An ocean ranching asset digitalization right transaction system and method based on acoustic tomography

Abstract

The present application relates to the field of computer and ocean monitoring technology, and discloses a marine ranch asset digitalization right transaction system and method based on acoustic tomography, aiming at solving the problems of existing visual monitoring being limited by environment, low biomass accounting accuracy and lack of public credibility of asset certificate. The method comprises the following steps: using an acoustic tomography array to perform all-around scanning to obtain multi-path acoustic projection original data; processing the data and applying a tomography algorithm to construct a three-dimensional voxel density model of biological distribution in a cage; combining Doppler frequency shift analysis to calculate real-time biomass evaluation value; generating an encrypted digitalization right certificate and triggering a transaction instruction. The system comprises an acoustic tomography array component, a three-dimensional reconstruction component, a value evaluation component and a transaction management component. Through acoustic three-dimensional reconstruction and non-contact health monitoring, all-weather high-precision biomass accounting is realized, and a financial-level right system with physical traceability is constructed.

Description

Technical Field

[0001] This invention belongs to the field of computer and marine monitoring technology, specifically a digital ownership confirmation and transaction system and method for marine ranch assets based on acoustic tomography. Background Technology

[0002] With the rapid development of the global marine economy, marine ranches, as an important carrier for realizing the industrialization and intelligentization of deep-sea aquaculture, have seen their digital asset management and ownership confirmation become a core link connecting the production end and the financial market. In the modern smart fishery system, accurately calculating the biomass in the cages and converting it into quantifiable digital assets is a fundamental prerequisite for carrying out spot trading of aquatic products, signing forward contracts, and financial mortgage loans.

[0003] Among them, the digital ownership confirmation and trading system for marine ranch assets based on acoustic tomography is an important technological direction for solving the problem of transparency of underwater hidden assets. This technology uses an array of acoustic sensors deployed in aquaculture waters to sense the distribution of organisms by utilizing the physical characteristics of sound waves propagating in the water medium. It aims to build a complete technological closed loop from physical perception to digital ownership confirmation and then to market transactions.

[0004] Existing technologies have the following main drawbacks in the confirmation and monitoring of marine ranch assets: Mainstream video surveillance technology is highly dependent on the optical environment of the water. The imaging quality is extremely poor when the seawater is turbid or the light is insufficient in deep water. Furthermore, the overlapping and obstruction of fish in the net cages makes it difficult to achieve full statistical analysis through visual recognition. Traditional contact-based manual sampling methods are not only inefficient, but the process can also cause serious stress to the fish, directly affecting the health and survival rate of aquaculture assets. Existing monitoring methods lack the ability to deeply analyze the spatial dimensions inside the cages, making it difficult to provide biomass accounting data with high confidence. The lack or distortion of underlying physical data leads to a lack of credibility in asset certificates generated by digital trading platforms, making it difficult to support advanced trading scenarios such as forward contracts and financial collateral, and easily generating credit default risks. These problems together limit the liquidity and financialization level of marine ranch assets.

[0005] Therefore, to address the above issues, a digital ownership confirmation and transaction system and method for marine ranch assets based on acoustic tomography is proposed. Summary of the Invention

[0006] The purpose of this invention is to address the shortcomings of existing technologies by establishing a digital ownership confirmation and trading system and method for marine ranch assets based on acoustic tomography, thereby solving the technical problems mentioned in the background art.

[0007] To address the above technical issues, the following technical solution is adopted: On the one hand, a digital ownership confirmation and transaction system for marine ranching assets based on acoustic tomography is proposed, which includes the following components: The multi-base station acoustic tomography array component is configured to deploy multiple ultrasonic transducers on a preset structural frame of a deep-sea cage in a marine ranch. By executing a one-to-many scanning mode, it collects the flight time attenuation data and scattering intensity data of the sound waves after passing through the biological population under test, and generates multipath acoustic projection characteristic signals. The acoustic signal processing and 3D reconstruction component, connected to the multi-base station acoustic tomography array component, is configured to preprocess and extract features from the acquired multipath acoustic projection feature signals, and use tomography algorithms to reconstruct the one-dimensional projection signal into a three-dimensional voxel density cloud map reflecting the distribution of organisms inside the cage. The biomass accounting and value assessment component, connected to the acoustic signal processing and three-dimensional reconstruction component, is configured to perform total biomass statistics based on the three-dimensional voxel density cloud map, and extract the Doppler frequency shift characteristics of the fish population by combining characteristic frequency analysis, so as to identify the physiological activity of the fish population and correct the value of biomass assets. The digital rights confirmation and transaction management component, connected to the biomass accounting and value assessment component, is configured to convert the corrected biomass data into standard biomass units, and generate encrypted digital rights confirmation certificates by combining geographical location information and timestamps, and distribute the digital rights confirmation certificates to the corresponding spot trading market or forward contract trading market.

[0008] To achieve the above objectives, the present invention provides the following technical solution: a method for a digital ownership confirmation and transaction system for marine ranching assets based on acoustic tomography, the method comprising the following steps: Step S110: Perform an all-round acoustic scan by deploying a multi-base station acoustic tomography array around the deep-water cage to obtain multipath acoustic projection raw data containing penetration attenuation and spatial scattering characteristics. Step S120: Perform clock synchronization calibration and environmental noise filtering on the original multipath acoustic projection data, and extract effective flight time data and scattering intensity feature values. Step S130: Apply the tomographic reconstruction algorithm to map the extracted feature values ​​to a three-dimensional spatial coordinate system to construct a three-dimensional voxel density distribution model of biological distribution inside the cage. Step S140: Extract the space occupancy rate and density gradient features from the three-dimensional voxel density distribution model, and combine them with the fish activity index obtained by Doppler frequency shift analysis to calculate the real-time biomass assessment value of the target marine ranch asset. Step S150: Convert the real-time biomass assessment value into a standardized digital asset certificate, and realize asset ownership confirmation based on distributed ledger technology. Trigger digital transaction instructions at the corresponding level based on the ownership confirmation result.

[0009] In the core system solution description, the multi-base station acoustic tomography array component, in its specific implementation, involves deploying multiple ultrasonic transducers at predetermined intervals on the top annular frame or vertical support of the deep-water cage. Each ultrasonic transducer has both transmitting and receiving functions. The system, through a controller, triggers one of the transducers to transmit ultrasonic pulses in turn according to a preset timing sequence, while the remaining transducers simultaneously enter the receiving state. This process achieves a full-coverage fan-shaped scan of the internal space of the cage, ensuring that the acquired acoustic projection data has multi-dimensional and multi-angle characteristics; the center frequency of the ultrasonic transducer is set within a preset frequency range to balance the penetration depth of sound waves in seawater with the resolution accuracy for small and medium-sized fish individuals.

