A big data-based emergency evolution reasoning method and system

Abstract

The invention discloses a big data-based emergency event evolution reasoning method and system. The method comprises the steps of integrating cases in a database to obtain a sub-event chain set; taking the sub-event chain set as an input of an algorithm; obtaining weak and strong rule item sets through the algorithm; obtaining the strong rule item set from sets higher than the support degree, and obtaining the weak rule item set from sets lower than or equal to the support degree, wherein the strong rule item set serves as a conventional emergency event evolution possible path, and the weak rule item set serves as a non-conventional emergency event evolution possible path; and by taking the weak and strong rule item sets as reasoning bases, mining a subsequent sub-event set with a maximum occurrence probability and a subsequent sub-event set with an extremely small occurrence probability from the case database. Therefore, the deduction that an Apriori algorithm only aims at the strong rule item set can be improved, and the weak and strong rule item sets in a calculation process are distinguished and both reserved as target data.

Description

technical field [0001] The invention belongs to the technical field of data mining, and more specifically, relates to a big data-based emergency evolution reasoning method and system. Background technique [0002] Data mining is also known as knowledge discovery in databases. The purpose of data mining is to abstract knowledge of interest from large databases. From the analysis of the abstract pattern, the decision-making process can be easily completed. Association rules are mainly based on the discovery of frequent itemsets. Retail stores often use association rules to assist marketing, advertising, inventory control, and predict failures in telecommunications networks. Among them, the association rule algorithm Apriori algorithm is mainly used. Apriori algorithm is a mining association The core idea of ​​regular frequent itemsets algorithm is to mine frequent itemsets through two stages of candidate set generation and downward closure detection of episodes. [0003] Wit...

AI Technical Summary

Problems solved by technology

[0003] With the occurrence of unconventional emergencies more and more frequently, however, most of the current emergency early warning and prediction methods start from the macro information of emergency cases (such as event type, resource type, etc.), using CBR and rule-based reasoning (Rule-Based Reasoning, RBR), Model-based Reasoning (Model-based Reasoning, MBR) and other methods, however, the above methods cannot dig out the original information relationship of the case, and at the same time ignore the regular burst The distinction between an event and an unconventional emergency has certain limitations

Images