Multidimensional data deep integration-based expressway risk early warning method

Abstract

The invention discloses a driving early warning method, in particular to a multidimensional data deep integration-based expressway risk early warning method. The multidimensional data deep integration-based expressway risk early warning method includes the following steps of: the collection and aggregation of the road network data of a key area; the collection and aggregation of the video monitoring data of the key area; the data synchronization of a meteorological department; and early warning information push. The method is characterized by including information collection and aggregation; research and judgment; and early warning information push. With the method of the invention adopted, the application of multi-dimensional big data fusion to the risk early warning of the key area of anexpressway can be enhanced; targeted and systematic solutions can be provided; cross-department information resource sharing is realized; an expressway key area risk early warning system is established; technologies such as big data and deep learning are applied; and risks can be sensed as early as possible, and the accurate push of early warning information can be realized.

Description

technical field [0001] The invention relates to a driving early warning method, in particular to a highway risk early warning method with deep fusion of multi-dimensional data. Background technique [0002] With the rapid development of the national economy, the rapid growth of motor vehicles and the increasingly busy road traffic tasks, the road traffic safety situation is facing increasingly severe challenges. The accident rate in key areas represented by tunnels and fog-prone road sections on expressways is very high. It is necessary to make special risk warnings for key areas. [0003] Although the corresponding basic application systems have reached a relatively high level of technology and application, there are still some problems and deficiencies: each application system is a solution for a specific type of data, and the data source is relatively single, or only for this type of data. In the data processing of the system, the phenomenon of "information islands" appe...

AI Technical Summary

Problems solved by technology

[0003] Although the corresponding basic application systems have reached a relatively high level of technology and application, there are still some problems and deficiencies: each application system is a solution for a specific type of data, and the data source is relatively single, or only for this type of data. In the data processing of the system, the phenomenon of "information islands" appears, and data fusion and information sharing cannot be realized. The functions of information services, coordinated command and public services are insufficient. There is a lack of information fusion analysis and mining, and the coordination and linkage of related systems cannot be carried out. The early warning efficiency is still not high. Significantly increased

[0004] Existing technology only uses a single data source, and the three parties along the road only rely on video surveillance data for traffic incident detection. The meteorological department only issues weather forecasts based on administrative divisions, and does not notify the three parties of abnormal weather conditions, resulting in the phenomenon of "information islands"

[0005] After the existing technology triggers the risk warning, it cannot push the warning information to the traffic participants in time

Due to the lack of access to the desensitized signaling location data of the basic telecommunications operators in the existing technology, it is impossible to accurately determine which users are driving to the early warning area, so the early warning information cannot be accurately pushed to the traffic participants who really need this early warning, resulting in some Mobile phone users are far away from the warning area but receive useless warning information. Some users drive into the warning area because they did not receive the warning information, resulting in avoidable congestion and accidents.

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