Ocean internal wave early warning method based on temperature-depth sensor carried by AUV (Autonomous Underwater Vehicle)

Abstract

The invention discloses an ocean internal wave early warning method based on an AUV carrying temperature-depth sensor, and relates to the technical field of ocean internal wave early warning. The ocean internal wave early warning method comprises the following steps: calculating the maximum floating frequency seawater depth; a water body temperature fluctuation time sequence is collected in real time in a sailing mode; intercepting background field data, and calculating to obtain the average water body fluctuation amplitude of the background field data; calculating the amplitude value of each frequency of the background field data; carrying out descending sorting on the amplitude values of the frequencies of the background field, and calculating an effective amplitude mean value of the background field; calculating an internal wave recognition threshold value by using the effective amplitude mean value of the background field; setting a sliding time window, and counting an effective amplitude mean value of a water body in the time window; drawing a water body effective amplitude mean value curve, and judging whether ocean internal waves exist or not; and if ocean internal waves are found in different navigation directions, floating and alarming are carried out.

Description

Technical Field

[0001] The present invention relates to the technical field of ocean internal wave early warning, and in particular to an ocean internal wave early warning method based on an AUV equipped with a temperature-depth sensor. Background Art

[0002] In the ocean, the prerequisite for the generation of internal waves is the vertical stratification of seawater density. The stratification phenomenon that is common in the ocean is caused by the difference in temperature and salinity of seawater. At a certain depth in the ocean, there are thermoclines and haloclines, which lead to the appearance of pycnoclines. In an ocean with vertical density stratification, since the density difference is much smaller than the density difference between the atmosphere and seawater, the vertical restoring force of the fluid particles inside the seawater is very small, so even a small disturbance may generate ocean internal waves with large amplitudes.

[0003] Ocean internal waves have a great impact on most marine activities, such as the exploitation of mineral resources such as offshore oil and natural gas, the development of fisheries and marine aquaculture, and the protection of the marine environment. Ocean internal waves can cause large fluctuations in the intensity and propagation speed of acoustic signals, and can cause significant changes in the surface flow field, exerting additional impact forces on marine engineering structures, equipment, and ships. Due to the existence of ocean internal waves, the flow of seawater above and below the density jump layer is in a shear state, which produces a shear effect on moving objects in the water that cross the jump layer, which may affect the movement posture and trajectory of the moving objects.

[0004] The main means of observing ocean internal waves at present include temperature chain, ADCP and satellite remote sensing technology. Temperature chain technology can obtain the record of the change of physical quantities of seawater in different water layers at a fixed position over time; ADCP method can obtain the change of physical quantities of seawater in the direction of sound beam propagation; remote sensing means, especially SAR images, can record the signals of large-scale sea surface flow fields modulated by internal waves, and analyze the characteristics of ocean internal waves from them.

[0005] With the development of unmanned marine equipment, autonomous unmanned underwater vehicles (AUVs) can collect ocean water data more efficiently and flexibly. Cazenave et al. proposed a method for tracking the vertical displacement of thermocline using AUV, which was successfully applied to the internal tidal wave survey in Monterey Bay, California in 2007. Petillo et al. proposed an autonomous thermocline tracking algorithm based on AUV, in which AUV sails in a zigzag trajectory restricted to the vertical direction (i.e., yo-yo trajectory) to observe internal waves in the ocean.

[0006] The above methods can all observe ocean internal waves, but they all have defects and shortcomings:

[0007] 1. Using temperature chain equipment to observe ocean internal waves can obtain high-resolution ocean water data, but because it is a fixed-point observation, it has certain limitations in spatial scale;

[0008] 2. Using ADCP to observe ocean internal waves, whether it is a bottom-mounted or underway type, has very high requirements for the beam angle excited by the transducer. If the sound wave is not emitted vertically in the seawater layer, errors will be generated in subsequent data processing, affecting the data quality;

[0009] 3. Remote sensing can only observe the internal wave characteristics of the sea surface, but cannot obtain data below the sea surface;

