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An early warning method for ship cable breakage under strong wind based on bp neural network

A BP neural network and ship technology, applied in neural learning methods, biological neural network models, ship construction, etc., can solve problems such as large gaps in complex situations, and achieve high accuracy.

Active Publication Date: 2022-03-04
SHANDONG JIAOTONG UNIV
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Problems solved by technology

[0004] The technical problem to be solved by the present invention is to provide a BP neural network-based early warning method for ship cable breakage under the action of strong winds, to solve the problem that the existing physical model test can only simulate the ship cable breakage situation in simple cases, which is far from the actual complex situation. big problem

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  • An early warning method for ship cable breakage under strong wind based on bp neural network
  • An early warning method for ship cable breakage under strong wind based on bp neural network
  • An early warning method for ship cable breakage under strong wind based on bp neural network

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[0033] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some, not all, embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.

[0034] A kind of ship cable breakage early warning method under the action of wind and waves based on BP neural network, comprises the steps: step 1, establishes physical model, obtains the data under different situations when the ship breaks cable as the required training data set of BP neural network model;

[0035] Step 1. Determine the input parameters and output parameters

[0036] Define the main factor affecting the breakage of the ship's cable as the i...

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Abstract

The invention discloses an early warning method for ship cable breakage under the action of strong wind based on BP neural network. The invention comprehensively utilizes physical model test, dimensionless parameterization and BP neural network method to create a system that only needs to input the length of the ship, the width of the ship, The seven data parameter values ​​of the ship's clear water height, the ship's freeboard height, the height of the dock where the berthing ship is located, the number of mooring cables and the wind speed can be used to calculate the maximum number of cables for ships berthing at the dock in the port for a period of time in the future under strong wind or typhoon weather. The numerical results of the force can be compared in real time by judging the force and the actual maximum force of the cable. The BP neural network has high accuracy and is closer to the facts.

Description

technical field [0001] The invention belongs to the field of ship navigation safety, in particular to a BP neural network-based early warning method for ship cable breakage under strong wind. Background technique [0002] With the development of large-scale ships, the problem of ship cable breakage has become an important cause of ship production accidents. At present, many ships are equipped with a force monitoring system for mooring cables. Real-time force data of the cables can be obtained from the system, but the future state of the cables changing with the external environment cannot be grasped. cable, causing an accident. Therefore, it is necessary to predict the future change of the mooring cable force of the ship, and to judge the maximum cable force that the mooring cable can carry under the action of wind, wave and current in advance is an important research direction to solve the problem of ship mooring safety. [0003] At present, the research on ship mooring c...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F30/15G06F30/27G06N3/08B63B71/10B63B71/20G06F119/14
CPCG06F30/15G06F30/27G06N3/084B63B71/10B63B71/20G06F2119/14
Inventor 马建文王波胡宴才
Owner SHANDONG JIAOTONG UNIV
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