Refining device pipe network integrity big data management system

Abstract

The invention relates to a refining device pipe network integrity big data management system. Comprising the following construction steps: step 1, construction of a big data management system of a refinery device pipe network integrity management system, step 2, design of an improved whale algorithm, and step 3, determination of a K-MAX clustering algorithm. 4, optimizing a K-MAX clustering algorithm based on the improved whale algorithm; and step 5, marking abnormal data of refinery device pipe network integrity management. By means of the constructed refining device integrity big data management system, abnormal data can be accurately found, then the defects existing in refining device pipe network integrity management can be found, and therefore the refining device pipe network integrity management system can be improved in time.

Description

technical field [0001] The invention relates to a big data management system for pipe network integrity, in particular to a big data management system for pipe network integrity of refinery equipment based on the improved whale algorithm-K-MAX clustering algorithm. Background technique [0002] With the continuous deepening of the integrity management of the pipeline network of refining and chemical equipment, the construction of an information system for the integrity management of the pipeline network of refining and chemical equipment has become a major trend, and the level of informatization construction is the key to improving the level of integrity management. At present, many softwares for pipe network integrity management have been developed, but the software mainly focuses on integrity management evaluation, and there are relatively few software related to integrity management business processes. In addition, the construction of the integrity management software sys...

AI Technical Summary

Problems solved by technology

In the big data environment, the data collection and analysis of the pipeline integrity management of refining and chemical equipment is very complicated, and an effective technology should be sought to process the big data of pipeline integrity management of refining and chemical equipment

In the process of continuous operation of refining and chemical equipment, the big data of pipe network integrity management is very complicated. Once a risk occurs, the operation data will also change accordingly. Effective detection of data anomalies can effectively prevent accidents in refining and chemical equipment. Data processing algorithms, such as C-means clustering algorithm, peak density algorithm, etc., have the defects of poor adaptability and low reliability

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