[0010] 4. The yo-yo AUV ocean internal wave detection method can continuously track the thermocline curve, but due to its working method, it has certain limitations in spatial resolution and cannot obtain continuous temperature-depth profiles. Summary of the invention

[0011] In order to solve the above technical problems, the present invention uses statistical methods to count the mean effective amplitude of the water temperature when the AUV is sailing at a fixed depth for a period of time. If internal waves appear, the mean effective amplitude of the water temperature at the same depth will definitely increase. When the mean effective amplitude of the water temperature is greater than the set threshold, it is considered that there are suspected ocean internal waves in the area. The navigation direction of the AUV is adjusted by the AUV autonomous heading adjustment algorithm. When the AUV finds ocean internal waves in different navigation directions, it will float up and alarm.

[0012] Specifically, the present invention proposes an ocean internal wave early warning method based on an AUV equipped with a temperature and depth sensor, comprising the following steps:

[0013] S1. Calculate the maximum floating frequency seawater depth;

[0014] S2, real-time collection of water temperature fluctuation time series in the navigation mode;

[0015] S3, intercepting the background field data, and calculating and obtaining the average water body fluctuation amplitude of the background field data;

[0016] S4, calculating the amplitude value of each frequency of the background field data;

[0017] S5, sorting the amplitude values ​​of each frequency of the background field in descending order, and obtaining the average effective amplitude of the background field;

[0018] S6. Calculating the internal wave recognition threshold using the background field effective amplitude mean;

[0019] S7, setting a sliding time window, and calculating the mean effective amplitude of the water body within the time window;

[0020] S8. Draw a curve of the effective amplitude mean of the water body to determine whether there are ocean internal waves;

[0021] S9, repeat steps S7 and S8, and if internal waves are found in different sailing directions, float up and sound an alarm.

[0022] In a preferred embodiment, in step S1, the AUV is used to measure the number of dives in the sea area to obtain the water temperature depth structure background, and the seawater depth corresponding to the maximum float frequency position is obtained by the float frequency calculation function of formula (1):

[0023]

[0024] Among them, N is the floating frequency, ρ is the ocean water density data measured in the previous dive, z is the depth data corresponding to ρ, and g is the gravitational acceleration.

[0025] In a preferred embodiment, in step S2, given a temperature data sequence x 1 ,x 1 ,x 1 ,···,x n And a window of time length k, calculate the standard deviation σ of the temperature data within the time length k:

[0026]

[0027] Among them, x i represents the i-th temperature data, μ represents the mean of the temperature data, and N represents the total number of temperature data.

[0028] In a preferred embodiment, if the temperature data x i If the following formula (3) is satisfied, then x i For wild values, use m i replace,

[0029] |x i -m i |>nσ (3);

[0030] Among them, nσ is the threshold, m i is the median of the data in a sliding window of length k.

[0031] In a preferred embodiment, in step S4, Fourier transform is performed on the background field water body temperature fluctuation data to obtain the amplitude values ​​of different frequencies of the background field;

[0032]

[0033] Among them, y(t) is the measured water temperature fluctuation data within a time length of k, that is, the background field data; y mean is the average water body fluctuation amplitude within a time length of k, F(ω) is the spectrum function after Fourier transformation, and its independent variable is the ω frequency, e -jωt is a complex signal;

[0034] Taking the real part of F(ω) we obtain real(F(ω)), which is the amplitude value of the background field at different frequencies.

[0035] In a preferred embodiment, in step S6, an internal wave discrimination coefficient is set and an internal wave recognition threshold is calculated;

[0036] Thre=coeff*back mean (5);

[0037] Among them, Thre is the internal wave recognition threshold, coeff is the internal wave discrimination coefficient, back mean is the mean effective amplitude of the background field.

[0038] In the preferred embodiment, in step S7, the sliding time window is set equal to the background field time window, the time length is k, step is the time window step, step < k, the time window is slid once every step, and the effective amplitude mean real of the water body in the time window k is calculated. j , and store it in the array effect[real 1 ,real 2 ,real 3 ,...,real j ]middle.

