A data processing-based fishing net detection method and system

Abstract

The application discloses a kind of based on data processing's fishing net detection method and system, comprising the following steps: by three-dimensional sonar detection device signal transmission, the three-dimensional sonar detection device receives signal and generates fishing net three-dimensional sonar image after processing, the fishing net three-dimensional sonar image is carried out grey processing and obtains fishing net damage area preliminary information, constructs fishing net information database, based on the fishing net information database and fishing net damage area preliminary information generates preset fishing net three-dimensional model and fishing net damage location real-time three-dimensional sonar model, by constructing three-dimensional coordinate system and virtual integration space, data integration and analysis are carried out to preset fishing net three-dimensional model and fishing net damage location real-time three-dimensional sonar model, and analysis result is obtained and fishing net safety repair suggestion is formulated.The application can improve the detection accuracy of the damaged area and the damage degree of the fishing net, and improve the economic benefit of deep-sea fishing.

Description

Technical Field

[0001] This invention relates to the field of fishing net detection technology, and in particular to a fishing net detection method and system based on data processing. Background Technology

[0002] Fishing nets are tools used to catch fish in the sea. They are essential tools for fishermen. High-quality fishing nets prevent fish from escaping through the mesh, resulting in better catches and schooling. Modern fishing nets are generally made of nylon or modified nylon monofilaments, multifilaments, or a combination of both, and can be woven with fibers such as polyester and polyvinylidene chloride.

[0003] During the use of fishing nets, fish, driven by their survival instinct, try to escape through the mesh openings, causing the mesh size to increase. Seawater, being a highly corrosive medium, is highly saline and oxygen-rich, which corrodes the woven fishing nets. A data-processing-based fishing net detection system can use sonar images and 3D model analysis to detect whether the mesh size meets standards and to assess the degree of corrosion caused by seawater, thereby improving economic efficiency and facilitating fishing for fishermen. Summary of the Invention

[0004] This invention overcomes the shortcomings of the prior art and provides a fishing net detection method and system based on data processing.

[0005] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0006] The first aspect of this invention provides a fishing net detection method based on data processing, comprising the following steps:

[0007] A three-dimensional sonar signal is emitted by a three-dimensional sonar detection device, and the reflected three-dimensional sonar signal is acquired. The reflected three-dimensional sonar signal is then sent back to a signal receiver. After the signal receiver processes the reflected three-dimensional sonar signal, a three-dimensional sonar image is generated by a signal image generation device.

[0008] The fishing net pixels in the three-dimensional sonar image are filtered out. After filtering out the fishing net pixels, the fishing net pixels are grayscaled to obtain a grayscale image. After noise reduction processing of the grayscale image, the information of the damaged area of ​​the fishing net is obtained.

[0009] The fishing net is subjected to laser flaw detection using a laser flaw detector to obtain dynamic pulse waveforms, and the location and condition of the damaged area are determined based on the dynamic pulse waveforms.

[0010] A preset three-dimensional model of the fishing net is established based on preset data, and a real-time three-dimensional model of the fishing net is obtained based on the complete three-dimensional sonar image of the fishing net and the dynamic pulse waveform.

[0011] The preset 3D model and the real-time 3D model of the fishing net are compared and analyzed to obtain the analysis results. Based on the analysis results, suggestions for safe repair of the fishing net are formulated.

[0012] Furthermore, in a preferred embodiment of the present invention, the step of transmitting a three-dimensional sonar signal through a three-dimensional sonar detection device, acquiring the reflected three-dimensional sonar signal, and sending the reflected three-dimensional sonar signal back to a signal receiver, wherein the signal receiver processes the reflected three-dimensional sonar signal, and a three-dimensional sonar image is generated by a signal image generation device, specifically as follows:

[0013] The device emits three-dimensional sonar signals and acquires three-dimensional sonar signals reflected from a preset area.

[0014] The three-dimensional sonar signal is divided into a high-frequency beam and a low-frequency beam. The high-frequency beam is processed by a high-frequency transducer and then received by a signal receiver. The low-frequency beam is processed by a low-frequency transducer and then received by a signal receiver. The high-frequency beam and the low-frequency beam are displayed on the display as waveform signals.

[0015] The signal image generation device analyzes the waveform signal to obtain the waveform signal analysis result, and generates a three-dimensional sonar image based on the waveform signal analysis result in the signal image generation device.

[0016] Furthermore, in a preferred embodiment of the present invention, the step of filtering the pixels of the three-dimensional sonar image, filtering out the fishing net pixels, performing grayscale processing on the fishing net pixels to obtain a grayscale image, and performing noise reduction processing on the grayscale image to obtain preliminary information on the damaged area of ​​the fishing net, specifically:

[0017] The pixels suspected to be living objects and fishing net pixels in the three-dimensional sonar image are screened and classified, the pixel features and the pixel distance between pixels of the same type are recorded, and the pixel distribution density is calculated; then the pixel distribution density is compared with the preset pixel distribution density.

[0018] If the pixel distribution density is greater than a preset pixel distribution density, then the pixels with a pixel distribution density greater than the preset pixel distribution density threshold are marked as fishing net pixels; if the pixel distribution density is not greater than the preset pixel distribution density, then the pixels with a pixel distribution density not greater than the preset pixel distribution density threshold are marked as suspected living objects pixels; extract the fishing net pixels, and form a three-dimensional sonar image of the fishing net from the fishing net pixels;

[0019] The red, green and blue color channels in the three-dimensional sonar image of the fishing net are weighted and averaged according to different weights to obtain gray values. A three-dimensional sonar grayscale image of the fishing net is then generated based on the three-dimensional sonar image of the fishing net and the gray values.

[0020] The mean filtering method is used to denoise the three-dimensional sonar image of the fishing net to obtain the processed three-dimensional sonar image of the fishing net. The granularity of the processed three-dimensional sonar image of the fishing net is reduced and the clarity is improved. Finally, the adaptive gray level compensation method is applied to the processed three-dimensional sonar image of the fishing net to obtain the preliminary three-dimensional sonar image of the fishing net.

[0021] The preliminary three-dimensional sonar grayscale image of the fishing net was analyzed to obtain preliminary information on the damaged area of ​​the fishing net.

