JP2022163442A - Prediction device for predicting utilization time of apparatus provided on vessel, prediction method, and prediction program - Google Patents

Abstract

To accurately predict utilization time of an apparatus provided on a vessel.SOLUTION: A prediction device 3 for predicting utilization time of an apparatus provided on a vessel S includes: a navigation plan acquirement unit 32 for acquiring a navigation plan for the vessel S; a weather information acquirement unit 34 for acquiring weather information for a sea route in the navigation plan; and a utilization time prediction unit 35 for predicting utilization time of the apparatus based on the weather information.SELECTED DRAWING: Figure 1

Description

TECHNICAL FIELD The present invention relates to a prediction device, a prediction method, and a prediction program for predicting operating time of equipment provided on a ship.

Equipment such as fresh water generators and oil-water separators installed on ships operating on the sea must be maintained (inspection or replacement of equipment parts) according to the cumulative operating time. Patent Document 1 discloses that a maintenance plan can be created based on daily operation time information of a marine engine, maintenance cycles for each unit component constituting the marine engine, maintenance history information, and annual planned operating hours of the marine engine. disclosed.

Patent No. 4909175

Since maintenance cannot be performed during navigation, it is necessary to perform maintenance before departure of the vessel so that there are no parts that require maintenance during navigation. However, since the operating conditions of equipment on ships change depending on the weather, the operating time of the equipment from departure of the ship to arrival at the destination is affected by the weather along the route. Therefore, it is difficult to determine whether or not maintenance is necessary based on the cumulative operation time of the equipment at the time of departure. In addition, if replacement parts for the equipment cannot be obtained due to forgetting to purchase them, and poor quality substitutes are used, the service life of the equipment may rather be shortened.

SUMMARY OF THE INVENTION It is an object of the present invention to solve the above problems by accurately predicting the operation time of equipment installed on a ship.

The above-described object of the present invention is a prediction device for predicting the operation time of equipment provided on a ship, which includes a navigation plan acquisition unit that acquires a navigation plan of the ship, and weather information on a route in the navigation plan. The problem is solved by a prediction device that includes a weather information acquisition unit and an operating time prediction unit that predicts the operating time of the device based on the weather information.

The above-described object of the present invention is a prediction method for predicting the operating time of equipment provided on a ship, comprising: a navigation plan obtaining step of obtaining a navigation plan of the ship; and acquiring weather information of a route in the navigation plan. The problem is solved by a prediction method comprising a weather information obtaining step and an operating time prediction step of predicting the operating time of the device based on the weather information.

In a preferred embodiment of the present invention, the weather information includes at least one of weather, wind speed, wind direction, wave height, sea surface temperature, current velocity and current direction.

In a preferred embodiment of the present invention, the prediction device further includes an equipment information acquisition unit that acquires equipment information regarding the ship and the equipment, and the operating time prediction unit obtains the equipment information based on the weather information and the equipment information. It is characterized by predicting the operating time of equipment. Further, the prediction method further includes an equipment information obtaining step of obtaining equipment information regarding the ship and the equipment, and in the operating time prediction step, the operating time of the equipment is predicted based on the weather information and the equipment information. It is characterized by

In a preferred embodiment of the present invention, the operating time prediction unit is characterized by predicting the operating time of the device using an operating time prediction model that has undergone machine learning. In addition, in the operating time prediction step, the operating time of the device is predicted using an operating time prediction model obtained by machine learning.

In a preferred embodiment of the present invention, the prediction device further comprises a maintenance prediction unit that predicts necessity of maintenance of the equipment based on the predicted operating time. Further, the prediction method is characterized by further comprising a maintenance prediction step of predicting necessity of maintenance of the equipment based on the predicted operating time.

In the above embodiment, the device may be a fresh water generator that produces fresh water from sea water.

In the above embodiment, the device may be an oil-water separator that separates oil from waste liquid.

