CN118568419B - Internet of things data optimization processing method and system based on deepwater net cage - Google Patents
Internet of things data optimization processing method and system based on deepwater net cage Download PDFInfo
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Abstract
本发明公开了一种基于深水网箱的物联网数据优化处理方法及系统，获取在待测深水网箱中所采集的物联网数据；基于预设的噪声模型对所述物联网数据进行噪声估计，获得所述待测深水网箱中的噪声数据；基于所述噪声数据确定逆滤波器参数；将所述物联网数据输入至所述逆滤波器参数所对应的逆滤波器进行去噪，以完成在所述待测深水网箱中所采集的物联网数据的噪声优化处理。本发明基于预设的噪声模型，针对待测深水网箱所采集到的物联网数据进行噪声估计，从而确认噪声数据，基于噪声数据进行逆滤波器的设置，实现了深水网箱的物联网数据去噪，提高了在深水网箱中物联网设备数据的采集准确率。
The present invention discloses a method and system for optimizing and processing Internet of Things data based on deep-water cages, which obtains Internet of Things data collected in a deep-water cage to be tested; estimates the noise of the Internet of Things data based on a preset noise model to obtain the noise data in the deep-water cage to be tested; determines the inverse filter parameters based on the noise data; and inputs the Internet of Things data into an inverse filter corresponding to the inverse filter parameters for denoising, so as to complete the noise optimization processing of the Internet of Things data collected in the deep-water cage to be tested. Based on a preset noise model, the present invention estimates the noise of the Internet of Things data collected by the deep-water cage to be tested, thereby confirming the noise data, and setting the inverse filter based on the noise data, thereby realizing the denoising of the Internet of Things data of the deep-water cage and improving the collection accuracy of the Internet of Things device data in the deep-water cage.
Description
Technical Field
The invention relates to the technical field of Internet of things, in particular to an Internet of things data optimization processing method and system based on a deepwater net cage.
Background
The deepwater net cage is commonly used for deepwater cultivation activities, and when the deepwater net cage is arranged, the data acquisition of the deepwater net cage is performed by arranging the Internet of things equipment, so that a user can grasp the condition of the deepwater net cage. However, due to the fact that the condition that the flow speed is too large or organisms gather easily occurs in the environment where the deepwater net cage is located, noise easily occurs in the acquired data of the Internet of things, and the problem that the data of the deepwater net cage is inaccurate is caused. The problem can be solved by the prior art, so that the problem of low data acquisition accuracy of the Internet of things equipment exists in the deepwater net cage.
Therefore, an optimization processing strategy for the data of the Internet of things is needed, so that the problem that the data acquisition accuracy of the equipment of the Internet of things is low in the deepwater net cage is solved.
Disclosure of Invention
The embodiment of the invention provides an Internet of things data optimization processing method and system based on a deepwater net cage, which are used for solving the problem that the data acquisition accuracy of Internet of things equipment is low in the deepwater net cage.
In order to solve the above problems, an embodiment of the present invention provides a method for optimizing data of internet of things based on a deepwater net cage, including:
acquiring Internet of things data acquired in a deepwater net cage to be detected;
Carrying out noise estimation on the Internet of things data based on a preset noise model to obtain noise data in the deepwater net cage to be detected; wherein the noise model comprises: a plurality of noise samples marked with signal intensity sample mean, signal intensity sample variance and signal spectrum sample density; analyzing noise samples based on the sound wave data, the video data and the flow data; collecting sound wave data at the deepwater net cage to be tested through a water sound sensor; collecting video data at the deepwater net cage to be tested through a camera; collecting flow data at the position of the deepwater net cage to be measured through a flow monitor; the acquisition time of the sound wave data, the video data and the flow data is kept consistent; the underwater acoustic sensor, the camera and the flow monitor are arranged on a telescopic rod;
Determining inverse filter parameters based on the noise data;
And inputting the Internet of things data into an inverse filter corresponding to the inverse filter parameters to perform denoising so as to finish noise optimization processing of the Internet of things data acquired in the deep water net cage to be detected.