[0022] Furthermore, in a preferred embodiment of the present invention, the step of performing laser flaw detection on the fishing net using a laser flaw detector to obtain a dynamic pulse waveform, and determining the location and state of the damaged area based on the analysis results of the dynamic pulse waveform and the preliminary information of the damaged area of ​​the fishing net, specifically involves:

[0023] The laser emitter in the laser flaw detector emits a pulsed laser, which is projected onto the fishing net through the optical path adjuster, and the pulsed laser scans the fishing net.

[0024] After the fishing net is laser-scanned, thermally excited ultrasonic waves are generated on the surface of the fishing net. These waves are received by the signal receiving device of the laser flaw detector to obtain a dynamic pulse waveform.

[0025] The amplitude and frequency of the dynamic pulse waveform are continuously analyzed to obtain analysis results. Based on the analysis results and preliminary information on the damaged area of ​​the fishing net, the location and damage status of the damaged area are determined.

[0026] Furthermore, in a preferred embodiment of the present invention, the step of establishing a fishing net information database based on preset data, constructing a preset three-dimensional sonar model of the fishing net based on the fishing net information database, obtaining a real-time three-dimensional sonar model of the fishing net damage location based on the location and state of the damaged area, and finally integrating and comparing the preset three-dimensional sonar model of the fishing net with the real-time three-dimensional sonar model of the fishing net damage location based on the fishing net information database, specifically involves:

[0027] Obtain data on the mesh size and weave thickness of fishing nets within the normal range;

[0028] A database is constructed by importing data on the size of fishing net mesh and the thickness of fishing net weave within the normal range into the database to obtain a fishing net information database. A preset three-dimensional sonar model of fishing net is constructed based on the fishing net information database.

[0029] Based on the location and condition of the fishing net damage, the net mesh size and weave thickness are analyzed and obtained. The net mesh size and weave thickness are then imported into the fishing net information database to obtain a real-time three-dimensional sonar model of the fishing net damage location.

[0030] Based on the fishing net information database, a virtual integration space is constructed, and a preset three-dimensional model of the fishing net and a real-time three-dimensional sonar model of the fishing net damage location are imported. In the virtual integration space, the preset three-dimensional model of the fishing net and the three-dimensional model of the fishing net damage location are integrated to obtain a real-time three-dimensional model of the fishing net.

[0031] A three-dimensional coordinate system is constructed, and based on the three-dimensional coordinate system, the preset three-dimensional model of the fishing net is compared with the real-time three-dimensional model of the fishing net to obtain the model deviation value;

[0032] The model deviation value is compared with a preset deviation threshold. If the model deviation value is greater than the preset deviation threshold, a first analysis result is generated; if the model deviation value is less than the preset deviation threshold, a second analysis result is generated.

[0033] If the analysis result is the first analysis result, then the size of the fishing net mesh and the thickness of the fishing net weave are in an abnormal state; if the analysis result is the second analysis result, then the size of the fishing net mesh and the thickness of the fishing net weave are in a normal state.

[0034] Furthermore, in a preferred embodiment of the present invention, the comparative analysis results based on the preset three-dimensional sonar model of the fishing net and the real-time three-dimensional sonar model of the fishing net damage location specifically include:

[0035] Based on the comparative analysis results of the preset three-dimensional sonar model of the fishing net and the real-time three-dimensional sonar model of the damaged location of the fishing net, safety repair suggestions for the fishing net are formulated. If the analysis results are in an abnormal state, the fishing net in the abnormal state needs to be maintained and repaired; if the results are in a normal state, the fishing net in the normal state does not need to be repaired for the time being.

[0036] A second aspect of the present invention also provides a fishing net detection system based on data processing. The fishing net detection system includes a memory and a processor. The memory stores a fishing net detection method program. When the fishing net detection method program is executed by the processor, it performs the following steps:

[0037] Furthermore, in a preferred embodiment of the present invention, the step of filtering the pixels of the three-dimensional sonar image, filtering out the fishing net pixels, performing grayscale processing on the fishing net pixels to obtain a grayscale image, and performing noise reduction processing on the grayscale image to obtain preliminary information on the damaged area of ​​the fishing net, specifically:

[0038] The pixels suspected to be living objects and fishing net pixels in the three-dimensional sonar image are screened and classified, the pixel features and the pixel distance between pixels of the same type are recorded, and the pixel distribution density is calculated; then the pixel distribution density is compared with the preset pixel distribution density.

[0039] If the pixel distribution density is greater than a preset pixel distribution density, then the pixels with a pixel distribution density greater than the preset pixel distribution density threshold are marked as fishing net pixels; if the pixel distribution density is not greater than the preset pixel distribution density, then the pixels with a pixel distribution density not greater than the preset pixel distribution density threshold are marked as suspected living objects pixels; extract the fishing net pixels, and form a three-dimensional sonar image of the fishing net from the fishing net pixels;

[0040] The red, green and blue color channels in the three-dimensional sonar image of the fishing net are weighted and averaged according to different weights to obtain gray values. A three-dimensional sonar grayscale image of the fishing net is then generated based on the three-dimensional sonar image of the fishing net and the gray values.

[0041] The mean filtering method is used to denoise the three-dimensional sonar image of the fishing net to obtain the processed three-dimensional sonar image of the fishing net. The granularity of the processed three-dimensional sonar image of the fishing net is reduced and the clarity is improved. Finally, the adaptive gray level compensation method is applied to the processed three-dimensional sonar image of the fishing net to obtain the preliminary three-dimensional sonar image of the fishing net.

[0042] The preliminary three-dimensional sonar grayscale image of the fishing net was analyzed to obtain preliminary information on the damaged area of ​​the fishing net.

[0043] Furthermore, in a preferred embodiment of the present invention, the step of performing laser flaw detection on the fishing net using a laser flaw detector to obtain a dynamic pulse waveform, and determining the location and state of the damaged area based on the analysis results of the dynamic pulse waveform and the preliminary information of the damaged area of ​​the fishing net, specifically involves:

[0044] The laser emitter in the laser flaw detector emits a pulsed laser, which is projected onto the fishing net through the optical path adjuster, and the pulsed laser scans the fishing net.

[0045] After the fishing net is laser-scanned, thermally excited ultrasonic waves are generated on the surface of the fishing net. These waves are received by the signal receiving device of the laser flaw detector to obtain a dynamic pulse waveform.

