Acoustic attracting method, device, medium and product for Sciaenidae based on otolith resonance

Abstract

The present invention discloses a method, device, medium and product for acoustic attracting of Sciaenidae based on otolith resonance, and relates to the technical field of acoustic attracting. The method comprises: generating a single-frequency signal group according to an initial preset resonance frequency point group, generating an orthogonal Chirp signal group according to the hearing threshold frequency range of target fish, superimposing the two to synthesize an acoustic attracting signal, calculating a digital signal and a frequency band sound pressure level through the feeding sound and otolith resonance sound of the target fish, calculating an amplitude adjustment coefficient according to the frequency band sound pressure level, replacing the initial preset resonance frequency point group with a measured resonance frequency point group determined by the frequency domain information of the digital signal, regenerating a single-frequency signal group, superimposing the single-frequency signal group with the orthogonal Chirp signal group, using the amplitude adjustment coefficient to adjust the amplitude of the updated acoustic attracting signal after superposition, and transmitting the amplitude-adjusted signal to effectively induce the fish to forage at a designated location, thereby improving the stability of acoustic attracting.

Description

Technical Field

[0001] The present invention relates to the technical field of acoustic trapping, and in particular to a method, device, medium and product for acoustic trapping of Sciaenidae based on otolith resonance. Background Art

[0002] my country is the world's largest aquaculture producer. Feeding costs dominate total aquaculture costs, accounting for approximately 70% of total costs. This significantly impacts the economic benefits of aquaculture and is a key factor in determining profitability. The Sciaenidae family is diverse, with 70 genera and over 270 species worldwide. Sciaenidae are one of my country's major commercial fish species. For example, the yellow croaker (Pseudosciaena spp.), a member of the Sciaenidae family in the order Perciformes, is the most productive marine fish species in my country. Currently, automated feeding is the predominant method. In this method, the feed amount for fish is preset based on theoretical growth models or practical experience. The system can manage feeding at scheduled and fixed times, reducing labor costs. However, this method still cannot fully achieve the precise requirements of scientific feeding, as feed delivery requires instant adjustments based on the fish's real-time physiological needs and environmental factors. In recent years, advances in computer technology have led to the development of feeding methods that combine vision and artificial intelligence. These use advanced algorithms to make feeding decisions, enabling more precise feeding. However, this method is highly dependent on good visual conditions. In low light or poor water quality, the accuracy of the system will be greatly reduced. However, the feeding method of acoustic technology is not affected by water quality and light intensity, and data collection and feedback are more effective.

[0003] In marine ranching, a sustainable, modern aquaculture model, strategically gathering fish schools is key to improving profitability. This not only optimizes feed utilization but also directly enhances aquaculture economics through refined management. While acoustic baiting may initially cause fish to move away from the noise source, over time, fish may adapt to the sound field, reducing the effectiveness of the baiting. In contrast, passive acoustic baiting methods require a distributed baiting strategy, which carries the risk of increased costs and bait waste, while also complicating water quality monitoring and assessment. Therefore, distributed baiting strategies must be re-examined and refined to enhance their feasibility. Research examining fish biology and their acoustic capabilities has revealed that Sciaenidae species, such as large yellow croaker, possess the ability to detect variations in sound signals across different frequencies and may have learned to discern specific acoustic signals that call them to feeding from irrelevant background noise. Therefore, using acoustic baiting techniques for Sciaenidae fish not only significantly improves baiting efficiency and accuracy but also offers excellent environmental benefits, effectively preventing blind feeding and reducing bait waste and water pollution.