The above-described problem of the present invention is a prediction program for causing a computer to function as a prediction device that predicts the operation time of equipment provided on a ship, comprising: a navigation plan acquisition unit for acquiring a navigation plan of the ship; is solved by a prediction program for causing a computer to function as a weather information acquisition unit that acquires weather information on a route in , and an operating time prediction unit that predicts the operating time of the equipment based on the weather information.

ADVANTAGE OF THE INVENTION According to this invention, the operation time of the apparatus provided in the ship can be estimated correctly.

1 is a block diagram of a management system according to one embodiment of the invention; FIG. An example of a route. It is an example of a region for acquiring route and weather information. It is an example of a weather chart showing wind speed and wind direction. It is an example of a weather chart showing wave heights and wave directions. It is an example of a weather chart showing sea surface temperature. It is an example of a meteorological map showing ocean current velocity and current direction. It is an example of device management information. It is a flowchart which shows operation|movement of a prediction apparatus.

BEST MODE FOR CARRYING OUT THE INVENTION Hereinafter, embodiments of the present invention will be described with reference to the accompanying drawings. In addition, this invention is not limited to the following embodiment.

[System configuration]
FIG. 1 is a block diagram of a management system 1 according to one embodiment of the invention. A management system 1 is a system for managing a ship S, and includes a control terminal 2 and a prediction device 3 .

The control terminal 2 is a terminal operated by the crew of the ship S, and includes an input device (not shown) such as a touch panel and a display device (not shown) such as a liquid crystal display. The control terminal 2 may be composed of a general-purpose computer, or may be composed of a dedicated computer such as a control panel. Alternatively, the control terminal 2 can be configured integrally with the fresh water generator 4 or the oil-water separator 5 . The control terminal 2 monitors the operating states of the fresh water generator 4 and the oil-water separator 5, and controls the fresh water generator 4 and the oil-water separator 5 according to the operation of the input device.

[Configuration of prediction device]
The prediction device 3 is a device for predicting the operation time of the fresh water generator 4 and the oil-water separator 5 which are devices provided on the ship S. The prediction device 3 is provided outside the ship, and is communicably connected to the control terminal 2 via a network N such as the Internet. The prediction device 3 can be configured with a general-purpose computer, and is managed by, for example, a management company that manages the ship S, a manufacturer of the fresh water generator 4 and the oil-water separator 5, or the like.

The prediction device 3 includes, as a hardware configuration, a processor such as a CPU or GPU, a main storage device (not shown) such as DRAM or SRAM, and an auxiliary storage device 31 such as HDD or SSD. The auxiliary storage device 31 stores various data such as a prediction program D1, an operating time prediction model D2, and equipment management information D3.

The prediction device 3 includes, as functional blocks, a navigation plan acquisition unit 32, an equipment information acquisition unit 33, a weather information acquisition unit 34, an operating time prediction unit 35, a maintenance prediction unit 36, and an external communication unit 37. I have it. Each of these units may be implemented in hardware such as a logic circuit, or may be implemented in software by the processor of the prediction device 3 . In the latter case, the processor reads out the prediction program D1 stored in the auxiliary storage device 31 into the main storage device and executes it, thereby implementing the respective units. The prediction program D1 may be downloaded to the prediction device 3 via the network N, or installed in the prediction device 3 via a computer-readable non-temporary recording medium such as a CD-ROM recording the prediction program D1. You may

The navigation plan acquisition unit 32 acquires the navigation plan of the ship S. The navigation plan includes the departure point, destination, scheduled date of departure, scheduled date of arrival, etc. of the vessel S, and in this embodiment, the crew of the vessel S inputs the navigation plan via the control terminal 2 . The input navigation plan data is transmitted to the prediction device 3 and acquired by the navigation plan acquisition unit 32 .