[0046] The amplitude and frequency of the dynamic pulse waveform are continuously analyzed to obtain analysis results. Based on the analysis results and preliminary information on the damaged area of ​​the fishing net, the location and damage status of the damaged area are determined.

[0047] Furthermore, in a preferred embodiment of the present invention, the step of establishing a fishing net information database based on preset data, constructing a preset three-dimensional sonar model of the fishing net based on the fishing net information database, obtaining a real-time three-dimensional sonar model of the fishing net damage location based on the location and state of the damaged area, and finally integrating and comparing the preset three-dimensional sonar model of the fishing net with the real-time three-dimensional sonar model of the fishing net damage location based on the fishing net information database, specifically involves:

[0048] Obtain data on the mesh size and weave thickness of fishing nets within the normal range;

[0049] A database is constructed by importing data on the size of fishing net mesh and the thickness of fishing net weave within the normal range into the database to obtain a fishing net information database. A preset three-dimensional sonar model of fishing net is constructed based on the fishing net information database.

[0050] Based on the location and condition of the fishing net damage, the net mesh size and weave thickness are analyzed and obtained. The net mesh size and weave thickness are then imported into the fishing net information database to obtain a real-time three-dimensional sonar model of the fishing net damage location.

[0051] Based on the fishing net information database, a virtual integration space is constructed, and a preset three-dimensional model of the fishing net and a real-time three-dimensional sonar model of the fishing net damage location are imported. In the virtual integration space, the preset three-dimensional model of the fishing net and the three-dimensional model of the fishing net damage location are integrated to obtain a real-time three-dimensional model of the fishing net.

[0052] A three-dimensional coordinate system is constructed, and based on the three-dimensional coordinate system, the preset three-dimensional model of the fishing net is compared with the real-time three-dimensional model of the fishing net to obtain the model deviation value;

[0053] The model deviation value is compared with a preset deviation threshold. If the model deviation value is greater than the preset deviation threshold, a first analysis result is generated; if the model deviation value is less than the preset deviation threshold, a second analysis result is generated.

[0054] If the analysis result is the first analysis result, then the size of the fishing net mesh and the thickness of the fishing net weave are in an abnormal state; if the analysis result is the second analysis result, then the size of the fishing net mesh and the thickness of the fishing net weave are in a normal state.

[0055] This invention addresses the technical deficiencies in the prior art and offers the following advantages: A three-dimensional sonar detection device emits signals, which are then received and processed to generate a three-dimensional sonar image of the fishing net. This image is then converted to grayscale to obtain preliminary information about the damaged area. A fishing net information database is constructed. Based on this database and the preliminary information about the damaged area, a preset three-dimensional model of the fishing net and a real-time three-dimensional sonar model of the damaged location are generated. A virtual integration space is constructed to integrate and analyze the data from these two models, yielding analysis results and recommendations for safe repair of the fishing net. This invention improves the accuracy of detecting the damaged area and extent of fishing net damage, thereby increasing the economic benefits of deep-sea fishing. Attached Figure Description

[0056] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other embodiments can be obtained from these drawings without creative effort.

[0057] Figure 1 A flowchart of a fishing net detection method based on data processing is shown;

[0058] Figure 2 A flowchart for the analysis of a three-dimensional model of a fishing net is shown;

[0059] Figure 3 A flowchart of a fishing net detection system based on data processing is shown. Detailed Implementation

[0060] To better understand the above-mentioned objectives, features, and advantages of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be noted that, unless otherwise specified, the embodiments and features described in these embodiments can be combined with each other.

[0061] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and therefore the scope of protection of the invention is not limited to the specific embodiments disclosed below.

[0062] like Figure 1 As shown, the first aspect of the present invention provides a fishing net detection method based on data processing, comprising the following steps:

[0063] S102, a three-dimensional sonar signal is emitted through a three-dimensional sonar detection device, and a three-dimensional sonar signal reflected in a preset area is acquired. The three-dimensional sonar signal reflected in the preset area is then sent back to a signal receiver. After the signal receiver processes the three-dimensional sonar signal reflected in the preset area, a three-dimensional sonar image is generated by a signal image generation device.

[0064] S104, the pixels of the three-dimensional sonar image are filtered out, and the fishing net pixels are then grayscaled to obtain a grayscale image. After noise reduction processing of the grayscale image, preliminary information on the damaged area of ​​the fishing net is obtained.

[0065] S106, Laser flaw detector is used to perform laser flaw detection on fishing nets to obtain dynamic pulse waveforms, and the location and state of damage are determined based on the dynamic pulse waveforms;

[0066] S108. Establish a fishing net information database based on preset data, construct a preset three-dimensional sonar model of the fishing net based on the fishing net information database, obtain a real-time three-dimensional sonar model of the fishing net damage location based on the location and state of the damaged area, and finally perform data integration and comparative analysis on the preset three-dimensional sonar model of the fishing net and the real-time three-dimensional sonar model of the fishing net damage location based on the fishing net information database.

[0067] S110, Based on the comparative analysis results of the preset three-dimensional sonar model of the fishing net and the real-time three-dimensional sonar model of the damaged location of the fishing net, formulate suggestions for safe repair of the fishing net.

[0068] It should be noted that this invention can improve the accuracy of detecting the damaged area and degree of damage to fishing nets, thereby increasing the economic benefits of deep-sea fishing.

[0069] Furthermore, in a preferred embodiment of the present invention, the step of transmitting a three-dimensional sonar signal through a three-dimensional sonar detection device, acquiring the reflected three-dimensional sonar signal, and sending the reflected three-dimensional sonar signal back to a signal receiver, wherein the signal receiver processes the reflected three-dimensional sonar signal, and a three-dimensional sonar image is generated by a signal image generation device, specifically as follows:

[0070] The device emits three-dimensional sonar signals and acquires three-dimensional sonar signals reflected from a preset area.

[0071] The three-dimensional sonar signal is divided into a high-frequency beam and a low-frequency beam. The high-frequency beam is processed by a high-frequency transducer and then received by a signal receiver. The low-frequency beam is processed by a low-frequency transducer and then received by a signal receiver. The high-frequency beam and the low-frequency beam are displayed on the display as waveform signals.

