JP2019012029A - Optimal route search method and device - Google Patents

Abstract

To provide a method and device for searching an optimal route with weather routing, in consideration of predication accuracy of sea weather predication data deteriorating with time.SOLUTION: An optimal route search method comprises causing a processor 12 of an optimal route search device 1 to, based on individual ship performance data M1 specific to a ship and sea weather predication data M2 indicating sea weather situation, search an optimal route of optimizing a predetermined evaluation index with weather routing, wherein the sea weather predication data is weighted so that weight is made gradually smaller with time over a forecasting period.SELECTED DRAWING: Figure 1

Description

  The present invention relates to an optimum route search method and apparatus for searching for an optimum route.

  In recent years, the importance of route selection technology called weather routing has increased in the shipbuilding and shipping industry. Weather routing predicts the sea weather that a ship will encounter while sailing from sea weather forecast data showing the current and future weather and the state of the sea (hereinafter referred to as “sea weather”), and ships in the predicted sea weather This is a technology that selects the optimum route from a given number of routes, taking into account the speed performance, hull motion, and fuel consumption. Here, as the optimum route, the safest route, the shortest time route, the minimum fuel consumption route, the maximum economical route, and combinations thereof have been proposed.

  In weather routing, sea weather that a ship encounters from departure time to arrival time is predicted based on long-term sea weather prediction data. Long-term sea weather forecast data is generally based on the calculation results of a numerical forecast model. Therefore, the forecast value for the same time may change between the sea weather forecast data used when calculating the optimum route before the voyage and the sea weather forecast data obtained thereafter.

  Therefore, in the optimum route search method described in Patent Document 1, an arrival point from a departure point of a certain sea area based on individual ship performance data unique to a ship and a predicted value of sea weather prediction data indicating a long-term sea weather condition. When calculating the optimal route up to the point, the forecast value of the sea weather prediction data that changes temporally and spatially at the calculated ship position is used every certain time until the arrival point is reached. .

JP 2008-145312 A

  The prediction accuracy of long-term sea weather forecast data decreases with the passage of time during the forecast period. That is, the prediction accuracy of two days ahead of the day ahead from the prediction time (calculation time) is lower, and the prediction accuracy of a few days ahead of the two days ahead is lower. However, in the conventional optimum route search technology including Patent Document 1, it is not considered that the prediction accuracy of the sea weather prediction data used for the calculation decreases with the passage of time.

  The present invention has been made in view of the above circumstances, and an object thereof is to provide a technique for searching for an optimum route by weather routing in consideration of the prediction accuracy of sea weather prediction data that decreases with the passage of time. There is to do.

An optimum route search method according to an aspect of the present invention is based on an individual route performance data unique to a ship and sea weather prediction data indicating sea weather conditions, and an optimum route that optimizes a predetermined evaluation index by weather routing. An optimum route search method for searching,
The marine weather forecast data is weighted so that the weight decreases as time passes over the forecast period.

Moreover, the optimum route search device according to one aspect of the present invention is provided.
A storage device storing individual ship performance data unique to the ship;
A storage device storing sea weather forecast data indicating the sea weather situation;
Route calculation for searching for an optimum route that optimizes a predetermined evaluation index by weather routing based on the sea weather forecast data and the individual vessel performance data that are weighted so that the weight decreases with the passage of time over the forecast period And a device.

  According to the above-mentioned optimum route search method and optimum route search device, the prediction accuracy of the sea weather forecast data decreases as time passes, in other words, the uncertainty of the sea weather forecast data is the time It is reflected to increase with progress. Therefore, it is possible to reduce the influence of the large uncertainty portion on the optimum route calculation in the sea weather prediction data than the influence of the small uncertainty portion on the optimum route calculation.

  In the optimum route search method and the optimum route search device, the difference between the sea weather prediction data and the average sea weather may be weighted.

  As a result, when the sea weather after the lapse of the forecast period is regarded as the average sea weather, the evaluation values during the forecast period and after the lapse of the forecast period can be smoothly continued.

  In the optimum route search method and the optimum route search device, the weight may change corresponding to a prediction probability of the sea weather prediction data.

  Thereby, the uncertainty of the sea weather forecast data can be reflected more accurately in the optimum route calculation.

An optimum route search method according to an aspect of the present invention is based on an individual route performance data unique to a ship and sea weather prediction data indicating sea weather conditions, and an optimum route that optimizes a predetermined evaluation index by weather routing. An optimum route search method for searching,
Addition from the departure time to the arrival time of an evaluation function, wherein the evaluation index includes, as control variables, predicted sea weather based on the sea weather forecast data and a weight that decreases with time over the forecast period of the sea weather forecast data Value or integral value.

