A Study Of Scenario-aware Travel Path Planning Algorithms

In recent years,for the rapid development of pervasive computing and wireless sensor technology,people’s lives has brought great benefits.More and more sensors are used to serve the community.The purpose of pervasive technology is to provide a solution for people.The development of sensors is used in our life,especially the development of smart phones,bringing a great communication convenience to people.Now the smart phone embedded a variety of sensors,can carry out environmental forecasting,environmental analysis and environmental perception and so on.This article based on the tourism scene information do the related research:(1).The accuracy of the original acceleration threshold step method is low,and the noise generated by the three-axis acceleration will reduce the accuracy of the threshold judgment.In this paper,the FFT method is used to filter out the abnormal data according to the influence of noise.Then,the adaptive threshold is to overcome the shortcomings of the fixed threshold and improve the accuracy of the step.And through the number of steps,length of step and Euler angle to estimate the course,you can get the movement distance.(2).Considering the existence of GPS positioning error,this paper uses the map correction algorithm,that is,the direction of the angle,the distance from the point to the road and the two sides of the road and the intersection of the angle as the attribute,and then give their adjustment parameters,Point on each section of the weight,the final point of positioning,thereby improving the accuracy of positioning.The previous road time prediction algorithm uses the historical data value calculation method,but the amount of historical data is large,and in the off-site,the amount of data is small,so the accuracy rate is low.According to this situation,this paper uses the step velocity update algorithm to calculate,and at the same time in the calculation of the first section of the route,one by one estimate,can improve the accuracy of the results.The GM(1,1)model is used for the estimation of tourist attractions.However,due to its shortcomings of poor prediction accuracy,the forecast residuals arc added to the model,and the prediction effect is improved obviously.(3).According to the multi-demand route planning and multi-site tourism route planning,this paper proposes a forward refinement refinement algorithm,which proposes a refinement mechanism based on the original greedy algorithm,which can effectively reduce the distance.According to the multi-site tourism route planning,this paper puts forward the algorithm of maximizing the value of the route,taking into account the attributes of the scenic spots,the popularity of scenic spots,the appropriate time of visit to the scenic spots,the order of visits between the attractions,and the movement of the routes plus the queuing time,Get a benefit to maximize the route.