[0010] In the core method scheme description, the tomographic reconstruction algorithm described in step S130 specifically adopts the algebraic reconstruction technique (ART) or the filtered back projection algorithm (FBP). When performing algebraic reconstruction, the system first divides the three-dimensional space inside the cage into several cubic voxels, each voxel representing an independent acoustic impedance contribution unit. By establishing a system of linear equations, the total attenuation value of each path is distributed to each voxel along the path. After multiple iterative calculations, the residual between the reconstructed voxel density distribution and the observed projection data reaches a preset convergence threshold. This process can effectively suppress signal occlusion interference caused by overlapping fish and realize the restoration of biological density inside the cage.

[0011] Preferably, the multi-base station acoustic tomography array component further includes an environmental parameter compensation module. The environmental parameter compensation module is configured to monitor the temperature, salinity, and hydrostatic pressure data of the aquaculture water area in real time. The environmental parameter compensation module calculates the real-time sound velocity using a preset empirical formula for sound velocity and dynamically corrects the flight time data collected in step S110 to ensure the physical consistency of acoustic projection data under different seasons and sea conditions.

[0012] Furthermore, when performing feature extraction, the acoustic signal processing and 3D reconstruction component uses sliding window Fourier transform or wavelet transform to perform time-frequency analysis on the original acoustic signal. By extracting the energy distribution characteristics of the signal in the frequency domain, it distinguishes the fish back reflection signal from the water background noise.

[0013] Preferably, the 3D reconstruction algorithm incorporates a regularization constraint term to smooth density abrupt changes between voxels and eliminate reconstruction artifacts caused by uneven sampling paths.

[0014] Furthermore, when applying characteristic frequency analysis (FFA), the biomass accounting and valuation component uses continuous wave or pulsed Doppler technology to monitor the Doppler frequency shift amplitude generated by the swimming of fish. The system compares the observed frequency shift characteristics with the preset benchmark movement modes of specific fish species at different growth stages. If the frequency shift characteristics show that the fish are in a preset healthy activity range, a preset premium correction coefficient is introduced into the biomass calculation model; if the frequency shift characteristics show abnormal decay, a health warning is triggered and the asset valuation value is reduced.

[0015] Furthermore, when generating digital ownership confirmation and transaction management components, the hash algorithm is used to calculate metadata including biomass assessment values, timestamps, transducer array geographic coordinates, and environmental parameters to generate a unique asset fingerprint. This certificate is encapsulated as a digital contract with tamper-proof characteristics.

[0016] Preferably, the system distributes asset data in a tiered manner based on the confidence level of the asset data: for data with high real-time performance and a deviation rate within a preset range, spot asset certificates are generated; for future biomass data predicted based on growth curve models, forward contract certificates are generated.

[0017] In addition, the digital rights confirmation and transaction management component also includes a dynamic quota management module. This module automatically adjusts the tradable position of the ranch on the trading platform based on the real-time confirmed biomass data. When the acoustic tomography system detects changes in assets due to fishing or natural depletion, the system updates the data and synchronizes it to the transaction matching engine within a predetermined response time to prevent excessive trading.

[0018] Preferably, the mounting bracket of the multi-base station acoustic tomography array component is made of a specific alloy material with an anti-bioadhesion coating. By bonding a piezoelectric film to the surface of the transducer's acoustic layer, a self-cleaning function is achieved using high-frequency vibration, ensuring that the system can operate stably for a long time without human intervention.

[0019] Furthermore, the acoustic signal processing and 3D reconstruction component also integrates a semantic segmentation module based on deep learning. After completing the reconstruction of the 3D voxel density cloud map, this module uses a pre-trained 3D convolutional neural network to perform instance segmentation on the biological targets in the cloud map, separating the fish targets from interfering targets such as the cage frame and underwater floating objects. By statistically analyzing the volume integral of each biological target after segmentation, the biomass calculation results are further calibrated, so that the final weight estimation error is controlled within the preset error range.

[0020] Furthermore, the digital rights confirmation and transaction management components support multi-dimensional visualization. The system combines the reconstructed 3D density cloud map with a Geographic Information System (GIS) to display the distribution heat map of each cage asset in real time on the trading terminal. Traders can click on specific coordinates to view the real-time acoustic scan image, health score, and historical growth curve of the assets in that area.

[0021] Furthermore, the system also includes an edge computing gateway. The massive amount of raw signal data collected by the multi-base station acoustic tomography array component is first processed locally at the edge computing gateway. The edge computing gateway uses a high-performance digital signal processor (DSP) or field-programmable gate array (FPGA) to perform real-time fast Fourier transform and primary tomographic reconstruction operations, and only uploads the extracted feature vectors and the reconstructed low-dimensional density matrix to the cloud server through a wireless communication module.

[0022] Furthermore, the digital rights confirmation and transaction management component combines historical meteorological data and water quality forecasting models when processing forward contract transactions. Based on the current biomass of the confirmed assets, the system predicts growth trends over a preset future time period. When it predicts that extreme weather risks may affect asset value, the system automatically adds a risk premium marker to the rights confirmation certificate.

[0023] On the other hand, a method for digital ownership confirmation and transaction system of marine ranching assets based on acoustic tomography, the specific steps of which are as follows: Step S110 involves performing a comprehensive acoustic scan using a multi-base station acoustic tomography array deployed around the perimeter of the deep-water cage. Specifically, the system control center activates the i-th of the N ultrasonic transducers as the transmitting source according to a preset scanning logic, while simultaneously instructing the remaining N-1 transducers to act as receiving elements. The amplitude attenuation matrix and phase shift matrix of the sound waves after passing through the biological population within the cage are collected to obtain raw multipath acoustic projection data containing penetration attenuation and spatial scattering characteristics.

[0024] Step S120: Clock synchronization calibration and environmental noise filtering are performed on the original data of the multipath acoustic projection. A high-precision synchronization mechanism is used to ensure that the sampling delay error of each receiving array element is lower than the preset error threshold. A bandpass filter is used to remove ocean background noise and ship power noise. Effective time of flight (ToF) data and scattering intensity (TS) feature values ​​are extracted. At the same time, the sound speed is corrected in real time according to the real-time monitored water temperature and salinity data, and the time quantity is converted into a precise distance quantity.