Moreover, the optimum route search device according to one aspect of the present invention is provided.
A storage device storing individual ship performance data unique to the ship;
A storage device storing sea weather forecast data indicating the sea weather situation;
A route calculation device for searching for an optimum route that optimizes a predetermined evaluation index by weather routing based on the individual ship performance data and the sea weather prediction data,
Addition from the departure time to the arrival time of an evaluation function, wherein the evaluation index includes, as control variables, predicted sea weather based on the sea weather forecast data and a weight that decreases with time over the forecast period of the sea weather forecast data Value or integral value.

  According to the above-mentioned optimum route search method and optimum route search device, the prediction accuracy of the sea weather forecast data decreases as time passes, in other words, the uncertainty of the sea weather forecast data is the time It is reflected to increase with progress. Therefore, it is possible to reduce the influence of the large uncertainty portion on the optimum route calculation in the sea weather prediction data than the influence of the small uncertainty portion on the optimum route calculation.

  In the optimum route search method and the optimum route search device, the weight may change corresponding to a prediction probability of the sea weather prediction data.

  Thereby, the uncertainty of the sea weather forecast data can be reflected more accurately in the optimum route calculation.

  In the optimum route search method and the optimum route search device, the weight may be attached to a difference between the evaluation value in the predicted sea weather and the evaluation value in plain water.

  As a result, when the evaluation index is calculated by regarding the sea weather after the forecast period as sea weather in the plain water, the evaluation values during the forecast period and after the forecast period can be smoothly continued.

  Alternatively, in the optimum route search method and the optimum route search device, the weight may be attached to a difference between the evaluation value in the predicted sea weather and the evaluation value in the average sea weather.

  As a result, when the evaluation index is calculated considering the sea weather after the lapse of the prediction period as the average sea weather, the evaluation values during the prediction period and after the lapse of the prediction period can be smoothly continued.

  ADVANTAGE OF THE INVENTION According to this invention, the technique which searches the optimal route by weather routing in consideration of the prediction accuracy of the sea weather prediction data which falls with progress of time can be provided.

FIG. 1 is a block diagram showing the overall configuration of a suitable route searching apparatus according to an embodiment of the present invention. 2A to 2G are tables showing examples of weighting functions. FIG. 3 is a chart showing changes in evaluation values over time, FIG. 3A shows weighted evaluation values, and FIG. 3B shows unweighted evaluation values for comparison. FIG. 4 is a chart showing changes in evaluation values over time, FIG. 4A shows weighted evaluation values, and FIG. 4B shows unweighted evaluation values for comparison. FIG. 5 is a chart showing a time-dependent change in weighted predicted sea weather. FIG. 6 is a chart showing a time-dependent change in weighted predicted sea weather.

[Configuration of Optimal Route Search Device 1]
FIG. 1 is a block diagram showing an overall configuration of an optimum route searching apparatus 1 according to an embodiment of the present invention. The optimum route searching device 1 shown in FIG. 1 includes a communication device 11, a processing device 12, an input device 21, a display device 22, and various storage devices M1 to M4. Each of the storage devices M1 to M4 may be configured as a separate storage device, or a plurality of storage devices may be configured as a single storage device.

  The processing device 12 is a computer having a calculation unit such as a CPU and a storage unit such as a ROM and a RAM, and the CPU executes a predetermined program (such as an optimum route search program) stored in advance in the storage unit. Thus, the operation of each part of the optimum route searching device 1 is controlled. The processing device 12 functions as a sea weather prediction data acquisition unit 15, a route calculation unit 16, and the like when the CPU executes an optimum route search program.

  The input device 21 includes a mouse and a keyboard, and is a unit that receives an input by a user operation. The input device 21 outputs input information by a user operation to the processing device 12.

  The display device 22 is composed of a display device such as a liquid crystal display, and displays information corresponding to display data provided from the processing device 12 on the screen.

  The communication device 11 is connected to the network 7. The processing device 12 controls the communication device 11 to acquire information from the server 50 of the external engine 5 via the network 7 and store it in the storage devices M1 to M3. In FIG. 1, one external engine 5 is shown as a representative, but the server 50 of the external engine 5 connected to the processing device 12 so as to be communicable may be plural or plural.

  The communication device 11 can communicate with the boat maneuvering device 20 mounted on the ship 2 via the ship-land communication system 9. The processing device 12 controls the communication device 11 to transmit information related to the optimum route to the ship maneuvering device 20 of the ship 2 via the ship-land communication system 9, and obtains information such as actual encounter sea weather. In the present embodiment, the optimum route search device 1 is installed on land, but may be installed on the ship 2.

  In the storage device M1, individual ship performance data unique to the ship 2 is stored. The individual ship performance data may be, for example, a hull performance model. The hull performance model is a numerical model or a simulation model for obtaining the hull water characteristics and the wave response characteristics (resistance increase characteristic and hull motion characteristic) of the hull. This hull performance model has performance obtained by adding the influence of disturbance in the actual sea area such as wind, wind, and swell to the performance of the hull in plain water.

