A target underwater falling point prediction method and device

Abstract

The application discloses a target underwater falling point prediction method and device, and the method comprises the following steps: S1, obtaining accident information; S2, determining marine environment information based on the accident time and the accident position; S3, performing sampling analysis and processing on the accident information and the marine environment information to obtain a target trajectory set; and S4, processing the accident position and the target trajectory set to obtain a falling point prediction result. The application can improve the efficiency and accuracy of target falling point prediction.

Description

Technical Field

[0001] This invention belongs to the field of underwater target search, specifically a method and apparatus for predicting the underwater landing point of a target. Background Technology

[0002] After a maritime accident, a rapid emergency response is required to quickly detect and salvage the lost target. However, deep-sea searches are like finding a needle in a haystack. There are two main reasons why searching for deep-sea targets is so difficult: first, the target is scattered over a wide area on the seabed, making the search area reach tens or even hundreds of kilometers; second, the depth and orientation of the target penetrating the seabed sediments bring certain difficulties to the detection and search work.

[0003] Therefore, after a target object is lost, it is essential to combine current field information from the accident area to calculate and analyze its underwater sinking location, thereby reducing the cost of deep-sea operations. Currently, the detection and search for deep-sea targets often relies on past search experience to determine the final sinking range of the target, thus conducting large-scale area searches. This search strategy depends too heavily on past experience, introduces significant uncertainty, and results in a low success rate for deep-sea target detection and search, leading to persistently high deep-sea operation costs. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to provide a method and apparatus for predicting the underwater landing point of a target object, which can improve the efficiency and accuracy of target object landing point prediction, improve search efficiency, and reduce the cost of deep-sea operations.

[0005] To address the aforementioned technical problems, this invention discloses a method for predicting the underwater landing point of a target object, the method comprising:

[0006] S1. Obtain accident information; the accident information includes the accident time, accident location, target mass, target volume, feature length, and feature width of the target object;

[0007] S2. Based on the time and location of the accident, determine the marine environmental information; the marine environmental information includes seawater density, wave height, wave amplitude, wave number, wave direction, and wave frequency;

[0008] S3. The accident information and the marine environment information are sampled, analyzed, and processed to obtain a set of target trajectories;

[0009] The target trajectory set includes N A sequence of target trajectories; N It is an integer greater than 1;

[0010] The target trajectory sequence includes several target locations;

[0011] S4. Process the crash location and the target trajectory set to obtain the landing point prediction result; the landing point prediction result includes the search and rescue area and the landing point probability matrix;

[0012] The search and rescue area includes Individual grid areas; and All are integers greater than 1;

[0013] The landing point probability matrix includes OK List.

[0014] As an optional implementation, in the first aspect of the present invention, determining marine environmental information based on the time and location of the accident includes:

[0015] S21. Using a preset marine environment prediction model, perform prediction processing based on the time of the accident and the location of the accident to obtain prediction results and prediction environmental information;

[0016] S22. Judge the prediction result;

[0017] When the prediction result is successful, the marine environmental information is determined to be the predicted environmental information; S3 is executed;

[0018] If the prediction result is unsuccessful, execute S23;

[0019] S23. Based on the time and location of the accident, query a preset historical environmental information database to obtain historical environmental information; determine the marine environmental information as the historical environmental information.

[0020] Both the predicted environmental information and the historical environmental information include the seawater density, wave height, wave amplitude, wave number, wave direction, and wave frequency.

[0021] As an optional implementation, in the first aspect of the present invention, the sampling and analysis processing of the accident information and the marine environmental information to obtain a target trajectory set includes:

[0022] S31. Establish random initial motion vectors ;

[0023] , and These are the random initial roll angle, random initial pitch angle, and random initial yaw angle, respectively. , and These are the random initial translational velocities of the target object along the X, Y, and Z axes of a preset global coordinate system, respectively. , and These are the random initial rotational angular velocities of the target object about the X-axis, Y-axis, and Z-axis of the global coordinate system, respectively.

[0024] S32, Perform the random initial motion vector... N Second sampling, obtained N Initial motion vectors ;

[0025] S33, respectively utilize N Using the initial motion vector, combined with the accident information and the marine environment information, a preset target trajectory model is solved to obtain... N A sequence of target trajectories; the target trajectory model includes a target motion sub-model and an ocean current sub-model.