[0004] As an innovative tool, acoustic attractant technology shows great potential for continuously optimizing fishery operations and protecting the marine environment. Consequently, it has garnered considerable attention and research in aquaculture in recent years. Currently, acoustic signals used in acoustic attractants are primarily divided into artificially synthesized sounds (such as sine waves, square waves, and pulse sounds) and natural biological noise (such as feeding and swimming sounds). Artificially synthesized sounds are widely used, but due to their relatively simple waveforms and frequencies, they lack the ability to adapt to environmental changes and adjust the signal in a timely manner. Long-term use can lead to decreased attractant effectiveness, and their universal applicability has not been verified through extensive comparative studies. In contrast, although biological noise can instinctively induce a positive sound-attracting response in fish, its limited functionality and poor adaptability to background noise make its attractant results unstable, limiting its practical application. Therefore, the challenge for acoustic attractant technology lies in developing adjustable attractant methods that are suitable for different fish species and aquaculture environments, and optimizing both efficiency and stability. Summary of the Invention

[0005] The purpose of the present invention is to provide a method, device, medium and product for acoustic attracting of Sciaenidae based on otolith resonance, which can accurately match the acoustic attractant sound wave signal by utilizing the natural frequency of the inner ear otoliths of target Sciaenidae fish, and can effectively induce them to go to a designated location to forage.

[0006] To achieve the above object, the present invention provides the following solutions:

[0007] A method for attracting Sciaenidae fish using sound based on otolith resonance, comprising:

[0008] Step 1: performing numerical modal analysis on the otoliths of target Sciaenidae fish to determine an initial preset resonant frequency point group, and generating a single-frequency signal group based on the initial preset resonant frequency point group; the initial preset resonant frequency point group includes a plurality of preset resonant frequency points; the single-frequency signal group is composed of a single-frequency signal corresponding to each preset resonant frequency point in the initial preset resonant frequency point group;

[0009] Step 2: generating an orthogonal Chirp signal group according to the hearing threshold frequency range of the target fish of the Sciaenidae family; the orthogonal Chirp signal group includes a plurality of orthogonal Chirp signals;

[0010] Step 3: superimposing the single frequency signal group and the orthogonal Chirp signal group to synthesize the acoustic trapping signal;

[0011] Step 4: using a cylindrical underwater acoustic transducer to transmit the acoustic attracting signal; the acoustic attracting signal is used to attract the target fish of the Sciaenidae family and to place bait at a fixed point;

[0012] Step 5: Receive feeding signals of the target Sciaenidae fish using a spherical hydrophone, pre-process the feeding signals to obtain digital signals, and calculate the frequency band sound pressure level of the feeding signals based on the feeding signals; the feeding signals include feeding sounds and otolith resonance sounds;

[0013] Step 6: Calculating the amplitude adjustment coefficient according to the sound pressure level of the frequency band;

[0014] Step 7: performing frequency domain conversion processing on the digital signal to obtain a frequency domain signal, and determining a measured resonant frequency point group according to the frequency domain signal; the measured resonant frequency point group includes a plurality of measured resonant frequency points;

[0015] Step 8: replacing the initial preset resonance frequency point group obtained in step 1 with the measured resonance frequency point group, and generating a measured single frequency signal group based on the measured resonance frequency point group;

[0016] Step 9: Superimposing the measured single-frequency signal group and the orthogonal chirp signal group obtained in step 2 to synthesize an updated acoustic decoy signal, performing amplitude adjustment on the updated acoustic decoy signal according to the amplitude adjustment coefficient to obtain an amplitude-adjusted signal, and transmitting the amplitude-adjusted signal using a cylindrical underwater acoustic transducer;

[0017] Step 10: Repeat steps 5 to 9 until the emission duration reaches the maximum sound trapping and baiting activity setting duration, and stop the sound emission.

[0018] A computer device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor. The processor executes the computer program to implement the steps of the above-mentioned method for acoustically attracting Sciaenidae based on otolith resonance.

[0019] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the above-mentioned method for acoustically attracting Sciaenidae based on otolith resonance.

[0020] A computer program product includes a computer program, which, when executed by a processor, implements the steps of the above-mentioned method for acoustically attracting Sciaenidae based on otolith resonance.