  Using this hull performance model, the processing device 12 can be used to implement real-time data such as a short-term response surface showing a hull resistance increase distribution (sea margin distribution), a short-term response surface of a wave load distribution, and a short-term response surface showing a vertical acceleration distribution. Sea area performance can be obtained. The hull resistance increase is a function of [incident wave direction, wave period, wave height, ship speed, drainage or draft, trim, wind direction, wind speed, propeller rotation speed, heading], and the wave load and vertical acceleration are Obtained from exercise.

  The storage device M2 stores sea weather prediction data. The sea weather prediction data acquisition unit 15 acquires sea weather prediction data that is updated a plurality of times (for example, 6 times) per day, and stores it in the storage device M2. The sea weather forecast data includes, for example, weather forecasts and sea forecasts at intervals of 30 minutes to 6 hours from 8 to 10 days ahead. For example, wind speed (average wind speed), wind direction (average wind direction), wind wave height (wind wave significant wave height), wind wave period (wind wave average wave period), wind wave direction (wind wave average wave direction), swell Wave height (swell significant wave height), swell wave period (swell average wave period), and swell wave direction (swell average wave direction), ocean current velocity, ocean current direction, tidal current velocity, tidal current direction, water temperature, Each item includes temperature and solar radiation. Such sea weather forecast data is expressed as, for example, a time-space map of sea weather at intervals of 6 hours from 8 to 10 days ahead, a wave height prediction sea surface distribution, and a time series table of wave height distribution prediction. Good. In the space-time map of sea weather, the sea area where the ship navigates is subdivided into meshes, and the flow velocity / direction of the ocean current at each point on the mesh, the wave height / wave direction / wave period of the wind wave, the wave height / wave direction / wave of the swell Information such as period, wind direction / wind speed, tidal flow velocity / direction, water temperature, temperature, and solar radiation amount may be indicated.

  In the storage device M3, data necessary for executing the optimum route search program is stored in addition to the data stored in the storage devices M1 and M2. The data may include, for example, a nautical chart, a tidal water temperature statistical data storing tidal water temperature statistical information in the sea area, and water depth information in the sea area.

  The storage device M4 stores data created when the optimum route search program is executed, that is, the optimum route and the like.

[Flow of planned route search processing]
Here, the flow of the planned route search process by the optimum route search device 1 having the above configuration will be described. The processing unit 12 of the optimum route search device 1 firstly starts with a departure point X0 (departure port), an arrival point Xf (arrival port), a departure time T0, an arrival time Tf, a constraint condition considering safe operation, and a type of optimum route calculation And, select conditions such as the ship's planned route. Restrictions that consider safe navigation include the upper limit value of the encounter wave height and the upper limit value of the hull motion. The type of optimal route calculation is selected from known optimal route problem solving methods such as isochronous curve method, dynamic programming, Dijkstra method, and variational method. These selection conditions may be input by a user's operation of the input device 21, or may be read from information stored in the storage device M3 in advance by the processing device 12.

  The route calculation unit 16 of the processing device 12 uses the weather routing technique based on the acquired selection conditions, the sea weather prediction data read from the storage devices M1 to M3, and the hull performance model, and the optimum route. Explore. The processing device 12 stores the searched optimum route in the storage device M4. The optimum route searched in this way is the network 7 or the inter-land communication system 9 from the optimum route search device 1 to the vessel maneuvering device 20 several times a day or in response to a request from the vessel maneuvering device 20 mounted on the ship 2. Delivered through.

  In the above, the “optimal route” means an optimum route in a voyage unit that sails from the departure point X0 (departure port) to the arrival point Xf (arrival port). However, the optimum route in the case of rerouting indicates the optimum route from the ship position at a certain time to the arrival point Xf. The “optimal” route is a route that optimizes a predetermined evaluation index, and types such as a safest route, a shortest time route, a minimum fuel consumption route, and a maximum economical route have been proposed. The optimum route described here is the minimum fuel consumption route that arrives at the arrival point Xf at the arrival time Tf and sets a predetermined evaluation index F, which will be described later, to an optimum value (that is, a minimum value). The type is not limited to this.

  In the route calculation unit 16 of the processing device 12, a simulation model that performs weather routing for searching for an optimum route is constructed. This weather routing simulation model is not limited to minimizing fuel consumption, but also includes constraints such as main engine power limits and operational limits that take into account safe operations, and is not limited to natural reductions in ship speed, and avoids rough weather. The conscious deceleration of the model is also incorporated. In the weather routing simulation, the ship speed, main engine output, hull movement, etc. are predicted under the sea weather that changes every moment during the voyage using the hull performance model and the sea weather forecast data. The optimum route is selected from a plurality of given routes up to the arrival point Xf. Since fuel consumption per unit time is proportional to the main engine output, the weather routing simulation that searches for the minimum fuel consumption route predicts the main engine output during the voyage and calculates the total fuel consumption to the destination. .