[0026] As an optional implementation, in the first aspect of the present invention, the expression of the target motion sub-model is:

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[0044] In the formula, ( x,y,z () represents the target location; t The time of the accident; 、 and These are roll angle, pitch angle, and yaw angle, respectively. , and These are the translational velocities of the target object along the X, Y, and Z axes of the global coordinate system, respectively. , and These are the rotational angular velocities of the target object about the X-axis, Y-axis, and Z-axis of the global coordinate system, respectively. m The target quality; The target volume; The density of the seawater; g It is the acceleration due to gravity; , , , These are the elements in the 2nd row and 2nd column, 3rd row and 3rd column, 5th row and 5th column, and 6th row and 6th column of the additional mass matrix, respectively. , and These are the elements in the first row and first column, the second row and second column, and the third row and third column of the moment of inertia matrix, respectively. υ The preset seawater kinematic viscosity coefficient; L and D These are the feature length and the feature width, respectively; , and These are the preset drag coefficients of the target object in the X, Y, and Z directions, respectively; , and These represent the drag forces acting on the target object in the X, Y, and Z directions, respectively. and These are the dragging torques experienced by the target object in the Y and Z directions, respectively; The lateral lifting force experienced by the target object in the Y direction; The vertical lifting force experienced by the target object in the Z direction; The vertical lifting torque of the target object in the Z direction; The vertical lifting torque of the target object in the Y direction; and These are the components of the relative velocity of the target object with respect to the wave in the Y-axis and Z-axis directions of the global coordinate system, respectively.

[0045] As an optional implementation, in the first aspect of the present invention, the expression of the ocean current sub-model is:

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[0052] In the formula, For the wave height, A For the amplitude, Wave phase angle; k For the wave number, For the wave direction, The frequency is the wave frequency; , and These are the components of the wave propagation speed in the X, Y, and Z axes of the global coordinate system, respectively. , and These are the components of the coupling velocity of waves and ocean currents in the X, Y, and Z axes of the global coordinate system, respectively. , and The components of the ocean current velocity in the X, Y, and Z axes of the global coordinate system.

[0053] As an optional implementation, in the first aspect of the present invention, the processing of the crash location and the target trajectory set to obtain the landing point prediction result includes:

[0054] S41. Based on the accident location, perform grid division processing to obtain the search and rescue area;

[0055] S42. Process the search and rescue area and the target trajectory set to obtain... N The coordinates of the landing point;

[0056] S43, to NThe probability estimation process is performed on the coordinates of the landing points to obtain the landing point probability distribution;

[0057] S44. Based on the probability matrix calculation model, the landing point probability distribution and the search and rescue area are processed to obtain the landing point probability matrix.

[0058] As an optional implementation, in the first aspect of the present invention, the method of... N The landing point coordinates are subjected to probability estimation processing to obtain the landing point probability distribution, including:

[0059] S431, Calculation N Statistical information of the coordinates of the landing points; the statistical information includes a first mean, a second mean, a first standard deviation, and a second standard deviation;

[0060] S432. Based on the statistical information, respectively... N The coordinates of the landing points are standardized to obtain... N A standardized coordinate system;

[0061] S433, to N The standardized coordinates are processed to obtain a distance matrix; the distance matrix is... N × N Matrix;

[0062] S434. Process the distance matrix to obtain N The set of nearest point indices and N Average distance;

[0063] The nearest point index set includes K One index value; K greater than 1 and less than N Integers; the index value is 1 to N Integers;

[0064] S435. Using the correlation value calculation model, for N The standardized coordinates and N The nearest point index set is processed to obtain N One relevant value;

[0065] The correlation value calculation model is as follows:

[0066]

[0067] In the formula, For the first ia The aforementioned relevant values, ia From 1 to N Integers; For the first iaThe set of nearest point indices; and The first ia The and the first ja The standardized coordinates described above;

[0068] S436. Construct a model using probability distributions, for N The average distance mentioned above, N The relevant values ​​and N The landing point coordinates are processed to obtain the landing point probability distribution. p ( xx , yy );

[0069] The probability distribution construction model is as follows:

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[0071] In the formula, p ( xx , yy ) represents the probability distribution of the landing point; For the first ia The average distance mentioned above; xx and yy These are the coordinate values ​​in the X and Y axes, respectively.

[0072] A second aspect of this invention discloses a device for predicting the underwater landing point of a target object. The device includes an accident information acquisition module, a marine environment prediction query module, a target trajectory prediction module, and a prediction result generation module.

[0073] The accident information acquisition module is used to acquire accident information;

[0074] The marine environment prediction and query module is used to determine marine environment information based on the time and location of the accident.

[0075] The target trajectory prediction module is used to sample and analyze the accident information and the marine environment information to obtain a set of target trajectories;

[0076] The prediction result generation module is used to process the crash location and the target trajectory set to obtain the landing point prediction result; the landing point prediction result includes the search and rescue area and the landing point probability matrix.

