Deep sea euryhaline fish multi-level screening and evaluation method

Abstract

The application discloses a deep-sea eurythermal fish multi-level screening and evaluation method, which comprises the following steps: based on the sea state parameters, hydrological conditions and bottom parameters of a target sea area, a first-level environment analysis model is established; according to the first-level analysis result, the type of deep-sea aquaculture facilities and the parameters of supporting equipment are matched to generate a second-level facility selection scheme; the biological characteristics, economic value and industry status data of local fish in the target sea area are integrated to construct a third-level local fish database; based on the comprehensive data of the first three levels, the eurythermal fish suitable for the target sea area and the facilities is screened through a weighted scoring method to form a fourth-level evaluation result. Through the construction of the five-level architecture, the multi-dimensional data such as sea state, hydrology, bottom, biological characteristics, economic value and the like are systematically integrated, the sea area environment and fish characteristics are dynamically matched through the weighted scoring method (core indexes such as biological adaptability and economic value), and the adaptability of the suitable breeding varieties is significantly improved.

Description

Technical Field

[0001] This invention relates to the field of deep-sea aquaculture technology, specifically a multi-level screening and evaluation method for deep-sea suitable fish species. Background Technology

[0002] Developing deep-sea aquaculture has become a crucial strategic direction for the transformation, upgrading, and high-quality development of my country's marine fisheries. The rapid development of large-scale deep-sea facilities such as deep-sea cages, large enclosures, and aquaculture vessels is continuously propelling marine aquaculture into deeper and more distant waters. "Safe facilities + intelligent equipment + superior species + advanced technology" are key to the success of deep-sea aquaculture. However, my country's deep-sea aquaculture development currently faces the predicament of a limited number of suitable species and insufficient technical support, urgently requiring the establishment of a system for selecting superior and suitable species and for developing advanced aquaculture technologies to support the high-quality development of deep-sea aquaculture.

[0003] Based on the current species and technology requirements of deep-sea aquaculture, a multi-level screening and evaluation method for suitable fish species in deep-sea aquaculture will be established to provide suitable species screening and evaluation, as well as land-sea relay aquaculture technology support for deep-sea aquaculture facilities in different sea areas. Summary of the Invention

[0004] The technical problem to be solved by this invention is to overcome the above-mentioned technical defects and provide a multi-level screening and evaluation method for deep-sea fish suitable for cultivation.

[0005] To address the aforementioned problems, the technical solution of this invention is a multi-level screening and evaluation method for deep-sea aquaculture fish, comprising the following steps:

[0006] S1. Based on the sea state parameters, hydrological conditions and sediment parameters of the target sea area, establish a first-level environmental analysis model;

[0007] S2. Based on the analysis results of the first level, the types of deep-sea aquaculture facilities and the parameters of supporting equipment are matched to generate a second-level facility selection scheme;

[0008] S3. Integrate data on the biological characteristics, economic value, and current status of native fish species in the target sea area to construct a third-level native fish database;

[0009] S4. Based on the comprehensive data of the first three levels, the fish species suitable for the target sea area and facilities are screened by weighted scoring method to form the fourth level of evaluation results.

[0010] S5. For the selected suitable fish species, design land-sea relay aquaculture technology and intelligent management solutions to generate the fifth level of aquaculture decision-making.

[0011] Furthermore, in the second-tier facility selection scheme, the deep-sea aquaculture facility types include at least one of the following: wind and wave resistant gravity cages, deep-sea bottom-mounted intelligent cages, submersible truss cages, and aquaculture vessels. The supporting equipment parameters include feed feeding systems, environmental monitoring devices, climate monitoring equipment, automatic net cleaning equipment, and sea-land relay transfer equipment.

[0012] Furthermore, the weighted scoring method for the fourth-level evaluation results includes setting four core indicators: biological adaptability, economic value, industrialization feasibility, and processing suitability. Dynamic weight coefficients are assigned to each indicator, and a comprehensive score is calculated through linear weighting to screen fish species with scores higher than a preset threshold.

[0013] Furthermore, the biological adaptability index is further refined into three sub-parameters: tolerance to low oxygen, disease resistance, and wind and wave resistance. The weight coefficients of each sub-parameter are dynamically adjusted according to the environmental characteristics of the target sea area.

[0014] Furthermore, the intelligent management solution collects aquaculture environment data in real time through IoT devices and uses digital twin technology to construct virtual aquaculture scenarios, monitor and calculate the growth status of fish under deep-sea aquaculture conditions, and optimize feed feeding strategies and emergency response plans for unforeseen circumstances.

[0015] Furthermore, the sea state parameters include wave height, ocean current speed, and storm frequency; the hydrological conditions include water temperature, salinity, dissolved oxygen, pH, and light intensity; and the sediment parameters include geological structure type and mud layer thickness. Multidimensional data fusion and visualization are achieved through a geographic information system.