[0025] Step S130: Apply tomographic reconstruction algorithm to construct a three-dimensional voxel density distribution model of biological distribution inside the net cage, discretize the space inside the net cage into cubic voxels with predetermined size, establish a linear equation system with voxel attenuation coefficient as unknown, and use algebraic reconstruction algorithm for iterative calculation. In each iteration, the theoretical projection value is calculated based on the currently estimated density field, and the voxel weights are corrected based on the deviation between the theoretical and measured values, until the structural similarity index of the reconstructed image reaches the preset convergence threshold.

[0026] Step S140: Extract the space occupancy rate and density gradient features from the three-dimensional voxel density distribution model. By performing integral calculations on the density distribution model, obtain the total volume distribution of the biological population in the cage. Combine the Doppler frequency shift of the fish swimming identified by Feature Frequency Analysis (FFA), calculate the instantaneous average speed and activity index of the fish population. Input the volume distribution, activity index, and preset species density model into the biomass calculation function to calculate the real-time total biomass weight and its distribution density of the target marine ranch asset.

[0027] Step S150: Asset ownership confirmation and transaction instruction triggering. The real-time biomass assessment value obtained in step S140 is combined with the cage's unique identification code (ID), geographical coordinates, and sampling time to generate a Standard Biomass Unit (SBU) data packet. The data packet is then signed using asymmetric encryption technology to generate an immutable digital ownership certificate. This certificate is broadcast to the trading platform via the blockchain network, automatically updating the corresponding asset holder's spot listing quantity or forward contract fulfillment limit.

[0028] The beneficial effects of this invention are: Achieving high penetration and all-weather monitoring, this invention utilizes acoustic tomography technology to maintain extremely high imaging quality even in turbid seawater, deep-water light-deficient conditions, and nighttime environments, enabling continuous monitoring of marine ranch assets.

[0029] This invention significantly improves the accuracy of biomass accounting. Through the multi-base station array's one-to-many transmission and multiple-to-receive mode and the three-dimensional reconstruction algorithm, it effectively solves the problem of overlapping and occlusion of fish in dense aquaculture conditions, significantly reducing the biomass accounting error to within the preset error range, and providing an accurate data foundation for asset ownership confirmation.

[0030] This invention enables non-contact health monitoring and value correction. By capturing the Doppler frequency shift of fish schools through characteristic frequency analysis, it can assess the physiological activity and health status of fish schools in real time without contact and convert them into correction coefficients for asset value.

[0031] A financial-grade, high-confidence rights confirmation system has been constructed. By deeply coupling physical sensing data with timestamps, geographical locations, and encryption algorithms, the digital rights confirmation certificate generated by this invention has extremely high credibility and immutability, effectively reducing credit risk in the process of transactions and financial collateral.

[0032] It improves the intelligence and automation level of marine ranch management. The system's integrated edge computing and dynamic quota management functions can automatically respond to asset changes and synchronize them to the trading market, thereby improving the efficiency of digital management of marine ranches. Attached Figure Description

[0033] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0034] In the attached diagram: Figure 1 This is a schematic diagram of the overall technical architecture of a digital ownership confirmation and transaction system for marine ranch assets based on acoustic tomography proposed in this invention. Figure 2 This is a flowchart of a method for digital ownership confirmation and transaction of marine ranch assets based on acoustic tomography proposed in this invention. Detailed Implementation

[0035] The exemplary embodiments of this disclosure are described below with reference to the accompanying drawings, including various details of the embodiments to aid understanding, and should be considered merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of this disclosure. Similarly, for clarity and brevity, descriptions of well-known functions and structures are omitted in the following description.

[0036] A digital ownership confirmation and transaction system for marine ranching assets based on acoustic tomography, comprising the following components: The multi-base station acoustic tomography array component is configured to deploy multiple ultrasonic transducers on a preset structural frame of a deep-sea cage in a marine ranch. By executing a one-to-many scanning mode, it collects the flight time attenuation data and scattering intensity data of the sound waves after passing through the biological population under test, and generates multipath acoustic projection characteristic signals. The acoustic signal processing and 3D reconstruction component, connected to the multi-base station acoustic tomography array component, is configured to preprocess and extract features from the acquired multipath acoustic projection feature signals, and use tomography algorithms to reconstruct the one-dimensional projection signal into a three-dimensional voxel density cloud map reflecting the distribution of organisms inside the cage. The biomass accounting and value assessment component, connected to the acoustic signal processing and three-dimensional reconstruction component, is configured to perform total biomass statistics based on the three-dimensional voxel density cloud map, and extract the Doppler frequency shift characteristics of the fish population by combining characteristic frequency analysis, so as to identify the physiological activity of the fish population and correct the value of biomass assets. The digital rights confirmation and transaction management component, connected to the biomass accounting and value assessment component, is configured to convert the corrected biomass data into standard biomass units, and generate encrypted digital rights confirmation certificates by combining geographical location information and timestamps, and distribute the digital rights confirmation certificates to the corresponding spot trading market or forward contract trading market.

[0037] To achieve the above objectives, the present invention provides the following technical solution: a method for a digital ownership confirmation and transaction system for marine ranching assets based on acoustic tomography, the method comprising the following steps: Step S110: Perform an all-round acoustic scan by deploying a multi-base station acoustic tomography array around the deep-water cage to obtain multipath acoustic projection raw data containing penetration attenuation and spatial scattering characteristics. Step S120: Perform clock synchronization calibration and environmental noise filtering on the original multipath acoustic projection data, and extract effective flight time data and scattering intensity feature values. Step S130: Apply the tomographic reconstruction algorithm to map the extracted feature values ​​to a three-dimensional spatial coordinate system to construct a three-dimensional voxel density distribution model of biological distribution inside the cage. Step S140: Extract the space occupancy rate and density gradient features from the three-dimensional voxel density distribution model, and combine them with the fish activity index obtained by Doppler frequency shift analysis to calculate the real-time biomass assessment value of the target marine ranch asset. Step S150: Convert the real-time biomass assessment value into a standardized digital asset certificate, and realize asset ownership confirmation based on distributed ledger technology. Trigger digital transaction instructions at the corresponding level based on the ownership confirmation result.

[0038] In the core system solution description, the multi-base station acoustic tomography array component is implemented by arranging multiple ultrasonic transducers at predetermined intervals on the top ring frame or vertical support of the deep-water cage. Each ultrasonic transducer has dual functions of transmitting and receiving. The system triggers one of the transducers to transmit ultrasonic pulses in turn according to a preset timing sequence through the controller, and the other transducers simultaneously enter the receiving state. This process achieves a full-coverage fan-shaped scan of the internal space of the net cage, ensuring that the acquired acoustic projection data has multi-dimensional and multi-angle characteristics. The center frequency of the ultrasonic transducer is set within a preset frequency range to balance the penetration depth of sound waves in seawater with the resolution accuracy for small and medium-sized fish.

