Data processing systems and programs for improving ship performance

Abstract

An acquisition means 1213 of a terminal system 12 acquires, for each of a plurality of vessels, sample data indicating values of various parameters influencing the performance of the vessel during actual navigation, and extraction condition data indicating extraction conditions related to these parameters. An extraction means 1212 extracts, as a population from the sample data, sample data satisfying the extraction conditions indicated by the extraction condition data. A generation means 1215 generates statistical data indicating a statistic of the value of the parameter of interest indicated by the sample data related to the plurality of vessels included in the population. A display instruction means 1217 causes a display device 122 to display the statistic indicated by the statistical data. A user can understand from the statistical value displayed on the display device 122 how the performance of the vessel changes according to the difference in the value of the parameter of interest.

Description

[Technical Field]

[0001] This invention relates to a technology for improving the performance of ships. [Background technology]

[0002] The performance of a ship is affected by the values ​​of several parameters, including parameters related to the environment surrounding the ship during navigation, such as wind speed and wind direction, and parameters related to the ship's attributes, such as the type of ship, the type of antifouling paint applied to the ship, and the type of fuel.

[0003] In this application, performance refers to the capabilities of a vessel determined by a combination of factors such as speed, fuel consumption, cargo capacity, carbon dioxide emissions, and sulfur oxide emissions. For example, consider the case where we focus on speed, fuel consumption, and cargo capacity. In this case, if two vessels with the same cargo capacity are sailing at the same speed, the vessel with lower fuel consumption can be said to have higher performance. Also, if two vessels with the same cargo capacity are sailing at the same fuel consumption, the vessel with higher speed can be said to have higher performance. Furthermore, if there are two vessels sailing at the same speed and with the same fuel consumption, the vessel with a larger cargo capacity can be said to have higher performance.

[0004] To improve the performance of a ship, a large amount of sample data showing combinations of values ​​for various parameters during the ship's actual voyages is collected. Based on the parameter values ​​shown in this sample data, statistical methods such as multivariate analysis are used to estimate how much each parameter influences the ship's performance.

[0005] For example, Patent Document 1 proposes a ship management device that improves ship performance by changing the trim, thereby reducing fuel consumption without changing ship speed or load, by calculating the rate of change in fuel consumption due to a change in trim from a large amount of data showing combinations of trim, ship speed, draft, and fuel consumption during ship navigation. [Prior art documents] [Patent Documents]

[0006] [Patent Document 1] Japanese Patent Publication No. 2015-83468 [Overview of the Initiative] [Problems that the invention aims to solve]

[0007] In the prior art exemplified by the ship management device described in Patent Document 1, it is proposed to change parameters to improve the performance of an individual ship (individual vessel) based on the values ​​of various parameters during actual voyages for that individual ship.

[0008] In the methods for improving the performance of conventional technologies as described above, the target vessel needs to undergo actual voyages with different values ​​for the same parameters. However, some parameters that affect the performance of a vessel cannot be easily changed.

[0009] For example, the type of vessel, country of construction, and shipyard of an individual vessel cannot be changed. While the presence or absence of energy-saving devices and scrubbers on an individual vessel can be changed, such changes are costly and therefore not easily implemented. Furthermore, while the type of antifouling paint applied to an individual vessel can be changed, repainting is typically done every few years. Therefore, obtaining values ​​for various parameters from actual voyages of the same vessel with different types of antifouling paint applied would take several years.

[0010] Therefore, with conventional technology, there is a problem in that it is not easy to know the degree to which the values ​​of parameters that are difficult to change as described above have an impact on the performance of the ship.

[0011] In view of the above background, the present invention provides a means to determine the degree to which the value of a parameter that affects the performance of a ship has an impact on the performance of a ship, even for parameters that are difficult to change on an individual ship. [Means for solving the problem]

[0012] To solve the above-mentioned problems, the present invention provides a data processing system comprising: acquisition means for acquiring sample data for each of a plurality of vessels, including parameters that indicate the performance of the vessel at the same time during the actual voyage of the vessel and parameters that affect said performance; extraction condition data indicating extraction conditions for one or more parameters selected from the plurality of parameters; extraction means for extracting one or more sample data that satisfies the extraction conditions indicated by the extraction condition data from the sample data for each of the plurality of vessels as a population; and generation means for generating statistical value data indicating statistical values ​​of the values ​​shown by the sample data included in the population for each of the parameters selected from a parameter group including the plurality of parameters and parameters identified based on one or more of the plurality of parameters.

[0013] Furthermore, the present invention provides a data processing system comprising: acquisition means for acquiring sample data for each of a plurality of vessels, including parameters that indicate the performance of the vessel at the same time during the vessel's actual voyage and parameters that affect said performance; and extraction condition data indicating extraction conditions for one or more parameters selected from the plurality of parameters; extraction means for extracting one or more sample data from the sample data for each of the plurality of vessels as a population that satisfies the extraction conditions indicated by the extraction condition data; and generation means for generating distribution data representing the distribution of values ​​indicated by the sample data included in the population for each of the plurality of vessels, with respect to one or more parameters selected from a group of parameters including the plurality of parameters and parameters identified based on one or more of the plurality of parameters.

[0014] Furthermore, the present invention provides a program for a computer to perform the following processes: acquiring sample data for each of a plurality of vessels, including parameters that indicate the performance of the vessel at the same time during its actual voyage and parameters that affect said performance; acquiring extraction condition data indicating extraction conditions for one or more parameters selected from the plurality of parameters; extracting one or more sample data from the sample data for each of the plurality of vessels that satisfy the extraction conditions indicated by the extraction condition data as a population; and generating statistical value data indicating statistical values ​​of the values ​​indicated by the sample data included in the population for each of the parameters selected from a group of parameters including the plurality of parameters and parameters identified based on one or more of the plurality of parameters.