The selected route (ie, the optimum route) minimizes the evaluation index F. The evaluation index F is, for example, an integrated value or an addition value from the departure time T0 to the arrival time Tf of the evaluation function f _i (i = 1) representing the fuel consumption, and the route that minimizes the evaluation index F is The route will have the lowest fuel consumption. The evaluation index F takes into account the prediction accuracy of the sea weather prediction data that decreases with the passage of time. Hereinafter, the evaluation index F in which the prediction accuracy of the sea weather prediction data that decreases with the passage of time is considered will be described with a specific example.

[Example 1 of evaluation index F]
In the evaluation index F according to Example 1, the evaluation function f _i is weighted with a weight g _i (t) that represents a decrease in prediction accuracy with the passage of time from the forecast time of weather forecast data.

The evaluation index F in Equation 1 is a value obtained by integrating the evaluation function f _i at the time t from the departure time T0 to the arrival time Tf. The evaluation index F in Equation 2 is a value obtained by adding the evaluation function f _i from the departure time T0 to the arrival time Tf. The evaluation index F is represented by the formula 1 or 2, and either may be adopted. The evaluation function f _i uses the predicted sea weather w _j (t), the voyage condition u _k (t), and the weight g _i (t) as control variables.

In the present embodiment, the evaluation function f _i is an evaluation function f ₁ related to fuel consumption, but an evaluation function f ₂ related to safest operation, an evaluation function f ₃ related to sea margin, an evaluation function f ₄ related to hull resistance,. It may be an evaluation function of various evaluation targets such as The predicted sea weather w _j (t) is the wind direction, wind speed, wave height, wave period, wave direction angle, current velocity, current direction, tidal current velocity, tidal current direction, seawater temperature, temperature, and time t Sea weather forecast data including the amount of solar radiation, etc. or sea weather forecast based on it. The predicted sea weather w _j may be various sea weather such as wind direction w ₁ , wind speed w ₂ , wind wave height w ₃ ,. The voyage condition u _k (t) represents the voyage condition such as the planned speed at the time t, the planned rotational speed, and the planned main engine output. The navigation condition u _k (t) may be various navigation conditions such as the planned ship speed u ₁ , the planned rotation speed u ₂ , the planned horsepower u ₃ ,.

The weight g _i (t) is a weight function for each of the predicted sea weather w _j (t) and the voyage condition u _k (t), which represents a decrease in prediction accuracy with the passage of time of the predicted sea weather at time t. is there. The weighting function may be set individually for the evaluation object (i) of the evaluation function f _i . The weight g _i (t) is a decreasing function that decreases with time over the forecast period of the sea weather forecast data. For example, as shown in FIG. 2A, the weight g _i (t) may be a decreasing function with a decreasing rate. Further, for example, as shown in FIG. 2B, the weight g _i (t) may be a decreasing function that increases the decreasing rate. For example, as shown in FIG. 2C, the weight g _i (t) may be a decreasing function with a constant decreasing rate, that is, a linear function. Further, for example, as shown in FIGS. 2D and 2E, the weight g _i (t) may be a decreasing function having an inflection point. Further, for example, as shown in FIG. 2 (f) and FIG. 2 (g), the weight g _i (t) may be a step-like decreasing function.

The weight g _i (t) as described above may be determined by a statistical value, for example. Further, the weight g _i (t) may correspond to the prediction probability associated with the sea weather prediction data provided by the sea condition / weather information provider.

The evaluation function f _i defined in this way is fuel consumption per unit time, ship speed, horsepower, ship resistance, ship motion, sea weather, solar power generation, sea margin, gas ship BOGR (Boil Off Gas). (Rate) or the like, or an evaluation value in consideration of the fuel price, the degree of impact on the safety of the hull and the load, or an evaluation value combining them may be expressed.

[Application 1 of Example 1]
In application 1 of Example 1, it is assumed that the predicted sea weather w _j (t) after the forecast period (for example, 8 days) of the sea weather forecast data has elapsed is the sea weather in plain water without waves. In this application, the weight g _i (t) is attached to the difference between the evaluation value in the predicted sea weather and the evaluation value in the plain water. In seawater in plain water, the ocean current, the wave height of the wind wave, the wave height / wave period of the swell, the wind speed, and the tide are all zero.

FIG. 3A shows a case where the weight g _i (t) changes from 1 to 0 (ie, weighting) where the vertical axis is the evaluation value and the horizontal axis is the elapsed time t from the departure time T0 (ie, the number of voyages). The evaluation value (that is, the calculation result of the evaluation function f _i ) over time. Also, in FIG. 3B, for comparison, the vertical axis is the evaluation value, the horizontal axis is the elapsed time t from the departure time T0 (ie, the number of days of voyage), and the weight g _i (t) is fixed at 1. This represents a change with time in the evaluation value in the case (that is, in the case where weighting is not performed). 3 (a) and 3 (b), it is assumed that the navigation condition u _k (t) is constant, and the evaluation value in plain water is represented by a chain line. The evaluation value in the plain water is obtained by calculating the evaluation function f _i (0, u _k (t), 1) with the predicted sea weather w _j (t) as 0 and the weight g _i (t) as 1. .