[0021] According to the specific embodiments provided by the present invention, the present invention discloses the following technical effects: the present invention provides a method, device, medium and product for acoustic attracting of Sciaenidae based on otolith resonance, which utilizes the inherent frequency of the otoliths in the inner ear of the target fish to accurately match the acoustic attracting sound wave signal, and can effectively induce the target fish to forage in a designated location. By superimposing a set of orthogonal Chirp signals of any combination (consistent with the hearing threshold range of the target fish) with a set of single-frequency signals that excite otolith resonance as an artificially synthesized acoustic attracting signal, the feedback adjustment mechanism of the acoustic attracting signal tracks the changes in the otolith resonance frequency in real time and dynamically adjusts the amplitude of the signal to maintain the variability of the frequency and amplitude of the emitted sound waves, and can continuously and stably attract Sciaenidae fish, thereby achieving higher economic benefits in aquaculture. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0023] Figure 1 A schematic diagram showing the principle of the acoustic trapping method for Sciaenidae based on otolith resonance provided in Example 1 of the present invention;

[0024] Figure 2 Schematic diagram of the synthesis and emission of acoustic decoy signals provided in Example 1 of the present invention;

[0025] Figure 3 Schematic diagram of feedback adjustment of the measured resonance frequency point update and amplitude adjustment coefficient k provided in Example 1 of the present invention;

[0026] Figure 4 A schematic diagram of the implementation flow of the hearing threshold grouping peak search algorithm provided in Example 1 of the present invention;

[0027] Figure 5 A schematic diagram showing a comparison of the radius of fish attracted by the acoustic attracting method for Sciaenidae based on otolith resonance provided in Example 1 of the present invention, the existing sinusoidal wave acoustic attracting method, and the square wave acoustic attracting method in the floating cage provided in Example 1 of the present invention;

[0028] Figure 6 This is a diagram of the internal structure of a computer device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0029] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0030] Sciaenidae fish possess exceptionally sensitive hearing, thanks to subtle vibrations of structures called otoliths in their inner ears. These otoliths, driven by underwater sound waves, vibrate, stimulating sensory cells to convert these signals into electrical signals, making them exceptionally sensitive to underwater sound detection. Located within the utricle and saccule of the inner ear, the otoliths not only facilitate the transmission of sound waves but also help maintain balance. These structures, formed through a series of extracellular deposition processes of inorganic salts such as calcium carbonate (CaCO3) on a protein matrix, endow Sciaenidae with remarkable auditory perception and balance. Due to the combined influence of genetic information and habitat, individual Sciaenidae fish vary significantly in size and shape during their growth, leading to variations in their natural frequencies and responses to acoustic stimuli. These differences in auditory sensitivity due to otolith morphology are most pronounced at low frequencies.

[0031] The purpose of the present invention is to provide a method, device, medium and product for acoustic attracting of Sciaenidae based on otolith resonance. The method aims to target the auditory physiological characteristics of Sciaenidae fish and utilize the inherent frequency of the otoliths in the inner ear of the target fish to accurately match the acoustic attracting sound wave signal, so as to effectively induce the fish to go to a designated location to forage. By superimposing a set of orthogonal chirp signals (consistent with the hearing threshold range of the target fish) with a set of single-frequency signals that excite otolith resonance as an artificially synthesized acoustic attracting signal, the feedback regulation mechanism of the acoustic attracting signal is used to track the changes in the otolith resonance frequency in real time and dynamically adjust the amplitude of the signal to maintain the variability of the frequency and amplitude of the emitted sound waves, thereby avoiding the fish's adaptation to continuous sound wave stimulation, which leads to weakened aggregation. The method can continuously and stably acoustically attract Sciaenidae fish, thereby achieving higher economic benefits in aquaculture.

[0032] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0033] Example 1

[0034] like Figure 1 As shown, this embodiment is used to provide a method for attracting Sciaenidae fish by sound based on otolith resonance, comprising:

[0035] Step 1: Determine an initial preset resonant frequency point group based on the otoliths of target Sciaenidae fish by performing numerical modal analysis, and generate a single-frequency signal group based on the initial preset resonant frequency point group; the initial preset resonant frequency point group includes a plurality of preset resonant frequency points; the single-frequency signal group is composed of a single-frequency signal corresponding to each preset resonant frequency point in the initial preset resonant frequency point group.

[0036] This embodiment takes large yellow croaker as an example to specifically introduce the acoustic attracting method for Sciaenidae based on otolith resonance provided by this embodiment, which is divided into two parts: designing the transmitting end and the feedback receiving end.

