Prediction device, prediction method, and prediction program for predicting the operating time of equipment installed on a ship

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]

[0001] The present invention relates to a prediction device, a prediction method, and a prediction program for predicting the operating time of equipment installed on a ship. [Background technology]

[0002] Equipment such as freshwater makers and oil-water separators installed on ships operating at sea require maintenance (inspection or replacement of equipment parts) according to the accumulated operating hours. Patent Document 1 discloses that a maintenance plan is created based on daily operating time information of the marine engine, the maintenance intervals for each unit part that constitutes the marine engine, maintenance history information, and the annual planned operating time of the marine engine. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Patent No. 4909175 Summary of the Invention [Problem to be solved by the invention]

[0004] Since maintenance cannot be performed while the ship is underway, it is necessary to carry out the maintenance before the ship departs to avoid any parts that need maintenance while the ship is underway. However, the operating conditions of the ship's equipment change depending on the weather, so the operating time of the equipment from the time the ship departs to the time it arrives at its destination is affected by the weather on the route. Therefore, it is difficult to determine whether maintenance is necessary from the accumulated operating time of the equipment at the time of departure. In addition, if replacement parts for the equipment cannot be obtained because they were forgotten to be purchased, and inferior substitutes are used, this may actually shorten the lifespan of the equipment.

[0005] The present invention has been made to solve the above problem, and an object of the present invention is to accurately predict the operating time of equipment installed on a ship. [Means for solving the problem]

[0006] The above-mentioned problem of the present invention is solved by a prediction device that predicts the operating time of equipment installed on a ship, the prediction device including a navigation plan acquisition unit that acquires a navigation plan for 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 equipment based on the weather information.

[0007] The above-mentioned problem of the present invention is solved by a prediction method for predicting the operating time of equipment installed on a ship, the prediction method including a navigation plan acquisition process for acquiring a navigation plan for the ship, a weather information acquisition process for acquiring weather information for a route in the navigation plan, and an operating time prediction process for predicting the operating time of the equipment based on the weather information.

[0008] In a preferred aspect of the present invention, the meteorological information includes at least one of weather, wind speed, wind direction, wave height, sea surface temperature, ocean current speed, and ocean current direction.

[0009] In a preferred aspect of the present invention, the prediction device further comprises an equipment information acquisition unit that acquires equipment information regarding the ship and equipment, and the operation time prediction unit predicts the operation time of the equipment based on the weather information and the equipment information. The prediction method further comprises an equipment information acquisition step that acquires equipment information regarding the ship and equipment, and the operation time prediction step predicts the operation time of the equipment based on the weather information and the equipment information.

[0010] In a preferred embodiment of the present invention, the operating time prediction unit predicts the operating time of the device using a machine learning operating time prediction model. Also, the operating time prediction step predicts the operating time of the device using a machine learning operating time prediction model.

[0011] In a preferred embodiment of the present invention, the prediction device further comprises a maintenance prediction unit for predicting whether or not maintenance of the equipment is required based on the predicted operation time. Also, the prediction method further comprises a maintenance prediction step for predicting whether or not maintenance of the equipment is required based on the predicted operation time.

[0012] In the above embodiment, the device may be a water generator that produces fresh water from seawater.

[0013] In the above embodiment, the device may be an oil-water separator that separates oil from waste liquid.

[0014] The above-mentioned problem of the present invention is solved by a prediction program for causing a computer to function as a prediction device that predicts the operating time of equipment installed on a ship, the prediction program causing a computer to function as a navigation plan acquisition unit that acquires a navigation plan for the ship, a weather information acquisition unit that acquires weather information for the route in the navigation plan, and an operating time prediction unit that predicts the operating time of the equipment based on the weather information. Effect of the Invention

[0015] According to the present invention, the operating time of equipment installed on a ship can be accurately predicted. [Brief description of the drawings]

[0016] [Figure 1] FIG. 1 is a block diagram of a management system according to an embodiment of the present invention. [Diagram 2] This is an example of a route. [Diagram 3] This is an example of an area for which route and weather information is obtained. [Figure 4] 1 is an example of a weather chart showing wind speed and direction. [Diagram 5] 1 is an example of a meteorological chart showing wave height and wave direction. [Figure 6] 1 is an example of a meteorological chart showing sea surface temperature. [Figure 7] 1 is an example of a meteorological chart showing ocean current speed and direction. [Figure 8] 4 is an example of device management information. [Figure 9] 13 is a flowchart showing the operation of the prediction device. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0017] Hereinafter, an embodiment of the present invention will be described with reference to the accompanying drawings. Note that the present invention is not limited to the following embodiment.

[0018] [System Configuration] 1 is a block diagram of a management system 1 according to an embodiment of the present invention. The management system 1 is a system for managing a ship S, and includes a control terminal 2 and a prediction device 3.

