Deep sea water acoustic data compression method and device based on FPGA

Abstract

This application relates to the field of acoustic wave data processing technology, specifically to a method and device for compressing deep-sea acoustic data based on an FPGA. The method comprises: performing segmented processing based on the energy distribution of IMF waves obtained by modal decomposition of deep-sea acoustic data to obtain each segment of sound data; obtaining a sound similarity measure based on the amplitude values and similarity of changes between any two segments of sound data; obtaining information density based on the sound similarity measure of sound data of the same time period on all different IMF waves and different sound data on the same IMF wave; and selecting a wavelet basis function group during the wavelet transform based on the information density, quantizing and encoding the wavelet coefficients of all IMF waves after wavelet transformation, and obtaining a compression result for the deep-sea acoustic data. This application can improve the compression quality of deep-sea acoustic data.

Description

Technical Field

[0001] The present application relates to the technical field of acoustic wave data processing, and in particular to a method and device for compressing deep sea water acoustic data based on FPGA. Background Art

[0002] Deep-sea acoustic data refers to acoustic wave signals collected in deep-sea environments by sonar and other equipment. Due to the enormous amount of data generated by deep-sea exploration and the limited communication bandwidth in deep-sea environments, data storage and transmission costs are high. This requires compression of deep-sea acoustic data for transmission to appropriate vessels or platforms.

[0003] Currently, lossless compression based on dictionary coding is a common method for compressing deep-sea acoustic data. However, due to the presence of various deep-sea noises distributed at different frequencies, direct compression after denoising can obscure some data features due to the complex noise distribution. This can lead to the loss of these features during the denoising process, thus affecting the compression quality of deep-sea acoustic data. Summary of the Invention

[0004] In order to solve the above technical problems, the purpose of this application is to provide a method and device for deep sea water acoustic data compression based on FPGA. The technical solutions adopted are as follows:

[0005] In a first aspect, an embodiment of the present application provides a method for compressing deep sea water acoustic data based on an FPGA, the method comprising the following steps:

[0006] S1, acquire deep sea acoustic data;

[0007] S2, modal decomposition of deep-sea acoustic data to obtain IMF waves, segmenting the sound data according to the energy distribution of each IMF wave, and obtaining the sound similarity measure of the two sound data segments based on the similarity of the amplitude values and changes of the two sound data segments;

[0008] S3, performing a threshold analysis on the sound similarity measures between the sound data of the same time period on all different IMF waves to obtain the sound residual similarity of each segment of sound data; combining the sound similarity measures and sound residual similarities of each segment of sound data with all other sound data on the same IMF wave to obtain the noise characteristic value of each segment of sound data;

[0009] S4, according to the proportion of all sound data on each IMF wave and the noise characteristic value, obtain the information density of each IMF wave; select the wavelet basis function group of each IMF wave in the wavelet transform process according to the information density of each IMF wave; according to the wavelet basis function group of each IMF wave, perform wavelet transformation on each IMF wave to obtain wavelet coefficients, quantize and encode the wavelet coefficients of all IMF waves, and obtain the compression result of deep sea acoustic data.

[0010] Furthermore, the modal decomposition of deep sea water acoustic data to obtain IMF waves is performed, and segment processing is performed according to the energy distribution of each IMF wave to obtain sound data of each segment, including:

[0011] The deep-sea acoustic data is modally decomposed to obtain a preset number of IMF waves; each IMF wave is framed to obtain each frame signal; each frame signal is judged in turn using the energy threshold method to filter out each segment of sound data.

[0012] Furthermore, the energy threshold method is used to determine each frame signal in turn to filter out each segment of sound data, including:

[0013] Calculate the energy of each frame signal; obtain the mode of the energy of all frame signals, and use one quarter of the mode as the energy threshold; record each frame signal with energy higher than the energy threshold as each segment of sound data.

[0014] Furthermore, for any two segments of sound data, the sound similarity measure of the two segments of sound data is obtained based on the similarity of the amplitude values and changes of the two segments of sound data, including:

[0015] ,

[0016] Where, Indicates sound data With sound data Sound similarity measure; Indicates sound data Middle Amplitude value at the moment; Indicates sound data Middle Amplitude value at the moment; Indicates sound data With sound data DTW distance; is an exponential function with a natural constant as its base; The number of moments collected in each segment of sound data.

