A method and system for predicting aquaculture capacity based on multi-source data

Abstract

The application discloses a kind of based on multi-source data's aquaculture capacity prediction method and system, it is related to aquaculture management technical field.A kind of based on multi-source data's aquaculture capacity prediction system, including have: data acquisition module, capacity prediction module, density adjustment analysis module, typhoon correction module, elastic capacity generation module and space mapping module.The application is by constructing typhoon influence correction model, using capacity correction weight is dynamically corrected to basic capacity, realizes the automatic adjustment of typhoon season prediction capacity, enhances the robustness of prediction result when facing extreme weather conditions;Based on recommended capacity, historical disaster data fitting model and risk preference setting, generate conservative, neutral, aggressive three kinds of capacity strategy interval, provide hierarchical selection for aquaculture decision of different risk tolerance, improve decision flexibility and risk control ability.

Description

Technical Field

[0001] This invention relates to the field of aquaculture management technology, and in particular to a method and system for predicting aquaculture capacity based on multi-source data. Background Technology

[0002] With the development of aquaculture along my country's coast, multi-species polyculture has gradually become the mainstream aquaculture model for improving aquaculture efficiency and enhancing ecological stability. Traditional methods for predicting aquaculture capacity are mostly based on empirical thresholds, single-factor estimations, or single-species models, which are difficult to fully reflect the dynamic impact of resource competition, interspecific relationships, and environmental changes on aquaculture capacity in complex ecosystems.

[0003] Especially in multi-age, mixed-species aquaculture scenarios, different aquaculture species and their life cycle characteristics vary significantly, resulting in different capacities and responses to spatial resources, water nutrients, and ecological loads. Simple linear models or averaging methods are insufficient to meet actual management needs. Furthermore, many current capacity forecasting methods lack consideration for natural disaster risks, particularly extreme weather events such as typhoons, leading to distorted forecasts during typhoon season and increasing decision-making risks.

[0004] In existing technologies, some studies have attempted to introduce ecological models for capacity simulation, but most focus on long-term ecological balance or resource assessment, lacking adaptability to actual aquaculture plans, and most are not deeply integrated with real-time environmental data or disaster forecast data. Furthermore, current aquaculture capacity assessment results lack spatial representation capabilities, making it difficult to support differentiated management strategies based on regional distribution. Summary of the Invention

[0005] This invention proposes an aquaculture capacity prediction method and system based on ecosystem modeling, integrating multi-source data input, possessing real-time disaster correction capabilities, and outputting visualized adjustment suggestions, in order to improve the intelligence level and risk response capabilities of aquaculture capacity management.

[0006] A method for predicting aquaculture capacity based on multi-source data includes:

[0007] Collect multi-source mixed-culture data for the target aquaculture area, including marine environmental data and mixed-culture plan data; the mixed-culture plan data includes information on the aquaculture species and their estimated age ranges for mixed-culture.

[0008] Multi-source polyculture data are input into a polyculture capacity prediction model established based on ecosystem modeling methods to perform capacity prediction calculations and output the basic capacity of each aquaculture species in the target aquaculture area at the estimated aquaculture age and the estimated capacity for the next aquaculture age.

[0009] Calculate the difference between the base capacity and the estimated capacity of aquaculture species, and output a stocking density adjustment strategy;

[0010] Real-time or forecast meteorological data for the typhoon season is acquired and input into a typhoon impact correction model constructed based on historical typhoon meteorological data. The typhoon impact correction model obtains capacity correction weight coefficients based on the capacity response relationship of each aquaculture species under different typhoon intensities, paths and frequencies, and dynamically corrects the basic capacity to obtain the recommended capacity for the typhoon season.

[0011] Based on the recommended capacity, combined with the economic loss prediction results fitted by historical disaster loss data and the preset risk preference strategy, a capacity elasticity range for the typhoon season is constructed. The capacity elasticity range includes conservative strategy capacity, neutral strategy capacity and aggressive strategy capacity.

[0012] The basic capacity, recommended capacity during the typhoon season, and capacity flexibility ranges for each aquaculture species are mapped onto a rasterized layer of the regional sea area to form a spatial distribution map of aquaculture capacity.

[0013] As a preferred technical solution of the present invention, the polyculture capacity prediction model includes functional group units, ecological process units, and spatial environment units constructed based on ecosystem modeling methods, and performs comprehensive capacity prediction calculations. The functional group units are used to simulate the physiological characteristics and resource requirements of various aquaculture species at different breeding ages, the ecological process units are used to describe the feeding, growth, excretion, and resource competition processes among aquaculture species, and the spatial environment units are used to express the spatial grid structure, hydrodynamic background, and environmental factor distribution characteristics of the target sea area.

[0014] As a preferred embodiment of the present invention, the comprehensive capacity prediction calculation includes:

[0015] Input the information of each aquaculture species and its estimated breeding age from the collected mixed culture plan data into the functional group unit to establish a set of individual parameters for each functional group corresponding to each species and age group.

[0016] Marine environmental data is input into the spatial environmental unit to generate a spatially rasterized environmental background field of the target aquaculture area, including spatial distribution data of water temperature, salinity, current velocity and nutrient concentration.

