Underwater acoustic environment observation instrument

Abstract

The invention discloses an underwater sound environment observation instrument which is novel and reasonable in structure, and from two aspects of structural design and signal processing, on one hand, flow-induced noise in a signal acquisition stage is reduced by adopting a seawater flow guiding and buffering means by utilizing a streamline design of a flow guiding frame and an elastic movable mounting mode of a flow guiding cover; on the other hand, non-acoustic interference such as flow-induced noise is further filtered out in the signal processing process through a digital signal processing algorithm, the non-acoustic interference can be filtered out to the maximum extent on the basis that effective information is reserved, and therefore the non-acoustic interference generated when underwater acoustic environment observation equipment works in the marine environment is overcome, and the service life of the underwater acoustic environment observation equipment is prolonged. And the signal-to-noise ratio of received signals and the quality of recorded data of the underwater acoustic environment observation instrument are improved. In addition, the dynamic power supply optimization management technology is adopted, so that the observation time of the equipment is greatly prolonged, the acquisition of high-quality data is ensured, the observation time is prolonged, the observation task can be ensured to be better completed, and the reliability of the observation equipment is improved.

Description

Technical Field

[0001] The invention belongs to the field of measurement, relates to underwater acoustic environment observation technology, and particularly to a novel underwater acoustic environment observation instrument which can effectively resist flow and reduce noise. Background Art

[0002] Acoustic observation equipment needs to be on duty underwater for a long time. In actual applications, affected by the wind and waves on the sea surface and its own structure, the equipment will generate a lot of self-noise and interference, which will reduce the signal-to-noise ratio of the received signal and seriously affect its measurement effect on the real noise of the marine environment and weak underwater acoustic signals. Some studies have shown that among these large amounts of self-noise and interference, noise caused by wind and waves accounts for a large part. When water flows through the underwater acoustic sensor of the equipment, a turbulent boundary layer is formed around the sensor. The pulsating pressure of the turbulent boundary layer compresses the sensor to produce noise, which is generally called "flow-induced noise", or "flow noise" for short. In addition, the shaking of the equipment and the structural connection caused by the impact of the ocean current will also cause the equipment to generate self-noise. All of the above can be called non-acoustic interference that is not needed in acoustic observation.

[0003] In recent years, acoustic observation equipment has made great progress in terms of function, but there are still some defects in performance, such as anti-flow noise reduction. Therefore, it is urgent to propose a new type of underwater acoustic environment observation instrument to solve the problem that the current equipment is still seriously disturbed by flow-induced noise during underwater acoustic signal collection and acoustic signal processing. Summary of the invention

[0004] The purpose of the present invention is to provide a new underwater acoustic environment observation instrument, which can use digital signal processing algorithms to further filter out flow-induced noise and equipment shaking caused by ocean current impact, self-noise generated by structural connections during signal processing. This method can filter out the above-mentioned non-acoustic interference to the maximum extent on the basis of retaining effective information, so as to overcome the non-acoustic interference when the equipment works in the marine environment and improve the signal-to-noise ratio of the received signal and the quality of recorded data of the observation instrument.

[0005] To achieve the above-mentioned purpose, the present invention provides the following scheme: The present invention provides an underwater acoustic environment observation instrument, comprising a guide frame, an instrument cabin and an underwater acoustic sensor, wherein: the underwater acoustic sensor is arranged on the guide frame for collecting underwater acoustic wave signals; the instrument cabin comprises an outer shell, a circuit module and a battery pack are arranged in the outer shell, the circuit module is communicatively connected with the underwater acoustic sensor, and the circuit module has a built-in digital signal processing algorithm, the circuit module can convert the analog signal output by the underwater acoustic sensor into a digital signal, and use the digital signal processing algorithm to filter out non-acoustic interference in the signal during the signal processing process.

[0006] In some embodiments, the circuit module includes a power supply board, a signal acquisition board, a signal processing board and a storage board. The power supply board is electrically connected to the battery pack. The signal acquisition board is communicatively connected to the hydroacoustic sensor and can convert the analog signal output by the hydroacoustic sensor into a digital signal, and then output it to the signal processing board. The signal processing board can use the digital signal processing algorithm to filter out non-acoustic interference in the signal and then output the signal to the storage board. The digital signal processing algorithm includes: Segment and superimpose the signal; Perform autocorrelation operation on the superimposed signal to obtain the autocorrelation function: ,in, Indicates signal, Indicates the delay amount, Indicates the number of sampling points; Perform Fourier transform on the autocorrelation function; The Fourier transform result is the power spectral density function: ,in, represents a complex exponential function, is an imaginary unit, Indicates frequency, Indicates the delay amount, Indicates the number of sampling points; Obtain the window width required for filtering according to the power spectrum density function; According to the selected window, stopband filtering is performed to obtain the signal after filtering out the non-acoustic interference.

[0007] In some implementations, the Fourier transform uses a fast Fourier transform algorithm.

[0008] In some implementations, the fast Fourier transform algorithm is as follows:

[0009] ,in, represents a signal sequence; The fast Fourier transform algorithm includes bit inversion, butterfly weight calculation, butterfly operations at various levels, and output of a fast Fourier transform sequence, wherein: The butterfly weight calculation: In each set of butterfly operations, the rotation factor needs to be calculated and stored. , N is the number of fast Fourier transform points, , according to Euler's formula: , convert the rotation factors into trigonometric function calculations, and when the program is initialized, the calculated results are stored in an array for direct call during the operation; Bit reversal and butterfly operations at all levels: According to the above fast Fourier transform algorithm, decompose step by step according to the following formula until the 2-point discrete Fourier transform: (k=0, 1, ..., N / 2-1) , (k=N / 2,...,N-1) , in, represents the signal sequence, and N is the number of signal sampling points participating in the fast Fourier transform.