  As shown in FIG. 3 (a), the evaluation value when weighted decreases with the passage of time, becomes a value close to or the same as the evaluation value in plain water in the middle or at the end of the forecast period, After the forecast period, it converges to the evaluation value in plain water. The evaluation value in the forecast period and the evaluation value after the forecast period have passed smoothly. Thus, the evaluation index F weighted so that the future evaluation value approaches the evaluation value in plain water is obtained.

  On the other hand, as shown in FIG. 3B, the evaluation value when weighting is not performed is discontinuous between the evaluation value in the forecast period and the evaluation value after the forecast period has elapsed.

As described above, the weight g _i (t) may be attached to the difference between the evaluation value in the predicted sea weather and the evaluation value in the plain water. As a result, when the evaluation index is calculated by regarding the sea weather after the forecast period as plain water, the assessment values during the forecast period and after the forecast period have elapsed can be smoothly continued.

[Application 2 of Example 1]
In application 2 of Example 1, the predicted sea weather w _j (t) after the sea weather forecast period (for example, 8 days) has elapsed is assumed to be the average sea weather. In this application, the weight g _i (t) is attached to the difference between the evaluation value in the predicted sea weather and the evaluation value in the average sea weather.

  Here, as the “average sea weather”, a monthly, seasonal, or yearly average sea weather of a local sea area including a region where the sea weather is predicted may be used. Alternatively, as the “average sea weather”, an oceanic monthly, seasonal, or yearly average sea weather including a region where the sea weather is predicted may be used. Alternatively, as the “average sea weather”, a predicted sea weather at a certain time within the forecast period after the forecast day (for example, a predicted sea weather at the end of the forecast period) may be used. Alternatively, as the “average sea weather”, an average sea weather corresponding to an average sea margin described later, or a constant value arbitrarily determined by an operator or designer of weather routing may be used.

FIG. 4A shows a case where the weight g _i (t) changes from 1 to 0 (ie, weighting) where the vertical axis is the evaluation value and the horizontal axis is the elapsed time t from the departure time T0 (ie, the number of voyages). The evaluation value (that is, the calculation result of the evaluation function f _i ) over time. Also, in FIG. 4B, for comparison, the vertical axis is the evaluation value, the horizontal axis is the elapsed time t from the departure time T0 (ie, the number of voyages), and the weight g _i (t) is fixed at 1. This represents a change with time in the evaluation value in the case (that is, in the case where weighting is not performed). 4 (a) and 4 (b), it is assumed that the navigation condition u _k (t) is constant, and the evaluation value in the average sea weather is represented by a chain line. The evaluation value in the mean sea weather is calculated by calculating the evaluation function f _i (m _j , u _k (t), 1) with the predicted sea weather w _j (t) as m _j and the weight g _i (t) as 1. It is a thing.

  As shown in FIG. 4A, the weighted evaluation value decreases with time, and is close to or the same value as the average sea weather evaluation value during or at the end of the forecast period. After the forecast period, it will converge to the average sea weather evaluation value. The evaluation value in the forecast period and the evaluation value after the forecast period have passed smoothly. In this way, the evaluation index F weighted so that the future evaluation value approaches the evaluation value in the average sea weather is obtained.

  On the other hand, as shown in FIG. 4B, the evaluation value when weighting is not performed is discontinuous between the evaluation value in the forecast period and the evaluation value after the forecast period has elapsed.

As described above, the weight g _i (t) may be attached to the difference between the evaluation value in the predicted sea weather and the evaluation value in the average sea weather. As a result, when the evaluation index is calculated considering the sea weather after the lapse of the prediction period as the average sea weather, the evaluation values during the prediction period and after the lapse of the prediction period can be smoothly continued.

[Application 3 of Example 1]
In application 3 of Example 1, the predicted sea weather w _j (t) after the forecast period (for example, 8 days) of the sea weather forecast data has passed is the sea weather (hereinafter, “ It is referred to as “sea weather corresponding to mean sea margin”. In this application, the weight g _i (t) is attached to the difference between the evaluation value in the predicted sea weather and the evaluation value in the sea weather corresponding to the average sea margin. As the “average sea margin”, a monthly, seasonal, or annual average sea margin of a local sea area including a region where the sea weather is predicted may be used. Alternatively, as the “average sea margin”, an average sea margin of the ocean including the region where the sea weather has been predicted may be used. Alternatively, a certain value arbitrarily determined by a weather routing operator or designer may be used as the “average sea margin”.

