Health Status Knowledge Representation And Service Technology

With the improvement of social life quality,people’s attention to health is not only limited to retreatment of illness,but also shifts to prevention-based health management.Disease does not occur without warning,but people often ignore the physical warning signs.By digitally quantifying health signals through multi-dimensional fusion of physical characteristics and establishing a health cognitive representation model,we can make comprehensive predictions to detect physical abnormalities timely and intervene early,and realize the integration of information and health care.In view of the difficulty in quantitative analysis of health assessment,this paper proposes the AEW-CTDW health prediction model.AHP model is used to construct a judgment matrix to quantify the importance of features to their membership levels,and the consistency index is used to verify the rationality of the health representation system,so as to obtain the subjective weight of the feature index.According to the EWM algorithm,the objective weight of the feature is obtained,and the weight fusion of the feature is realized by combining the AHP and the EWM model.The feature threshold is determined according to medical and health knowledge,and the center point triangular whitening weight function model is introduced for classification and identification,and the weight vector and the whitening weight function value of the category are weighted and summed to construct a multi-dimensional feature weight fusion health classification model AEW-CTDW.Experiment shows that the model construction meets the consistency requirement,and the classification prediction accuracy rate reaches 83.89%,and the comparative experiments are carried out to verify the reliability of the algorithm.The health status is ranked separately according to the closeness of the positive ideal solution of the TOPSIS algorithm and the geometric similarity of the curves between the evaluation sequence and the reference master sequence of the GRA algorithm,and select the TOPSIS model with good effect to improve.The feature fusion weights are embedded in the TOPSIS model,and the Euclidean distance between the data set and the ideal solution is improved according to the principle of orthogonal projection to the vertical distance between the eigenvector and the parallel plane where the ideal solution is located,and the proximity to the ideal solution is measured,thus,an improved OP-TOPSIS model is proposed.The result shows that the OP-TOPSIS model improves the accuracy and simplifies the algorithm steps.Finally,based on the above algorithm research,the mobile terminal system is designed and implemented,and the model is applied to the software to provide health visualization representation services and realize real-time health detection and early warning.