Drift range prediction method, device, computing equipment and storage medium for floating objects

Abstract

The present disclosure provides a floating object drift range prediction method, device, computing equipment and storage medium. The floating object drift range prediction method comprises: determining a first predicted sea state parameter of a floating object to be predicted at a position of an i-th prediction time, i=1, 2, …, N; performing random disturbance on the first predicted sea state parameter to obtain a plurality of second predicted sea state parameters; calculating a predicted drift speed based on each second predicted sea state parameter respectively, and calculating a position of the floating object to be predicted at an (i+1)-th prediction time according to the predicted drift speed and the position of the floating object to be predicted at the i-th prediction time; and determining a predicted drift range of the floating object to be predicted according to the position of the floating object to be predicted at each preset time. The scheme provided by the embodiment of the present disclosure can randomly determine the drift position of the floating object to be predicted, so as to characterize the randomness in actual drift when predicting the drift range of the floating object to be predicted, and improve the prediction accuracy of the drift range.

Description

Technical Field

[0001] This disclosure relates to the field of maritime technology, and in particular to a method, apparatus, computing device, and storage medium for predicting the drift range of floating objects. Background Technology

[0002] Maritime accidents can generate various types of floating debris. For example, an oil spill may produce oil slicks. Similarly, a collision may produce debris such as wrecked parts, life rafts, and other floating objects.

[0003] To quickly salvage and clear floating debris or conduct search and rescue operations after a maritime accident, it is necessary to determine the approximate sea area where the debris is located. Determining the approximate sea area requires predicting the drift range of the debris. How to reasonably predict the drift range of floating debris is a technical problem that current technology needs to solve. Summary of the Invention

[0004] To solve the above-mentioned technical problems, or at least partially solve them, this disclosure provides a method, apparatus, computing device, and storage medium for predicting the drift range of floating objects.

[0005] In a first aspect, embodiments of this disclosure provide a method for predicting the drift range of a floating object, including:

[0006] Determine the first predicted sea state parameter at the location of the floating object to be predicted at the i-th prediction time, i = 1, 2, ..., N;

[0007] Randomly perturb the first predicted sea state parameter to obtain multiple second predicted sea state parameters;

[0008] The predicted drift velocity of the object to be predicted is calculated based on each of the second predicted sea state parameters, and the position of the object to be predicted at the (i+1)th prediction time is calculated based on each of the predicted drift velocities and the position of the object to be predicted at the i-th prediction time.

[0009] The predicted drift range of the object to be predicted is determined based on its position at each preset time.

[0010] Optionally, determining the predicted drift range of the object to be predicted based on its position at each preset time includes:

[0011] Based on the drift position of the object to be predicted at various times, determine multiple drift trajectories of the object to be predicted;

[0012] The predicted drift range of the object to be predicted is determined based on multiple drift trajectories.

[0013] Optionally, the second predicted sea state parameters include surface current characteristic parameters, wind characteristic parameters, and wave characteristic parameters;

[0014] The calculation of the predicted drift velocity of the object to be predicted based on each of the second predicted sea state parameters includes:

[0015] The corresponding flow-induced drift velocity is determined based on the surface water flow characteristic parameters;

[0016] The corresponding wind-induced drift velocity is determined based on the wind characteristic parameters;

[0017] The corresponding wave-induced drift velocity is determined based on the wave characteristic parameters;

[0018] The predicted drift velocity is calculated using the current-induced drift velocity, wind-induced drift velocity, and wave-induced drift velocity corresponding to each of the second predicted sea state parameters.

[0019] Optionally, determining the corresponding wave-induced drift velocity based on the wave characteristic parameters includes:

[0020] The wave feature parameters are input into a pre-trained wave-induced drift deep learning model to obtain the corresponding wave-induced drift velocity.

[0021] The wave-induced drift deep learning model is trained based on the first sample drift data, which includes the sample drift velocity observation value of the sample floating object and the corresponding sample surface water flow characteristic parameters, sample wind characteristic parameters and sample wave characteristic parameters.

[0022] Optionally, determining the corresponding flow-induced drift velocity based on the surface water flow characteristic parameters includes: calculating the flow-induced drift velocity according to the surface water flow characteristic parameters and the flow-induced drift coefficient;

[0023] Determining the corresponding wind-induced drift velocity based on the wind characteristic parameters includes: calculating the wind-induced drift velocity according to the wind characteristic parameters and the wind-induced drift coefficient;

[0024] The flow-induced drift coefficient and the wind-induced drift coefficient are obtained during the training of the wave-induced drift deep learning model.

[0025] Optionally, before calculating the flow-induced drift velocity based on the surface water flow characteristic parameters and the flow-induced drift coefficient, the method further includes: adding a random perturbation to the flow-induced drift coefficient to obtain the perturbed flow-induced drift coefficient;

[0026] Before calculating the wind-induced drift velocity based on the wind characteristic parameters and the wind-induced drift coefficient, the method further includes: adding a random perturbation to the wind-induced drift coefficient to obtain the perturbed wind-induced drift coefficient.

[0027] Optionally, before calculating the wind-induced drift velocity based on the wind characteristic parameters and the wind-induced drift coefficient, the method further includes:

[0028] The second predicted sea state parameter is input into the pre-trained drift bias deep learning model to determine the predicted drift bias of the floating object to be predicted.

[0029] The wind-induced drift coefficient corresponding to the second predicted sea state parameter is determined based on the predicted drift bias.

[0030] The biased deep learning model is trained based on the second sample drift data, which includes the drift biased observations of the sample floating objects and the sample sea state parameters.

