Fish health condition monitoring, early warning and regulating method, device, equipment, medium and product based on fish sound signal

Abstract

The invention discloses a fish health condition monitoring, early warning and regulating method, device and equipment based on fish sound signals, a medium and a product, and relates to the field of fish health monitoring, and the method comprises the steps: obtaining sound signals of healthy fishes under suitable / non-suitable living environment conditions, and constructing a first sound database and a second sound database; sound signals of sick fishes in different sick stages are obtained, and a third sound database is constructed; acquiring a sound signal of the target research fish school; determining the similarity between the sound signals of the target research fish school and the sound signals in the first sound database, the second sound database and the third sound database by adopting a similarity measurement method; and obtaining a health condition monitoring result of the target research fish school based on the similarity, and generating a regulation and control strategy based on the health condition monitoring result. According to the invention, high-efficiency and accurate fish early-stage pathology identification is realized in a low-visibility environment, so that the real-time performance and accuracy of fish health condition monitoring, early warning and regulation are improved.

Description

Technical Field

[0001] This application relates to the field of fish health monitoring, and in particular to a method, device, equipment, medium, and product for monitoring, early warning, and regulating fish health status based on fish sound signals. Background Technology

[0002] Currently, monitoring fish disease status mainly relies on visual observation of surface lesions and abnormal swimming behavior. However, by the time these symptoms appear, fish have often entered the middle or late stages of the disease, at which point pathological intervention is usually less effective. Furthermore, traditional behavioral monitoring methods are difficult to implement effectively in turbid aquaculture water. Therefore, there is an urgent need to develop a highly efficient and accurate fish health monitoring technology, especially for early pathological identification in low-visibility environments. Summary of the Invention

[0003] The purpose of this application is to provide a method, device, equipment, medium, and product for monitoring, early warning, and regulation of fish health status based on fish sound signals, which can achieve efficient and accurate early pathological identification of fish in low visibility environments, thereby improving the real-time performance and accuracy of fish health status monitoring, early warning, and regulation.

[0004] To achieve the above objectives, this application provides the following solution:

[0005] In a first aspect, this application provides a method for monitoring, early warning, and regulating fish health status based on fish acoustic signals, including:

[0006] Under suitable living conditions, acquire sound signals from healthy fish and construct the first sound database;

[0007] Acquire sound signals from healthy fish under unsuitable living conditions to construct a second sound database;

[0008] Acquire acoustic signals from diseased fish at different stages of disease and construct a third acoustic database;

[0009] Acquire the acoustic signals of the target fish population and construct a fourth acoustic database;

[0010] A similarity measurement method was used to determine the similarity between the sound signals of the target fish group in the fourth sound database and the sound signals in the first sound database, the second sound database, and the third sound database, respectively.

[0011] Based on the similarity, the health status monitoring results of the target fish population were obtained, and a regulation strategy was generated based on the health status monitoring results; the regulation strategy includes suggestions for improving the living environment and treatment suggestions.

[0012] Optionally, acoustic signals of diseased fish at different stages of the disease are acquired to construct a third acoustic database, including:

[0013] For each stage of the disease, a corresponding sub-database is generated based on the acoustic signals of the diseased fish.

[0014] Optionally, the similarity measurement method can be any one of similarity statistics methods, machine learning methods, and deep learning methods; the similarity statistics method includes correlation coefficient matrix similarity calculation, mutual information calculation, or relative entropy calculation; the machine learning method includes feature vector comparison, cluster analysis, or similarity learning; and the deep learning method includes database embedding, graph neural networks, or attention mechanisms for similarity determination.

[0015] Optionally, based on the similarity, the health status monitoring results of the target fish population are obtained, and a control strategy is generated based on the health status monitoring results, including:

[0016] When the sound signal of the target fish population has the highest similarity to the sound signal in the first sound database, the health status monitoring result is healthy, and no further action is taken.

[0017] When the similarity between the sound signal of the target fish population and the sound signal in the second sound database is the highest, the health status monitoring result indicates that the fish is in a stressful state of an unsuitable living environment, and suggestions for improving the living environment are provided.

[0018] When the similarity between the sound signal of the target fish population and the sound signal in the third sound database is the highest, the health status monitoring result is considered to be diseased, and treatment suggestions are provided.