[0015] Furthermore, the present invention provides a program for a computer to perform the following processes: acquiring sample data for each of a plurality of vessels, including parameters that indicate the performance of the vessel at the same time during its actual voyage and parameters that affect said performance; acquiring extraction condition data indicating extraction conditions for one or more parameters selected from the plurality of parameters; extracting one or more sample data from the sample data for each of the plurality of vessels that satisfy the extraction conditions indicated by the extraction condition data as a population; and generating distribution data representing the distribution of values ​​indicated by the sample data included in the population for each of the parameters selected from a group of parameters including the plurality of parameters and parameters identified based on one or more of the plurality of parameters. [Effects of the Invention]

[0016] According to the present invention, by using data of a plurality of ships instead of individual ships, for any parameter, information indicating the level of performance according to the value of the parameter is generated. As a result, among the parameters that affect the performance of a ship, even for parameters that are difficult to change in an individual ship, the degree of influence of the value of the parameter on the performance of the ship can be understood.

Brief Description of the Drawings

[0017] [Figure 1] FIG. 1 is a diagram showing the overall configuration of a data processing system according to an embodiment of the present invention. [Figure 2] FIG. 2 is a diagram showing the functional configuration of a terminal system according to an embodiment of the present invention. [Figure 3] FIG. 3 is a diagram schematically showing a parameter definition screen displayed by a display device in an embodiment of the present invention. [Figure 4] FIG. 4 is a diagram schematically showing an extraction condition input screen displayed by a display device in an embodiment of the present invention. [Figure 5] FIG. 5 is a diagram schematically showing an output template setting screen displayed by a display device in an embodiment of the present invention. [Figure 6] FIG. 6 is an example of a bar graph displayed by a display device in an embodiment of the present invention. [Figure 7] FIG. 7 is an example of a scatter diagram displayed by a display device in an embodiment of the present invention. [Figure 8] FIG. 8 is an example of a scatter diagram displayed by a display device in an embodiment of the present invention. [Figure 9] FIG. 9 is an example of a scatter diagram displayed by a display device in an embodiment of the present invention. [Figure 10] FIG. 10 is an example of a table displayed by a display device in an embodiment of the present invention. [Figure 11] FIG. 11 is a diagram illustrating an estimated performance display screen displayed by a display device in a modification of the present invention. [Figure 12]Figure 12 shows an example of a scatter plot and a trendline displayed by a display device in one modified example of the present invention. [Figure 13] Figure 13 shows an example of a scatter plot and a trendline displayed by a display device in one modified example of the present invention. [Modes for carrying out the invention]

[0018] [Embodiment] Figure 1 shows the overall configuration of a data processing system 1 according to one embodiment of the present invention. The data processing system 1 is a system that provides users with information to improve the performance of a ship.

[0019] The data processing system 1 comprises a server device 11 and a terminal system 12. The server device 11 and the terminal system 12 send and receive data from each other via a communication network.

[0020] Server device 11 is a server device that functions as a data center. That is, server device 11 stores various types of data, transmits data to terminal system 12 in response to requests from terminal system 12, and also stores data transmitted from terminal system 12.

[0021] The data stored in the server device 11 includes a ship navigation log TB (Table). The ship navigation log TB is a collection of sample data, which is data for a predetermined time unit from actual voyages of various ships. The sample data included in the ship navigation log TB includes values ​​for parameters that indicate the performance of a ship at the same time during an actual voyage (ship speed, fuel consumption, cargo capacity, etc.) and values ​​for parameters that affect the performance of the ship (Beaufort wind stage, wind direction, type of antifouling paint, etc.), as well as data showing cost values ​​(e.g., cost of antifouling paint) corresponding to those parameters (parameters that affect the performance of the ship, e.g., type of antifouling paint).

[0022] The sample data included in the ship navigation log TB consists of data showing parameters or cost values ​​corresponding to the following item names. Note that UWC (Underwater Cleaning) refers to the operation of removing fouling from the bottom of a ship underwater. Ship name Ship type Sailing date Load capacity Cruising distance Ship speed relative to land Ship speed over water fuel consumption fuel cost horsepower Main engine rotation speed Beaufort Wind Stages undulation Wind direction Final antifouling paint application date Name of antifouling paint Total sailing distance (since the date of application of the final antifouling paint) Cost of antifouling paint UWC count (after the final application date of the antifouling paint) Final UWC date Total distance traveled (since the last UWC event) UWC cost

[0023] Hereinafter, the parameters (including cost values) included in the sample data contained in the ship navigation log TB will be referred to as "original parameters" and distinguished from user-defined parameters (referred to as "user-defined parameters") as described later.

[0024] The frequency at which the values ​​of multiple parameters shown in the sample data are obtained differs for each parameter. For example, the value of one parameter may be obtained every minute, another every hour, and yet another every day. The timing at which the parameter values ​​are obtained also differs for each parameter. In this embodiment, the values ​​of multiple parameters shown in the sample data are, for example, values ​​aggregated into daily values. That is, for parameters from which multiple values ​​are obtained in a single day, the sample data shows, for example, the statistical value of those multiple values ​​(for example, the average value after removing outliers). Furthermore, for parameters from which only one value is obtained over multiple days, the sample data shows, for example, the value for a target day identified by interpolating multiple values ​​obtained over a long period of time, such as several days or tens of days, using a known interpolation method (linear interpolation, spline interpolation, etc.).

[0025] In this embodiment, the ship navigation log TB is assumed to consist of a single table, but the ship navigation log TB may be configured as a database containing multiple normalized tables. Furthermore, the data items that the ship navigation log TB has as described above are illustrative examples, and the ship navigation log TB may have data items different from those described above, such as the name of the country of construction of the ship, the name of the shipyard, the type of fuel, the name of the additives used, the type of energy-saving equipment installed, the presence or absence of a scrubber, etc.

[0026] Although the server device 11 shown in Figure 1 consists of a single device, the server device 11 may be configured as a system composed of multiple devices that work in coordination with each other. However, in the following description, the server device 11 will be assumed to consist of a single device.

[0027] The hardware of the server device 11 is, for example, a general-purpose computer for server devices. The computer used as the hardware of the server device 11 includes memory for storing various data, a processor for performing various data processing according to the programs stored in memory, and a communication interface for data communication with external devices (for example, a terminal system 12).