[0031] Secondly, embodiments of this disclosure provide a device for predicting the drift range of a floating object, comprising:

[0032] The sea state parameter acquisition unit is used to determine the first predicted sea state parameter at the location of the floating object to be predicted at the i-th prediction time, i = 1, 2, ..., N;

[0033] The disturbance parameter acquisition unit is used to randomly disturb the first predicted sea state parameter to obtain multiple second predicted sea state parameters.

[0034] The position calculation unit is used to calculate the predicted drift velocity of the floating object to be predicted based on each of the second predicted sea state parameters, and to calculate the position of the floating object to be predicted at the (i+1)th prediction time based on each of the predicted drift velocities and the position of the floating object to be predicted at the i-th prediction time.

[0035] The drift range determination unit is used to determine the predicted drift range of the floating object to be predicted based on its position at each preset time.

[0036] Thirdly, embodiments of this disclosure provide a computing device, including:

[0037] A memory and a processor, wherein the memory stores a computer program that, when executed by the processor, implements the previously described method for predicting the drift range of floating objects.

[0038] Fourthly, embodiments of this disclosure provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the method for predicting the drift range of a floating object as described in any of the preceding claims.

[0039] The solution provided in this disclosure has the following advantages: By using the solution provided in this disclosure, multiple second predicted sea state parameters with added random perturbations are obtained based on the first predicted sea state parameters calculated at the i-th prediction time. The position of the floating object to be predicted at the (i+1)-th time is then predicted based on the second predicted sea state parameters. This allows for the random determination of multiple drift trajectories of the floating object to be predicted, and the drift range of the floating object to be predicted is then predicted based on these multiple drift trajectories. Because the second predicted sea state parameters are sea state parameters obtained with added random perturbations, the solution provided in this disclosure can randomly determine the drift position of the floating object to be predicted. This makes the prediction of the drift range of the floating object characterize the randomness in the actual drift, thus improving the prediction accuracy of the drift range. Attached Figure Description

[0040] The accompanying drawings, which are incorporated in and form a part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure.

[0041] To more clearly illustrate the technical solutions in the embodiments of this disclosure or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0042] Figure 1 This is a flowchart of the method for predicting the drift range of floating objects provided in the embodiments of this disclosure;

[0043] Figure 2 This is a flowchart of the method for obtaining the wave-induced drift deep learning model used in some embodiments of this disclosure;

[0044] Figure 3 This is a flowchart of a method for determining the wind-induced drift coefficient provided in some embodiments of this disclosure;

[0045] Figure 4 This is a schematic diagram of the structure of the drift range prediction device for floating objects provided in the embodiments of this disclosure;

[0046] Figure 5 This is a schematic diagram of the structure of the computing device provided in the embodiments of this disclosure. Detailed Implementation

[0047] To better understand the above-mentioned objectives, features, and advantages of this disclosure, the solutions disclosed herein will be further described below. It should be noted that, unless otherwise specified, the embodiments and features described herein can be combined with each other.

[0048] Embodiments of this disclosure will now be described in more detail with reference to the accompanying drawings. While some embodiments of this disclosure are shown in the drawings, it should be understood that this disclosure can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of this disclosure. It should be understood that the accompanying drawings and embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of protection of this disclosure.

[0049] The term "comprising" and its variations as used herein are open-ended, meaning "including but not limited to". The term "based on" means "at least partially based on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". Definitions of other terms will be given in the description below. It should be noted that the concepts of "first", "second", etc., used in this disclosure are only used to distinguish different devices, modules, or units, and are not intended to limit the order of functions performed by these devices, modules, or units or their interdependencies.

[0050] It should be noted that the terms "a" and "a plurality of" used in this disclosure are illustrative rather than restrictive, and those skilled in the art should understand that, unless otherwise expressly indicated in the context, they should be understood as "one or more".

[0051] This disclosure provides a method for predicting the drift range of floating objects, used to determine the drift range of floating objects on water. In practical applications, the drift range prediction method provided by this disclosure can be applied to the maritime field to determine the drift range of floating objects at sea. Of course, the drift range prediction method provided by this disclosure can also be applied to determining the drift range of floating objects in inland rivers and lakes.

[0052] Figure 1 This is a flowchart of a method for predicting the drift range of floating objects provided in an embodiment of this disclosure. Figure 1 As shown, the method for predicting the drift range of floating objects provided in this embodiment may include steps S110-S140.

[0053] It should be noted that the method for predicting the drift range of floating objects provided in this embodiment is executed by a computing device. The computing device may be a server dedicated to data processing, or it may be a computing device such as a laptop computer, a shipborne terminal, a personal digital assistant (PDA), or a wearable device for rescue personnel.

[0054] S110: Determine the first predicted sea state parameter at the location of the floating object to be predicted at the i-th prediction time, i = 1, 2, ..., N.

[0055] In this embodiment of the disclosure, each prediction time is a time within the time period to be predicted. Determining the first predicted sea state parameter at the location of the floating object to be predicted at the i-th preset time involves first determining the location of the floating object to be predicted at the i-th preset time, and then determining the first predicted sea state parameter based on the location of the floating object at the i-th preset time and the predicted sea state parameters of the preset grid points at the i-th prediction time.

[0056] In this embodiment of the disclosure, the position of the i-th preset time is calculated based on the position of the (i-1)-th predicted time (or the initial time of the period to be predicted when i=1) and the predicted sea state parameters. The specific calculation method is as follows:

[0057] In this embodiment of the disclosure, determining the first predicted sea state parameter at the location of the floating object to be predicted at the i-th prediction time can be achieved by performing dynamic simulation based on a pre-determined sea state simulation model to determine the predicted sea state parameters of each preset grid point at the i-th prediction time. Then, based on the location of the floating object to be predicted at the i-th preset time, neighboring grid points are selected from the preset grid points, and the first predicted sea state parameter is calculated based on the location of the neighboring grid points, the predicted sea state parameters at their locations, and the location at the i-th prediction time.