[0010] In some implementations, a space-time conversion method is used to integrate and optimize the bit reversal operation with the butterfly operation, and the optimized operation process is as follows: first, two single-precision floating-point arrays of length N, DataReal and DataImag, are established when the program is initialized, where N is the number of signal sampling points participating in the fast Fourier transform, and the initial value is initialized to zero; secondly, during the execution of the program, the collected digital signal is stored in the DataReal array as the real part of the fast Fourier transform; then, during the fast Fourier transform operation, the array subscripts of the vector elements participating in the operation in the DataReal array are calculated; finally, during the butterfly operation, the required vector elements are directly called by a table lookup method according to the array subscripts.

[0011] In some implementations, the signal cable of the hydroacoustic sensor is communicatively connected to the instrument cabin via a watertight joint.

[0012] In some embodiments, a temperature sensor and a voltage acquisition chip are further configured in the instrument cabin, the temperature sensor is used to monitor the temperature in the instrument cabin in real time, and the voltage acquisition chip is used to monitor the power supply voltage of the battery pack in real time; the temperature sensor and the voltage acquisition chip are both communicatively connected to the signal acquisition board, and the signal processing board can estimate the remaining capacity of the battery pack in real time based on the power supply voltage of the battery pack and the temperature in the instrument cabin, in combination with the discharge curve of the battery pack, so as to adjust the observation mode of the underwater acoustic environment observer in real time.

[0013] In some implementations, the observation modes of the underwater acoustic environment observation instrument include: , , and ,in, To adopt a sampling frequency of 15 minutes / hour, To adopt the sampling frequency of 15 minutes / 2 hours, To adopt 200KHz sampling rate, The sampling rate is 40KHz.

[0014] In some embodiments, the underwater acoustic environment observer further includes a flow deflector, which is a hollow mesh cover, and the hydroacoustic sensor is disposed in the flow deflector, and the flow deflector is movably mounted on the flow deflector frame by a spring.

[0015] In some embodiments, the outside of the deflector is also covered with a water-permeable nylon sheath.

[0016] Compared with the prior art, the present invention has achieved the following technical effects: the underwater acoustic environment observation instrument proposed in the present invention has a novel and reasonable structure, and uses a digital signal processing algorithm to further filter out flow-induced noise and equipment shaking caused by ocean current impact, and self-noise generated by structural connections during the signal processing process. This method can filter out the above-mentioned non-acoustic interference to the maximum extent on the basis of retaining effective information, thereby overcoming the non-acoustic interference of the underwater acoustic environment observation equipment when working in the marine environment, and improving the signal-to-noise ratio of the received signal and the quality of recorded data of the underwater acoustic environment observation instrument.

[0017] In some technical solutions disclosed in the present invention, the structural design is also used to further reduce the flow-induced noise in the signal collection stage of the underwater acoustic sensor, that is, the streamlined design of the guide frame and the elastic and movable installation method of the guide cover are used to reduce the flow-induced noise by means of seawater diversion buffering. This flow-induced noise reduction method is combined with the aforementioned method of reducing flow-induced noise by signal processing, which can greatly improve the signal-to-noise ratio of the received signal and the quality of recorded data of the underwater acoustic environment observation instrument.

[0018] In some technical solutions disclosed in the present invention, dynamic power optimization management technology is used to significantly extend the observation time of the equipment, which not only ensures the acquisition of high-quality data but also extends the observation time, can ensure better completion of the observation task and improve the reliability of the observation equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0020] Figure 1 The figure is a schematic diagram of the overall structure of the underwater acoustic environment observer disclosed in an embodiment of the present invention.

[0021] Figure 2 The figure is a schematic diagram of the internal structure of the instrument cabin in the underwater acoustic environment observation instrument disclosed in an embodiment of the present invention.

[0022] Figure 3The present invention is a schematic diagram of the internal structure of an underwater acoustic sensor in an underwater acoustic environment observer disclosed in an embodiment of the present invention.

[0023] Figure 4 The present invention is a flowchart of a non-acoustic interference signal filtering algorithm for an underwater acoustic environment observer disclosed in an embodiment of the present invention.

[0024] Figure 5 It is a schematic diagram of the FFTcos array obtained after initialization in the butterfly weight calculation disclosed in the embodiment of the present invention.

[0025] Figure 6 The diagram is a schematic diagram of the FFTsin array obtained after initialization in the butterfly weight calculation disclosed in the embodiment of the present invention.

[0026] Figure 7 This is a flowchart of the fast Fourier transform algorithm disclosed in an embodiment of the present invention.

[0027] Figure 8 A schematic diagram of the original signal in the simulation of the fast Fourier transform algorithm disclosed in the embodiment of the present invention.

[0028] Fig. 9 The figure is a schematic diagram of filtering the original signal in the simulation of the fast Fourier transform algorithm disclosed in the embodiment of the present invention.

[0029] Fig.10 This is a schematic diagram of the simulation results of the fast Fourier transform algorithm disclosed in an embodiment of the present invention.