[0019] The control terminal 2 is a terminal operated by a crew member or the like of the ship S, and is equipped with 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 configured as a general-purpose computer, or may be configured as a dedicated computer such as a control panel. Alternatively, the control terminal 2 may be configured integrally with the freshwater generator 4 or the oil-water separator 5. The control terminal 2 monitors the operating status of the freshwater generator 4 and the oil-water separator 5, and controls the freshwater generator 4 and the oil-water separator 5 in response to operations on the input device.

[0020] [Configuration of prediction device] The prediction device 3 is a device that predicts the operating times of the freshwater generator 4 and the oil-water separator 5, which are equipment installed on the ship S. The prediction device 3 is installed outside the ship and is communicatively 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, for example, by a management company that manages the ship S, or a manufacturer of the freshwater generator 4 and the oil-water separator 5.

[0021] The prediction device 3 includes, as its hardware configuration, a processor such as a CPU or a GPU, a main memory device (not shown) such as a DRAM or an SRAM, and an auxiliary memory device 31 such as an HDD or an SSD. The auxiliary memory device 31 stores various data such as a prediction program D1, an operating time prediction model D2, and device management information D3.

[0022] The prediction device 3 also includes, as functional blocks, a route 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. Each of these units may be realized in hardware using a logic circuit or the like, or in software using a processor in the prediction device 3. In the latter case, each of the above units can be realized by having the processor read out the prediction program D1 stored in the auxiliary storage device 31 into the main storage device and execute it. The prediction program D1 may be downloaded to the prediction device 3 via the network N, or may be installed in the prediction device 3 via a computer-readable non-transitory recording medium such as a CD-ROM on which the prediction program D1 is recorded.

[0023] The navigation plan acquisition unit 32 acquires a navigation plan of the ship S. The navigation plan includes the departure point, destination, scheduled departure date, scheduled arrival date, etc. of the ship S, and in this embodiment, the crew of the ship S inputs the navigation plan via the control terminal 2. The inputted navigation plan data is transmitted to the prediction device 3 and acquired by the navigation plan acquisition unit 32.

[0024] The equipment information acquisition unit 33 acquires equipment information related to the ship S, the freshwater generator 4, and the oil-water separator 5. Specifically, the equipment information includes the type, size, and age of the ship S, the serial number and amount of water in the produced water tank of the freshwater generator 4, and the serial number and amount of water in the waste liquid treatment tank of the oil-water separator 5. In this embodiment, the serial number of the freshwater 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 acquires other equipment information such as the type of the ship S by checking the serial number against an external server of the manufacturer.

[0025] The weather information acquisition unit 34 acquires weather information for the route in the navigation plan of the ship S. For example, as shown in Fig. 2, the navigation plan is to depart from port A on March 1 and arrive at port B on March 3. In this case, the weather information acquisition unit 34 acquires weather information (forecast) from March 1 to March 3 in the shaded area R including the route P shown in Fig. 3.

[0026] The route P may be a standard route from port A to port B, or may be determined by the crew of the ship S. The width of the area R may be set appropriately, taking into account the error between the route P and the actual route of the ship S.

[0027] Weather information can be obtained from weather information providers such as the Japan Meteorological Agency via the network N. The type of weather information is not particularly limited as long as it affects the operating status of the equipment, and examples of the weather information include weather, wind speed, wind direction, wave height, sea surface temperature, ocean current speed, and ocean current direction.

[0028] 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 there is a strong headwind, 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 slow sailing. In addition, when the ship S is a cargo ship, after sailing at a slow speed, it may switch to high-speed sailing to make up for delays in schedule. The sea surface temperature affects the amount of water produced by the water maker 4.

[0029] Examples of meteorological information are shown in Figures 4 to 7. Figure 4 is an example of a meteorological chart showing wind speed and wind direction, Figure 5 is an example of a meteorological chart showing wave height and wave direction, Figure 6 is an example of a meteorological chart showing sea surface temperature, and Figure 7 is an example of a meteorological chart showing ocean current speed and direction. Note that Figures 4 to 7 are schematic meteorological charts, and more detailed information is provided in reality.

[0030] The operation time prediction unit 35 predicts the operation times of the fresh water generator 4 and the oil-water separator 5 based on the weather information and the equipment information. In this embodiment, the operation time prediction unit 35 predicts the operation times of the fresh water generator 4 and the oil-water separator 5 using an operation time prediction model D2 that has been machine-learned. That is, the operation 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 operation time prediction model D2, and acquires the operation times of the fresh water generator 4 and the oil-water separator 5 output from the operation time prediction model D2 in response to this as prediction results.