[0017] The outputs of the functional group units and the spatial environment units serve as the inputs of the ecological process units. The ecological process units dynamically simulate the feeding, growth, metabolism, excretion, and interaction processes of each functional group under the background field, and output the basic capacity values ​​of each functional group under the estimated breeding age.

[0018] Based on the simulation output of the current age group, the ecological process unit infers the biomass change trend of the next breeding age group, and combines the growth rate of resource demand per unit weight during the age group growth process to output the capacity estimate corresponding to the next breeding age group.

[0019] As a preferred embodiment of the present invention, the output stocking density adjustment strategy includes:

[0020] Based on the difference between the base capacity of each aquaculture species at the estimated breeding age and the estimated capacity of its next breeding age, the capacity change rate per unit area or per grid is calculated. The capacity change rate is compared with a preset density adjustment threshold to determine whether a breeding density adjustment operation needs to be performed. When the capacity change rate exceeds the threshold, an adjustment strategy is generated, including diluting the breeding density or staged harvesting, based on the growth cycle of each aquaculture species and the local spatial environmental capacity status. The adjustment strategy includes the time node for strategy implementation, the target density range, and the recommended operation area.

[0021] As a preferred embodiment of the present invention, the typhoon impact correction model includes a typhoon event feature extraction unit, an aquaculture species response parameter unit, and a capacity correction calculation unit. The typhoon event feature extraction unit is used to match event features based on typhoon season data. The aquaculture species response parameter unit is used to construct the capacity impact response relationship of different aquaculture species and their aquaculture age groups under different typhoon event features using historical typhoon season data, forming a corresponding correction weight library. The capacity correction calculation unit is used to obtain the capacity correction weight coefficient and calculate the recommended capacity of the basic capacity during the typhoon season.

[0022] As a preferred embodiment of the present invention, the recommended capacity of the computational base capacity during the typhoon season includes:

[0023] Real-time or forecast meteorological data for the typhoon season is input into the typhoon event feature extraction unit to obtain parameter features of the current typhoon event's intensity, path, landfall frequency, duration, and landfall location, and to match the corresponding event features. The event features are used as search conditions to match the corresponding capacity correction weight coefficients in the correction weight library. The capacity correction weight coefficients are then weighted and corrected according to the basic capacity of each aquaculture species by species and age group to obtain the recommended capacity under the current typhoon season conditions. The calculation of the recommended capacity takes into account the regional environmental carrying capacity limit and the synergistic relationship between species. If the total capacity exceeds the regional ecological carrying capacity limit, proportional compression is performed based on the capacity adjustment priority of each species.

[0024] As a preferred embodiment of the present invention, the construction of the capacity flexibility range for the typhoon season includes:

[0025] The recommended capacity for each aquaculture species during the typhoon season is used as the baseline capacity value. Based on historical typhoon damage data, a fitting model is constructed to establish the relationship between capacity changes and economic losses for each species, thereby obtaining the trend of loss changes corresponding to capacity reduction or exceeding limits. Risk preference levels are set, with each level corresponding to the capacity adjustment range and the acceptable economic loss range. Based on the fitting model and risk preference levels, the capacity fluctuation range corresponding to each species during the typhoon season is calculated to form the capacity elasticity range.

[0026] A multi-source data-based aquaculture capacity prediction system includes:

[0027] Data acquisition module: used to collect multi-source mixed culture data of the target aquaculture area;

[0028] Capacity prediction module: Used to input multi-source polyculture data into the polyculture capacity prediction model and output the basic capacity and estimated capacity of each aquaculture species;

[0029] Density Adjustment Analysis Module: Used to generate recommendations for adjusting stocking density based on the difference between the baseline capacity and the estimated capacity;

[0030] Typhoon Correction Module: Used to acquire real-time or forecast meteorological data for the typhoon season, and dynamically correct the base capacity using the typhoon impact correction model, outputting the recommended capacity for the typhoon season.

[0031] Elastic capacity generation module: used to construct the capacity elastic range for the typhoon season based on the recommended capacity;

[0032] The spatial mapping module is used to map the basic capacity, recommended capacity during the typhoon season, and capacity flexibility ranges of each aquaculture species to a rasterized layer of the regional sea area, forming a spatialized aquaculture capacity distribution map.

[0033] The present invention has the following advantages:

[0034] This invention constructs a mixed-species capacity prediction model based on ecosystem modeling methods, combines marine environmental data and mixed-species planning data, and outputs capacity prediction results for each aquaculture species in the current age group and the next age group, thereby improving the accuracy and adaptability of capacity assessment in mixed-species scenarios.

[0035] This invention supports the combined output of the basic capacity of the current age group and the predicted capacity of the next age group for each aquaculture species, making the adjustment of aquaculture density more forward-looking and facilitating the timely formulation of harvesting timing or control strategies. It automatically generates aquaculture density adjustment strategies based on the difference between the current capacity and the capacity of the next age group, including suggestions for dilution density or phased harvesting, and outputs them in a targeted manner based on spatial environmental conditions, thereby improving the operability of the plan.