[0017] Furthermore, the method for obtaining the residual similarity of the sound data of each segment includes:

[0018] For any IMF wave, any segment of sound data on the IMF wave is recorded as the target data, and the sound data on other IMF waves with the same time period as the target data is recorded as the co-location data of the target data; the number of sound similarity measurements between the target data and all its co-location data that is greater than or equal to the preset similarity threshold is obtained and recorded as the similarity number; the ratio of the similarity number to the number of all IMF waves is taken as the sound residual similarity of the target data.

[0019] Furthermore, the method for obtaining the noise characteristic value of each segment of sound data includes:

[0020] Obtain an average value of the sound similarity measure between any segment of sound data and all other sound data on the same IMF wave; and multiply the average value by the sound residual similarity of the any segment of sound data as the noise characteristic value of the any segment of sound data.

[0021] Furthermore, the method for obtaining the information density of each IMF wave includes: The information density of an IMF wave is denoted as , Where, Indicates the length of each segment of sound data; Indicates sound data The noise characteristic value of Indicates the The total amount of sound data contained in an IMF wave; Indicates the The total length of an IMF wave.

[0022] Furthermore, the wavelet basis function group of each IMF wave in the wavelet transform process is selected according to the information density of each IMF wave, including:

[0023] Obtaining a normalized value of the information concentration of each IMF wave; rounding up a preset multiple of the normalized value to obtain an information level of each IMF wave;

[0024] Randomly select several groups of wavelet basis function groups; the number of wavelet basis function groups is consistent with the number of information level, and each group of wavelet basis function groups has two orthogonal wavelet bases;

[0025] All wavelet basis function groups are sorted in order from small to large according to the scale of the wavelet basis, and the serial number of each wavelet basis function group is recorded as the scale level of each wavelet basis function group;

[0026] Among the scale levels of all wavelet basis function groups, the wavelet basis function group with the same value as the information level of each IMF wave is used as the wavelet basis function group of each IMF wave in the wavelet transform process.

[0027] Furthermore, the method of performing wavelet transformation on each IMF wave according to the wavelet basis function group of each IMF wave to obtain wavelet coefficients, quantizing and encoding the wavelet coefficients of all IMF waves to obtain the compression result of deep sea water acoustic data specifically includes:

[0028] Each IMF wave is decomposed and transformed using its wavelet basis function group in the wavelet transform process to obtain wavelet coefficients; the wavelet coefficients are uniformly quantized; the uniformly quantized wavelet coefficients are encoded using the entropy coding method to obtain the encoded data of each IMF wave; the encoded data of all IMF waves are combined into a coding table for deep sea acoustic data.

[0029] In a second aspect, an embodiment of the present application further provides an FPGA-based deep sea water acoustic data compression device, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, wherein when the processor executes the computer program, the steps of any one of the above-mentioned FPGA-based deep sea water acoustic data compression methods are implemented.

[0030] This application has at least the following beneficial effects:

[0031] This application analyzes the problem of prior art that some non-noise key information is lost when de-noising and compressing deep-sea acoustic data. By leveraging the high-speed parallel processing capabilities and flexible programmable features of deep-sea acoustic data, this application achieves efficient compression of deep-sea acoustic signals. First, the original acoustic data is decomposed to obtain the intrinsic mode functions of different frequencies, which facilitates more efficient and accurate identification of possible noise data in the acoustic data. The relationship between the similarity between different sound data segments in each IMF wave and the noise content is further analyzed to obtain the noise characteristic value, which more accurately evaluates the amount of noise each IMF wave may contain. The information concentration is further calculated by combining the sound data ratio and the noise characteristic value on each IMF wave. The information content of each IMF wave is evaluated, so that a different wavelet basis can be selected for each IMF wave to perform wavelet transform on the IMF wave. This can achieve more accurate compression of noise data without deleting the noise data, effectively preserving other key information and data features. Finally, a quantization technique is combined to quantize and encode the numerous wavelet coefficients and IMF wave coefficients to complete the compression of deep-sea acoustic data, which can effectively improve the compression quality of deep-sea acoustic data in deep-sea operations. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] In order to more clearly illustrate the technical solutions and advantages of the embodiments of the present application or the prior art, the following is a brief introduction to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0033] Figure 1 A flowchart of the steps of a deep sea water acoustic data compression method based on FPGA provided in one embodiment of the present application;

[0034] Figure 2 A block diagram of a wavelet basis function group provided in one embodiment of the present application. DETAILED DESCRIPTION

[0035] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. In the absence of conflict, the embodiments of this application and the technical features in the embodiments can be combined with each other. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0036] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.