[0039] In the core method scheme description, the tomographic reconstruction algorithm described in step S130 specifically adopts the algebraic reconstruction technique (ART) or the filtered back projection algorithm (FBP). When performing algebraic reconstruction, the system first divides the three-dimensional space inside the cage into several cubic voxels, each voxel representing an independent acoustic impedance contribution unit. By establishing a system of linear equations, the total attenuation value observed along each path is distributed to each voxel along the path. After multiple iterative calculations, the residual between the reconstructed voxel density distribution and the observed projection data reaches a preset convergence threshold. This process effectively suppresses signal occlusion interference caused by overlapping fish populations, thus restoring the biological density within the net cage.

[0040] The multi-base station acoustic tomography array component also includes an environmental parameter compensation module, which is configured to monitor the temperature, salinity and hydrostatic pressure data of the aquaculture water area in real time. The environmental parameter compensation module calculates the real-time sound speed using a preset empirical formula and dynamically corrects the flight time data collected in step S110 to ensure the physical consistency of acoustic projection data under different seasons and sea conditions.

[0041] Furthermore, when performing feature extraction, the acoustic signal processing and 3D reconstruction component uses sliding window Fourier transform or wavelet transform to perform time-frequency analysis on the original acoustic signal; by extracting the energy distribution characteristics of the signal in the frequency domain, it distinguishes the fish back reflection signal from the water background noise. The 3D reconstruction algorithm introduces a regularization constraint term to smooth density abrupt changes between voxels and eliminate reconstruction artifacts caused by uneven sampling paths.

[0042] In addition, when applying characteristic frequency analysis (FFA), the biomass accounting and value assessment component uses continuous wave or pulsed Doppler technology to monitor the Doppler frequency shift amplitude generated by the swimming of fish schools; The system compares the observed frequency shift characteristics with the preset baseline movement modes of specific fish species at different growth stages; if the frequency shift characteristics show that the fish population is in the preset healthy activity range, a preset premium correction coefficient is introduced into the biomass calculation model; if the frequency shift characteristics show abnormal decay, a health warning is triggered and the asset valuation is lowered.

[0043] Furthermore, when generating digital ownership confirmation and transaction management components, the hash algorithm is used to calculate metadata including biomass assessment values, timestamps, transducer array geographic coordinates, and environmental parameters to generate a unique asset fingerprint. This certificate is encapsulated as a digital contract with tamper-proof characteristics. The system distributes asset data in a tiered manner based on the confidence level of the data: for data with high real-time performance and a deviation rate within a preset range, spot asset certificates are generated; for future biomass data predicted based on growth curve models, forward contract certificates are generated.

[0044] Furthermore, the digital rights confirmation and transaction management component also includes a dynamic quota management module. This module automatically adjusts the ranch's tradable position on the trading platform based on real-time confirmed biomass data. When the acoustic tomography system detects changes in assets due to fishing or natural attrition, the system updates the data and synchronizes it to the transaction matching engine within a predetermined response time to prevent excessive or unauthorized transactions.

[0045] The mounting bracket for the multi-base station acoustic tomography array assembly is made of a specific alloy material with an anti-bioadhesion coating. By laminating a piezoelectric film onto the surface of the transducer's acoustic layer, a self-cleaning function is achieved using high-frequency vibration, ensuring that the system can operate stably for a long time without human intervention.

[0046] Furthermore, the acoustic signal processing and 3D reconstruction component also integrates a semantic segmentation module based on deep learning. After completing the reconstruction of the 3D voxel density cloud map, this module uses a pre-trained 3D convolutional neural network to segment biological targets in the cloud map, separating fish targets from interfering targets such as cage frames and underwater floating objects. By statistically analyzing the volume integral of each biological target after segmentation, the biomass calculation results are further calibrated, so that the final weight estimation error is controlled within the preset error range.

[0047] Furthermore, the digital rights confirmation and transaction management components support multi-dimensional visualization. The system combines the reconstructed 3D density cloud map with a Geographic Information System (GIS) to display the distribution heat map of each cage asset in real time on the trading terminal; traders can click on specific coordinates to view the real-time acoustic scan image, health score, and historical growth curve of the assets in that area.

[0048] Furthermore, the system also includes an edge computing gateway; the massive amount of raw signal data collected by the multi-base station acoustic tomography array component is first processed locally at the edge computing gateway; the edge computing gateway uses a high-performance digital signal processor (DSP) or field-programmable gate array (FPGA) to perform real-time fast Fourier transform and primary tomography reconstruction operations; only the extracted feature vectors and the reconstructed low-dimensional density matrix are uploaded to the cloud server through the wireless communication module.

[0049] In addition, when processing forward contract transactions, the digital rights confirmation and transaction management component combines historical meteorological data and water quality forecasting models. The system predicts the growth trend in a preset future time period based on the current biomass of the confirmed rights. When it is predicted that extreme weather risks may affect asset value in the future, the system automatically adds a risk premium mark to the certificate of ownership.

[0050] A method for digital ownership confirmation and transaction system of marine ranching assets based on acoustic tomography, the specific steps of which are as follows: Step S110 involves performing an all-around acoustic scan using a multi-base station acoustic tomography array deployed around the perimeter of the deep-water cage. Specifically, the system control center activates the i-th of the N ultrasonic transducers as the transmitting source according to a preset scanning logic, while simultaneously instructing the remaining N-1 transducers as receiving elements. This process acquires the amplitude attenuation matrix and phase shift matrix of the sound waves after they pass through the biological population within the cage, obtaining raw multipath acoustic projection data that includes penetration attenuation and spatial scattering characteristics.

[0051] Step S120: Clock synchronization calibration and environmental noise filtering are performed on the original data of the multipath acoustic projection. A high-precision synchronization mechanism is used to ensure that the sampling delay error of each receiving array element is lower than the preset error threshold. A bandpass filter is used to remove ocean background noise and ship power noise. Effective time of flight (ToF) data and scattering intensity (TS) feature values ​​are extracted. At the same time, the sound speed is corrected in real time according to the real-time monitored water temperature and salinity data, and the time quantity is converted into a precise distance quantity.

[0052] Step S130: Apply tomographic reconstruction algorithm to construct a three-dimensional voxel density distribution model of biological distribution inside the net cage, discretize the space inside the net cage into cubic voxels with predetermined size, establish a linear equation system with voxel attenuation coefficient as unknown, and use algebraic reconstruction algorithm for iterative calculation. In each iteration, the theoretical projection value is calculated based on the currently estimated density field, and the voxel weights are corrected based on the deviation between the theoretical and measured values, until the structural similarity index of the reconstructed image reaches the preset convergence threshold.

