A hazardous chemical transportation risk prediction system based on big data

Abstract

The invention relates to a hazardous chemical transportation risk prediction system based on big data, which is characterized in that the system comprises a data expression module, a risk factor statistics and analysis module, a prediction module and a simulation experiment and system realization module. The invention makes full use of the interdisciplinary advantages of the internet of things technology, data mining technology, statistical management technology, mobile communication technology and other disciplines, performs in-depth study of hazardous chemical transport oriented data warehouse models, risk factor analysis methods and risk prediction mechanisms, and strive to make breakthrough in the study of key issues about the regional hazardous chemical transport risk analysis and prediction.

Description

technical field [0001] The invention relates to a big data-based dangerous chemical transportation risk prediction system. Background technique [0002] With the rapid development of China's economy, hazardous chemicals have become indispensable and important materials in national defense construction, industrial and agricultural production and people's daily life. However, during the road transportation of hazardous chemicals, due to equipment defects, impact, extrusion, etc., containers containing flammable, explosive, and toxic dangerous goods and related auxiliary facilities may be broken down, broken, damaged, and leaked out. A large number of flammable, explosive, and toxic chemicals are transported, which in turn leads to major accidents such as fire, explosion, and poisoning. Especially in national central cities like Beijing, road transport vehicles for hazardous chemicals inevitably need to pass through populated areas, posing a potentially huge threat to resident...

AI Technical Summary

Problems solved by technology

[0009] The present invention designs a hazardous chemicals transportation risk prediction system based on big data, and the technical problems it solves are: (1) The prior art lacks an effective multi-dimensional and multi-source data expression method, and cannot provide risk analysis for hazardous chemicals transportation Provide comprehensive data support; (2) The existing technology lacks risk analysis methods based on modern information technology, and most of the current risk analysis adopts the traditional probability statistics method of the theorem of large numbers; (3) The existing technology lacks real-time dynamic risk prediction At present, most of the existing research is still oriented to the risk prediction of deterministic transportation risks, and considering the uncertain factors of transportation risks and the dynamic prediction method through the feedback mechanism will make the risk analysis more realistic; (4) the existing The technology lacks a complete hazardous chemical transportation risk prediction software system, does not introduce spatial information into all aspects of risk analysis, and lacks the information representation of the risk space-time distribution map of hazardous chemical risk accidents

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