[0031] The operating time prediction model D2 is generated, for example, as follows. First, for a plurality of ships that have previously navigated the route in the navigation plan of the ship S, equipment information and actual weather information for the route are associated with the actual operating times of the freshwater generator and the oil-water separator to generate teacher data (learning data set). Then, the operating time prediction model D2 is generated by machine learning an artificial intelligence model such as a neural network using the teacher data. The weather information in the teacher data may be information extracted from a weather chart for a predetermined range of area including the route. For example, when the route is route P shown in FIG. 3, the weather information in the teacher data may be weather information for area R. In addition, when generating an operating time prediction model D2 specialized for specific equipment, the equipment information may be omitted from the teacher data.

[0032] The maintenance prediction unit 36 ​​predicts whether or not maintenance is required for the fresh water generator 4 and the oil-water separator 5, based on the operation time predicted by the operation time prediction unit 35 (hereinafter referred to as "predicted operation time"). In this embodiment, the maintenance prediction unit 36 ​​predicts whether or not inspection or replacement is required for each part of the fresh water generator 4 and the oil-water separator 5, with reference to the equipment management information D3.

[0033] 8 is an example of the equipment management information D3. The equipment management information D3 includes the factory number, model number, consumable name, estimated replacement time, and accumulated operation time of the equipment. The estimated replacement time is the time from when the part is replaced until it becomes necessary to replace it again (maintenance cycle). The accumulated operation time is the accumulated time the equipment has been operating since the part was replaced, and is periodically acquired by the prediction device 3 from the control terminal 2 of the ship S.

[0034] For example, if the predicted operating time is 15 hours, the accumulated operating time of part A will exceed the estimated replacement time while the ship S is sailing. Therefore, the maintenance prediction unit 36 ​​predicts that part A needs to be replaced.

[0035] 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 enables the crew of the ship S to know which parts require maintenance.

[0036] The prediction by the operation time prediction unit 35 and the maintenance prediction unit 36 ​​may be performed before the ship S sails or while the ship S is sailing. Even if the prediction is performed before the ship S sails, if the weather forecast changes or the route is changed while the ship S is sailing, it is preferable to acquire information by the navigation plan acquisition unit 32, the equipment information acquisition unit 33, and the weather information acquisition unit 34 while the ship S is sailing, and to perform the prediction by the operation time prediction unit 35 and the maintenance prediction unit 36.

[0037] The external communication unit 37 can communicate with an external contact T via the network N or another network. For example, as described above, when the maintenance prediction unit 36 ​​predicts that the part A needs to be replaced, the external communication unit 37 can communicate with the external contact T and take measures such as delivering the part A to the nearest port of call. In this embodiment, the external contact T is, for example, a ship management company, a replacement parts distributor, a product manufacturer, or the like.

[0038] [Processing Procedure] FIG. 9 is a flowchart showing the operation of the prediction device 3. In the prediction device 3, the navigation plan acquisition unit 32 acquires the navigation plan of the ship S from the control terminal 2 of the ship S (navigation plan acquisition step S1). At the same time, the equipment information acquisition unit 33 acquires equipment information related to the ship S, the freshwater generator 4, and the oil-water separator 5 from the control terminal 2 (equipment information acquisition step S2). Next, the weather information acquisition unit 34 acquires weather information for the route in the navigation plan of the ship S (weather information acquisition step S3). Next, the operation time prediction unit 35 predicts the operation time of the freshwater generator 4 and the oil-water separator 5 based on the weather information and the equipment information (operation time prediction step S4). Furthermore, the maintenance prediction unit 36 ​​predicts the necessity of maintenance of the freshwater generator 4 and the oil-water separator 5 based on the predicted operation time (maintenance prediction step S5).

[0039] The order of steps S1 to S3 is not particularly limited. Also, as long as each step is executed by a computer, the subject of each step is not limited to the above. For example, at least a part of steps S1 to S5 may be executed by the control terminal 2.

[0040] [Summary] As described above, in this embodiment, the operation time prediction model D2 obtained by machine learning is used to predict the operation time of each of the equipment of the freshwater generator 4 and the oil-water separator 5 based on meteorological information for the route in the navigation plan. Therefore, it is possible to accurately predict the operation time of the equipment that is affected by the weather. Furthermore, it is possible to predict whether or not the equipment needs maintenance based on the predicted operation time.

[0041] [Variations] Although the embodiment of the present invention has been described above, the present invention is not limited to the above embodiment, and various modifications are possible without departing from the spirit of the present invention.

[0042] In the above embodiment, the water maker and the oil-water separator are exemplified as the equipment installed in the ship, but the present invention is not limited thereto. The equipment may be a main engine or the like, but the present invention is mainly directed to auxiliary equipment.

