Ship-hull management method and ship-hull management device

Abstract

The present invention addresses the problem of providing a ship-hull management method and a ship-hull management device with which an appropriate treatment can be taken by predicting a fouling state of a hull, and the problem is solved by the ship-hull management method and the ship-hull management device of a ship in which a fouling-growth prediction model is constructed through training by training data associated with a ship-hull size, a ship speed, a surface state of the ship-hull, and an increase rate of frictional resistance and having a correlation between the increase rate of the frictional resistance and the surface state of the ship-hull; on the basis of an output of a ship of the same type as the target ship in a specific ship route in the past, ideal fuel consumption and actual fuel consumption, the output at the time of a voyage in the specific ship route of the target ship, the ideal fuel consumption at that time, inclination of the actual fuel consumption, and the resistance of a channel in the ship route are presumed, and the increase rate of the frictional resistance is estimated; the ship-hull size of the ship, the presumed increase rate of the frictional resistance rate are input into the constructed fouling-growth prediction model; and the fouling-growth prediction data indicating a relationship between a fouling rate and the number of days in a corresponding relationship with the surface state of the ship hull is generated.

Description

Ship hull management method and ship hull management device

[0001] The present invention relates to a ship hull management method and a ship hull management device, and more particularly to a ship hull management method and a ship hull management device that can predict the state of hull fouling in advance and take appropriate measures while the hull is underway.

[0002] The impact of transboundary movement of organisms on ecosystems has become an international environmental issue, and progress is being made in establishing international legislation regarding the management of "ship ballast water" and "organisms attached to ship hulls," which are believed to originate from ships.

[0003] Regarding the management of ships' ballast water, the International Convention for the Control and Management of Ships' Ballast Water and Sediments (2004) came into force on September 8, 2017. Regarding the management of hull fouling organisms, the Guidelines for the Prevention of Hull Fouling Organisms (Resolution MEPC.207(62)) were formulated in 2011, and from 2020, a review of these guidelines was carried out to evaluate their practicality and effectiveness. In 2023, the IMO / International Maritime Organization adopted a revised version of the Guidelines on Hull Fouling Organisms (Resolution MEPC.378(80)).

[0004] The new guidelines, revised in 2023, state that a five-level pollution assessment will be conducted based on the results of underwater inspections, and that cleaning methods will be selected based on the pollution assessment.

[0005] If the fouling rating is low, the deposits are removed using a soft brush or a water jet, but if the fouling rating is high, the deposits must be removed and collected, which significantly increases the amount of work involved.In addition, in New Zealand and Australia, local regulations require proof that the hull has been cleaned within a certain period of time.

[0006] On the other hand, hull fouling caused by organisms adhering to the hull is problematic as it reduces operational performance, specifically, increasing hull resistance, resulting in worsening fuel efficiency, increasing operational costs, and increasing greenhouse gas (GHG) emissions. Non-Patent Document 1 reports a study on the impact of long-term ship demurrage on hull fouling, and the summary states:

[0007] The article states, "Recently, the effects of long periods of delay at Australian coal and iron ore export ports due to port congestion over the past three years are becoming evident in the biological fouling and fuel consumption of ships on their return journeys. Australia exports a large amount of coal to Japan, and many coal carriers bound for Japan are being forced to wait for long periods of time at Australian loading ports. This, coupled with rising fuel oil prices, is becoming a major problem for ship operators. Biofouling is also thought to be a cause of the transportation of aquatic organisms. The need for measures to combat biofouling is also being discussed at the IMO. The authors therefore investigated the arrival of ships bound for Japan at the major coal export ports of Newcastle and Gladstone over the past 10 years. They also investigated the operating history of coal carriers in service in Australia. They also analyzed the impact of biological fouling on ships, using the fuel consumption coefficient per unit time as an evaluation value."

[0008] https: / / www.jstage.jst.go.jp / article / jin / 121 / 0 / 121_KJ00005822061 / _pdf / -char / ja

[0009] Patent Publication No. 2022-526652 (Monitoring module) Patent Publication No. 2022-526655 (Monitoring module) Patent Publication No. 2022-519354 (Method and system for reducing ship fuel consumption)

[0010] Conventionally, Patent Documents 1 and 2 disclose technologies for controlling a robot configured to clean the hull of a ship while it is moving. However, these technologies do not perform a fouling evaluation, and only disclose that cleaning can be performed by a robot, but are unable to indicate when cleaning is possible while the fouling evaluation is low.