As an improvement of the above solution, the noise estimating the internet of things data based on the preset noise model to obtain noise data in the deepwater net cage to be detected includes:
Extracting a signal intensity mean value and a signal intensity variance corresponding to the data of the Internet of things;
Performing Fourier transform on the data of the Internet of things to obtain signal spectrum density;
Based on the signal intensity mean value, the signal intensity variance and the signal spectrum density of the data of the Internet of things, matching is carried out in a preset noise model, and noise matching degree is obtained; wherein the noise model comprises: a plurality of noise samples marked with signal intensity sample mean, signal intensity sample variance and signal spectrum sample density;
and marking signals with noise matching degree larger than a matching degree threshold value in the data of the Internet of things as noise, and summarizing to obtain noise data of the deepwater net cage to be detected.
As an improvement of the above solution, the generating of the noise model includes:
Receiving sound wave data, video data and flow data acquired and obtained at the deepwater net cage to be detected; based on the characteristic of up-and-down movement of the telescopic rod, data acquisition is carried out at different depths outside the deepwater net cage to be measured;
Judging the video data and the flow data;
If the video data meets the first noise environment condition or the flow data meets the second noise environment condition, recording target acquisition time, calling sound wave data corresponding to the target acquisition time, and performing signal preprocessing to obtain a plurality of noise samples;
Extracting a signal intensity sample mean value and a signal intensity sample variance of each noise sample;
Performing Fourier transform on each noise sample to obtain signal spectrum sample density;
And storing each noise sample and a signal intensity sample mean value, a signal intensity sample variance and a signal spectrum sample density corresponding to each noise sample in a database to obtain a noise model.
As an improvement of the above solution, the determining the video data and the traffic data further includes:
If the video data does not meet the first noise environment condition and the flow data does not meet the second noise environment condition, selecting the current sound wave data as normal sound wave data;
after judging normal sound wave data, making the telescopic rod rotate in the deepwater net cage so that a flow monitor of the telescopic rod can acquire flow data in the deepwater net cage to be detected;
the surface wind power data of the area where the deepwater net cage to be detected is located is called;
Based on the water depth of the flow monitor on the telescopic rod, a preset flow rate calculation formula and the wind power data, calculating the basic flow rate of the water body of the water depth of the flow monitor; the flow rate calculation formula specifically comprises the following steps:
；
wherein V is the basic flow velocity of the water body, U is the surface wind power data, The drift coefficient of the surface Ekman is D, the water depth is D, and H is an attenuation depth parameter;
Marking a target water depth based on the currently acquired flow data of the flow monitor and the current basic flow rate of the water body, so that a user can determine the water depth position of the culture based on the target water depth; when the difference value between the current acquired flow data of the flow monitor and the current basic flow velocity of the water body is larger than a flow velocity difference threshold value, marking the water depth where the flow monitor is positioned as a target water depth.
As an improvement of the above solution, the video data satisfying a first noise environmental condition includes:
Identifying every two frames of pictures of the video data;
If the area of the pixel change area between every two frames of pictures is larger than the area change threshold value and the RGB difference value of the pixel change area is larger than the RGB change threshold value, the video data corresponding to the current two frames of pictures meet the first noise environment condition, and the acquisition time of the current two frames of pictures is selected as the target acquisition time;
Otherwise, no operation is performed.
As an improvement of the above solution, the flow data satisfies a second noise environmental condition, including:
In the flow data, selecting the flow rate with the maximum time duty ratio as a flow rate threshold;
identifying the flow speed of the flow data;
If the flow velocity is greater than or equal to the flow velocity threshold, the flow data with the flow velocity greater than the flow velocity threshold meets the second noise environment condition, and the acquisition time of the current flow data is selected as the target acquisition time.
As an improvement of the above, the inverse filter parameters include: the stopband range of the band-stop filter and the passband width of the band-pass filter; the determining inverse filter parameters based on the noise data includes:
extracting a first signal frequency range corresponding to the noise data, and determining the stop band range of the band-stop filter based on the first signal frequency range;
Summarizing normal sound wave data to obtain a normal sound wave data set;
And extracting a second signal frequency range corresponding to the normal sound wave data set, and determining the passband width of the band-pass filter based on the second signal frequency range.
