计算机工程与应用
計算機工程與應用
계산궤공정여응용
COMPUTER ENGINEERING AND APPLICATIONS
2015年
3期
265-270
,共6页
钻井工程风险%决策%本体%案例推理%案例检索
鑽井工程風險%決策%本體%案例推理%案例檢索
찬정공정풍험%결책%본체%안례추리%안례검색
drilling engineering risk%decision%ontology%case-based reasoning%case retrieval
为了实现钻井工程风险智能决策,从钻井专家决策过程的特征出发,针对传统的CBR技术的不足,提出并构建了一种基于本体和CBR的钻井工程风险决策模型。通过对风险决策案例的分析,设计了钻井风险决策案例的结构,并结合本体技术构建了风险决策案例本体,使案例表示更规范化、语义化,同时为案例知识提供了良好的可扩展性与共享性;为了提高案例检索效率,按照本体中风险的类别对案例进行分层组织,建立了风险案例库;针对现有的案例相似度计算模型的缺陷,提出了一种改进的基于本体的语义相似度计算模型,并由此构建了风险案例检索模型,经现场实例测试,结果表明该模型有效地提高了案例检索的查准率和查全率。在以上基础上,开发了钻井工程风险决策原型系统,为钻井专家、技术人员提供了高效的决策支持。
為瞭實現鑽井工程風險智能決策,從鑽井專傢決策過程的特徵齣髮,針對傳統的CBR技術的不足,提齣併構建瞭一種基于本體和CBR的鑽井工程風險決策模型。通過對風險決策案例的分析,設計瞭鑽井風險決策案例的結構,併結閤本體技術構建瞭風險決策案例本體,使案例錶示更規範化、語義化,同時為案例知識提供瞭良好的可擴展性與共享性;為瞭提高案例檢索效率,按照本體中風險的類彆對案例進行分層組織,建立瞭風險案例庫;針對現有的案例相似度計算模型的缺陷,提齣瞭一種改進的基于本體的語義相似度計算模型,併由此構建瞭風險案例檢索模型,經現場實例測試,結果錶明該模型有效地提高瞭案例檢索的查準率和查全率。在以上基礎上,開髮瞭鑽井工程風險決策原型繫統,為鑽井專傢、技術人員提供瞭高效的決策支持。
위료실현찬정공정풍험지능결책,종찬정전가결책과정적특정출발,침대전통적CBR기술적불족,제출병구건료일충기우본체화CBR적찬정공정풍험결책모형。통과대풍험결책안례적분석,설계료찬정풍험결책안례적결구,병결합본체기술구건료풍험결책안례본체,사안례표시경규범화、어의화,동시위안례지식제공료량호적가확전성여공향성;위료제고안례검색효솔,안조본체중풍험적유별대안례진행분층조직,건립료풍험안례고;침대현유적안례상사도계산모형적결함,제출료일충개진적기우본체적어의상사도계산모형,병유차구건료풍험안례검색모형,경현장실례측시,결과표명해모형유효지제고료안례검색적사준솔화사전솔。재이상기출상,개발료찬정공정풍험결책원형계통,위찬정전가、기술인원제공료고효적결책지지。
In order to realize intelligent decision of drilling engineering risk, considering the characteristic of drilling expert decision making, aiming at the shortcomings of traditional CBR technology, a kind of decision-making model of drilling engineering risk based on ontology and CBR is put forward. Through analyzing risk case knowledge, this paper designs the case structure and builds the case ontology of drilling risk decision-making by the ontology technology, making case repre-sentation standardization and semantization, at the same time, providing good scalability and sharing for case knowledge. In order to improve the efficiency of risk case retrieval, it organizes all cases hierarchically according to the risk classification in the case ontology, builds a risk case base. Aiming at the defects of exiting case similarity calculation models, it puts for-ward a kind of improved semantic similarity calculation model based on ontology, and based on this model, constructs the risk case retrieval model. Through site test, the results show that compared with traditional models, this model can improve the precision ratio and recall ratio of case retrieval effectively. On the basis of above, a prototype system for drilling engi-neering risk decision is developed, providing more efficient decision support for drilling experts and technicians.