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Machine learning-based vehicle abnormal trajectory real-time recognition method

A recognition method and machine learning technology, which is applied in the field of abnormal vehicle trajectory recognition, can solve the problems of abnormal trajectory recognition models that cannot be updated and low recognition accuracy, and achieve real-time and efficient data processing, high detection efficiency, and improved recognition accuracy.

Inactive Publication Date: 2019-05-21
成都古河云科技有限公司
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] The purpose of the present invention is: the present invention provides a real-time recognition method for vehicle abnormal trajectory based on machine learning, which solves the problem that the existing vehicle abnormal trajectory recognition model cannot be updated, resulting in low recognition accuracy

Method used

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  • Machine learning-based vehicle abnormal trajectory real-time recognition method
  • Machine learning-based vehicle abnormal trajectory real-time recognition method
  • Machine learning-based vehicle abnormal trajectory real-time recognition method

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Experimental program
Comparison scheme
Effect test

Embodiment 1

[0066] Explanation of technical terms:

[0067] PMML, the full name of Predictive Model Markup Language (Predictive Model Markup Language), uses XML to describe and store data mining models. PMML is a de facto standard language for presenting data mining models.

[0068] Hadoop is a distributed system infrastructure developed by the Apache Foundation.

[0069] HDFS, Hadoop implements a distributed file system (Hadoop Distributed File System), referred to as HDFS. HDFS has the characteristics of high fault tolerance and is designed to be deployed on low-cost (low-cost) hardware; and it provides high throughput (high throughput) to access application data, suitable for those with large data sets (large data set) applications.

[0070] Hive, a Hadoop-based data warehouse tool, can map structured data files into a database table, and provides a simple SQL query function, which can convert SQL statements into MapReduce tasks for execution.

[0071] Spark, Apache Spark is a fast ...

Embodiment 2

[0108] On the basis of Embodiment 1 of the present invention, the refinement model is updated, such as Figure 4 As shown, the details are as follows:

[0109]Step 4. Model update:

[0110] Automatically updating and correcting the model according to the feedback information of the car owner includes the following steps:

[0111] Step aaa: based on the historical data and the latest data of the vehicle, automatically perform regular training on the outlier detection model to obtain a new model;

[0112] Step bbb: Automatically detect whether the accuracy of the new model meets the standard, if it meets the standard, skip to step ccc to update the model, if not, do not update the model;

[0113] Step ccc: Automatically replace and deploy the model that meets the detection standard.

[0114] In step aaa, the automatic regular training of the outlier detection model includes the following steps:

[0115] Build the training data set of model, described training data set compri...

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Abstract

The invention discloses a machine learning-based vehicle abnormal trajectory real-time recognition method and belongs to the field of vehicle trajectory anomaly recognition. The method includes the following steps that: collected data are cleaned, so that complete, non-repetitive, abnormal value-free training data are obtained; an unsupervised isolated forest method and training data are used to perform model training, so that an anomaly detection model is obtained; the anomaly detection model is put into a flow calculation engine for real-time prediction, and a prediction result is sent to avehicle owner; and the model is automatically updated and corrected according to the feedback information of the vehicle owner, and the updated model is put into the flow calculation engine for real-time prediction, and a prediction result is sent to the vehicle owner. According to the method of the invention, the vehicle information is periodically collected; the unsupervised isolated forest algorithm is adopted; real-time prediction analysis is performed on vehicle trajectories in the flow calculation engine; the probability value of the abnormal behavior of a vehicle is rendered; the modelis periodically adjusted according to the feedback data given by the vehicle user; the dynamic update of the model is realized; and the recognition accuracy of the model is improved.

Description

technical field [0001] The invention relates to the field of abnormal vehicle trajectory identification, in particular to a machine learning-based real-time identification method for abnormal vehicle trajectory. Background technique [0002] With the development of China's social economy, the number of vehicles (cars, electric vehicles, motorcycles) in the Chinese market is increasing, and the problem of vehicle loss has become a more difficult social security management problem. At present, car owners generally adopt the method of reporting to the police after the event, tracing the historical track and monitoring after the event. However, many theft gangs have relatively large and mature sales networks, and the speed of selling stolen goods is very fast. Once the vehicle is lost, the probability of recovering the vehicle after reporting to the police is relatively low. , and the labor costs and social resources spent in recovering stolen goods are relatively high. [0003...

Claims

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Application Information

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IPC IPC(8): G08B21/24G06F17/50
Inventor 彭安
Owner 成都古河云科技有限公司
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