[0019] Secondly, this application provides a fish health status monitoring, early warning, and control device based on fish acoustic signals, comprising:

[0020] The signal acquisition system is used to collect sound signals from healthy fish and target fish populations under suitable and unsuitable living conditions, respectively.

[0021] The storage system, connected to the signal acquisition system, includes a first sound database, a second sound database, a third sound database, and a fourth sound database; the first sound database is used to store sound signals of healthy fish collected under suitable living conditions; the second sound database is used to store sound signals of healthy fish collected under unsuitable living conditions; the third sound database is used to store sound signals of fish at different disease stages; and the fourth sound database is used to store sound signals of the target research fish population.

[0022] The signal processing system, connected to both the signal acquisition system and the storage system, is used to implement the fish health status monitoring, early warning, and regulation method based on fish sound signals provided above, to generate feedback regulation instructions; the feedback regulation instructions are used to execute regulation strategies; the regulation strategies include suggestions for improving the living environment and suggestions for treatment.

[0023] Optionally, the signal acquisition system is an underwater microphone or hydrophone.

[0024] Optionally, the fish health status monitoring, early warning, and control device based on fish sound signals further includes:

[0025] A feedback execution system, connected to the signal processing system, is used to execute the feedback adjustment command;

[0026] The remote terminal interacts with the signal processing system.

[0027] Thirdly, this application provides a computer device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the above-described method for monitoring, warning, and regulating fish health status based on fish sound signals.

[0028] Fourthly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-described method for monitoring, warning, and regulating fish health status based on fish sound signals.

[0029] Fifthly, this application provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the above-described method for monitoring, warning, and regulating fish health status based on fish sound signals.

[0030] According to the specific embodiments provided in this application, this application has the following technical effects:

[0031] This application achieves fish health status monitoring, early warning, and regulation by comparing the similarity of fish sound signals with pre-collected sample sound signals stored in different databases. It solves the problem that optical methods (such as cameras) cannot achieve efficient and accurate early pathological identification of fish in low visibility environments, thereby improving the real-time performance and accuracy of fish health status monitoring, early warning, and regulation. Attached Figure Description

[0032] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0033] Figure 1 A flowchart illustrating a method for monitoring, early warning, and regulating fish health status based on fish acoustic signals, provided in an embodiment of this application;

[0034] Figure 2 This is a schematic diagram of the acoustic signal of disease-free fish provided in an embodiment of this application;

[0035] Figure 3 This is a schematic diagram of the acoustic signal of a diseased fish provided in an embodiment of this application;

[0036] Figure 4 A schematic diagram of the implementation architecture of a fish health status monitoring, early warning and control system based on fish sound signals provided in an embodiment of this application;

[0037] Figure 5 This is a schematic diagram of the structure of a computer device provided in an embodiment of this application. Detailed Implementation

[0038] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0039] In the early stages of disease, fish often exhibit subtle but detectable changes in their vocal behavior. These changes typically precede visible lesions or behavioral abnormalities. For example, healthy fish emit regular pecking sounds while feeding, but in the early stages of disease, these sounds may become rapid, chaotic, or abnormally frequent. Similarly, specific acoustic signals during courtship may weaken or disappear, while abnormal vocalizations under stress (such as high-frequency rapid sounds) may increase significantly. These changes in acoustic characteristics are closely related to the physiological state of fish and can serve as reliable biomarkers for early health warnings.

[0040] By employing highly sensitive hydrophone equipment and intelligent acoustic analysis algorithms, the sound signals of fish in aquaculture waters can be collected and processed in real time, and a health baseline model can be established using machine learning technology. Once an abnormal acoustic pattern is detected, an early warning can be triggered immediately, prompting aquaculture personnel to take targeted measures (such as adjusting water quality, isolating diseased fish, or administering medication), thereby intervening in the early stages of disease and significantly reducing the risk of disease outbreaks. Compared to traditional methods relying on visual observation, this application provides an acoustic monitoring-based technology with advantages such as fast response, high sensitivity, and strong non-invasiveness, making it particularly suitable for large-scale, high-density aquaculture or complex environments such as turbid water bodies.