[0028] The hardware of the terminal system 12 is a typical terminal system comprising, for example, a computer 121 for terminal devices, a display device 122 connected to the computer 121, and an input device 123 (e.g., keyboard, mouse, etc.).

[0029] Although the computer 121, display device 122, and input device 123 of the terminal system 12 shown in Figure 1 are separate components, two or more of them may be integrated into a single unit. For example, if the hardware of the terminal system 12 is a notebook PC (Personal Computer) or a tablet PC, the computer 121, display device 122, and input device 123 are configured as a single device.

[0030] Like the server device 11, the computer 121 includes a memory for storing various data, a processor for performing various data processing according to the programs stored in the memory, and a communication interface for communicating data with an external device (for example, the server device 11).

[0031] Figure 2 shows the functional configuration of the terminal system 12. That is, the computer 121 constituting the terminal system 12 functions as a data processing device with the configuration shown in Figure 2 by performing data processing according to the program of this embodiment using its processor. The functional configuration of the terminal system 12 will be described below.

[0032] The memory means 1211 stores various types of data.

[0033] The extraction means 1212 extracts one or more sample data that satisfy the extraction conditions specified by the user from among multiple sample data included in the ship navigation log TB, and uses this as the population. In this embodiment, the extraction means 1212 extracts the population by transmitting the extraction condition data to the server device 11 and having the acquisition means 1213 (described later) receive one or more sample data transmitted from the server device 11 in response.

[0034] The extraction condition data transmitted by the extraction means 1212 to the server device 11 is data indicating extraction conditions, which are the conditions for extracting sample data from the ship navigation log TB. Specifically, the extraction condition data indicates extraction conditions (e.g., "Beaufort wind speed is 5 or less") related to one or more parameters (e.g., "Beaufort wind speed is 5 or less") selected from among multiple parameters included in multiple data items of the ship navigation log TB.

[0035] When the server device 11 receives extraction condition data transmitted from the terminal system 12, it extracts sample data from the ship's navigation log TB that satisfies the extraction conditions indicated by the extraction condition data, and transmits one or more of the extracted sample data to the terminal system 12.

[0036] The acquisition means 1213 acquires various types of data. The data acquired by the acquisition means 1213 is stored in the storage means 1211.

[0037] The data acquired by the acquisition means 1213 includes one or more sample data sent from the server device 11 in accordance with the extraction condition data transmitted by the extraction means 1212, and extraction condition data generated by the input device 123 in response to user operations. The extraction means 1212 transmits the extraction condition data acquired by the acquisition means 1213 from the input device 123 and temporarily stored in the storage means 1211 to the server device 11.

[0038] The identification means 1214 identifies a new parameter (user-defined parameter) for each of the one or more sample data acquired by the acquisition means 1213 from the server device 11, based on one or more values ​​of a plurality of parameters (original parameters) that the sample data has, according to a predetermined rule (for example, a predetermined calculation formula).

[0039] As an example, let's explain the case where the identifying means 1214 identifies a user-defined parameter called the Fouling coefficient. The Fouling coefficient is an index value that indicates the degree of the fuel consumption reduction effect of the antifouling paint.

[0040] The identification means 1214 first extracts sample data relating to the target vessel (hereinafter referred to as vessel X) from the population. Subsequently, based on the date of the last application of antifouling paint and the name of the antifouling paint indicated by the sample data relating to vessel X, the identification means 1214 identifies which antifouling paint was applied to vessel X during which period and under what conditions it sailed.

[0041] As an example, let's assume that ship X entered service on date D0 with antifouling paint A applied during construction, then began sailing on date D1 with antifouling paint B applied, and then began sailing again on date D2 with antifouling paint C applied, and has been sailing ever since. In this case, the identifying means 1214 identifies the following information. Period 1 (Dates D0-D1): Antifouling paint A Period 2 (Dates D1-D2): Antifouling paint B Third period (Date D2 to present): Antifouling paint C

[0042] Next, the identification means 1214 identifies a predetermined period from the start date as the post-painting period TA for each of the first and second periods, excluding the third period whose final day is not yet determined, from among the one or more periods identified as described above. The post-painting period TA is, for example, a period whose end date is the day after a certain number of days have elapsed since the start date. However, the post-painting period TA may also be defined as, for example, a period whose end date is the day on which the total cruising distance since the start date reaches a predetermined threshold.

[0043] Hereinafter, the period immediately following painting TA shall be defined as the period from the start date until 30 days have elapsed. In this case, the identifying means 1214 identifies the 30 days from date D0 onwards as the period immediately following painting TA for the first period, and identifies the 30 days from date D1 onwards as the period immediately following painting TA for the second period.

[0044] Next, the identification means 1214 identifies the following for the first period based on the cruising distance and fuel consumption shown in the sample data relating to vessel X: Fuel consumption per unit distance traveled during the period immediately following painting in the first period E1 Total fuel consumption G1 during the first period Total distance traveled during the first period: N1

[0045] Next, the specific means 1214 calculates the Fouling coefficient F1(%) for the first period according to the following calculation formula. F1=((G1-E1×N1)÷(E1×N1))×100

[0046] Furthermore, the identification means 1214, based on the cruising distance and fuel consumption shown in the sample data relating to vessel X, identifies the following with respect to the second period: Fuel consumption per unit distance during the period immediately following painting in the second period E2 Total fuel consumption G2 in the second period Total distance traveled during the second period: N2

[0047] Next, the specific means 1214 calculates the Fouling coefficient F2(%) for the second period according to the following calculation formula. F2=((G2-E2×N2)÷(E2×N2))×100

[0048] As described above, the identification means 1214 identifies the fouling coefficient F1 of the antifouling paint A that was painted on the ship X during the first period and the fouling coefficient F2 of the antifouling paint B that was painted on the ship X during the second period as values ​​for a new parameter (fouling coefficient) related to the ship X.

[0049] Furthermore, the Fouling coefficient, as identified above, becomes smaller as the performance of the antifouling paint increases.