[0072] The signal image generation device analyzes the waveform signal to obtain the waveform signal analysis result, and generates a three-dimensional sonar image based on the waveform signal analysis result in the signal image generation device.

[0073] It should be noted that the three-dimensional sonar detection device consists of a high-frequency transducer, a low-frequency transducer, a beam transmitter, a signal receiver, a display, and a signal image generation device. The beam transmitter emits a pulsed acoustic beam of 265 MHz at a frequency of 1.36 MHz. Due to environmental influences, the three-dimensional sonar signal experiences harmonic distortion during transmission. This harmonic distortion introduces harmonic components into the three-dimensional sonar signal, which are superimposed on the original signal, forming a distorted waveform signal. The following are methods to reduce harmonic distortion:

[0074] Apply appropriate voltage positive feedback or current negative feedback to the waveform distortion signal within the signal receiver;

[0075] The signal receiver uses amplification devices with high characteristic frequency, low noise figure, and good linearity.

[0076] Increase the power of the signal receiver.

[0077] This method can reduce harmonic distortion of dynamic pulse waveforms.

[0078] Furthermore, in a preferred embodiment of the present invention, the step of filtering the pixels of the three-dimensional sonar image, filtering out the fishing net pixels, performing grayscale processing on the fishing net pixels to obtain a grayscale image, and performing noise reduction processing on the grayscale image to obtain preliminary information on the damaged area of ​​the fishing net, specifically:

[0079] The pixels suspected to be living objects and fishing net pixels in the three-dimensional sonar image are screened and classified, the pixel features and the pixel distance between pixels of the same type are recorded, and the pixel distribution density is calculated; then the pixel distribution density is compared with the preset pixel distribution density.

[0080] If the pixel distribution density is greater than a preset pixel distribution density, then the pixels with a pixel distribution density greater than the preset pixel distribution density threshold are marked as fishing net pixels; if the pixel distribution density is not greater than the preset pixel distribution density, then the pixels with a pixel distribution density not greater than the preset pixel distribution density threshold are marked as suspected living objects pixels; extract the fishing net pixels, and form a three-dimensional sonar image of the fishing net from the fishing net pixels;

[0081] The red, green and blue color channels in the three-dimensional sonar image of the fishing net are weighted and averaged according to different weights to obtain gray values. A three-dimensional sonar grayscale image of the fishing net is then generated based on the three-dimensional sonar image of the fishing net and the gray values.

[0082] The mean filtering method is used to denoise the three-dimensional sonar image of the fishing net to obtain the processed three-dimensional sonar image of the fishing net. The granularity of the processed three-dimensional sonar image of the fishing net is reduced and the clarity is improved. Finally, the adaptive gray level compensation method is applied to the processed three-dimensional sonar image of the fishing net to obtain the preliminary three-dimensional sonar image of the fishing net.

[0083] The preliminary three-dimensional sonar grayscale image of the fishing net was analyzed to obtain preliminary information on the damaged area of ​​the fishing net.

[0084] It should be noted that the adaptive grayscale compensation method may result in insufficient grayscale compensation. In such cases, manual grayscale compensation is required to improve the grayscale level of the image. Additionally, due to the aforementioned harmonic distortion issue, the preliminary 3D sonar grayscale image of the fishing net may be incomplete. Therefore, the obtained information on the damaged area of ​​the fishing net is only preliminary; complete information on the damaged area requires combining it with the dynamic pulse waveform from a laser flaw detector. This method enables adaptive compensation of the 3D sonar grayscale image of the fishing net.

[0085] Furthermore, in a preferred embodiment of the present invention, the step of performing laser flaw detection on the fishing net using a laser flaw detector to obtain a dynamic pulse waveform, and determining the location and state of the damaged area based on the analysis results of the dynamic pulse waveform and the preliminary information of the damaged area of ​​the fishing net, specifically involves:

[0086] The laser emitter in the laser flaw detector emits a pulsed laser, which is projected onto the fishing net through the optical path adjuster, and the pulsed laser scans the fishing net.

[0087] After the fishing net is laser-scanned, thermally excited ultrasonic waves are generated on the surface of the fishing net. These waves are received by the signal receiving device of the laser flaw detector to obtain a dynamic pulse waveform.

[0088] The amplitude and frequency of the dynamic pulse waveform are continuously analyzed to obtain analysis results. Based on the analysis results and preliminary information on the damaged area of ​​the fishing net, the location and damage status of the damaged area are determined.

[0089] It should be noted that the pulse width of the pulsed laser emitted by the laser emitter should be less than 0.25 seconds, and the pulse energy intensity should be within the acceptable range to prevent damage to the fishing net material from excessively high pulse energy. Additionally, it should be noted that due to environmental influences, the three-dimensional sonar signal generates harmonic distortion during transmission. This harmonic distortion introduces harmonic components into the three-dimensional sonar signal, which are superimposed on the original signal, forming a distorted waveform. The following are methods to reduce harmonic distortion:

[0090] Apply appropriate voltage positive feedback or current negative feedback to the waveform distortion signal within the signal receiver;

[0091] The signal receiver uses amplification devices with high characteristic frequency, low noise figure, and good linearity.

[0092] Increase the power of the signal receiver.

[0093] This method can protect fishing nets, reduce harmonic distortion, and make dynamic pulse waveforms more complete.

[0094] Furthermore, in a preferred embodiment of the present invention, such as Figure 2 As shown, the process involves establishing a fishing net information database based on preset data, constructing a preset three-dimensional sonar model of the fishing net based on the database, obtaining a real-time three-dimensional sonar model of the fishing net damage location based on the location and state of the damaged area, and finally integrating and comparing the preset three-dimensional sonar model of the fishing net with the real-time three-dimensional sonar model of the fishing net damage location based on the fishing net information database. Based on the comparison analysis results, recommendations for safe repair of the fishing net are formulated, specifically as follows:

[0095] S202, Construct a pre-defined three-dimensional sonar model of fishing nets based on the fishing net information database;

[0096] S204, Construct a real-time three-dimensional sonar model of the location of the fishing net damage based on the location and state of the damage area;

[0097] S206, Based on the fishing net information database, the preset three-dimensional sonar model of the fishing net and the real-time three-dimensional sonar model of the location of the fishing net damage are integrated and compared.