[0036] This invention constructs a typhoon impact correction model and uses capacity correction weights to dynamically correct the base capacity, thereby achieving automatic adjustment of the predicted capacity during the typhoon season and enhancing the robustness of the prediction results under extreme weather conditions. Based on the recommended capacity, historical disaster loss data fitting model, and risk preference settings, it generates three capacity strategy ranges: conservative, neutral, and aggressive, providing graded selection for aquaculture decisions with different risk tolerance levels and improving decision-making flexibility and risk control capabilities.

[0037] This invention maps basic capacity, recommended capacity, and flexible capacity to a marine raster layer to form a spatialized aquaculture capacity distribution map, supports the formulation of strategies based on regional differences, and realizes the visualization and platform application of aquaculture capacity prediction results. Attached Figure Description

[0038] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings in the following description are only schematic diagrams of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0039] Figure 1 This is a schematic diagram of the structure of an aquaculture capacity prediction system based on multi-source data used in an embodiment of the present invention. Detailed Implementation

[0040] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this invention, and not all embodiments. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0041] Example 1: A method for predicting aquaculture capacity based on multi-source data, comprising the following steps:

[0042] Step S1: Collect multi-source mixed aquaculture data for the target aquaculture area, including marine environmental data and mixed aquaculture plan data; the mixed aquaculture plan data includes information on the aquaculture species and their estimated age ranges for mixed aquaculture.

[0043] Marine environmental data: used to represent the hydrological, physical, and chemical ecological background of the target aquaculture area, for use in the spatial environment and ecological process modeling in the capacity prediction model (forming a rasterized environmental background field of the target marine area), including at least the following tables:

[0044]

[0045] It also includes hydrodynamic structure data (such as tidal currents, wave propagation direction, wind speed and direction, etc.) obtained through remote sensing data platforms or local marine forecasting systems, used to analyze the permeability and material exchange characteristics of the sea area; spatial environmental factors, including the coordinate boundaries of the aquaculture sea area, topographic features (whether it is nearshore / bay mouth / open area), and seabed type (silt, reef, etc.) and other geographical background information.

[0046] Mixed-species aquaculture plan data (taking "large yellow croaker + mussel + kelp" combination as an example): refers to a mixed-species aquaculture structure plan set for a target aquaculture area, providing core information such as species types, initial parameters, and spatial locations required by the model, for example:

[0047] Mixed-species combination: large yellow croaker, mussels, and kelp;

[0048] Stocking density: Large yellow croaker - 5 fish / square meter; Mussels - 20 per cage; Kelp - 10 kg / rope;

[0049] Estimated age range for cultivation: Large yellow croaker - early fattening stage (6 months); Mussels - mid-stage; Kelp - adult stage;

[0050] Mixed-culture ratio: Large yellow croaker: mussels: kelp = 2:2:1;

[0051] Spatial distribution and numbering: The cages are numbered F1 to F30 and are located in the sea area of ​​N26°10' to 26°12'. The coordinates of the numbers match the GIS layer.

[0052] Source and update method: Exported from the aquaculture management system, or entered by on-site inspectors via mobile device.

[0053] This type of multi-species combination is used in nearshore cage culture, where kelp absorbs eutrophic salts to reduce the risk of eutrophication, mussels filter particulate matter to purify the water, and it complements the large yellow croaker, making it a typical example.

[0054] The aforementioned marine environmental data and mixed-species planning data will be input according to a unified data format standard (JSON or structured tables) to form a complete multi-source input dataset, which will be used by the mixed-species capacity prediction model in the next step. The system will automatically connect parameter fields to the model input interface to achieve seamless data transfer.

[0055] Step S2: Input the multi-source polyculture data into the polyculture capacity prediction model established based on the ecosystem modeling method, perform capacity prediction calculation, and output the basic capacity of each aquaculture species in the target aquaculture area at the estimated aquaculture age and the estimated capacity for the next aquaculture age.

[0056] The ecosystem modeling method refers to a method based on ecological dynamics theory that uses structured models to quantitatively express the nutrient uptake, growth and metabolism, interspecific interactions, and environmental feedback mechanisms of multiple species in aquaculture systems. This modeling method typically adopts a modular structure and possesses the following characteristics:

[0057] Using functional groups as the basic simulation unit, individuals with similar ecological behaviors are aggregated to simplify computational complexity;

[0058] It emphasizes the coupling relationship between organisms and the environment, and demonstrates the regulatory role of biological behavior on ecological capacity through bidirectional dynamic coupling with environmental factors;

[0059] It supports data input from multiple age groups, multiple varieties, and multiple spatial grid points, making it suitable for complex scenarios such as high-density mixed farming and three-dimensional space utilization.

[0060] Common modeling framework packages such as EwE, Farm-AEM, and AquaFarm have good ecological process simulation and visualization capabilities.

[0061] In this embodiment, the ecosystem modeling method is used to construct a mixed-species capacity prediction model, which is decomposed into three parts: functional group units, ecological process units, and spatial environment units, and capacity prediction simulation is carried out in a coordinated manner.