Correspondingly, an embodiment of the invention also provides an internet of things data optimization processing system based on the deepwater net cage, which comprises the following steps: the system comprises a data acquisition module, a noise estimation module, a data determination module and a denoising module;
the data acquisition module is used for acquiring the data of the Internet of things acquired in the deepwater net cage to be detected;
The noise estimation module is used for carrying out noise estimation on the data of the Internet of things based on a preset noise model to obtain noise data in the deepwater net cage to be detected; wherein the noise model comprises: a plurality of noise samples marked with signal intensity sample mean, signal intensity sample variance and signal spectrum sample density; analyzing noise samples based on the sound wave data, the video data and the flow data; collecting sound wave data at the deepwater net cage to be tested through a water sound sensor; collecting video data at the deepwater net cage to be tested through a camera; collecting flow data at the position of the deepwater net cage to be measured through a flow monitor; the acquisition time of the sound wave data, the video data and the flow data is kept consistent; the underwater acoustic sensor, the camera and the flow monitor are arranged on a telescopic rod;
the data determining module is used for determining inverse filter parameters based on the noise data;
The denoising module is used for inputting the internet of things data into an inverse filter corresponding to the inverse filter parameters to perform denoising so as to finish noise optimization processing of the internet of things data acquired in the deepwater net cage to be detected.
Correspondingly, an embodiment of the invention also provides a computer terminal device, which comprises a processor, a memory and a computer program stored in the memory and configured to be executed by the processor, wherein the data optimization processing method based on the deepwater net cage is realized when the processor executes the computer program.
Correspondingly, an embodiment of the invention also provides a computer readable storage medium, which comprises a stored computer program, wherein when the computer program runs, equipment where the computer readable storage medium is located is controlled to execute the data optimization processing method based on the deepwater net cage.
From the above, the invention has the following beneficial effects:
The invention provides an Internet of things data optimization processing method based on a deepwater net cage, which is used for acquiring Internet of things data acquired in the deepwater net cage to be detected; carrying out noise estimation on the Internet of things data based on a preset noise model to obtain noise data in the deepwater net cage to be detected; determining inverse filter parameters based on the noise data; and inputting the Internet of things data into an inverse filter corresponding to the inverse filter parameters to perform denoising so as to finish noise optimization processing of the Internet of things data acquired in the deep water net cage to be detected. According to the method, based on the preset noise model, the noise estimation is performed on the Internet of things data acquired by the deepwater net cage to be detected, so that the noise data is confirmed, the inverse filter is set based on the noise data, the denoising of the Internet of things data of the deepwater net cage is realized, and the acquisition accuracy of the Internet of things equipment data in the deepwater net cage is improved.
Drawings
Fig. 1 is a flow chart of an internet of things data optimization processing method based on a deepwater net cage according to an embodiment of the present invention;
Fig. 2 is a schematic structural diagram of an internet of things data optimization processing system based on a deepwater net cage according to an embodiment of the present invention;
Fig. 3 is a schematic structural diagram of a terminal device according to an embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Example 1
Referring to fig. 1, fig. 1 is a flow chart of an internet of things data optimization processing method based on a deepwater net cage according to an embodiment of the present invention, as shown in fig. 1, the embodiment includes steps 101 to 104, where each step specifically includes:
step 101: and acquiring the data of the Internet of things acquired in the deepwater net cage to be detected.
In this embodiment, the internet of things data of the deepwater net cage to be detected is collected through the internet of things device, and the corresponding internet of things device is selected according to the water quality, depth, temperature and water pressure of the deepwater net cage, so that the stability and waterproof performance of the internet of things device are ensured. And the internet of things equipment performs data transmission in a wired mode, so that the internet of things data is transmitted to a device/equipment for executing the internet of things data optimization processing method based on the deepwater net cage.
