Multimodal wave physics field separation method

Abstract

The present application discloses a method for separating the physical field of multimodal fluctuations, which relates to the field of ship and ocean engineering technology. The method comprises: obtaining physical field time series data of the multimodal fluctuations in a geodetic coordinate system, then extracting the propagation velocity of each mode of the multimodal fluctuations through a physical quantity time cloud diagram, and finally establishing a plurality of satellite coordinate systems, wherein the movement velocity of each satellite coordinate system in a target direction corresponds to the propagation velocity of a mode of the multimodal fluctuations, and numerically averaging the physical quantities of each point in each satellite coordinate system so as to separate the physical field data of each mode of the multimodal fluctuations. In addition, the multimodal fluctuation physical field data with a smaller field of view in the geodetic coordinate system is reconstructed in the satellite coordinate system to obtain a large-scale fluctuation velocity field, thereby facilitating accurate research on the fluctuation characteristics of the multimodal fluctuations, and improving the accuracy of multi-physical field detection. The method is of great significance for the application of multi-physical field feature detection and tracking technology for wakes of navigation bodies.

Description

Technical Field

[0001] The present application relates to the field of ship and ocean engineering technology, and in particular to a method for separating multi-modal wave physical fields. Background Art

[0002] Multimodal fluctuations of a vehicle are the water waves generated by a vehicle navigating in an ocean stratified by temperature, salinity, and density. These multimodal fluctuations can propagate very far in the ocean. Detecting the wake of these multimodal fluctuations allows for the detection and tracking of the vehicle, which is of great value in the fields of shipbuilding and ocean engineering.

[0003] Multimodal fluctuations include surface waves and several orders of internal waves. Due to the continuous vertical stratification of fluid density, each multimodal fluctuation has a different propagation speed, and the information of each order of multimodal fluctuations is mixed in the physical field at each moment, making it difficult to analyze the physical characteristics of a single multimodal fluctuation. Summary of the Invention

[0004] In response to the above problems and technical requirements, the applicant has proposed a multi-modal wave physical field separation method. The technical solution of this application is as follows:

[0005] A multimodal wave physical field separation method, the method comprising:

[0006] Obtaining the global physical field time series data of the multimodal fluctuations induced by the navigation of the vehicle in the geodetic coordinate system. The global physical field time series data in the geodetic coordinate system includes the physical quantities of each coordinate point in the fluid calculation domain at each time in the geodetic coordinate system.

[0007] Extract the propagation speed of multi-modal fluctuations in the target direction based on the global physical field time series data acquired in the geodetic coordinate system;

[0008] The global physical field time series data in the geodetic coordinate system are converted into the global physical field time series data in each satellite coordinate system, and each satellite coordinate system moves along the target direction at a corresponding propagation speed;

[0009] The physical field data of one mode in the multimodal fluctuation is extracted based on the global physical field time series data in each body coordinate system, so as to separate the global physical field data to obtain the physical field data of each mode in the multimodal fluctuation in the geodetic coordinate system;

[0010] Among them, the target direction is one or more of the x direction, y direction and z direction. The x direction, y direction and z direction are perpendicular to each other and constitute a three-dimensional coordinate system. The plane formed by the x direction and y direction is parallel to the seabed, and the z direction is perpendicular to the seabed. The navigation body moves along the x direction or the y direction.

[0011] A further technical solution is to extract the physical field data of one mode in the multimodal fluctuation based on the global physical field time series data in each body coordinate system, including:

[0012] The physical quantities of each coordinate point in the fluid calculation domain in the geodetic coordinate system at different sampling times are numerically averaged to extract the physical field data of a mode.

[0013] A further technical solution is to extract multiple propagation velocities of multimodal fluctuations in the target direction based on the acquired global physical field time series data in the geodetic coordinate system, including:

[0014] Extract the physical quantity time cloud map of multimodal fluctuations in the target direction from the global physical field time series data in the geodetic coordinate system. The grayscale value of any pixel point in the i-th row and j-th column in the physical quantity time cloud map corresponds to the physical quantity of the j-th coordinate point along the target direction at the i-th moment, and the physical quantity is positively correlated with the corresponding grayscale value. i and j are both integer parameters.