[0058] In some embodiments of this disclosure, the preset grid points are triangular grid points. Assuming the position coordinates of the floating object to be predicted at the i-th prediction time are (x, y), and the position coordinates of the three neighboring grid points are (x1, y1), (x2, y2), and (x3, y3), then based on the coordinates of the four points, the weight coefficients for the corresponding three neighboring grid points can be a, b, and c, respectively. The estimated sea state characteristic parameters of the floating object to be predicted at the location of the i-th prediction time are z = a×z1 + b×z2 + c×z3, where z1, z2 and z3 are the predicted sea state parameters of the three neighboring preset grid points, respectively.

[0059] As mentioned earlier, a sea state simulation model is needed for numerical simulation to determine the preset sea state parameters for each preset grid point at the i-th prediction time. In this embodiment, the sea state simulation model can employ various data assimilation methods to assimilate the observed sea state parameters, resulting in an assimilated sea state simulation model. For example, the computing device can use cyclic three-dimensional variational assimilation methods, ensemble Kalman filter assimilation methods, adaptive optimal interpolation assimilation methods, etc., to assimilate the observed sea state parameters to obtain the sea state simulation model.

[0060] Step S120: Randomly perturb the first predicted sea state parameter to obtain multiple second predicted sea state parameters.

[0061] In this embodiment of the present disclosure, after obtaining the first predicted sea state parameter, the computing device randomly perturbs the first predicted sea state parameter according to a preset random perturbation method, so that random perturbation factors are added to the first predicted parameter, and a second predicted sea state parameter is obtained.

[0062] In practice, the computing device can pre-store the fluctuation range of the disturbance parameter. When it is necessary to randomly disturb the first predicted sea state parameter, the computing device randomly selects a disturbance parameter within the fluctuation range of the disturbance parameter, and obtains the second predicted sea state parameter based on the disturbance parameter and the first preset sea state parameter.

[0063] For example, in some embodiments, the aforementioned fluctuation range of the disturbance parameter can be a proportional range, which is a proportional range obtained by performing a probabilistic analysis based on the predicted sea state and the actual sea state. For example, it can be between [0.95, 1.05]. In this case, after selecting the disturbance parameter within the proportional range, the disturbance parameter is multiplied by the first predicted sea state parameter to obtain the second predicted sea state parameter.

[0064] For example, in some embodiments, after calculating the first predicted sea state parameter, the corresponding range of disturbance parameters can be found based on the first predicted sea state parameter. Then, the first predicted sea state parameter is added to a disturbance parameter randomly selected within the disturbance parameter range to obtain the second predicted sea state parameter.

[0065] In this embodiment of the disclosure, the computing device performs the aforementioned random perturbation steps multiple times to obtain multiple second predicted sea state parameters, and the multiple second predicted sea state parameters are likely to be parameters with different values.

[0066] Step S130: Calculate the predicted drift velocity of the object to be predicted based on each of the second predicted sea state parameters, and calculate the position of the object to be predicted at the (i+1)th predicted time based on each predicted drift velocity and the position of the object to be predicted at the i-th preset time.

[0067] After determining the various second predicted sea state parameters, the predicted drift velocity of the floating object to be predicted can be calculated according to the second predicted sea state parameters and the preset calculation method.

[0068] In some embodiments of this disclosure, the second predicted sea state parameters include surface current characteristic parameters, wind characteristic parameters, and wave characteristic parameters. Surface current characteristic parameters are parameters characterizing the fluidity characteristics of surface water flow. These parameters may include the flow velocity and direction of the surface water. Wind characteristic parameters are parameters used to characterize the wind characteristics above the water surface. These parameters may include wind speed and direction. In specific applications, the wind characteristic parameters may use the wind speed and direction at a height of 10m above the water surface. Wave characteristic parameters are parameters used to characterize the wave characteristics of the water surface. These parameters may include at least one of wave height, wave period, and wave direction.

[0069] In this embodiment of the disclosure, the predicted drift velocity of the floating object to be predicted is calculated based on each second predicted sea state parameter, including steps S131-S132.

[0070] Step S131: Determine the corresponding flow-induced drift velocity based on surface water flow characteristic parameters, determine the corresponding wind-induced drift velocity based on wind characteristic parameters, and determine the corresponding wave-induced drift velocity based on wave characteristic parameters.

[0071] Flow-induced drift velocity is the velocity acquired by the floating object due to the action of surface water flow. The direction of the flow-induced drift velocity is the same as the direction of the surface water flow, and the magnitude of the flow-induced drift velocity is directly proportional to the velocity of the surface water flow.

[0072] The calculation device can calculate the flow-induced drift velocity using a pre-set flow-induced drift coefficient and the surface water velocity. Specifically, it can use V... F-surface-current =λ c ·V c The flow-induced drift velocity V was calculated. F-surface-current , where λ c V is the first-order flow-induced drift coefficient. c This indicates surface water flow.

[0073] Wind-induced drift velocity is the speed at which a floating object, to be predicted, drifts due to the action of wind. It can be calculated using wind-induced drift coefficients and wind characteristic parameters. Wind-induced drift velocity includes both along-wind direction and perpendicular-wind direction drift velocities. Calculating wind-induced drift velocity based on wind characteristic parameters involves calculating both along-wind direction and perpendicular-wind direction drift velocities, with corresponding wind-induced drift coefficients for both directions.

[0074] Wave-induced drift speed is the drift speed acquired by a floating object due to the action of ocean waves. In some embodiments of this disclosure, wave-induced drift speed is calculated based on wave feature parameters. This can be achieved by inputting at least one parameter from the estimated sea state feature parameters into a pre-trained deep learning model of wave-induced drift to obtain the wave-induced drift speed.

