An acoustic data extraction and storage method and system for a fishing boat fish finder

Abstract

The application discloses an acoustic data extraction and storage method and system for a fishing boat fish finder. The method comprises the following steps: acquiring the working data of the fishing boat fish finder, and performing reliability evaluation; formulating a time-sharing working scheme according to the evaluation result; performing fish school detection on a target water area according to the scheme, extracting and preprocessing acoustic data; then performing fish species identification, and storing the identification result into a data storage system. The application optimizes the working of the fish finder through reliability evaluation and a time-sharing working scheme, improves the efficiency, effectively manages the acoustic data through the data storage system, and provides support for fishery resource monitoring and sustainable management. The technical scheme has practicability and innovation, and is suitable for the fields of fishery resource monitoring, management and scientific research.

Description

Technical Field

[0001] This invention relates to the field of fisheries data processing technology, and in particular to a method and system for acoustic data extraction and storage for fish finders on fishing vessels. Background Technology

[0002] With the increasing demand for fishery resource management, fish finders on fishing vessels, as an important tool for water monitoring, are widely used in fishery detection and resource management. However, existing fish finders on fishing vessels still face many challenges in terms of signal interference, data processing, and storage, affecting their detection efficiency and the value of the data.

[0003] When multiple fish finders operate in the same waters, interference between detection signals becomes a common and serious challenge. Since fish finders typically use similar or identical frequencies for underwater detection, overlapping or interference between signals when multiple devices operate simultaneously can lead to signal attenuation and even prevent effective detection of target fish schools. This interference not only affects the accuracy of the fish finders but also impacts the accuracy of subsequent processing and analysis. Existing data preprocessing techniques are relatively simple, often relying on manual filtering and basic algorithms, which are inefficient and prone to inaccuracies.

[0004] Meanwhile, due to the massive amount of data generated by fish finders on fishing vessels, how to efficiently store, manage, and access this data has become an urgent problem to be solved. Existing storage methods typically employ traditional database storage. Furthermore, the fish species and location data collected by fish finders on fishing vessels usually require further analysis and identification, but existing fish identification technologies rely on human experience, resulting in low accuracy and poor real-time performance, making it difficult to meet the needs of efficient fisheries monitoring.

[0005] Therefore, there is an urgent need for an innovative method for acoustic data extraction and storage that can improve the working efficiency of fish finders on fishing vessels in complex aquatic environments, optimize the management of detection signal interference, enhance data processing and storage capabilities, and ensure the accuracy and real-time nature of data analysis through efficient fish species identification technology, thereby providing more reliable technical support for the monitoring, management, and sustainable utilization of fishery resources. Summary of the Invention

[0006] To address at least one of the aforementioned technical problems, this invention proposes a method and system for acoustic data extraction and storage using a fish finder on a fishing vessel.

[0007] The first aspect of this invention provides a method for acoustic data extraction and storage for a fish finder on a fishing vessel, comprising:

[0008] Acquire the working data of the fish finder on the fishing vessel in the target water area, and conduct a reliability assessment of the fish finder on the fishing vessel based on the working data to obtain the reliability assessment result.

[0009] Based on the reliability assessment results, the working time of the fish finder on the fishing boat in the target water area is allocated to obtain the time-sharing working scheme of the fish finder on the fishing boat in the target water area.

[0010] According to the time-sharing working plan, fish schools are detected in the target waters, the raw acoustic data of the fish finder on the fishing boat is extracted, and the raw acoustic data is preprocessed to obtain preprocessed acoustic data.

[0011] Fish identification is performed based on the preprocessed acoustic data to obtain fish identification results. A data storage system is then constructed to store the fish identification results in the acoustic data storage system.

[0012] In this solution, the process of acquiring the operating data of the fish finder on the fishing vessel within the target waters, and then conducting a reliability assessment of the fish finder based on this data to obtain the reliability assessment result, is as follows:

[0013] The fishing vessel AIS system is used to acquire the working data of the fish finder in the target water area. The working data of the fish finder includes the detection signal coverage, detection signal strength and fish finder location information of the fish finders of all fishing vessels in the target water area.

[0014] Acquire information on the intensity variation of the detection signal of the fish finder on the fishing vessel within the detection coverage area, and draw a schematic diagram of the detection signal of the fish finder in the target water area based on the working data and intensity variation information of the fish finder on the fishing vessel.

[0015] The interference intensity of the fish finder's detection signal on each fishing boat in the target water area is determined based on the aforementioned detection signal diagram.

[0016] The reliability of the fish finder on the fishing vessel was evaluated based on the interference intensity of the detection signal, and the reliability evaluation results were obtained.