[0098] S208. Based on the comparative analysis results of the preset three-dimensional sonar model of the fishing net and the real-time three-dimensional sonar model of the damaged location of the fishing net, formulate suggestions for safe repair of the fishing net.

[0099] Furthermore, in a preferred embodiment of the present invention, the step of constructing a preset three-dimensional sonar model of a fishing net based on a fishing net information database specifically includes:

[0100] Data on the size of fishing net mesh within the normal range and the thickness of fishing net weaving within the normal range are obtained to construct a database. The data on the size of fishing net mesh within the normal range and the thickness of fishing net weaving within the normal range are imported into the database to obtain a fishing net information database. A preset three-dimensional sonar model of fishing net is constructed based on the fishing net information database.

[0101] It should be noted that the data on the normal range of fishing net mesh size refers to the normal range of fishing net mesh diameter. Fishing net mesh diameters within the normal range are more effective at catching fish, making it less likely for fish to escape through the mesh. If the fishing net mesh diameter is larger than the normal range, fish can easily escape through the mesh. The data on the normal range of fishing net weaving thickness refers to the normal range of fishing net weaving rope diameter. Fishing net weaving ropes within the normal range are stronger, more resilient, and less prone to breakage or tearing. If the fishing net weaving rope diameter is smaller than the normal range, the fishing net weaves easily break and tear, and fish can escape through the broken or torn openings in the net.

[0102] Furthermore, in a preferred embodiment of the present invention, the step of constructing a real-time three-dimensional sonar model of the fishing net damage location based on the location and state of the damaged area specifically involves:

[0103] Based on the analysis of the dynamic pulse waveform of the laser detector and the preliminary information of the damaged area of ​​the fishing net, the location and state of the damaged area of ​​the fishing net are obtained. According to the location and state of the damaged area, the data of the size of the net's pores and the thickness of the net's weave are obtained. The data of the size of the net's pores and the thickness of the net's weave are imported into the fishing net information database to obtain a real-time three-dimensional sonar model of the location of the damaged area of ​​the fishing net.

[0104] It should be noted that the three-dimensional sonar model derived from the fishing net information database based on the location and condition of the damaged area is only a real-time three-dimensional sonar model of the damaged area. Based on the fishing net information database, this real-time three-dimensional sonar model of the damaged area is integrated with the preset three-dimensional sonar model of the fishing net to obtain a real-time three-dimensional model of the fishing net. This method enables the construction of a real-time three-dimensional model of the fishing net, thereby providing a direct understanding of the location and condition of the damaged area.

[0105] Furthermore, in a preferred embodiment of the present invention, the data integration and comparative analysis of the preset three-dimensional sonar model of the fishing net and the real-time three-dimensional sonar model of the fishing net damage location based on the fishing net information database specifically includes:

[0106] Based on the fishing net information database, a virtual integration space is constructed, and a preset three-dimensional model of the fishing net and a real-time three-dimensional sonar model of the fishing net damage location are imported. In the virtual integration space, the preset three-dimensional model of the fishing net and the three-dimensional model of the fishing net damage location are integrated to obtain a real-time three-dimensional model of the fishing net.

[0107] A three-dimensional coordinate system is constructed, and based on the three-dimensional coordinate system, the preset three-dimensional model of the fishing net is compared with the real-time three-dimensional model of the fishing net to obtain the model deviation value;

[0108] The model deviation value is compared with a preset deviation threshold. If the model deviation value is greater than the preset deviation threshold, a first analysis result is generated; if the model deviation value is less than the preset deviation threshold, a second analysis result is generated.

[0109] If the analysis result is the first analysis result, then the size of the fishing net mesh and the thickness of the fishing net weave are in an abnormal state; if the analysis result is the second analysis result, then the size of the fishing net mesh and the thickness of the fishing net weave are in a normal state.

[0110] It should be noted that the three-dimensional coordinate system and virtual integrated space are constructed using 3D modeling software. The simulated 3D model of the fishing net is compared with the real-time 3D model of the fishing net to obtain a model deviation value. This model deviation value is then compared with a preset deviation threshold. If the model deviation value is greater than the preset deviation threshold, it indicates that the diameter of the fishing net mesh and the thickness of the fishing net rope are greater than the normal range, indicating that the fishing net has a significant opening, and fish can easily escape through these openings. If the model deviation value is not greater than the preset deviation threshold, it indicates that the diameter of the fishing net mesh and the thickness of the fishing net rope are not greater than the normal range, indicating that the fishing net does not have a significant opening, the fishing net is within a safe range, and fishing operations can proceed normally. This method can detect the diameter of the fishing net mesh and the thickness of the fishing net rope to determine whether the fishing net has a significant opening.

[0111] Furthermore, in a preferred embodiment of the present invention, the step of formulating a fishing net safety repair suggestion based on the comparative analysis results of the preset fishing net three-dimensional sonar model and the real-time fishing net three-dimensional sonar model specifically includes:

[0112] Based on the comparative analysis results of the preset three-dimensional sonar model of the fishing net and the real-time three-dimensional sonar model of the fishing net, safety repair suggestions for the fishing net are formulated. If the analysis results are in an abnormal state, the fishing net in the abnormal state needs to be maintained and repaired; if the results are in a normal state, the fishing net in the normal state does not need to be repaired for the time being.

[0113] Furthermore, the aforementioned data processing-based fishing net detection method also includes the following steps:

[0114] The analysis results are obtained by comparing and analyzing the preset three-dimensional sonar model of the fishing net with the real-time three-dimensional sonar model of the fishing net. If the analysis result is the first analysis result, the size of the fishing net mesh and the thickness of the fishing net weave are in an abnormal state. If the analysis result is the second analysis result, the size of the fishing net mesh and the thickness of the fishing net weave are in a normal state.

[0115] The results of the first analysis are analyzed to determine the diameter of the opening where the fishing net breaks or tears.

[0116] Based on the diameter of the broken or torn openings in the fishing net, recommendations for safe repair of the fishing net are formulated.