[0077] A third aspect of this invention discloses another underwater landing point prediction device for a target object, the device comprising:

[0078] Memory containing executable program code;

[0079] A processor coupled to the memory;

[0080] The processor calls the executable program code stored in the memory to execute some or all of the steps in the underwater landing point prediction method for the target object disclosed in the first aspect of the present invention.

[0081] The fourth aspect of the present invention discloses a computer storage medium storing computer instructions, which, when invoked, are used to execute some or all of the steps in the underwater landing point prediction method for a target object disclosed in the first aspect of the present invention.

[0082] Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0083] This invention obtains multiple target trajectory sequences through sampling analysis and combines them with fine grid division for landing point prediction, which can improve the efficiency and accuracy of landing point prediction, thereby improving search efficiency and reducing deep-sea operation costs. Attached Figure Description

[0084] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0085] Figure 1 This is a flowchart illustrating a method for predicting the underwater landing point of a target object, as disclosed in an embodiment of the present invention.

[0086] Figure 2 This is a schematic diagram of the underwater landing point prediction device for a target object disclosed in an embodiment of the present invention.

[0087] Figure 3 This is a schematic diagram of another underwater landing point prediction device for a target object disclosed in an embodiment of the present invention. Detailed Implementation

[0088] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0089] In the description of this invention, it should be noted that the terms "center," "upper," "lower," "left," "right," "vertical," "horizontal," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are used only for the convenience of describing the invention and for simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0090] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0091] Example 1

[0092] Please see Figure 1 . Figure 1 This is a flowchart illustrating a method for predicting the underwater landing point of a target object, as disclosed in an embodiment of the present invention. Figure 1 The described underwater impact point prediction method is applied to underwater target search, such as underwater impact point prediction for wrecked ships, aircraft, etc., and the embodiments of this invention are not limited thereto. Figure 1 As shown, the method includes:

[0093] S1. Obtain crash information; the crash information includes the crash time, crash location, target mass, target volume, feature length, and feature width of the target object.

[0094] It should be noted that the aforementioned target object can be a wrecked ship or aircraft, and this embodiment of the invention does not limit it.

[0095] It should be noted that the above-mentioned crash locations are expressed in latitude and longitude.

[0096] It should be noted that the feature length and feature width mentioned above are the maximum and minimum lengths of the planar projection of the target object, respectively.

[0097] S2. Based on the above-mentioned time and location of the accident, determine the marine environmental information; the above-mentioned marine environmental information includes seawater density, wave height, wave amplitude, wave number, wave direction and wave frequency.

[0098] S3. The above-mentioned accident information and marine environmental information are sampled, analyzed and processed to obtain the target trajectory set.

[0099] The aforementioned set of target trajectories includes N A sequence of target trajectories; N It is an integer greater than 1.

[0100] The above target trajectory sequence includes several target locations.

[0101] It should be noted that the target positions in the above target trajectory sequence represent the underwater coordinates of the target object as time increases after it falls into the water.

[0102] S4. Process the above-mentioned accident location and the above-mentioned target trajectory set to obtain the landing point prediction result; the above-mentioned landing point prediction result includes the search and rescue area and the landing point probability matrix.

[0103] The aforementioned search and rescue area includes Individual grid areas; and All are integers greater than 1.

[0104] The above landing probability matrix includes OK List.

[0105] It should be noted that the search and rescue area mentioned above is a rectangular area, which is divided along its two sides into... OK Column, get Individual grid areas.

[0106] It should be noted that in the above landing probability matrix, the first... ii Line number jj The elements of the column represent the number of the target object located in the search and rescue area. ii Line number jj The probability values ​​of the subgrid regions of the column, where ii From 1 to integers, jj From 1 to Integers.

[0107] In an optional embodiment, the determination of marine environmental information based on the aforementioned time and location of the accident includes:

[0108] S21. Using a pre-set marine environment prediction model, based on the above-mentioned accident time and accident location, perform prediction processing to obtain prediction results and prediction environment information.

[0109] It should be noted that the above-mentioned marine environment prediction model can be the "Wenhai" marine environment intelligent forecasting model, the "Xihe" global eddy distinguishable marine environment forecasting model, or the "Pangu" marine model, etc., and the embodiments of the present invention are not limited thereto.

[0110] It should be noted that the above prediction results are either successful or unsuccessful.

[0111] S22. Judge the above prediction results;

[0112] If the above prediction result is successful, the above marine environmental information is determined to be the predicted environmental information; proceed to step S3;

[0113] If the above prediction result is unsuccessful, execute S23.