[0039] In a preferred embodiment, in step S8, when effect[real 1 ,real 2 ,real 3 ,...,real j ] In the array, 5 consecutive real j When three of the values ​​are greater than Thre, it is judged that there are suspected internal waves in the area, and the AUV autonomous heading adjustment algorithm is started to adjust the navigation direction of the AUV, and the AUV continues to navigate at a fixed depth to collect water temperature data in real time.

[0040] Compared with the prior art, the present invention has the following beneficial technical effects:

[0041] (1) The ocean internal wave early warning method based on the AUV equipped with a temperature and depth sensor provided by the present invention can be flexibly deployed in various sea areas for ocean internal wave early warning compared with the traditional ocean internal wave detection method;

[0042] (2) The AUV in the present invention adopts a strategy of constant depth navigation, which can continuously collect temperature data at a constant depth, and can effectively improve the spatial resolution;

[0043] (3) The present invention adopts a method of first analyzing the background field data and then analyzing the depth determination data, and can automatically adjust the internal wave recognition threshold according to the actual seawater temperature change characteristics;

[0044] (4) The present invention can identify different types of ocean internal waves by adjusting the internal wave discrimination coefficient according to the needs of the detection purpose;

[0045] (5) Based on the generation characteristics of ocean internal waves, the present invention can effectively identify ocean internal waves and reduce the false alarm rate by automatically adjusting the navigation angle of the AUV. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 This is a flow chart of the ocean internal wave early warning method based on the temperature and depth sensor carried by AUV;

[0047] Figure 2 This is a schematic diagram of the ocean internal wave early warning work based on AUV;

[0048] Figure 3 The floating frequency curve of this sea area is measured by CTD;

[0049] Figure 4 Temperature and depth data of AUV working process;

[0050] Figure 5 This is the temperature-depth data curve of the AUV sailing at a fixed depth of 30 meters;

[0051] Figure 6 It is the amplitude value and sorting of each frequency of the background field;

[0052] Figure 7 To remove outliers from the temperature data of AUV during fixed-depth navigation;

[0053] Figure 8 Calculate the water temperature fluctuation amplitude for the sliding time window;

[0054] Fig. 9 is the effective amplitude mean curve of water body;

[0055] Fig.10 This is the AUV navigation trajectory diagram. DETAILED DESCRIPTION

[0056] The technical scheme in the embodiment of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiment of the present invention. Obviously, the described embodiment is only a part of the embodiment of the present invention, not all of the embodiments. Based on the embodiment of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0057] Example 1

[0058] This embodiment 1 proposes an ocean internal wave early warning method based on an AUV equipped with a temperature and depth sensor, such as Figure 1 As shown, the specific steps are as follows:

[0059] (1) Determine the sea depth with the maximum floating frequency

[0060] By conducting a submerged measurement in the sea area with AUV, the data of ocean water temperature, salinity and density changing with depth were obtained, X(z, t, s, ρ), where z represents depth, t represents temperature, s represents salinity and ρ represents density. The floating frequency changing curve with depth can be obtained by the floating frequency calculation formula of formula (1). When the floating frequency is the largest, the corresponding seawater depth is the position where the amplitude of the up-and-down vibration of the stratified seawater is the largest.

[0061]

[0062] Among them, N is the floating frequency, ρ is the ocean water density data measured in the previous dive, z is the depth data corresponding to ρ, and g is the gravitational acceleration.

[0063] (2) The water temperature fluctuation time series S(t) is collected in real time by cruising mode.

[0064] Extract the temperature data of AUV fixed depth navigation and remove the outliers on the temperature data;

[0065] Given a temperature data sequence x 1 ,x 1 ,x 1 ,…,x n and a window of time length k, calculate the standard deviation of the temperature data within the time length k,

[0066]

[0067] Where σ is the standard deviation, x i represents the i-th temperature data, μ represents the mean of the temperature data, and N represents the total number of temperature data.

[0068] If the temperature data x i If the following formula (3) is satisfied, then x i For wild values, use m i replace:,

[0069] |x i -m i |>nσ (3);

[0070] Where nσ is the given threshold, m i is the median of the data in a sliding window of length k.

