A dynamic logistics big data effective information extraction algorithm

Abstract

The invention discloses an effective information extraction algorithm of dynamic logistics big data, which belongs to the technical field of dynamic logistics big data, including S101: collecting dynamic logistics data to form a data set; S102: performing regional classification on the data set obtained in the step S101, The regional classification data set is a normal data set; S103: classify the data set obtained in the step S101, and the category classification data set is a dynamic data set; S104: invalid information setting, which includes the operator's own Setting also includes system automatic setting; S105: extracting valid information. It not only saves the time for extracting effective information, but also facilitates the diversified processing of dynamic logistics information; the extracted effective information is highly accurate, organized, concise, and free of messy information interference; the effectiveness of information can be artificially controlled.

Description

technical field [0001] The invention relates to the technical field of dynamic logistics big data, in particular to an effective information extraction algorithm for dynamic logistics big data. Background technique [0002] In recent years, with the development of data information science and technology, human beings have entered the era of data visualization and cloud computing. With the continuous improvement of people's quality of life, logistics activities have gradually been welcomed by more and more people. With the continuous advancement of the cloud computing era, especially the rapid expansion of e-commerce such as data collection, online payment, status monitoring and remote control, the amount of data and information currently possessed by many logistics companies has increased rapidly. , a large amount of data information, the use of traditional static information data mining technology to analyze can no longer meet the requirements of current practical applicati...

AI Technical Summary

Problems solved by technology

[0007] 1. The extraction of effective information is not convertible. It is extracted and generated after one setting, which leads to the need to perform data processing and analysis again when performing different types of logistics data in practical applications, which not only wastes the time of information extraction, but also affects the processing of logistics information Stereotyping;

[0008] 2. According to the invalid data set by the system as a reference, find out and eliminate the invalid data in the overall database to achieve the purpose of extracting valid information. Among them, the elimination can only be eliminated according to a one-to-one reference, such as the set If the invalid information is "soap", the objects to be eliminated are "XX soap", "soap XX", and for "XX soap", "soap XX" cannot be eliminated, the processing accuracy of the data is low, and the effective information obtained is disorderly sorted, disorganized;

[0009] 3. If you want to control the amount of effective information extracted and the effective value of effective information, you need to add, delete, and change the extraction words set in the initial effective information. On the premise that the extraction words of effective information are limited and cannot be changed Under the circumstances, it is impossible to control the quantity of effective information and the effective value of effective information

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