[0037] Within the acoustic attraction range of large yellow croaker, numerical modal analysis of the otoliths of large yellow croaker at different growth stages was performed to obtain a set of preset resonant frequency points, and a set of single-frequency signals (SGS1) with specific frequency, amplitude and phase corresponding to the preset resonant frequency points were generated, and this set of single-frequency signals was transferred to the register.

[0038] Otolith morphology in fish is primarily regulated by two factors: genetics plays a dominant role, while environmental factors also play a significant role. Within the same fish species, otolith morphology can vary depending on the environment and conditions in which they grow. Otolith morphology only stabilizes after the fish reaches a certain stage of growth. This complex morphogenesis process results in a unique frequency distribution in the otoliths of each Sciaenidae species. To minimize this variability in otolith natural frequencies, numerical modal analysis of the otoliths of target fish species is performed to identify the frequencies that influence their natural frequencies.

[0039] Numerical modal analysis is performed by establishing a geometric model of the target object, determining its material properties and boundary conditions, and using matrix equations to solve the eigenvalues, i.e., the natural frequencies of the object, and identify the object's sensitive frequency range. Based on the numerical modal analysis of the otoliths of large yellow croaker, a set of preset resonant frequency points is obtained: 200Hz, 1000Hz, 1500Hz, 2800Hz, and 3500Hz. Based on this set of resonant frequency points, a set of corresponding single-frequency signals (SGS1) is generated. The normalized representation of the single-frequency signal group in step 1 is as follows:

[0040]

[0041] Among them, SGS1(t) is the single frequency signal group, f 1,m represents the mth frequency corresponding to the preset resonant frequency point, t represents time, T represents the period of the signal, φ represents a specific phase, and M represents the total number of corresponding resonant frequency points. In this embodiment, M=5.

[0042] Step 2: Generate an orthogonal Chirp signal group according to the hearing threshold frequency range of the target fish of the Sciaenidae family; the orthogonal Chirp signal group includes a plurality of orthogonal Chirp signals.

[0043] According to the hearing threshold frequency range of large yellow croaker (100Hz~4kHz), a set of orthogonal chirp signals (OCDS) were generated as the base signal of the acoustic attractant signal.

[0044] It should be noted that the hearing threshold frequency range of the target fish is determined using the Auditory Brainstem Response (ABR) method. ABR offers the advantages of rapid measurement, fish-safety, and adaptability to both size and species. ABR measures the hearing threshold frequency range by recording synchronized neural activity in the vestibulocochlear nerve and brainstem in response to sound stimulation, deriving auditory-related potentials from the fish's skull, and assessing the fish's response based on these potential changes.

[0045] Fish have a wide range of hearing thresholds, with significant differences between species. Therefore, understanding the hearing threshold range of specific species is crucial for successful acoustic trapping. Certain fish species may be more sensitive to specific frequencies or sound levels, depending on their habitat and ecological habits. For large yellow croaker, the hearing threshold curve exhibits a characteristic "V" shape. In the 100-300 Hz frequency range, the hearing threshold decreases, while the hearing sensitivity increases. The frequency range of 500-800 Hz is the most sensitive for large yellow croaker, with the peak and valley of the hearing threshold at 500 Hz. In the 1-4 kHz range, the hearing threshold increases, while the hearing sensitivity decreases.

[0046] The frequency range of the base signal composed of orthogonal Chirp signals is set according to the hearing threshold range of large yellow croaker, the period of the Chirp signal is set to T, and N is the number of curves in the group of Chirp signals. i The normalized OCDS signal generated in the i-th round represented by (t), that is, the orthogonal Chirp signal group is expressed as follows:

[0047]

[0048] The freely combinable nature of orthogonal chirp signals offers significant applicability in customizing optimal acoustic lure signaling strategies. The number of orthogonal chirp signal curves, N, can be optimized based on the specific environment and requirements of the baiting system. Crucially, this optimization process does not increase processing complexity. Furthermore, the frequency content of orthogonal chirp signals varies over time, significantly enhancing the efficiency of the baiting system.

[0049] Step 3: Superimpose the single-frequency signal group and the orthogonal Chirp signal group to synthesize the acoustic decoy signal.

