TW202239664A - Prediction apparatus, prediction method, and prediction program for predicting operating time of device provided on ship - Google Patents

Abstract

The present invention provides a prediction apparatus for accurately predicting operation time of device provided on ship. The prediction apparatus 3 of the present invention is used to predict the operation time of device provided on a ship S, and comprises a sailing plan acquiring portion 32 for acquiring the sailing plan of the ship S; a meteorological information acquiring portion 34 for acquiring the meteorological information of the sea route in the sailing plan; and an operation time prediction portion 35 for predicting the operation time of device based on the meteorological information.

Description

Prediction device, method and computer program for predicting operation time of equipment installed on ship

The present invention relates to a forecasting device, a forecasting method and a forecasting computer program for predicting the operating time of a machine installed on a ship.

Machines such as water generating devices and oil-water separators installed on ships operating at sea need to be maintained (inspection or replacement of machine parts) based on the accumulated operating hours. Patent Document 1 discloses that a maintenance plan is established based on the daily operating time information of the marine engine, the maintenance cycle of each unit component constituting the marine engine, maintenance history information, and the annual planned operating time of the marine engine. [Prior Technical Literature] [Patent Document]

[Patent Document 1] Japanese Patent No. 4909175

[Technical problem to be solved by the invention]

Since the maintenance cannot be carried out during the voyage, it is necessary to carry out the maintenance before the ship sails to prevent the parts from entering the maintenance period during the voyage. However, the operating conditions of the machinery of the ship change with the weather, so the operating time of the machinery from the departure of the ship to the arrival at the destination will be affected by the weather of the route. Therefore, it is difficult to judge whether maintenance is required from the cumulative operating time of the machine when sailing. In addition, when the replacement parts of the machine cannot be obtained due to forgetting to purchase, etc., and low-quality substitutes are used, the life of the machine may be shortened.

The present invention is made to solve the above-mentioned problems, and its object is to accurately predict the operation time of the equipment installed on the ship. [Technical means]

The above problems of the present invention are solved by a predicting device for predicting the operation time of a machine installed on a ship, and having: a voyage plan obtaining unit for obtaining a voyage plan of the ship The weather information obtaining part is used to obtain the weather information of the route in the aforementioned flight plan; and the operating time prediction part is used to predict the operating time of the aforementioned machine based on the aforementioned weather information.

The above-mentioned problem of the present invention is solved by a kind of prediction method, and the above-mentioned prediction method is to predict the operation time of the machine installed on the ship, and has: the voyage plan acquisition step, is to obtain the voyage plan of the aforementioned ship; weather information acquisition The step of obtaining the weather information of the route in the aforementioned flight plan; and the step of predicting the operating time is to predict the operating time of the aforementioned machine based on the aforementioned weather information.

In an ideal embodiment of the present invention, the aforementioned weather information is characterized by: including at least any one of weather, wind speed, wind direction, wave height, sea surface temperature, sea current speed, and sea current direction.

In an ideal embodiment of the present invention, the aforementioned forecasting device is characterized in that: it is further equipped with a machine material information acquisition unit for acquiring machine material information related to the aforementioned ship and machine; and the aforementioned operation time prediction unit is based on the aforementioned weather information and the aforementioned machine material information To predict the operating time of the aforementioned machines. In addition, the aforementioned forecasting method is characterized in that it further includes a machine information acquisition step of acquiring machine material information related to the aforementioned ship and the aforementioned machine; and the aforementioned operation time prediction step is to predict the operating time of the aforementioned machine based on the aforementioned weather information and the aforementioned machine material information.

In an ideal embodiment of the present invention, the operation time prediction unit is characterized in that it uses a machine-learned operation time prediction model to predict the operation time of the aforementioned machine. In addition, the feature of the aforementioned operation time prediction step is: using a machine-learned operation time prediction model to predict the operation time of the aforementioned machine.

In an ideal embodiment of the present invention, the aforementioned predicting device is characterized in that it further includes a maintenance predicting unit, and the aforementioned maintenance predicting unit predicts whether the aforementioned machine needs to be maintained based on the aforementioned predicted operating time. In addition, the aforementioned forecasting method is characterized in that it further includes a maintenance forecasting step, and the aforementioned maintenance forecasting step is to predict whether the aforementioned machines need to be maintained based on the aforementioned predicted operating hours.