[0017] In this scheme, the allocation of working time for fish finders on fishing vessels within the target waters based on the reliability assessment results yields a time-sharing working scheme for the fish finders within the target waters, specifically as follows:

[0018] Acquire the detection accuracy requirement data of the fish finder on each fishing vessel in the target water area, and determine the reliability threshold of the fish finder on each fishing vessel based on the detection accuracy requirement data.

[0019] Based on the reliability assessment results, fish finders on fishing vessels with working reliability not lower than the reliability threshold in the target waters are classified as Class I fish finders, and fish finders on fishing vessels with working reliability lower than the reliability threshold are classified as Class II fish finders.

[0020] For each Class II fishing vessel fish finder, connect the Class II fishing vessel fish finder to the fish finder that is interfering with the Class II fishing vessel fish finder, and construct a limited map of the interference intensity of the detection signal of the Class II fishing vessel fish finder in the target water area.

[0021] In the graph where the interference intensity of the detection signal is limited, the nodes represent fish finders on the second type of fishing vessel, the edges and their directions represent the interference relationship between the fish finders on the second type of fishing vessel and the fish finders from the interference source, and the connection weights of the edges represent the interference intensity of the fish finders from the interference source on the fish finders on the second type of fishing vessel.

[0022] If the fish finder on the fishing vessel is a Class I fish finder, real-time detection operation is performed according to the detection requirements of the Class I fish finder. If the fish finder on the fishing vessel is a Class II fish finder, the working combination and working time of the Class II fish finder are determined according to the limited interference intensity diagram of the detection signal, so as to obtain the time-sharing working scheme of the fish finder on the fishing vessel in the target water area.

[0023] In this scheme, determining the working combination and working time of the fish finder for Class II fishing vessels based on the limited interference intensity map of the detection signal specifically involves:

[0024] S1, randomly select a node from the detection signal interference intensity finite map for access;

[0025] S2, mark the visited nodes as visited nodes, sort the edges pointing to the visited nodes in descending order of connection weight, extract the sorted edges and their connected nodes in turn, evaluate the working reliability of the visited nodes for each extracted edge and connected node, until the working reliability of the visited nodes is not lower than the reliability threshold, stop the extraction of edges and connected nodes, mark the visited nodes as visited nodes, mark the visited nodes as non-extractable and non-repeated nodes, and update the graph with limited interference intensity of the detection signal.

[0026] S3, randomly select an unvisited node from the updated detection signal interference intensity finite map to visit and repeat step S2. When there are no nodes to visit in the detection signal interference intensity finite map, output the connection nodes that have not been removed in the detection signal interference intensity finite map as the working combination of the fish finder for the second type of fishing vessel.

[0027] S4, reconstruct the limited map of interference intensity of the probe signal from the removed connection nodes, and execute steps S2-S3 to output a new working combination;

[0028] S5, repeat S4 until all nodes in the detection signal interference intensity finite graph have been visited, and output multiple working combinations;

[0029] S6, obtain the number of work combinations, divide the preset time length into equal lengths according to the number of work combinations, and determine the working time of each work combination within the preset time length.

[0030] In this scheme, the step of detecting fish schools in the target waters according to the time-sharing working scheme, extracting the raw acoustic data from the fish finder on the fishing boat, and preprocessing the raw acoustic data to obtain preprocessed acoustic data specifically involves:

[0031] The fish finder on the fishing boat in the target water area will detect fish schools in the target water area according to the time-sharing working scheme, and the original acoustic data of the fish finder on the fishing boat will be extracted.

[0032] The original acoustic data is converted into frequency domain acoustic data based on the Fourier transform algorithm. The spectral information of the frequency domain acoustic data is calculated. A spectrogram is constructed based on the spectral information. The energy distribution of the spectrogram is determined. The frequency band data contained in the original acoustic data is determined based on the energy distribution.

[0033] If the number of frequency bands is greater than 1, the independent component analysis method is introduced. Based on the independent component analysis method, multi-band acoustic data is extracted from the original acoustic data to obtain the acoustic data of each detection frequency band of the fish finder on the fishing boat.

[0034] The acoustic data of each detection frequency band of the fish finder on the fishing vessel is compressed using the Huffman coding method to obtain preprocessed acoustic data.

[0035] In this solution, the step of performing fish identification based on the preprocessed acoustic data, obtaining fish identification results, constructing a data storage system, and storing the fish identification results in the acoustic data storage system specifically involves:

[0036] A fish species identification model is constructed based on a deep learning algorithm. Historical acoustic data of different fish species are obtained, the historical acoustic data is labeled with fish species, and the labeled historical acoustic data is imported into the fish species identification model for training.

