Method, device and equipment for inverting ocean current search and rescue forecast based on table drift data

Abstract

The invention discloses a method, a device and equipment for inverting ocean current search and rescue forecast based on surface drift data, relates to the technical field of inverting ocean current search and rescue forecast, and is used for solving the problems that an ocean current model has system errors and a drift prediction result occurring in open sea is difficult to verify and improve in the prior art. Comprising the steps of obtaining multiple groups of surface floating trajectory data of a preset sea area; performing quality control on the obtained surface drift data, and calculating the instantaneous drift speed of the surface drift track by using the surface drift data subjected to the quality control; acquiring the simulated flow velocity of the surface drift track position through an interpolation method by using ocean current data of business forecast; and establishing a regression model of the simulated flow velocity and the instantaneous drifting velocity of the surface drifting, correcting the simulated flow velocity, carrying out search and rescue forecast by using the corrected simulated flow velocity, and calculating and outputting a rolling forecast result. The drifting trajectory of surface drifting can be simulated more accurately, the overall forecast error is better reduced, and a certain guiding effect can be provided for search and rescue forecast work.

Description

Technical Field

[0001] This invention relates to the field of ocean current search and rescue forecasting technology, and in particular to methods, apparatus and equipment for ocean current search and rescue forecasting based on surface drift data. Background Technology

[0002] Currently, search and rescue forecasting work is carried out by using drift trajectory models and numerical forecasting results of wind, current, and wave fields to predict the drift trajectories of various targets. This method has been widely used both domestically and internationally. Countries such as the United States and Norway have formed relatively mature operational systems for search and rescue forecasting. my country has established multiple comprehensive emergency auxiliary decision-making systems and the "National Maritime Search and Rescue Environmental Service Support Platform" to provide support for search and rescue emergency work in various sea areas.

[0003] While current methods can predict the exact location and velocity of search targets, practical applications are subject to numerous uncertainties. These include forecasting errors in wind and currents, measurement errors in observation data, model calculation errors, errors introduced by subgrids, errors introduced by model parameterization, errors arising from inaccuracies in the predicted starting position of the target, and incomplete or uncertain information regarding the initial state and physical shape characteristics of the search target. All of these factors contribute to errors in the drift trajectory model. Furthermore, the development and sea trials of domestic search and rescue prediction systems are primarily concentrated in near-shore areas, with limited sea trials conducted in the open ocean. This makes it difficult to verify and improve drift prediction results in the open ocean.

[0004] Therefore, there is an urgent need to provide a more reliable scheme for ocean current search and rescue forecasting based on surface drift data inversion. Summary of the Invention

[0005] The purpose of this invention is to provide a method, apparatus, and equipment for ocean current search and rescue forecasting based on drift data inversion, which solves the problem that existing ocean current models have systematic errors and that drift prediction results occurring in the open ocean are difficult to verify and improve.

[0006] To achieve the above objectives, the present invention provides the following technical solution:

[0007] In a first aspect, the present invention provides a method for retrieving ocean current search and rescue forecasts based on surface drift data, the method comprising:

[0008] Acquire multiple sets of surface drift trajectory data for a preset sea area;

[0009] The acquired drift data is subjected to quality control, and the instantaneous drift velocity of the drift trajectory is calculated using the quality-controlled drift data; the instantaneous drift velocity includes at least the instantaneous drift velocities in the x and y directions.

[0010] Using operational ocean current data, simulated current velocities at the location of the surface drift trajectory are obtained through interpolation methods;

[0011] A regression model of simulated flow velocity and instantaneous drift velocity of the surface is established to correct the simulated flow velocity. The corrected simulated flow velocity is then applied to search and rescue forecasts, and rolling forecast results are calculated and output.

[0012] Optionally, a regression model is established between the simulated flow velocity and the instantaneous drift velocity of the surface to correct the simulated flow velocity, specifically including:

[0013] Based on the simulated current velocity and the instantaneous current velocity calculated from the surface drift trajectory, regression models were established for different preset sea areas, with the simulated velocity as the independent variable and the calculated instantaneous drift velocity of the surface drift as the dependent variable.

[0014] Use the least squares method to determine the coefficients of the regression model so that the sum of squared residuals is minimized;

[0015] Based on the coefficient of determination, highly correlated floating data are selected to construct regression models for different preset sea areas.

[0016] Optionally, the acquired drift data is subjected to quality control, and the instantaneous drift velocity of the drift trajectory is calculated using the quality-controlled drift data, specifically including:

[0017]

[0018] Calculate the instantaneous drift velocity of the drift trajectory;

[0019] Where i represents the number of times the table floats, and n is the nth time point. Let represent the instantaneous drift velocity of the i-th float in the x-direction at time n. This represents the instantaneous drift velocity of the i-th float in the y-direction at time n, lon and lat represent the latitude and longitude of the float's position, t represents the time of the float data, and Rad is the Earth's radius.

[0020] Optionally, using operationally forecasted ocean current data, simulated current velocities at the location of the surface drift trajectory can be obtained through interpolation methods, specifically including:

[0021] Determine the temporal and positional relationships between operational forecast ocean current data and surface drift data;

[0022] Based on the temporal and spatial relationships, linear interpolation is used in time and bilinear interpolation is used in space;

[0023] Based on the position of the drift, determine the nearest preset known point and the corresponding ocean current component in the operational forecast ocean current data;

[0024] The simulated flow velocity at the location of the drift trajectory is calculated using linear interpolation.

[0025] Optionally, establishing a regression model for the simulated flow velocity and the instantaneous drift velocity of the surface specifically includes:

[0026] A univariate linear regression model was constructed with simulated velocity as the independent variable and instantaneous drift velocity of the surface as the dependent variable.