  The sea margin is the horsepower required for navigating the actual sea area at the same ship speed Vs as the ship speed Vs in flat water, and the horsepower due to the disturbance effect from the horsepower necessary for navigating at the ship speed Vs in flat water. Represents the rate of increase. The sea margin is expressed as a ratio of “increase in horsepower due to disturbance” and “horsepower required to travel at a boat speed Vs in flat water”.

  The horsepower required for sailing at a boat speed Vs in flat water can be obtained using an individual ship performance model. Further, the increase in horsepower due to disturbance can be obtained using a private ship performance model and sea weather. Therefore, the sea weather corresponding to the average sea margin can be obtained from the average sea margin.

As described above, the weight g _i (t) may be attached to the difference between the evaluation value in the predicted sea weather and the evaluation value in the sea weather corresponding to the average sea margin. As a result, when the evaluation index is calculated considering sea weather after the forecast period as sea weather corresponding to the average sea margin, evaluation values during the forecast period and after the forecast period have passed can be smoothly continued. .

[Application 4 of Example 1]
In application 4 of Example 1, the evaluation function f _i is a function for obtaining an evaluation value of a sea margin (for example, i = 3) at time t, and a weight g _i ( t) is attached. The weight g _i (t) is a value between the evaluation value in the predicted sea weather and the average sea margin so that the evaluation value after the forecast period (for example, 8 days) of the sea weather prediction data has passed becomes the average sea margin. It is attached to the difference.

As described above, the weight g _i (t) may be attached to the difference between the evaluation value (that is, the sea margin) in the predicted sea weather and the average sea margin. As a result, when the evaluation index is calculated by regarding the sea margin after the lapse of the expected period as the average sea margin, the evaluation values during the expected period and after the lapse of the expected period can be smoothly continued.

As described above, the optimum route search method according to the present embodiment provides a predetermined evaluation index by weather routing based on the individual ship performance data unique to the ship 2 and the sea weather prediction data indicating the sea weather condition. An optimum route search method for searching an optimum route to be optimized, wherein the evaluation index F is small as time passes over the forecast sea weather w _j (t) based on the sea weather forecast data and the forecast period of the sea weather forecast data. The evaluation function f _i is an addition value or an integrated value from the departure time T0 to the arrival time Tf of the evaluation function f _i including the weight g _i (t) as a control variable.

In addition, the optimum route searching device 1 according to the present embodiment includes a storage device M1 that stores individual ship performance data unique to the ship 2, a storage device M2 that stores sea weather prediction data indicating a sea weather condition, and a private ship. A route calculation unit 16 (route calculation device) that searches for an optimum route that optimizes a predetermined evaluation index by weather routing based on the performance data and the sea weather prediction data is provided. The evaluation index F includes a predicted sea weather w _j (t) based on the sea weather forecast data and a weight g _i (t) that decreases as time passes over the forecast period of the sea weather forecast data as control variables. The function f _i is an added value or integrated value from the departure time T0 to the arrival time Tf.

  In general, there is a difference in the information distributed by the provider of the sea weather forecast data. Even so, the difference in forecasts from each provider is relatively small in the most recent forecast from the forecast time, and the difference in forecasts from each provider tends to increase as time passes from the forecast time. Therefore, in the optimum route search method and the optimum route search apparatus 1 described above, the prediction accuracy of the sea weather prediction data decreases with the passage of time in the evaluation index F, in other words, the uncertainty of the sea weather prediction data. Is reflected as time passes. As a result, the influence of the uncertain portion of the predicted sea weather on the optimum route calculation is reduced. Therefore, the influence on the optimum route due to differences in the companies providing the sea weather forecast data is reduced. In addition, a route that excessively detours due to uncertain forecast sea weather in the future is not calculated, and an optimal route that can appropriately avoid stormy weather can be calculated while suppressing fuel consumption.

In the optimum route search method and the optimum route search device 1 according to the present embodiment, the weight g _i (t) may change corresponding to the prediction probability of the sea weather prediction data.

  The forecast probability provided by the company providing the sea weather forecast data is highly related to the uncertainty of the sea weather forecast data, and weighting based on such forecast probabilities further reduces the uncertainty of the forecast sea weather forecast. It is possible to accurately reflect the optimum route calculation.

[Example 2 of evaluation index F]
In the evaluation index F according to Example 2, the predicted sea weather W _j (t) weighted with the weight g _j (t) representing the decrease in the prediction accuracy with the passage of time from the forecast time of the sea weather forecast data is used. An evaluation function f _i is obtained. In other words, the sea weather forecast data used in the weather routing is weighted so that the weight decreases as time passes over the forecast period of the sea weather forecast data. Below, the description which overlaps with the evaluation parameter | index F which concerns on the said Example 1 is abbreviate | omitted.