[0062] The mixed-culture capacity prediction model includes functional group units, ecological process units, and spatial environment units constructed based on ecosystem modeling methods, and comprehensively performs capacity prediction calculations.

[0063] Functional unit: used to simulate the physiological characteristics and resource requirements of various aquaculture species at different age stages;

[0064] Physiological characteristics include the individual growth rate, feeding capacity, excretion rate, and metabolic heat flux of the breed at the current age.

[0065] Resource requirements include the average daily consumption of key resources such as particulate organic matter, dissolved oxygen, and nutrients.

[0066] For example, in the combination of "large yellow croaker + mussels + kelp": the feeding amount parameters and ammonia excretion rate parameters of large yellow croaker (early fattening stage); the metabolic load parameters of mussels that rely on filter feeding on planktonic particles; and the consumption rate parameters of kelp that use nutrients (nitrates and phosphates) as its main resources, with photosynthesis dominating its growth process.

[0067] The functional group unit organizes the above parameters into a standardized set of functional group objects, which serve as the basic unit for ecological simulation.

[0068] Ecological process unit: used to describe the feeding, growth, excretion and resource competition processes among farmed species;

[0069] The input consists of biological parameters output by the functional group unit and background field data from the space environment unit;

[0070] Using a population ecological dynamics model, the following processes are dynamically simulated: feeding efficiency and growth of each functional group under the current environment; the impact of excretions such as ammonia nitrogen produced by respiratory metabolism on the local environment; growth inhibition, competition, and synergistic effects of various populations under resource constraints; the simulation results include the ecological balance capacity per unit grid and for each functional group at the current age.

[0071] Spatial environment unit: used to express the spatial grid structure, hydrodynamic background and distribution characteristics of environmental factors of the target sea area;

[0072] The sea area is divided into several spatial grid units (e.g., 500m × 500m), and each grid point is bound to the following environmental information: water temperature, salinity, water flow velocity, flow direction; nitrate, phosphate, dissolved oxygen concentration; water depth, substrate type, water exchange frequency, etc. The environmental background field (spatial grid structure of the target sea area) constructed by this unit will be used to constrain and drive subsequent ecological process simulations.

[0073] The comprehensive capacity prediction calculation includes:

[0074] Input the information of each aquaculture species and its estimated breeding age from the collected mixed culture plan data into the functional group unit to establish a set of individual parameters for each functional group corresponding to each species and age group.

[0075] For example, the 6-month-old group of large yellow croaker was configured with the following parameters: feed intake rate per unit body weight of 0.08 kg / kg·day, metabolic ammonia excretion of 0.015 g / kg·day, and optimal water temperature range of 21~26℃.

[0076] Each parameter comes from measured data, aquaculture manuals, or model literature databases (such as the EcopathwithEcosim parameter set).

[0077] Marine environmental data is input into the spatial environmental unit to generate a spatially rasterized environmental background field of the target aquaculture area, including spatial distribution data of water temperature, salinity, current velocity and nutrient concentration.

[0078] For each spatial grid, a set of environmental factor vectors is established. For example, grid point G_001: water temperature 23.2℃, salinity 30.5‰, NO3=0.38 mg / L;

[0079] The outputs of the functional group units and the spatial environment units serve as the inputs of the ecological process units. The ecological process units dynamically simulate the feeding, growth, metabolism, excretion, and interaction processes of each functional group under the background field, and output the basic capacity values ​​of each functional group under the estimated breeding age.

[0080] For example: In grid G_001, the basic capacity of large yellow croaker is 150 kg, mussels are 80 kg, and kelp is 200 kg;

[0081] If there are regional limitations on nutrients or dissolved oxygen, the model will automatically adjust the stocking density to maintain ecological balance.

[0082] Based on the simulation output of the current age group, the ecological process unit infers the biomass change trend of the next breeding age group, and combines the growth rate of resource demand per unit weight during the age group growth process to output the capacity estimate corresponding to the next breeding age group.

[0083] For example, when large yellow croakers transition from 6 months to 8 months of age, their food intake per unit body weight increases by 12%, but if the background nutrient level is insufficient, their volume decreases to 130 kg.

[0084] This estimated value can be used to develop aquaculture adjustment plans in advance (such as optimizing fishing and stocking).

[0085] Step S3: Calculate the difference between the basic capacity and the estimated capacity of the aquaculture species, and output the stocking density adjustment strategy;

[0086] The output stocking density adjustment strategy includes:

[0087] Based on the difference between the base capacity of each aquaculture species at the estimated breeding age and the estimated capacity of its next breeding age, the capacity change rate per unit area or per grid is calculated. The capacity change rate is compared with a preset density adjustment threshold to determine whether a breeding density adjustment operation needs to be performed. When the capacity change rate exceeds the threshold, an adjustment strategy is generated, including diluting the breeding density or staged harvesting, based on the growth cycle of each aquaculture species and the local spatial environmental capacity status. The adjustment strategy includes the time node for strategy implementation, the target density range, and the recommended operation area.

[0088] Density adjustment determination mechanism: (determines whether or not stocking density adjustment is necessary)

[0089] The capacity change rate is the difference between the current age group capacity and the next age group capacity within a unit space, used to assess whether there is an overload risk or resource redundancy.