[0015] Based on the grayscale value of each pixel at each moment, multiple straight lines are fitted from the physical quantity time cloud diagram, and the absolute value of the slope of each straight line is calculated to obtain multiple propagation velocities in the target direction.

[0016] A further technical solution is that the difference in grayscale values of pixel points included in each straight line obtained by fitting from the physical quantity time cloud diagram does not exceed a difference threshold.

[0017] A further technical solution is that the absolute value of the slope of each straight line |k| = |Δε / Δt|, where Δε is the span distance of the fitted straight line in the row direction, and Δt is the span distance of the fitted straight line in the column direction.

[0018] A further technical solution is to obtain multiple propagation velocities in the target direction, including:

[0019] The absolute values of the slopes of the straight lines are clustered into several clusters, and a propagation speed in the target direction is obtained based on the absolute value of the slope of the straight lines in each cluster.

[0020] A further technical solution is to extract the time cloud diagram of the physical quantity of multimodal fluctuations in the target direction from the global physical field time series data in the geodetic coordinate system, including:

[0021] The physical quantities of each coordinate point along the target direction at each moment are extracted from the global physical field time series data in the geodetic coordinate system. After interpolation processing of the physical quantities of each coordinate point at each moment, the imagesc function is used to convert the physical quantities of each coordinate point at each moment into the grayscale values of the corresponding pixel points to obtain the time cloud map of the physical quantity in the target direction.

[0022] A further technical solution is to perform interpolation processing on the physical quantities of each extracted coordinate point at each moment, including:

[0023] For each coordinate point, spline interpolation and smooth fitting are performed between the physical quantities of any two moments of the coordinate point to obtain a smooth curve of the physical quantities of each coordinate point at different moments.

[0024] A further technical solution is that the method further comprises:

[0025] The separated physical field data of the same mode in the geodetic coordinate system are spatially connected in sequence along the target direction to obtain the full-field physical field spatial data of the mode.

[0026] A further technical solution is that the acquired global physical field time series data includes global velocity field time series data, global vortex field time series data, global density field time series data and global pressure field time series data.

[0027] The beneficial technical effects of this application are:

[0028] The present application discloses a method for separating the physical field of multimodal fluctuations. The method first obtains the global physical field time series data of the multimodal fluctuations in the geodetic coordinate system, then extracts the propagation speed of each multimodal fluctuation through the physical quantity time cloud map, and finally establishes several satellite coordinate systems. The speed of each satellite coordinate system corresponds to the propagation speed of a mode of the multimodal fluctuation. The physical quantities of each point in each satellite coordinate system are numerically averaged, so that the physical field data of each mode of the multimodal fluctuation are separated, thereby facilitating the accurate study of the fluctuation characteristics of each mode of the multimodal fluctuation, and is conducive to improving the accuracy of multi-physical field detection.

[0029] This application also improves the method for calculating the propagation speed of multimodal fluctuations. By extracting the characteristic straight lines of multimodal fluctuations on the physical quantity time cloud diagram, the propagation speed of each mode of multimodal fluctuations is calculated. Not only is the anti-interference ability greatly enhanced, but it also has a better signal gain effect.

[0030] This application reconstructs physical field data with a smaller field of view in the geodetic coordinate system to obtain physical field data over a large range, and can further obtain the changes of different modes with space, so as to deeply understand the spatial characteristics and laws of the multi-modal fluctuations of the vehicle, which is of great significance for the application of vehicle wake detection and tracking technology. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 It is a flowchart of an embodiment of the present application.

[0032] Figure 2 This is a plan view of the test pool xoy in an example of the present application.

[0033] Figure 3 This is a plan view of the test pool xoz in an example of the present application.