In the above-mentioned embodiment, the aforementioned machine can also be a water-making device for producing fresh water from seawater.

In the above embodiment, the aforementioned machine can also be an oil-water separator for separating oil from waste liquid.

The above-mentioned problems of the present invention are solved by a forecasting computer program, which is executed by a computer to make the computer function as a forecasting device for predicting the operating time of a machine installed on a ship, and to use it The above-mentioned computer is used as: the voyage plan acquisition part, which is used to obtain the voyage plan of the aforementioned ship; the weather information acquisition part, which is used to obtain the weather information of the route in the aforementioned voyage plan; and the operation time prediction part, which is based on the aforementioned weather Information to predict the operating time of the aforementioned machines, so as to function. [Effect of the invention]

According to the present invention, it is possible to accurately predict the operation time of the equipment installed on the ship.

Hereinafter, embodiments of the present invention will be described with reference to the attached drawings. In addition, the present invention is not limited to the following embodiments.

[System Components] Fig. 1 shows a block diagram of a management system 1 of an embodiment of the present invention. The management system 1 is a system for managing the ship S, and includes a control terminal 2 and a forecasting device 3 .

The control terminal 2 is a terminal operated by the crew of the ship S, etc., and the control terminal 2 includes an input device such as a touch panel (not shown), and a display device such as a liquid crystal display (not shown). The control terminal 2 may be constituted by a general-purpose computer, or may be constituted by a dedicated computer such as a control panel. Alternatively, the control terminal 2 may also be integrated with the water generation device 4 or the oil-water separator 5 . The control terminal 2 controls the water generating device 4 and the oil-water separator 5 according to the operation of the input device while monitoring the operating status of the water generating device 4 and the oil-water separator 5 .

[Structure of the predictive device] The prediction device 3 is a device for predicting the operation time of the equipment installed on the ship S, that is, the water generating device 4 and the oil-water separator 5 . The prediction device 3 is installed 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 constituted by a general-purpose computer, and managed by, for example, a management company that manages the ship S or a manufacturing company that manufactures the water generating device 4 and the oil-water separator 5 .

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

In addition, the forecasting device 3 includes: a flight plan acquisition unit 32 , an equipment information acquisition unit 33 , a weather information acquisition unit 34 , an operation time prediction unit 35 , a maintenance prediction unit 36 , and an external communication unit 37 as functional blocks of the forecasting device 3 . Each of these parts can be implemented in hardware by a logic circuit or the like, or can be implemented in software by a processor of the prediction device 3 . In the latter case, the aforementioned parts can be realized by the processor reading the predictive computer program D1 stored in the auxiliary memory 31 into the main memory and executing it. The prediction computer program D1 can be downloaded to the prediction device 3 via the network N, and can also be installed to the prediction device 3 via a computer-readable non-transitory recording medium such as a CD-ROM on which the prediction computer program D1 is recorded.

The voyage plan obtaining unit 32 is for obtaining the voyage plan of the ship S. The voyage plan includes: the place of departure, the destination, the scheduled date of departure, the scheduled arrival date of the ship S, etc. In this embodiment, the crew of the ship S inputs the voyage plan through the control terminal 2 . The data of the input flight plan is sent to the prediction device 3 and obtained by the flight plan obtaining unit 32 .

The machine material information obtaining unit 33 is used to obtain machine material information related to the ship S, the water making device 4 and the oil-water separator 5 . Specifically, the machine material information includes: the ship type, size and age of the ship S; the manufacturing number of the water generating device 4 and the water volume of the manufactured water tank; and the manufacturing number of the oil-water separator 5 and the water volume of the waste liquid treatment tank. In this embodiment, the manufacturing number of the water generating device 4 or the oil-water separator 5 is sent from the control terminal 2 together with the data of the flight plan, and the equipment information acquisition unit 33 checks the manufacturing number on the external server of the manufacturing company, In this way, other machine and equipment information such as the ship type of the ship S can be obtained.

