JP2021088430A - Device for automatically transferring heavy machine for marine vessel in cargo handling equipment - Google Patents

Abstract

To provide a device for automatically transferring a heavy machine for a marine vessel in cargo handling equipment, which can efficiently carry the heavy machine into the inside of a hold, and leads to a complete automatic operation in the future.SOLUTION: A device for automatically transferring a heavy machine for a marine vessel in cargo handling equipment comprises: a detector 100 for detecting a heavy machine, a hatch, and a bulk cargo; a machine learning computer 200 which stores data on the heavy machine, the hatch, and the bulk cargo, and repeats the machine learning until the data on the heavy machine, the hatch, and the bulk cargo can be recognized; and a control device 300 for supervising the operation of the cargo handling equipment. When a heavy machine lifting mode is selected, the machine learning computer 200 recognizes a heavy machine position based on data of the heavy machine detected by the detector 100 at that time, and moves a trolley to the heavy machine position and stops there. When a heavy machine carry-in mode is selected, the machine learning computer lifts the heavy machine, determines a heavy machine carry-in position based on data of the hatch and the bulk cargo detected by the detector 100 at that time, moves the trolley to a position above the heavy machine carry-in position and stops there, hangs the heavy machine after the trolley is stopped, and stops hanging the heavy machine after the heavy machine lands.SELECTED DRAWING: Figure 1

Description

The present invention relates to an automatic transfer device for heavy machinery for ships of cargo handling machinery.

Generally, in a cargo handling machine such as an unloader or a container crane, an operator operates a controller in the driver's cab to perform cargo handling work.

Since the time required for cargo handling work varies greatly depending on the skill of the operator and has a great influence on cargo handling efficiency, interest in autonomous driving is increasing. Further, in order to reduce the burden on the operator, automatic operation of cargo handling work is very important.

On the other hand, in the case of a ship loaded with loose objects such as coal, the opening area of the hatch is smaller than the internal space of the hold, so the range of loose objects that can be grasped by the grab bucket of the cargo handling machine is that of the hold. It is limited to the area around the area projected downward from the hatch. For this reason, the loose objects remaining inside the hold are scraped by heavy machinery such as a bulldozer and a backhoe.

Conventionally, the heavy machine is lifted from the pier by a cargo handling machine manually operated by an operator, and is thrown into the hold from the hatch.

For example, Patent Document 1 indicates a general technical level related to the cargo handling machine.

Japanese Unexamined Patent Publication No. 2000-255482

However, it takes a very long time to lift a heavy machine from a pier by a cargo handling machine manually operated by an operator and put it into the hold from a hatch as in the conventional case, which may lead to a decrease in cargo handling efficiency.

The present invention has been made in view of the above-mentioned conventional problems, and provides an automatic transfer device for heavy machinery for ships, which can efficiently put heavy machinery into the hold and can be connected to fully automatic operation in the future. It is something to try.

The present invention is an automatic transfer device for heavy machinery for ships, which is a cargo handling machine that can move along a ship in which heavy machinery is loaded from a hatch into a hold on which loose objects are loaded.
A detector that is installed in the cargo handling machine and catches heavy machinery, hatches, and loose objects.
A machine learning computer that accumulates heavy equipment data, hatch data, and loose object data captured by the detector, and repeats machine learning until the heavy machinery, hatch, and loose objects can be recognized based on the data.
It is equipped with a control device that controls the operation of the cargo handling machine.
When the heavy equipment lifting mode is selected, the machine learning computer recognizes the heavy equipment position based on the heavy equipment data captured by the detector at that time, and controls the trolley movement / stop command for moving / stopping the trolley to the heavy equipment position. Output to the device, and when the heavy equipment loading mode is selected, the heavy equipment lifting command to lift the heavy equipment is output to the control device, and the heavy equipment loading position is determined based on the hatch data and loose object data captured by the detector at that time. A trolley movement / stop command for determining and moving / stopping the trolley to above the heavy equipment loading position is output to the control device, and after the trolley is stopped, a heavy equipment suspension command for suspending the heavy equipment is output to the control device. The present invention relates to an automatic transfer device for heavy equipment for ships of a cargo handling machine that outputs a heavy equipment suspension stop command for stopping the suspension of the heavy equipment to the control device after the heavy equipment has landed.

