Oil chromatography online monitoring data visualization system and method for multi-scale time series analysis

Abstract

The invention discloses an oil-chromatogram on-line monitoring data visualization system of multi-scale time sequence analysis and a method thereof. The system comprises a data acquisition server, a data display client machine, an HBase client machine and an application server, wherein the data acquisition server transmits data of detected equipment to the HBase client machine, wherein the data of the detected equipment is collected by an oil-chromatogram on-line monitoring device; the HBase client machine stores the data in a corresponding distributed storage server; the data display client machine receives interaction operation of a user and converts the interaction operation into a data request instruction, the application server receives a data request of the data display client machine and sends the data request to the HBase client machine; the HBase client machine reads data from the corresponding distributed storage server according to the data request and returns the data to the application server; the application server transmits the read data to the data display client machine; and the data display client machine finally displays a final data display interface. By using the system and the method, an influence of data distortion on data analysis is reduced so that a data rule can be conveniently analyzed from multiple levels and multiple scales.

Description

technical field [0001] The invention relates to a visualization system and method for oil chromatography on-line monitoring data of multi-scale time series analysis. Background technique [0002] The operation of the online monitoring device for dissolved gas in oil makes up for the shortcomings of the long analysis period of the traditional DGA and the inability to continuously monitor the equipment. However, due to the limitation of sensor technology, the reliability and sensitivity of the online monitoring device have not yet reached satisfactory levels. It is difficult to correctly judge the status of the equipment through the conventional threshold setting method. With the large-scale operation of online monitoring devices and the continuous accumulation of monitoring data, people's ability to analyze monitoring data has lagged far behind the ability to obtain data. The massive, high-dimensional, and dynamic characteristics of online monitoring data pose a huge challen...

AI Technical Summary

Problems solved by technology

[0004] With the advancement of technologies such as computers, communications, sensors, polymer materials, chromatography, and spectroscopy, the on-line monitoring technology for dissolved gases in transformer oil has also been continuously updated and developed. Sensitivity and other aspects have not reached a satisfactory level

The temperature additional error is an important but difficult indicator among various technical indicators of the sensor. The online monitoring device is generally installed outdoors, and the on-site temperature condition is relatively bad. At the same time, the substation online monitoring device is located in the strong electromagnetic interference environment of the high-voltage bus of the substation and a large number of primary equipment. Electromagnetic interference may affect the sampling accuracy of the online monitoring equipment, resulting in unstable fluctuations in the online monitoring data and data distortion.

[0005] The above reasons lead to large differences in the measurement results of the online monitoring device, and it is difficult to effectively judge the equipment status through conventional statistics and threshold early warning methods

In addition, with the extensive operation and continuous monitoring of online monitoring devices, the data scale of monitoring data continues to increase, and the traditional relational database storage method has gradually been unable to meet the rapid query of massive data, which also provides staff with rapid analysis. Difficulties posed by processing monitoring data

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