[0011] Patent Document 3 states that the proportion of time a ship spends at sea in optimal draft, trim, and speed conditions can be increased, but on the other hand, it only mentions that hull fouling reduces the accuracy of the process and points out a decrease in fuel efficiency.

[0012] Therefore, an object of the present invention is to provide a ship hull management method and a ship hull management device that can predict the state of fouling of the hull and take appropriate measures.

[0013] Other objects of the present invention will become apparent from the following description.

[0014] The above problems are solved by the following inventions.

[0015] 1. A ship hull management method comprising: constructing a fouling growth prediction model by learning it using teacher data in which hull size, ship speed, hull surface condition, and rate of increase in frictional resistance are previously associated with each other, and which has a correlation between the rate of increase in frictional resistance and the hull surface condition; estimating the rate of increase in frictional resistance of the target ship by estimating the output of the target ship when sailing the route on which the target ship is scheduled to sail, the trends in the ideal fuel efficiency and actual fuel efficiency of that output, and the resistance of straits on the route on which the target ship is scheduled to sail, based on the output of similar ships to the target ship that have previously sailed the same route as the route on which the target ship is scheduled to sail, the trends in the ideal fuel efficiency and actual fuel efficiency of that output, and the resistance of straits on the route on which the target ship is scheduled to sail; inputting the hull size of the target ship and the estimated rate of increase in frictional resistance into the constructed fouling growth prediction model; and generating fouling growth prediction data showing the relationship between the fouling rate, which corresponds to the hull surface condition, and the number of days. 1. A ship hull management method as set forth in claim 1, characterized in that, when the number of days expected to arrive in a destination country and a fouling rate that meets the standards of the destination country are input, the method calculates, based on the fouling growth prediction data, a cleaning timing that requires the minimum number of cleanings using a predetermined cleaning method so that the fouling rate meets the standards of the destination country, in relation to the number of days until a specified fouling rate, which is the timing when cleaning can be done using a predetermined cleaning method. 3. A ship hull management method as set forth in claim 2, characterized in notifying the calculated cleaning timing that requires the minimum number of cleanings. 4. A ship hull management method as set forth in claim 2, characterized in that: a cleaning cost using a predetermined cleaning method is set in advance, a fuel consumption cost if cleaning is not done using the predetermined cleaning method is calculated, and a total cost is calculated by adding the fuel consumption cost if cleaning is done and the cleaning cost, and a cost difference is calculated by comparing the total cost with the fuel consumption cost if cleaning is not done, and the notification unit notifies the total cost and the fuel consumption cost if cleaning is not done, and notifies the cost difference.5. A ship hull management method according to any one of items 1 to 4, characterized in that operation profile data that records at least the history of the ship's track, sea area, ship speed, and water temperature while the ship is moving from port to port is associated with image data of the ship's hull and saved. a data generation unit that constructs a fouling growth prediction model by learning from teacher data in which hull size, ship speed, hull surface condition, and frictional resistance increase rate are associated in advance, and which has a correlation between the frictional resistance increase rate and the hull surface condition; an estimation unit that estimates the target ship's output, the ideal fuel efficiency and actual fuel efficiency trends for the output, and the resistance of straits on the planned route based on the output of similar ships to the target ship that have previously navigated the same route as the target ship's planned route, and the resistance of straits on the route; 6. A ship hull management device as set forth in claim 5, further comprising an acquisition unit that acquires the number of days until arrival at a destination country and a fouling rate that satisfies the standards of the destination country, and a calculation unit that calculates, based on the fouling growth prediction data, a cleaning timing that requires the minimum number of cleanings using a predetermined cleaning method so that a fouling rate that satisfies the standards of the destination country will be achieved, in relation to the number of days until a specified fouling rate that is the timing at which cleaning can be performed using a predetermined cleaning method will be achieved. 7. A ship hull management device as set forth in claim 5, further comprising an acquisition unit that acquires the number of days until arrival at a destination country and a fouling rate that satisfies the standards of the destination country, based on the fouling growth prediction data, in relation to the number of days until a specified fouling rate that is the timing at which cleaning can be performed using a predetermined cleaning method will be achieved. 8. A ship hull management device as set forth in claim 5, further comprising a notification unit that notifies the predicted cleaning timing that requires the minimum number of cleanings.9. The ship hull management device according to any of paragraphs 7 to 9, characterized in that the calculation unit presets a cleaning cost according to a predetermined cleaning method, calculates the fuel cost if cleaning is not performed using the predetermined cleaning method, calculates a total cost by adding the fuel cost if cleaning is performed and the cleaning cost, and also calculates a cost difference by comparing the total cost with the fuel cost if cleaning is not performed, and the notification unit notifies the total cost and the fuel cost if cleaning is not performed, as well as notifying the cost difference. 10. The ship hull management device according to any of paragraphs 6 to 9, characterized in that the acquisition unit, upon acquiring image data of the hull surface of the target ship, is provided with a ship data storage unit in which operation profile data of the ship, which is stored in advance on the target ship and records a history of at least the course, sea area, ship speed, and water temperature while traveling from port to port, is associated with and stored in association with the image data.