In the ship heavy equipment automatic transfer device of the cargo handling machine, the detector can be a camera that photographs heavy equipment, a hatch, and a loose object and captures image data.

Further, in the heavy equipment automatic transfer device for ships of the cargo handling machine, the detector measures scattered light for laser irradiation emitted in a pulse shape, analyzes data of the distance and shape to the heavy equipment, and the distance and shape to the hatch. It can also be used as a laser image detector that captures the analysis data of the above and the analysis data of the distance to the loose object and the shape.

According to the heavy equipment automatic transfer device for ships of the cargo handling machine of the present invention, the heavy equipment can be efficiently put into the hold and can be connected to the fully automatic operation in the future, which is an excellent effect.

It is a block diagram which shows the Example of the heavy equipment automatic transfer apparatus for ship of the cargo handling machine of this invention. It is a flowchart which shows the machine learning for the heavy machine, the hatch, and the loose object recognition in the Example of the heavy machine automatic transfer device for a ship of the cargo handling machine of this invention. It is a flowchart which shows the operation at the time of selection of the heavy machine lifting mode in the Example of the heavy machine automatic transfer device for a ship of the cargo handling machine of this invention. It is a flowchart which shows the operation at the time of selection of the heavy machine loading mode in the Example of the heavy machine automatic transfer device for a ship of the cargo handling machine of this invention. It is an overall view which shows the grab bucket type unloader as an example of a cargo handling machine to which the heavy equipment automatic transfer apparatus for a ship of this invention is applied. FIG. 5 is a view taken along the line VI-VI of FIG.

Hereinafter, embodiments of the present invention will be described with reference to the accompanying drawings.

1 to 6 are examples of the heavy equipment automatic transfer device for ships of the cargo handling machine of the present invention.

As shown in FIG. 1, the ship heavy equipment automatic transfer device of the cargo handling machine of this embodiment includes a detector 100, a machine learning computer 200, a control device 300, a transmitter 400, and a receiver 500, for example. , Used in the grab bucket type unloader 600 as a cargo handling machine as shown in FIGS. 5 and 6. However, the heavy equipment automatic transfer device for ships of this embodiment can be used not only for the grab bucket type unloader 600 but also for cargo handling machines such as container cranes.

First, the grab bucket type unloader 600 will be described.

As shown in FIG. 5, the grab bucket type unloader 600 has a machine body 604 having a sea leg 601 on the sea side and a land leg 602 on the land side and traveling on a rail 603 on the pier 800, and the machine body 604. A boom 606 that can be raised from the girder 605 provided on the upper land side to the sea side and can be raised around the pin 606a, a trolley 607 that traverses along the longitudinal direction of the boom 606 and the girder 605, and suspended from the trolley 607. It has a grab bucket 608 that is lowered to raise and lower and open and close. A heavy equipment hook 609 for transferring the heavy equipment 710 is suspended from the trolley 607.

A hopper 610 is provided in the machine main body 604, an in-flight conveyor 611 is provided under the hopper 610, and a conveyor 612 installed in the pier 800 is connected to the downstream end of the in-flight conveyor 611. Has been done.

In the boom 606, a driver's cab 613 on which an operator who operates the grab bucket type unloader 600 is boarded is slidably arranged along the boom 606. In FIG. 5, 700 is a ship, the grab bucket 608 is suspended from the hatch 701 of the ship 700 into the hold 702, and a loose object 900 such as coal as a load loaded on the ship 700 is suspended from the grab bucket 608. It is designed to be grabbed by.