[0037] A data storage system is constructed to store the preprocessed acoustic data and the fish species identification model. When fish species identification is required on the preprocessed acoustic data, the preprocessed acoustic data is decompressed, and the decompressed preprocessed acoustic data is imported into the fish species identification model for fish identification. The fish location information is determined based on the detection location information of the fish finder on the fishing boat, and the fish identification result is obtained.

[0038] The fish identification results are classified according to fish location and fish identification type. The classified fish identification results are then visualized to obtain a schematic diagram of fish distribution in the target water area.

[0039] Import the fish distribution diagram into the data storage system.

[0040] A second aspect of the present invention also provides an acoustic data extraction and storage system for a fish finder on a fishing vessel. The system includes a memory and a processor. The memory includes a method program for acoustic data extraction and storage for the fish finder on a fishing vessel. When the processor executes the method program for acoustic data extraction and storage for the fish finder on a fishing vessel, it performs the following steps:

[0041] Acquire the working data of the fish finder on the fishing vessel in the target water area, and conduct a reliability assessment of the fish finder on the fishing vessel based on the working data to obtain the reliability assessment result.

[0042] Based on the reliability assessment results, the working time of the fish finder on the fishing boat in the target water area is allocated to obtain the time-sharing working scheme of the fish finder on the fishing boat in the target water area.

[0043] According to the time-sharing working plan, fish schools are detected in the target waters, the raw acoustic data of the fish finder on the fishing boat is extracted, and the raw acoustic data is preprocessed to obtain preprocessed acoustic data.

[0044] Fish identification is performed based on the preprocessed acoustic data to obtain fish identification results. A data storage system is then constructed to store the fish identification results in the acoustic data storage system.

[0045] This invention discloses a method and system for acoustic data extraction and storage using a fish finder on a fishing vessel. The method includes the following steps: acquiring the operating data of the fish finder and conducting a reliability assessment; developing a time-sharing operating plan based on the assessment results; detecting fish schools in the target waters according to the plan, extracting and preprocessing the acoustic data; then identifying the fish species and storing the identification results in a data storage system. This invention optimizes the operation of the fish finder through reliability assessment and a time-sharing operating plan, improving efficiency, and effectively manages acoustic data through a data storage system, providing support for fisheries resource monitoring and sustainable management. This technical solution is practical and innovative, applicable to the fields of fisheries resource monitoring, management, and scientific research. Attached Figure Description

[0046] Figure 1 A flowchart of an acoustic data extraction and storage method for a fish finder on a fishing vessel according to the present invention is shown;

[0047] Figure 2 The flowchart illustrating the preprocessed acoustic data obtained according to the present invention is shown;

[0048] Figure 3 This invention illustrates a flowchart of storing fish identification results to an acoustic data storage system.

[0049] Figure 4 A block diagram of an acoustic data extraction and storage system for a fish finder on a fishing vessel, according to the present invention, is shown. Detailed Implementation

[0050] 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.

[0051] 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.

[0052] Figure 1 The flowchart illustrates a method for acoustic data extraction and storage for a fish finder on a fishing vessel according to the present invention.

[0053] like Figure 1 As shown, the first aspect of the present invention provides a method for acoustic data extraction and storage for a fish finder on a fishing vessel, comprising:

[0054] S102, acquire the working data of the fish finder on the fishing vessel in the target water area, and conduct a reliability assessment of the fish finder on the fishing vessel based on the working data of the fish finder to obtain the reliability assessment result.

[0055] S104. Based on the reliability assessment results, the working time of the fish finder on the fishing boat in the target water area is allocated to obtain the time-sharing working scheme of the fish finder on the fishing boat in the target water area.

[0056] S106, according to the time-sharing working scheme, fish school detection is carried out in the target water area, the original acoustic data of the fish finder on the fishing boat is extracted, and the original acoustic data is preprocessed to obtain preprocessed acoustic data.

[0057] S108, fish species are identified based on the preprocessed acoustic data to obtain fish species identification results, a data storage system is constructed, and the fish identification results are stored in the acoustic data storage system.

[0058] It should be noted that by acquiring operational data from fish finders on fishing vessels within the target waters and conducting reliability assessments, the operational status of each fish finder can be objectively evaluated, ensuring their reliability under different operating conditions. Secondly, scheduling the time-sharing operation of fish finders on fishing vessels based on the reliability assessment results helps to rationally allocate working time, avoid signal interference between multiple fish finders, and maximize the overall efficiency and detection accuracy of fish finders within the waters. Preprocessing the raw acoustic data effectively improves data quality and enhances the accuracy and efficiency of subsequent analysis. Next, analyzing the preprocessed data using fish species identification technology enables accurate identification of different fish species, providing crucial data support for the monitoring and management of fishery resources. Finally, constructing an acoustic data storage system to systematically store the fish identification results not only improves the efficiency of data storage and management but also facilitates subsequent data retrieval, analysis, and visualization, promoting the long-term utilization of the data.