[0016] According to the present invention, it is possible to provide a ship hull management method and a ship hull management device that can predict the state of fouling of the hull and take appropriate measures while the hull is underway.

[0017] Furthermore, the present invention can provide a ship hull management method and a ship hull management device that require only the minimum number of cleanings necessary, can indicate when cleaning is possible while the fouling assessment is still low, and can reduce fuel costs.

[0018] Flowchart showing an example of a hull management method of the present invention. Diagram showing the correspondence between hull size, ship speed, hull surface condition, and frictional resistance increase rate. Diagram showing the relationship between hull surface condition and fouling rate. Diagram showing an example of fouling growth prediction data of the present invention. Diagram showing an example of correction of fouling growth prediction data of the present invention. Flowchart showing another embodiment of the present invention. Diagram showing another example of another embodiment of the present invention. Flowchart showing yet another embodiment of the present invention. Diagram showing an example of a hull management device of the present invention.

[0019] Preferred embodiments of the present invention will now be described.

[0020] An example of a hull management method of the present invention will be described with reference to Fig. 1. Fig. 1 is a flowchart showing an example of a hull management method of the present invention.

[0021] First, a fouling growth prediction model is constructed by learning from training data that correlates the hull size, hull speed, hull surface condition, and frictional resistance increase rate, and that has a correlation between the frictional resistance increase rate and the hull surface condition (S1).

[0022] FIG. 2 is a diagram showing the correspondence between hull size, ship speed, hull surface condition, and frictional resistance increase rate, and FIG. 3 is a diagram showing the relationship between hull surface condition (Description) and fouling rate (FR).

[0023] As shown in Figure 2, the hull size, ship speed, hull surface condition, and frictional resistance increase rate are correlated. In the illustrated example, the hull sizes are shown as an example of a 230m container ship and an example of a 175m bulk carrier, and their speeds are also shown. The relationship between the frictional resistance rate and the hull surface condition is also shown.

[0024] This table shows that frictional resistance increases with the rate of biofilm adhesion. It also shows the frictional resistance of a ship with an AF coating (Anti-Fouling Coating), and the frictional resistance of the surface of the ship with biofilm attached. These findings suggest that there is a correlation between the surface condition of the ship's hull and the rate of increase in frictional resistance.

[0025] The fouling growth prediction model for a hull is constructed by learning from the data in the table shown in Figure 2 as training data. In this embodiment, for example, the training data shown in Figure 2 is only an example, and there is data (not shown) showing the relationship between various hull sizes, speeds, hull surface conditions, and frictional resistance increase rates. By using this as training data, the fouling growth prediction model can generate fouling growth prediction data (described below) for various ship types.

[0026] Furthermore, Figure 3 shows the correspondence between the hull surface condition and the fouling rate. Since Figure 3 shows the surface condition and its fouling rate, by using this as training data to learn, the fouling growth prediction model can grasp the relationship between the surface condition shown in Figure 2 and the surface condition shown in Figure 3. As a result, it is possible to output the hull surface condition as a fouling rate.