[0071] (3) Intercept the background field data and calculate the average water body fluctuation amplitude of the background field data.

[0072] The measured water temperature fluctuation data within a time length of k after the AUV fixed depth navigation is intercepted as the background field data y(t) (the intercepted data must be able to reflect the current sea temperature background field), and the average water body fluctuation amplitude y of the background field data is calculated. mean ;

[0073] (4) Obtain the amplitude value of each frequency of the background field data

[0074] Perform Fourier transform on the background field water temperature fluctuation data to obtain the amplitude values ​​of different frequencies of the background field;

[0075]

[0076] Among them, y(t) is the measured water temperature fluctuation data within a time length of k, that is, the background field data; y mean is the average water body fluctuation amplitude within a time length of k; F(ω) is the spectrum function after Fourier transformation, and its independent variable is the ω frequency, e -jωt It is a complex signal.

[0077] Taking the real part of F(ω) we obtain real(F(ω)), which is the amplitude value of the background field at different frequencies.

[0078] (5) Sort the amplitude values ​​of each frequency of the background field in descending order and calculate the mean effective amplitude of the background field.

[0079] The amplitude values ​​of different frequencies of the background field are sorted in descending order. According to the effective wave height theory, the first 1 / 3 of the data is intercepted and its average value is calculated as the effective amplitude mean value of the background field. mean ;

[0080] (6) Calculate the internal wave recognition threshold

[0081] Set the internal wave discrimination coefficient and calculate the internal wave recognition threshold;

[0082] Thre=coeff*back mean (5);

[0083] Among them, Thre is the internal wave recognition threshold, coeff is the internal wave discrimination coefficient, back mean is the mean effective amplitude of the background field.

[0084] The internal wave discrimination coefficient is mainly set based on statistical laws and the distribution laws of different types of ocean internal waves in different sea areas.

[0085] First, the mean back of the effective amplitude of ocean water fluctuations within a certain period of time is calculated. mean This mean value indicates that the amplitude of the water body fluctuation in the current sea area contains random fluctuations in the back meanIt can be used as a characteristic value to describe the amplitude of water fluctuation in the current sea area.

[0086] Secondly, different internal wave discrimination coefficients are set according to the characteristics of different types of ocean internal waves in different sea areas.

[0087] 1) For the ocean internal wave formation areas such as straits, the ocean internal wave amplitude is relatively small, and the internal wave discrimination coefficient can be set to 1.1 to 1.5;

[0088] 2) For the internal waves in the continental shelf area, the amplitude of the internal waves increases due to the effect of tidal ground, and the internal wave discrimination coefficient can be set to 1.2 to 2.0;

[0089] 3) For the ocean internal waves in the offshore area, affected by the terrain and propagation distance, the ocean internal waves will be dispersed and the energy will gradually dissipate. The internal wave discrimination coefficient can be set to 1.02 to 1.2, while reducing the background field time.

[0090] (7) Set a sliding time window and calculate the mean effective amplitude of the water body within the time window

[0091] Set the sliding time window to be equal to the background field time window, with a time length of k, step is the time window step (step < k), slide the time window once every step, and calculate the effective amplitude mean real of the water body in time window k j , and store it in the array effect[real 1 ,real 2 ,real 3 ,...,real j ]middle.

[0092] (8) Draw the mean effective amplitude curve of the water body to determine whether there are internal waves in the ocean.

[0093] When effect[real 1 ,real 2 ,real 3 ,...,real j ] In the array, 5 consecutive real j When three of the values ​​are greater than Thre, it is judged that there are suspected internal waves in the area, and the AUV autonomous heading adjustment algorithm is started to adjust the navigation direction of the AUV, and the AUV continues to navigate at a fixed depth to collect water temperature data in real time.

[0094] (9) Repeat the process of (7)-(8). If the AUV detects internal waves in different navigation directions, it will surface and sound an alarm.