[0041] The widespread application of this technology will bring revolutionary changes to the aquaculture industry: on the one hand, precise early warning can reduce antibiotic overuse and promote green and healthy aquaculture; on the other hand, combined with an IoT platform, it can achieve remote intelligent management, improving aquaculture efficiency and economic benefits. In the future, with the improvement of acoustic databases and the optimization of artificial intelligence algorithms, this technology is expected to become a standard tool for fish health management and provide innovative solutions for marine ecological monitoring, endangered species protection, and other fields.

[0042] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0043] In one exemplary embodiment, this application provides a method for monitoring, warning, and regulating fish health status based on fish acoustic signals. This method is executed by a computer device, specifically a terminal or server, or both. In this embodiment, the method is described using a server as an example. Figure 1 As shown, the method includes:

[0044] Step 100: Under suitable living conditions, acquire the sound signals of healthy fish to construct the first sound database. The sound signals of disease-free fish (i.e., healthy fish) are as follows: Figure 2 As shown, Figure 2 Part (a) shows the sound signal waveforms of disease-free fish. Figure 2 Part (b) is the acoustic signal spectrum of disease-free fish. The main behavioral characteristics of healthy fish are as follows:

[0045] (1) Active feeding: Regularly gathers to forage for food, and the hunting actions are coordinated.

[0046] (2) Swimming posture: The dorsal fin is extended and the movement trajectory is smooth.

[0047] (3) Social behavior: group synchronized movement.

[0048] Step 101: Under unsuitable living conditions, acquire sound signals from healthy fish to construct a second sound database. The types of unsuitable living environments mainly include:

[0049] (1) Water quality deterioration, mainly including insufficient dissolved oxygen (hypoxic environment) and ammonia nitrogen (NH3 / NH4). + Excessive levels of nitrite (NO2) - Accumulation and abnormal pH levels, etc.

[0050] (2) Temperature discomfort, mainly including high temperature stress and low temperature stress.

[0051] (3) Abnormal physical environment, mainly including unsuitable lighting, insufficient water flow and space, and excessive suspended matter.

[0052] (4) Chemical pollution, mainly including heavy metal pollution (Cu 2+ Zn 2+ (etc.), pesticide / antibiotic residues, algal toxins (cyanobacterial blooms), etc.

[0053] Step 102: Obtain the acoustic signals of diseased fish at different stages of the disease and construct a third acoustic database. Specifically, for the acoustic signals of diseased fish used at different stages of the disease, corresponding sub-databases can be constructed. The acoustic signals of diseased fish are as follows: Figure 3 As shown, Figure 2 Part (a) is a waveform diagram of the sound signals of the diseased fish. Figure 3 Part (b) is the sound signal spectrum of the diseased fish.

[0054] In practical applications, the disease stage can include the incubation period, early stage, middle stage, and late stage. The main causes of fish diseases include: (1) bacteria, such as Aeromonas hydrophila, Pseudomonas fluorescens, Edwardsiella tarda, Gram-positive bacteria, Flavobacterium columnare, Vibrio, etc.; (2) viruses, such as toxemia virus, neuronecrosis virus, iridovirus, reovirus, lymphocystis virus, etc.; (3) parasites, such as Ichthyophthirius multifiliis, flagellates, worms, tapeworms, nematodes, myxosporidia, etc.

[0055] Different stages of the disease are mainly determined by observing the following behaviors:

[0056] (1) Incubation period (the pathogen colonizes and there are no symptoms), the main behavioral manifestations are: slight decrease in food intake (reduction of 5-15%), occasional rapid head shaking (initial attachment of parasites), and weakened tropism (delayed response to avoid water flow impact).

[0057] (2) Early stage (abnormal physiological indicators, no obvious lesions), the main behavioral manifestations are: increased feeding selectivity (refusal to eat bottom feed), rubbing against the pool wall / net cage (external parasite stimulation), alternating between accelerated swimming and sudden cessation, etc.

[0058] (3) Mid-stage (typical symptoms appear), the main behavioral manifestations are: spiral swimming (neurogenic pathogen infection), gill cover opening (gill swelling), "air swallowing" at the water surface (hypoxia compensation behavior), etc.

[0059] (4) Late stage (organ failure), the main behavioral manifestations are: rolling to the side / belly up, no response to stimuli (such as not escaping when caught in a net), large area of ​​ulcers on the body surface, etc.

