JP7428509B2 - Heavy machinery automatic transfer device for ships of cargo handling machinery - Google Patents

Description

TECHNICAL FIELD The present invention relates to a heavy equipment automatic transfer device for ships of cargo handling machines.

Generally, in cargo handling machines such as unloaders and container cranes, an operator operates a controller in a driver's cab to perform cargo handling work.

The time required for cargo handling operations varies greatly depending on the skill of the operator, which has a significant impact on cargo handling efficiency, which is why there is growing interest in automated driving. Furthermore, automatic operation of cargo handling operations is extremely important in reducing the burden on operators.

On the other hand, in the case of ships loaded with bulk materials such as coal, the opening area of the hatch is smaller than the interior space of the hold, so the range of bulk materials that can be grabbed by the grab bucket of the cargo handling machine is limited to the area of the cargo hold. Limited to the area projected downward from the hatch. For this reason, loose materials remaining inside the ship's hold are scraped up by heavy machinery such as bulldozers and backhoes.

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

Note that, for example, Patent Document 1 shows the general technical level related to the cargo handling machine.

Japanese Patent Application Publication No. 2000-255482

However, as in the past, lifting heavy machinery from a pier with a cargo handling machine manually operated by an operator and loading it into a ship's hold through a hatch is very time consuming and 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 heavy equipment transfer device for ships for cargo handling equipment that can efficiently load heavy equipment into the interior of a ship's hold and can lead to future fully automatic operation. This is what I am trying to do.

The present invention is an automatic ship heavy equipment transfer device for a cargo handling machine that is movable along a ship in which heavy equipment is introduced from a hatch into a ship's hold where bulk goods are loaded,
a detector disposed on the cargo handling machine and detecting 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 based on the data until it can recognize the heavy equipment, the hatch, and the loose object;
and a control device that supervises the operation of the cargo handling machine,
When the heavy equipment lifting mode is selected, the machine learning computer recognizes the position of the heavy equipment based on the heavy equipment data captured by the detector at that time, and controls the trolley movement/stop command to move and stop the trolley to the heavy equipment position. When the heavy machinery loading mode is selected, a heavy machinery lifting command to lift the heavy machinery is output to the control device, and the heavy machinery loading position is determined based on hatch data and bulk material data detected by the detector at that time. determine, output a trolley movement/stop command to the control device to move and stop the trolley above the heavy equipment loading position, output a heavy equipment suspension command to the control device to suspend the heavy equipment after the trolley has stopped, and The present invention relates to an automatic heavy equipment transfer device for a ship of a cargo handling machine that outputs a heavy equipment suspension stop command to the control device to stop the suspension of the heavy equipment after the heavy equipment has landed on the floor.

In the automatic ship heavy equipment transfer device for cargo handling equipment, the detector may be a camera that photographs the heavy equipment, the hatch, and loose items and captures image data.

Further, in the automatic ship heavy equipment transfer device for cargo handling equipment, the detector measures scattered light in response to pulsed laser irradiation, and analyzes data on the distance and shape to the heavy equipment and the distance and shape to the hatch. It is also possible to use a laser image detector that captures analysis data on the distance and shape of the loose object.

According to the automatic ship heavy equipment transfer device for cargo handling machines of the present invention, it is possible to efficiently introduce heavy equipment into a ship's hold, and it is possible to achieve the excellent effect of being able to lead to future fully automatic operation.

BRIEF DESCRIPTION OF THE DRAWINGS FIG. 1 is a block diagram showing an embodiment of a heavy equipment automatic transfer device for ships of a cargo handling machine according to the present invention. 1 is a flowchart showing machine learning for recognizing heavy machinery, hatches, and loose objects in an embodiment of the automatic heavy machinery transfer device for ships of cargo handling machines of the present invention. It is a flowchart which shows the operation|movement when the heavy equipment hoisting mode is selected in the Example of the heavy equipment automatic transfer apparatus for ships of the cargo handling machine of this invention. It is a flowchart which shows the operation|movement when heavy machinery input mode is selected in the Example of the heavy machinery automatic transfer apparatus for ships of a cargo handling machine of this invention. 1 is an overall view showing a grab bucket type unloader as an example of a cargo handling machine to which the automatic ship heavy equipment transfer device of the present invention is applied. 6 is a view taken along the line VI-VI in FIG. 5. FIG.

Embodiments of the present invention will be described below with reference to the accompanying drawings.

FIGS. 1 to 6 show embodiments of the automatic heavy equipment transfer system for ships of cargo handling machines according to the present invention.

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

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

As shown in FIG. 5, the grab bucket type unloader 600 includes a machine body 604 that has a sea leg 601 on the sea side and a land leg 602 on the land side and runs on a rail 603 on a pier 800, and a machine body 604 that runs on a rail 603 on a pier 800. A boom 606 that can be raised and raised from a girder 605 provided on the land side of the upper part to the sea side about a pin 606a, a trolley 607 that runs horizontally along the longitudinal direction of the boom 606 and the girder 605, and a boom that is suspended from the trolley 607. It has a grab bucket 608 that is lowered and can be raised and lowered and opened and closed. Note that a heavy equipment hook 609 for transporting heavy equipment 710 is suspended from the trolley 607.