The evaluation index F in Equation 3 is a value obtained by integrating the evaluation function f _i at the time t from the departure time T0 to the arrival time Tf. The evaluation index F in Equation 4 is a value obtained by adding the evaluation function f _i from the departure time T0 to the arrival time Tf. The evaluation index F according to Example 2 is expressed by the formula 3 or 4, and any of them may be adopted. The evaluation function f _i uses the predicted sea weather W _j (t) weighted with the weight g _j (t) and the navigation condition u _k (t) as control variables. The added value from the departure time T0 to the arrival time Tf of the evaluation function f _i becomes the evaluation index F. Here, the weighted predicted sea weather W _j (t) can be obtained by the following equation.
[Weighted predicted sea weather W _j (t)] = [Predicted sea weather w _j (t)] × [Weight g _j (t)]

The weight g _j (t) represents a decrease in prediction accuracy with the passage of time of the sea weather prediction data at time t. The weight g _j (t) may change corresponding to the prediction probability of the sea weather prediction data. The weighted predicted sea weather W _j (t) is the wind direction, wind speed, wave height, wave period, wave direction angle, ocean current velocity, ocean current velocity, tidal current velocity, tidal current direction, t Represents sea weather forecast data including water temperature, temperature, and solar radiation, or sea weather predicted based on it, but the current velocity, current direction, tidal current velocity, and tidal current direction are Since the prediction accuracy does not decrease even after elapses, they need not be weighted.

5, the weight of the vertical axis g _j (t) in the weighted prediction sea weather W _j (t), as the course of the horizontal axis from the starting time T0 time t (i.e., cruise days), the predicted sea weather W _j This represents the change over time. As shown in the chart of FIG. 5, the predicted sea weather W _j (t) weighted with the weight g _j (t) decreases with the passage of time and becomes zero (ie, flat water) at the end of the forecast period. Converge. That is, the weighted predicted sea weather W _j (t) is weighted to the difference between the sea weather forecast data and the sea weather in plain water. By adding or integrating an evaluation function f _i including such a weighted predicted sea weather W _j (t) as a control variable, a weighted evaluation is performed so that the future predicted sea weather approaches the sea weather in plain water. An index F is obtained.

In the above description, the entire predicted value of the sea weather prediction data is weighted, but the difference from the average sea weather among the predicted values of the sea weather prediction data may be weighted. More specifically, in the evaluation function f _i shown in Equation 2, the weighted predicted sea weather W _j (t) is weighted to the difference between the predicted sea weather and the average sea weather based on the sea weather prediction data. It may be a thing. That is, the weighted predicted sea weather W _j (t) may be obtained by the following equation.
[Weighted predicted sea weather W _j (t)] = [Predicted sea weather w _j (t) −average sea weather m _j ] × [weight g _j (t)] + [average sea weather m _j ]

6, the weight of the vertical axis g _j (t) in the weighted prediction sea weather W _j (t), as the course of the horizontal axis from the starting time T0 time t (i.e., cruise days), the predicted sea weather W _j This represents the change over time. In FIG. 6, the chain line represents the average sea weather m _j . As shown in FIG. 6, the predicted sea weather W _j (t) weighted by the difference between the predicted sea weather and the average sea weather decreases with the passage of time, and the average sea weather at the end of the forecast period. converge to m _j . By adding or integrating the evaluation function f _i including the weighted predicted sea weather W _j (t) as a control variable, the evaluation index F weighted so that the future predicted sea weather approaches the average sea weather. Is obtained. As a result, when the sea weather after the lapse of the forecast period is regarded as the average sea weather, the evaluation values during the forecast period and after the lapse of the forecast period can be smoothly continued.

Moreover, you may weight the difference with the sea weather corresponding to an average sea margin among the predicted values of sea weather prediction data. More specifically, in the evaluation function f _i shown in Equation 3 or 4, the weighted predicted sea weather W _j (t) is the difference between the predicted sea weather based on the sea weather forecast data and the sea weather corresponding to the average sea margin. A weight may be added. That is, the weighted predicted sea weather W _j (t) may be obtained by the following equation.
[Weighted predicted sea weather W _j (t)] = [Predicted sea weather w _j (t) −Sea weather corresponding to average sea margin] × [Weight g _j (t)] + [Sea weather corresponding to average sea margin]

Thus, the predicted sea weather W _j (t) weighted by the difference between the predicted sea weather and the sea weather corresponding to the average sea margin decreases with time, and corresponds to the average sea margin at the end of the forecast period. Converge to sea weather. By adding or integrating the evaluation function f _i including such a weighted predicted sea weather W _j (t) as a control variable, the future predicted sea weather is weighted so as to approach the average sea margin-corresponding sea weather. An evaluation index F is obtained. As a result, when the sea weather after the forecast period is regarded as the sea weather corresponding to the average sea margin, the evaluation value during the forecast period and after the forecast period can be smoothly continued.