[0075] The wave-induced drift deep learning model is a deep learning model that predicts wave-induced drift velocity based on wave characteristic parameters. In this embodiment, the wave-induced drift deep learning model is trained based on first sample drift data. The first sample drift data includes the observed drift velocity of sample floating objects and the corresponding sample surface water flow characteristic parameters, sample wind characteristic parameters, and sample wave characteristic parameters. How the wave-induced drift deep learning model is trained based on the first sample drift data will be analyzed later.

[0076] Step S132: Calculate the corresponding predicted drift velocity using the current-induced drift velocity, wind-induced drift velocity, and wave-induced drift velocity corresponding to each of the second predicted sea state parameters.

[0077] After obtaining the current-induced drift velocity, wind-induced drift velocity, and wave-induced drift velocity corresponding to each of the second predicted sea state parameters, the current-induced drift velocity, wind-induced drift velocity, and wave-induced drift velocity are added together to obtain the predicted drift velocity.

[0078] After calculating the predicted drift velocity, an integral can be performed based on the predicted drift velocity to determine the drift distance from the i-th time point to the (i+1)-th prediction time point. After determining the drift distance, combined with the position of the object to be predicted at the i-th prediction time point, the position of the object to be predicted at the (i+1)-th prediction time point can be obtained. Specifically, the position at the (i+1)-th prediction time point is s2 = s1 + v·(t2 - t1), where s2 is the position of the object to be predicted at the (i+1)-th prediction time point, v is the predicted drift velocity, t2 is the (i+1)-th prediction time point, and t1 is the i-th prediction time point.

[0079] In this embodiment of the disclosure, a corresponding position at the next prediction time is calculated for different second predicted sea state parameters.

[0080] In this embodiment of the disclosure, the aforementioned steps S110-S130 can be repeatedly executed to calculate the location of the floating object to be predicted at multiple consecutive time points.

[0081] Step S140: Determine the predicted drift range of the floating object based on its position at each preset time.

[0082] After determining the location of the predicted floating object at each preset time, the predicted drift range of the floating object can be determined based on its location.

[0083] In some embodiments of this disclosure, determining the predicted drift range based on the location at each preset time can be achieved using steps S141-S142.

[0084] Step S141: Determine multiple drift trajectories of the object to be predicted based on its drift position at various consecutive moments.

[0085] In this embodiment of the disclosure, the number of drift trajectories is much greater than the number of drift locations. For example, if three second predicted sea state parameters are obtained based on a first predicted sea state parameter, and the number of times to be predicted is N, then the number of drift trajectories obtained can be 3. N indivual.

[0086] Step S142: Determine the predicted drift range of the floating object to be predicted based on multiple drift trajectories.

[0087] After determining each drift trajectory, the area defined by each drift trajectory can be used as the predicted drift range of the object to be predicted. In some embodiments of this disclosure, after obtaining multiple drift trajectories, the Andrew algorithm can be used to process the multiple drift trajectories to obtain the drift range of the object to be predicted.

[0088] The method for predicting the drift range of a floating object provided in this disclosure involves obtaining multiple second predicted sea state parameters with added random perturbations based on a first predicted sea state parameter calculated and determined at the i-th prediction time. The position of the floating object to be predicted at the (i+1)-th time is then predicted based on these second predicted sea state parameters. This allows for the random determination of multiple drift trajectories of the floating object, and the prediction of its drift range based on these multiple drift trajectories. Because the second predicted sea state parameters are sea state parameters obtained with added random perturbations, the scheme provided in this disclosure can randomly determine the drift position of the floating object, thus making the prediction of the drift range reflect the randomness of the actual drift and improving the accuracy of the drift range prediction.

[0089] As mentioned earlier, in this embodiment of the disclosure, the wave-induced drift speed is calculated using a wave-induced drift deep learning model. Figure 2 This is a flowchart illustrating the method for obtaining the wave-induced drift deep learning model used in some embodiments of this disclosure. Figure 2 As shown, in some embodiments of this disclosure, the method for obtaining a wave-induced drift deep learning model based on first sample data includes steps S210-S250.

[0090] Figure 2This is a flowchart illustrating a method for training a wave-induced drift deep learning model based on first sample data, as described in this embodiment of the disclosure. Figure 2 As shown, the method for training a wave-induced drift deep learning model based on the first sample data includes steps S210-S250.

[0091] Step S210: Obtain the flow-induced drift coefficient and the wind-induced drift coefficient.

[0092] In some embodiments of this disclosure, the computing device may directly use the flow-induced drift coefficient and wind-induced drift coefficient provided by various other technical documents or related software products as the flow-induced drift coefficient and wind-induced drift coefficient, or it may calculate the flow-induced drift coefficient and wind-induced drift coefficient using the methods mentioned below.

[0093] Step S220: Based on the surface water flow characteristic parameters and flow-induced drift coefficient of the sample, calculate the corresponding estimated value of the sample flow-induced drift velocity.

[0094] Step S230: Calculate the corresponding estimated value of sample wind-induced drift velocity based on sample wind characteristic parameters and wind-induced drift coefficient.

[0095] Step S240: Based on the sample drift velocity observation, the corresponding sample flow-induced drift velocity estimate and the sample wind-induced drift velocity estimate, calculate the corresponding sample wave-induced drift velocity estimate.

[0096] The estimated flow-induced velocity of a sample is calculated by multiplying the flow-induced drift coefficient by the sample surface flow velocity. Similarly, the estimated wind-induced velocity of a sample is calculated by multiplying the wind-induced drift coefficient by the sample wind characteristic parameters.