[0053] Step S140: Extract the space occupancy rate and density gradient features from the three-dimensional voxel density distribution model. By performing integral calculations on the density distribution model, obtain the total volume distribution of the biological population in the net cage. Combine the Doppler frequency shift of the fish swimming identified by Feature Frequency Analysis (FFA) to calculate the instantaneous average speed and activity index of the fish population. By inputting the volume distribution, activity index, and preset species density model into the biomass accounting function, the real-time total biomass weight and its distribution density of the target marine ranch asset are calculated.

[0054] Step S150: Asset ownership confirmation and transaction instruction triggering are achieved by combining the real-time biomass assessment value obtained in step S140 with the unique identification code (ID) of the cage, geographical coordinates and sampling time to generate a standard biomass unit (SBU) data packet. The data packet is signed using asymmetric encryption technology to generate an immutable digital ownership certificate. This certificate is broadcast to the trading platform through the blockchain network to automatically update the spot listing quantity or forward contract performance limit of the corresponding asset holder.

[0055] Example 1 This embodiment uses the cultivation of large yellow croaker in deep-sea large circular net cages as a specific application scenario to explain in detail the specific implementation of a digital ownership confirmation and transaction system for marine ranch assets based on acoustic tomography. In this scenario, the net cages are 30 meters in diameter and 15 meters deep, with a high stocking density, and the aquatic environment is significantly affected by seasonal ocean currents and suspended matter.

[0056] At the system hardware level, the multi-base station acoustic tomography array component has 16 ultrasonic transducers arranged at equal intervals of 2.5 meters on the top ring frame of the deep-water cage. Each ultrasonic transducer is made of piezoelectric ceramic material with high pressure resistance, and its center frequency is precisely set to 200 kHz. This frequency selection can effectively balance the penetration distance of sound waves in seawater and the ability to distinguish individual large yellow croakers, ensuring that sound waves can penetrate dense schools of fish without producing severe scattering distortion. Each transducer is connected to the edge computing gateway located above the cage via a double-shielded waterproof cable.

[0057] The multi-base station acoustic tomography array component performs a one-to-many scanning mode during operation; The system controller triggers transducers 1 through 16 sequentially according to a preset nanosecond-level timing logic. When the No. 1 transducer emits an ultrasonic signal with a pulse width of 50 microseconds, the other 15 transducers simultaneously switch to high-sensitivity receiving mode. This process completes 16 transmissions and 240 receptions in 1 second, generating multipath acoustic projection characteristic signals covering the entire area of ​​the cage. These raw signals contain data on the time-of-flight decay of sound waves after passing through the biological community, as well as data on the scattering intensity caused by lateral reflections from the fish.

[0058] During the data acquisition process, the environmental parameter compensation module plays a real-time role. This module is integrated into the sensor group at the bottom of the cage, which includes a platinum resistance temperature sensor, a conductivity and salinity meter, and a high-precision hydrostatic pressure gauge. The environmental parameter compensation module collects water temperature, salinity, and depth data at a frequency of 10 Hz and inputs them into the built-in empirical formula for sound speed. The system dynamically calibrates the sound wave flight time data based on the real-time calculated sound speed value to eliminate refraction errors caused by seawater stratification.

[0059] After receiving the calibrated signal, the acoustic signal processing and 3D reconstruction component first uses a field-programmable gate array to perform a fast Fourier transform, converting the time-domain signal into frequency-domain features. Through a sliding window algorithm, the system extracts the energy envelope of the signal near the 200 kHz center frequency and identifies the strong signal feature value generated by the reflection from the back of the fish. Subsequently, the system applied algebraic reconstruction technology to discretize the internal space of the cage into cubic voxel units with a side length of 15 centimeters, constructing a computational matrix of approximately 120,000 independent voxels.

[0060] When executing the algebraic reconstruction algorithm, the system establishes the following system of linear equations for iteration: in, Representing the Total attenuation observed along the acoustic path Representing the The path passes through the first The weighting factor of individual elements The first one to be solved Acoustic impedance density value of individual units; The system performs more than 50 iterations to make the residual between the reconstructed voxel density distribution and the measured projection data less than the convergence threshold of 0.001. The process outputs a three-dimensional voxel density cloud map reflecting the food distribution of large yellow croaker inside the cage.

[0061] The biomass accounting and value assessment component performs integral calculations on the generated three-dimensional voxel density cloud map, counts the total number of voxels whose density values ​​exceed a preset threshold, and thus calculates the total volume of the fish population. To further improve accuracy, the system utilizes continuous wave Doppler technology to monitor frequency shift characteristics in the signal. When the large yellow croaker is swimming rapidly, the received signal exhibits a significant frequency shift. The system compares the observed Doppler frequency shift amplitude with a pre-set database of healthy movement patterns of large yellow croaker in real time. If the frequency shift characteristics show that the average swimming speed of the fish is between 0.5 m / s and 0.8 m / s, the system determines that the fish are in a healthy state with high activity and introduces a premium correction coefficient of 1.08 into the biomass calculation model; if the frequency shift characteristics show that the swimming is stagnant or abnormally violent, a health warning is triggered.

[0062] After receiving the corrected biomass data, the digital rights confirmation and transaction management component converts it into standard biomass units; The system calls a hash algorithm to encapsulate the biomass value, the geographical coordinates of the net cage, the timestamp accurate to milliseconds, and the current sound speed compensation parameters together to generate a 256-bit asset fingerprint. This fingerprint, as a unique digital certificate of ownership, is written into a blockchain node based on distributed ledger technology.

[0063] In the spot trading scenario of this embodiment, when the confidence score of the digital ownership certificate is higher than 95%, the system automatically lists the corresponding biomass share in the spot trading market. Traders can view the three-dimensional density distribution heat map of the fish cage in real time through the terminal. The heat map intuitively shows the gathering area and density gradient of the fish through the color depth. When a transaction is matched, the dynamic quota management module deducts the tradable position of the net cage in real time according to the transaction result and updates it to the blockchain ledger in a synchronous manner to ensure the uniqueness and immutability of asset ownership.

[0064] The method steps in this embodiment strictly follow the following process: In step S110, an all-round acoustic scan is performed by a multi-base station acoustic tomography array deployed around the deep-water cage. The system control center drives 16 ultrasonic transducers to emit in turn to acquire 240 sets of multipath acoustic projection raw data containing penetration attenuation and spatial scattering characteristics.