Step 102: carrying out noise estimation on the Internet of things data based on a preset noise model to obtain noise data in the deepwater net cage to be detected; wherein the noise model comprises: a plurality of noise samples marked with signal intensity sample mean, signal intensity sample variance and signal spectrum sample density; analyzing noise samples based on the sound wave data, the video data and the flow data; collecting sound wave data at the deepwater net cage to be tested through a water sound sensor; collecting video data at the deepwater net cage to be tested through a camera; collecting flow data at the position of the deepwater net cage to be measured through a flow monitor; the acquisition time of the sound wave data, the video data and the flow data is kept consistent; the underwater acoustic sensor, the camera and the flow monitor are arranged on a telescopic rod.
It should be noted that the outer surface of the deepwater net cage to be tested is provided with a concave groove, so that the telescopic rod can move up and down and rotate left and right in the groove.
In this embodiment, the performing noise estimation on the internet of things data based on the preset noise model to obtain noise data in the deepwater net cage to be detected includes:
Extracting a signal intensity mean value and a signal intensity variance corresponding to the data of the Internet of things;
Performing Fourier transform on the data of the Internet of things to obtain signal spectrum density;
Based on the signal intensity mean value, the signal intensity variance and the signal spectrum density of the data of the Internet of things, matching is carried out in a preset noise model, and noise matching degree is obtained; wherein the noise model comprises: a plurality of noise samples marked with signal intensity sample mean, signal intensity sample variance and signal spectrum sample density;
and marking signals with noise matching degree larger than a matching degree threshold value in the data of the Internet of things as noise, and summarizing to obtain noise data of the deepwater net cage to be detected.
In a specific embodiment, the mean and variance of signal intensities of the data of the internet of things are extracted: the mean and variance of the signal intensities are extracted from the internet of things data. For example: each data point of the internet of things data contains time, location and signal strength. Calculating the Mean and Variance of the signal intensities of all the data points to obtain a Mean (Mean) of the signal intensities and a Variance (Variance) of the signal intensities;
and carrying out Fourier transform on the data of the Internet of things to obtain frequency spectrum representation of the signals and display the energy distribution condition of the signals on different frequencies.
It can be understood that the noise matching degree is calculated, specifically: and calculating the noise matching degree by comparing the signal intensity mean value, variance and spectrum density of the data of the Internet of things with the values of corresponding samples in the noise model. Some distance measure or similarity index may be used to evaluate the degree of matching, such as euclidean distance or correlation coefficient; and the matching degree threshold value can be a value adaptively set based on different water depth environments.
In this embodiment, the generating of the noise model includes:
Receiving sound wave data, video data and flow data acquired and obtained at the deepwater net cage to be detected; based on the characteristic of up-and-down movement of the telescopic rod, data acquisition is carried out at different depths outside the deepwater net cage to be measured;
Judging the video data and the flow data;
If the video data meets the first noise environment condition or the flow data meets the second noise environment condition, recording target acquisition time, calling sound wave data corresponding to the target acquisition time, and performing signal preprocessing to obtain a plurality of noise samples;
Extracting a signal intensity sample mean value and a signal intensity sample variance of each noise sample;
Performing Fourier transform on each noise sample to obtain signal spectrum sample density;
And storing each noise sample and a signal intensity sample mean value, a signal intensity sample variance and a signal spectrum sample density corresponding to each noise sample in a database to obtain a noise model.
In a specific embodiment, the underwater acoustic sensor, the camera and the flow monitor are initially installed on the deepwater net cage to be detected, so that the water flow condition and the biological dynamics of the deepwater net cage to be detected can be recorded, and the influence between the water flow condition and the biological dynamics based on the acoustic data acquired by the underwater acoustic sensor by a user is facilitated.
It can be understood that the invention takes the signal intensity mean value, the signal intensity variance and the signal spectrum density as the characteristics of noise, so that the signal of the data of the Internet of things can be judged based on three dimensions when the noise is matched, and the accuracy of the noise estimation of the data of the Internet of things is further improved.