[0030] Fig.11 This is a schematic diagram of the original signal in the actual measurement of the fast Fourier transform algorithm disclosed in the embodiment of the present invention.

[0031] Fig.12 The figure is a schematic diagram of filtering the original signal in actual measurement of the fast Fourier transform algorithm disclosed in the embodiment of the present invention.

[0032] Fig.13 This is a schematic diagram of the measured results of the fast Fourier transform algorithm disclosed in an embodiment of the present invention.

[0033] Fig.14 The figure is a schematic diagram of the temperature monitoring principle of the instrument cabin disclosed in the embodiment of the present invention.

[0034] Fig.15 The battery pack voltage monitoring principle disclosed in the embodiment of the present invention is shown in FIG. Figure 1 .

[0035] Fig.16 The battery pack voltage monitoring principle disclosed in the embodiment of the present invention is shown in FIG. Figure 2 .

[0036] Fig.17This is a control principle diagram of the main control chip IC100C disclosed in an embodiment of the present invention.

[0037] Fig.18 This is a control principle diagram of the main control chip IC100B disclosed in an embodiment of the present invention.

[0038] Fig.19 This is a discharge curve diagram of the 18650 battery pack disclosed in an embodiment of the present invention.

[0039] Fig. 20 A schematic diagram of the battery capacity in a battery pack disclosed in an embodiment of the present invention.

[0040] Fig.21 This is a flow chart of dynamic power optimization management of a battery pack disclosed in an embodiment of the present invention.

[0041] Fig. 22 This is the comprehensive spectrum of underwater noise in the ocean.

[0042] In the figure, the reference numerals are: 100, underwater acoustic environment observation instrument; 1, guide frame; 2, instrument cabin; 21, end cover one; 22, end cover two; 23, columnar shell; 24, circuit module; 241, power supply board; 242, signal acquisition board; 243, signal processing board; 244, storage board; 245, socket connector; 246, SD card; 247, main control chip; 248, temperature sensor; 249, voltage acquisition chip; 25, battery pack; 26, data interface; 3, underwater acoustic sensor; 31, piezoelectric composite material; 32, waterproof and sound-permeable layer; 33, signal cable; 34, watertight joint; 4, guide cover; 5, spring; 6, lifting ring; 7, limit ring. DETAILED DESCRIPTION

[0043] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0044] The purpose of the present invention is to provide a new underwater acoustic environment observation instrument, which can further filter out flow-induced noise in the signal processing process by using a digital signal processing algorithm. This method can filter out non-acoustic noise to the maximum extent on the basis of retaining effective information, so as to overcome the flow-induced noise interference when the equipment is working in the marine environment, and improve the signal-to-noise ratio of the received signal and the quality of recorded data of the observation instrument.

[0045] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0046] Example 1: Figure 1~Figure 3 As shown, this embodiment provides an underwater acoustic environment observer 100, which includes a guide frame 1, on which an instrument cabin 2 and a hydroacoustic sensor 3 are arranged. The hydroacoustic sensor 3 is a finished standard part, which mainly includes a piezoelectric composite material 31, a waterproof and sound-permeable layer 32, a signal cable 33 and a watertight joint 34, wherein: the piezoelectric composite material 31 can convert mechanical energy into electrical energy; the waterproof and sound-permeable layer 32 protects the piezoelectric composite material 31 while allowing sound waves to pass through, ensuring that the hydroacoustic sensor 3 can normally receive sound wave signals; the signal cable 33 is located at the rear end of the piezoelectric composite material 31, and connects the output signal line of the piezoelectric composite material 31 to the inner core of the watertight joint 34; the watertight joint 34 is connected to the instrument cabin 2, protects the sealing of the hydroacoustic sensor 3, and prevents moisture from entering and affecting the sensor performance. The instrument cabin 2 includes an outer shell and a circuit module 24 and a battery pack 25 arranged inside the outer shell, wherein: the outer shell includes an end cover 1 21, an end cover 22 and a cylindrical shell 23, and the end cover 1 21 and the end cover 22 are respectively sealed and connected at both ends of the cylindrical shell 23 to ensure the sealing inside the outer shell; the circuit module 24 and the battery pack 25 are loaded inside the cylindrical shell 23, and the outer shell can protect the circuit module 24 and the battery pack 25 from moisture.

[0047] In some embodiments, Figure 2 As shown, the circuit module 24 includes a power board 241, a signal acquisition board 242, a signal processing board 243 and a storage board 244. The boards are connected to each other by a socket connector 245 for transmission of power signals and digital signals. The power board 241 is electrically connected to the battery pack 25, and can convert the electric energy of the battery pack 25 into the DC voltage required by each board and output it to each board; the signal acquisition board 242 is connected to the watertight connector 34 of the hydroacoustic sensor 3, and can convert the analog signal output by the hydroacoustic sensor 3 into a digital signal after passing through the pre-circuit, and then output it to the signal processing board 243, and the signal processing board 243 filters out the non-acoustic interference in the signal and outputs the signal to the storage board 244; the storage board 244 can store the signal in the SD card 246.

[0048] In some embodiments, the battery pack 25 preferably includes at least one group of rechargeable polymer lithium batteries, which can provide power for the circuit module 24 and the hydroacoustic sensor 3 .