[0059] According to an embodiment of the present invention, the step of acquiring the working data of the fish finder on the fishing vessel in the target waters, and conducting a reliability assessment of the fish finder based on the working data to obtain a reliability assessment result, specifically includes:

[0060] The fishing vessel AIS system is used to obtain the working data of the fish finder in the target water area. The working data of the fish finder includes the detection signal coverage, detection signal strength and fish finder location information of the fish finders of all fishing vessels in the target water area.

[0061] Acquire information on the intensity variation of the detection signal of the fish finder on the fishing vessel within the detection coverage area, and draw a schematic diagram of the detection signal of the fish finder in the target water area based on the working data and intensity variation information of the fish finder on the fishing vessel.

[0062] The interference intensity of the fish finder's detection signal on each fishing boat in the target water area is determined based on the aforementioned detection signal diagram.

[0063] The reliability of the fish finder on the fishing vessel was evaluated based on the interference intensity of the detection signal, and the reliability evaluation results were obtained.

[0064] It should be noted that the detection signal diagram of the fish finder in the target water area is drawn by using the working data of the fish finder in the target water area. Based on the detection signal diagram, the interference intensity of the detection signal of each fish finder in the target water area is evaluated. Finally, the reliability of the fish finder is evaluated based on the interference intensity. The greater the interference intensity, the lower the reliability. This method can objectively evaluate the working status and detection effect of the fish finder in complex water environment.

[0065] According to an embodiment of the present invention, the step of allocating the working time of the fish finder on the fishing vessel in the target water area based on the reliability assessment results to obtain a time-sharing working scheme for the fish finder on the fishing vessel in the target water area is specifically as follows:

[0066] Acquire the detection accuracy requirement data of the fish finder on each fishing vessel in the target water area, and determine the reliability threshold of the fish finder on each fishing vessel based on the detection accuracy requirement data.

[0067] Based on the reliability assessment results, fish finders on fishing vessels with working reliability not lower than the reliability threshold in the target waters are classified as Class I fish finders, and fish finders on fishing vessels with working reliability lower than the reliability threshold are classified as Class II fish finders.

[0068] For each Class II fishing vessel fish finder, connect the Class II fishing vessel fish finder to the fish finder that is interfering with the Class II fishing vessel fish finder, and construct a limited map of the interference intensity of the detection signal of the Class II fishing vessel fish finder in the target water area.

[0069] In the graph where the interference intensity of the detection signal is limited, the nodes represent fish finders on the second type of fishing vessel, the edges and their directions represent the interference relationship between the fish finders on the second type of fishing vessel and the fish finders from the interference source, and the connection weights of the edges represent the interference intensity of the fish finders from the interference source on the fish finders on the second type of fishing vessel.

[0070] If the fish finder on the fishing vessel is a Class I fish finder, real-time detection operation is performed according to the detection requirements of the Class I fish finder. If the fish finder on the fishing vessel is a Class II fish finder, the working combination and working time of the Class II fish finder are determined according to the limited interference intensity diagram of the detection signal, so as to obtain the time-sharing working scheme of the fish finder on the fishing vessel in the target water area.