[0065] Step S120: Clock synchronization calibration and environmental noise filtering are performed on the original data of multipath acoustic projection. The high-precision crystal oscillator in the edge computing gateway is used to ensure that the sampling delay error is less than 10 nanoseconds. A bandpass digital filter is used to remove ocean background noise with frequencies below 150 kHz and above 250 kHz. Effective flight time data and scattering intensity feature values ​​are extracted.

[0066] Step S130: Apply tomographic reconstruction algorithm to construct a three-dimensional voxel density distribution model of biological distribution inside the net cage. Divide the three-dimensional space inside the net cage into cubic voxels, establish a system of linear equations, and use algebraic reconstruction algorithm to perform iterative calculations until the structural similarity index of the reconstructed image reaches the convergence threshold of 0.98.

[0067] Step S140: Extract the space occupancy rate and density gradient features from the three-dimensional voxel density distribution model, combine them with the fish activity index obtained from the feature frequency analysis, input the volume distribution and activity index into the biomass accounting function, and calculate the real-time total biomass weight of the target large yellow croaker asset.

[0068] Step S150: Convert the real-time biomass assessment value into a standardized digital asset certificate, and realize asset ownership confirmation based on distributed ledger technology. The ownership certificate contains a cryptographic signature to ensure that the data is not tampered with during the transmission to the transaction matching engine.

[0069] Example 2 This embodiment describes a large truss-type offshore aquaculture platform (such as the "Ningde No. 1" type structure), which is characterized by its huge cage volume and complex frame structure. To meet the imaging requirements in large-scale spaces, this embodiment differs significantly from Embodiment 1 in terms of technical approach and hardware deployment.

[0070] In terms of hardware structure, the multi-base station acoustic tomography array component in this embodiment does not adopt a ring layout, but uses the four main vertical pillars of the truss structure as transducer mounting bases. Eight ultrasonic transducers are installed vertically at equal intervals on each pillar to form a 4x8 vertical sensor array. This layout method realizes the layered scanning of a huge water space. In addition, to prevent deep-sea organisms from attaching and affecting the accuracy of sound wave transmission, the transducer mounting bracket is made of titanium alloy with an anti-biofouling coating. A 0.5 mm thick piezoelectric film is laminated on the surface of the transducer's acoustic layer. The system automatically triggers a 40 kHz high-frequency vibration every 12 hours to achieve surface self-cleaning through cavitation effect, ensuring long-term stable operation of the system without human intervention.

[0071] In terms of algorithm logic, since the scanning path in this embodiment exhibits non-uniform sector characteristics, the system abandons algebraic reconstruction technology and instead adopts a filtering back projection algorithm that is more computationally efficient and more suitable for large-scale projection data. The acoustic signal processing and 3D reconstruction components first perform the inverse operation of Radon transform on the original projection signal. The system introduces an RL filter or Hamming window function in the frequency domain to perform high-pass filtering on the projection signal in order to eliminate star-shaped artifacts generated during the 3D reconstruction process.

[0072] The specific calculation process of the filtered back projection algorithm is as follows: in, For the reconstructed voxel density function, This is the projected signal after filtering. To determine the scanning angle, the system stacks two-dimensional slices at different depths to synthesize a three-dimensional density cloud map that reflects the distribution of organisms inside the entire large truss cage. When processing voxels of millions, the algorithm's computation speed is more than 5 times faster than iterative algorithms.

[0073] In terms of biomass accounting and value assessment, this embodiment focuses on the value prediction of forward contracts. The biomass accounting and value assessment component not only collects real-time acoustic data, but also accesses historical meteorological data and water quality forecasting models through an edge computing gateway. Based on the current biomass of the confirmed rights, combined with the water temperature prediction curve for the next 30 days in the sea area, the system uses a biological growth model to predict future weight gain trends. When it is predicted that the risk of typhoons or red tides may affect the value of assets in the future, the system will automatically add a risk premium mark to the certificate of ownership, providing a scientific basis for the pricing of forward contracts.

[0074] In this embodiment, the digital rights confirmation and transaction management component incorporates a dynamic quota management module. This module is deeply coupled with the trading platform's matching engine. When the acoustic tomography system detects asset changes due to natural attrition or staggered harvesting, the system updates the data and regenerates the ownership certificate within 30 seconds, simultaneously adjusting the ranch's tradable position on the trading platform. This millisecond-level synchronization mechanism effectively prevents credit risk of over-listing during periods of significant asset volatility.

[0075] The method steps in this embodiment exhibit unique logic in stages S130 and S140: Step S110: Perform cross-sector acoustic scanning by an array deployed on vertical pillars to obtain raw data containing multi-level position projection features.

[0076] Step S120: A high-precision synchronization mechanism is used to ensure that the sampling phase error of the 32 transducers is less than 5 degrees. Based on real-time monitored hydrostatic pressure data, vertical gradient correction is performed on the sound velocity distribution at different depths.

[0077] Step S130: Apply the filtered back-projection algorithm. The system first performs a one-dimensional Fourier transform on the projection data of each layer, and after filtering in the frequency domain, performs back-projection accumulation to construct a three-dimensional voxel density distribution model.

[0078] Step S140: Extract the density gradient features from the model. Due to the uneven distribution of fish in large net cages, the system identifies the core cluster area and sparse area through density gradient. Combined with the physiological activity obtained by Doppler frequency shift analysis, the baseline biomass assessment value required for the long-term contract is calculated.

[0079] Step S150: Generate a digital certificate of ownership with a risk premium mark, triggering a listing instruction in the forward contract market.

[0080] Example 3 This embodiment is for a multi-species mixed-culture marine ranch scenario, such as the mixed culture of fish and shellfish in the same net cage and the presence of a large number of underwater floating disturbances (such as kelp debris); The technical difference in this embodiment lies in the introduction of a semantic segmentation module based on deep learning to solve the problem of biological target stripping in complex backgrounds.

[0081] In this embodiment, the acoustic signal processing and 3D reconstruction components integrate a semantic segmentation module based on deep learning; After completing the initial reconstruction of the 3D voxel density cloud map, the module calls a pre-trained 3D convolutional neural network. The network takes a 128x128x128 voxel matrix as input and extracts spatial features through 5 layers of convolution. The semantic segmentation module can perform pixel-level classification and separation of effective fish targets in the cloud map from the cage frame, underwater ropes, and floating debris. By statistically integrating the volume of each biological target after segmentation, the system can accurately eliminate acoustic impedance interference caused by non-biological factors, so that the final weight estimation error is controlled within 3%.

