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KNN algorithm-based voyage number box entering amount distribution condition prediction method and system

A technology of KNN algorithm and prediction method, which is applied in the computer field, can solve the problems of efficiently utilizing the physical space resources of the yard, and achieve the effect of efficient freight service and good throughput capacity

Pending Publication Date: 2022-02-08
SHANGHAI JIAO TONG UNIV
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AI Technical Summary

Problems solved by technology

[0005] However, the existing technology only has a coarse-grained prediction of the total container volume, but lacks a finer-grained daily incoming container volume prediction, which makes it difficult to use the physical space resources of the yard more efficiently.

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  • KNN algorithm-based voyage number box entering amount distribution condition prediction method and system
  • KNN algorithm-based voyage number box entering amount distribution condition prediction method and system
  • KNN algorithm-based voyage number box entering amount distribution condition prediction method and system

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Embodiment

[0050] The invention provides a KNN-based method for predicting the quantity of boxes entering a voyage. According to the weighted summation of the distances of multiple feature dimensions between voyages, multiple voyages that are most similar to the predicted voyage are matched, and then according to the box intake of these similar voyages According to the rules to infer the box-in rules of the predicted voyage, the box volume prediction results can be obtained. Finally, the genetic algorithm is used to further optimize the weight of the features. For the detailed process, see figure 1 . The prediction method mainly includes three parts: similar voyage matching, kernel density estimation, and genetic algorithm, detailed as follows:

[0051] Similar voyage matching: According to the encoded feature information, the weighted distance measure is obtained to find multiple voyages that are most similar to the predicted voyage;

[0052] Kernel density estimation: According to th...

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Abstract

The invention provides a KNN algorithm-based voyage number box entering amount distribution condition prediction method and system. The method comprises the steps: 1, carrying out the coding of voyage number features, and carrying out the similar voyage number matching; 2, performing kernel density estimation on similar voyage numbers, and sampling in a kernel density estimation result; 3, performing kernel density estimation on the sampled data to obtain a prediction result; and step 4, optimizing a prediction result through a genetic algorithm. According to the method, the container feeding amount of the voyage number can be predicted on the basis of the finer granularity every day or even every hour, so that the handling capacity of a wharf is better excavated, the freight service is efficiently provided, the cost is reduced, and the market competitiveness of waterway shipping is improved.

Description

technical field [0001] The present invention relates to the field of computer technology, in particular, to a method and system for predicting the distribution of incoming containers during a voyage based on the KNN algorithm. Background technique [0002] Compared with land transportation and air transportation services, ship shipping services have many advantages such as large cargo capacity, low unit transportation cost, low construction investment cost, less energy consumption, less environmental pollution, and safety and economy. Ocean shipping occupies a unique and important position in my country's foreign economic and trade. More than 90% of my country's foreign trade goods are transported by ocean, which is a powerful pillar for the development of international trade. Therefore, improving the efficiency of shipping services is of great significance to the country and society. [0003] Forecasting the daily container volume of container ship voyages is of great signi...

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Application Information

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IPC IPC(8): G06Q10/04G06Q10/08G06K9/62G06N3/12
CPCG06Q10/04G06Q10/083G06N3/126G06F18/24147
Inventor 曹健申世通钱诗友
Owner SHANGHAI JIAO TONG UNIV
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