[0034] Figure 4 This is a physical quantity-time cloud diagram of an example of this application.

[0035] Figure 5 This is a spatial connection diagram of an example of this application.

[0036] Explanation of the accompanying symbols: 1. Navigation body; 2. Multimodal fluctuation; 3. Straight line with y0 coordinate in the y direction; 4. Test tank; 5. Geodetic coordinate system; 6. Body-attached coordinate system. DETAILED DESCRIPTION

[0037] The specific implementation of this application will be further described below with reference to the accompanying drawings.

[0038] like Figure 1 As shown, in one embodiment, the multimodal wave physical field separation method of the present application includes:

[0039] Step S110: Acquire global physical field time series data of multimodal fluctuations induced by the navigation of the vehicle in the geodetic coordinate system. The global physical field time series data in the geodetic coordinate system includes physical quantities of each coordinate point in the fluid calculation domain at each time in the geodetic coordinate system.

[0040] The method of the present application is applicable to various types of physical fields, and the global physical field time series data includes: global velocity field time series data, global vorticity field time series data, global density field time series data and global pressure field time series data, and the corresponding physical quantities obtained are velocity, vorticity, density and pressure.

[0041] Step S120 , extracting multiple propagation velocities of multimodal fluctuations in a target direction based on the acquired global physical field time series data in the geodetic coordinate system.

[0042] The target direction is one or more of the x-direction, y-direction and z-direction. The x-direction, y-direction and z-direction are perpendicular to each other and form a three-dimensional coordinate system. The plane formed by the x-direction and y-direction is parallel to the horizontal plane of the geodetic coordinate system. The z-direction is perpendicular to the horizontal plane of the geodetic coordinate system. The navigation body moves along the x-direction or the y-direction, such as Figure 2 and Figure 3 shown.

[0043] Step S130 , converting the global physical field time series data in the earth coordinate system into the global physical field time series data in each satellite coordinate system, each satellite coordinate system moves along the target direction at a corresponding propagation speed.

[0044] In step S140, the physical field data of one mode in the multimodal fluctuations is extracted based on the global physical field time series data in each body coordinate system, so as to separate the global physical field time series data to obtain the physical field data of each mode in the multimodal fluctuations in the geodetic coordinate system. The multimodal fluctuations include internal waves and surface waves.

[0045] The multimodal fluctuation sequence physical field separation method of the present application extracts the propagation velocity of the multimodal fluctuation by acquiring the global physical field time series data under the multimodal fluctuation, establishes the corresponding body coordinate system according to the different propagation velocities, and separates the physical field data of each mode of the multimodal fluctuation based on the propagation velocity difference characteristics of different modes of the navigation body, thereby facilitating the accurate study of the fluctuation characteristics of each mode of the multimodal fluctuation, and is conducive to improving the accuracy of multi-physical field detection.

[0046] In order to more clearly illustrate the multi-modal wave physical field separation method of the present application, another embodiment of the present application is described in detail below with reference to the accompanying drawings.

[0047] Step S210: obtaining the global physical field time series data of the multimodal fluctuations induced by the navigation of the vehicle in the geodetic coordinate system.

[0048] like Figure 2 and Figure 3 As shown, a test platform is typically constructed in a test pool 4 to acquire global physical field time series data. A detection device is provided in the test pool 4 to sense physical quantities at different coordinates within the fluid computational domain. As the vehicle 1 navigates the test pool 4, the detection device performs multiple sampling operations, thereby acquiring global physical field time series data of the multimodal fluctuation 2 in the geodetic coordinate system.

[0049] For different types of global physical field time series data, it is often necessary to use a detection device that matches the physical quantity to be obtained to obtain the global physical field time series data, such as using a PIV system to obtain global velocity field time series data and global vortex field time series, such as using a conductivity meter array to obtain global density field time series data, and using a pressure sensor array to obtain global pressure field time series data.

[0050] Step S220 , extracting multiple propagation velocities of multimodal fluctuations in a target direction based on the acquired global physical field time series data in the geodetic coordinate system.