[0071] It should be noted that in complex aquatic environments, when multiple fish finders operate simultaneously, signal interference and detection accuracy become significant factors affecting detection effectiveness and resource management efficiency. By obtaining the detection accuracy requirements of each fish finder, a reliability threshold is determined. When the operational reliability of a fish finder is not lower than the reliability threshold, it indicates that the fish finder is less affected by signal interference from other fish finders and still meets operational reliability requirements. Therefore, this type of fish finder is labeled as Category I fish finders, and real-time detection can be performed according to operational needs without further processing. Fish finders with operational reliability below the reliability threshold are labeled as Category II fish finders. By constructing a limited interference intensity graph for Category II fish finders, the interference relationships between fish finders can be clearly understood, and the interference intensity is reflected by the edge weights. This graphical information structure helps the system comprehensively identify which fish finders have strong interference, thus preventing them from operating simultaneously. By scientifically and rationally allocating working time, the impact of interference sources on the detection results of the target fish finder is reduced, thereby improving the accuracy of data acquisition. By utilizing the direction and weight information of edges in a finite graph, the working combination of fish finders on Class II fishing vessels can be precisely determined. Specifically, fish finders with high interference intensity will not be scheduled to work in the same time period, while fish finders with less interference can work simultaneously. In this way, Class II fishing vessel fish finders can conduct detection during periods of less interference, thereby maximizing detection quality and the overall efficiency of the system. The interfering source fish finder refers to other Class II fishing vessel fish finders that cause signal interference to the current Class II fishing vessel fish finder. The detection signal interference intensity priority graph shows the intensity of detection signal interference to the Class II fishing vessel fish finder from other Class II fishing vessel fish finders. In the detection signal interference intensity priority graph, the nodes represent Class II fishing vessel fish finders, and the edges represent the interference relationship between the Class II fishing vessel fish finder and the interfering source fish finder. If an edge points from the interfering source fish finder to the Class II fishing vessel fish finder, it means that the interfering source fish finder is causing detection signal interference to the Class II fishing vessel fish finder. The weight of the edge is the interference intensity of the interfering source fish finder on the Class II fishing vessel fish finder. The detection requirements refer to the operating time requirements of the fish finders on fishing vessels. The time-sharing work plan includes the operating time periods for each fish finder on each fishing vessel.

[0072] According to an embodiment of the present invention, the step of determining the working combination and working time of the fish finder for Class II fishing vessels based on the interference intensity finiteness map of the detection signal specifically includes:

[0073] S1, randomly select a node from the detection signal interference intensity finite map for access;

[0074] S2, mark the visited nodes as visited nodes, sort the edges pointing to the visited nodes in descending order of connection weight, extract the sorted edges and their connected nodes in turn, evaluate the working reliability of the visited nodes for each extracted edge and connected node, until the working reliability of the visited nodes is not lower than the reliability threshold, stop the extraction of edges and connected nodes, mark the visited nodes as visited nodes, mark the visited nodes as non-extractable and non-repeated nodes, and update the graph with limited interference intensity of the detection signal.

[0075] S3, randomly select an unvisited node from the updated detection signal interference intensity finite map to visit and repeat step S2. When there are no nodes to visit in the detection signal interference intensity finite map, output the connection nodes that have not been removed in the detection signal interference intensity finite map as the working combination of the fish finder for the second type of fishing vessel.

[0076] S4, reconstruct the limited map of interference intensity of the probe signal from the removed connection nodes, and execute steps S2-S3 to output a new working combination;

[0077] S5, repeat S4 until all nodes in the detection signal interference intensity finite graph have been visited, and output multiple working combinations;

[0078] S6, obtain the number of work combinations, divide the preset time length into equal lengths according to the number of work combinations, and determine the working time of each work combination within the preset time length.

[0079] It should be noted that updating the finite interference intensity map of the detection signal refers to removing nodes and marking visited nodes as visited nodes; the working combination represents a group of Class II fishing vessel fish finders. When this group of Class II fishing vessel fish finders works simultaneously, the mutual detection signal interference between them will not cause the working reliability of the Class II fishing vessel fish finders in this working combination to fall below the reliability threshold; the preset time length is one minute, and the working time is the length of time that each working combination works simultaneously within the preset time period. The working principle of the method of this invention is to first randomly select a Class II fishing vessel fish finder from the finite interference intensity map of the detection signal, and then delete (remove) the fish finders that interfere with the selected Class II fishing vessel fish finder one by one until a working combination of Class II fishing vessel fish finders that will not fall below the reliability threshold when working simultaneously is obtained; since the Class II fishing vessel fish finders deleted from the finite interference intensity map of the detection signal still need to be detected in reality, they cannot be truly deleted. It is necessary to continue analysis and construct a new finite interference intensity map of the detection signal to continue generating working combinations. Each working group operates in a time-sharing manner to avoid signal interference caused by all fish finders on Class II fishing vessels operating simultaneously, which would result in the reliability of the fish finders on Class II fishing vessels falling below the reliability threshold.

[0080] Figure 2 A flowchart illustrating the preprocessed acoustic data obtained according to the present invention is shown.

[0081] According to an embodiment of the present invention, the step of detecting fish schools in the target waters according to the time-sharing working scheme, extracting the raw acoustic data from the fish finder on the fishing boat, and preprocessing the raw acoustic data to obtain preprocessed acoustic data specifically involves:

[0082] S202, the fish finder on the fishing boat in the target water area is used to detect fish in the target water area according to the time-sharing working plan, and the original acoustic data of the fish finder on the fishing boat is extracted.