[0049] In some embodiments, the instrument cabin 2 is fixed to the guide frame 1 by a limit ring 7; the hydroacoustic sensor 3 is connected to the instrument cabin 2 through its watertight joint 34 to ensure that the hydroacoustic sensor 3 is communicatively connected to the circuit module 24 without destroying the sealing of the outer shell of the instrument cabin 2.

[0050] The above-mentioned underwater acoustic environment observer 100 can switch between the real-time transmission working mode and the self-contained working mode. When the system is set to the real-time transmission working mode, the underwater acoustic sensor 3 inputs the analog signal into the signal acquisition board 242 through the watertight joint 34, and the analog signal is filtered and amplified by the front circuit of the signal acquisition board 242, and then the analog signal is converted into a digital signal through the AD conversion circuit, and output to the signal processing board 243. The signal processing board 243 mainly filters out the non-acoustic interference in the signal, and then transmits the processed signal back to the host computer through the data interface 26, and transmits the signal to the storage board 244, and the storage board 244 stores the signal in the SD card 246. When the system is set to the self-contained working mode, the signal processing board 243 transmits the processed signal to the storage board 244, and the storage board 244 stores the signal in the SD card 246. The underwater acoustic environment observer 100 works underwater for a long time and mostly adopts the self-contained working mode.

[0051] The principle of non-acoustic interference filtering of the above-mentioned signal processing board 243 is as follows: According to research, if Fig. 22 The ocean underwater comprehensive noise spectrum shown in the figure shows that the non-acoustic interference that has a greater impact on the observation of the underwater acoustic environment observer 100 is mainly the flow-induced noise and self-noise caused by wind and waves, etc., which are mainly distributed in the low frequency band (100Hz). The signal processing board 243 mainly performs stop-band filtering on the signal through the dynamic window width selection method. The window width is obtained using the power spectrum density function of the signal, because the power spectrum density function can reflect the following characteristics of the signal: 1. It shows which frequency components are contained in the signal; 2. The power distribution of the signal at different frequency components; 3. The stability and periodicity of the signal can be understood. For example, a periodic signal will show sharp spectral lines at the corresponding frequency points, while a non-periodic signal will show a continuous power distribution in a wider frequency range. The flow-induced noise involved in this scheme can be identified as periodic interference noise, so by analyzing the power spectrum density function, the frequency range and power distribution of the flow-induced noise can be obtained. This scheme uses the signal autocorrelation function to obtain the power spectrum density function, because the Fourier transform of the signal autocorrelation function is equivalent to its power spectrum density. As Figure 4 As shown, it is a flow chart of the principle of non-acoustic interference filtering of the signal processing board 243, and the specific steps are as follows: 1. Segment and superimpose the signal. In this scheme, 1 minute of data is divided into four segments, each of 15 seconds. Because segment superposition can enhance the signal energy, but the noise contained in the signal does not increase its energy due to its randomness, so this step can improve the signal-to-noise ratio; 2. Perform autocorrelation operation on the superimposed signal to obtain the autocorrelation function:

[0052] in, Indicates signal, Indicates the delay amount, Indicates the number of sampling points; 3. Perform Fourier transform on the autocorrelation function; 4. The Fourier transform result is the power spectrum density function:

[0053] in, represents a complex exponential function, is an imaginary unit, Indicates frequency, Indicates the delay amount, Indicates the number of sampling points; 5. Obtain the window width required for filtering based on the power spectrum density function. Find the peak point Max(a, b) of the power spectrum density in the range of 0~100 Hz, where a represents the frequency and b represents the power amplitude. Select the window width as 0~a*2(Hz). If there is no obvious peak point in the range of 0~100 Hz, select the window width as 0~100 Hz; 6. According to the selected window, stopband filtering is performed to obtain the signal after filtering out the non-acoustic interference. The characteristic of this algorithm is that the filter window width is dynamically selected for each group of signals, which avoids excessive calculations or omissions caused by fixed window width, ensures the observation quality, and saves system power consumption.

[0054] In order to improve the system's computing time efficiency and save power consumption, the Fourier transform program in the operation is optimized. The Fourier transform program optimization is as follows: Fourier transform can be optimized into fast Fourier transform algorithm. Fast Fourier transform algorithm mainly includes bit inversion, butterfly weight calculation, butterfly operation at each level and output of fast Fourier transform sequence. The specific algorithm is as follows:

[0055] in, Represents a signal sequence.

[0056] ①Butterfly weight calculation From the above fast Fourier transform algorithm, we can see that in each set of butterfly operations in the fast Fourier transform algorithm, the corresponding rotation factors need to be calculated and stored. , N is the number of fast Fourier transform points, , if the rotation factor is calculated in real time according to the formula , the amount of calculation and complexity are huge, which increases the CPU computing time and slows down the system computing speed. Therefore, this solution is based on Euler's formula:

[0057] Convert the rotation factors into trigonometric calculations, and store the calculated results in an array when the program is initialized. The calculation process can be directly called to reduce the calculation cycle and optimize the program. The code is as follows: tmpomega = SEGMENT_SIZE / 2 for (i = 0; i <tmpomega; i++) { Omega = -1 *PI *i / tmpomega; FFTcos[i] = Math.Cos(Omega); FFTsin[i] = Math.Sin(Omega); } Among them, SEGMENT_SIZE is the number of fast Fourier transform points, FFTcos[i] and FFTsin[i] are arrays with a length of SEGMENT_SIZE / 2 and initialized to zero. Figure 5 , Figure 6 Shown in the figure are the FFTcos and FFTsin arrays obtained after initialization, PI = 3.141592653589793.