[0117] It should be noted that excessively large mesh size and excessively fine weave of the fishing net can lead to excessively large tears and breaks, affecting fishing efficiency. Based on the analysis, the diameter of the net's breakage and tear is determined. A preset normal value for the net's breakage and tear diameter is established. If the diameter is not greater than this preset value, fishing operations are immediately stopped, and professionals are dispatched to repair the net. After repair, the net can function normally, and the fishing boat retrieves the net upon docking. If the diameter is greater than the preset normal value, fishing operations are immediately stopped, and the net is retrieved. After docking, professionals are dispatched to repair the net. This invention maximizes the profitability of fishing nets.

[0118] like Figure 3 As shown, a second aspect of the present invention provides a fishing net detection system based on data processing. The fishing net detection system includes a memory 31 and a processor 32. The memory 31 stores a fishing net detection method program. When the fishing net detection method program is executed by the processor 32, it performs the following steps:

[0119] A three-dimensional sonar signal is emitted by a three-dimensional sonar detection device, and the three-dimensional sonar signal reflected in a preset area is acquired. The three-dimensional sonar signal reflected in the preset area is then sent back to a signal receiver. After the signal receiver processes the three-dimensional sonar signal reflected in the preset area, a three-dimensional sonar image is generated by a signal image generation device.

[0120] The pixels of the three-dimensional sonar image are filtered out. After the fishing net pixels are filtered out, the fishing net pixels are grayscaled to obtain a grayscale image. After the grayscale image is denoised, preliminary information on the damaged area of ​​the fishing net is obtained.

[0121] The fishing net is subjected to laser flaw detection using a laser flaw detector to obtain dynamic pulse waveforms, and the location and condition of the damaged area are determined based on the dynamic pulse waveforms.

[0122] A fishing net information database is established based on preset data. A preset three-dimensional sonar model of the fishing net is constructed based on the fishing net information database. A real-time three-dimensional sonar model of the fishing net damage location is obtained based on the location and state of the damaged area. Finally, the preset three-dimensional sonar model of the fishing net and the real-time three-dimensional sonar model of the fishing net damage location are compared and analyzed based on the fishing net information database.

[0123] The results of the comparative analysis are based on the preset three-dimensional sonar model of the fishing net and the real-time three-dimensional sonar model of the damaged location of the fishing net.

[0124] Furthermore, in a preferred embodiment of the present invention, the step of establishing a fishing net information database based on preset data, constructing a preset three-dimensional sonar model of the fishing net based on the fishing net information database, obtaining a real-time three-dimensional sonar model of the fishing net damage location based on the location and state of the damaged area, and finally integrating and comparing the preset three-dimensional sonar model of the fishing net with the real-time three-dimensional sonar model of the fishing net damage location based on the fishing net information database, specifically involves:

[0125] Obtain data on the mesh size and weave thickness of fishing nets within the normal range;

[0126] A database is constructed by importing data on the size of fishing net mesh and the thickness of fishing net weave within the normal range into the database to obtain a fishing net information database. A preset three-dimensional sonar model of fishing net is constructed based on the fishing net information database.

[0127] Based on the location and condition of the fishing net damage, the net mesh size and weave thickness are analyzed and obtained. The net mesh size and weave thickness are then imported into the fishing net information database to obtain a real-time three-dimensional sonar model of the fishing net damage location.

[0128] Based on the fishing net information database, a virtual integration space is constructed, and a preset three-dimensional model of the fishing net and a real-time three-dimensional sonar model of the fishing net damage location are imported. In the virtual integration space, the preset three-dimensional model of the fishing net and the three-dimensional model of the fishing net damage location are integrated to obtain a real-time three-dimensional model of the fishing net.

[0129] A three-dimensional coordinate system is constructed, and based on the three-dimensional coordinate system, the preset three-dimensional model of the fishing net is compared with the real-time three-dimensional model of the fishing net to obtain the model deviation value;

[0130] The model deviation value is compared with a preset deviation threshold. If the model deviation value is greater than the preset deviation threshold, a first analysis result is generated; if the model deviation value is less than the preset deviation threshold, a second analysis result is generated.

[0131] If the analysis result is the first analysis result, then the size of the fishing net mesh and the thickness of the fishing net weave are in an abnormal state; if the analysis result is the second analysis result, then the size of the fishing net mesh and the thickness of the fishing net weave are in a normal state.

[0132] It should be noted that the three-dimensional coordinate system and virtual integrated space are constructed using 3D modeling software. The simulated 3D model of the fishing net is compared with the real-time 3D model of the fishing net to obtain a model deviation value. This model deviation value is then compared with a preset deviation threshold. If the model deviation value is greater than the preset deviation threshold, it indicates that the diameter of the fishing net mesh and the thickness of the fishing net rope are greater than the normal range, indicating that the fishing net has a significant opening, and fish can easily escape through these openings. If the model deviation value is not greater than the preset deviation threshold, it indicates that the diameter of the fishing net mesh and the thickness of the fishing net rope are not greater than the normal range, indicating that the fishing net does not have a significant opening, the fishing net is within a safe range, and fishing operations can proceed normally. This method can detect the diameter of the fishing net mesh and the thickness of the fishing net rope to determine whether the fishing net has a significant opening.

[0133] This invention addresses the technical deficiencies in the prior art and offers the following advantages: A three-dimensional sonar detection device emits signals, which are then received and processed to generate a three-dimensional sonar image of the fishing net. This image is then converted to grayscale to obtain preliminary information about the damaged area. A fishing net information database is constructed. Based on this database and the preliminary information about the damaged area, a preset three-dimensional model of the fishing net and a real-time three-dimensional sonar model of the damaged location are generated. A virtual integration space is constructed to integrate and analyze the data from these two models, yielding analysis results and recommendations for safe repair of the fishing net. This invention improves the accuracy of detecting the damaged area and extent of fishing net damage, thereby increasing the economic benefits of deep-sea fishing.