[0083] S204, Based on the Fourier transform algorithm, the original acoustic data is converted into frequency domain acoustic data, the spectral information of the frequency domain acoustic data is calculated, a spectrum diagram is constructed based on the spectral information, the energy distribution of the spectrum diagram is determined, and the frequency band data contained in the original acoustic data is determined based on the energy distribution;

[0084] S206. If the number of frequency bands is greater than 1, introduce the independent component analysis method and extract multi-band acoustic data from the original acoustic data based on the independent component analysis method to obtain the acoustic data of each detection frequency band of the fish finder on the fishing boat.

[0085] S208, based on the Huffman coding method, the acoustic data of each detection frequency band of the fish finder on the fishing boat is compressed to obtain preprocessed acoustic data.

[0086] It should be noted that, according to the time-sharing working scheme for fish detection in target waters, the fish finder on the fishing vessel can extract accurate raw acoustic data within a reasonable time period, avoiding interference and duplication of work between multiple fish finders, thus improving the efficiency and quality of data acquisition. Since the fish finder on the fishing vessel may simultaneously detect fish using multiple frequency bands, the acquired raw acoustic data is a mixed-frequency signal. Therefore, Fourier transform is used to convert the raw acoustic data into frequency domain data and calculate its spectral information. This allows for more accurate analysis and judgment of the energy distribution of the acoustic signal, helping to identify information in different frequency bands of the sound wave and extract frequency band data containing effective detection information. When the number of frequency bands is greater than one, Independent Component Analysis (ICA) is introduced to extract multiple frequency bands from the raw acoustic data. This separates different frequency band components in the signal, avoiding interference between different frequency band signals, allowing the acoustic data of each frequency band to be analyzed independently, thereby improving data quality and ensuring the accuracy of fish detection and species identification. Huffman coding is used to compress the acoustic data of each detection frequency band, significantly reducing the space requirements for data storage and transmission. This compression process not only improves data storage efficiency but also reduces processing latency and network burden that may result from large data volumes. The raw acoustic data refers to the acoustic signal data acquired by the fish finder.

[0087] Figure 3 The flowchart illustrating the present invention stores fish identification results into an acoustic data storage system is shown.

[0088] According to an embodiment of the present invention, the step of identifying fish species based on the preprocessed acoustic data, obtaining fish species identification results, constructing a data storage system, and storing the fish identification results in the acoustic data storage system specifically includes:

[0089] S302, Construct a fish species identification model based on a deep learning algorithm, obtain historical acoustic data of different fish species, label the historical acoustic data with fish species, and import the labeled historical acoustic data into the fish species identification model for training.

[0090] S304, Construct a data storage system, import the preprocessed acoustic data and the fish species identification model into the acoustic data storage system for storage, when it is necessary to identify fish species from the preprocessed acoustic data, decompress the preprocessed acoustic data, import the decompressed preprocessed acoustic data into the fish species identification model for fish identification, and determine the fish location information based on the detection location information of the fish finder on the fishing boat to obtain the fish identification result;

[0091] S306, The fish identification results are classified according to fish location and fish identification type, and the classified fish identification results are visualized to obtain a fish distribution diagram of the target water area.

[0092] S308, Import the fish distribution diagram into the data storage system.

[0093] It should be noted that the fish species identification model built based on deep learning algorithms allows the system to learn and train on historical acoustic data of different fish species, enabling the identification model to accurately identify fish species in target waters during practical applications. Through training the deep learning model, the system can automatically extract features from acoustic data and identify fish populations in complex environments, significantly improving the accuracy and efficiency of identification. By importing preprocessed acoustic data and the fish species identification model into the acoustic data storage system, efficient storage and management of acoustic data are achieved. When fish identification is required, the system decompresses the preprocessed acoustic data and inputs it into the identification model, enabling rapid processing and generation of identification results, avoiding the waste of redundant data storage and improving storage space utilization. During the fish identification process, the system combines the detection location information from the fish finder on the fishing vessel to accurately determine the location of the fish. By associating location with species, the system can generate detailed fish distribution maps. Fish identification results are categorized according to location and species, and a fish distribution diagram is generated through visualization, allowing users to intuitively understand the fish population distribution in target waters. By storing and visualizing the fish species identification results, fisheries managers can more accurately grasp the fish population status in the waters and take timely measures for resource protection or adjust fishing plans. This not only contributes to the sustainable use of fishery resources but also optimizes the fisheries industry chain and improves fisheries production efficiency. The deep learning algorithms include convolutional neural networks and recurrent neural network algorithms.

[0094] Figure 4 A block diagram of an acoustic data extraction and storage system for a fish finder on a fishing vessel, according to the present invention, is shown.