[0050] Combine the single frequency signal group SGS1(t) in the register of step 1 with a group of orthogonal Chirp signals generated in step 2, i.e., the orthogonal Chirp signal group ψ i (t) Perform superposition synthesis of sound trapping signal Y i (t);

[0051] Y i (t) = SGS i (t)+ψ i (t) (3);

[0052] Where i represents the i-th round of acoustic emission in the acoustic trapping activity. In the first round of acoustic emission, i=1, then the acoustic trapping signal Y1(t)=SGS1(t)+ψ1(t) in the first round of emission.

[0053] Step 4: emitting the acoustic luring signal by using a cylindrical underwater acoustic transducer; the acoustic luring signal is used to lure the target fish of the Sciaenidae family to a fixed point for baiting.

[0054] The superimposed signal Y in step 3 i (t) The acoustic signal is transmitted by a cylindrical underwater acoustic transducer and is used to attract large yellow croakers by fixed-point baiting. The synthesis and transmission process of the acoustic attracting signal is as follows: Figure 2 shown.

[0055] Step 5: Receive feeding signals from the target Sciaenidae fish using a spherical hydrophone, preprocess the feeding signals to obtain digital signals, and calculate the frequency band sound pressure level of the feeding signals based on the feeding signals. The feeding signals include feeding sounds and otolith resonance sounds. Preprocessing includes preamplification, anti-aliasing filtering, and analog-to-digital conversion.

[0056] The spherical hydrophone receives the feeding sound of large yellow croaker and the otolith resonance sound. The signal received by the hydrophone is pre-amplified, anti-aliased filtered and analog-to-digital converted to obtain the digital signal x[p], and the frequency band sound pressure level L of the feeding sound of large yellow croaker and the otolith resonance sound is calculated. pf ;

[0057] Frequency band sound pressure level L of feeding sound and otolith resonance sound pf , the calculation formula is as follows:

[0058]

[0059] Among them, L pf is the frequency band sound pressure level, P f is the sound pressure value of feeding sound and otolith resonance sound within the bandwidth, in Pa; P0 is the reference sound pressure, which is usually 1uPa in underwater environment.

[0060] Step 6: Calculate the amplitude adjustment coefficient according to the sound pressure level of the frequency band.

[0061] The frequency band sound pressure level L obtained in step 5 pf To calculate the amplitude adjustment coefficient k; the measured frequency band sound pressure level L of the feeding sound and otolith resonance sound pf It changes in real time with the baiting process, and the amplitude adjustment coefficient k is continuously calculated and updated. The formula is as follows:

[0062]

[0063] Among them, k is the amplitude adjustment coefficient, V max A is the maximum voltage that the cylindrical underwater acoustic transducer can accept. max is the maximum amplitude of the superimposed signal, A i is the amplitude of the superimposed signal adjusted for the i-th time, T max is the time parameter.

[0064] In order to update the amplitude adjustment coefficient k more efficiently, the time parameter T is introduced max Set the maximum value T of the acoustic trap signal emission time max , which not only enables more real-time feedback, but also helps reduce the power consumption of signal transmission. The frequency band sound pressure level L of feeding sound and otolith resonance sound pf As the acoustic decoy signal emission time T changes, these changes will be fed back and interact with each other to adjust the amplitude adjustment coefficient k.

[0065] Step 7: Perform frequency domain conversion processing on the digital signal to obtain a frequency domain signal, and determine a measured resonant frequency point group according to the frequency domain signal; the measured resonant frequency point group includes a plurality of measured resonant frequency points.

[0066] Determining the measured resonant frequency point group according to the frequency domain signal specifically includes: using an auditory threshold grouping peak search algorithm to determine the measured resonant frequency point group according to the frequency domain signal.

[0067] The digital signal x[p] obtained in step 5 is transformed into a frequency domain signal X[r] by discrete Fourier transform (DFT) in the frequency domain, and five measured resonant frequency points are found in the frequency domain by the hearing threshold grouping peak search algorithm. Figure 3 The diagram below shows the feedback adjustment of the measured resonance frequency point update and amplitude adjustment coefficient k. The basic expression of DFT calculation is:

[0068]

[0069] Where X[r] is the rth element of the transformed frequency domain sequence, x[p] is the pth element of the digital signal sequence, and Q is the length of the sequence.