[0090] The density adjustment threshold is a system preset parameter, set (±15%) based on past experience or model sensitivity analysis, and is used to define the acceptable range of capacity fluctuation;

[0091] If the rate of change is greater than the positive threshold, it indicates a significant increase in biomass, which may cause resource overload, and a density dilution recommendation should be triggered.

[0092] If the rate of change is less than the negative threshold, it indicates insufficient growth or increased ecological constraints, and early harvesting or structural optimization can be considered.

[0093] Strategy generation process description: (Generates adjustment strategies including diluting aquaculture density or phased harvesting)

[0094] Inputs: basic capacity, estimated capacity, environmental capacity limits, and life cycle information for each age group;

[0095] Processing logic: Determine the direction and magnitude of capacity fluctuations; analyze the growth cycle and inflection point of the corresponding age group; check whether the environmental capacity of the corresponding space unit can accommodate the changes; match strategies according to the set strategy template;

[0096] Output content: Dilution operation suggestions (such as moving boxes, reducing density); Harvesting time window (such as starting harvesting 1 month earlier, gradually reducing weight); Suggestions for optimizing mixed culture structure (such as postponing the next cycle of stocking).

[0097] Taking the mixed culture of "large yellow croaker + kelp" in a certain grid as an example:

[0098]

[0099] The system determined that the growth rate of large yellow croaker exceeded the upper limit (the threshold is set at ±15%), triggering a density dilution recommendation; the kelp did not exceed the lower limit, but because it is an auxiliary species and its nutrients are slightly insufficient, it is recommended to end the early stage of harvesting or reduce the continuous stocking.

[0100] The final policy output is:

[0101] Grid number: G_005, Adjustment type: Combined;

[0102] Recommendations for large yellow croaker: {Reduce density by 20%, postpone feeding by 2 weeks, recommended harvesting period: week 7 to week 8};

[0103] Kelp recommendation: {Maintain current density or reduce seedling ratio to 80%};

[0104] Step S4: Obtain real-time or forecast meteorological data for the typhoon season and input it into the typhoon impact correction model constructed based on historical typhoon meteorological data. The typhoon impact correction model obtains the capacity correction weight coefficient according to the capacity response relationship of each aquaculture species under different typhoon intensities, paths and frequencies, and dynamically corrects the basic capacity to obtain the recommended capacity for the typhoon season.

[0105] The typhoon impact correction model includes a typhoon event feature extraction unit, an aquaculture species response parameter unit, and a capacity correction calculation unit. These three units work together to form a complete typhoon impact correction mechanism, which is used to simulate the compression effect of typhoon disturbances on the capacity of different aquaculture species at specific age groups.

[0106] Typhoon Event Feature Extraction Unit: This unit receives real-time or forecast typhoon season data from external meteorological systems (NOAA, regional marine meteorological service platforms), analyzes and extracts the following five key typhoon parameters: intensity level (e.g., tropical storm, severe tropical storm, typhoon, severe typhoon); track (latitude and longitude sequence or track number); landfall frequency (number of events per unit time); duration (e.g., hours or days); and landfall location coordinates (affected area or center point).

[0107] After the event features are extracted, a standardized typhoon event description object will be formed and input into the downstream module for matching processing.

[0108] The aquaculture species response parameter unit is used to establish a typhoon capacity impact response model. Its main function is to fit and analyze the extracted event characteristics with historical aquaculture capacity change data to build a capacity correction weight library.

[0109] The process of constructing corrected weights involves: compiling records of actual losses or production capacity declines in the target sea area during multiple past typhoon events; comparing the impact of event intensity, path, and production capacity of different aquatic species and age groups; fitting different combinations of event characteristics and corresponding capacity impact factors for species and age groups; and storing these as key-value structures to form a corrected weight database.

[0110] Definition of capacity correction weighting coefficient: The correction coefficient is a real number in the interval [0,1], representing the proportion of capacity retained under the influence of typhoon.

[0111] For example:

[0112]

[0113] Capacity correction calculation unit: used to find matching coefficients in the correction weight library based on the characteristics of the current typhoon event, and to correct the basic capacity of each aquaculture species.

[0114] The recommended capacity for the basic computing capacity during the typhoon season includes:

[0115] Real-time or forecast meteorological data for the typhoon season is input into the typhoon event feature extraction unit to obtain parameter features of the current typhoon event's intensity, path, landfall frequency, duration, and landfall location, and to match the corresponding event features. The event features are used as search conditions to match the corresponding capacity correction weight coefficients in the correction weight library. The capacity correction weight coefficients are then weighted and corrected according to the basic capacity of each aquaculture species by species and age group to obtain the recommended capacity under the current typhoon season conditions. The calculation of the recommended capacity takes into account the regional environmental carrying capacity limit and the synergistic relationship between species. If the total capacity exceeds the regional ecological carrying capacity limit, proportional compression is performed based on the capacity adjustment priority of each species.