[0027] Next, the trend of the rate of increase in frictional resistance of the target hull is estimated (S2). The trend of the rate of increase in frictional resistance can be estimated, for example, before the ship sets sail.

[0028] For example, a ship management system (not shown) accumulates operation histories of ships similar to the target ship that have previously sailed the same route as the target ship's planned route. The accumulated operation history includes at least data on trends in ship speed and power output. By understanding the trends in ship speed and power output accumulated in the ship management system and understanding the trends in ideal fuel economy and actual fuel economy for that power output trend, the trend in the rate of increase in frictional resistance can be estimated based on these data. In this embodiment, similar ships that have sailed the same route may include both the target ship and ships other than the target ship. Therefore, the operation history of the target ship's past sailings can be used, or the operation history of ships other than the target ship can be used. Ships similar to the target ship may be manufactured by the same or different manufacturers as the target ship. Ships with the same or similar cargo capacity, hull size, etc. as the target ship are included in the category of similar ships.

[0029] In this embodiment, in addition to the above, when estimating the trend in the rate of increase in frictional resistance, if there is any data necessary for understanding the rate of increase in frictional resistance, that data can also be acquired from the operation history, etc., of a ship management system (not shown). As a result, the trend in the rate of increase in frictional resistance of the target ship on a particular sea route can be estimated from the past operation history, etc., of similar ships other than the target ship. In this embodiment, the trend in the rate of increase in frictional resistance on a particular sea route can also be estimated based on the operation history of the target ship itself when it has operated on that sea route in the past.

[0030] In this embodiment, the estimated rate of increase in frictional resistance can be corrected while the target vessel is actually navigating the route. For example, if there is a difference between the output, actual fuel consumption, etc. actually acquired data for the target vessel and the output, actual fuel consumption, etc. in the vessel's past operating history, the rate of increase in frictional resistance may be corrected based on that difference.

[0031] In this way, once the route that the target ship is scheduled to travel has been decided, it is possible to estimate the trend in the rate of increase in frictional resistance on that route. Furthermore, even while the target ship is traveling on a specific route, if the target ship's actual fuel consumption differs from the output trends and actual fuel consumption trends of ships with past operating histories, these can be taken into account and the trend in the rate of increase in frictional resistance for the target ship while it is traveling can be corrected. This makes it possible to estimate the trend in the rate of increase in frictional resistance more accurately, which has the effect of increasing the accuracy of the fouling growth prediction data described below.

[0032] Next, fouling growth prediction data showing the relationship between the fouling rate and the number of days for the fouling rate is generated (S3). An example of the fouling growth prediction data is shown in FIG. 4. The fouling growth prediction data shown in FIG. 4 is an example created based on estimated data before departure. In this embodiment, as described above, the increase rate of frictional resistance of the target ship, which serves as input data, can be corrected during navigation. An example of corrected fouling growth prediction data is shown in FIG. 5. As shown in FIG. 5, if the increase rate of fouling resistance changes at a certain point in time, as a result of the correction, for example, if the increase rate of frictional resistance increases, the frictional resistance will increase, and the fouling rate will be higher than the estimate before navigation, resulting in a graph like the two-dot chain line in FIG. 5. On the other hand, if the increase rate of frictional resistance decreases, the frictional resistance will decrease, and the fouling rate will be lower than the estimate before navigation, resulting in a graph like the one-dot chain line in FIG. 5. As described above, by acquiring operating data during navigation, correcting the input data, and inputting the corrected data into the fouling growth prediction model, fouling growth prediction data can be generated that is more based on operating data during navigation.

[0033] The fouling growth prediction model takes the hull size of the target ship and the trend in the rate of increase in frictional resistance as input, and outputs fouling growth prediction data showing the relationship between the number of days and the rate of increase in frictional resistance based on past operating history prior to the voyage.

[0034] This fouling growth prediction data indicates, for example, the number of days during navigation that the fouling rate will reach during navigation, on a fouling rate scale of 0 to 100 shown in Figure 3. In other words, it indicates the degree of fouling growth on the hull.