[0097] After obtaining the estimated values ​​of the sample flow-induced drift velocity and the sample wind-induced drift velocity, the estimated values ​​of the sample flow-induced drift velocity and the estimated values ​​of the sample wind-induced drift velocity are subtracted from the sample drift velocity observation values ​​to obtain the estimated value of the sample wave-induced drift velocity.

[0098] Step S250: Train a deep learning model for wave-induced drift using sample wave feature parameters and corresponding sample wave-induced drift velocity estimates.

[0099] After obtaining the estimated sample wave-induced drift velocity, the sample wave parameters and the corresponding estimated sample wave-induced drift velocity are then used as training samples to train the wave-induced drift velocity, thus obtaining the wave-induced drift deep learning model.

[0100] The wave-drift deep learning model can be any of the possible models known in the art, such as a BP neural network model, a recurrent neural network model, etc., and the embodiments disclosed herein are not particularly limited.

[0101] Specifically, in order to train a better wave-drift deep learning model, multiple pre-built deep learning models can be established in this embodiment of the disclosure, and each neural network model can be trained separately. The quality of the training results determines which model to sample as the final wave-drift deep learning model to use.

[0102] In some embodiments of this disclosure, the wind-induced drift coefficient and the flow-induced drift coefficient can also be determined during the training of the wave-induced drift deep learning model. Determining the wind-induced drift coefficient and the flow-induced drift coefficient during the training of the wave-induced drift deep learning model involves fitting the observed sample drift velocities as approximations of the sum of the flow-induced drift velocities and the wind-induced drift velocities to the flow-induced drift coefficients and the wind-induced drift coefficients.

[0103] To construct formulas An approximate relationship is established between the observed sample drift velocity, the sum of the flow-induced drift velocity and the wind-induced drift velocity. Wherein, V ox-s and V oy-s These are the components of the sample drift velocity observations along the x and y axes (the x-axis can be the latitude direction, and the y-axis can be the longitude direction), V F-surface-current-sx and V F-surface-current-sy L represents the components of the flow-induced drift velocity of the sample floating object on the x and y axes. sx and L sy The wind-induced drift velocity of the sample floating object is represented by the components on the x and y axes.

[0104] Based on the relationship between flow-induced drift velocity and surface water flow characteristic parameters, the formula can be used. Determine V F-surface-current-sx and V F-surface-current-sy , where λ c V is the flow-induced drift coefficient. csx and V csy These are the components of the surface flow velocity on the x-axis and y-axis, respectively.

[0105] Based on the relationship between wind-induced drift velocity and wind characteristic parameters, the formula can be used. Calculate and determine L sx and L sy ,in Among them W 10mwind-s For sample wind speed, The wind-induced drift speed along the wind direction, a is the wind-induced drift velocity perpendicular to the wind direction. d b d ac b c ε is the wind-induced drift coefficient. d Let ε be the wind-induced random fluctuation coefficient along the wind direction. c This represents the random fluctuation coefficient when the wind direction is deflected to the left and right.

[0106] By fitting the aforementioned formula with the first sample drift data, the flow-induced drift coefficient λ can be determined. c And wind-induced drift coefficient a d b d a c b c ε d and ε c Specifically, the least squares method can be used for parameter fitting to obtain the aforementioned flow-induced drift coefficient and wind-induced drift coefficient.

[0107] In specific implementations of this disclosure, multiple sets of first sample drift data may be included. Based on each set of first sample drift data, a set of flow-induced drift coefficients λ can be obtained. c And wind-induced drift coefficient a d b d a c b c ε d and ε c After obtaining multiple sets of the aforementioned drift coefficients, the mean and standard deviation of each drift coefficient are calculated, and data meeting preset conditions are selected based on the mean and standard deviation. Finally, the flow-induced drift coefficient λ is calculated based on the data meeting the preset conditions. c And wind-induced drift coefficient a d b d a c b c ε d and ε c .

[0108] In some embodiments of this disclosure, in order to ensure that the calculated flow-induced drift velocity and wind-induced drift velocity reflect the random characteristics of force and velocity changes during the actual drift of the object to be predicted, the following steps can be performed before calculating the flow-induced drift velocity based on the aforementioned flow-induced drift coefficient and surface water velocity: adding a random perturbation to the flow-induced drift coefficient to obtain a perturbed flow-induced drift coefficient. By adding a random perturbation to the flow-induced drift coefficient, the calculated flow-induced drift velocity also becomes random. In specific implementations, the perturbation range for adding random perturbation to the flow-induced drift coefficient is determined in advance, specifically during the calculation of the flow-induced drift coefficient.

[0109] Similarly, before performing the aforementioned calculation of wind-induced drift velocity based on wind characteristic parameters and wind-induced drift coefficient, the following steps can be performed: Add a random perturbation to the wind-induced drift coefficient to obtain a perturbed wind-induced drift coefficient. By adding a random perturbation to the wind-induced drift coefficient, the calculated wind-induced drift velocity becomes random. In specific implementation, the range of adding a random perturbation to the wind-induced drift coefficient can be determined according to ε. d and ε c Sure.

[0110] Figure 3 This is a flowchart illustrating a method for determining the wind-induced drift coefficient provided in some embodiments of this disclosure. Figure 3 As shown, in some embodiments of this disclosure, steps S310-S320 may be performed before calculating the wind-induced drift velocity based on wind characteristic parameters and wind-induced drift coefficient.

[0111] Step S310: Determine the predicted drift bias based on the estimated sea state characteristic parameters of the floating object at the location of the object to be predicted at the time of prediction.

[0112] In some embodiments of this disclosure, determining the drift bias of a floating object to be predicted may involve inputting estimated sea state characteristic parameters into a pre-trained deep learning model of drift bias to determine the predicted drift bias.