[0095] Example 2

[0096] The main operation process of an ocean internal wave early warning method based on AUV equipped with temperature and depth sensors is as follows:

[0097] (1) AUV descent phase:

[0098] 1) Using the AUV in-situ observation method, the AUV is equipped with a temperature and depth sensor and deployed in the observation sea area;

[0099] 2) Observe the temperature-depth profile of the sea area. The AUV conducts a submerged measurement in the sea area. The maximum measurement depth can reach the maximum depth of the current sea area as much as possible to obtain the vertical temperature-depth profile of the sea water in the sea area;

[0100] 3) Calculate the vertical floating frequency curve of the area through the measured vertical temperature-depth profile of the seawater to obtain the water depth corresponding to the maximum floating frequency point;

[0101] (4) The AUV navigates at a fixed depth of 3-10 m below the water depth corresponding to the maximum buoyancy point, and collects temperature and depth data in real time during the navigation process;

[0102] (2) AUV ascent phase:

[0103] The AUV rises to a position 3-10m below the water depth corresponding to the maximum buoyancy point and begins depth-fixing navigation;

[0104] (3) AUV fixed depth stage:

[0105] 1) The AUV conducts depth-determining navigation in the observation area and uses the ocean internal wave discrimination algorithm to determine whether there are ocean internal waves in the area;

[0106] 2) Control the navigation direction of the AUV through the AUV autonomous heading adjustment algorithm;

[0107] 4) AUV surfacing stage:

[0108] When the ocean internal wave warning conditions are met, the AUV surfaces and sounds an alarm.

[0109] This example takes the actual sea trial data on July 31, 2024 as an example:

[0110] (1) Figure 2 As shown in the figure, the AUV's ocean internal wave warning working mode is divided into three parts. The first part is the descending section, in order to obtain the current sea water body buoyancy frequency curve; the second part is the ascending section, rising to the vicinity of the maximum buoyancy frequency position; the third part is the depth-fixing section, including depth-fixing navigation and turning; the fourth part is the buoyancy section, after confirming the ocean internal wave, the buoyancy alarm is sounded.

[0111] (2) Figure 3The figure shows the temperature-depth change curve and floating frequency conversion curve of the current sea area measured by the temperature-salinity-depth meter (CTD). The jump layer position in the current sea area is between 20m and 100m, and the maximum floating frequency position is about 31m, so the AUV fixed depth navigation depth is selected to be 30m.

[0112] (3) Figure 4 The figure shows the temperature and depth data collected during the operation of the AUV. It can be seen from the figure that the AUV began to descend at 16:50, dived to the deepest position of 80m at 16:55, and then began to rise to 30m. The period from 16:57 to 17:54 was the depth-fixing navigation stage, and it surfaced to sound the alarm at 17:56.

[0113] (4) Figure 5 The depth and temperature data of the depth-fixing navigation are shown in Figure 1. The depth-fixing duration is about 60min, the depth is 30m, the sampling frequency is 0.6Hz, and the depth-fixing accuracy of this type of AUV is ±0.5m.

[0114] (5) Figure 6 As shown, the first 4 minutes of data are taken as the background field data of this survey line, the effective amplitude range is 1 / 3, the average amplitude of the background field is calculated to be 0.0032m, the discrimination coefficient is set to 1.3, and the internal wave recognition threshold is calculated to be 0.0042m.

[0115] (6) Figure 7 As shown in the figure, the AUV temperature data is processed to remove wild values. The AUV and temperature-depth sensor are stable at depth, and the temperature-depth data are smooth without mutation.

[0116] (7) Figure 8 As shown, 600 sampling points (6 minutes) are used as the sliding window length, 200 sampling points (2 minutes) are used as the sliding window step, and the mean effective amplitude of the water body of the survey line is calculated by the sliding window method;

[0117] (8) Fig. 9 As shown in the figure, the mean effective amplitude of the water body of the survey line is calculated and compared with the internal wave detection threshold. At 17:17, if 3 of the 5 consecutive mean effective amplitudes of the water body are greater than the threshold, it is considered that there are suspected ocean internal waves in the area; the AUV turns left 150° and continues to sail. At 17:50, it is confirmed that there are ocean internal waves in the area. Fig.10 The figure shows the navigation track of the AUV. The AUV turned left 150° at 17:17 and continued to navigate. It surfaced and sounded an alarm at 17:50.