[0043] In the above embodiment, the operation time prediction unit 35 predicts the operation time of the equipment based on meteorological information and equipment information, but if the operation time prediction model D2 is a model specialized for a specific equipment, the operation time prediction unit 35 may predict the operation time of the equipment based only on meteorological information. In this case, the equipment information acquisition unit 33 can be omitted.

[0044] In the above embodiment, the operation time prediction unit 35 predicts the operation time of the equipment using the operation time prediction model D2 that has been machine-learned, i.e., using artificial intelligence, but the operation time of the equipment may be predicted without using artificial intelligence. In this case, for example, the operation time of the freshwater generator can be calculated by the following formula.

[0045] Operating time = Required water volume / Estimated water production volume Here, the required amount of water is calculated by multiplying the average consumption by the number of days at sea by α, where α is a safety factor, and is set taking into consideration the periods immediately after departure and immediately before arrival in port (reserve days) when the water production system cannot be used. Furthermore, the predicted amount of water production can be predicted from the engine load and seawater surface temperature, and the engine load depends on the weather (particularly wave height and wind speed).

[0046] Furthermore, in the above embodiment, the maintenance prediction unit 36 ​​of the prediction device 3 automatically predicts whether or not equipment maintenance is required, but the prediction device 3 may also perform the operation time prediction unit 35 to predict the operation time of the equipment, and a user (such as a crew member of the ship S) may determine whether or not the equipment requires maintenance based on the predicted operation time. [Explanation of symbols]

[0047] 1 Management System 2 Control terminal 3. Prediction Device 31 Auxiliary storage device 32 Route Plan Acquisition Department 33 Equipment Information Acquisition Department 34 Weather Information Acquisition Department 35 Operating Time Prediction Section 36 Maintenance Forecasting Department 4 Water generator 5 Oil-water separator D1 Prediction Program D2 Operating Time Prediction Model D3 Device management information N Network P route R area S ship

Claims

1. A prediction device for predicting an operating time of equipment installed on a ship, comprising: A navigation plan acquisition unit that acquires a navigation plan of the ship; A weather information acquisition unit that acquires weather information for the route in the navigation plan; an operation time prediction unit that predicts an operation time of the device based on the meteorological information; A prediction device comprising:

2. The prediction device according to claim 1 , wherein the meteorological information includes at least one of weather, wind speed, wind direction, wave height, sea surface temperature, ocean current speed, and ocean current direction.

3. An equipment information acquisition unit for acquiring equipment information relating to the ship and the equipment, The prediction device according to claim 1 , wherein the operation time prediction unit predicts an operation time of the equipment based on the meteorological information and the equipment information.

4. The prediction device according to claim 1 , wherein the operating time prediction unit predicts the operating time of the equipment using an operating time prediction model trained by machine learning.

5. The prediction device according to claim 1 , further comprising a maintenance prediction unit that predicts whether or not maintenance of the device is required based on the predicted operating time.

6. 6. The prediction device according to claim 1, wherein the device is a fresh water generator that produces fresh water from seawater.

7. The prediction device according to any one of claims 1 to 5, wherein the equipment is an oil-water separator that separates oil from waste liquid.

8. A method for predicting an operating time of equipment installed on a ship, comprising the steps of: a navigation plan acquisition step of acquiring a navigation plan for the ship; a weather information acquisition step of acquiring weather information for the route in the navigation plan; an operation time prediction step of predicting an operation time of the device based on the meteorological information; A prediction method comprising:

9. The prediction method according to claim 8 , wherein the meteorological information includes at least one of weather, wind speed, wind direction, wave height, sea surface temperature, ocean current speed, and ocean current direction.

10. An equipment information acquisition step of acquiring equipment information relating to the ship and the equipment, The prediction method according to claim 8 , wherein the operation time prediction step predicts an operation time of the equipment based on the meteorological information and the equipment information.

11. The prediction method according to any one of claims 8 to 10, wherein the operating time prediction step predicts the operating time of the equipment using a machine learning operating time prediction model.

12. The prediction method according to claim 9 or 10, further comprising a maintenance prediction step of predicting whether or not maintenance of the device is required based on the predicted operating time.

13. The prediction device according to any one of claims 8 to 12, wherein the equipment is a fresh water generator that produces fresh water from seawater.

14. The prediction device according to any one of claims 8 to 12, wherein the equipment is an oil-water separator that separates oil from waste liquid.

15. A prediction program for causing a computer to function as a prediction device for predicting an operating time of equipment installed on a ship, the prediction program comprising: A navigation plan acquisition unit that acquires a navigation plan of the ship; a weather information acquisition unit for acquiring weather information for the route in the navigation plan; and an operation time prediction unit that predicts an operation time of the device based on the meteorological information; A predictive program for making a computer function as a.

Images