In this embodiment, the determining the video data and the traffic data further includes:
If the video data does not meet the first noise environment condition and the flow data does not meet the second noise environment condition, selecting the current sound wave data as normal sound wave data;
after judging normal sound wave data, controlling the telescopic rod to rotate in the deepwater net cage so that a flow monitor of the telescopic rod can acquire flow data in the deepwater net cage to be detected;
the surface wind power data of the area where the deepwater net cage to be detected is located is called;
Based on the water depth of the flow monitor on the telescopic rod, a preset flow rate calculation formula and the wind power data, calculating the basic flow rate of the water body of the water depth of the flow monitor; the flow rate calculation formula specifically comprises the following steps:
；
wherein V is the basic flow velocity of the water body, U is the surface wind power data, The drift coefficient of the surface Ekman is D, the water depth is D, and H is an attenuation depth parameter;
Marking a target water depth based on the currently acquired flow data of the flow monitor and the current basic flow rate of the water body, so that a user can determine the water depth position of the culture based on the target water depth; when the difference value between the current acquired flow data of the flow monitor and the current basic flow velocity of the water body is larger than a flow velocity difference threshold value, marking the water depth where the flow monitor is positioned as a target water depth.
After the target water depth of the culture is determined, the collected data are all related data of the culture because no noise influence exists, and a user can put feed at fixed points or perform other culture activities at the target water depth, so that the culture cost is saved.
In a specific embodiment, the attenuation depth parameter H represents a Ekman drift factor along the vertical directionThe specific value of H depends on the characteristics of the sea/water and the environmental conditions, typically between tens and hundreds of meters, to the depth required for attenuation to 1/e of the original value (bottom of natural logarithm). Determining the surface Ekman drift coefficients is dependent on a large amount of observation and research effort, and requires a full knowledge of the sea/water environment and characteristics, and user-adaptive settings based on actual conditions. The flow rate difference threshold is adaptively set by the user based on the habit of the culture.
In this embodiment, the video data satisfies a first noise environment condition, including:
Identifying every two frames of pictures of the video data;
If the area of the pixel change area between every two frames of pictures is larger than the area change threshold value and the RGB difference value of the pixel change area is larger than the RGB change threshold value, the video data corresponding to the current two frames of pictures meet the first noise environment condition, and the acquisition time of the current two frames of pictures is selected as the target acquisition time;
Otherwise, no operation is performed.
In a specific embodiment, the bio-aggregation or movement may cause bio-noise of the deepwater net cage, and the identification of the bio-noise generation condition of the deepwater net cage is performed through the area of the pixel change region and the RGB difference value of the pixel change region, which is used as the first noise environment condition.
It should be noted that, the area of the pixel change area can identify the environmental change, but the pixel change area may occur due to the weather problem, but this is not caused by biological noise; the pixel change areas caused by the weather problem are all changes of light in water, the RGB difference values are not large, and biological noise caused by biological movement can be accurately identified by identifying the RGB difference values of the pixel change areas, so that the influence of the weather condition can be eliminated.
In this embodiment, the flow data satisfies a second noise environmental condition, including:
In the flow data, selecting the flow rate with the maximum time duty ratio as a flow rate threshold;
identifying the flow speed of the flow data;
If the flow velocity is greater than or equal to the flow velocity threshold, the flow data with the flow velocity greater than the flow velocity threshold meets the second noise environment condition, and the acquisition time of the current flow data is selected as the target acquisition time.
In a specific embodiment, when water flows too much due to the influence of meteorological conditions or hydrologic influences, water flow noise is also generated, and because the water flow speeds of different areas are different, the method and the device can adapt to water flow noise detection of different areas by selecting the flow speed with the largest time ratio as the flow speed threshold, and further judge the water flow noise by identifying the data of the Internet of things when the time is larger than the flow speed threshold.
Preferably, the noise model needs to be updated in time to meet the condition of flow velocity change in different seasons in different areas, so that the problem of inaccurate noise model caused by large weather environment difference is avoided.
It is understood that the flow rate threshold may be a range of flow rate maxima and flow rate minima over time.
Step 103: an inverse filter parameter is determined based on the noise data.