[0027] Use the least squares method to determine the coefficients of the regression model to minimize the sum of squared residuals;

[0028] Regression models for different preset sea areas were constructed by selecting highly correlated drift data.

[0029] Optionally, the step of determining the coefficients of the regression model using the least squares method specifically includes:

[0030] The objective function is set as minimizing the sum of squared residuals:

[0031]

[0032] Among them, u cn Let n be the actual instantaneous drift velocity in the x-direction. Here is the correction value for the nth model prediction, where b1 is the intercept, a1 is the slope, and u is the slope. in For the simulated velocity in the nth x-direction, sum the squared residuals over all observation points from n=1 to m to obtain the residual sum of squares. The smaller the RSS, the better. The closer to u cn ;

[0033] By finding the minimum value of the objective function, the slope and intercept of the regression model are determined.

[0034] Formula used:

[0035]

[0036] Calculate the coefficient of determination R 2 ;

[0037] Where TSS represents the total sum of squares:

[0038]

[0039] For u c The mean of the data indicates the degree of dispersion of the data itself;

[0040] ESS is the sum of squares of the regression:

[0041]

[0042] Characterization model prediction correction value with the mean The degree of deviation.

[0043] Optionally, the application of the corrected simulated flow velocity for search and rescue forecasting, and the calculation and output of rolling forecast results, specifically include:

[0044] The corrected simulated flow velocity was applied to the drift trajectory calculation model;

[0045] Calculate and output rolling forecast results for different time periods, including the average distance error for different preset time periods;

[0046] Compare the rolling forecast results before and after the correction to evaluate the correction effect.

[0047] Compared with existing technologies, this invention provides a method for search and rescue forecasting based on surface drift data inversion of ocean currents. This method involves acquiring multiple sets of surface drift trajectory data for a predetermined sea area; performing quality control on the acquired surface drift data and calculating the instantaneous drift velocity of the surface drift trajectory using the quality-controlled data; using operationally forecasted ocean current data, obtaining the simulated current velocity at the surface drift trajectory position through interpolation; establishing a regression model between the simulated current velocity and the instantaneous drift velocity of the surface drift; correcting the simulated current velocity; and applying the corrected simulated current velocity to search and rescue forecasts, calculating and outputting rolling forecast results. Establishing a regression model to explore the relationship between simulated current velocity and the instantaneous drift velocity of the surface drift enables more accurate simulation of the surface drift trajectory, better reduces overall forecast errors, and can provide guidance for search and rescue forecasting work.

[0048] Secondly, the present invention provides an apparatus for retrieving ocean current search and rescue forecasts based on surface drift data, the apparatus comprising:

[0049] The surface drift trajectory data acquisition module is used to acquire multiple sets of surface drift trajectory data for a preset sea area;

[0050] The instantaneous drift velocity determination module is used to perform quality control on the acquired drift data and calculate the instantaneous drift velocity of the drift trajectory using the quality-controlled drift data; the instantaneous drift velocity includes at least the instantaneous drift velocity in the x-direction and the y-direction.

[0051] The simulated current velocity determination module is used to obtain the simulated current velocity at the position of the surface drift trajectory by interpolation method using operationally forecasted ocean current data.

[0052] The search and rescue forecast module is used to establish a regression model between the simulated flow velocity and the instantaneous drift velocity of the surface, correct the simulated flow velocity, apply the corrected simulated flow velocity to perform search and rescue forecasts, and calculate and output rolling forecast results.

[0053] Thirdly, this invention provides a device for retrieving ocean current search and rescue forecasts based on surface drift data, the device comprising:

[0054] The system includes a memory, a processor, and a communication interface coupled to the processor; the memory stores a computer program that can be run by the processor; when the processor runs the computer program, it executes the aforementioned method for ocean current search and rescue forecasting based on surface drift data inversion.

[0055] Fourthly, the present invention provides a computer storage medium storing instructions that, when executed, implement the above-described method for ocean current search and rescue forecasting based on surface drift data inversion.

[0056] The technical effects achieved by the device-type solution provided in the second aspect, the equipment-type solution provided in the third aspect, and the computer storage medium solution provided in the fourth aspect are the same as those achieved by the method-type solution provided in the first aspect, and will not be repeated here. Attached Figure Description

[0057] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this invention, illustrate exemplary embodiments of the invention and are used to explain the invention, but do not constitute an undue limitation of the invention. In the drawings:

[0058] Figure 1 This is a schematic diagram of the method for retrieving ocean current search and rescue forecasts based on surface drift data provided by the present invention;

[0059] Figure 2 This is a schematic diagram of the interpolation method provided by the present invention;

[0060] Figure 3 A comparison chart of rolling forecast results before and after correction processing;

[0061] Figure 4 A schematic diagram of the device structure for ocean current search and rescue forecasting based on surface drift data provided by the present invention;

[0062] Figure 5 A schematic diagram of the equipment structure for ocean current search and rescue forecasting based on surface drift data provided by the present invention. Detailed Implementation

[0063] To facilitate a clear description of the technical solutions in the embodiments of the present invention, the terms "first" and "second" are used to distinguish identical or similar items with essentially the same function and effect. For example, the first threshold and the second threshold are merely used to distinguish different thresholds and do not limit their order. Those skilled in the art will understand that the terms "first" and "second" do not limit the quantity or execution order, and that the terms "first" and "second" are not necessarily different.

[0064] It should be noted that in this invention, the terms "exemplary" or "for example" are used to indicate examples, illustrations, or descriptions. Any embodiment or design described as "exemplary" or "for example" in this invention should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of terms such as "exemplary" or "for example" is intended to present the relevant concepts in a concrete manner.