The weather information obtaining unit 34 is used to obtain the weather information of the route in the ship S's voyage plan. For example, as shown in Figure 2, the voyage plan is to sail from port A on March 1 and arrive at port B on March 3. In this case, the weather information obtaining unit 34 can obtain the weather information (forecast) from March 1 to March 3 in the area R including the route P shown in FIG. 3 and indicated by oblique lines.

Also, the route P may be a standard route for sailing from port A to port B, and may be determined by the crew of the ship S or the like. In addition, 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. FIG.

Weather information can be obtained from meteorological information providers such as the Meteorological Bureau through the network N. The type of weather is not particularly limited as long as it affects the operation of the machine, such as: weather, wind speed, wind direction, wave height, sea surface temperature, sea current speed, and sea current direction.

Weather, wind speed, wind direction, wave height, sea current speed, and sea current direction will 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; when the wave height is high, the load on the main engine tends to decrease due to low speed sailing. In addition, when the ship S is a cargo ship, after sailing at a low speed, it may switch to a high speed sailing in order to catch up with a delay in the shipping schedule. The sea surface water temperature can affect the water production capacity of the water production device 4 .

Examples of weather information are shown in FIGS. 4 to 7 . Figure 4 shows an example of a weather map of wind speed and wind direction; Figure 5 shows an example of a weather map of wave height and wave direction; Figure 6 shows an example of a weather map of sea surface water temperature; Figure 7 shows an example of a weather map of sea current speed and direction . Also, Figures 4 to 7 are rough weather maps, which actually provide more detailed information.

The operation time prediction unit 35 predicts the operation time of the water generation device 4 and the oil-water separator 5 based on the weather information and machine material information. In this embodiment, the operation time prediction unit 35 uses the machine-learned operation time prediction model D2 to predict the operation time of the water generation device 4 and the oil-water separator 5 . That is, the operation time prediction unit 35 inputs the weather information obtained by the weather information acquisition unit 34 and the equipment information obtained by the equipment information acquisition unit 33 into the operation time prediction model D2, and then obtains the information from the operation time prediction model D2 accordingly. The output operation time of the water generating device 4 and the oil-water separator 5 is used as the prediction result.

The operation time prediction model D2 is generated as follows, for example. First of all, for the multiple ships that have sailed in the route of the ship S’s voyage plan in the past, match the equipment information, the actual weather information on the route, and the actual operating time of the water generating device and the oil-water separator, so as to formulate Instructional Materials (Learning Collections). Then, using the teaching data, machine learning is carried out by artificial intelligence models such as neural networks, thereby generating the operation time prediction model D2. The meteorological information in the teaching materials can be extracted from the weather map to cover the information of the specified area of the route. For example, when the route is the route P shown in Figure 3, only the weather information in the area R is sufficient. In addition, in the case of generating the operating time prediction model D2 specialized for a specific device, the device information can also be omitted from the teaching materials.

The maintenance predicting unit 36 predicts whether the water generating device 4 and the oil-water separator 5 need to be maintained based on the operating time predicted by the operating time predicting unit 35 (hereinafter referred to as “predicted operating time”). In this embodiment, the maintenance prediction unit 36 refers to the equipment management information D3 to predict whether it is necessary to inspect or replace the parts of the water generation device 4 and the oil-water separator 5 .

FIG. 8 shows an example of the equipment management information D3. The machine management information D3 includes: machine number, model, name of consumables, replacement estimated time and accumulated operating time. The estimated replacement time is the time from when the part is replaced until it needs to be replaced again (maintenance cycle). The accumulative operating time is the accumulative operating time of the machine after replacing the part, and the forecasting device 3 will obtain it from the control terminal 2 of the ship S on a regular basis.

For example, when the predicted operating time is 15 hours, the cumulative operating time of part A during the voyage of ship S will exceed the estimated replacement time. Therefore, the maintenance predicting unit 36 predicts that the part A needs to be replaced.

The prediction result is sent from the prediction device to the control terminal 2 and displayed on the display device of the control terminal 2 . Thereby, the crew of the ship S can grasp the parts requiring maintenance.