[0134] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A fishing net detection method based on data processing, characterized in that, Includes the following steps: A three-dimensional sonar signal is emitted by a three-dimensional sonar detection device, and the three-dimensional sonar signal reflected in a preset area is acquired. The three-dimensional sonar signal reflected in the preset area is then sent back to a signal receiver. After the signal receiver processes the three-dimensional sonar signal reflected in the preset area, a three-dimensional sonar image is generated by a signal image generation device. The pixels of the three-dimensional sonar image are filtered out. After the fishing net pixels are filtered out, the fishing net pixels are grayscaled to obtain a grayscale image. After the grayscale image is denoised, preliminary information on the damaged area of ​​the fishing net is obtained. The fishing net is subjected to laser flaw detection using a laser flaw detector to obtain dynamic pulse waveforms, and the location and condition of the damaged area are determined based on the dynamic pulse waveforms. A fishing net information database is established based on preset data. A preset three-dimensional sonar model of the fishing net is constructed based on the fishing net information database. According to the location and state of the damaged area, a real-time three-dimensional sonar model of the fishing net damage location is obtained. Finally, based on the fishing net information database, the preset three-dimensional sonar model of the fishing net and the real-time three-dimensional sonar model of the fishing net damage location are integrated and compared. Specifically: Obtain data on the mesh size and weave thickness of fishing nets within the normal range; A database is constructed by importing data on the size of fishing net mesh and the thickness of fishing net weave within the normal range into the database to obtain a fishing net information database. A preset three-dimensional sonar model of fishing net is constructed based on the fishing net information database. Based on the location and condition of the fishing net damage, the net mesh size and weave thickness are analyzed and obtained. The net mesh size and weave thickness are then imported into the fishing net information database to obtain a real-time three-dimensional sonar model of the fishing net damage location. Based on the fishing net information database, a virtual integration space is constructed, and a preset three-dimensional model of the fishing net and a real-time three-dimensional sonar model of the fishing net damage location are imported. In the virtual integration space, the preset three-dimensional model of the fishing net and the three-dimensional model of the fishing net damage location are integrated to obtain a real-time three-dimensional model of the fishing net. A three-dimensional coordinate system is constructed, and based on the three-dimensional coordinate system, the preset three-dimensional model of the fishing net is compared with the real-time three-dimensional model of the fishing net to obtain the model deviation value; The model deviation value is compared with a preset deviation threshold. If the model deviation value is greater than the preset deviation threshold, a first analysis result is generated. If the model deviation value is less than the preset deviation threshold, a second analysis result is generated; If the analysis result is the first analysis result, then the size of the fishing net mesh and the thickness of the fishing net weave are in an abnormal state; If the analysis result is the second analysis result, then the size of the fishing net mesh and the thickness of the fishing net weave are in a normal state; Based on the comparative analysis results of the preset three-dimensional sonar model of the fishing net and the real-time three-dimensional sonar model of the damaged location of the fishing net, suggestions for safe repair of the fishing net are formulated.

2. The fishing net detection method based on data processing according to claim 1, characterized in that, The process involves emitting a three-dimensional sonar signal via a three-dimensional sonar detection device, acquiring the reflected three-dimensional sonar signal, and sending the reflected three-dimensional sonar signal back to a signal receiver. The signal receiver processes the reflected three-dimensional sonar signal, and a signal image generation device generates a three-dimensional sonar image. Specifically: The device emits three-dimensional sonar signals and acquires three-dimensional sonar signals reflected from a preset area. The three-dimensional sonar signal is divided into a high-frequency beam and a low-frequency beam. The high-frequency beam is processed by a high-frequency transducer and then received by a signal receiver. The low-frequency beam is processed by a low-frequency transducer and then received by a signal receiver. The high-frequency beam and the low-frequency beam are displayed on the display as waveform signals. The signal image generation device analyzes the waveform signal to obtain the waveform signal analysis result, and generates a three-dimensional sonar image based on the waveform signal analysis result in the signal image generation device.

3. The fishing net detection method based on data processing according to claim 1, characterized in that, The process involves filtering the pixels of the three-dimensional sonar image, identifying fishing net pixels, converting these pixels to grayscale to obtain a grayscale image, and then performing noise reduction on the grayscale image to obtain preliminary information about the damaged area of ​​the fishing net. Specifically: The pixels suspected to be living objects and fishing net pixels in the three-dimensional sonar image are screened and classified, the pixel features and the pixel distance between pixels of the same type are recorded, and the pixel distribution density is calculated; then the pixel distribution density is compared with the preset pixel distribution density. If the pixel distribution density is greater than a preset pixel distribution density, then the pixels with a pixel distribution density greater than the preset pixel distribution density threshold are marked as fishing net pixels; if the pixel distribution density is not greater than the preset pixel distribution density, then the pixels with a pixel distribution density not greater than the preset pixel distribution density threshold are marked as suspected living objects pixels; extract the fishing net pixels, and form a three-dimensional sonar image of the fishing net from the fishing net pixels; The red, green and blue color channels in the three-dimensional sonar image of the fishing net are weighted and averaged according to different weights to obtain gray values. A three-dimensional sonar grayscale image of the fishing net is then generated based on the three-dimensional sonar image of the fishing net and the gray values. The mean filtering method is used to denoise the three-dimensional sonar image of the fishing net to obtain the processed three-dimensional sonar image of the fishing net. The granularity of the processed three-dimensional sonar image of the fishing net is reduced and the clarity is improved. Finally, the adaptive gray level compensation method is applied to the processed three-dimensional sonar image of the fishing net to obtain the preliminary three-dimensional sonar image of the fishing net. The preliminary three-dimensional sonar grayscale image of the fishing net was analyzed to obtain preliminary information on the damaged area of ​​the fishing net.

4. The fishing net detection method based on data processing according to claim 1, characterized in that, The process involves using a laser flaw detector to perform laser flaw detection on the fishing net to obtain dynamic pulse waveforms. Based on the analysis results of the dynamic pulse waveforms and the preliminary information on the damaged areas of the fishing net, the location and state of the damaged areas are determined. Specifically: The laser emitter in the laser flaw detector emits a pulsed laser, which is projected onto the fishing net through the optical path adjuster, and the pulsed laser scans the fishing net. After the fishing net is laser-scanned, thermally excited ultrasonic waves are generated on the surface of the fishing net. These waves are received by the signal receiving device of the laser flaw detector to obtain a dynamic pulse waveform. The amplitude and frequency of the dynamic pulse waveform are continuously analyzed to obtain analysis results. Based on the analysis results and preliminary information on the damaged area of ​​the fishing net, the location and damage status of the damaged area are determined.