[0065] In this invention, "at least one" refers to one or more, and "more than one" refers to two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, or B alone, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, or c can represent: a, b, c, a combination of a and b, a combination of a and c, a combination of b and c, or a, b, and c, where a, b, and c can be single or multiple.

[0066] Next, the solutions provided in the embodiments of this specification will be described in conjunction with the accompanying drawings:

[0067] like Figure 1 As shown, the process may include the following steps:

[0068] Step 110: Obtain multiple sets of surface drift trajectory data for the preset sea area.

[0069] Acquiring multiple sets of surface float trajectory data within a predetermined sea area aims to collect trajectory information of multiple sets of surface floats within that specific area. Besides surface floats, other surface floating objects (mostly or completely submerged in seawater) can also be included, considering only the influence of ocean currents. Appropriate observation methods (such as GPS positioning, satellite tracking, and ship observation) are used to obtain the position data of these surface floating objects at different times, thus forming their motion trajectories.

[0070] The preset sea area can be the South China Sea or the Indian Ocean. Collecting multiple sets of surface drift trajectory data in the preset sea area can ensure that subsequent analysis is representative and reliable, and can reflect the overall characteristics and changes of ocean currents in that sea area.

[0071] For example, using the four sets of surface drift data from the South China Sea and the ten sets of surface drift data from the Indian Ocean, the instantaneous drift velocity of the observed surface drift trajectories in the South China Sea and the Indian Ocean can be calculated respectively.

[0072] Step 120: Perform quality control on the acquired drift data, and use the quality-controlled drift data to calculate the instantaneous drift velocity of the drift trajectory; the instantaneous drift velocity includes at least the instantaneous drift velocities in the x and y directions.

[0073] Quality control of the acquired table data may specifically include:

[0074] Remove duplicate data from the same time point;

[0075] Remove abnormal data that has an excessively high drift speed or a drift trajectory that deviates from natural drift.

[0076] The drift trajectory with good time continuity was selected for calculation.

[0077] Data quality control involves checking, filtering, and correcting multiple sets of data, removing or correcting erroneous data, and retaining high-quality, reliable data to ensure the accuracy of subsequent analysis results. Common data quality control methods include data range checking (removing values ​​outside the reasonable range), time series analysis (detecting abnormal time variation patterns), and comparison and verification with other data sources.

[0078] Step 130: Using operational forecast ocean current data, obtain the simulated current velocity at the position of the surface drift trajectory through interpolation.

[0079] Operational ocean current forecasts consist of periodically released current forecasts for specific sea areas by specialized ocean forecasting agencies or systems. These data are typically provided according to a spatial grid and time intervals. However, the location of the drift trajectory may not necessarily lie precisely at these grid points. Therefore, interpolation methods are needed to estimate the simulated current velocity at the drift's location based on current forecast data from known grid points around the drift trajectory's location. There are many interpolation methods, such as linear interpolation, bilinear interpolation, and cubic spline interpolation. Their purpose is to reasonably infer the data value at the unknown location based on information from known data points, so that the simulated current velocity can reflect the ocean current conditions at the drift's location as accurately as possible.

[0080] Step 140: Establish a regression model between simulated flow velocity and instantaneous drift velocity of the surface, correct the simulated flow velocity, apply the corrected simulated flow velocity to search and rescue forecasts, and calculate and output the rolling forecast results.

[0081] When establishing a regression model, the simulated current velocity is obtained by interpolation based on operational forecast data, while the instantaneous drift velocity of the surface drift is the actual observed drift velocity. Due to various factors (such as errors in ocean current forecast models and the inherent characteristics of the surface drift), there may be some difference between the simulated current velocity and the instantaneous drift velocity. By establishing a regression model, a quantitative relationship between the simulated current velocity and the instantaneous drift velocity can be found. The regression model can be a linear regression model (such as univariate linear regression or multivariate linear regression) or a nonlinear regression model, the specific choice depending on the data characteristics and analytical requirements. By fitting the regression model, a functional relationship between the simulated current velocity and the instantaneous drift velocity of the surface drift can be obtained, which can be used to correct the simulated current velocity.

[0082] When correcting the simulated current velocity, a pre-established regression model is used to calculate the corresponding correction amount based on the simulated current velocity value. This correction amount is then added to the original simulated current velocity to obtain the corrected simulated current velocity. The corrected simulated current velocity is closer to the actual ocean current velocity reflected by the surface drift motion, thus improving the accuracy of ocean current forecasts.

[0083] By utilizing corrected simulated current velocities and combining information such as the initial position and movement time of surface drift, search and rescue drift trajectories can be predicted. This allows for the simulation of the movement trajectory of surface drifts (representing potentially missing objects) over a future period. Rolling forecasts involve continuously acquiring new surface drift data over time and repeatedly predicting search and rescue drift trajectories, providing more accurate real-time forecast information to adapt to dynamic changes in ocean currents and surface drift movement, thereby improving the success rate of search and rescue operations. The calculated forecast results are output in an appropriate format.

[0084] Figure 1 The method described herein involves acquiring multiple sets of float drift trajectory data for a predetermined sea area; performing quality control on the acquired float drift data and calculating the instantaneous drift velocity of the float drift trajectory using the quality-controlled float drift data; using operationally forecasted ocean current data, obtaining the simulated current velocity at the float drift trajectory position through interpolation; establishing a regression model between the simulated current velocity and the instantaneous drift velocity of the float drift; correcting the simulated current velocity; and applying the corrected simulated current velocity to search and rescue forecasts, calculating and outputting rolling forecast results. Establishing a regression model to explore the relationship between simulated current velocity and the instantaneous drift velocity of the float drift enables more accurate simulation of the float drift trajectory, better reduces the overall forecast error, and can provide certain guidance for search and rescue forecasting work.