[0070] Through the hearing threshold grouping peak search method, the frequency components in the signal can be accurately grasped, and then the five measured resonance frequency points that have the most significant impact on the resonance of the large yellow croaker otolith can be identified. The peak search algorithm is mainly divided into three categories: sliding window search method, global threshold method and local adaptive threshold method. Among them, the sliding window search method needs to rely on a large amount of data to maintain stable performance, and if the window is not selected properly, it may cause inaccurate peak detection results. The global threshold method has very high requirements for the selection of thresholds. If the threshold is set too high or too low, it may cause inaccurate peak detection results. This embodiment adopts an improved local adaptive threshold method, which is as follows:

[0071] The frequency range for peak search directly references the hearing threshold of the target fish, encompassing the frequency search range from the lower to upper threshold. In frequency domain analysis, a sliding window of appropriate length and a sliding step size are first set based on the frequency domain signal X[r]. Based on numerical modal analysis of the target fish's otoliths, the resonant frequency points in the measured signal that are influential on the target fish's otoliths do not deviate significantly from the numerical modal analysis results. Therefore, the average of two adjacent resonant frequency points is used as the endpoint of the search frequency range. This step yields five frequency ranges based on the five resonant frequency points. For each frequency range, the average is calculated and used as the threshold for that range. Values ​​within that range that fall below the corresponding threshold are then set to zero. Within the sliding window, the value with the largest amplitude is found; this value is considered a peak within that frequency range. Once a peak is identified, other values ​​within the window are suppressed to prevent duplicate detection of the same peak. After completing the peak search for all windows, the frequency corresponding to the maximum peak in each frequency range is extracted and considered the resonant frequency point. After the above process, 5 measured resonant frequency points can be obtained from the five groups of frequency intervals. The block diagram of the hearing threshold group peak search algorithm is as follows: Figure 4 shown.

[0072] Step 8: Replace the initial preset resonance frequency point group obtained in step 1 with the measured resonance frequency point group, and generate a measured single frequency signal group based on the measured resonance frequency point group.

[0073] The set of measured resonance frequency points obtained in step 7 replaces the preset resonance frequency points, and generates a set of normalized single-frequency signals (measured single-frequency signal group) SGS with specific frequency, amplitude and phase corresponding to the measured resonance frequency points of the i-th round. i (t)(i≥2), then store it in the register and update SGS i (t) The replica, i.e. the measured single frequency signal group is as follows:

[0074]

[0075] Among them, fi,m is a set of measured resonant frequency points obtained in step 7, i.e., a measured resonant frequency point group, i represents the frequency obtained in the i-th round, m represents the sequence number in this round of frequencies, φ represents a specific phase, and M represents the total number of corresponding resonant frequency points.

[0076] Step 9: Superimpose the measured single-frequency signal group and the orthogonal chirp signal group obtained in step 2 to synthesize an updated acoustic decoy signal, adjust the amplitude of the updated acoustic decoy signal according to the amplitude adjustment coefficient to obtain an amplitude-adjusted signal, and transmit the amplitude-adjusted signal using a cylindrical underwater acoustic transducer.

[0077] The measured single frequency signal group SGS obtained in step 8 i (t) and the orthogonal Chirp signal ψ obtained in step 2 i (t) is superimposed to obtain the updated acoustic trapping signal Y i (t), the updated acoustic trapping signal Y after superposition i (t) According to the amplitude adjustment coefficient k obtained in step 6, the maximum amplitude value of the signal is adjusted. The signal after amplitude adjustment is A i Y i (t), the amplitude-adjusted signal A is transmitted by the cylindrical underwater acoustic transducer i Y i (t), used to lure large yellow croakers and place bait at fixed points.

[0078] Step 10: Repeat steps 5 to 9 until the emission duration reaches the maximum sound trapping and baiting activity setting duration, and stop the sound emission.