[0051] The physical quantity time cloud diagram of multimodal fluctuations in the target direction is extracted from the global physical field time series data in the geodetic coordinate system. The grayscale value of any pixel point in the i-th row and j-th column in the physical quantity time cloud diagram corresponds to the physical quantity of the j-th coordinate point along the target direction at the i-th moment, and the physical quantity is positively correlated with the corresponding grayscale value. i and j are both integer parameters.

[0052] Extracting a time cloud diagram of the physical quantities of multimodal fluctuations in the target direction includes: first, extracting the physical quantities of each coordinate point along the target direction at each time in the global physical field time series data in the geodetic coordinate system. In one embodiment, the physical quantities of each coordinate point on line 3 with y0 as the y-direction coordinate in the fluid calculation domain are taken at each time. Because the turbulent wake in the central wake region of the vehicle is dominant, some modes (such as internal wake waves) have not yet fully formed. Therefore, the line should be selected to deviate from the central wake region of the vehicle, and the distance between the line and the center of the vehicle should be greater than the wake radius of the vehicle.

[0053] Then, the physical quantities of each coordinate point at each moment are interpolated, including: for each coordinate point, spline interpolation and smooth fitting are performed between the physical quantities of any two moments of the coordinate point to obtain a smooth curve of the physical quantities of each coordinate point at different moments.

[0054] Finally, the imagesc function is used to convert the physical quantity of each coordinate point at each moment into the grayscale value of the corresponding pixel point, and the physical quantity time cloud map in the target direction is obtained. In an example, the schematic diagram of the obtained physical quantity time cloud map is as follows Figure 4 shown.

[0055] Based on the grayscale values of each pixel at each moment, multiple straight lines are fitted from the physical quantity time cloud diagram, and the difference in the grayscale values of each pixel on each straight line does not exceed the difference threshold. The difference in the grayscale values of each pixel on each straight line can be determined by a pre-set method, such as the variance or standard deviation of the grayscale values of all pixels on a straight line, or the ratio of the pixels on a straight line whose grayscale values are within a predetermined range to the total number of pixels on the straight line. For example, Figure 4 In the physical quantity time cloud diagram shown in the figure, the 6 straight lines obtained by fitting are l1 to l6. Straight lines l1 to l6 are just the straight lines from Figure 4 Some straight lines are fitted in the physical quantity-time cloud diagram, and other straight lines are not shown one by one.

[0056] Calculate the absolute value of the slope of each line: |k| = |Δε / Δt|, where Δε is the distance the fitted line spans in the row direction, and Δt is the distance the fitted line spans in the column direction. For ease of observation and accurate values, choose either the peak line (with the lowest grayscale value) or the trough line (with the highest grayscale value) as the calculation target.

[0057] The multiple straight lines fitted from the physical quantity time cloud map correspond to the various modes of the multimodal fluctuations. Each mode corresponds to multiple fitted straight lines. In theory, the absolute values of the slopes of the lines corresponding to the same mode are equal. However, due to the certain errors in selecting straight lines based on image features and the continuous grayscale values of images, the slopes of each line often vary. Therefore, after obtaining the absolute values of the slopes of each line, these absolute values are clustered into several clusters. Based on the absolute values of the slopes of the lines in each cluster, a propagation velocity in the target direction is obtained. This includes taking the absolute value of the slope of any line in the same cluster as a propagation velocity, or taking the average of the absolute values of the slopes of all lines in the same cluster as a propagation velocity. Based on this method, multiple propagation velocities can be extracted, each corresponding to a mode of the multimodal fluctuations.

[0058] By improving the calculation method of the propagation velocity of multimodal fluctuations, extracting the characteristic straight lines of multimodal fluctuations on the physical quantity time cloud diagram, and calculating the propagation velocity of each mode of multimodal fluctuations, not only the anti-interference ability is greatly enhanced, but also a better signal gain effect is achieved.