[0085] based on Figure 1 In addition to the method described herein, this specification also provides some specific implementation methods of this method, which will be described below.

[0086] In step 140, a regression model is established between the simulated flow velocity and the instantaneous drift velocity of the surface float to correct the simulated flow velocity. Specifically, this may include:

[0087] Based on the simulated current velocity and the instantaneous current velocity calculated from the surface drift trajectory, regression models were established for different preset sea areas, with the simulated velocity as the independent variable and the calculated instantaneous drift velocity of the surface drift as the dependent variable.

[0088] Use the least squares method to determine the coefficients of the regression model so that the sum of squared residuals is minimized;

[0089] Based on the coefficient of determination, highly correlated floating data are selected to construct regression models for different preset sea areas.

[0090] In step 120, the acquired drift data undergoes quality control, and the instantaneous drift velocity of the drift trajectory is calculated using the quality-controlled drift data. Specifically, this may include:

[0091]

[0092] Calculate the instantaneous drift velocity of the drift trajectory;

[0093] Where i represents the number of times the table floats, and n is the nth time point. Let represent the instantaneous drift velocity of the i-th float in the x-direction at time n. This represents the instantaneous drift velocity of the i-th float in the y-direction at time n, lon and lat represent the latitude and longitude of the float's position, t represents the time of the float data, and Rad is the Earth's radius.

[0094] Further, in step 130, using operationally forecasted ocean current data, the simulated current velocity at the position of the surface drift trajectory is obtained through interpolation methods, which may specifically include:

[0095] Determine the temporal and positional relationships between operational forecast ocean current data and surface drift data;

[0096] Based on the temporal and spatial relationships, linear interpolation is used in time and bilinear interpolation is used in space;

[0097] Based on the position of the drift, determine the nearest preset known point and the corresponding ocean current component in the operational forecast ocean current data;

[0098] The simulated flow velocity at the location of the drift trajectory is calculated using linear interpolation.

[0099] Further, in step 140, establishing a regression model for the simulated flow velocity and the instantaneous drift velocity of the surface may specifically include:

[0100] A univariate linear regression model was constructed with simulated velocity as the independent variable and instantaneous drift velocity of the surface as the dependent variable.

[0101] Use the least squares method to determine the coefficients of the regression model to minimize the sum of squared residuals;

[0102] Regression models for different preset sea areas were constructed by selecting highly correlated drift data.

[0103] Specifically, in step 140, applying the corrected simulated flow velocity for search and rescue forecasting, and calculating and outputting the rolling forecast results, may include:

[0104] The corrected simulated flow velocity was applied to the drift trajectory calculation model;

[0105] Calculate and output rolling forecast results for different time periods, including the average distance error for different preset time periods;

[0106] Compare the rolling forecast results before and after the correction to evaluate the correction effect.

[0107] Next, in order to further elaborate Figure 1 The implementation of the Chinese solution will be explained using specific examples:

[0108] For step 120, for example: using the four sets of surface drift data from the South China Sea and the ten sets of surface drift data from the Indian Ocean, calculate the instantaneous drift velocity of the observed surface drift trajectories in the South China Sea and the Indian Ocean respectively. First, perform quality control on the acquired data, removing data from the same time period and data with obvious trajectory abnormalities (e.g., excessive drift velocity, drift trajectory different from natural drift), and select drift trajectories with good time continuity to calculate the corresponding instantaneous drift velocity, as shown in formulas (1) and (2):

[0109]

[0110] Calculate the instantaneous drift velocity of the float drift trajectory; where i represents the 1st to 14th float drift, n is the nth time; lon and lat represent the latitude and longitude of the float drift position, respectively; t represents the time of the float drift data; u represents the instantaneous drift velocity in the x-direction, v represents the instantaneous drift velocity in the y-direction; Rad is the Earth's radius, taken as 6371 kilometers. The instantaneous drift velocities in the x and y directions of 14 sets of float drift trajectories are calculated.

[0111] Calculations lead to the following conclusions: Floaters released between 60°E and 120°E longitude and 1°N and 10°S latitude exhibit a large current velocity u, which can reach a maximum of 1.35 m / s. The distribution of their trajectories also shows that floaters 10202, 10204, 10206, 10209, 10211, and 10215 released in this region all flow eastward under the influence of a large current velocity u, and then exhibit a significant northwestward turn around 85°E.

[0112] For steps 130 and 140, for example: In order to use the observed surface drift data in actual calculations to obtain more accurate calculation results, the existing operational forecast ocean current data is interpolated to the corresponding surface drift position at the corresponding time. Linear interpolation is used in time and bilinear interpolation is used in space to obtain the simulated current velocity (u, v) at the surface drift trajectory position. A regression model is established to explore the relationship between the simulated current velocity and the instantaneous drift velocity of the surface drift, in order to simulate the drift trajectory of the surface drift more accurately.

[0113] like Figure 2 Taking existing operational surface current forecast data from a trusted agency as an example, the data time interval is 1 hour, the spatial resolution is 5 km, and the range is the Northwest Pacific Ocean. It is known that at time t... b Timetable drift positions x0, y0, determination time t b Located between t1 and t2 of operational surface current forecast data, with times t1 and t2 corresponding to current data with a horizontal grid of 1098×1084, spatial bilinear interpolation can be performed on the u and v components of the surface drift positions x0 and y0 at the two times. First, at time t1, based on the known surface drift positions x0 and y0, the four nearest known points Q are determined. 11 = (x1, y1), Q 12 = (x1, y2), Q 21 = (x2, y1), Q 22 = (x2, y2), the ocean current u-components corresponding to the four points are u(Q) and u(Q) respectively. 11 ), u(Q 12 ), u(Q 21 ), u(Q 22 Now we interpolate at point P = (x, y), where x1 ≤ x ≤ x2 and y1 ≤ y ≤ y2.