[0114] S23. Based on the above-mentioned accident time and accident location, query the preset historical environmental information database to obtain historical environmental information; determine the above-mentioned marine environmental information as the above-mentioned historical environmental information.

[0115] The aforementioned predicted environmental information and the aforementioned historical environmental information both include the aforementioned seawater density, the aforementioned wave height, the aforementioned wave amplitude, the aforementioned wave number, the aforementioned wave direction, and the aforementioned wave frequency.

[0116] It should be noted that the aforementioned historical environmental information database includes marine environmental information at different historical times and locations. When the time and location of the accident are entered, the historical environmental information closest to the time and location of the accident can be retrieved.

[0117] In another optional embodiment, the above-mentioned sampling and analysis of the accident information and the above-mentioned marine environmental information to obtain a target trajectory set includes:

[0118] S31. Establish random initial motion vectors .

[0119] , and These are the random initial roll angle, random initial pitch angle, and random initial yaw angle, respectively. , and These are the random initial translational velocities of the aforementioned target objects along the X, Y, and Z axes of a preset global coordinate system, respectively. , and These are the random initial rotational angular velocities of the target object around the X, Y, and Z axes of the global coordinate system, respectively.

[0120] It should be noted that the above-mentioned random initial motion vector is used to represent the initial motion state of the target object when it falls into the water, and each component in the random initial motion vector is an independent random variable.

[0121] Optionally, each component in the above-mentioned random initial motion vector follows a uniform distribution.

[0122] Preferably, the above , and The value range is [0, 150], and the unit is m / s.

[0123] Preferably, the above , and The value range is [-120, 120], and the unit is rad / s.

[0124] Preferably, the range of the above-mentioned random initial roll angle is [ , ].

[0125] Preferably, the range of the above-mentioned random initial pitch angle is [ , ]

[0126] Preferably, the range of the above-mentioned random initial yaw angle is [ , ]

[0127] It should be noted that, in order to describe the motion of the target object and the waves, a global coordinate system is established. This global coordinate system is a Cartesian coordinate system, in which the X-axis, Y-axis, Z-axis and origin are defined as follows: (1) The X-axis is a horizontal coordinate axis, which can be due east, due north or a certain horizontal reference direction. This embodiment of the invention does not limit this. (2) The Y-axis is a horizontal coordinate axis perpendicular to the X-axis. (3) The Z-axis is a coordinate axis perpendicular to the horizontal plane. It can be vertically upward or vertically downward. This embodiment of the invention does not limit this. When the Z-axis is vertically upward, Z=0 represents the mean sea level or free water surface; Z>0 represents the area above the water surface; Z<0 represents the area below the water surface. The corresponding coordinate values ​​represent the water depth. (3) The origin is a certain reference point, such as the coastline, the seabed or a certain fixed structure. This embodiment of the invention does not limit this.

[0128] S32. Perform the above-mentioned random initial motion vector... N Second sampling, obtained N Initial motion vectors .

[0129] S33, respectively utilize NBased on the aforementioned initial motion vectors, combined with the aforementioned accident information and marine environmental information, the preset target trajectory model is solved to obtain... N A sequence of target trajectories; the above target trajectory model includes a target motion sub-model and an ocean current sub-model.

[0130] The above-mentioned target trajectory model is obtained by substituting any initial motion vector, as well as the crash information and ocean environment information, into the target motion sub-model and ocean current sub-model to obtain two sets of equations, which are then solved simultaneously to obtain multiple target positions.

[0131] As can be seen, by simultaneously solving multiple target trajectory models based on the initial motion state of the target obtained from multiple samplings, multiple target trajectory sequences can be quickly obtained, providing sufficient data input for subsequent landing point prediction and improving the efficiency and accuracy of landing point prediction.

[0132] In yet another optional embodiment, the expression for the above target motion sub-model is:

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[0150] In the formula, ( x, y, z () represents the target location mentioned above; t The time of the accident; 、 and These are roll angle, pitch angle, and yaw angle, respectively. , and These are the translational velocities of the aforementioned target object along the X, Y, and Z axes of the aforementioned global coordinate system, respectively. , and These are the rotational angular velocities of the aforementioned target object about the X, Y, and Z axes of the aforementioned global coordinate system, respectively. m For the aforementioned target quality; The target volume is as described above; The density of the seawater is as described above; g It is the acceleration due to gravity; , , , These are the elements in the 2nd row and 2nd column, 3rd row and 3rd column, 5th row and 5th column, and 6th row and 6th column of the additional mass matrix, respectively. , and These are the elements in the first row and first column, the second row and second column, and the third row and third column of the moment of inertia matrix, respectively.

