Optimized execution estimation method for massive data processing and storage processes in industrial processes

A mass data processing, industrial process technology, applied in the direction of electrical digital data processing, special data processing applications, relational databases, etc., can solve the problems of increasing system operating load, reducing system performance indicators, low data storage efficiency, etc., to improve Efficiency, saving storage space, optimizing the effect of collecting and storing processes

Inactive Publication Date: 2018-08-17
CHONGQING UNIV
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Problems solved by technology

The traditional industrial data acquisition system needs to spend more resources and time when extracting massive data and processing data, and the data storage efficiency is relatively low. The data acquisition system...

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  • Optimized execution estimation method for massive data processing and storage processes in industrial processes

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Embodiment

[0052] Example: such as figure 1 Shown; an optimal execution estimation method for mass data processing and storage in industrial processes, taking the parameter acquisition process in the operation of a wind power plant as an example, the specific implementation steps are as follows:

[0053] S1: Select PMSG permanent magnet synchronous wind turbine equipment as the parameter acquisition system for modeling;

[0054] The specific steps of step S1 are as follows: determine the key process in the data collection process of wind power equipment, that is, the original data comes from each equipment device of the wind farm, clarify the time and space relationship between the parameter collection process, and use the simulation software to analyze the parameter collection process. The transmission process of the general transmission interface, the parameter signal conditioning, the digital-to-analog conversion process and the parameter storage process are simulated and modeled.

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Abstract

The invention discloses an optimized execution estimation method for massive data processing and storage process in an industrial process. The method includes S1: structural modeling of industrial data acquisition systems; S2: calculating desired performance indicators of industrial data processing and storage processes according to the model; S3: a strategy for establishing a massive data processing layer, a transport layer, and a storage layer; S4: extracting process parameters related to performance indicators from the database; S5: calculating actual performance indicators; S6: comparing the expected performance index and the actual performance index, and adjusting the data processing and storage mechanism of the next cycle until the performance of the industrial data acquisition system meets the requirements. The beneficial effects obtained by the method are: the efficiency of the whole data collection and storage process can be improved, the data can be sorted and excavated in anorderly manner, and the storage space is saved, optimizing the collection and storage process of massive data in industrial processes, and preparing for subsequent data analysis.

Description

technical field [0001] The invention relates to the technical field of computer data collection, transmission and storage, in particular to a method for optimizing execution estimation of massive data processing and storage processes in industrial processes. Background technique [0002] In the Industrial Internet of Things, a variety of sensors are widely deployed in industrial production environments, and large-scale industrial data acquisition systems require data acquisition, processing and storage with high concurrency and high real-time performance. The traditional industrial data acquisition system needs to spend more resources and time when extracting massive data and processing data, and the data storage efficiency is relatively low. The data acquisition system has the problems of lack of data fusion ability, expansion ability, general ability and flexibility. These all reduce the performance index of the whole system, and increase the operating load of the system a...

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Application Information

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IPC IPC(8): G06F17/30
CPCG06F16/284G06F16/22G06F16/27
Inventor 张可柴毅张悦胡月郑雯
Owner CHONGQING UNIV
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