Design And Implementation Of Real-time Intelligent Positioning System Based On Machine Learning

Spatial location-based services are widely used in personal security,tourism,medical care,transportation,public security,and communications,which greatly satisfy people's demand for information consumption.While enjoying the convenience,people also put new demands on the accuracy of positioning systems.Machine learning,as the core of artificial intelligence,is increasingly used in positioning technology.At present,the flow of statisticians mainly relies on the positioning of the base station.The low positioning accuracy leads to large errors in the flow of people and does not support the refined park management.The establishment of a real-time location system will be used primarily for fine-grained network optimization analysis and scenic spot flow analysis and public safety early warning,which is bound to have a wide range of application value and practical significance.This article compares the domestic and foreign research results and status quo of the positioning technology and selects the position fingerprinting technology that is applicable to this system.Then study the system characteristics and user characteristics,then do a detailed analysis of the main functional modules,and then give the use case description.Combining the application of machine learning in this field,the architecture and positioning process of the real-time intelligent positioning system based on machine learning was designed,and the database design and interface design of the system were carried out to provide the system solution.Then,while designing the main modules of the system and describing their logical implementation,the key factors affecting the positioning performance are analyzed according to functional modules,such as fingerprint feature selection,fingerprint interval,model hyperparameter,matching rules,smooth noise data,etc.The algorithm model is selected based on four aspects:positioning accuracy,real-time performance of the algorithm,system stability,and system scalability.The author puts forward the fingerprint search technology based on ECI to avoid searching the global fingerprint database and shorten the online fingerprint matching time.The minimum value is selected by the local clustering algorithm,which is used to denoise the positioning result,and to assign different weights to the neighbors.In the development phase of the model algorithm,real-time high concurrency of the system is achieved using redis clusters,kafka clusters,and multi-process mechanisms.Through the verification of the system function and performance in the actual environment,it shows that the average positioning data processing volume can reach 1M/s,and the positioning accuracy can reach 60m 80%on average,basically meeting the system positioning requirements.