[0060] Step 103: Acquire the acoustic signals of the target fish population and construct a fourth acoustic database. Specifically, an acoustic database (i.e., the fourth acoustic database) can be constructed based on the acquired acoustic signals of the target fish population.

[0061] Step 104: Using a similarity measurement method, determine the similarity between the sound signals of the target fish group in the fourth sound database and the sound signals in the first, second, and third sound databases, respectively.

[0062] Step 105: Obtain the health status monitoring results of the target fish population based on similarity, and generate a control strategy based on the health status monitoring results. The control strategy includes suggestions for improving the living environment and treatment suggestions.

[0063] By implementing steps 100-105 above, this application can improve the real-time nature and accuracy of fish health status monitoring, early warning, and control.

[0064] In another exemplary embodiment of this application, in order to improve the accuracy of fish disease monitoring and achieve precise control, during step 102 above, a corresponding sub-database can be generated for the acoustic signals of diseased fish acquired at each stage of the disease. Then, during the similarity comparison process, the stage of the fish disease can be determined.

[0065] In another exemplary embodiment of this application, the similarity measurement method used in step 104 above can be any one of the similarity statistical method, machine learning method, and deep learning method. The similarity statistical method includes: (1) Correlation coefficient matrix similarity calculation: calculating the Pearson / Spearman correlation coefficient between sound signals. (2) Mutual information calculation: measuring the nonlinear dependency between sound signals. (3) Relative entropy calculation: measuring the difference between the probability distributions of two sound signals.

[0066] Machine learning methods include: (1) Feature vector comparison: After representing each database as a feature vector, the distance between sound signals is calculated. (2) Cluster analysis: K-means or hierarchical clustering is used to observe each sound signal. (3) Similarity learning: Siamese network is trained to learn similarity measures.

[0067] Deep learning methods include: (1) Database embedding: using autoencoders or BERT-like models to generate vector representations between sound signals. (2) Graph neural networks: using GNNs for similarity calculation. (3) Attention mechanism similarity determination: used to identify the similarity contribution between sound signals.

[0068] In another exemplary embodiment of this application, the health status and stress etiology of the target fish population are determined primarily based on similarity measurement results between different databases. Therefore, the implementation process of step 104 above can be described as follows:

[0069] When the similarity measurement result between the sound signal of the target fish population and the sound signal of healthy fish in a suitable environment (i.e., the first sound database) is the highest, the target fish population is judged to be in a healthy state and no further action is taken.

[0070] When the similarity measurement result of the sound signal of the target fish population and the sound signal of fish at different disease stages (i.e., the third sound database) is the highest for a certain disease at a specific disease stage, it is determined that the target fish population is at a specific disease stage of that disease, and treatment suggestions are provided.

[0071] When the similarity measurement result of the sound signal of the target fish population and the sound signal of fish in an unsuitable living environment (i.e., the second sound database) is the highest for a certain unsuitable living environment, it is determined that the target fish population is under stress in that unsuitable living environment, and suggestions for improving the living environment are provided.

[0072] Based on the above description, fish sounds may precede visible symptoms (such as lesions on the body surface) and abnormal swimming postures, which helps to detect diseases or water quality problems at an early stage. Based on this, this application uses changes in fish sounds (such as those related to feeding and courtship) as a sensitive indicator. Abnormal behaviors such as sound frequency, rhythm, or intensity can reflect stresses such as hypoxia, pollution, or disease (such as parasitic infection), enabling precise monitoring and control 24 / 7.

[0073] Furthermore, data traceability and integration.

[0074] By constructing a sound database, historical data can be accumulated to help track patterns of health changes or environmental impacts, and long-term trend analysis can be conducted. It can also be linked with water quality sensor data (dissolved oxygen, pH, etc.) to achieve multimodal fusion and improve the accuracy of early warning.

[0075] Based on the same inventive concept, this application also provides a fish health status monitoring, early warning, and control device based on fish sound signals for implementing the above-mentioned method for monitoring, early warning, and control of fish health status based on fish sound signals. The solution provided by this device is similar to the solution described in the above method. Therefore, the specific limitations of one or more embodiments of the fish health status monitoring, early warning, and control device based on fish sound signals provided below can be found in the limitations of the fish health status monitoring, early warning, and control method based on fish sound signals described above, and will not be repeated here.