In this embodiment, the inverse filter parameters include: the stopband range of the band-stop filter and the passband width of the band-pass filter; the determining inverse filter parameters based on the noise data includes:
extracting a first signal frequency range corresponding to the noise data, and determining the stop band range of the band-stop filter based on the first signal frequency range;
Summarizing normal sound wave data to obtain a normal sound wave data set;
And extracting a second signal frequency range corresponding to the normal sound wave data set, and determining the passband width of the band-pass filter based on the second signal frequency range.
In a specific embodiment, the frequency component of the normal sound wave signal is reserved and noise is suppressed by setting the passband width of the filter (if the normal sound wave frequency in the data of the Internet of things is 100Hz to 10kHz, the passband width is 100Hz to 10 kHz).
It will be appreciated that to better illustrate the setting of the stopband range of the bandstop filter, the following examples are given: the high-frequency noise of the deepwater net cage to be detected is subjected to spectrum analysis, the high-frequency noise is mainly concentrated in the frequency range above 10kHz, and possibly has higher power spectral density, in order to weaken the high-frequency noise, the stop band range of the band-stop filter can be set to be above 10kHz, and the stop band range of the filter can be adjusted according to the estimation of the power spectral density of the noise.
Using these adjusted inverse filters, we can pass the original signal through the filters to attenuate the noise component as much as possible and preserve the frequency component of the music signal. By adjusting the frequency range and noise characteristics, we can better adapt to the characteristics of signals and noise and realize more effective inverse wave denoising effect. It should be noted that specific adjustment parameters need to be adjusted and optimized according to actual situations and requirements.
Step 104: and inputting the Internet of things data into an inverse filter corresponding to the inverse filter parameters to perform denoising so as to finish noise optimization processing of the Internet of things data acquired in the deep water net cage to be detected.
In a specific embodiment, the data of the internet of things is input into MATLAB, denoising is performed through the MATLAB inverse filter function, and denoising accuracy of the inverse filter is improved through setting the stop band range of the band-stop filter and the pass band width of the band-pass filter.
Referring to fig. 2, fig. 2 is a schematic structural diagram of an internet of things data optimization processing system based on a deepwater net cage according to an embodiment of the present invention, including: a data acquisition module 201, a noise estimation module 202, a data determination module 203 and a denoising module 204;
the data acquisition module is used for acquiring the data of the Internet of things acquired in the deepwater net cage to be detected;
The noise estimation module is used for carrying out noise estimation on the data of the Internet of things based on a preset noise model to obtain noise data in the deepwater net cage to be detected; wherein the noise model comprises: a plurality of noise samples marked with signal intensity sample mean, signal intensity sample variance and signal spectrum sample density; analyzing noise samples based on the sound wave data, the video data and the flow data; collecting sound wave data at the deepwater net cage to be tested through a water sound sensor; collecting video data at the deepwater net cage to be tested through a camera; collecting flow data at the position of the deepwater net cage to be measured through a flow monitor; the acquisition time of the sound wave data, the video data and the flow data is kept consistent; the underwater acoustic sensor, the camera and the flow monitor are arranged on a telescopic rod;
the data determining module is used for determining inverse filter parameters based on the noise data;
The denoising module is used for inputting the internet of things data into an inverse filter corresponding to the inverse filter parameters to perform denoising so as to finish noise optimization processing of the internet of things data acquired in the deepwater net cage to be detected.
The embodiment of the system item corresponds to the embodiment of the method item of the invention, and the method for optimizing the data of the internet of things based on the deepwater net cage provided by any one of the embodiment of the method item of the invention can be realized.
According to the embodiment, the data of the Internet of things acquired in the deepwater net cage to be detected are acquired; carrying out noise estimation on the Internet of things data based on a preset noise model to obtain noise data in the deepwater net cage to be detected; determining inverse filter parameters based on the noise data; and inputting the Internet of things data into an inverse filter corresponding to the inverse filter parameters to perform denoising so as to finish noise optimization processing of the Internet of things data acquired in the deep water net cage to be detected. According to the method, based on the preset noise model, the noise estimation is performed on the Internet of things data acquired by the deepwater net cage to be detected, so that the noise data is confirmed, the inverse filter is set based on the noise data, the denoising of the Internet of things data of the deepwater net cage is realized, and the acquisition accuracy of the Internet of things equipment data in the deepwater net cage is improved.