[0113] The aforementioned drift bias deep learning model is trained based on second sample drift data, which includes drift bias observations of sample floating objects and corresponding estimated sea state characteristic parameters.

[0114] In practical applications, various types of floating objects can be sampled in specific aquatic environments to conduct floating object drift experiments. The drift trajectory of the floating objects can be tracked using tracking observation vessels and positioning buoys. The corresponding surface water flow characteristic parameters, wind characteristic parameters, and wave characteristic parameters of the sample can be obtained using equipment such as acoustic Doppler current profilers, meteorological sensors, and wave sensors carried on the tracking observation vessels.

[0115] After obtaining a large amount of sample floating object drift experimental data, the data can be analyzed to determine the observed drift bias of the sample floating objects. In practical applications, the observed drift bias of the sample floating objects can be determined by subtracting the surface water velocity from the observed drift velocity of the sample floating objects to roughly obtain the wind-induced drift velocity. Then, the drift bias of the sample floating objects can be determined using the direction of the wind-induced drift velocity and the wind direction. Subsequently, a second sample drift data set can be constructed using the observed drift bias of the sample floating objects, along with the corresponding surface water flow characteristic parameters, wind characteristic parameters, and wave characteristic parameters.

[0116] After obtaining the second sample drift data, the pre-built biased deep learning model can be trained using the second sample drift data to obtain a trained biased deep learning model. In the application of this disclosure embodiment, the biased deep learning model can be various possible models, such as widely used neural network models like the BP neural network model.

[0117] Step S320: Select the corresponding wind-induced drift coefficient based on the predicted drift bias.

[0118] In this embodiment of the disclosure, different predicted drift biases correspond to different wind-induced drift coefficients, specifically different wind-induced drift coefficients perpendicular to the wind direction. For example, when the bias is left-leaning, the aforementioned wind-induced drift coefficient a... c b c ε c a c+ b c+ ε c+ However, when the bias is right-leaning, the aforementioned wind-induced drift coefficient a c b c ε c a c- b c- and ε c- .

[0119] It should be noted that when selecting the corresponding wind-induced drift coefficient based on the predicted drift bias, the aforementioned formula is applied to fit the flow-induced drift coefficient and the wind-induced drift coefficient. Specifically Where δ is 1, it indicates that the drift deflection is perpendicular to the wind direction and deflects to the left, that is, when the drift deflection is to the left... To be used for fitting calculation a c+ b c+ ε c+ When δ is 0, it indicates that the drift deflection is to the right of the vertical wind direction. Used for fitting calculation a c- b c- and ε c- .

[0120] In addition to providing the aforementioned method for predicting the drift range of floating objects, this disclosure also provides a device for predicting the drift range of floating objects. Figure 4 This is a schematic diagram of the structure of the drift range prediction device for floating objects provided in an embodiment of this disclosure. Figure 4 As shown, the drift range prediction device 400 for floating objects provided in this embodiment includes a sea state parameter acquisition unit 401, a disturbance parameter acquisition unit 402, a position calculation unit 403, and a drift range determination unit 404.

[0121] The sea state parameter acquisition unit 401 is used to determine the first predicted sea state parameter at the location of the floating object to be predicted at the i-th prediction time, i = 1, 2, ..., N.

[0122] The disturbance parameter acquisition unit 402 is used to randomly disturb the first predicted sea state parameter to obtain multiple second predicted sea state parameters.

[0123] The position calculation unit 403 is used to calculate the predicted drift velocity of the floating object to be predicted based on each of the second predicted sea state parameters, and to calculate the position of the floating object to be predicted at the (i+1)th predicted time based on each predicted drift velocity and the position of the floating object to be predicted at the i-th predicted time.

[0124] The drift range determination unit 404 is used to determine the predicted drift range of the floating object based on its position at each preset time.

[0125] In some embodiments of this disclosure, the drift range determination unit 404 includes a drift trajectory determination subunit and a drift range determination subunit. The drift trajectory determination subunit is used to determine multiple drift trajectories of the object to be predicted based on its drift position at various times. The drift range determination subunit is used to determine the predicted drift range of the object to be predicted based on the multiple drift trajectories.

[0126] In some embodiments of this disclosure, the second predicted sea state parameters include surface current characteristic parameters, wind characteristic parameters, and wave characteristic parameters. The position calculation unit 403 includes a current-induced drift velocity calculation subunit, a wind-induced drift velocity calculation subunit, a wave-induced drift velocity calculation subunit, and a drift velocity calculation subunit.

[0127] The current-induced drift velocity calculation subunit is used to determine the corresponding current-induced drift velocity based on surface water flow characteristic parameters; the wind-induced drift velocity calculation subunit is used to determine the corresponding wind-induced drift velocity based on wind characteristic parameters; the wave-induced drift velocity calculation subunit is used to determine the corresponding wave-induced drift velocity based on wave characteristic parameters; and the drift velocity calculation subunit is used to calculate the corresponding predicted drift velocity using the current-induced drift velocity, wind-induced drift velocity, and wave-induced drift velocity corresponding to each of the second predicted sea state parameters.

[0128] In some embodiments of this disclosure, the wave-induced drift velocity calculation subunit inputs wave feature parameters into a pre-trained wave-induced drift deep learning model to obtain the corresponding wave-induced drift velocity; wherein, the wave-induced drift deep learning model is trained based on first sample drift data, the first sample drift data including sample drift velocity observations of sample floating objects and corresponding sample surface water flow feature parameters, sample wind feature parameters and sample wave feature parameters.