[0116] Recommended capacity calculation process: Use typhoon event characteristics as search conditions, call the corrected weights provided by the response parameter unit; apply the corrected weights to the basic capacity of each variety and its age group to obtain the recommended capacity;

[0117] If the total recommended capacity exceeds the ecological carrying capacity limit of the area (such as limits on dissolved oxygen, nutrient load, and habitat space), the capacity compression mechanism will be activated: priority adjustment will be made according to the capacity adjustment priority of the species (preset strategy or economic value weight); the proportion of all species will be compressed or partially compressed to ensure that the total capacity falls back below the ecological threshold; the final recommended capacity and compression explanation information will be output.

[0118] Capacity Correction Weighting Coefficient: Used to measure the proportion of actual aquaculture capacity that each type of aquaculture unit may retain under the influence of a typhoon, reflecting the sensitivity to typhoon disturbance;

[0119] Recommended capacity: This represents the reasonable and safe carrying capacity for aquaculture under the current typhoon event interference assumptions, and is used to generate adjustment suggestions or investment plans;

[0120] Ecological carrying capacity limit: refers to the maximum capacity of aquaculture species populations to sustainably exist under current hydrological and ecological conditions, preventing system overload or ecological degradation.

[0121] Step S5: Based on the recommended capacity, combined with the economic loss prediction results fitted from historical disaster loss data and the preset risk preference strategy, construct the capacity elasticity range for the typhoon season. The capacity elasticity range includes conservative strategy capacity, neutral strategy capacity and aggressive strategy capacity.

[0122] Recommended capacity: The appropriate input capacity calculated by the typhoon impact correction model;

[0123] Capacity flexibility range: The upper and lower capacity fluctuation range formed by combining the recommended capacity with loss prediction and risk tolerance range;

[0124] Fitting model: A model that fits historical aquaculture loss data to the degree of capacity shift as a function, used to quantify risk;

[0125] The capacity flexibility range for the typhoon season includes:

[0126] The recommended capacity for each aquaculture species during the typhoon season is used as the baseline capacity value, which is the corrected acceptable capacity output by the typhoon impact correction model in step S4.

[0127] A fitting model was constructed based on historical typhoon damage data to establish the relationship between capacity changes and economic losses for various types of crops.

[0128] The historical disaster damage data includes: actual production losses in aquaculture during typhoons; the proportion of production reduction due to disasters; the production density at the time of the typhoon; and post-disaster repair costs.

[0129] Data sources: aquaculture enterprise ledgers, insurance claim records, and post-disaster assessment files from local marine and fisheries departments.

[0130] Fitting methods: based on nonlinear functions of capacity offset magnitude and loss amount, such as piecewise linear regression, logistic regression, or loss-risk response curves; to obtain the trend of loss changes corresponding to capacity decline or over-limit;

[0131] Set risk appetite levels, with each level corresponding to a capacity adjustment range and an acceptable economic loss range;

[0132] Risk appetite strategies are set according to the investor's / manager's willingness to bear losses, with typical levels including: conservative (loss tolerance <5%), neutral (tolerance 5–15%), and aggressive (tolerance 15–30%).

[0133] Each level will be mapped to an adjustment range of the recommended capacity, such as: conservative, 85%–100% of the recommended capacity; neutral, 70%–115% of the recommended capacity; and aggressive, 50%–130% of the recommended capacity.

[0134] Also consider: the uncertainty of typhoon forecasts; the differences in the risk resistance of aquaculture species; and the economic profit and loss turning point (critical capacity).

[0135] Based on the fitted model and risk preference level, the capacity fluctuation range of each variety during the typhoon season is calculated to form the capacity elasticity range.

[0136] For each variety, establish the following mapping: ,in C 低 This indicates a conservative lower limit for the recommended capacity; C 高 This indicates the upper limit of risk compensation for the recommended capacity; R Indicates the level of risk preference; L ( C () represents the estimated potential loss within the corresponding range. f () indicates the mapping process.

[0137] The output capacity flexibility ranges are named as follows:

[0138] Conservative strategy capacity: It is recommended to invest within a smaller offset range (such as 90% of the recommended value) to prioritize the protection of principal and resources;

[0139] Neutral strategy capacity: Maintain the recommended capacity with fluctuations of 10-20%, balancing stability and returns;

[0140] Aggressive strategy capacity: This can exceed the recommended capacity by a certain percentage in exchange for higher returns, but it also carries the risk of post-disaster production reduction or environmental compression.

[0141] Step S6: Map the basic capacity, recommended capacity during the typhoon season, and capacity flexibility ranges of each aquaculture species to the regional sea area rasterized layer to form a spatialized aquaculture capacity distribution map.

[0142] Definition of noun:

[0143] Rasterized layer: Discretizes a continuous sea area into grid cells, each cell containing location and attribute data;

[0144] Capacity types include three categories: basic capacity, recommended capacity, and elastic capacity.

[0145] Spatial mapping: refers to the process of binding non-spatial data (such as capacity) to specific geographical locations;

[0146] Visual coding: Using colors, legends, shapes, and other forms to express numerical changes, enhancing the intuitive communication of information;

[0147] Distribution map: A graphical representation of capacity data across a spatial region, providing intuitive interpretation and data support.