[0059] Step S230 , converting the global physical field time series data in the earth coordinate system into the global physical field time series data in each satellite coordinate system, each satellite coordinate system moves along the target direction at a corresponding propagation speed.

[0060] Through the coordinate mapping relationship between the geodetic coordinate system and the body coordinate system, the physical quantity of each coordinate point in the geodetic coordinate system can be mapped to the body coordinate system at each sampling moment, thereby obtaining the global physical field time series data in each body coordinate system.

[0061] Step S240, based on the global physical field time series data in each body coordinate system, the physical field data of one mode in the multimodal fluctuation is extracted to separate the global physical field time series data to obtain the physical field data of each mode in the multimodal fluctuation in the geodetic coordinate system.

[0062] This involves numerically averaging the physical quantities of each point in the fluid computational domain in the geodetic coordinate system at different sampling times in the body coordinate system. Because the body coordinate system moves at a specific propagation velocity, the physical field data for a single mode corresponding to that propagation velocity can be extracted, while the physical field time series data for other modes is weakened and filtered out. This allows the extraction of global physical field data for each mode, separating the global physical field data for each mode of multimodal fluctuations.

[0063] For example, in one embodiment, assuming that the propagation velocities C1 and C2 of the multimodal fluctuation along the target direction are extracted, the body coordinate system O1 moves along the target direction according to C1, and the body coordinate system O1 of the multimodal fluctuation located at P(x 0j ,y 0i ) point and perform numerical averaging to obtain the time-averaged data of the physical field corresponding to the propagation velocity C1 for the first-order mode. The propagation velocity of the second-order mode in the multimodal fluctuation is C2, which is inconsistent with the velocity C1 of the body coordinate system O1. After the above sampling and averaging, the fluctuation information of the second-order mode is greatly weakened. When the number of sampling times exceeds a certain value, the influence of the second-order mode is considered negligible.

[0064] In step S250 , the separated physical field data of the same mode in the geodetic coordinate system are spatially connected in sequence along the target direction to obtain the full-field physical field spatial data of the mode.

[0065] After obtaining the full-field spatial data of the modal physical field, we can further extract the modal's multi-physical field fluctuation characteristics such as wavelength, aperture angle, wave height, etc.

[0066] This application reconstructs the physical field time series data with a small field of view in the geodetic coordinate system into a large-scale full-field data of the physical field space in the body coordinate system, and can further obtain the spatial changes of different modes of multimodal fluctuations, so as to deeply understand the spatial characteristics and laws of the multimodal fluctuations of the vehicle, which is of great significance for the application of multi-physical field feature detection and tracking technology of the vehicle wake.

[0067] In practical applications, this application is commonly used to separate global velocity field time series data. The following is an example of separating global velocity field data of different modes of multimodal fluctuations, with reference to the accompanying drawings, and includes the following steps:

[0068] Step S310, obtaining the global velocity field time series data of the multimodal fluctuations induced by the navigation of the vehicle in the geodetic coordinate system, wherein the global velocity field time series data in the geodetic coordinate system includes the velocity of each coordinate point in the fluid calculation domain in the geodetic coordinate system at each moment.

[0069] like Figure 2 and Figure 3 As shown, a test platform is built in the test pool 4 to obtain global velocity field time series data.

[0070] A detection device is installed in the test tank 4. In this embodiment, the detection device can be a PIV system, which uses a laser or ordinary light source to illuminate a horizontal surface and a camera or CCD to capture particle images of the fluid calculation domain in the test tank 4. In one embodiment, the detection device detects a plane perpendicular to the z-direction, with the navigation direction of the vehicle 1 being the x-direction and the width of the vehicle 1 being the y-direction. While the vehicle 1 is navigating in the test tank 4, the detection device performs multiple sampling to obtain global velocity field time series data of the multimodal fluctuation 2 in the geodetic coordinate system.

[0071] Step S320 : extracting multiple propagation velocities of the multimodal fluctuations in the target direction based on the acquired global velocity field time series data in the geodetic coordinate system.