5. The fishing net detection method based on data processing according to claim 1, characterized in that, Based on the comparative analysis results of the preset three-dimensional sonar model of the fishing net and the real-time three-dimensional sonar model of the damaged location of the fishing net, safety repair suggestions for the fishing net are formulated, specifically as follows: Based on the comparative analysis results of the preset three-dimensional sonar model of the fishing net and the real-time three-dimensional sonar model of the damaged location of the fishing net, safety repair suggestions for the fishing net are formulated. If the analysis results are in an abnormal state, the fishing net in the abnormal state needs to be maintained and repaired; if the results are in a normal state, the fishing net in the normal state does not need to be repaired for the time being.

6. A fishing net detection system based on data processing, characterized in that, The fishing net detection system includes a memory and a processor. The memory stores a fishing net detection method program. When the fishing net detection method program is executed by the processor, it performs the following steps: A three-dimensional sonar signal is emitted by a three-dimensional sonar detection device, and the three-dimensional sonar signal reflected in a preset area is acquired. The three-dimensional sonar signal reflected in the preset area is then sent back to a signal receiver. After the signal receiver processes the three-dimensional sonar signal reflected in the preset area, a three-dimensional sonar image is generated by a signal image generation device. The pixels of the three-dimensional sonar image are filtered out. After the fishing net pixels are filtered out, the fishing net pixels are grayscaled to obtain a grayscale image. After the grayscale image is denoised, preliminary information on the damaged area of ​​the fishing net is obtained. The fishing net is subjected to laser flaw detection using a laser flaw detector to obtain dynamic pulse waveforms, and the location and condition of the damaged area are determined based on the dynamic pulse waveforms. A fishing net information database is established based on preset data. A preset three-dimensional sonar model of the fishing net is constructed based on the fishing net information database. According to the location and state of the damaged area, a real-time three-dimensional sonar model of the fishing net damage location is obtained. Finally, based on the fishing net information database, the preset three-dimensional sonar model of the fishing net and the real-time three-dimensional sonar model of the fishing net damage location are integrated and compared. Specifically: Obtain data on the mesh size and weave thickness of fishing nets within the normal range; A database is constructed by importing data on the size of fishing net mesh and the thickness of fishing net weave within the normal range into the database to obtain a fishing net information database. A preset three-dimensional sonar model of fishing net is constructed based on the fishing net information database. Based on the location and condition of the fishing net damage, the net mesh size and weave thickness are analyzed and obtained. The net mesh size and weave thickness are then imported into the fishing net information database to obtain a real-time three-dimensional sonar model of the fishing net damage location. Based on the fishing net information database, a virtual integration space is constructed, and a preset three-dimensional model of the fishing net and a real-time three-dimensional sonar model of the fishing net damage location are imported. In the virtual integration space, the preset three-dimensional model of the fishing net and the three-dimensional model of the fishing net damage location are integrated to obtain a real-time three-dimensional model of the fishing net. A three-dimensional coordinate system is constructed, and based on the three-dimensional coordinate system, the preset three-dimensional model of the fishing net is compared with the real-time three-dimensional model of the fishing net to obtain the model deviation value; The model deviation value is compared with a preset deviation threshold. If the model deviation value is greater than the preset deviation threshold, a first analysis result is generated. If the model deviation value is less than the preset deviation threshold, a second analysis result is generated; If the analysis result is the first analysis result, then the size of the fishing net mesh and the thickness of the fishing net weave are in an abnormal state; If the analysis result is the second analysis result, then the size of the fishing net mesh and the thickness of the fishing net weave are in a normal state; Based on the comparative analysis results of the preset three-dimensional sonar model of the fishing net and the real-time three-dimensional sonar model of the damaged location of the fishing net, suggestions for safe repair of the fishing net are formulated.

7. A fishing net detection system based on data processing according to claim 6, characterized in that, The process involves filtering the pixels of the three-dimensional sonar image, identifying fishing net pixels, converting these pixels to grayscale to obtain a grayscale image, and then performing noise reduction on the grayscale image to obtain preliminary information about the damaged area of ​​the fishing net. Specifically: The pixels suspected to be living objects and fishing net pixels in the three-dimensional sonar image are screened and classified, the pixel features and the pixel distance between pixels of the same type are recorded, and the pixel distribution density is calculated; then the pixel distribution density is compared with the preset pixel distribution density. If the pixel distribution density is greater than a preset pixel distribution density, then the pixels with a pixel distribution density greater than the preset pixel distribution density threshold are marked as fishing net pixels; if the pixel distribution density is not greater than the preset pixel distribution density, then the pixels with a pixel distribution density not greater than the preset pixel distribution density threshold are marked as suspected living objects pixels; extract the fishing net pixels, and form a three-dimensional sonar image of the fishing net from the fishing net pixels; The red, green and blue color channels in the three-dimensional sonar image of the fishing net are weighted and averaged according to different weights to obtain gray values. A three-dimensional sonar grayscale image of the fishing net is then generated based on the three-dimensional sonar image of the fishing net and the gray values. The mean filtering method is used to denoise the three-dimensional sonar image of the fishing net to obtain the processed three-dimensional sonar image of the fishing net. The granularity of the processed three-dimensional sonar image of the fishing net is reduced and the clarity is improved. Finally, the adaptive gray level compensation method is applied to the processed three-dimensional sonar image of the fishing net to obtain the preliminary three-dimensional sonar image of the fishing net. The preliminary three-dimensional sonar grayscale image of the fishing net was analyzed to obtain preliminary information on the damaged area of ​​the fishing net.

8. A fishing net detection system based on data processing according to claim 6, characterized in that, The process involves using a laser flaw detector to perform laser flaw detection on the fishing net to obtain dynamic pulse waveforms. Based on the analysis results of the dynamic pulse waveforms and the preliminary information on the damaged areas of the fishing net, the location and state of the damaged areas are determined. Specifically: The laser emitter in the laser flaw detector emits a pulsed laser, which is projected onto the fishing net through the optical path adjuster, and the pulsed laser scans the fishing net. After the fishing net is laser-scanned, thermally excited ultrasonic waves are generated on the surface of the fishing net. These waves are received by the signal receiving device of the laser flaw detector to obtain a dynamic pulse waveform. The amplitude and frequency of the dynamic pulse waveform are continuously analyzed to obtain analysis results. Based on the analysis results and preliminary information on the damaged area of ​​the fishing net, the location and damage status of the damaged area are determined.

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