[0058] ②Bit reversal and butterfly operations at all levels According to the above fast Fourier transform algorithm, the following formula is used to decompose the data step by step until a 2-point discrete Fourier transform is obtained: (k=0, 1, ..., N / 2-1) (k=N / 2,...,N-1) in, represents the signal sequence, and N is the number of signal sampling points participating in the fast Fourier transform. In order to realize the above fast Fourier transform algorithm from left to right operation process and obtain the frequency domain sequence, the time domain sequence must be reordered first, that is, the bit inversion operation. (Bit inversion: Assume , ,but ,say for However, before the butterfly operation, the bit reversal operation alone not only increased the amount of calculation but also required double the storage space, increasing the burden on system operation.

[0059] This solution uses the space-time conversion method to integrate and optimize the bit reversal operation and butterfly operation. The operation process is: First, when the program is initialized, two single-precision floating-point arrays DataReal and DataImag with a length of N are created. N is the number of signal sampling points participating in the fast Fourier transform, and the initial value is initialized to zero. Secondly, during the execution of the program, the collected digital signal is stored in the DataReal array as the real part of the fast Fourier transform; then, during the fast Fourier transform operation, the array subscripts of the vector elements involved in the operation in the DataReal array are calculated; Finally, during the butterfly operation, the required vector elements are directly called by the table lookup method according to the array index. The above fast Fourier transform algorithm program flow chart is as follows Figure 7 shown.

[0060] The core part of the fast Fourier transform algorithm has three layers of loops. The outer loop is the series step of the butterfly operation. FFT_STEP_NUMBER= , N is the number of signal sampling points participating in the fast Fourier transform; the middle-level loop is the number of element vectors of each group participating in each level of butterfly operation, , the number of element vectors in each group is calculated and stored in the array when the program is initialized; the inner loop is the number of element vector groups participating in each level of butterfly operation, , the number of element vector groups is calculated and stored in the array when the program is initialized. The code execution order is: first, the first level 0~ -1 group i-th point and i+ Point to perform butterfly operation; in the next cycle, execute the second level 0~ -1 group i-th point and i+ Click the butterfly operation; and so on until the operation is completed.

[0061] In butterfly operation, complex number operation is involved. Since the embedded system cannot process complex numbers, the complex numbers are split into the real part DataReal array and the imaginary part DataImag array, and the array length is N. During the operation, DataReal[Index_a], DataImag[Index_a] and DataReal[Index_b], DataImag[Index_b] are called according to the calculated array index to participate in the operation according to the real part and imaginary part respectively. Each level of butterfly operation calculates a new sequence value to replace the input sequence value of that level, avoiding the opening of new storage space and effectively saving storage space.

[0062] The simulation results of the above fast Fourier transform algorithm are as follows Figure 8~Figure 10 As shown: The input signal is a 10Hz single-frequency sine wave plus Levy noise. The original signal is as follows Figure 8As shown in the figure, it can be seen from the spectrum that in the 0~20Hz frequency band, in addition to the known signal 10Hz, many frequency components also exist clearly. After segmentation, superposition, and autocorrelation operations, the peak at 10Hz can be clearly seen in the power spectrum density. The 0~20Hz frequency band is selected as the stopband window width to filter the signal, and the result is Figure 3 From the results shown, we can see that except for the known signal of 10Hz in the 0~20Hz frequency band, other frequency components are well suppressed.

[0063] The measured signal effect of the above fast Fourier transform algorithm is as follows Figure 11~Figure 13 As shown: The measured signal is a cruise ship signal: It is known that there is a 0.4Hz ocean current impact signal at the scene, which masks the 7.5Hz cruise ship signal. After signal processing, the low-frequency band of the signal is well smoothed, and the envelope of the signal can also be observed in the time domain.

[0064] In summary, this scheme uses digital signal processing algorithms to further filter out flow-induced noise during the signal processing process. This method can filter out non-acoustic noise to the maximum extent while retaining effective information, thereby overcoming the flow-induced noise interference of underwater acoustic environment observation equipment when working in the marine environment, and improving the signal-to-noise ratio of the received signal and the quality of recorded data of the underwater acoustic environment observation instrument.

[0065] Embodiment 2: This embodiment proposes an underwater acoustic environment observer 100. The underwater acoustic environment observer 100 is an underwater self-contained long-term observation device. In order to reduce the size of the device, achieve miniaturization, and reduce the size of the rechargeable lithium battery, the control of the system power consumption must be fully considered during the operation of the system. Based on the above embodiment 1, this solution adopts dynamic power optimization management technology. On the premise of ensuring the observation effect of the underwater acoustic environment observer 100, a method combining multiple observation modes is adopted to achieve dynamic power optimization management, and try to ensure the optimal combination of observation data duration and resolution.