[0050] The identification means 1214 adds, for example, data showing the value of the Fouling coefficient F1 for the first period of vessel X to each of the sample data relating to vessel X that pertain to the first period. The identification means 1214 also adds, for example, data showing the value of the Fouling coefficient F2 for the second period of vessel X to each of the sample data relating to vessel X that pertain to the second period.

[0051] As described above, the acquisition means 1213 acquires sample data (all showing the same parameter values) for each of several different time periods of the vessel X (an example of the same vessel), and the identification means 1214 identifies the Fouling coefficient based on the same parameters (fuel consumption, cruising distance, etc.) for each of the multiple time periods shown in those sample data.

[0052] The acquisition means 1213 performs the same processing as described above for ship X, but also for each of several different ships such as ship Y and ship Z. The identification means 1214 also performs the same processing as described above for ship X, but also for each of several different ships such as ship Y and ship Z. As a result, a large number of fouling coefficients are identified, and these fouling coefficients include, for example, the fouling coefficient when ship X is painted with antifouling paint A, the fouling coefficient when ship Y is painted with antifouling paint A, the fouling coefficient when ship X is painted with antifouling paint B, the fouling coefficient when ship Y is painted with antifouling paint B, and so on, relating to various combinations of ships and various antifouling paints.

[0053] The Fouling coefficient is just one example of a parameter (user-defined parameter) identified by the identification means 1214, and the values ​​of various other types of parameters may be identified by the identification means 1214, as long as they are based on the values ​​of one or more parameters shown in the sample data. This concludes the explanation of the identification means 1214.

[0054] The generation means 1215 generates statistical data representing the statistical values ​​of the values ​​shown by the sample data included in the population related to each of the one or more parameters selected from among a plurality of parameters (original parameters and user-defined parameters). The generation means 1215 also generates distribution data representing the distribution of the values ​​shown by the sample data included in the population related to each of the one or more parameters selected from among a plurality of parameters (original parameters and user-defined parameters).

[0055] The calculation means 1216 calculates various indicator values ​​related to the performance and cost of a ship using the values ​​indicated by the sample data. For example, the calculation means 1216 uses the statistical values ​​indicated by the statistical data generated by the generation means 1215 (for example, the average value of the Fouling coefficient for each antifouling paint name) and the cost values ​​indicated by the sample data (for example, the cost value of the antifouling paint) to calculate a cost value corresponding to the population (for example, the population for each antifouling paint name) according to a predetermined calculation formula.

[0056] The display instruction means 1217 instructs the display device 122 to display various information. The information that the display instruction means 1217 instructs the display device 122 to display includes statistical values ​​shown in the statistical data generated by the generation means 1215, distributions represented in the distribution data generated by the generation means 1215, and various index values ​​calculated by the calculation means 1216. The above is a description of the functional configuration of the terminal system 12.

[0057] Figure 3 schematically shows the parameter definition screen displayed by the display device 122 in accordance with the instructions of the display instruction means 1217. The parameter definition screen is a screen for the user to define new parameters (user-defined parameters).

[0058] On the parameter definition screen, the user enters a conditional expression, calculation formula, etc. (hereinafter referred to as "parameter definition information") in the "Parameter Definition" field, which shows the procedure for identifying a new parameter using an existing parameter, and enters a parameter name that identifies the parameter according to that parameter definition information in the "Parameter Name" field, and then clicks the "Register" button. This operation stores the parameter definition data that the user entered. The parameter names corresponding to the stored parameter definition data are displayed in the "User Defined Parameters" list, and the user can read and reuse the stored parameter definition data by selecting one of the parameter names from the "User Defined Parameters" list and clicking the "Confirm" button.

[0059] Furthermore, when users enter parameter definition information in the "Parameter Definition" field, they can select existing parameters (original parameters and user-defined parameters) from the "Parameter" dropdown menu, and then enter the parameter name of the selected parameter as a variable in the "Parameter Definition" field.

[0060] In the parameter definition screen, the parameters defined by the user are identified by the identification means 1214 using the values ​​of the parameters (original parameters) included in the sample data extracted from the ship navigation log TB.

[0061] Therefore, the Fouling coefficient described above is an example of a parameter defined by the user on the parameter definition screen (user-defined parameter).

[0062] Figure 4 is a schematic diagram showing the extraction condition input screen displayed by the display device 122 in accordance with the instructions of the display instruction means 1217. The extraction condition input screen is a screen for the user to input extraction conditions.

[0063] The extraction criteria input screen displays checkboxes and input boxes corresponding to each parameter (original parameters and user-defined parameters).

[0064] The checkbox is a box used to select a parameter to include in the extraction criteria.

[0065] The input boxes consist of, for example, a single input box for entering one or more values ​​for a corresponding parameter, or a pair of input boxes for entering a range of values ​​for a corresponding parameter (one input box for entering a lower limit and one input box for entering an upper limit).

[0066] Depending on the type of parameter, the input box may be of an appropriate type, such as a text box where the user can freely enter text, or a pull-down menu where the user can select from multiple options presented.

[0067] The user enters the extraction conditions on the extraction conditions input screen, enters an extraction condition name to identify the entered conditions, and then clicks the "Register" button or similar. This operation stores the extraction condition data representing the extraction conditions entered by the user. The extraction condition names corresponding to the stored extraction condition data are displayed in the "Extraction Conditions" list, and the user can read and reuse the stored extraction condition data by selecting an extraction condition name from the "Extraction Conditions" list and clicking the "Confirm" button or similar.

[0068] Figure 5 schematically shows the output template setting screen displayed by the display device 122 in accordance with the instructions of the display instruction means 1217. The output template setting screen is a screen for the user to set an output template that indicates the format of the information (graphs, lists, etc.) to be displayed by the display device 122.

[0069] On the output template settings screen, the user first selects the type of output template they want to set from the "Template Type" dropdown menu. Depending on the type of output template selected by the user, the input boxes and other elements displayed in area R1 of the output template settings screen will change. In Figure 5, area R1 displays, as an example, the "Parameter of Interest" dropdown menu, the "X-axis" dropdown menu, and the "Y-axis" dropdown menu.

