Path planning method and system based on scene classification

Abstract

The invention relates to the technical field of intelligent terminals, and especially relates to a path planning method and system based on scene classification. The method comprises the steps: obtaining a sample data set X; selecting a core sample data set M from the sample data set X; carrying out the cluster dividing of the core sample data set M, and obtaining a cluster dividing set C; inputting the cluster dividing set C into a corresponding navigation system, and carrying out the path planning; and outputting a path planning result. Each cluster in the cluster dividing set C is correspondingly taken as one scene class. According to the invention, the method is based on a DBSCAN clustering algorithm, and achieves the dividing of the sample data, i.e., the position data. The method achieves the optimization of a neighbor parameter calculating method in a conventional clustering algorithm, and an Epsilon and MinPts model is built through a core function, so as to achieve the correctand effective dividing of the clusters in a clustering process. When a central point of one cluster is selected and inputted into the navigation system, the method can effectively achieve the recommendation of the optimal path.

Description

technical field [0001] The present application relates to the technical field of intelligent terminals, in particular to a method and system for path planning based on scene classification. Background technique [0002] Data clustering analysis belongs to the field of data mining technology. With the deepening of data mining technology, its practicability is also expanding, including recommendation system, communication, business intelligence, search technology, biology, social security, image recognition and other fields. Some applications include e-commerce data mining, scientific and production engineering data mining, financial data analysis, market economic forecasting, Web data mining, telecommunications and retail data mining and other scenarios. [0003] The commonly used data clustering methods include random selection clustering method, K-means clustering method, hierarchical clustering method, self-organizing map method, and density-based clustering method. Commo...

AI Technical Summary

Problems solved by technology

Commonly used clustering methods often have low data classification accuracy. When there are disturbing data, wrong or missing data in the data set, the clusters divided by data clustering are inaccurate, resulting in more and smaller clusters; Or the clustering and division of aggregated data becomes more sparse and the main characteristics of the sample data are lost. Due to the existence of these defects, few data clustering methods are applied to the field of map navigation technology in the prior art, because map navigation has a great impact on data. The accuracy requirement is higher, and the current data clustering method cannot meet the needs of the map navigation system

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