[0072] Since velocity is a vector, in order to facilitate the analysis of the fluctuation characteristics of multimodal fluctuations, the acquired global velocity field time series data needs to be decomposed into the x-, y-, and z-directions. In this embodiment, assuming that the x-direction is the u-component velocity, taking the u-component velocity field time series data as an example, the u-component velocity time cloud map of the multimodal fluctuation in the x-direction is extracted from the u-component velocity field time series data in the geodetic coordinate system. The grayscale value of any pixel in the i-th row and j-th column of the u-component velocity time cloud map corresponds to the u-component velocity of the j-th coordinate point along the x-direction at the i-th moment, and the u-component velocity is positively correlated with the corresponding grayscale value, where i and j are both integer parameters.

[0073] Extracting a time cloud of the physical quantities of multimodal fluctuations in the target direction includes first extracting the physical quantities of each coordinate point along the target direction at each moment in the global physical field time series data in the geodetic coordinate system. In this embodiment, the u-component velocity of each coordinate point on line 3 with y0 in the y-direction in the fluid computational domain is taken at each moment. Because the turbulent wake in the central wake region of the vehicle is dominant, some modes (such as internal waves in the wake) have not yet fully formed. Therefore, the line should be selected away from the central wake region of the vehicle, and the distance between the line and the center of the vehicle should be greater than the wake radius of the vehicle.

[0074] Then, the physical quantities of each coordinate point at each moment are interpolated, including: for each coordinate point, spline interpolation and smooth fitting are performed between the u component velocities of any two moments of the coordinate point to obtain a smooth curve of the u component velocities of each coordinate point at different moments. Finally, the imagesc function is used to convert the u component velocities of each coordinate point at each moment into the grayscale values of the corresponding pixel points to obtain the u component velocity time cloud map in the target direction. In one example, the schematic diagram of the obtained physical quantity time cloud map is as follows: Figure 4 In another embodiment, a straight line with coordinate x0 in the x direction in the fluid calculation domain may be used to obtain the time nephogram of the v component velocity in the y direction. The method is the same as the method for obtaining the time nephogram of the u component velocity in the x direction, and will not be repeated here.

[0075] Based on the grayscale value of each pixel at each moment, multiple straight lines are fitted from the u component velocity time cloud map, such as Figure 4 The 6 straight lines obtained by fitting are l1 to l6. Straight lines l1 to l6 are just the straight lines from Figure 4 Some straight lines are fitted in the physical quantity-time cloud diagram, and other straight lines are not shown one by one.

[0076] Calculate the absolute value of the slope of each straight line |k|=|Δε / Δt|, for example Figure 4 In the example, the average values of the absolute values of the slopes of the fitted straight lines are shown in Table 1. The absolute values of the slopes of the six straight lines are clustered. Straight lines l1, l2, and l3 are in one cluster, and the propagation speed is taken as the average value of the absolute values of the slopes, which is 1.12; straight lines l4, l5, and l6 are in another cluster, and the propagation speed is taken as the average value of the absolute values of the slopes, which is 2.42.

[0077] Table 1 Slope of the linear graph of physical quantity time

[0078] Line number l1 l2 l3 l4 l5 l6 Slope of a straight line 1.12 -1.18 1.07 2.45 2.39 -2.43

[0079] By improving the calculation method of the propagation velocity of multimodal fluctuations, extracting the characteristic straight lines of multimodal fluctuations on the physical quantity time cloud diagram, and calculating the propagation velocity of each mode of multimodal fluctuations, not only the anti-interference ability is greatly enhanced, but also a better signal gain effect is achieved.

[0080] Step S330 : Convert the global velocity field time series data in the geodetic coordinate system to obtain the global velocity field time series data in each satellite coordinate system. Each satellite coordinate system moves along the target direction at a corresponding propagation velocity.