[0016] Furthermore, in the third-level local fish database, the biological characteristics include growth rate, flow tolerance, and feed protein requirements; the economic value includes market price, market demand, and export potential; and the industry status includes the distribution of major production areas, farming models, and annual output data. The data is dynamically updated and trend predicted using machine learning algorithms.

[0017] Furthermore, in the fifth-level aquaculture decision-making process, the land-sea relay aquaculture technology includes the connection parameters between the land-based seedling stage and the deep-sea aquaculture stage, specifically covering water temperature transition gradient, transportation time control, and environmental adaptability training programs; the intelligent management program includes a remote environmental monitoring module, a risk early warning system, and an aquaculture expert decision support module.

[0018] Furthermore, the method also includes a blockchain-based aquaculture data storage module, which is used to encrypt and store the screening process, evaluation results, and decision-making basis at each level to ensure the immutability and full traceability of the data.

[0019] The advantages of this invention compared to existing technologies are:

[0020] 1. This invention provides a multi-level screening and evaluation method for deep-sea aquaculture fish, constructing a five-layer architecture (environmental analysis → facility selection → database → weighted scoring → intelligent decision-making). It systematically integrates multi-dimensional data such as sea conditions, hydrology, seabed sediment, biological characteristics of aquaculture species, economic value, and market demand. Through a weighted scoring method (core indicators such as biological suitability, economic value, and market demand), it dynamically matches the marine environment with fish characteristics, significantly improving the suitability of aquaculture species. Based on GIS multi-dimensional data fusion, it accurately recommends facility types such as truss cages and aquaculture vessels, reducing facility damage rates in extreme environments, increasing aquaculture survival rates, and promoting the growth of aquaculture species.

[0021] 2. This invention provides a multi-level screening and evaluation method for deep-sea aquaculture fish. It deploys IoT devices to collect environmental data such as water temperature and dissolved oxygen in real time, uses digital twins to construct virtual aquaculture scenarios, monitors and calculates the growth status of fish under deep-sea aquaculture conditions, optimizes feeding strategies and disaster response plans, and enables all-weather management through remote monitoring and risk early warning systems, reducing human intervention. By simulating extreme sea conditions (such as storms and low oxygen), it can formulate emergency plans in advance to reduce losses. Attached Figure Description

[0022] Figure 1 This is a flowchart of the multi-level screening and evaluation method for deep-sea aquaculture fish of the present invention. Detailed Implementation

[0023] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numerals in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this disclosure. Rather, they are merely examples of apparatuses consistent with some aspects of this disclosure as detailed in the appended claims.

[0024] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0025] like Figure 1 As shown, this embodiment proposes a multi-level screening and evaluation method for deep-sea aquaculture fish, including the following steps:

[0026] S1. Based on the sea state parameters, hydrological conditions and sediment parameters of the target sea area, establish a first-level environmental analysis model;

[0027] S2. Based on the analysis results of the first level, match the types of deep-sea aquaculture facilities and the parameters of supporting equipment to generate a second-level facility selection scheme;

[0028] S3. Integrate data on the biological characteristics, economic value, and current status of native fish species in the target sea area to construct a third-level native fish database;

[0029] S4. Based on the comprehensive data from the first three levels, a weighted scoring method is used to screen suitable fish species for the target sea area and facilities, forming the fourth level of evaluation results.

[0030] S5. For the selected suitable fish species, design land-sea relay aquaculture technology and intelligent management solutions to generate the fifth level of aquaculture decision-making.

[0031] Furthermore, the sea state parameters include wave height, current speed, and storm frequency; the hydrological conditions include water temperature, salinity, dissolved oxygen, pH, and light intensity; and the sediment parameters include geological structure type and mud layer thickness. Multidimensional data fusion and visualization are achieved through a geographic information system.

[0032] Furthermore, in the second-tier facility selection scheme, the types of deep-sea aquaculture facilities include at least one of the following: wind and wave resistant gravity cages, deep-sea bottom-mounted intelligent cages, submersible truss cages, and aquaculture vessels. The supporting equipment parameters include feed feeding systems, environmental monitoring devices, climate monitoring equipment, automatic net cleaning equipment, and sea-land relay transfer equipment.

[0033] Furthermore, in the third-tier local fish database, biological characteristics include growth rate, flow tolerance, and feed protein requirements; economic value includes market price, market demand, and export potential; and industry status includes distribution of major production areas, farming models, and annual output data. The data is dynamically updated and trend predicted using machine learning algorithms.

[0034] Furthermore, the weighted scoring method for the fourth-level evaluation results includes setting four core indicators: biological adaptability, economic value, industrialization feasibility, and processing suitability. Dynamic weight coefficients are assigned to each indicator, and a comprehensive score is calculated through linear weighting to screen fish species with scores higher than a preset threshold.