[0079] Repeat the process from step 5 to step 9 continuously. After i rounds of sound luring signal emission, when the emission duration reaches the duration set for this sound luring baiting activity, stop the sound emission and this baiting activity ends.

[0080] Figure 5 The present invention compares the effects of attracting large yellow croaker by using an acoustic attracting method based on otolith resonance and other artificially synthesized sounds (this embodiment uses square wave acoustic attracting signals and sine wave acoustic attracting signals as artificially synthesized sounds) in floating cages. The experimental results show that as the emission time of the acoustic attracting signal increases, the radius of the attracted fish school obtained by the acoustic attracting method based on otolith resonance is larger than that of the fish school attracted by other artificially synthesized sounds, and the attracting effect is more stable. Therefore, this embodiment can significantly improve the efficiency of fixed-point feeding in aquaculture.

[0081] This embodiment has the following beneficial effects:

[0082] 1. The acoustic lure signal, designed using the principle of otolith resonance, avoids the transmission of non-resonant frequency signals by precisely matching the resonant frequency of the otoliths of target Sciaenidae fish, effectively reducing transmission power and improving the effectiveness of targeted baiting for acoustic lure targeting Sciaenidae fish. This strategy precisely activates the behavioral responses of Sciaenidae fish, making baiting more focused and effective. Because the acoustic lure signal generation algorithm is relatively simple, the equipment design and implementation process are also simplified. Furthermore, this embodiment uses amplitude feedback regulation for power control to prevent the emitted acoustic lure signal from exceeding the hearing threshold of the target fish and damaging their auditory system, ensuring optimal transmission power efficiency and eco-friendliness.

[0083] 2. To avoid a decrease in attracting effectiveness due to a single acoustic attracting signal, this embodiment employs a feedback control mechanism to update the otolith resonant frequency in real time, precisely adjusting the attracting signal to continuously optimize the baiting process. Furthermore, a set of orthogonal chirp signals forming the base signal can be freely combined during each attracting event, thus achieving signal variability and maintaining optimal attracting effectiveness.

[0084] Example 2

[0085] A computer device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor. The processor executes the computer program to implement the steps of a method for acoustically attracting Sciaenidae based on otolith resonance in Example 1.

[0086] Example 3

[0087] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of a method for acoustically attracting Sciaenidae based on otolith resonance in Example 1.

[0088] Example 4

[0089] A computer program product includes a computer program, which, when executed by a processor, implements the steps of the method for attracting Sciaenidae fish based on otolith resonance in embodiment 1.

[0090] Example 5

[0091] A computer device, which may be a database, may have an internal structure as shown in FIG. Figure 6As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, abbreviated as I / O) and a communication interface. The processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store pending transactions. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a method for attracting Sciaenidae based on otolith resonance in Example 1 is implemented.

[0092] It should be noted that the object information (including but not limited to object device information, object personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in the present invention are all information and data authorized by the object or fully authorized by all parties, and the collection, use and processing of relevant data must comply with the relevant laws, regulations and standards of relevant countries and regions.

[0093] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, database or other media used in the embodiments provided by the present invention 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 (ReRAM), 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 be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided by the present invention may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, distributed databases based on blockchains. The processor involved in each embodiment provided by the present invention may be a general-purpose processor, a central processing unit, a graphics processing unit, a digital signal processor, a programmable logic unit, a data processing logic unit based on quantum computing, etc., but are not limited to these.

[0094] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, 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 the present invention. The above examples are only intended to help understand the method and core concept of the present invention. At the same time, those skilled in the art will find that the specific implementation methods and application scopes may vary based on the concept of the present invention. In summary, the contents of this specification should not be construed as limiting the present invention.