[0035] The inventors have studied the effect of fouling on the surface of a hull on fuel efficiency, and have found that, in terms of the hull's output, the rate of increase in frictional resistance can be determined in relation to the actual fuel efficiency relative to the ideal fuel efficiency, excluding resistance due to power resistance, resistance in straits, etc., and that this rate of increase in frictional resistance is the fuel efficiency caused by the resistance of the hull surface in the actual fuel efficiency.

[0036] Therefore, by understanding the trend in the rate of increase in frictional resistance, it is possible to understand the trend in the condition of the hull surface.

[0037] In this embodiment, the operation history of the target ship can be added to the training data for the fouling growth prediction model, which increases the training data for the fouling growth prediction model, enabling more accurate predictions.

[0038] Next, a further embodiment of the present invention will be described with reference to Fig. 6. Fig. 6 is a flowchart showing the cleaning timing calculation process based on the soiling growth prediction data of the present invention.

[0039] Once the fouling growth prediction data indicating the relationship between the fouling rate and the number of days for the fouling rate has been generated, the number of days for the target ship to arrive at the destination country and the fouling rate that meets the criteria of the destination country are input (S10). The input means is not particularly limited, and the data may be input by a ship management system (not shown).

[0040] Next, ship data is acquired at predetermined time intervals to estimate the current trend between ideal fuel consumption and actual fuel consumption, and the rate of increase in frictional resistance is estimated (S11). This makes it possible to grasp the trend in the condition of the hull surface.

[0041] Next, once the frictional resistance increase rate has been estimated, it is input into a contamination growth prediction model, and the contamination growth prediction data is corrected. Based on the corrected contamination growth prediction data, the cleaning time when the minimum number of cleanings using the specified cleaning method will be required is calculated so that the contamination rate will meet the standards of the destination country, in relation to the number of days required to reach the specified contamination rate, which is the timing when cleaning can be done using the specified cleaning method (S12).

[0042] Here, the predetermined cleaning method can be selected from proactive cleaning and reactive cleaning.

[0043] Proactive cleaning is a cleaning method primarily used to treat initial hull fouling (soft fouling), and involves cleaning with soft brushes and water jets, etc., to minimize damage to the antifouling paint applied to the hull. Proactive cleaning is an underwater hull cleaning method recommended in the revised guidelines on hull fouling organisms, and has the advantage of not requiring the removal of fouling matter.

[0044] Reactive cleaning is a cleaning method used for hard fouling on a ship's hull, and involves scraping off severe fouling such as barnacles using tools such as hard wire brushes and spatulas. This reactive cleaning method has the advantage of being able to clean even severe fouling, but it has the disadvantage of requiring the removal of the deposits and also scraping off the antifouling paint on the hull.

[0045] In this embodiment, taking into consideration costs and labor, proactive cleaning is preferably used for cleaning the ship while it is sailing. The following describes the case where the predetermined cleaning method is proactive cleaning.

[0046] 7 is a diagram showing an example of calculating the cleaning timing when the number of proactive cleanings required is minimized before the arrival at destination country A. FIG. 7 shows an example of a reference soiling rate for destination country A and a designated cleaning rate for proactive cleaning.

[0047] The standard contamination rate for destination country A is set so that the contamination rate must be below the standard when the cargo arrives at destination country A.

[0048] In this case, once the designated contamination rate for proactive cleaning is known, in order to achieve the contamination rate that meets the standard for destination country A by the scheduled number of days to arrive at destination country A, as shown in the figure, if the ship is not cleaned after the second cleaning timing, the contamination growth forecast data indicates that the standard for destination country A will be exceeded. In this case, in order to be able to enter destination country A, cleaning must be performed immediately before entry. As a result, it is clear that three cleanings are required. The timing for performing these three cleanings can also be calculated based on the contamination growth forecast data. This makes it possible to minimize cleaning costs while preventing the ship from being unable to enter the destination country.

[0049] 8 is a diagram showing an example of calculating the cleaning timing for performing proactive cleaning with the minimum number of cleanings required before arriving at destination country B. In the case of destination country B, the base rate is higher than the designated soiling rate for proactive cleaning compared to the case of destination country A shown in FIG.