[0035] Furthermore, in the fifth level of aquaculture decision-making, the land-sea relay aquaculture technology includes the connection parameters between the land-based seedling stage and the deep-sea aquaculture stage, specifically covering water temperature transition gradient, transportation time control, and environmental adaptability training programs; the intelligent management solution includes a remote environmental monitoring module, a risk early warning system, and an aquaculture expert decision support module.

[0036] Furthermore, the biological adaptability index is further refined into three sub-parameters: tolerance to low oxygen, disease resistance, and wind and wave resistance. The weight coefficients of each sub-parameter are dynamically adjusted according to the environmental characteristics of the target sea area.

[0037] Furthermore, the method also includes a blockchain-based aquaculture data storage module, which is used to encrypt and store the screening process, evaluation results, and decision-making basis at each level to ensure the immutability and full traceability of the data.

[0038] Furthermore, the intelligent management solution collects aquaculture environment data in real time through IoT devices and uses digital twin technology to build virtual aquaculture scenarios, monitor and calculate the growth status of fish under different aquaculture conditions, and optimize feed feeding strategies and emergency response plans for unexpected situations.

[0039] For specific usage, please refer to... Figure 1 As shown,

[0040] S1. Establish the first-level environmental analysis model.

[0041] S1.1 Data Acquisition and Parameter Definition

[0042] Sea state parameters include wave height (unit: meters, measuring equipment: wave radar), ocean current speed (unit: meters / second, measuring equipment: acoustic Doppler current profiler), and storm frequency (historical meteorological data statistics, unit: times / year).

[0043] Hydrological conditions: water temperature (unit: °C, real-time monitoring by sensor), salinity (unit: ‰, salinity meter), dissolved oxygen (unit: mg / L, dissolved oxygen sensor), pH, light intensity (unit: Lux, light sensor).

[0044] Seabed parameters: geological structure type (classified as sandy, muddy, rocky, etc. through seabed geological exploration), mud layer thickness (unit: meters, through sediment sampling and analysis).

[0045] S1.2 Data Fusion and Model Building

[0046] The above parameters are spatially overlaid and analyzed using a Geographic Information System (GIS) to generate a multidimensional environmental data layer.

[0047] Environmental analysis model formula:

[0048]

[0049] Where, p i For each parameter (e.g., wave height, salinity); p max The maximum allowed threshold for the parameter (set based on historical data or industry standards); W iThe parameters are weighted (wave height weight 0.3, salinity weight 0.2, dissolved oxygen weight 0.25, etc., and are dynamically adjusted according to the characteristics of the sea area).

[0050] S2. Generate a second-level facility selection plan.

[0051] S2.1 Facility Type Matching Rules

[0052] Gravity-resistant gabion cages: suitable for sea areas with wave height ≤ 5 meters and current speed ≤ 1.5 meters / second;

[0053] Deep-sea bottom-mounted intelligent cages: suitable for sea areas with muddy bottoms and mud layers ≥ 2 meters thick;

[0054] Submersible truss cages: suitable for sea areas with storm frequency ≥ 10 times / year and requiring rapid disaster avoidance;

[0055] S2.2 Equipment Parameter Configuration

[0056] Feeding system: Feeding amount formula

[0057] Q feed =N×W fish ×R growth ×C protein

[0058] Where N is the number of fish, W fish R represents the average body weight (kg). growth C represents the daily weight gain rate (%). protein This is the feed protein requirement coefficient (set according to fish species).

[0059] S3. Construct a third-level local fish database.

[0060] S3.1 Data Integration and Dynamic Updates

[0061] Biological characteristics: growth rate (g / day), flow tolerance (flow tolerance threshold, unit: m / s), feed protein requirement (%) (each parameter is scored from 0 to 10).

[0062] Economic value: market price (RMB / kg), export potential (scored by export volume percentage, 0-10 points).

[0063] Industry status: Distribution of main production areas (GIS coordinates), annual output (tons / year).

[0064] S3.2 Machine Learning Trend Prediction

[0065] Using time series modeling (ARIMA) to predict market demand:

[0066] D t+1 =αDt +βS t +γE t

[0067] Among them, D t Based on current market demand, S t As a seasonal factor, E t This is an economic environment index.

[0068] S4. Weighted scoring method for screening suitable fish species

[0069] S4.1 Core Indicators and Weight Allocation

[0070] Biological compatibility (weight 0.4), economic value (weight 0.3), industrialization feasibility (weight 0.2), and processing suitability (weight 0.1).

[0071] Dynamic weight adjustment rule: If the target sea area has a high frequency of storms, the biological adaptability weight is increased to 0.5.