[0148] The spatialized aquaculture capacity distribution map includes:

[0149] Constructing a spatial rasterized base map of the regional sea area: The target aquaculture sea area is divided into equal-scale spatial raster units according to latitude and longitude or sea area coordinate system;

[0150] Each grid unit is bound to a unique spatial identifier (GridID), which corresponds to the number of the mixed-species facility (such as a net cage or raft).

[0151] seabed Figure 1 It is generally constructed by GIS system (such as ArcGIS, QGIS), and the underlying data sources include nautical charts, remote sensing images and marine utilization planning maps.

[0152] Establish a mapping relationship between capacity data and spatial grids: Match the basic capacity output in step S2, the recommended capacity in step S4, and the capacity elasticity range in step S5 to the corresponding grids according to the spatial distribution coordinates of the aquaculture facilities; for cases where multiple species are mixed-cultured within the same grid, display their mixed-culture capacity status in a hierarchical or synthetic index manner.

[0153] If the capacity value of a raster differs significantly under different strategies (conservative / neutral / aggressive), then color gradients or sign changes can be used for visual differentiation.

[0154] Three types of capacity layers are generated: a basic capacity layer, reflecting the basic biomass distribution suitable for aquaculture of various species under the current or near-term marine ecosystem; a recommended capacity layer, considering capacity correction results after typhoon season disturbances, serving as the core basis for guiding short-term aquaculture planning; and a flexible capacity layer, displaying the range of upper and lower capacity limits based on risk preferences, used for dynamic decision support. Capacity value coding and visualization:

[0155] The numerical fields of each layer are stored in the following format: {Raster ID|Variety|Age Group|Capacity Type|Value (kg or tail / raster)};

[0156] Visualization methods: Different varieties are distinguished by legend colors; different capacity types are represented by layer overlay; flexible ranges can be marked with shaded areas or error band icons.

[0157] The final result is a spatialized aquaculture capacity distribution map, enabling visualization of capacity layout across multiple strategies, species, and age groups. This map can be used for the following applications: capacity allocation and adjustment in aquaculture areas; risk zone identification and early warning deployment during typhoon season; spatial decision-making for dilution or expansion strategies; and layer integration with government regulatory or scientific research model analysis platforms.

[0158] Example 2: An aquaculture capacity prediction system based on multi-source data, see [link to example]. Figure 1 As shown, it includes the following units:

[0159] The data acquisition module is used to collect multi-source mixed aquaculture data of the target aquaculture area, including marine environmental data and mixed aquaculture plan data. The mixed aquaculture plan data includes information on the aquaculture species and their estimated age ranges for mixed aquaculture.

[0160] The capacity prediction module is used to input the multi-source polyculture data into the polyculture capacity prediction model established based on the ecosystem modeling method, and output the basic capacity of each aquaculture species in the estimated breeding age and the estimated capacity in the next breeding age.

[0161] The density adjustment analysis module is used to generate recommendations for adjusting aquaculture density based on the difference between the baseline capacity and the estimated capacity.

[0162] The typhoon correction module is used to acquire real-time or forecast meteorological data for the typhoon season, and dynamically correct the base capacity based on the typhoon impact correction model built on historical typhoon data, and output the recommended capacity for the typhoon season.

[0163] The elastic capacity generation module is used to generate capacity elasticity ranges, including conservative strategy capacity, neutral strategy capacity, and aggressive strategy capacity, based on the recommended capacity, economic loss fitting model, and risk preference level.

[0164] The spatial mapping module is used to map the basic capacity, recommended capacity during the typhoon season, and capacity flexibility ranges of each aquaculture species to a rasterized layer of the regional sea area, forming a spatialized aquaculture capacity distribution map.