[0050] In this case, as shown in Figure 8, after the timing of the second cleaning, it is predicted that the fouling rate will not exceed the standard on the day of arrival at destination country B even without cleaning. In this case, to be able to enter destination country B, it is not necessary to perform cleaning immediately before entry. As a result, only two cleanings are required, and the timing of the second cleanings can be calculated based on the fouling growth prediction data. This makes it possible to minimize cleaning costs and prevent situations where the ship is unable to enter the destination country.

[0051] In this embodiment, after the first cleaning, the fouling rate of the hull will decrease. New fouling growth prediction data can be generated from the fouling rate and number of days after the decrease.

[0052] In this case, ship data is acquired at predetermined time intervals to determine the current trend between ideal fuel efficiency and actual fuel efficiency, and the rate of increase in frictional resistance is calculated, which can then be input into the fouling growth prediction model, enabling new predictions.Fouling growth prediction data created by the fouling growth prediction model based on data acquired from the above-mentioned ship management system (not shown) before the ship sails can also be used.

[0053] Furthermore, the designated contamination rate for proactive cleaning may be any rate that can be cleaned by proactive cleaning, i.e., any contamination rate that is not beyond the range of contamination that cannot be removed. Therefore, the designated rate can be freely set within an acceptable range.

[0054] Next, the calculated cleaning time when the minimum number of cleanings will be required is notified (S13).

[0055] In this embodiment, a system may be established in which, based on the cleaning time calculated in S12, a reservation with a cleaning company is made simultaneously with the notification in S13.

[0056] Next, a further embodiment of the present invention will be described with reference to Fig. 9. Fig. 9 is a flowchart showing yet another embodiment of the present invention.

[0057] A predetermined cleaning method and its corresponding cost are acquired in advance (S20).

[0058] Next, the cost of washing the minimum number of times calculated in S12 of the washing timing calculation process using a predetermined washing method is calculated, and the fuel cost of washing is calculated, along with the total cost (S21).

[0059] The fuel consumption cost when cleaning is considered because cleaning resets the rate of increase in frictional resistance and improves actual fuel consumption. In this case, the fuel consumption cost can be calculated by calculating the actual fuel consumption trend with reference to the timing up to the first cleaning. This makes it possible to calculate the cleaning cost and fuel consumption cost based on the predicted data, and also to calculate the total cost by combining these costs.

[0060] Next, the fuel cost when cleaning is not performed using the predetermined cleaning method is calculated (S22). The fuel cost when cleaning is not performed can be calculated based on, for example, the tendency of the actual fuel consumption used to calculate the rate of increase in frictional resistance estimated before departure.

[0061] In this embodiment, the case where cleaning is not performed using the predetermined cleaning method in S22 does not mean that cleaning is not performed at all, but rather that the predetermined cleaning method is used and the cleaning timing is not calculated. In order to meet the standards of the destination country, the hull must be cleaned before entry. In this case, cleaning must be performed reliably; for example, reactive cleaning must be performed at least to meet the entry standards of the destination country. The cleaning cost in this case is included in the fuel cost when cleaning is not performed.

[0062] Next, the total cost calculated in S21 and the fuel cost calculated in S22 are displayed side by side and notified (S23). For example, the notification may be displayed on a display unit (not shown), which makes it easier to visually compare the costs.

[0063] Next, the cost difference between the total cost calculated in S21 and the fuel cost calculated in S22 is calculated (S24), and the calculated cost difference is notified (S25).

[0064] The differential cost notified in S25 is displayed together with the total cost calculated in S21 and notified side by side in S23, and the fuel cost calculated in S22, making it easier to compare and consider measures for managing the hull from a cost perspective.

[0065] In this embodiment, it is preferable to record at least the history of the ship's track, sea area, ship speed, and water temperature while it travels from port to port. It is preferable to create operation profile data that includes these histories. In addition, it is preferable to acquire image data of the ship's hull and associate and save the operation history profile data with the image data. It is preferable to save this together with the fouling growth prediction data created for the target ship. This allows the operation management of the target ship to be saved together with the hull condition, making it possible to properly manage the hull condition.