[0095] A second aspect of the present invention also provides an acoustic data extraction and storage system 4 for a fish finder on a fishing vessel. The system includes a memory 41 and a processor 42. The memory includes a method program for acoustic data extraction and storage for the fish finder on a fishing vessel. When the processor executes the method program for acoustic data extraction and storage for the fish finder on a fishing vessel, it performs the following steps:

[0096] Acquire the working data of the fish finder on the fishing vessel in the target water area, and conduct a reliability assessment of the fish finder on the fishing vessel based on the working data to obtain the reliability assessment result.

[0097] Based on the reliability assessment results, the working time of the fish finder on the fishing boat in the target water area is allocated to obtain the time-sharing working scheme of the fish finder on the fishing boat in the target water area.

[0098] According to the time-sharing working plan, fish schools are detected in the target waters, the raw acoustic data of the fish finder on the fishing boat is extracted, and the raw acoustic data is preprocessed to obtain preprocessed acoustic data.

[0099] Fish identification is performed based on the preprocessed acoustic data to obtain fish identification results. A data storage system is then constructed to store the fish identification results in the acoustic data storage system.

[0100] This invention discloses a method and system for acoustic data extraction and storage using a fish finder on a fishing vessel. The method includes the following steps: acquiring the operating data of the fish finder and conducting a reliability assessment; developing a time-sharing operating plan based on the assessment results; detecting fish schools in the target waters according to the plan, extracting and preprocessing the acoustic data; then identifying the fish species and storing the identification results in a data storage system. This invention optimizes the operation of the fish finder through reliability assessment and a time-sharing operating plan, improving efficiency, and effectively manages acoustic data through a data storage system, providing support for fisheries resource monitoring and sustainable management. This technical solution is practical and innovative, applicable to the fields of fisheries resource monitoring, management, and scientific research.

[0101] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods, such as: multiple units or components can be combined, or integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the various components shown or discussed can be through some interfaces, and the indirect coupling or communication connection between devices or units can be electrical, mechanical, or other forms.

[0102] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units. They may be located in one place or distributed across multiple network units. Some or all of the units may be selected to achieve the purpose of this embodiment according to actual needs.

[0103] In addition, in the various embodiments of the present invention, each functional unit can be integrated into one processing unit, or each unit can be a separate unit, or two or more units can be integrated into one unit; the integrated unit can be implemented in hardware or in the form of hardware plus software functional units.

[0104] Those skilled in the art will understand that all or part of the steps of the above method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it performs the steps of the above method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0105] Alternatively, if the integrated units of this invention are implemented as software functional modules and sold or used as independent products, they can also be stored in a computer-readable storage medium. Based on this understanding, the technical solutions of the embodiments of this invention, or the parts that contribute to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, ROM, RAM, magnetic disks, or optical disks.

[0106] 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 method for extracting and storing acoustic data for fish finders on fishing vessels, characterized in that, Includes the following steps: Acquire the working data of the fish finder on the fishing vessel in the target water area, and conduct a reliability assessment of the fish finder on the fishing vessel based on the working data to obtain the reliability assessment result. Based on the reliability assessment results, the working time of the fish finder on the fishing boat in the target water area is allocated to obtain the time-sharing working scheme of the fish finder on the fishing boat in the target water area. According to the time-sharing working plan, fish schools are detected in the target waters, the raw acoustic data of the fish finder on the fishing boat is extracted, and the raw acoustic data is preprocessed to obtain preprocessed acoustic data. Fish identification is performed based on the preprocessed acoustic data to obtain fish identification results. A data storage system is then constructed to store the fish identification results in the acoustic data storage system. The working time of the fish finders on fishing vessels in the target waters is allocated based on the reliability assessment results, resulting in a time-sharing working scheme for the fish finders in the target waters, specifically as follows: When multiple fish finders on fishing boats are working simultaneously, the detection accuracy requirement data of each fish finder on fishing boats in the target water area is acquired, and the reliability threshold of each fish finder on fishing boats is determined based on the detection accuracy requirement data. Fish finders on fishing vessels with working reliability not lower than the reliability threshold in the target waters are classified as Class I fish finders, and fish finders on fishing vessels with working reliability lower than the reliability threshold are classified as Class II fish finders. Connect the fish finder on the second-class fishing vessel to the fish finder on the second-class fishing vessel that is interfering with the fish finder on the second-class fishing vessel, and construct a limited interference intensity diagram of the detection signal of the fish finder on the second-class fishing vessel in the target water area. In the graph where the interference intensity of the detection signal is limited, the nodes represent fish finders on the second type of fishing vessel, the edges and their directions represent the interference relationship between the fish finders on the second type of fishing vessel and the fish finders from the interference source, and the connection weights of the edges represent the interference intensity of the fish finders from the interference source on the fish finders on the second type of fishing vessel. If the fish finder on the fishing vessel is a Class I fish finder, real-time detection operation shall be carried out according to the detection requirements of Class I fish finders. If the fish finder is a Class II fish finder, first randomly select a Class II fish finder from the limited interference intensity diagram of the detection signal, and then delete the fish finders that interfere with the selected Class II fish finder one by one until a working combination of Class II fish finders that will not fall below the reliability threshold when working simultaneously is obtained. The fish finders on the deleted fishing boats were further analyzed to construct a new map of limited interference intensity of the detection signal. Working combinations were then generated, resulting in multiple working combinations, and the working time for each working combination was assigned.