[0082] In terms of hardware support, this embodiment employs a high-performance edge computing gateway. The gateway integrates a digital signal processor with 4096 computing units, dedicated to performing inference calculations for convolutional neural networks. Feature vector extraction and primary tomography reconstruction of the raw acoustic signal are performed locally, with only the segmented feature matrix uploaded to the cloud server via a low-power wide-area network. This architecture significantly reduces data transmission bandwidth pressure, ensuring the system's real-time response capability in remote sea areas.

[0083] The digital asset ownership verification and transaction management components provide multi-dimensional visualization at the presentation layer. The system integrates reconstructed 3D density cloud maps with a geographic information system (GIS), allowing traders to not only view asset numerical certificates on the client side but also retrieve real-time 3D distribution heatmaps of the corresponding asset cages by clicking on specific coordinates on the GIS map. The system supports viewing correlation analysis between historical growth curves and real-time health scores, enhancing the transparency and credibility of digital assets through data visualization.

[0084] The method steps in this embodiment incorporate intelligent recognition logic in steps S130 and S140: Step S110: Perform an all-around acoustic scan. For mixed-species scenarios, the system adopts a multi-frequency composite scanning mode, using 150 kHz and 300 kHz sound waves to obtain multi-spectral acoustic features.

[0085] Step S120: The multispectral data is fused. Wavelet transform is used to extract the energy distribution of the signal at different scales to distinguish the scattering differences between fish and shells.

[0086] Step S130: Apply an algebraic reconstruction algorithm to generate a multispectral voxel density model.

[0087] Step S140: Use a three-dimensional convolutional neural network to perform semantic segmentation on the model, identify and extract the voxel set of the target fish group, calculate the activity level by combining Doppler frequency shift analysis, input the segmented pure biomass volume into the kernel function, and obtain the real-time biomass assessment value after eliminating interference.

[0088] Step S150 generates a digital certificate of ownership containing three-dimensional semantic features. This certificate not only contains total data, but also contains topological features of biological distribution, providing a comprehensive asset profile for advanced financial mortgage business.

[0089] In all the above embodiments, the system has a complete anomaly handling mechanism. When the multi-base station acoustic tomography array component detects a signal interruption or voltage abnormality in a transducer, the controller will automatically replan the scanning path and use the remaining transducers to form a redundant array for compensation and reconstruction. At the same time, the system will automatically mark "data quality warning" in the generated digital rights confirmation certificate and lower the confidence level of the batch of data, guiding the trading platform to enter the manual review process, thereby ensuring the security of the entire rights confirmation trading system.

[0090] The digital asset ownership confirmation and transaction management component incorporates a time-series smoothing algorithm when processing asset ownership confirmation. The system compares the currently collected biomass data with historical trends over the past 24 hours. If an illogical data mutation occurs (such as a sudden 50% drop in biomass without a harvesting order), the system will determine it as a sensor malfunction or a risk of cage damage, immediately locking the asset's transaction function and sending an instant alert to the administrator. This multi-level linkage logic ensures a high degree of consistency between physical layer data and digital asset certificates.

[0091] The communication protocol of the entire system follows the standard industrial IoT standard. The edge computing gateway and the cloud server use the encrypted MQTT protocol for data interaction. Each data packet contains a cyclic redundancy check code to ensure the integrity of data transmission in the harsh marine electromagnetic environment. The digital ownership certificate is stored using a sharded storage strategy. The asset's metadata is stored in a distributed database, while its hash fingerprint is anchored on the blockchain mainnet, balancing query efficiency and data immutability.

[0092] Through the detailed description of the above three embodiments, the flexible application and in-depth implementation of the present invention under different breeding environments, different hardware deployments and different algorithm requirements are demonstrated. By constructing a closed-loop system encompassing physical sensing, signal processing, value assessment, and rights confirmation and trading, the system effectively addresses the pain points of marine ranch assets being invisible, difficult to quantify, and easily falsified, laying a solid physical foundation for the financialization of modern smart fisheries.

[0093] In the description of this invention, it should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this disclosure can be executed in parallel, sequentially, or in different orders, as long as the desired results of the technical solutions provided in this disclosure can be achieved, and no limitation is imposed herein.

[0094] The above description is merely a preferred embodiment of the present invention and does not constitute a limitation on the scope of protection of this disclosure. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this disclosure should be included within the scope of protection of this disclosure.

Claims

1. An acoustic tomography-based digitalization of mariculture assets for the purpose of transactional rights system, characterized in that, The system includes: The multi-base station acoustic tomography array component is configured to deploy multiple ultrasonic transducers on a preset structural frame of a deep-sea cage in a marine ranch. The controller triggers one of the ultrasonic transducers to emit ultrasonic pulses in turn according to a preset timing sequence, and instructs the other transducers to switch to the receiving state synchronously to perform a one-to-many scanning mode, collect flight time attenuation data and scattering intensity data of the sound waves after passing through the biological population under test, and generate multipath acoustic projection characteristic signals. The acoustic signal processing and 3D reconstruction component, connected to the multi-base station acoustic tomography array component, is configured to perform clock synchronization calibration and environmental noise filtering on the acquired multipath acoustic projection feature signals, extract effective time-of-flight data and scattering intensity feature values, and use tomography algorithms to map the one-dimensional projection signal to a three-dimensional spatial coordinate system to reconstruct a three-dimensional voxel density cloud map reflecting the distribution status of organisms inside the cage. The biomass accounting and value assessment component, connected to the acoustic signal processing and three-dimensional reconstruction component, is configured to perform integral calculations on the three-dimensional voxel density cloud map to count the total biological distribution, and extract the Doppler frequency shift characteristics of the fish population by combining characteristic frequency analysis. By comparing the Doppler frequency shift characteristics with preset motion modes, the physiological activity of the fish population is identified and the value of the biomass assets is corrected, and a real-time biomass assessment value is calculated. The digital rights confirmation and transaction management component is connected to the biomass accounting and value assessment component. It is configured to convert the corrected real-time biomass assessment value into a standard biomass unit, and use a hash algorithm to calculate the metadata, including the biomass assessment value, geographical location information and timestamp, to generate an encrypted digital rights confirmation certificate, and distribute the digital rights confirmation certificate to the corresponding trading market.

2. The marine ranching asset digitization and right transaction system based on acoustic tomography according to claim 1, wherein, The multi-base station acoustic tomography array component also includes an environmental parameter compensation module; The environmental parameter compensation module is configured to monitor the temperature, salinity, and hydrostatic pressure data of the aquaculture water area in real time, and input the monitored temperature, salinity, and hydrostatic pressure data into a preset empirical formula for sound velocity to calculate the real-time sound velocity. The environmental parameter compensation module is also configured to dynamically calibrate the flight time attenuation data based on the real-time sound speed to eliminate sound wave refraction errors caused by water stratification and ensure the physical consistency of acoustic projection data under different seasons and sea conditions.