[0118] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. The embodiments should therefore be considered exemplary and non-limiting, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

Claims

1. A method for early warning of internal ocean waves based on an AUV equipped with a temperature and depth sensor, characterized in that: The steps include: S1. Calculate the maximum floating frequency seawater depth; S2, real-time collection of water temperature fluctuation time series in the navigation mode; S3, intercepting the background field data, and calculating and obtaining the average water body fluctuation amplitude of the background field data; S4, calculating the amplitude value of each frequency of the background field data; S5, sorting the amplitude values ​​of each frequency of the background field in descending order, and obtaining the average effective amplitude of the background field; S6. Calculating the internal wave recognition threshold using the background field effective amplitude mean; S7, setting a sliding time window, and calculating the mean effective amplitude of the water body within the time window; S8. Draw a curve of the effective amplitude mean of the water body to determine whether there are ocean internal waves; S9, repeat steps S7 and S8, and if internal waves are found in different sailing directions, float up and sound an alarm.

2. The ocean internal wave early warning method based on the AUV equipped with a temperature and depth sensor according to claim 1 is characterized in that: In step S1, the AUV is used to measure the number of dives in the sea area to obtain the water temperature depth structure background, and the seawater depth corresponding to the maximum float frequency position is obtained through the float frequency calculation function of formula (1): Among them, N is the floating frequency, ρ is the ocean water density data measured in the previous dive, z is the depth data corresponding to ρ, and g is the gravitational acceleration.

3. The ocean internal wave early warning method based on AUV equipped with temperature and depth sensor according to claim 1 is characterized in that: In step S2, given a temperature data sequence x1,x1,x1,···,x n And a window of time length k, calculate the standard deviation σ of the temperature data within the time length k: Among them, x i represents the i-th temperature data, μ represents the mean of the temperature data, and N represents the total number of temperature data.

4. The ocean internal wave early warning method based on the AUV equipped with a temperature and depth sensor according to claim 3 is characterized in that: If the temperature data x i If the following formula (3) is satisfied, then x i For wild values, use m i replace, |x i -m i |>nσ (3); Among them, nσ is the threshold, m i is the median of the data in a sliding window of length k.

5. The ocean internal wave early warning method based on the AUV equipped with a temperature and depth sensor according to claim 3 is characterized in that: In the step S4, Fourier transform is performed on the background field water body temperature fluctuation data to obtain the amplitude values ​​of different frequencies of the background field; Among them, y(t) is the measured water temperature fluctuation data within a time length of k, that is, the background field data; y mean is the average water body fluctuation amplitude within a time length of k, F(ω) is the spectrum function after Fourier transformation, and its independent variable is the ω frequency, e -jωt is a complex signal; Taking the real part of F(ω) we obtain real(F(ω)), which is the amplitude value of the background field at different frequencies.

6. The ocean internal wave early warning method based on the AUV equipped with a temperature and depth sensor according to claim 1 is characterized in that: In the step S6, the internal wave discrimination coefficient is set and the internal wave recognition threshold is calculated; Thre=coeff*back mean (5); Among them, Thre is the internal wave recognition threshold, coeff is the internal wave discrimination coefficient, back mean is the mean effective amplitude of the background field.

7. The ocean internal wave early warning method based on AUV equipped with temperature and depth sensor according to claim 1 is characterized in that: In step S7, the sliding time window is set to be equal to the background field time window, the time length is k, step is the time window step length, step < k, the time window is slid once every step length, and the effective amplitude mean value real of the water body in the time window k is calculated. j , and store them in the array effect[real1,real2,real3,...,real j ]middle.

8. The ocean internal wave early warning method based on the AUV equipped with a temperature and depth sensor according to claim 7 is characterized in that: In step S8, when effect[real1,real2,real3,...,real j ] In the array, 5 consecutive real j When three of the values ​​are greater than Thre, it is judged that there are suspected internal waves in the area, and the AUV autonomous heading adjustment algorithm is started to adjust the navigation direction of the AUV, and the AUV continues to navigate at a fixed depth to collect water temperature data in real time.

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