[0066] A ship hull management system according to the present invention will be described with reference to Fig. 10. Fig. 10 is a block diagram showing an example of a ship hull management system according to the present invention.

[0067] As shown in FIG. 10 , the ship hull management device includes a data generation unit 100 , an estimation unit 101 , an acquisition unit 102 , a calculation unit 103 , a notification unit 104 , and a ship data storage unit 105 .

[0068] The data generation unit 100 stores a fouling growth prediction model, and the fouling growth prediction model is constructed by learning from training data that corresponds in advance to the hull size, ship speed, hull surface condition, and frictional resistance increase rate, and has a correlation between the frictional resistance increase rate and the hull surface condition.The fouling growth prediction model is then used to generate fouling growth prediction data that shows the relationship between the fouling rate on the hull surface and the number of days.

[0069] The estimation unit 101 receives from the ship underway the ship's power output during the voyage, its ideal and actual fuel consumption at that time, and the resistance of the strait during the voyage, and estimates the rate of increase in frictional resistance. The estimation unit 101 also obtains in advance from the operation history of past ships of the same type and size as the target ship that have sailed on the same route the trends in ship speed and power output at that time, and acquires data on the trends in ideal fuel consumption and actual fuel consumption for that power output trend from a hull management system (not shown) or the like that has accumulated such data, and can estimate the rate of increase in frictional resistance based on this data.

[0070] The acquisition unit 102 acquires the estimated number of days to arrive at the destination country and the fouling rate that meets the standards of the destination country. The acquisition unit 102 also acquires image data of the hull surface of the target ship. The acquisition unit 102 can also send data acquired from outside to the data generation unit 100, the estimation unit 101, and the calculation unit 103.

[0071] Based on the contamination growth prediction data generated by the data generation unit 100, the calculation unit 103 can calculate the cleaning time when the minimum number of cleanings using a specified cleaning method will be required so that the contamination rate meets the standards of the destination country, in relation to the number of days required to reach a specified contamination rate, which is the timing when cleaning can be done using a specified cleaning method.

[0072] The calculation unit 103 also has a memory unit (not shown) that stores corresponding data between predetermined cleaning methods and their costs, and calculates the fuel consumption cost when cleaning is not performed using the predetermined cleaning method, calculates a total cost by adding up the fuel consumption cost when cleaning is performed and the cleaning cost, and also calculates the cost difference by comparing the total cost with the fuel consumption cost when cleaning is not performed.

[0073] The notification unit 104 can be, for example, a display unit having a display function, or a transmission unit having a transmission function. The notification unit 104 can display the cleaning time when the minimum number of cleanings will be required using the predetermined cleaning method calculated by the calculation unit 103. This allows for smooth arrangements for cleaning.

[0074] The ship data storage unit 105 stores the ship's operation profile data, which records at least the history of the ship's track, sea area, ship speed, and water temperature while the ship is traveling from port to port, in association with the image data of the ship's hull surface.

[0075] Preferably, the operational profile data is stored as data is transferred between ports of call, as this data can be used as reference data for other ships of interest.

[0076] Image data of the ship's hull surface can be stored in association with mapping data of the entire ship generated while the ship is docked, and then stored in association with operational profile data, thereby recording image data of the ship's hull surface condition between docks. This data can also be used as reference data when selecting a hull paint for the ship when it is docked.

[0077] Regarding the method of using the ship hull management device according to the present invention, please refer to the above-mentioned ship hull management method, and the explanation thereof will be omitted.

[0078] 100 Data generation unit 101 Estimation unit 102 Acquisition unit 103 Calculation unit 104 Notification unit 105 Ship data storage unit

Claims

1. First, a fouling growth prediction model is learned and constructed using teacher data in which the hull size, ship speed, hull surface condition, and the increase rate of frictional resistance are associated in advance and there is a correlation between the increase rate of frictional resistance and the hull surface condition. Based on the output of a ship similar to the target ship that has sailed the same route as the planned route of the target ship in the past, the ideal fuel consumption and actual fuel consumption of the output, and the resistance of the strait in the route, the output of the target ship during navigation on the planned route of the target ship, the trend of the ideal fuel consumption and the trend of the actual fuel consumption of the output, and the resistance of the strait in the planned route of navigation are inferred to estimate the increase rate of the frictional resistance of the target ship. The hull size of the target ship and the estimated increase rate of the frictional resistance rate are input into the constructed fouling growth prediction model to generate fouling growth prediction data showing the relationship between the fouling rate and the number of days in correspondence with the hull surface condition. A method for hull management of a ship, characterized in that.