[0151] It should be noted that the above-mentioned time of the accident t This indicates the time elapsed since the moment of the accident, i.e., the moment of the accident. t The value is 0.

[0152] It should be noted that the roll angle, pitch angle, and yaw angle mentioned above represent the angles by which the target object rotates around the X-axis, Y-axis, and Z-axis of the global coordinate system, respectively.

[0153] It should be noted that in fluid mechanics, the added mass matrix can be represented by a 6×6 symmetric matrix, where each component corresponds to the inertial effect of the object in its six degrees of freedom (three translational degrees of freedom and three rotational degrees of freedom). Among them: (1) This represents the added mass of the target object when it translates along the Y-axis. When the target object accelerates or decelerates along the Y-axis, the fluid needs to be accelerated or decelerated, generating a corresponding inertial force, the magnitude of which is... Proportional. (2) This represents the added mass of the target object when it translates along the Z-axis. When the target object accelerates or decelerates along the Z-axis, the fluid needs to be accelerated or decelerated, generating a corresponding inertial force, the magnitude of which is... Proportional. (3) This represents the added mass (i.e., pitch mass) when the target object rotates about the Y-axis. As the target object rotates about the Y-axis, the fluid also moves accordingly, producing a pitch mass of similar magnitude to... Proportional torque. (4) This represents the added mass (i.e., yaw mass) when the target object rotates around the Z-axis. As the target object rotates around the Z-axis, the fluid also moves accordingly, producing a mass similar in magnitude to... The torque of inertia is proportional to the torque of inertia.

[0154] It should be noted that the above-mentioned additional mass matrix can be solved by the surface element method, which is an existing technology and will not be described in detail in this embodiment of the invention.

[0155] It should be noted that the moment of inertia of an object in water can be represented by a 3x3 symmetric moment of inertia matrix, where... , and Located on the diagonal of the moment of inertia matrix, these represent the moments of inertia of the target object about the Y-axis, X-axis, and Z-axis, respectively.

[0156] It should be noted that the above-mentioned moment of inertia matrix can be obtained through the mass distribution of the target object, specifically by integrating the product of the mass element in the target object and the square of the distance from the rotation axis. This method is existing technology and is not limited in the embodiments of the present invention.

[0157] This is the preset seawater kinematic viscosity coefficient.

[0158] Preferably, the kinematic viscosity coefficient of the seawater is 1.14 × .

[0159] L and D These are the feature length and feature width mentioned above, respectively; , and These are the drag coefficients of the aforementioned target object in the X, Y, and Z directions, respectively.

[0160] Optional, , and The value range is [1.0, 1.8].

[0161] , and These represent the drag forces exerted on the target object in the X, Y, and Z directions, respectively.

[0162] and These represent the dragging torques experienced by the aforementioned target object in the Y and Z directions, respectively.

[0163] The lateral lifting force exerted on the target object in the Y direction is given by the above-mentioned force. This represents the vertical lifting force exerted on the target object in the Z direction.

[0164] The vertical lifting torque of the aforementioned target object in the Z direction; This is the vertical lifting torque of the aforementioned target object in the Y direction.

[0165] and These are the components of the relative velocity of the target object with respect to the waves in the Y-axis and Z-axis directions of the global coordinate system.

[0166] It should be noted that the above target motion sub-model is a 6-DOF motion equation, which describes the underwater unpowered sinking law of the target after it falls into the water. It comprehensively considers the translational and rotational motion of the target in the X, Y and Z directions, thus improving the accuracy of the target trajectory sequence.

[0167] In yet another optional embodiment, the expression for the above-mentioned ocean current sub-model is:

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[0174] In the formula, For the above wave height, A For the above amplitude, Wave phase angle; k For the above wave number, For the aforementioned wave direction, The aforementioned frequency; , and These represent the components of the wave propagation speed along the X, Y, and Z axes of the aforementioned global coordinate system; , and These are the components of the coupling velocity of waves and ocean currents in the X, Y, and Z axes of the aforementioned global coordinate system, respectively. , and These are the components of the ocean current velocity in the X, Y, and Z axes of the aforementioned global coordinate system.

[0175] As can be seen, the above ocean current sub-model further describes the velocity of the target object relative to the waves under the action of ocean currents. Thus, based on the target motion sub-model, the role of ocean currents in the sinking process of the target object can be further considered, thereby improving the accuracy of the target trajectory sequence.