The machine main body 604 is provided with a hopper 610, an in-machine conveyor 611 is provided below the hopper 610, and a conveyor 612 installed on the pier 800 is connected to the downstream end of the in-machine conveyor 611. has been done.

An operator's cab 613 in which an operator who operates the grab bucket type unloader 600 is seated is disposed on the boom 606 so as to be slidable along the boom 606. In FIG. 5, 700 is a ship, and the grab bucket 608 is suspended from the hatch 701 of the ship 700 into the hold 702, and bulk materials 900 such as coal loaded on the ship 700 are transported to the grab bucket 608. It is designed to be grasped.

The detector 100 in this embodiment is disposed on the grab bucket type unloader 600 so as to detect the heavy equipment 710, the hatch 701, and the loose items 900 in the hold 702. In the example shown in FIGS. 5 and 6, four detectors 100 are disposed 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 installed on the boom 606 diagonally downward toward the pier 800 to detect the heavy equipment 710 on the pier 800. Another detector 100 shown in FIG. 5 is installed vertically downward on the boom 606 and captures the hatch 701 and the loose object 900 directly below, and the remaining two detectors 100, as shown in FIG. It is provided diagonally downward on the boom 606 to capture the left and right hatches 701. However, it goes without saying that the location can be selected as appropriate. Further, the number of detectors 100 may be one or two or more as long as the shapes of the heavy machinery 710, the hatch 701, and the loose items 900 can be detected. As the detector 100, it is possible to employ a camera that photographs the heavy machinery 710, the hatch 701 of the ship 700, and the loose items 900 and captures image data. The detector 100 measures scattered light in response to pulsed laser irradiation, and collects analysis data on the distance and shape to the heavy machinery 710, analysis data on the distance and shape to the hatch 701, and the loose objects 900. It is also possible to use a laser image detector such as LIDAR (Laser Imaging Detection and Ranging) that captures analysis data on the distance and shape.

The machine learning computer 200 accumulates the data of the heavy machinery 710, the data of the hatch 701, and the data of the loose object 900 detected by the detector 100, and identifies the heavy machinery 710, the hatch 701, and the loose object 900 based on the data. Machine learning is repeated until it can be recognized.

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 a wireless transmitter 400 is connected to the control device 300 . The transmitter 400 is installed in the operator's cab 613, for example, and outputs a trigger signal when the operator presses a button or the like when selecting the heavy equipment lifting mode or the heavy equipment loading mode. However, instead of installing the transmitter 400 in the operator's cab 613, it may be installed in a central control room (not shown) or the like.

Furthermore, when the heavy equipment lifting mode is selected, the machine learning computer 200 recognizes the position of the heavy equipment 710 based on the data of the heavy equipment 710 detected by the detector 100 at that time, and moves and stops the trolley 607 to the position of the heavy equipment 710. A trolley movement/stop command is output to the control device 300. Furthermore, when the heavy equipment insertion mode is selected, the machine learning computer 200 outputs a heavy equipment lifting command to the control device 300 to lift the heavy equipment 710, and at that time, the machine learning computer 200 outputs the data of the hatch 701 and loose objects detected by the detector 100. It recognizes the image of the hatch 701 and the image of the loose items 900 based on the data 900, determines the heavy equipment loading position, and outputs a trolley movement/stop command to the control device 300 to move and stop the trolley 607 above the heavy equipment loading position. do. Furthermore, the machine learning computer 200 outputs a heavy equipment suspension command to the control device 300 to suspend the heavy equipment 710 after the trolley 607 is stopped, and stops the lifting of the heavy equipment 710 after the heavy equipment 710 lands on the floor. A heavy equipment suspension stop command is output to the control device 300.

Next, the operation of the above embodiment will be explained.

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 employed as the detector 100, the camera photographs the heavy machinery 710, the hatch 701 of the ship 700, and the bulk items 900 in the hold 702, and captures image data. Furthermore, when a laser image detector such as LIDAR is adopted as the detector 100, the laser image detector measures scattered light in response to pulsed laser irradiation, and analyzes data on the distance and shape to the heavy equipment 710. Then, analysis data on the distance and shape to the hatch 701 and analysis data on the distance and shape to the loose items 900 in the hold 702 are imported (see step S20 in FIG. 2).

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

In the machine learning computer 200, machine learning is performed to recognize the heavy machinery 710, the hatch 701, and the loose items 900 (see step S40 in FIG. 2).

After the machine learning computer 200 performs machine learning, it is determined whether the heavy machinery 710, the hatch 701, and the loose items 900 can be recognized (see step S50 in FIG. 2). If the heavy machinery 710, the hatch 701, and the loose items 900 cannot be recognized, the operations in steps S10 to S40 are continued.

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

Next, automatic transport of the heavy machinery 710 after the machine learning has been repeated will be explained.