[0070] The "Focus Parameter" pull-down menu is a pull-down menu for the user to specify a focus parameter. A focus parameter is a parameter used to group a population consisting of sample data extracted according to extraction conditions (hereinafter referred to as the "parent population") into multiple populations (hereinafter referred to as "child populations"). For example, if "Anti-fouling paint name" is specified as the focus parameter, the parent population will be grouped into multiple child populations corresponding to each of the different anti-fouling paint names. Then, a graph corresponding to each of these child populations, that is, a graph corresponding to each of the anti-fouling paint names, will be displayed by the display device 122.

[0071] The "X-axis" dropdown menu allows the user to specify the parameter value or statistical value of the parameter represented by the X-axis (an example of a coordinate axis) of a graph placed in coordinate space. Similarly, the "Y-axis" dropdown menu allows the user to specify the parameter value or statistical value of the parameter represented by the Y-axis (an example of a coordinate axis) of a graph placed in coordinate space.

[0072] In area R1 of the output template settings screen, the user enters the information necessary to set the output template, enters an output template name to identify the set output template, and then clicks the "Register" button, etc. This operation stores the output template data representing the output template set by the user. The output template names corresponding to the stored output template data are displayed in the "Output Templates" list, and the user can read and reuse the stored output template data by selecting an output template name from the "Output Templates" list and clicking the "Confirm" button, etc.

[0073] The display device 122 displays graphs, lists, etc., according to the information entered by the user on the extraction condition input screen, parameter definition screen, and output template setting screen. When the user changes information on the extraction condition input screen, parameter definition screen, or output template setting screen, the displayed graphs, tables, etc., are updated according to the changed information. Examples of graphs, etc., displayed on the display device 122 are shown below.

[0074] Figure 6 is a bar graph where the X-axis represents the name of the antifouling paint and the Y-axis represents the average Fouling coefficient. The graph in Figure 6 shows the average Fouling coefficient for various ships for each of the four populations corresponding to the four types of antifouling paints, distinguished by the names "A," "B," "C," and "D," as indicated by the height of the bars. As previously mentioned, a smaller Fouling coefficient indicates a higher effectiveness of the antifouling paint. Therefore, from the graph in Figure 6, users can easily see, for example, that antifouling paint "A" is the most effective of the four antifouling paints.

[0075] The point to note here is that, as shown in the graph in Figure 6, the average value of the Fouling coefficient, which is a statistical value for each of the antifouling coatings that is the parameter of interest, is a statistical value of multiple Fouling coefficients for multiple different vessels (however, these multiple Fouling coefficients may include multiple Fouling coefficients for the same vessel). In other words, the statistical value shown in the graph in Figure 6 is not a statistical value for an individual vessel, but a statistical value for a group of vessels that includes multiple different vessels.

[0076] Figure 7 is a scatter plot where the X-axis shows the mean value of the Fouling coefficient and the Y-axis shows the standard deviation of the Fouling coefficient. The four plots included in the scatter plot of Figure 7 (an example of a display) correspond to four types of antifouling paints distinguished by the names "A," "B," "C," and "D." The size of the plot indicates the number of sample data points included in the population corresponding to that plot. The sample data used to calculate the statistical values ​​shown by each of these plots (in this case, the mean value of the Fouling coefficient and the standard deviation of the Fouling coefficient) is a collection of sample data from multiple different ships (however, multiple sample data from the same ship may be included in these multiple sample data). From the graph in Figure 7, the user can easily see, for example, that antifouling paint "A" shows a higher effect on average compared to antifouling paint "B," but that there is variability in that effect.

[0077] The graph in Figure 8 differs from the graph in Figure 7 in that the Y-axis shows the cost of antifouling paint (for example, the price of antifouling paint per unit area). Regarding the plots shown in Figure 8, the sample data used to calculate the statistical values ​​shown for each plot (in this case, the average Fouling coefficient and the cost of antifouling paint) is a collection of sample data from multiple different vessels (however, these multiple sample data may include multiple sample data from the same vessel). From the graph in Figure 8, users can easily see, for example, that antifouling paint "A" is more effective but more expensive than antifouling paint "B".

[0078] The four graphs shown in Figure 9 are all scatter plots where the X-axis represents ship speed over land and the Y-axis represents fuel consumption. Each plot in these scatter plots corresponds to a sample data set. This sample data is a collection of sample data from multiple different ships (however, multiple sample data from the same ship may be included in these multiple sample data sets). In these graphs, the further to the lower right the distribution area of ​​the plots is located, the higher the ship's navigation performance is, and the further to the upper left the distribution area is located, the lower the ship's navigation performance is.

[0079] Figures 9(A) and 9(B) are scatter plots of sample data from various vessels painted with the antifouling paint "A". However, Figure 9(A) is a scatter plot of sample data over a predetermined time period (e.g., 30 days) immediately after painting, while Figure 9(B) is a scatter plot of sample data over a predetermined time period (e.g., 30 days) after a certain period of time (e.g., 3 years) has elapsed since painting.

[0080] Figures 9(C) and 9(D) are scatter plots of sample data from various vessels painted with the antifouling paint "B". However, Figure 9(C) is a scatter plot of sample data for a predetermined time period immediately after painting, while Figure 9(D) is a scatter plot of sample data for a predetermined time period after a certain period has elapsed since immediately after painting.

[0081] The four graphs shown in Figure 9 indicate that ships painted with antifouling paint "A" show a smaller difference in sailing performance immediately after painting compared to ships painted with antifouling paint "B". The main factor in the difference in sailing performance immediately after painting and sailing performance after a predetermined time has elapsed is hull fouling due to biological fouling. In other words, the smaller the difference in sailing performance, the more effective the antifouling paint is. Therefore, users can infer from the four graphs shown in Figure 9 that antifouling paint "A" is more effective than antifouling paint "B".

[0082] Note that while the graphs in Figures 6 to 8 show statistical values ​​of the values ​​shown by sample data included in the population, the graph in Figure 9 shows the distribution of the values ​​shown by sample data included in the population. Both of these graphs have in common that they are graphs of a population that is a collection of sample data from multiple different vessels, rather than graphs of a population that is a collection of sample data from individual vessels.