In addition, the prediction by the operation time prediction unit 35 and the maintenance prediction unit 36 may be performed before the ship S sails, or may be performed during the ship S's sailing. In addition, even if the above-mentioned prediction is made before the voyage of the ship S, when the weather forecast changes or the route changes during the voyage of the ship S, it is desirable to use the voyage plan acquisition unit 32 and the equipment information acquisition unit during the voyage. 33 and the weather information acquisition unit 34 perform information acquisition, and the operation time prediction unit 35 and the maintenance prediction unit 36 perform prediction.

The external communication unit 37 can communicate with the external contact person T via the network N or other networks. For example, as mentioned 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 person T, and take measures such as delivering the part A to the nearest berth. In this embodiment, the external contact person T can be, for example, a ship management company, a replacement part seller, a product manufacturing company, and the like.

[Procedure] FIG. 9 shows a flowchart of the operation of the prediction device 3 . In the prediction device 3 , the voyage plan acquisition unit 32 acquires the voyage plan of the ship S from the control terminal 2 of the ship S (voyage plan acquisition step S1 ). At the same time, the machine material information acquisition unit 33 acquires machine material information related to the ship S, the water generation device 4 and the oil-water separator 5 from the control terminal 2 (machine material information acquisition step S2 ). Next, the weather information acquisition unit 34 acquires weather information of 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 water generating device 4 and the oil-water separator 5 based on the weather information and the equipment information (operation time prediction step S4 ). Next, the maintenance predicting unit 36 predicts whether the water generation device 4 and the oil-water separator 5 need to be maintained based on the predicted operating time (maintenance predicting step S5 ).

In addition, the order of steps S1 to S3 is not particularly limited. In addition, as long as each step is executed by a computer, the operating subject of each step is not limited to the above. For example, at least a part of steps S1 to S5 can also be executed by the control terminal 2 .

[summary] As mentioned above, in this embodiment, based on the weather information of the flight route in the flight plan, the operation time of each machine of the water generation device 4 and the oil-water separator 5 is predicted by using the machine-learned operation time prediction model D2. Therefore, the operating time of machines affected by weather can be accurately predicted. Also, it is possible to predict whether a machine needs to be maintained based on the predicted operating hours.

[Modification] Although the embodiments of the present invention have been described above, the present invention is not limited to the above embodiments, and various changes can be made without departing from the gist of the present invention.

In the above-mentioned embodiment, although the water generating device and the oil-water separator were exemplified as the equipment installed on the ship, the present invention is not limited thereto. The machine may be a main machine such as an engine, but in the present invention, the auxiliary machine is mainly targeted.

In the above-mentioned implementation mode, the operation time prediction part 35 predicts the operation time of the machine based on the weather information and the equipment information, but in the case that the operation time prediction model D2 is a model that has been specialized on a specific machine material, the operation time prediction part 35. It is also possible to predict the operating time of the machine based on weather information only. In this case, the machine material information acquisition unit 33 can be omitted.

In addition, 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 machine, but it can also predict the operating time of the machine without using artificial intelligence. In this case, for example, the operation time of the water generation device can be calculated by the following formula.

Operating time = required water volume / water production forecast Here, the required amount of water is calculated by the following: average consumption × number of sailing days × α. α is the safety factor, which is set in consideration of the period (preparation days) shortly after sailing and before arrival at the port when the water generating device cannot be used. In addition, the prediction of water creation can be predicted from the engine load and sea surface temperature, and the engine load depends on the weather (especially wave height and wind speed).

In addition, in the above-mentioned embodiment, the maintenance forecasting unit 36 of the forecasting device 3 will automatically predict whether the machine needs to be maintained, but in the forecasting device 3, it can also be carried out until the operating time of the machine is predicted by the operating time predicting unit 35, and based on the forecasted operation time, allowing the user (the crew of the ship S, etc.) to judge whether the machine needs to be maintained.