[0129] In some embodiments of this disclosure, the flow-induced drift velocity calculation subunit calculates the flow-induced drift velocity based on surface water flow characteristic parameters and the flow-induced drift coefficient; the wind-induced drift velocity calculation subunit calculates the wind-induced drift velocity based on wind characteristic parameters and the wind-induced drift coefficient; the flow-induced drift coefficient and the wind-induced drift coefficient are obtained during the training of the wave-induced drift deep learning model.

[0130] In some embodiments of this disclosure, the drift range prediction device 400 for floating objects further includes a drift coefficient perturbation unit. The drift coefficient perturbation unit is used to add random perturbations to the flow-induced drift coefficient to obtain a perturbed flow-induced drift coefficient, and to add random perturbations to the wind-induced drift coefficient to obtain a perturbed wind-induced drift coefficient. A flow-induced drift velocity calculation subunit uses the perturbed flow-induced drift coefficient to calculate the flow-induced drift velocity. A wind-induced drift velocity calculation subunit uses the perturbed wind-induced drift coefficient to calculate the wind-induced drift velocity.

[0131] In some embodiments of this disclosure, the drift range prediction device 400 for floating objects further includes a wind-induced drift bias determination subunit and a wind-induced drift coefficient selection unit. The wind-induced drift bias determination subunit inputs a second predicted sea state parameter into a pre-trained drift bias deep learning model to determine the predicted drift bias of the floating object to be predicted; wherein, the bias deep learning model is trained based on second sample drift data, which includes the observed drift bias of the sample floating object and sample sea state parameters. Correspondingly, the wind-induced drift coefficient selection unit determines the wind-induced drift coefficient corresponding to the second predicted sea state parameter based on the predicted drift bias.

[0132] This disclosure also provides a computing device, which includes a processor and a memory, wherein the memory stores a computer program, and when the computer program is executed by the processor, it can perform the above-described functions. Figure 1 -The method of any embodiment in Figure 3.

[0133] Figure 5 This is a schematic diagram of the structure of the computing device provided in the embodiments of this disclosure. For example... Figure 5 As shown, the computing device 500 may include a processing unit (e.g., a central processing unit, a graphics processing unit, etc.) 501, which can perform various appropriate actions and processes based on a program stored in a read-only memory (ROM) 502 or a program loaded from a storage device 508 into a random access memory (RAM) 503. The RAM 503 also stores various programs and data required for the operation of the computing device 500. The processing unit 501, ROM 502, and RAM 503 are interconnected via a bus 504. An input / output (I / O) interface 505 is also connected to the bus 504.

[0134] Typically, the following devices can be connected to I / O interface 505: input devices 506 including, for example, touchscreens, touchpads, keyboards, mice, cameras, microphones, accelerometers, gyroscopes, etc.; output devices 507 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices 508 including, for example, magnetic tapes, hard disks, etc.; and communication devices 509. Communication device 509 allows computing device 500 to communicate wirelessly or wiredly with other devices to exchange data. Although Figure 5 A computing device 500 with various devices is shown; however, it should be understood that it is not required to implement or have all of the devices shown. More or fewer devices may be implemented or included alternatively.

[0135] In particular, based on embodiments of this disclosure, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this disclosure include a computer program product comprising a computer program carried on a non-transitory computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication device 509, or installed from storage device 508, or installed from ROM 502. When the computer program is executed by processing device 501, it performs the functions defined in the methods of embodiments of this disclosure.

[0136] It should be noted that the computer-readable medium described in this disclosure can be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this disclosure, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in connection with an instruction execution system, apparatus, or device. In this disclosure, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium can be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wires, optical fibers, RF (radio frequency), etc., or any suitable combination thereof.

[0137] In some implementations, clients and servers can communicate using any currently known or future-developed network protocol such as HTTP (Hypertext Transfer Protocol) and can interconnect with digital data communication (e.g., communication networks) of any form or medium. Examples of communication networks include local area networks (“LANs”), wide area networks (“WANs”), the Internet (e.g., the Internet of Things), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any currently known or future-developed networks.

[0138] The aforementioned computer-readable medium may be included in the aforementioned computing device; or it may exist independently and not assembled into the computing device.

[0139] The aforementioned computer-readable medium carries one or more programs that, when executed by the computing device, cause the computing device to: determine a first predicted sea state parameter, i = 1, 2, ..., N, for the location of the object to be predicted at the i-th predicted time; randomly perturb the first predicted sea state parameter to obtain a plurality of second predicted sea state parameters; calculate the predicted drift velocity of the object to be predicted based on each of the second predicted sea state parameters, and calculate the location of the object to be predicted at the (i+1)-th predicted time based on each predicted drift velocity and the location of the object to be predicted at the i-th predicted time; and determine the predicted drift range of the object to be predicted based on the location of the object to be predicted at each preset time. Computer program code for performing the operations of this disclosure can be written in one or more programming languages ​​or a combination thereof, including but not limited to object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as C or similar programming languages. The program code can execute entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or it can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0140] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0141] The units described in the embodiments of this disclosure can be implemented in software or hardware. The names of the units are not, in some cases, intended to limit the specific unit.

[0142] The functions described above in this document can be performed, at least in part, by one or more hardware logic components. For example, exemplary types of hardware logic components that can be used, without limitation, include: Field Programmable Gate Arrays (FPGAs), Application-Specific Integrated Circuits (ASICs), Application Standard Products (ASSPs), System-on-Chip (SoCs), Complex Programmable Logic Devices (CPLDs), and so on.

[0143] In the context of this disclosure, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. Machine-readable media can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0144] This disclosure also provides a computer-readable storage medium storing a computer program that, when executed by a processor, can perform the above-described functions. Figures 1-4 The methods in any of the embodiments are similar in execution and beneficial effects, and will not be described again here.

[0145] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes the element.

