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.
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[0065] Example 1
[0066] Explanation of professional terms:
[0067] PMML, the full name of 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 hardware; and it provides high throughput to access application data, suitable for those with large data sets (large data sets). set) application.
[0070] Hive, a data warehouse tool based on Hadoop, can map structured data files to a database table and provide simple SQL query functions, which can convert SQL statements into MapReduce tasks for execution.
[0071] Spark, Apache Spark is a fast and universal computing engine designed for large-...
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[0107] Example 2
[0108] On the basis of the first embodiment of the present invention, the refined model is updated, such as Figure 4 As shown, the details are as follows:
[0109] Step 4. Model update:
[0110] Automatically update and revise the model according to the vehicle owner’s feedback, including 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 it does not meet the standard, the model is not updated;
[0113] Step ccc: Automatically replace and deploy the models that meet the standards.
[0114] In step aaa, the automatic regular training of the outlier detection model includes the following steps:
[0115] Form a training data set of the model, where the training data ...
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