Mass data management system for engine state monitoring

Abstract

The invention discloses a mass data management system for engine state monitoring. The system comprises a data layer, a service logic layer, a presentation layer and a client layer, wherein the data layer forms a two-stage storage mode by using a relational database and a distributed file system, so that the dynamic coupling of the basic data and the mass monitoring data is realized; the businesslogic layer is responsible for analyzing the engine state monitoring data provided by the data layer and providing an analysis result to the presentation layer; the presentation layer is responsible for the alarm management and trend analysis of the state of the engine; and the client layer is responsible for providing a unified service interface for a system user. According to the scheme, by analyzing the actual needs of the airline, the relational database and the distributed file system are used for forming the two-stage storage mode, so that the actual needs of the airline for efficientlystoring and managing the mass monitoring data are effectively met, and the dynamic coupling of the basic data and the monitoring data of the aeroengine is achieved.

Description

technical field [0001] The invention belongs to the technical field of big data management, and relates to a massive data management system for engine state monitoring. Background technique [0002] As a core component of aircraft, the health management of aero-engines is the top priority of airlines' daily work. Aeroengine is a very complex system, and its daily operation will generate a large amount of status monitoring data. These monitoring data can help engineers understand the current status of the engine, determine whether the engine is faulty, and formulate corresponding maintenance plans based on the current status. Therefore, realizing the effective monitoring and management of aero-engine data has become an urgent need for airlines. [0003] In the past, the status monitoring data of aero-engines were stored in relational databases. When the amount of stored data was small, traditional relational databases were used for retrieval, and the retrieval speed could me...

AI Technical Summary

Problems solved by technology

[0004] Although the performance of relational databases is very good, traditional relational databases require fixed patterns to describe data, so it is difficult to adapt to the changing characteristics of working data patterns, and it is difficult to expand horizontally

The demand for capacity expansion can only be realized through downtime maintenance and data migration, and the time and financial costs are high

In addition, traditional relational databases are difficult to meet the needs of high concurrent read and write, and the results returned by simple queries are not fast enough and require high hardware performance

[0005] Due to these defects, only relying on the index of the relational database itself or methods such as partitioning and sub-tables to store the increasingly large-scale engine monitoring data, its storage and usage efficiency will become very low, and even cause the database server to crash in severe cases

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