Claims

1. A method for attracting Sciaenidae fish by acoustic trapping based on otolith resonance, characterized in that: include: Step 1: performing numerical modal analysis on the otoliths of target Sciaenidae fish to determine an initial preset resonant frequency point group, and generating a single-frequency signal group based on the initial preset resonant frequency point group; the initial preset resonant frequency point group includes a plurality of preset resonant frequency points; the single-frequency signal group is composed of a single-frequency signal corresponding to each preset resonant frequency point in the initial preset resonant frequency point group; Step 2: generating an orthogonal Chirp signal group according to the hearing threshold frequency range of the target fish of the Sciaenidae family; the orthogonal Chirp signal group includes a plurality of orthogonal Chirp signals; Step 3: superimposing the single frequency signal group and the orthogonal Chirp signal group to synthesize the acoustic trapping signal; Step 4: using a cylindrical underwater acoustic transducer to transmit the acoustic attracting signal; the acoustic attracting signal is used to attract the target fish of the Sciaenidae family and to place bait at a fixed point; Step 5: Receive feeding signals of the target Sciaenidae fish using a spherical hydrophone, pre-process the feeding signals to obtain digital signals, and calculate the frequency band sound pressure level of the feeding signals based on the feeding signals; the feeding signals include feeding sounds and otolith resonance sounds; Step 6: Calculating the amplitude adjustment coefficient according to the sound pressure level of the frequency band; Step 7: performing frequency domain conversion processing on the digital signal to obtain a frequency domain signal, and determining a measured resonant frequency point group according to the frequency domain signal; the measured resonant frequency point group includes a plurality of measured resonant frequency points; Step 8: replacing the initial preset resonance frequency point group obtained in step 1 with the measured resonance frequency point group, and generating a measured single frequency signal group based on the measured resonance frequency point group; Step 9: Superimposing the measured single-frequency signal group and the orthogonal chirp signal group obtained in step 2 to synthesize an updated acoustic decoy signal, performing amplitude adjustment on the updated acoustic decoy signal according to the amplitude adjustment coefficient to obtain an amplitude-adjusted signal, and transmitting the amplitude-adjusted signal using a cylindrical underwater acoustic transducer; Step 10: Repeat steps 5 to 9 until the emission duration reaches the maximum sound trapping and baiting activity setting duration, and then stop the sound emission.

2. The method for attracting Sciaenidae fish by acoustic trapping based on otolith resonance according to claim 1, characterized in that: The single frequency signal group in step 1 is: Among them, SGS1(t) is the single frequency signal group, f 1,m represents the mth frequency corresponding to the preset resonant frequency point, t represents time, T represents the period of the signal, φ represents the phase, and M represents the total number of corresponding resonant frequency points.

3. The method for attracting Sciaenidae fish based on otolith resonance according to claim 1, characterized in that: The orthogonal Chirp signal group is: Among them, ψ i (t) is the orthogonal Chirp signal group of the i-th round.

4. The method for attracting Sciaenidae fish by acoustic trapping based on otolith resonance according to claim 1, characterized in that: The pre-processing includes pre-amplification, anti-aliasing filtering and analog-to-digital conversion.

5. The method for attracting Sciaenidae fish based on otolith resonance according to claim 1, characterized in that: The calculation formula for the frequency band sound pressure level is as follows: Among them, L pf is the frequency band sound pressure level, P f is the sound pressure value of feeding sound and otolith resonance sound within the bandwidth, in Pa, and P0 is the reference sound pressure.

6. The method for attracting Sciaenidae fish by acoustic trapping based on otolith resonance according to claim 1, characterized in that: The calculation formula of the amplitude adjustment coefficient is as follows: Among them, k is the amplitude adjustment coefficient, L pf is the frequency band sound pressure level, V max A is the maximum voltage that the cylindrical underwater acoustic transducer can accept. max is the maximum amplitude of the superimposed signal, A i is the amplitude of the superimposed signal adjusted for the i-th time, T max is the time parameter.

7. The method for attracting Sciaenidae fish by acoustic trapping based on otolith resonance according to claim 1, characterized in that: Determining a measured resonant frequency point group according to the frequency domain signal specifically includes: The measured resonant frequency point group is determined according to the frequency domain signal using an auditory threshold grouping peak search algorithm.

8. A computer device comprising: 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 method for acoustically attracting Sciaenidae based on otolith resonance as described in any one of claims 1 to 7.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method for attracting Sciaenidae based on otolith resonance as described in any one of claims 1 to 7 are implemented.

10. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method for attracting Sciaenidae based on otolith resonance as described in any one of claims 1 to 7 are implemented.

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