[0083] Figure 10 is a table showing the average Fouling coefficient and the total cost (an example of a cost value corresponding to the population) calculated according to the following formula for each of the antifouling coatings "A" to "D". Total cost = Antifouling paint cost + (Standard daily fuel consumption × Standard operating rate × Standard period length × Standard fuel price × Fouling coefficient) + UWC cost

[0084] The sample data used to calculate the statistical values ​​shown in the table in Figure 10 (in this case, the average Fouling coefficient and the total cost) is a collection of sample data from multiple different vessels (however, these multiple sample data may include multiple sample data from the same vessel).

[0085] The antifouling paint cost and UWC cost used in the above calculation formula are the average values ​​shown in the sample data, and the Fouling coefficient is the average value identified from the values ​​shown in the sample data. All other values ​​are constants as exemplified below. Standard daily fuel consumption: 50 MT / day Standard availability: 0.6 Standard period length: 2.5 years Standard fuel price: US$489

[0086] The total cost is an estimate of the increased costs due to biological fouling of the hull, taking into account the costs associated with applying antifouling paint and implementing UWC (Untreated Water Conditioning). Therefore, a lower total cost indicates higher cost-effectiveness of the antifouling paint.

[0087] From the table in Figure 10, users can see that if they prioritize reducing fuel consumption, they should choose antifouling paint "A," which has the smallest fouling coefficient; however, if they prioritize cost-effectiveness, they should choose antifouling paint "C," which has the lowest total cost.

[0088] The above is a description of data processing system 1. According to data processing system 1 described above, users can find out the degree to which the value of a parameter that affects the performance of a ship has an impact on the ship's performance, even for parameters that are difficult to change on an individual ship.

[0089] [Differentiation] The embodiments described above can be modified in various ways. Examples of these modifications are shown below. The embodiments described above and the modifications shown below may be combined as appropriate.

[0090] (1) The calculation means 1216 of the terminal system 12 of the data processing system 1 may be configured to calculate a value indicating the performance that a ship will acquire when the value of a parameter of interest for that ship is changed, using the statistical values ​​indicated by the statistical data generated by the generation means 1215.

[0091] Figure 11 is an example of the estimated performance display screen shown by the display device 122 in the data processing system 1 according to this modified example. The estimated performance display screen shows a "ship" list, an "antifouling paint" list, and a coordinate region R2 where the X axis represents ship speed relative to the ground and the Y axis represents fuel consumption.

[0092] The "Ship" list displays the ship's name and the name of the antifouling paint applied to the ship identified by that name. The "Antifouling Paint" list displays the name of the antifouling paint and the average Fouling coefficient corresponding to the antifouling paint identified by that name.

[0093] Here, assume that the user wants to know the performance of ship Y (an example of a ship according to the sample data included in the first population) coated with antifouling paint "A" when coated with antifouling paint "B".

[0094] In this case, the user selects ship Y from the "Ship" list and selects antifouling paint "B" from the "Antifouling Paint" list.

[0095] In response to the user's operation of selecting ship Y, the extraction means 1212 of the terminal system 12 extracts, as the sub-population PA, the sample data regarding the period immediately after the coating of ship Y from the sample data included in the population that the acquisition means 1213 has already acquired from the server device 11. Further, the extraction means 1212 extracts, as the sub-population PB, the sample data regarding the most recent predetermined period of ship Y from the sample data included in the population that the acquisition means 1213 has already acquired from the server device 11.

[0096] Subsequently, the generation means 1215 generates a scatter diagram SA in which plots corresponding to each of the sample data included in the sub-population PA are scattered. Further, the generation means 1215 generates a scatter diagram SB in which plots corresponding to each of the sample data included in the sub-population PB are scattered.

[0097] Subsequently, the calculation means 1216 calculates an approximate curve CA of the plots shown in the scatter diagram SA and an approximate curve CB of the plots shown in the scatter diagram SB. Hereinafter, the fuel consumption on the approximate curve CA when the ship's speed relative to the ground is X is Y A and the fuel consumption on the approximate curve CB when the ship's speed relative to the ground is X is Y B .

[0098] Subsequently, the calculation means 1216 calculates the fuel consumption Y C in accordance with the following calculation formula for various ship speeds X relative to the ground. Y C = Y A +(Y B - Y A )×(F B / F A ) However, F A F is the average value of the fouling coefficient of the antifouling paint "A". B This is the average value of the fouling coefficient for the antifouling paint "B".

[0099] Next, the calculation means 1216 calculates the fuel consumption Y for various ground speeds X. C The coordinates (X,Y) are determined by the combination of the ship's speed X relative to the ground. c Calculate the approximate curve CC of the plots when the ) are plotted.

[0100] The generation means 1215 generates graph data representing scatter plot SA, scatter plot SB, approximation curve CA, approximation curve CB, and approximation curve CC. The display instruction means 1217 instructs the display device 122 to display the graph represented by the graph data. As a result, the coordinate region R2 of the estimated performance display screen displays a graph as shown in Figure 11.

[0101] The approximation curve CC shows the estimated performance that ship Y would have exhibited over the most recent specified period if it had been painted with antifouling paint "B". From the graph shown in Figure 11, users can easily see how much the performance would change if ship Y were painted with antifouling paint "B" compared to the currently applied antifouling paint "A".

[0102] In the example above, the parameter of interest is the name of the antifouling paint. Also, the average value F of the Fouling coefficient for antifouling paint "A" is also considered. A This is a statistical value (an example of a statistical value shown in the first statistical value data) generated by the generation means 1215 with respect to a population (an example of the first population) consisting of sample data that satisfy the extraction condition (an example of extracted data shown in the first extraction condition data) that the name of the antifouling paint is "A", and is the average value F of the Fouling coefficient for antifouling paint "B". BThis is a statistical value (an example of a statistical value shown in the second statistical value data) generated by the generation means 1215 with respect to a population (an example of a second population) consisting of sample data that satisfy the extraction condition (an example of extracted data shown in the second extraction condition data) that the name of the antifouling paint is "B".