[0114] Taking the ocean current u component as an example, linear interpolation is performed on y1 and y2 in the x direction to obtain the ocean current u components at positions P1 and P2 as u(P1) and u(P2), respectively, i.e. u(x, y1) and u(x, y2), as shown in formulas (3) and (4):

[0115]

[0116] Linear interpolation is performed on the u-components of the ocean current at points P1 and P2 in the y-direction to obtain the u-component of the ocean current at position P as u(P), i.e., u(x, y), as shown in formula (5):

[0117]

[0118] Using the same method, the ocean current v component v(x, y) at location P is obtained, from which the simulated current velocity u, v at the location of the surface drift trajectory is obtained.

[0119] A regression model was established based on the simulated flow velocity and the instantaneous flow velocity calculated from the float drift trajectory. There are a total of 14 float drift data. Regression models were constructed for the Indian Ocean and the South China Sea buoys respectively, South China Sea (M_S) and Indian Ocean (M_I), as shown in formula (6).

[0120]

[0121] Among them, u c v c The instantaneous drift velocities u in the x and y directions, respectively, are the calculated drift velocities. i , vi are the simulated velocities in the x and y directions, respectively, and a1, a2 and b1, b2 represent the coefficients of the regression model.

[0122] Taking the u-component of ocean currents as an example, this paper introduces the method of constructing a univariate linear regression model, using u as an example. i That is, the simulated velocity in the x-direction is the independent variable, and u is the value of the simulated velocity. c That is, the calculated true instantaneous drift velocity in the x-direction is the dependent variable, a1 is the slope, and b1 is the intercept.

[0123] Using the least squares method, the goal is to find a1 and b1 such that the residual sum of squares (RSS) is minimized, as shown in formula (7):

[0124]

[0125] Among them, u cn For the nth u c (The actual instantaneous drift velocity in the x-direction of the drift) Here is the correction value for the nth model prediction, where b1 is the intercept, a1 is the slope, and u is the slope. in For the nth u i (Simulated velocity in the x-direction). The sum of squared residuals is obtained by summing the squared residuals for all observation points from n=1 to m. The smaller the RSS, the better the correction value of the model prediction. The closer to the true value u cn The better the fit, the better.

[0126] The coefficient of determination R is calculated using formula (8). 2 :

[0127]

[0128] Where TSS is the total sum of squares (TSS), as shown in formula (9):

[0129]

[0130] ucn For the nth u c (The actual instantaneous drift velocity in the x-direction of the drift) For u c The mean represents the degree of dispersion of the data itself.

[0131] ESS is the explained sum of squares (ESS), as shown in formula (10):

[0132]

[0133] For the nth model prediction correction value, The mean of uc (the true instantaneous drift velocity in the x-direction of the drift) is used to measure the model prediction correction. with the mean The degree of deviation.

[0134] Coefficient of determination R 2 The closer the value is to 1, the better the model fit. Based on the above calculation formula, determine a1 and b1 to minimize the residual sum of squares (RSS). Using the same method, determine the slope a2 and intercept b2 of the v component to minimize the residual sum of squares (RSS) of the v component.

[0135] First, regression models were established for the simulated and calculated current velocities of various surface drifts. Surface drifts with high correlations between simulated and calculated current velocities were selected, and regression models for the South China Sea and Indian Ocean were constructed using these highly correlated data. After screening, for the Indian Ocean, drifts 10202, 10210, and 10207 showed R-squared (coefficient of determination) greater than 0.7, indicating a high correlation. For the South China Sea, drifts 10205, 10212, and 10213 showed R-squared (coefficient of determination) greater than 0.8. Furthermore, during rolling forecasts, a significant underestimation of the simulated velocity v for the South China Sea current was observed. Therefore, surface drifts with high v component certainty were used to construct the regression model.

[0136] Based on the simulated flow velocity obtained by interpolation and the calculated instantaneous flow velocity, the coefficients of the two regression models are fitted using the least squares method described above, as shown in Table 1.

[0137] Table 1. Coefficients of independent variables in the u and v directions in the regression model.

[0138]

[0139]

[0140] The coefficients obtained above are applied to the drift trajectory calculation model, and the corresponding u and v values ​​are corrected accordingly. This yields new rolling forecast results, which are compared with the previous uncorrected rolling forecast results in the table below. Case 1 represents calculations performed directly using simulated ocean currents without any processing; Case 2 represents prioritizing only components with larger R-square values, such as the u component in the Indian Ocean (R-square greater than 85%, indicating high correlation), where only the u component is corrected; and the v component in the South China Sea (R-square greater than 85%), where only the v component is corrected; Case 3 represents corrections for both u and v values ​​being considered in the drift model.

[0141] The correction process involves applying the above formula to adjust the values ​​of u and v using the least squares method. The coefficients of u and v are then multiplied by the simulated ocean current by the slope and intercept. The result is considered a revised value that approximates the true value.