1: Management system 2: Control terminal 3: Prediction device 31: Auxiliary memory 32: Flight plan acquisition department 33:Machinery Information Acquisition Department 34: Meteorological Information Acquisition Department 35: Operation Time Forecasting Department 36:Maintenance Forecast Department 37: External Communications Department 4: Water making device 5: Oil-water separator D1: Prediction computer program D2: Operating Time Prediction Model D3: Machine Management Information N: network P: route R: area S: ship T: external contact

[FIG. 1] A block diagram showing a management system of an embodiment of the present invention. [FIG. 2] shows an example of a flight route. [Fig. 3] shows an example of the area where air route and weather information are obtained. [Fig. 4] An example of a weather map showing wind speed and direction. [Fig. 5] An example of a weather map showing wave height and wave direction. [Fig. 6] An example of a weather map showing sea surface water temperature. [Fig. 7] An example of a weather map showing ocean current velocity and ocean current direction. [FIG. 8] shows an example of device management information. [FIG. 9] A flow chart showing the operation of the prediction device.

1: Management system

2: Control terminal

3: Prediction device

31: Auxiliary memory

32: Flight plan acquisition department

33:Machinery Information Acquisition Department

34: Meteorological Information Acquisition Department

35: Operation Time Forecasting Department

36:Maintenance Forecast Department

37: External Communications Department

4: Water making device

5: Oil-water separator

D1: Prediction computer program

D2: Operating Time Prediction Model

D3: Machine Management Information

N: network

S: ship

T: external contact

Claims (15)

A forecasting device, which is used to predict the operating time of a machine installed on a ship; its features are: The voyage plan acquisition part is used to obtain the voyage plan of the ship; The meteorological information obtaining unit is used to obtain the meteorological information of the route in the flight plan; and The operation time forecasting part predicts the operation time of the machine based on the weather information. The forecasting device according to Claim 1, wherein the meteorological information includes at least any one of weather, wind speed, wind direction, wave height, sea surface temperature, sea current speed, and sea current direction. The forecasting device as described in Claim 1 or 2, further comprising a machinery information acquisition unit for obtaining machinery information related to the ship and the machine; and The operating time forecasting unit predicts the operating time of the machine based on the weather information and the machine material information. The prediction device according to claim 1 or 2, wherein the operation time prediction unit uses a machine-learned operation time prediction model to predict the operation time of the machine. The forecasting device according to claim 1 or 2, further comprising a maintenance forecasting unit that predicts whether the machine needs to be maintained based on the predicted operating time. The prediction device according to claim 1 or 2, wherein the machine is a water generation device for producing fresh water from seawater. The prediction device according to claim 1 or 2, wherein the machine is an oil-water separator for separating oil from waste liquid. A forecasting method for forecasting the operating time of a machine installed on a ship; characterized by: The step of obtaining the voyage plan is to obtain the voyage plan of the ship; The step of obtaining weather information is to obtain the weather information of the route in the flight plan; and The operation time prediction step is to predict the operation time of the machine based on the weather information. The forecasting method according to Claim 8, wherein the meteorological information includes at least any one of weather, wind speed, wind direction, wave height, sea surface temperature, sea current speed, and sea current direction. The forecasting method as described in Claim 8 or 9, further comprising a step of acquiring machine material information related to the ship and the machine; and The operation time prediction step is to predict the operation time of the machine based on the weather information and the machine material information. The forecasting method as described in Claim 8 or 9, wherein the operation time prediction step is to use a machine-learned operation time prediction model to predict the operation time of the machine. The forecasting method according to Claim 9, further comprising a maintenance forecasting step, wherein the maintenance forecasting step is to predict whether the machine needs to be maintained based on the predicted operating time. The forecasting method according to claim 8 or 9, wherein the machine is a water generation device for producing fresh water from seawater. The prediction method as described in claim 8 or 9, wherein the machine is an oil-water separator for separating oil from waste liquid. A predictive computer program, which is loaded into a computer and executed, so that the computer functions as a forecasting device for predicting the operating time of machines installed on ships; the feature is that the computer functions as follows: The voyage plan acquisition part is used to obtain the voyage plan of the ship; The meteorological information obtaining unit is used to obtain the meteorological information of the route in the flight plan; and The operation time forecasting part predicts the operation time of the machine based on the weather information.

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