The equipment information acquisition unit 33 acquires equipment information regarding the ship S, the fresh water generator 4 and the oil-water separator 5 . Specifically, the equipment information includes the ship type, size and age of the ship S, the serial number of the fresh water generator 4 and the water volume of the produced water tank, the serial number of the oil-water separator 5 and the volume of the waste liquid treatment tank, and the like. In this embodiment, the manufacturing number of the fresh water generator 4 or the oil-water separator 5 is transmitted from the control terminal 2 along with the navigation plan data, and the equipment information acquisition unit 33 checks the manufacturing number against the external server of the manufacturing company. Other equipment information such as the type of ship S is acquired by.

The weather information acquisition unit 34 acquires weather information of the route in the navigation plan of the ship S. For example, as shown in FIG. 2, assume that the sailing plan is to depart Port A on March 1st and arrive at Port B on March 3rd. In this case, the weather information acquiring unit 34 acquires weather information (forecast) for March 1st to March 3rd in the hatched area R including the route P shown in FIG.

The route P may be a standard route from port A to port B, or may be determined by the crew of ship S or the like. Further, the width of the region R can be appropriately set in consideration of the error between the route P and the actual route of the ship S.

Weather information can be obtained through the network N from a weather information provider such as the Japan Meteorological Agency. The type of weather is not particularly limited as long as it affects the operational status of the equipment, but includes, for example, weather, wind speed, wind direction, wave height, sea surface temperature, ocean current velocity, and ocean current direction.

Weather, wind speed, wind direction, wave height, ocean current speed and ocean current direction affect the load (ship speed) of the main engine (engine) of the ship S. For example, when the headwind is strong, the load on the main engine tends to increase, and when the wave height is large, the load on the main engine tends to decrease due to low-speed operation. Further, when the ship S is a cargo ship, after sailing at low speed, it may switch to sailing at high speed in order to make up for delays in the schedule. The sea surface temperature affects the amount of fresh water produced by the fresh water generator 4 .

Examples of weather information are shown in FIGS. 4 to 7. FIG. FIG. 4 is an example of a weather chart showing wind speed and direction, FIG. 5 is an example of a weather chart showing wave height and direction, FIG. 6 is an example of a weather chart showing sea surface temperature, and FIG. is an example of a weather map showing current velocity and current direction. 4 to 7 are schematic meteorological maps, and more detailed information is actually provided.

The operating time prediction unit 35 predicts the operating time of the fresh water generator 4 and the oil-water separator 5 based on weather information and equipment information. In this embodiment, the operating time prediction unit 35 predicts the operating time of the fresh water generator 4 and the oil-water separator 5 using the machine-learned operating time prediction model D2. That is, the operating time prediction unit 35 inputs the weather information acquired by the weather information acquisition unit 34 and the equipment information acquired by the equipment information acquisition unit 33 into the operating time prediction model D2, and accordingly, the operating time prediction model D2. The operation time of the fresh water generator 4 and the oil-water separator 5 output from is acquired as a prediction result.

The operating time prediction model D2 is generated, for example, as follows. First, training data ( training dataset). Then, an operating time prediction model D2 is generated by performing machine learning on an artificial intelligence model such as a neural network using the training data. The weather information in the teacher data may be information obtained by extracting a predetermined area including the route from the weather map. For example, if the route is route P shown in FIG. Further, when generating an operating time prediction model D2 specialized for a specific piece of equipment, the piece of equipment information may be omitted from the teacher data.

The maintenance prediction unit 36 predicts the necessity of maintenance of the fresh water generator 4 and the oil-water separator 5 based on the operating time predicted by the operating time prediction unit 35 (hereinafter referred to as "predicted operating time"). In the present embodiment, the maintenance prediction unit 36 references the device management information D3 and predicts whether or not each component of the fresh water generator 4 and the oil-water separator 5 needs to be inspected or replaced.

