基于P-图的流程知识自动生成方法

曹健; 牟鹏; 顾祥柏; 朱群雄

doi:10.11949/j.issn.0438-1157.20181353

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基于P-图的流程知识自动生成方法

过程系统工程 | 更新时间：2026-07-24

- 基于P-图的流程知识自动生成方法
- Automatic generation method of process knowledge based on P-graph
- 化工学报 2019年70卷第2期页码：467-474
- 作者机构：
  
  1.北京化工大学信息科学与技术学院，北京 100029
  2.智能过程系统工程教育部工程研究中心，北京100029
  3.中石化炼化工程（集团）股份有限公司，北京100101
- 作者简介：
  
  曹健（1987—），男，博士研究生，caojianbuct@163.com
  朱群雄（1960—），男，博士，教授，zhuqx@mail.buct.edu.cn
- 基金信息：
  
  国家自然科学基金重点项目(61533003)
- DOI：10.11949/j.issn.0438-1157.20181353
  中图分类号： TP 391.1
- 收稿：2018-11-16，
  
  修回：2018-11-26，
  
  网络首发：2020-06-16，
  
  纸质出版：2019-02-05
- 稿件说明：
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曹健, 牟鹏, 顾祥柏, 朱群雄. 基于P-图的流程知识自动生成方法[J]. 化工学报, 2019, 70(2): 467-474

CAO Jian, MU Peng, GU Xiangbai, ZHU Qunxiong. Automatic generation method of process knowledge based on P-graph[J]. CIESC Journal, 2019, 70(2): 467-474
曹健, 牟鹏, 顾祥柏, 朱群雄. 基于P-图的流程知识自动生成方法[J]. 化工学报, 2019, 70(2): 467-474 DOI： 10.11949/j.issn.0438-1157.20181353.

CAO Jian, MU Peng, GU Xiangbai, ZHU Qunxiong. Automatic generation method of process knowledge based on P-graph[J]. CIESC Journal, 2019, 70(2): 467-474 DOI： 10.11949/j.issn.0438-1157.20181353.

摘要

知识生成是工业知识自动化的基础，流程工业知识通常由定向任务驱动生成，如优化调度、优化操作、故障诊断等，其解决方案生成除需要了解运行机理及生产数据外，同时更依赖领域专家经验，此类知识表示形式难统一，移植性差，不便于共享及重用。针对乙烯裂解炉炉群资源调度优化问题，采用P-图求解方法，构建解决方案的超结构模型，设计用于表示知识的P-图本体以及数据库映射为知识的规则，自动生成RDF(resource description framework)表示的解决方案知识，建立知识库。最后利用乙烯生产厂的实际生产数据，验证了提出方法的可行性与实用性。

Abstract

Knowledge generation is the basis of industrial knowledge automation. Process industry knowledge is usually generated by directed tasks

such as optimization scheduling

optimization operation

fault diagnosis

etc

. The solution generation requires not only understanding the operation mechanism and production data

but also relying on domain expert experience. Such forms of knowledge representation are difficult to unify

poorly ported

and inconvenient to share and reuse. Aiming at the problem of resource scheduling optimization of ethylene cracking furnace group

the P-graph method is used to construct the superstructure model of the solution

which is designed to represent the knowledge of P-graph ontology and database mapping into knowledge rules

and automatically generate RDF (resource description framework) represents the solution knowledge and builds a knowledge repository. Finally

the actual production data of the ethylene production plant was used to verify the feasibility and practicability of the proposed method.

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references

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