[0081] In this embodiment, through the coordinate mapping relationship between the geodetic coordinate system and the body coordinate system, the u-component velocity of each coordinate point in the geodetic coordinate system can be mapped to the body coordinate system at each sampling moment, thereby obtaining the global velocity field time series data in each body coordinate system.

[0082] Step S340: Extract the u-component velocity field data of one mode in the multimodal fluctuation based on the u-component velocity field time series data in each body coordinate system, and separate the u-component velocity field time series data to obtain the u-component velocity field data of each mode in the multimodal fluctuation in the geodetic coordinate system.

[0083] This involves numerically averaging the physical quantities of each point in the fluid computational domain in the geodetic coordinate system at different sampling times in the body coordinate system. Because the body coordinate system moves at a specific propagation velocity, the physical field data for a mode corresponding to that propagation velocity can be extracted, while the physical field data for other modes is weakened and filtered out. This allows the physical field data for each mode to be extracted, separating the physical field data for each mode of multimodal fluctuation.

[0084] For example, in one embodiment, assuming that the propagation velocities C1 and C2 of the multimodal fluctuation along the target direction are extracted, the body coordinate system O1 moves along the target direction according to C1, and the body coordinate system O1 of the multimodal fluctuation located at P(x 0j ,y 0i ) is sampled multiple times and numerically averaged, and the velocity component u data of the first-order mode corresponding to the propagation velocity C1 can be obtained. The propagation velocity of the second-order mode in the multimodal state is C2, which is inconsistent with the motion velocity C1 of the body coordinate system O1. After the above sampling and averaging, the fluctuation information of the second-order mode is greatly weakened. When the number of sampling times exceeds a certain value, the influence of the second-order mode fluctuation is considered to be negligible. In addition, after separating the physical field data of each mode, the multi-physical field fluctuation characteristics such as the wavelength, opening angle, and wave height of the mode can be further extracted.

[0085] In one embodiment, taking the first-order mode of multimodal fluctuations as an example, when the body coordinate system O1 corresponding to the first-order mode moves a certain distance in the x-direction at a speed C1, the movement time is recorded, and the product of the movement time and the preset sampling frequency is rounded to the integer to obtain the number of sampling times.

[0086] Step S350: The u-component velocity field data of the same mode obtained by separation in the geodetic coordinate system are spatially connected in sequence along the target direction to obtain continuous u-component velocity field data, such as Figure 5 shown.

[0087] In this embodiment, the spatial connection direction is the x direction. When t changes from 0 to t1 and then to t2, the coordinates of the body coordinate system in the x direction also change continuously. j For example, when t=0, x j The point corresponds to x in the body coordinate system 0j When the coordinate system moves with the body, 0j The points are continuously moved in the continuous direction in the geodetic coordinate system, and the image combination technology is used to splice them into a large-scale image in the x direction in chronological order.

[0088] This application reconstructs the velocity field time series data with a small field of view in the geodetic coordinate system into a large-scale velocity field data in the body coordinate system, and can further obtain the spatial changes of different modes of multimodal fluctuations, so as to deeply understand the spatial characteristics and laws of multimodal fluctuations of the vehicle, which is of great significance for the application of vehicle wake detection and tracking technology.

[0089] It should be noted that the separation method of various physical fields is similar to the above-mentioned velocity field embodiment. Except for the detection device and the obtained physical quantity time cloud diagram, the other steps are the same. Those skilled in the art can conduct experiments based on this, so it will not be repeated here.

[0090] The above are only preferred embodiments of the present application, and the present application is not limited to the above embodiments. It is understood that other improvements and variations directly derived or associated with those skilled in the art without departing from the spirit and concept of the present application should be considered to be included in the scope of protection of the present application.