[0176] In another optional embodiment, the above-mentioned processing of the crash location and the target trajectory set to obtain the landing point prediction result includes:

[0177] S41. Based on the above-mentioned accident location, a grid division process is performed to obtain the above-mentioned search and rescue area.

[0178] S42. Process the above-mentioned search and rescue area and the above-mentioned target trajectory set to obtain... N The coordinates of the landing point.

[0179] S43, to N The above-mentioned landing point coordinates are subjected to probability estimation processing to obtain the landing point probability distribution.

[0180] S44. Based on the probability matrix calculation model, the above landing point probability distribution and the above search and rescue area are processed to obtain the above landing point probability matrix.

[0181] It is evident that by using fine grid division and estimation of landing point probability distribution, we can accurately obtain the probability value of the target object being located in each grid by leveraging the concept of probability. This avoids blindly conducting search work, improves search efficiency, and reduces the cost of deep-sea operations.

[0182] In another optional embodiment, the above-mentioned grid division process based on the crash location yields the above-mentioned search and rescue area, including:

[0183] S411. Establish a system with a length of [length missing] centered on the crash site. Width is The rectangular area to be searched, where This is the preset grid side length.

[0184] Preferably, the side length of the grid is 5 km.

[0185] S412. Divide the rectangular region to be searched into sections along the length and width directions. OK List three square subgrid regions, where the side length of each subgrid region is . .

[0186] In another optional embodiment, the above-mentioned search and rescue area and the above-mentioned target trajectory set are processed to obtain... N The coordinates of the landing point include:

[0187] S421, respectively for N The above target trajectory sequences are subjected to curve fitting processing to obtain... N A trajectory curve.

[0188] Preferably, the above curve fitting process is a polynomial curve fitting.

[0189] S422. Based on a preset seabed elevation database, query the seabed elevation of the search and rescue area to obtain an elevation set; the elevation set includes the seabed elevation of several query locations within the search and rescue area.

[0190] S423. Perform surface fitting processing on all the above query locations and corresponding seabed elevations to obtain the seabed surface.

[0191] Preferably, the above surface fitting process is polynomial surface fitting.

[0192] S424, calculate separately N The intersection of the trajectory curve and the seabed surface yields... N The coordinates of the landing point.

[0193] It should be noted that the intersection point calculated above is a three-dimensional coordinate in the global coordinate system. Removing the Z-axis coordinate along the vertical direction from this three-dimensional coordinate system yields the two-dimensional landing point coordinate.

[0194] In yet another alternative embodiment, the above-described... N The above-mentioned landing point coordinates are subjected to probability estimation processing to obtain the landing point probability distribution, including:

[0195] S431, Calculation N Statistical information on the coordinates of the aforementioned landing points; the aforementioned statistical information includes the first mean. Second mean First standard deviation Second standard deviation ;

[0196] The expression for calculation is:

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[0199] In the formula, For the first ia The coordinates of the above landing points, ia From 1 to N Integers.

[0200] S432. Based on the above statistical information, respectively... N The coordinates of the above landing points are standardized to obtain N A standardized coordinate system;

[0201] The expression for calculation is:

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[0203] In the formula, For the first ia The above-mentioned standardized coordinates.

[0204] S433, to N The above standardized coordinates are processed to obtain a distance matrix; the above distance matrix is... N × N The matrix.

[0205] It should be noted that the distance matrix above is a symmetric matrix, where the element in the k1-th row and k2-th column represents the Euclidean distance between the k1-th and k2-th standardized coordinates, where k1 and k2 are both integers from 1 to N.

[0206] S434. Process the above distance matrix to obtain... N The set of nearest point indices and N Average distance.

[0207] The above set of nearest point indices includes K One index value; K greater than 1 and less than N Integers; the index value above is 1 to N Integers.

[0208] S435. Using the correlation value calculation model, for N The above standardized coordinates and N Process the above set of nearest point indices to obtain N One relevant value;

[0209] The above-mentioned correlation value calculation model is as follows:

[0210]

[0211] In the formula, For the first ia The aforementioned related values; For the first ia The above set of nearest point indices; and The first ia The and the first jaThe above-mentioned standardized coordinates.

[0212] S436. Construct a model using probability distributions, for N The above average distance, N The above-mentioned relevant values ​​and N The above-mentioned landing point coordinates are processed to obtain the above-mentioned landing point probability distribution. p ( xx , yy );

[0213] The above probability distribution is constructed as follows:

[0214]

[0215] In the formula, p ( xx , yy ) represents the above probability distribution of landing points; For the first ia The above average distance; xx and yy These are the coordinate values ​​in the X and Y axes, respectively.

