一种应急预案本体模型及其应用研究*

目前,应急预案是分布于多个单位的非结构化文档,缺乏统一的建模方法,造成对预案描述的领域知识进行表示和利用存在困难。为解决以上问题,提出一种基于本体的知识建模方法,对应急预案文本进行建模,并实现应急领域知识共享。根据对应急预案文本结构特征进行分析,在重用现有本体模型的基础上提出了各主要要素的本体模型,形成一种应急预案本体模型（emergency plan ontology model,EPOM）。通过利用该方法对某区域防汛应急预案进行本体知识建模,开展应用研究。应用结果表明,该方法能够有效建模应急预案描述的     

自然灾害应急预案形式化表示技术

自然灾害应急预案描述了应急响应事件处置的领域知识,是高效应急反应的基础.当前应急预案大多以非结构化文本形式存在,不利于应急决策者快速准确地获取应急处置知识,阻碍了信息技术在自然灾害应急管理中的应用.针对这一现状,对文本应急预案内容进行分析,结合应急组织协调联动过程,提出了一种基于本体的自然灾害应急预案形式化表示方法,并通过系统实现来说明本方法的有效性.     

数字化应急预案知识模板存储的设计与实现

文中研究了数字化应急预案中基于XML语言的应急知识模板的设计及存储。分析了应急预案的总体结构,提出了将所有文档统一标记的具体模板结构;利用UML关系的多样性,采用了类模型来表示众多应急预案中潜在的结构化信息;结合应急预案的特性,给出了UML类模型中类与关系在XML Schema的映射方法;通过XMLSpy软件编译应急预案的XML Schema文档并验证其有效性。最终抽取出适用于大部分预案的知识模板,并在此基础上对应急预案进行XML标注,实现应急预案的数字化存储。     

基于预案模板的HTN规划知识建模方法及其应用

应急决策是应急管理的基础和核心,H TN( Hierarchy Mask Nct)规划为应急决策提供了一种有效手段,是目前研究的热点问题。针对基于HTN规划求解应急决策问题存在领域知识难于建模的问题,在利用基于PetriNet的工作流模型对应急预案进行建模的基础上,提出了一种将预案模板转化为H`I'N规划领域知识模型的方法。在此基础上,将基于预案模板的应急决策问题建模为应急任务规划问题,利用HTN规划系统SHOP2 (Simple Hierarchical Ordered Planner 2)进行规划求解,实现复杂应急态势条件下通过选择和组织应急计划片段科学制定应对方案,并以某区域洪灾应急响应为例开展了应用研究。     

基于密度峰值聚类理念的大群体应急模糊决策模型

针对决策者权重未知情形下重大突发事件应急决策问题,提出一种SFN-CFSFDP-Borda-MULTIMOORA模型.首先,采用球形模糊数描述决策者对应急备选方案的评价信息;其次,基于密度峰值聚类理念对大决策群体进行聚类;然后,提出可扩展的群体综合冲突优化模型,根据聚类结果进行冲突测度,求解决策者及聚集的权重并实现意见融合;再次,利用改进Borda-MULTIMOORA法决策最优方案;最后,以黑龙江东湖水库事件为例,对模型的有效性与实用性给予验证.结果表明,SFN-CFSFDP-Borda-MULTIMOORA模型能够充分考虑决策者的心理特征,强化在重大突发不确定情景下决策者的知识表达能力,同时明确聚类中心的选择方法,达到聚集内部差异小、聚集间差异大的聚类效果,通过考虑群体综合冲突及实际决策情形对决策者权重和聚集权重进行设置更符合实际,有效实现群体冲突融合,并提高决策效率,为重大突发事件大群体应急决策提供理论支持.     

