摘要
应急决策体系可视为具有多自主、多因素、多尺度、多变性特征的一个开放的复杂巨系统,包含着丰富而深刻的复杂性科学问题。本文围绕范维澄院士所提出的未来5-10年内,我国应急管理基础研究迫切需要解决的五大板块关键科学问题之一——“复杂条件下应急决策的科学问题”中的“多目标应急决策生成理论和方法”展开理论研究和实际工程背景的应用。
本文研究工作主要从以下四个方面展开。
(1)通过剖析典型应急案例,深刻反思应急决策支持系统(Emergency Decision Support System, EDSS)所面临的极大挑战。在查阅大量国内外相关文献资料的基础上,综述EDSS前沿动态,遵循大师西蒙所提出的“有限理性”的决策原理,提出融合时态逻辑和本体技术的多维知识表示的共享数据为信息平台层,以双基因变异混合进化策略的多目标应急决策为业务处理层,可根据不同需求设计适合各类用户使用的用户接口层的三层应急决策模型。
(2)在信息平台层,围绕如何解决提高决策知识表示的准确性,对时态知识、本体知识两大主流知识表示建立模型。
①围绕时态逻辑,首先,深入讨论了应急决策系统中的时态知识模型构建基础理论与时态知识表示方法,从时态表示的逻辑基础着手,概括介绍了时态基本元素和时态逻辑相关理论,以及知识的定义及层次、表示方法论和常用的时态知识表示方法。其次,把应急系统中的时态知识分成两类:一是描述事实的时态知识,二是描述事件关系的时态知识,设计了五元组的时态知识表示模型,用以描述应急决策系统中与时间关联的时序信息,形式化地表示应急决策系统中的各种时间信息,可更加准确地表示时序关系的知识,提高决策方案的准确性。
②围绕本体技术,首先介绍了本体论基础理论,包括本体定义、描述逻辑、本体建模元语、本体描述语言和编辑工具,提出一种基于本体论的应急决策领域知识图示化建模方法;具有图示化建模直观、便捷的特点。其次,着重研究并构建了应急预案和消防应急本体知识模型,实例本体知识的建立验证了本文所提出的基于本体论的应急决策领域知识图示化建模方法的可操作性。这样有利于开发人员和分析人员的沟通,大大提高了领域本体构建的规范性和准确性。
(3)在业务处理层,本文针对决策自动生成的快速性难点,研究应急决策生成的进化推理机制,以及并行串匹配算法的实现机理。
①围绕本体匹配实现时的并行串匹配算法,创新性的提出基于连续r位匹配规则的并行串匹配算法。从理论上分析并计算了连续r位模式串与文本串的匹配概率,并在并行集群环境下编程实现该算法,实验结果表明:在数据规模增加时,可加快本体匹配速度。将并行串匹配算法应用到本体匹配中,可以使并行算法在本体匹配计算中充分发挥快速计算的优势,为并行计算领域和本体领域找到新的结合点。
②围绕进化策略的推理机制,把应急决策自动生成归结为寻找最佳决策规则参数的优化问题,提出一种基于(μ+λ+κ)?ES和(μ+λ) ? ES的双基因变异混合进化策略算法。该算法采用Gauss算子和Cauchy算子的混合变异方法,对目标函数值最优的父代个体采用Gauss变异,对目标函数值最差的父代个体采用Cauchy变异,对搜索域进行比较细致的搜索,无论是理论证明还是实验验证,都说明:在保持群体多样性的前提下,可提高算法的收敛速度和准确度。
(4)建立了响应时间最短与损失程度最小的多目标消防应急决策优化数学模型,理论证明其正确性,并将改进的双基因变异混合进化策略算法与本体基础数据相结合,实现了基于本体的多目标消防应急决策快速进化推理过程。同时还解决了具体实现中的技术难点,如案例预处理、本体推理接口以及算法模块对本体数据的操作等。
本文虽然取得一定研究成果,但围绕本课题还有一些方面值得深入研究和探讨:
(1)本文知识模型的建立是基于静态知识元素的,动态描述逻辑的研究可用来完善规则性知识和过程性知识的形式化问题,进一步理论证明多维知识模型的可满足性,构建应急决策动态知识模型,这样,应急决策知识才满足知识的完备性。
(2)本体匹配的研究和应用逐渐成为一个热点,具有许多有待解决的挑战性课题。进一步的工作可以通过计算语义距离、加入本体知识属性或权重因子等,发现更加丰富的语义匹配问题;此外,在纯文本、数据库或XML向本体过渡的过程中,本体和其他形式的数据模型之间的匹配,也有待进一步研究。
Emergency decision system could be regarded as an open complex giant system with characteristics of multi-autonomy, multi-factor, multi-scale and multi-variability. It includes rich and deep complicated scientific problems. Five key scientific urgent problems with Chinese emergency management research in 5 to 10 years had been proposed by academician Fan Wei-Cheng. Theoretical research and application in real circumstance given by this paper are centering on and spreading out from one of the five, that is, theory and method of multi-target emergency decision-making in the scientific problems in a complex environment.