[0216] It should be noted that the above landing point probability distribution is a probability density function, which represents the probability density of the target landing point at each location within the search and rescue area. The location of the search and rescue area is represented by the coordinates of the X and Y axes of the global coordinate system.

[0217] In yet another optional embodiment, the distance matrix is ​​processed as described above to obtain... N The set of nearest point indices and N The average distance includes:

[0218] S4341. Process the above distance matrix to obtain... N A set of nearest point indices.

[0219] Optionally, the distance matrix is ​​processed as described above to obtain... N A set of nearest point indices, used to calculate the distance matrix. N The largest in each row K The column indices corresponding to each element are combined to form the nearest point index set for that row.

[0220] S4342. Using the average distance calculation model, the above distance matrix and N Process the above set of nearest point indices to obtain N Average distance;

[0221] The above average distance calculation model is as follows:

[0222]

[0223] In the formula, For the first ia The above average distance, ia From 1 to N Integers; For the first ia The above set of nearest point indices; For the above distance matrix, the first... ia Line number ja The elements of the column.

[0224] In yet another optional embodiment, the expression for the above probability matrix calculation model is:

[0225]

[0226] In the formula, The landing probability matrix is ​​the first... ii Line number jj Column elements; For the first search and rescue area ii Line number jj The subgrid region of the column; ii From 1 to integers, jj From 1 to Integers.

[0227] As can be seen, the above probability matrix calculation model obtains the probability value of the target object landing within each sub-grid region by integrating the landing probability distribution. Furthermore, the landing probability matrix can effectively identify sub-grid regions with high probability values, helping search and rescue personnel determine priority search areas and routes.

[0228] As can be seen, the underwater landing point prediction method for targets described in the embodiments of the present invention, by obtaining multiple target trajectory sequences through sampling analysis and combining them with fine grid division for landing point prediction, can improve the efficiency and accuracy of landing point prediction, thereby improving search efficiency and reducing deep-sea operation costs.

[0229] Example 2

[0230] Please see Figure 2 , Figure 2 This is a schematic diagram of the structure of a target object underwater landing point prediction device disclosed in an embodiment of the present invention. Figure 2 The described underwater target landing point prediction device can be applied to underwater target search, such as underwater landing point prediction for wrecked ships, aircraft, etc., and the embodiments of the present invention are not limited thereto. Figure 2 As shown, the device may include an accident information acquisition module 201, a marine environment prediction query module 202, a target trajectory prediction module 203, and a prediction result generation module 204.

[0231] The aforementioned accident information acquisition module 201 is used to acquire accident information.

[0232] The aforementioned marine environment prediction query module 202 is used to determine marine environment information based on the aforementioned accident time and accident location.

[0233] The aforementioned target trajectory prediction module 203 is used to sample, analyze, and process the aforementioned accident information and marine environmental information to obtain a set of target trajectories.

[0234] The prediction result generation module 204 is used to process the above-mentioned accident location and the above-mentioned target trajectory set to obtain the landing point prediction result; the landing point prediction result includes the search and rescue area and the landing point probability matrix.

[0235] It is evident that implementing the underwater landing point prediction device for targets described in the embodiments of the present invention can improve the efficiency and accuracy of landing point prediction, thereby improving search efficiency and reducing deep-sea operation costs.

[0236] Example 3

[0237] Please see Figure 3 , Figure 3 This is a schematic diagram of another underwater landing point prediction device for targets disclosed in an embodiment of the present invention. Figure 3 The described underwater target landing point prediction device can be applied to underwater target search, such as underwater landing point prediction for wrecked ships, aircraft, etc., and the embodiments of the present invention are not limited thereto. Figure 3 As shown, the underwater impact point prediction device for the target object may include the following parts:

[0238] Memory 301 storing executable program code;

[0239] Processor 302 coupled to memory 301;

[0240] The processor 302 calls the executable program code stored in the memory 301 to execute the steps in the underwater landing point prediction method for the target object described in Embodiment 1.

[0241] Example 4

[0242] This invention discloses a computer read storage medium that stores a computer program for electronic data interchange, wherein the computer program causes a computer to execute the steps in the underwater landing point prediction method for a target object described in Embodiment 1.

[0243] The device embodiments described above are merely illustrative. The modules described as separate components may or may not be physically separate, and the components shown as modules may or may not be physical modules; that is, they may be located in one place or distributed across multiple network modules. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0244] Through the detailed description of the above embodiments, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, including read-only memory (ROM), random access memory (RAM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), one-time programmable read-only memory (OTPROM), electrically-Erasable Programmable Read-Only Memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disc storage, disk storage, magnetic tape storage, or any other computer-readable medium that can be used to carry or store data.