[0076] In one exemplary embodiment, a fish health status monitoring, early warning, and control device based on fish sound signals is provided, comprising: a signal acquisition system, a storage system, and a signal processing system.

[0077] The signal acquisition system is used to collect the acoustic signals of healthy fish and the acoustic signals of target fish populations under suitable and unsuitable living conditions.

[0078] The storage system is connected to the signal acquisition system. The storage system includes a first sound database, a second sound database, a third sound database, and a fourth sound database. The first sound database stores sound signals from healthy fish collected under suitable living conditions. The second sound database stores sound signals from healthy fish collected under unsuitable living conditions. The third sound database stores sound signals from fish at different stages of disease. The fourth sound database stores sound signals from the target research fish population.

[0079] The signal processing system is connected to both the signal acquisition system and the storage system. The signal processing system is used to implement the fish health status monitoring, early warning, and control method based on fish acoustic signals provided in this application, to generate feedback regulation instructions. These feedback regulation instructions are used to execute regulation strategies. The regulation strategies include recommendations for improving the living environment and recommendations for treatment. Based on this, the execution of the feedback regulation instructions includes: when the target research fish population is determined to be in a diseased state, using appropriate drug treatment to intervene in the pathological drug treatment of the target research fish population; when the target research fish population is determined to be in a state of environmental stress, using appropriate drug treatment to improve the environment of the target research fish population.

[0080] As an optional implementation, to reduce invasiveness and stress, the signal acquisition system used in this application can be an underwater microphone or hydrophone. Acquiring sound signals via an underwater microphone or hydrophone eliminates the need to capture or touch the fish, avoiding stress or injury caused by manipulation. It also allows for continuous observation of fish behavior without affecting their natural living conditions, making it suitable for long-term monitoring.

[0081] Based on the above setup, this application, by employing acoustic sensors such as underwater microphones or hydrophones and combining them with artificial intelligence algorithms, can automatically analyze large amounts of data, achieving 24 / 7 monitoring. This reduces the time and labor costs associated with traditional manual observation or sampling, thereby lowering labor costs. Furthermore, acoustic signals remain effective in low-light or high-turbidity environments, while optical methods (such as cameras) may be limited. Therefore, the device provided in this application can be deployed in aquaculture farms, natural waters, or large net cages, covering a wide area and enabling large-scale application. The investment in hydrophones and signal processing systems is lower than that of some high-end biological detection technologies, offering the advantage of low equipment cost. It is particularly suitable for resource-limited aquaculture farms or field research stations, easy to promote, and possesses good economic efficiency and scalability.

[0082] As an optional implementation, to facilitate real-time viewing and control of monitoring and control information by users, the fish health status monitoring, early warning, and control device based on fish sound signals provided in this application may further include: a feedback execution system and a remote terminal. The feedback execution system is connected to the signal processing system. The remote terminal interacts with the signal processing system.

[0083] Feedback execution systems are used to execute feedback control commands. For example, when the monitored living environment is hypoxic, the aerator in the feedback execution system can perform oxygenation until the monitored environment meets the requirements for suitable living.

[0084] Furthermore, the solution provided in this application eliminates the need for reagents or markers, thus avoiding secondary pollution of water bodies. It is suitable for research in sensitive ecological areas or on endangered species and can protect biodiversity.

[0085] Based on the above description, the implementation architecture of the fish health status monitoring, early warning, and control device based on fish acoustic signals provided in this application is as follows: Figure 4 As shown.

[0086] In one exemplary embodiment, a computer device is provided, which may be a server or a terminal, and its internal structure diagram may be as follows. Figure 5As shown, the computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a device bus, and the communication interface is connected to the device bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores operating devices, computer programs, and a database. The internal memory provides an environment for the operation of the operating devices and computer programs stored in the non-volatile storage media. The database stores data on fish health monitoring, early warning, and control based on fish acoustic signals. The I / O interfaces are used for exchanging information between the processor and external devices. The communication interface is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements a method for monitoring, warning, and controlling fish health based on fish acoustic signals.

[0087] Those skilled in the art will understand that Figure 5 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0088] In one exemplary embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments.

[0089] In one exemplary embodiment, a computer-readable storage medium is provided storing a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0090] In one exemplary embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0091] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0092] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (RRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).