Then, the detector 100 in this embodiment is arranged in the grab bucket type unloader 600 so as to catch the heavy machine 710, the hatch 701, and the loose object 900 of the hold 702. In the examples shown in FIGS. 5 and 6, four detectors 100 are arranged at the tip of the boom 606 of the grab bucket type unloader 600. As shown in FIG. 5, one of the four detectors 100 is provided on the boom 606 diagonally downward toward the pier 800 to catch the heavy equipment 710 on the pier 800. The other detector 100 shown in FIG. 5 is provided vertically downward on the boom 606 to catch the hatch 701 and the loose object 900 directly below, and the remaining two detectors 100 are as shown in FIG. The boom 606 is provided diagonally downward to catch the left and right hatches 701. However, it goes without saying that the arrangement location can be appropriately selected. Further, the number of detectors 100 may be one or a plurality of detectors 100 as long as the shapes of the heavy machine 710, the hatch 701, and the loose object 900 can be captured. As the detector 100, a camera that captures the heavy equipment 710, the hatch 701 of the ship 700, and the loose object 900 and captures the image data can be adopted. Further, the detector 100 measures scattered light for laser irradiation that emits light in a pulse shape, analyzes data on the distance and shape to the heavy machine 710, analysis data on the distance and shape to the hatch 701, and a loose object 900. It can also be a laser image detector such as LIDAR (Laser Imaging Detection and Ranging) that captures the distance and shape analysis data.

The machine learning computer 200 accumulates the data of the heavy machine 710, the data of the hatch 701, and the data of the loose object 900 captured by the detector 100, and based on the data, the heavy machine 710, the hatch 701, and the loose object 900 are combined. Machine learning is repeated until it becomes recognizable.

The control device 300 controls the operation of the grab bucket type unloader 600, and is a programmable logic controller (PLC). A receiver 500 that receives a trigger signal from the wireless transmitter 400 is connected to the control device 300. The transmitter 400 is provided in the driver's cab 613, for example, and outputs a trigger signal by pressing a button or the like when the operator selects the heavy equipment lifting mode or the heavy equipment loading mode. However, the transmitter 400 may be installed in a centralized control room or the like (not shown) instead of being installed in the driver's cab 613.

Further, when the heavy machine lifting mode is selected, the machine learning computer 200 recognizes the heavy machine 710 position based on the data of the heavy machine 710 captured by the detector 100 at that time, and moves / stops the trolley 607 to the heavy machine 710 position. A trolley move / stop command is output to the control device 300. Further, when the heavy equipment loading mode is selected, the machine learning computer 200 outputs a heavy equipment lifting command for lifting the heavy equipment 710 to the control device 300, and the data of the hatch 701 captured by the detector 100 at that time and loose objects. Based on the data of 900, the image of the hatch 701 and the image of the loose object 900 are recognized, the heavy equipment loading position is determined, and the trolley movement / stop command for moving / stopping the trolley 607 above the heavy equipment loading position is output to the control device 300. To do. Furthermore, the machine learning computer 200 outputs a heavy equipment suspension command for suspending the heavy equipment 710 to the control device 300 after the trolley 607 is stopped, and stops the suspension of the heavy equipment 710 after the heavy equipment 710 has landed. The heavy equipment suspension stop command to be caused is output to the control device 300.

Next, the operation of the above-described embodiment will be described.

First, the detector 100 is installed at the tip of the boom 606 of the grab bucket type unloader 600 (see step S10 in FIG. 2).

When a camera is adopted as the detector 100, the heavy machine 710, the hatch 701 of the ship 700, and the loose object 900 of the hold 702 are photographed by the camera, and image data is captured. When a laser image detector such as LIDAR is adopted as the detector 100, the scattered light for the laser irradiation emitted in a pulse shape is measured by the laser image detector, and the distance and shape analysis data to the heavy machine 710 are measured. Then, the analysis data of the distance and shape to the hatch 701 and the analysis data of the distance and shape to the loose object 900 of the shipyard 702 are taken in (see step S20 in FIG. 2).