  As described above, the optimum route search method according to the present embodiment provides a predetermined evaluation index by weather routing based on the individual ship performance data unique to the ship 2 and the sea weather prediction data indicating the sea weather condition. An optimum route search method for searching for an optimum route to be optimized is characterized in that the sea weather forecast data is weighted so that the weight decreases over time over the forecast period.

  In addition, the optimum route searching device 1 according to the present embodiment includes a storage device M1 that stores individual ship performance data unique to the ship 2, a storage device M2 that stores sea weather prediction data indicating the sea weather condition, and a forecast period. A route calculation unit 16 (route) that searches for an optimum route that optimizes a predetermined evaluation index by weather routing based on sea weather forecast data and individual vessel performance data that are weighted so that the weight decreases over time. And a computing device).

  According to the optimum route search method and the optimum route search device 1, the prediction accuracy of the sea weather prediction data decreases as the evaluation index with time, in other words, the uncertainty of the sea weather prediction data is the time. It is reflected that it increases as time passes. As a result, the influence of the large uncertainty portion on the optimum route calculation in the sea weather forecast data can be made smaller than the influence of the small uncertainty portion on the optimum route calculation. Therefore, the influence on the optimum route due to differences in the companies providing the sea weather forecast data is reduced. In addition, a route that excessively detours due to uncertain forecast sea weather in the future is not calculated, and an optimal route that can appropriately avoid stormy weather can be calculated while suppressing fuel consumption.

  The preferred embodiments of the present invention have been described above, but the present invention may include modifications in the specific structure and / or function details of the above embodiments without departing from the spirit of the present invention. .

1: Optimal route search device 2: Ship 20: Ship maneuvering device 5: External engine 50: Server 7: Network 9: Inter-ship communication system 11: Communication device 12: Processing device 15: Sea weather forecast data acquisition unit 16: Route calculation Unit 21: Input device 22: Display devices M1 to M4: Storage device

Claims (14)

An optimum route search method for searching for an optimum route that optimizes a predetermined evaluation index by weather routing based on individual ship performance data unique to a ship and sea weather prediction data indicating a sea weather condition,
The sea weather forecast data is weighted so that the weight decreases with the passage of time over the forecast period.
Optimal route search method. Weighting the difference from the average sea weather among the sea weather forecast data,
The optimal route search method of Claim 1. The weight changes corresponding to the prediction probability of the sea weather prediction data.
The optimal route search method of Claim 1 or 2. A storage device storing individual ship performance data unique to the ship;
A storage device storing sea weather forecast data indicating the sea weather situation;
Route calculation for searching for an optimum route that optimizes a predetermined evaluation index by weather routing based on the sea weather forecast data and the individual vessel performance data weighted so that the weight becomes smaller as time passes over the forecast period A device,
Optimal route search device. Weighting the difference from the average sea weather among the sea weather forecast data,
The optimum route search device according to claim 4. The weight changes corresponding to the prediction probability of the sea weather prediction data.
The optimum route search device according to claim 4 or 5. An optimum route search method for searching for an optimum route that optimizes a predetermined evaluation index by weather routing based on individual ship performance data unique to a ship and sea weather prediction data indicating a sea weather condition,
Addition from the departure time to the arrival time of an evaluation function, wherein the evaluation index includes, as control variables, predicted sea weather based on the sea weather forecast data and a weight that decreases with time over the forecast period of the sea weather forecast data Value or integral value,
Optimal route search method. The weight is attached to the difference between the evaluation value in the predicted sea weather and the evaluation value in plain water,
The optimal route search method of Claim 7. The weight is attached to the difference between the evaluation value in the predicted sea weather and the evaluation value in the average sea weather,
The optimal route search method of Claim 7. The weight changes corresponding to the prediction probability of the sea weather prediction data.
The optimal route search method as described in any one of Claims 7-9. A storage device storing individual ship performance data unique to the ship;
A storage device storing sea weather forecast data indicating the sea weather situation;
A route calculation device for searching for an optimum route that optimizes a predetermined evaluation index by weather routing based on the individual ship performance data and the sea weather prediction data,
Addition from the departure time to the arrival time of an evaluation function, wherein the evaluation index includes, as control variables, predicted sea weather based on the sea weather forecast data and a weight that decreases with time over the forecast period of the sea weather forecast data Value or integral value,
Optimal route search device. The weight is attached to the difference between the evaluation value in the predicted sea weather and the evaluation value in plain water,
The optimal route search apparatus of Claim 11. The weight is attached to the difference between the evaluation value in the predicted sea weather and the evaluation value in the average sea weather,
The optimal route search apparatus of Claim 11. The weight changes corresponding to the prediction probability of the sea weather prediction data.
The optimal route search apparatus as described in any one of Claims 11-13.

Images