2. The method for acoustic data extraction and storage for a fish finder on a fishing vessel according to claim 1, characterized in that, The process involves acquiring operational data from the fish finder on fishing vessels within the target waters, conducting a reliability assessment of the fish finder based on this data, and obtaining the reliability assessment results. Specifically: The fishing vessel AIS system is used to obtain the working data of the fish finder in the target water area. The working data of the fish finder includes the detection signal coverage, detection signal strength and fish finder location information of the fish finders of all fishing vessels in the target water area. Acquire information on the intensity variation of the detection signal of the fish finder on the fishing vessel within the detection coverage area, and draw a schematic diagram of the detection signal of the fish finder in the target water area based on the working data and intensity variation information of the fish finder on the fishing vessel. The interference intensity of the fish finder's detection signal on each fishing boat in the target water area is determined based on the aforementioned detection signal diagram. The reliability of the fish finder on the fishing vessel was evaluated based on the interference intensity of the detection signal, and the reliability evaluation results were obtained.

3. The method for acoustic data extraction and storage for a fish finder on a fishing vessel according to claim 1, characterized in that, The step involves detecting fish schools in the target waters according to the time-sharing working scheme, extracting the raw acoustic data from the fish finder on the fishing boat, and preprocessing the raw acoustic data to obtain preprocessed acoustic data. Specifically, this process includes: The fish finder on the fishing boat in the target water area will detect fish schools in the target water area according to the time-sharing working scheme, and the original acoustic data of the fish finder on the fishing boat will be extracted. The original acoustic data is converted into frequency domain acoustic data based on the Fourier transform algorithm. The spectral information of the frequency domain acoustic data is calculated. A spectrogram is constructed based on the spectral information. The energy distribution of the spectrogram is determined. The frequency band data contained in the original acoustic data is determined based on the energy distribution. If the number of frequency bands is greater than 1, the independent component analysis method is introduced. Based on the independent component analysis method, multi-band acoustic data is extracted from the original acoustic data to obtain the acoustic data of each detection frequency band of the fish finder on the fishing boat. The acoustic data of each detection frequency band of the fish finder on the fishing vessel is compressed using the Huffman coding method to obtain preprocessed acoustic data.

4. The method for acoustic data extraction and storage for a fish finder on a fishing vessel according to claim 1, characterized in that, The step of identifying fish based on the preprocessed acoustic data, obtaining fish identification results, constructing a data storage system, and storing the fish identification results in the acoustic data storage system specifically involves: A fish species identification model is constructed based on a deep learning algorithm. Historical acoustic data of different fish species are obtained, the historical acoustic data is labeled with fish species, and the labeled historical acoustic data is imported into the fish species identification model for training. A data storage system is constructed to store the preprocessed acoustic data and the fish species identification model. When fish species identification is required on the preprocessed acoustic data, the preprocessed acoustic data is decompressed, and the decompressed preprocessed acoustic data is imported into the fish species identification model for fish identification. The fish location information is determined based on the detection location information of the fish finder on the fishing boat, and the fish identification result is obtained. The fish identification results are classified according to fish location and fish identification type. The classified fish identification results are then visualized to obtain a schematic diagram of fish distribution in the target water area. Import the fish distribution diagram into the data storage system.

5. An acoustic data extraction and storage system for a fish finder on a fishing vessel, characterized in that, The acoustic data extraction and storage system for a fish finder on a fishing vessel includes a storage unit and a processor. The storage unit includes a program for an acoustic data extraction and storage method for a fish finder on a fishing vessel. When the program for an acoustic data extraction and storage method for a fish finder on a fishing vessel is executed by the processor, it implements the steps of the acoustic data extraction and storage method for a fish finder on a fishing vessel as described in any one of claims 1 to 4.

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