[0142]

[0143] Among them, u c v c The instantaneous drift velocities u in the x and y directions, respectively, are the calculated drift velocities. i v i These represent the simulated velocities in the x and y directions, respectively, and a1, a2 and b1, b2 represent the coefficients of the regression model. For example... Figure 3 In the graph, the values ​​highlighted in yellow represent the optimal results among the three cases. Based on the average distance error over 12 hours, Case 1, without any processing, had the best result with only 2 results; Case 2, which only corrected the larger R-square component, had the best result with 5 results; and Case 3, which corrected both u and v, had the best result with 7 results. Based on the average distance error over 24 hours, Case 1 had the best result with only 1 result; Case 2 had the best result with 4 results; and Case 3, which corrected both u and v, had the best result with 9 results. Based on the average distance error over 48 hours, Case 1 had the best result with only 2 results; Case 2 had the best result with 7 results; and Case 3 had the best result with 5 results.

[0144] Comparing the average distance errors over 12 hours and 24 hours, Case 3 is clearly superior. Furthermore, except for the best results from Case 1 (3 sets of data in total for 12 hours and 24 hours), Case 3 has a smaller error rate than Case 1 for all other results (25 sets). Comparing the average errors over 48 hours, Case 2 has an advantage in the number of best predictions (7 sets), but Case 2 has 4 sets of error data that are greater than Case 1, while Case 3 has 3 sets of error data that are greater than Case 1.

[0145] From the perspective of the mean of the average distance errors of 14 groups of surface drift data, the means of the average distance errors of the 14 groups of surface drift data in Case3 at 12h, 24h, and 48h are the lowest. In ascending order of the mean of the average distance error, it is Case3 < Case2 < Case1. Based on the above calculations, Case3 reduces the overall prediction error to a certain extent and can provide certain guidance for the search and rescue prediction work.

[0146] Based on the same idea, the present invention also provides a device for inverse sea current search and rescue prediction based on surface drift data, as Figure 4 shown. The device may include:

[0147] A surface drift trajectory data acquisition module 410 for acquiring multiple groups of surface drift trajectory data in a preset sea area;

[0148] An instantaneous drift velocity determination module 420 for performing quality control on the acquired surface drift data and calculating the instantaneous drift velocity of the surface drift trajectory using the surface drift data after quality control; the instantaneous drift velocity at least includes the instantaneous drift velocities in the x direction and the y direction;

[0149] A simulated flow velocity determination module 430 for obtaining the simulated flow velocity at the surface drift trajectory position by interpolation using the sea current data of operational forecasting;

[0150] A search and rescue prediction module 440 for establishing a regression model between the simulated flow velocity and the instantaneous drift velocity of the surface drift, correcting the simulated flow velocity, and applying the corrected simulated flow velocity for search and rescue prediction, calculating and outputting the rolling prediction result.

[0151] Based on Figure 4 the device in, some specific implementation units may also be included:

[0152] Optionally, the search and rescue prediction module 440 may specifically be used for:

[0153] Based on the simulated flow velocity and the instantaneous flow velocity calculated according to the surface drift trajectory, establish regression models for different preset sea areas respectively, with the simulated velocity as the independent variable and the calculated instantaneous drift velocity of the surface drift as the dependent variable;

[0154] Use the least squares method to determine the coefficients of the regression model to minimize the sum of squared residuals;

[0155] Select the surface drift data with high correlation according to the determined coefficients for constructing regression models for different preset sea areas.

[0156] Optionally, the instantaneous drift velocity determination module 420 may specifically be used for:

[0157]

[0158] Calculate the instantaneous drift velocity of the drift trajectory;

[0159] Where i represents the number of times the table floats, and n is the nth time point. Let represent the instantaneous drift velocity of the i-th float in the x-direction at time n. This represents the instantaneous drift velocity of the i-th float in the y-direction at time n, lon and lat represent the latitude and longitude of the float's position, t represents the time of the float data, and Rad is the Earth's radius.

[0160] Optionally, the instantaneous drift speed determination module 420 can also be used for:

[0161] Determine the temporal and positional relationships between operational forecast ocean current data and surface drift data;

[0162] Based on the temporal and spatial relationships, linear interpolation is used in time and bilinear interpolation is used in space;

[0163] Based on the position of the drift, determine the nearest preset known point and the corresponding ocean current component in the operational forecast ocean current data;

[0164] The simulated flow velocity at the location of the drift trajectory is calculated using linear interpolation.

[0165] Optionally, the search and rescue forecast module can be used specifically for:

[0166] A univariate linear regression model was constructed with simulated velocity as the independent variable and instantaneous drift velocity of the surface as the dependent variable.

[0167] Use the least squares method to determine the coefficients of the regression model to minimize the sum of squared residuals;

[0168] Regression models for different preset sea areas were constructed by selecting highly correlated drift data.

[0169] Optionally, the step of determining the coefficients of the regression model using the least squares method may specifically include:

[0170] The objective function is set as minimizing the sum of squared residuals:

[0171]

[0172] Among them, u cn Let n be the actual instantaneous drift velocity in the x-direction. Here is the correction value for the nth model prediction, where b1 is the intercept, a1 is the slope, and u is the slope. in For the simulated velocity in the nth x-direction, sum the squared residuals over all observation points from n=1 to m to obtain the residual sum of squares. The smaller the RSS, the better. The closer to u cn ;

[0173] By finding the minimum value of the objective function, the slope and intercept of the regression model are determined.

[0174] Formula used:

[0175]

[0176] Calculate the coefficient of determination R 2 ;

[0177] Where TSS represents the total sum of squares:

[0178]

[0179] For u c The mean of the data indicates the degree of dispersion of the data itself;

[0180] ESS is the sum of squares of the regression:

[0181]

[0182] Characterization model prediction correction value with the mean The degree of deviation.

[0183] Optionally, the search and rescue forecast module 440 can be used for:

[0184] The corrected simulated flow velocity was applied to the drift trajectory calculation model;

[0185] Calculate and output rolling forecast results for different time periods, including the average distance error for different preset time periods;

[0186] Compare the rolling forecast results before and after the correction process to evaluate the effectiveness of the correction.