2. When the estimated number of days to reach the destination country and the fouling rate that meets the criteria of the destination country are input, based on the fouling growth prediction data, the number of days to reach the specified fouling rate, which is the timing when cleaning can be performed by a predetermined cleaning method, is calculated so that the fouling rate that meets the criteria of the destination country is reached. A method for hull management of a ship according to claim 1, characterized in that the cleaning timing that requires the minimum number of cleaning times is calculated.

3. A method for hull management of a ship according to claim 2, characterized in that the calculated cleaning timing that requires the minimum number of cleaning times is notified.

4. The cleaning cost by a predetermined cleaning method is set in advance, the fuel cost when not cleaned by a predetermined cleaning method is calculated, and the total cost obtained by adding the fuel cost when cleaning is performed and the cleaning cost is calculated. At the same time, the cost difference is calculated by comparing the total cost with the fuel cost when not cleaning. The notification unit notifies the total cost and the fuel cost when not cleaning, and also notifies the cost difference. A method for hull management of a ship according to claim 2, characterized in that.

5. The ship hull management method according to any one of claims 1 to 4, characterized in that operation profile data for recording at least the history of the track, sea area, ship speed, and water temperature during the movement of the ship from the port of call to the port of call is associated with and stored together with the image data of the photographed ship hull.

6. A data generation unit is provided which pre-associates the hull size, ship speed, surface condition of the hull, and the increase rate of frictional resistance, and learns and constructs a fouling growth prediction model using teacher data having a correlation between the increase rate of frictional resistance and the surface condition of the hull. Based on the output of a ship similar to the target ship that has sailed on the same route as the route planned for navigation of the target ship in the past, the ideal fuel consumption and actual fuel consumption of the output, and the resistance of the strait in the route, the output of the target ship during navigation on the route planned for navigation of the target ship, the trend of the ideal fuel consumption and the trend of the actual fuel consumption of the output, and the resistance of the strait in the planned navigation route are estimated, and an estimation unit for estimating the increase rate of the frictional resistance of the target ship is provided. The data generation unit inputs the hull size of the target ship estimated by the estimation unit and the increase rate of the estimated frictional resistance rate into the constructed fouling growth prediction model, thereby generating fouling growth prediction data indicating the relationship between the fouling rate of the hull surface and the number of days. A ship hull management device characterized by this.

7. The ship hull management device according to claim 6, further comprising an acquisition unit for acquiring the estimated number of days of arrival in the destination country and the fouling rate satisfying the standard of the destination country. Based on the fouling growth prediction data, a calculation unit is provided which calculates the cleaning timing such that the number of cleanings required to reach the specified fouling rate, which is the timing at which cleaning can be performed using a predetermined cleaning method, is minimized so as to achieve the fouling rate satisfying the standard of the destination country.

8. The ship hull management device according to claim 7, comprising a notification unit for notifying the predicted cleaning timing that requires the minimum number of cleanings.

9. The calculation unit presets the cleaning cost by a predetermined cleaning method, calculates the fuel cost when cleaning is not performed by the predetermined cleaning method, calculates the total cost by adding the fuel cost when cleaning is performed and the cleaning cost, and calculates the cost difference by comparing the total cost with the fuel cost when cleaning is not performed. The notification unit notifies the total cost and the fuel cost when cleaning is not performed, and notifies the cost difference. The hull management device for a ship according to claim 7, characterized in that.

10. When the acquisition unit acquires the imaging data of the hull surface of the target ship, it includes a ship data storage unit in which the operation profile data of the ship that records at least the history of the track, sea area, ship speed, and water temperature during the movement from the port of call to the port of call, which is pre-stored in the target ship, is associated with the imaging data and stored. The hull management device for a ship according to any one of claims 6 to 9, characterized in that.

Images