Coal mine worker pneumoconiosis risk early warning method based on multi-source data fusion

Abstract

The invention relates to a coal mine operator pneumoconiosis risk early warning method based on multi-source data fusion, and belongs to the technical field of coal mines. The method comprises the following steps: S1, establishing a multi-source data early warning index set which comprises a job feature set, a management feature set and an individual and group feature set; s2, comprehensively considering coal mine operation characteristics, coal mine operation site management characteristics and coal mine operation personnel individual and group characteristics, establishing a multi-source data risk early warning model, and calculating a risk value; and S3, according to the risk value obtained in the step S2, calculating the risk level by adopting a quantitative analysis method. The method is complete in consideration factors, high in integration and simple and rapid in prediction.

Description

technical field [0001] The invention belongs to the technical field of coal mines, and relates to a pneumoconiosis risk warning method for coal mine workers based on multi-source data fusion. Background technique [0002] At this stage, health screening is adopted as the basis for evaluating the prevalence of pneumoconiosis, and the prediction and early warning of pneumoconiosis and the management of dust-exposed personnel are lagging behind. Scientific evaluation of the risk of pneumoconiosis among workers is a prerequisite for targeted prevention and treatment of pneumoconiosis. Therefore, an effective early warning method for pneumoconiosis risk in coal mine workplaces is urgently needed, so as to inform coal mine occupational hazard monitoring and supervision agencies, Coal production enterprises and dust collectors issued forecasts and warnings. [0003] Some scholars have also carried out related research, such as Lu Guodong, etc., through the analysis of discrete var...

AI Technical Summary

Problems solved by technology

However, none of the above studies systematically combined the characteristics of coal mine workplaces, operation management, and individual and group characteristics of workers, and only considered a small number of factors that cause pneumoconiosis, with obvious limitations.

In addition, most of the existing methods are based on existing statistical data and cannot adapt to changing operating conditions and the actual environment of dust-exposed personnel.

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