[0093] The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0094] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0095] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A fish health condition monitoring, early warning and regulation method based on fish sound signals, characterized in that, The method comprises the following steps: acquiring sound signals of healthy fish under suitable living environment conditions to build a first sound database; acquiring sound signals of healthy fish under non-suitable living environment conditions to build a second sound database; acquiring sound signals of fish in different disease stages to build a third sound database; acquiring sound signals of a target fish group to build a fourth sound database; determining the similarity between the sound signals of the target fish group in the fourth sound database and the sound signals in the first sound database, the second sound database and the third sound database respectively by using a similarity measurement method; obtaining a health condition monitoring result of the target fish group based on the similarity, and generating a control strategy based on the health condition monitoring result; the control strategy comprises living environment improvement suggestions and treatment suggestions. 2.The fish health condition monitoring, early warning and regulating method based on fish sound signals according to claim 1, characterized in that, The method for acquiring sound signals of fish in different disease stages to build a third sound database comprises the following steps: a corresponding sub-database is generated for the sound signals of fish in each disease stage. 3.The fish health condition monitoring, early warning and regulating method based on fish sound signals according to claim 1, characterized in that, The similarity measurement method is any one of a similarity statistical method, a machine learning method and a deep learning method; the similarity statistical method comprises correlation coefficient matrix similarity calculation, mutual information calculation or relative entropy calculation; the machine learning method comprises feature vector comparison, clustering analysis or similarity learning; and the deep learning method comprises database embedding, graph neural network or attention mechanism similarity determination. 4.The fish health condition monitoring, early warning and regulating method based on fish sound signals according to claim 1, characterized in that, The method for obtaining a health condition monitoring result of the target fish group based on the similarity, and generating a control strategy based on the health condition monitoring result comprises the following steps: when the similarity between the sound signals of the target fish group and the sound signals in the first sound database is the highest, the health condition monitoring result is healthy, and no treatment is needed; when the similarity between the sound signals of the target fish group and the sound signals in the second sound database is the highest, the health condition monitoring result is that the fish is under stress in a non-suitable living environment, and living environment improvement suggestions are provided; when the similarity between the sound signals of the target fish group and the sound signals in the third sound database is the highest, the health condition monitoring result is that the fish is diseased, and treatment suggestions are provided.

5. A fish health condition monitoring, early warning and regulating device based on fish sound signals, characterized in that, The method comprises the following steps: a signal acquisition system is used to acquire sound signals of healthy fish under suitable living environment conditions and non-suitable living environment conditions, and to acquire sound signals of a target fish group; a storage system is connected with the signal acquisition system, and comprises a first sound database, a second sound database, a third sound database and a fourth sound database; the first sound database is used to store sound signals of healthy fish acquired under suitable living environment conditions; the second sound database is used to store sound signals of healthy fish acquired under non-suitable living environment conditions; the third sound database is used to store sound signals of fish in different disease stages; the fourth sound database is used to store sound signals of a target fish group; A signal processing system is connected with the signal collecting system and the storage system respectively, and is used to implement the fish health condition monitoring, early warning and regulation method based on fish sound signals according to any one of claims 1-4 to generate feedback regulation instructions; the feedback regulation instructions are used to execute a regulation strategy; The regulation strategy includes survival environment improvement suggestions and treatment suggestions.

6. The fish health condition monitoring, early warning and regulating device based on fish sound signals according to claim 5, characterized in that, The signal collecting system is an underwater microphone or a hydrophone.

7. The fish health condition monitoring, early warning and regulating device based on fish sound signals according to claim 5, characterized in that, The fish health condition monitoring, early warning and regulation device based on fish sound signals further includes: A feedback execution system connected with the signal processing system to execute the feedback regulation instructions; A remote terminal to interact with the signal processing system.

8. A computer device comprising: A memory, a processor, and a computer program stored in the memory and capable of running on the processor, characterized in that the processor executes the computer program to implement the fish health condition monitoring, early warning and regulation method based on fish sound signals according to any one of claims 1-4.

9. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to implement the fish health condition monitoring, early warning and regulation method based on fish sound signals according to any one of claims 1-4.

10. A computer program product comprising a computer program, characterized in that, The computer program is executed by the processor to implement the fish health condition monitoring, early warning and regulation method based on fish sound signals according to any one of claims 1-4.

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