Study On Environmental Quality And Occupational Health Damage Assessment In Mechanical Processing Workshop

Government departments,managers and workers are paying more and more attention to the environmental quality of production workshop and the occupational health of workers.Evaluating the environmental quality of workshop and the impact on human health accords with the urgent needs of green manufacturing.Only by comprehensively evaluating the environmental quality and the impact on occupational health of the mechanical processing workshop,can we achieve the goal of comprehensive control of environmental quality and workers’ occupational health,promote the transformation of traditional workshop to green workshop,and provide strong support for the construction of green factory.In the support of the green manufacturing system integration project of Ministry of Industry and Information Technology in 2017,"Green Process Innovation and Integrated Demonstration Factory for High Precision and Complex Casting Products",this paper studies and explores the environmental quality and occupational health damage assessment of mechanical processing workshop by using BP neural network evaluation method and Combination of Health Risk and Health Damage Assessment.According to the selection principle of environmental quality attributes and environmental indicators of machine shop,the environmental indicators system of machine shop is established,and the monitoring scheme is put forward based on sensors,gateways and other equipment.The environmental index data passes through the perception layer,the collection layer,the network layer and the data layer to reach the SQL Server server.The principles and monitoring schemes of monitoring sites for environmental indicators are determined,realizing the wireless monitoring of the environmental index data.The evaluation method of environmental quality of machining workshop based on BP neural network is established.Through the investigation of relevant national standards and industries,the environmental quality and specific environmental indicators of mechanical processing workshop are classified into different grades.The numerical values of each grade of environmental indicators are used as the training set of BP neural network.Through the analysis of mean square error and training steps,the final structure of BP neural network evaluation model is determined.Taking environmental data of a monitoring area as an example,the practicability of BP neural network evaluation model is shown.Combining the evaluation methods of occupational health risk and health damage,the evaluation model of occupational health damage in mechanical processing workshop is established.This paper analyses the hazard factors of occupational health in mechanical processing workshop.Calculates the actual exposure concentration of pollutants and health risk hazard index according to the parameters of working years and respiratory rate of workers.Assigns health risk to the specific effect terminal,takes the year of health life loss(DALY)as the evaluation index.Realizes the quantification of occupational health damage.Taking environmental data of a monitoring area as an example,the practicability of occupational health damage assessment model is shown.Taking a mechanical processing workshop as an example,the monitoring scheme of environmental indicators and the assessment model of environmental quality and occupational health damage in mechanical processing workshop are analyzed and visualized.Real-time monitoring of environmental indicators in different processing areas is carried out,and connect FineReport software with database to visualize the data of environmental indicators.The environmental monitoring data in one week in March are selected.The environmental quality and occupational health damage in workshop are evaluated by using BP neural network evaluation model and occupational health damage evaluation model.The horizontal and vertical analysis of the evaluation results can provide effective suggestions for managers and workers.