[0165] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for predicting aquaculture capacity based on multi-source data, characterized in that, The method comprises the following steps: Collecting multi-source mixed culture data of the target breeding sea area, including sea area environment data and mixed culture plan data; The mixed culture plan data includes the mixed culture aquatic species and their estimated breeding age information; Inputting the multi-source mixed culture data into a mixed culture capacity prediction model based on an ecosystem modeling method to perform capacity prediction calculation, and outputting the basic capacity of each aquatic species in the target breeding sea area in the estimated breeding age stage and the estimated capacity in the next breeding age stage; Calculating the difference between the basic capacity and the estimated capacity of the aquatic species, and outputting the breeding density adjustment strategy; The output breeding density adjustment strategy comprises: According to the difference between the basic capacity of each aquatic species in the estimated breeding age stage and the estimated capacity in the next breeding age stage, the capacity change rate per unit area or per unit grid is calculated; the capacity change rate is compared with the preset density adjustment threshold to determine whether the breeding density adjustment operation needs to be performed; when the capacity change rate exceeds the threshold, the adjustment strategy including diluting the breeding density or stage fishing is generated according to the age growth period of each breeding species and the local space environment capacity state, and the adjustment strategy includes the time node of strategy implementation, the target density interval and the recommended operation area; Obtaining real-time or forecast typhoon season meteorological data and inputting them into a typhoon influence correction model constructed based on historical typhoon meteorological data, the typhoon influence correction model obtains capacity correction weight coefficients according to the capacity response relationship of each breeding species under different typhoon intensity, path and frequency conditions, dynamically corrects the basic capacity, and obtains the recommended capacity in the typhoon season; The typhoon influence correction model comprises a typhoon event feature extraction unit, an aquatic species response parameter unit and a capacity correction calculation unit; the typhoon event feature extraction unit is used to match event features according to typhoon season data; the aquatic species response parameter unit is used to construct the capacity influence response relationship of different breeding species and their breeding age stages under different typhoon event features through historical typhoon season data, and form a corresponding correction weight library; the capacity correction calculation unit is used to obtain the capacity correction weight coefficients and calculate the recommended capacity of the basic capacity in the typhoon season; The calculation of the recommended capacity of the basic capacity in the typhoon season comprises: Inputting real-time or forecast typhoon season meteorological data into the typhoon event feature extraction unit to obtain the intensity, path, landing frequency, duration and landing location parameter features of the current typhoon event, and match the corresponding event features; using the event features as the retrieval condition to match the corresponding capacity correction weight coefficients in the correction weight library; weighting and correcting the capacity correction weight coefficients and the basic capacity of each aquatic species by species and age to obtain the recommended capacity of the basic capacity under the current typhoon season condition; the calculation of the recommended capacity considers the regional environmental carrying limit and the species coordination relationship, and in the case that the total capacity value exceeds the regional ecological carrying limit, the capacity is proportionally compressed based on the priority of each species. Based on the recommended capacity, the economic loss estimation result fitted based on historical disaster data, and the preset risk preference strategy, a capacity elasticity interval for the typhoon season is constructed, which includes a conservative strategy capacity, a neutral strategy capacity, and an aggressive strategy capacity; The basic capacity of each aquaculture species, the recommended capacity for the typhoon season, and each capacity elasticity interval are respectively mapped to a regional sea area raster layer to form a spatialized aquaculture capacity distribution map.

2. The method of claim 1, wherein, The polyculture capacity prediction model comprises a functional group unit, an ecological process unit, and a spatial environment unit constructed based on an ecosystem modeling method, and comprehensively performs capacity prediction calculation; the functional group unit is used to simulate the physiological characteristics and resource demand of each aquaculture species at different culture ages; the ecological process unit is used to describe the processes of feeding, growth, excretion, and resource competition among the aquaculture species; and the spatial environment unit is used to express the spatial grid structure, hydrodynamic background, and environmental factor distribution characteristics of the target sea area.

3. The method of claim 2, wherein, The comprehensive capacity prediction calculation includes: inputting the collected polyculture plan data of each aquaculture species and its estimated culture age stage information into the functional group unit to establish a functional group individual parameter set corresponding to each species and age stage; inputting the sea area environmental data into the spatial environment unit to generate a spatially gridded environmental background field of the target culture sea area, including the spatial distribution data of water temperature, salinity, flow velocity, and nutrient salt concentration; the outputs of the functional group unit and the spatial environment unit are used as the inputs of the ecological process unit, which dynamically simulates the processes of feeding, growth, metabolism, excretion, and interaction of each functional group in the background field, and outputs the basic capacity value of each functional group at the estimated culture age stage; based on the simulation output result of the current age stage, the ecological process unit deduces the biomass change trend to the next culture age stage, and outputs the capacity estimation value corresponding to the next culture age stage in combination with the unit weight resource demand growth rate during the age stage growth process. 4.The method of claim 1, wherein, The construction of the capacity elasticity interval for the typhoon season includes: taking the recommended capacity of each aquaculture species in the typhoon season as the reference capacity value; constructing a fitting model between the capacity variation and economic loss of each species based on historical typhoon disaster data to obtain the loss change trend corresponding to the capacity decline or over-limit; setting a risk preference level, each level corresponding to a capacity adjustment range and an acceptable economic loss interval; calculating the capacity floating interval of each species corresponding to the typhoon season according to the fitting model and the risk preference level to form the capacity elasticity interval. 5.A system for aquaculture capacity prediction based on multi-source data, characterized in that, The system applies the polyculture capacity prediction method based on multiple data sources in any one of claims 1 to 4, which includes: a data acquisition module for acquiring multiple polyculture data of the target culture sea area; a capacity prediction module for inputting the multiple polyculture data into the polyculture capacity prediction model to output the basic capacity and estimated capacity of each aquaculture species; a density adjustment analysis module for generating a culture density adjustment suggestion according to the difference between the basic capacity and the estimated capacity; a typhoon correction module for obtaining real-time or forecast typhoon season meteorological data, and dynamically correcting the basic capacity through a typhoon influence correction model to output the recommended capacity for the typhoon season; and a capacity elasticity interval correction module for correcting the capacity elasticity interval according to the recommended capacity for the typhoon season. an elastic capacity generating module, configured to construct a capacity elastic interval of the typhoon season according to the recommended capacity; a space mapping module, configured to map the basic capacity of each aquaculture species, the recommended capacity of the typhoon season, and each capacity elastic interval to a regional sea grid layer respectively, to form a spatialized aquaculture capacity distribution map.

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