[0066] The dynamic power optimization management technology mainly monitors the supply voltage of the battery pack 25 and the internal temperature of the instrument compartment 2 in real time, and the monitoring of temperature and voltage is completed in the signal acquisition board 242 of the circuit module 24. Among them: (i) Temperature monitoring uses a temperature sensor 248, such as a temperature sensor IC300, such as Fig.14 As shown, its pins SCL_275 and SDA_275 are respectively connected to the PB6 and PB7 pins of the main control chip 247 of the signal acquisition board 242. The main control chip 247 is an IC100B chip, and a STM32F207 processor can be used specifically; (ii) Fig.15As shown, the voltage monitoring battery output is connected to the Vin16.8_BD pin, and the Vin16.8_BD pin is connected to R403, R404, and operational amplifier IC404. R403 and R404 divide the output voltage of the battery, and operational amplifier IC404 reduces the AD input impedance. Fig.16 As shown, the output BDV_ADIN is connected to the voltage acquisition chip 249, which can be an IC405 chip and is responsible for sampling the output voltage BDV_ADIN. Fig.17 As shown, the SCK_AD2O, SDO_AD2O, and CS_AD2O pins of the voltage acquisition chip 249 are connected to the PA8, PC8, and PC9 pins of the main control chip IC100C.

[0067] This solution uses 18650 rechargeable lithium batteries to form battery pack 25, with a 4.2V / cell and 2200mAh / cell lithium battery. The battery pack 25 has a voltage of 16.8V and a capacity of 8800mAh by connecting 4 batteries in parallel and 4 batteries in series. According to previous work experience, the capacity of battery pack 25 needs to have a 20% redundancy, and the available capacity of battery pack 25 is 8800*80%=7040mAh. According to the actual measured data of the system, the current of the system is 68mA at a high sampling rate of 16.8V, and the current is 57mA at a low sampling rate of 16.8V.

[0068] Dynamic power optimization management technology combines the battery supply voltage and the internal temperature of the instrument compartment 2 with Fig.19 The discharge curve of the battery pack 25 shown in the figure estimates the remaining capacity of the battery pack 25 in the system and adjusts the observation mode in real time. The two factors that have a greater impact on the capacity of the battery pack 25 are: signal sampling frequency and signal sampling rate ,in: Indicates the sampling frequency of the signal, which determines the observation time. Studies have shown that a single sampling of more than 15 minutes (including 15 minutes) can better reflect the stability of the sound signal. Therefore, when this system is set to I=1, the system uses high-frequency sampling, 15 minutes / hour; when I=2, the system uses low-frequency sampling, 15 minutes / 2 hours.

[0069] Indicates the signal sampling rate, which determines the resolution of the observed signal. The frequency range that the human ear can hear is 20~20KHz. According to the Nyquist theorem, when the sampling rate is higher than 40KHz (including 40KHz), the 20~20KHz bandwidth signal can be covered. Therefore, when this system is set to J=1, the system uses a high sampling rate of 200KHz; when J=2, the system uses a low sampling rate of 40KHz.

[0070] According to the principle of pairwise combination, the observation mode of the system is determined as: , , and Four modes. Figure 20~Figure 21 As shown, when the battery is fully charged (available capacity 100%~50%, 3520mAh), the observation is Mode, observation time 200 hours; when the battery power is medium (available capacity 50%~30%, 1408mAh), observation is carried out using mode, the observation time is 200 hours; when the battery is low (available capacity 30%~10%, 1408mAh), the observation is mode, observation time is 160 hours; when the battery is low (available capacity <10%, 704mAh), observation is performed using mode, the observation time is 96 hours, and the cumulative observation time is 656 hours. If the system adopts high-frequency sampling and high-sampling rate working mode, mode, the observation time was 412 hours. It can be seen that the use of dynamic power optimization management technology has extended the observation time by 37%, which not only ensures the acquisition of high-quality data but also extends the observation time, and can ensure better completion of the observation task.

[0071] Embodiment 3: This embodiment proposes an underwater acoustic environment observation instrument 100, which, based on Embodiment 1 or Embodiment 2, further proposes a structural improvement design, mainly for further reducing the flow-induced noise in the signal acquisition stage of the hydroacoustic sensor 3. Among them, the shape of the guide frame 1 adopts a streamlined design, and its two ends are tapered, which ensures that the observation instrument maintains a vertical posture underwater. The instrument cabin 2 is fixed on the guide frame 1, and the guide frame 1 is also provided with a guide cover 4 specifically for installing the hydroacoustic sensor 3. The deflector 4 is a hollow mesh cover. The hydroacoustic sensor 3 can be directly placed in the deflector 4, or fixed inside the deflector 4 by bonding or bolting. The mesh holes of the deflector 4 can allow seawater to enter and exit, but the hydroacoustic sensor 3 cannot fall off from the mesh holes of the deflector 4; the hollow mesh cover is movably mounted on the deflector frame 1 by means of springs 5 ​​to ensure that when seawater flows through the hollow mesh cover, the hollow mesh cover can swing relative to the deflector frame 1 under the action of the springs 5, thereby reducing the impact of seawater on the hydroacoustic sensor 3 in the hollow mesh cover, and the buffering effect of the mesh holes of the hollow mesh cover on the water flow is combined with the water flow to avoid the water flow directly flowing through the hydroacoustic sensor 3 and generating strong flow noise, thereby greatly reducing the interference of flow-induced noise on the hydroacoustic sensor 3 during the signal acquisition stage.

[0072] In some embodiments, the hollow mesh cover is a hollow columnar mesh cover, and mesh holes are arranged on the side wall and both end surfaces of the hollow mesh cover. Specifically, the hollow mesh cover can be a cylindrical mesh cover or a prismatic mesh cover.