The paper is including four parts:
(1) Through the analysis of typical emergency cases, the self-rethinking of great challenges in emergency decision support system (EDSS) would be done. Based on consulting a large number of literatures, trends and current issues in EDSS are summarized. Then, following the decision principle“bounded rationality”given by master Simon, this paper puts forward a three-layer emergency decision model which includes information platform layer, business layer, and user interface layer. The information platform layer is the shared data represented by multi-dimension knowledge integrated temporal logic and ontology technique; the business layer is multi-target emergency decision based on the hybrid evolutionary strategy with double-gene mutation; the user interface layer, its different designs would be given to satisfy users with different needs.
(2) At the information platform layer, two mainstream knowledge representations, temporal knowledge and ontology knowledge have been modeled to increase the accuracy of decision knowledge representation.
①Centering on the temporal logic, the modeling fundamental theory and representations of temporal knowledge in the emergency decision system have been deeply discussed. From the logical foundation of temporal representation, related theories such as temporal elements and temporal logic, definition and layers of knowledge, representation methodology and common temporal knowledge representations have been briefly introduced. Then, temporal knowledge in the emergency decision system is divided into two parts: one is to describe the fact; the other is to describe the event relationship. A quintuple knowledge representation has been designed to show temporal information correlated with time in this system. It also affords the formalized representation of different time information to represent sequential relationship knowledge and make decision more accurately.
②Surrounding the ontology technique, fundamental theories including definition, description logic, modeling primitives, description language and editing tool have been introduced, and a modeling method, domain knowledge of emergency decision, which is straightforward and convenient, has been proposed in this paper. Then, the emergency plans and the ontology knowledge model of fire emergency have been studied and built particularly, and the applicability of this modeling method has been validated by the creation of significant ontology knowledge. This method gives a better interaction between developers and analysts, and greatly increases the normativity and accuracy of domain ontology establishment.
(3) At the business layer, the evolutionary reasoning mechanism of emergency decision-making and the implement of parallel string matching algorithm have been studied in this paper to conquer the difficulty in the rapidity of automatic decision-making.
①First, a novel parallel string matching algorithm based on r- continuous bits matching rule has been presented. Second, the matching probability between r-continuous bits pattern string and text string has been analyzed and computed in theory. This algorithm is implemented in cluster environment. The experiment result shows, the new algorithm can accelerate ontology matching with the increase of data size. The application of parallel string matching algorithm in ontology matching can take advantage of parallelism, and new bounding points between parallel field and ontology field would be found.
②Focusing on reasoning mechanism of evolutionary strategy, a hybrid evolutionary algorithm with double-gene mutation based on (μ+λ+κ)?ES and (μ+λ) ? ES has been proposed by translating the automatic emergency decision-making to an optimize problem of finding the best decision rule parameters. There are two operators in this algorithm: Gauss and Cauchy. Gauss mutation operator is used in the parent population with the best objective function value, and Cauchy mutation operator is used in the one with the worst. A careful search is preceded. Both theories and experiments could validate that the hybrid mutation operators can increase convergence speed and accuracy on the premise of keeping population variety.
(4) A multi-target fire emergency decision optimization mathematics model, which aims at the shortest response time and the minority damage, has been built. It has been proved to be valid. A multi-target fire emergency decision rapid evolutionary reasoning process has been implemented by the combination of the improved hybrid evolutionary algorithm and the ontology data. Meanwhile, technical difficulties in practice such as the case preprocessing, the ontology reasoning interface and the data operation in the algorithm module etc. have been solved.
This paper has got some research findings already, but some aspects of this subject are still worth deeply studying and exploring:
(1) In this paper, the establishment of knowledge model is based on the static knowledge elements, the study of dynamic description logic can be used to perfect the formalization of regularity knowledge and procedure knowledge. Then, the satisfiability of multi-dimension knowledge model has been validated in theoretic and emergency decision dynamic knowledge model is given. In this case, the knowledge completeness could be achieved by emergency decision knowledge.
(2) The research and application of ontology matching become a hot topic with many challenging tasks. Further work lies in finding richer semantic matching problems by computing semantic distance, adding ontology knowledge attributes or weight factors etc. Moreover, the matching between ontology and other kinds of data models during the process of the transformation from plain text, database or XML to ontology will come under review.
引文
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