[0072] S4.2, Comprehensive Scoring Formula

[0073]

[0074] Among them, S i The score is calculated based on the following indicators: (Biological adaptability score = hypoxia tolerance × 0.3 + disease resistance × 0.3 + wind and wave adaptability × 0.4);

[0075] Filtering threshold: S core ≥80 points.

[0076] S5. Generate fifth-level aquaculture decisions.

[0077] S5.1 Design of Land-Sea Relay Process Parameters

[0078] Water temperature transition gradient: temperature difference ≤ 2℃, formula:

[0079] T target =T base +ΔT×t

[0080] Among them, T base The water temperature for land-based seedling cultivation is ΔT, which represents the daily temperature rise.

[0081] Transportation time control: The maximum transportation time is set according to the fish's tolerance time to hypoxia (unit: hours).

[0082] S5.2, Intelligent Management Module

[0083] Digital twin simulation: Verifying feeding strategies and optimizing formulas through virtual scenarios.

[0084] (Feed costs + growth delay losses)

[0085] Blockchain-based evidence storage: Data at each level is encrypted using the SHA-256 algorithm to ensure it is tamper-proof.

[0086] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0087] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

[0088] The present invention and its embodiments have been described above. This description is not restrictive, and the accompanying drawings are only one embodiment of the present invention; the actual structure is not limited thereto. In conclusion, if those skilled in the art are inspired by this description and design similar structures and embodiments without departing from the spirit of the invention, such designs should fall within the protection scope of the present invention.

Claims

1. A multi-level screening and evaluation method for deep-sea cultured fish, characterized in that, Includes the following steps: S1. Based on the sea state parameters, hydrological conditions and sediment parameters of the target sea area, establish a first-level environmental analysis model; S2. Based on the analysis results of the first level, match the types of deep-sea aquaculture facilities and the parameters of supporting equipment to generate a second-level facility selection scheme; S3. Integrate data on the biological characteristics, economic value, and current status of native fish species in the target sea area to construct a third-level native fish database; S4. Based on the comprehensive data from the first three levels, a weighted scoring method is used to screen suitable fish species for the target sea area and facilities, forming the fourth level of evaluation results. S5. For the selected suitable fish species, design land-sea relay aquaculture technology and intelligent management solutions to generate fifth-level aquaculture decision-making. In the second-tier facility selection scheme, the deep-sea aquaculture facility types include at least one of the following: wind and wave resistant gravity cages, deep-sea bottom-mounted intelligent cages, submersible truss cages, and aquaculture workboats. The supporting equipment includes a feed delivery system, an environmental monitoring device, a climate monitoring device, and a relay transportation device. The biological characteristics in the third-level local fish database include growth rate, flow tolerance and feed protein requirements; the economic value includes market price, market demand and export potential; the industry status includes distribution of main production areas, farming models and annual output data; and the data is dynamically updated and trend predicted through machine learning algorithms. The weighted scoring method for the fourth-level evaluation results includes setting four core indicators: biological adaptability, economic value, industrialization feasibility, and processing suitability. Dynamic weight coefficients are assigned to each indicator, and a comprehensive score is calculated through linear weighting to screen fish with scores higher than a preset threshold. In the fifth level of aquaculture decision-making, the land-sea relay aquaculture technology includes the connection parameters between the land-based seedling stage and the deep-sea aquaculture stage, specifically covering water temperature transition gradient, transportation time control, and environmental adaptability training programs; the intelligent management solution includes a remote environmental monitoring module, a risk early warning system, and an aquaculture expert decision support module.

2. The method for multi-level screening and evaluation of deep-sea aquaculture fish according to claim 1, characterized in that: The sea state parameters include wave height, current speed, and storm frequency; the hydrological conditions include water temperature, salinity, dissolved oxygen, and light intensity; and the sediment parameters include geological structure type and mud layer thickness. Multidimensional data fusion and visualization are achieved through a geographic information system.

3. The method for multi-level screening and evaluation of deep-sea aquaculture fish according to claim 1, characterized in that: The biological adaptability index is further refined into three sub-parameters: tolerance to low oxygen, disease resistance, and wind and wave resistance. The weight coefficients of each sub-parameter are dynamically adjusted according to the environmental characteristics of the target sea area.

4. The method for multi-level screening and evaluation of deep-sea aquaculture fish according to claim 1, characterized in that: The method also includes a blockchain-based aquaculture data storage module, which is used to encrypt and store the screening process, evaluation results and decision-making basis at each level to ensure the immutability and full traceability of the data.

5. The method for multi-level screening and evaluation of deep-sea aquaculture fish according to claim 1, characterized in that: The intelligent management solution collects aquaculture environment data in real time through IoT devices and uses digital twin technology to construct virtual aquaculture scenarios, simulating fish growth under different aquaculture conditions, and optimizing feed feeding strategies and disaster emergency response plans.

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