Claims

1. A multimodal wave physical field separation method, characterized in that: The method comprises: Acquire global physical field time series data of multimodal fluctuations induced by the navigation of the vehicle in the geodetic coordinate system, wherein the global physical field time series data in the geodetic coordinate system includes physical quantities of each coordinate point in the fluid calculation domain at each time in the geodetic coordinate system; Extracting the respective propagation velocities of the multimodal fluctuations in the target direction based on the acquired global physical field time series data in the geodetic coordinate system; Converting the global physical field time series data in the geodetic coordinate system to obtain the global physical field time series data in each satellite coordinate system, each satellite coordinate system moving along the target direction at a corresponding propagation speed; Extracting physical field data of one mode in the multimodal fluctuation based on global physical field time series data in each satellite coordinate system, so as to separate the global physical field time series data to obtain physical field data of each mode in the multimodal fluctuation in the geodetic coordinate system, including: numerically averaging physical quantities of each coordinate point in the fluid calculation domain in the geodetic coordinate system at different sampling times in the satellite coordinate system to extract physical field data of one mode; The separated physical field data of the same mode in the geodetic coordinate system are sequentially connected in space along the target direction to obtain the full-field physical field spatial data of the mode; The target direction is x direction, y Direction and z One or more directions, x direction, y Direction and z The directions are perpendicular to each other and form a three-dimensional coordinate system. x Direction and y The plane formed by the directions is parallel to the horizontal plane of the geodetic coordinate system. z The direction is perpendicular to the horizontal plane of the geodetic coordinate system, and the navigation body moves along x Direction or y Direction movement.

2. The multimodal wave physical field separation method according to claim 1, characterized in that: The extracting the respective propagation velocities of the multimodal fluctuations in the target direction based on the acquired global physical field time series data in the geodetic coordinate system includes: Extracting a physical quantity time cloud map of the multimodal fluctuation in the target direction from the global physical field time series data in the geodetic coordinate system, wherein the grayscale value of any pixel point in the i-th row and j-th column in the physical quantity time cloud map corresponds to the physical quantity of the j-th coordinate point along the target direction at the i-th moment, and the physical quantity is positively correlated with the corresponding grayscale value, where i and j are both integer parameters; Based on the grayscale value of each pixel point at each moment, a plurality of straight lines are fitted from the physical quantity time cloud diagram, and the absolute value of the slope of each straight line is calculated to obtain a plurality of propagation speeds in the target direction.

3. The multimodal wave physical field separation method according to claim 2, characterized in that: The difference in grayscale values of pixel points included in each straight line obtained by fitting the physical quantity time cloud diagram does not exceed a difference threshold.

4. The multimodal wave physical field separation method according to claim 2, characterized in that: The absolute value of the slope of each straight line ,in, is the distance spanned by the fitted straight line in the row direction, is the distance spanned by the fitted straight line in the column direction.

5. The multimodal wave physical field separation method according to claim 2, characterized in that: The obtaining of a plurality of propagation velocities in the target direction includes: The absolute values of the slopes of the straight lines are clustered into a number of clusters, and a propagation speed in the target direction is obtained based on the absolute value of the slope of the straight lines in each cluster.

6. The multimodal wave physical field separation method according to claim 2, characterized in that: The step of extracting a physical quantity time cloud diagram of the multimodal fluctuation in a target direction from the global physical field time series data in the geodetic coordinate system includes: The physical quantities of each coordinate point along the target direction at each moment are extracted from the global physical field time series data in the geodetic coordinate system; after interpolation processing is performed on the physical quantities of each coordinate point at each moment, the imagesc function is used to convert the physical quantities of each coordinate point at each moment into the grayscale values of the corresponding pixel points to obtain a time cloud map of the physical quantities in the target direction.

7. The multimodal wave physical field separation method according to claim 6, characterized in that: The interpolation processing of the physical quantities of each extracted coordinate point at each moment includes: For each coordinate point, spline interpolation and smooth fitting are performed between the physical quantities of any two moments of the coordinate point to obtain a smooth curve of the physical quantities of each coordinate point at different moments.

8. The multimodal wave physical field separation method according to claim 1, characterized in that: The acquired global physical field time series data includes global velocity field time series data, global vorticity field time series data, global density field time series data and global pressure field time series data.

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