社会网络环境下基于公众行为大数据属性挖掘的大群体应急决策方法及应用

针对社会网络环境下复杂大群体应急决策中决策属性信息难以获得问题,提出社会网络环境下公众行为大数据驱动的大群体应急决策方法.首先,通过挖掘社交平台上的公众行为大数据,利用TF-IDF、Word2vec技术进行关键词提取、聚类及其影响力分析,从大量行为数据中挖掘大群体决策属性信息以辅助专家决策,使决策结果具有更高的科学性和有效性;其次,构建决策者间基于信任关系和观点相似度的社会网络,采用同时考虑信任和相似度的聚类方法对决策者进行聚类,并基于社会网络分析获得决策者权重;然后,提出基于决策者间信任关系的共识调整方法进行共识调整以获得最终群体决策矩阵和方案排序,通过引入决策者客观自信度避免个别决策者过分自信行为的影响;最后,通过一个新冠疫情案例分析说明方法的可行性和有效性.     

基于本体的民航应急决策知识表达与推理方法研究

针对民航突发事件应急决策知识表达与管理中的问题,利用本体技术在知识获取和建模方面的优势,本文设计了一个基于领域本体的民航突发事件应急决策知识推理模型;以民航突发事件应急预案、应急案例、应急资源和应急规则等应急知识为基础,构建了民航应急管理领域词典,采用领域本体四元组建模方法给出了基于领域词典的本体构建过程;运用SWRL规则语言建立了民航突发事件应急决策所需的基本规则,采用Protégé本体编辑工具和Jena推理机实现了基于本体的规则推理,为民航突发事件应急决策知识的管理与应用提供了良好的方法与技术支持。     

基于文档结构与深度学习的金融公告信息抽取

针对金融类公告中的结构化数据难以被高效快速提取的问题,提出一种基于文档结构与Bi-LSTM-CRF网络模型的信息抽取方法。自定义一种文档结构树生成算法,利用规则从文档结构树中抽取所需节点信息;构建基于信息句触发词的局部句子规则,抽取包含结构化字段信息的信息句;将字段的结构化信息抽取看作序列标注问题,分词时加入领域知识词典,构建基于Bi-LSTM-CRF的神经网络模型进行字段信息识别。实验结果表明,该信息抽取方法可以满足多类型公告的结构化信息提取,最终的信息句与字段信息抽取的平均F1值均可达到91%以上,验证了该方法在产品业务中的可行性和实用性。     

基于应急案例本体的信息抽取的模型及应用

为了对重大突发性危机事件的应急预警并及时有效的处理,提出了一个基于应急案例本体的信息抽取的模型的设计思想和实现方法,该模型首先使用本体来描述应急知识领域中的案例,再在领域知识的指导下抽取Web资源中的相关案例并构建其案例库,供决策者或领域专家选用。应急决策系统既利用了特征模式匹配的快速性,同时又利用了同一数据项在层次结构中的优良特性,从资源中抽取了更为精确的知识,取得了较好的效果     

基于框架结构的智能预案相似度匹配研究

目前国内外数字化预案尚没有统一的标准,各类预案特别是操作性预案还不完备,而且大都以纸质文档或文本方式存储,结构多样化、复杂化,很难帮助决策者进行信息筛选与整合。本文通过对现有应急预案和应急响应过程的分析,对应急预案的知识利用框架技术进行表示,研究了预案的匹配算法,给出了预案相似度以及价值评估的计算方法。     

Interval-valued Pythagorean fuzzy linguistic TODIM based on PCA and its application for emergency decision

Nowadays the emergency decision has become a hot topic in the field of decision-making with interval-valued Pythagorean fuzzy linguistic (IVPFL) information. Moreover, with the increase of attributes and decision makers, the complexity of the operation is a great challenge for making decision. How to overcome multicollinearity is a crucial link in the emergency decision modeling process. In this paper, we treat the attributes and DMs as IVPFL variables and construct the IVPFL principal component analysis (IVPFL-PCA) model to overcome the multicollinearity. Then, a novel TODIM (abbreviation for interactive and multicriterial decision-making in Portuguese) method is proposed to tackle the IVPFL information under several new variables that are independent of each other (i.e., the PCs) and the reasonable weights of PCs obtained based on the IVPFL-PCA model. Finally, a case study on earthquake emergency decision is presented to show the applicability of the proposed approach and some comparisons are presented to illustrate its superiorities.     