[0073] In some embodiments, the hollow mesh cover is assembled and connected to the guide frame 1 through a spring 5 group. Specifically, the spring 5 group includes a plurality of springs 5, all of which are evenly distributed on the outer periphery of the hollow mesh cover, and one end of any spring 5 is connected to the hollow mesh cover (the connection method can be hanging, welding, etc.), and the other end of any spring 5 is connected to the guide frame 1 (the connection method can be hanging, welding, etc.). As a preferred solution, one end of the spring 5 can be hung with the hollow mesh cover through a ring, and a hanging ring is provided on the hollow mesh cover for the ring to be hung; the other end of the spring 5 can also be hung with the guide frame 1 through a ring, and a hanging ring is provided on the guide frame 1 for the ring to be hung. The two ends of the spring 5 are respectively hung with the hollow mesh cover and the guide frame 1 through the ring, which increases the flexibility of the spring 5 relative to the hollow mesh cover and the guide frame 1, and can further enhance the buffering and diversion effect of the hollow mesh cover on seawater.

[0074] In some embodiments, the hollow mesh cover is coaxially mounted at the center of the guide frame 1, and the two ends of the hollow mesh cover are respectively connected to the guide frame 1 through a plurality of evenly distributed springs 5. For example, the two ends of the hollow mesh cover are respectively connected to the guide frame 1 through two symmetrically arranged springs 5, so that the four corners of the hollow mesh cover are respectively connected to the guide frame 1 through a spring 5.

[0075] In some implementations, the air guide cover 4 may be a metal air guide cover 4 or a polypropylene material air guide cover 4 .

[0076] In some embodiments, in order to further enhance the buffering effect of the deflector 4, at least one layer of water-permeable nylon sheath may be provided on the outside of the deflector 4. The nylon sheath has a denser structure, which can not only protect the deflector 4, but also further alleviate the impact of water flow.

[0077] In some embodiments, the outer shell of the instrument cabin 2 is directly fixed in the guide frame 1 by a limiting ring 7. The limiting ring 7 includes two relatively arranged half rings. After the two half rings clamp the outer shell of the instrument cabin 2, the two ends of the two half rings are fastened by bolts to clamp the outer shell of the instrument cabin 2, and then the limiting ring 7 is fixed to the guide frame 1 by bolts and other structures, thereby completing the installation and fixation of the instrument cabin 2 on the guide frame 1. In order to ensure firm installation, the outer shell of the instrument cabin 2 can be connected and fixed to the guide frame 1 by multiple limiting rings 7.

[0078] In some embodiments, lifting rings 6 are respectively provided at both ends of the guide frame 1. During the system deployment process, the upper lifting ring 6 is connected to the lifting rope, and the lower lifting ring 6 is connected to the lead fish, which can ensure the posture of the entire observation instrument underwater.

[0079] In some embodiments, the instrument cabin 2 and the air deflector 4 are arranged along the axial direction of the air deflector 1, and in a static state, the instrument cabin 2, the air deflector 4 and the air deflector 1 are arranged substantially coaxially. The battery pack 25 in the instrument cabin 2 is preferably arranged near the bottom of the air deflector 1 (i.e., away from the air deflector 4 and the hydroacoustic sensor 3), and correspondingly, the circuit module 24 is arranged near the top of the air deflector 1 (i.e., near the air deflector 4 and the hydroacoustic sensor 3).

[0080] In summary, in view of the problem that the structure of the underwater acoustic sensor 3 itself does not include a noise reduction function and is seriously disturbed by flow-induced noise during signal acquisition and signal processing, the underwater acoustic environment observer 100 proposed in the present invention starts from two aspects: structural design and signal processing. On the one hand, the streamlined design of the guide frame 1 and the elastic and movable installation method of the guide cover 4 are utilized to reduce the flow-induced noise in the signal acquisition stage by means of seawater diversion buffering. On the other hand, a digital signal processing algorithm is used to further filter out the flow-induced noise during the signal processing process. This method can filter out non-acoustic noise to the maximum extent while retaining effective information, thereby overcoming the flow-induced noise interference when the underwater acoustic environment observation equipment is working in the marine environment, and improving the signal-to-noise ratio of the received signal and the quality of recorded data of the underwater acoustic environment observer.

[0081] It should be noted that the structures, proportions, sizes, etc. drawn in the drawings of this specification are only used to match the contents disclosed in the specification for people familiar with this technology to understand and read, and are not used to limit the limiting conditions for the implementation of the present invention, so they have no substantial technical significance. Any modification of the structure, change of the proportional relationship or adjustment of the size should still fall within the scope of the technical content disclosed by the present invention without affecting the effects and purposes that can be achieved by the present invention. At the same time, the terms such as "upper", "lower", "left", "right", "middle" and "one" quoted in this specification are only for the convenience of description, and are not used to limit the scope of the implementation of the present invention. The change or adjustment of their relative relationship should also be regarded as the scope of the implementation of the present invention without substantially changing the technical content.

[0082] The present invention uses specific examples to illustrate the principles and implementation methods of the present invention. The above examples are only used to help understand the method and core ideas of the present invention. At the same time, for those skilled in the art, according to the ideas of the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting the present invention.