The data of the heavy machine 710 (image data of the heavy machine 710 or the analysis data of the distance and shape to the heavy machine 710) and the data of the hatch 701 (the image data of the hatch 701 or the distance and shape to the hatch 701) captured by the detector 100. (Analysis data) and data of the loose object 900 (image data of the loose object 900 or analysis data of the distance and shape to the loose object 900) are stored in the machine learning computer 200 (see step S30 in FIG. 2).

In the machine learning computer 200, machine learning for recognizing the heavy machine 710, the hatch 701, and the loose object 900 is performed (see step S40 in FIG. 2).

After machine learning, the machine learning computer 200 determines whether or not the heavy machine 710, the hatch 701, and the loose object 900 can be recognized (see step S50 in FIG. 2). If the heavy machine 710, the hatch 701, and the loose object 900 cannot be recognized, the operations of steps S10 to S40 are continued.

When it is determined in step S50 that the heavy machine 710, the hatch 701, and the loose object 900 can be recognized, the machine learning ends.

Next, the automatic transfer of the heavy machine 710 after the machine learning is repeated will be described.

When the operator selects the heavy equipment lifting mode by pressing a button or the like of the transmitter 400, a trigger signal is output from the transmitter 400. The trigger signal is received by the receiver 500, and the control device 300 switches the grab bucket type unloader 600 to the heavy equipment lifting mode (see step S100 in FIG. 3).

When the grab bucket type unloader 600 is switched to the heavy equipment lifting mode, the heavy equipment 710 is captured by the detector 100, and the position of the heavy equipment 710 is recognized by the machine learning computer 200 (see step S110 in FIG. 3).

When the position of the heavy machine 710 is recognized, the machine learning computer 200 outputs a trolley move / stop command to the control device 300, and the trolley 607 moves to the position of the heavy machine 710 and stops (step S120 in FIG. 3). reference).

When the trolley 607 moves to the position of the heavy machine 710 and stops, the heavy machine hook 609 is hung from the trolley 607, and the slinging operation of hanging the heavy machine 710 on the heavy machine hook 609 is performed (step of FIG. 3). See S130). The slinging work is performed by a worker.

After that, when the operator selects the heavy equipment closing mode by pressing the button or the like of the transmitter 400, the trigger signal is output from the transmitter 400. The trigger signal is received by the receiver 500, and the control device 300 switches the grab bucket type unloader 600 to the heavy equipment loading mode (see step S200 in FIG. 4).

When the grab bucket type unloader 600 is switched to the heavy equipment loading mode, the machine learning computer 200 outputs a heavy equipment lifting command to the control device 300, and the heavy equipment 710 is lifted (see step S210 in FIG. 4).

When the heavy machine 710 is lifted, the data of the hatch 701 captured by the detector 100 at that time (image data of the hatch 701 or analysis data of the distance and shape to the hatch 701) and the data of the loose object 900 (the loose object 900). The hatch 701 and the loose object 900 are recognized by the machine learning computer 200 based on the image data of the above or the analysis data of the distance and shape to the loose object 900 (see step S220 in FIG. 4).

The machine learning computer 200 determines a position to put the heavy machine 710 on the loose object 900 inside the hatch 701 (see step S230 in FIG. 4).

When the machine learning computer 200 determines the position to put the heavy machine 710 on the loose object 900 inside the hatch 701, the machine learning computer 200 outputs a trolley move / stop command to the control device 300. The trolley 607 moves to the loading position of the heavy machine 710 and stops (see step S240 in FIG. 4).

When the trolley 607 moves to the loading position of the heavy machine 710 and stops, the machine learning computer 200 outputs a heavy machine suspension command to the control device 300, and the heavy machine 710 is suspended (see step S250 in FIG. 4).

When the heavy machine 710 is suspended, it is determined whether or not the heavy machine 710 has landed on the loose object 900 (see step S260 in FIG. 4). This determination is made, for example, by detecting the load on a load cell (not shown) attached to the hoisting mechanism of the trolley 607. If it is determined that the heavy machine 710 has not landed on the loose object 900, the process returns to step S250, and the heavy machine 710 is continuously suspended.