[0037] The specific scheme of the deep sea water acoustic data compression method and device based on FPGA provided by the present application is described in detail below with reference to the accompanying drawings.

[0038] See also Figure 1 , which shows a flowchart of a deep sea water acoustic data compression method based on FPGA provided by an embodiment of the present application, the method comprising the following steps:

[0039] S1, obtain deep sea acoustic data.

[0040] The implementation scenario of this application is to compress the deep-sea water acoustic data collected and fed back when locating or detecting deep-sea terrain, organisms and other objects in the deep sea by relying on sound waves and sonar devices. A deep-sea distributed acoustic receiving device is set on a deep-sea submersible, including an unmanned underwater submersible, and then the submersible is lowered to the deep-sea target position to collect deep-sea water acoustic data. Then, the deep-sea water acoustic data is input into a field programmable gate array, and the acoustic signal is pre-processed by its built-in digital signal processing module. The data received by multiple acoustic collection devices is integrated and divided to obtain several segments of integrated deep-sea water acoustic data. The collection time of each segment of deep-sea water acoustic data is 1 hour, and the deep-sea water acoustic data is normalized.

[0041] S2, modally decompose the deep sea water acoustic data to obtain IMF waves, and segment them according to the energy distribution of each IMF wave to obtain each segment of sound data; for any two segments of sound data, the sound similarity measure of the two segments of sound data is obtained based on the similarity of the amplitude values and changes of the two segments of sound data.

[0042] When collecting deep-sea acoustic waves, we often encounter various noise sources, including those caused by seawater movement, wind and atmospheric effects on the sea surface, shifting or melting water layers, changes in seafloor geological structures, sounds produced by marine life, and human-induced noise. These noises can be low-frequency or high-frequency, widely distributed, and occur at irregular intervals. EMD decomposition is typically used to derive component signals at different frequencies, and the noise level of each component is then analyzed. The EMD empirical mode decomposition algorithm is well known, and the detailed implementation is omitted here.

[0043] Considering that noise may be more prevalent in certain signal components or less prevalent in others, wavelet bases of different sizes can be selected when performing wavelet transforms on these components. Specifically, for components with more noise, a larger wavelet can be selected to improve the compression ratio and reduce the proportion of noise in the final transmitted information. Conversely, a smaller wavelet base can preserve more detailed information in the underwater acoustics. Finally, quantization technology is used to encode the coefficients in the EMD decomposition and wavelet transform to complete the compression of deep-sea acoustic data.

[0044] Specifically, this embodiment decomposes deep-sea acoustic data using the EMD (Empirical Mode Decomposition) algorithm to obtain a preset number of intrinsic modal components, or IMF waves. In this embodiment, 10 IMF waves are decomposed. To further analyze the IMF waves obtained from the EMD decomposition, a suitable wavelet basis is selected. First, the likelihood that each small segment of sound data represents noise is analyzed. Each IMF wave segment is then framed, with a preset frame size of 20 milliseconds and a 50% overlap between frames. This framing process helps extract local signal features, and the signal characteristics within each frame can be considered relatively stable. Each frame is then evaluated sequentially using an energy threshold method, eliminating frames with energy below the threshold and filtering out the individual segments of sound data. In this embodiment, the energy of each frame is first calculated, and then the mode of the energy of all frames is selected, with one-quarter of this mode being used as the energy threshold. Signal energy calculation is well known and will not be further described. Frames with energy above the threshold are recorded as individual segments of sound data.

[0045] Deep-sea environments are prone to a wide range of noise, distributed across a wide range of frequencies. The noise levels vary across frequencies, and since noise sources typically come from a few fixed sources, these noises share similarities, reflected in aspects like volume and pitch. The relevant characteristics of each noise also overlap in the time domain. Based on this, this embodiment analyzes the likelihood that each small segment of sound data is noise.

[0046] First, for any two sound data segments, the smaller the difference in volume between the two sound data segments at many moments and the higher the similarity in their pitch changes, the more similar the two sound data segments are. Considering that this process only considers the similarity at a certain moment, the similarity of the two sound segments is summed up to obtain the similarity of the two sound segments. Based on this, the similarity between the two sound data segments is calculated to evaluate the similarity between the two sound segments. The calculation formula is:

[0047] ,

[0048] Where, Indicates sound data With sound data Sound similarity measure; Indicates sound data Middle Amplitude value at the moment; Indicates sound data Middle Amplitude value at the moment; Indicates sound data With sound data DTW distance; is an exponential function with a natural constant as its base; The number of moments collected in each segment of sound data.