[0146] The above are merely specific embodiments of this disclosure, enabling those skilled in the art to understand or implement this disclosure. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this disclosure. Therefore, this disclosure is not to be limited to these embodiments, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for predicting the drift range of a floating object, characterized in that, include: Determine the floating object to be predicted in the first... i The first predicted sea state parameter at the location of the predicted time. i=1,2,3，……, N ; Randomly perturb the first predicted sea state parameter to obtain multiple second predicted sea state parameters; The predicted drift velocity of the object to be predicted is calculated based on each of the second predicted sea state parameters, and the predicted drift velocity is calculated based on each of the predicted drift velocities and the object to be predicted in the [missing information]. i The position of the floating object to be predicted at the predicted time is calculated. i +1 Predicted position; The predicted drift range of the floating object to be predicted is determined based on its position at each preset time. Determining the predicted drift range of the object to be predicted based on its position at each preset time includes: Based on the drift position of the object to be predicted at various times, determine multiple drift trajectories of the object to be predicted; The predicted drift range of the object to be predicted is determined based on multiple drift trajectories. The second predicted sea state parameters include surface current characteristic parameters, wind characteristic parameters, and wave characteristic parameters; The calculation of the predicted drift velocity of the object to be predicted based on each of the second predicted sea state parameters includes: The corresponding flow-induced drift velocity is determined based on the surface water flow characteristic parameters; The corresponding wind-induced drift velocity is determined based on the wind characteristic parameters; The corresponding wave-induced drift velocity is determined based on the wave characteristic parameters; The predicted drift velocity is calculated using the current-induced drift velocity, wind-induced drift velocity, and wave-induced drift velocity corresponding to each of the second predicted sea state parameters.

2. The method according to claim 1, characterized in that, The step of determining the corresponding wave-induced drift velocity based on the wave characteristic parameters includes: The wave feature parameters are input into a pre-trained wave-induced drift deep learning model to obtain the corresponding wave-induced drift velocity. The wave-induced drift deep learning model is trained based on the first sample drift data, which includes the sample drift velocity observation value of the sample floating object and the corresponding sample surface water flow characteristic parameters, sample wind characteristic parameters and sample wave characteristic parameters.

3. The method according to claim 2, characterized in that, The step of determining the corresponding flow-induced drift velocity based on the surface water flow characteristic parameters includes: calculating the flow-induced drift velocity according to the surface water flow characteristic parameters and the flow-induced drift coefficient; Determining the corresponding wind-induced drift velocity based on the wind characteristic parameters includes: calculating the wind-induced drift velocity according to the wind characteristic parameters and the wind-induced drift coefficient; The flow-induced drift coefficient and the wind-induced drift coefficient are obtained during the training of the wave-induced drift deep learning model.

4. The method according to claim 3, characterized in that, Before calculating the flow-induced drift velocity based on the surface water flow characteristic parameters and the flow-induced drift coefficient, the method further includes: adding a random perturbation to the flow-induced drift coefficient to obtain the perturbed flow-induced drift coefficient; Before calculating the wind-induced drift velocity based on the wind characteristic parameters and the wind-induced drift coefficient, the method further includes: adding a random perturbation to the wind-induced drift coefficient to obtain the perturbed wind-induced drift coefficient.

5. The method according to claim 3, characterized in that, Before calculating the wind-induced drift velocity based on the wind characteristic parameters and the wind-induced drift coefficient, the method further includes: The second predicted sea state parameter is input into the pre-trained drift bias deep learning model to determine the predicted drift bias of the floating object to be predicted. The wind-induced drift coefficient corresponding to the second predicted sea state parameter is determined based on the predicted drift bias. The biased deep learning model is trained based on the second sample drift data, which includes the drift biased observations of the sample floating objects and the sample sea state parameters.

6. A device for predicting the drift range of a floating object, characterized in that, include: The sea state parameter acquisition unit is used to determine the sea state parameter of the object to be predicted in the [missing information - likely a specific timeframe]. i The first predicted sea state parameter at the location of the predicted time. i = 1, 2, 3, ..., N ; The disturbance parameter acquisition unit is used to randomly disturb the first predicted sea state parameter to obtain multiple second predicted sea state parameters. The position calculation unit is used to calculate the predicted drift velocity of the object to be predicted based on each of the second predicted sea state parameters, and to calculate the predicted drift velocity of the object to be predicted based on each of the predicted drift velocities and the position of the object in the second predicted sea state parameter. i The position of the floating object to be predicted at the predicted time is calculated. i+1 The location at each predicted moment; A drift range determination unit is used to determine the predicted drift range of the floating object to be predicted based on its position at each preset time. Determining the predicted drift range of the object to be predicted based on its position at each preset time includes: Based on the drift position of the object to be predicted at various times, determine multiple drift trajectories of the object to be predicted; The predicted drift range of the object to be predicted is determined based on multiple drift trajectories. The second predicted sea state parameters include surface current characteristic parameters, wind characteristic parameters, and wave characteristic parameters; The calculation of the predicted drift velocity of the object to be predicted based on each of the second predicted sea state parameters includes: The corresponding flow-induced drift velocity is determined based on the surface water flow characteristic parameters; The corresponding wind-induced drift velocity is determined based on the wind characteristic parameters; The corresponding wave-induced drift velocity is determined based on the wave characteristic parameters; The predicted drift velocity is calculated using the current-induced drift velocity, wind-induced drift velocity, and wave-induced drift velocity corresponding to each of the second predicted sea state parameters.

7. A computing device, characterized in that, include: A memory and a processor, wherein the memory stores a computer program that, when executed by the processor, implements the method as described in any one of claims 1-5.

8. A computer-readable storage medium, characterized in that, The storage medium stores a computer program that, when executed by a processor, implements the method as described in any one of claims 1-5.

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