Leakage prevention data analysis system

Abstract

The invention belongs to the field of data services, and in particular relates to a data analysis system for preventing leaks, which includes a sequentially connected client, server, security computer, data collection terminal, and cloud data module; wherein, the server module includes a data analysis module, Knowledge base, model base, database; said data analysis module is connected with knowledge base, model base respectively; said database is connected with data analysis module, knowledge base, model base respectively. The present invention realizes the function of anti-leakage by setting up a security computer between the server and the data collection terminal, and adopts one-way point-to-point data transmission. The data is analyzed to obtain the data analysis results. On the one hand, the results are imported into the knowledge base as a knowledge reserve, and on the other hand, the model base is corrected and enriched through the data analysis results. A dynamic cycle relationship is formed between the three.

Description

technical field [0001] The invention belongs to the field of data services, and in particular relates to a leak-proof data analysis system. Background technique [0002] In order to discover knowledge from data and use it to guide people's decision-making, it is necessary to conduct in-depth analysis of data instead of just generating simple reports. These complex analyzes must rely on complex analysis models, which are difficult to express in SQL. Collectively referred to as in-depth analysis. Big data is characterized by its complexity, mass, low density, and rapid generation, making in-depth analysis of big data necessary to obtain useful information or intelligence. [0003] The rapid development of big data is also accompanied by huge challenges, including the problem of data modeling. One of the core contents of data analysis is data modeling. By analyzing the statistical and semantic characteristics of existing data, find out the laws, Then generalize it into an abs...

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Problems solved by technology

[0003] The rapid development of big data is also accompanied by huge challenges, including the problem of data modeling. One of the core contents of data analysis is data modeling. By analyzing the statistical and semantic characteristics of existing data, find out the laws, Then generalize it into an abstract data analysis model, and then provide a basis for data analysis. Big data analysis deals with massive data. Due to the existence of massive data, a single or a few model combinations in the past can no longer meet the needs of big data analysis. , building a model library composed of many models is an effective way to solve massive data analysis

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