[0049] in, Used to describe the similarity between two sound data in terms of time domain and pitch. The smaller the DTW distance, the higher the similarity between the two. It is used for inverse proportional normalization to describe the similarity between two sounds in terms of quantity and pitch. The closer the amplitudes of the two sounds at each moment are, the higher the similarity between the two sounds, and the larger the sound similarity measure obtained.

[0050] S3, perform threshold analysis on the sound similarity measures between the sound data of the same time period on all different IMF waves to obtain the sound residual similarity of each segment of sound data; combine the sound similarity measures and sound residual similarities of each segment of sound data with all other sound data on the same IMF wave to obtain the noise characteristic value of each segment of sound data.

[0051] Based on the characteristics of deep-sea sound, each sound segment is analyzed to determine whether it represents deep-sea noise. Submersibles navigating the deep ocean collect complex sound data, but after EMD decomposition of each noise segment, some residual components remain. This leads to persistent repetitive noise in other components. This residual similarity is then calculated for the target segment.

[0052] First, the corresponding sound data segments of each sound data segment on other IMF waves are obtained, and the corresponding time points of the two sound data segments on different IMFs are the same.

[0053] For any IMF wave, any segment of sound data on the IMF wave is recorded as the target data, and the sound data on other IMF waves with the same time period as the target data is recorded as the co-location data of the target data; the number of sound similarity measurements between the target data and all its co-location data that is greater than or equal to the preset similarity threshold is obtained and recorded as the similarity number; the ratio of the similarity number to the number of all IMF waves is taken as the sound residual similarity of the target data.

[0054] Among them, by presetting the similarity threshold The sound residual similarity obtained by screening describes the sound fluctuation similarity between the target data and the data on other IMF waves in the same time period.

[0055] As deep-sea noise is more repetitive than other sounds, the likelihood of its inclusion is determined by combining the similarity between sound data on the same IMF wave. Specifically, the average of the sound similarity measures for any given sound segment and all other sound segments on the same IMF wave is calculated. The product of this average and the residual sound similarity for that particular sound segment is then used as the noise characteristic value for that particular sound segment.

[0056] The noise eigenvalue describes the likelihood that a segment of data represents deep-sea noise. It is calculated by combining the acoustic similarity measures of the sound data with those of other sound data on the same IMF wave. Specifically, the average of the acoustic similarity measures of any segment of sound data and all other sound data on the same IMF wave is obtained. The product of this average and the residual acoustic similarity of the segment is used as the noise eigenvalue for that segment of sound data.

[0057] S4, according to the proportion of all sound data on each IMF wave and the noise characteristic value, obtain the information density of each IMF wave; select the wavelet basis function group of each IMF wave in the wavelet transform process according to the information density of each IMF wave; according to the wavelet basis function group of each IMF wave, perform wavelet transformation on each IMF wave to obtain wavelet coefficients, quantize and encode the wavelet coefficients of all IMF waves, and obtain the compression result of deep sea acoustic data.

[0058] Because the bandwidth for transmitting information is limited in deep-sea environments, and in order to preserve more deep-sea acoustic data, it is necessary to distinguish the ratio of valid data to noise data in each IMF wave. Deep-sea acoustic data contains noise at all frequencies. For each IMF wave of a corresponding frequency, the less noise it contains, the more valid acoustic data it contains. In other words, the IMF wave with more valid acoustic data in its corresponding component is more important. Based on this, this embodiment evaluates the importance of each IMF wave:

[0059] ,

[0060] Where, Indicates the The information density of an IMF wave; Indicates the length of each segment of sound data; Indicates sound data The noise characteristic value of Indicates the The total amount of sound data contained in an IMF wave; Indicates the The total length of an IMF wave.

[0061] Information density is used to evaluate the importance of valid underwater acoustic data contained in the IMF wave; the noise eigenvalue indicates the possibility that the sound data contains valid data; the less noise, the more information it contains; the greater the proportion of sound data in the IMF wave, the smaller the degree of noise interference; by taking the inverse normalized value of the noise eigenvalue as the weight and performing weighted averaging on the duration of the sound data, the greater the information density, the more valid information the IMF wave contains.