When it is determined that the heavy machine 710 has landed on the loose object 900, the machine learning computer 200 outputs a heavy machine suspension stop command to the control device 300, and the heavy machine 710 is stopped from being suspended (FIG. FIG. See step S270 in step 4).

As a result, unlike the conventional case, the grab bucket type unloader 600 as a cargo handling machine can lift the heavy machine 710 from the pier 800 and put it into the hold 702 from the hatch 701 in a short time without the operator manually operating it. It becomes possible and the cargo handling efficiency is improved.

In this way, the heavy machine 710 can be efficiently put into the hold 702, which can be connected to the fully automatic operation in the future.

Then, in the case of this embodiment, the detector 100 can be a camera that captures the heavy machine 710, the hatch 701, and the loose object 900 and captures the image data. If the detector 100 is configured by a camera, the system can be easily constructed.

Further, the detector 100 measures the scattered light for the laser irradiation that emits in a pulse shape, and analyzes the distance and shape to the heavy machine 710, the analysis data of the distance and shape to the hatch 701, and the loose object 900. It can also be a laser image detector that captures the distance and shape analysis data. When the detector 100 is composed of a laser image detector, the heavy machine 710, the hatch 701, and the loose object 900 can be captured with extremely high optical resolution, and the accuracy can be improved.

It should be noted that the heavy equipment automatic transfer device for ships of the cargo handling machine of the present invention is not limited to the above-described embodiment, and it goes without saying that various modifications can be made without departing from the gist of the present invention.

100 Detector 200 Machine learning computer 300 Control device 400 Transmitter 500 Receiver 600 Grab bucket type unloader (cargo handling machine)
601 Sea leg 602 Land leg 603 Rail 604 Machine body 605 Girder 606 Boom 606a Pin 607 Trolley 608 Grab bucket 609 Heavy equipment hook 610 Hopper 611 In-flight conveyor 612 Conveyor conveyor 613 Driver's cab 700 Ship 701 Hatch 702 Hold 710 Heavy equipment 800 Pier 900

Claims (3)

A heavy equipment automatic transfer device for ships, which is a cargo handling machine that can move along the ship where heavy equipment is loaded from the hatch into the hold where loose objects are loaded.
A detector that is installed in the cargo handling machine and catches heavy machinery, hatches, and loose objects.
A machine learning computer that accumulates heavy equipment data, hatch data, and loose object data captured by the detector, and repeats machine learning until the heavy machinery, hatch, and loose objects can be recognized based on the data.
It is equipped with a control device that controls the operation of the cargo handling machine.
When the heavy equipment lifting mode is selected, the machine learning computer recognizes the heavy equipment position based on the heavy equipment data captured by the detector at that time, and controls the trolley movement / stop command for moving / stopping the trolley to the heavy equipment position. Output to the device, and when the heavy equipment loading mode is selected, the heavy equipment lifting command to lift the heavy equipment is output to the control device, and the heavy equipment loading position is determined based on the hatch data and loose object data captured by the detector at that time. A trolley movement / stop command for determining and moving / stopping the trolley to above the heavy equipment loading position is output to the control device, and after the trolley is stopped, a heavy equipment suspension command for suspending the heavy equipment is output to the control device. An automatic transfer device for heavy equipment for ships of a cargo handling machine that outputs a heavy equipment suspension stop command for stopping the suspension of the heavy equipment to the control device after the heavy equipment has landed. The automatic transfer device for heavy machinery for ships according to claim 1, wherein the detector is a camera that captures images of heavy machinery, hatches, and loose objects and captures image data. The detector measures the scattered light for laser irradiation that emits in a pulse shape, and analyzes the distance and shape analysis data to the heavy machine, the distance and shape analysis data to the hatch, and the distance and shape analysis to the loose object. The automatic transfer device for heavy machinery for ships according to claim 1, which is a laser image detector that captures data.

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