Example two
Referring to fig. 3, fig. 3 is a schematic structural diagram of a terminal device according to an embodiment of the present invention.
A terminal device of this embodiment includes: a processor 301, a memory 302 and a computer program stored in said memory 302 and executable on said processor 301. The processor 301 executes the computer program to implement the steps in the embodiment of the method for optimizing data of the internet of things based on a deepwater net cage, for example, all the steps of the method for optimizing data of the internet of things based on a deepwater net cage shown in fig. 1. Or the processor, when executing the computer program, performs the functions of the modules in the system embodiments described above, for example: all modules of the data optimization processing system of the Internet of things based on the deepwater net cage shown in fig. 2.
In addition, the embodiment of the invention also provides a computer readable storage medium, which comprises a stored computer program, wherein when the computer program runs, equipment where the computer readable storage medium is located is controlled to execute the data optimization processing method based on the deepwater net cage according to any embodiment.
It will be appreciated by those skilled in the art that the schematic diagram is merely an example of a terminal device and does not constitute a limitation of the terminal device, and may include more or less components than illustrated, or may combine certain components, or different components, e.g., the terminal device may further include an input-output device, a network access device, a bus, etc.
The Processor 301 may be a central processing unit (Central Processing Unit, CPU), but may also be other general purpose processors, digital signal processors (DIGITAL SIGNAL Processor, DSP), application SPECIFIC INTEGRATED Circuit (ASIC), off-the-shelf Programmable gate array (Field-Programmable GATE ARRAY, FPGA) or other Programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like. The general purpose processor may be a microprocessor or the processor may be any conventional processor or the like, and the processor 301 is a control center of the terminal device, and connects various parts of the entire terminal device using various interfaces and lines.
The memory 302 may be used to store the computer program and/or module, and the processor 301 may implement various functions of the terminal device by running or executing the computer program and/or module stored in the memory and invoking data stored in the memory 302. The memory 302 may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program (such as a sound playing function, an image playing function, etc.) required for at least one function, and the like; the storage data area may store data (such as audio data, phonebook, etc.) created according to the use of the handset, etc. In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as a hard disk, memory, plug-in hard disk, smart memory card (SMART MEDIA CARD, SMC), secure Digital (SD) card, flash memory card (FLASH CARD), at least one disk storage device, flash memory device, or other volatile solid-state storage device.
Wherein the terminal device integrated modules/units may be stored in a computer readable storage medium if implemented in the form of software functional units and sold or used as stand alone products. Based on such understanding, the present invention may implement all or part of the flow of the method of the above embodiment, or may be implemented by a computer program to instruct related hardware, where the computer program may be stored in a computer readable storage medium, and when the computer program is executed by a processor, the computer program may implement the steps of each of the method embodiments described above. Wherein the computer program comprises computer program code which may be in source code form, object code form, executable file or some intermediate form etc. The computer readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), an electrical carrier signal, a telecommunications signal, a software distribution medium, and so forth.
It should be noted that the system embodiments described above are merely illustrative, and that the units described as separate units may or may not be physically separate, and that units shown as units may or may not be physical units, may be located in one place, or may be distributed over a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment. In addition, in the system embodiment of the present invention, the connection relationship between the modules represents that there is a communication connection between them, and may be specifically implemented as one or more communication buses or signal lines. Those of ordinary skill in the art will understand and implement the present invention without undue burden.
While the foregoing is directed to the preferred embodiments of the present invention, it will be appreciated by those skilled in the art that changes and modifications may be made without departing from the principles of the invention, such changes and modifications are also intended to be within the scope of the invention.
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| CN116612735A (en) * | 2023-03-15 | 2023-08-18 | 中国人民解放军国防科技大学 | Underwater sound audio denoising method |
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| CN116612735A (en) * | 2023-03-15 | 2023-08-18 | 中国人民解放军国防科技大学 | Underwater sound audio denoising method |
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