[0062] Due to the variety of noise in the deep sea, different degrees of noise exist on each IMF wave after decomposition. For IMF waves with more noise, they contain relatively less effective information. Therefore, their compression degree can be increased to make room for transmission bandwidth and storage space containing more effective information. Therefore, in order to select the appropriate wavelet basis for each IMF wave during wavelet transform, this embodiment first constructs the wavelet basis level required for selection based on the information density of each IMF wave:

[0063] ,

[0064] Where, Indicates the The information level of each IMF wave; Indicates the Information density of an IMF wave Use maximum and minimum normalization to normalize the results; Represents the ceiling function.

[0065] Among them, the information level is used to describe the level of the wavelet basis that needs to be selected; The information density of the first IMF wave is projected onto all the information densities to observe their relative importance. Then, the information density is amplified tenfold and rounded up to obtain the first The information level of an IMF wave ranges from 1 to 10, that is, 10 levels. The closer to 10, the more effective information the IMF wave contains.

[0066] In order to adaptively adjust the compression ratio of the wavelet transform, each IMF wave is transformed using a biorthogonal wavelet. Therefore, multiple different wavelet basis function groups are preselected, each of which has two orthogonal wavelet bases. The number of wavelet basis function groups is consistent with the number of information levels, that is, 10 levels.

[0067] In order to select a larger wavelet basis for IMF waves with higher information content when decomposing IMF waves, so as to compress noise data to a greater extent, and conversely select a smaller wavelet basis to retain more detailed features in deep sea acoustic data, which can effectively remove noise. Therefore, the scale level corresponding to the wavelet basis function with a smaller scale is higher. In this embodiment, all wavelet basis function groups are encoded from 1 to 10 in order from the scale of the wavelet basis to the largest, and encoded as the scale level of the wavelet basis function group. Among the scale levels of all wavelet basis function groups, the wavelet basis function group with the same value as the information level of each IMF wave is used as the wavelet basis function group for each IMF wave in the wavelet transform process; that is, the wavelet basis function group of the same level can be selected for each IMF wave for transformation.

[0068] The selection box diagram of the wavelet basis function group, such as Figure 2 shown.

[0069] The wavelet basis functions required for each IMF wave are obtained above. Each IMF wave is decomposed using these wavelet basis functions to obtain the corresponding wavelet coefficients. Each IMF wave has a unique level of information content, and the wavelet basis level selected during wavelet transform is also uniquely determined. Furthermore, since each IMF wave is derived from the decomposition and splitting of deep-sea acoustic data, the noise-reduced deep-sea acoustic data is obtained by transforming and superimposing the IMF waves. Therefore, this embodiment quantizes and encodes the wavelet coefficients of each IMF wave to obtain the encoded data for each IMF wave. The encoded data of all IMF waves is then combined into a coding table for deep-sea acoustic data, completing the compression of the deep-sea acoustic data. The wavelet coefficients are quantized using uniform quantization, and the quantized wavelet coefficients are compressed using entropy coding. The specific implementation procedures are well-known techniques and will not be detailed in this application.

[0070] Based on the same inventive concept as the above method, an embodiment of the present application also provides an FPGA-based deep sea water acoustic data compression device, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, the steps of any one of the above-mentioned FPGA-based deep sea water acoustic data compression methods are implemented.

[0071] Through the above description of the implementation method in combination with the accompanying drawings, technical personnel in the relevant field can understand that for the convenience and simplicity of description, only the division of the above-mentioned functional modules is used as an example. In actual applications, the above-mentioned functions can be distributed and completed by different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above.

[0072] The above content is only a specific implementation method of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with this technical field can easily think of changes or replacements within the technical scope disclosed in this application, and they should all be covered by the protection scope of the present application.

Claims

1. A deep sea water acoustic data compression method based on FPGA, characterized in that: The method comprises the following steps: S1, acquire deep sea acoustic data; S2, modal decomposition of deep-sea acoustic data to obtain IMF waves, segmenting the sound data according to the energy distribution of each IMF wave, and obtaining the sound similarity measure of the two sound data segments based on the similarity of the amplitude values and changes of the two sound data segments; S3, performing a threshold analysis on the sound similarity measures between the sound data of the same time period on all different IMF waves to obtain the sound residual similarity of each segment of sound data; combining the sound similarity measures and sound residual similarities of each segment of sound data with all other sound data on the same IMF wave to obtain the noise characteristic value of each segment of sound data; S4, obtaining the information density of each IMF wave based on the proportion of all sound data on each IMF wave and the noise characteristic value; selecting a wavelet basis function group for each IMF wave in the wavelet transform process based on the information density of each IMF wave; performing wavelet transformation on each IMF wave based on the wavelet basis function group to obtain wavelet coefficients, quantizing and encoding the wavelet coefficients of all IMF waves to obtain the compression result of deep sea acoustic data; For any two segments of sound data, the sound similarity measure of the two segments of sound data is obtained based on the similarity of the amplitude values and changes of the two segments of sound data, including: ； Where, Indicates sound data With sound data Sound similarity measure; Indicates sound data Middle Amplitude value at the moment; Indicates sound data Middle Amplitude value at the moment; Indicates sound data With sound data DTW distance; is an exponential function with a natural constant as its base; The number of moments collected in each segment of sound data.

