Research On The Logistics Industry Full-time Drivers’ Health Risk Evaluation System

The logistics industry professional driver is not only important in logistics transportation, but also directly effects the efficiency of logistics. Therefore, it is an urgent need for the full-time driver to have the health risk assessment. Only if they have healthy body, can the safety and efficiency of the logistics be ensured. Health risk assessment model is established to track full-time driver’s personal health, and to give specific risk evaluation so that health risks can be eliminated in bud stage. This method can reduce traffic accident loss for logistics enterprises due to full-time drivers’ health factors.In recent years many health management institutions at home and abroad are developing health management information system based on Internet of things. Because the development time of the health management information system based on Internet of things is not long enough so the health assessment technology is not perfect, and can be improved in many ways. At this stage, most of the information system is based on simple questionnaire evaluation. Then, through priority weight assessment for evaluation which cannot predict users’ potential health risks effectively. Therefore it has vital importance for health management industry to build a system which can provide users with health risk assessment.In this paper, on the basis of health risk assessment system, the content of the health risk assessment is concluded through the demand analysis. The content of the health risk assessment mainly includes four parts: physical health assessment, psychological health survey, survey of diet and exercise care. The sample data is obtained through two ways: the questionnaire according to these four parts and the intelligent detection equipment. This paper gives an example of the risk of hypertension, By referencing to relevant literature and cases we analyze the weight of various influence factors and build twelve indexes system of full-time drivers about the hypertension risk. On the basis of the multi-index system, a risk assessment model can be established using BP neural network. The model uses twelve indexes: age, diet, smoking, drinking, meditation time, intensity, and family history of high blood pressure, mental factor, BMI, blood pressure, triglyceride(TG), total cholesterol(CHOL) as input layer nodes and risk assessment results as output layer nodes. Eventually the risk of high blood pressure of drivers are predicted. Meanwhile, samples of the model should be trained and parameters should be adjusted to minimize error so we can achieve the best prediction effect.Finally, 20 groups of the driver’s health data are used to verify the exactness of the neural network prediction model, and the empirical analysis are conducted. The test results show that the average error of the 20 drivers verified 15 times are less than 0.12, which indicates that the driver’s health risk assessment model based on Internet of things is feasible. What’s more, the results also proved that using BP artificial neural network model to evaluate the driver’s health risk is more intelligent and scientific than traditional way of scoring.