When the operator selects the heavy equipment lifting mode by pressing a button or the like on the transmitter 400, the transmitter 400 outputs a trigger signal. The trigger signal is received by the receiver 500, and the grab bucket type unloader 600 is switched to the heavy equipment lifting mode by the control device 300 (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 machinery 710 is recognized, a trolley movement/stop command is output from the machine learning computer 200 to the control device 300, and the trolley 607 moves to the position of the heavy machinery 710 and stops (step S120 in FIG. 3). reference).

When the trolley 607 moves to the position of the heavy equipment 710 and stops, the heavy equipment hook 609 is suspended from the trolley 607, and a slinging operation is performed to suspend the heavy equipment 710 from the heavy equipment hook 609 (step in FIG. 3). (See S130). Note that the slinging work is performed by a worker.

Thereafter, when the operator selects the heavy equipment loading mode by pressing a button or the like on the transmitter 400, the transmitter 400 outputs a trigger signal. The trigger signal is received by the receiver 500, and the grab bucket type unloader 600 is switched to the heavy equipment loading mode by the control device 300 (see step S200 in FIG. 4).

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

When the heavy equipment 710 is lifted, the data of the hatch 701 (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) are captured by the detector 100 at that time. The hatch 701 and the loose object 900 are recognized by the machine learning computer 200 (see step S220 in FIG. 4).

The machine learning computer 200 determines the position at which the heavy equipment 710 is to be placed on top of the bulk goods 900 inside the hatch 701 (see step S230 in FIG. 4).

When the machine learning computer 200 determines the position where the heavy equipment 710 is to be thrown onto the bulk goods 900 inside the hatch 701, the machine learning computer 200 outputs a trolley movement/stop command to the control device 300, The trolley 607 moves to the loading position of the heavy equipment 710 and stops (see step S240 in FIG. 4).

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

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

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

As a result, unlike in the past, the grab bucket type unloader 600 as a cargo handling machine can lift the heavy equipment 710 from the pier 800 and throw it into the cargo hold 702 from the hatch 701 in a short time without manual operation by the operator. This makes it possible to improve cargo handling efficiency.

In this way, the heavy machinery 710 can be efficiently loaded into the cargo hold 702, leading to full automatic operation in the future.

In the case of this embodiment, the detector 100 can be a camera that photographs the heavy machinery 710, the hatch 701, and the loose items 900 and captures image data. If the detector 100 is configured with a camera, the system can be easily constructed.

Further, the detector 100 measures scattered light in response to laser irradiation emitted in a pulsed manner, and provides analysis data on the distance and shape to the heavy equipment 710, analysis data on the distance and shape to the hatch 701, and analysis data on the distance and shape to the loose object 900. It may also be a laser image detector that captures distance and shape analysis data. When the detector 100 is configured with a laser image detector, the heavy equipment 710, the hatch 701, and the loose objects 900 can be detected with extremely high optical resolution, and accuracy can be improved.

It should be noted that the automatic heavy equipment transfer device for ships of cargo handling machines of the present invention is not limited to the above-described embodiments, and it goes without saying that various changes 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 Garder 606 boom 606 A pin 607 torori 608 grab bucket 609 heavy machine hooks 610 hopper 611 In -air conveyor 613 Transportation room 613 driving room 701 boat 701 boat 701 boat Kura 710 heavy machine 800 pier 900 roses

Claims (3)

An automatic ship heavy equipment transfer device for a cargo handling machine that is movable along a ship in which heavy equipment is introduced from a hatch into a ship's hold where bulk goods are loaded,
a detector disposed on the cargo handling machine and detecting 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 based on the data until it can recognize the heavy equipment, the hatch, and the loose object;
and a control device that supervises the operation of the cargo handling machine,
When the heavy equipment lifting mode is selected, the machine learning computer recognizes the position of the heavy equipment based on the heavy equipment data captured by the detector at that time, and controls the trolley movement/stop command to move and stop the trolley to the heavy equipment position. When the heavy machinery loading mode is selected, a heavy machinery lifting command to lift the heavy machinery is output to the control device, and the heavy machinery loading position is determined based on hatch data and bulk material data detected by the detector at that time. determine, output a trolley movement/stop command to the control device to move and stop the trolley above the heavy equipment loading position, output a heavy equipment suspension command to the control device to suspend the heavy equipment after the trolley has stopped, and A heavy equipment automatic transfer device for ships of cargo handling equipment that outputs a heavy equipment suspension stop command to the control device to stop the lifting of the heavy equipment after the heavy equipment lands on the floor. 2. The automatic heavy equipment transfer system for ships of cargo handling equipment according to claim 1, wherein the detector is a camera that photographs the heavy equipment, the hatch, and the loose items and captures the image data. The detector measures scattered light in response to pulsed laser irradiation, and analyzes the distance and shape of the heavy equipment, the distance and shape of the hatch, and the distance and shape of loose objects. 2. The automatic heavy equipment transfer system for a ship for a cargo handling machine according to claim 1, which is a laser image detector that captures data.

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