Claims

1. An underwater acoustic environment observation instrument, characterized in that: The invention comprises a flow guide frame (1), an instrument cabin (2) and an underwater acoustic sensor (3), wherein: the underwater acoustic sensor (3) is arranged on the flow guide frame (1) and is used to collect underwater acoustic wave signals; the instrument cabin (2) comprises a shell, a circuit module (24) and a battery pack (25) are arranged in the shell, the circuit module (24) is communicatively connected with the underwater acoustic sensor (3), and the circuit module (24) has a built-in digital signal processing algorithm, the circuit module (24) can convert the analog signal output by the underwater acoustic sensor (3) into a digital signal, and use the digital signal processing algorithm to filter out non-acoustic interference in the signal during the signal processing process.

2. The underwater acoustic environment observation instrument according to claim 1, characterized in that: The circuit module (24) comprises a power supply board (241), a signal acquisition board (242), a signal processing board (243) and a storage board (244); the power supply board (241) is electrically connected to the battery pack (25); the signal acquisition board (242) is communicatively connected to the hydroacoustic sensor (3) and is capable of converting an analog signal output by the hydroacoustic sensor (3) into a digital signal and then outputting the digital signal to the signal processing board (243); the signal processing board (243) is capable of filtering out non-acoustic interference in the signal using the digital signal processing algorithm and then outputting the signal to the storage board (244); the digital signal processing algorithm comprises: Segment and superimpose the signal; Perform autocorrelation operation on the superimposed signal to obtain the autocorrelation function: ,in, Indicates signal, Indicates the delay amount, Indicates the number of sampling points; Perform Fourier transform on the autocorrelation function; The Fourier transform result is the power spectral density function: ,in, represents a complex exponential function, is an imaginary unit, Indicates frequency, Indicates the delay amount, Indicates the number of sampling points; Obtain the window width required for filtering according to the power spectrum density function; According to the selected window, stopband filtering is performed to obtain the signal after filtering out the non-acoustic interference.

3. The underwater acoustic environment observation instrument according to claim 2, characterized in that: The Fourier transform adopts a fast Fourier transform algorithm.

4. The underwater acoustic environment observation instrument according to claim 3, characterized in that: The Fast Fourier Transform algorithm is as follows: in, represents a signal sequence; The fast Fourier transform algorithm includes bit inversion, butterfly weight calculation, butterfly operations at various levels, and output of a fast Fourier transform sequence, wherein: The butterfly weight calculation: In each set of butterfly operations, the rotation factor needs to be calculated and stored. , N is the number of fast Fourier transform points, , according to Euler's formula: , convert the rotation factors into trigonometric function calculations, and when the program is initialized, the calculated results are stored in an array for direct call during the operation; Bit reversal and butterfly operations at all levels: According to the above fast Fourier transform algorithm, decompose step by step according to the following formula until the 2-point discrete Fourier transform: (k=0, 1, ..., N / 2-1) ， (k=N / 2,...,N-1) ， in, represents the signal sequence, and N is the number of signal sampling points participating in the fast Fourier transform.

5. The underwater acoustic environment observation instrument according to claim 4, characterized in that: The space-time conversion method is adopted to integrate and optimize the bit reversal operation with the butterfly operation. The optimized operation process is as follows: first, two single-precision floating-point arrays of length N, DataReal and DataImag, are established when the program is initialized. N is the number of signal sampling points participating in the fast Fourier transform, and the initial value is initialized to zero; secondly, during the execution of the program, the collected digital signal is stored in the DataReal array as the real part of the fast Fourier transform; then, during the fast Fourier transform operation, the array subscripts of the vector elements participating in the operation in the DataReal array are calculated; finally, during the butterfly operation, the required vector elements are directly called by the table lookup method according to the array subscripts.

6. The underwater acoustic environment observation instrument according to any one of claims 1 to 5, characterized in that: The signal cable (33) of the hydroacoustic sensor (3) is communicatively connected to the instrument cabin (2) via a watertight joint (34).

7. The underwater acoustic environment observation instrument according to any one of claims 1 to 5, characterized in that: The instrument cabin (2) is also provided with a temperature sensor (248) and a voltage acquisition chip (249); the temperature sensor (248) is used to monitor the temperature in the instrument cabin (2) in real time, and the voltage acquisition chip (249) is used to monitor the supply voltage of the battery pack (25) in real time; the temperature sensor (248) and the voltage acquisition chip (249) are both communicatively connected to the signal acquisition board (242); the signal processing board (243) can estimate the remaining capacity of the battery pack (25) in real time based on the supply voltage of the battery pack (25) and the temperature in the instrument cabin (2), in combination with the discharge curve of the battery pack (25), so as to adjust the observation mode of the underwater acoustic environment observation instrument (100) in real time.

8. The underwater acoustic environment observation instrument according to claim 7, characterized in that: The observation modes of the underwater acoustic environment observation instrument (100) include: , , and ,in, To adopt a sampling frequency of 15 minutes / hour, To adopt the sampling frequency of 15 minutes / 2 hours, To adopt 200KHz sampling rate, The sampling rate is 40KHz.

9. The underwater acoustic environment observation instrument according to any one of claims 1 to 5, characterized in that: It also comprises a flow deflector (4), the flow deflector (4) being a hollow mesh cover, the hydroacoustic sensor (3) being arranged in the flow deflector (4), and the flow deflector (4) being movably mounted on the flow deflector frame (1) via a spring (5).

10. The underwater acoustic environment observation instrument according to claim 9, characterized in that: The outside of the deflector cover (4) is also covered with a water-permeable nylon sheath.

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