FIG. 8 is an example of the device management information D3. The equipment management information D3 includes the equipment serial number, model number, consumable item name, estimated replacement time, and accumulated operating time. The estimated replacement time is the time (maintenance cycle) from when the part is replaced until the part needs to be replaced again. The cumulative operating time is the cumulative operating time of the device after the replacement of the part, and is periodically obtained by the prediction device 3 from the control terminal 2 of the ship S.

For example, if the predicted operating time is 15 hours, the cumulative operating time of the component A will exceed the recommended replacement time while the ship S is sailing. Therefore, the maintenance prediction unit 36 predicts that the part A needs to be replaced.

The prediction result is transmitted from the prediction device to the control terminal 2 and displayed on the display device of the control terminal 2 . This allows the crew of the ship S to grasp the parts that require maintenance.

The prediction by the operating time prediction unit 35 and the maintenance prediction unit 36 may be performed before the ship S travels, or may be performed while the ship S travels. Further, even if the prediction is made before the ship S sails, if the weather forecast is changed or the route is changed while the ship S is sailing, the navigation plan acquiring unit 32, It is preferable that the equipment information acquisition unit 33 and the weather information acquisition unit 34 acquire information, and the operating time prediction unit 35 and the maintenance prediction unit 36 make predictions.

External communication unit 37 can communicate with external contact T via network N or another network. For example, as described above, when the maintenance prediction unit 36 predicts that replacement of the part A is necessary, the external communication unit 37 communicates with the external contact T to deliver the part A to the nearest port of call. can take measures such as In this embodiment, the external contact T is, for example, a ship management company, a replacement parts dealer, a product manufacturer, or the like.

[Processing procedure]
FIG. 9 is a flow chart showing the operation of the prediction device 3. As shown in FIG. In the prediction device 3, the navigation plan acquiring unit 32 acquires the navigation plan of the vessel S from the control terminal 2 of the vessel S (navigation plan acquisition step S1). At the same time, the equipment information acquisition unit 33 acquires equipment information about the ship S, the fresh water generator 4, and the oil-water separator 5 from the control terminal 2 (equipment information acquisition step S2). Subsequently, the weather information acquisition unit 34 acquires the weather information of the route in the navigation plan of the ship S (weather information acquisition step S3). Subsequently, the operating time prediction unit 35 predicts the operating time of the fresh water generator 4 and the oil-water separator 5 based on the weather information and the equipment information (operating time prediction step S4). Further, the maintenance prediction unit 36 predicts necessity of maintenance of the fresh water generator 4 and the oil-water separator 5 based on the predicted operating time (maintenance prediction step S5).

The order of steps S1 to S3 is not particularly limited. Also, if each step is executed by a computer, the subject of each step is not limited to the above. For example, at least part of the steps S1-S5 may be performed by the control terminal 2. FIG.

[Brief Summary]
As described above, in the present embodiment, the operating time of each device of the fresh water generator 4 and the oil-water separator 5 is predicted using the machine-learned operating time prediction model D2 based on the weather information of the route in the navigation plan. ing. Therefore, it is possible to accurately predict the operation time of equipment affected by weather. Furthermore, it is possible to predict the necessity of maintenance of the device based on the predicted operating time.

[Modification]
Although the embodiments of the present invention have been described above, the present invention is not limited to the above embodiments, and various modifications are possible without departing from the scope of the invention.

In the above-described embodiment, a fresh water generator and an oil-water separator are exemplified as devices provided on a ship, but the present invention is not limited to these. The device may be a main machine such as an engine, but the present invention mainly targets auxiliary machines.

In the above embodiment, the operating time prediction unit 35 predicts the operating time of equipment based on weather information and equipment information. The operating time prediction unit 35 may predict the operating time of the device based only on weather information. In this case, the equipment information acquisition unit 33 can be omitted.

In the above embodiment, the operating time prediction unit 35 uses the machine-learned operating time prediction model D2, that is, uses artificial intelligence to predict the operating time of the device. You may predict the operating time of the equipment in advance. In this case, for example, the operation time of the fresh water generator can be calculated by the following formula.