2. The deep sea water acoustic data compression method based on FPGA according to claim 1, characterized in that: The modal decomposition of deep sea water acoustic data to obtain IMF waves is performed, and segment processing is performed according to the energy distribution of each IMF wave to obtain sound data of each segment, including: The deep-sea acoustic data is modally decomposed to obtain a preset number of IMF waves; each IMF wave is framed to obtain each frame signal; each frame signal is judged in turn using the energy threshold method to filter out each segment of sound data.

3. The deep sea water acoustic data compression method based on FPGA according to claim 2, characterized in that: The energy threshold method is used to determine each frame signal in turn, and each segment of sound data is obtained by screening, including: Calculate the energy of each frame signal; obtain the mode of the energy of all frame signals, and use one quarter of the mode as the energy threshold; record each frame signal with energy higher than the energy threshold as each segment of sound data.

4. The deep sea water acoustic data compression method based on FPGA according to claim 1, characterized in that: The method for obtaining the sound residual similarity of each segment of sound data includes: For any IMF wave, any segment of sound data on the IMF wave is recorded as the target data, and the sound data on other IMF waves with the same time period as the target data is recorded as the co-location data of the target data; the number of sound similarity measurements between the target data and all its co-location data that is greater than or equal to the preset similarity threshold is obtained and recorded as the similarity number; the ratio of the similarity number to the number of all IMF waves is taken as the sound residual similarity of the target data.

5. The deep sea water acoustic data compression method based on FPGA according to claim 1, characterized in that: The method for obtaining the noise characteristic value of each segment of sound data includes: Obtain an average value of the sound similarity measure between any segment of sound data and all other sound data on the same IMF wave; and multiply the average value by the sound residual similarity of the any segment of sound data as the noise characteristic value of the any segment of sound data.

6. The deep sea water acoustic data compression method based on FPGA according to claim 1, characterized in that: The method for obtaining the information density of each IMF wave comprises: The information density of an IMF wave is recorded as , Where, Indicates the length of each segment of sound data; Indicates sound data The noise characteristic value of Indicates the The total amount of sound data contained in an IMF wave; Indicates the The total length of an IMF wave.

7. The deep sea water acoustic data compression method based on FPGA according to claim 1, characterized in that: The wavelet basis function group of each IMF wave in the wavelet transform process is selected according to the information density of each IMF wave, including: Obtaining a normalized value of the information concentration of each IMF wave; rounding up a preset multiple of the normalized value to obtain an information level of each IMF wave; Randomly select several groups of wavelet basis function groups; the number of wavelet basis function groups is consistent with the number of information level, and each group of wavelet basis function groups has two orthogonal wavelet bases; All wavelet basis function groups are sorted in order from small to large according to the scale of the wavelet basis, and the serial number of each wavelet basis function group is recorded as the scale level of each wavelet basis function group; Among the scale levels of all wavelet basis function groups, the wavelet basis function group with the same value as the information level of each IMF wave is used as the wavelet basis function group of each IMF wave in the wavelet transform process.

8. The deep sea water acoustic data compression method based on FPGA according to claim 1, characterized in that: The method of performing wavelet transformation on each IMF wave according to the wavelet basis function group of each IMF wave to obtain wavelet coefficients, and quantizing and encoding the wavelet coefficients of all IMF waves to obtain the compression result of deep sea water acoustic data specifically includes: Each IMF wave is decomposed and transformed using its wavelet basis function group in the wavelet transform process to obtain wavelet coefficients; the wavelet coefficients are uniformly quantized; the uniformly quantized wavelet coefficients are encoded using the entropy coding method to obtain the encoded data of each IMF wave; the encoded data of all IMF waves are combined into a coding table for deep sea acoustic data.

9. A deep sea water acoustic data compression device based on FPGA, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the steps of the deep sea water acoustic data compression method based on FPGA as claimed in any one of claims 1 to 8 are implemented.

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