[0187] Following the same approach, embodiments of this specification also provide equipment for retrieving ocean current search and rescue forecasts based on surface drift data. For example... Figure 5 As shown, the device includes:

[0188] The system includes a memory, a processor, and a communication interface coupled to the processor; the memory stores a computer program that can be run by the processor; when the processor runs the computer program, it executes the aforementioned method for ocean current search and rescue forecasting based on surface drift data inversion.

[0189] like Figure 5As shown, the processor described above can be a general-purpose central processing unit (CPU), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits used to control the execution of the program of the present invention. The communication interface described above can be one or more. The communication interface can use any transceiver-like device for communicating with other devices or communication networks.

[0190] like Figure 5 As shown, the terminal device described above may also include a communication line. The communication line may include a path for transmitting information between the components described above.

[0191] Optional, such as Figure 5 As shown, the terminal device may further include a memory. The memory stores a computer program that can be executed by the processor; when the processor executes the computer program, it implements the method provided in the embodiments of the present invention.

[0192] like Figure 5 As shown, the memory can be read-only memory (ROM) or other types of static storage devices capable of storing static information and instructions, random access memory (RAM) or other types of dynamic storage devices capable of storing information and instructions, or electrically erasable programmable read-only memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compressed discs, laser discs, optical discs, universal optical discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but not limited to these. The memory can exist independently and be connected to the processor via communication lines. The memory can also be integrated with the processor.

[0193] Optionally, the computer execution instructions in the embodiments of the present invention may also be referred to as application code, and the embodiments of the present invention do not specifically limit this.

[0194] In a specific implementation, as one example, such as Figure 5 As shown, a processor may include one or more CPUs, such as Figure 5 CPU0 and CPU1 in the CPU.

[0195] In a specific implementation, as one example, such as Figure 5 As shown, the terminal device may include multiple processors, such as Figure 5 The processors in the system. Each of these processors can be a single-core processor or a multi-core processor.

[0196] Based on the same idea, this specification also provides a computer storage medium corresponding to the above embodiments. The computer storage medium stores instructions that, when executed, implement the methods in the above embodiments.

[0197] The foregoing mainly describes the solutions provided by the embodiments of the present invention from the perspective of the interaction between various modules. It is understood that each module, in order to achieve the above functions, includes corresponding hardware structures and / or software units for executing each function. Those skilled in the art should readily recognize that, in conjunction with the units and algorithm steps of the various examples described in the embodiments disclosed herein, the present invention can be implemented in hardware or a combination of hardware and computer software. Whether a function is executed by hardware or by computer software driving hardware depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present invention.

[0198] The embodiments of the present invention can divide functional modules according to the above method examples. For example, each function can be divided into its own functional module, or two or more functions can be integrated into one processing module. The integrated module can be implemented in hardware or as a software functional module. It should be noted that the module division in the embodiments of the present invention is illustrative and only represents one logical functional division; other division methods may be used in actual implementation.

[0199] The processor described in this specification may also function as a memory. The memory stores computer execution instructions for carrying out the present invention, and its execution is controlled by the processor. The processor executes the computer execution instructions stored in the memory, thereby implementing the method provided in the embodiments of the present invention.

[0200] The memory can be read-only memory (ROM) or other types of static storage devices capable of storing static information and instructions, random access memory (RAM) or other types of dynamic storage devices capable of storing information and instructions, or electrically erasable programmable read-only memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compressed discs, laser discs, optical discs, universal optical discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but not limited to these. The memory can exist independently and be connected to the processor via communication lines. The memory can also be integrated with the processor.

[0201] Optionally, the computer execution instructions in the embodiments of the present invention may also be referred to as application code, and the embodiments of the present invention do not specifically limit this.

[0202] The methods disclosed in the above embodiments of the present invention can be applied to a processor or implemented by a processor. The processor may be an integrated circuit chip with signal processing capabilities. During implementation, each step of the above methods can be completed by integrated logic circuits in the processor's hardware or by instructions in software form. The processor can be a general-purpose processor, a digital signal processor (DSP), an ASIC, a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of the present invention. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of the present invention can be directly embodied in the execution of a hardware decoding processor, or executed by a combination of hardware and software modules in the decoding processor. The software modules can reside in random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. This storage medium is located in memory; the processor reads information from the memory and, in conjunction with its hardware, completes the steps of the above methods.

[0203] Although the invention has been described herein in conjunction with various embodiments, those skilled in the art will understand and implement other variations of the disclosed embodiments by reviewing the accompanying drawings, the disclosure, and the appended claims in carrying out the claimed invention. In the claims, the word "comprising" does not exclude other components or steps, and "a" or "an" does not exclude a plurality. A single processor or other unit can implement several functions listed in the claims. While different dependent claims may recite certain measures, this does not mean that these measures cannot be combined to produce good results.

[0204] Although the invention has been described in conjunction with specific features and embodiments, it is obvious that various modifications and combinations can be made therein without departing from the spirit and scope of the invention. Accordingly, this specification and drawings are merely exemplary descriptions of the invention as defined by the appended claims, and are considered to cover any and all modifications, variations, combinations, or equivalents within the scope of the invention. Clearly, those skilled in the art can make various alterations and modifications to the invention without departing from its spirit and scope. Thus, if such modifications and modifications of the invention fall within the scope of the claims and their equivalents, the invention is also intended to include such modifications and modifications.