3. The marine ranching asset digitization and right transaction system based on acoustic tomography according to claim 1, characterized in that, The acoustic signal processing and 3D reconstruction components are configured to apply algebraic reconstruction techniques when executing tomographic imaging algorithms; The acoustic signal processing and 3D reconstruction component divides the 3D space inside the cage into multiple independent voxels, each voxel representing an independent acoustic impedance contribution unit, and establishes a system of linear equations with the voxel attenuation coefficient as the unknown, distributing the observed total attenuation value of each path to each voxel along the path. The acoustic signal processing and 3D reconstruction components perform multiple iterative calculations to make the residual between the reconstructed voxel density distribution and the observed projection data reach a preset convergence threshold, thereby restoring the distribution density of organisms inside the cage.

4. The marine ranching asset digitization and right transaction system based on acoustic tomography according to claim 1, characterized in that, The acoustic signal processing and 3D reconstruction components are configured to apply a filtered back projection algorithm when executing the tomography algorithm. The acoustic signal processing and 3D reconstruction component performs the inverse operation of Radon transform on the original projection signal and introduces a preset filter in the frequency domain to perform high-pass filtering on the projection signal in order to eliminate star artifacts generated during the 3D reconstruction process. The acoustic signal processing and 3D reconstruction components synthesize a 3D density cloud map reflecting the biological distribution inside a large truss cage by stacking 2D slices at different depth levels.

5. The marine ranching asset digitization and right transaction system based on acoustic tomography according to claim 1, characterized in that, The acoustic signal processing and 3D reconstruction component also integrates a semantic segmentation module based on deep learning; The semantic segmentation module is configured to, after completing the reconstruction of the three-dimensional voxel density cloud map, use a pre-trained three-dimensional convolutional neural network to perform instance segmentation on the biological targets in the three-dimensional voxel density cloud map, and separate the fish targets from the cage frame and underwater floating interference targets. The semantic segmentation module calibrates the calculation results generated by the biomass calculation and value assessment component by statistically integrating the volume of each biological target after segmentation, so as to eliminate acoustic impedance interference caused by non-biological factors.

6. The marine ranching asset digitization and right transaction system based on acoustic tomography according to claim 1, characterized in that, The system also includes an edge computing gateway; The edge computing gateway is connected to the multi-base station acoustic tomography array component and is configured to receive the raw signal data collected by the multi-base station acoustic tomography array component and perform fast Fourier transform and primary tomography reconstruction operations using the built-in high-performance digital signal processor. The edge computing gateway only uploads the extracted feature vectors and the reconstructed low-dimensional density matrix to the cloud server via the wireless communication module, thereby reducing the bandwidth pressure on massive amounts of raw data during transmission.

7. The digital ownership confirmation and transaction system for marine ranching assets based on acoustic tomography as described in claim 1, characterized in that, The digital rights confirmation and transaction management component includes a dynamic quota management module; The dynamic quota management module is connected to the trading matching engine of the trading platform and is configured to automatically adjust the tradable position of the corresponding marine ranch on the trading platform based on the real-time confirmed biomass data. When the acoustic signal processing and 3D reconstruction components detect changes in assets due to fishing or natural losses, the dynamic quota management module updates the data and synchronizes it to the transaction matching engine within a predetermined response time to prevent over-listing during asset fluctuations.

8. The digital ownership confirmation and transaction system for marine ranching assets based on acoustic tomography as described in claim 1, characterized in that, The ultrasonic transducers in the multi-base station acoustic tomography array assembly are mounted on an alloy support with an anti-bioadhesion coating. The ultrasonic transducer has a piezoelectric thin film on its acoustic layer surface. The controller is configured to periodically trigger the piezoelectric thin film to generate high-frequency vibrations at a preset frequency, thereby achieving self-cleaning of the acoustic layer surface through cavitation effect to ensure the accuracy of sound wave transmission and reception.

9. The method of the marine ranching asset digitization and right confirmation transaction system based on acoustic tomography, applied to the marine ranching asset digitization and right confirmation transaction system based on acoustic tomography of any one of claims 1-8, characterized in that, The method includes the following steps: By performing an all-round acoustic scan using a multi-base station acoustic tomography array deployed around the deep-water cage, the control center sequentially activates a specific transducer among multiple ultrasonic transducers as a transmission source according to the preset scanning logic, and instructs the remaining transducers as receiving array elements to obtain multipath acoustic projection raw data containing penetration attenuation characteristics and spatial scattering characteristics. The original data of the multipath acoustic projection is clocked and filtered for environmental noise. A bandpass filter is used to remove ocean background noise. The sound speed is corrected in real time based on the real-time monitored water temperature and salinity data. The time is converted into the distance and the effective flight time data and scattering intensity feature value are extracted. By applying tomographic reconstruction algorithm, the extracted feature values ​​are mapped to a three-dimensional spatial coordinate system, the internal space of the net cage is discretized into cubic voxels with predetermined size, a linear equation system with voxel attenuation coefficient as unknown is established and iterative calculation is performed to construct a three-dimensional voxel density distribution model that reflects the distribution of organisms inside the net cage. The space occupancy rate and density gradient features in the three-dimensional voxel density distribution model are extracted, and the fish swimming Doppler frequency shift identified by characteristic frequency analysis is combined to calculate the fish activity index. The volume distribution, activity index and preset species density model are input into the biomass accounting function to calculate the real-time biomass assessment value of the target marine ranch asset. The real-time biomass assessment value is converted into a standardized digital asset certificate, and asset ownership is confirmed based on distributed ledger technology. Asymmetric encryption technology is used to sign the data packet containing the biomass assessment value, unique identification code and geographic coordinates to generate an immutable digital ownership certificate. Based on the ownership confirmation result, the corresponding level of digital transaction instructions are triggered.

10. The method of the marine ranching asset digitization and rights transaction system based on acoustic tomography according to claim 9, characterized in that, The steps for realizing asset ownership confirmation and triggering transaction instructions also include a hierarchical distribution logic based on confidence level; The digital ownership confirmation and transaction management component classifies assets based on the quality assessment results of the asset data: For data whose real-time performance exceeds a preset threshold and whose deviation rate is within a preset range, generate spot asset certificates. For future biomass data predicted by growth curve models, trend analysis is performed by combining historical meteorological data and water quality forecast models to generate forward contract certificates with risk premium tags.

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