[0245] Finally, it should be noted that the underwater landing point prediction method and apparatus for a target object disclosed in the embodiments of the present invention are merely preferred embodiments of the present invention and are only used to illustrate the technical solutions of the present invention, not to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for predicting a fall point of a target under water, characterized by, include: S1. Obtain crash information; crash information includes the crash time, crash location, target mass, target volume, feature length, and feature width of the target object; S2. Based on the time and location of the accident, determine the marine environmental information; the marine environmental information includes seawater density, wave height, wave amplitude, wave number, wave direction, and wave frequency; S3. Sample and analyze the accident information and marine environmental information to obtain the target trajectory set; a set of target trajectories, comprising N a target trajectory sequence; N is an integer greater than 1. The target trajectory sequence includes several target locations; S4. Process the accident location and target trajectory set to obtain the landing point prediction result; the landing point prediction result includes the search and rescue area and the landing point probability matrix; Search and rescue area, including Individual grid areas; and All are integers greater than 1; Landing probability matrix, including OK List; S4 includes: S41. Based on the accident location, perform grid division to obtain the search and rescue area; S42. Process the search and rescue area and target trajectory set to obtain... N The coordinates of the landing point; S43, to N The probability distribution of landing points is obtained by performing probability estimation on the coordinates of each landing point. S44. Based on the probability matrix calculation model, the landing point probability distribution and search and rescue area are processed to obtain the landing point probability matrix; S43 includes: S431, Calculation N Statistical information on the coordinates of each landing point; the statistical information includes the first mean, the second mean, the first standard deviation, and the second standard deviation; S432. Based on statistical information, respectively... N The coordinates of each landing point are standardized to obtain N A standardized coordinate system; S433, to N The distance matrix is ​​obtained by processing the standardized coordinates; the distance matrix is... N × N Matrix; S434. Process the distance matrix to obtain... N The set of nearest point indices and N Average distance; The nearest point index set includes K One index value; K greater than 1 and less than N Integers; index values ​​from 1 to N Integers; S435. Using the correlation value calculation model, for N A standardized coordinate and N Process the set of nearest point indices to obtain N One relevant value; For the first ia One relevant value, ia From 1 to N Integers; For the first ia A set of nearest point indices; and The first ia The and the first ja A standardized coordinate system; S436. Construct a model using probability distributions, for N Average distance N Each relevant value and N The landing point coordinates are processed to obtain the landing point probability distribution. p ( xx , yy ); For the first ia Average distance; xx and yy These are the coordinate values ​​in the X and Y axes, respectively.

2. The method for predicting the underwater landing point of a target object according to claim 1, characterized in that, The determination of marine environmental information based on the time and location of the accident includes: S21. Using a preset marine environment prediction model, perform prediction processing based on the time of the accident and the location of the accident to obtain prediction results and prediction environmental information; S22. Judge the prediction result; When the prediction result is successful, the marine environmental information is determined to be the predicted environmental information; S3 is executed; If the prediction result is unsuccessful, execute S23; S23. Based on the time and location of the accident, query a preset historical environmental information database to obtain historical environmental information; determine the marine environmental information as the historical environmental information. Both the predicted environmental information and the historical environmental information include the seawater density, wave height, wave amplitude, wave number, wave direction, and wave frequency.

3. A device for predicting the underwater landing point of a target object, characterized in that, The apparatus for implementing the underwater impact point prediction method for targets as described in any one of claims 1-2 includes an accident information acquisition module, a marine environment prediction query module, a target trajectory prediction module, and a prediction result generation module. The accident information acquisition module is used to acquire accident information; The marine environment prediction and query module is used to determine marine environment information based on the time and location of the accident. The target trajectory prediction module is used to sample and analyze the accident information and the marine environment information to obtain a set of target trajectories; The prediction result generation module is used to process the crash location and the target trajectory set to obtain the landing point prediction result; the landing point prediction result includes the search and rescue area and the landing point probability matrix.

4. A device for predicting the underwater landing point of a target object, characterized in that, The device includes: Memory containing executable program code; A processor coupled to the memory; The processor calls the executable program code stored in the memory to execute the underwater landing point prediction method for the target object as described in any one of claims 1-2.

5. A computer-storable medium, characterized in that, The computer storage medium stores computer instructions, which, when invoked, are used to execute the underwater landing point prediction method for the target object as described in any one of claims 1-2.

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