[0103] (2) In the above embodiment, the server device 11 may perform some of the processing that the terminal system 12 would otherwise perform. Also, the terminal system 12 may store the ship navigation log TB, and the data processing system 1 may not have a server device 11.

[0104] (3) In the embodiments described above, the Fouling coefficient was used as a user-defined parameter, but it is possible to arbitrarily change which parameters are used as original parameters and which are used as user-defined parameters. For example, the Fouling coefficient may be stored in the ship navigation log TB as an original parameter in advance.

[0105] (4) In the above-described embodiment, examples were given of the information that the data processing system 1 presents to the user when focusing on changes in ship performance due to differences in antifouling paint. However, the information that the data processing system 1 presents to the user may also show changes in ship performance due to differences in parameters other than antifouling paint. For example, the data processing system 1 may present to the user information showing how the performance of a ship changes when the country of construction, shipyard, fuel type, additive type, presence or absence of energy-saving devices, presence or absence of scrubbers, presence or absence of paint undercoat treatment by blasting, etc., are different.

[0106] For example, Figure 12 is an example of a graph displayed in the data processing system 1 to compare the performance of a specific type of vessel manufactured by shipyard P with that of a similar vessel manufactured by shipyard Q. In Figure 12, the X-axis represents the ship's speed over land, and the Y-axis represents fuel consumption.

[0107] The black square plots shown in Figure 12 represent combinations of ground speed and fuel consumption observed in sample data for multiple vessels that satisfy all of the following conditions, for example. The shipyard that manufactured it is Shipyard P. The ship is a Cape-sized dry bulker. This is within the three-month period immediately following manufacture. Beaufort is 3 or less.

[0108] The curve CP shown in Figure 12 is an approximation curve for those black square plots.

[0109] Furthermore, the white square plots shown in Figure 12 represent combinations of ground speed and fuel consumption shown by sample data for multiple vessels that satisfy all of the following conditions, for example. The shipyard that manufactured it is Shipyard Q. The ship is a Cape-sized dry bulker. This is within the three-month period immediately following manufacture. Beaufort is 3 or less.

[0110] The curve CQ shown in Figure 12 is an approximation curve of those white square plots.

[0111] In the example in Figure 12, the curve CP representing the performance of ships manufactured by shipyard P is located lower and to the right than the curve CQ representing the performance of ships manufactured by shipyard Q. This indicates that, statistically, ships manufactured by shipyard P have higher performance than ships manufactured by shipyard Q.

[0112] For example, Figure 13 is an example of a graph displayed in the data processing system 1 to compare the performance of a ship with and without blasting (hereinafter referred to as "blasting treatment") before painting the hull. In Figure 13, the X axis represents the ship's speed over the ground, and the Y axis represents fuel consumption.

[0113] The black square plots shown in Figure 13 represent combinations of ground speed and fuel consumption observed in sample data for multiple vessels that satisfy all of the following conditions, for example. Blasting is being performed. The ship is a Cape-sized dry bulker. This period is within three months immediately following painting. Beaufort is 3 or less.

[0114] The curve CY shown in Figure 13 is the approximate curve for those black square plots.

[0115] Furthermore, the white square plots shown in Figure 13 represent combinations of ground speed and fuel consumption observed in sample data for multiple vessels that satisfy all of the following conditions, for example. No blasting treatment has been performed. The ship is a Cape-sized dry bulker. This period is within three months immediately following painting. Beaufort is 3 or less.

[0116] The curve CN shown in Figure 13 is the approximate curve for those white square plots.

[0117] Since the curve CY in Figure 13 is located lower and to the right of the curve CN, it can be seen that blast treatment improves the performance of the ship. However, the difference between them is not very large.

[0118] For example, the data processing system 1 calculates an estimated fuel cost reduction if blasting is performed when painting a specific vessel (e.g., vessel X, a Cape-size dry bulk vessel) selected by the user, based on the distribution of vessel speeds during past voyages and the reduction in fuel consumption calculated from the difference between curve CY and curve CN. The user can then compare the estimated fuel cost reduction calculated by the data processing system 1 with the costs associated with blasting (costs incurred for the blasting itself, opportunity costs, etc.) to determine whether or not to blast vessel X.

[0119] (5) The present invention can be understood as a program that causes a computer 121, which constitutes a data processing system as exemplified in the data processing system 1 and a terminal system 12, to execute processing performed by the terminal system 12. Furthermore, the program according to the present invention may be provided in a state recorded on a recording medium and read from the recording medium to a computer, or downloaded to a computer via a communication network. [Explanation of Symbols]

[0120] 1...Data processing system, 11...Server device, 12...Terminal system, 121...Computer, 122...Display device, 123...Input device, 1211...Storage means, 1212...Extraction means, 1213...Acquisition means, 1214...Specification means, 1215...Generation means, 1216...Calculation means, 1217...Display instruction means.

Claims

1. Acquisition means for acquiring sample data showing the values ​​of each of a plurality of parameters, including parameters that indicate the performance of each vessel at the same time during the actual voyage of the vessel and parameters that affect said performance, and extraction condition data showing extraction conditions for one or more parameters selected from the plurality of parameters, An extraction means for extracting one or more sample data that satisfy the extraction conditions indicated by the extraction condition data from the sample data relating to each of the plurality of vessels, as a population; A means for identifying a new parameter that cannot be easily changed, based on one or more of the values ​​of the plurality of parameters contained in the extracted sample data, which indicates the degree to which the value of the parameter affecting the performance of the ship has an impact on the performance of the ship, A generation means for generating statistical data that shows statistical values ​​of the values ​​indicated by sample data included in the population with respect to at least one or more parameters, including the new parameter, A storage means for storing the generated statistical data and A data processing system equipped with the following features.

2. The acquisition means acquires multiple extraction condition data, The extraction means extracts one or more sample data that satisfy the extraction conditions indicated by each of the multiple extraction condition data as the population. The generation means generates statistical data showing the statistical values ​​of the values ​​indicated by the sample data included in each of the multiple populations, The system includes a display instruction means that instructs a display device to display the statistical value indicated by each of the plurality of statistical value data generated by the generation means. The data processing system according to claim 1.

