Horizontal transportation task AGV dynamic time estimation method

A technology of horizontal transportation and dynamic time, applied in neural learning methods, forecasting, instruments, etc., can solve problems such as large deviation, idle resource efficiency, low efficiency, etc., and achieve the effect of improving the results of time prediction

Pending Publication Date: 2021-04-30
SHANGHAI ZHENHUA HEAVY IND
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AI Technical Summary

Problems solved by technology

[0005] The purpose of the present invention is to provide a method for predicting the dynamic time of AGV for horizontal transportation tasks, using machine learning models and neural network models to dynamically predict the completion time of AGVs, so as to solve the problem of static time matrix prediction results and actual results in the prior art Large deviations lead to technical problems in all aspects of port operations that result in idle resources or inefficiencies

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  • Horizontal transportation task AGV dynamic time estimation method
  • Horizontal transportation task AGV dynamic time estimation method
  • Horizontal transportation task AGV dynamic time estimation method

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Embodiment

[0049] figure 1 Shown is the flow chart of the method for predicting the dynamic time of the horizontal transport task AGV of the present invention. In combination with its path, characteristics, traffic conditions in the field, etc., the method for predicting the dynamic time of the horizontal transport task AGV is specifically divided into the following steps: figure 1 Shown:

[0050]Step 1: feature selection: use computer program algorithm to select and screen the features of AGV operation instructions for horizontal transportation tasks, and integrate feature data;

[0051] figure 2 A screenshot showing data extraction processing order (sequence) table; image 3 A screenshot showing data extraction processing the track table; Figure 4 A screenshot of the data table after feature extraction is shown;

[0052] Wherein: the feature selection in step 1 includes the following steps:

[0053] S11: Process the data in the order table such as figure 2 As shown, the record...

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Abstract

The invention discloses a horizontal transport task AGV dynamic time estimation method in the technical field of dynamic time estimation. The horizontal transport task AGV dynamic time estimation method comprises the following steps: 1, feature selection; 2, data processing; 3, machine learning; 4, a neural network; 5, verifying the dynamic time estimation model; according to the horizontal transportation task AGV dynamic time estimation method, a machine learning model and a neural network model are used to dynamically predict the task completion time of the AGV, and the influence of various factors on the AGV in the horizontal transportation process of a wharf is described. Therefore, the time estimation result is improved, and more accurate basic data is provided for other application scenes.

Description

technical field [0001] The present invention relates to the technical field of dynamic time prediction, and more specifically, relates to a method for dynamic time prediction of an AGV for horizontal transport tasks. Background technique [0002] AGV (Automated Guided Vehicles), also known as unmanned guided vehicles, automatic guided vehicles, and laser guided vehicles, is characterized by unmanned driving. AGV unmanned vehicle loading and unloading operations are widely used in the current port industry. During AGV operation, AGV time estimation has a wide range of application scenarios. AGV running time is the key basic data of the scheduling system, which can be applied to AGV task selection and scheduling, actual task AGV operation time estimation, overall port efficiency optimization, and traffic conflict priority during AGV operation. In terms of level selection, TOS task distribution in automated ports, matching decision between vehicles and tasks, conflict optimiza...

Claims

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Application Information

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IPC IPC(8): G06Q10/04G06K9/62G06N3/04G06N3/08
CPCG06Q10/04G06N3/084G06N3/047G06N3/045G06F18/23
Inventor 王涵晟张峥炜赵云王小进陈波
Owner SHANGHAI ZHENHUA HEAVY IND
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