Operating time=required amount of water/estimated amount of fresh water production Here, the required amount of water is calculated by the average consumption amount×the number of sailing days×α. α is a safety factor, which is set in consideration of the period immediately after departure and immediately before entering port (preliminary days) in which the fresh water generator cannot be used. Also, the predicted amount of fresh water production can be predicted from the engine load and the seawater surface temperature, and the engine load depends on the weather (especially wave height and wind speed).

In the above-described embodiment, the maintenance prediction unit 36 of the prediction device 3 automatically predicts whether or not maintenance is necessary for the equipment. A user (such as a crew member of the ship S) may determine the necessity of maintenance of the equipment based on the estimated operating time.

1 Management system 2 Control terminal 3 Prediction device 31 Auxiliary storage device 32 Navigation plan acquisition unit 33 Equipment information acquisition unit 34 Weather information acquisition unit 35 Operating time prediction unit 36 Maintenance prediction unit 4 Fresh water generator 5 Oil-water separator D1 Prediction program D2 Operation Time prediction model D3 Equipment management information N Network P Route R Area S Ship

Claims (15)

A prediction device for predicting the operation time of equipment provided on a ship,
a navigation plan acquisition unit that acquires the navigation plan of the ship;
a weather information acquisition unit that acquires weather information for a route in the navigation plan;
an operating time prediction unit that predicts the operating time of the device based on the weather information;
A prediction device comprising a The prediction device according to claim 1, wherein the weather information includes at least one of weather, wind speed, wind direction, wave height, sea surface temperature, ocean current velocity and ocean current direction. further comprising an equipment information acquisition unit that acquires equipment information related to the ship and equipment;
The prediction device according to claim 1 or 2, wherein the operating time prediction unit predicts the operating time of the equipment based on the weather information and the equipment information. The prediction device according to any one of claims 1 to 3, wherein the operating time prediction unit predicts the operating time of the device using a machine-learned operating time prediction model. The prediction device according to any one of claims 1 to 4, further comprising a maintenance prediction unit that predicts necessity of maintenance of said equipment based on said predicted operating time. The prediction device according to any one of claims 1 to 5, wherein the device is a fresh water generator that produces fresh water from sea water. The prediction device according to any one of claims 1 to 5, wherein the device is an oil-water separator that separates oil from waste liquid. A prediction method for predicting the operation time of equipment provided on a ship,
a navigation plan acquisition step of acquiring a navigation plan of the ship;
a weather information acquiring step of acquiring weather information of a route in the navigation plan;
an operating time prediction step of predicting the operating time of the device based on the weather information;
A forecasting method comprising 9. The prediction method according to claim 8, wherein said weather information includes at least one of weather, wind speed, wind direction, wave height, sea surface temperature, current velocity and current direction. further comprising an equipment information acquisition step of acquiring equipment information related to the vessel and equipment;
10. The prediction method according to claim 8, wherein in said operating time prediction step, the operating time of said device is predicted based on said weather information and said equipment information. The prediction method according to any one of claims 8 to 10, wherein in the operating time prediction step, the operating time of the device is predicted using a machine-learned operating time prediction model. The prediction method according to claim 9 or 10, further comprising a maintenance prediction step of predicting necessity of maintenance of said equipment based on said predicted operating time. The prediction device according to any one of claims 8 to 12, wherein the device is a fresh water generator that produces fresh water from sea water. The prediction device according to any one of claims 8 to 12, wherein the device is an oil-water separator that separates oil from waste liquid. A prediction program for causing a computer to function as a prediction device that predicts the operating time of equipment provided on a ship,
a navigation plan acquisition unit that acquires the navigation plan of the ship;
a weather information acquisition unit that acquires weather information for a route in the navigation plan; and an operating time prediction unit that predicts the operating time of the device based on the weather information;
Prediction programs for computers to function as

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