;模型能够兼容稀疏性数据,对数据要求低;构造的粗糙四维规则挖掘模型可行有效,且对新用户价值等级的预测准确性高。综上,SC-RS模型能够对电力用户大数据实现价值情报挖掘与利用,是为我国电力企业智慧营销管理赋能的有力工具。     

Is Risk Analysis Scientific?

This article discusses to what extent risk analysis is scientific in view of a set of commonly used definitions and criteria. We consider scientific knowledge to be characterized by its subject matter, its success in developing the best available knowledge in its fields of study, and the epistemic norms and values that guide scientific investigations. We proceed to assess the field of risk analysis according to these criteria. For this purpose, we use a model for risk analysis in which science is used as a base for decision making on risks, which covers the five elements evidence, knowledge base, broad risk evaluation, managerial review and judgment, and the decision; and that relates these elements to the domains experts and decisionmakers, and to the domains fact‐based or value‐based. We conclude that risk analysis is a scientific field of study, when understood as consisting primarily of (i) knowledge about risk‐related phenomena, processes, events, etc., and (ii) concepts, theories, frameworks, approaches, principles, methods and models to understand, assess, characterize, communicate, and manage risk, in general and for specific applications (the instrumental part).     

基于公众偏好大数据分析的大群体应急决策质量动态演化研究

针对突发事件大群体应急决策质量及其影响因素的问题，探讨了公众参与视角下突发事件演化的全生命周期，以及各阶段下公众风险感知对应急决策质量的影响，并以“8·12天津港爆炸”为背景案例进行分析。首先，阐述了公众偏好大数据的获取及处理方式，并以此对“8·12天津港爆炸”案例的公众偏好大数据进行预处理和可视化；其次，通过TF-IDF算法提取并量化公众风险感知，建立大群体应急决策质量打分函数，衡量不同阶段的应急决策质量；最后，基于“8·12天津港爆炸”案例的微博数据进行大群体应急决策质量的动态演化分析，得出研究结论和启示。实证表明，所提的模型与方法具有较新颖的研究视角，能够为大数据背景下的应急响应和政府决策提供理论支持和方法辅助。     

基于偏好DEA模型的中国纺织业效率评价

基于具有输入输出指标偏好信息的数据包络分析模型评价中国纺织工业的效率,给出了描述规模报酬不变和规模报酬可变假设的DEA模型及其对偶规划的一种形式。模型克服传统DEA模型应用中常见的决策单元权重为零进而高估决策单元效率的不合理现象,有效地测算了中国31个地区纺织工业的经济运行效率,并分析了地区间效率水平差异的原因。利用各决策单元在经验生产前沿面上的投影点,估计了中国纺织工业的随机生产前沿函数,最后用效率弹性线性递减模型分析了销售收入、人均资产、台港澳和外商投资对各地区效率的微观影响关系。     

Bayesian Reanalysis of the Challenger O-Ring Data

A Bayesian forecasting model is developed to quantify uncertainty about the postflight state of a field-joint primary O-ring (not damaged or damaged), given the O-ring temperature at the time of launch of the space shuttle Challenger in 1986. The crux of this problem is the enormous extrapolation that must be performed: 23 previous shuttle flights were launched at temperatures between 53 °F and 81 °F, but the next launch is planned at 31 °F. The fundamental advantage of the Bayesian model is its theoretic structure, which remains correct over the entire sample space of the predictor and that affords flexibility of implementation. A novel approach to extrapolating the input elements based on expert judgment is presented; it recognizes that extrapolation is equivalent to changing the conditioning of the model elements. The prior probability of O-ring damage can be assessed subjectively by experts following a nominal-interacting process in a group setting. The Bayesian model can output several posterior probabilities of O-ring damage, each conditional on the given temperature and on a different strength of the temperature effect hypothesis. A lower bound on, or a value of, the posterior probability can be selected for decision making consistently with expert judgment, which encapsulates engineering information, knowledge, and experience. The Bayesian forecasting model is posed as a replacement for the logistic regression and the nonparametric approach advocated in earlier analyses of the Challenger O-ring data. A comparison demonstrates the inherent deficiency of the generalized linear models for risk analyses that require (1) forecasting an event conditional on a predictor value outside the sampling interval, and (2) combining empirical evidence with expert judgment.     

Integrated Uncertainty Analysis for Ambient Pollutant Health Risk Assessment: A Case Study of Ozone Mortality Risk

The U.S. Environmental Protection Agency (EPA) uses health risk assessment to help inform its decisions in setting national ambient air quality standards (NAAQS). EPA's standard approach is to make epidemiologically‐based risk estimates based on a single statistical model selected from the scientific literature, called the “core” model. The uncertainty presented for “core” risk estimates reflects only the statistical uncertainty associated with that one model's concentration‐response function parameter estimate(s). However, epidemiologically‐based risk estimates are also subject to “model uncertainty,” which is a lack of knowledge about which of many plausible model specifications and data sets best reflects the true relationship between health and ambient pollutant concentrations. In 2002, a National Academies of Sciences (NAS) committee recommended that model uncertainty be integrated into EPA's standard risk analysis approach. This article discusses how model uncertainty can be taken into account with an integrated uncertainty analysis (IUA) of health risk estimates. It provides an illustrative numerical example based on risk of premature death from respiratory mortality due to long‐term exposures to ambient ozone, which is a health risk considered in the 2015 ozone NAAQS decision. This example demonstrates that use of IUA to quantitatively incorporate key model uncertainties into risk estimates produces a substantially altered understanding of the potential public health gain of a NAAQS policy decision, and that IUA can also produce more helpful insights to guide that decision, such as evidence of decreasing incremental health gains from progressive tightening of a NAAQS.     

风险条件下研发项目改进DEA选择模型

研发项目是企业提高竞争力的关键,研发项目选择关系到企业的战略和未来发展趋势。研发项目在生命周期内面临技术风险、突发风险和市场风险,本文考虑到研发项目的风险特征,结合企业战略,构建了基于效率排序的研发项目选择模型。首先,本文对研发项目的有效性和相对于其它备选项目的相对优势进行分析;然后针对数据包络分析模型排序的不足,结合平衡计分卡理念,规范研发项目投入的比例,引入实物期权理论,改进数据包络分析模型,实现了对研发项目的效率排序;最后通过实际算例对模型进行验证、横向对比和敏感性分析,结果表明：相对于标准数据包络分析模型,本文提出的模型具有更高的效率区分度,并且能够敏感地捕捉到研发项目投入和产出的变化对效率影响的演变过程。