Classification of business documents for real-time application

In this paper, we present a new document classification based on physical layout features and graph b-coloring modeling. In order to reduce the computing time and to increase the performance of our automatic reading system, we propose to pre-classify the business documents by introducing an Automatic Recognition of Documents stage as a pre-analysis phase. This phase guides others involved in the recognition process of the documents contents. Once the document type is identified, the reading system will use its corresponding information source to improve the recognition of its logical layout, the selection and parameterization of the OCR, and the final decision of sorting. The graph coloring model is introduced for both layout analysis and document classification. The proposed method is reliable, robust to various constraints and guarantees a real-time answer to the sorting of business documents.     

Machine Learning for Intelligent Processing of Printed Documents

A paper document processing system is an information system component which transforms information on printed or handwritten documents into a computer-revisable form. In intelligent systems for paper document processing this information capture process is based on knowledge of the specific layout and logical structures of the documents. This article proposes the application of machine learning techniques to acquire the specific knowledge required by an intelligent document processing system, named WISDOM++, that manages printed documents, such as letters and journals. Knowledge is represented by means of decision trees and first-order rules automatically generated from a set of training documents. In particular, an incremental decision tree learning system is applied for the acquisition of decision trees used for the classification of segmented blocks, while a first-order learning system is applied for the induction of rules used for the layout-based classification and understanding of documents. Issues concerning the incremental induction of decision trees and the handling of both numeric and symbolic data in first-order rule learning are discussed, and the validity of the proposed solutions is empirically evaluated by processing a set of real printed documents.     

Eager interpretation of on-line hand-drawn structured documents: The DALI methodology

On-line hand-drawn structured document interpretation is a complex problem of pattern recognition. This paper deals with eager strategies for such purpose, i.e. consisting in updating the analyzed document after each input stroke and providing a corresponding feedback to the user. We have designed a new class of visual grammars for the modeling of structured document composition: the context-driven constraint multiset grammars (CD-CMG). Their main originalities are to model the structural context in which a production can be reduced and to take into account the hand-drawn nature of the considered data. Its associated parser exploits the formalized knowledge for predictive purposes and couples bottom-up and top-down strategies. Their context-sensitiveness helps reducing significantly the combinatory associated to the analysis process. We use the fuzzy set framework to evaluate each possible interpretation on a qualitative way. Reject options are exploited to increase the decision making robustness and to detect the need for stroke segmentation. The parser is also able to wait for more information before making a decision by using a branch and bound algorithm. In this paper, we provide experimental results showing that the method is efficient enough to be used in real-time applications. We illustrate this point by focusing on a commercialized pen-based system that is based on the DALI method we present in this paper.     

Using Physical and Logical Constraints for Invoice Understanding

This work presents a methodology for invoice understanding. The invoices of our domain can be grouped into classes according to their logo. The understanding phase is based on two knowledge levels: a specific knowledge for each class, called a document model; and knowledge on the whole domain of interest, called a domain model. The invoices of a known class are understood by its document model, while the invoices of an unknown class are understood by using the domain model. The main contribution of this work is related to the use of the physical and logical constraints of the domain of interest for document understanding, without using an OCR system. Our approach has been tested by some experiments that are intended to identify some regions within invoices of unknown classes. In most cases, the results have shown the reliability of the approach.     

Style control in the quill document editing system

A critical problem in the design of editors for structured documents is that of style control, i.e. mapping the logical elements of the documents to their physical appearance on pages. This paper presents a novel approach to style control, used in the Quill document editing system that has been prototyped at the IBM Almaden Research Center. In our approach, the style control mechanism is an integral part of the editing system and consistent with the overall system architecture, in both its inner structure and its user interface. Properties that specify the formatting process, together with action routines for specifying complex semantics, are the basic style control primitives in the proposed approach.     

一种工作流技术实现的应急预案生成方法研究

本文提出了一种通过工作流技术生成应急预案的方法。基于一种网结构工作流模型,设计了所需的角色、任务及状态转换规则,对应急预案的生成过程进行建模。还将工作流多实例技术,应用于重复结构的子流程建模,从而大幅减少了信息系统的开发工作量,提高了系统的可维护性。