3. The generation means generates statistical data representing each of several different types of statistical values ​​representing the values ​​shown by the sample data included in each of the multiple populations, The display instruction means instructs the display device to display the statistical values ​​shown by each of the multiple statistical data sets by arranging display objects corresponding to each of the multiple populations in a coordinate space having multiple coordinate axes corresponding to each of the multiple types of statistical values. The data processing system according to claim 2.

4. The acquisition means acquires, for each of a plurality of vessels, the values ​​of each of a plurality of parameters, including parameters that indicate the performance of the vessel at the same time during the vessel's actual voyage and parameters that affect said performance, as well as sample data showing the cost value of the parameters that affect said performance according to the type of antifouling paint. The system includes a display instruction means that instructs a display device to display the statistical values ​​shown by the statistical data, using an image in which a display object corresponding to the population is placed in a coordinate space having coordinate axes corresponding to the statistical values ​​shown by the statistical data and coordinate axes corresponding to the cost values ​​shown by the sample data. The data processing system according to claim 1.

5. The acquisition means acquires sample data showing the same parameter values ​​for each of multiple different time periods of the same vessel, The generation means generates statistical data representing the statistical values ​​of the values ​​shown by the sample data included in the population, with respect to parameters identified based on the same parameters for each of the multiple time periods shown by the sample data. The data processing system according to claim 1.

6. The acquisition means acquires, for each of a plurality of vessels, the values ​​of each of a plurality of parameters, including parameters that indicate the performance of the vessel at the same time during the vessel's actual voyage and parameters that affect said performance, as well as sample data showing the cost value of the parameters that affect said performance according to the type of antifouling paint. The system includes a calculation means for calculating a cost value appropriate to the population using statistical values ​​shown in statistical data and cost values ​​shown in sample data. The data processing system according to claim 1.

7. The acquisition means acquires first extraction condition data and second extraction condition data, which indicate extraction conditions with different conditions regarding the parameter of interest. The extraction means extracts one or more sample data that satisfy the extraction conditions indicated by the first extraction condition data as a first population, and extracts one or more sample data that satisfy the extraction conditions indicated by the second extraction condition data as a second population. The generation means generates first statistical data showing statistical values ​​of the values ​​shown by the sample data included in the first population, and generates second statistical data showing statistical values ​​of the values ​​shown by the sample data included in the second population. The system includes a calculation means that calculates a value indicating the performance of a vessel corresponding to sample data included in the first population, based on the statistical values ​​indicated by the first statistical data and the statistical values ​​indicated by the second statistical data, when the conditions for the parameter of interest are set to the conditions indicated by the second extraction condition data. The data processing system according to claim 1.

8. Acquisition means for acquiring sample data showing the values ​​of each of a plurality of parameters, including parameters that indicate the performance of each vessel at the same time during the actual voyage of the vessel and parameters that affect said performance, and extraction condition data showing extraction conditions for one or more parameters selected from the plurality of parameters, An extraction means for extracting one or more sample data that satisfy the extraction conditions indicated by the extraction condition data from the sample data relating to each of the plurality of vessels, as a population; A means for identifying a new parameter that cannot be easily changed, based on one or more of the values ​​of the plurality of parameters contained in the extracted sample data, which indicates the degree to which the value of the parameter affecting the performance of the ship has an impact on the performance of the ship, A generation means for generating distribution data representing the distribution of values ​​shown by sample data included in the population with respect to at least one or more parameters, including the new parameter, A storage means for storing the generated distribution data and A data processing system equipped with the following features.

9. The acquisition means acquires multiple extraction condition data, The extraction means extracts one or more sample data that satisfy the extraction conditions indicated by each of the multiple extraction condition data as the population. The generation means generates distribution data representing the distribution of values ​​shown by the sample data included in each of the multiple populations, The system includes a display instruction means that instructs the display device to display the distribution represented by each of the multiple distribution data. The data processing system according to claim 8.

10. The acquisition means acquires sample data showing the same parameter values ​​for each of multiple different time periods of the same vessel, The generation means generates distribution data that represents the distribution of values ​​shown by the sample data included in the population, with respect to parameters identified based on the same parameters for each of multiple time periods shown by the sample data. The data processing system according to claim 8.

11. On the computer, A process for obtaining sample data for each of several vessels, including parameters that indicate the performance of the vessel at the same time during its actual voyage and parameters that affect that performance, A process to obtain extraction condition data indicating extraction conditions for one or more parameters selected from the aforementioned multiple parameters, A process of extracting one or more sample data that satisfy the extraction conditions indicated by the extraction condition data from the sample data relating to each of the aforementioned multiple vessels, and using this as the population; A process to identify a new parameter that cannot be easily changed, based on one or more of the values ​​of the multiple parameters contained in the extracted sample data, which indicates the degree to which the value of the parameter affecting the ship's performance has an impact on the ship's performance, A process for generating statistical data that shows the statistical value of the value indicated by the sample data included in the population with respect to at least one or more parameters, including the new parameter; A process for storing the generated statistical data and A program to execute.

12. On the computer, A process for obtaining sample data for each of several vessels, including parameters that indicate the performance of the vessel at the same time during its actual voyage and parameters that affect that performance, A process to obtain extraction condition data indicating extraction conditions for one or more parameters selected from the aforementioned multiple parameters, A process of extracting one or more sample data that satisfy the extraction conditions indicated by the extraction condition data from the sample data relating to each of the aforementioned multiple vessels, and using this as the population; A process to identify a new parameter that cannot be easily changed, based on one or more of the values ​​of the multiple parameters contained in the extracted sample data, which indicates the degree to which the value of the parameter affecting the ship's performance has an impact on the ship's performance, A process for generating distribution data representing the distribution of values ​​shown by sample data included in the population with respect to at least one or more parameters, including the new parameter, A process for storing the generated distribution data and A program to execute.

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