Claims

1. A method for retrieving ocean current search and rescue forecasts based on surface drift data, characterized in that, The methods include: Acquire multiple sets of surface drift trajectory data for a preset sea area; The acquired drift data is subjected to quality control, and the instantaneous drift velocity of the drift trajectory is calculated using the quality-controlled drift data; the instantaneous drift velocity includes at least the instantaneous drift velocities in the x and y directions; Using operational ocean current data, simulated current velocities at the location of the surface drift trajectory are obtained through interpolation methods; A regression model of simulated flow velocity and instantaneous drift velocity of the surface is established to correct the simulated flow velocity. The corrected simulated flow velocity is then applied to search and rescue forecasts, and rolling forecast results are calculated and output.

2. The method for ocean current search and rescue forecasting based on surface drift data according to claim 1, characterized in that, Establish a regression model between simulated flow velocity and instantaneous surface drift velocity, and correct the simulated flow velocity, specifically including: Based on the simulated current velocity and the instantaneous current velocity calculated from the surface drift trajectory, regression models were established for different preset sea areas, with the simulated velocity as the independent variable and the calculated instantaneous drift velocity of the surface drift as the dependent variable. Use the least squares method to determine the coefficients of the regression model so that the sum of squared residuals is minimized; Based on the coefficient of determination, highly correlated floating data are selected to construct regression models for different preset sea areas.

3. The method for ocean current search and rescue forecasting based on surface drift data according to claim 1, characterized in that, The acquired drift data undergoes quality control, and the instantaneous drift velocity of the drift trajectory is calculated using the quality-controlled drift data. Specifically, this includes: Calculate the instantaneous drift velocity of the drift trajectory; Where i represents the number of times the table floats, and n is the nth time point. Let represent the instantaneous drift velocity of the i-th drift in the x-direction at time n. This represents the instantaneous drift velocity of the i-th float in the y-direction at time n, lon and lat represent the latitude and longitude of the float's position, t represents the time of the float data, and Rad is the Earth's radius.

4. The method for ocean current search and rescue forecasting based on surface drift data according to claim 1, characterized in that, Using operationally forecasted ocean current data, simulated current velocities at the location of surface drift trajectories are obtained through interpolation methods, specifically including: Determine the temporal and positional relationships between operational forecast ocean current data and surface drift data; Based on the temporal and spatial relationships, linear interpolation is used in time and bilinear interpolation is used in space; Based on the position of the drift, determine the nearest preset known point and the corresponding ocean current component in the operational forecast ocean current data; The simulated flow velocity at the location of the drift trajectory is calculated using linear interpolation.

5. The method for ocean current search and rescue forecasting based on surface drift data according to claim 1, characterized in that, The establishment of the regression model for the simulated flow velocity and the instantaneous drift velocity of the surface specifically includes: A univariate linear regression model was constructed with simulated velocity as the independent variable and instantaneous drift velocity of the surface as the dependent variable. Use the least squares method to determine the coefficients of the regression model to minimize the sum of squared residuals; Regression models for different preset sea areas were constructed by selecting highly correlated drift data.

6. The method for ocean current search and rescue forecasting based on surface drift data according to claim 5, characterized in that, The steps for determining the coefficients of the regression model using the least squares method specifically include: The objective function is set as minimizing the sum of squared residuals: Among them, u cn Let n be the actual instantaneous drift velocity in the x-direction. Here is the correction value for the nth model prediction, where b1 is the intercept, a1 is the slope, and u is the slope. in For the simulated velocity in the nth x-direction, sum the squared residuals over all observation points from n=1 to m to obtain the residual sum of squares. The smaller the RSS, the better. The closer to u cn ; By finding the minimum value of the objective function, the slope and intercept of the regression model are determined. Formula used: Calculate the coefficient of determination R 2 ; Where TSS represents the total sum of squares: For u c The mean of the data indicates the degree of dispersion of the data itself; ESS is the sum of squares of the regression: Characterization model prediction correction value with the mean The degree of deviation.

7. The method for ocean current search and rescue forecasting based on surface drift data according to claim 1, characterized in that, The application of the modified simulated flow velocity for search and rescue forecasting, and the calculation and output of rolling forecast results, specifically include: The corrected simulated flow velocity was applied to the drift trajectory calculation model; Calculate and output rolling forecast results for different time periods, including the average distance error for different preset time periods; Compare the rolling forecast results before and after the correction to evaluate the correction effect.

8. A device for retrieving ocean current search and rescue forecasts based on surface drift data, characterized in that, The device includes: The surface drift trajectory data acquisition module is used to acquire multiple sets of surface drift trajectory data for a preset sea area; The instantaneous drift velocity determination module is used to perform quality control on the acquired drift data and calculate the instantaneous drift velocity of the drift trajectory using the quality-controlled drift data; the instantaneous drift velocity includes at least the instantaneous drift velocity in the x-direction and the y-direction. The simulated current velocity determination module is used to obtain the simulated current velocity at the position of the surface drift trajectory by interpolation method using operationally forecasted ocean current data. The search and rescue forecast module is used to establish a regression model between the simulated flow velocity and the instantaneous drift velocity of the surface, correct the simulated flow velocity, apply the corrected simulated flow velocity to perform search and rescue forecasts, and calculate and output rolling forecast results.

9. A device for retrieving ocean current search and rescue forecasts based on surface drift data, characterized in that the device... include: Memory, processor, and communication interface coupled to the processor; The memory stores computer programs that can be executed by the processor; When the processor runs the computer program, it performs the method for ocean current search and rescue forecasting based on surface drift data as described in any one of claims 1 to 7.

10. A computer storage medium, characterized in that, The computer storage medium stores instructions that, when executed by the processor, implement the method for ocean current search and rescue forecasting based on surface drift data as described in any one of claims 1 to 7.

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